A national scale monitoring network for nutrients in agriculture dominated headwaters in the Netherlands

NASA Astrophysics Data System (ADS)

Broers, H. P.; Rozemeijer, J.; Klein, J.

2012-04-01

Although specific monitoring networks exist in the Netherlands which assess the leaching of nutrients to surface waters and groundwater, none of them was capable to quantify the effects of nutrient reduction schemes to agriculture dominated headwaters. Thus, an important link was missing which relates the nutrient concentrations measured in shallow groundwater at farm scale to nutrient concentrations measured at the scale of Water Framework Directive water bodies. A new network was composed using existing monitoring locations and water quality time series owned by the 24 water boards in the Netherlands. Only monitoring locations were selected where no other pollution sources , such as water sewage treatment plants were influencing water quality. Eventually, 168 monitoring locations were selected to assess compliance to environmental standards and 80 for trend analysis. Compliance was tested applying environmental quality standards (EQS) based on summer averaged concentrations, which are set by the water boards and which are water type and location dependent. Compliance was strongly weather dependent, and only 24% of the locations complied for N and P under all weather conditions. Trends were assessed using a combination of seasonal Mann-Kendall tests and Theil-Sen robust lines for individual time series, and aggregating those trends to acquire median and average trend slopes for the sand, clay and peat regions in the Netherlands. Significant downward trends were demonstrated for N and P over the whole period (slopes between -0,55 mgN/l and -0.015 and 0.02 mg P/l per 10 year). Slopes were even more pronounced for winter concentrations of N (-0.89 mg N/l per 10 year). The slopes were relevant and environmentally significant in relation to the height of the EQS and were attributed to the effective reduction of nutrient leaching as the result of adapted farming practices. The presentation will highlight and evaluate choices in the design of the newly composed network, including the use of existing monitoring data and its probable effect on the outcomes of the network.

Tolerance for Nutrient Imbalance in an Intermittently Feeding Herbivorous Cricket, the Wellington Tree Weta

PubMed Central

Wehi, Priscilla M.; Raubenheimer, David; Morgan-Richards, Mary

2013-01-01

Organisms that regulate nutrient intake have an advantage over those that do not, given that the nutrient composition of any one resource rarely matches optimal nutrient requirements. We used nutritional geometry to model protein and carbohydrate intake and identify an intake target for a sexually dimorphic species, the Wellington tree weta (Hemideina crassidens). Despite pronounced sexual dimorphism in this large generalist herbivorous insect, intake targets did not differ by sex. In a series of laboratory experiments, we then investigated whether tree weta demonstrate compensatory responses for enforced periods of imbalanced nutrient intake. Weta pre-fed high or low carbohydrate: protein diets showed large variation in compensatory nutrient intake over short (<48 h) time periods when provided with a choice. Individuals did not strongly defend nutrient targets, although there was some evidence for weak regulation. Many weta tended to select high and low protein foods in a ratio similar to their previously identified nutrient optimum. These results suggest that weta have a wide tolerance to nutritional imbalance, and that the time scale of weta nutrient balancing could lie outside of the short time span tested here. A wide tolerance to imbalance is consistent with the intermittent feeding displayed in the wild by weta and may be important in understanding weta foraging patterns in New Zealand forests. PMID:24358369

Human and riverine impacts on the dynamics of biogeochemical parameters in Kwangyang Bay, South Korea revealed by time-series data and multivariate statistics.

PubMed

Kim, Tae-Wook; Kim, Dongseon; Baek, Seung Ho; Kim, Young Ok

2015-01-15

The successful management of sustainable coastal environments that are beneficial to both humans and marine ecosystems requires knowledge about factors that are harmful to such environments. Here, we investigated seawater nutrient and carbon parameters between 2010 and 2012 in Kwangyang Bay, Korea, a coastal environment that has been exposed to intensive anthropogenic activities. The data were analyzed using cluster and factor analysis. We found that the biogeochemical cycles of nutrients and carbon were determined by river discharge into the bay and biological activity. However, the impacts of these factors varied both spatially and seasonally. During the past 10 years, nutrient loads from the river and industrial complexes to the bay have decreased. The impacts of this decrease are visible in the phosphate concentration, which has fallen to a third of its initial value. We also examined the potential role of atmospheric nitrogen deposition in nitrogen cycling in the study area. Copyright © 2014 Elsevier Ltd. All rights reserved.

The stocks and flows of nitrogen, phosphorus and potassium across a 30-year time series for agriculture in Huantai county, China.

PubMed

Bellarby, Jessica; Surridge, Ben W J; Haygarth, Philip M; Liu, Kun; Siciliano, Giuseppina; Smith, Laurence; Rahn, Clive; Meng, Fanqiao

2018-04-01

In order to improve the efficiency of nutrient use whilst also meeting projected changes in the demand for food within China, new nutrient management frameworks comprised of policy, practice and the means of delivering change are required. These frameworks should be underpinned by systemic analyses of the stocks and flows of nutrients within agricultural production. In this paper, a 30-year time series of the stocks and flows of nitrogen (N), phosphorus (P) and potassium (K) are reported for Huantai county, an exemplar area of intensive agricultural production in the North China Plain. Substance flow analyses were constructed for the major crop systems in the county across the period 1983-2014. On average across all production systems between 2010 and 2014, total annual nutrient inputs to agricultural land in Huantai county remained high at 18.1kt N, 2.7kt P and 7.8kt K (696kg N ha -1 ; 104kgP ha -1 ; 300kgK ha -1 ). Whilst the application of inorganic fertiliser dominated these inputs, crop residues, atmospheric deposition and livestock manure represented significant, yet largely unrecognised, sources of nutrients, depending on the individual production system and the period of time. Whilst nutrient use efficiency (NUE) increased for N and P between 1983 and 2014, future improvements in NUE will require better alignment of nutrient inputs and crop demand. This is particularly true for high-value fruit and vegetable production, in which appropriate recognition of nutrient supply from sources such as manure and from soil reserves will be required to enhance NUE. Aligned with the structural organisation of the public agricultural extension service at county-scale in China, our analyses highlight key areas for the development of future agricultural policy and farm advice in order to rebalance the management of natural resources from a focus on production and growth towards the aims of efficiency and sustainability. Copyright © 2017 Elsevier B.V. All rights reserved.

Are all temperate lakes eutrophying in a warmer world?

NASA Astrophysics Data System (ADS)

Paltsev, A.; Creed, I. F.

2017-12-01

Freshwater lakes are at risk of eutrophication due to climate change and intensification of human activities on the planet. In relatively undisturbed areas of the temperate forest biome, lakes are "sentinels" of the effects of rising temperatures. We hypothesise that rising temperatures are driving a shift from nutrient-poor oligotrophic states to nutrient-rich eutrophic states. To test this hypothesis, we examined a time series of satellite based chlorophyll-a (a proxy of algal biomass) of 12,000+ lakes over 30 years in the Canadian portion of the Laurentian Great Lakes basin. From the time series, non-stationary trends (detected by Mann-Kendall analysis) and stationary cycles (revealed through Morlet wavelet analysis) were removed, and the standard deviation (SD) of the remaining residuals was used as an indicator of lake stability. Four classes of lake stability were identified: (1) stable (SD is consistently low); (2) destabilizing (SD increases over time); (3) unstable (SD is consistently high); and (4) stabilizing lakes (SD decreases over time). Stable lakes were either oligotrophic or eutrophic indicating the presence of two stable states in the region. Destabilizing lakes were shifting from oligotrophic to lakes with a higher trophic status (indicating eutrophication), unstable lakes were mostly mesotrophic, and stabilizing lakes were shifting from eutrophic to the lakes with lower trophic status (indicating oligotrophication). In contrast to common expectations, while many lakes (2142) were shifting from oligotrophic to eutrophic states, more lakes (3199) were showing the opposite trend and shifting from eutrophic to oligotrophic states. This finding reveals a complexity of lake responses to rising temperatures and the need to improve understanding of why some lakes shift while others do not. Future work is focused on exploring the interactive effects of global, regional, and local drivers of lake trophic states.

Ecological stages of the Venice Lagoon analysed using landing time series data

NASA Astrophysics Data System (ADS)

Libralato, Simone; Pranovi, Fabio; Raicevich, Saša; Da Ponte, Filippo; Giovanardi, Otello; Pastres, Roberto; Torricelli, Patrizia; Mainardi, Danilo

2004-11-01

The time series of landings in the Venice Lagoon from 1945 to 2001 were analysed with the aim of explaining the ecosystem changes occurred. The comparative analysis of the total landings and mean Trophic Level (mTL) time series allowed to identify four different stages in the lagoon ecosystem. The first period, from 1945 to 1973, was characterised by increasing trends in the landings and their mTL. The second one, from 1974 to 1989, showed a decrease in the landings but still an increase in the mTL. The third period, from 1990 to 1998, had again a positive trend in the landings, but the mTL showed a sharp decline. After 1998, a slight decreasing trend in both mTL and landings was observed: the analyses of the artisanal fishery landings only date back to 1995 this effect. The presence of four distinct periods was also confirmed by the analysis of the trends of other indices estimated using landings data: the Fishing in Balance index (FiB), the Trophic Efficiency (TE) and the Pelagic on Demersal landings ratio (P/D). In the first period, the increasing fishing pressure, along with no evidence of ecosystem crisis, suggested that an increased nutrient discharge was supporting it; analogously, the bottom-up effects had driven the dynamics of the ecosystem also in the second period, when the decrease in nutrient loads caused a shift of the primary producers from planktonic to macrobenthic. The spreading of the Manila clam, a non-native species, and the development of its massive mechanical exploitation have been the main forces driving the ecosystem during the third period, for which, however, no signs of crises were detected. The fourth period showed evidence of the "fishing down the food web" effect. Possible causes of such an effect were investigated and allowed us to conclude that not overfishing, but the effects of mechanical harvesting of the Manila clam had caused relevant impacts on habitat and benthic communities, concluding that the present level of exploitation of the stock of Manila clam is not sustainable in the long term. Our findings were also compared with the general evolution of enclosed seas, subjected to high nutrient loads, fishing pressure and invasion by non-native species.

Aggregate Measures of Watershed Health from Reconstructed ...

EPA Pesticide Factsheets

Risk-based indices such as reliability, resilience, and vulnerability (R-R-V), have the potential to serve as watershed health assessment tools. Recent research has demonstrated the applicability of such indices for water quality (WQ) constituents such as total suspended solids and nutrients on an individual basis. However, the calculations can become tedious when time-series data for several WQ constituents have to be evaluated individually. Also, comparisons between locations with different sets of constituent data can prove difficult. In this study, data reconstruction using relevance vector machine algorithm was combined with dimensionality reduction via variational Bayesian noisy principal component analysis to reconstruct and condense sparse multidimensional WQ data sets into a single time series. The methodology allows incorporation of uncertainty in both the reconstruction and dimensionality-reduction steps. The R-R-V values were calculated using the aggregate time series at multiple locations within two Indiana watersheds. Results showed that uncertainty present in the reconstructed WQ data set propagates to the aggregate time series and subsequently to the aggregate R-R-V values as well. serving as motivating examples. Locations with different WQ constituents and different standards for impairment were successfully combined to provide aggregate measures of R-R-V values. Comparisons with individual constituent R-R-V values showed that v

Global patterns of phytoplankton dynamics in coastal ecosystems

USGS Publications Warehouse

Paerl, H.; Yin, Kedong; Cloern, J.

2011-01-01

Scientific Committee on Ocean Research Working Group 137 Meeting; Hangzhou, China, 17-21 October 2010; Phytoplankton biomass and community structure have undergone dramatic changes in coastal ecosystems over the past several decades in response to climate variability and human disturbance. These changes have short- and long-term impacts on global carbon and nutrient cycling, food web structure and productivity, and coastal ecosystem services. There is a need to identify the underlying processes and measure the rates at which they alter coastal ecosystems on a global scale. Hence, the Scientific Committee on Ocean Research (SCOR) formed Working Group 137 (WG 137), "Global Patterns of Phytoplankton Dynamics in Coastal Ecosystems: A Comparative Analysis of Time Series Observations" (http://wg137.net/). This group evolved from a 2007 AGU-sponsored Chapman Conference entitled "Long Time-Series Observations in Coastal Ecosystems: Comparative Analyses of Phytoplankton Dynamics on Regional to Global Scales.".

A Coupled Epipelagic-Meso/Bathypelagic Particle Flux Model for the Bermuda Atlantic Time-series Station (BATS)/Oceanic Flux Program (OFP) Site

NASA Astrophysics Data System (ADS)

Glover, D. M.; Conte, M.

2002-12-01

Of considerable scientific interest is the role remineralization plays in the global carbon cycle. It is the ``biological pump'' that fixes carbon in the upper water column and exports it for long time periods to the deep ocean. From a global carbon cycle point-of-view, it is the processes that govern remineralization in the mid- to deep-ocean waters that provide the feedback to the biogeochemical carbon cycle. In this study we construct an ecosystem model that serves as a mechanistic link between euphotic processes and mesopelagic and bathypelagic processes. We then use this prognostic model to further our understanding of the unparalleled time-series of deep-water sediment traps (21+ years) at the Oceanic Flux Program (OFP) and the euphotic zone measurements (10+ years) at the Bermuda Atlantic Time-series Site (BATS). At the core of this mechanistic ecosystem model of the mesopelagic zone is a model that consists of an active feeding habit zooplankton, a passive feeding habit zooplankton, large detritus (sinks), small detritus (non-sinking), and a nutrient pool. As the detritus, the primary source of food, moves through the water column it is fed upon by the active/passive zooplankton pair and undergoes bacterially mediated remineralization into nutrients. The large detritus pool at depth gains material from the formation of fecal pellets from the passive and active zooplankton. Sloppy feeding habits of the active zooplankton contribute to the small detrital pool. Zooplankton mortality (both classes) also contribute directly to the large detritus pool. Aggregation and disaggregation transform detrital particles from one pool to the other and back again. The nutrients at each depth will gain from detrital remineralization and zooplankton excretion. The equations that model the active zooplankton, passive zooplankton, large detritus, small detritus, and nutrients will be reviewed, results shown and future model modifications discussed.

Nutrient budgets in the subtropical ocean gyres dominated by lateral transport

NASA Astrophysics Data System (ADS)

Letscher, Robert T.; Primeau, François; Moore, J. Keith

2016-11-01

Ocean circulation replenishes surface nutrients depleted by biological production and export. Vertical processes are thought to dominate, but estimated vertical nutrient fluxes are insufficient to explain observed net productivity in the subtropical ocean gyres. Lateral inputs help balance the North Atlantic nutrient budget, but their importance for other gyres has not been demonstrated. Here we use an ocean model that couples circulation and ecosystem dynamics to show that lateral transport and biological uptake of inorganic and organic forms of nitrogen and phosphorus from the gyre margins exceeds the vertical delivery of nutrients, supplying 24-36% of the nitrogen and 44-67% of the phosphorus required to close gyre nutrient budgets. At the Bermuda and Hawaii time-series sites, nearly half of the annual lateral supply by lateral transport occurs during the summer-to-fall stratified period, helping explain seasonal patterns of inorganic carbon drawdown and nitrogen fixation. Our study confirms the importance of upper-ocean lateral nutrient transport for understanding the biological cycles of carbon and nutrients in the ocean's largest biome.

Velocity landscape correlation resolves multiple flowing protein populations from fluorescence image time series.

PubMed

Pandžić, Elvis; Abu-Arish, Asmahan; Whan, Renee M; Hanrahan, John W; Wiseman, Paul W

2018-02-16

Molecular, vesicular and organellar flows are of fundamental importance for the delivery of nutrients and essential components used in cellular functions such as motility and division. With recent advances in fluorescence/super-resolution microscopy modalities we can resolve the movements of these objects at higher spatio-temporal resolutions and with better sensitivity. Previously, spatio-temporal image correlation spectroscopy has been applied to map molecular flows by correlation analysis of fluorescence fluctuations in image series. However, an underlying assumption of this approach is that the sampled time windows contain one dominant flowing component. Although this was true for most of the cases analyzed earlier, in some situations two or more different flowing populations can be present in the same spatio-temporal window. We introduce an approach, termed velocity landscape correlation (VLC), which detects and extracts multiple flow components present in a sampled image region via an extension of the correlation analysis of fluorescence intensity fluctuations. First we demonstrate theoretically how this approach works, test the performance of the method with a range of computer simulated image series with varying flow dynamics. Finally we apply VLC to study variable fluxing of STIM1 proteins on microtubules connected to the plasma membrane of Cystic Fibrosis Bronchial Epithelial (CFBE) cells. Copyright © 2018 Elsevier Inc. All rights reserved.

[TMOSKOVHE COMPARATIVE CHARACTERISTIC OF GROWTH MEDIUMS FOR SEPARATION OF CORYNEBACTERIA].

PubMed

Shepelin, A P; Polosenko, O V; Borisova, O Yu; Pimenova, A S; Gadua, N T

2016-01-01

The comparative tests of growth mediums for isolation and accumulation of diphtheria bacteria were implemented. The testing consisted of six series of growth medium "Corynebacagar" produced by the state research center of applied microbiology and biotechnology and three series of blood tellurite agar. The concluding results of identification of biological indicators of all series of growth nutrient mediums are presented The "Corynebacagar" is recommended for application in health care practice for primary inoculation of pathological material during implementation of cultural analysis on diphtheria.

Biogeochemistry from Gliders at the Hawaii Ocean Times-Series

NASA Astrophysics Data System (ADS)

Nicholson, D. P.; Barone, B.; Karl, D. M.

2016-02-01

At the Hawaii Ocean Time-series (HOT) autonomous, underwater gliders equipped with biogeochemical sensors observe the oceans for months at a time, sampling spatiotemporal scales missed by the ship-based programs. Over the last decade, glider data augmented by a foundation of time-series observations have shed light on biogeochemical dynamics occuring spatially at meso- and submesoscales and temporally on scales from diel to annual. We present insights gained from the synergy between glider observations, time-series measurements and remote sensing in the subtropical North Pacific. We focus on diel variability observed in dissolved oxygen and bio-optics and approaches to autonomously quantify net community production and gross primary production (GPP) as developed during the 2012 Hawaii Ocean Experiment - DYnamics of Light And Nutrients (HOE-DYLAN). Glider-based GPP measurements were extended to explore the relationship between GPP and mesoscale context over multiple years of Seaglider deployments.

Winter nutrient behaviours in the Pearl River estuary

NASA Astrophysics Data System (ADS)

Wang, G.; Jin, S.; Du, M.

2017-12-01

Nutrient (nitrate, nitrite, ammonium, phosphate, and silicate) mapping and time-series investigation were carried out in winter in the Pearl River estuary, China. These nutrients behaved non-conservatively in the upper estuary. In the middle and lower estuary, however, nitrate and silicate seemed to be controled by physical mixing, while additions of nitrite, ammonium, and phosphate were found in the middle estuary. Nitrate was the dominant disslved inorganic nitrogen, with a fraction of more than 2/3. From the upper to the lower estuary the N:P ratio decreased from more than 200 to near the Redfield ratio of 16. Nutrients near the surface behaved almost the same as near the bottom in the water column at both the uppper and lower estuary. During a tidal cycle these nutrients seemed to be regulated more by physical mixing than by other processes.

Primary production export flux in Marguerite Bay (Antarctic Peninsula): Linking upper water-column production to sediment trap flux

NASA Astrophysics Data System (ADS)

Weston, Keith; Jickells, Timothy D.; Carson, Damien S.; Clarke, Andrew; Meredith, Michael P.; Brandon, Mark A.; Wallace, Margaret I.; Ussher, Simon J.; Hendry, Katharine R.

2013-05-01

A study was carried out to assess primary production and associated export flux in the coastal waters of the western Antarctic Peninsula at an oceanographic time-series site. New, i.e., exportable, primary production in the upper water-column was estimated in two ways; by nutrient deficit measurements, and by primary production rate measurements using separate 14C-labelled radioisotope and 15N-labelled stable isotope uptake incubations. The resulting average annual exportable primary production estimates at the time-series site from nutrient deficit and primary production rates were 13 and 16 mol C m-2, respectively. Regenerated primary production was measured using 15N-labelled ammonium and urea uptake, and was low throughout the sampling period. The exportable primary production measurements were compared with sediment trap flux measurements from 2 locations; the time-series site and at a site 40 km away in deeper water. Results showed ˜1% of the upper mixed layer exportable primary production was exported to traps at 200 m depth at the time-series site (total water column depth 520 m). The maximum particle flux rate to sediment traps at the deeper offshore site (total water column depth 820 m) was lower than the flux at the coastal time-series site. Flux of particulate organic carbon was similar throughout the spring-summer high flux period for both sites. Remineralisation of particulate organic matter predominantly occurred in the upper water-column (<200 m depth), with minimal remineralisation below 200 m, at both sites. This highly productive region on the Western Antarctic Peninsula is therefore best characterised as 'high recycling, low export'.

Relationships among net primary productivity, nutrients and climate in tropical rain forest: A pan-tropical analysis

USGS Publications Warehouse

Cleveland, Cory C.; Townsend, Alan R.; Taylor, Philip; Alvarez-Clare, Silvia; Bustamante, Mercedes M.C.; Chuyong, George; Dobrowski, Solomon Z.; Grierson, Pauline; Harms, Kyle E.; Houlton, Benjamin Z.; Marklein, Alison; Parton, William; Porder, Stephen; Reed, Sasha C.; Sierra, Carlos A.; Silver, Whendee L.; Tanner, Edmund V.J.; Wieder, William R.

2011-01-01

Tropical rain forests play a dominant role in global biosphere-atmosphere CO2 exchange. Although climate and nutrient availability regulate net primary production (NPP) and decomposition in all terrestrial ecosystems, the nature and extent of such controls in tropical forests remain poorly resolved. We conducted a meta-analysis of carbon-nutrient-climate relationships in 113 sites across the tropical forest biome. Our analyses showed that mean annual temperature was the strongest predictor of aboveground NPP (ANPP) across all tropical forests, but this relationship was driven by distinct temperature differences between upland and lowland forests. Within lowland forests (< 1000 m), a regression tree analysis revealed that foliar and soil-based measurements of phosphorus (P) were the only variables that explained a significant proportion of the variation in ANPP, although the relationships were weak. However, foliar P, foliar nitrogen (N), litter decomposition rate (k), soil N and soil respiration were all directly related with total surface (0–10 cm) soil P concentrations. Our analysis provides some evidence that P availability regulates NPP and other ecosystem processes in lowland tropical forests, but more importantly, underscores the need for a series of large-scale nutrient manipulations – especially in lowland forests – to elucidate the most important nutrient interactions and controls.

The use of linear programming to determine whether a formulated complementary food product can ensure adequate nutrients for 6- to 11-month-old Cambodian infants.

PubMed

Skau, Jutta K H; Bunthang, Touch; Chamnan, Chhoun; Wieringa, Frank T; Dijkhuizen, Marjoleine A; Roos, Nanna; Ferguson, Elaine L

2014-01-01

A new software tool, Optifood, developed by the WHO and based on linear programming (LP) analysis, has been developed to formulate food-based recommendations. This study discusses the use of Optifood for predicting whether formulated complementary food (CF) products can ensure dietary adequacy for target populations in Cambodia. Dietary data were collected by 24-h recall in a cross-sectional survey of 6- to 11-mo-old infants (n = 78). LP model parameters were derived from these data, including a list of foods, median serving sizes, and dietary patterns. Five series of LP analyses were carried out to model the target population's baseline diet and 4 formulated CF products [WinFood (WF), WinFood-Lite (WF-L), Corn-Soy-Blend Plus (CSB+), and Corn-Soy-Blend Plus Plus (CSB++)], which were added to the diet in portions of 33 g/d dry weight (DW) for infants aged 6-8 mo and 40 g/d DW for infants aged 9-11 mo. In each series of analyses, the nutritionally optimal diet and theoretical range, in diet nutrient contents, were determined. The LP analysis showed that baseline diets could not achieve the Recommended Nutrient Intake (RNI) for thiamin, riboflavin, niacin, folate, vitamin B-12, calcium, iron, and zinc (range: 14-91% of RNI in the optimal diets) and that none of the formulated CF products could cover the nutrient gaps for thiamin, niacin, iron, and folate (range: 22-86% of the RNI). Iron was the key limiting nutrient, for all modeled diets, achieving a maximum of only 48% of the RNI when CSB++ was included in the diet. Only WF and WF-L filled the nutrient gap for calcium. WF-L, CSB+, and CSB++ filled the nutrient gap for zinc (9- to 11-mo-olds). The formulated CF products improved the nutrient adequacy of complementary feeding diets but could not entirely cover the nutrient gaps. These results emphasize the value of using LP to evaluate special CF products during the intervention planning phase. The WF study was registered at controlled-trials.com as ISRCTN19918531.

Logging residue removal after thinning in boreal forests: long-term impact on the nutrient status of Norway spruce and Scots pine needles.

PubMed

Luiro, Jukka; Kukkola, Mikko; Saarsalmi, Anna; Tamminen, Pekka; Helmisaari, Heljä-Sisko

2010-01-01

The aim of this study was to compare how conventional stem harvesting (CH) and whole-tree harvesting (WTH) in the first, and in some cases also in the second, thinning affect the needle nutrient status of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) stands in Finland. A series of 12 long-term field experiments was studied. The experiments were established during 1978-86. The effects of logging residue removal after thinnings on the needle nutrient concentrations were generally minor and without any overall trends, but there were differences between experiments. Trees tend to maintain their current needle nutrient concentrations at the same level by re-utilizing the nutrients stored in the older tissues and by changing C allocation in the whole tree. Thus, needle analysis should be combined with stem growth data in order to achieve a more comprehensive understanding of the effects of WTH on the nutrient status of trees.

Solubility of aerosol trace elements: Sources and deposition fluxes in the Canary Region

NASA Astrophysics Data System (ADS)

López-García, Patricia; Gelado-Caballero, María Dolores; Collado-Sánchez, Cayetano; Hernández-Brito, José Joaquín

2017-01-01

African dust inputs have important effects on the climate and marine biogeochemistry of the subtropical North Atlantic Ocean. The impact of dust inputs on oceanic carbon uptake and climate is dependent on total dust deposition fluxes as well as the bioavailability of nutrients and metals in the dust. In this work, the solubility of trace metals (Fe, Al, Mn, Co and Cu) and ions (Ca, sulphate, nitrate and phosphate) has been estimated from the analysis of a long-time series of 109 samples collected over a 3-year period in the Canary Islands. Solubility is primarily a function of aerosol origin, with higher solubility values corresponding to aerosols with more anthropogenic influence. Using soluble fractions of trace elements measured in this work, atmospheric deposition fluxes of soluble metals and nutrients have been calculated. Inputs of dissolved nutrients (P, N and Fe) have been estimated for the mixed layer. Considering that P is the limiting factor when ratios of these elements are compared with phytoplankton requirements, an increase of 0.58 nM of P in the mixed layer (∼150 m depth) and in a year can be estimated, which can support an increase of 0.02 μg Chla L-1 y-1. These atmospheric inputs of trace metals and nutrients appear to be significant relative to the concentrations reported in this region, especially during the summer months when the water column is more stratified and deep-water nutrient inputs are reduced.

Feasibility of Estimating Relative Nutrient Contributions of Agriculture and Forests Using MODIS Time Series

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; Gasser, Gerald; Spiering, Bruce

2010-01-01

Around the Gulf of Mexico, high-input crops in several regions make a significant contribution to nutrient loading of small to medium estuaries and to the near-shore Gulf. Some crops cultivated near the coast include sorghum in Texas, rice in Texas and Louisiana, sugarcane in Florida and Louisiana, citrus orchards in Florida, pecan orchards in Mississippi and Alabama, and heavy sod and ornamental production around Mobile and Tampa Bay. In addition to crops, management of timberlands in proximity to the coasts also plays a role in nutrient loading. In the summer of 2008, a feasibility project is planned to explore the use of NASA data to enhance the spatial and temporal resolution of near-coast nutrient source information available to the coastal community. The purpose of this project is to demonstrate the viability of nutrient source information products applicable to small to medium watersheds surrounding the Gulf of Mexico. Conceptually, these products are intended to complement estuarine nutrient monitoring.

Feasibility of Estimating Relative Nutrient Contributions of Agriculture using MODIS Time Series

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; Gasser, Gerald; Spiering, Bruce

2008-01-01

Around the Gulf of Mexico, high-input crops in several regions make a significant contribution to nutrient loading of small to medium estuaries and to the near-shore Gulf. Some crops cultivated near the coast include sorghum in Texas, rice in Texas and Louisiana, sugarcane in Florida and Louisiana, citrus orchards in Florida, pecan orchards in Mississippi and Alabama, and heavy sod and ornamental production around Mobile and Tampa Bay. In addition to crops, management of timberlands in proximity to the coasts also plays a role in nutrient loading. In the summer of 2008, a feasibility project is planned to explore the use of NASA data to enhance the spatial and temporal resolution of near-coast nutrient source information available to the coastal community. The purpose of this project is to demonstrate the viability of nutrient source information products applicable to small to medium watersheds surrounding the Gulf of Mexico. Conceptually, these products are intended to complement estuarine nutrient monitoring.

Rapid Ecological Change in Two Contrasting Lake Ecosystems: Evidence of Threshold Responses, Altered Species Dynamics, and Perturbed Patterns of Variability

NASA Astrophysics Data System (ADS)

Simpson, G. L.

2015-12-01

Studying threshold responses to environmental change is often made difficult due to the paucity of monitoring data prior to and during change. Progress has been made via theoretical models of regime shifts or experimental manipulation but natural, real world, examples of threshold change are limited and in many cases inconclusive. Lake sediments provide the potential to examine abrupt ecological change by directly observing how species, communities, and biogeochemical proxies responded to environmental perturbation or recorded ecosystem change. These records are not problem-free; age uncertainties, uneven and variable temporal resolution, and time-consuming taxonomic work all act to limit the scope and scale of the data or complicate its analysis. Here I use two annually laminated records 1. Kassjön, a seasonally anoxic mesotrophic lake in N Sweden, and2. Baldeggersee, a nutrient rich, hardwater lake on the central Swiss Plateau to investigate lake ecosystem responses to abrupt environmental change using ideal paleoecological time series. Rapid cooling 2.2kyr ago in northern Sweden significantly perturbed the diatom community of Kassjön. Using wavelet analysis, this amelioration in climate also fundamentally altered patterns of variance in diatom abundances, suppressing cyclicity in species composition that required several hundred years to reestablish. Multivariate wavelet analysis of the record showed marked switching between synchronous and asynchronous species dynamics in response to rapid climatic cooling and subsequent warming. Baldeggersee has experienced a long history of eutrophication and the diatom record has been used as a classic illustration of a regime shift in response to nutrient loading. Time series analysis of the record identified some evidence of a threshold-like response in the diatoms. A stochastic volatility model identified increasing variance in composition prior to the threshold, as predicted from theory, and a switch from compensatory to synchronous species dynamics, concomitant with eutrophication, was observed. These results document in high resolution how two aquatic systems reacted to abrupt change and demonstrate that under ideal conditions sediments can preserve valuable evidence of rapid ecological change.

Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

PubMed

Wang, Xuelei; Wang, Qiao; Yang, Shengtian; Zheng, Donghai; Wu, Chuanqing; Mannaerts, C M

2011-06-01

Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling. Copyright © 2011 Elsevier B.V. All rights reserved.

History of nutrient inputs to the northeastern United States, 1930-2000

NASA Astrophysics Data System (ADS)

Hale, Rebecca L.; Hoover, Joseph H.; Wollheim, Wilfred M.; Vörösmarty, Charles J.

2013-04-01

Humans have dramatically altered nutrient cycles at local to global scales. We examined changes in anthropogenic nutrient inputs to the northeastern United States (NE) from 1930 to 2000. We created a comprehensive time series of anthropogenic N and P inputs to 437 counties in the NE at 5 year intervals. Inputs included atmospheric N deposition, biological N2 fixation, fertilizer, detergent P, livestock feed, and human food. Exports included exports of feed and food and volatilization of ammonia. N inputs to the NE increased throughout the study period, primarily due to increases in atmospheric deposition and fertilizer. P inputs increased until 1970 and then declined due to decreased fertilizer and detergent inputs. Livestock consistently consumed the majority of nutrient inputs over time and space. The area of crop agriculture declined during the study period but consumed more nutrients as fertilizer. We found that stoichiometry (N:P) of inputs and absolute amounts of N matched nutritional needs (livestock, humans, crops) when atmospheric components (N deposition, N2 fixation) were not included. Differences between N and P led to major changes in N:P stoichiometry over time, consistent with global trends. N:P decreased from 1930 to 1970 due to increased inputs of P, and increased from 1970 to 2000 due to increased N deposition and fertilizer and decreases in P fertilizer and detergent use. We found that nutrient use is a dynamic product of social, economic, political, and environmental interactions. Therefore, future nutrient management must take into account these factors to design successful and effective nutrient reduction measures.

A model for growth of a single fungal hypha based on well-mixed tanks in series: simulation of nutrient and vesicle transport in aerial reproductive hyphae.

PubMed

Balmant, Wellington; Sugai-Guérios, Maura Harumi; Coradin, Juliana Hey; Krieger, Nadia; Furigo Junior, Agenor; Mitchell, David Alexander

2015-01-01

Current models that describe the extension of fungal hyphae and development of a mycelium either do not describe the role of vesicles in hyphal extension or do not correctly describe the experimentally observed profile for distribution of vesicles along the hypha. The present work uses the n-tanks-in-series approach to develop a model for hyphal extension that describes the intracellular transport of nutrient to a sub-apical zone where vesicles are formed and then transported to the tip, where tip extension occurs. The model was calibrated using experimental data from the literature for the extension of reproductive aerial hyphae of three different fungi, and was able to describe different profiles involving acceleration and deceleration of the extension rate. A sensitivity analysis showed that the supply of nutrient to the sub-apical vesicle-producing zone is a key factor influencing the rate of extension of the hypha. Although this model was used to describe the extension of a single reproductive aerial hypha, the use of the n-tanks-in-series approach to representing the hypha means that the model has the flexibility to be extended to describe the growth of other types of hyphae and the branching of hyphae to form a complete mycelium.

Developmental Strategy For Effective Sampling To Detect Possible Nutrient Fluxes In Oligotrophic Coastal Reef Waters In The Caribbean

NASA Astrophysics Data System (ADS)

Mendoza, W. G.; Corredor, J. E.; Ko, D.; Zika, R. G.; Mooers, C. N.

2008-05-01

The increasing effort to develop the coastal ocean observing system (COOS) in various institutions has gained momentum due to its high value to climate, environmental, economic, and health issues. The stress contributed by nutrients to the coral reef ecosystem is among many problems that are targeted to be resolved using this system. Traditional nutrient sampling has been inadequate to resolve issues on episodic nutrient fluxes in reef regions due to temporal and spatial variability. This paper illustrates sampling strategy using the COOS information to identify areas that need critical investigation. The area investigated is within the Puerto Rico subdomain (60-70oW, 15-20oN), and Caribbean Time Series (CaTS), World Ocean Circulation Experiment (WOCE), Intra-America Sea (IAS) ocean nowcast/forecast system (IASNFS), and other COOS-related online datasets are utilized. Nutrient profile results indicate nitrate is undetectable in the upper 50 m apparently due to high biological consumption. Nutrients are delivered in Puerto Rico particularly in the CaTS station either via a meridional jet formed from opposing cyclonic and anticyclonic eddies or wind-driven upwelling. The strong vertical fluctuation in the upper 50 m demonstrates a high anomaly in temperature and salinity and a strong cross correlation signal. High chlorophyll a concentration corresponding to seasonal high nutrient influx coincides with higher precipitation accumulation rates and apparent riverine input from the Amazon and Orinoco Rivers during summer (August) than during winter (February) seasons. Non-detectability of nutrients in the upper 50 m is a reflection of poor sampling frequency or the absence of a highly sensitive nutrient analysis method to capture episodic events. Thus, this paper was able to determine the range of depths and concentrations that need to be critically investigated to determine nutrient fluxes, nutrient sources, and climatological factors that can affect nutrient delivery. It also provides some insight into needed sampling rates and temporal and spatial domain choices. Finally, it demonstrates a scientific reconnaissance for a field study that is now possible with online in-situ and remote sensing observations and numerical simulations, as a consequence of IOOS in general and COOS in particular.

Estimation of nutrient discharge from the Yangtze River to the East China Sea and the identification of nutrient sources.

PubMed

Tong, Yindong; Bu, Xiaoge; Chen, Junyue; Zhou, Feng; Chen, Long; Liu, Maodian; Tan, Xin; Yu, Tao; Zhang, Wei; Mi, Zhaorong; Ma, Lekuan; Wang, Xuejun; Ni, Jing

2017-01-05

Based on a time-series dataset and the mass balance method, the contributions of various sources to the nutrient discharges from the Yangtze River to the East China Sea are identified. The results indicate that the nutrient concentrations vary considerably among different sections of the Yangtze River. Non-point sources are an important source of nutrients to the Yangtze River, contributing about 36% and 63% of the nitrogen and phosphorus discharged into the East China Sea, respectively. Nutrient inputs from non-point sources vary among the sections of the Yangtze River, and the contributions of non-point sources increase from upstream to downstream. Considering the rice growing patterns in the Yangtze River Basin, the synchrony of rice tillering and the wet seasons might be an important cause of the high nutrient discharge from the non-point sources. Based on our calculations, a reduction of 0.99Tg per year in total nitrogen discharges from the Yangtze River would be needed to limit the occurrences of harmful algal blooms in the East China Sea to 15 times per year. The extensive construction of sewage treatment plants in urban areas may have only a limited effect on reducing the occurrences of harmful algal blooms in the future. Copyright © 2016 Elsevier B.V. All rights reserved.

An introduction to high-frequency nutrient and biogeochemical monitoring for the Sacramento–San Joaquin Delta, northern California

USGS Publications Warehouse

Kraus, Tamara E.C.; Bergamaschi, Brian A.; Downing, Bryan D.

2017-07-11

Executive SummaryThis report is the first in a series of three reports that provide information about high-frequency (HF) nutrient and biogeochemical monitoring in the Sacramento–San Joaquin Delta of northern California (Delta). This first report provides an introduction to the reasons for and fundamental concepts behind collecting HF measurements, and describes the benefits associated with a real-time, continuous, HF, multi-parameter water quality monitoring station network that is co-located with flow stations. It then provides examples of how HF nutrient measurements have improved our understating of nutrient sources and cycling in aquatic systems worldwide, followed by specific examples from the Delta. These examples describe the ways in which HF instrumentation may be used for both fixed-station and spatial assessments. The overall intent of this document is to describe how HF measurements currently (2017) are being used in the Delta to examine the relationship between nutrient concentrations, nutrient cycling, and aquatic habitat conditions.The second report in the series (Downing and others, 2017) summarizes information about HF nutrient and associated biogeochemical monitoring in the northern Delta. The report synthesizes data available from the nutrient and water quality monitoring network currently operated by the U.S. Geological Survey in this ecologically important region of the Delta. In the report, we present and discuss the available data at various timescales—first, at the monthly, seasonal, and inter-annual timescales; and, second, for comparison, at the tidal and event (for example, storms, reservoir releases, phytoplankton blooms) timescales. As expected, we determined that there is substantial variability in nitrate concentrations at short timescales within hours, but also significant variability at longer timescales such as months or years. This multi-scale, high variability affects calculation of fluxes and loads, indicating that HF monitoring is necessary for understanding and assessing flux-based processes and outcomes in tidal environments, such as the Delta.The third report in the series (Bergamaschi and others, 2017) provides information about how to design HF nutrient and biogeochemical monitoring for assessment of nutrient inputs and dynamics in the Delta. The report provides background, principles, and considerations for designing an HF nutrient-monitoring network for the Sacramento–San Joaquin Delta to address high-priority, nutrient-management questions. The report starts with high-priority management questions to be addressed, continues with questions and considerations that place demands and constraints on network design, discusses the principles applicable to network design, and concludes with the presentation of three example nutrient‑monitoring network designs for the Delta. For the three example networks, we assess how they would address high-priority questions identified by the Delta Regional Monitoring Program (Delta Regional Monitoring Program Technical Advisory Committee, 2015).

Yield Gap, Indigenous Nutrient Supply and Nutrient Use Efficiency for Maize in China.

PubMed

Xu, Xinpeng; Liu, Xiaoyan; He, Ping; Johnston, Adrian M; Zhao, Shicheng; Qiu, Shaojun; Zhou, Wei

2015-01-01

Great achievements have been attained in agricultural production of China, while there are still many difficulties and challenges ahead that call for put more efforts to overcome to guarantee food security and protect environment simultaneously. Analyzing yield gap and nutrient use efficiency will help develop and inform agricultural policies and strategies to increase grain yield. On-farm datasets from 2001 to 2012 with 1,971 field experiments for maize (Zea mays L.) were collected in four maize agro-ecological regions of China, and the optimal management (OPT), farmers' practice (FP), a series of nutrient omission treatments were used to analyze yield gap, nutrient use efficiency and indigenous nutrient supply by adopting meta-analysis and ANOVA analysis. Across all sites, the average yield gap between OPT and FP was 0.7 t ha-1, the yield response to nitrogen (N), phosphorus (P), and potassium (K) were 1.8, 1.0, and 1.2 t ha-1, respectively. The soil indigenous nutrient supply of N, P, and K averaged 139.9, 33.7, and 127.5 kg ha-1, respectively. As compared to FP, the average recovery efficiency (RE) of N, P, and K with OPT increased by percentage point of 12.2, 5.5, and 6.5, respectively. This study indicated that there would be considerable potential to further improve yield and nutrient use efficiency in China, and will help develop and inform agricultural policies and strategies, while some management measures such as soil, plant and nutrient are necessary and integrate with advanced knowledge and technologies.

Yield Gap, Indigenous Nutrient Supply and Nutrient Use Efficiency for Maize in China

PubMed Central

Xu, Xinpeng; Liu, Xiaoyan; He, Ping; Johnston, Adrian M.; Zhao, Shicheng; Qiu, Shaojun; Zhou, Wei

2015-01-01

Great achievements have been attained in agricultural production of China, while there are still many difficulties and challenges ahead that call for put more efforts to overcome to guarantee food security and protect environment simultaneously. Analyzing yield gap and nutrient use efficiency will help develop and inform agricultural policies and strategies to increase grain yield. On-farm datasets from 2001 to 2012 with 1,971 field experiments for maize (Zea mays L.) were collected in four maize agro-ecological regions of China, and the optimal management (OPT), farmers’ practice (FP), a series of nutrient omission treatments were used to analyze yield gap, nutrient use efficiency and indigenous nutrient supply by adopting meta-analysis and ANOVA analysis. Across all sites, the average yield gap between OPT and FP was 0.7 t ha-1, the yield response to nitrogen (N), phosphorus (P), and potassium (K) were 1.8, 1.0, and 1.2 t ha-1, respectively. The soil indigenous nutrient supply of N, P, and K averaged 139.9, 33.7, and 127.5 kg ha-1, respectively. As compared to FP, the average recovery efficiency (RE) of N, P, and K with OPT increased by percentage point of 12.2, 5.5, and 6.5, respectively. This study indicated that there would be considerable potential to further improve yield and nutrient use efficiency in China, and will help develop and inform agricultural policies and strategies, while some management measures such as soil, plant and nutrient are necessary and integrate with advanced knowledge and technologies. PMID:26484543

The Nutrient Pool of Five Important Bottomland Hardwood Soils

Treesearch

John K. Francis

1988-01-01

Heretofore, with the exception of N, the concentration of total nutrients and the amount of variation in nutrient concentrations among and within soil series and depths within the rooting zone of forested alluvial soils of the South was unknown. Information about total nutrient concentrations is important in studying the danger of nutrient depletion posed by total tree...

Recovery of a top predator mediates negative eutrophic effects on seagrass

USGS Publications Warehouse

Hughes, Brent B.; Eby, Ron; Van Dyke, Eric; Tinker, M. Tim; Marks, Corina I.; Johnson, Kenneth S.; Wasson, Kerstin

2013-01-01

A fundamental goal of the study of ecology is to determine the drivers of habitat-forming vegetation, with much emphasis given to the relative importance to vegetation of “bottom-up” forces such as the role of nutrients and “top-down” forces such as the influence of herbivores and their predators. For coastal vegetation (e.g., kelp, seagrass, marsh, and mangroves) it has been well demonstrated that alterations to bottom-up forcing can cause major disturbances leading to loss of dominant vegetation. One such process is anthropogenic nutrient loading, which can lead to major changes in the abundance and species composition of primary producers, ultimately affecting important ecosystem services. In contrast, much less is known about the relative importance of apex predators on coastal vegetated ecosystems because most top predator populations have been depleted or lost completely. Here we provide evidence that an unusual four-level trophic cascade applies in one such system, whereby a top predator mitigates the bottom-up influences of nutrient loading. In a study of seagrass beds in an estuarine ecosystem exposed to extreme nutrient loading, we use a combination of a 50-y time series analysis, spatial comparisons, and mesocosm and field experiments to demonstrate that sea otters (Enhydra lutris) promote the growth and expansion of eelgrass (Zostera marina) through a trophic cascade, counteracting the negative effects of agriculturally induced nutrient loading. Our results add to a small but growing body of literature illustrating that significant interactions between bottom-up and top-down forces occur, in this case with consequences for the conservation of valued ecosystem services provided by seagrass.

Recovery of a top predator mediates negative eutrophic effects on seagrass

PubMed Central

Hughes, Brent B.; Eby, Ron; Van Dyke, Eric; Tinker, M. Tim; Marks, Corina I.; Johnson, Kenneth S.; Wasson, Kerstin

2013-01-01

A fundamental goal of the study of ecology is to determine the drivers of habitat-forming vegetation, with much emphasis given to the relative importance to vegetation of “bottom-up” forces such as the role of nutrients and “top-down” forces such as the influence of herbivores and their predators. For coastal vegetation (e.g., kelp, seagrass, marsh, and mangroves) it has been well demonstrated that alterations to bottom-up forcing can cause major disturbances leading to loss of dominant vegetation. One such process is anthropogenic nutrient loading, which can lead to major changes in the abundance and species composition of primary producers, ultimately affecting important ecosystem services. In contrast, much less is known about the relative importance of apex predators on coastal vegetated ecosystems because most top predator populations have been depleted or lost completely. Here we provide evidence that an unusual four-level trophic cascade applies in one such system, whereby a top predator mitigates the bottom-up influences of nutrient loading. In a study of seagrass beds in an estuarine ecosystem exposed to extreme nutrient loading, we use a combination of a 50-y time series analysis, spatial comparisons, and mesocosm and field experiments to demonstrate that sea otters (Enhydra lutris) promote the growth and expansion of eelgrass (Zostera marina) through a trophic cascade, counteracting the negative effects of agriculturally induced nutrient loading. Our results add to a small but growing body of literature illustrating that significant interactions between bottom-up and top-down forces occur, in this case with consequences for the conservation of valued ecosystem services provided by seagrass. PMID:23983266

GPS Position Time Series @ JPL

NASA Technical Reports Server (NTRS)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

A novel coupled system of non-local integro-differential equations modelling Young's modulus evolution, nutrients' supply and consumption during bone fracture healing

NASA Astrophysics Data System (ADS)

Lu, Yanfei; Lekszycki, Tomasz

2016-10-01

During fracture healing, a series of complex coupled biological and mechanical phenomena occurs. They include: (i) growth and remodelling of bone, whose Young's modulus varies in space and time; (ii) nutrients' diffusion and consumption by living cells. In this paper, we newly propose to model these evolution phenomena. The considered features include: (i) a new constitutive equation for growth simulation involving the number of sensor cells; (ii) an improved equation for nutrient concentration accounting for the switch between Michaelis-Menten kinetics and linear consumption regime; (iii) a new constitutive equation for Young's modulus evolution accounting for its dependence on nutrient concentration and variable number of active cells. The effectiveness of the model and its predictive capability are qualitatively verified by numerical simulations (using COMSOL) describing the healing of bone in the presence of damaged tissue between fractured parts.

A Model for Growth of a Single Fungal Hypha Based on Well-Mixed Tanks in Series: Simulation of Nutrient and Vesicle Transport in Aerial Reproductive Hyphae

PubMed Central

Balmant, Wellington; Sugai-Guérios, Maura Harumi; Coradin, Juliana Hey; Krieger, Nadia; Furigo Junior, Agenor; Mitchell, David Alexander

2015-01-01

Current models that describe the extension of fungal hyphae and development of a mycelium either do not describe the role of vesicles in hyphal extension or do not correctly describe the experimentally observed profile for distribution of vesicles along the hypha. The present work uses the n-tanks-in-series approach to develop a model for hyphal extension that describes the intracellular transport of nutrient to a sub-apical zone where vesicles are formed and then transported to the tip, where tip extension occurs. The model was calibrated using experimental data from the literature for the extension of reproductive aerial hyphae of three different fungi, and was able to describe different profiles involving acceleration and deceleration of the extension rate. A sensitivity analysis showed that the supply of nutrient to the sub-apical vesicle-producing zone is a key factor influencing the rate of extension of the hypha. Although this model was used to describe the extension of a single reproductive aerial hypha, the use of the n-tanks-in-series approach to representing the hypha means that the model has the flexibility to be extended to describe the growth of other types of hyphae and the branching of hyphae to form a complete mycelium. PMID:25785863

Geochemical and geophysical examination of submarine groundwater discharge and associated nutrient loading estimates into Lynch Cove, Hood Canal, WA

USGS Publications Warehouse

Swarzenski, P.W.; Simonds, F.W.; Paulson, A.J.; Kruse, S.; Reich, C.

2007-01-01

Geochemical tracer data (i.e., 222Rn and four naturally occurring Ra isotopes), electromagnetic (EM) seepage meter results, and high-resolution, stationary electrical resistivity images were used to examine the bi-directional (i.e., submarine groundwater discharge and recharge) exchange of a coastal aquifer with seawater. Our study site for these experiments was Lynch Cove, the terminus of Hood Canal, WA, where fjord-like conditions dramatically limit water column circulation that can lead to recurring summer-time hypoxic events. In such a system a precise nutrient budget may be particularly sensitive to groundwater-derived nutrient loading. Shore-perpendicular time-series subsurface resistivity profiles show clear, decimeter-scale tidal modulation of the coastal aquifer in response to large, regional hydraulic gradients, hydrologically transmissive glacial terrain, and large (4-5 m) tidal amplitudes. A 5-day 222Rn time-series shows a strong inverse covariance between 222Rn activities (0.5−29 dpm L-1) and water level fluctuations, and provides compelling evidence for tidally modulated exchange of groundwater across the sediment/water interface. Mean Rn-derived submarine groundwater discharge (SGD) rates of 85 ± 84 cm d-1 agree closely in the timing and magnitude with EM seepage meter results that showed discharge during low tide and recharge during high tide events. To evaluate the importance of fresh versus saline SGD, Rn-derived SGD rates (as a proxy of total SGD) were compared to excess 226Ra-derived SGD rates (as a proxy for the saline contribution of SGD). The calculated SGD rates, which include a significant (>80%) component of recycled seawater, are used to estimate associated nutrient (NH4+, Si, PO43-, NO3 + NO2, TDN) loads to Lynch Cove. The dissolved inorganic nitrogen (DIN = NH4 + NO2 + NO3) SGD loading estimate of 5.9 × 104 mol d-1 is 1−2 orders of magnitude larger than similar estimates derived from atmospheric deposition and surface water runoff, respectively.

Hydrothermal carbonization of food waste for nutrient recovery and reuse.

PubMed

Idowu, Ifeolu; Li, Liang; Flora, Joseph R V; Pellechia, Perry J; Darko, Samuel A; Ro, Kyoung S; Berge, Nicole D

2017-11-01

Food waste represents a rather large and currently underutilized source of potentially available and reusable nutrients. Laboratory-scale experiments evaluating the hydrothermal carbonization of food wastes collected from restaurants were conducted to understand how changes in feedstock composition and carbonization process conditions influence primary and secondary nutrient fate. Results from this work indicate that at all evaluated reaction times and temperatures, the majority of nitrogen, calcium, and magnesium remain integrated within the solid-phase, while the majority of potassium and sodium reside in the liquid-phase. The fate of phosphorus is dependent on reaction times and temperatures, with solid-phase integration increasing with higher reaction temperature and longer time. A series of leaching experiments to determine potential solid-phase nutrient availability were also conducted and indicate that, at least in the short term, nitrogen release from the solids is small, while almost all of the phosphorus present in the solids produced from carbonizing at 225 and 250°C is released. At a reaction temperature of 275°C, smaller fractions of the solid-phase total phosphorus are released as reaction times increase, likely due to increased solids incorporation. Using these data, it is estimated that up to 0.96% and 2.30% of nitrogen and phosphorus-based fertilizers, respectively, in the US can be replaced by the nutrients integrated within hydrochar and liquid-phases generated from the carbonization of currently landfilled food wastes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Controlling factors of the seasonal variability of productivity in the southern Humboldt Current System (30-40°S): A biophysical modeling approach

NASA Astrophysics Data System (ADS)

Vergara, Odette A.; Echevín, Vincent; Sepúlveda, Héctor Hito; Quiñones, Renato A.

2017-09-01

The spatial and seasonal variability of nutrients and chlorophyll in the southern Humboldt Current System were assessed using a high-resolution regional ocean circulation model (ROMS) coupled to a biogeochemical model (Pelagic-Interactions Scheme for carbon and Ecosystem Studies; PISCES). The simulated nutrients and chlorophyll fields were validated using satellite and in situ observations at a continental shelf time-series station. The annual cycles of modeled chlorophyll and nutrients were consistent with the highest values observed in spring and summer, which is in agreement with enhanced upwelling observations. Co-limitation of phytoplankton growth by nutrients and light was analyzed for diatoms, the dominant phytoplankton group in the simulations. The results showed that co-limitation, near the coast, was governed in autumn and winter by light, and by silicate in spring and summer, whereas other nutrients were limiting offshore between January and April. Nutrient transport in the surface layer was analyzed. Vertical advection reflected areas with higher coastal upwelling, and was partly offset by horizontal processes related to eddy-induced transport from the nearshore to the open ocean. Vertical mixing was shown to play a key role in replenishing the surface layer with nutrients.

Dynamic investigation of nutrient consumption and injection strategy in microbial enhanced oil recovery (MEOR) by means of large-scale experiments.

PubMed

Song, Zhiyong; Zhu, Weiyao; Sun, Gangzheng; Blanckaert, Koen

2015-08-01

Microbial enhanced oil recovery (MEOR) depends on the in situ microbial activity to release trapped oil in reservoirs. In practice, undesired consumption is a universal phenomenon but cannot be observed effectively in small-scale physical simulations due to the scale effect. The present paper investigates the dynamics of oil recovery, biomass and nutrient consumption in a series of flooding experiments in a dedicated large-scale sand-pack column. First, control experiments of nutrient transportation with and without microbial consumption were conducted, which characterized the nutrient loss during transportation. Then, a standard microbial flooding experiment was performed recovering additional oil (4.9 % Original Oil in Place, OOIP), during which microbial activity mostly occurred upstream, where oil saturation declined earlier and steeper than downstream in the column. Subsequently, more oil remained downstream due to nutrient shortage. Finally, further research was conducted to enhance the ultimate recovery by optimizing the injection strategy. An extra 3.5 % OOIP was recovered when the nutrients were injected in the middle of the column, and another additional 11.9 % OOIP were recovered by altering the timing of nutrient injection.

Scale and legacy controls on catchment nutrient export regimes

NASA Astrophysics Data System (ADS)

Howden, N. J. K.; Burt, T.; Worrall, F.

2017-12-01

Nutrient dynamics in river catchments are complex: water and chemical fluxes are highly variable in low-order streams, but this variability declines as fluxes move through higher-order reaches. This poses a major challenge for process understanding as much effort is focussed on long-term monitoring of the main river channel (a high-order reach), and therefore the data available to support process understanding are predominantly derived from sites where much of the transient response of nutrient export is masked by the effect of averaging over both space and time. This may be further exacerbated at all scales by the accumulation of legacy nutrient sources in soils, aquifers and pore waters, where historical activities have led to nutrient accumulation where the catchment system is transport limited. Therefore it is of particular interest to investigate how the variability of nutrient export changes both with catchment scale (from low to high-order catchment streams) and with the presence of legacy sources, such that the context of infrequent monitoring on high-order streams can be better understood. This is not only a question of characterising nutrient export regimes per se, but also developing a more thorough understanding of how the concepts of scale and legacy may modify the statistical characteristics of observed responses across scales in both space and time. In this paper, we use synthetic data series and develop a model approach to consider how space and timescales combine with impacts of legacy sources to influence observed variability in catchment export. We find that: increasing space and timescales tend to reduce the observed variance in nutrient exports, due to an increase in travel times and greater mixing, and therefore averaging, of sources; increasing the influence of legacy sources inflates the variance, with the level of inflation dictated by the residence time of the respective sources.

PHYTOPLANKTON DYNAMICS IN A GULF OF MEXICO ESTUARY: TIME SERIES OF SIZE STRUCTURE, NUTRIENTS, VARIABLE FLUORESCENCE AND ALGAL PHOSPHATASE ACTIVITY

EPA Science Inventory

Relationships between phytoplankton dynamics and physiology, and environmental conditions were studied in Santa Rosa Sound, Florida, USA, at near-weekly intervals during 2001. Santa Rosa Sound is a component of the Pensacola Bay estuary in the northern Gulf of Mexico. Parameters ...

Long-term MODIS observations of cyanobacterial dynamics in Lake Taihu: Responses to nutrient enrichment and meteorological factors

PubMed Central

Shi, Kun; Zhang, Yunlin; Zhou, Yongqiang; Liu, Xiaohan; Zhu, Guangwei; Qin, Boqiang; Gao, Guang

2017-01-01

We developed and validated an empirical model for estimating chlorophyll a concentrations (Chla) in Lake Taihu to generate a long-term Chla and algal bloom area time series from MODIS-Aqua observations for 2003 to 2013. Then, based on the long-term time series data, we quantified the responses of cyanobacterial dynamics to nutrient enrichment and climatic conditions. Chla showed substantial spatial and temporal variability. In addition, the annual mean cyanobacterial surface bloom area exhibited an increasing trend across the entire lake from 2003 to 2013, with the exception of 2006 and 2007. High air temperature and phosphorus levels in the spring can prompt cyanobacterial growth, and low wind speeds and low atmospheric pressure levels favor cyanobacterial surface bloom formation. The sensitivity of cyanobacterial dynamics to climatic conditions was found to vary by region. Our results indicate that temperature is the most important factor controlling Chla inter-annual variability followed by phosphorus and that air pressure is the most important factor controlling cyanobacterial surface bloom formation followed by wind speeds in Lake Taihu. PMID:28074871

Depletion and Redistribution of Soil Nutrients in Response to Wind Erosion in Desert Grasslands of the Southwestern United States

NASA Astrophysics Data System (ADS)

Li, J.; Okin, G.; Hartman, L.; Epstein, H.

2005-12-01

Wind is a key abiotic factor that determines the spatial distribution of soil nutrients in arid grasslands with large unvegetated gaps, such as those found in the southwestern US. On the landscape scale, basic relationships such as wind erosion rate vs. vegetative cover, and soil nutrient removal rate vs. vegetative cover have not yet been extensively studied. In a series of experiments conducted in the Jornada Experimental Range near Las Cruces, New Mexico, we have examined these relationships to determine the impact of wind erosion and dust emission on pools of soil nutrients. In the experiments, varying levels of cover were achieved by vegetation removal on 25 m x 50 m plots. Intense surface soil sampling was conducted to monitor spatial distribution of soil nutrients. Large numbers of aeolian sediment samplers were installed to obtain estimates of vertical and horizontal dust flux. Available data from one wind erosion season show that: 1) total organic C (TOC) and total N (TN) content in the windblown sediment collected at the height of 1 m were 2.2 to 7.2 times larger than those of nutrients in the surface soil (enrichment ratio); 2) enrichment ratio generally increases with the increase of vegetative cover, indicating biotic processes continually add nutrients to surface soil in high-cover treatments, while nutrients are depleted in low-cover treatments; 3) average horizontal mass flux is 12 times larger in the bare plot than in the control plot, indicating the extreme importance of vegetative cover in protecting soil nutrient loss caused by wind erosion; 4) detectable soil nutrient depletion happened within one windy season in plots with vegetation removal, especially for TOC and TN, reflecting the importance of biotic processes in maintaining nutrient pools in the surface soil; and, 5) after only a single windy season, wind erosion can significantly alter the spatial pattern of soil nutrients.

Ocean-color remote sensing of the Nile delta shelf and SE Levantine basin and possible linkage to some mesoscale circulation features and Nile river run-off

NASA Astrophysics Data System (ADS)

Moufaddal, Wahid; Lavender, Samantha

To date, and despite the passage of more than 30 years since the launch of the first satellite based ocean-color sensor, no systematic study of the variability of chlorophyll in the Egyptian Mediterranean coast off the Nile delta has been undertaken using this kind of data. Meantime, available in-situ measurements on chlorophyll and other nutrient parameters along this coast are indeed very modest and scarce. The lack of data has in turn created a large gap in our knowledge on the biogeochemical characteristics of the coastal water and impacts of the Aswan High Dam and other land-use changes on the marine ecosystems and nutrient budget in the Nile delta shelf and the SE Mediterranean. The present study aims to fill part of this gap through application of ocean-color remote sensing and satellite retrieval of phytoplankton chlorophyll. For this purpose a 10-year (1997-2006) monthly satellite dataset from ESA Globcolour project (an ESA Data User Element project: http://www.globcolour.info) was retrieved and subjected to time-series analysis. Results of this analysis revealed that the oceanic and coastal parts off the Nile delta coast and SE Mediterranean manifest from time to time some of the most interesting and dynamical marine features including meso-scale gyres, coastal filaments, localized algal blooms and higher concentration of phytoplankton chlorophyll. These features together with certain physical pro-cesses and surface run-off from Nile mouthes and other land-based sources were found to exert pronounced effects on the nutrient supply and quality of the coastal and oceanic surface waters in this region. Results reveled also that there has been a general upward trend in concentration of surface chlorophyll during the 10-year period from 1997 to 2006 with a coincident rise of the coastal fisheries implying that improvement of nutrient supply is most likely responsible for this rise. Results confirmed also shift of the Nile phytoplankton bloom in space and time after construc-tion of the Aswan High Dam and other subsequent anthropogenic activities Conclusions and results achieved by this study show the importance of ocean-color satellite data for monitoring of biogeochemical impacts of land-use changes on coastal ecosystems and the role which it can play in assessment of the marine productivity and state of the marine fisheries.

Mathematical analysis of a nutrient-plankton system with delay.

PubMed

Rehim, Mehbuba; Zhang, Zhenzhen; Muhammadhaji, Ahmadjan

2016-01-01

A mathematical model describing the interaction of nutrient-plankton is investigated in this paper. In order to account for the time needed by the phytoplankton to mature after which they can release toxins, a discrete time delay is incorporated into the system. Moreover, it is also taken into account discrete time delays which indicates the partially recycled nutrient decomposed by bacteria after the death of biomass. In the first part of our analysis the sufficient conditions ensuring local and global asymptotic stability of the model are obtained. Next, the existence of the Hopf bifurcation as time delay crosses a threshold value is established and, meanwhile, the phenomenon of stability switches is found under certain conditions. Numerical simulations are presented to illustrate the analytical results.

Nutrient Management Certification for Delaware: Developing a Water Quality Curriculum

ERIC Educational Resources Information Center

Hansen, David J.; Binford, Gregory D.

2004-01-01

Water quality is a critical environmental, social, and political issue in Delaware. In the late 1990s, a series of events related to water quality issues led to the passage of a state nutrient management law. This new law required nutrient management planning and established a state certification program for nutrient users in the agricultural and…

Highly comparative time-series analysis: the empirical structure of time series and their methods.

PubMed

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Highly comparative time-series analysis: the empirical structure of time series and their methods

PubMed Central

Fulcher, Ben D.; Little, Max A.; Jones, Nick S.

2013-01-01

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines. PMID:23554344

C : N : P stoichiometry at the Bermuda Atlantic Time-series Study station in the North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Singh, A.; Baer, S. E.; Riebesell, U.; Martiny, A. C.; Lomas, M. W.

