Energy profiles of four American states

NASA Astrophysics Data System (ADS)

Song, Jiamei

2018-06-01

Energy production and usage are the major portion of any economy. With the constant consumption of the polluting energy and the deteriorating environment, people are paying more and more attention to clean, renewable energy. Based on autoregressive model and TOPSIS, though analyzing the past data, this paper establishes the energy profiles of four American states from 1960 to 2009, predict the energy profiles for 2025 and 2050 and obtain the ideal criteria for future clean, renewable energy usage at last. This study finds that by analyzing and predicting the energy profile, human beings can better understand and grasp the trend of energy development and take appropriate measures to deal with future energy trends.

Future Declines of Coronary Heart Disease Mortality in England and Wales Could Counter the Burden of Population Ageing

PubMed Central

Guzman Castillo, Maria; Gillespie, Duncan O. S.; Allen, Kirk; Bandosz, Piotr; Schmid, Volker; Capewell, Simon; O’Flaherty, Martin

2014-01-01

Background Coronary Heart Disease (CHD) remains a major cause of mortality in the United Kingdom. Yet predictions of future CHD mortality are potentially problematic due to population ageing and increase in obesity and diabetes. Here we explore future projections of CHD mortality in England & Wales under two contrasting future trend assumptions. Methods In scenario A, we used the conventional counterfactual scenario that the last-observed CHD mortality rates from 2011 would persist unchanged to 2030. The future number of deaths was calculated by applying those rates to the 2012–2030 population estimates. In scenario B, we assumed that the recent falling trend in CHD mortality rates would continue. Using Lee-Carter and Bayesian Age Period Cohort (BAPC) models, we projected the linear trends up to 2030. We validate our methods using past data to predict mortality from 2002–2011. Then, we computed the error between observed and projected values. Results In scenario A, assuming that 2011 mortality rates stayed constant by 2030, the number of CHD deaths would increase 62% or approximately 39,600 additional deaths. In scenario B, assuming recent declines continued, the BAPC model (the model with lowest error) suggests the number of deaths will decrease by 56%, representing approximately 36,200 fewer deaths by 2030. Conclusions The decline in CHD mortality has been reasonably continuous since 1979, and there is little reason to believe it will soon halt. The commonly used assumption that mortality will remain constant from 2011 therefore appears slightly dubious. By contrast, using the BAPC model and assuming continuing mortality falls offers a more plausible prediction of future trends. Thus, despite population ageing, the number of CHD deaths might halve again between 2011 and 2030. This has implications for how the potential benefits of future cardiovascular strategies might best be calculated and presented. PMID:24918442

Utilizing Multi-Ensemble of Downscaled CMIP5 GCMs to Investigate Trends and Spatial and Temporal Extent of Drought in Willamette Basin

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Beal, B.; Moradkhani, H.

2015-12-01

Changing climate and potential future increases in global temperature are likely to have impacts on drought characteristics and hydrologic cylce. In this study, we analyze changes in temporal and spatial extent of meteorological and hydrological droughts in future, and their trends. Three statistically downscaled datasets from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), Multivariate Adaptive Constructed Analogs (MACA), and Bias Correction and Spatial Disagregation (BCSD-PSU) each consisting of 10 CMIP5 Global Climate Models (GCM) are utilized for RCP4.5 and RCP8.5 scenarios. Further, Precipitation Runoff Modeling System (PRMS) hydrologic model is used to simulate streamflow from GCM inputs and assess the hydrological drought characteristics. Standard Precipitation Index (SPI) and Streamflow Drought Index (SDI) are the two indexes used to investigate meteorological and hydrological drought, respectively. Study is done for Willamette Basin with a drainage area of 29,700 km2 accommodating more than 3 million inhabitants and 25 dams. We analyze our study for annual time scale as well as three future periods of near future (2010-2039), intermediate future (2040-2069), and far future (2070-2099). Large uncertainty is found from GCM predictions. Results reveal that meteorological drought events are expected to increase in near future. Severe to extreme drought with large areal coverage and several years of occurance is predicted around year 2030 with the likelihood of exceptional drought for both drought types. SPI is usually showing positive trends, while SDI indicates negative trends in most cases.

21st Century Trends in the Potential for Ozone Depletion

NASA Astrophysics Data System (ADS)

Hurwitz, M. M.; Newman, P. A.

2009-05-01

We find robust trends in the area where Antarctic stratospheric temperatures are below the threshold for polar stratospheric cloud (PSC) formation in Goddard Earth Observing System (GEOS) chemistry-climate model (CCM) simulations of the 21st century. In late winter (September-October-November), cold area trends are consistent with the respective trends in equivalent effective stratospheric chlorine (EESC), i.e. negative cold area trends in 'realistic future' simulations where EESC decreases and the ozone layer recovers. In the early winter (April through June), regardless of EESC scenario, we find an increasing cold area trend in all simulations; multiple linear regression analysis shows that this early winter cooling trend is associated with the predicted increase in greenhouse gas concentrations in the future. We compare the seasonality of the potential for Antarctic ozone depletion in two versions of the GEOS CCM and assess the impact of the above-mentioned cold area trends on polar stratospheric chemistry.