2015-06-01

Nitrogen (N) and phosphorus (P) availability determine the strength of the ocean's carbon (C) uptake, and variation in the N : P ratio in inorganic nutrients is key to phytoplankton growth. A similarity between C : N : P ratios in the plankton biomass and deep-water nutrients was observed by Alfred C. Redfield around 80 years ago and suggested that biological processes in the surface ocean controlled deep ocean chemistry. Recent studies have emphasized the role of inorganic N : P ratios in governing biogeochemical processes, particularly the C : N : P ratio in suspended particulate organic matter (POM), with somewhat less attention given to exported POM and dissolved organic matter (DOM). Herein, we extend the discussion on ecosystem C : N : P stoichiometry but also examine temporal variation of stoichiometric relationships. We have analysed elemental stoichiometry in the suspended POM and total (POM + DOM) organic matter (TOM) pools in the upper 100 m, and in the exported POM and sub-euphotic zone (100-500 m) inorganic nutrient pools from the monthly data collected at the Bermuda Atlantic Time-series Study (BATS) site located in the western part of the North Atlantic Ocean. C : N : P ratios in the TOM pool were more than twice that in the POM pool. Observed C : N ratios in suspended POM were approximately equal to the canonical Redfield Ratio (C : N : P = 106 : 16 : 1), while N : P and C : P ratios in the same pool were more than twice the Redfield Ratio. Average N : P ratios in the subsurface inorganic nutrient pool were ~ 26 : 1, squarely between the suspended POM ratio and the Redfield ratio. We have further linked variation in elemental stoichiometry with that of phytoplankton cell abundance observed at the BATS site. Findings from this study suggest that the variation elemental ratios with depth in the euphotic zone was mainly due to different growth rates of cyanobacterial cells. These time-series data have also allowed us to examine the potential role of climate variability on C : N : P stoichiometry. This study strengthens our understanding of elemental stoichiometry in different organic matter pools and should improve biogeochemical models by constraining the range of non-Redfield stoichiometry.

Phosphorus and nitrogen trajectories in the Mediterranean Sea (1950-2030): Diagnosing basin-wide anthropogenic nutrient enrichment

NASA Astrophysics Data System (ADS)

Powley, Helen R.; Krom, Michael D.; Van Cappellen, Philippe

2018-03-01

Human activities have significantly modified the inputs of land-derived phosphorus (P) and nitrogen (N) to the Mediterranean Sea (MS). Here, we reconstruct the external inputs of reactive P and N to the Western Mediterranean Sea (WMS) and Eastern Mediterranean Sea (EMS) over the period 1950-2030. We estimate that during this period the land derived P and N loads increased by factors of 3 and 2 to the WMS and EMS, respectively, with reactive P inputs peaking in the 1980s but reactive N inputs increasing continuously from 1950 to 2030. The temporal variations in reactive P and N inputs are imposed in a coupled P and N mass balance model of the MS to simulate the accompanying changes in water column nutrient distributions and primary production with time. The key question we address is whether these changes are large enough to be distinguishable from variations caused by confounding factors, specifically the relatively large inter-annual variability in thermohaline circulation (THC) of the MS. Our analysis indicates that for the intermediate and deep water masses of the MS the magnitudes of changes in reactive P concentrations due to changes in anthropogenic inputs are relatively small and likely difficult to diagnose because of the noise created by the natural circulation variability. Anthropogenic N enrichment should be more readily detectable in time series concentration data for dissolved organic N (DON) after the 1970s, and for nitrate (NO3) after the 1990s. The DON concentrations in the EMS are predicted to exhibit the largest anthropogenic enrichment signature. Temporal variations in annual primary production over the 1950-2030 period are dominated by variations in deep-water formation rates, followed by changes in riverine P inputs for the WMS and atmospheric P deposition for the EMS. Overall, our analysis indicates that the detection of basin-wide anthropogenic nutrient concentration trends in the MS is rendered difficult due to: (1) the Atlantic Ocean contributing the largest reactive P and N inputs to the MS, hence diluting the anthropogenic nutrient signatures, (2) the anti-estuarine circulation removing at least 45% of the anthropogenic nutrients inputs added to both basins of the MS between 1950 and 2030, and (3) variations in intermediate and deep water formation rates that add high natural noise to the P and N concentration trajectories.

Modeling ecosystem processes with variable freshwater inflow to the Caloosahatchee River Estuary, southwest Florida. II. Nutrient loading, submarine light, and seagrasses

NASA Astrophysics Data System (ADS)

Buzzelli, Christopher; Doering, Peter; Wan, Yongshan; Sun, Detong

2014-12-01

Short- and long-term changes in estuarine biogeochemical and biological attributes are consequences of variations in both the magnitude and composition of freshwater inputs. A common conceptualization of estuaries depicts nutrient loading from coastal watersheds as the stressor that promotes algal biomass, decreases submarine light penetration, and degrades seagrass habitats. Freshwater inflow depresses salinity while simultaneously introducing colored dissolved organic matter (color or CDOM) which greatly reduces estuarine light penetration. This is especially true for sub-tropical estuaries. This study applied a model of the Caloosahatchee River Estuary (CRE) in southwest Florida to explore the relationships between freshwater inflow, nutrient loading, submarine light, and seagrass survival. In two independent model series, the loading of dissolved inorganic nitrogen and phosphorus (DIN and DIP) was reduced by 10%, 20%, 30%, and 50% relative to the base model case from 2002 to 2009 (2922 days). While external nutrient loads were reduced by lowering inflow (Q0) in the first series (Q0 series), reductions were accomplished by decreasing the incoming concentrations of DIN and DIP in the second series (NP Series). The model also was used to explore the partitioning of submarine light extinction due to chlorophyll a, CDOM, and turbidity. Results suggested that attempting to control nutrient loading by decreasing freshwater inflow could have minor effects on water column concentrations but greatly influence submarine light and seagrass biomass. This is because of the relative importance of Q0 to salinity and submarine light. In general, light penetration and seagrass biomass decreased with increased inflow and CDOM. Increased chlorophyll a did account for more submarine light extinction in the lower estuary. The model output was used to help identify desirable levels of inflow, nutrient loading, water quality, salinity, and submarine light for seagrass in the lower CRE. These findings provide information essential to the development of a resource-based approach to improve the management of both freshwater inflow and estuarine biotic resources.

Diatoms morphology and gene expression in turbulence

NASA Astrophysics Data System (ADS)

Iudicone, D.; Amato, A.; Ferrante, M. I.; Ribera, M.

2016-02-01

Diatoms are an ecologically important algal group that prospers in turbulent environments. Despite previous efforts, a general scenario that explains mesocosm and in situ observations is missing. Importantly, according to the present theories, the main effect of microscale turbulence is the increase of nutrient uptake efficiency in nutrient-depleted conditions and for very large cells. We will present evidences from laboratory experiments in nutrient-repleted conditions that chain-forming diatoms sense and respond to turbulence by varying their chain length spectra and tuning their metabolism. Further, we compared growth and gene expressions of small-sized cells in turbulent and in still conditions and found that turbulence sensing activates a series of pathways suggesting a partial metabolic switch in response to agitation. In addition, a long-time exposure experiment showed that when silicates become depleted these same diatoms take up other nutrients differently than in still condition. This implies that in natural environment prolonged turbulence would shape the phytoplankton community structure and succession.

Zooplankton time-series in the Balearic Sea (Western Mediterranean): Variability during the decade 1994 2003

NASA Astrophysics Data System (ADS)

Fernández de Puelles, Maria Luz; Alemany, Francisco; Jansá, Javier

2007-08-01

Studies of plankton time-series from the Balearic islands waters are presented for the past decade, with main emphasis on the variability of zooplankton and how it relates to the environment. The seasonal and interannual patterns of temperature, salinity, nutrients, chlorophyll concentration and zooplankton abundance are described with data obtained between 1994 and 2003. Samples were collected every 10 days at a monitoring station in the Mallorca channel, an area with marked hydrographic variability in the Western Mediterranean. Mesoscale variability was also assessed using data from monthly sampling survey carried out between 1994 and 1999 in a three station transect located in the same study area. The copepods were the most abundant group with three higher peaks (March, May and September) distinguished during the annual cycle and a clear coastal-offshore decreasing gradient. Analysis of the zooplankton community revealed two distinct periods: the mixing period during winter and early spring, where copepods, siphonophores and ostracods were most abundant and, the stratified period characterised by an increase of cladocerans and meroplankton abundances. Remarkable interannual zooplankton variability was observed in relation to hydrographic regime with higher abundances of main groups during cool years, when northern Mediterranean waters prevailed in the area. The warmer years showed the lowest zooplankton abundances, associated with the inflow of less saline and nutrient-depleted Atlantic Waters. Moreover, the correlation found between copepod abundance and large scale climatic factors (e.g., NAO) suggested that they act as main driver of the zooplankton variability. Therefore, the seasonal but particularly the interannual variation observed in plankton abundance and structure patterns of the Balearic Sea seems to be highly modulated by large-scale forcing and can be considered an ideal place where to investigate potential consequences of global climate change.

The Impact of Long-Term Climate Change on Nitrogen Runoff at the Watershed Scale.

NASA Astrophysics Data System (ADS)

Dorley, J.; Duffy, C.; Arenas Amado, A.

2017-12-01

The impact of agricultural runoff is a major concern for water quality of mid-western streams. This concern is largely due to excessive use of agricultural fertilizer, a major source of nutrients in many Midwestern watersheds. In order to improve water quality in these watersheds, understanding the long-term trends in nutrient concentration and discharge is an important water quality problem. This study attempts to analyze the role of long-term temperature and precipitation on nitrate runoff in an agriculturally dominated watershed in Iowa. The approach attempts to establish the concentration-discharge (C-Q) signature for the watershed using time series analysis, frequency analysis and model simulation. The climate data is from the Intergovernmental Panel on Climate Change (IPCC), model GFDL-CM3 (Geophysical Fluid Dynamic Laboratory Coupled Model 3). The historical water quality data was made available by the IIHR-Hydroscience & Engineering at the University of Iowa for the clear creek watershed (CCW). The CCW is located in east-central Iowa. The CCW is representative of many Midwestern watersheds with humid-continental climate with predominantly agricultural land use. The study shows how long-term climate changes in temperature and precipitation affects the C-Q dynamics and how a relatively simple approach to data analysis and model projections can be applied to best management practices at the site.

Energy and Nutrient Intake Monitoring

NASA Technical Reports Server (NTRS)

Luckey, T. D.; Venugopal, B.; Hutcheson, D. P.

1975-01-01

A passive system to determine the in-flight intake of nutrients is developed. Nonabsorbed markers placed in all foods in proportion to the nutrients selected for study are analyzed by neutron activation analysis. Fecal analysis for each market indicates how much of the nutrients were eaten and apparent digestibility. Results of feasibility tests in rats, mice, and monkeys indicate the diurnal variation of several markers, the transit time for markers in the alimentary tract, the recovery of several markers, and satisfactory use of selected markers to provide indirect measurement of apparent digestibility. Recommendations are provided for human feasibility studies.

New insights into biogeochemical processing gained from sub-daily river monitoring

NASA Astrophysics Data System (ADS)

Halliday, S. J.; Wade, A. J.; Skeffington, R. A.; Bowes, M.; Palmer-Felgate, E.; Loewenthal, M.; Jarvie, H.; Neal, C.; Reynolds, B.; Gozzard, E.; Newman, J.

2012-12-01

This talk will focus on the insights obtained from sub-daily hydrochemical monitoring for a sustained time periods (> 1 year), at multiple sites within a catchment and across different catchment types. Sub-daily instream hydrochemical dynamics were investigated, using non-stationary time-series analysis techniques, for two catchments representative of upland and lowland UK. The River Hafren at Plynlimon, mid-Wales drains an upland catchment where half the land cover is unmanaged moorland and the other half is first generation plantation forestry. The Hafren was monitored at two sites on a 7-hourly basis, between March 2007 and January 2009, using a Xian automatic sampler. The River Enborne, Berkshire, southeast England, is a rural lowland catchment, impacted by agricultural runoff, and septic tank and sewage treatment works discharges. The Enborne was monitored on an hourly basis between November 2009 and February 2012, using in situ field deployable analytical equipment to measure: Total Reactive Phosphorus (TRP: Systea Micromac C), Nitrate (Hach-Lange Nitratax), pH, dissolved oxygen, conductivity and water temperature (YSI 6600 Multi-parameter sonde). The results reveal complex diurnal patterns which exhibit seasonal changes in phase and amplitude, and are influenced by both flow conditions and nutrient sources. The comparison of the upland and lowland nitrate time series highlights how the different nitrogen sources within each system results in marked differences in the seasonal and diurnal dynamics, with a seasonal maximum in winter and a single peak diurnal cycle in the upland system, compared to a summer maximum and a two peak diurnal cycle in the lowland system. The analysis of TRP and nitrate concentrations in the Enborne catchment, in combination with flow, pH, dissolved oxygen, conductivity and water temperature, allowed the main processes controlling the observed sub-daily nutrient dynamics to be investigated. The different monitoring approaches adopted revealed the complexities involved in the accurate extraction of diurnal dynamics under lower frequency sampling, and the inherent issues of aliasing. Monitoring for 2 years also allowed an initial assessment of the inter-annual variability in the observed dynamics.

Nutrient transporter gene expression in poultry, livestock and fish

USDA-ARS?s Scientific Manuscript database

The absorption of nutrients such as amino acids, peptides, monosaccharides and minerals by cells and tissues is mediated by a series of membrane bound transporters that are members of the solute carrier (SLC) gene family. These transporters regulate the influx and efflux of nutrients in a wide vari...

Analysis of Nonstationary Time Series for Biological Rhythms Research.

PubMed

Leise, Tanya L

2017-06-01

This article is part of a Journal of Biological Rhythms series exploring analysis and statistics topics relevant to researchers in biological rhythms and sleep research. The goal is to provide an overview of the most common issues that arise in the analysis and interpretation of data in these fields. In this article on time series analysis for biological rhythms, we describe some methods for assessing the rhythmic properties of time series, including tests of whether a time series is indeed rhythmic. Because biological rhythms can exhibit significant fluctuations in their period, phase, and amplitude, their analysis may require methods appropriate for nonstationary time series, such as wavelet transforms, which can measure how these rhythmic parameters change over time. We illustrate these methods using simulated and real time series.

Water quality and trend analysis of Colorado--Big Thompson system reservoirs and related conveyances, 1969 through 2000

USGS Publications Warehouse

Stevens, Michael R.

2003-01-01

The U.S. Geological Survey, in an ongoing cooperative monitoring program with the Northern Colorado Water Conservancy District, Bureau of Reclamation, and City of Fort Collins, has collected water-quality data in north-central Colorado since 1969 in reservoirs and conveyances, such as canals and tunnels, related to the Colorado?Big Thompson Project, a water-storage, collection, and distribution system. Ongoing changes in water use among agricultural and municipal users on the eastern slope of the Rocky Mountains in Colorado, changing land use in reservoir watersheds, and other water-quality issues among Northern Colorado Water Conservancy District customers necessitated a reexamination of water-quality trends in the Colorado?Big Thompson system reservoirs and related conveyances. The sampling sites are on reservoirs, canals, and tunnels in the headwaters of the Colorado River (on the western side of the transcontinental diversion operations) and the headwaters of the Big Thompson River (on the eastern side of the transcontinental diversion operations). Carter Lake Reservoir and Horsetooth Reservoir are off-channel water-storage facilities, located in the foothills of the northern Colorado Front Range, for water supplied from the Colorado?Big Thompson Project. The length of water-quality record ranges from approximately 3 to 30 years depending on the site and the type of measurement or constituent. Changes in sampling frequency, analytical methods, and minimum reporting limits have occurred repeatedly over the period of record. The objective of this report was to complete a retrospective water-quality and trend analysis of reservoir profiles, nutrients, major ions, selected trace elements, chlorophyll-a, and hypolimnetic oxygen data from 1969 through 2000 in Lake Granby, Shadow Mountain Lake, and the Granby Pump Canal in Grand County, Colorado, and Horsetooth Reservoir, Carter Lake, Lake Estes, Alva B. Adams Tunnel, and Olympus Tunnel in Larimer County, Colorado. This report summarizes and assesses: Water-quality and field-measurement profile data collected by the U.S. Geological Survey and stored in the U.S. Geological Survey National Water Information System, Time-series trends of chemical constituents and physical properties, Trends in oxygen deficits in the hypolimnion of the reservoirs in the late summer season by the seasonal Kendall trend test method, Nutrient limitation and trophic status indicators, and Water-quality data in terms of Colorado water-quality standards. Water quality was generally acceptable for primary uses throughout the Colorado?Big Thompson system over the site periods of record, which are all within the span of 1969 to 2000. Dissolved solids and nutrient concentrations were low and typical of a forested/mountainous/crystalline bedrock hydrologic setting. Most of the more toxic trace elements were rarely detected or were found in low concentrations, due at least in part to a relative lack of ore-mineral deposits within the drainage areas of the Colorado?Big Thompson Project. Constituent concentrations consistently met water-quality standard thresholds set by the State of Colorado. Trophic-State Index Values indicated mesotrophic conditions generally prevailed at reservoirs, based on available Secchi depth, total phosphorus concentrations, and chlorophyll-a concentrations. Based on plots of time-series values and concentrations and seasonal Kendall nonparametric trends testing, dissolved solids and most major ions are decreasing at most sites. Many of the nutrient data did not meet the minimum criteria for time-series testing; but for those that did, nutrient concentrations were generally stable (no statistical trend) or decreasing (ammonia plus organic nitrogen and total phosphorus). Iron and manganese concentrations were stable or decreasing at most sites that met testing criteria. Chlorophyll-a data were only collected for 11 years but generally indicated quasi-stable or d

Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection

PubMed Central

Kirchner, James W.; Neal, Colin

2013-01-01

The chemical dynamics of lakes and streams affect their suitability as aquatic habitats and as water supplies for human needs. Because water quality is typically monitored only weekly or monthly, however, the higher-frequency dynamics of stream chemistry have remained largely invisible. To illuminate a wider spectrum of water quality dynamics, rainfall and streamflow were sampled in two headwater catchments at Plynlimon, Wales, at 7-h intervals for 1–2 y and weekly for over two decades, and were analyzed for 45 solutes spanning the periodic table from H+ to U. Here we show that in streamflow, all 45 of these solutes, including nutrients, trace elements, and toxic metals, exhibit fractal 1/fα scaling on time scales from hours to decades (α = 1.05 ± 0.15, mean ± SD). We show that this fractal scaling can arise through dispersion of random chemical inputs distributed across a catchment. These 1/f time series are non–self-averaging: monthly, yearly, or decadal averages are approximately as variable, one from the next, as individual measurements taken hours or days apart, defying naive statistical expectations. (By contrast, stream discharge itself is nonfractal, and self-averaging on time scales of months and longer.) In the solute time series, statistically significant trends arise much more frequently, on all time scales, than one would expect from conventional t statistics. However, these same trends are poor predictors of future trends—much poorer than one would expect from their calculated uncertainties. Our results illustrate how 1/f time series pose fundamental challenges to trend analysis and change detection in environmental systems. PMID:23842090

Universal fractal scaling in stream chemistry and its implications for solute transport and water quality trend detection

NASA Astrophysics Data System (ADS)

Kirchner, James W.; Neal, Colin

2013-07-01

The chemical dynamics of lakes and streams affect their suitability as aquatic habitats and as water supplies for human needs. Because water quality is typically monitored only weekly or monthly, however, the higher-frequency dynamics of stream chemistry have remained largely invisible. To illuminate a wider spectrum of water quality dynamics, rainfall and streamflow were sampled in two headwater catchments at Plynlimon, Wales, at 7-h intervals for 1-2 y and weekly for over two decades, and were analyzed for 45 solutes spanning the periodic table from H+ to U. Here we show that in streamflow, all 45 of these solutes, including nutrients, trace elements, and toxic metals, exhibit fractal 1/fα scaling on time scales from hours to decades (α = 1.05 ± 0.15, mean ± SD). We show that this fractal scaling can arise through dispersion of random chemical inputs distributed across a catchment. These 1/f time series are non-self-averaging: monthly, yearly, or decadal averages are approximately as variable, one from the next, as individual measurements taken hours or days apart, defying naive statistical expectations. (By contrast, stream discharge itself is nonfractal, and self-averaging on time scales of months and longer.) In the solute time series, statistically significant trends arise much more frequently, on all time scales, than one would expect from conventional t statistics. However, these same trends are poor predictors of future trends-much poorer than one would expect from their calculated uncertainties. Our results illustrate how 1/f time series pose fundamental challenges to trend analysis and change detection in environmental systems.

Dissolved Organic Carbon Degradation in Response to Nutrient Amendments in Southwest Greenland Lakes

NASA Astrophysics Data System (ADS)

Burpee, B. T.; Northington, R.; Simon, K. S.; Saros, J. E.

2014-12-01

Aquatic ecosystems across the Arctic are currently experiencing rapid shifts in biotic, chemical, and physical factors in response to climate change. Preliminary data from multiple lakes in southwestern Greenland indicate decreasing dissolved organic carbon (DOC) concentrations over the past decade. Though several factors may be contributing to this phenomenon, this study attempts to elucidate the potential of heterotrophic bacteria to degrade DOC in the presence of increasing nutrient concentrations. In certain Arctic regions, nutrient subsidies have been released into lakes due to permafrost thaw. If this is occurring in southwestern Greenland, we hypothesized that increased nutrient concentrations will relieve nutrient limitation, thereby allowing heterotrophic bacteria to utilize DOC as an energy source. This prediction was tested using experimental DOC degradation assays from four sample lakes. Four nutrient amendment treatments (control, N, P, and N + P) were used to simulate in situ subsidies. Five time points were sampled during the incubation: days 0, 3, 6, 14, and 60. Total organic carbon (TOC) and parallel factor (PARAFAC) analysis were used to monitor the relative concentrations of different DOC fractions over time. In addition, samples for extracellular enzyme activity (EEA) analysis were collected at every time point. Early analysis of fulvic and humic pools of DOC do not indicate any significant change from days 0 to 14. This could be due to the fact that these DOC fractions are relatively recalcitrant. This study will be important in determining whether bacterial degradation could be a contributing factor to DOC decline in arctic lakes.

Monitoring Phenology as Indicator for Timing of Nutrient Inputs in Northern Gulf Watersheds

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; Spiering, Bruce A.; Kalcic, Maria T.

2009-01-01

Nutrient over-enrichment defined by the U.S. Environmental Protection Agency as the anthropogenic addition of nutrients, in addition to any natural processes, causing adverse effects or impairments to the beneficial uses of a water body has been identified as one of the most significant environmental problems facing sensitive estuaries and coastal waters. Understanding the timing of nutrient inputs into those waters through remote sensing observables helps define monitoring and mitigation strategies. Remotely sensed data products can trace both forcings and effects of the nutrient system from landscape to estuary. This project is focused on extracting nutrient information from the landscape. The timing of nutrients entering coastal waters from the land boundary is greatly influenced by hydrologic processes, but can also be affected by the timing of nutrient additions across the landscape through natural or anthropogenic means. Non-point source nutrient additions to watersheds are often associated with specific seasonal cycles, such as decomposition of organic materials in fall and winter or addition of fertilizers to crop lands in the spring. These seasonal cycles or phenology may in turn be observed through the use of satellite sensors. Characterization of the phenology of various land cover types may be of particular interest in Gulf of Mexico estuarine systems with relatively short pathways between intensively managed systems and the land/estuarine boundary. The objective of this study is to demonstrate the capability of monitoring phenology of specific classes of land, such as agriculture and managed timberlands, at a refined watershed level. The extraction of phenological information from the Moderate Resolution Imaging Spectroradiometer (MODIS) data record is accomplished using analytical tools developed for NASA at Stennis Space Center: the Time Series Product Tool and the Phenological Parameters Estimation Tool. MODIS reflectance data (product MOD09) were used to compute the Normalized Difference Vegetation Index, which is sensitive to changes in vegetation canopies. The project team is working directly with the Mississippi Department of Environmental Quality to understand end-user requirements for this type of information product. Initial focus areas are identification of time frames for pre-plant fertilizer applications (prior to start of season), side-dress fertilizer applications (during rapid green-up), and periods of plant decomposition (during and after senescence). Prototypical maps of phenological stages related to these time frames have been generated for watersheds in the northern Gulf of Mexico. Where feasible, these maps have been compared to existing in situ nutrient monitoring data, but the in situ data is temporally sparse (monthly frequency or less), which makes interpretation challenging. Future work will include integrating effects of rainfall and seeking couplings with estuarine remote sensing.

Front-Eddy Influence on Water Column Properties, Phytoplankton Community Structure, and Cross-Shelf Exchange of Diatom Taxa in the Shelf-Slope Area off Concepción (˜36-37°S)

NASA Astrophysics Data System (ADS)

Morales, Carmen E.; Anabalón, Valeria; Bento, Joaquim P.; Hormazabal, Samuel; Cornejo, Marcela; Correa-Ramírez, Marco A.; Silva, Nelson

2017-11-01

In eastern boundary current systems (EBCSs), submesoscale to mesocale variability contributes to cross-shore exchanges of water properties, nutrients, and plankton. Data from a short-term summer survey and satellite time series (January-February 2014) were used to characterize submesoscale variability in oceanographic conditions and phytoplankton distribution across the coastal upwelling and coastal transition zones north of Punta Lavapié, and to explore cross-shelf exchanges of diatom taxa. A thermohaline front (FRN-1) flanked by a mesoscale anticyclonic intrathermocline eddy (ITE-1), or mode-water eddy, persisted during the time series and the survey was undertaken during a wind relaxation event. At the survey time, ITE-1 contributed to an onshore intrusion of warm oceanic waters (southern section) and an offshore advection of cold coastal waters (northern section), with the latter forming a cold, high chlorophyll-a filament. In situ phytoplankton and diatom biomasses were highest at the surface in FRN-1 and at the subsurface in ITE-1, whereas values in the coastal zone were lower and dominated by smaller cells. Diatom species typical of the coastal zone and species dominant in oceanic waters were both found in the FRN-1 and ITE-1 interaction area, suggesting that this mixture was the result of both offshore and onshore advection. Overall, front-eddy interactions in EBCSs could enhance cross-shelf exchanges of coastal and oceanic plankton, as well as sustain phytoplankton growth in the slope area through localized upward injections of nutrients in the frontal zone, combined with ITE-induced advection and vertical nutrient inputs to the surface layer.

Differential niche dynamics among major marine invertebrate clades

PubMed Central

Hopkins, Melanie J; Simpson, Carl; Kiessling, Wolfgang

2014-01-01

The degree to which organisms retain their environmental preferences is of utmost importance in predicting their fate in a world of rapid climate change. Notably, marine invertebrates frequently show strong affinities for either carbonate or terrigenous clastic environments. This affinity is due to characteristics of the sediments as well as correlated environmental factors. We assessed the conservatism of substrate affinities of marine invertebrates over geological timescales, and found that niche conservatism is prevalent in the oceans, and largely determined by the strength of initial habitat preference. There is substantial variation in niche conservatism among major clades with corals and sponges being among the most conservative. Time-series analysis suggests that niche conservatism is enhanced during times of elevated nutrient flux, whereas niche evolution tends to occur after mass extinctions. Niche evolution is not necessarily elevated in genera exhibiting higher turnover in species composition. PMID:24313951

Temporal variation and trends of inorganic nutrients in the coastal upwelling of the NW Spain (Atlantic Galician rías)

NASA Astrophysics Data System (ADS)

Doval, M. D.; López, A.; Madriñán, M.

2016-02-01

The temporal variability of inorganic nutrients (nitrate, nitrite, ammonium, phosphate and silicate) in five coastal upwelling Galician rías (Ría de Vigo, Ría de Pontevedra, Ría de Arousa, Ría de Muros and Ría de Ares-Betanzos) was assessed by considering biweekly values at 10 oceanographic stations for the decade 2002-2011. The long trends (1993-2011) of biweekly and annual average series for one station (mouth of Ría de Arousa, A0) were compared with decadal trends. A marked seasonal variability in all surface inorganic nutrients was observed. The average of the ten annual cycles (2002-2011) of each variable was calculated in order to obtain the average seasonal cycles. Maximum surface values occurred from the end of October to the end of February (8-24 μmol L- 1 dissolved inorganic nitrogen, 5-22 μmol L- 1 silicate and 0.5-1.8 μmol L- 1 phosphate). Minimum surface values (0.9-3.0 μmol L- 1 dissolved inorganic nitrogen, 0.6-2.5 μmol L- 1 silicate and 0.15-0.6 μmol L- 1 phosphate) detected from the beginning of May to the end of September. The highest nutrient concentrations (> 35%) recorded in the inner stations (V3, P3, A3, M2 and L3), as compared to the outer stations (V5, P4, A0, M5 and L1), indicated a more estuarine behaviour of these areas. Trends of the deseasonalised nutrient data were highly dependent on factors as length of the series, the initial year and the time integration period considered. The results found for these trends should be taken with caution due to the change in the sign of the slopes and the moderate accuracy of the models. Considering the variables whose trends did not change sign, ammonium and silicate showed a slightly positive trend for the biweekly-averaged series in A0 station during the periods 2002-2011 and 1993-2011.

Policy Guidance From a Multi-scale Suite of Natural Field and Digital Laboratories of Change: Hydrological Catchment Studies of Nutrient and Pollutant Source Releases, Waterborne Transport-Transformations and Mass Flows in Water Ecosystems

NASA Astrophysics Data System (ADS)

Destouni, G.

2008-12-01

Continental fresh water transports and loads excess nutrients and pollutants from various land surface sources, through the landscape, into downstream inland and coastal water environments. Our ability to understand, predict and control the eutrophication and the pollution pressures on inland, coastal and marine water ecosystems relies on our ability to quantify these mass flows. This paper synthesizes a series of hydro- biogeochemical studies of nutrient and pollutant sources, transport-transformations and mass flows in catchment areas across a range of scales, from continental, through regional and national, to individual drainage basin scales. Main findings on continental scales include correlations between country/catchment area, population and GDP and associated pollutant and nutrient loading, which differ significantly between world regions with different development levels. On regional scales, essential systematic near-coastal gaps are identified in the national monitoring of nutrient and pollutant loads from land to the sea. Combination of the unmonitored near-coastal area characteristics with the relevant regional nutrient and pollutant load correlations with these characteristics shows that the unmonitored nutrient and pollutant mass loads to the sea may often be as large as, or greater than the monitored river loads. Process studies on individual basin- scales show long-term nutrient and pollutant memories in the soil-groundwater systems of the basins, which may continue to uphold large mass loading to inland and coastal waters long time after mitigation of the sources. Linked hydro-biogeochemical-economic model studies finally demonstrate significant comparative advantages of policies that demand explicit quantitative account of the uncertainties implied by these monitoring gaps and long-term nutrient-pollution memories and time lags, and other knowledge, data and model limitations, instead of the now common neglect or subjective implicit handling of such uncertainties in strategies and practices for combating water pollution and eutrophication.

Designing a high-frequency nutrient and biogeochemical monitoring network for the Sacramento–San Joaquin Delta, northern California

USGS Publications Warehouse

Bergamaschi, Brian A.; Downing, Bryan D.; Kraus, Tamara E.C.; Pellerin, Brian A.

2017-07-11

Executive SummaryThis report is the third in a series of three reports that provide information about how high-frequency (HF) nutrient monitoring may be used to assess nutrient inputs and dynamics in the Sacramento–San Joaquin Delta, California (Delta). The purpose of this report is to provide the background, principles, and considerations for designing an HF nutrient-monitoring network for the Delta to address high-priority, nutrient-management questions. The report starts with discussion of the high-priority management questions to be addressed, continues through discussion of the questions and considerations that place demands and constraints on network design, discusses the principles applicable to network design, and concludes with the presentation of three example nutrient-monitoring network designs for the Delta. For three example network designs, we assess how they would address high-priority questions that have been identified by the Delta Regional Monitoring Program (Delta Regional Monitoring Program Technical Advisory Committee, 2015).This report, along with the other two reports of this series (Kraus and others, 2017; Downing and others, 2017), was drafted in cooperation with the Delta Regional Monitoring Program to help scientists, managers, and planners understand how HF data improve our understanding of nutrient sources and sinks, drivers, and effects in the Delta. The first report in the series (Kraus and others, 2017) provides an introduction to the reasons for and fundamental concepts behind using HF monitoring measurements, including a brief summary of nutrient status and trends in the Delta and an extensive literature review showing how and where other research and monitoring programs have used HF monitoring to improve our understanding of nutrient cycling. The report covers the various technologies available for HF nutrient monitoring and presents the different ways HF monitoring instrumentation may be used for both fixed station and spatial assessments. Finally, it presents numerous examples of how HF measurements are currently (2017) being used in the Delta to examine how nutrients and nutrient cycling are related to aquatic habitat conditions.The second report in the series (Downing and others, 2017) summarizes information about HF nutrient and associated biogeochemical monitoring in the north Delta. The report synthesizes data available from the nutrient and water quality monitoring network currently (2017) operated by the U.S. Geological Survey in this ecologically important region of the Delta. In the report, we present and discuss the available data at various timescales—first at the monthly, seasonal, and inter-annual timescales; and, second, for comparison, at the tidal and event timescales. As expected, we determined that there is substantial variability in nitrate concentrations at short timescales, such as within a few hours, but also significant variability at longer timescales such as months or years. This high variability affects calculation of fluxes and loads, indicating that HF monitoring is necessary for understanding and assessing flux-based processes and outcomes in Delta tidal environments.

Plant ecosystem responses to rising atmospheric CO2: applying a "two-timing" approach to assess alternative hypotheses for mechanisms of nutrient limitation

NASA Astrophysics Data System (ADS)

Medlyn, B.; Jiang, M.; Zaehle, S.

2017-12-01

There is now ample experimental evidence that the response of terrestrial vegetation to rising atmospheric CO2 concentration is modified by soil nutrient availability. How to represent nutrient cycling processes is thus a key consideration for vegetation models. We have previously used model intercomparison to demonstrate that models incorporating different assumptions predict very different responses at Free-Air CO2 Enrichment experiments. Careful examination of model outputs has provided some insight into the reasons for the different model outcomes, but it is difficult to attribute outcomes to specific assumptions. Here we investigate the impact of individual assumptions in a generic plant carbon-nutrient cycling model. The G'DAY (Generic Decomposition And Yield) model is modified to incorporate alternative hypotheses for nutrient cycling. We analyse the impact of these assumptions in the model using a simple analytical approach known as "two-timing". This analysis identifies the quasi-equilibrium behaviour of the model at the time scales of the component pools. The analysis provides a useful mathematical framework for probing model behaviour and identifying the most critical assumptions for experimental study.

Investigating Stream Metabolism and Nutrient Dynamics in Contrasting Ecosystems: The Role of Hydrologic Compartments

NASA Astrophysics Data System (ADS)

Gonzalez-Pinzon, R.; Riveros-Iregui, D. A.; Covino, T. P.

2015-12-01

The interactions between mobile and less mobile hydrologic compartments affect the quality and quantity of water in streams and aquifers, and the cycling of dissolved carbon and nutrients. As new laboratory and field techniques become available, new questions and challenges emerge, including: What do we measure, where, and for how long to fully characterize a system? and, What is the ideal cost-maintenance-benefit relationship that we should strive for to maximize knowledge gained in different field settings? We recently performed a series of field experiments to measure aquatic metabolism and nutrient dynamics in two highly contrasting hydrologic systems, i.e., 1) a wetland-stream alpine, tropical system in Colombia (South America) and 2) a dryland river continuum (1st - 5th stream orders) in New Mexico. In this presentation we discuss how multiple lines of evidence can support the analysis of key aquatic processes and how co-interpretation provides a more complete picture of stream complexity. For this analysis, we deployed YSI EXO2 and 6920 sondes, Turner Designs C-sense and C6 sensors, and Onset HOBO water quality data loggers. Parameters measured by these instruments include conductivity, temperature, dissolved oxygen, pH, turbidity, pCO2, chlorophyll-a, phycocyanin, fluorescein, CDOM, brighteners and water depth. We also injected conservative tracers (i.e., NaCl and NaBr) and the bioreactive tracer resazurin in both experimental sites, and NO3 in the dryland river continuum. NO3 was measured in-situ with Satlantic Submersible Ultraviolet Nitrate Analyzers (SUNA) sensors and in the laboratory using Ion Chromatograph techniques using stream grab samples. Our results highlight the role of both residence times and chemical fluxes in regulating the effective processing of carbon and nutrients. Our results also demonstrate that stream stimuli from controlled experiments are ideal for maximizing the information content derived from short (hours to days) and mid-term (weeks) sensor deployment campaigns.

Meat Consumption, Related Nutrients, Obesity and Risk of Prostate Cancer: a Case-Control Study in Uruguay.

PubMed

Stefani, Eduardo De; Boffetta, Paolo L; Ronco, Alvaro; Deneo-Pellegrini, Hugo

2016-01-01

In order to determine the role of meat consumption and related nutrients in the etiology of prostate cancer we conducted a case-control study among Uruguayan men in the time period 1998-2007. The study included 464 cases and 472 controls, frequency matched for age and residence. Both series were drawn from the four major public hospitals in Montevideo. Unconditional logistic regression was used to estimate odds ratios (ORs) and 95 % confidence intervals (95 % CI) of prostate cancer by quartiles of meat intake and related nutrients. The highest vs. the lowest quartile of intake of total meat (OR = 5.19, 95 % CI 3.46-7.81), red meat (OR = 4.64, 95 % CI 3.10-6.95), and processed meat (OR = 1.78, 95% CI 1.22-2.59) were associated with increased risk of prostate cancer. Meat nutrients were directly associated with the risk of prostate cancer (OR for cholesterol 5.61, 95 % CI 3.75-8.50). Moreover, both total meat and red meat displayed higher risks among obese patients. This study suggests that total and red meat and meat nutrients may play a role in the etiology of prostate cancer in Uruguay.

Validity of electronic diet recording nutrient estimates compared to dietitian analysis of diet records: A randomized controlled trial

USDA-ARS?s Scientific Manuscript database

Background: Dietary intake assessment with diet records (DR) is a standard research and practice tool in nutrition. Manual entry and analysis of DR is time-consuming and expensive. New electronic tools for diet entry by clients and research participants may reduce the cost and effort of nutrient int...

The land-use legacy effect: Towards a mechanistic understanding of time-lagged water quality responses to land use/cover.

PubMed

Martin, Sherry L; Hayes, Daniel B; Kendall, Anthony D; Hyndman, David W

2017-02-01

Numerous studies have linked land use/land cover (LULC) to aquatic ecosystem responses, however only a few have included the dynamics of changing LULC in their analysis. In this study, we explicitly recognize changing LULC by linking mechanistic groundwater flow and travel time models to a historical time series of LULC, creating a land-use legacy map. We then illustrate the utility of legacy maps to explore relationships between dynamic LULC and lake water chemistry. We tested two main concepts about mechanisms linking LULC and lake water chemistry: groundwater pathways are an important mechanism driving legacy effects; and, LULC over multiple spatial scales is more closely related to lake chemistry than LULC over a single spatial scale. We applied statistical models to twelve water chemistry variables, ranging from nutrients to relatively conservative ions, to better understand the roles of biogeochemical reactivity and solubility on connections between LULC and aquatic ecosystem response. Our study illustrates how different areas can have long groundwater pathways that represent different LULC than what can be seen on the landscape today. These groundwater pathways delay the arrival of nutrients and other water quality constituents, thus creating a legacy of historic land uses that eventually reaches surface water. We find that: 1) several water chemistry variables are best fit by legacy LULC while others have a stronger link to current LULC, and 2) single spatial scales of LULC analysis performed worse for most variables. Our novel combination of temporal and spatial scales was the best overall model fit for most variables, including SRP where this model explained 54% of the variation. We show that it is important to explicitly account for temporal and spatial context when linking LULC to ecosystem response. Copyright © 2016. Published by Elsevier B.V.

Duality between Time Series and Networks

PubMed Central

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Physical habitat simulation system reference manual: version II

USGS Publications Warehouse

Milhous, Robert T.; Updike, Marlys A.; Schneider, Diane M.

1989-01-01

There are four major components of a stream system that determine the productivity of the fishery (Karr and Dudley 1978). These are: (1) flow regime, (2) physical habitat structure (channel form, substrate distribution, and riparian vegetation), (3) water quality (including temperature), and (4) energy inputs from the watershed (sediments, nutrients, and organic matter). The complex interaction of these components determines the primary production, secondary production, and fish population of the stream reach. The basic components and interactions needed to simulate fish populations as a function of management alternatives are illustrated in Figure I.1. The assessment process utilizes a hierarchical and modular approach combined with computer simulation techniques. The modular components represent the "building blocks" for the simulation. The quality of the physical habitat is a function of flow and, therefore, varies in quality and quantity over the range of the flow regime. The conceptual framework of the Incremental Methodology and guidelines for its application are described in "A Guide to Stream Habitat Analysis Using the Instream Flow Incremental Methodology" (Bovee 1982). Simulation of physical habitat is accomplished using the physical structure of the stream and streamflow. The modification of physical habitat by temperature and water quality is analyzed separately from physical habitat simulation. Temperature in a stream varies with the seasons, local meteorological conditions, stream network configuration, and the flow regime; thus, the temperature influences on habitat must be analysed on a stream system basis. Water quality under natural conditions is strongly influenced by climate and the geological materials, with the result that there is considerable natural variation in water quality. When we add the activities of man, the possible range of water quality possibilities becomes rather large. Consequently, water quality must also be analysed on a stream system basis. Such analysis is outside the scope of this manual, which concentrates on simulation of physical habitat based on depth, velocity, and a channel index. The results form PHABSIM can be used alone or by using a series of habitat time series programs that have been developed to generate monthly or daily habitat time series from the Weighted Usable Area versus streamflow table resulting from the habitat simulation programs and streamflow time series data. Monthly and daily streamflow time series may be obtained from USGS gages near the study site or as the output of river system management models.

Introduction and application of the multiscale coefficient of variation analysis.

PubMed

Abney, Drew H; Kello, Christopher T; Balasubramaniam, Ramesh

2017-10-01

Quantifying how patterns of behavior relate across multiple levels of measurement typically requires long time series for reliable parameter estimation. We describe a novel analysis that estimates patterns of variability across multiple scales of analysis suitable for time series of short duration. The multiscale coefficient of variation (MSCV) measures the distance between local coefficient of variation estimates within particular time windows and the overall coefficient of variation across all time samples. We first describe the MSCV analysis and provide an example analytical protocol with corresponding MATLAB implementation and code. Next, we present a simulation study testing the new analysis using time series generated by ARFIMA models that span white noise, short-term and long-term correlations. The MSCV analysis was observed to be sensitive to specific parameters of ARFIMA models varying in the type of temporal structure and time series length. We then apply the MSCV analysis to short time series of speech phrases and musical themes to show commonalities in multiscale structure. The simulation and application studies provide evidence that the MSCV analysis can discriminate between time series varying in multiscale structure and length.

Remote Sensing Analysis of Malawi's Agricultural Inputs Subsidy and Climate Variability Impacts on Productivity

NASA Astrophysics Data System (ADS)

Galford, G. L.; Fiske, G. J.; Sedano, F.; Michelson, H.

2016-12-01

Agriculture in sub-Saharan Africa is characterized by smallholder production and low yields ( 1 ton ha-1 year-1 since records began in 1961) for staple food crops such as maize (Zea mays). Many years of low-input farming have depleted much of the region's agricultural land of critical soil carbon and nitrogen, further reducing yield potentials. Malawi is a 98,000 km2 subtropical nation with a short rainy season from November to May, with most rainfall occurring between December and mid-April. This short growing season supports the cultivation of one primary crop, maize. In Malawi, many smallholder farmers face annual nutrient deficits as nutrients removed as grain harvest and residues are beyond replenishment levels. As a result, Malawi has had stagnant maize yields averaging 1.2 ton ha-1 year-1 for decades. After multiple years of drought and widespread hunger in the early 2000s, Malawi introduced an agricultural input support program (fertilizer and seed subsidy) in time for the 2006 harvest that was designed to restore soil nutrients, improve maize production, and decrease dependence on food aid. Malawi's subsidy program targets 50-67% of smallholder farmers who cultivate half a hectare or less, yet collectively supply 80% of the country's maize. The country has achieved significant increases in crop yields (now 2 tons/ha/year) and, as our analysis shows, benefited from a new resilience against drought. We utilized Landsat time series to determine cropland extent from 2000-present and identify areas of marginal and/or intermittent production. We found a strong latitudinal gradient of precipitation variability from north to south in CHIRPS data. We used the precipitation variability to normalize trends in a productivity proxy derived from MODIS EVI. After normalization of productivity to precipitation variability, we found significant productivity trends correlated to subsidy distribution. This work was conducted with Google's Earth Engine, a cloud-based platform for data storage and analysis with unprecedented speed and efficient computing by making use of Google's computing infrastructure.

Drivers of coastal bacterioplankton community diversity and structure along a nutrient gradient in the East China Sea

NASA Astrophysics Data System (ADS)

He, Jiaying; Wang, Kai; Xiong, Jinbo; Guo, Annan; Zhang, Demin; Fei, Yuejun; Ye, Xiansen

2017-04-01

Anthropogenic nutrient discharge poses widespread threats to coastal ecosystems and has increased environmental gradients from coast to sea. Bacterioplankton play crucial roles in coastal biogeochemical cycling, and a variety of factors affect bacterial community diversity and structure. We used 16S rRNA gene pyrosequencing to investigate the spatial variation in bacterial community composition (BCC) across five sites on a coast-offshore gradient in the East China Sea. Overall, bacterial alpha-diversity did not differ across sites, except that richness and phylogenetic diversity were lower in the offshore sites, and the highest alpha-diversity was found in the most landward site, with Chl-a being the main factor. BCCs generally clustered into coastal and offshore groups. Chl-a explained 12.3% of the variation in BCCs, more than that explained by either the physicochemical (5.7%) or spatial (8.5%) variables. Nutrients (particularly nitrate and phosphate), along with phytoplankton abundance, were more important than other physicochemical factors, co-explaining 20.0% of the variation in BCCs. Additionally, a series of discriminant families (primarily affiliated with Gammaproteobacteria and Alphaproteobacteria), whose relative abundances correlated with Chl-a, DIN, and phosphate concentrations, were identified, implying their potential to indicate phytoplankton blooms and nutrient enrichment in this marine ecosystem. This study provides insight into bacterioplankton response patterns along a coast-offshore gradient, with phytoplankton abundance increasing in the offshore sites. Time-series sampling across multiple transects should be performed to determine the seasonal and spatial patterns in bacterial diversity and community structure along this gradient.

Drivers of coastal bacterioplankton community diversity and structure along a nutrient gradient in the East China Sea

NASA Astrophysics Data System (ADS)

He, Jiaying; Wang, Kai; Xiong, Jinbo; Guo, Annan; Zhang, Demin; Fei, Yuejun; Ye, Xiansen

2018-03-01

Anthropogenic nutrient discharge poses widespread threats to coastal ecosystems and has increased environmental gradients from coast to sea. Bacterioplankton play crucial roles in coastal biogeochemical cycling, and a variety of factors affect bacterial community diversity and structure. We used 16S rRNA gene pyrosequencing to investigate the spatial variation in bacterial community composition (BCC) across five sites on a coast-offshore gradient in the East China Sea. Overall, bacterial alpha-diversity did not differ across sites, except that richness and phylogenetic diversity were lower in the offshore sites, and the highest alpha-diversity was found in the most landward site, with Chl-a being the main factor. BCCs generally clustered into coastal and offshore groups. Chl-a explained 12.3% of the variation in BCCs, more than that explained by either the physicochemical (5.7%) or spatial (8.5%) variables. Nutrients (particularly nitrate and phosphate), along with phytoplankton abundance, were more important than other physicochemical factors, co-explaining 20.0% of the variation in BCCs. Additionally, a series of discriminant families (primarily affiliated with Gammaproteobacteria and Alphaproteobacteria), whose relative abundances correlated with Chl-a, DIN, and phosphate concentrations, were identified, implying their potential to indicate phytoplankton blooms and nutrient enrichment in this marine ecosystem. This study provides insight into bacterioplankton response patterns along a coast-offshore gradient, with phytoplankton abundance increasing in the offshore sites. Time-series sampling across multiple transects should be performed to determine the seasonal and spatial patterns in bacterial diversity and community structure along this gradient.

Fringing coral reef condition decline: assembling the puzzle of human impact associated to coastal development.

NASA Astrophysics Data System (ADS)

Garza-Perez, J. R.; Lopez-Patoni, A.; Naranjo-Garcia, M. J.

2014-12-01

Coral cover at Akumal fringing coral reef decreased 50% in a 13 yr. period, while the adjacent coastal zone increased its human-modified surface (associated to urban-tourist development) in 192%. In the same period, the number of local residents only increased 20% (1088 to1362) but the visitors did in 50% from ca. 200,000 to ca. 300,000. In this coastal zone, the phreatic acts as a storage of nutrients and pollutants from sources related to human activity, thus having a chronic run-off towards the reef, with acute episodes during the rainy season, specially during the anomalous rainy season of 2013. Using videotransects for monitoring the benthic reef components, changes were detected: from 2000 to 2013 the algae cover increased 166%, the reef condition and the reef structure indexes decreased in 50%, and coral diseases incidence increased 25% after a spike increment of 150% in 2010. The role of anthropogenic-stress indicators (population, modified land area, nutrients) was explored along reef condition indicators (reef structure and diversity indexes, topographic complexity, benthic cover and coral diseases incidence) via spatial analysis and multivariate statistics. Spatial patterns of the change in reef condition derived from high-resolution satellite imagery also provided insight for the stressors analysis and their relationships along the study period. Stress indicators (land-modified area and population) are correlated to decreases in coral cover and in reef structure. Direct stressors as sedimentation, nutrients and pollutants seem to be related to the decrease in overall reef condition, although time-series data is lacking; the contextual interpretation of their effects, paired with benthic condition characteristics suggest a strong relationship between these stressors and the decrease in the condition of the reef.

Coupling End-Member Mixing Analysis and Isotope Mass Balancing (222-Rn) for Differentiation of Fresh and Recirculated Submarine Groundwater Discharge Into Knysna Estuary, South Africa

NASA Astrophysics Data System (ADS)

Petermann, E.; Knöller, K.; Rocha, C.; Scholten, J.; Stollberg, R.; Weiß, H.; Schubert, M.

2018-02-01

Quantification of submarine groundwater discharge (SGD) is essential for evaluating the vulnerability of coastal water bodies to groundwater pollution and for understanding water body material cycles response due to potential discharge of nutrients, organic compounds, or heavy metals. Here we present an environmental tracer-based methodology for quantifying SGD into Knysna Estuary, South Africa. Both components of SGD, (1) fresh, terrestrial (FSGD) and (2) saline, recirculated (RSGD), were differentiated. We conducted an end-member mixing analysis for radon (222Rn) and salinity time series of estuary water over two tidal cycles to determine fractions of seawater, riverwater, FSGD, and RSGD. The mixing analysis was treated as a constrained optimization problem for finding the end-member mixing ratio that is producing the best fit to observations at every time step. Results revealed highest FSGD and RSGD fractions in the estuary during peak low tide. Over a 24 h time series, the portions of FSGD and RSGD in the estuary water were 0.2% and 0.8% near the estuary mouth and the FSGD/RSGD ratio was 1:3.3. We determined a median FSGD of 41,000 m³ d-1 (1.4 m³ d-1 per m shoreline) and a median RSGD of 135,000 m³ d-1 (4.5 m³ d-1 per m shoreline) which suggests that SGD exceeds river discharge by a factor of 1.0-2.1. By comparison to other sources, this implies that SGD is responsible for 28-73% of total DIN fluxes into Knysna Estuary.

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Determining Nutrient Requirements For Intensively Managed Loblolly Pine Stands Using the SSAND (Soil Supply and Nutrient Demand) Model

Treesearch

Hector G. Adegbidi; Nicholas B. Comerford; Hua Li; Eric J. Jokela; Nairam F. Barros

2002-01-01

Nutrient management represents a central component of intensive silvicultural systems that are designed to increase forest productivity in southern pine stands. Forest soils throughout the South are generally infertile, and fertilizers may be applied one or more times over the course of a rotation. Diagnostic techniques, such as foliar analysis and soil testing are...

Health and Nutrient Content Claims in Food Advertisements on Hispanic and Mainstream Prime-Time Television

ERIC Educational Resources Information Center

Abbatangelo-Gray, Jodie; Byrd-Bredbenner, Carol; Austin, S. Bryn

2008-01-01

Objective: Characterize frequency and type of health and nutrient content claims in prime-time weeknight Spanish- and English-language television advertisements from programs shown in 2003 with a high viewership by women aged 18 to 35 years. Design: Comparative content analysis design was used to analyze 95 hours of Spanish-language and 72 hours…

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

An Ontology for the Discovery of Time-series Data

NASA Astrophysics Data System (ADS)

Hooper, R. P.; Choi, Y.; Piasecki, M.; Zaslavsky, I.; Valentine, D. W.; Whitenack, T.

2010-12-01

An ontology was developed to enable a single-dimensional keyword search of time-series data collected at fixed points, such as stream gage records, water quality observations, or repeated biological measurements collected at fixed stations. The hierarchical levels were developed to allow navigation from general concepts to more specific ones, terminating in a leaf concept, which is the specific property measured. For example, the concept “nutrient” has child concepts of “nitrogen”, “phosphorus”, and “carbon”; each of these children concepts are then broken into the actual constituent measured (e.g., “total kjeldahl nitrogen” or “nitrate + nitrite”). In this way, a non-expert user can find all nutrients containing nitrogen without knowing all the species measured, but an expert user can go immediately to the compound of interest. In addition, a property, such as dissolved silica, can appear as a leaf concept under nutrients or weathering products. This flexibility allows users from various disciplines to find properties of interest. The ontology can be viewed at http://water.sdsc.edu/hiscentral/startree.aspx. Properties measured by various data publishers (e.g., universities and government agencies) are tagged with leaf concepts from this ontology. A discovery client, HydroDesktop, creates a search request by defining the spatial and temporal extent of interest and a keyword taken from the discovery ontology. Metadata returned from the catalog describes the time series which meet the specified search criteria. This ontology is considered to be an initial description of physical, chemical and biological properties measured in water and suspended sediment. Future plans call for creating a moderated forum for the scientific community to add to and to modify this ontology. Further information for the Hydrologic Information Systems project, of which this is a part, is available at http://his.cuahsi.org.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Time averaging, ageing and delay analysis of financial time series

NASA Astrophysics Data System (ADS)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Visibility Graph Based Time Series Analysis.

PubMed

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Synergetic Use of Sentinel-1 and 2 to Improve Agro-Hydrological Modeling

NASA Astrophysics Data System (ADS)

Ferrant, Sylvain; Kerr, Yann; Al-Bitar, Ahmad; Le Page, Michel; Selles, Adrien; Mermoz, Stephane; Bouvet, Alexandre; Marechal, Jean-Christophe; Tomer, Sat; Sekhar, Muddu; Dedieu, Gerard; Le Toan, Thuy; Bustillo, Vincent

2016-08-01

In the context of global changes and population growth, agricultural activities are a growing factor influencing water resources availability in term of quantity and quality. Water management strategies have to be analyzed at a regional catchment scales. Yet, agricultural practices, crop water and nutrient consumption that drive the main water and nutrient fluxes at the catchment scale have to be monitored at a high spatial (crop extension) and temporal resolution (crop growth period). This proceeding describes some advances in the framework of a co-funded ESA Living Planet Fellowship project, called ―agro-hydrology from space‖, which aims at demonstrating the improvement brought by synergetic observations of Sentinel-1 (S1) and Sentinel-2 (S2) satellite mission in agro- hydrological studies. Geo-information time-series of vegetation and water index with multi-spectral optical detection S2 together with surface roughness time series with C-band radar detection S1 are used to re-set soil water holding capacity parameters (depth, porosity) and agricultural practices (sowing date, irrigated area extent) of a crop model coupled with a hydrological model in two contrasted water management issues: stream water nitrate pollution in Gascogne region in south-west of France and groundwater depletion and shortages for irrigation in Deccan Plateau, in south-India.

Nitrogen enrichment and speciation in a coral reef lagoon driven by groundwater inputs of bird guano

NASA Astrophysics Data System (ADS)

McMahon, Ashly; Santos, Isaac R.

2017-09-01

While the influence of river inputs on coral reef biogeochemistry has been investigated, there is limited information on nutrient fluxes related to submarine groundwater discharge (SGD). Here, we investigate whether significant saline groundwater-derived nutrient inputs from bird guano drive coral reef photosynthesis and calcification off Heron Island (Great Barrier Reef, Australia). We used multiple experimental approaches including groundwater sampling, beach face transects, and detailed time series observations to assess the dynamics and speciation of groundwater nutrients as they travel across the island and discharge into the coral reef lagoon. Nitrogen speciation shifted from nitrate-dominated groundwater (>90% of total dissolved nitrogen) to a coral reef lagoon dominated by dissolved organic nitrogen (DON; ˜86%). There was a minimum input of nitrate of 2.1 mmol m-2 d-1 into the lagoon from tidally driven submarine groundwater discharge estimated from a radon mass balance model. An independent approach based on the enrichment of dissolved nutrients during isolation at low tide implied nitrate fluxes of 5.4 mmol m-2 d-1. A correlation was observed between nitrate and daytime net ecosystem production and calcification. We suggest that groundwater nutrients derived from bird guano may offer a significant addition to oligotrophic coral reef lagoons and fuel ecosystem productivity and the coastal carbon cycle near Heron Island. The large input of groundwater nutrients in Heron Island may serve as a natural ecological analogue to other coral reefs subject to large nutrient inputs from anthropogenic sources.

Spectral analysis for GNSS coordinate time series using chirp Fourier transform

NASA Astrophysics Data System (ADS)

Feng, Shengtao; Bo, Wanju; Ma, Qingzun; Wang, Zifan

2017-12-01

Spectral analysis for global navigation satellite system (GNSS) coordinate time series provides a principal tool to understand the intrinsic mechanism that affects tectonic movements. Spectral analysis methods such as the fast Fourier transform, Lomb-Scargle spectrum, evolutionary power spectrum, wavelet power spectrum, etc. are used to find periodic characteristics in time series. Among spectral analysis methods, the chirp Fourier transform (CFT) with less stringent requirements is tested with synthetic and actual GNSS coordinate time series, which proves the accuracy and efficiency of the method. With the length of series only limited to even numbers, CFT provides a convenient tool for windowed spectral analysis. The results of ideal synthetic data prove CFT accurate and efficient, while the results of actual data show that CFT is usable to derive periodic information from GNSS coordinate time series.

Changes in distributional patterns of plaice Pleuronectes platessa in the central and eastern North Sea; do declining nutrient loadings play a role?

NASA Astrophysics Data System (ADS)

Støttrup, Josianne G.; Munk, Peter; Kodama, Masashi; Stedmon, Colin

2017-09-01

Since the beginning of the 1990s, there has been a change in the relative distribution of smaller age-classes of plaice Pleuronectes platessa (age 1-3) in the North Sea. The abundances have increased in deeper, more offshore areas, while coastal abundances have been stagnant or declining. For the same time period available time series data on nutrient conditions in the coastal North Sea area show that the freshwater nitrogen loading has decreased by about 50%. While nutrient concentrations in the ambient environment have been shown to influence growth in juvenile plaice through influence on their prey, we here inspect the potential linkage between distributional changes in plaice and the decline in nutrient loading. We compare plaice observations in coastal areas in the eastern North Sea, which have experienced large changes in eutrophication, with observations for the Dogger Bank, a large sandbank in a shallow offshore area of the North Sea. The Dogger Bank, was used as a reference location assuming this area has been less influenced from coastal eutrophication but similar regional climate conditions, and here we found no changes in the abundances of juvenile plaice. The increase in the use of offshore habitats as nursery areas by juvenile plaice in the North Sea appears not related to water depth per se but driven by specific processes dominating in near-shore areas and may be related to changes in nutrient loadings. This point to the importance of separating more general depth-related factors from conditions specific for near-shore areas, such as nutrient loadings in coastal waters and export offshore. The concurrent changes in environment and in distribution of juvenile plaice may have implications for environmental and fisheries management.

A comparison of eutrophication impacts in two harbours in Hong Kong with different hydrodynamics

NASA Astrophysics Data System (ADS)

Xu, J.; Yin, K.; Liu, H.; Lee, J. H. W.; Anderson, D. M.; Ho, A. Y. T.; Harrison, P. J.

2010-11-01

Eutrophication impacts may vary spatially and temporally due to different physical processes. Using a 22-year time series data set (1986-2007), a comparison was made of eutrophication impacts between the two harbours with very different hydrodynamic conditions. Victoria Harbour (Victoria) receives sewage effluent and therefore nutrients are abundant. In the highly-flushed Victoria, the highest monthly average Chl a (13 μg L -1) occurred during the period of strongest stratification in summer as a result of rainfall, runoff and the input of the nutrient-rich Pearl River estuarine waters, but the high flushing rate restricted nutrient utilization and further accumulation of algal biomass. In other seasons, vertical mixing induced light limitation and horizontal dilution led to low Chl a (< 2 μg L -1) and no spring bloom. Few hypoxic events (DO < 2 mg L -1) occurred due to re-aeration and limited accumulation at depth due to flushing and vertical mixing. Therefore, Victoria is resilient to nutrient enrichment. In contrast, in the weakly-flushed Tolo Harbour (Tolo), year long stratification, long residence times and weak tidal currents favored algal growth, resulting in a spring diatom bloom and high Chl a (10-30 μg L -1) all year and frequent hypoxic events in summer. Hence, Tolo is susceptible to nutrient enrichment and responded to nutrient reduction after sewage diversion in 1997. Sewage diversion from Tolo resulted in a 32-38% decrease in algal biomass in Tolo, but not in Victoria. There has been a significant increase (11-22%) in bottom DO in both harbours. Our findings demonstrate that an understanding of the role of physical processes is critical in order to predict the effectiveness of sewage management strategies in reducing eutrophication impacts.

Is nutritional quality of food-at-home purchases improving? 1969-2010: 40 years of household consumption surveys in France.

PubMed

Caillavet, France; Darmon, Nicole; Létoile, Flavie; Nichèle, Véronique

2018-02-01

The rise of nutrition-related diseases in developed countries prompts investigation into the role played by changing food patterns. Our aim was to observe changes in food-at-home purchases by French households and their impacts on nutritional quality over the past 40 years (1969-2010). Time-series of food-at-home purchases from representative samples of French households were built based on two sources of data: the INSEE National Food Survey (1969-1991) and the Kantar Food Consumption Panel (1989-2010). Food-at-home purchases were converted into energy and nutrients using the French CIQUAL food composition table. The nutritional quality of food-at-home purchases was estimated using the mean adequacy ratio (MAR) for 15 key nutrients. MAR was expressed per 2000 kcal to assess the nutrient density of food-at-home purchases. Between 1969 and 2010, food-at-home purchases showed dramatic changes in many food groups, with increasing processed vs raw products. The purchase of calories increased (+6.7%) and nutrient density improved (MAR per 2000 kcal + 12.9 points). However, this overall trend harbors heterogeneous patterns: food-at-home calories decreased and nutrient density improved up to 2002, but then calories increased while nutrient density stabilized. The nutritional quality of French households' food-at-home purchases improved over the last 40 years, as shown by increasing nutrient density. However, during the last decade, nutrient density ceased to increase and the purchase of calories increased, advocating a need for public action to promote healthier food purchasing patterns.

Non-parametric characterization of long-term rainfall time series

NASA Astrophysics Data System (ADS)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Secondary analysis of a marketing research database reveals patterns in dairy product purchases over time.

PubMed

Van Wave, Timothy W; Decker, Michael

2003-04-01

Development of a method using marketing research data to assess food purchase behavior and consequent nutrient availability for purposes of nutrition surveillance, evaluation of intervention effects, and epidemiologic studies of diet-health relationships. Data collected on household food purchases accrued over a 13-week period were selected by using Universal Product Code numbers and household characteristics from a marketing research database. Universal Product Code numbers for 39,408 dairy product purchases were linked to a standard reference for food composition to estimate the nutrient content of foods purchased over time. Two thousand one hundred sixty-one households located in Victoria, Texas, and surrounding communities who were active members of a frequent shopper program. Demographic characteristics of sample households and the nutrient content of their dairy product purchases were analyzed using frequency distribution, cross tabulation, analysis of variance, and t test procedures. A method for using marketing research data was successfully used to estimate household purchases of specific foods and their nutrient content from a marketing database containing hundreds of thousands of records. Distribution of dairy product purchases and their concomitant nutrients between Hispanic and non-Hispanic households were significant (P<.01, P<.001, respectively) and sustained over time. Purchase records from large, nationally representative panels of shoppers, such as those maintained by major market research companies, might be used to accomplish detailed longitudinal epidemiologic studies or surveillance of national food- and nutrient-purchasing patterns within and between countries and segments of their respective populations.

Long-Term Time Series of Remote Sensing Observations for Development of Regulatory Water Quality Standards

NASA Technical Reports Server (NTRS)

Blonski, Slawomir; Spiering, Bruce A.; Holekamp, Kara L.

2010-01-01

Water quality standards in the U.S. consist of: designated uses (the services that a water body provides; e.g., drinking water, aquatic life, harvestable species, recreation) . criteria that define the environmental conditions that must be maintained to support the uses For estuaries and coastal waters in the Gulf of Mexico, there are no numeric (quantitative) criteria to protect designated uses from effects of nutrients. This is largely due to the absence of adequate data that would quantitatively link biological conditions to nutrient concentrations. The Gulf of Mexico Alliance, an organization fostering collaboration between the Gulf States and U.S. Federal agencies, has identified the development of the numeric nutrient criteria as a major step leading to reduction in MODIS Products Figure 6. Map of the Mobile Bay with a yellow patch indicating the Bon Secour Bay area selected in this study for averaging water clarity parameters retrieved from MODIS datasets. nutrient inputs to coastal ecosystems. Nutrient enrichment in estuaries and coastal waters can be quantified based on response variables that measure phytoplankton biomass and water clarity. Long-term, spatially and temporally resolved measurements of chlorophyll a concentration, total concentration of suspended solids, and water clarity are needed to establish reference conditions and to quantify stressor-response relationships.

ENSO-driven nutrient variability recorded by central equatorial Pacific corals

NASA Astrophysics Data System (ADS)

LaVigne, M.; Nurhati, I. S.; Cobb, K. M.; McGregor, H. V.; Sinclair, D. J.; Sherrell, R. M.

2012-12-01

Recent evidence for shifts in global ocean primary productivity suggests that surface ocean nutrient availability is a key link between global climate and ocean carbon cycling. Time-series records from satellite, in situ buoy sensors, and bottle sampling have documented the impact of the El Niño Southern Oscillation (ENSO) on equatorial Pacific hydrography and broad changes in biogeochemistry since the late 1990's, however, data are sparse prior to this. Here we use a new paleoceanographic nutrient proxy, coral P/Ca, to explore the impact of ENSO on nutrient availability in the central equatorial Pacific at higher-resolution than available from in situ nutrient data. Corals from Christmas (157°W 2°N) and Fanning (159°W 4°N) Islands recorded a well-documented decrease in equatorial upwelling as a ~40% decrease in P/Ca during the 1997-98 ENSO cycle, validating the application of this proxy to Pacific Porites corals. We compare the biogeochemical shifts observed through the 1997-98 event with two pre-TOGA-TAO ENSO cycles (1982-83 and 1986-87) reconstructed from a longer Christmas Island core. All three corals revealed ~30-40% P/Ca depletions during ENSO warming as a result of decreased regional wind stress, thermocline depth, and equatorial upwelling velocity. However, at the termination of each El Niño event, surface nutrients did not return to pre-ENSO levels for ~4-12 months after, SST as a result of increased biological draw down of surface nutrients. These records demonstrate the utility of high-resolution coral nutrient archives for understanding the impact of tropical Pacific climate on the nutrient and carbon cycling of this key region.

Improving dynamic phytoplankton reserve-utilization models with an indirect proxy for internal nitrogen.

PubMed

Malerba, Martino E; Heimann, Kirsten; Connolly, Sean R

2016-09-07

Ecologists have often used indirect proxies to represent variables that are difficult or impossible to measure directly. In phytoplankton, the internal concentration of the most limiting nutrient in a cell determines its growth rate. However, directly measuring the concentration of nutrients within cells is inaccurate, expensive, destructive, and time-consuming, substantially impairing our ability to model growth rates in nutrient-limited phytoplankton populations. The red chlorophyll autofluorescence (hereafter "red fluorescence") signal emitted by a cell is highly correlated with nitrogen quota in nitrogen-limited phytoplankton species. The aim of this study was to evaluate the reliability of including flow cytometric red fluorescence as a proxy for internal nitrogen status to model phytoplankton growth rates. To this end, we used the classic Quota model and designed three approaches to calibrate its model parameters to data: where empirical observations on cell internal nitrogen quota were used to fit the model ("Nitrogen-Quota approach"), where quota dynamics were inferred only from changes in medium nutrient depletion and population density ("Virtual-Quota approach"), or where red fluorescence emission of a cell was used as an indirect proxy for its internal nitrogen quota ("Fluorescence-Quota approach"). Two separate analyses were carried out. In the first analysis, stochastic model simulations were parameterized from published empirical relationships and used to generate dynamics of phytoplankton communities reared under nitrogen-limited conditions. Quota models were fitted to the dynamics of each simulated species with the three different approaches and the performance of each model was compared. In the second analysis, we fit Quota models to laboratory time-series and we calculate the ability of each calibration approach to describe the observed trajectories of internal nitrogen quota in the culture. Results from both analyses concluded that the Fluorescence-Quota approach including per-cell red fluorescence as a proxy of internal nitrogen substantially improved the ability of Quota models to describe phytoplankton dynamics, while still accounting for the biologically important process of cell nitrogen storage. More broadly, many population models in ecology implicitly recognize the importance of accounting for storage mechanisms to describe the dynamics of individual organisms. Hence, the approach documented here with phytoplankton dynamics may also be useful for evaluating the potential of indirect proxies in other ecological systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.

PubMed

Fulcher, Ben D; Jones, Nick S

2017-11-22

Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Comparative analysis of phytochemicals and nutrient availability in two contrasting cultivars of sweet potato (Ipomoea batatas L.).

PubMed

Shekhar, Shubhendu; Mishra, Divya; Buragohain, Alak Kumar; Chakraborty, Subhra; Chakraborty, Niranjan

2015-04-15

Sweet potato ranks as the world's seventh most important food crop, and has major contribution to energy and phytochemical source of nutrition. To unravel the molecular basis for differential nutrient availability, and to exploit the natural genetic variation(s) of sweet potato, a series of physiochemical and proteomics experiment was conducted using two contrasting cultivars, an orange-fleshed sweet potato (OFSP) and a white-fleshed sweet potato (WFSP). Phytochemical screening revealed high percentage of carbohydrate, reducing sugar and phenolics in WFSP, whereas OFSP showed increased levels of total protein, flavonoids, anthocyanins, and carotenoids. The rate of starch and cellulose degradation was found to be less in OFSP during storage, indicating tight regulation of gene(s) responsible for starch-degradation. Comparative proteomics displayed a cultivar-dependent expression of proteins along with evolutionarily conserved proteins. These results suggest that cultivar-specific expression of proteins and/or their interacting partners might play a crucial role for nutrient acquisition in sweet potato. Copyright © 2014 Elsevier Ltd. All rights reserved.

A novel water quality data analysis framework based on time-series data mining.

PubMed

Deng, Weihui; Wang, Guoyin

2017-07-01

The rapid development of time-series data mining provides an emerging method for water resource management research. In this paper, based on the time-series data mining methodology, we propose a novel and general analysis framework for water quality time-series data. It consists of two parts: implementation components and common tasks of time-series data mining in water quality data. In the first part, we propose to granulate the time series into several two-dimensional normal clouds and calculate the similarities in the granulated level. On the basis of the similarity matrix, the similarity search, anomaly detection, and pattern discovery tasks in the water quality time-series instance dataset can be easily implemented in the second part. We present a case study of this analysis framework on weekly Dissolve Oxygen time-series data collected from five monitoring stations on the upper reaches of Yangtze River, China. It discovered the relationship of water quality in the mainstream and tributary as well as the main changing patterns of DO. The experimental results show that the proposed analysis framework is a feasible and efficient method to mine the hidden and valuable knowledge from water quality historical time-series data. Copyright © 2017 Elsevier Ltd. All rights reserved.

More on Time Series Designs: A Reanalysis of Mayer and Kozlow's Data.

ERIC Educational Resources Information Center

Willson, Victor L.

1982-01-01

Differentiating between time-series design and time-series analysis, examines design considerations and reanalyzes data previously reported by Mayer and Kozlow in this journal. The current analysis supports the analysis performed by Mayer and Kozlow but puts the results on a somewhat firmer statistical footing. (Author/JN)

Climate effects on phytoplankton floral composition in Chesapeake Bay

NASA Astrophysics Data System (ADS)

Harding, L. W.; Adolf, J. E.; Mallonee, M. E.; Miller, W. D.; Gallegos, C. L.; Perry, E. S.; Johnson, J. M.; Sellner, K. G.; Paerl, H. W.

2015-09-01

Long-term data on floral composition of phytoplankton are presented to document seasonal and inter-annual variability in Chesapeake Bay related to climate effects on hydrology. Source data consist of the abundances of major taxonomic groups of phytoplankton derived from algal photopigments (1995-2004) and cell counts (1985-2007). Algal photopigments were measured by high-performance liquid chromatography (HPLC) and analyzed using the software CHEMTAX to determine the proportions of chlorophyll-a (chl-a) in major taxonomic groups. Cell counts determined microscopically provided species identifications, enumeration, and dimensions used to obtain proportions of cell volume (CV), plasma volume (PV), and carbon (C) in the same taxonomic groups. We drew upon these two independent data sets to take advantage of the unique strengths of each method, using comparable quantitative measures to express floral composition for the main stem bay. Spatial and temporal variability of floral composition was quantified using data aggregated by season, year, and salinity zone. Both time-series were sufficiently long to encompass the drought-flood cycle with commensurate effects on inputs of freshwater and solutes. Diatoms emerged as the predominant taxonomic group, with significant contributions by dinoflagellates, cryptophytes, and cyanobacteria, depending on salinity zone and season. Our analyses revealed increased abundance of diatoms in wet years compared to long-term average (LTA) or dry years. Results are presented in the context of long-term nutrient over-enrichment of the bay, punctuated by inter-annual variability of freshwater flow that strongly affects nutrient loading, chl-a, and floral composition. Statistical analyses generated flow-adjusted diatom abundance and showed significant trends late in the time series, suggesting current and future decreases of nutrient inputs may lead to a reduction of the proportion of biomass comprised by diatoms in an increasingly diverse flora.

Enrichment in a stoichiometric model of two producers and one consumer.

PubMed

Lin, Laurence Hao-Ran; Peckham, Bruce B; Stech, Harlan W; Pastor, John

2012-01-01

We consider a stoichiometric population model of two producers and one consumer. Stoichiometry can be thought of as the tracking of food quality in addition to food quantity. Our model assumes a reduced rate of conversion of biomass from producer to consumer when food quality is low. The model is open for carbon but closed for nutrient. The introduction of the second producer, which competes with the first, leads to new equilibria, new limit cycles, and new bifurcations. The focus of this paper is on the bifurcations which are the result of enrichment. The primary parameters we vary are the growth rates of both producers. Secondary variable parameters are the total nutrients in the system, and the producer nutrient uptake rates. The possible equilibria are: no-life, one-producer, coexistence of both producers, the consumer coexisting with either producer, and the consumer coexisting with both producers. We observe limit cycles in the latter three coexistence combinations. Bifurcation diagrams along with corresponding representative time series summarize the behaviours observed for this model.

Repeat synoptic sampling reveals drivers of change in carbon and nutrient chemistry of Arctic catchments

NASA Astrophysics Data System (ADS)

Zarnetske, J. P.; Abbott, B. W.; Bowden, W. B.; Iannucci, F.; Griffin, N.; Parker, S.; Pinay, G.; Aanderud, Z.

2017-12-01

Dissolved organic carbon (DOC), nutrients, and other solute concentrations are increasing in rivers across the Arctic. Two hypotheses have been proposed to explain these trends: 1. distributed, top-down permafrost degradation, and 2. discrete, point-source delivery of DOC and nutrients from permafrost collapse features (thermokarst). While long-term monitoring at a single station cannot discriminate between these mechanisms, synoptic sampling of multiple points in the stream network could reveal the spatial structure of solute sources. In this context, we sampled carbon and nutrient chemistry three times over two years in 119 subcatchments of three distinct Arctic catchments (North Slope, Alaska). Subcatchments ranged from 0.1 to 80 km2, and included three distinct types of Arctic landscapes - mountainous, tundra, and glacial-lake catchments. We quantified the stability of spatial patterns in synoptic water chemistry and analyzed high-frequency time series from the catchment outlets across the thaw season to identify source areas for DOC, nutrients, and major ions. We found that variance in solute concentrations between subcatchments collapsed at spatial scales between 1 to 20 km2, indicating a continuum of diffuse- and point-source dynamics, depending on solute and catchment characteristics (e.g. reactivity, topography, vegetation, surficial geology). Spatially-distributed mass balance revealed conservative transport of DOC and nitrogen, and indicates there may be strong in-stream retention of phosphorus, providing a network-scale confirmation of previous reach-scale studies in these Arctic catchments. Overall, we present new approaches to analyzing synoptic data for change detection and quantification of ecohydrological mechanisms in ecosystems in the Arctic and beyond.

A time-series approach to dynamical systems from classical and quantum worlds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fossion, Ruben

2014-01-08

This contribution discusses some recent applications of time-series analysis in Random Matrix Theory (RMT), and applications of RMT in the statistial analysis of eigenspectra of correlation matrices of multivariate time series.

Sequential nutrient uptake as a potential mechanism for phytoplankton to maintain high primary productivity and balanced nutrient stoichiometry

NASA Astrophysics Data System (ADS)

Yin, Kedong; Liu, Hao; Harrison, Paul J.

2017-05-01

We hypothesize that phytoplankton have the sequential nutrient uptake strategy to maintain nutrient stoichiometry and high primary productivity in the water column. According to this hypothesis, phytoplankton take up the most limiting nutrient first until depletion, continue to draw down non-limiting nutrients and then take up the most limiting nutrient rapidly when it is available. These processes would result in the variation of ambient nutrient ratios in the water column around the Redfield ratio. We used high-resolution continuous vertical profiles of nutrients, nutrient ratios and on-board ship incubation experiments to test this hypothesis in the Strait of Georgia. At the surface in summer, ambient NO3- was depleted with excess PO43- and SiO4- remaining, and as a result, both N : P and N : Si ratios were low. The two ratios increased to about 10 : 1 and 0. 45 : 1, respectively, at 20 m. Time series of vertical profiles showed that the leftover PO43- continued to be removed, resulting in additional phosphorus storage by phytoplankton. The N : P ratios at the nutricline in vertical profiles responded differently to mixing events. Field incubation of seawater samples also demonstrated the sequential uptake of NO3- (the most limiting nutrient) and then PO43- and SiO4- (the non-limiting nutrients). This sequential uptake strategy allows phytoplankton to acquire additional cellular phosphorus and silicon when they are available and wait for nitrogen to become available through frequent mixing of NO3- (or pulsed regenerated NH4). Thus, phytoplankton are able to maintain high productivity and balance nutrient stoichiometry by taking advantage of vigorous mixing regimes with the capacity of the stoichiometric plasticity. To our knowledge, this is the first study to show the in situ dynamics of continuous vertical profiles of N : P and N : Si ratios, which can provide insight into the in situ dynamics of nutrient stoichiometry in the water column and the inference of the transient status of phytoplankton nutrient stoichiometry in the coastal ocean.

[FEATURES OF MASS-SPECTROMETRIC PROTEIN PROFILES OF STRAINS OF BRUCELLOSIS CAUSATIVE AGENT DURING PREPARATION OF CULTURE ON VARIOUS NUTRIENT MEDIA].

PubMed

Ulshina, D V; Kovalev, D A; Zhirov, A M; Zharinova, N V; Khudoleev, A A; Kogotkova, O I; Efremenko, V I; Evchenko, N I; Kulichenko, A N

2016-01-01

Carry out comparative analysis using time-of-flight mass-spectrometry with matrix laser desorption/ionization (MALDI-TOF MS) of protein profiles of brucellosis causative agents (Brucella melitensis Rev-1 and Brucella abortus 19BA), cultivated in various nutrient media: Albimi agar, brucellagar and erythrit-agar. Vaccine,strains: Brucella melitensis Rev-1 and Brucella abortus 19BA. Protein profiling in linear mode on Microflex "Bruker Daltonics" MALDI-TOF mass-spectrometer. A number of characteristic features of brucella mass-spectra was detected: in particular, preservation of the total qualitative composition of protein profiles of cultures and significant differences in the intensity of separate peaks depending on the nutrient medium used. Based on the analysis of the data obtained, use of Albimi agar as the nutrient medium for preparation of brucella culture samples for mass-spectrometric analysis was shown to be optimal.

Synthesis of data from high-frequency nutrient and associated biogeochemical monitoring for the Sacramento–San Joaquin Delta, northern California

USGS Publications Warehouse

Downing, Bryan D.; Bergamaschi, Brian A.; Kraus, Tamara E.C.

2017-07-11

Executive SummaryThis report is the second in a series of three reports that provide information about high-frequency (HF) nutrient and biogeochemical monitoring in the Sacramento–San Joaquin Delta of northern California (Delta). The purpose of this report is to synthesize the data available from a nutrient and water-quality HF (about every 15 minutes) monitoring network operated by the U.S. Geological Survey in the northern Delta. In this report, we describe the network and focus on the purpose of each station. We then present and discuss the available data, at various timescales—first at the monthly, seasonal, and inter-annual timescales, and second, for comparison, at the tidal and event timescales. As expected, we determined that there is substantial variability in nitrate-N concentrations at short timescales within hours, but also significant variability at longer timescales such as months or years. Resolving this variability is made possible by the HF data, with the largest variability caused by storms, tides, and diel biological processes. Given this large temporal variability, calculations of cumulative nutrient fluxes (for example, daily, monthly, or annual loads) is difficult without HF data. For example, in the Cache Slough, calculation of the annual load without the tidal variability resulted in a 30 percent underestimation of the true annual load value. We conclude that HF measurements are important for accurate determination of fluxes and loads in tidal environments, but, more importantly, provide important insights into processes and rates of nutrient cycling.This report, along with the other two reports of this series (Bergamaschi and others, 2017; Kraus, Bergamaschi, and others, 2017), was drafted in cooperation with the Delta Regional Monitoring Program to help scientists, managers, and planners understand how HF data improve our understanding of nutrient sources and sinks, drivers, and effects in the Delta. The first report in the series (Kraus, Bergamaschi, and others, 2017) provides an introduction to the reasons for and fundamental concepts behind using HF monitoring measurements, including a brief summary of nutrient status and trends in the Delta and an extensive literature review showing how and where other research and monitoring programs have used HF monitoring to improve our understanding of nutrient cycling. The report covers the various technologies available for HF nutrient monitoring and presents the different ways HF monitoring instrumentation may be used for fixed station and spatial assessments. Finally, it presents numerous examples of how HF measurements are currently (2017) being used in the Delta to examine how nutrients and nutrient cycling are related to aquatic habitat conditions.The third report in the series (Bergamaschi and others, 2017) provides the background, principles, and considerations for designing an HF nutrient-monitoring network for the Delta to address high-priority, nutrient-management questions. The report starts with discussion of the high‑priority management questions to be addressed, continues through discussion of the questions and considerations that place demands and constraints on network design, discusses the principles applicable to network design, and concludes with the presentation of three example nutrient-monitoring network designs for the Delta, proposed to address high-priority questions identified by the Delta Regional Monitoring Program (Delta Regional Monitoring Program Technical Advisory Committee, 2015).

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

Visibility Graph Based Time Series Analysis

PubMed Central

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it’s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. PMID:26571115

Nonlinear Dynamics, Poor Data, and What to Make of Them?

NASA Astrophysics Data System (ADS)

Ghil, M.; Zaliapin, I. V.

2005-12-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict variability in the geosciences. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this talk we will describe the connections between time series analysis and nonlinear dynamics, discuss signal-to-noise enhancement, and present some of the novel methods for spectral analysis. These fall into two broad categories: (i) methods that try to ferret out regularities of the time series; and (ii) methods aimed at describing the characteristics of irregular processes. The former include singular-spectrum analysis (SSA), the multi-taper method (MTM), and the maximum-entropy method (MEM). The various steps, as well as the advantages and disadvantages of these methods, will be illustrated by their application to several important climatic time series, such as the Southern Oscillation Index (SOI), paleoclimatic time series, and instrumental temperature time series. The SOI index captures major features of interannual climate variability and is used extensively in its prediction. The other time series cover interdecadal and millennial time scales. The second category includes the calculation of fractional dimension, leading Lyapunov exponents, and Hurst exponents. More recently, multi-trend analysis (MTA), binary-decomposition analysis (BDA), and related methods have attempted to describe the structure of time series that include both regular and irregular components. Within the time available, I will try to give a feeling for how these methods work, and how well.

The Effects of Groundwater-associated Nutrients on Benthic Community Composition in Maunalua Bay, Hawaíi

NASA Astrophysics Data System (ADS)

La Valle, F. F.; Thomas, F. I. M.

2016-02-01

As populations grow and development efforts continue in coastal regions throughout the world, eutrophication is one of the leading issues surrounding coastal ecosystems. Currently, studies on subterranean groundwater discharge (SGD) are confirming that SGD can contain substantial nutrient concentrations due to agricultural activities, urbanization, leaky septic and sewer systems, and use of fertilizers. Thus, it is important for SGD with high nutrient concentrations to be monitored for its impact on coastal dynamics. Coral reef systems are especially sensitive to changes in nutrient concentrations which can change community composition by creating advantageous biochemical environments for specific algal species. Excess nutrients along with decreased herbivory have been attributed to phase shifts from coral dominated to algal dominated reefs. In this study we mapped algal cover and nutrient load with respect to the groundwater in two fringing reefs (Black Point and Wailupe) in Maunalua Bay, Oahu, Hawaíi. We established relationships between salinity and nutrient concentrations for the two sites by sampling synoptically on an onshore to offshore transect from the SGD seeps (n = 48 Black Point, n = 40 Wailupe, R2 > 0.965). The groundwater end members at the two sites have different nutrient signatures: concentrations at Black Point averaged 167.3 uM N+N (NO3- + NO2-) and 3.57 uM PO43-, while at Wailupe nutrient concentrations averaged 68.7 uM N+N and 1.96 uM PO43-. We used these relationships to calculate nutrient time series after deploying 23 autonomous salinity sensors for one month across the benthos at each site respectively. Benthic surveys taken over 2 seasons indicate that the algal composition and distribution relative to the groundwater sources differ at the two sites. Growth rates of some major macroalgal species also differ with distance from SGD source. Further studies on the biological effects of high SGD-associated nutrients on coastal systems are warranted.

Legacy nutrient dynamics and patterns of catchment response under changing land use and management

NASA Astrophysics Data System (ADS)

Attinger, S.; Van, M. K.; Basu, N. B.

2017-12-01

Watersheds are complex heterogeneous systems that store, transform, and release water and nutrients under a broad distribution of both natural and anthropogenic controls. Many current watershed models, from complex numerical models to simpler reservoir-type models, are considered to be well-developed in their ability to predict fluxes of water and nutrients to streams and groundwater. They are generally less adept, however, at capturing watershed storage dynamics. In other words, many current models are run with an assumption of steady-state dynamics, and focus on nutrient flows rather than changes in nutrient stocks within watersheds. Although these commonly used modeling approaches may be able to adequately capture short-term watershed dynamics, they are unable to represent the clear nonlinearities or hysteresis responses observed in watersheds experiencing significant changes in nutrient inputs. To address such a lack, we have, in the present work, developed a parsimonious modeling approach designed to capture long-term catchment responses to spatial and temporal changes in nutrient inputs. In this approach, we conceptualize the catchment as a biogeochemical reactor that is driven by nutrient inputs, characterized internally by both biogeochemical degradation and residence or travel time distributions, resulting in a specific nutrient output. For the model simulations, we define a range of different scenarios to represent real-world changes in land use and management implemented to improve water quality. We then introduce the concept of state-space trajectories to describe system responses to these potential changes in anthropogenic forcings. We also increase model complexity, in a stepwise fashion, by dividing the catchment into multiple biogeochemical reactors, coupled in series or in parallel. Using this approach, we attempt to answer the following questions: (1) What level of model complexity is needed to capture observed system responses? (2) How can we explain different patterns of nonlinearity in watershed nutrient dynamics? And finally, how does the accumulation of nutrient legacies within watersheds impact current and future water quality?

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.

Clustering Financial Time Series by Network Community Analysis

NASA Astrophysics Data System (ADS)

Piccardi, Carlo; Calatroni, Lisa; Bertoni, Fabio

In this paper, we describe a method for clustering financial time series which is based on community analysis, a recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of N time series. The weight of the link (i, j), which quantifies the similarity between the two corresponding time series, is defined according to a metric based on symbolic time series analysis, which has recently proved effective in the context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then time series) with strong similarity. A quantitative assessment of the significance of the obtained partition is also provided. The method is applied to two distinct case-studies concerning the US and Italy Stock Exchange, respectively. In the US case, the stability of the partitions over time is also thoroughly investigated. The results favorably compare with those obtained with the standard tools typically used for clustering financial time series, such as the minimal spanning tree and the hierarchical tree.

Time series analysis of InSAR data: Methods and trends

NASA Astrophysics Data System (ADS)

Osmanoğlu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cabral-Cano, Enrique

2016-05-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ;unwrapping; of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Time Series Analysis of Insar Data: Methods and Trends

NASA Technical Reports Server (NTRS)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Impact of Climate Variability and Landscape Patterns on Water Budget and Nutrient Loads in a Peri-urban Watershed: A Coupled Analysis Using Process-based Hydrological Model and Landscape Indices.

PubMed

Li, Chongwei; Zhang, Yajuan; Kharel, Gehendra; Zou, Chris B

2018-06-01

Nutrient discharge into peri-urban streams and reservoirs constitutes a significant pressure on environmental management, but quantitative assessment of non-point source pollution under climate variability in fast changing peri-urban watersheds is challenging. Soil and Water Assessment Tool (SWAT) was used to simulate water budget and nutrient loads for landscape patterns representing a 30-year progression of urbanization in a peri-urban watershed near Tianjin metropolis, China. A suite of landscape pattern indices was related to nitrogen (N) and phosphorous (P) loads under dry and wet climate using CANOCO redundancy analysis. The calibrated SWAT model was adequate to simulate runoff and nutrient loads for this peri-urban watershed, with Nash-Sutcliffe coefficient (NSE) and coefficient of determination (R 2 ) > 0.70 and percentage bias (PBIAS) between -7 and +18 for calibration and validation periods. With the progression of urbanization, forest remained the main "sink" landscape while cultivated and urban lands remained the main "source" landscapes with the role of orchard and grassland being uncertain and changing with time. Compared to 1984, the landscape use pattern in 2013 increased nutrient discharge by 10%. Nutrient loads modelled under wet climate were 3-4 times higher than that under dry climate for the same landscape pattern. Results indicate that climate change could impose a far greater impact on runoff and nutrient discharge in a peri-urban watershed than landscape pattern change.

Impact of Climate Variability and Landscape Patterns on Water Budget and Nutrient Loads in a Peri-urban Watershed: A Coupled Analysis Using Process-based Hydrological Model and Landscape Indices

NASA Astrophysics Data System (ADS)

Li, Chongwei; Zhang, Yajuan; Kharel, Gehendra; Zou, Chris B.

2018-06-01

Nutrient discharge into peri-urban streams and reservoirs constitutes a significant pressure on environmental management, but quantitative assessment of non-point source pollution under climate variability in fast changing peri-urban watersheds is challenging. Soil and Water Assessment Tool (SWAT) was used to simulate water budget and nutrient loads for landscape patterns representing a 30-year progression of urbanization in a peri-urban watershed near Tianjin metropolis, China. A suite of landscape pattern indices was related to nitrogen (N) and phosphorous (P) loads under dry and wet climate using CANOCO redundancy analysis. The calibrated SWAT model was adequate to simulate runoff and nutrient loads for this peri-urban watershed, with Nash-Sutcliffe coefficient (NSE) and coefficient of determination ( R 2) > 0.70 and percentage bias (PBIAS) between -7 and +18 for calibration and validation periods. With the progression of urbanization, forest remained the main "sink" landscape while cultivated and urban lands remained the main "source" landscapes with the role of orchard and grassland being uncertain and changing with time. Compared to 1984, the landscape use pattern in 2013 increased nutrient discharge by 10%. Nutrient loads modelled under wet climate were 3-4 times higher than that under dry climate for the same landscape pattern. Results indicate that climate change could impose a far greater impact on runoff and nutrient discharge in a peri-urban watershed than landscape pattern change.

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

Multifractal analysis of the Korean agricultural market

NASA Astrophysics Data System (ADS)

Kim, Hongseok; Oh, Gabjin; Kim, Seunghwan

2011-11-01

We have studied the long-term memory effects of the Korean agricultural market using the detrended fluctuation analysis (DFA) method. In general, the return time series of various financial data, including stock indices, foreign exchange rates, and commodity prices, are uncorrelated in time, while the volatility time series are strongly correlated. However, we found that the return time series of Korean agricultural commodity prices are anti-correlated in time, while the volatility time series are correlated. The n-point correlations of time series were also examined, and it was found that a multifractal structure exists in Korean agricultural market prices.

Multiple Indicator Stationary Time Series Models.

ERIC Educational Resources Information Center

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

Post-Disaster Food and Nutrition from Urban Agriculture: A Self-Sufficiency Analysis of Nerima Ward, Tokyo.

PubMed

Sioen, Giles Bruno; Sekiyama, Makiko; Terada, Toru; Yokohari, Makoto

2017-07-10

Background : Post-earthquake studies from around the world have reported that survivors relying on emergency food for prolonged periods of time experienced several dietary related health problems. The present study aimed to quantify the potential nutrient production of urban agricultural vegetables and the resulting nutritional self-sufficiency throughout the year for mitigating post-disaster situations. Methods : We estimated the vegetable production of urban agriculture throughout the year. Two methods were developed to capture the production from professional and hobby farms: Method I utilized secondary governmental data on agricultural production from professional farms, and Method II was based on a supplementary spatial analysis to estimate the production from hobby farms. Next, the weight of produced vegetables [t] was converted into nutrients [kg]. Furthermore, the self-sufficiency by nutrient and time of year was estimated by incorporating the reference consumption of vegetables [kg], recommended dietary allowance of nutrients per capita [mg], and population statistics. The research was conducted in Nerima, the second most populous ward of Tokyo's 23 special wards. Self-sufficiency rates were calculated with the registered residents. Results : The estimated total vegetable production of 5660 tons was equivalent to a weight-based self-sufficiency rate of 6.18%. The average nutritional self-sufficiencies of Methods I and II were 2.48% and 0.38%, respectively, resulting in an aggregated average of 2.86%. Fluctuations throughout the year were observed according to the harvest seasons of the available crops. Vitamin K (6.15%) had the highest self-sufficiency of selected nutrients, while calcium had the lowest (0.96%). Conclusions : This study suggests that depending on the time of year, urban agriculture has the potential to contribute nutrients to diets during post-disaster situations as disaster preparedness food. Emergency responses should be targeted according to the time of year the disaster takes place to meet nutrient requirements in periods of low self-sufficiency and prevent gastrointestinal symptoms and cardiovascular diseases among survivors.

Post-Disaster Food and Nutrition from Urban Agriculture: A Self-Sufficiency Analysis of Nerima Ward, Tokyo

PubMed Central

Sekiyama, Makiko; Terada, Toru; Yokohari, Makoto

2017-01-01

Background: Post-earthquake studies from around the world have reported that survivors relying on emergency food for prolonged periods of time experienced several dietary related health problems. The present study aimed to quantify the potential nutrient production of urban agricultural vegetables and the resulting nutritional self-sufficiency throughout the year for mitigating post-disaster situations. Methods: We estimated the vegetable production of urban agriculture throughout the year. Two methods were developed to capture the production from professional and hobby farms: Method I utilized secondary governmental data on agricultural production from professional farms, and Method II was based on a supplementary spatial analysis to estimate the production from hobby farms. Next, the weight of produced vegetables [t] was converted into nutrients [kg]. Furthermore, the self-sufficiency by nutrient and time of year was estimated by incorporating the reference consumption of vegetables [kg], recommended dietary allowance of nutrients per capita [mg], and population statistics. The research was conducted in Nerima, the second most populous ward of Tokyo’s 23 special wards. Self-sufficiency rates were calculated with the registered residents. Results: The estimated total vegetable production of 5660 tons was equivalent to a weight-based self-sufficiency rate of 6.18%. The average nutritional self-sufficiencies of Methods I and II were 2.48% and 0.38%, respectively, resulting in an aggregated average of 2.86%. Fluctuations throughout the year were observed according to the harvest seasons of the available crops. Vitamin K (6.15%) had the highest self-sufficiency of selected nutrients, while calcium had the lowest (0.96%). Conclusions: This study suggests that depending on the time of year, urban agriculture has the potential to contribute nutrients to diets during post-disaster situations as disaster preparedness food. Emergency responses should be targeted according to the time of year the disaster takes place to meet nutrient requirements in periods of low self-sufficiency and prevent gastrointestinal symptoms and cardiovascular diseases among survivors. PMID:28698515

Interrupted Time Series Versus Statistical Process Control in Quality Improvement Projects.

PubMed

Andersson Hagiwara, Magnus; Andersson Gäre, Boel; Elg, Mattias

2016-01-01

To measure the effect of quality improvement interventions, it is appropriate to use analysis methods that measure data over time. Examples of such methods include statistical process control analysis and interrupted time series with segmented regression analysis. This article compares the use of statistical process control analysis and interrupted time series with segmented regression analysis for evaluating the longitudinal effects of quality improvement interventions, using an example study on an evaluation of a computerized decision support system.

Historical Changes and remediation Measures of Agricultural Streams

NASA Astrophysics Data System (ADS)

Wörman, Anders; Riml, Joakim; Morén, Ida

2017-04-01

Changes in landscapes and climate during the last centuries in Sweden can be tracer in dramatic changes in the runoff pattern over large areas. Particularly, extensive drainage works aimed at expanding arable land and reduce risks for local floods. The availability of long-term monitoring runoff time series make it possible to distinguish the effects of landscape changes from climate fluctuations. However, it is expected that these changes also have an effect on retention and attenuation of nutrients in agricultural streams. This work focuses on design approaches for remediation actions in streams that can restore some of the previous self-purifying capacity and, hence, contribute to improved eutrophication status of the Baltic Sea. For analysis of historical time-series we propose a separation of the power spectral response of runoff in watersheds in terms of the product of the power spectra of precipitation and the impulse response function for the watershed. This allows a formal separation of the spectral response in climatic factors - the precipitation - from those of land-use change and regulation - the impulse response function. We found periodic fluctuations in runoff all over Sweden that can be explained by various climate indices. In addition, we found that the intra-annual variation in runoff was primarily affected by the land-use change in 79 unregulated catchments with up to century-long time series of measured daily discharge. Finally, we developed a design approach for stream remediation actions that restored the self-purification capacity while also increasing the risk for local floods. It is shown that step-structures, like check dams, are effective measures for inducing hyporheic exchange and thereby increasing potential for adsorption of phosphorus to soil and denitrification of nitrogen in biofilms.

A simple model of variable residence time flow and nutrient transport in the chalk

NASA Astrophysics Data System (ADS)

Jackson, Bethanna M.; Wheater, Howard S.; Mathias, Simon A.; McIntyre, Neil; Butler, Adrian P.

2006-10-01

SummaryA basic problem of modelling flow and transport in Chalk catchments arises from the existence of a deep unsaturated zone, with complex interactions between flow in fractures and water held in the fine pores of the rock matrix. The response of the water table to major infiltration episodes is rapid (of the order of days). However, chemical signals are strongly damped, suggesting that this water is of varying age, with a corresponding mixed history of nutrient loading. Clearly this effect should be represented in any model of nutrients in Chalk systems. The applicability of simplified physically-based model formulations to represent the dual response in an integrated way has been investigated by a variety of researchers, but it has been shown that these approximations break down in application to the Chalk. Mathias et al. [Mathias, S., Butler, A.P., Jackson, B.M., Wheater, H.S., this issue. Characterising flow in the Chalk unsaturated zone. In: Wheater, H.S., Peach, D., Neal, C, editors, Hydrology on LOCAR in the Pang/Lambourn, special issue of J. Hydrol, doi:10.1016/j.jhydrol.2006.04.010] present a dual permeability model that explains the observed response, but such complex formulations are not readily incorporated in catchment-scale nutrient models. This paper reviews previous approaches to modelling the Chalk and then presents a pragmatic approach, with transport of solute and water through the unsaturated zone treated separately, and combined at the water table. Varying residence times are included through considering the distance between the water table and the soil surface, and the history of nutrient application at the surface. If an average rate of downwards migration of the nutrients is assumed, it is possible to derive a travel time distribution of nitrate transport to the water table using a DTM (digital terrain model) map of elevation and information on groundwater levels. This distribution can then be implemented through difference equations. The rationale behind the model and the resulting algorithm is described, and the algorithm then applied to a hypothetical case study of nutrient loading located in the Lambourn, a groundwater-dominated Chalk catchment in Southern England. Simulated groundwater concentrations are very similar in magnitude and variability to observed Chalk groundwater series, suggesting that this simple conceptual model may well be able to capture the dominant responses of nutrient transport through the Chalk.

Detecting seasonal and cyclical trends in agricultural runoff water quality-hypothesis tests and block bootstrap power analysis.

PubMed

Uddameri, Venkatesh; Singaraju, Sreeram; Hernandez, E Annette

2018-02-21

Seasonal and cyclic trends in nutrient concentrations at four agricultural drainage ditches were assessed using a dataset generated from a multivariate, multiscale, multiyear water quality monitoring effort in the agriculturally dominant Lower Rio Grande Valley (LRGV) River Watershed in South Texas. An innovative bootstrap sampling-based power analysis procedure was developed to evaluate the ability of Mann-Whitney and Noether tests to discern trends and to guide future monitoring efforts. The Mann-Whitney U test was able to detect significant changes between summer and winter nutrient concentrations at sites with lower depths and unimpeded flows. Pollutant dilution, non-agricultural loadings, and in-channel flow structures (weirs) masked the effects of seasonality. The detection of cyclical trends using the Noether test was highest in the presence of vegetation mainly for total phosphorus and oxidized nitrogen (nitrite + nitrate) compared to dissolved phosphorus and reduced nitrogen (total Kjeldahl nitrogen-TKN). Prospective power analysis indicated that while increased monitoring can lead to higher statistical power, the effect size (i.e., the total number of trend sequences within a time-series) had a greater influence on the Noether test. Both Mann-Whitney and Noether tests provide complementary information on seasonal and cyclic behavior of pollutant concentrations and are affected by different processes. The results from these statistical tests when evaluated in the context of flow, vegetation, and in-channel hydraulic alterations can help guide future data collection and monitoring efforts. The study highlights the need for long-term monitoring of agricultural drainage ditches to properly discern seasonal and cyclical trends.

Attempting to link hydro-morphology, transient storage and metabolism in streams: Insights from reactive tracer experiments

NASA Astrophysics Data System (ADS)

Kurz, Marie J.; Schmidt, Christian; Blaen, Phillip; Knapp, Julia L. A.; Drummond, Jennifer D.; Martí, Eugenia; Zarnetske, Jay P.; Ward, Adam S.; Krause, Stefan

2016-04-01

In-stream transient storage zones, including the hyporheic zone and vegetation beds, can be hotspots of biogeochemical processing in streams, enhancing ecosystem functions such as metabolism and nutrient uptake. The spatio-temporal dynamics and reactivity of these storage zones are influenced by multiple factors, including channel geomorphology, substrate composition and hydrology, and by anthropogenic modifications to flow regimes and nutrient loads. Tracer injections are a commonly employed method to evaluate solute transport and transient storage in streams; however, reactive tracers are needed to differentiate between metabolically active and inactive transient storage zones. The reactive stream tracer resazurin (Raz), a weakly fluorescent dye which irreversibly transforms to resorufin (Rru) under mildly reducing conditions, provides a proxy for aerobic respiration and an estimate of the metabolic activity associated with transient storage zones. Across a range of lotic ecosystems, we try to assess the influence of stream channel hydro-morphology, morphologic heterogeneity, and substrate type on reach (103 m) and sub-reach (102 m) scale transient storage, respiration, and nutrient uptake. To do so, we coupled injections of Raz and conservative tracers (uranine and/or salt) at each study site. The study sites included: vegetated mesocosms controlled for water depth; vegetated and un-vegetated sediment-filled mesocosms fed by waste-water effluent; a contrasting sand- vs. gravel-bedded lowland stream (Q = 0.08 m3/s); and a series of upland streams with varying size (Q = 0.1 - 1.5 m3/s) and prevalence of morphologic features. Continuous time-series of tracer concentrations were recorded using in-situ fluorometers and EC loggers. At the stream sites, time-series were recorded at multiple downstream locations in order to resolve sub-reach dynamics. Analyses yielded highly variable transport metrics and Raz-Rru transformation between study sites and between sub-reaches within stream sites. Higher Raz-Rru transformation rates were typically observed in smaller streams, in sub-reaches with higher prevalence of morphologic features known to promote hyporheic exchange, and in mesocosms with higher water depth, vegetation density and retention time. However, relationships between transformation rates and common metrics of transient storage were not consistent among study cases, indicating the existence of yet unrealized complexities in the relationships between water and solute transport and metabolism. Further insights were also gained related to the utility of Raz and improved tracer test practices.

A high resolution paleo-record of export production using deep-sea coral stable isotope values from a unique HNLC zone on the California Margin

NASA Astrophysics Data System (ADS)

Wright, Z.

2015-12-01

The Sur Ridge, located ~30 km off the Big Sur coast of central California, represents a unique system within the highly productive California Current ecosystem. Its unique high nutrient, but low chlorophyll characteristics are not fully understood. Time series of bulk stable carbon (δ13C) and stable nitrogen (δ15N) isotopes can help us better understand past changes in nutrient dynamics and phytoplankton community baselines for this region in order to better predict future changes. Deep-sea proteinaceous corals are particularly powerful paleoarchives of past ocean conditions. These organisms serve as "living sediment traps," incorporating the stable isotope values of exported particulate organic material (POM) from the surface into their growth layers. The longevity of bamboo corals (Isidella, up to 400 years) makes them excellent resources for creating high resolution, centennial time series of δ13C and δ15N dynamics. Bamboo corals used in this study were harvested during summer of 2014 from 1220 to 1300 m depths. Two corals were milled in sub-millimeter intervals to generate a 200 year time series at approximately three year temporal resolution. Over the past 200 years, deep-sea coral δ13C values ranged from -15.7 to -19.0‰ and δ15N values ranged from 14.4 to 15.9‰, consistent with earlier data from the CA margin. The δ13C records were characterized by long periods of remarkable stability, contrasted with several large shifts (~1900 and ~1960) in δ13C of approximately 1‰. We hypothesize that these shifts likely reflect changes in plankton composition or production associated with regional climate shifts. The δ15N data were more dynamic, including several large shifts (1940 - 1960), as well as periods of apparent decadal scale oscillation (1825 - 1925 and 1965 - present). These shifts may reflect changes in the source or utilization of nitrogen at the base of the food web. Together, these data give us a first look at baseline stability of biogeochemical systems in this unique region, and will be crucial in connecting potential future system changes in climate and upwelling to possible shifts in nutrient dynamics and phytoplankton species composition. 

A general framework for time series data mining based on event analysis: application to the medical domains of electroencephalography and stabilometry.

PubMed

Lara, Juan A; Lizcano, David; Pérez, Aurora; Valente, Juan P

2014-10-01

There are now domains where information is recorded over a period of time, leading to sequences of data known as time series. In many domains, like medicine, time series analysis requires to focus on certain regions of interest, known as events, rather than analyzing the whole time series. In this paper, we propose a framework for knowledge discovery in both one-dimensional and multidimensional time series containing events. We show how our approach can be used to classify medical time series by means of a process that identifies events in time series, generates time series reference models of representative events and compares two time series by analyzing the events they have in common. We have applied our framework on time series generated in the areas of electroencephalography (EEG) and stabilometry. Framework performance was evaluated in terms of classification accuracy, and the results confirmed that the proposed schema has potential for classifying EEG and stabilometric signals. The proposed framework is useful for discovering knowledge from medical time series containing events, such as stabilometric and electroencephalographic time series. These results would be equally applicable to other medical domains generating iconographic time series, such as, for example, electrocardiography (ECG). Copyright © 2014 Elsevier Inc. All rights reserved.

Time-series modeling of long-term weight self-monitoring data.

PubMed

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

Couplings of watersheds and coastal waters: Sources and consequences of nutrient enrichment in Waquoit Bay, Massachusetts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valiela, I.; Foreman, K.; LaMontagne, M.

1992-12-01

Human activities on coastal watersheds provide the major sources of nutrients entering shallow coastal ecosystems. Nutrient loadings from watersheds alter structure and function of receiving aquatic ecosystems. To investigate this coupling of land to marine systems, a series of subwatersheds of Waquoit Bay differing in degree of urbanization and with widely different nutrient loading rates was studied. The subwatersheds differ in septic tanks numbers and forest acreage. Ground water is the major mechanism that transports nutrients to coastal waters. Some attenuation of nutrient concentrations within the aquifer or at the sediment-water interface, but significant increases in the nutrient content ofmore » groundwater arriving at the shore's edge are in urbanized areas. The groundwater flows through the sediment-water boundary, and sufficient groundwater-borne nutrients (nitrogen in particular) traverse the sediment-water boundary to cause significant changes in the aquatic ecosystem. These loading-dependent alterations include increased nutrients in water, greater primary production by phytoplankton, and increased macroalgal biomass and growth. The increased macroalgal biomass dominates the bay ecosystem through second- or third-order effects such as alterations of nutrient status of water columns and increasing frequency of anoxic events. The increases in seaweeds have decreased the areas covered by eelgrass habitats. The change in habitat type, plus the increased frequency of anoxic events, change the composition of the benthic fauna. The importance of bottom-up control in shallow coastal food webs is evident. The coupling of land to sea by groundwater-borne nutrient transport is mediated by a complex series of steps, making it unlikely to find a one-to-one relation between land use and conditions in the aquatic ecosystem. Appropriate models may provide a way to deal with the complexities of the coupling. 22 refs., 14 figs., 5 tabs.« less

Limnology of Blue Mesa, Morrow Point, and Crystal Reservoirs, Curecanti National Recreation area, during 1999, and a 25-year retrospective of nutrient conditions in Blue Mesa Reservoir, Colorado

USGS Publications Warehouse

Bauch, Nancy J.; Malick, Matt

2003-01-01

The U.S. Geological Survey and the National Park Service conducted a water-quality investigation in Curecanti National Recreation Area in Colorado from April through December 1999. Current (as of 1999) limnological characteristics, including nutrients, phytoplankton, chlorophyll-a, trophic status, and the water quality of stream inflows and reservoir outflows, of Blue Mesa, Morrow Point, and Crystal Reservoirs were assessed, and a 25-year retrospective of nutrient conditions in Blue Mesa Reservoir was conducted. The three reservoirs are in a series on the Gunnison River, with an upstream to downstream order of Blue Mesa, Morrow Point, and Crystal Reservoirs. Physical properties and water-quality samples were collected four times during 1999 from reservoir, inflow, and outflow sites in and around the recreation area. Samples were analyzed for nutrients, phytoplankton and chlorophyll-a (reservoir sites only), and suspended sediment (stream inflows only). Nutrient concentrations in the reservoirs were low; median total nitrogen and phosphorus concentrations were less than 0.4 and 0.06 milligram per liter, respectively. During water-column stratification, samples collected at depth had higher nutrient concentrations than photic-zone samples. Phytoplankton community and density were affected by water temperature, nutrients, and water residence time. Diatoms were the dominant phytoplankton throughout the year in Morrow Point and Crystal Reservoirs and during spring and early winter in Blue Mesa Reservoir. Blue-green algae were dominant in Blue Mesa Reservoir during summer and fall. Phytoplankton density was highest in Blue Mesa Reservoir and lowest in Crystal Reservoir. Longer residence times and warmer temperatures in Blue Mesa Reservoir were favorable for phytoplankton growth and development. Shorter residence times and cooler temperatures in the downstream reservoirs probably limited phytoplankton growth and development. Median chlorophyll-a concentrations were higher in Blue Mesa Reservoir than Morrow Point or Crystal Reservoirs. Blue Mesa Reservoir was mesotrophic in upstream areas and oligotrophic downstream. Both Morrow Point and Crystal Reservoirs were oligotrophic. Trophic-state index values were determined for total phosphorus, chlorophyll-a, and Secchi depth for each reservoir by the Carlson method; all values ranged between 29 and 55. Only the upstream areas in Blue Mesa Reservoir had total phosphorus and chlorophyll-a indices above 50, reflecting mesotrophic conditions. Nutrient inflows to Blue Mesa Reservoir, which were derived primarily from the Gunnison River, varied on a seasonal basis, whereas nutrient inflows to Morrow Point and Crystal Reservoirs, which were derived primarily from deep water releases from the respective upstream reservoir, were steady throughout the sampling period. Total phosphorus concentrations were elevated in many stream inflows. A comparison of current (as of 1999) and historical nutrient, chlorophyll-a, and trophic conditions in Blue Mesa Reservoir and its tributaries indicated that the trophic status in Blue Mesa Reservoir has not changed over the last 25 years, and more recent nutrient enrichment has not occurred.

Modeling the effects of free-living marine bacterial community composition on heterotrophic remineralization rates and biogeochemical carbon cycling

NASA Astrophysics Data System (ADS)

Teel, E.; Liu, X.; Cram, J. A.; Sachdeva, R.; Fuhrman, J. A.; Levine, N. M.

2016-12-01

Global oceanic ecosystem models either disregard fluctuations in heterotrophic bacterial remineralization or vary remineralization as a simple function of temperature, available carbon, and nutrient limitation. Most of these models were developed before molecular techniques allowed for the description of microbial community composition and functional diversity. Here we investigate the impact of a dynamic heterotrophic community and variable remineralization rates on biogeochemical cycling. Specifically, we integrated variable microbial remineralization into an ecosystem model by utilizing molecular community composition data, association network analysis, and biogeochemical rate data from the San Pedro Ocean Time-series (SPOT) station. Fluctuations in free-living bacterial community function and composition were examined using monthly environmental and biological data collected at SPOT between 2000 and 2011. On average, the bacterial community showed predictable seasonal changes in community composition and peaked in abundance in the spring with a one-month lag from peak chlorophyll concentrations. Bacterial growth efficiency (BGE), estimated from bacterial production, was found to vary widely at the site (5% to 40%). In a multivariate analysis, 47.6% of BGE variability was predicted using primary production, bacterial community composition, and temperature. A classic Nutrient-Phytoplankton-Zooplankton-Detritus model was expanded to include a heterotroph module that captured the observed relationships at the SPOT site. Results show that the inclusion of dynamic bacterial remineralization into larger oceanic ecosystem models can significantly impact microzooplankton grazing, the duration of surface phytoplankton blooms, and picophytoplankton primary production rates.

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hidden Process Models

DTIC Science & Technology

2009-12-18

cannot be detected with univariate techniques, but require multivariate analysis instead (Kamitani and Tong [2005]). Two other time series analysis ...learning for time series analysis . The historical record of DBNs can be traced back to Dean and Kanazawa [1988] and Dean and Wellman [1991], with...Rev. 8-98) Prescribed by ANSI Std Z39-18 Keywords: Hidden Process Models, probabilistic time series modeling, functional Magnetic Resonance Imaging

Use of Time-Series, ARIMA Designs to Assess Program Efficacy.

ERIC Educational Resources Information Center

Braden, Jeffery P.; And Others

1990-01-01

Illustrates use of time-series designs for determining efficacy of interventions with fictitious data describing drug-abuse prevention program. Discusses problems and procedures associated with time-series data analysis using Auto Regressive Integrated Moving Averages (ARIMA) models. Example illustrates application of ARIMA analysis for…

Water-quality assessment of the Rio Grande Valley, Colorado, New Mexico, and Texas; occurrence and distribution of selected pesticides and nutrients at selected surface-water sites in the Mesilla Valley, 1994-95

USGS Publications Warehouse

Healy, D.F.

1996-01-01

The Rio Grande Valley study unit of the U.S. Geological Survey National Water-Quality Assessment Program conducted a two-phase synoptic study of the occurrence and distribution of pesticides and nutrients in the surface water of the Mesilla Valley, New Mexico and Texas. Phase one, conducted in April-May 1994 during the high-flow irrigation season, consisted of a 6-week time- series sampling event during which 17 water-column samples were collected at 3 main-stem sites on the Rio Grande and a synoptic irrigation-run sampling event during which 19 water-column samples were collected at 7 main-stem sites, 10 drain sites, and 2 sites at the discharges of wastewater-treatment plants. Three samples are included in both the time-series and irrigation-run events. Phase two, conducted in January 1995 during the low-flow non-irrigation season, consisted of a non-irrigation synoptic sampling event during which 18 water-column samples were collected at seven main-stem sites, nine drain sites, and two sites at the discharges of wastewater-treatment plants and a bed- material sampling event during which 6 bed-material samples were collected at six sites near the mouths of drains that discharge to the Rio Grande. The 51 water-column samples were analyzed for 78 pesticides and metabolites and 8 nutrients along with other constituents. The six bed-material samples were analyzed for 21 pesticides and metabolites, gross polychlorinated biphenyls, and gross polychlorinated naphthalenes. The presence of dissolved pesticides in the surface water of the Mesilla Valley is erratic. A total of 100 detections of 17 different pesticides were detected in 44 of the water-column samples. As many as 38 percent of these detections may be attributed to pesticide use upstream from the valley or to nonagricultural pesticide use within the valley. There were 29 detections of 10 different pesticides in 17 samples during the irrigation run and 41 detections of 13 pesticides in 16 samples during the non-irrigation run. Nine pesticides were detected during both phases of the study. The most commonly detected pesticides in the water-column samples were DCPA, which was detected in 29 samples, and metolachlor, which was detected in 17 of the samples. DCPA was detected throughout the Mesilla Valley, whereas metolachlor was detected mainly in the northern and central parts of the valley. The maximum pesticide concentration found during the study was 0.75 microgram per liter of carbofuran, which was detected at the East Side Drain site during the irrigation run. No water-column pesticide concentration exceeded U.S. Environmental Protection Agency's drinking-water standards or any applicable Federal or State criteria or guidelines. A total of 21 occurrences of six pesticides and metabolites were found in the bed-material samples. Chlordane, diazinon, and methyl parathion were detected once each, whereas DDD, DDE, and DDT were detected at all six bed-material sites. Water-column samples for the analysis of nutrient concentrations were collected at all sampling sites during both phases of the study. The concentrations of each nutrient ranged from at or below the individual minimum reporting level to as much as two or three orders of magnitude larger than the minimum reporting level. The concentration of each nutrient was left skewed with most of the values toward the lower end of the range. The larger concentrations of each nutrient, except dissolved nitrite plus nitrate, were associated with wastewater-treatment- plant sites 4 and 16. The larger concentrations of dissolved nitrite plus nitrate were generally associated with the non- irrigation run; however, the largest concentration was at site 4 during the irrigation run. During this study, the Mesilla Valley as a unit was a source of nutrients to the Rio Grande. Wi

The "Chaos Theory" and nonlinear dynamics in heart rate variability analysis: does it work in short-time series in patients with coronary heart disease?

PubMed

Krstacic, Goran; Krstacic, Antonija; Smalcelj, Anton; Milicic, Davor; Jembrek-Gostovic, Mirjana

2007-04-01

Dynamic analysis techniques may quantify abnormalities in heart rate variability (HRV) based on nonlinear and fractal analysis (chaos theory). The article emphasizes clinical and prognostic significance of dynamic changes in short-time series applied on patients with coronary heart disease (CHD) during the exercise electrocardiograph (ECG) test. The subjects were included in the series after complete cardiovascular diagnostic data. Series of R-R and ST-T intervals were obtained from exercise ECG data after sampling digitally. The range rescaled analysis method determined the fractal dimension of the intervals. To quantify fractal long-range correlation's properties of heart rate variability, the detrended fluctuation analysis technique was used. Approximate entropy (ApEn) was applied to quantify the regularity and complexity of time series, as well as unpredictability of fluctuations in time series. It was found that the short-term fractal scaling exponent (alpha(1)) is significantly lower in patients with CHD (0.93 +/- 0.07 vs 1.09 +/- 0.04; P < 0.001). The patients with CHD had higher fractal dimension in each exercise test program separately, as well as in exercise program at all. ApEn was significant lower in CHD group in both RR and ST-T ECG intervals (P < 0.001). The nonlinear dynamic methods could have clinical and prognostic applicability also in short-time ECG series. Dynamic analysis based on chaos theory during the exercise ECG test point out the multifractal time series in CHD patients who loss normal fractal characteristics and regularity in HRV. Nonlinear analysis technique may complement traditional ECG analysis.

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Collaborative Research with Chinese, Indian, Filipino and North European Research Organizations on Infectious Disease Epidemics.

PubMed

Sumi, Ayako; Kobayashi, Nobumichi

2017-01-01

In this report, we present a short review of applications of time series analysis, which consists of spectral analysis based on the maximum entropy method in the frequency domain and the least squares method in the time domain, to the incidence data of infectious diseases. This report consists of three parts. First, we present our results obtained by collaborative research on infectious disease epidemics with Chinese, Indian, Filipino and North European research organizations. Second, we present the results obtained with the Japanese infectious disease surveillance data and the time series numerically generated from a mathematical model, called the susceptible/exposed/infectious/recovered (SEIR) model. Third, we present an application of the time series analysis to pathologic tissues to examine the usefulness of time series analysis for investigating the spatial pattern of pathologic tissue. It is anticipated that time series analysis will become a useful tool for investigating not only infectious disease surveillance data but also immunological and genetic tests.

Use of orange peel extract for mixotrophic cultivation of Chlorella vulgaris: increased production of biomass and FAMEs.

PubMed

Park, Won-Kun; Moon, Myounghoon; Kwak, Min-Su; Jeon, Seungjib; Choi, Gang-Guk; Yang, Ji-Won; Lee, Bongsoo

2014-11-01

Mass cultivation of microalgae is necessary to achieve economically feasible production of microalgal biodiesel, but the high cost of nutrients is a major limitation. In this study, orange peel extract (OPE) was used as an inorganic and organic nutrient source for the cultivation of Chlorella vulgaris OW-01. Chemical composition analysis of the OPE indicated that it contains sufficient nutrients for mixotrophic cultivation of C. vulgaris OW-01. Analysis of biomass and FAME production showed that microalgae grown in OPE medium produced 3.4-times more biomass and 4.5-times more fatty acid methyl esters (FAMEs) than cells cultured in glucose-supplemented BG 11 medium (BG-G). These results suggest that growth of microalgae in an OPE-supplemented medium increases lipid production and that OPE has potential for use in the mass cultivation of microalgae. Copyright © 2014 Elsevier Ltd. All rights reserved.

C : N : P stoichiometry at the Bermuda Atlantic Time-series Study station in the North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Singh, A.; Baer, S. E.; Riebesell, U.; Martiny, A. C.; Lomas, M. W.

2015-11-01

Nitrogen (N) and phosphorus (P) availability, in addition to other macro- and micronutrients, determine the strength of the ocean's carbon (C) uptake, and variation in the N : P ratio of inorganic nutrient pools is key to phytoplankton growth. A similarity between C : N : P ratios in the plankton biomass and deep-water nutrients was observed by Alfred C. Redfield around 80 years ago and suggested that biological processes in the surface ocean controlled deep-ocean chemistry. Recent studies have emphasized the role of inorganic N : P ratios in governing biogeochemical processes, particularly the C : N : P ratio in suspended particulate organic matter (POM), with somewhat less attention given to exported POM and dissolved organic matter (DOM). Herein, we extend the discussion on ecosystem C : N : P stoichiometry but also examine temporal variation in stoichiometric relationships. We have analyzed elemental stoichiometry in the suspended POM and total (POM + DOM) organic-matter (TOM) pools in the upper 100 m and in the exported POM and subeuphotic zone (100-500 m) inorganic nutrient pools from the monthly data collected at the Bermuda Atlantic Time-series Study (BATS) site located in the western part of the North Atlantic Ocean. C : N and N : P ratios in TOM were at least twice those in the POM, while C : P ratios were up to 5 times higher in TOM compared to those in the POM. Observed C : N ratios in suspended POM were approximately equal to the canonical Redfield ratio (C : N : P = 106 : 16 : 1), while N : P and C : P ratios in the same pool were more than twice the Redfield ratio. Average N : P ratios in the subsurface inorganic nutrient pool were ~ 26 : 1, squarely between the suspended POM ratio and the Redfield ratio. We have further linked variation in elemental stoichiometry to that of phytoplankton cell abundance observed at the BATS site. Findings from this study suggest that elemental ratios vary with depth in the euphotic zone, mainly due to different growth rates of cyanobacterial cells. We have also examined the role of the Arctic Oscillation on temporal patterns in C : N : P stoichiometry. This study strengthens our understanding of the variability in elemental stoichiometry in different organic-matter pools and should improve biogeochemical models by constraining the range of non-Redfield stoichiometry and the net relative flow of elements between pools.

Phytoplankton and nutrient distributions in a front-eddy area adjacent to the coastal upwelling zone off Concepcion (Chile): implications for ecosystem productivity.

NASA Astrophysics Data System (ADS)

Morales, Carmen; Anabalón, Valeria; Hormazábal, Samuel; Cornejo, Marcela; Bento, Joaquim; Silva, Nelson

2016-04-01

The impact that sub-mesoscale (1-10 km) to mesocale (50-100 km) oceanographic variability has on plankton and nutrient distributions (horizontal and vertical) in the coastal upwelling and transition zones off Concepcion was the focus of this study. Satellite time-series data (wind, sea-surface temperature (SST), and altimetry) were used to understand the dynamic context of in situ data derived from a short-term front survey (3 d) during the upwelling period (3-6 February, 2014). The survey included two transects perpendicular to the coast, covering the shelf and shelf-break areas just north of Punta Lavapie, a main upwelling center (˜37° S). Wind and SST time-series data indicated that the survey was undertaken just after a moderate upwelling event (end of January) which lead to a relaxation phase during early February. A submesoscale thermal front was detected previous to and during the survey and results from an eddy tracking algorithm based on altimetry data indicated that this front (F1) was flanked on its oceanic side by an anticyclonic, mesoscale eddy (M1), which was ˜25 d old at the sampling time. M1 strengthened the thermal gradient of F1 by bringing warmer oceanic water nearer to the colder coastal upwelling zone. The distributions of hydrographic variables and nutrients in the water column (<300 m depth) also denoted these two features. Phytoplankton biomass (Chl-a) and diatom abundance were highest in the surface layer (<20 m depth) between the coast and F1, with primary maxima in the latter, whereas they were highest at the subsurface (20-40 m depth) towards M1 and associated with secondary maxima. The distribution of dominant diatoms in the top layer (<100 m depth) indicated that both coastal and oceanic species were aggregated at F1 and in M1. These results suggest that the front-eddy interaction creates a complex field of submesoscale processes in the top layer, including vertical nutrient injections and lateral stirring, which contributes to the exportation of coastal communities to the open ocean in this region. We discuss how this interaction might affect ecosystem productivity in the coastal band.

Variable primary producer responses to nutrient and ...

EPA Pesticide Factsheets

Mesocosm experiments have been used to evaluate the impacts of nutrient loading on estuarine plant communities in order to develop nutrient response relationships. Mesocosm eutrophication studies tend to focus on long residence time systems. In the Pacific Northwest, many estuaries have high nutrient loads, short water residence times, seasonal macroalgal blooms, while intertidal seagrass meadows persist under what appear to be largely naturally-derived eutrophic conditions. Using experimental mesocosms, we examined how primary producer communities in rapidly flushed systems respond to a range of temperature (10 and 20 °C) and nutrient loads (ambient, 1.5, 3 and 6 x ambient). Thermal and nutrient loading regimes were maintained for three sets of 3 week-duration experiments during the summer of 2013. Statistical analysis was performed using an information criterion approach to evaluate the best fit model. Green macroalgal (GMA) growth and tissue N increased in response to nutrient loading. Irrespective of nutrient load, GMA at 10 °C remained intercalated among seagrass shoots, but at 20 °C formed floating mats that overtopped seagrass. Outgassing of O2 in combination with photosynthetic O2 production likely induced floating mat formation. No phytoplankton blooms were observed. Zostera japonica leaf biomass and C:N responded to temperature while other metrics exhibited no statistically significant difference. Z. marina growth, wasting disease, and morphological

Modeling of Engine Parameters for Condition-Based Maintenance of the MTU Series 2000 Diesel Engine

DTIC Science & Technology

2016-09-01

are suitable. To model the behavior of the engine, an autoregressive distributed lag (ARDL) time series model of engine speed and exhaust gas... time series model of engine speed and exhaust gas temperature is derived. The lag length for ARDL is determined by whitening of residuals using the...15 B. REGRESSION ANALYSIS ....................................................................15 1. Time Series Analysis

Nutrient Retention in Restored Streams and Floodplains: A ...

EPA Pesticide Factsheets

Abstract: Excess nitrogen (N) and phosphorus (P) from human activities have contributed to degradation of coastal waters globally. A growing body of work suggests that hydrologically restoring streams and floodplains in agricultural and urban watersheds has potential to increase nitrogen and phosphorus retention, but rates and mechanisms have not yet been synthesized and compared across studies. We conducted a review of nutrient retention within hydrologically reconnected streams and floodplains including 79 studies. Overall, 62% of results were positive, 26% were neutral, and 12% were negative. The studies we reviewed used a variety of methods to analyze nutrients cycling. We did a further intensive meta-analysis on nutrient spiraling studies because this method was the most consistent and comparable between studies. A meta-analysis of 240 experimental additions of ammonium (NH4+), nitrate (NO3-), and soluble reactive phosphorus (SRP) was synthesized from 15 nutrient spiraling studies. Overall, we found that rates of uptake were variable along stream reaches over space and time. Our results indicate that the size of the stream restoration (total surface area) and hydrologic residence time can be key drivers in influencing N and P uptake at broader watershed scales or along the urban watershed continuum. Excess nitrogen and phosphorus from human activities contributes to the degradation of water quality in streams and coastal areas nationally and globally.

A Study on Predictive Analytics Application to Ship Machinery Maintenance

DTIC Science & Technology

2013-09-01

Looking at the nature of the time series forecasting method , it would be better applied to offline analysis . The application for real- time online...other system attributes in future. Two techniques of statistical analysis , mainly time series models and cumulative sum control charts, are discussed in...statistical tool employed for the two techniques of statistical analysis . Both time series forecasting as well as CUSUM control charts are shown to be

A non linear analysis of human gait time series based on multifractal analysis and cross correlations

NASA Astrophysics Data System (ADS)

Muñoz-Diosdado, A.

2005-01-01

We analyzed databases with gait time series of adults and persons with Parkinson, Huntington and amyotrophic lateral sclerosis (ALS) diseases. We obtained the staircase graphs of accumulated events that can be bounded by a straight line whose slope can be used to distinguish between gait time series from healthy and ill persons. The global Hurst exponent of these series do not show tendencies, we intend that this is because some gait time series have monofractal behavior and others have multifractal behavior so they cannot be characterized with a single Hurst exponent. We calculated the multifractal spectra, obtained the spectra width and found that the spectra of the healthy young persons are almost monofractal. The spectra of ill persons are wider than the spectra of healthy persons. In opposition to the interbeat time series where the pathology implies loss of multifractality, in the gait time series the multifractal behavior emerges with the pathology. Data were collected from healthy and ill subjects as they walked in a roughly circular path and they have sensors in both feet, so we have one time series for the left foot and other for the right foot. First, we analyzed these time series separately, and then we compared both results, with direct comparison and with a cross correlation analysis. We tried to find differences in both time series that can be used as indicators of equilibrium problems.

Multiscale multifractal time irreversibility analysis of stock markets

NASA Astrophysics Data System (ADS)

Jiang, Chenguang; Shang, Pengjian; Shi, Wenbin

2016-11-01

Time irreversibility is one of the most important properties of nonstationary time series. Complex time series often demonstrate even multiscale time irreversibility, such that not only the original but also coarse-grained time series are asymmetric over a wide range of scales. We study the multiscale time irreversibility of time series. In this paper, we develop a method called multiscale multifractal time irreversibility analysis (MMRA), which allows us to extend the description of time irreversibility to include the dependence on the segment size and statistical moments. We test the effectiveness of MMRA in detecting multifractality and time irreversibility of time series generated from delayed Henon map and binomial multifractal model. Then we employ our method to the time irreversibility analysis of stock markets in different regions. We find that the emerging market has higher multifractality degree and time irreversibility compared with developed markets. In this sense, the MMRA method may provide new angles in assessing the evolution stage of stock markets.

Phase walk analysis of leptokurtic time series.

PubMed

Schreiber, Korbinian; Modest, Heike I; Räth, Christoph

2018-06-01

The Fourier phase information play a key role for the quantified description of nonlinear data. We present a novel tool for time series analysis that identifies nonlinearities by sensitively detecting correlations among the Fourier phases. The method, being called phase walk analysis, is based on well established measures from random walk analysis, which are now applied to the unwrapped Fourier phases of time series. We provide an analytical description of its functionality and demonstrate its capabilities on systematically controlled leptokurtic noise. Hereby, we investigate the properties of leptokurtic time series and their influence on the Fourier phases of time series. The phase walk analysis is applied to measured and simulated intermittent time series, whose probability density distribution is approximated by power laws. We use the day-to-day returns of the Dow-Jones industrial average, a synthetic time series with tailored nonlinearities mimicing the power law behavior of the Dow-Jones and the acceleration of the wind at an Atlantic offshore site. Testing for nonlinearities by means of surrogates shows that the new method yields strong significances for nonlinear behavior. Due to the drastically decreased computing time as compared to embedding space methods, the number of surrogate realizations can be increased by orders of magnitude. Thereby, the probability distribution of the test statistics can very accurately be derived and parameterized, which allows for much more precise tests on nonlinearities.

Dynamic shear-stress-enhanced rates of nutrient consumption in gas-liquid semi-continuous-flow suspensions

NASA Astrophysics Data System (ADS)

Belfiore, Laurence A.; Volpato, Fabio Z.; Paulino, Alexandre T.; Belfiore, Carol J.

2011-12-01

The primary objective of this investigation is to establish guidelines for generating significant mammalian cell density in suspension bioreactors when stress-sensitive kinetics enhance the rate of nutrient consumption. Ultra-low-frequency dynamic modulations of the impeller (i.e., 35104 Hz) introduce time-dependent oscillatory shear into this transient analysis of cell proliferation under semi-continuous creeping flow conditions. Greater nutrient consumption is predicted when the amplitude A of modulated impeller rotation increases, and stress-kinetic contributions to nutrient consumption rates increase linearly at higher modulation frequency via an application of fluctuation-dissipation response. Interphase mass transfer is required to replace dissolved oxygen as it is consumed by aerobic nutrient consumption in the liquid phase. The theory and predictions described herein could be important at small length scales in the creeping flow regime where viscous shear is significant at the interface between the nutrient medium and isolated cells in suspension. Two-dimensional flow around spherically shaped mammalian cells, suspended in a Newtonian culture medium, is analyzed to calculate the surface-averaged magnitude of the velocity gradient tensor and modify homogeneous rates of nutrient consumption that are stimulated by viscous shear, via the formalism of stress-kinetic reciprocal relations that obey Curie's theorem in non-equilibrium thermodynamics. Time constants for stress-free free and stress-sensitive stress nutrient consumption are defined and quantified to identify the threshold (i.e., stress,threshold) below which the effect of stress cannot be neglected in accurate predictions of bioreactor performance. Parametric studies reveal that the threshold time constant for stress-sensitive nutrient consumption stress,threshold decreases when the time constant for stress-free nutrient consumption free is shorter. Hence, stress,threshold depends directly on free. In other words, the threshold rate of stress-sensitive nutrient consumption is higher when the stress-free rate of nutrient consumption increases. Modulated rotation of the impeller, superimposed on steady shear, increases stress,threshold when free is constant, and stress,threshold depends directly on the amplitude A of these angular velocity modulations.

Abiotic and biotic responses to Milankovitch-forced megamonsoon and glacial cycles recorded in South China at the end of the Late Paleozoic Ice Age

NASA Astrophysics Data System (ADS)

Fang, Qiang; Wu, Huaichun; Hinnov, Linda A.; Tian, Wenqian; Wang, Xunlian; Yang, Tianshui; Li, Haiyan; Zhang, Shihong

2018-04-01

At the end of the Late Paleozoic Ice Age (LPIA) from late Early Permian to early Late Permian, the global climate was impacted by a prevailing megamonsoon and Gondwanan deglaciation. To better understand the abiotic and biotic responses to Milankovitch-forced climate changes during this time period, multi-element X-ray fluorescence (XRF) geochemistry analyses were conducted on 948 samples from the late Early-late Middle Permian Maokou Formation at Shangsi, South China. The Fe/Ti, S/Ti, Ba/Ti and Ca time series, which were calibrated with an existing "floating" astronomical time scale (ATS), show the entire suite of Milankovitch rhythms including 405 kyr long eccentricity, 128 and 95 kyr short eccentricity, 33 kyr obliquity and 20 kyr precession. Spectral coherency and cross-phase analysis reveals that chemical weathering (monitored by Fe/Ti) and upwelling (captured by S/Ti and Ba/Ti) are nearly antiphase in the precession band, which suggests a contrast between summer and winter monsoon intensities. Strong obliquity signal in the Ba/Ti series is proposed to derive from changes in thermohaline circulation intensity from glaciation dynamics in southern Gondwana. The abundance of foraminifer, brachiopod and ostracod faunas within the Maokou Formation were mainly controlled by the 1.1 Myr obliquity modulation cycle. The obliquity-forced high-nutrient and oxygen-depleted conditions generally produced a benthic foraminifer bloom, but threatened the brachiopod and ostracod faunas.

Trends and seasonality of river nutrients in agricultural catchments: 18years of weekly citizen science in France.

PubMed

Abbott, Benjamin W; Moatar, Florentina; Gauthier, Olivier; Fovet, Ophélie; Antoine, Virginie; Ragueneau, Olivier

2018-05-15

Agriculture and urbanization have disturbed three-quarters of global ice-free land surface, delivering huge amounts of nitrogen and phosphorus to freshwater ecosystems. These excess nutrients degrade habitat and threaten human food and water security at a global scale. Because most catchments are either currently subjected to, or recovering from anthropogenic nutrient loading, understanding the short- and long-term responses of river nutrients to changes in land use is essential for effective management. We analyzed a never-published, 18-year time series of anthropogenic (NO 3 - and PO 4 3- ) and naturally derived (dissolved silica) riverine nutrients in 13 catchments recovering from agricultural pollution in western France. In a citizen science initiative, high-school students sampled catchments weekly, which ranged from 26 to 1489km 2 . Nutrient concentrations decreased substantially over the period of record (19 to 50% for NO 3 - and 14 to 80% for PO 4 3- ), attributable to regional, national, and international investment and regulation, which started immediately prior to monitoring. For the majority of catchments, water quality during the summer low-flow period improved faster than during winter high-flow conditions, and annual minimum concentrations improved relatively faster than annual maximum concentrations. These patterns suggest that water-quality improvements were primarily due to elimination of discrete nutrient sources with seasonally-constant discharge (e.g. human and livestock wastewater), agreeing with available land-use and municipal records. Surprisingly, long-term nutrient decreases were not accompanied by changes in nutrient seasonality in most catchments, attributable to persistent, diffuse nutrient stocks. Despite decreases, nutrient concentrations in almost all catchments remained well above eutrophication thresholds, and because additional improvements will depend on decreasing diffuse nutrient sources, future gains may be much slower than initial rate of recovery. These findings demonstrate the value of citizen science initiatives in quantifying long-term and seasonal consequences of changes in land management, which are necessary to identify sustainable limits and predict recovery timeframes. Copyright © 2017 Elsevier B.V. All rights reserved.

Fluctuations in Cerebral Hemodynamics

DTIC Science & Technology

2003-12-01

Determination of scaling properties Detrended Fluctuations Analysis (see (28) and references therein) is commonly used to determine scaling...pressure (averaged over a cardiac beat) of a healthy subject. First 1000 values of the time series are shown. (b) Detrended fluctuation analysis (DFA...1000 values of the time series are shown. (b) Detrended fluctuation analysis of the time series shown in (a). Fig . 3 Side-by-side boxplot for the

Biophysical Controls on Carbon Cycling in Restored and Unrestored Urban Streams

NASA Astrophysics Data System (ADS)

Larsen, L. G.; Harvey, J. W.; Singh, J. D.; Sinclair, G. A.; Langston, T.; Maglio, M. M.

2012-12-01

Stream restoration is a multibillion dollar industry, yet how restoration impacts the ecological functioning of streams remains poorly understood. Because stream restoration may alter numerous biophysical controls, including light availability (through tree removal during bank regrading), hydraulics, sediment characteristics, and/or nutrient concentrations, it can be challenging to achieve a general understanding of how different aspects of stream restoration design influence ecosystem function (e.g., carbon cycling). In this study we combined strategies of continuously monitoring hydrology, turbidity, and dissolved oxygen at a station with spatially distributed but temporally sparse synoptic sampling to understand how restoration and land-use impact carbon fixation and respiration in urban streams. The study was performed over three years in three adjacent 3rd-4th order stream reaches in the urban Chesapeake Bay watershed, one of which was restored in 2002 using the ubiquitous Natural Channel Design method. By parsing the dissolved oxygen time series into contributions from respiration and gross primary production, we found the unrestored urban reach to be the most heterotrophic. It removed two times more carbon from the stream to the atmosphere than an unrestored suburban stream that was nutrient impacted and five times more carbon than the restored urban stream. The synoptic sampling revealed that nutrients, light, and hydrodynamic disturbance were the primary controls on carbon fixation and respiration, with fine sediment also exhibiting importance, likely as a vehicle for nutrient transport. Low rates of net carbon removal in the restored stream arose from high light availability resulting in high primary production, combined with low fine sediment availability restricting respiration. Thus, while restoration may have been effective for stream stabilization, it has decreased the functionality of the stream for net carbon removal to the atmosphere. Surprisingly, streambed potential respiration rates were indistinguishable between different geomorphic zones within the streams, suggesting that large-scale factors (i.e., nutrient and fine sediment supply) were more dominant controls than geomorphically controlled local variability.

Carbon Stable Isotope Values in Plankton and Mussels Reflect Changes in Carbonate Chemistry Associated with Nutrient Enhanced Net Production

EPA Science Inventory

Coastal ecosystems are inherently complex and potentially adaptive as they respond to changes in nutrient loads and climate. We documented the role that carbon stable isotope (δ13C) measurements could play in understanding that adaptation with a series of three Ecostat (i.e...

Gridded Surface Subsurface Hydrologic Analysis Modeling for Analysis of Flood Design Features at the Picayune Strand Restoration Project

DTIC Science & Technology

2016-08-01

the POI. ............................................................... 17  Figure 9. Discharge time series for the Miller pump system...2. In C2, the Miller Canal pump system was implicitly simulated by a time series of outflows assigned to model cells. This flow time series was...representative of how the pump system would operate during the storm events simulated in this work (USACE 2004). The outflow time series for the Miller

Extending nonlinear analysis to short ecological time series.

PubMed

Hsieh, Chih-hao; Anderson, Christian; Sugihara, George

2008-01-01

Nonlinearity is important and ubiquitous in ecology. Though detectable in principle, nonlinear behavior is often difficult to characterize, analyze, and incorporate mechanistically into models of ecosystem function. One obvious reason is that quantitative nonlinear analysis tools are data intensive (require long time series), and time series in ecology are generally short. Here we demonstrate a useful method that circumvents data limitation and reduces sampling error by combining ecologically similar multispecies time series into one long time series. With this technique, individual ecological time series containing as few as 20 data points can be mined for such important information as (1) significantly improved forecast ability, (2) the presence and location of nonlinearity, and (3) the effective dimensionality (the number of relevant variables) of an ecological system.

Detection of Anomalies in Citrus Leaves Using Laser-Induced Breakdown Spectroscopy (LIBS).

PubMed

Sankaran, Sindhuja; Ehsani, Reza; Morgan, Kelly T

2015-08-01

Nutrient assessment and management are important to maintain productivity in citrus orchards. In this study, laser-induced breakdown spectroscopy (LIBS) was applied for rapid and real-time detection of citrus anomalies. Laser-induced breakdown spectroscopy spectra were collected from citrus leaves with anomalies such as diseases (Huanglongbing, citrus canker) and nutrient deficiencies (iron, manganese, magnesium, zinc), and compared with those of healthy leaves. Baseline correction, wavelet multivariate denoising, and normalization techniques were applied to the LIBS spectra before analysis. After spectral pre-processing, features were extracted using principal component analysis and classified using two models, quadratic discriminant analysis and support vector machine (SVM). The SVM resulted in a high average classification accuracy of 97.5%, with high average canker classification accuracy (96.5%). LIBS peak analysis indicated that high intensities at 229.7, 247.9, 280.3, 393.5, 397.0, and 769.8 nm were observed of 11 peaks found in all the samples. Future studies using controlled experiments with variable nutrient applications are required for quantification of foliar nutrients by using LIBS-based sensing.

NASA Satellite Monitoring of Water Clarity in Mobile Bay for Nutrient Criteria Development

NASA Technical Reports Server (NTRS)

Blonski, Slawomir; Holekamp, Kara; Spiering, Bruce A.

2009-01-01

This project has demonstrated feasibility of deriving from MODIS daily measurements time series of water clarity parameters that provide coverage of a specific location or an area of interest for 30-50% of days. Time series derived for estuarine and coastal waters display much higher variability than time series of ecological parameters (such as vegetation indices) derived for land areas. (Temporal filtering often applied in terrestrial studies cannot be used effectively in ocean color processing). IOP-based algorithms for retrieval of diffuse light attenuation coefficient and TSS concentration perform well for the Mobile Bay environment: only a minor adjustment was needed in the TSS algorithm, despite generally recognized dependence of such algorithms on local conditions. The current IOP-based algorithm for retrieval of chlorophyll a concentration has not performed as well: a more reliable algorithm is needed that may be based on IOPs at additional wavelengths or on remote sensing reflectance from multiple spectral bands. CDOM algorithm also needs improvement to provide better separation between effects of gilvin (gelbstoff) and detritus. (Identification or development of such algorithm requires more data from in situ measurements of CDOM concentration in Gulf of Mexico coastal waters (ongoing collaboration with the EPA Gulf Ecology Division))

Coupling detrended fluctuation analysis for analyzing coupled nonstationary signals.

PubMed

Hedayatifar, L; Vahabi, M; Jafari, G R

2011-08-01

When many variables are coupled to each other, a single case study could not give us thorough and precise information. When these time series are stationary, different methods of random matrix analysis and complex networks can be used. But, in nonstationary cases, the multifractal-detrended-cross-correlation-analysis (MF-DXA) method was introduced for just two coupled time series. In this article, we have extended the MF-DXA to the method of coupling detrended fluctuation analysis (CDFA) for the case when more than two series are correlated to each other. Here, we have calculated the multifractal properties of the coupled time series, and by comparing CDFA results of the original series with those of the shuffled and surrogate series, we can estimate the source of multifractality and the extent to which our series are coupled to each other. We illustrate the method by selected examples from air pollution and foreign exchange rates.

Coupling detrended fluctuation analysis for analyzing coupled nonstationary signals

NASA Astrophysics Data System (ADS)

Hedayatifar, L.; Vahabi, M.; Jafari, G. R.

2011-08-01

When many variables are coupled to each other, a single case study could not give us thorough and precise information. When these time series are stationary, different methods of random matrix analysis and complex networks can be used. But, in nonstationary cases, the multifractal-detrended-cross-correlation-analysis (MF-DXA) method was introduced for just two coupled time series. In this article, we have extended the MF-DXA to the method of coupling detrended fluctuation analysis (CDFA) for the case when more than two series are correlated to each other. Here, we have calculated the multifractal properties of the coupled time series, and by comparing CDFA results of the original series with those of the shuffled and surrogate series, we can estimate the source of multifractality and the extent to which our series are coupled to each other. We illustrate the method by selected examples from air pollution and foreign exchange rates.

Relations between phytoplankton growth rates and nutrient dynamics in Lake Norman, North Carolina. Technical report series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rodriguez, M.S.

A baseline study of phytoplankton production and nutrient dynamics was conducted on Lake Norman, NC, a 13000-ha, warm-monomictic reservoir, prior to the initiation of thermal inputs from an 1180-MW nuclear electric generation facility. The objective of the study was to identify the major physical, chemical and biological processes controlling nutrient dynamics in Lake Norman, with specific reference to the impact of phytoplankton production on the cycling of carbon, nitrogen and phosphorus.

Exploring the long-term response of undisturbed Mediterranean catchments to changes in atmospheric inputs through time series analysis.

PubMed

Bernal, S; Belillas, C; Ibáñez, J J; Àvila, A

2013-08-01

The aim of this study was to gain insights on the potential hydrological and biogeochemical mechanisms controlling the response of two nested Mediterranean catchments to long-term changes in atmospheric inorganic nitrogen and sulphate deposition. One catchment was steep and fully forested (TM9, 5.9 ha) and the other one had gentler slopes and heathlands in the upper part while side slopes were steep and forested (TM0, 205 ha). Both catchments were highly responsive to the 45% decline in sulphate concentration measured in atmospheric deposition during the 1980s and 1990s, with stream concentrations decreasing by 1.4 to 3.4 μeq L(-1) y(-1). Long-term changes in inorganic nitrogen in both, atmospheric deposition and stream water were small compared to sulphate. The quick response to changes in atmospheric inputs could be explained by the small residence time of water (4-5 months) in these catchments (inferred from chloride time series variance analysis), which was attributed to steep slopes and the role of macropore flow bypassing the soil matrix during wet periods. The estimated residence time for sulphate (1.5-3 months) was substantially lower than for chloride suggesting unaccounted sources of sulphate (i.e., dry deposition, or depletion of soil adsorbed sulphate). In both catchments, inorganic nitrogen concentration in stream water was strongly damped compared to precipitation and its residence time was of the order of decades, indicating that this essential nutrient was strongly retained in these catchments. Inorganic nitrogen concentration tended to be higher at TM0 than at TM9 which was attributed to the presence of nitrogen fixing species in the heathlands. Our results indicate that these Mediterranean catchments react rapidly to environmental changes, which make them especially vulnerable to changes in atmospheric deposition. Copyright © 2013 Elsevier B.V. All rights reserved.

Interactive Digital Signal Processor

NASA Technical Reports Server (NTRS)

Mish, W. H.

1985-01-01

Interactive Digital Signal Processor, IDSP, consists of set of time series analysis "operators" based on various algorithms commonly used for digital signal analysis. Processing of digital signal time series to extract information usually achieved by applications of number of fairly standard operations. IDSP excellent teaching tool for demonstrating application for time series operators to artificially generated signals.

G14A-06- Analysis of the DORIS, GNSS, SLR, VLBI and Gravimetric Time Series at the GGOS Core Sites

NASA Technical Reports Server (NTRS)

Moreaux, G.; Lemoine, F.; Luceri, V.; Pavlis, E.; MacMillan, D.; Bonvalot, S.; Saunier, J.

2017-01-01

Analysis of the time series at the 3-4 multi-technique GGOS sites to analyze and compare the spectral content of the space geodetic and gravity time series. Evaluate the level of agreement between the space geodesy measurements and the physical tie vectors.

Nitrate supply from deep to near-surface waters of the North Pacific subtropical gyre.

PubMed

Johnson, Kenneth S; Riser, Stephen C; Karl, David M

2010-06-24

Concentrations of dissolved inorganic carbon (DIC) decrease in the surface mixed layers during spring and summer in most of the oligotrophic ocean. Mass balance calculations require that the missing DIC is converted into particulate carbon by photosynthesis. This DIC uptake represents one of the largest components of net community production in the world ocean. However, mixed-layer waters in these regions of the ocean typically contain negligible concentrations of plant nutrients such as nitrate and phosphate. Combined nutrient supply mechanisms including nitrogen fixation, diffusive transport and vertical entrainment are believed to be insufficient to supply the required nutrients for photosynthesis. The basin-scale potential for episodic nutrient transport by eddy events is unresolved. As a result, it is not understood how biologically mediated DIC uptake can be supported in the absence of nutrients. Here we report on high-resolution measurements of nitrate (NO(3)(-)) and oxygen (O(2)) concentration made over 21 months using a profiling float deployed near the Hawaii Ocean Time-series station in the North Pacific subtropical gyre. Our measurements demonstrate that as O(2) was produced and DIC was consumed over two annual cycles, a corresponding seasonal deficit in dissolved NO(3)(-) appeared in water at depths from 100 to 250 m. The deep-water deficit in NO(3)(-) was in near-stoichiometric balance with the fixed nitrogen exported to depth. Thus, when the water column from the surface to 250 m is considered as a whole, there is near equivalence between nutrient supply and demand. Short-lived transport events (<10 days) that connect deep stocks of nitrate to nutrient-poor surface waters were clearly present in 12 of the 127 vertical profiles.

Transformation-cost time-series method for analyzing irregularly sampled data

NASA Astrophysics Data System (ADS)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data.

PubMed

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Providing web-based tools for time series access and analysis

NASA Astrophysics Data System (ADS)

Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane

2014-05-01

Time series information is widely used in environmental change analyses and is also an essential information for stakeholders and governmental agencies. However, a challenging issue is the processing of raw data and the execution of time series analysis. In most cases, data has to be found, downloaded, processed and even converted in the correct data format prior to executing time series analysis tools. Data has to be prepared to use it in different existing software packages. Several packages like TIMESAT (Jönnson & Eklundh, 2004) for phenological studies, BFAST (Verbesselt et al., 2010) for breakpoint detection, and GreenBrown (Forkel et al., 2013) for trend calculations are provided as open-source software and can be executed from the command line. This is needed if data pre-processing and time series analysis is being automated. To bring both parts, automated data access and data analysis, together, a web-based system was developed to provide access to satellite based time series data and access to above mentioned analysis tools. Users of the web portal are able to specify a point or a polygon and an available dataset (e.g., Vegetation Indices and Land Surface Temperature datasets from NASA MODIS). The data is then being processed and provided as a time series CSV file. Afterwards the user can select an analysis tool that is being executed on the server. The final data (CSV, plot images, GeoTIFFs) is visualized in the web portal and can be downloaded for further usage. As a first use case, we built up a complimentary web-based system with NASA MODIS products for Germany and parts of Siberia based on the Earth Observation Monitor (www.earth-observation-monitor.net). The aim of this work is to make time series analysis with existing tools as easy as possible that users can focus on the interpretation of the results. References: Jönnson, P. and L. Eklundh (2004). TIMESAT - a program for analysing time-series of satellite sensor data. Computers and Geosciences 30, 833-845. Verbesselt, J., R. Hyndman, G. Newnham and D. Culvenor (2010). Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. DOI: 10.1016/j.rse.2009.08.014 Forkel, M., N. Carvalhais, J. Verbesselt, M. Mahecha, C. Neigh and M. Reichstein (2013). Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. Remote Sensing 5, 2113-2144.

The Irish Sea: Is it eutrophic?

NASA Astrophysics Data System (ADS)

Gowen, R. J.; Tett, P.; Kennington, K.; Mills, D. K.; Shammon, T. M.; Stewart, B. M.; Greenwood, N.; Flanagan, C.; Devlin, M.; Wither, A.

2008-01-01

The question of whether the Irish Sea is eutrophic is addressed by reviewing the evidence for anthropogenic nutrient enrichment, elevated phytoplankton production and biomass and undesirable disturbance in the context of the EU and OSPAR definitions of eutrophication. Winter concentrations of dissolved available inorganic phosphate (DAIP), nitrogen (DAIN as nitrate and nitrite) and silicate (Si) in coastal waters and concentrations of DAIP and Si in offshore waters of the Irish Sea are elevated relative to winter Celtic Sea shelf break concentrations (0.5 μM DAIP, 7.7 μM DAIN and 2.7 μM Si). Significant, negative nutrient salinity relationships and analysis of the Isle of Man nutrient time-series indicate that the elevated Irish Sea levels of DAIP and DAIN are the result of anthropogenic enrichment with highest concentrations (≈2.0 μM DAIP, 30 μM DAIN and 17 μM Si) measured in near shore eastern Irish Sea waters. Summer levels of phytoplankton chlorophyll (Chl) range from <0.1 to 11.4 mg m -3 (mean: 3.4 mg m -3) and from <0.1 to 16.4 mg m -3 (mean: 2.2 mg m -3) in coastal and offshore waters of the western Irish Sea, respectively. Offshore eastern Irish Sea summer chlorophyll levels range from 0.3 to 3.8 mg m -3 (mean: 1.8 mg m -3). Higher levels of spring (up to 43.9 mg m -3) and summer (up to 22.7 mg m -3) biomass in Liverpool Bay are attributed to nutrient enrichment. Estimates of spring and summer production in different regions of the Irish Sea are ≤194 g C m -2. The absence of: (a) oxygen depletion in near shore and open waters of the Irish Sea (except the seasonally isolated western Irish Sea bottom water); (b) trends in the frequency of Phaeocystis spp. blooms and occurrence of toxin producing algae; and (c) changes in the dominant life form of pelagic primary producers, point to a lack of undesirable disturbance and hence argue against anthropogenic eutrophication in the Irish Sea. This conclusion is discussed in the context of future trends in anthropogenic nutrient inputs.

Characterization of the Kootenai River Algae Community and Primary Productivity Before and After Experimental Nutrient Addition, 2004–2007 [Chapter 2, Kootenai River Algal Community Characterization, 2009 KTOI REPORT].

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holderman, Charlie; Bonners Ferry, ID; Anders, Paul

2009-07-01

The Kootenai River ecosystem (spelled Kootenay in Canada) has experienced numerous ecological changes since the early 1900s. Some of the largest impacts to habitat, biological communities, and ecological function resulted from levee construction along the 120 km of river upstream from Kootenay Lake, completed by the 1950s, and the construction and operation of Libby Dam on the river near Libby Montana, completed in 1972. Levee construction isolated tens of thousands of hectares of historic functioning floodplain habitat from the river channel downstream in Idaho and British Columbia (B.C.) severely reducing natural biological productivity and habitat diversity crucial to large river-floodplainmore » ecosystem function. Libby Dam greatly reduces sediment and nutrient transport to downstream river reaches, and dam operations cause large changes in the timing, duration, and magnitude of river flows. These and other changes have contributed to the ecological collapse of the post-development Kootenai River ecosystem and its native biological communities. In response to large scale loss of nutrients, experimental nutrient addition was initiated in the North Arm of Kootenay Lake in 1992, in the South Arm of Kootenay Lake in 2004, and in the Kootenai River at the Idaho-Montana border during 2005. This report characterizes baseline chlorophyll concentration and accrual (primary productivity) rates and diatom and algal community composition and ecological metrics in the Kootenai River for four years, one (2004) before, and three (2005 through 2007) after nutrient addition. The study area encompassed a 325 km river reach from the upper Kootenay River at Wardner, B.C. (river kilometer (rkm) 445) downstream through Montana and Idaho to Kootenay Lake in B.C. (rkm 120). Sampling reaches included an unimpounded reach furthest upstream and four reaches downstream from Libby Dam affected by impoundment: two in the canyon reach (one with and one without nutrient addition), a braided reach, and a meandering reach. The study design included 14 sampling sites: an upstream, unimpounded reference site (KR-14), four control (non-fertilized) canyon sites downstream from Libby Dam, but upstream from nutrient addition (KR-10 through KR-13), two treatment sites referred to collectively as the nutrient addition zone (KR-9 and KR-9.1, located at and 5 km downstream from the nutrient addition site), two braided reach sites (KR-6 and KR-7), and four meander reach sites (KR-1 through KR-4). A series of qualitative evaluations and quantitative analyses were used to assess baseline conditions and effects of experimental nutrient addition treatments on chlorophyll, primary productivity, and taxonomic composition and metric arrays for the diatom and green algae communities. Insufficient density in the samples precluded analyses of bluegreen algae taxa and metrics for pre- and post-nutrient addition periods. Chlorophyll a concentration (mg/m{sup 2}), chlorophyll accrual rate (mg/m{sup 2}/30d), total chlorophyll concentration (chlorophyll a and b) (mg/m{sup 2}), and total chlorophyll accrual rate (mg/m{sup 2}/30d) were calculated. Algal taxa were identified and grouped by taxonomic order as Cyanophyta (blue-greens), Chlorophyta (greens), Bacillariophyta (diatoms), Chrysophyta (goldens), and dominant species from each sample site were identified. Algal densities (number/ml) in periphyton samples were calculated for each sample site and sampling date. Principal Component Analysis (PCA) was performed to reduce the dimension of diatom and algae data and to determine which taxonomic groups and metrics were contributing significantly to the observed variation. PCA analyses were tabulated to indicate eigenvalues, proportion, and cumulative percent variation, as well as eigenvectors (loadings) for each of the components. Biplot graphic displays of PCA axes were also generated to characterize the pattern and structure of the underlying variation. Taxonomic data and a series of biological and ecological metrics were used with PCA for diatoms and algae. Algal metrics included a suite of abundance, diversity, richness, dominance, and other measures, whereas additional trophic status and chemical limnology metrics, Van Dam indices and morphological groupings were employed in diatom PCAs. Analysis of Variance (ANOVA) was carried out using chlorophyll metrics and taxa and metric arrays for the diatom and green algae community data for comparing site differences from 2004 through 2007. Clear, statistically significant, biological responses from chlorophyll metrics, and taxa and metrics of the diatom and algal communities were revealed following experimental nutrient addition in the Kootenai River. Chlorophyll metric responses were more often significant and generally greater in magnitude than diatom and green algae taxa and metric responses.« less

State space model approach for forecasting the use of electrical energy (a case study on: PT. PLN (Persero) district of Kroya)

NASA Astrophysics Data System (ADS)

Kurniati, Devi; Hoyyi, Abdul; Widiharih, Tatik

2018-05-01

Time series data is a series of data taken or measured based on observations at the same time interval. Time series data analysis is used to perform data analysis considering the effect of time. The purpose of time series analysis is to know the characteristics and patterns of a data and predict a data value in some future period based on data in the past. One of the forecasting methods used for time series data is the state space model. This study discusses the modeling and forecasting of electric energy consumption using the state space model for univariate data. The modeling stage is began with optimal Autoregressive (AR) order selection, determination of state vector through canonical correlation analysis, estimation of parameter, and forecasting. The result of this research shows that modeling of electric energy consumption using state space model of order 4 with Mean Absolute Percentage Error (MAPE) value 3.655%, so the model is very good forecasting category.

EMC: Air Quality Forecast Home page

Science.gov Websites

archive NAM Verification Meteorology Error Time Series EMC NAM Spatial Maps Real Time Mesoscale Analysis Precipitation verification NAQFC VERIFICATION CMAQ Ozone & PM Error Time Series AOD Error Time Series HYSPLIT Smoke forecasts vs GASP satellite Dust and Smoke Error Time Series HYSPLIT WCOSS Upgrade (July

[Local fractal analysis of noise-like time series by all permutations method for 1-115 min periods].

PubMed

Panchelyuga, V A; Panchelyuga, M S

2015-01-01

Results of local fractal analysis of 329-per-day time series of 239Pu alpha-decay rate fluctuations by means of all permutations method (APM) are presented. The APM-analysis reveals in the time series some steady frequency set. The coincidence of the frequency set with the Earth natural oscillations was demonstrated. A short review of works by different authors who analyzed the time series of fluctuations in processes of different nature is given. We have shown that the periods observed in those works correspond to the periods revealed in our study. It points to a common mechanism of the phenomenon observed.

Automated Bayesian model development for frequency detection in biological time series.

PubMed

Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

2011-06-24

A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure.

Automated Bayesian model development for frequency detection in biological time series

PubMed Central

2011-01-01

Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time series often deviate significantly from the requirements of optimality for Fourier transformation. In this paper we present an alternative approach based on Bayesian inference. We show the value of placing spectral analysis in the framework of Bayesian inference and demonstrate how model comparison can automate this procedure. PMID:21702910

Impact of drought on morphological, physiological and nutrient use efficiency of elite cacao genotypes from Bahia-Brazil, Tarapoto-Peru and Puerto Rico-USA

USDA-ARS?s Scientific Manuscript database

Worldwide, drought is considered one of the most limiting abiotic stress factors for cacao growth, development and production. A series of greenhouse and growth chamber experiments were undertaken to assess drought effects on early cacao morphological and physiological traits and nutrient use effici...

The microcomputer scientific software series 8: the SYCOOR users manual.

Treesearch

Edgar E. Gutierrez-Espeleta; Gary J. Brand

1993-01-01

Describes how to use SYCOOR, an interactive Macintosh program written in BASIC for computing and adjusting synecological coordinates. Site synecological coordinates are indices of moisture, nutrients, heat, and light computed from lists of plant species present at the site. Graphs of a species` distribution in moisture-nutrient and heat-light space are also displayed...

Analysis of Time-Series Quasi-Experiments. Final Report.

ERIC Educational Resources Information Center

Glass, Gene V.; Maguire, Thomas O.

The objective of this project was to investigate the adequacy of statistical models developed by G. E. P. Box and G. C. Tiao for the analysis of time-series quasi-experiments: (1) The basic model developed by Box and Tiao is applied to actual time-series experiment data from two separate experiments, one in psychology and one in educational…

A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis.

PubMed

Hayashi, Hideaki; Shibanoki, Taro; Shima, Keisuke; Kurita, Yuichi; Tsuji, Toshio

2015-12-01

This paper proposes a probabilistic neural network (NN) developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a continuous-density hidden Markov model with a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into an NN, which is named a time-series discriminant component network (TSDCN), so that parameters of dimensionality reduction and classification can be obtained simultaneously as network coefficients according to a backpropagation through time-based learning algorithm with the Lagrange multiplier method. The TSDCN is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. The validity of the TSDCN is demonstrated for high-dimensional artificial data and electroencephalogram signals in the experiments conducted during the study.

Rapid Shifts in Soil Nutrients and Decomposition Enzyme Activity in Early Succession Following Forest Fire

DOE PAGES

Knelman, Joseph E.; Graham, Emily B.; Ferrenberg, Scott; ...

2017-09-15

In post-disturbance landscapes nutrient availability has proven a major control on ecological succession. In this study, we examined variation in connections between soil nutrient availability and decomposition extracellular enzyme activity (EEA) across post fire secondary succession in forest soils as well as after a secondary flood disturbance. We also examined possible linkages between edaphic properties and bacterial communities based on 16S rRNA gene analysis. We found that with advancing succession in a post-fire landscape, the relationship between soil nutrients and EEA became stronger over time. In general, late successional soils showed stronger connections between EEA and soil nutrient status, whilemore » early successional soils were marked by a complete decoupling of nutrients and EEA. We also found that soil moisture and bacterial communities of post-fire disturbance soils were susceptible to change following the secondary flood disturbance, while undisturbed, reference forest soils were not. Our results demonstrate that nutrient pools correlating with EEA change over time. While past work has largely focused on ecosystem succession on decadal timescales, our work suggests that nutrients shift in their relative importance as a control of decomposition EEA in the earliest stages of secondary succession. Furthermore, this work emphasizes the relevance of successional stage, even on short timescales, in predicting rates of carbon and nitrogen cycling, especially as disturbances become more frequent in a rapidly changing world.« less

Rapid Shifts in Soil Nutrients and Decomposition Enzyme Activity in Early Succession Following Forest Fire

DOE Office of Scientific and Technical Information (OSTI.GOV)

Knelman, Joseph E.; Graham, Emily B.; Ferrenberg, Scott

In post-disturbance landscapes nutrient availability has proven a major control on ecological succession. In this study, we examined variation in connections between soil nutrient availability and decomposition extracellular enzyme activity (EEA) across post fire secondary succession in forest soils as well as after a secondary flood disturbance. We also examined possible linkages between edaphic properties and bacterial communities based on 16S rRNA gene analysis. We found that with advancing succession in a post-fire landscape, the relationship between soil nutrients and EEA became stronger over time. In general, late successional soils showed stronger connections between EEA and soil nutrient status, whilemore » early successional soils were marked by a complete decoupling of nutrients and EEA. We also found that soil moisture and bacterial communities of post-fire disturbance soils were susceptible to change following the secondary flood disturbance, while undisturbed, reference forest soils were not. Our results demonstrate that nutrient pools correlating with EEA change over time. While past work has largely focused on ecosystem succession on decadal timescales, our work suggests that nutrients shift in their relative importance as a control of decomposition EEA in the earliest stages of secondary succession. Furthermore, this work emphasizes the relevance of successional stage, even on short timescales, in predicting rates of carbon and nitrogen cycling, especially as disturbances become more frequent in a rapidly changing world.« less

Beyond linear methods of data analysis: time series analysis and its applications in renal research.

PubMed

Gupta, Ashwani K; Udrea, Andreea

2013-01-01

Analysis of temporal trends in medicine is needed to understand normal physiology and to study the evolution of disease processes. It is also useful for monitoring response to drugs and interventions, and for accountability and tracking of health care resources. In this review, we discuss what makes time series analysis unique for the purposes of renal research and its limitations. We also introduce nonlinear time series analysis methods and provide examples where these have advantages over linear methods. We review areas where these computational methods have found applications in nephrology ranging from basic physiology to health services research. Some examples include noninvasive assessment of autonomic function in patients with chronic kidney disease, dialysis-dependent renal failure and renal transplantation. Time series models and analysis methods have been utilized in the characterization of mechanisms of renal autoregulation and to identify the interaction between different rhythms of nephron pressure flow regulation. They have also been used in the study of trends in health care delivery. Time series are everywhere in nephrology and analyzing them can lead to valuable knowledge discovery. The study of time trends of vital signs, laboratory parameters and the health status of patients is inherent to our everyday clinical practice, yet formal models and methods for time series analysis are not fully utilized. With this review, we hope to familiarize the reader with these techniques in order to assist in their proper use where appropriate.

Quantification of submarine groundwater discharge and its short-term dynamics by linking time-variant end-member mixing analysis and isotope mass balancing (222-Rn)

NASA Astrophysics Data System (ADS)

Petermann, Eric; Knöller, Kay; Stollberg, Reiner; Scholten, Jan; Rocha, Carlos; Weiß, Holger; Schubert, Michael

2017-04-01

Submarine groundwater discharge (SGD) plays a crucial role for the water quality of coastal waters due to associated fluxes of nutrients, organic compounds and/or heavy-metals. Thus, the quantification of SGD is essential for evaluating the vulnerability of coastal water bodies with regard to groundwater pollution as well as for understanding the matter cycles of the connected water bodies. Here, we present a scientific approach for quantifying discharge of fresh groundwater (GWf) and recirculated seawater (SWrec), including its short-term temporal dynamics, into the tide-affected Knysna estuary, South Africa. For a time-variant end-member mixing analysis we conducted time-series observations of radon (222Rn) and salinity within the estuary over two tidal cycles in combination with estimates of the related end-members for seawater, river water, GWf and SWrec. The mixing analysis was treated as constrained optimization problem for finding an end-member mixing ratio that simultaneously fits the observed data for radon and salinity best for every time-step. Uncertainty of each mixing ratio was quantified by Monte Carlo simulations of the optimization procedure considering uncertainty in end-member characterization. Results reveal the highest GWf and SWrec fraction in the estuary during peak low tide with averages of 0.8 % and 1.4 %, respectively. Further, we calculated a radon mass balance that revealed a daily radon flux of 4.8 * 108 Bq into the estuary equivalent to a GWf discharge of 29.000 m3/d (9.000-59.000 m3/d for 25th-75th percentile range) and a SWrec discharge of 80.000 m3/d (45.000-130.000 m3/d for 25th-75th percentile range). The uncertainty of SGD reflects the end-member uncertainty, i.e. the spatial heterogeneity of groundwater composition. The presented approach allows the calculation of mixing ratios of multiple uncertain end-members for time-series measurements of multiple parameters. Linking these results with a tracer mass balance allows conversion of end-member fractions to end-member fluxes.

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be studied through the copula theory. As to the parameter estimation, the maximum likelihood estimation (MLE) will be applied. To illustrate the method, the univariate time series model and the dependence structure will be determined and tested using the monthly discharge time series of Cuyahoga River Basin.

Behavioral tracing demonstrates dietary nutrient discrimination in two-spotted crickets Gryllus bimaculatus.

PubMed

Fukumura, Keisuke; Nagata, Shinji

2017-10-01

Animals select appropriate diets to meet their nutritional requirements. Here, we demonstrate the availability for analysis of feeding preference using an orthopteran, the two-spotted cricket Gryllus bimaculatus. A time-course study of these insects, involving continuous recording and tracing behavior for 9 h, allowed us to monitor discrimination of diet that contained various nutrients.

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-07-01

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

A 45-year time series of Saharan dune mobility from remote sensing

NASA Astrophysics Data System (ADS)

Vermeesch, P.

2012-04-01

Decadal trends in the aeolian dust record of the Sahara affect the global climate system and the nutrient budget of the Atlantic Ocean. One proposed cause of these trends are changes in the frequency and intensity of dust storms, which have hitherto been hard to quantify. Because sand flux scales with the cube of wind speed, dune migration rates can be used as a proxy for storminess. Relative changes in the storminess of the Sahara can thus be monitored by tracking the migration rates of individual sand dunes over time. The Bodélé Depression of northern Chad was selected as a target area for this method, because it is the most important point-source of aeolian dust on the planet and features the largest and fastest dunes on Earth. A collection of co-registered Landsat, SPOT, and ASTER scenes, combined with declassified American spy satellite images was used to construct a 45 year record of dune migration in the Bodélé Depression. One unexpected outcome of the study was the observation of binary dune interactions in the imagery sequence, which reveals that when two barchan dunes collide, a transfer of mass occurs so that one dune appears to travel through the other unscathed, like a solitary wave. This confirms a controversial numerical model prediction and settles a decade-old debate in aeolian geomorphology. The COSI-Corr change detection method was used to measure the dune migration rates from 1984 until 1987, 1990, 1996, 2000, 2003, 2005, 2007, 2008, 2009, and 2010. An algorithm was developed to automatically warp the resulting displacement fields back to a common point in time. Thus, individual image pixels of a dune field were tracked over time, allowing the extraction of a time series from the co-registered satellite images without further human intervention. The automated analysis was extended further back into the past by comparison of the 1984 image with declassified American spy satellite (Corona) images from 1965 and 1970. Due to the presence of specks of dust as well as image distortions caused by shrinking of the photographic film, it was not possible to automatically measure the dune displacements of these scenes with COSI-Corr. Instead, the image was georeferenced and coregistered to the 1984 Landsat imagery by third order polynomial fits to 531 tie points, and the displacements of ten large barchan dunes were measured by hand. Thanks to the 19-year time lapse between the two images used for these 'analog' measurements, their precision is better than 5%, which is comparable with that of the automated COSI-Corr analysis. The resulting dune celerities are identical to the automated measurements, which themselves show little or no temporal variability over the subsequent 26 years. The lack of any trend in the time series of dune celerity paints a picture of remarkably stable dune mobility over the past 45 years. None of the distributions fall outside the overall average of 25m/yr. The constant dune migration rates resulting from our study indicate that there has been no change in the storminess of the Sahara over the past 45 years. The observed dust trends are therefore caused by changes in vegetation cover, which in turn reflect changes in precipitation and land usage. This work highlights the importance of the hyper-arid Bodélé Depression, which provides a steady but finite supply of aeolian dust to the atmosphere without which nutrient fluxes and terrestrial albedo would be more variable than they are today.

A simple and fast representation space for classifying complex time series

NASA Astrophysics Data System (ADS)

Zunino, Luciano; Olivares, Felipe; Bariviera, Aurelio F.; Rosso, Osvaldo A.

2017-03-01

In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

PubMed

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates.

PubMed

Xia, Li C; Steele, Joshua A; Cram, Jacob A; Cardon, Zoe G; Simmons, Sheri L; Vallino, Joseph J; Fuhrman, Jed A; Sun, Fengzhu

2011-01-01

The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

PubMed Central

2011-01-01

Background The increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval. Results We extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. Conclusions The extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa. PMID:22784572

On the equivalence of case-crossover and time series methods in environmental epidemiology.

PubMed

Lu, Yun; Zeger, Scott L

2007-04-01

The case-crossover design was introduced in epidemiology 15 years ago as a method for studying the effects of a risk factor on a health event using only cases. The idea is to compare a case's exposure immediately prior to or during the case-defining event with that same person's exposure at otherwise similar "reference" times. An alternative approach to the analysis of daily exposure and case-only data is time series analysis. Here, log-linear regression models express the expected total number of events on each day as a function of the exposure level and potential confounding variables. In time series analyses of air pollution, smooth functions of time and weather are the main confounders. Time series and case-crossover methods are often viewed as competing methods. In this paper, we show that case-crossover using conditional logistic regression is a special case of time series analysis when there is a common exposure such as in air pollution studies. This equivalence provides computational convenience for case-crossover analyses and a better understanding of time series models. Time series log-linear regression accounts for overdispersion of the Poisson variance, while case-crossover analyses typically do not. This equivalence also permits model checking for case-crossover data using standard log-linear model diagnostics.

Defense Applications of Signal Processing

DTIC Science & Technology

1999-08-27

class of multiscale autoregressive moving average (MARMA) processes. These are generalisations of ARMA models in time series analysis , and they contain...including the two theoretical sinusoidal components. Analysis of the amplitude and frequency time series provided some novel insight into the real...communication channels, underwater acoustic signals, radar systems , economic time series and biomedical signals [7]. The alpha stable (aS) distribution has

Detection of wine grape nutrient levels using visible and near infrared 1nm spectral resolution remote sensing

NASA Astrophysics Data System (ADS)

Anderson, Grant; van Aardt, Jan; Bajorski, Peter; Vanden Heuvel, Justine

2016-05-01

The grape industry relies on regular crop assessment to aid in the day-to-day and seasonal management of their crop. More specifically, there are six key nutrients of interest to viticulturists in the growing of wine grapes, namely nitrogen, potassium, phosphorous, magnesium, zinc and boron. Traditional methods of determining the levels of these nutrients are through collection and chemical analysis of petiole samples from the grape vines themselves. We collected ground-level observations of the spectra of the grape vines, using a hyperspectral spectrometer (0.4-2.5um), at the same time that petioles samples were harvested. We then interpolated the data into a consistent 1 nm spectral resolution before comparing it to the nutrient data collected. This nutrient data came from both the industry standard petiole analysis, as well as an additional leaf-level analysis. The data were collected for two different grape cultivars, both during bloom and veraison periods to provide variability, while also considering the impact of temporal/seasonal change. A narrow-band NDI (Normalized Difference Index) approach, as well as a simple ratio index, was used to determine the correlation of the reflectance data to the nutrient data. This analysis was limited to the silicon photodiode range to increase the utility of our approach for wavelength-specific cameras (via spectral filters) in a low cost drone platform. The NDI generated correlation coefficients were as high as 0.80 and 0.88 for bloom and veraison, respectively. The ratio index produced correlation coefficient results that are the same at two decimal places with 0.80 and 0.88. These results bode well for eventual non-destructive, accurate and precise assessment of vineyard nutrient status.

An Observation Analysis Tool for time-series analysis and sensor management in the FREEWAT GIS environment for water resources management

NASA Astrophysics Data System (ADS)

Cannata, Massimiliano; Neumann, Jakob; Cardoso, Mirko; Rossetto, Rudy; Foglia, Laura; Borsi, Iacopo

2017-04-01

In situ time-series are an important aspect of environmental modelling, especially with the advancement of numerical simulation techniques and increased model complexity. In order to make use of the increasing data available through the requirements of the EU Water Framework Directive, the FREEWAT GIS environment incorporates the newly developed Observation Analysis Tool for time-series analysis. The tool is used to import time-series data into QGIS from local CSV files, online sensors using the istSOS service, or MODFLOW model result files and enables visualisation, pre-processing of data for model development, and post-processing of model results. OAT can be used as a pre-processor for calibration observations, integrating the creation of observations for calibration directly from sensor time-series. The tool consists in an expandable Python library of processing methods and an interface integrated in the QGIS FREEWAT plug-in which includes a large number of modelling capabilities, data management tools and calibration capacity.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Simulating high frequency water quality monitoring data using a catchment runoff attenuation flux tool (CRAFT).

PubMed

Adams, Russell; Quinn, Paul F; Perks, Matthew; Barber, Nicholas J; Jonczyk, Jennine; Owen, Gareth J

2016-12-01

High resolution water quality data has recently become widely available from numerous catchment based monitoring schemes. However, the models that can reproduce time series of concentrations or fluxes have not kept pace with the advances in monitoring data. Model performance at predicting phosphorus (P) and sediment concentrations has frequently been poor with models not fit for purpose except for predicting annual losses. Here, the data from the Eden Demonstration Test Catchments (DTC) project have been used to calibrate the Catchment Runoff Attenuation Flux Tool (CRAFT), a new, parsimonious model developed with the aim of modelling both the generation and attenuation of nutrients and sediments in small to medium sized catchments. The CRAFT has the ability to run on an hourly timestep and can calculate the mass of sediments and nutrients transported by three flow pathways representing rapid surface runoff, fast subsurface drainage and slow groundwater flow (baseflow). The attenuation feature of the model is introduced here; this enables surface runoff and contaminants transported via this pathway to be delayed in reaching the catchment outlet. It was used to investigate some hypotheses of nutrient and sediment transport in the Newby Beck Catchment (NBC) Model performance was assessed using a suite of metrics including visual best fit and the Nash-Sutcliffe efficiency. It was found that this approach for water quality models may be the best assessment method as opposed to using a single metric. Furthermore, it was found that, when the aim of the simulations was to reproduce the time series of total P (TP) or total reactive P (TRP) to get the best visual fit, that attenuation was required. The model will be used in the future to explore the impacts on water quality of different mitigation options in the catchment; these will include attenuation of surface runoff. Copyright © 2016 Elsevier B.V. All rights reserved.

Enabling Web-Based Analysis of CUAHSI HIS Hydrologic Data Using R and Web Processing Services

NASA Astrophysics Data System (ADS)

Ames, D. P.; Kadlec, J.; Bayles, M.; Seul, M.; Hooper, R. P.; Cummings, B.

2015-12-01

The CUAHSI Hydrologic Information System (CUAHSI HIS) provides open access to a large number of hydrological time series observation and modeled data from many parts of the world. Several software tools have been designed to simplify searching and access to the CUAHSI HIS datasets. These software tools include: Desktop client software (HydroDesktop, HydroExcel), developer libraries (WaterML R Package, OWSLib, ulmo), and the new interactive search website, http://data.cuahsi.org. An issue with using the time series data from CUAHSI HIS for further analysis by hydrologists (for example for verification of hydrological and snowpack models) is the large heterogeneity of the time series data. The time series may be regular or irregular, contain missing data, have different time support, and be recorded in different units. R is a widely used computational environment for statistical analysis of time series and spatio-temporal data that can be used to assess fitness and perform scientific analyses on observation data. R includes the ability to record a data analysis in the form of a reusable script. The R script together with the input time series dataset can be shared with other users, making the analysis more reproducible. The major goal of this study is to examine the use of R as a Web Processing Service for transforming time series data from the CUAHSI HIS and sharing the results on the Internet within HydroShare. HydroShare is an online data repository and social network for sharing large hydrological data sets such as time series, raster datasets, and multi-dimensional data. It can be used as a permanent cloud storage space for saving the time series analysis results. We examine the issues associated with running R scripts online: including code validation, saving of outputs, reporting progress, and provenance management. An explicit goal is that the script which is run locally should produce exactly the same results as the script run on the Internet. Our design can be used as a model for other studies that need to run R scripts on the web.

Improved hydrological-model design by integrating nutrient and water flow

NASA Astrophysics Data System (ADS)

Arheimer, B.; Lindstrom, G.

2013-12-01

The potential of integrating hydrologic and nutrient concentration data to better understand patterns of catchment response and to better design hydrological modeling was explored using a national multi-basin model system for Sweden, called ';S-HYPE'. The model system covers more than 450 000 km2 and produce daily values of nutrient concentration and water discharge in 37 000 catchments from 1961 and onwards. It is based on the processed-based and semi-distributed HYdrological Predictions for the Environment (HYPE) code. The model is used operationally for assessments of water status or climate change impacts and for forecasts by the national warning service of floods, droughts and fire. The first model was launched in 2008, but S-HYPE is continuously improved and released in new versions every second year. Observations are available in 400 sites for daily water discharge and some 900 sites for monthly grab samples of nutrient concentrations. The latest version (2012) has an average NSE for water discharge of 0.7 and an average relative error of 5%, including both regulated and unregulated rivers with catchments from ten to several thousands of km2 and various landuse. The daily relative errors of nutrient concentrations are on average 20% for total Nitrogen and 35% for total Phosphorus. This presentation will give practical examples of how the nutrient data has been used to trace errors or inadequate parameter values in the hydrological model. Since 2008 several parts of the model structure has been reconsidered both in the source code, parameter values and input data of catchment characteristics. In this process water quality has been guiding much of the overall model design of catchment hydrological functions and routing along the river network. The model structure has thus been developed iteratively when evaluating results and checking time-series. Examples of water quality driven improvements will be given for estimation of vertical flow paths, such as separation of the hydrograph in surface flow, snow melt and baseflow, as well as horizontal flow paths in the landscape, such as mixing from various land use, impact from lakes and river channel volume. Overall, the S-HYPE model performance of water discharge increased from NSE 0.55 to 0.69 as an average for 400 gauges between the version 2010 and 2012. Most of this improvement, however, can be referred to improved regulations routines, rating curves for major lakes and parameters correcting ET and precipitation. Nevertheless, integrated water and nutrient modeling put constraints on the hydrological parameter values, which reduce equifinality for the hydrological part without reducing the model performance. The examples illustrates that the credibility of the hydrological model structure is thus improved by integrating water and nutrient flow. This lead to improved understanding of flow paths and water-nutrient process interactions in Sweden, which in turn will be very useful in further model analysis on impact of climate change or measures to reduce nutrient load from rivers to the Baltic Sea.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

Real-time nutrient monitoring in rivers: adaptive sampling strategies, technological challenges and future directions

NASA Astrophysics Data System (ADS)

Blaen, Phillip; Khamis, Kieran; Lloyd, Charlotte; Bradley, Chris

2016-04-01

Excessive nutrient concentrations in river waters threaten aquatic ecosystem functioning and can pose substantial risks to human health. Robust monitoring strategies are therefore required to generate reliable estimates of river nutrient loads and to improve understanding of the catchment processes that drive spatiotemporal patterns in nutrient fluxes. Furthermore, these data are vital for prediction of future trends under changing environmental conditions and thus the development of appropriate mitigation measures. In recent years, technological developments have led to an increase in the use of continuous in-situ nutrient analysers, which enable measurements at far higher temporal resolutions than can be achieved with discrete sampling and subsequent laboratory analysis. However, such instruments can be costly to run and difficult to maintain (e.g. due to high power consumption and memory requirements), leading to trade-offs between temporal and spatial monitoring resolutions. Here, we highlight how adaptive monitoring strategies, comprising a mixture of temporal sample frequencies controlled by one or more 'trigger variables' (e.g. river stage, turbidity, or nutrient concentration), can advance our understanding of catchment nutrient dynamics while simultaneously overcoming many of the practical and economic challenges encountered in typical in-situ river nutrient monitoring applications. We present examples of short-term variability in river nutrient dynamics, driven by complex catchment behaviour, which support our case for the development of monitoring systems that can adapt in real-time to rapid environmental changes. In addition, we discuss the advantages and disadvantages of current nutrient monitoring techniques, and suggest new research directions based on emerging technologies and highlight how these might improve: 1) monitoring strategies, and 2) understanding of linkages between catchment processes and river nutrient fluxes.

HydroClimATe: hydrologic and climatic analysis toolkit

USGS Publications Warehouse

Dickinson, Jesse; Hanson, Randall T.; Predmore, Steven K.

2014-01-01

The potential consequences of climate variability and climate change have been identified as major issues for the sustainability and availability of the worldwide water resources. Unlike global climate change, climate variability represents deviations from the long-term state of the climate over periods of a few years to several decades. Currently, rich hydrologic time-series data are available, but the combination of data preparation and statistical methods developed by the U.S. Geological Survey as part of the Groundwater Resources Program is relatively unavailable to hydrologists and engineers who could benefit from estimates of climate variability and its effects on periodic recharge and water-resource availability. This report documents HydroClimATe, a computer program for assessing the relations between variable climatic and hydrologic time-series data. HydroClimATe was developed for a Windows operating system. The software includes statistical tools for (1) time-series preprocessing, (2) spectral analysis, (3) spatial and temporal analysis, (4) correlation analysis, and (5) projections. The time-series preprocessing tools include spline fitting, standardization using a normal or gamma distribution, and transformation by a cumulative departure. The spectral analysis tools include discrete Fourier transform, maximum entropy method, and singular spectrum analysis. The spatial and temporal analysis tool is empirical orthogonal function analysis. The correlation analysis tools are linear regression and lag correlation. The projection tools include autoregressive time-series modeling and generation of many realizations. These tools are demonstrated in four examples that use stream-flow discharge data, groundwater-level records, gridded time series of precipitation data, and the Multivariate ENSO Index.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

The Timeseries Toolbox - A Web Application to Enable Accessible, Reproducible Time Series Analysis

NASA Astrophysics Data System (ADS)

Veatch, W.; Friedman, D.; Baker, B.; Mueller, C.

2017-12-01

The vast majority of data analyzed by climate researchers are repeated observations of physical process or time series data. This data lends itself of a common set of statistical techniques and models designed to determine trends and variability (e.g., seasonality) of these repeated observations. Often, these same techniques and models can be applied to a wide variety of different time series data. The Timeseries Toolbox is a web application designed to standardize and streamline these common approaches to time series analysis and modeling with particular attention to hydrologic time series used in climate preparedness and resilience planning and design by the U. S. Army Corps of Engineers. The application performs much of the pre-processing of time series data necessary for more complex techniques (e.g. interpolation, aggregation). With this tool, users can upload any dataset that conforms to a standard template and immediately begin applying these techniques to analyze their time series data.

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

Developement of watershed and reference loads for a TMDL in Charleston Harbor System, SC.

Treesearch

Silong Lu; Devenra Amatya; Jamie Miller

2005-01-01

It is essential to determine point and non-point source loads and their distribution for development of a dissolved oxygen (DO) Total Maximum Daily Load (TMDL). A series of models were developed to assess sources of oxygen-demand loadings in Charleston Harbor, South Carolina. These oxygen-demand loadings included nutrients and BOD. Stream flow and nutrient...

Feasibility analysis of marine ecological on-line integrated monitoring system

NASA Astrophysics Data System (ADS)

Chu, D. Z.; Cao, X.; Zhang, S. W.; Wu, N.; Ma, R.; Zhang, L.; Cao, L.

2017-08-01

The in-situ water quality sensors were susceptible to biological attachment. Moreover, sea water corrosion and wave impact damage, and many sensors scattered distribution would cause maintenance inconvenience. The paper proposed a highly integrated marine ecological on-line integrated monitoring system, which can be used inside monitoring station. All sensors were reasonably classified, the similar in series, the overall in parallel. The system composition and workflow were described. In addition, the paper proposed attention issues of the system design and corresponding solutions. Water quality multi-parameters and 5 nutrient salts as the verification index, in-situ and systematic data comparison experiment were carried out. The results showed that the data consistency of nutrient salt, PH and salinity was better. Temperature and dissolved oxygen data trend was consistent, but the data had deviation. Turbidity fluctuated greatly; the chlorophyll trend was similar with it. Aiming at the above phenomena, three points system optimization direction were proposed.

Vertical nutrient fluxes, turbulence and the distribution of chlorophyll a in the north-eastern North Sea

NASA Astrophysics Data System (ADS)

Bendtsen, Jørgen; Richardson, Katherine

2017-04-01

During summer the northern North Sea is characterized by nutrient rich bottom water masses and nutrient poor surface layers. This explains the distribution of chlorophyll a in the water column where a subsurface maximum, referred to as the deep chlorophyll maximum (DCM), often is present during the growth season. Vertical transport of nutrients between bottom water masses and the well lit surface layer stimulates phytoplankton growth and this generally explains the location of the DCM. However, a more specific understanding of the interplay between vertical transports, nutrient fluxes and phytoplankton abundance is required for identifying the nature of the vertical transport processes, e.g the role of advection versus vertical turbulent diffusion or the role of localized mixing associated with mesoscale eddies. We present results from the VERMIX study in the north-eastern North Sea where nutrients, chlorophyll a and turbulence profiles were measured along five north-south directed transects in July 2016. A high-resolution sampling program, with horizontal distances of 1-10 km between CTD-stations, resolved the horizontal gradients of chlorophyll a across the steep bottom slope from the relatively shallow central North Sea ( 50-80 m) towards the deep Norwegian Trench (>700 m). Low oxygen concentrations in the bottom water masses above the slope indicated enhanced biological production where vertical mixing would stimulate phytoplankton growth around the DCM. Measurements of variable fluorescence (Fv/Fm) showed elevated values in the DCM which demonstrates a higher potential for electron transport in the Photosystem II in the phytoplankton cells, i.e. an indication of nutrient-rich conditions favorable for phytoplankton production. Profiles of the vertical shear and microstructure of temperature and salinity were measured by a VMP-250 turbulence profiler and the vertical diffusion of nutrients was calculated from the estimated vertical turbulent diffusivity and the distributions of nutrients. Results from the five transects and two time-series stations, where vertical profiles were made at hourly intervals, showed that vertical mixing processes above the slope increased the vertical transport of nutrients significantly and mixing above the slope can explain the hydrographic features and the distribution of the DCM in the area.

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

PubMed

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Impact of physiology, structure and BRDF in hyperspectral time series of a Citrus orchard

NASA Astrophysics Data System (ADS)

Stuckens, J.; Dzikiti, S.; Verstraeten, W. W.; Verreynne, J. S.; Swinnen, R.; Coppin, P.

2010-05-01

Monitoring of plant production systems using remote sensing requires an understanding of the mechanisms in which physiological and structural changes as well as the quality and direction of incident light alter the measured canopy reflectance. Due to the evergreen nature of Citrus, the benefits of year-round monitoring of spectral changes are counterweighted by more subtle changes and seasonal trends than in other perennials. This study presents the results of a 14 months field measurement campaign in a commercial Citrus sinensis ‘Midknight Valencia' orchard in Wellington, Western Cape Province, South-Africa. Hyperspectral data were collected of canopy and leaf reflectance (350 - 2500 nm) of 16 representative trees at monthly intervals and supplemented with local climatology, orchard management records, sap stream, water potential and leaf and soil nutrient analysis. The aim of this research is to translate spectral changes and trends at the leaf and at canopy levels into physiological processes such as plant nutrient and carbohydrate balances and stress responses. Specific research questions include the spectral detection of flowering (date of anthesis, flowering intensity), fruit drop, fruit number and coloration, vegetative flushes, leaf senescence and drop and pruning. Attention is paid to the detection and the impact of sunburn (photo-damage). In order to separate physiological and structural changes from changes caused by seasonal changes in solar elevation during measurement time (bidirectional reflectance) a normalization function is constructed using simulated and measured data. Additional research is done to up-scale measurements from tree level to orchard level, which includes the tree variability, the influence of soil and weeds and different amounts of shading.

Frequency-phase analysis of resting-state functional MRI

PubMed Central

Goelman, Gadi; Dan, Rotem; Růžička, Filip; Bezdicek, Ondrej; Růžička, Evžen; Roth, Jan; Vymazal, Josef; Jech, Robert

2017-01-01

We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience. PMID:28272522

Numerical modelling of biophysicochemical effects on multispecies reactive transport in porous media involving Pseudomonas putida for potential microbial enhanced oil recovery application.

PubMed

Sivasankar, P; Rajesh Kanna, A; Suresh Kumar, G; Gummadi, Sathyanarayana N

2016-07-01

pH and resident time of injected slug plays a critical role in characterizing the reservoir for potential microbial enhanced oil recovery (MEOR) application. To investigate MEOR processes, a multispecies (microbes-nutrients) reactive transport model in porous media was developed by coupling kinetic and transport model. The present work differs from earlier works by explicitly determining parametric values required for kinetic model by experimental investigations using Pseudomonas putida at different pH conditions and subsequently performing sensitivity analysis of pH, resident time and water saturation on concentrations of microbes, nutrients and biosurfactant within reservoir. The results suggest that nutrient utilization and biosurfactant production are found to be maximum at pH 8 and 7.5 respectively. It is also found that the sucrose and biosurfactant concentrations are highly sensitive to pH rather than reservoir microbial concentration, while at larger resident time and water saturation, the microbial and nutrient concentrations were lesser due to enhanced dispersion. Copyright © 2016 Elsevier Ltd. All rights reserved.

Recurrence plots and recurrence quantification analysis of human motion data

NASA Astrophysics Data System (ADS)

Josiński, Henryk; Michalczuk, Agnieszka; Świtoński, Adam; Szczesna, Agnieszka; Wojciechowski, Konrad

2016-06-01

The authors present exemplary application of recurrence plots, cross recurrence plots and recurrence quantification analysis for the purpose of exploration of experimental time series describing selected aspects of human motion. Time series were extracted from treadmill gait sequences which were recorded in the Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology in Bytom, Poland by means of the Vicon system. Analysis was focused on the time series representing movements of hip, knee, ankle and wrist joints in the sagittal plane.

Analysis of Site Position Time Series Derived From Space Geodetic Solutions

NASA Astrophysics Data System (ADS)

Angermann, D.; Meisel, B.; Kruegel, M.; Tesmer, V.; Miller, R.; Drewes, H.

2003-12-01

This presentation deals with the analysis of station coordinate time series obtained from VLBI, SLR, GPS and DORIS solutions. We also present time series for the origin and scale derived from these solutions and discuss their contribution to the realization of the terrestrial reference frame. For these investigations we used SLR and VLBI solutions computed at DGFI with the software systems DOGS (SLR) and OCCAM (VLBI). The GPS and DORIS time series were obtained from weekly station coordinates solutions provided by the IGS, and from the joint DORIS analysis center (IGN-JPL). We analysed the time series with respect to various aspects, such as non-linear motions, periodic signals and systematic differences (biases). A major focus is on a comparison of the results at co-location sites in order to identify technique- and/or solution related problems. This may also help to separate and quantify possible effects, and to understand the origin of still existing discrepancies. Technique-related systematic effects (biases) should be reduced to the highest possible extent, before using the space geodetic solutions for a geophysical interpretation of seasonal signals in site position time series.

77 FR 47383 - Annual Assessment of the Status of Competition in the Market for the Delivery of Video Programming

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-08

... monitor trends on an annual basis. To continue our time-series analysis, we request data as of June 30... information and time- series data we should collect for the analysis of various MVPD performance metrics. In... revenues, cash flows, and margins. To the extent possible, we seek five-year time-series data to allow us...

Nonstationary time series prediction combined with slow feature analysis

NASA Astrophysics Data System (ADS)

Wang, G.; Chen, X.

2015-01-01

Almost all climate time series have some degree of nonstationarity due to external driving forces perturbations of the observed system. Therefore, these external driving forces should be taken into account when reconstructing the climate dynamics. This paper presents a new technique of combining the driving force of a time series obtained using the Slow Feature Analysis (SFA) approach, then introducing the driving force into a predictive model to predict non-stationary time series. In essence, the main idea of the technique is to consider the driving forces as state variables and incorporate them into the prediction model. To test the method, experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted. The results showed improved and effective prediction skill.

Nonlinear Analysis of Surface EMG Time Series

NASA Astrophysics Data System (ADS)

Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

2004-04-01

Applications of nonlinear analysis of surface electromyography time series of patients with and without low back pain are presented. Limitations of the standard methods based on the power spectrum are discussed.

Spatially explicit modeling of particulate nutrient flux in Large global rivers

NASA Astrophysics Data System (ADS)

Cohen, S.; Kettner, A.; Mayorga, E.; Harrison, J. A.

2017-12-01

Water, sediment, nutrient and carbon fluxes along river networks have undergone considerable alterations in response to anthropogenic and climatic changes, with significant consequences to infrastructure, agriculture, water security, ecology and geomorphology worldwide. However, in a global setting, these changes in fluvial fluxes and their spatial and temporal characteristics are poorly constrained, due to the limited availability of continuous and long-term observations. We present results from a new global-scale particulate modeling framework (WBMsedNEWS) that combines the Global NEWS watershed nutrient export model with the spatially distributed WBMsed water and sediment model. We compare the model predictions against multiple observational datasets. The results indicate that the model is able to accurately predict particulate nutrient (Nitrogen, Phosphorus and Organic Carbon) fluxes on an annual time scale. Analysis of intra-basin nutrient dynamics and fluxes to global oceans is presented.

Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data.

PubMed

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S

2016-06-01

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

Species interactions and response time to climate change: ice-cover and terrestrial run-off shaping Arctic char and brown trout competitive asymmetries

NASA Astrophysics Data System (ADS)

Finstad, A. G.; Palm Helland, I.; Jonsson, B.; Forseth, T.; Foldvik, A.; Hessen, D. O.; Hendrichsen, D. K.; Berg, O. K.; Ulvan, E.; Ugedal, O.

2011-12-01

There has been a growing recognition that single species responses to climate change often mainly are driven by interaction with other organisms and single species studies therefore not are sufficient to recognize and project ecological climate change impacts. Here, we study how performance, relative abundance and the distribution of two common Arctic and sub-Arctic freshwater fishes (brown trout and Arctic char) are driven by competitive interactions. The interactions are modified both by direct climatic effects on temperature and ice-cover, and indirectly through climate forcing of terrestrial vegetation pattern and associated carbon and nutrient run-off. We first use laboratory studies to show that Arctic char, which is the world's most northernmost distributed freshwater fish, outperform trout under low light levels and also have comparable higher growth efficiency. Corresponding to this, a combination of time series and time-for-space analyses show that ice-cover duration and carbon and nutrient load mediated by catchment vegetation properties strongly affected the outcome of the competition and likely drive the species distribution pattern through competitive exclusion. In brief, while shorter ice-cover period and decreased carbon load favored brown trout, increased ice-cover period and increased carbon load favored Arctic char. Length of ice-covered period and export of allochthonous material from catchments are major, but contrasting, climatic drivers of competitive interaction between these two freshwater lake top-predators. While projected climate change lead to decreased ice-cover, corresponding increase in forest and shrub cover amplify carbon and nutrient run-off. Although a likely outcome of future Arctic and sub-arctic climate scenarios are retractions of the Arctic char distribution area caused by competitive exclusion, the main drivers will act on different time scales. While ice-cover will change instantaneously with increasing temperature, changes in catchment vegetation, such as forest-line or shrub advancement affecting carbon and nutrient transport into lakes, act on considerably longer time-scales. This study therefore emphasizes the recurring challenge for ecological climate change studies related to species interactions within and across ecosystem compartments and the response time of ecosystems.

Robust extrema features for time-series data analysis.

PubMed

Vemulapalli, Pramod K; Monga, Vishal; Brennan, Sean N

2013-06-01

The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

Adaptive time-variant models for fuzzy-time-series forecasting.

PubMed

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Time series models on analysing mortality rates and acute childhood lymphoid leukaemia.

PubMed

Kis, Maria

2005-01-01

In this paper we demonstrate applying time series models on medical research. The Hungarian mortality rates were analysed by autoregressive integrated moving average models and seasonal time series models examined the data of acute childhood lymphoid leukaemia.The mortality data may be analysed by time series methods such as autoregressive integrated moving average (ARIMA) modelling. This method is demonstrated by two examples: analysis of the mortality rates of ischemic heart diseases and analysis of the mortality rates of cancer of digestive system. Mathematical expressions are given for the results of analysis. The relationships between time series of mortality rates were studied with ARIMA models. Calculations of confidence intervals for autoregressive parameters by tree methods: standard normal distribution as estimation and estimation of the White's theory and the continuous time case estimation. Analysing the confidence intervals of the first order autoregressive parameters we may conclude that the confidence intervals were much smaller than other estimations by applying the continuous time estimation model.We present a new approach to analysing the occurrence of acute childhood lymphoid leukaemia. We decompose time series into components. The periodicity of acute childhood lymphoid leukaemia in Hungary was examined using seasonal decomposition time series method. The cyclic trend of the dates of diagnosis revealed that a higher percent of the peaks fell within the winter months than in the other seasons. This proves the seasonal occurrence of the childhood leukaemia in Hungary.

Information retrieval for nonstationary data records

NASA Technical Reports Server (NTRS)

Su, M. Y.

1971-01-01

A review and a critical discussion are made on the existing methods for analysis of nonstationary time series, and a new algorithm for splitting nonstationary time series, is applied to the analysis of sunspot data.

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik; Marwan, Norbert; Dijkstra, Henk; Kurths, Jürgen

2016-04-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology. pyunicorn is available online at https://github.com/pik-copan/pyunicorn. Reference: J.F. Donges, J. Heitzig, B. Beronov, M. Wiedermann, J. Runge, Q.-Y. Feng, L. Tupikina, V. Stolbova, R.V. Donner, N. Marwan, H.A. Dijkstra, and J. Kurths, Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package, Chaos 25, 113101 (2015), DOI: 10.1063/1.4934554, Preprint: arxiv.org:1507.01571 [physics.data-an].

Persistence analysis of extreme CO, NO2 and O3 concentrations in ambient air of Delhi

NASA Astrophysics Data System (ADS)

Chelani, Asha B.

2012-05-01

Persistence analysis of air pollutant concentration and corresponding exceedance time series is carried out to examine for temporal evolution. For this purpose, air pollutant concentrations, namely, CO, NO2 and O3 observed during 2000-2009 at a traffic site in Delhi are analyzed using detrended fluctuation analysis. Two types of extreme values are analyzed; exceeded concentrations to a threshold provided by national pollution controlling agency and time interval between two exceedances. The time series of three pollutants is observed to possess persistence property whereas the extreme value time series of only primary pollutant concentrations is found to be persistent. Two time scaling regions are observed to be significant in extreme time series of CO and NO2, mainly attributed to implementation of CNG in vehicles. The presence of persistence in three pollutant concentration time series is linked to the property of self-organized criticality. The observed persistence in the time interval between two exceeded levels is a matter of concern as persistent high concentrations can trigger health problems.

Water-quality trend analysis and sampling design for the Souris River, Saskatchewan, North Dakota, and Manitoba

USGS Publications Warehouse

Vecchia, Aldo V.

2000-01-01

The Souris River Basin is a 24,600-square-mile basin located in southeast Saskatchewan, north-central North Dakota, and southwest Manitoba.  The Souris River Bilateral Water Quality Monitoring Group, formed in 1989 by the governments of Canada and the United States, is responsible for documenting trends in water quality in the Souris River and making recommendations for monitoring future water-quality conditions.  This report presents results of a study conducted for the Bilateral Water Quality Monitoring Group by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, to analyze historic trends in water quality in the Souris River and to determine efficient sampling designs for monitoring future trends.  U.S. Geological Survey and Environment Canada water-quality data collected during 1977-96 from four sites near the boundary crossings between Canada and the United States were included in the trend analysis. A parametric time-series model was developed for detecting trends in historic constituent concentration data.  The model can be applied to constituents that have at least 90 percent of observations above detection limits of the analyses, which, for the Souris River, includes most major ions and nutrients and many trace elements.  The model can detect complex nonmonotonic trends in concentration in the presence of complex interannual and seasonal variability in daily discharge.  A key feature of the model is its ability to handle highly irregular sampling intervals.  For example, the intervals between concentration measurements may be be as short as 10 days to as long as several months, and the number of samples in any given year can range from zero to 36. Results from the trend analysis for the Souris River indicated numerous trends in constituent concentration.  The most significant trends at the two sites located near the upstream boundary crossing between Saskatchewan and North Dakota consisted of increases in concentrations of most major ions, dissolved boron, and dissolved arsenic during 1987-91 and decreases in concentrations of the same constituents during 1992-96.  Significant trends at the two sites located near the downstream boundary crossing between North Dakota and Manitoba included increases in dissolved sodium, dissolved chloride, and total phosphorus during 1977-86, decreases in dissolved oxygen and dissolved boron and increases in total phosphorus and dissolved iron during 1987-91, and a decrease in total phosphorus during 1992-96. The time-series model also was used to determine the sensitivity of various sampling designs for monitoring future water-quality trends in the Souris River.  It was determined that at least two samples per year are required in each of three seasons--March through June, July through October, and November through February--to obtain reasonable sensitivity for detecting trends in each season.  In addition, substantial improvements occurred in sensitivity for detecting trends by adding a third sample for major ions and trace elements in March through June, adding a third sample for nutrients in July through October, and adding a third sample for nutrients, trace elements, and dissolved oxygen in November through February.

Estimating short-run and long-run interaction mechanisms in interictal state.

PubMed

Ozkaya, Ata; Korürek, Mehmet

2010-04-01

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.

Entropic Analysis of Electromyography Time Series

NASA Astrophysics Data System (ADS)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Nutrient cycling Microbial Ecosystems: Assembly, Function and Targeted Design

DTIC Science & Technology

2017-05-05

different chemical transformations, converting potentially harmful chemicals via a series of intermediates, to harmless waste products. This shuttling of...Report: Nutrient-cycling Microbial Ecosystems: Assembly, Function and Targeted Design The views, opinions and/or findings contained in this report...are those of the author(s) and should not contrued as an official Department of the Army position, policy or decision, unless so designated by other

National Aquatic Resource Surveys (NARS) N/P Values for Streams - Wadeable Streams Assessment

EPA Pesticide Factsheets

The National Aquatic Resource Survey (NARS) findings for nutrients in streams and lakes highlight that nutrient pollution is widespread across the United States and impacts biological communities. The NARS analysis examined the range of values for nutrients in least-disturbed sites in a WSA region [WSA regions are modified Level III ecoregions from Omernik (1987)] and used this distribution for nitrogen (N) and phosphorus (P) to separate sites into those having high, medium, or low concentrations of nutrients. Sites identified as high were worse (i.e., had higher nutrient concentrations) than 95% of the sites used to define least-disturbed condition. Similarly, the 75th percentile of the least-disturbed distribution was used to distinguish between sites in medium and low condition. This means that sites reported as being as low were as good as or better than 75% of the sites used to define least-disturbed condition. A relative risk analysis of the data from this survey found that nationally streams and lakes have more than two times greater risk of having degraded biological communities when nutrient concentrations are high than when they are low. For more information, please consult the National Wadeable Streams Assessment (WSA) Report available online at: https://www.epa.gov/national-aquatic-resource-surveys/nrsa:

Phytoplankton Assemblage Patterns in the Southern Mid-Atlantic Bight

NASA Technical Reports Server (NTRS)

Makinen, Carla; Moisan, Tiffany A. (Editor)

2012-01-01

As part of the Wallops Coastal Oceans Observing Laboratory (Wa-COOL) Project, we sampled a time-series transect in the southern Mid-Atlantic Bight (MAB) biweekly. Our 2-year time-series data included physical parameters, nutrient concentrations, and chlorophyll a concentrations. A detailed phytoplankton assemblage structure was examined in the second year. During the 2-year study, chlorophyll a concentration (and ocean color satellite imagery) indicated that phytoplankton blooms occurred in January/February during mixing conditions and in early autumn under stratified conditions. The chlorophyll a concentrations ranged from 0.25 microgram 1(exp -1) to 15.49 microgram 1(exp -1) during the 2-year period. We were able to discriminate approximately 116 different species under phase contrast microscopy. Dominant phytoplankton included Skeletonema costatum, Rhizosolenia spp., and Pseudo-nitzschia pungens. In an attempt to determine phytoplankton species competition/succession within the assemblage, we calculated a Shannon Weaver diversity index for our diatom microscopy data. Diatom diversity was greatest during the winter and minimal during the spring. Diatom diversity was also greater at nearshore stations than at offshore stations. Individual genera appeared patchy, with surface and subsurface patches appearing abruptly and persisting for only 1-2 months at a time. The distribution of individual species differed significantly from bulk variables of the assemblage (chlorophyll a ) and total phytoplankton assemblage (cells), which indicates that phytoplankton species may be limited in growth in ways that differ from those of the total assemblage. Our study demonstrated a highly diverse phytoplankton assemblage throughout the year, with opportunistic species dominating during spring and fall in response to seasonal changes in temperature and nutrients in the southern MAB.

Response kinetics of tethered bacteria to stepwise changes in nutrient concentration.

PubMed

Chernova, Anna A; Armitage, Judith P; Packer, Helen L; Maini, Philip K

2003-09-01

We examined the changes in swimming behaviour of the bacterium Rhodobacter sphaeroides in response to stepwise changes in a nutrient (propionate), following the pre-stimulus motion, the initial response and the adaptation to the sustained concentration of the chemical. This was carried out by tethering motile cells by their flagella to glass slides and following the rotational behaviour of their cell bodies in response to the nutrient change. Computerised motion analysis was used to analyse the behaviour. Distributions of run and stop times were obtained from rotation data for tethered cells. Exponential and Weibull fits for these distributions, and variability in individual responses are discussed. In terms of parameters derived from the run and stop time distributions, we compare the responses to stepwise changes in the nutrient concentration and the long-term behaviour of 84 cells under 12 propionate concentration levels from 1 nM to 25 mM. We discuss traditional assumptions for the random walk approximation to bacterial swimming and compare them with the observed R. sphaeroides motile behaviour.

How well do ecosystem indicators communicate the effects of anthropogenic eutrophication?

NASA Astrophysics Data System (ADS)

McQuatters-Gollop, Abigail; Gilbert, Alison J.; Mee, Laurence D.; Vermaat, Jan E.; Artioli, Yuri; Humborg, Christoph; Wulff, Fred

2009-05-01

Anthropogenic eutrophication affects the Mediterranean, Black, North and Baltic Seas to various extents. Responses to nutrient loading and methods of monitoring relevant indicators vary regionally, hindering interpretation of ecosystem state changes and preventing a straightforward pan-European assessment of eutrophication symptoms. Here we summarize responses to nutrient enrichment in Europe's seas, comparing existing time-series of selected pelagic (phytoplankton biomass and community composition, turbidity, N:P ratio) and benthic (macro flora and faunal communities, bottom oxygen condition) indicators based on their effectiveness in assessing eutrophication effects. Our results suggest that the Black Sea and Northern Adriatic appear to be recovering from eutrophication due to economic reorganization in the Black Sea catchment and nutrient abatement measures in the case of the Northern Adriatic. The Baltic is most strongly impacted by eutrophication due to its limited exchange and the prevalence of nutrient recycling. Eutrophication in the North Sea is primarily a coastal problem, but may be exacerbated by climatic changes. Indicator interpretation is strongly dependent on sea-specific knowledge of ecosystem characteristics, and no single indicator can be employed to adequately compare eutrophication state between European seas. Communicating eutrophication-related information to policy-makers could be facilitated through the use of consistent indicator selection and monitoring methodologies across European seas. This work is discussed in the context of the European Commission's recently published Marine Strategy Directive.

Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

PubMed

Krafty, Robert T

2016-07-01

Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.

The physiology analysis system: an integrated approach for warehousing, management and analysis of time-series physiology data.

PubMed

McKenna, Thomas M; Bawa, Gagandeep; Kumar, Kamal; Reifman, Jaques

2007-04-01

The physiology analysis system (PAS) was developed as a resource to support the efficient warehousing, management, and analysis of physiology data, particularly, continuous time-series data that may be extensive, of variable quality, and distributed across many files. The PAS incorporates time-series data collected by many types of data-acquisition devices, and it is designed to free users from data management burdens. This Web-based system allows both discrete (attribute) and time-series (ordered) data to be manipulated, visualized, and analyzed via a client's Web browser. All processes occur on a server, so that the client does not have to download data or any application programs, and the PAS is independent of the client's computer operating system. The PAS contains a library of functions, written in different computer languages that the client can add to and use to perform specific data operations. Functions from the library are sequentially inserted into a function chain-based logical structure to construct sophisticated data operators from simple function building blocks, affording ad hoc query and analysis of time-series data. These features support advanced mining of physiology data.

Oxygen and diverse nutrients influence the water kefir fermentation process.

PubMed

Laureys, David; Aerts, Maarten; Vandamme, Peter; De Vuyst, Luc

2018-08-01

Eight water kefir fermentation series differing in the presence of oxygen, the nutrient concentration, and the nutrient source were studied during eight consecutive backslopping steps. The presence of oxygen allowed the proliferation of acetic acid bacteria, resulting in high concentrations of acetic acid, and decreased the relative abundance of Bifidobacterium aquikefiri. Low nutrient concentrations resulted in slow water kefir fermentation and high pH values, which allowed the growth of Comamonas testosteroni/thiooxydans. Further, low nutrient concentrations favored the growth of Lactobacillus hilgardii and Dekkera bruxellensis, whereas high nutrient concentrations favored the growth of Lactobacillus nagelii and Saccharomyces cerevisiae. Dried figs, dried apricots, and raisins resulted in stable water kefir fermentation. Water kefir fermentation with dried apricots resulted in the highest pH and water kefir grain growth, whereas that with raisins resulted in the lowest pH and water kefir grain growth. Further, water kefir fermentation with raisins resembled fermentations with low nutrient concentrations, that with dried apricots resembled fermentations with normal nutrient concentrations, and that with fresh figs or a mixture of yeast extract and peptone resembled fermentations with high nutrient concentrations. Copyright © 2018 Elsevier Ltd. All rights reserved.

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kevin McCarthy; Milos Manic

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presentsmore » an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.« less

Financial Time-series Analysis: a Brief Overview

NASA Astrophysics Data System (ADS)

Chakraborti, A.; Patriarca, M.; Santhanam, M. S.

Prices of commodities or assets produce what is called time-series. Different kinds of financial time-series have been recorded and studied for decades. Nowadays, all transactions on a financial market are recorded, leading to a huge amount of data available, either for free in the Internet or commercially. Financial time-series analysis is of great interest to practitioners as well as to theoreticians, for making inferences and predictions. Furthermore, the stochastic uncertainties inherent in financial time-series and the theory needed to deal with them make the subject especially interesting not only to economists, but also to statisticians and physicists [1]. While it would be a formidable task to make an exhaustive review on the topic, with this review we try to give a flavor of some of its aspects.

A comparative analysis of spectral exponent estimation techniques for 1/fβ processes with applications to the analysis of stride interval time series

PubMed Central

Schaefer, Alexander; Brach, Jennifer S.; Perera, Subashan; Sejdić, Ervin

2013-01-01

Background The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f) = 1/fβ. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline. New Method This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. Results The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. Conclusions The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. PMID:24200509

A comparative analysis of spectral exponent estimation techniques for 1/f(β) processes with applications to the analysis of stride interval time series.

PubMed

Schaefer, Alexander; Brach, Jennifer S; Perera, Subashan; Sejdić, Ervin

2014-01-30

The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum S(f)=1/f(β). The scaling exponent β is thus often interpreted as a "biomarker" of relative health and decline. This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared. The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances. The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Time Series in Education: The Analysis of Daily Attendance in Two High Schools

ERIC Educational Resources Information Center

Koopmans, Matthijs

2011-01-01

This presentation discusses the use of a time series approach to the analysis of daily attendance in two urban high schools over the course of one school year (2009-10). After establishing that the series for both schools were stationary, they were examined for moving average processes, autoregression, seasonal dependencies (weekly cycles),…

Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, V.

2018-06-01

Effective modelling and prediction of smaller time step rainfall is reported to be very difficult owing to its highly erratic nature. Accurate forecast of daily rainfall for longer duration (multi time step) may be exceptionally helpful in the efficient planning and management of water resources systems. Identification of inherent patterns in a rainfall time series is also important for an effective water resources planning and management system. In the present study, Singular Spectrum Analysis (SSA) is utilized to forecast the daily rainfall time series pertaining to Koyna watershed in Maharashtra, India, for 365 days after extracting various components of the rainfall time series such as trend, periodic component, noise and cyclic component. In order to forecast the time series for longer time step (365 days-one window length), the signal and noise components of the time series are forecasted separately and then added together. The results of the study show that the method of SSA could extract the various components of the time series effectively and could also forecast the daily rainfall time series for longer duration such as one year in a single run with reasonable accuracy.

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Scaling of physical constraints at the root-soil interface to macroscopic patterns of nutrient retention in ecosystems.

PubMed

Gerber, Stefan; Brookshire, E N Jack

2014-03-01

Nutrient limitation in terrestrial ecosystems is often accompanied with maintaining a nearly closed vegetation-soil nutrient cycle. The ability to retain nutrients in an ecosystem requires the capacity of the plant-soil system to draw down nutrient levels in soils effectually such that export concentrations in soil solutions remain low. Here we address the physical constraints of plant nutrient uptake that may be limited by the diffusive movement of nutrients in soils, by the uptake at the root/mycorrhizal surface, and from interactions with soil water flow. We derive an analytical framework of soil nutrient transport and uptake and predict levels of plant available nutrient concentration and residence time. Our results, which we evaluate for nitrogen, show that the physical environment permits plants to lower soil solute concentration substantially. Our analysis confirms that plant uptake capacities in soils are considerable, such that water movement in soils is generally too small to significantly erode dissolved plant-available nitrogen. Inorganic nitrogen concentrations in headwater streams are congruent with the prediction of our theoretical framework. Our framework offers a physical-based parameterization of nutrient uptake in ecosystem models and has the potential to serve as an important tool toward scaling biogeochemical cycles from individual roots to landscapes.

Water intake and post-exercise cognitive performance: an observational study of long-distance walkers and runners.

PubMed

Benefer, Martin D; Corfe, Bernard M; Russell, Jean M; Short, Richard; Barker, Margo E

2013-03-01

The impact of diet on endurance performance and cognitive function has been extensively researched in controlled settings, but there are limited observational data in field situations. This study examines relationships between nutrient intake and cognitive function following endurance exercise amongst a group of 33 recreational runners and walkers. All participants (mean age of 43.2 years) took part in a long-distance walking event and completed diet diaries to estimate nutrient intake across three-time periods (previous day, breakfast and during the event). Anthropometric measurements were recorded. Cognitive tests, covering word recall, ruler drop and trail making tests (TMT) A and B were conducted pre- and post-exercise. Participants rated their exercise level on a validated scale. Nutrient intake data were summarised using principal components analysis to identify a nutrient intake pattern loaded towards water intake across all time periods. Regression analysis was used to ascertain relationships between water intake component scores and post-exercise cognitive function, controlling for anthropometric measures and exercise metrics (distance, duration and pace). Participants rated their exercise as 'hard-heavy' (score 14.4, ±3.2). Scores on the water intake factor were associated with significantly faster TMT A (p = 0.001) and TMT B (p = 0.005) completion times, and a tendency for improved short-term memory (p = 0.090). Water intake scores were not associated with simple reaction time (assessed via the ruler drop test). These data are congruent with experimental research demonstrating a benefit of hydration on cognitive function. Further field research to confirm this relationship, supported with precise measures of body weight, is needed.

Filter-based multiscale entropy analysis of complex physiological time series.

PubMed

Xu, Yuesheng; Zhao, Liang

2013-08-01

Multiscale entropy (MSE) has been widely and successfully used in analyzing the complexity of physiological time series. We reinterpret the averaging process in MSE as filtering a time series by a filter of a piecewise constant type. From this viewpoint, we introduce filter-based multiscale entropy (FME), which filters a time series to generate multiple frequency components, and then we compute the blockwise entropy of the resulting components. By choosing filters adapted to the feature of a given time series, FME is able to better capture its multiscale information and to provide more flexibility for studying its complexity. Motivated by the heart rate turbulence theory, which suggests that the human heartbeat interval time series can be described in piecewise linear patterns, we propose piecewise linear filter multiscale entropy (PLFME) for the complexity analysis of the time series. Numerical results from PLFME are more robust to data of various lengths than those from MSE. The numerical performance of the adaptive piecewise constant filter multiscale entropy without prior information is comparable to that of PLFME, whose design takes prior information into account.

Falcon: A Temporal Visual Analysis System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A.

2016-09-05

Flexible visible exploration of long, high-resolution time series from multiple sensor streams is a challenge in several domains. Falcon is a visual analytics approach that helps researchers acquire a deep understanding of patterns in log and imagery data. Falcon allows users to interactively explore large, time-oriented data sets from multiple linked perspectives. Falcon provides overviews, detailed views, and unique segmented time series visualizations with multiple levels of detail. These capabilities are applicable to the analysis of any quantitative time series.

Changes in Streamflow and the Flux of Nutrients in the Mississippi-Atchafalaya River Basin, USA, 1980-2007

USGS Publications Warehouse

Battaglin, William A.; Aulenbach, Brent T.; Vecchia, Aldo; Buxton, Herbert T.

2010-01-01

Nutrients and freshwater delivered by the Mississippi and Atchafalaya Rivers drive algal production in the northern Gulf of Mexico, which eventually results in the widespread occurrence of hypoxic bottom waters along the Louisiana and Texas coast. Researchers have demonstrated a relation between the extent of the hypoxic zone and the magnitude of streamflow, nutrient fluxes, and nutrient concentrations in the Mississippi River, with springtime streamflows and fluxes being the most predictive. In 1999 the U.S. Geological Survey (USGS) estimated the flux of nitrogen, phosphorus, and silica at selected sites in the Mississippi Basin and to the Gulf of Mexico for 1980-1996. These flux estimates provided the baseline information used by the Mississippi River/Gulf of Mexico Watershed Nutrient Task Force to develop an Action Plan for reducing hypoxia in the northern Gulf of Mexico. The primary goal of the Action Plan was to achieve a reduction in the size (areal extent) of the hypoxic zone from an average of approximately 14,000 square kilometers in 1996-2000 to a 5-year moving average of less than 5,000 square kilometers by 2015. Improved statistical models and adjusted maximum likelihood estimation using USGS Load Estimator (LOADEST) software were used to estimate annual and seasonal nutrient fluxes for 1980-2007 at selected sites on the Mississippi River and its tributaries. These data provide a means to evaluate the influence of natural and anthropogenic effects on delivery of water and nutrients to the Gulf of Mexico; to define subbasins that are the most important contributors of nutrients to the gulf; and to investigate the relations among streamflow, nutrient fluxes, and the size and duration of the Gulf of Mexico hypoxic zone. A comparative analysis between the baseline period of 1980-1996 and 5-year moving averages thereafter indicate that the average annual streamflow and fluxes of total nitrogen, nitrate, orthophosphate, and silica to the Gulf of Mexico have decreased. However, the flux of total phosphorus between the baseline period and subsequent 5-year periods has increased. The average spring (April, May, and June) streamflow and fluxes of silica, total nitrogen, nitrate, and orthophosphate to the Gulf of Mexico also decreased, whereas the spring flux of total phosphorus has increased. Similar changes in streamflow and nutrient flux were observed at many sites Buxtonwithin the basin. The inputs of water, total nitrogen, and total phosphorus from the major subbasins of the Mississippi-Atchafalaya River Basin as a percentage of the to-the-gulf totals have increased from the Ohio River Basin, decreased from the Missouri River Basin, and remained relatively unchanged from the Upper Mississippi, Red, and Arkansas River Basins. Changes in streamflow and nutrient fluxes are related, but short-term variations in sources of streamflow and nutrients complicate the interpretation of factors that affect nutrient delivery to the Gulf of Mexico. Parametric time-series models are used to try and separate natural variability in nutrient flux from changes due to other causes. Results indicate that the decrease in annual nutrient fluxes that has occurred between the 1980-1996 baseline period and more recent years can be largely attributed to natural causes (climate and streamflow) and not management actions or other human controlled activities in the Mississippi-Atchafalaya River Basin. The downward trends in total nitrogen, nitrate, ammonium, and orthophosphate that were detected at either the Mississippi River near St. Francisville, La., or the Atchafalaya River at Melville, La., occurred prior to 1995. In spite of the general decrease in nutrient flux, the average size of the Gulf of Mexico hypoxic zone has increased between 1997 and 2007. The reasons for this are not clear but could be due to the type or nature of nutrient delivery. Whereas the annual flux of total nitrogen to the Gulf of Mexico has decreased, the proporti

Drainage Basins as Large-Scale Field Laboratories of Change: Hydro-biogeochemical- economic Model Study Support for Water Pollution and Eutrophication Management Under Uncertainty

NASA Astrophysics Data System (ADS)

Destouni, G.

2008-12-01

Excess nutrient and pollutant releases from various point and diffuse sources at and below the land surface, associated with land use, industry and households, pose serious eutrophication and pollution risks to inland and coastal water ecosystems worldwide. These risks must be assessed, for instance according to the EU Water Framework Directive (WFD). The WFD demands economically efficient, basin-scale water management for achieving and maintaining good physico-chemical and ecological status in all the inland and coastal waters of EU member states. This paper synthesizes a series of hydro-biogeochemical and linked economic efficiency studies of basin-scale waterborne nutrient and pollutant flows, the development over the last decades up to the current levels of these flows, the main monitoring and modelling uncertainties associated with their quantification, and the effectiveness and economic efficiency of different possible abatement strategies for abating them in order to meet WFD requirements and other environmental goals on local, national and international levels under climate and other regional change. The studies include different Swedish and Baltic Sea drainage basins. Main findings include quantification of near-coastal monitoring gaps and long-term nutrient and pollutant memory in the subsurface (soil-groundwater-sediment) water systems of drainage basins. The former may significantly mask nutrient and pollutant loads to the sea while the latter may continue to uphold large loads to inland and coastal waters long time after source mitigation. A methodology is presented for finding a rational trade-off between the two resource-demanding options to reduce, or accept and explicitly account for the uncertainties implied by these monitoring gaps and long-term nutrient-pollution memories and time lags, and other knowledge, data and model uncertainties that limit the effectiveness and efficiency of water pollution and eutrophication management.

Single-Case Time Series with Bayesian Analysis: A Practitioner's Guide.

ERIC Educational Resources Information Center

Jones, W. Paul

2003-01-01

This article illustrates a simplified time series analysis for use by the counseling researcher practitioner in single-case baseline plus intervention studies with a Bayesian probability analysis to integrate findings from replications. The C statistic is recommended as a primary analysis tool with particular relevance in the context of actual…

Time series regression studies in environmental epidemiology.

PubMed

Bhaskaran, Krishnan; Gasparrini, Antonio; Hajat, Shakoor; Smeeth, Liam; Armstrong, Ben

2013-08-01

Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed ('lagged') associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.

Multiresolution analysis of Bursa Malaysia KLCI time series

NASA Astrophysics Data System (ADS)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Symplectic geometry spectrum regression for prediction of noisy time series

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates; Sivakumar, Bellie; Mengersen, Kerrie

2016-05-01

We present the symplectic geometry spectrum regression (SGSR) technique as well as a regularized method based on SGSR for prediction of nonlinear time series. The main tool of analysis is the symplectic geometry spectrum analysis, which decomposes a time series into the sum of a small number of independent and interpretable components. The key to successful regularization is to damp higher order symplectic geometry spectrum components. The effectiveness of SGSR and its superiority over local approximation using ordinary least squares are demonstrated through prediction of two noisy synthetic chaotic time series (Lorenz and Rössler series), and then tested for prediction of three real-world data sets (Mississippi River flow data and electromyographic and mechanomyographic signal recorded from human body).

Interannual variability in phytoplankton pigment distribution during the spring transition along the west coast of North America

NASA Technical Reports Server (NTRS)

Thomas, A. C.; Strub, P. T.

1989-01-01

A 5-year time series of coastal zone color scanner imagery (1980-1983, 1986) is used to examine changes in the large-scale pattern of chlorophyll pigment concentration coincident with the spring transition in winds and currents along the west coast of North America. The data show strong interannual variability in the timing and spatial patterns of pigment concentration at the time of the transition event. Interannual variability in the response of pigment concentration to the spring transition appears to be a function of spatial and temporal variability in vertical nutrient flux induced by wind mixing and/or the upwelling initiated at the time of the transition. Interannual differences in the mixing regime are illustrated with a one-dimensional mixing model.

A chain reaction approach to modelling gene pathways.

PubMed

Cheng, Gary C; Chen, Dung-Tsa; Chen, James J; Soong, Seng-Jaw; Lamartiniere, Coral; Barnes, Stephen

2012-08-01

BACKGROUND: Of great interest in cancer prevention is how nutrient components affect gene pathways associated with the physiological events of puberty. Nutrient-gene interactions may cause changes in breast or prostate cells and, therefore, may result in cancer risk later in life. Analysis of gene pathways can lead to insights about nutrient-gene interactions and the development of more effective prevention approaches to reduce cancer risk. To date, researchers have relied heavily upon experimental assays (such as microarray analysis, etc.) to identify genes and their associated pathways that are affected by nutrient and diets. However, the vast number of genes and combinations of gene pathways, coupled with the expense of the experimental analyses, has delayed the progress of gene-pathway research. The development of an analytical approach based on available test data could greatly benefit the evaluation of gene pathways, and thus advance the study of nutrient-gene interactions in cancer prevention. In the present study, we have proposed a chain reaction model to simulate gene pathways, in which the gene expression changes through the pathway are represented by the species undergoing a set of chemical reactions. We have also developed a numerical tool to solve for the species changes due to the chain reactions over time. Through this approach we can examine the impact of nutrient-containing diets on the gene pathway; moreover, transformation of genes over time with a nutrient treatment can be observed numerically, which is very difficult to achieve experimentally. We apply this approach to microarray analysis data from an experiment which involved the effects of three polyphenols (nutrient treatments), epigallo-catechin-3-O-gallate (EGCG), genistein, and resveratrol, in a study of nutrient-gene interaction in the estrogen synthesis pathway during puberty. RESULTS: In this preliminary study, the estrogen synthesis pathway was simulated by a chain reaction model. By applying it to microarray data, the chain reaction model computed a set of reaction rates to examine the effects of three polyphenols (EGCG, genistein, and resveratrol) on gene expression in this pathway during puberty. We first performed statistical analysis to test the time factor on the estrogen synthesis pathway. Global tests were used to evaluate an overall gene expression change during puberty for each experimental group. Then, a chain reaction model was employed to simulate the estrogen synthesis pathway. Specifically, the model computed the reaction rates in a set of ordinary differential equations to describe interactions between genes in the pathway (A reaction rate K of A to B represents gene A will induce gene B per unit at a rate of K; we give details in the "method" section). Since disparate changes of gene expression may cause numerical error problems in solving these differential equations, we used an implicit scheme to address this issue. We first applied the chain reaction model to obtain the reaction rates for the control group. A sensitivity study was conducted to evaluate how well the model fits to the control group data at Day 50. Results showed a small bias and mean square error. These observations indicated the model is robust to low random noises and has a good fit for the control group. Then the chain reaction model derived from the control group data was used to predict gene expression at Day 50 for the three polyphenol groups. If these nutrients affect the estrogen synthesis pathways during puberty, we expect discrepancy between observed and expected expressions. Results indicated some genes had large differences in the EGCG (e.g., Hsd3b and Sts) and the resveratrol (e.g., Hsd3b and Hrmt12) groups. CONCLUSIONS: In the present study, we have presented (I) experimental studies of the effect of nutrient diets on the gene expression changes in a selected estrogen synthesis pathway. This experiment is valuable because it allows us to examine how the nutrient-containing diets regulate gene expression in the estrogen synthesis pathway during puberty; (II) global tests to assess an overall association of this particular pathway with time factor by utilizing generalized linear models to analyze microarray data; and (III) a chain reaction model to simulate the pathway. This is a novel application because we are able to translate the gene pathway into the chemical reactions in which each reaction channel describes gene-gene relationship in the pathway. In the chain reaction model, the implicit scheme is employed to efficiently solve the differential equations. Data analysis results show the proposed model is capable of predicting gene expression changes and demonstrating the effect of nutrient-containing diets on gene expression changes in the pathway. One of the objectives of this study is to explore and develop a numerical approach for simulating the gene expression change so that it can be applied and calibrated when the data of more time slices are available, and thus can be used to interpolate the expression change at a desired time point without conducting expensive experiments for a large amount of time points. Hence, we are not claiming this is either essential or the most efficient way for simulating this problem, rather a mathematical/numerical approach that can model the expression change of a large set of genes of a complex pathway. In addition, we understand the limitation of this experiment and realize that it is still far from being a complete model of predicting nutrient-gene interactions. The reason is that in the present model, the reaction rates were estimated based on available data at two time points; hence, the gene expression change is dependent upon the reaction rates and a linear function of the gene expressions. More data sets containing gene expression at various time slices are needed in order to improve the present model so that a non-linear variation of gene expression changes at different time can be predicted.

Macroscopic Spatial Complexity of the Game of Life Cellular Automaton: A Simple Data Analysis

NASA Astrophysics Data System (ADS)

Hernández-Montoya, A. R.; Coronel-Brizio, H. F.; Rodríguez-Achach, M. E.

In this chapter we present a simple data analysis of an ensemble of 20 time series, generated by averaging the spatial positions of the living cells for each state of the Game of Life Cellular Automaton (GoL). We show that at the macroscopic level described by these time series, complexity properties of GoL are also presented and the following emergent properties, typical of data extracted complex systems such as financial or economical come out: variations of the generated time series following an asymptotic power law distribution, large fluctuations tending to be followed by large fluctuations, and small fluctuations tending to be followed by small ones, and fast decay of linear correlations, however, the correlations associated to their absolute variations exhibit a long range memory. Finally, a Detrended Fluctuation Analysis (DFA) of the generated time series, indicates that the GoL spatial macro states described by the time series are not either completely ordered or random, in a measurable and very interesting way.

Tissue classification using depth-dependent ultrasound time series analysis: in-vitro animal study

NASA Astrophysics Data System (ADS)

Imani, Farhad; Daoud, Mohammad; Moradi, Mehdi; Abolmaesumi, Purang; Mousavi, Parvin

2011-03-01

Time series analysis of ultrasound radio-frequency (RF) signals has been shown to be an effective tissue classification method. Previous studies of this method for tissue differentiation at high and clinical-frequencies have been reported. In this paper, analysis of RF time series is extended to improve tissue classification at the clinical frequencies by including novel features extracted from the time series spectrum. The primary feature examined is the Mean Central Frequency (MCF) computed for regions of interest (ROIs) in the tissue extending along the axial axis of the transducer. In addition, the intercept and slope of a line fitted to the MCF-values of the RF time series as a function of depth have been included. To evaluate the accuracy of the new features, an in vitro animal study is performed using three tissue types: bovine muscle, bovine liver, and chicken breast, where perfect two-way classification is achieved. The results show statistically significant improvements over the classification accuracies with previously reported features.

Memory and betweenness preference in temporal networks induced from time series

NASA Astrophysics Data System (ADS)

Weng, Tongfeng; Zhang, Jie; Small, Michael; Zheng, Rui; Hui, Pan

2017-02-01

We construct temporal networks from time series via unfolding the temporal information into an additional topological dimension of the networks. Thus, we are able to introduce memory entropy analysis to unravel the memory effect within the considered signal. We find distinct patterns in the entropy growth rate of the aggregate network at different memory scales for time series with different dynamics ranging from white noise, 1/f noise, autoregressive process, periodic to chaotic dynamics. Interestingly, for a chaotic time series, an exponential scaling emerges in the memory entropy analysis. We demonstrate that the memory exponent can successfully characterize bifurcation phenomenon, and differentiate the human cardiac system in healthy and pathological states. Moreover, we show that the betweenness preference analysis of these temporal networks can further characterize dynamical systems and separate distinct electrocardiogram recordings. Our work explores the memory effect and betweenness preference in temporal networks constructed from time series data, providing a new perspective to understand the underlying dynamical systems.

Multiscale multifractal detrended cross-correlation analysis of financial time series

NASA Astrophysics Data System (ADS)

Shi, Wenbin; Shang, Pengjian; Wang, Jing; Lin, Aijing

2014-06-01

In this paper, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). The method allows us to extend the description of the cross-correlation properties between two time series. MM-DCCA may provide new ways of measuring the nonlinearity of two signals, and it helps to present much richer information than multifractal detrended cross-correlation analysis (MF-DCCA) by sweeping all the range of scale at which the multifractal structures of complex system are discussed. Moreover, to illustrate the advantages of this approach we make use of the MM-DCCA to analyze the cross-correlation properties between financial time series. We show that this new method can be adapted to investigate stock markets under investigation. It can provide a more faithful and more interpretable description of the dynamic mechanism between financial time series than traditional MF-DCCA. We also propose to reduce the scale ranges to analyze short time series, and some inherent properties which remain hidden when a wide range is used may exhibit perfectly in this way.

Comparative Model Evaluation Studies of Biogenic Trace Gas Fluxes in Tropical Forests

NASA Technical Reports Server (NTRS)

Potter, C. S.; Peterson, David L. (Technical Monitor)

1997-01-01

Simulation modeling can play a number of important roles in large-scale ecosystem studies, including synthesis of patterns and changes in carbon and nutrient cycling dynamics, scaling up to regional estimates, and formulation of testable hypotheses for process studies. Recent comparative studies have shown that ecosystem models of soil trace gas exchange with the atmosphere are evolving into several distinct simulation approaches. Different levels of detail exist among process models in the treatment of physical controls on ecosystem nutrient fluxes and organic substrate transformations leading to gas emissions. These differences are is in part from distinct objectives of scaling and extrapolation. Parameter requirements for initialization scalings, boundary conditions, and time-series driven therefore vary among ecosystem simulation models, such that the design of field experiments for integration with modeling should consider a consolidated series of measurements that will satisfy most of the various model requirements. For example, variables that provide information on soil moisture holding capacity, moisture retention characteristics, potential evapotranspiration and drainage rates, and rooting depth appear to be of the first order in model evaluation trials for tropical moist forest ecosystems. The amount and nutrient content of labile organic matter in the soil, based on accurate plant production estimates, are also key parameters that determine emission model response. Based on comparative model results, it is possible to construct a preliminary evaluation matrix along categories of key diagnostic parameters and temporal domains. Nevertheless, as large-scale studied are planned, it is notable that few existing models age designed to simulate transient states of ecosystem change, a feature which will be essential for assessment of anthropogenic disturbance on regional gas budgets, and effects of long-term climate variability on biosphere-atmosphere exchange.

Time Series Model Identification by Estimating Information, Memory, and Quantiles.

DTIC Science & Technology

1983-07-01

Standards, Sect. D, 68D, 937-951. Parzen, Emanuel (1969) "Multiple time series modeling" Multivariate Analysis - II, edited by P. Krishnaiah , Academic... Krishnaiah , North Holland: Amsterdam, 283-295. Parzen, Emanuel (1979) "Forecasting and Whitening Filter Estimation" TIMS Studies in the Management...principle. Applications of Statistics, P. R. Krishnaiah , ed. North Holland: Amsterdam, 27-41. Box, G. E. P. and Jenkins, G. M. (1970) Time Series Analysis

A model of phytoplankton blooms.

PubMed

Huppert, Amit; Blasius, Bernd; Stone, Lewi

2002-02-01

A simple model that describes the dynamics of nutrient-driven phytoplankton blooms is presented. Apart from complicated simulation studies, very few models reported in the literature have taken this "bottom-up" approach. Yet, as discussed and justified from a theoretical standpoint, many blooms are strongly controlled by nutrients rather than by higher trophic levels. The analysis identifies an important threshold effect: a bloom will only be triggered when nutrients exceed a certain defined level. This threshold effect should be generic to both natural blooms and most simulation models. Furthermore, predictions are given as to how the peak of the bloom Pmax is determined by initial conditions. A number of counterintuitive results are found. In particular, it is shown that increasing initial nutrient or phytoplankton levels can act to decrease Pmax. Correct predictions require an understanding of such factors as the timing of the bloom and the period of nutrient buildup before the bloom.

Trophic State Evolution and Nutrient Trapping Capacity in a Transboundary Subtropical Reservoir: A 25-Year Study

NASA Astrophysics Data System (ADS)

Cunha, Davi Gasparini Fernandes; Benassi, Simone Frederigi; de Falco, Patrícia Bortoletto; do Carmo Calijuri, Maria

2016-03-01

Artificial reservoirs have been used for drinking water supply, other human activities, flood control and pollution abatement worldwide, providing overall benefits to downstream water quality. Most reservoirs in Brazil were built during the 1970s, but their long-term patterns of trophic status, water chemistry, and nutrient removal are still not very well characterized. We aimed to evaluate water quality time series (1985-2010) data from the riverine and lacustrine zones of the transboundary Itaipu Reservoir (Brazil/Paraguay). We examined total phosphorus and nitrogen, chlorophyll a concentrations, water transparency, and phytoplankton density to look for spatial and temporal trends and correlations with trophic state evolution and nutrient retention. There was significant temporal and spatial water quality variation ( P < 0.01, ANCOVA). The results indicated that the water quality and structure of the reservoir were mainly affected by one internal force (hydrodynamics) and one external force (upstream cascading reservoirs). Nutrient and chlorophyll a concentrations tended to be lower in the lacustrine zone and decreased over the 25-year timeframe. Reservoir operational features seemed to be limiting primary production and phytoplankton development, which exhibited a maximum density of 6050 org/mL. The relatively small nutrient concentrations in the riverine zone were probably related to the effect of the cascade reservoirs upstream of Itaipu and led to relatively low removal percentages. Our study suggested that water quality problems may be more pronounced immediately after the filling phase of the artificial reservoirs, associated with the initial decomposition of drowned vegetation at the very beginning of reservoir operation.

Trophic State Evolution and Nutrient Trapping Capacity in a Transboundary Subtropical Reservoir: A 25-Year Study.

PubMed

Cunha, Davi Gasparini Fernandes; Benassi, Simone Frederigi; de Falco, Patrícia Bortoletto; Calijuri, Maria do Carmo

2016-03-01

Artificial reservoirs have been used for drinking water supply, other human activities, flood control and pollution abatement worldwide, providing overall benefits to downstream water quality. Most reservoirs in Brazil were built during the 1970s, but their long-term patterns of trophic status, water chemistry, and nutrient removal are still not very well characterized. We aimed to evaluate water quality time series (1985-2010) data from the riverine and lacustrine zones of the transboundary Itaipu Reservoir (Brazil/Paraguay). We examined total phosphorus and nitrogen, chlorophyll a concentrations, water transparency, and phytoplankton density to look for spatial and temporal trends and correlations with trophic state evolution and nutrient retention. There was significant temporal and spatial water quality variation (P < 0.01, ANCOVA). The results indicated that the water quality and structure of the reservoir were mainly affected by one internal force (hydrodynamics) and one external force (upstream cascading reservoirs). Nutrient and chlorophyll a concentrations tended to be lower in the lacustrine zone and decreased over the 25-year timeframe. Reservoir operational features seemed to be limiting primary production and phytoplankton development, which exhibited a maximum density of 6050  org/mL. The relatively small nutrient concentrations in the riverine zone were probably related to the effect of the cascade reservoirs upstream of Itaipu and led to relatively low removal percentages. Our study suggested that water quality problems may be more pronounced immediately after the filling phase of the artificial reservoirs, associated with the initial decomposition of drowned vegetation at the very beginning of reservoir operation.

Spatio-temporal dynamics in phytobenthos structural properties reveal insights into agricultural catchment dynamics and nutrient fluxes

NASA Astrophysics Data System (ADS)

Reaney, S. M.; Snell, M. A.; Barker, P. A.; Aftab, A.; Barber, N. J.; Benskin, C.; Burke, S.; Cleasby, W.; Haygarth, P.; Jonczyk, J. C.; Owen, G. J.; Perks, M. T.; Quinn, P. F.; Surridge, B.

2016-12-01

Low order streams are spatially extensive, temporally dynamic, systems within the agricultural landscape. This dynamism extends to the aquatic communities within these streams, including the phytobentos, which demonstrates considerable resilience to diffuse anthropogenic nutrient pressures and changing climate dynamics. The phytobenthos community can substantially contribute to the food web, in particular diatoms, which dominate photo-autotrophic assemblages in low order streams. Diatoms are widely used in ecological monitoring because of their high sensitivity to environmental condition, but knowledge is limited on the ecological effects of winter disturbances and variance introduced by multiple and interacting pressures (N, P, sediment), introducing bias in understanding temporal dynamics in benthic diatom communities. Using the environmental time series data from long term monitoring within the River Eden Demonstration Test Catchment programme, we assess the impact of multiple hydro-chemical stressors on phytobenthic community resilience, and synthesize the impact of an extreme winter event. Monthly data from diatom communities collected in the Eden DTC from March 2011 to present show that river flow, strongly coupled to precipitation, is a key driver of these communities. Discharge has a direct effect on communities through scouring, but is also tightly correlated to nutrient delivery, such that 80% of the annual TP load arrives in 10% of the time. Trophic Diatom Index (TDI) values demonstrated considerable resilience by the stability of inter-monthly TDI scores over 5 seasonal cycles against the characterised highly variable hydrological regime. This research demonstrates that well characterised winter disturbances are critical to understanding drivers of aquatic dynamics. This has implications for catchment diffuse pollution policy, farm management and economics, given the climate projections of increases in frequency and intensity of extreme winter events, which may alter instream nutrient fluxes.

Coastal hypoxia responses to remediation

NASA Astrophysics Data System (ADS)

Kemp, W. M.; Testa, J. M.; Conley, D. J.; Gilbert, D.; Hagy, J. D.

2009-07-01

The incidence and intensity of hypoxic waters in coastal aquatic ecosystems has been expanding in recent decades coincident with eutrophication of the coastal zone. Because of the negative effects hypoxia has on many organisms, extensive efforts have been made to reduce the size and duration of hypoxia in many coastal waters. Although it has been broadly assumed that reductions in nutrient loading rates would reverse eutrophication and consequently, hypoxia, recent analyses of historical data from European and North American coastal systems suggest little evidence for simple linear response trajectories. We review existing data, analyses, and models that relate variations in the extent and intensity of hypoxia to changes in loading rates for inorganic nutrients and labile organic matter. We also assess existing knowledge of physical and ecological factors regulating oxygen in coastal marine waters and examine a broad range of examples where hypoxia responses to reductions in nutrient (or organic matter) inputs have been documented. Of the 22 systems identified where concurrent time series of loading and O2 were available, half displayed relatively clear and direct recoveries following remediation. We explored in detail 5 well-studied systems that have exhibited complex, non-linear responses to loading, including apparent "regime shifts." A summary of these analyses suggests that O2 conditions improved rapidly and linearly in systems where remediation focused on organic inputs from sewage plants, which were the primary drivers of hypoxia. In larger more open systems where diffuse nutrient loads are more important in fueling O2 depletion and where climatic influences are pronounced, responses to remediation tend to follow non-linear trends that may include hysteresis and time-lags. Improved understanding of hypoxia remediation requires that future studies use comparative approaches and consider multiple regulating factors including: (1) the dominant temporal scales of the hypoxia, (2) the relative contributions of inorganic and organic nutrients, (3) the influence of shifts in climatic and oceanographic processes, and (4) the roles of feedback interactions whereby O2-sensitive biogeochemistry, food-webs, and habitats influence the nutrient and algal dynamics that regulate O2 levels.

Nutrient fluxes and stoichiometry in a large impounded river-bay system

NASA Astrophysics Data System (ADS)

Klump, J. V.; Waples, J. T.; Able, L. M.; Anderson, P. D.; Weckerly, K.; Szmania, D. C.

2003-04-01

Reservoir-induced aging of continental runoff has been shown to an anthropogenically induced global phenomenon with estimates that the mean age of river water reaching the coastal ocean has likely tripled historically. This aging is hypothesized to have a significant biogeochemical impact on land-margin systems by altering flow regimes, net water balances and residence times, reaeration of surface waters, carbon cycling processes, and sediment storage and transport. The Fox-Wolf watershed system contains more than 20 reservoirs, impoundments and lakes on the main stems of the two principal rivers that feed Green Bay and Lake Michigan. Consequently, this hydrologic system can be conceived as functioning as a series of linked biogeochemical reactors which retard flow, retain particles, significantly attenuate the flux of materials into sequential downstream "pools", and both process and repackage nutrients via tightly coupled benthic-pelagic biotic interactions. This successional transformation process results in a poorly understood delivery of nutrients, soils and contaminants from upstream sources to downstream receptors in Green Bay and ultimately -- Lake Michigan. Nutrient reprocessing (defined as the sum of all processes affecting nutrients, i.e. fixation, remineralization, repackaging, sedimentation, etc.) within each pool is hypothesized to be primarily a function of: (1) particle-solute and hydraulic residence times, (2) the quality and quantity of inputs, and (3) the food web structure. Overlaid on these dynamics are very strong seasonal forcing factors, including annual temperature cycles that induce order of magnitude variations in temperature dependent reaction rates, and winter ice cover on the upper pool lakes, reservoirs and Green Bay, that halts run off from the land and reduces within-basin mixing. These short term and seasonal loading dynamics result in considerable temporal stochasticity in the capacity of the biotic component of the ecosystem to assimilate, transform, and attenuate the flux of materials through the land margin system. We report here preliminary results on the nature of elemental riverine fluxes (e.g. carbon, nitrogen, phosphorus, silica), the shift in their composition and stoichiometry as these materials move downstream, and on the role of impoundments as nutrient traps.

Comparison of detrending methods for fluctuation analysis in hydrology

NASA Astrophysics Data System (ADS)

Zhang, Qiang; Zhou, Yu; Singh, Vijay P.; Chen, Yongqin David

2011-03-01

SummaryTrends within a hydrologic time series can significantly influence the scaling results of fluctuation analysis, such as rescaled range (RS) analysis and (multifractal) detrended fluctuation analysis (MF-DFA). Therefore, removal of trends is important in the study of scaling properties of the time series. In this study, three detrending methods, including adaptive detrending algorithm (ADA), Fourier-based method, and average removing technique, were evaluated by analyzing numerically generated series and observed streamflow series with obvious relative regular periodic trend. Results indicated that: (1) the Fourier-based detrending method and ADA were similar in detrending practices, and given proper parameters, these two methods can produce similarly satisfactory results; (2) detrended series by Fourier-based detrending method and ADA lose the fluctuation information at larger time scales, and the location of crossover points is heavily impacted by the chosen parameters of these two methods; and (3) the average removing method has an advantage over the other two methods, i.e., the fluctuation information at larger time scales is kept well-an indication of relatively reliable performance in detrending. In addition, the average removing method performed reasonably well in detrending a time series with regular periods or trends. In this sense, the average removing method should be preferred in the study of scaling properties of the hydrometeorolgical series with relative regular periodic trend using MF-DFA.

Multi-complexity ensemble measures for gait time series analysis: application to diagnostics, monitoring and biometrics.

PubMed

Gavrishchaka, Valeriy; Senyukova, Olga; Davis, Kristina

2015-01-01

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

Long Term Precipitation Pattern Identification and Derivation of Non Linear Precipitation Trend in a Catchment using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, Vinayakam

2017-04-01

Precipitation is the major component in the hydrologic cycle. Awareness of not only the total amount of rainfall pertaining to a catchment, but also the pattern of its spatial and temporal distribution are equally important in the management of water resources systems in an efficient way. Trend is the long term direction of a time series; it determines the overall pattern of a time series. Singular Spectrum Analysis (SSA) is a time series analysis technique that decomposes the time series into small components (eigen triples). This property of the method of SSA has been utilized to extract the trend component of the rainfall time series. In order to derive trend from the rainfall time series, we need to select components corresponding to trend from the eigen triples. For this purpose, periodogram analysis of the eigen triples have been proposed to be coupled with SSA, in the present study. In the study, seasonal data of England and Wales Precipitation (EWP) for a time period of 1766-2013 have been analyzed and non linear trend have been derived out of the precipitation data. In order to compare the performance of SSA in deriving trend component, Mann Kendall (MK) test is also used to detect trends in EWP seasonal series and the results have been compared. The result showed that the MK test could detect the presence of positive or negative trend for a significance level, whereas the proposed methodology of SSA could extract the non-linear trend present in the rainfall series along with its shape. We will discuss further the comparison of both the methodologies along with the results in the presentation.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series.

PubMed

Marken, John P; Halleran, Andrew D; Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C; Golino, Caroline A; Kemper, Peter; Saha, Margaret S

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features.

Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis.

PubMed

Basatnia, Nabee; Hossein, Seyed Abbas; Rodrigo-Comino, Jesús; Khaledian, Yones; Brevik, Eric C; Aitkenhead-Peterson, Jacqueline; Natesan, Usha

2018-04-29

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO 2 ), nitrate (NO 3 ), biological oxygen demand (BOD), phosphate (PO 4 ), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R 2 , RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.

The parametric modified limited penetrable visibility graph for constructing complex networks from time series

NASA Astrophysics Data System (ADS)

Li, Xiuming; Sun, Mei; Gao, Cuixia; Han, Dun; Wang, Minggang

2018-02-01

This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields.

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodge, Brian S; Chartan, Erol Kevin; Feng, Cong

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the importantmore » information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.« less

77 FR 76135 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing of...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-26

... 500 Index option series in the pilot: (1) A time series analysis of open interest; and (2) an analysis... issue's total market share value, which is the share price times the number of shares outstanding. These... other series. Strike price intervals would be set no less than 5 points apart. Consistent with existing...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamışlıoğlu, Miraç, E-mail: m.kamislioglu@gmail.com; Külahcı, Fatih, E-mail: fatihkulahci@firat.edu.tr

Nonlinear time series analysis techniques have large application areas on the geoscience and geophysics fields. Modern nonlinear methods are provided considerable evidence for explain seismicity phenomena. In this study nonlinear time series analysis, fractal analysis and spectral analysis have been carried out for researching the chaotic behaviors of release radon gas ({sup 222}Rn) concentration occurring during seismic events. Nonlinear time series analysis methods (Lyapunov exponent, Hurst phenomenon, correlation dimension and false nearest neighbor) were applied for East Anatolian Fault Zone (EAFZ) Turkey and its surroundings where there are about 35,136 the radon measurements for each region. In this paper weremore » investigated of {sup 222}Rn behavior which it’s used in earthquake prediction studies.« less

Time Series Analysis Based on Running Mann Whitney Z Statistics

USDA-ARS?s Scientific Manuscript database

A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...

Submarine ground water discharge and fate along the coast of Kaloko-Honokohau National Historical Park, Hawai‘i: Part I: time-series measurements of currents, waves, salinity and temperature: November, 2005-July, 2007

USGS Publications Warehouse

Presto, M. Katherine; Storlazzi, Curt D.; Logan, Joshua B.; Grossman, Eric E.

2007-01-01

The impending development for the west Hawai‘i coastline adjacent to Kaloko-Honokōhau National Historical Park (KAHO) may potentially alter coastal hydrology and water quality in the marine waters of the park. Water resources are perhaps the most significant natural and cultural resource component in the park, and are critical to the health and well being of six federally listed species. KAHO contains ecosystems of brackish anchialine pools, two 11-acre fishponds, and 596 acres of coral reef habitats, all fed by groundwater originating upslope. The steep gradients on high islands, combined with typically porous substrates and high rainfall levels at upper elevations, make these settings especially vulnerable to shifts in submarine groundwater discharge (SGD) and its entrained nutrients and pollutants. Little is known about the magnitude, rate, frequency, and variability of SGD and its influence on contaminant loading to Hawaiian coastal environments. Recent studies show that groundwater flux through the park is vital to many ecosystem components including anchialine ponds and wetland biota. The function of these ecosystems may be vulnerable to changes in groundwater flow stemming from natural changes (climate and sea level) and land use (groundwater pumping and contamination). Oki and others (1999) showed that increased groundwater withdrawals for urban development since 1978 likely decreased groundwater flux to the coast by 50%. During this same time, the quality of groundwater has been vulnerable to increases in contaminant and nutrient/fertilizer additions associated with industrial, commercial and residential use upslope from KAHO (Oki and others, 1999). High-resolution measurements of waves, currents, water levels, temperature and salinity were collected in the marine portion of the park from November, 2005, through July, 2006, to establish baseline information on the magnitude, rate, frequency, and variability of SGD. These data are intended to help researchers and resource managers better understand the hydrodynamics of the oceanographic environment in the park’s coastal waters as it pertains to the pathway of SGD and associated nutrient and contaminant input to the park’s coral reef ecosystem. Measurements were made of the oceanographic environment (waves, tides, currents, salinity and temperature) using hydrodynamic techniques to characterize and quantify the distribution, input and throughput of freshwater and associated nutrient/contaminant within the near shore environment of KAHO through the emplacement of a series of bottom-mounted instruments deployed in water depths less than 15 m. This study was conducted in support of the National Park Service (NPS) by the U.S. Geological Survey (USGS) Coastal and Marine Geology Program’s Coral Reef Project. These measurements support the ongoing studies of the Coral Reef Project to better understand the transport mechanisms of sediment, larvae, nutrients, pollutants and other particles on Pacific coral reefs. Subsequent reports will address the spatial and temporal variability in groundwater input and the associated nutrient flux in the park’s waters.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Seasonal progression of uranium series isotopes in subglacial meltwater: Implications for subglacial storage time

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arendt, Carli A.; Aciego, Sarah M.; Sims, Kenneth W. W.

The residence time of subglacial meltwater impacts aquifer recharge, nutrient production, and chemical signals that reflect underlying bedrock/substrate, but is inaccessible to direct observation. We report the seasonal evolution of subglacial meltwater chemistry from the 2011 melt season at the terminus of the Athabasca Glacier, Canada. We also measured major and trace analytes and U-series isotopes for twenty-nine bulk meltwater samples collected over the duration of the melt season. This dataset, which is the longest time-series record of ( 234U/ 238U) isotopes in a glacial meltwater system, provides insight into the hydrologic evolution of the subglacial system during active melting.more » Meltwater samples, measured from the outflow, were analyzed for ( 238U), ( 222Rn) and ( 234U/ 238U)activity, conductivity, alkalinity, pH and major cations. Subglacial meltwater varied in [238U] and (222Rn) from 23 to 832 ppt and 9 to 171 pCi/L, respectively. Activity ratios of ( 234U/ 238U) ranged from 1.003 to 1.040, with the highest ( 238U), ( 222Rn) and ( 234U/ 238U)activity values occurring in early May when delayed-flow basal meltwater composed a significant portion of the bulk melt. Furthemore, from the chemical evolution of the meltwater, we posit that the relative subglacial water residence times decrease over the course of the melt season. This decrease in qualitative residence time during active melt is consistent with prior field studies and model-predicted channel switching from a delayed, distributed network to a fast, channelized network flow. As such, our study provides support for linking U-series isotopes to storage lengths of meltwater beneath glacial systems as subglacial hydrologic networks evolve with increased melting and channel network efficiency.« less

Seasonal progression of uranium series isotopes in subglacial meltwater: Implications for subglacial storage time

DOE PAGES

Arendt, Carli A.; Aciego, Sarah M.; Sims, Kenneth W. W.; ...

2017-07-31

The residence time of subglacial meltwater impacts aquifer recharge, nutrient production, and chemical signals that reflect underlying bedrock/substrate, but is inaccessible to direct observation. We report the seasonal evolution of subglacial meltwater chemistry from the 2011 melt season at the terminus of the Athabasca Glacier, Canada. We also measured major and trace analytes and U-series isotopes for twenty-nine bulk meltwater samples collected over the duration of the melt season. This dataset, which is the longest time-series record of ( 234U/ 238U) isotopes in a glacial meltwater system, provides insight into the hydrologic evolution of the subglacial system during active melting.more » Meltwater samples, measured from the outflow, were analyzed for ( 238U), ( 222Rn) and ( 234U/ 238U)activity, conductivity, alkalinity, pH and major cations. Subglacial meltwater varied in [238U] and (222Rn) from 23 to 832 ppt and 9 to 171 pCi/L, respectively. Activity ratios of ( 234U/ 238U) ranged from 1.003 to 1.040, with the highest ( 238U), ( 222Rn) and ( 234U/ 238U)activity values occurring in early May when delayed-flow basal meltwater composed a significant portion of the bulk melt. Furthemore, from the chemical evolution of the meltwater, we posit that the relative subglacial water residence times decrease over the course of the melt season. This decrease in qualitative residence time during active melt is consistent with prior field studies and model-predicted channel switching from a delayed, distributed network to a fast, channelized network flow. As such, our study provides support for linking U-series isotopes to storage lengths of meltwater beneath glacial systems as subglacial hydrologic networks evolve with increased melting and channel network efficiency.« less

Using machine learning to identify structural breaks in single-group interrupted time series designs.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

Analysis and generation of groundwater concentration time series

NASA Astrophysics Data System (ADS)

Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae

2018-01-01

Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.

Use of point-of-sale data to assess food and nutrient quality in remote stores.

PubMed

Brimblecombe, Julie; Liddle, Robyn; O'Dea, Kerin

2013-07-01

To examine the feasibility of using point-of-sale data to assess dietary quality of food sales in remote stores. A multi-site cross-sectional assessment of food and nutrient composition of food sales. Point-of-sale data were linked to Australian Food and Nutrient Data and compared across study sites and with nutrient requirements. Remote Aboriginal Australia. Six stores. Point-of-sale data were readily available and provided a low-cost, efficient and objective assessment of food and nutrient sales. Similar patterns in macronutrient distribution, food expenditure and key food sources of nutrients were observed across stores. In all stores, beverages, cereal and cereal products, and meat and meat products comprised approximately half of food sales (range 49–57 %). Fruit and vegetable sales comprised 10.4 (SD 1.9) % on average. Carbohydrate contributed 54.4 (SD 3.0) % to energy; protein 13.5 (SD 1.1) %; total sugars 28.9 (SD 4.3) %; and the contribution of total saturated fat to energy ranged from 11.0 to 14.4% across stores. Mg, Ca, K and fibre were limiting nutrients, and Na was four to five times higher than the midpoint of the average intake range. Relatively few foods were major sources of nutrients. Point-of-sale data enabled an assessment of dietary quality within stores and across stores with no burden on communities and at no cost, other than time required for analysis and reporting. Similar food spending patterns and nutrient profiles were observed across the six stores. This suggests potential in using point-of-sale data to monitor and evaluate dietary quality in remote Australian communities.

Uncertainty Of Stream Nutrient Transport Estimates Using Random Sampling Of Storm Events From High Resolution Water Quality And Discharge Data

NASA Astrophysics Data System (ADS)

Scholefield, P. A.; Arnscheidt, J.; Jordan, P.; Beven, K.; Heathwaite, L.

2007-12-01

The uncertainties associated with stream nutrient transport estimates are frequently overlooked and the sampling strategy is rarely if ever investigated. Indeed, the impact of sampling strategy and estimation method on the bias and precision of stream phosphorus (P) transport calculations is little understood despite the use of such values in the calibration and testing of models of phosphorus transport. The objectives of this research were to investigate the variability and uncertainty in the estimates of total phosphorus transfers at an intensively monitored agricultural catchment. The Oona Water which is located in the Irish border region, is part of a long term monitoring program focusing on water quality. The Oona Water is a rural river catchment with grassland agriculture and scattered dwelling houses and has been monitored for total phosphorus (TP) at 10 min resolution for several years (Jordan et al, 2007). Concurrent sensitive measurements of discharge are also collected. The water quality and discharge data were provided at 1 hour resolution (averaged) and this meant that a robust estimate of the annual flow weighted concentration could be obtained by simple interpolation between points. A two-strata approach (Kronvang and Bruhn, 1996) was used to estimate flow weighted concentrations using randomly sampled storm events from the 400 identified within the time series and also base flow concentrations. Using a random stratified sampling approach for the selection of events, a series ranging from 10 through to the full 400 were used, each time generating a flow weighted mean using a load-discharge relationship identified through log-log regression and monte-carlo simulation. These values were then compared to the observed total phosphorus concentration for the catchment. Analysis of these results show the impact of sampling strategy, the inherent bias in any estimate of phosphorus concentrations and the uncertainty associated with such estimates. The estimates generated using the full time series underestimate the flow weighted mean concentration of total phosphorus. This work compliments other contemporary work in the area of load estimation uncertainty in the UK (Johnes, 2007). Johnes P,J. 2007, Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density, Journal of hydrology 332 (1- 2): 241-258 Jordan, P., Arnscheidt, J., McGrogan, H & McCormick, S., 2007. Characterising phosphorus transfers in rural transfers using a continuous bank-side analyser. Hydrology and Earth System Science 11, 372-381 Kronvang B & Bruhn, A. J, 1996. Choice of sampling strategy and estimation method for calculating nitrogen and phosphorus transport in small lowland streams , Hydrological processes 10 (11): 1483-1501

Shifted hot spots and nutrient imbalance in global fertilizer use for agriculture production in the past half century

NASA Astrophysics Data System (ADS)

Tian, H.; Lu, C.

2016-12-01

In addition to enhance agricultural productivity, synthetic nitrogen (N) and phosphorous (P) fertilizer application in croplands dramatically altered global nutrient budget, water quality, greenhouse gas balance, and their feedbacks to the climate system. However, due to the lack of geospatial fertilizer input data, current Earth system/land surface modeling studies have to ignore or use over-simplified data (e.g., static, spatially uniform fertilizer use) to characterize agricultural N and P input over decadal or century-long period. In this study, we therefore develop a global time-series gridded data of annual synthetic N and P fertilizer use rate in croplands, matched with HYDE 3,2 historical land use maps, at a resolution of 0.5º latitude by longitude during 1900-2013. Our data indicate N and P fertilizer use rates increased by approximately 8 times and 3 times, respectively, since the year 1961, when IFA (International Fertilizer Industry Association) and FAO (Food and Agricultural Organization) survey of country-level fertilizer input were available. Considering cropland expansion, increase of total fertilizer consumption amount is even larger. Hotspots of agricultural N fertilizer use shifted from the U.S. and Western Europe in the 1960s to East Asia in the early 21st century. P fertilizer input show the similar pattern with additional hotspot in Brazil. We find a global increase of fertilizer N/P ratio by 0.8 g N/g P per decade (p< 0.05) during 1961-2013, which may have important global implication of human impacts on agroecosystem functions in the long run. Our data can serve as one of critical input drivers for regional and global assessment on agricultural productivity, crop yield, agriculture-derived greenhouse gas balance, global nutrient budget, land-to-aquatic nutrient loss, and ecosystem feedback to the climate system.

Empirical Records of Environmental Change across the Archean-Proterozoic Transition

NASA Astrophysics Data System (ADS)

Kaufman, A. J.

2011-12-01

Time-series geochemical analyses of scientific drill cores intersecting the Archean-Proterozoic transition suggest a coupling of environmental and biological change that culminated in the pervasive oxygenation of Earth's atmosphere and oceans. Elemental and multiple isotope measurements of sedimentary archives, including carbonate, shale, and banded iron-formation from Western Australia, South Africa, Brazil, and southern Canada, indicate important changes in the carbon, sulfur, and nitrogen cycles that monitor the redox state of the oceans and the cyanobacterial buildup of atmospheric oxygen and ozone. In response, continental weathering would have increased, resulting in the enhanced delivery of sulfate and nutrients to seawater, further stimulating photoautotrophic fluxes of oxygen to surface environments. The positive feedback may additionally be responsible for the decline of atmospheric methane and surface refrigeration, represented by a series of discrete ice ages beginning around 2.4 billion years ago, due to the loss of greenhouse capacity during a time of lower solar luminosity. While speculative, the linkage of surface oxidation with enhanced nutrient supply and development of stratospheric sunscreen soon after the Archean-Proterozoic boundary suggests that the earliest perturbation in the carbon cycle may be associated with the rapid expansion of single-celled eukaryotes. Both sterol synthesis in eukaryotes and aerobic respiration require significant levels of oxygen in the ambient environment. Hence, Earth's earliest ice age(s) and onset of a modern and far more energetic carbon cycle may have been directly related to the global expansion of cyanobacteria that released oxygen to the environment, and of eukaryotes that respired it.

Comparative transcriptome analysis to investigate the high starch accumulation of duckweed (Landoltia punctata) under nutrient starvation.

PubMed

Tao, Xiang; Fang, Yang; Xiao, Yao; Jin, Yan-Ling; Ma, Xin-Rong; Zhao, Yun; He, Kai-Ze; Zhao, Hai; Wang, Hai-Yan

2013-05-08

Duckweed can thrive on anthropogenic wastewater and produce tremendous biomass production. Due to its relatively high starch and low lignin percentage, duckweed is a good candidate for bioethanol fermentation. Previous studies have observed that water devoid of nutrients is good for starch accumulation, but its molecular mechanism remains unrevealed. This study globally analyzed the response to nutrient starvation in order to investigate the starch accumulation in duckweed (Landoltia punctata). L. punctata was transferred from nutrient-rich solution to distilled water and sampled at different time points. Physiological measurements demonstrated that the activity of ADP-glucose pyrophosphorylase, the key enzyme of starch synthesis, as well as the starch percentage in duckweed, increased continuously under nutrient starvation. Samples collected at 0 h, 2 h and 24 h time points respectively were used for comparative gene expression analysis using RNA-Seq. A comprehensive transcriptome, comprising of 74,797 contigs, was constructed by a de novo assembly of the RNA-Seq reads. Gene expression profiling results showed that the expression of some transcripts encoding key enzymes involved in starch biosynthesis was up-regulated, while the expression of transcripts encoding enzymes involved in starch consumption were down-regulated, the expression of some photosynthesis-related transcripts were down-regulated during the first 24 h, and the expression of some transporter transcripts were up-regulated within the first 2 h. Very interestingly, most transcripts encoding key enzymes involved in flavonoid biosynthesis were highly expressed regardless of starvation, while transcripts encoding laccase, the last rate-limiting enzyme of lignifications, exhibited very low expression abundance in all three samples. Our study provides a comprehensive expression profiling of L. punctata under nutrient starvation, which indicates that nutrient starvation down-regulated the global metabolic status, redirects metabolic flux of fixed CO2 into starch synthesis branch resulting in starch accumulation in L. punctata.

Comparative transcriptome analysis to investigate the high starch accumulation of duckweed (Landoltia punctata) under nutrient starvation

PubMed Central

2013-01-01

Background Duckweed can thrive on anthropogenic wastewater and produce tremendous biomass production. Due to its relatively high starch and low lignin percentage, duckweed is a good candidate for bioethanol fermentation. Previous studies have observed that water devoid of nutrients is good for starch accumulation, but its molecular mechanism remains unrevealed. Results This study globally analyzed the response to nutrient starvation in order to investigate the starch accumulation in duckweed (Landoltia punctata). L. punctata was transferred from nutrient-rich solution to distilled water and sampled at different time points. Physiological measurements demonstrated that the activity of ADP-glucose pyrophosphorylase, the key enzyme of starch synthesis, as well as the starch percentage in duckweed, increased continuously under nutrient starvation. Samples collected at 0 h, 2 h and 24 h time points respectively were used for comparative gene expression analysis using RNA-Seq. A comprehensive transcriptome, comprising of 74,797 contigs, was constructed by a de novo assembly of the RNA-Seq reads. Gene expression profiling results showed that the expression of some transcripts encoding key enzymes involved in starch biosynthesis was up-regulated, while the expression of transcripts encoding enzymes involved in starch consumption were down-regulated, the expression of some photosynthesis-related transcripts were down-regulated during the first 24 h, and the expression of some transporter transcripts were up-regulated within the first 2 h. Very interestingly, most transcripts encoding key enzymes involved in flavonoid biosynthesis were highly expressed regardless of starvation, while transcripts encoding laccase, the last rate-limiting enzyme of lignifications, exhibited very low expression abundance in all three samples. Conclusion Our study provides a comprehensive expression profiling of L. punctata under nutrient starvation, which indicates that nutrient starvation down-regulated the global metabolic status, redirects metabolic flux of fixed CO2 into starch synthesis branch resulting in starch accumulation in L. punctata. PMID:23651472

On statistical inference in time series analysis of the evolution of road safety.

PubMed

Commandeur, Jacques J F; Bijleveld, Frits D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

2013-11-01

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Cell-specific CO2 fixation rates of two distinct groups of plastidic protists in the Atlantic Ocean remain unchanged after nutrient addition.

PubMed

Grob, Carolina; Jardillier, Ludwig; Hartmann, Manuela; Ostrowski, Martin; Zubkov, Mikhail V; Scanlan, David J

2015-04-01

To assess the role of open-ocean ecosystems in global CO2 fixation, we investigated how picophytoplankton, which dominate primary production, responded to episodic increases in nutrient availability. Previous experiments have shown nitrogen alone, or in combination with phosphorus or iron, to be the proximate limiting nutrient(s) for total phytoplankton grown over several days. Much less is known about how nutrient upshift affects picophytoplankton CO2 fixation over the duration of the light period. To address this issue, we performed a series of small volume (8-60 ml) - short term (10-11 h) nutrient addition experiments in different regions of the Atlantic Ocean using NH4 Cl, FeCl3 , K medium, dust and nutrient-rich water from 300 m depth. We found no significant nutrient stimulation of group-specific CO2 fixation rates of two taxonomically and size-distinct groups of plastidic protists. The above was true regardless of the region sampled or nutrient added, suggesting that this is a generic phenomenon. Our findings show that at least in the short term (i.e. daylight period), nutrient availability does not limit CO2 fixation by the smallest plastidic protists, while their taxonomic composition does not determine their response to nutrient addition. © 2014 Society for Applied Microbiology and John Wiley & Sons Ltd.

Modelling short time series in metabolomics: a functional data analysis approach.

PubMed

Montana, Giovanni; Berk, Maurice; Ebbels, Tim

2011-01-01

Metabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two or more experimental conditions (e.g. a control and drug-treated group), thus producing time course data. Models from traditional time series analysis are often unsuitable because, by design, only very few time points are available and there are a high number of missing values. We propose a functional data analysis approach for modelling short time series arising in metabolomic studies which overcomes these obstacles. Our model assumes that each observed time series is a smooth random curve, and we propose a statistical approach for inferring this curve from repeated measurements taken on the experimental units. A test statistic for detecting differences between temporal profiles associated with two experimental conditions is then presented. The methodology has been applied to NMR spectroscopy data collected in a pre-clinical toxicology study.

Identifying arsenic trioxide (ATO) functions in leukemia cells by using time series gene expression profiles.

PubMed

Yang, Hong; Lin, Shan; Cui, Jingru

2014-02-10

Arsenic trioxide (ATO) is presently the most active single agent in the treatment of acute promyelocytic leukemia (APL). In order to explore the molecular mechanism of ATO in leukemia cells with time series, we adopted bioinformatics strategy to analyze expression changing patterns and changes in transcription regulation modules of time series genes filtered from Gene Expression Omnibus database (GSE24946). We totally screened out 1847 time series genes for subsequent analysis. The KEGG (Kyoto encyclopedia of genes and genomes) pathways enrichment analysis of these genes showed that oxidative phosphorylation and ribosome were the top 2 significantly enriched pathways. STEM software was employed to compare changing patterns of gene expression with assigned 50 expression patterns. We screened out 7 significantly enriched patterns and 4 tendency charts of time series genes. The result of Gene Ontology showed that functions of times series genes mainly distributed in profiles 41, 40, 39 and 38. Seven genes with positive regulation of cell adhesion function were enriched in profile 40, and presented the same first increased model then decreased model as profile 40. The transcription module analysis showed that they mainly involved in oxidative phosphorylation pathway and ribosome pathway. Overall, our data summarized the gene expression changes in ATO treated K562-r cell lines with time and suggested that time series genes mainly regulated cell adhesive. Furthermore, our result may provide theoretical basis of molecular biology in treating acute promyelocytic leukemia. Copyright © 2013 Elsevier B.V. All rights reserved.

Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection

PubMed Central

Flamm, Christoph; Graef, Andreas; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred

2013-01-01

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures. PMID:23354014

Analysis of Land Subsidence Monitoring in Mining Area with Time-Series Insar Technology

NASA Astrophysics Data System (ADS)

Sun, N.; Wang, Y. J.

2018-04-01

Time-series InSAR technology has become a popular land subsidence monitoring method in recent years, because of its advantages such as high accuracy, wide area, low expenditure, intensive monitoring points and free from accessibility restrictions. In this paper, we applied two kinds of satellite data, ALOS PALSAR and RADARSAT-2, to get the subsidence monitoring results of the study area in two time periods by time-series InSAR technology. By analyzing the deformation range, rate and amount, the time-series analysis of land subsidence in mining area was realized. The results show that InSAR technology could be used to monitor land subsidence in large area and meet the demand of subsidence monitoring in mining area.

Mobile Visualization and Analysis Tools for Spatial Time-Series Data

NASA Astrophysics Data System (ADS)

Eberle, J.; Hüttich, C.; Schmullius, C.

2013-12-01

The Siberian Earth System Science Cluster (SIB-ESS-C) provides access and analysis services for spatial time-series data build on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and climate data from meteorological stations. Until now a webportal for data access, visualization and analysis with standard-compliant web services was developed for SIB-ESS-C. As a further enhancement a mobile app was developed to provide an easy access to these time-series data for field campaigns. The app sends the current position from the GPS receiver and a specific dataset (like land surface temperature or vegetation indices) - selected by the user - to our SIB-ESS-C web service and gets the requested time-series data for the identified pixel back in real-time. The data is then being plotted directly in the app. Furthermore the user has possibilities to analyze the time-series data for breaking points and other phenological values. These processings are executed on demand of the user on our SIB-ESS-C web server and results are transfered to the app. Any processing can also be done at the SIB-ESS-C webportal. The aim of this work is to make spatial time-series data and analysis functions available for end users without the need of data processing. In this presentation the author gives an overview on this new mobile app, the functionalities, the technical infrastructure as well as technological issues (how the app was developed, our made experiences).

76 FR 12775 - Self-Regulatory Organizations; C2 Options Exchange, Incorporated; Notice of Filing of a Proposed...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-08

... series in the pilot: (1) A time series analysis of open interest; and (2) An analysis of the distribution... times the number of shares outstanding. These are summed for all 500 stocks and divided by a... below $3.00 and $0.10 for all other series. Strike price intervals would be set no less than 5 points...

Time series behaviour of the number of Air Asia passengers: A distributional approach

NASA Astrophysics Data System (ADS)

Asrah, Norhaidah Mohd; Djauhari, Maman Abdurachman

2013-09-01

The common practice to time series analysis is by fitting a model and then further analysis is conducted on the residuals. However, if we know the distributional behavior of time series, the analyses in model identification, parameter estimation, and model checking are more straightforward. In this paper, we show that the number of Air Asia passengers can be represented as a geometric Brownian motion process. Therefore, instead of using the standard approach in model fitting, we use an appropriate transformation to come up with a stationary, normally distributed and even independent time series. An example in forecasting the number of Air Asia passengers will be given to illustrate the advantages of the method.

Regional Landslide Mapping Aided by Automated Classification of SqueeSAR™ Time Series (Northern Apennines, Italy)

NASA Astrophysics Data System (ADS)

Iannacone, J.; Berti, M.; Allievi, J.; Del Conte, S.; Corsini, A.

2013-12-01

Space borne InSAR has proven to be very valuable for landslides detection. In particular, extremely slow landslides (Cruden and Varnes, 1996) can be now clearly identified, thanks to the millimetric precision reached by recent multi-interferometric algorithms. The typical approach in radar interpretation for landslides mapping is based on average annual velocity of the deformation which is calculated over the entire times series. The Hotspot and Cluster Analysis (Lu et al., 2012) and the PSI-based matrix approach (Cigna et al., 2013) are examples of landslides mapping techniques based on average annual velocities. However, slope movements can be affected by non-linear deformation trends, (i.e. reactivation of dormant landslides, deceleration due to natural or man-made slope stabilization, seasonal activity, etc). Therefore, analyzing deformation time series is crucial in order to fully characterize slope dynamics. While this is relatively simple to be carried out manually when dealing with small dataset, the time series analysis over regional scale dataset requires automated classification procedures. Berti et al. (2013) developed an automatic procedure for the analysis of InSAR time series based on a sequence of statistical tests. The analysis allows to classify the time series into six distinctive target trends (0=uncorrelated; 1=linear; 2=quadratic; 3=bilinear; 4=discontinuous without constant velocity; 5=discontinuous with change in velocity) which are likely to represent different slope processes. The analysis also provides a series of descriptive parameters which can be used to characterize the temporal changes of ground motion. All the classification algorithms were integrated into a Graphical User Interface called PSTime. We investigated an area of about 2000 km2 in the Northern Apennines of Italy by using SqueeSAR™ algorithm (Ferretti et al., 2011). Two Radarsat-1 data stack, comprising of 112 scenes in descending orbit and 124 scenes in ascending orbit, were processed. The time coverage lasts from April 2003 to November 2012, with an average temporal frequency of 1 scene/month. Radar interpretation has been carried out by considering average annual velocities as well as acceleration/deceleration trends evidenced by PSTime. Altogether, from ascending and descending geometries respectively, this approach allowed detecting of 115 and 112 potential landslides on the basis of average displacement rate and 77 and 79 landslides on the basis of acceleration trends. In conclusion, time series analysis resulted to be very valuable for landslide mapping. In particular it highlighted areas with marked acceleration in a specific period in time while still being affected by low average annual velocity over the entire analysis period. On the other hand, even in areas with high average annual velocity, time series analysis was of primary importance to characterize the slope dynamics in terms of acceleration events.

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades

PubMed Central

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-01-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter. PMID:28813566

A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades.

PubMed

Dai, Weiwei; Selesnick, Ivan; Rizzo, John-Ross; Rucker, Janet; Hudson, Todd

2017-08-01

The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.

Time Series Econometrics for the 21st Century

ERIC Educational Resources Information Center

Hansen, Bruce E.

2017-01-01

The field of econometrics largely started with time series analysis because many early datasets were time-series macroeconomic data. As the field developed, more cross-sectional and longitudinal datasets were collected, which today dominate the majority of academic empirical research. In nonacademic (private sector, central bank, and governmental)…

A Spatial and Temporal Assessment of Non-Point Groundwater Pollution Sources, Tutuila Island, American Samoa

NASA Astrophysics Data System (ADS)

Shuler, C. K.; El-Kadi, A. I.; Dulaiova, H.; Glenn, C. R.; Fackrell, J.

2015-12-01

The quality of municipal groundwater supplies on Tutuila, the main island in American Samoa, is currently in question. A high vulnerability for contamination from surface activities has been recognized, and there exists a strong need to clearly identify anthropogenic sources of pollution and quantify their influence on the aquifer. This study examines spatial relationships and time series measurements of nutrients and other tracers to identify predominant pollution sources and determine the water quality impacts of the island's diverse land uses. Elevated groundwater nitrate concentrations are correlated with areas of human development, however, the mixture of residential and agricultural land use in this unique village based agrarian setting makes specific source identification difficult using traditional geospatial analysis. Spatial variation in anthropogenic impact was assessed by linking NO3- concentrations and δ15N(NO3) from an extensive groundwater survey to land-use types within well capture zones and groundwater flow-paths developed with MODFLOW, a numerical groundwater model. Land use types were obtained from high-resolution GIS data and compared to water quality results with multiple-regression analysis to quantify the impact that different land uses have on water quality. In addition, historical water quality data and new analyses of δD and δ18O in precipitation, groundwater, and mountain-front recharge waters were used to constrain the sources and mechanisms of contamination. Our analyses indicate that groundwater nutrient levels on Tutuila are controlled primarily by residential, not agricultural activity. Also a lack of temporal variation suggests that episodic pollution events are limited to individual water sources as opposed to the entire aquifer. These results are not only valuable for water quality management on Tutuila, but also provide insight into the sustainability of groundwater supplies on other islands with similar hydrogeology and land use history.

Comparative Fatigue Lives of Rubber and PVC Wiper Cylindrical Coatings

NASA Technical Reports Server (NTRS)

Vlcek, Brian L.; Hendricks, Robert C.; Zaretsky, Erwin V.; Savage, Michael

2002-01-01

Three coating materials for rotating cylindrical-coated wiping rollers were fatigue tested in 2 Intaglio printing presses. The coatings were a hard, cross-linked, plasticized PVC thermoset (P-series); a plasticized PVC (A-series); and a hard, nitryl rubber (R-series). Both 2- and 3-parameter Weibull analyses as well as a cost-benefit analysis were performed. The mean value of life for the R-series coating is 24 and 9 times longer than the P- and A-series coatings, respectively. Both the cost and replacement rate for the R-series coating was significantly less than those for the P- and A-series coatings. At a very high probability of survival the R-series coating is approximately 2 and 6 times the lives of the P- and A-series, respectively, before the first failure occurs. Where all coatings are run to failure, using the mean (life) time between removal (MTBR) for each coating to calculate the number of replacements and costs provides qualitatively similar results to those using a Weibull analysis.

Stochastic modeling of hourly rainfall times series in Campania (Italy)

NASA Astrophysics Data System (ADS)

Giorgio, M.; Greco, R.

2009-04-01

Occurrence of flowslides and floods in small catchments is uneasy to predict, since it is affected by a number of variables, such as mechanical and hydraulic soil properties, slope morphology, vegetation coverage, rainfall spatial and temporal variability. Consequently, landslide risk assessment procedures and early warning systems still rely on simple empirical models based on correlation between recorded rainfall data and observed landslides and/or river discharges. Effectiveness of such systems could be improved by reliable quantitative rainfall prediction, which can allow gaining larger lead-times. Analysis of on-site recorded rainfall height time series represents the most effective approach for a reliable prediction of local temporal evolution of rainfall. Hydrological time series analysis is a widely studied field in hydrology, often carried out by means of autoregressive models, such as AR, ARMA, ARX, ARMAX (e.g. Salas [1992]). Such models gave the best results when applied to the analysis of autocorrelated hydrological time series, like river flow or level time series. Conversely, they are not able to model the behaviour of intermittent time series, like point rainfall height series usually are, especially when recorded with short sampling time intervals. More useful for this issue are the so-called DRIP (Disaggregated Rectangular Intensity Pulse) and NSRP (Neymann-Scott Rectangular Pulse) model [Heneker et al., 2001; Cowpertwait et al., 2002], usually adopted to generate synthetic point rainfall series. In this paper, the DRIP model approach is adopted, in which the sequence of rain storms and dry intervals constituting the structure of rainfall time series is modeled as an alternating renewal process. Final aim of the study is to provide a useful tool to implement an early warning system for hydrogeological risk management. Model calibration has been carried out with hourly rainfall hieght data provided by the rain gauges of Campania Region civil protection agency meteorological warning network. ACKNOWLEDGEMENTS The research was co-financed by the Italian Ministry of University, by means of the PRIN 2006 PRIN program, within the research project entitled ‘Definition of critical rainfall thresholds for destructive landslides for civil protection purposes'. REFERENCES Cowpertwait, P.S.P., Kilsby, C.G. and O'Connell, P.E., 2002. A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes, Water Resources Research, 38(8):1-14. Salas, J.D., 1992. Analysis and modeling of hydrological time series, in D.R. Maidment, ed., Handbook of Hydrology, McGraw-Hill, New York. Heneker, T.M., Lambert, M.F. and Kuczera G., 2001. A point rainfall model for risk-based design, Journal of Hydrology, 247(1-2):54-71.

Forecasting and analyzing high O3 time series in educational area through an improved chaotic approach

NASA Astrophysics Data System (ADS)

Hamid, Nor Zila Abd; Adenan, Nur Hamiza; Noorani, Mohd Salmi Md

2017-08-01

Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and Cao method identified the presence of low-dimensional chaotic dynamics in the observed O3 time series. Results showed that at least seven factors affect the studied O3 time series, which is consistent with the listed factors from the diurnal variations investigation and the sensitivity analysis from past studies. In conclusion, chaotic approach has been successfully forecast and analyzes the O3 time series in educational area of Shah Alam. These findings are expected to help stakeholders such as Ministry of Education and Department of Environment in having a better air pollution management.

Application of the Allan Variance to Time Series Analysis in Astrometry and Geodesy: A Review.

PubMed

Malkin, Zinovy

2016-04-01

The Allan variance (AVAR) was introduced 50 years ago as a statistical tool for assessing the frequency standards deviations. For the past decades, AVAR has increasingly been used in geodesy and astrometry to assess the noise characteristics in geodetic and astrometric time series. A specific feature of astrometric and geodetic measurements, as compared with clock measurements, is that they are generally associated with uncertainties; thus, an appropriate weighting should be applied during data analysis. In addition, some physically connected scalar time series naturally form series of multidimensional vectors. For example, three station coordinates time series X, Y, and Z can be combined to analyze 3-D station position variations. The classical AVAR is not intended for processing unevenly weighted and/or multidimensional data. Therefore, AVAR modifications, namely weighted AVAR (WAVAR), multidimensional AVAR (MAVAR), and weighted multidimensional AVAR (WMAVAR), were introduced to overcome these deficiencies. In this paper, a brief review is given of the experience of using AVAR and its modifications in processing astrogeodetic time series.

Analysis of Zenith Tropospheric Delay above Europe based on long time series derived from the EPN data

NASA Astrophysics Data System (ADS)

Baldysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; Kroszczynski, Krzysztof; Araszkiewicz, Andrzej

2015-04-01

In recent years, the GNSS system began to play an increasingly important role in the research related to the climate monitoring. Based on the GPS system, which has the longest operational capability in comparison with other systems, and a common computational strategy applied to all observations, long and homogeneous ZTD (Zenith Tropospheric Delay) time series were derived. This paper presents results of analysis of 16-year ZTD time series obtained from the EPN (EUREF Permanent Network) reprocessing performed by the Military University of Technology. To maintain the uniformity of data, analyzed period of time (1998-2013) is exactly the same for all stations - observations carried out before 1998 were removed from time series and observations processed using different strategy were recalculated according to the MUT LAC approach. For all 16-year time series (59 stations) Lomb-Scargle periodograms were created to obtain information about the oscillations in ZTD time series. Due to strong annual oscillations which disturb the character of oscillations with smaller amplitude and thus hinder their investigation, Lomb-Scargle periodograms for time series with the deleted annual oscillations were created in order to verify presence of semi-annual, ter-annual and quarto-annual oscillations. Linear trend and seasonal components were estimated using LSE (Least Square Estimation) and Mann-Kendall trend test were used to confirm the presence of linear trend designated by LSE method. In order to verify the effect of the length of time series on the estimated size of the linear trend, comparison between two different length of ZTD time series was performed. To carry out a comparative analysis, 30 stations which have been operating since 1996 were selected. For these stations two periods of time were analyzed: shortened 16-year (1998-2013) and full 18-year (1996-2013). For some stations an additional two years of observations have significant impact on changing the size of linear trend - only for 4 stations the size of linear trend was exactly the same for two periods of time. In one case, the nature of the trend has changed from negative (16-year time series) for positive (18-year time series). The average value of a linear trends for 16-year time series is 1,5 mm/decade, but their spatial distribution is not uniform. The average value of linear trends for all 18-year time series is 2,0 mm/decade, with better spatial distribution and smaller discrepancies.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

NPKS uptake, sensing, and signaling and miRNAs in plant nutrient stress.

PubMed

Nath, Manoj; Tuteja, Narendra

2016-05-01

Sessile nature of higher plants consequently makes it highly adaptable for nutrient absorption and acquisition from soil. Plants require 17 essential elements for their growth and development which include 14 minerals (macronutrients: N, P, K, Mg, Ca, S; micronutrients: Cl, Fe, B, Mn, Zn, Cu, Ni, Mo) and 3 non-mineral (C, H, O) elements. The roots of higher plants must acquire these macronutrients and micronutrients from rhizosphere and further allocate to other plant parts for completing their life cycle. Plants evolved an intricate series of signaling and sensing cascades to maintain nutrient homeostasis and to cope with nutrient stress/availability. The specific receptors for nutrients in root, root system architecture, and internal signaling pathways help to develop plasticity in response to the nutrient starvation. Nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) are essential for various metabolic processes, and their deficiency negatively effects the plant growth and yield. Genes coding for transporters and receptors for nutrients as well as some small non-coding RNAs have been implicated in nutrient uptake and signaling. This review summarizes the N, P, K, and S uptake, sensing and signaling events in nutrient stress condition especially in model plant Arabidopsis thaliana and involvement of microRNAs in nutrient deficiency. This article also provides a framework of uptake, sensing, signaling and to highlight the microRNA as an emerging major players in nutrient stress condition. Nutrient-plant-miRNA cross talk may help plant to cope up nutrient stress, and understanding their precise mechanism(s) will be necessary to develop high yielding smart crop with low nutrient input.

MEM spectral analysis for predicting influenza epidemics in Japan.

PubMed

Sumi, Ayako; Kamo, Ken-ichi

2012-03-01

The prediction of influenza epidemics has long been the focus of attention in epidemiology and mathematical biology. In this study, we tested whether time series analysis was useful for predicting the incidence of influenza in Japan. The method of time series analysis we used consists of spectral analysis based on the maximum entropy method (MEM) in the frequency domain and the nonlinear least squares method in the time domain. Using this time series analysis, we analyzed the incidence data of influenza in Japan from January 1948 to December 1998; these data are unique in that they covered the periods of pandemics in Japan in 1957, 1968, and 1977. On the basis of the MEM spectral analysis, we identified the periodic modes explaining the underlying variations of the incidence data. The optimum least squares fitting (LSF) curve calculated with the periodic modes reproduced the underlying variation of the incidence data. An extension of the LSF curve could be used to predict the incidence of influenza quantitatively. Our study suggested that MEM spectral analysis would allow us to model temporal variations of influenza epidemics with multiple periodic modes much more effectively than by using the method of conventional time series analysis, which has been used previously to investigate the behavior of temporal variations in influenza data.

Modified cross sample entropy and surrogate data analysis method for financial time series

NASA Astrophysics Data System (ADS)

Yin, Yi; Shang, Pengjian

2015-09-01

For researching multiscale behaviors from the angle of entropy, we propose a modified cross sample entropy (MCSE) and combine surrogate data analysis with it in order to compute entropy differences between original dynamics and surrogate series (MCSDiff). MCSDiff is applied to simulated signals to show accuracy and then employed to US and Chinese stock markets. We illustrate the presence of multiscale behavior in the MCSDiff results and reveal that there are synchrony containing in the original financial time series and they have some intrinsic relations, which are destroyed by surrogate data analysis. Furthermore, the multifractal behaviors of cross-correlations between these financial time series are investigated by multifractal detrended cross-correlation analysis (MF-DCCA) method, since multifractal analysis is a multiscale analysis. We explore the multifractal properties of cross-correlation between these US and Chinese markets and show the distinctiveness of NQCI and HSI among the markets in their own region. It can be concluded that the weaker cross-correlation between US markets gives the evidence for the better inner mechanism in the US stock markets than that of Chinese stock markets. To study the multiscale features and properties of financial time series can provide valuable information for understanding the inner mechanism of financial markets.

A better understanding of long-range temporal dependence of traffic flow time series

NASA Astrophysics Data System (ADS)

Feng, Shuo; Wang, Xingmin; Sun, Haowei; Zhang, Yi; Li, Li

2018-02-01

Long-range temporal dependence is an important research perspective for modelling of traffic flow time series. Various methods have been proposed to depict the long-range temporal dependence, including autocorrelation function analysis, spectral analysis and fractal analysis. However, few researches have studied the daily temporal dependence (i.e. the similarity between different daily traffic flow time series), which can help us better understand the long-range temporal dependence, such as the origin of crossover phenomenon. Moreover, considering both types of dependence contributes to establishing more accurate model and depicting the properties of traffic flow time series. In this paper, we study the properties of daily temporal dependence by simple average method and Principal Component Analysis (PCA) based method. Meanwhile, we also study the long-range temporal dependence by Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). The results show that both the daily and long-range temporal dependence exert considerable influence on the traffic flow series. The DFA results reveal that the daily temporal dependence creates crossover phenomenon when estimating the Hurst exponent which depicts the long-range temporal dependence. Furthermore, through the comparison of the DFA test, PCA-based method turns out to be a better method to extract the daily temporal dependence especially when the difference between days is significant.

Detecting PM2.5's Correlations between Neighboring Cities Using a Time-Lagged Cross-Correlation Coefficient.

PubMed

Wang, Fang; Wang, Lin; Chen, Yuming

2017-08-31

In order to investigate the time-dependent cross-correlations of fine particulate (PM2.5) series among neighboring cities in Northern China, in this paper, we propose a new cross-correlation coefficient, the time-lagged q-L dependent height crosscorrelation coefficient (denoted by p q (τ, L)), which incorporates the time-lag factor and the fluctuation amplitude information into the analogous height cross-correlation analysis coefficient. Numerical tests are performed to illustrate that the newly proposed coefficient ρ q (τ, L) can be used to detect cross-correlations between two series with time lags and to identify different range of fluctuations at which two series possess cross-correlations. Applying the new coefficient to analyze the time-dependent cross-correlations of PM2.5 series between Beijing and the three neighboring cities of Tianjin, Zhangjiakou, and Baoding, we find that time lags between the PM2.5 series with larger fluctuations are longer than those between PM2.5 series withsmaller fluctuations. Our analysis also shows that cross-correlations between the PM2.5 series of two neighboring cities are significant and the time lags between two PM2.5 series of neighboring cities are significantly non-zero. These findings providenew scientific support on the view that air pollution in neighboring cities can affect one another not simultaneously but with a time lag.

Multiscale structure of time series revealed by the monotony spectrum.

PubMed

Vamoş, Călin

2017-03-01

Observation of complex systems produces time series with specific dynamics at different time scales. The majority of the existing numerical methods for multiscale analysis first decompose the time series into several simpler components and the multiscale structure is given by the properties of their components. We present a numerical method which describes the multiscale structure of arbitrary time series without decomposing them. It is based on the monotony spectrum defined as the variation of the mean amplitude of the monotonic segments with respect to the mean local time scale during successive averagings of the time series, the local time scales being the durations of the monotonic segments. The maxima of the monotony spectrum indicate the time scales which dominate the variations of the time series. We show that the monotony spectrum can correctly analyze a diversity of artificial time series and can discriminate the existence of deterministic variations at large time scales from the random fluctuations. As an application we analyze the multifractal structure of some hydrological time series.

Cross-Sectional Time Series Designs: A General Transformation Approach.

ERIC Educational Resources Information Center

Velicer, Wayne F.; McDonald, Roderick P.

1991-01-01

The general transformation approach to time series analysis is extended to the analysis of multiple unit data by the development of a patterned transformation matrix. The procedure includes alternatives for special cases and requires only minor revisions in existing computer software. (SLD)

Multidimensional stock network analysis: An Escoufier's RV coefficient approach

NASA Astrophysics Data System (ADS)

Lee, Gan Siew; Djauhari, Maman A.

2013-09-01

The current practice of stocks network analysis is based on the assumption that the time series of closed stock price could represent the behaviour of the each stock. This assumption leads to consider minimal spanning tree (MST) and sub-dominant ultrametric (SDU) as an indispensible tool to filter the economic information contained in the network. Recently, there is an attempt where researchers represent stock not only as a univariate time series of closed price but as a bivariate time series of closed price and volume. In this case, they developed the so-called multidimensional MST to filter the important economic information. However, in this paper, we show that their approach is only applicable for that bivariate time series only. This leads us to introduce a new methodology to construct MST where each stock is represented by a multivariate time series. An example of Malaysian stock exchange will be presented and discussed to illustrate the advantages of the method.

Programmable Logic Application Notes

NASA Technical Reports Server (NTRS)

Katz, Richard

2000-01-01

This column will be provided each quarter as a source for reliability, radiation results, NASA capabilities, and other information on programmable logic devices and related applications. This quarter will continue a series of notes concentrating on analysis techniques with this issue's section discussing: Digital Timing Analysis Tools and Techniques. Articles in this issue include: SX and SX-A Series Devices Power Sequencing; JTAG and SXISX-AISX-S Series Devices; Analysis Techniques (i.e., notes on digital timing analysis tools and techniques); Status of the Radiation Hard reconfigurable Field Programmable Gate Array Program, Input Transition Times; Apollo Guidance Computer Logic Study; RT54SX32S Prototype Data Sets; A54SX32A - 0.22 micron/UMC Test Results; Ramtron FM1608 FRAM; and Analysis of VHDL Code and Synthesizer Output.

Weighted combination of LOD values oa splitted into frequency windows

NASA Astrophysics Data System (ADS)

Fernandez, L. I.; Gambis, D.; Arias, E. F.

In this analysis a one-day combined time series of LOD(length-of-day) estimates is presented. We use individual data series derived by 7 GPS and 3 SLR analysis centers, which routinely contribute to the IERS database over a recent 27-month period (Jul 1996 - Oct 1998). The result is compared to the multi-technique combined series C04 produced by the Central Bureau of the IERS that is commonly used as a reference for the study of the phenomena of Earth rotation variations. The Frequency Windows Combined Series procedure brings out a time series, which is close to C04 but shows an amplitude difference that might explain the evident periodic behavior present in the differences of these two combined series. This method could be useful to generate a new time series to be used as a reference in the high frequency variations of the Earth rotation studies.

Adventures in Modern Time Series Analysis: From the Sun to the Crab Nebula and Beyond

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey

2014-01-01

With the generation of long, precise, and finely sampled time series the Age of Digital Astronomy is uncovering and elucidating energetic dynamical processes throughout the Universe. Fulfilling these opportunities requires data effective analysis techniques rapidly and automatically implementing advanced concepts. The Time Series Explorer, under development in collaboration with Tom Loredo, provides tools ranging from simple but optimal histograms to time and frequency domain analysis for arbitrary data modes with any time sampling. Much of this development owes its existence to Joe Bredekamp and the encouragement he provided over several decades. Sample results for solar chromospheric activity, gamma-ray activity in the Crab Nebula, active galactic nuclei and gamma-ray bursts will be displayed.

Population dietary habits and physical activity modification with age.

PubMed

Schröder, H; Marrugat, J; Covas, M; Elosua, R; Pena, A; Weinbrenner, T; Fito, M; Vidal, M A; Masia, R

2004-02-01

The aim of the present study was to analyse the relation between age and both dietary habits and leisure-time physical activity, and to determine nutrient inadequacy of aged groups in our population. Cross-sectional study. A random sample of the 25-74-y-old population of Gerona, Spain. A total of 838 men and 910 women were selected from among the general population according to the 1991 census. Analysis of dietary habits, including amount and type of alcohol consumption, and detailed evaluation of leisure-time physical activity. Nutrient densities of carbohydrates, vitamin B(1), vitamin B(12), vitamin C, vitamin E, folate, potassium, iron, magnesium, copper, and dietary fiber increased significantly (P<0.05) with age in both genders, whereas an inverse trend was observed for total fat, saturated fatty acids, cholesterol, and sodium. Multiple linear regression analysis revealed a direct association of healthy dietary habits, characterized through a composite dietary score, with age after adjusting for several confounders both in men and women (P<0.001). This score was composed of folate, vitamin C, vitamin E, beta-carotene, dietary fibre, cholesterol, saturated fatty acids, and sodium. In all, 29 and 10% of male and female subjects aged 65-74 y, respectively, reported inadequate intakes of six or more of 16 nutrients. Total leisure-time physical activity increased with age in men (P<0.002), and was not different among female age groups. Dietary behaviours and levels of physical activity spent during leisure time indicate a healthy lifestyle of the aged men and women in the present population. Nutrient inadequacy observed in some aged men and women, however, deserves particular intervention of health-care programmes for this growing part of our society.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

PubMed

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

A Space Affine Matching Approach to fMRI Time Series Analysis.

PubMed

Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili

2016-07-01

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.

Statistical tools for analysis and modeling of cosmic populations and astronomical time series: CUDAHM and TSE

NASA Astrophysics Data System (ADS)

Loredo, Thomas; Budavari, Tamas; Scargle, Jeffrey D.

2018-01-01

This presentation provides an overview of open-source software packages addressing two challenging classes of astrostatistics problems. (1) CUDAHM is a C++ framework for hierarchical Bayesian modeling of cosmic populations, leveraging graphics processing units (GPUs) to enable applying this computationally challenging paradigm to large datasets. CUDAHM is motivated by measurement error problems in astronomy, where density estimation and linear and nonlinear regression must be addressed for populations of thousands to millions of objects whose features are measured with possibly complex uncertainties, potentially including selection effects. An example calculation demonstrates accurate GPU-accelerated luminosity function estimation for simulated populations of $10^6$ objects in about two hours using a single NVIDIA Tesla K40c GPU. (2) Time Series Explorer (TSE) is a collection of software in Python and MATLAB for exploratory analysis and statistical modeling of astronomical time series. It comprises a library of stand-alone functions and classes, as well as an application environment for interactive exploration of times series data. The presentation will summarize key capabilities of this emerging project, including new algorithms for analysis of irregularly-sampled time series.

A novel weight determination method for time series data aggregation

NASA Astrophysics Data System (ADS)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

A Markovian Entropy Measure for the Analysis of Calcium Activity Time Series

PubMed Central

Rahman, Atiqur; Odorizzi, Laura; LeFew, Michael C.; Golino, Caroline A.; Kemper, Peter; Saha, Margaret S.

2016-01-01

Methods to analyze the dynamics of calcium activity often rely on visually distinguishable features in time series data such as spikes, waves, or oscillations. However, systems such as the developing nervous system display a complex, irregular type of calcium activity which makes the use of such methods less appropriate. Instead, for such systems there exists a class of methods (including information theoretic, power spectral, and fractal analysis approaches) which use more fundamental properties of the time series to analyze the observed calcium dynamics. We present a new analysis method in this class, the Markovian Entropy measure, which is an easily implementable calcium time series analysis method which represents the observed calcium activity as a realization of a Markov Process and describes its dynamics in terms of the level of predictability underlying the transitions between the states of the process. We applied our and other commonly used calcium analysis methods on a dataset from Xenopus laevis neural progenitors which displays irregular calcium activity and a dataset from murine synaptic neurons which displays activity time series that are well-described by visually-distinguishable features. We find that the Markovian Entropy measure is able to distinguish between biologically distinct populations in both datasets, and that it can separate biologically distinct populations to a greater extent than other methods in the dataset exhibiting irregular calcium activity. These results support the benefit of using the Markovian Entropy measure to analyze calcium dynamics, particularly for studies using time series data which do not exhibit easily distinguishable features. PMID:27977764

Delay differential analysis of time series.

PubMed

Lainscsek, Claudia; Sejnowski, Terrence J

2015-03-01

Nonlinear dynamical system analysis based on embedding theory has been used for modeling and prediction, but it also has applications to signal detection and classification of time series. An embedding creates a multidimensional geometrical object from a single time series. Traditionally either delay or derivative embeddings have been used. The delay embedding is composed of delayed versions of the signal, and the derivative embedding is composed of successive derivatives of the signal. The delay embedding has been extended to nonuniform embeddings to take multiple timescales into account. Both embeddings provide information on the underlying dynamical system without having direct access to all the system variables. Delay differential analysis is based on functional embeddings, a combination of the derivative embedding with nonuniform delay embeddings. Small delay differential equation (DDE) models that best represent relevant dynamic features of time series data are selected from a pool of candidate models for detection or classification. We show that the properties of DDEs support spectral analysis in the time domain where nonlinear correlation functions are used to detect frequencies, frequency and phase couplings, and bispectra. These can be efficiently computed with short time windows and are robust to noise. For frequency analysis, this framework is a multivariate extension of discrete Fourier transform (DFT), and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short or sparse time series and can be extended to cross-trial and cross-channel spectra if multiple short data segments of the same experiment are available. Together, this time-domain toolbox provides higher temporal resolution, increased frequency and phase coupling information, and it allows an easy and straightforward implementation of higher-order spectra across time compared with frequency-based methods such as the DFT and cross-spectral analysis.

Statistical Analysis of Time-Series from Monitoring of Active Volcanic Vents

NASA Astrophysics Data System (ADS)

Lachowycz, S.; Cosma, I.; Pyle, D. M.; Mather, T. A.; Rodgers, M.; Varley, N. R.

2016-12-01

Despite recent advances in the collection and analysis of time-series from volcano monitoring, and the resulting insights into volcanic processes, challenges remain in forecasting and interpreting activity from near real-time analysis of monitoring data. Statistical methods have potential to characterise the underlying structure and facilitate intercomparison of these time-series, and so inform interpretation of volcanic activity. We explore the utility of multiple statistical techniques that could be widely applicable to monitoring data, including Shannon entropy and detrended fluctuation analysis, by their application to various data streams from volcanic vents during periods of temporally variable activity. Each technique reveals changes through time in the structure of some of the data that were not apparent from conventional analysis. For example, we calculate the Shannon entropy (a measure of the randomness of a signal) of time-series from the recent dome-forming eruptions of Volcán de Colima (Mexico) and Soufrière Hills (Montserrat). The entropy of real-time seismic measurements and the count rate of certain volcano-seismic event types from both volcanoes is found to be temporally variable, with these data generally having higher entropy during periods of lava effusion and/or larger explosions. In some instances, the entropy shifts prior to or coincident with changes in seismic or eruptive activity, some of which were not clearly recognised by real-time monitoring. Comparison with other statistics demonstrates the sensitivity of the entropy to the data distribution, but that it is distinct from conventional statistical measures such as coefficient of variation. We conclude that each analysis technique examined could provide valuable insights for interpretation of diverse monitoring time-series.

A Plasmonic Mass Spectrometry Approach for Detection of Small Nutrients and Toxins

NASA Astrophysics Data System (ADS)

Wu, Shu; Qian, Linxi; Huang, Lin; Sun, Xuming; Su, Haiyang; Gurav, Deepanjali D.; Jiang, Mawei; Cai, Wei; Qian, Kun

2018-07-01

Nutriology relies on advanced analytical tools to study the molecular compositions of food and provide key information on sample quality/safety. Small nutrients detection is challenging due to the high diversity and broad dynamic range of molecules in food samples, and a further issue is to track low abundance toxins. Herein, we developed a novel plasmonic matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) approach to detect small nutrients and toxins in complex biological emulsion samples. Silver nanoshells (SiO2@Ag) with optimized structures were used as matrices and achieved direct analysis of 6 nL of human breast milk without any enrichment or separation. We performed identification and quantitation of small nutrients and toxins with limit-of-detection down to 0.4 pmol (for melamine) and reaction time shortened to minutes, which is superior to the conventional biochemical method currently in use. The developed approach contributes to the near-future application of MALDI MS in a broad field and personalized design of plasmonic materials for real-case bio-analysis.[Figure not available: see fulltext.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Microstructural and associated chemical changes during the composting of a high temperature biochar: Mechanisms for nitrate, phosphate and other nutrient retention and release.

PubMed

Joseph, Stephen; Kammann, Claudia I; Shepherd, Jessica G; Conte, Pellegrino; Schmidt, Hans-Peter; Hagemann, Nikolas; Rich, Anne M; Marjo, Christopher E; Allen, Jessica; Munroe, Paul; Mitchell, David R G; Donne, Scott; Spokas, Kurt; Graber, Ellen R

2018-03-15

Recent studies have demonstrated the importance of the nutrient status of biochar and soils prior to its inclusion in particular agricultural systems. Pre-treatment of nutrient-reactive biochar, where nutrients are loaded into pores and onto surfaces, gives improved yield outcomes compared to untreated biochar. In this study we have used a wide selection of spectroscopic and microscopic techniques to investigate the mechanisms of nutrient retention in a high temperature wood biochar, which had negative effects on Chenopodium quinoa above ground biomass yield when applied to the system without prior nutrient loading, but positive effects when applied after composting. We have compared non-composted biochar (BC) with composted biochar (BCC) to elucidate the differences which may have led to these results. The results of our investigation provide evidence for a complex series of reactions during composting, where dissolved nutrients are first taken up into biochar pores along a concentration gradient and through capillary action, followed by surface sorption and retention processes which block biochar pores and result in deposition of a nutrient-rich organomineral (plaque) layer. The lack of such pretreatment in the BC samples would render it reactive towards nutrients in a soil-fertilizer system, making it a competitor for, rather than provider of, nutrients for plant growth. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis of native water, bed material, and elutriate samples of major Louisiana waterways, 1975

USGS Publications Warehouse

Demas, Charles R.

1976-01-01

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, conducted a series of elutriate studies in selected reaches of major navigable waterways of Louisiana. As defined by the U.S. Environmental Protection Agency, an elutriate is the supernatant resulting from the vigorous 30-minute shaking of one part bottom sediment from the dredging site with four parts water (vol/vol) collected from the dredging site followed by one hour settling time and appropriate centrifugation and a 0.45-micron filtration. The elutriate studies were initiated to evaluate possible environmental effects of proposed dredging activities in selected reaches of Louisiana waterways. The waterways investigated were the Mississippi River-Gulf Outlet, Breton Sound, Mississippi River downstream from Baton Rouge, Bayou Long, Intracoastal Waterway (east and west of the Harvey Canal), Three Rivers area, Ouachita River, Barataria Bay, Houma Navigation Canal, Atchafalaya Bay (Ship Channel), Berwick Bay, Intracoastal Waterway (Port Allen to Morgan City), Petite Anse area, and Calcasieu River and Ship Channel. The Geological Survey collected 227 samples of native water and bed (bottom) material from 130 different sites. These samples (as well as elutriates prepared from mixtures of native water and bed material) were analyzed for selected metal, pesticide, nutrient, and organic constituents. An additional 116 bed samples collected at 58 sites were analyzed for selected pesticides; and 4 additional native-water samples from 2 sites were analyzed for selected metal pesticide, nutrient, and organic constituents. (Woodard-USGS)

Wavelet application to the time series analysis of DORIS station coordinates

NASA Astrophysics Data System (ADS)

Bessissi, Zahia; Terbeche, Mekki; Ghezali, Boualem

2009-06-01

The topic developed in this article relates to the residual time series analysis of DORIS station coordinates using the wavelet transform. Several analysis techniques, already developed in other disciplines, were employed in the statistical study of the geodetic time series of stations. The wavelet transform allows one, on the one hand, to provide temporal and frequential parameter residual signals, and on the other hand, to determine and quantify systematic signals such as periodicity and tendency. Tendency is the change in short or long term signals; it is an average curve which represents the general pace of the signal evolution. On the other hand, periodicity is a process which is repeated, identical to itself, after a time interval called the period. In this context, the topic of this article consists, on the one hand, in determining the systematic signals by wavelet analysis of time series of DORIS station coordinates, and on the other hand, in applying the denoising signal to the wavelet packet, which makes it possible to obtain a well-filtered signal, smoother than the original signal. The DORIS data used in the treatment are a set of weekly residual time series from 1993 to 2004 from eight stations: DIOA, COLA, FAIB, KRAB, SAKA, SODB, THUB and SYPB. It is the ign03wd01 solution expressed in stcd format, which is derived by the IGN/JPL analysis center. Although these data are not very recent, the goal of this study is to detect the contribution of the wavelet analysis method on the DORIS data, compared to the other analysis methods already studied.

Pesticides in the Nation's Streams and Ground Water, 1992-2001

USGS Publications Warehouse

Gilliom, Robert J.; Barbash, Jack E.; Crawford, Charles G.; Hamilton, Pixie A.; Martin, Jeffrey D.; Nakagaki, Naomi; Nowell, Lisa H.; Scott, Jonathan C.; Stackelberg, Paul E.; Thelin, Gail P.; Wolock, David M.

2006-01-01

This report is one of a series of publications, The Quality of Our Nation's Waters, that describe major findings of the NAWQA Program on water-quality issues of regional and national concern. This report presents evaluations of pesticides in streams and ground water based on findings for the first decadal cycle of NAWQA. 'Pesticides in the Nation's Streams and Ground Water, 1992-2001' greatly expands the analysis of pesticides presented in 'Nutrients and Pesticides,' which was the first report in the series and was based on early results from 1992 to 1995. Other reports in this series cover additional water-quality constituents of concern, such as volatile organic compounds and trace elements, as well as physical and chemical effects on aquatic ecosystems. Each report builds toward a more comprehensive understanding of regional and national water resources. The information in this series is intended primarily for those interested or involved in resource management, conservation, regulation, and policymaking at regional and national levels. In addition, the information might interest those at a local level who wish to know more about the general quality of streams and ground water in areas near where they live and how that quality compares with other areas across the Nation.

Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction

NASA Astrophysics Data System (ADS)

Moeeni, Hamid; Bonakdari, Hossein; Fatemi, Seyed Ehsan

2017-04-01

Because time series stationarization has a key role in stochastic modeling results, three methods are analyzed in this study. The methods are seasonal differencing, seasonal standardization and spectral analysis to eliminate the periodic effect on time series stationarity. First, six time series including 4 streamflow series and 2 water temperature series are stationarized. The stochastic term for these series obtained with ARIMA is subsequently modeled. For the analysis, 9228 models are introduced. It is observed that seasonal standardization and spectral analysis eliminate the periodic term completely, while seasonal differencing maintains seasonal correlation structures. The obtained results indicate that all three methods present acceptable performance overall. However, model accuracy in monthly streamflow prediction is higher with seasonal differencing than with the other two methods. Another advantage of seasonal differencing over the other methods is that the monthly streamflow is never estimated as negative. Standardization is the best method for predicting monthly water temperature although it is quite similar to seasonal differencing, while spectral analysis performed the weakest in all cases. It is concluded that for each monthly seasonal series, seasonal differencing is the best stationarization method in terms of periodic effect elimination. Moreover, the monthly water temperature is predicted with more accuracy than monthly streamflow. The criteria of the average stochastic term divided by the amplitude of the periodic term obtained for monthly streamflow and monthly water temperature were 0.19 and 0.30, 0.21 and 0.13, and 0.07 and 0.04 respectively. As a result, the periodic term is more dominant than the stochastic term for water temperature in the monthly water temperature series compared to streamflow series.

Assessment of nutrient enrichment by use of algal-, invertebrate-, and fish-community attributes in wadeable streams in ecoregions surrounding the Great Lakes

USGS Publications Warehouse

Frey, Jeffrey W.; Bell, Amanda H.; Hambrook Berkman, Julie A.; Lorenz, David L.

2011-01-01

The algal, invertebrate, and fish taxa and community attributes that best reflect the effects of nutrients along a gradient of low to high nutrient concentrations in wadeable, primarily midwestern streams were determined as part of the U.S. Geological Suvey's National Water-Quality Assessment (NAWQA) Program. Nutrient data collected from 64 sampling sites that reflected reference, agricultural, and urban influences between 1993 and 2006 were used to represent the nutrient gradient within Nutrient Ecoregion VI (Cornbelt and Northern Great Plains), VII (Mostly Glaciated Dairy Region), and VIII (Nutrient Poor Largely Glaciated Upper Midwest and Northeast). Nutrient Ecoregions VII and VIII comprise the Glacial North diatom ecoregion (GNE) and Nutrient Ecoregion VI represents the Central and Western Plains diatom ecoregion (CWPE). The diatom-ecoregion groupings were used chiefly for data analysis. The total nitrogen (TN) and total phosphorus (TP) data from 64 sites, where at least 6 nutrient samples were collected within a year at each site, were used to classify the sites into low-, medium-, and high-nutrient categories based upon the 10th and 75th percentiles of for sites within each Nutrient Ecoregion. In general, TN and TP concentrations were 3-5 times greater in Nutrient Ecoregion VI than in Nutrient Ecoregions VII and VIII. A subgroup of 54 of these 64 sites had algal-, invertebrate-, and fish-community data that were collected within the same year as the nutrients; these sites were used to assess the effects of nutrients on the biological communities. Multidimensional scaling was used to determine whether the entire region could be assessed together or whether there were regional differences between the algal, invertebrate, and fish communities. The biological communities were significantly different between the northern sites, primarily in the GNE and the southern sites, primarily in the CWPE. In the higher nutrient concentration gradient in the streams of the CWPE, algae exhibited greater differences than invertebrates and fish between all of the nutrient categories for both TN and TP; however, in the lower nutrient gradient in the streams of the GNE, invertebrates exhibited greater differences between the nutrient categories. Certain species of algae, invertebrates, and fish were more prevalent in low- and high-nutrient categories within each of the diatom ecoregions. Breakpoint analysis was used to identify the concentration at which the relations between the response variable (biological attribute) and the stressor variable (TN and TP) change. There were significant breakpoints for nutrients (TN and TP) and multiple attributes for algae, invertebrates, and fish communities within the CWPE and GNE diatom ecoregions. In general, more significant breakpoints, with lower concentrations, were found in the GNE than the more nutrient-rich CWPE. The breakpoints from all biological communities were generally about 3-5 times higher in the south (CWPE) than the north (GNE). In the north, breakpoints with similar lower concentrations were found for TN from all biological communities (around 0.60 milligram per liter) and for TP (between 0.02 and 0.03 milligram per liter) for the algae and invertebrate communities. The findings from our study suggest that the range in breakpoints for TN and TP from the GNE can be used as oligotrophic and eutrophic boundaries derived from biological response based on this ecoregion having (1) a gradient with sufficiently low to high nutrient concentrations, (2) distinctive differences in the biological communities in the low- to high-nutrient streams, (3) similarity of breakpoints within algal, invertebrate, and fish communities, (4) significant attributes with either direct relations to nutrients or traditional changes in community structure (that is, decreases in sensitive species or increases in tolerant species), and (5) similar breakpoints in other studies in this and other regions. In nutrie

From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation

NASA Astrophysics Data System (ADS)

Scarsoglio, Stefania; Cazzato, Fabio; Ridolfi, Luca

2017-09-01

A network-based approach is presented to investigate the cerebrovascular flow patterns during atrial fibrillation (AF) with respect to normal sinus rhythm (NSR). AF, the most common cardiac arrhythmia with faster and irregular beating, has been recently and independently associated with the increased risk of dementia. However, the underlying hemodynamic mechanisms relating the two pathologies remain mainly undetermined so far; thus, the contribution of modeling and refined statistical tools is valuable. Pressure and flow rate temporal series in NSR and AF are here evaluated along representative cerebral sites (from carotid arteries to capillary brain circulation), exploiting reliable artificially built signals recently obtained from an in silico approach. The complex network analysis evidences, in a synthetic and original way, a dramatic signal variation towards the distal/capillary cerebral regions during AF, which has no counterpart in NSR conditions. At the large artery level, networks obtained from both AF and NSR hemodynamic signals exhibit elongated and chained features, which are typical of pseudo-periodic series. These aspects are almost completely lost towards the microcirculation during AF, where the networks are topologically more circular and present random-like characteristics. As a consequence, all the physiological phenomena at the microcerebral level ruled by periodicity—such as regular perfusion, mean pressure per beat, and average nutrient supply at the cellular level—can be strongly compromised, since the AF hemodynamic signals assume irregular behaviour and random-like features. Through a powerful approach which is complementary to the classical statistical tools, the present findings further strengthen the potential link between AF hemodynamic and cognitive decline.

Spent mushroom substrate biochar as a potential amendment in pig manure and rice straw composting processes.

PubMed

Chang, Ken-Lin; Chen, Xi-Mei; Sun, Jian; Liu, Jing-Yong; Sun, Shui-Yu; Yang, Zuo-Yi; Wang, Yin

2017-07-01

Spent mushroom substrate (SMS) is a bulky waste byproduct of commercial mushroom production, which can cause serious environmental problems and, therefore, poses a significant barrier to future expansion of the mushroom industry. In the present study, we explored the use of SMS as a biochar to improve the quality of bio-fertilizer. Specifically, we performed a series of experiments using composting reactors to investigate the effects of SMS biochar on the physio-chemical properties of bio-fertilizer. Biochar was derived from dry SMS pyrolysed at 500°C and mixed with pig manure and rice straw. Results from this study demonstrate that the addition of biochar significantly reduced electrical conductivity and loss of organic matter in compost material. Nutrient analysis revealed that the SMS-derived biochar is rich in fertilizer nutrients such as P, K, Na, and N. All of these findings suggest that SMS biochar could be an excellent medium for compost.

An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions

PubMed Central

Hickey, Barbara M.; Kudela, Raphael M.; Lefebvre, Kathi A.; Adams, Nicolaus G.; Bill, Brian D.; Gulland, Frances M. D.; Thomson, Richard E.; Cochlan, William P.; Trainer, Vera L.

2016-01-01

Abstract A coastwide bloom of the toxigenic diatom Pseudo‐nitzschia in spring 2015 resulted in the largest recorded outbreak of the neurotoxin, domoic acid, along the North American west coast. Elevated toxins were measured in numerous stranded marine mammals and resulted in geographically extensive and prolonged closures of razor clam, rock crab, and Dungeness crab fisheries. We demonstrate that this outbreak was initiated by anomalously warm ocean conditions. Pseudo‐nitzschia australis thrived north of its typical range in the warm, nutrient‐poor water that spanned the northeast Pacific in early 2015. The seasonal transition to upwelling provided the nutrients necessary for a large‐scale bloom; a series of spring storms delivered the bloom to the coast. Laboratory and field experiments confirming maximum growth rates with elevated temperatures and enhanced toxin production with nutrient enrichment, together with a retrospective analysis of toxic events, demonstrate the potential for similarly devastating ecological and economic disruptions in the future. PMID:27917011

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1990-01-01

Descriptive and analytical techniques for NASA trend analysis applications are presented in this standard. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. This document should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend analysis is neither a precise term nor a circumscribed methodology: it generally connotes quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this document. The basic ideas needed for qualitative and quantitative assessment of trends along with relevant examples are presented.

Stream-subsurface nutrient dynamics in a groundwater-fed stream

NASA Astrophysics Data System (ADS)

Rezanezhad, F.; Niederkorn, A.; Parsons, C. T.; Van Cappellen, P.

2015-12-01

The stream-riparian-aquifer interface plays a major role in the regional flow of nutrients and contaminants due to a strong physical-chemical gradient that promotes the transformation, retention, elimination or release of biogenic elements. To better understand the effect of the near-stream zones on stream biogeochemistry, we conducted a field study on a groundwater-fed stream located in the rare Charitable Research Reserve, Cambridge, Ontario, Canada. This study focused on monitoring the spatial and temporal distributions of nutrient elements within the riparian and hyporheic zones of the stream. Several piezometer nests and a series of passive (diffusion) water samplers, known as peepers, were installed along longitudinal and lateral transects centered on the stream to obtain data on the groundwater chemistry. Groundwater upwelling along the stream resulted in distinctly different groundwater types and associated nitrate concentrations between small distances in the riparian zone (<4m). After the upstream source of the stream surface water, concentrations of nutrients (NO3-, NH4+, SO42- and carbon) did not significantly change before the downstream outlet. Although reduction of nitrate and sulphate were found in the riparian zone of the stream, this did not significantly influence the chemistry of the adjacent stream water. Also, minimal retention in the hyporheic zones limited reduction of reactive compounds (NO3- and SO42-) within the stream channel. The results showed that the dissolved organic carbon (DOC) and residence time of water in the hyporheic zone and in surface water limited denitrification.

What Time-Series Designs May Have to Offer Educational Researchers.

ERIC Educational Resources Information Center

Kratochwill, Thomas R.; Levin, Joel R.

1978-01-01

The promise of time-series designs for educational research and evaluation is reviewed. Ten time-series designs are presented and discussed in the context of threats to internal and external validity. The advantages and disadvantages of various visual and statistical data-analysis techniques are presented. A bibliography is appended. (Author/RD)

FATS: Feature Analysis for Time Series

NASA Astrophysics Data System (ADS)

Nun, Isadora; Protopapas, Pavlos; Sim, Brandon; Zhu, Ming; Dave, Rahul; Castro, Nicolas; Pichara, Karim

2017-11-01

FATS facilitates and standardizes feature extraction for time series data; it quickly and efficiently calculates a compilation of many existing light curve features. Users can characterize or analyze an astronomical photometric database, though this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.

The Prediction of Teacher Turnover Employing Time Series Analysis.

ERIC Educational Resources Information Center

Costa, Crist H.

The purpose of this study was to combine knowledge of teacher demographic data with time-series forecasting methods to predict teacher turnover. Moving averages and exponential smoothing were used to forecast discrete time series. The study used data collected from the 22 largest school districts in Iowa, designated as FACT schools. Predictions…

Information and Complexity Measures Applied to Observed and Simulated Soil Moisture Time Series

USDA-ARS?s Scientific Manuscript database

Time series of soil moisture-related parameters provides important insights in functioning of soil water systems. Analysis of patterns within these time series has been used in several studies. The objective of this work was to compare patterns in observed and simulated soil moisture contents to u...

A likelihood-based time series modeling approach for application in dendrochronology to examine the growth-climate relations and forest disturbance history

EPA Science Inventory

A time series intervention analysis (TSIA) of dendrochronological data to infer the tree growth-climate-disturbance relations and forest disturbance history is described. Maximum likelihood is used to estimate the parameters of a structural time series model with components for ...

Testing for nonlinearity in non-stationary physiological time series.

PubMed

Guarín, Diego; Delgado, Edilson; Orozco, Álvaro

2011-01-01

Testing for nonlinearity is one of the most important preprocessing steps in nonlinear time series analysis. Typically, this is done by means of the linear surrogate data methods. But it is a known fact that the validity of the results heavily depends on the stationarity of the time series. Since most physiological signals are non-stationary, it is easy to falsely detect nonlinearity using the linear surrogate data methods. In this document, we propose a methodology to extend the procedure for generating constrained surrogate time series in order to assess nonlinearity in non-stationary data. The method is based on the band-phase-randomized surrogates, which consists (contrary to the linear surrogate data methods) in randomizing only a portion of the Fourier phases in the high frequency domain. Analysis of simulated time series showed that in comparison to the linear surrogate data method, our method is able to discriminate between linear stationarity, linear non-stationary and nonlinear time series. Applying our methodology to heart rate variability (HRV) records of five healthy patients, we encountered that nonlinear correlations are present in this non-stationary physiological signals.

The Effect on Non-Normal Distributions on the Integrated Moving Average Model of Time-Series Analysis.

ERIC Educational Resources Information Center

Doerann-George, Judith

The Integrated Moving Average (IMA) model of time series, and the analysis of intervention effects based on it, assume random shocks which are normally distributed. To determine the robustness of the analysis to violations of this assumption, empirical sampling methods were employed. Samples were generated from three populations; normal,…

Teaching Earth Signals Analysis Using the Java-DSP Earth Systems Edition: Modern and Past Climate Change

ERIC Educational Resources Information Center

Ramamurthy, Karthikeyan Natesan; Hinnov, Linda A.; Spanias, Andreas S.

2014-01-01

Modern data collection in the Earth Sciences has propelled the need for understanding signal processing and time-series analysis techniques. However, there is an educational disconnect in the lack of instruction of time-series analysis techniques in many Earth Science academic departments. Furthermore, there are no platform-independent freeware…

A new methodological approach for worldwide beryllium-7 time series analysis

NASA Astrophysics Data System (ADS)

Bianchi, Stefano; Longo, Alessandro; Plastino, Wolfango

2018-07-01

Time series analyses of cosmogenic radionuclide 7Be and 22Na atmospheric activity concentrations and meteorological data observed at twenty-five International Monitoring System (IMS) stations of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO) have shown great variability in terms of noise structures, harmonic content, cross-correlation patterns and local Hurst exponent behaviour. Noise content and its structure has been extracted and characterised for the two radionuclides time series. It has been found that the yearly component, which is present in most of the time series, is not stationary, but has a percentage weight that varies with time. Analysis of atmospheric activity concentrations of 7Be, measured at IMS stations, has shown them to be influenced by distinct meteorological patterns, mainly by atmospheric pressure and temperature.

Integrated method for chaotic time series analysis

DOEpatents

Hively, Lee M.; Ng, Esmond G.

1998-01-01

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

Parameter motivated mutual correlation analysis: Application to the study of currency exchange rates based on intermittency parameter and Hurst exponent

NASA Astrophysics Data System (ADS)

Cristescu, Constantin P.; Stan, Cristina; Scarlat, Eugen I.; Minea, Teofil; Cristescu, Cristina M.

2012-04-01

We present a novel method for the parameter oriented analysis of mutual correlation between independent time series or between equivalent structures such as ordered data sets. The proposed method is based on the sliding window technique, defines a new type of correlation measure and can be applied to time series from all domains of science and technology, experimental or simulated. A specific parameter that can characterize the time series is computed for each window and a cross correlation analysis is carried out on the set of values obtained for the time series under investigation. We apply this method to the study of some currency daily exchange rates from the point of view of the Hurst exponent and the intermittency parameter. Interesting correlation relationships are revealed and a tentative crisis prediction is presented.

A study of sound generation in subsonic rotors, volume 2

NASA Technical Reports Server (NTRS)

Chalupnik, J. D.; Clark, L. T.

1975-01-01

Computer programs were developed for use in the analysis of sound generation by subsonic rotors. Program AIRFOIL computes the spectrum of radiated sound from a single airfoil immersed in a laminar flow field. Program ROTOR extends this to a rotating frame, and provides a model for sound generation in subsonic rotors. The program also computes tone sound generation due to steady state forces on the blades. Program TONE uses a moving source analysis to generate a time series for an array of forces moving in a circular path. The resultant time series are than Fourier transformed to render the results in spectral form. Program SDATA is a standard time series analysis package. It reads in two discrete time series and forms auto and cross covariances and normalizes these to form correlations. The program then transforms the covariances to yield auto and cross power spectra by means of a Fourier transformation.

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature.

PubMed

Bhandari, Siddhartha; Bergmann, Neil; Jurdak, Raja; Kusy, Branislav

2017-05-26

Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.

On the characterization of vegetation recovery after fire disturbance using Fisher-Shannon analysis and SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series

NASA Astrophysics Data System (ADS)

Lasaponara, Rosa; Lanorte, Antonio; Lovallo, Michele; Telesca, Luciano

2015-04-01

Time series can fruitfully support fire monitoring and management from statistical analysis of fire occurrence (Tuia et al. 2008) to danger estimation (lasaponara 2005), damage evaluation (Lanorte et al 2014) and post fire recovery (Lanorte et al. 2014). In this paper, the time dynamics of SPOT-VEGETATION Normalized Difference Vegetation Index (NDVI) time series are analyzed by using the statistical approach of the Fisher-Shannon (FS) information plane to assess and monitor vegetation recovery after fire disturbance. Fisher-Shannon information plane analysis allows us to gain insight into the complex structure of a time series to quantify its degree of organization and order. The analysis was carried out using 10-day Maximum Value Composites of NDVI (MVC-NDVI) with a 1 km × 1 km spatial resolution. The investigation was performed on two test sites located in Galizia (North Spain) and Peloponnese (South Greece), selected for the vast fires which occurred during the summer of 2006 and 2007 and for their different vegetation covers made up mainly of low shrubland in Galizia test site and evergreen forest in Peloponnese. Time series of MVC-NDVI have been analyzed before and after the occurrence of the fire events. Results obtained for both the investigated areas clearly pointed out that the dynamics of the pixel time series before the occurrence of the fire is characterized by a larger degree of disorder and uncertainty; while the pixel time series after the occurrence of the fire are featured by a higher degree of organization and order. In particular, regarding the Peloponneso fire, such discrimination is more evident than in the Galizia fire. This suggests a clear possibility to discriminate the different post-fire behaviors and dynamics exhibited by the different vegetation covers. Reference Lanorte A, R Lasaponara, M Lovallo, L Telesca 2014 Fisher-Shannon information plane analysis of SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) time series to characterize vegetation recovery after fire disturbanceInternational Journal of Applied Earth Observation and Geoinformation 26 441-446 Lanorte A, M Danese, R Lasaponara, B Murgante 2014 Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis International Journal of Applied Earth Observation and Geoinformation 20, 42-51 Tuia D, F Ratle, R Lasaponara, L Telesca, M Kanevski 2008 Scan statistics analysis of forest fire clusters Communications in Nonlinear Science and Numerical Simulation 13 (8), 1689-1694 Telesca L, R Lasaponara 2006 Pre and post fire behavioral trends revealed in satellite NDVI time series Geophysical Research Letters 33 (14) Lasaponara R 2005 Intercomparison of AVHRR based fire susceptibility indicators for the Mediterranean ecosystems of southern Italy International Journal of Remote Sensing 26 (5), 853-870

Allan deviation analysis of financial return series

NASA Astrophysics Data System (ADS)

Hernández-Pérez, R.

2012-05-01

We perform a scaling analysis for the return series of different financial assets applying the Allan deviation (ADEV), which is used in the time and frequency metrology to characterize quantitatively the stability of frequency standards since it has demonstrated to be a robust quantity to analyze fluctuations of non-stationary time series for different observation intervals. The data used are opening price daily series for assets from different markets during a time span of around ten years. We found that the ADEV results for the return series at short scales resemble those expected for an uncorrelated series, consistent with the efficient market hypothesis. On the other hand, the ADEV results for absolute return series for short scales (first one or two decades) decrease following approximately a scaling relation up to a point that is different for almost each asset, after which the ADEV deviates from scaling, which suggests that the presence of clustering, long-range dependence and non-stationarity signatures in the series drive the results for large observation intervals.

Modelling bloom formation of the toxic dinoflagellates Dinophysis acuminata and Dinophysis caudata in a highly modified estuary, south eastern Australia

NASA Astrophysics Data System (ADS)

Ajani, Penelope; Larsson, Michaela E.; Rubio, Ana; Bush, Stephen; Brett, Steve; Farrell, Hazel

2016-12-01

Dinoflagellates belonging to the toxigenic genus Dinophysis are increasing in abundance in the Hawkesbury River, south-eastern Australia. This study investigates a twelve year time series of abundance and physico-chemical data to model these blooms. Four species were reported over the sampling campaign - Dinophysis acuminata, Dinophysis caudata, Dinophysis fortii and Dinophysis tripos-with D. acuminata and D. caudata being most abundant. Highest abundance of D. acuminata occurred in the austral spring (max. abundance 4500 cells l-1), whilst highest D. caudata occurred in the summer to autumn (max. 12,000 cells l-1). Generalised additive models revealed abundance of D. acuminata was significantly linked to season, thermal stratification and nutrients, whilst D. caudata was associated with nutrients, salinity and dissolved oxygen. The models' predictive capability was up to 60% for D. acuminata and 53% for D. caudata. Altering sampling strategies during blooms accompanied with in situ high resolution monitoring will further improve Dinophysis bloom prediction capability.

Retrospective analysis of bottlenose dolphin foraging: a legacy of anthropogenic ecosystem disturbance

USGS Publications Warehouse

Rossman, Sam; Barros, Nélio B.; Ostrom, Peggy H.; Stricker, Craig A.; Hohn, Aleta A.; Gandhi, Hasand; Wells, Randall S.

2013-01-01

We used stable isotope analysis to investigate the foraging ecology of coastal bottlenose dolphins (Tursiops truncatus) in relation to a series of anthropogenic disturbances. We first demonstrated that stable isotopes are a faithful indicator of habitat use by comparing muscle isotope values to behavioral foraging data from the same individuals. δ13C values increased, while δ34S and δ15N values decreased with the percentage of feeding observations in seagrass habitat. We then utilized stable isotope values of muscle to assess temporal variation in foraging habitat from 1991 to 2010 and collagen from tooth crown tips to assess the time period 1944 to 2007. From 1991 to 2010, δ13C values of muscle decreased while δ34S values increased indicating reduced utilization of seagrass habitat. From 1944 to 1989 δ13C values of the crown tip declined significantly, likely due to a reduction in the coverage of seagrass habitat and δ15N values significantly increased, a trend we attribute to nutrient loading from a rapidly increasing human population. Our results demonstrate the utility of using marine mammal foraging habits to retrospectively assess the extent to which anthropogenic disturbance impacts coastal food webs.

Annual trend patterns of phytoplankton species abundance belie homogeneous taxonomical group responses to climate in the NE Atlantic upwelling.

PubMed

Bode, Antonio; Estévez, M Graciela; Varela, Manuel; Vilar, José A

2015-09-01

Phytoplankton is a sentinel of marine ecosystem change. Composed by many species with different life-history strategies, it rapidly responds to environment changes. An analysis of the abundance of 54 phytoplankton species in Galicia (NW Spain) between 1989 and 2008 to determine the main components of temporal variability in relation to climate and upwelling showed that most of this variability was stochastic, as seasonality and long term trends contributed to relatively small fractions of the series. In general, trends appeared as non linear, and species clustered in 4 groups according to the trend pattern but there was no defined pattern for diatoms, dinoflagellates or other groups. While, in general, total abundance increased, no clear trend was found for 23 species, 14 species decreased, 4 species increased during the early 1990s, and only 13 species showed a general increase through the series. In contrast, series of local environmental conditions (temperature, stratification, nutrients) and climate-related variables (atmospheric pressure indices, upwelling winds) showed a high fraction of their variability in deterministic seasonality and trends. As a result, each species responded independently to environmental and climate variability, measured by generalized additive models. Most species showed a positive relationship with nutrient concentrations but only a few showed a direct relationship with stratification and upwelling. Climate variables had only measurable effects on some species but no common response emerged. Because its adaptation to frequent disturbances, phytoplankton communities in upwelling ecosystems appear less sensitive to changes in regional climate than other communities characterized by short and well defined productive periods. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Kalantari, Mahdi; Yarmohammadi, Masoud; Hassani, Hossein; Silva, Emmanuel Sirimal

Missing values in time series data is a well-known and important problem which many researchers have studied extensively in various fields. In this paper, a new nonparametric approach for missing value imputation in time series is proposed. The main novelty of this research is applying the L1 norm-based version of Singular Spectrum Analysis (SSA), namely L1-SSA which is robust against outliers. The performance of the new imputation method has been compared with many other established methods. The comparison is done by applying them to various real and simulated time series. The obtained results confirm that the SSA-based methods, especially L1-SSA can provide better imputation in comparison to other methods.

Nitrogen and organic carbon cycling processes in tidal marshes and shallow estuarine habitats

NASA Astrophysics Data System (ADS)

Bergamaschi, B. A.; Downing, B. D.; Pellerin, B. A.; Kraus, T. E. C.; Fleck, J.; Fujii, R.

2016-02-01

Tidal wetlands and shallow water habitats can be sites of high aquatic productivity, and they have the potential of exchanging this newly produced organic carbon with adjacent deeper habitats. Indeed, export of organic carbon from wetlands and shallow water habitats to pelagic food webs is one of the primary ecosystem functions targeted in tidal wetland restorations. Alternatively, wetlands and shallow water habitats can function as retention areas for nutrients due to the nutrient demand of emergent macrophytes and denitrification in anoxic zones. They can also remove phytoplankton and non-algal particles from the aquatic food webs because the shallower waters can result in higher rates of benthic grazing and higher settling due to lower water velocities. We conducted studies in wetland and channel sites in the San Francisco estuary (USA) to investigate the dynamics of nutrients and carbon production at a variety of temporal scales. We collected continuous time series of nutrients, oxygen, chlorophyll and pH in conjunction with continuous acoustic measurement of water velocity and discharge to provide mass controls and used simple biogeochemical models to assess rates. We found a high degree of temporal variability in individual systems, corresponding to, for example, changes in nutrient supply, water level, light level, wind, wind direction, and other physical factors. There was also large variability among the different systems, probably due to differences in flows and geomorphic features. We compare the aquatic productivity of theses environments and speculate as to the formative elements of each. Our findings demonstrate the complex interaction between physical, chemical, and biological factors that determine the type of production and degree of export from tidal wetlands and shallow water habitats, suggesting that a clearer picture of these processes is important for guiding future large scale restoration efforts.

Getting to the root of plant biology: impact of the Arabidopsis genome sequence on root research

PubMed Central

Benfey, Philip N.; Bennett, Malcolm; Schiefelbein, John

2010-01-01

Summary Prior to the availability of the genome sequence, the root of Arabidopsis had attracted a small but ardent group of researchers drawn to its accessibility and developmental simplicity. Roots are easily observed when grown on the surface of nutrient agar media, facilitating analysis of responses to stimuli such as gravity and touch. Developmental biologists were attracted to the simple radial organization of primary root tissues, which form a series of concentric cylinders around the central vascular tissue. Equally attractive was the mode of propagation, with stem cells at the tip giving rise to progeny that were confined to cell files. These properties of root development reduced the normal four-dimensional problem of development (three spatial dimensions and time) to a two-dimensional problem, with cell type on the radial axis and developmental time along the longitudinal axis. The availability of the complete Arabidopsis genome sequence has dramatically accelerated traditional genetic research on root biology, and has also enabled entirely new experimental strategies to be applied. Here we review examples of the ways in which availability of the Arabidopsis genome sequence has enhanced progress in understanding root biology. PMID:20409273

Dynamics of pollution-indicator and heterotrophic bacteria in sewage treatment lagoons.

PubMed Central

Legendre, P; Baleux, B; Troussellier, M

1984-01-01

The spatio-temporal dynamics of pollution-indicator bacteria and aerobic heterotrophic bacteria were studied in the sewage treatment lagoons of an urban wastewater center after 26 months of biweekly sampling at eight stations in these lagoons. Robust statistical methods of time-series analysis were used to study successional steps (through chronological clustering) and rhythmic behavior through time (through contingency periodogram). The aerobic heterotrophic bacterial community showed two types of temporal evolution: in the first four stations, it seems mainly controlled by the nutrient support capacity of the sewage input, whereas in the remaining part of the lagoon, it seems likely that the pollution-indicator bacteria are gradually replaced by other bacterial types that are better adapted to this environment. On the other hand, the pollution-indicator bacteria showed an annual cycle which increased in amplitude at distances further from the wastewater source. The main events in this cycle were produced simultaneously at all stations, indicating control of these bacterial populations by climatic factors, which act through physical and chemical factors, and also through other biological components of this ecosystem (phytoplankton and zooplankton). Finally, we use results from this study to suggest a modified design for a future study program. Images PMID:6497372

Dynamics of pollution-indicator and heterotrophic bacteria in sewage treatment lagoons.

PubMed

Legendre, P; Baleux, B; Troussellier, M

1984-09-01

The spatio-temporal dynamics of pollution-indicator bacteria and aerobic heterotrophic bacteria were studied in the sewage treatment lagoons of an urban wastewater center after 26 months of biweekly sampling at eight stations in these lagoons. Robust statistical methods of time-series analysis were used to study successional steps (through chronological clustering) and rhythmic behavior through time (through contingency periodogram). The aerobic heterotrophic bacterial community showed two types of temporal evolution: in the first four stations, it seems mainly controlled by the nutrient support capacity of the sewage input, whereas in the remaining part of the lagoon, it seems likely that the pollution-indicator bacteria are gradually replaced by other bacterial types that are better adapted to this environment. On the other hand, the pollution-indicator bacteria showed an annual cycle which increased in amplitude at distances further from the wastewater source. The main events in this cycle were produced simultaneously at all stations, indicating control of these bacterial populations by climatic factors, which act through physical and chemical factors, and also through other biological components of this ecosystem (phytoplankton and zooplankton). Finally, we use results from this study to suggest a modified design for a future study program.

TimesVector: a vectorized clustering approach to the analysis of time series transcriptome data from multiple phenotypes.

PubMed

Jung, Inuk; Jo, Kyuri; Kang, Hyejin; Ahn, Hongryul; Yu, Youngjae; Kim, Sun

2017-12-01

Identifying biologically meaningful gene expression patterns from time series gene expression data is important to understand the underlying biological mechanisms. To identify significantly perturbed gene sets between different phenotypes, analysis of time series transcriptome data requires consideration of time and sample dimensions. Thus, the analysis of such time series data seeks to search gene sets that exhibit similar or different expression patterns between two or more sample conditions, constituting the three-dimensional data, i.e. gene-time-condition. Computational complexity for analyzing such data is very high, compared to the already difficult NP-hard two dimensional biclustering algorithms. Because of this challenge, traditional time series clustering algorithms are designed to capture co-expressed genes with similar expression pattern in two sample conditions. We present a triclustering algorithm, TimesVector, specifically designed for clustering three-dimensional time series data to capture distinctively similar or different gene expression patterns between two or more sample conditions. TimesVector identifies clusters with distinctive expression patterns in three steps: (i) dimension reduction and clustering of time-condition concatenated vectors, (ii) post-processing clusters for detecting similar and distinct expression patterns and (iii) rescuing genes from unclassified clusters. Using four sets of time series gene expression data, generated by both microarray and high throughput sequencing platforms, we demonstrated that TimesVector successfully detected biologically meaningful clusters of high quality. TimesVector improved the clustering quality compared to existing triclustering tools and only TimesVector detected clusters with differential expression patterns across conditions successfully. The TimesVector software is available at http://biohealth.snu.ac.kr/software/TimesVector/. sunkim.bioinfo@snu.ac.kr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Simultaneous determination of radionuclides separable into natural decay series by use of time-interval analysis.

PubMed

Hashimoto, Tetsuo; Sanada, Yukihisa; Uezu, Yasuhiro

2004-05-01

A delayed coincidence method, time-interval analysis (TIA), has been applied to successive alpha- alpha decay events on the millisecond time-scale. Such decay events are part of the (220)Rn-->(216)Po ( T(1/2) 145 ms) (Th-series) and (219)Rn-->(215)Po ( T(1/2) 1.78 ms) (Ac-series). By using TIA in addition to measurement of (226)Ra (U-series) from alpha-spectrometry by liquid scintillation counting (LSC), two natural decay series could be identified and separated. The TIA detection efficiency was improved by using the pulse-shape discrimination technique (PSD) to reject beta-pulses, by solvent extraction of Ra combined with simple chemical separation, and by purging the scintillation solution with dry N(2) gas. The U- and Th-series together with the Ac-series were determined, respectively, from alpha spectra and TIA carried out immediately after Ra-extraction. Using the (221)Fr-->(217)At ( T(1/2) 32.3 ms) decay process as a tracer, overall yields were estimated from application of TIA to the (225)Ra (Np-decay series) at the time of maximum growth. The present method has proven useful for simultaneous determination of three radioactive decay series in environmental samples.

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction

PubMed Central

Carleton, W. Christopher; Campbell, David

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating—the most common chronometric technique in archaeological and palaeoenvironmental research—creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20–30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence. PMID:29351329

Radiocarbon dating uncertainty and the reliability of the PEWMA method of time-series analysis for research on long-term human-environment interaction.

PubMed

Carleton, W Christopher; Campbell, David; Collard, Mark

2018-01-01

Statistical time-series analysis has the potential to improve our understanding of human-environment interaction in deep time. However, radiocarbon dating-the most common chronometric technique in archaeological and palaeoenvironmental research-creates challenges for established statistical methods. The methods assume that observations in a time-series are precisely dated, but this assumption is often violated when calibrated radiocarbon dates are used because they usually have highly irregular uncertainties. As a result, it is unclear whether the methods can be reliably used on radiocarbon-dated time-series. With this in mind, we conducted a large simulation study to investigate the impact of chronological uncertainty on a potentially useful time-series method. The method is a type of regression involving a prediction algorithm called the Poisson Exponentially Weighted Moving Average (PEMWA). It is designed for use with count time-series data, which makes it applicable to a wide range of questions about human-environment interaction in deep time. Our simulations suggest that the PEWMA method can often correctly identify relationships between time-series despite chronological uncertainty. When two time-series are correlated with a coefficient of 0.25, the method is able to identify that relationship correctly 20-30% of the time, providing the time-series contain low noise levels. With correlations of around 0.5, it is capable of correctly identifying correlations despite chronological uncertainty more than 90% of the time. While further testing is desirable, these findings indicate that the method can be used to test hypotheses about long-term human-environment interaction with a reasonable degree of confidence.

Resolving variability of phytoplankton species composition and blooms in coastal ecosystems

NASA Astrophysics Data System (ADS)

Klais, Riina; Cloern, James E.; Harrison, Paul J.

2015-09-01

The contributions to this special volume focus on phytoplankton dynamics in coastal ecosystems, where perturbations from terrestrial, atmospheric, oceanic sources and human activities converge to cause changes in phytoplankton communities. Analyses of phytoplankton time series across the range of coastal sites, either as meta-analyses or single site based studies, complete our general understanding of the ecology of coastal phytoplankton dynamics. The role of short-term variability of the phytoplankton community appears to be more important for the annual primary production than previously thought, especially during the high biomass spring bloom period (Gallegos and Neale, 2015). Diel vertical migration of motile species is commonplace even in shallow and presumably well-mixed estuaries (Hall et al., 2015). Comparing phytoplankton patterns in various sites reveals contrasting long-term trends in the last two decades, reflecting the recent history of economic growth in related coastal areas. In Chesapeake Bay Estuary (US east coast) and Thau Lagoon (southern France), oligotrophication has been achieved by different nutrient reduction measures (Gowen et al., 2015; Harding et al., 2015), while in the Patos Lagoon Estuary (Brazil) and SE coast of Arabian Sea, the last two decades showed signs of eutrophication, following the more recent period of economic growth in the area (Haraguchi et al., 2015; Godhe et al., 2015). The global meta-analyses in this volume exposed the great challenges involved when working with this type of data, due to the diversity of idiosyncrasies characteristic to most phytoplankton time series, for example, the taxonomic practices, cell volume calculations (Harrison et al., 2015), volume to carbon conversions (Carstensen et al., 2015; Olli et al., 2015). But also the diversity of the patterns themselves makes analyses challenging (Carstensen et al., 2015; Thompson et al., 2015). To begin to move towards more similar practices in plankton monitoring, or at least to describe the sampling procedure in sufficient detail, Zingone et al. (2015) outlined the most critical suggestions for quality control and quality assurance and detailed metadata that would increase the usability and intercomparability of data for other researchers with little or no extra cost. Recurrent intercalibrations between taxonomists and different institutions are critical to harmonize the sample analysis method and ensure the consistency of the information that is being produced (Jakobsen et al., 2015), however, similar workshops could be convened to unify all critical steps of time series collection, i.e. sampling, sample analysis, and the structure and description of data. Despite of the preliminary difficulties in using the existing phytoplankton time series data for cross system comparisons, most studies that have been global to date have used either model outputs or satellite observations, and even the latter do not come close to the level of information that the microscopic analysis of a phytoplankton sample by experienced taxonomist can provide. On the downside, this issue witnesses the continuing bias of the studies towards the northern hemisphere.

Developmental transitions in C. elegans larval stages.

PubMed

Rougvie, Ann E; Moss, Eric G

2013-01-01

Molecular mechanisms control the timing, sequence, and synchrony of developmental events in multicellular organisms. In Caenorhabditis elegans, these mechanisms are revealed through the analysis of mutants with "heterochronic" defects: cell division or differentiation patterns that occur in the correct lineage, but simply at the wrong time. Subsets of cells in these mutants thus express temporal identities normally restricted to a different life stage. A seminal finding arising from studies of the heterochronic genes was the discovery of miRNAs; these tiny miRNAs are now a defining feature of the pathway. A series of sequentially expressed miRNAs guide larval transitions through stage-specific repression of key effector molecules. The wild-type lineage patterns are executed as discrete modules programmed between temporal borders imposed by the molting cycles. How these successive events are synchronized with the oscillatory molting cycle is just beginning to come to light. Progression through larval stages can be specifically, yet reversibly, halted in response to environmental cues, including nutrient availability. Here too, heterochronic genes and miRNAs play key roles. Remarkably, developmental arrest can, in some cases, either mask or reveal timing defects associated with mutations. In this chapter, we provide an overview of how the C. elegans heterochronic gene pathway guides developmental transitions during continuous and interrupted larval development. © 2013 Elsevier Inc. All rights reserved.

A review of mathematical modeling and simulation of controlled-release fertilizers.

PubMed

Irfan, Sayed Ameenuddin; Razali, Radzuan; KuShaari, KuZilati; Mansor, Nurlidia; Azeem, Babar; Ford Versypt, Ashlee N

2018-02-10

Nutrients released into soils from uncoated fertilizer granules are lost continuously due to volatilization, leaching, denitrification, and surface run-off. These issues have caused economic loss due to low nutrient absorption efficiency and environmental pollution due to hazardous emissions and water eutrophication. Controlled-release fertilizers (CRFs) can change the release kinetics of the fertilizer nutrients through an abatement strategy to offset these issues by providing the fertilizer content in synchrony with the metabolic needs of the plants. Parametric analysis of release characteristics of CRFs is of paramount importance for the design and development of new CRFs. However, the experimental approaches are not only time consuming, but they are also cumbersome and expensive. Scientists have introduced mathematical modeling techniques to predict the release of nutrients from the CRFs to elucidate fundamental understanding of the dynamics of the release processes and to design new CRFs in a shorter time and with relatively lower cost. This paper reviews and critically analyzes the latest developments in the mathematical modeling and simulation techniques that have been reported for the characteristics and mechanisms of nutrient release from CRFs. The scope of this review includes the modeling and simulations techniques used for coated, controlled-release fertilizers. Copyright © 2017 Elsevier B.V. All rights reserved.

Quality of groundwater and surface water, Wood River Valley, south-central Idaho, July and August 2012

USGS Publications Warehouse

Hopkins, Candice B.; Bartolino, James R.

2013-01-01

Residents and resource managers of the Wood River Valley of south-central Idaho are concerned about the effects that population growth might have on the quality of groundwater and surface water. As part of a multi-phase assessment of the groundwater resources in the study area, the U.S. Geological Survey evaluated the quality of water at 45 groundwater and 5 surface-water sites throughout the Wood River Valley during July and August 2012. Water samples were analyzed for field parameters (temperature, pH, specific conductance, dissolved oxygen, and alkalinity), major ions, boron, iron, manganese, nutrients, and Escherichia coli (E.coli) and total coliform bacteria. This study was conducted to determine baseline water quality throughout the Wood River Valley, with special emphasis on nutrient concentrations. Water quality in most samples collected did not exceed U.S. Environmental Protection Agency standards for drinking water. E. coli bacteria, used as indicators of water quality, were detected in all five surface-water samples and in two groundwater samples collected. Some analytes have aesthetic-based recommended drinking water standards; one groundwater sample exceeded recommended iron concentrations. Nitrate plus nitrite concentrations varied, but tended to be higher near population centers and in agricultural areas than in tributaries and less populated areas. These higher nitrate plus nitrite concentrations were not correlated with boron concentrations or the presence of bacteria, common indicators of sources of nutrients to water. None of the samples collected exceeded drinking-water standards for nitrate or nitrite. The concentration of total dissolved solids varied considerably in the waters sampled; however a calcium-magnesium-bicarbonate water type was dominant (43 out of 50 samples) in both the groundwater and surface water. Three constituents that may be influenced by anthropogenic activity (chloride, boron, and nitrate plus nitrite) deviate from this pattern and show a wide distribution of concentrations in the unconfined aquifer, indicating possible anthropogenic influence. Time-series plots of historical water-quality data indicated that nitrate does not seem to be increasing or decreasing in groundwater over time; however, time-series plots of chloride concentrations indicate that chloride may be increasing in some wells. The small amount of temporal variability in nitrate concentrations indicates a lack of major temporal changes to groundwater inputs.

Dietary patterns of rural older adults are associated with weight and nutritional status.

PubMed

Ledikwe, Jenny H; Smiciklas-Wright, Helen; Mitchell, Diane C; Miller, Carla K; Jensen, Gordon L

2004-04-01

To characterize dietary patterns of rural older adults and relate patterns to weight and nutritional status. Cross-sectional. Rural Pennsylvania. One hundred seventy-nine community-dwelling adults aged 66 to 87 years. A home visit was conducted to collect demographic, health behavior, and anthropometric data and a blood sample. Five 24-hour dietary recall were administered. Cluster analysis classified participants into dietary patterns using food subgroup servings. Chi-square, analysis of covariance, and logistic regression were used to assess differences across clusters. A low-nutrient-dense cluster (n=107), with higher intake of breads, sweet breads/desserts, dairy desserts, processed meats, eggs, and fats/oils, and a high-nutrient-dense cluster (n=72) with higher intake of cereals, dark green/yellow vegetables, other vegetables, citrus/melons/berries, fruit juices, other fruits, milks, poultry, fish, and beans, were identified. Those in the high-nutrient-dense cluster had lower energy intake; higher energy-adjusted intake of fiber, iron, zinc, folate, and vitamins B(6), B(12), and D; higher Healthy Eating Index scores; higher plasma vitamin B(12) levels; and a lower waist circumference. Those with a low-nutrient-dense dietary pattern were twice as likely to be obese, twice as likely to have low plasma vitamin B(12) levels, and three to 17 times more likely to have low nutrient intake. This study provides support for recommending a high-nutrient-dense dietary pattern for older adults. Behavioral interventions encouraging diets characterized by high-nutrient-dense foods may improve weight and nutritional status of older adults.

Phosphorylated Akt Protein at Ser473 Enables HeLa Cells to Tolerate Nutrient-Deprived Conditions

PubMed

Fathy, Moustafa; Awale, Suresh; Nikaido, Toshio

2017-12-29

Background: Despite angiogenesis, many tumours remain hypovascular and starved of nutrients while continuing to grow rapidly. The specific biochemical mechanisms associated with starvation resistance, austerity, may be new biological characters of cancer that are critical for cancer progression. Objective: This study aim was to investigate the effect of nutrient starvation on HeLa cells and the possible mechanism by which the cells are able to tolerate nutrient-deprived conditions. Methods: Nutrient starvation was achieved by culturing HeLa cells in nutrient-deprived medium (NDM) and cell survival was estimated by using cell counting kit-8. The effect of starvation on cell cycle distribution and the quantitative analysis of apoptotic cells were investigated by flow cytometry using propidium iodide staining. Western blotting was used to detect the expression levels of Akt and phosphorylated Akt at Ser473 (Ser473p-Akt) proteins. Results: HeLa cells displayed extremely long survival when cultured in NDM. The percentage of apoptotic HeLa cells was significantly increased by starvation in a time-dependent manner. A significant increase in the expression of Ser473p-Akt protein after starvation was also observed. Furthermore, it was found that Akt inhibitor III molecule inhibited the cells proliferation in a concentration- and time-dependent manner. Conclusion: Results of the present study provide evidence that Akt activation may be implicated in the tolerance of HeLa cells for nutrient starvation and may help to suggest new therapeutic strategies designed to prevent austerity of cervical cancer cells through inhibition of Akt activation. Creative Commons Attribution License

Unveiling signatures of interdecadal climate changes by Hilbert analysis

NASA Astrophysics Data System (ADS)

Zappalà, Dario; Barreiro, Marcelo; Masoller, Cristina

2017-04-01

A recent study demonstrated that, in a class of networks of oscillators, the optimal network reconstruction from dynamics is obtained when the similarity analysis is performed not on the original dynamical time series, but on transformed series obtained by Hilbert transform. [1] That motivated us to use Hilbert transform to study another kind of (in a broad sense) "oscillating" series, such as the series of temperature. Actually, we found that Hilbert analysis of SAT (Surface Air Temperature) time series uncovers meaningful information about climate and is therefore a promising tool for the study of other climatological variables. [2] In this work we analysed a large dataset of SAT series, performing Hilbert transform and further analysis with the goal of finding signs of climate change during the analysed period. We used the publicly available ERA-Interim dataset, containing reanalysis data. [3] In particular, we worked on daily SAT time series, from year 1979 to 2015, in 16380 points arranged over a regular grid on the Earth surface. From each SAT time series we calculate the anomaly series and also, by using the Hilbert transform, we calculate the instantaneous amplitude and instantaneous frequency series. Our first approach is to calculate the relative variation: the difference between the average value on the last 10 years and the average value on the first 10 years, divided by the average value over all the analysed period. We did this calculations on our transformed series: frequency and amplitude, both with average values and standard deviation values. Furthermore, to have a comparison with an already known analysis methods, we did these same calculations on the anomaly series. We plotted these results as maps, where the colour of each site indicates the value of its relative variation. Finally, to gain insight in the interpretation of our results over real SAT data, we generated synthetic sinusoidal series with various levels of additive noise. By applying Hilbert analysis to the synthetic data, we uncovered a clear trend between mean amplitude and mean frequency: as the noise level grows, the amplitude increases while the frequency decreases. Research funded in part by AGAUR (Generalitat de Catalunya), EU LINC project (Grant No. 289447) and Spanish MINECO (FIS2015-66503-C3-2-P).

"Observation Obscurer" - Time Series Viewer, Editor and Processor

NASA Astrophysics Data System (ADS)

Andronov, I. L.

The program is described, which contains a set of subroutines suitable for East viewing and interactive filtering and processing of regularly and irregularly spaced time series. Being a 32-bit DOS application, it may be used as a default fast viewer/editor of time series in any compute shell ("commander") or in Windows. It allows to view the data in the "time" or "phase" mode, to remove ("obscure") or filter outstanding bad points; to make scale transformations and smoothing using few methods (e.g. mean with phase binning, determination of the statistically opti- mal number of phase bins; "running parabola" (Andronov, 1997, As. Ap. Suppl, 125, 207) fit and to make time series analysis using some methods, e.g. correlation, autocorrelation and histogram analysis: determination of extrema etc. Some features have been developed specially for variable star observers, e.g. the barycentric correction, the creation and fast analysis of "OC" diagrams etc. The manual for "hot keys" is presented. The computer code was compiled with a 32-bit Free Pascal (www.freepascal.org).

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

An Energy-Based Similarity Measure for Time Series

NASA Astrophysics Data System (ADS)

Boudraa, Abdel-Ouahab; Cexus, Jean-Christophe; Groussat, Mathieu; Brunagel, Pierre

2007-12-01

A new similarity measure, called SimilB, for time series analysis, based on the cross-[InlineEquation not available: see fulltext.]-energy operator (2004), is introduced. [InlineEquation not available: see fulltext.] is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED) or the Pearson correlation coefficient (CC), SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of [InlineEquation not available: see fulltext.] are presented. Particularly, we show that [InlineEquation not available: see fulltext.] as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Bark analysis as a guide to cassava nutrition in Sierra Leone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Godfrey-Sam-Aggrey, W.; Garber, M.J.

1979-01-01

Cassava main stem barks from two experiments in which similar fertilizers were applied directly in a 2/sup 5/ confounded factorial design were analyzed and the bark nutrients used as a guide to cassava nutrition. The application of multiple regression analysis to the respective root yields and bark nutrient concentrations enable nutrient levels and optimum adjusted root yields to be derived. Differences in bark nutrient concentrations reflected soil fertility levels. Bark analysis and the application of multiple regression analysis to root yields and bark nutrients appear to be useful tools for predicting fertilizer recommendations for cassava production.

Impact of change in climate and policy from 1988 to 2007 on environmental and microbial variables at the time series station Boknis Eck, Baltic Sea

NASA Astrophysics Data System (ADS)

Hoppe, H.-G.; Giesenhagen, H. C.; Koppe, R.; Hansen, H.-P.; Gocke, K.

2013-07-01

Phytoplankton and bacteria are sensitive indicators of environmental change. The temporal development of these key organisms was monitored from 1988 to the end of 2007 at the time series station Boknis Eck in the western Baltic Sea. This period was characterized by the adaption of the Baltic Sea ecosystem to changes in the environmental conditions caused by the conversion of the political system in the southern and eastern border states, accompanied by the general effects of global climate change. Measured variables were chlorophyll, primary production, bacteria number, -biomass and -production, glucose turnover rate, macro-nutrients, pH, temperature and salinity. Negative trends with time were recorded for chlorophyll, bacteria number, bacterial biomass and bacterial production, nitrate, ammonia, phosphate, silicate, oxygen and salinity while temperature, pH, and the ratio between bacteria numbers and chlorophyll increased. Strongest reductions with time occurred for the annual maximum values, e.g. for chlorophyll during the spring bloom or for nitrate during winter, while the annual minimum values remained more stable. In deep water above sediment the negative trends of oxygen, nitrate, phosphate and bacterial variables as well as the positive trend of temperature were similar to those in the surface while the trends of salinity, ammonia and silicate were opposite to those in the surface. Decreasing oxygen, even in the surface layer, was of particular interest because it suggested enhanced recycling of nutrients from the deep hypoxic zones to the surface by vertical mixing. The long-term seasonal patterns of all variables correlated positively with temperature, except chlorophyll and salinity. Salinity correlated negatively with all bacterial variables (as well as precipitation) and positively with chlorophyll. Surprisingly, bacterial variables did not correlate with chlorophyll, which may be inherent with the time lag between the peaks of phytoplankton and bacteria during spring. Compared to the 20-yr averages of the environmental and microbial variables, the strongest negative deviations of corresponding annual averages were measured about ten years after political change for nitrate and bacterial secondary production (~ -60%), followed by chlorophyll (-50%) and bacterial biomass (-40%). Considering the circulation of surface currents in the Baltic Sea we interpret the observed patterns of the microbial variables at the Boknis Eck time series station as a consequence of the improved management of water resources after 1989 and - to a minor extent - the trends of the climate variables salinity and temperature.

Impact of change in climate and policy from 1988 to 2007 on environmental and microbial variables at the time series station Boknis Eck, Baltic Sea

NASA Astrophysics Data System (ADS)

Hoppe, H.-G.; Giesenhagen, H. C.; Koppe, R.; Hansen, H.-P.; Gocke, K.

2012-12-01

Phytoplankton and bacteria are sensitive indicators of environmental change. The temporal development of these key organisms was monitored from 1988 to the end of 2007 at the time series station Boknis Eck in the Western Baltic Sea. This period was characterized by the adaption of the Baltic Sea ecosystem to changes in the environmental conditions caused by the collapse and conversion of the political system in the Southern and Eastern Border States, accompanied by the general effects of global climate change. Measured variables were chlorophyll, primary production, bacteria number, -biomass and -production, glucose turnover rate, macro-nutrients, pH, temperature and salinity. Negative trends with time were recorded for chlorophyll, the bacterial variables, nitrate, ammonia, phosphate, silicate, oxygen and salinity while temperature, pH, and the ratio between bacteria numbers and chlorophyll increased. The strongest reductions with time occurred for the annual maximum values, e.g. for chlorophyll during the spring bloom or for nitrate during winter, while the annual minimum values remained more stable. In deep water above sediment the negative trends of oxygen, nitrate, phosphate and bacterial variables as well as the positive trend of temperature were similar to those in the surface while the trends of salinity, ammonia and silicate were opposite to those in the surface. Decreasing oxygen even in the surface layer was of particular interest because it suggested enhanced recycling of nutrients from the deep hypoxic zones to the surface by vertical mixing. In the long run all variables correlated positively with temperature, except chlorophyll and salinity. Salinity correlated negatively with all bacterial variables as well as precipitation and positively with chlorophyll. Surprisingly, bacterial variables did not correlate with chlorophyll which may be inherent with the time lag between the peaks of phytoplankton and bacteria during spring. Compared to the 20-yr averages of the environmental and microbial variables, the strongest negative deviations of corresponding annual averages were measured about ten years after political change for nitrate and bacterial secondary production (~ -60%), followed by chlorophyll (-50%) and bacterial biomass (-40%). Considering the circulation of surface currents in the Baltic Sea we conclude that the improved management of water resources after 1989 together with the trends of the climate variables salinity and temperature were responsible for the observed patterns of the microbial variables at the Boknis Eck time series station.

Relating interesting quantitative time series patterns with text events and text features

NASA Astrophysics Data System (ADS)

Wanner, Franz; Schreck, Tobias; Jentner, Wolfgang; Sharalieva, Lyubka; Keim, Daniel A.

2013-12-01

In many application areas, the key to successful data analysis is the integrated analysis of heterogeneous data. One example is the financial domain, where time-dependent and highly frequent quantitative data (e.g., trading volume and price information) and textual data (e.g., economic and political news reports) need to be considered jointly. Data analysis tools need to support an integrated analysis, which allows studying the relationships between textual news documents and quantitative properties of the stock market price series. In this paper, we describe a workflow and tool that allows a flexible formation of hypotheses about text features and their combinations, which reflect quantitative phenomena observed in stock data. To support such an analysis, we combine the analysis steps of frequent quantitative and text-oriented data using an existing a-priori method. First, based on heuristics we extract interesting intervals and patterns in large time series data. The visual analysis supports the analyst in exploring parameter combinations and their results. The identified time series patterns are then input for the second analysis step, in which all identified intervals of interest are analyzed for frequent patterns co-occurring with financial news. An a-priori method supports the discovery of such sequential temporal patterns. Then, various text features like the degree of sentence nesting, noun phrase complexity, the vocabulary richness, etc. are extracted from the news to obtain meta patterns. Meta patterns are defined by a specific combination of text features which significantly differ from the text features of the remaining news data. Our approach combines a portfolio of visualization and analysis techniques, including time-, cluster- and sequence visualization and analysis functionality. We provide two case studies, showing the effectiveness of our combined quantitative and textual analysis work flow. The workflow can also be generalized to other application domains such as data analysis of smart grids, cyber physical systems or the security of critical infrastructure, where the data consists of a combination of quantitative and textual time series data.

Influence of denitrification reactor retention time distribution (RTD) on dissolved oxygen control and nitrogen removal efficiency.

PubMed

Raboni, Massimo; Gavasci, Renato; Viotti, Paolo

2015-01-01

Low concentrations of dissolved oxygen (DO) are usually found in biological anoxic pre-denitrification reactors, causing a reduction in nitrogen removal efficiency. Therefore, the reduction of DO in such reactors is fundamental for achieving good nutrient removal. The article shows the results of an experimental study carried out to evaluate the effect of the anoxic reactor hydrodynamic model on both residual DO concentration and nitrogen removal efficiency. In particular, two hydrodynamic models were considered: the single completely mixed reactor and a series of four reactors that resemble plug-flow behaviour. The latter prove to be more effective in oxygen consumption, allowing a lower residual DO concentration than the former. The series of reactors also achieves better specific denitrification rates and higher denitrification efficiency. Moreover, the denitrification food to microrganism (F:M) ratio (F:MDEN) demonstrates a relevant synergic action in both controlling residual DO and improving the denitrification performance.

Terrain Dynamics Analysis Using Space-Time Domain Hypersurfaces and Gradient Trajectories Derived From Time Series of 3D Point Clouds

DTIC Science & Technology

2015-08-01

optimized space-time interpolation method. Tangible geospatial modeling system was further developed to support the analysis of changing elevation surfaces...Evolution Mapped by Terrestrial Laser Scanning, talk, AGU Fall 2012 *Hardin E, Mitas L, Mitasova H., Simulation of Wind -Blown Sand for...Geomorphological Applications: A Smoothed Particle Hydrodynamics Approach, GSA 2012 *Russ, E. Mitasova, H., Time series and space-time cube analyses on

Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series

NASA Astrophysics Data System (ADS)

Pal, Mayukha; Madhusudana Rao, P.; Manimaran, P.

2014-12-01

We apply the recently developed multifractal detrended cross-correlation analysis method to investigate the cross-correlation behavior and fractal nature between two non-stationary time series. We analyze the daily return price of gold, West Texas Intermediate and Brent crude oil, foreign exchange rate data, over a period of 18 years. The cross correlation has been measured from the Hurst scaling exponents and the singularity spectrum quantitatively. From the results, the existence of multifractal cross-correlation between all of these time series is found. We also found that the cross correlation between gold and oil prices possess uncorrelated behavior and the remaining bivariate time series possess persistent behavior. It was observed for five bivariate series that the cross-correlation exponents are less than the calculated average generalized Hurst exponents (GHE) for q<0 and greater than GHE when q>0 and for one bivariate series the cross-correlation exponent is greater than GHE for all q values.

Emerging spectra of singular correlation matrices under small power-map deformations

NASA Astrophysics Data System (ADS)

Vinayak; Schäfer, Rudi; Seligman, Thomas H.

2013-09-01

Correlation matrices are a standard tool in the analysis of the time evolution of complex systems in general and financial markets in particular. Yet most analysis assume stationarity of the underlying time series. This tends to be an assumption of varying and often dubious validity. The validity of the assumption improves as shorter time series are used. If many time series are used, this implies an analysis of highly singular correlation matrices. We attack this problem by using the so-called power map, which was introduced to reduce noise. Its nonlinearity breaks the degeneracy of the zero eigenvalues and we analyze the sensitivity of the so-emerging spectra to correlations. This sensitivity will be demonstrated for uncorrelated and correlated Wishart ensembles.

Emerging spectra of singular correlation matrices under small power-map deformations.

PubMed

Vinayak; Schäfer, Rudi; Seligman, Thomas H

2013-09-01

Correlation matrices are a standard tool in the analysis of the time evolution of complex systems in general and financial markets in particular. Yet most analysis assume stationarity of the underlying time series. This tends to be an assumption of varying and often dubious validity. The validity of the assumption improves as shorter time series are used. If many time series are used, this implies an analysis of highly singular correlation matrices. We attack this problem by using the so-called power map, which was introduced to reduce noise. Its nonlinearity breaks the degeneracy of the zero eigenvalues and we analyze the sensitivity of the so-emerging spectra to correlations. This sensitivity will be demonstrated for uncorrelated and correlated Wishart ensembles.

Inhomogeneous scaling behaviors in Malaysian foreign currency exchange rates

NASA Astrophysics Data System (ADS)

Muniandy, S. V.; Lim, S. C.; Murugan, R.

2001-12-01

In this paper, we investigate the fractal scaling behaviors of foreign currency exchange rates with respect to Malaysian currency, Ringgit Malaysia. These time series are examined piecewise before and after the currency control imposed in 1st September 1998 using the monofractal model based on fractional Brownian motion. The global Hurst exponents are determined using the R/ S analysis, the detrended fluctuation analysis and the method of second moment using the correlation coefficients. The limitation of these monofractal analyses is discussed. The usual multifractal analysis reveals that there exists a wide range of Hurst exponents in each of the time series. A new method of modelling the multifractal time series based on multifractional Brownian motion with time-varying Hurst exponents is studied.

Time irreversibility and intrinsics revealing of series with complex network approach

NASA Astrophysics Data System (ADS)

Xiong, Hui; Shang, Pengjian; Xia, Jianan; Wang, Jing

2018-06-01

In this work, we analyze time series on the basis of the visibility graph algorithm that maps the original series into a graph. By taking into account the all-round information carried by the signals, the time irreversibility and fractal behavior of series are evaluated from a complex network perspective, and considered signals are further classified from different aspects. The reliability of the proposed analysis is supported by numerical simulations on synthesized uncorrelated random noise, short-term correlated chaotic systems and long-term correlated fractal processes, and by the empirical analysis on daily closing prices of eleven worldwide stock indices. Obtained results suggest that finite size has a significant effect on the evaluation, and that there might be no direct relation between the time irreversibility and long-range correlation of series. Similarity and dissimilarity between stock indices are also indicated from respective regional and global perspectives, showing the existence of multiple features of underlying systems.

The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series

NASA Astrophysics Data System (ADS)

Du, Kongchang; Zhao, Ying; Lei, Jiaqiang

2017-09-01

In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt 'future' values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of 'future' values. These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.

78 FR 40104 - Endangered and Threatened Wildlife; 12-Month Finding on Petitions To List the Northeastern...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-03

...). Across the entire time series of available logbook data (1981-2011), CPUE in this fishery appears to have... abundance over this period. The time series of annual abundance estimates from this analysis showed there... been documented at Southeast Farallon Island since the 1980s, providing a relatively long time series...

A Computer Evolution in Teaching Undergraduate Time Series

ERIC Educational Resources Information Center

Hodgess, Erin M.

2004-01-01

In teaching undergraduate time series courses, we have used a mixture of various statistical packages. We have finally been able to teach all of the applied concepts within one statistical package; R. This article describes the process that we use to conduct a thorough analysis of a time series. An example with a data set is provided. We compare…

Analysis of Complex Intervention Effects in Time-Series Experiments.

ERIC Educational Resources Information Center

Bower, Cathleen

An iterative least squares procedure for analyzing the effect of various kinds of intervention in time-series data is described. There are numerous applications of this design in economics, education, and psychology, although until recently, no appropriate analysis techniques had been developed to deal with the model adequately. This paper…

A Comparison of Missing-Data Procedures for Arima Time-Series Analysis

ERIC Educational Resources Information Center

Velicer, Wayne F.; Colby, Suzanne M.

2005-01-01

Missing data are a common practical problem for longitudinal designs. Time-series analysis is a longitudinal method that involves a large number of observations on a single unit. Four different missing-data methods (deletion, mean substitution, mean of adjacent observations, and maximum likelihood estimation) were evaluated. Computer-generated…

Interrupted Time Series Analysis: A Research Technique for Evaluating Social Programs for the Elderly

ERIC Educational Resources Information Center

Calsyn, Robert J.; And Others

1977-01-01

After arguing that treatment programs for the elderly need to be evaluated with better research designs, the authors illustrate how interrupted time series analysis can be used to evaluate programs for the elderly when random assignment to experimental and control groups is not possible. (Author)

Time series analysis of monthly pulpwood use in the Northeast

Treesearch

James T. Bones

1980-01-01

Time series analysis was used to develop a model that depicts pulpwood use in the Northeast. The model is useful in forecasting future pulpwood requirements (short term) or monitoring pulpwood-use activity in relation to past use patterns. The model predicted a downturn in use during 1980.

Nonlinear Analysis of Surface EMG Time Series of Back Muscles

NASA Astrophysics Data System (ADS)

Dolton, Donald C.; Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

2004-10-01

A nonlinear analysis of surface electromyography time series of subjects with and without low back pain is presented. The mean-square displacement and entropy shows anomalous diffusive behavior on intermediate time range 10 ms < t < 1 s. This behavior implies the presence of correlations in the signal. We discuss the shape of the power spectrum of the signal.

Integrated method for chaotic time series analysis

DOEpatents

Hively, L.M.; Ng, E.G.

1998-09-29

Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data are disclosed. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated. 8 figs.

Interrupted time series regression for the evaluation of public health interventions: a tutorial.

PubMed

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-02-01

Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

Interrupted time series regression for the evaluation of public health interventions: a tutorial

PubMed Central

Bernal, James Lopez; Cummins, Steven; Gasparrini, Antonio

2017-01-01

Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design. PMID:27283160

Long-range memory and multifractality in gold markets

NASA Astrophysics Data System (ADS)

Mali, Provash; Mukhopadhyay, Amitabha

2015-03-01

Long-range correlation and fluctuation in the gold market time series of the world's two leading gold consuming countries, namely China and India, are studied. For both the market series during the period 1985-2013 we observe a long-range persistence of memory in the sequences of maxima (minima) of returns in successive time windows of fixed length, but the series, as a whole, are found to be uncorrelated. Multifractal analysis for these series as well as for the sequences of maxima (minima) is carried out in terms of the multifractal detrended fluctuation analysis (MF-DFA) method. We observe a weak multifractal structure for the original series that mainly originates from the fat-tailed probability distribution function of the values, and the multifractal nature of the original time series is enriched into their sequences of maximal (minimal) returns. A quantitative measure of multifractality is provided by using a set of ‘complexity parameters’.

Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases.

PubMed

Altiparmak, Fatih; Ferhatosmanoglu, Hakan; Erdal, Selnur; Trost, Donald C

2006-04-01

An effective analysis of clinical trials data involves analyzing different types of data such as heterogeneous and high dimensional time series data. The current time series analysis methods generally assume that the series at hand have sufficient length to apply statistical techniques to them. Other ideal case assumptions are that data are collected in equal length intervals, and while comparing time series, the lengths are usually expected to be equal to each other. However, these assumptions are not valid for many real data sets, especially for the clinical trials data sets. An addition, the data sources are different from each other, the data are heterogeneous, and the sensitivity of the experiments varies by the source. Approaches for mining time series data need to be revisited, keeping the wide range of requirements in mind. In this paper, we propose a novel approach for information mining that involves two major steps: applying a data mining algorithm over homogeneous subsets of data, and identifying common or distinct patterns over the information gathered in the first step. Our approach is implemented specifically for heterogeneous and high dimensional time series clinical trials data. Using this framework, we propose a new way of utilizing frequent itemset mining, as well as clustering and declustering techniques with novel distance metrics for measuring similarity between time series data. By clustering the data, we find groups of analytes (substances in blood) that are most strongly correlated. Most of these relationships already known are verified by the clinical panels, and, in addition, we identify novel groups that need further biomedical analysis. A slight modification to our algorithm results an effective declustering of high dimensional time series data, which is then used for "feature selection." Using industry-sponsored clinical trials data sets, we are able to identify a small set of analytes that effectively models the state of normal health.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1988-01-01

This Standard presents descriptive and analytical techniques for NASA trend analysis applications. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. Use of this Standard is not mandatory; however, it should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend Analysis is neither a precise term nor a circumscribed methodology, but rather connotes, generally, quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this Standard. The document presents the basic ideas needed for qualitative and quantitative assessment of trends, together with relevant examples. A list of references provides additional sources of information.

Combined Vocal Exercises for Rehabilitation After Supracricoid Laryngectomy: Evaluation of Different Execution Times.

PubMed

Silveira, Hevely Saray Lima; Simões-Zenari, Marcia; Kulcsar, Marco Aurélio; Cernea, Claudio Roberto; Nemr, Kátia

2017-10-27

The supracricoid partial laryngectomy allows the preservation of laryngeal functions with good local cancer control. To assess laryngeal configuration and voice analysis data following the performance of a combination of two vocal exercises: the prolonged /b/vocal exercise combined with the vowel /e/ using chest and arm pushing with different durations among individuals who have undergone supracricoid laryngectomy. Eleven patients undergoing partial laryngectomy supracricoid with cricohyoidoepiglottopexy (CHEP) were evaluated using voice recording. Four judges performed separately a perceptive-vocal analysis of hearing voices, with random samples. For the analysis of intrajudge reliability, repetitions of 70% of the voices were done. Intraclass correlation coefficient was used to analyze the reliability of the judges. For an analysis of each judge to the comparison between zero time (time point 0), after the first series of exercises (time point 1), after the second series (time point 2), after the third series (time point 3), after the fourth series (time point 4), and after the fifth and final series (time point 5), the Friedman test was used with a significance level of 5%. The data relative to the configuration of the larynx were subjected to a descriptive analysis. In the evaluation, were considered the judge results 1 which have greater reliability. There was an improvement in the general level of vocal, roughness, and breathiness deviations from time point 4 [T4]. The prolonged /b/vocal exercise, combined with the vowel /e/ using chest- and arm-pushing exercises, was associated with an improvement in the overall grade of vocal deviation, roughness, and breathiness starting at minute 4 among patients who had undergone supracricoid laryngectomy with CHEP reconstruction. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data

PubMed Central

Takayasu, Hideki; Takayasu, Misako

2017-01-01

We extend the concept of statistical symmetry as the invariance of a probability distribution under transformation to analyze binary sign time series data of price difference from the foreign exchange market. We model segments of the sign time series as Markov sequences and apply a local hypothesis test to evaluate the symmetries of independence and time reversion in different periods of the market. For the test, we derive the probability of a binary Markov process to generate a given set of number of symbol pairs. Using such analysis, we could not only segment the time series according the different behaviors but also characterize the segments in terms of statistical symmetries. As a particular result, we find that the foreign exchange market is essentially time reversible but this symmetry is broken when there is a strong external influence. PMID:28542208

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.

PubMed

Xia, Li C; Ai, Dongmei; Cram, Jacob A; Liang, Xiaoyi; Fuhrman, Jed A; Sun, Fengzhu

2015-09-21

Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies. By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns. The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

Hotspots of human-induced biomass productivity decline and their social-ecological types toward supporting national policy and local studies on combating land degradation

NASA Astrophysics Data System (ADS)

Vu, Quyet Manh; Le, Quang Bao; Vlek, Paul L. G.

2014-10-01

Identification and social-ecological characterization of areas that experience high levels of persistent productivity decline are essential for planning appropriate management measures. Although land degradation is mainly induced by human actions, the phenomenon is concurrently influenced by global climate changes that need to be taken into account in land degradation assessments. This study aims to delineate the geographic hotspots of human-induced land degradation in the country and classify the social-ecological characterizations of each specific degradation hotspot type. The research entailed a long-term time-series (1982-2006) of Normalized Difference Vegetation Index to specify the extents of areas with significant biomass decline or increase in Vietnam. Annual rainfall and temperature time-series were then used to separate areas of human-induced biomass productivity decline from those driven by climate dynamics. Next, spatial cluster analyses identified social-ecological types of degradation for guiding further investigations at regional and local scales. The results show that about 19% of the national land mass experienced persistent declines in biomass productivity over the last 25 years. Most of the degraded areas are found in the Southeast and Mekong River Delta (17,984 km2), Northwest Mountains (14,336 km2), and Central Highlands (13,504 km2). We identified six and five social-ecological types of degradation hotspots in agricultural and forested zones, respectively. Constraints in soil nutrient availability and nutrient retention capability are widely spreading in all degradation hotspot types. These hotspot types are different from each other in social and ecological conditions, suggesting that region-specific strategies are needed for the formulation of land degradation combating policy.

An examination of the nutrient content and on-package marketing of novel beverages.

PubMed

Dachner, Naomi; Mendelson, Rena; Sacco, Jocelyn; Tarasuk, Valerie

2015-02-01

Changing regulatory approaches to fortification in Canada have enabled the expansion of the novel beverage market, but the nutritional implications of these new products are poorly understood. This study assessed the micronutrient composition of energy drinks, vitamin waters, and novel juices sold in Canadian supermarkets, and critically examined their on-package marketing at 2 time points: 2010-2011, when they were regulated as Natural Health Products, and 2014, when they fell under food regulations. We examined changes in micronutrient composition and on-package marketing among a sample of novel beverages (n = 46) over time, compared micronutrient content with Dietary Reference Intakes and the results of the 2004 Canadian Community Health Survey to assess potential benefits, and conducted a content analysis of product labels. The median number of nutrients per product was 4.5, with vitamins B6, B12, C, and niacin most commonly added. Almost every beverage provided at least 1 nutrient in excess of requirements, and most contained 3 or more nutrients at such levels. With the exception of vitamin C, there was no discernible prevalence of inadequacy among young Canadian adults for the nutrients. Product labels promoted performance and emotional benefits related to nutrient formulations that go beyond conventional nutritional science. Label graphics continued to communicate these attributes even after reformatting to comply with food regulations. In contrast with the on-package marketing of novel beverages, there is little evidence that consumers stand to benefit from the micronutrients most commonly found in these products.

Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory

NASA Astrophysics Data System (ADS)

Wang, Na; Li, Dong; Wang, Qiwen

2012-12-01

The visibility graph approach and complex network theory provide a new insight into time series analysis. The inheritance of the visibility graph from the original time series was further explored in the paper. We found that degree distributions of visibility graphs extracted from Pseudo Brownian Motion series obtained by the Frequency Domain algorithm exhibit exponential behaviors, in which the exponential exponent is a binomial function of the Hurst index inherited in the time series. Our simulations presented that the quantitative relations between the Hurst indexes and the exponents of degree distribution function are different for different series and the visibility graph inherits some important features of the original time series. Further, we convert some quarterly macroeconomic series including the growth rates of value-added of three industry series and the growth rates of Gross Domestic Product series of China to graphs by the visibility algorithm and explore the topological properties of graphs associated from the four macroeconomic series, namely, the degree distribution and correlations, the clustering coefficient, the average path length, and community structure. Based on complex network analysis we find degree distributions of associated networks from the growth rates of value-added of three industry series are almost exponential and the degree distributions of associated networks from the growth rates of GDP series are scale free. We also discussed the assortativity and disassortativity of the four associated networks as they are related to the evolutionary process of the original macroeconomic series. All the constructed networks have “small-world” features. The community structures of associated networks suggest dynamic changes of the original macroeconomic series. We also detected the relationship among government policy changes, community structures of associated networks and macroeconomic dynamics. We find great influences of government policies in China on the changes of dynamics of GDP and the three industries adjustment. The work in our paper provides a new way to understand the dynamics of economic development.

Characterization of chaotic attractors under noise: A recurrence network perspective

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2016-12-01

We undertake a detailed numerical investigation to understand how the addition of white and colored noise to a chaotic time series changes the topology and the structure of the underlying attractor reconstructed from the time series. We use the methods and measures of recurrence plot and recurrence network generated from the time series for this analysis. We explicitly show that the addition of noise obscures the property of recurrence of trajectory points in the phase space which is the hallmark of every dynamical system. However, the structure of the attractor is found to be robust even upto high noise levels of 50%. An advantage of recurrence network measures over the conventional nonlinear measures is that they can be applied on short and non stationary time series data. By using the results obtained from the above analysis, we go on to analyse the light curves from a dominant black hole system and show that the recurrence network measures are capable of identifying the nature of noise contamination in a time series.

Investigation of the 16-year and 18-year ZTD Time Series Derived from GPS Data Processing

NASA Astrophysics Data System (ADS)

Bałdysz, Zofia; Nykiel, Grzegorz; Figurski, Mariusz; Szafranek, Karolina; KroszczyńSki, Krzysztof

2015-08-01

The GPS system can play an important role in activities related to the monitoring of climate. Long time series, coherent strategy, and very high quality of tropospheric parameter Zenith Tropospheric Delay (ZTD) estimated on the basis of GPS data analysis allows to investigate its usefulness for climate research as a direct GPS product. This paper presents results of analysis of 16-year time series derived from EUREF Permanent Network (EPN) reprocessing performed by the Military University of Technology. For 58 stations Lomb-Scargle periodograms were performed in order to obtain information about the oscillations in ZTD time series. Seasonal components and linear trend were estimated using Least Square Estimation (LSE) and Mann—Kendall trend test was used to confirm the presence of a linear trend designated by LSE method. In order to verify the impact of the length of time series on trend value, comparison between 16 and 18 years were performed.

Multiscale entropy-based methods for heart rate variability complexity analysis

NASA Astrophysics Data System (ADS)

Silva, Luiz Eduardo Virgilio; Cabella, Brenno Caetano Troca; Neves, Ubiraci Pereira da Costa; Murta Junior, Luiz Otavio

2015-03-01

Physiologic complexity is an important concept to characterize time series from biological systems, which associated to multiscale analysis can contribute to comprehension of many complex phenomena. Although multiscale entropy has been applied to physiological time series, it measures irregularity as function of scale. In this study we purpose and evaluate a set of three complexity metrics as function of time scales. Complexity metrics are derived from nonadditive entropy supported by generation of surrogate data, i.e. SDiffqmax, qmax and qzero. In order to access accuracy of proposed complexity metrics, receiver operating characteristic (ROC) curves were built and area under the curves was computed for three physiological situations. Heart rate variability (HRV) time series in normal sinus rhythm, atrial fibrillation, and congestive heart failure data set were analyzed. Results show that proposed metric for complexity is accurate and robust when compared to classic entropic irregularity metrics. Furthermore, SDiffqmax is the most accurate for lower scales, whereas qmax and qzero are the most accurate when higher time scales are considered. Multiscale complexity analysis described here showed potential to assess complex physiological time series and deserves further investigation in wide context.

Modeling BAS Dysregulation in Bipolar Disorder.

PubMed

Hamaker, Ellen L; Grasman, Raoul P P P; Kamphuis, Jan Henk

2016-08-01

Time series analysis is a technique that can be used to analyze the data from a single subject and has great potential to investigate clinically relevant processes like affect regulation. This article uses time series models to investigate the assumed dysregulation of affect that is associated with bipolar disorder. By formulating a number of alternative models that capture different kinds of theoretically predicted dysregulation, and by comparing these in both bipolar patients and controls, we aim to illustrate the heuristic potential this method of analysis has for clinical psychology. We argue that, not only can time series analysis elucidate specific maladaptive dynamics associated with psychopathology, it may also be clinically applied in symptom monitoring and the evaluation of therapeutic interventions.

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Diffuse nutrient losses and the impact factors determining their regional differences in four catchments from North to South China

NASA Astrophysics Data System (ADS)

Zhang, Yongyong; Zhou, Yujian; Shao, Quanxi; Liu, Hongbin; Lei, Qiuliang; Zhai, Xiaoyan; Wang, Xuelei

2016-12-01

Diffuse nutrient loss mechanism is complicated and shows remarkably regional differences due to spatial heterogeneities of underlying surface conditions, climate and agricultural practices. Moreover, current available observations are still hard to support the identification of impact factors due to different time or space steps. In this study, an integrated water system model (HEQM) was adopted to obtain the simulated loads of diffuse components (carriers: runoff and sediment; nutrient: total nitrogen (TN) and total phosphorous (TP)) with synchronous scales. Multivariable statistical analysis approaches (Analysis of Similarity and redundancy analysis) were used to assess the regional differences, and to identify impact factors as well as their contributions. Four catchments were selected as our study areas, i.e., Xiahui and Zhangjiafen Catchments of Miyun Basin in North China, Yuliang and Tunxi Catchments of Xin'anjiang Basin in South China. Results showed that the model performances of monthly processes were very good for runoff and good for sediment, TN and TP. The annual average coefficients of all the diffuse components in Xin'anjiang Basin were much greater than those in Miyun Basin, and showed significantly regional differences. All the selected impact factors interpreted 72.87-82.16% of the regional differences of carriers, and 62.72-71.62% of those of nutrient coefficients, respectively. For individual impact factor categories, the critical category was geography, followed by land-use/cover, carriers, climate, as well as soil and agricultural practices in Miyun Basin, or agricultural practices and soil in Xin'anjiang Basin. For individual factors, the critical factors were locations for the carrier regional differences, and carriers or chemical fertilizer for the nutrient regional differences. This study is expected to promote further applications of integrated water system model and multivariable statistical analysis in the diffuse nutrient studies, and provide a scientific support for the diffuse pollution control and management in China.

Foliar nutrient analysis of sugar maple decline: retrospective vector diagnosis

Treesearch

Victor R. Timmer; Yuanxin Teng

1999-01-01

Accuracy of traditional foiiar analysis of nutrient disorders in sugar maple (Acer saccharum Marsh) is limited by lack of validation and confounding by nutrient interactions. Vector nutrient diagnosis is relatively free of these problems. The technique is demonstrated retrospectively on four case studies. Diagnostic interpretations consistently...

Are nutrient databases and nutrient analysis systems ready for the International implications of nutrigenomics?

USDA-ARS?s Scientific Manuscript database

Our objective is to discuss the implications internationally of the increased focus on nutrigenomics as the underlying basis for individualized health promotion and chronic disease prevention and the challenges presented to existing nutrient database and nutrient analysis systems by these trends. De...

76 FR 25345 - Annual Assessment of the Status of Competition in the Market for the Delivery of Video Programming

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-04

... as of June 30 of the relevant year to monitor trends on an annual basis. To continue our time-series... video programming? 24. MVPD Performance. We seek comment on the information and time- series data we... Television Performance. We seek information and time- series data for the analysis of various performance...

EO-1 Hyperion reflectance time series at calibration and validation sites: stability and sensitivity to seasonal dynamics

Treesearch

Petya K. Entcheva Campbell; Elizabeth M. Middleton; Kurt J. Thome; Raymond F. Kokaly; Karl Fred Huemmrich; David Lagomasino; Kimberly A. Novick; Nathaniel A. Brunsell

2013-01-01

This study evaluated Earth Observing 1 (EO-1) Hyperion reflectance time series at established calibration sites to assess the instrument stability and suitability for monitoring vegetation functional parameters. Our analysis using three pseudo-invariant calibration sites in North America indicated that the reflectance time series are devoid of apparent spectral trends...

Analysis in natural time domain of geoelectric time series monitored prior two strong earthquakes occurred in Mexico

NASA Astrophysics Data System (ADS)

Ramírez-Rojas, A.; Flores-Marquez, L. E.

2009-12-01

The short-time prediction of seismic phenomena is currently an important problem in the scientific community. In particular, the electromagnetic processes associated with seismic events take in great interest since the VAN method was implemented. The most important features of this methodology are the seismic electrical signals (SES) observed prior to strong earthquakes. SES has been observed in the electromagnetic series linked to EQs in Greece, Japan and Mexico. By mean of the so-called natural time domain, introduced by Varotsos et al. (2001), they could characterize signals of dichotomic nature observed in different systems, like SES and ionic current fluctuations in membrane channels. In this work we analyze SES observed in geoelectric time series monitored in Guerrero, México. Our analysis concern with two strong earthquakes occurred, on October 24, 1993 (M=6.6) and September 14, 1995 (M=7.3). The time series of the first one displayed a seismic electric signal six days before the main shock and for the second case the time series displayed dichotomous-like fluctuations some months before the EQ. In this work we present the first results of the analysis in natural time domain for the two cases which seems to be agreeing with the results reported by Varotsos. P. Varotsos, N. Sarlis, and E. Skordas, Practica of the Athens Academy 76, 388 (2001).

Mathematical Sciences Division 1992 Programs

DTIC Science & Technology

1992-10-01

statistical theory that underlies modern signal analysis . There is a strong emphasis on stochastic processes and time series , particularly those which...include optimal resource planning and real- time scheduling of stochastic shop-floor processes. Scheduling systems will be developed that can adapt to...make forecasts for the length-of-service time series . Protocol analysis of these sessions will be used to idenify relevant contextual features and to

Multiscale Poincaré plots for visualizing the structure of heartbeat time series.

PubMed

Henriques, Teresa S; Mariani, Sara; Burykin, Anton; Rodrigues, Filipa; Silva, Tiago F; Goldberger, Ary L

2016-02-09

Poincaré delay maps are widely used in the analysis of cardiac interbeat interval (RR) dynamics. To facilitate visualization of the structure of these time series, we introduce multiscale Poincaré (MSP) plots. Starting with the original RR time series, the method employs a coarse-graining procedure to create a family of time series, each of which represents the system's dynamics in a different time scale. Next, the Poincaré plots are constructed for the original and the coarse-grained time series. Finally, as an optional adjunct, color can be added to each point to represent its normalized frequency. We illustrate the MSP method on simulated Gaussian white and 1/f noise time series. The MSP plots of 1/f noise time series reveal relative conservation of the phase space area over multiple time scales, while those of white noise show a marked reduction in area. We also show how MSP plots can be used to illustrate the loss of complexity when heartbeat time series from healthy subjects are compared with those from patients with chronic (congestive) heart failure syndrome or with atrial fibrillation. This generalized multiscale approach to Poincaré plots may be useful in visualizing other types of time series.

Quantifying memory in complex physiological time-series.

PubMed

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Quantifying Memory in Complex Physiological Time-Series

PubMed Central

Shirazi, Amir H.; Raoufy, Mohammad R.; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R.; Amodio, Piero; Jafari, G. Reza; Montagnese, Sara; Mani, Ali R.

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of “memory length” was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are ‘forgotten’ quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations. PMID:24039811

Fate and effects of nitrogen and phosphorus in shallow vegetated aquatic ecosystems

USGS Publications Warehouse

Fairchild, James F.; Vradenburg, Leigh Ann

2006-01-01

Nitrate concentrations have greatly increased in streams and rivers draining agricultural regions of the Midwestern United States, increasing nitrate transport to the Gulf of Mexico has been implicated in the hypoxic conditions that threaten the productivity of marine fisheries. Increases in nitrate concentrations have been attributed to a combination of factors including agricultural expansion, increased nitrogen application rates, increased tile drainage, and loss of riparian Wetlands, These landscape-level changes have resulted in a decreased natural capacity for nitrogen uptake, removal, and cycling back to the atmosphere. Land managers are increasingly interested in using wetland construction and rehabilitation as a management practice to reduce loss of nitrate from the terrestrial systems. Yet, relatively little is known about the limnological factors involved in nitrate removal by Wetland systems.We conducted a series of studies from 1999-2000 to investigate the functional capacity of shallow, macrophyte-dominated pond wetland systems for uptake, assimilation, and retention of nitrogen (N) and phosphorus (P). We evaluated four factors that were hypothesized to influence nutrient uptake and assimilation: 1) nitrate loading rates; 2) nitrogen to phosphorus (N.P) ratios; 3) frequency of dosing/application; and 4) timing of dose initiation.Nutrient assimilation was rapid; store than 90% of added nutrients were removed from the water column in all treatments. Neither variation in N:P ratios (evaluated range, <13:1 to -114.1), frequency of application (weekly or bi-weekly), nor liming of dose initiation relative to macrophyte development (0%, 15-25%, or 75-90% maximum biomass) had significant effects on nutrient assimilation of wetland community dynamics. Maximum loading of nitrate (60 g N/m2 2.4 g P/m2) applied as six weekly doses stimulated algal communities, but inhibited macrophyte communities.Predicted shifts from a stable state of macrophyte- to phytoplankton-dominance did not occur due to nutrient additions. Macrophytes, phytoplankton, and the sediment surface were all significant factors in the removal of nitrate from the Water column. Overall, these shallow macrophyte-dominated systems provided an efficient means of removing nutrients from the water column. Construction or rehabilitation of shallow, vegetated wetlands may offer promise as land management practices for nutrient removal in agricultural watersheds.

Time-series RNA-seq analysis package (TRAP) and its application to the analysis of rice, Oryza sativa L. ssp. Japonica, upon drought stress.

PubMed

Jo, Kyuri; Kwon, Hawk-Bin; Kim, Sun

2014-06-01

Measuring expression levels of genes at the whole genome level can be useful for many purposes, especially for revealing biological pathways underlying specific phenotype conditions. When gene expression is measured over a time period, we have opportunities to understand how organisms react to stress conditions over time. Thus many biologists routinely measure whole genome level gene expressions at multiple time points. However, there are several technical difficulties for analyzing such whole genome expression data. In addition, these days gene expression data is often measured by using RNA-sequencing rather than microarray technologies and then analysis of expression data is much more complicated since the analysis process should start with mapping short reads and produce differentially activated pathways and also possibly interactions among pathways. In addition, many useful tools for analyzing microarray gene expression data are not applicable for the RNA-seq data. Thus a comprehensive package for analyzing time series transcriptome data is much needed. In this article, we present a comprehensive package, Time-series RNA-seq Analysis Package (TRAP), integrating all necessary tasks such as mapping short reads, measuring gene expression levels, finding differentially expressed genes (DEGs), clustering and pathway analysis for time-series data in a single environment. In addition to implementing useful algorithms that are not available for RNA-seq data, we extended existing pathway analysis methods, ORA and SPIA, for time series analysis and estimates statistical values for combined dataset by an advanced metric. TRAP also produces visual summary of pathway interactions. Gene expression change labeling, a practical clustering method used in TRAP, enables more accurate interpretation of the data when combined with pathway analysis. We applied our methods on a real dataset for the analysis of rice (Oryza sativa L. Japonica nipponbare) upon drought stress. The result showed that TRAP was able to detect pathways more accurately than several existing methods. TRAP is available at http://biohealth.snu.ac.kr/software/TRAP/. Copyright © 2014 Elsevier Inc. All rights reserved.

Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces

NASA Astrophysics Data System (ADS)

Qian, Xi-Yuan; Liu, Ya-Min; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

2015-06-01

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography.

PubMed

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

Testing for intracycle determinism in pseudoperiodic time series.

PubMed

Coelho, Mara C S; Mendes, Eduardo M A M; Aguirre, Luis A

2008-06-01

A determinism test is proposed based on the well-known method of the surrogate data. Assuming predictability to be a signature of determinism, the proposed method checks for intracycle (e.g., short-term) determinism in the pseudoperiodic time series for which standard methods of surrogate analysis do not apply. The approach presented is composed of two steps. First, the data are preprocessed to reduce the effects of seasonal and trend components. Second, standard tests of surrogate analysis can then be used. The determinism test is applied to simulated and experimental pseudoperiodic time series and the results show the applicability of the proposed test.

Application of computational mechanics to the analysis of natural data: an example in geomagnetism.

PubMed

Clarke, Richard W; Freeman, Mervyn P; Watkins, Nicholas W

2003-01-01

We discuss how the ideal formalism of computational mechanics can be adapted to apply to a noninfinite series of corrupted and correlated data, that is typical of most observed natural time series. Specifically, a simple filter that removes the corruption that creates rare unphysical causal states is demonstrated, and the concept of effective soficity is introduced. We believe that computational mechanics cannot be applied to a noisy and finite data series without invoking an argument based upon effective soficity. A related distinction between noise and unresolved structure is also defined: Noise can only be eliminated by increasing the length of the time series, whereas the resolution of previously unresolved structure only requires the finite memory of the analysis to be increased. The benefits of these concepts are demonstrated in a simulated times series by (a) the effective elimination of white noise corruption from a periodic signal using the expletive filter and (b) the appearance of an effectively sofic region in the statistical complexity of a biased Poisson switch time series that is insensitive to changes in the word length (memory) used in the analysis. The new algorithm is then applied to an analysis of a real geomagnetic time series measured at Halley, Antarctica. Two principal components in the structure are detected that are interpreted as the diurnal variation due to the rotation of the Earth-based station under an electrical current pattern that is fixed with respect to the Sun-Earth axis and the random occurrence of a signature likely to be that of the magnetic substorm. In conclusion, some useful terminology for the discussion of model construction in general is introduced.

Assessing the impacts of salinity and nutrient stress to Ruppia ...

EPA Pesticide Factsheets

Healthy seagrass beds were once found throughout the shallow areas of Narragansett Bay, R.I. but have disappeared due to infilling, pollution and disease. In Greenwich Bay, a highly developed embayment within Narragansett Bay, Ruppia maritima has colonized an area on the northern shore historically dominated by Zostera marina. Ruppia is extremely salinity tolerant, and may also be more nutrient tolerant than Zostera. To test this hypothesis 6-week microcosm experiments were conducted in the summers of 2014 and 2015. Microcosms were renewed daily to simulate tidal flushing and the water column was dosed with a 15N tracer for the first week of the experiments. In the 2014 microcosm experiment two salinity (20, 30 ppt) and four nutrient (0, 5, 10, 30 µM inorganic N) levels were used to test the species’ relative tolerance. This experiment yielded structurally significant results for Ruppia but no significant differences were detected for Zostera. In 2015 this experiment was performed for a second time with lower salinity (5, 30 ppt) and higher nutrients (0, 30, 100, 300, 1000 µM inorganic N) in order to determine Zostera’s tolerance to nutrient and salinity stress and confirm the previously observed Ruppia results. Both species had significant structural responses to the nutrient and salinity variables. Isotopic analysis run on above-ground tissue indicated that with increasing nutrient levels δ15N in the seagrass shoots increased, suggesting that nutrients

GATE: software for the analysis and visualization of high-dimensional time series expression data.

PubMed

MacArthur, Ben D; Lachmann, Alexander; Lemischka, Ihor R; Ma'ayan, Avi

2010-01-01

We present Grid Analysis of Time series Expression (GATE), an integrated computational software platform for the analysis and visualization of high-dimensional biomolecular time series. GATE uses a correlation-based clustering algorithm to arrange molecular time series on a two-dimensional hexagonal array and dynamically colors individual hexagons according to the expression level of the molecular component to which they are assigned, to create animated movies of systems-level molecular regulatory dynamics. In order to infer potential regulatory control mechanisms from patterns of correlation, GATE also allows interactive interroga-tion of movies against a wide variety of prior knowledge datasets. GATE movies can be paused and are interactive, allowing users to reconstruct networks and perform functional enrichment analyses. Movies created with GATE can be saved in Flash format and can be inserted directly into PDF manuscript files as interactive figures. GATE is available for download and is free for academic use from http://amp.pharm.mssm.edu/maayan-lab/gate.htm

The Microbial Database for Danish wastewater treatment plants with nutrient removal (MiDas-DK) - a tool for understanding activated sludge population dynamics and community stability.

PubMed

Mielczarek, A T; Saunders, A M; Larsen, P; Albertsen, M; Stevenson, M; Nielsen, J L; Nielsen, P H

2013-01-01

Since 2006 more than 50 Danish full-scale wastewater treatment plants with nutrient removal have been investigated in a project called 'The Microbial Database for Danish Activated Sludge Wastewater Treatment Plants with Nutrient Removal (MiDas-DK)'. Comprehensive sets of samples have been collected, analyzed and associated with extensive operational data from the plants. The community composition was analyzed by quantitative fluorescence in situ hybridization (FISH) supported by 16S rRNA amplicon sequencing and deep metagenomics. MiDas-DK has been a powerful tool to study the complex activated sludge ecosystems, and, besides many scientific articles on fundamental issues on mixed communities encompassing nitrifiers, denitrifiers, bacteria involved in P-removal, hydrolysis, fermentation, and foaming, the project has provided results that can be used to optimize the operation of full-scale plants and carry out trouble-shooting. A core microbial community has been defined comprising the majority of microorganisms present in the plants. Time series have been established, providing an overview of temporal variations in the different plants. Interestingly, although most microorganisms were present in all plants, there seemed to be plant-specific factors that controlled the population composition thereby keeping it unique in each plant over time. Statistical analyses of FISH and operational data revealed some correlations, but less than expected. MiDas-DK (www.midasdk.dk) will continue over the next years and we hope the approach can inspire others to make similar projects in other parts of the world to get a more comprehensive understanding of microbial communities in wastewater engineering.

Defect-Repairable Latent Feature Extraction of Driving Behavior via a Deep Sparse Autoencoder

PubMed Central

Taniguchi, Tadahiro; Takenaka, Kazuhito; Bando, Takashi

2018-01-01

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE. PMID:29462931

The application of complex network time series analysis in turbulent heated jets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charakopoulos, A. K.; Karakasidis, T. E., E-mail: thkarak@uth.gr; Liakopoulos, A.

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topologicalmore » properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.« less

The application of complex network time series analysis in turbulent heated jets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charakopoulos, A. K.; Karakasidis, T. E., E-mail: thkarak@uth.gr; Liakopoulos, A.

2014-06-15

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topologicalmore » properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.« less

A method for analyzing temporal patterns of variability of a time series from Poincare plots.

PubMed

Fishman, Mikkel; Jacono, Frank J; Park, Soojin; Jamasebi, Reza; Thungtong, Anurak; Loparo, Kenneth A; Dick, Thomas E

2012-07-01

The Poincaré plot is a popular two-dimensional, time series analysis tool because of its intuitive display of dynamic system behavior. Poincaré plots have been used to visualize heart rate and respiratory pattern variabilities. However, conventional quantitative analysis relies primarily on statistical measurements of the cumulative distribution of points, making it difficult to interpret irregular or complex plots. Moreover, the plots are constructed to reflect highly correlated regions of the time series, reducing the amount of nonlinear information that is presented and thereby hiding potentially relevant features. We propose temporal Poincaré variability (TPV), a novel analysis methodology that uses standard techniques to quantify the temporal distribution of points and to detect nonlinear sources responsible for physiological variability. In addition, the analysis is applied across multiple time delays, yielding a richer insight into system dynamics than the traditional circle return plot. The method is applied to data sets of R-R intervals and to synthetic point process data extracted from the Lorenz time series. The results demonstrate that TPV complements the traditional analysis and can be applied more generally, including Poincaré plots with multiple clusters, and more consistently than the conventional measures and can address questions regarding potential structure underlying the variability of a data set.

Time-Series Analysis: Assessing the Effects of Multiple Educational Interventions in a Small-Enrollment Course

NASA Astrophysics Data System (ADS)

Warren, Aaron R.

2009-11-01

Time-series designs are an alternative to pretest-posttest methods that are able to identify and measure the impacts of multiple educational interventions, even for small student populations. Here, we use an instrument employing standard multiple-choice conceptual questions to collect data from students at regular intervals. The questions are modified by asking students to distribute 100 Confidence Points among the options in order to indicate the perceived likelihood of each answer option being the correct one. Tracking the class-averaged ratings for each option produces a set of time-series. ARIMA (autoregressive integrated moving average) analysis is then used to test for, and measure, changes in each series. In particular, it is possible to discern which educational interventions produce significant changes in class performance. Cluster analysis can also identify groups of students whose ratings evolve in similar ways. A brief overview of our methods and an example are presented.

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.

PubMed

Monserud, R A; Marshall, J D

2001-09-01

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.

Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG.

PubMed

van Mierlo, Pieter; Lie, Octavian; Staljanssens, Willeke; Coito, Ana; Vulliémoz, Serge

2018-04-26

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.

Functional Foods Baseline and Requirements Analysis

NASA Technical Reports Server (NTRS)

Cooper, M. R.; Bermudez-Aguirre, L. D.; Douglas, G.

2015-01-01

Current spaceflight foods were evaluated to determine if their nutrient profile supports positioning as a functional food and if the stability of the bioactive compound within the food matrix over an extended shelf-life correlated with the expected storage duration during the mission. Specifically, the research aims were: Aim A. To determine the amount of each nutrient in representative spaceflight foods immediately after processing and at predetermined storage time to establish the current nutritional state. Aim B. To identify the requirements to develop foods that stabilize these nutrients such that required concentrations are maintained in the space food system throughout long duration missions (up to five years). Aim C. To coordinate collaborations with health and performance groups that may require functional foods as a countermeasure.

Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

NASA Astrophysics Data System (ADS)

Lyons, Mitchell B.; Roelfsema, Chris M.; Phinn, Stuart R.

2013-03-01

The spatial and temporal dynamics of seagrasses have been well studied at the leaf to patch scales, however, the link to large spatial extent landscape and population dynamics is still unresolved in seagrass ecology. Traditional remote sensing approaches have lacked the temporal resolution and consistency to appropriately address this issue. This study uses two high temporal resolution time-series of thematic seagrass cover maps to examine the spatial and temporal dynamics of seagrass at both an inter- and intra-annual time scales, one of the first globally to do so at this scale. Previous work by the authors developed an object-based approach to map seagrass cover level distribution from a long term archive of Landsat TM and ETM+ images on the Eastern Banks (≈200 km2), Moreton Bay, Australia. In this work a range of trend and time-series analysis methods are demonstrated for a time-series of 23 annual maps from 1988 to 2010 and a time-series of 16 monthly maps during 2008-2010. Significant new insight was presented regarding the inter- and intra-annual dynamics of seagrass persistence over time, seagrass cover level variability, seagrass cover level trajectory, and change in area of seagrass and cover levels over time. Overall we found that there was no significant decline in total seagrass area on the Eastern Banks, but there was a significant decline in seagrass cover level condition. A case study of two smaller communities within the Eastern Banks that experienced a decline in both overall seagrass area and condition are examined in detail, highlighting possible differences in environmental and process drivers. We demonstrate how trend and time-series analysis enabled seagrass distribution to be appropriately assessed in context of its spatial and temporal history and provides the ability to not only quantify change, but also describe the type of change. We also demonstrate the potential use of time-series analysis products to investigate seagrass growth and decline as well as the processes that drive it. This study demonstrates clear benefits over traditional seagrass mapping and monitoring approaches, and provides a proof of concept for the use of trend and time-series analysis of remotely sensed seagrass products to benefit current endeavours in seagrass ecology.

Beyond trend analysis: How a modified breakpoint analysis enhances knowledge of agricultural production after Zimbabwe's fast track land reform

NASA Astrophysics Data System (ADS)

Hentze, Konrad; Thonfeld, Frank; Menz, Gunter

2017-10-01

In the discourse on land reform assessments, a significant lack of spatial and time-series data has been identified, especially with respect to Zimbabwe's ;Fast-Track Land Reform Programme; (FTLRP). At the same time, interest persists among land use change scientists to evaluate causes of land use change and therefore to increase the explanatory power of remote sensing products. This study recognizes these demands and aims to provide input on both levels: Evaluating the potential of satellite remote sensing time-series to answer questions which evolved after intensive land redistribution efforts in Zimbabwe; and investigating how time-series analysis of Normalized Difference Vegetation Index (NDVI) can be enhanced to provide information on land reform induced land use change. To achieve this, two time-series methods are applied to MODIS NDVI data: Seasonal Trend Analysis (STA) and Breakpoint Analysis for Additive Season and Trend (BFAST). In our first analysis, a link of agricultural productivity trends to different land tenure regimes shows that regional clustering of trends is more dominant than a relationship between tenure and trend with a slightly negative slope for all regimes. We demonstrate that clusters of strong negative and positive productivity trends are results of changing irrigation patterns. To locate emerging and fallow irrigation schemes in semi-arid Zimbabwe, a new multi-method approach is developed which allows to map changes from bimodal seasonal phenological patterns to unimodal and vice versa. With an enhanced breakpoint analysis through the combination of STA and BFAST, we are able to provide a technique that can be applied on large scale to map status and development of highly productive cropping systems, which are key for food production, national export and local employment. We therefore conclude that the combination of existing and accessible time-series analysis methods: is able to achieve both: overcoming demonstrated limitations of MODIS based trend analysis and enhancing knowledge of Zimbabwe's FTLRP.

Complex-valued time-series correlation increases sensitivity in FMRI analysis.

PubMed

Kociuba, Mary C; Rowe, Daniel B

2016-07-01

To develop a linear matrix representation of correlation between complex-valued (CV) time-series in the temporal Fourier frequency domain, and demonstrate its increased sensitivity over correlation between magnitude-only (MO) time-series in functional MRI (fMRI) analysis. The standard in fMRI is to discard the phase before the statistical analysis of the data, despite evidence of task related change in the phase time-series. With a real-valued isomorphism representation of Fourier reconstruction, correlation is computed in the temporal frequency domain with CV time-series data, rather than with the standard of MO data. A MATLAB simulation compares the Fisher-z transform of MO and CV correlations for varying degrees of task related magnitude and phase amplitude change in the time-series. The increased sensitivity of the complex-valued Fourier representation of correlation is also demonstrated with experimental human data. Since the correlation description in the temporal frequency domain is represented as a summation of second order temporal frequencies, the correlation is easily divided into experimentally relevant frequency bands for each voxel's temporal frequency spectrum. The MO and CV correlations for the experimental human data are analyzed for four voxels of interest (VOIs) to show the framework with high and low contrast-to-noise ratios in the motor cortex and the supplementary motor cortex. The simulation demonstrates the increased strength of CV correlations over MO correlations for low magnitude contrast-to-noise time-series. In the experimental human data, the MO correlation maps are noisier than the CV maps, and it is more difficult to distinguish the motor cortex in the MO correlation maps after spatial processing. Including both magnitude and phase in the spatial correlation computations more accurately defines the correlated left and right motor cortices. Sensitivity in correlation analysis is important to preserve the signal of interest in fMRI data sets with high noise variance, and avoid excessive processing induced correlation. Copyright © 2016 Elsevier Inc. All rights reserved.

Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations.

PubMed

Buras, Allan; van der Maaten-Theunissen, Marieke; van der Maaten, Ernst; Ahlgrimm, Svenja; Hermann, Philipp; Simard, Sonia; Heinrich, Ingo; Helle, Gerd; Unterseher, Martin; Schnittler, Martin; Eusemann, Pascal; Wilmking, Martin

2016-01-01

This paper introduces a new approach-the Principal Component Gradient Analysis (PCGA)-to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA.

Tuning the Voices of a Choir: Detecting Ecological Gradients in Time-Series Populations

PubMed Central

Buras, Allan; van der Maaten-Theunissen, Marieke; van der Maaten, Ernst; Ahlgrimm, Svenja; Hermann, Philipp; Simard, Sonia; Heinrich, Ingo; Helle, Gerd; Unterseher, Martin; Schnittler, Martin; Eusemann, Pascal; Wilmking, Martin

2016-01-01

This paper introduces a new approach–the Principal Component Gradient Analysis (PCGA)–to detect ecological gradients in time-series populations, i.e. several time-series originating from different individuals of a population. Detection of ecological gradients is of particular importance when dealing with time-series from heterogeneous populations which express differing trends. PCGA makes use of polar coordinates of loadings from the first two axes obtained by principal component analysis (PCA) to define groups of similar trends. Based on the mean inter-series correlation (rbar) the gain of increasing a common underlying signal by PCGA groups is quantified using Monte Carlo Simulations. In terms of validation PCGA is compared to three other existing approaches. Focusing on dendrochronological examples, PCGA is shown to correctly determine population gradients and in particular cases to be advantageous over other considered methods. Furthermore, PCGA groups in each example allowed for enhancing the strength of a common underlying signal and comparably well as hierarchical cluster analysis. Our results indicate that PCGA potentially allows for a better understanding of mechanisms causing time-series population gradients as well as objectively enhancing the performance of climate transfer functions in dendroclimatology. While our examples highlight the relevance of PCGA to the field of dendrochronology, we believe that also other disciplines working with data of comparable structure may benefit from PCGA. PMID:27467508

Identification of Appropriate Reference Genes for Normalization of miRNA Expression in Grafted Watermelon Plants under Different Nutrient Stresses

PubMed Central

Wu, Weifang; Deng, Qin; Shi, Pibiao; Yang, Jinghua; Hu, Zhongyuan; Zhang, Mingfang

2016-01-01

Watermelon (Citrullus lanatus) is a globally important crop belonging to the family Cucurbitaceae. The grafting technique is commonly used to improve its tolerance to stress, as well as to enhance its nutrient uptake and utilization. It is believed that miRNA is most likely involved in its nutrient-starvation response as a graft-transportable signal. The quantitative real-time reverse transcriptase polymerase chain reaction is the preferred method for miRNA functional analysis, in which reliable reference genes for normalization are crucial to ensure the accuracy. The purpose of this study was to select appropriate reference genes in scion (watermelon) and rootstocks (squash and bottle gourd) of grafted watermelon plants under normal growth conditions and nutrient stresses (nitrogen and phosphorus starvation). Under nutrient starvation, geNorm identified miR167c and miR167f as two most stable genes in both watermelon leaves and squash roots. miR166b was recommended by both geNorm and NormFinder as the best reference in bottle gourd roots under nutrient limitation. Expression of a new Cucurbitaceae miRNA, miR85, was used to validate the reliability of candidate reference genes under nutrient starvation. Moreover, by comparing several target genes expression in qRT-PCR analysis with those in RNA-seq data, miR166b and miR167c were proved to be the most suitable reference genes to normalize miRNA expression under normal growth condition in scion and rootstock tissues, respectively. This study represents the first comprehensive survey of the stability of miRNA reference genes in Cucurbitaceae and provides valuable information for investigating more accurate miRNA expression involving grafted watermelon plants. PMID:27749935

Identification of Appropriate Reference Genes for Normalization of miRNA Expression in Grafted Watermelon Plants under Different Nutrient Stresses.

PubMed

Wu, Weifang; Deng, Qin; Shi, Pibiao; Yang, Jinghua; Hu, Zhongyuan; Zhang, Mingfang

2016-01-01

Watermelon (Citrullus lanatus) is a globally important crop belonging to the family Cucurbitaceae. The grafting technique is commonly used to improve its tolerance to stress, as well as to enhance its nutrient uptake and utilization. It is believed that miRNA is most likely involved in its nutrient-starvation response as a graft-transportable signal. The quantitative real-time reverse transcriptase polymerase chain reaction is the preferred method for miRNA functional analysis, in which reliable reference genes for normalization are crucial to ensure the accuracy. The purpose of this study was to select appropriate reference genes in scion (watermelon) and rootstocks (squash and bottle gourd) of grafted watermelon plants under normal growth conditions and nutrient stresses (nitrogen and phosphorus starvation). Under nutrient starvation, geNorm identified miR167c and miR167f as two most stable genes in both watermelon leaves and squash roots. miR166b was recommended by both geNorm and NormFinder as the best reference in bottle gourd roots under nutrient limitation. Expression of a new Cucurbitaceae miRNA, miR85, was used to validate the reliability of candidate reference genes under nutrient starvation. Moreover, by comparing several target genes expression in qRT-PCR analysis with those in RNA-seq data, miR166b and miR167c were proved to be the most suitable reference genes to normalize miRNA expression under normal growth condition in scion and rootstock tissues, respectively. This study represents the first comprehensive survey of the stability of miRNA reference genes in Cucurbitaceae and provides valuable information for investigating more accurate miRNA expression involving grafted watermelon plants.

Community temporal variability increases with fluctuating resource availability

PubMed Central

Li, Wei; Stevens, M. Henry H.

2017-01-01

An increase in the quantity of available resources is known to affect temporal variability of aggregate community properties. However, it is unclear how might fluctuations in resource availability alter community-level temporal variability. Here we conduct a microcosm experiment with laboratory protist community subjected to manipulated resource pulses that vary in intensity, duration and time of supply, and examine the impact of fluctuating resource availability on temporal variability of the recipient community. The results showed that the temporal variation of total protist abundance increased with the magnitude of resource pulses, as protist community receiving infrequent resource pulses (i.e., high-magnitude nutrients per pulse) was relatively more unstable than community receiving multiple resource pulses (i.e., low-magnitude nutrients per pulse), although the same total amounts of nutrients were added to each community. Meanwhile, the timing effect of fluctuating resources did not significantly alter community temporal variability. Further analysis showed that fluctuating resource availability increased community temporal variability by increasing the degree of community-wide species synchrony and decreasing the stabilizing effects of dominant species. Hence, the importance of fluctuating resource availability in influencing community stability and the regulatory mechanisms merit more attention, especially when global ecosystems are experiencing high rates of anthropogenic nutrient inputs. PMID:28345592

Community temporal variability increases with fluctuating resource availability

NASA Astrophysics Data System (ADS)

Li, Wei; Stevens, M. Henry H.

2017-03-01

An increase in the quantity of available resources is known to affect temporal variability of aggregate community properties. However, it is unclear how might fluctuations in resource availability alter community-level temporal variability. Here we conduct a microcosm experiment with laboratory protist community subjected to manipulated resource pulses that vary in intensity, duration and time of supply, and examine the impact of fluctuating resource availability on temporal variability of the recipient community. The results showed that the temporal variation of total protist abundance increased with the magnitude of resource pulses, as protist community receiving infrequent resource pulses (i.e., high-magnitude nutrients per pulse) was relatively more unstable than community receiving multiple resource pulses (i.e., low-magnitude nutrients per pulse), although the same total amounts of nutrients were added to each community. Meanwhile, the timing effect of fluctuating resources did not significantly alter community temporal variability. Further analysis showed that fluctuating resource availability increased community temporal variability by increasing the degree of community-wide species synchrony and decreasing the stabilizing effects of dominant species. Hence, the importance of fluctuating resource availability in influencing community stability and the regulatory mechanisms merit more attention, especially when global ecosystems are experiencing high rates of anthropogenic nutrient inputs.

Application of modern tests for stationarity to single-trial MEG data: transferring powerful statistical tools from econometrics to neuroscience.

PubMed

Kipiński, Lech; König, Reinhard; Sielużycki, Cezary; Kordecki, Wojciech

2011-10-01

Stationarity is a crucial yet rarely questioned assumption in the analysis of time series of magneto- (MEG) or electroencephalography (EEG). One key drawback of the commonly used tests for stationarity of encephalographic time series is the fact that conclusions on stationarity are only indirectly inferred either from the Gaussianity (e.g. the Shapiro-Wilk test or Kolmogorov-Smirnov test) or the randomness of the time series and the absence of trend using very simple time-series models (e.g. the sign and trend tests by Bendat and Piersol). We present a novel approach to the analysis of the stationarity of MEG and EEG time series by applying modern statistical methods which were specifically developed in econometrics to verify the hypothesis that a time series is stationary. We report our findings of the application of three different tests of stationarity--the Kwiatkowski-Phillips-Schmidt-Schin (KPSS) test for trend or mean stationarity, the Phillips-Perron (PP) test for the presence of a unit root and the White test for homoscedasticity--on an illustrative set of MEG data. For five stimulation sessions, we found already for short epochs of duration of 250 and 500 ms that, although the majority of the studied epochs of single MEG trials were usually mean-stationary (KPSS test and PP test), they were classified as nonstationary due to their heteroscedasticity (White test). We also observed that the presence of external auditory stimulation did not significantly affect the findings regarding the stationarity of the data. We conclude that the combination of these tests allows a refined analysis of the stationarity of MEG and EEG time series.

Quantifying the capacity of compost buffers for treating agricultural runoff

NASA Astrophysics Data System (ADS)

Naranjo, S. A.; Beighley, R. E.; Buyuksonmez, F.

2007-12-01

Agricultural operations, specifically, avocado and commercial nurseries require frequent and significant fertilizing and irrigating which tends to result in excessive nutrient leaching and off-site runoff. The increased runoff contains high concentrations of nutrients which negatively impacts stream water quality. Researcher has demonstrated that best management practices such as compost buffers can be effective for reducing nutrient and sediment concentrations in agricultural runoff. The objective of this research is to evaluate both the hydraulic capacity and the nutrient removal efficiency of: (a) compost buffers and (b) buffers utilizing a combination of vegetation and compost. A series of experiments will be performed in the environmental hydraulics laboratory at San Diego State University. A tilting flume 12-m long, 27-cm wide and 25-cm deep will be used. Discharge is propelled by an axial flow pump powered by a variable speed motor with a maximum capacity of 30 liters per second. The experiments are designed to measure the ratio compost mass per flow rate per linear width. Two different discharges will be measured: (a) treatment discharge (maximum flow rate such that the buffer decreases the incoming nitrogen and phosphorus concentrations below a maximum allowable limit) and (b) breaking discharge (maximum flow rate the buffer can tolerate without structural failure). Experimental results are presented for the hydraulic analysis, and preliminary results are presented for the removal of nitrogen and phosphorus from runoff. The results from this project will be used to develop guidelines for installing compost buffers along the perimeters of nursery sites and avocado groves in southern California.

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

NASA Astrophysics Data System (ADS)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis.

PubMed

Lee, Hyokyeong; Moody-Davis, Asher; Saha, Utsab; Suzuki, Brian M; Asarnow, Daniel; Chen, Steven; Arkin, Michelle; Caffrey, Conor R; Singh, Rahul

2012-01-01

Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery. We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis. We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs. The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind.

Quantification and clustering of phenotypic screening data using time-series analysis for chemotherapy of schistosomiasis

PubMed Central

2012-01-01

Background Neglected tropical diseases, especially those caused by helminths, constitute some of the most common infections of the world's poorest people. Development of techniques for automated, high-throughput drug screening against these diseases, especially in whole-organism settings, constitutes one of the great challenges of modern drug discovery. Method We present a method for enabling high-throughput phenotypic drug screening against diseases caused by helminths with a focus on schistosomiasis. The proposed method allows for a quantitative analysis of the systemic impact of a drug molecule on the pathogen as exhibited by the complex continuum of its phenotypic responses. This method consists of two key parts: first, biological image analysis is employed to automatically monitor and quantify shape-, appearance-, and motion-based phenotypes of the parasites. Next, we represent these phenotypes as time-series and show how to compare, cluster, and quantitatively reason about them using techniques of time-series analysis. Results We present results on a number of algorithmic issues pertinent to the time-series representation of phenotypes. These include results on appropriate representation of phenotypic time-series, analysis of different time-series similarity measures for comparing phenotypic responses over time, and techniques for clustering such responses by similarity. Finally, we show how these algorithmic techniques can be used for quantifying the complex continuum of phenotypic responses of parasites. An important corollary is the ability of our method to recognize and rigorously group parasites based on the variability of their phenotypic response to different drugs. Conclusions The methods and results presented in this paper enable automatic and quantitative scoring of high-throughput phenotypic screens focused on helmintic diseases. Furthermore, these methods allow us to analyze and stratify parasites based on their phenotypic response to drugs. Together, these advancements represent a significant breakthrough for the process of drug discovery against schistosomiasis in particular and can be extended to other helmintic diseases which together afflict a large part of humankind. PMID:22369037

Topological data analysis of financial time series: Landscapes of crashes

NASA Astrophysics Data System (ADS)

Gidea, Marian; Katz, Yuri

2018-02-01

We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional time series. Using a sliding window, we extract time-dependent point cloud data sets, to which we associate a topological space. We detect transient loops that appear in this space, and we measure their persistence. This is encoded in real-valued functions referred to as a 'persistence landscapes'. We quantify the temporal changes in persistence landscapes via their Lp-norms. We test this procedure on multidimensional time series generated by various non-linear and non-equilibrium models. We find that, in the vicinity of financial meltdowns, the Lp-norms exhibit strong growth prior to the primary peak, which ascends during a crash. Remarkably, the average spectral density at low frequencies of the time series of Lp-norms of the persistence landscapes demonstrates a strong rising trend for 250 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. Our study suggests that TDA provides a new type of econometric analysis, which complements the standard statistical measures. The method can be used to detect early warning signals of imminent market crashes. We believe that this approach can be used beyond the analysis of financial time series presented here.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

PubMed

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Estimation of Parameters from Discrete Random Nonstationary Time Series

NASA Astrophysics Data System (ADS)

Takayasu, H.; Nakamura, T.

For the analysis of nonstationary stochastic time series we introduce a formulation to estimate the underlying time-dependent parameters. This method is designed for random events with small numbers that are out of the applicability range of the normal distribution. The method is demonstrated for numerical data generated by a known system, and applied to time series of traffic accidents, batting average of a baseball player and sales volume of home electronics.

A New Modified Histogram Matching Normalization for Time Series Microarray Analysis.

PubMed

Astola, Laura; Molenaar, Jaap

2014-07-01

Microarray data is often utilized in inferring regulatory networks. Quantile normalization (QN) is a popular method to reduce array-to-array variation. We show that in the context of time series measurements QN may not be the best choice for this task, especially not if the inference is based on continuous time ODE model. We propose an alternative normalization method that is better suited for network inference from time series data.

The promise of the state space approach to time series analysis for nursing research.

PubMed

Levy, Janet A; Elser, Heather E; Knobel, Robin B

2012-01-01

Nursing research, particularly related to physiological development, often depends on the collection of time series data. The state space approach to time series analysis has great potential to answer exploratory questions relevant to physiological development but has not been used extensively in nursing. The aim of the study was to introduce the state space approach to time series analysis and demonstrate potential applicability to neonatal monitoring and physiology. We present a set of univariate state space models; each one describing a process that generates a variable of interest over time. Each model is presented algebraically and a realization of the process is presented graphically from simulated data. This is followed by a discussion of how the model has been or may be used in two nursing projects on neonatal physiological development. The defining feature of the state space approach is the decomposition of the series into components that are functions of time; specifically, slowly varying level, faster varying periodic, and irregular components. State space models potentially simulate developmental processes where a phenomenon emerges and disappears before stabilizing, where the periodic component may become more regular with time, or where the developmental trajectory of a phenomenon is irregular. The ultimate contribution of this approach to nursing science will require close collaboration and cross-disciplinary education between nurses and statisticians.

How bootstrap can help in forecasting time series with more than one seasonal pattern

NASA Astrophysics Data System (ADS)

Cordeiro, Clara; Neves, M. Manuela

2012-09-01

The search for the future is an appealing challenge in time series analysis. The diversity of forecasting methodologies is inevitable and is still in expansion. Exponential smoothing methods are the launch platform for modelling and forecasting in time series analysis. Recently this methodology has been combined with bootstrapping revealing a good performance. The algorithm (Boot. EXPOS) using exponential smoothing and bootstrap methodologies, has showed promising results for forecasting time series with one seasonal pattern. In case of more than one seasonal pattern, the double seasonal Holt-Winters methods and the exponential smoothing methods were developed. A new challenge was now to combine these seasonal methods with bootstrap and carry over a similar resampling scheme used in Boot. EXPOS procedure. The performance of such partnership will be illustrated for some well-know data sets existing in software.

Assessing backscatter change due to backscatter gradient over the Greenland ice sheet using Envisat and SARAL altimetry

NASA Astrophysics Data System (ADS)

Su, Xiaoli; Luo, Zhicai; Zhou, Zebing

2018-06-01

Knowledge of backscatter change is important to accurately retrieve elevation change time series from satellite radar altimetry over continental ice sheets. Previously, backscatter coefficients generated in two cases, namely with and without accounting for backscatter gradient (BG), are used. However, the difference between backscatter time series obtained separately in these two cases and its impact on retrieving elevation change are not well known. Here we first compare the mean profiles of the Ku and Ka band backscatter over the Greenland ice sheet (GrIS), with results illustrating that the Ku-band backscatter is 3 ∼ 5 dB larger than that of the Ka band. We then conduct statistic analysis about time series of backscatter formed separately in the above two cases for both Ku and Ka bands over two regions in the GrIS. It is found that the standard deviation of backscatter time series becomes slightly smaller after removing the BG effect, which suggests that the method for the BG correction is effective. Furthermore, the impact on elevation change from backscatter change due to the BG effect is separately assessed for both Ku and Ka bands over the GrIS. We conclude that Ka band altimetry would benefit from a BG induced backscatter analysis (∼10% over region 2). This study may provide a reference to form backscatter time series towards refining elevation change time series from satellite radar altimetry over ice sheets using repeat-track analysis.

How long will the traffic flow time series keep efficacious to forecast the future?

NASA Astrophysics Data System (ADS)

Yuan, PengCheng; Lin, XuXun

2017-02-01

This paper investigate how long will the historical traffic flow time series keep efficacious to forecast the future. In this frame, we collect the traffic flow time series data with different granularity at first. Then, using the modified rescaled range analysis method, we analyze the long memory property of the traffic flow time series by computing the Hurst exponent. We calculate the long-term memory cycle and test its significance. We also compare it with the maximum Lyapunov exponent method result. Our results show that both of the freeway traffic flow time series and the ground way traffic flow time series demonstrate positively correlated trend (have long-term memory property), both of their memory cycle are about 30 h. We think this study is useful for the short-term or long-term traffic flow prediction and management.

Nutrient controls on new production in the Bodega Bay, California, coastal upwelling plume

NASA Astrophysics Data System (ADS)

Dugdale, R. C.; Wilkerson, F. P.; Hogue, V. E.; Marchi, A.

2006-12-01

A theoretical framework for the time-dependent processes leading to the high rates of new production in eastern boundary upwelling systems has been assembled from a series of past upwelling studies. As part of the CoOP WEST (Wind Events and Shelf Transport) study, new production in the Bodega Bay upwelling area and it's control by ambient nitrate and ammonium concentrations and the advective wind regime are described. Data and analyses are focused primarily on the WEST 2001 cruise (May-June 2001) when the two legs differed greatly in wind regimes but not nutrient concentrations. Elevated concentrations of ammonium in upwelled water with high nitrate were observed in both legs. Nitrate uptake by phytoplankton as a function of nitrate concentration was linear rather than Michaelis-Menten-like, modulated by inhibitory levels of ammonium, yielding coefficients that enable the specific nitrate uptake element of new production to be estimated from nutrient concentrations. The range of specific nitrate uptake rates for the two legs of WEST 2001 were similar, essentially a physiological response to nutrient conditions. However, the low "realization" of new production i.e. incorporation of biomass as particulate nitrogen that occurred in this system compared to the theoretical maximum possible was determined by the strong advective and turbulent conditions that dominated the second leg of the WEST 2001 study. These data are compared with other upwelling areas using a physiological shift-up model [Dugdale, R.C., Wilkerson, F.P., Morel, A. 1990. Realization of new production in coastal upwelling areas: a means to compare relative performance. Limnology and Oceanography 35, 822-829].

Effect of increased temperature, CO2, and iron on nitrate uptake and primary productivity in the coastal Ross Sea

NASA Astrophysics Data System (ADS)

Bronk, D. A.; Spackeen, J.; Sipler, R. E.; Bertrand, E. M.; Roberts, Q. N.; Xu, K.; Baer, S. E.; McQuaid, J.; Zhu, Z.; Walworth, N. G.; Hutchins, D. A.; Allen, A. E.

2016-02-01

Western Antarctic Seas are rapidly changing as a result of elevated concentrations of CO2 and rising sea surface temperatures. It is critical to determine how the structure and function of microbial communities will be impacted by these changes in the future because the Southern Ocean has seasonally high rates of primary production, is an important sink for anthropogenic CO2, and supports a diverse assemblage of higher trophic level organisms. During the Austral summer of 2013 and 2015, a collaborative research group conducted a series of experiments to understand how the individual and combined effects of temperature, CO2, and iron impact Ross Sea microorganisms. Our project used a variety of approaches, including batch experiments, semi-continuous experiments, and continuous-culturing over extended time intervals, to determine how future changes may shift Ross Sea microbial communities and how nutrient cycling and carbon biogeochemistry may subsequently be altered. Chemical and biological parameters were measured throughout the experiments to assess changes in community composition and nutrient cycling, including uptake rate measurements of nitrate and bicarbonate by different size fractions of microorganisms. Relative to the control, nitrate uptake rates significantly increased when temperature and iron were elevated indicating that temperature and iron are important physical drivers that influence nutrient cycling. Elevations in temperature and iron independently and synergistically produced higher rates than elevated CO2. Our nutrient uptake results also suggest that the physiology of large microorganisms will be more impacted by climate change variables than small microorganisms.

A role of vertical mixing on nutrient supply into the subsurface chlorophyll maximum in the shelf region of the East China Sea

NASA Astrophysics Data System (ADS)

Lee, Keunjong; Matsuno, Takeshi; Endoh, Takahiro; Ishizaka, Joji; Zhu, Yuanli; Takeda, Shigenobu; Sukigara, Chiho

2017-07-01

In summer, Changjiang Diluted Water (CDW) expands over the shelf region of the northern East China Sea. Dilution of the low salinity water could be caused by vertical mixing through the halocline. Vertical mixing through the pycnocline can transport not only saline water, but also high nutrient water from deeper layers to the surface euphotic zone. It is therefore very important to quantitatively evaluate the vertical mixing to understand the process of primary production in the CDW region. We conducted extensive measurements in the region during the period 2009-2011. Detailed investigations of the relative relationship between the subsurface chlorophyll maximum (SCM) and the nitracline suggested that there were two patterns relating to the N/P ratio. Comparing the depths of the nitracline and SCM, it was found that the SCM was usually located from 20 to 40 m and just above the nitracline, where the N/P ratio within the nitracline was below 15, whereas it was located from 10 to 30 m and within the nitracline, where the N/P ratio was above 20. The large value of the N/P ratio in the latter case suggests the influence of CDW. Turbulence measurements showed that the vertical flux of nutrients with vertical mixing was large (small) where the N/P ratio was small (large). A comparison with a time series of primary production revealed a consistency with the pattern of snapshot measurements, suggesting that the nutrient supply from the lower layer contributes considerably to the maintenance of SCM.

Time-series analysis to study the impact of an intersection on dispersion along a street canyon.

PubMed

Richmond-Bryant, Jennifer; Eisner, Alfred D; Hahn, Intaek; Fortune, Christopher R; Drake-Richman, Zora E; Brixey, Laurie A; Talih, M; Wiener, Russell W; Ellenson, William D

2009-12-01

This paper presents data analysis from the Brooklyn Traffic Real-Time Ambient Pollutant Penetration and Environmental Dispersion (B-TRAPPED) study to assess the transport of ultrafine particulate matter (PM) across urban intersections. Experiments were performed in a street canyon perpendicular to a highway in Brooklyn, NY, USA. Real-time ultrafine PM samplers were positioned on either side of an intersection at multiple locations along a street to collect time-series number concentration data. Meteorology equipment was positioned within the street canyon and at an upstream background site to measure wind speed and direction. Time-series analysis was performed on the PM data to compute a transport velocity along the direction of the street for the cases where background winds were parallel and perpendicular to the street. The data were analyzed for sampler pairs located (1) on opposite sides of the intersection and (2) on the same block. The time-series analysis demonstrated along-street transport, including across the intersection when background winds were parallel to the street canyon and there was minimal transport and no communication across the intersection when background winds were perpendicular to the street canyon. Low but significant values of the cross-correlation function (CCF) underscore the turbulent nature of plume transport along the street canyon. The low correlations suggest that flow switching around corners or traffic-induced turbulence at the intersection may have aided dilution of the PM plume from the highway. This observation supports similar findings in the literature. Furthermore, the time-series analysis methodology applied in this study is introduced as a technique for studying spatiotemporal variation in the urban microscale environment.

Iron fertilization of the Subantarctic Ocean during the last ice age

NASA Astrophysics Data System (ADS)

Martinez-Garcia, A.

2015-12-01

Dust has the potential to modify global climate by influencing the radiative balance of the atmosphere and by supplying iron and other essential limiting micronutrients to the ocean. The scarcity of iron limits marine productivity and carbon uptake in one-quarter of the world ocean where the concentration of major nutrients (phosphorus and nitrogen) is perennially high. The Southern Ocean is the region where variations in iron availability can have the largest effect on Earth's carbon cycle through its fertilizing effect on marine ecosystems. Paleoceanographic records from the Subantarctic Atlantic have revealed a remarkable correlation between phytoplankton productivity and aeolian iron flux during glacial periods supporting the iron fertilization hypothesis. In addition, a recent study has shown that peak glacial times and millennial cold events were nearly universally associated not only with increases in dust flux and export production, but also with an increase in nutrient consumption (the last indicated by higher foraminifera-bound δ15N) (Martinez-Garcia et al. 2014). This combination of changes is uniquely consistent with ice age iron fertilization of the Subantarctic Atlantic. The strengthening of the biological pump associated with the observed increase in Subantarctic nutrient consumption during the high-dust intervals of the last two ice ages can explain up to ~40 ppm of the CO2 decrease that characterizes the transitions from mid-climate states to full ice age conditions. However, the impact of iron fertilization in other sectors of the Southern Ocean characterized by lower ice age dust fluxes than the Atlantic remains unclear. A series of recently published records from the Subantarctic Pacific indicate that dust deposition and marine export production were three times higher during glacial periods than during interglacials (Lamy et al. 2014). Here we present new measurements of foraminifera-bound nitrogen isotopes in a sediment core located in the Subantarctic Pacific (PS75/56-1), which allow us to evaluate the impact of iron fertilization on major nutrient consumption in the largest Southern Ocean sector.