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

PubMed

Rajagopalan, Ramesh; Litvan, Irene; Jung, Tzyy-Ping

2017-11-01

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems.

Assessing spatiotemporal changes in forest carbon turnover times in observational data and models

NASA Astrophysics Data System (ADS)

Yu, K.; Smith, W. K.; Trugman, A. T.; van Mantgem, P.; Peng, C.; Condit, R.; Anderegg, W.

2017-12-01

Forests influence global carbon and water cycles, biophysical land-atmosphere feedbacks, and atmospheric composition. The capacity of forests to sequester atmospheric CO2 in a changing climate depends not only on the response of carbon uptake (i.e., gross primary productivity) but also on the simultaneous change in carbon residence time. However, changes in carbon residence with climate change are uncertain, impacting the accuracy of predictions of future terrestrial carbon cycle dynamics. Here, we use long-term forest inventory data representative of tropical, temperate, and boreal forests; satellite-based estimates of net primary productivity and vegetation carbon stock; and six models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to investigate spatiotemporal trends in carbon residence time and its relation to climate. Forest inventory and satellite-based estimates of carbon residence time show a pervasive decreasing trend across global forests. In contrast, the CMIP5 models diverge in predicting historical and future trends in carbon residence time. Divergence across CMIP5 models indicate carbon turnover times are not well constrained by observations, which likely contributes to large variability in future carbon cycle projections.

Projecting the Demand for Dental Care in 2040.

PubMed

Manski, Richard J; Meyerhoefer, Chad D

2017-08-01

The purpose of this study was to provide a forward-thinking assessment of the underlying factors likely to impact trends in dental care demand and the need for dental providers in 2020, 2025, and beyond. Dental workforce trends and their likely impact on the need for dentists are a function of predicted dental care demand, which will in turn be determined by the size and characteristics of our population size, economic outlook, the state of public and private dental care insurance, trends in dental care delivery, professionally determined dental care need, and population health beliefs. Projecting rates of dental care utilization far into the future is difficult because projections must be made using historical data, and established trends may not persist if there is structural change in the future. Nonetheless, when structural change occurs, it does not typically affect all aspects of the economy, so there is value in describing the likely future impact of current trends. This article was written as part of the project "Advancing Dental Education in the 21 st Century."

Nuclear Medicine.

ERIC Educational Resources Information Center

Badawi, Ramsey D.

2001-01-01

Describes the use of nuclear medicine techniques in diagnosis and therapy. Describes instrumentation in diagnostic nuclear medicine and predicts future trends in nuclear medicine imaging technology. (Author/MM)

Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions

PubMed Central

Rajagopalan, Ramesh; Jung, Tzyy-Ping

2017-01-01

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems. PMID:29104256

Measurement of historical cliff-top changes and estimation of future trends using GIS data between Bridlington and Hornsea - Holderness Coast (UK)

NASA Astrophysics Data System (ADS)

Castedo, Ricardo; de la Vega-Panizo, Rogelio; Fernández-Hernández, Marta; Paredes, Carlos

2015-02-01

A key requirement for effective coastal zone management is good knowledge of historical rates of change and the ability to predict future shoreline evolution, especially for rapidly eroding areas. Historical shoreline recession analysis was used for the prediction of future cliff shoreline positions along a section of 9 km between Bridlington and Hornsea, on the northern area of the Holderness Coast, UK. The analysis was based on historical maps and aerial photographs dating from 1852 to 2011 using the Digital Shoreline Analysis System (DSAS) 4.3, extension of ESRI's ArcInfo 10.×. The prediction of future shorelines was performed for the next 40 years using a variety of techniques, ranging from extrapolation from historical data, geometric approaches like the historical trend analysis, to a process-response numerical model that incorporates physically-based equations and geotechnical stability analysis. With climate change and sea-level rise implying that historical rates of change may not be a reliable guide for the future, enhanced visualization of the evolving coastline has the potential to improve awareness of these changing conditions. Following the IPCC, 2013 report, two sea-level rise rates, 2 mm/yr and 6 mm/yr, have been used to estimate future shoreline conditions. This study illustrated that good predictive models, once their limitations are estimated or at least defined, are available for use by managers, planners, engineers, scientists and the public to make better decisions regarding coastal management, development, and erosion-control strategies.

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.

Oklahoma Study of Educator Supply and Demand: Trends and Projections

ERIC Educational Resources Information Center

Berg-Jacobson, Alex; Levin, Jesse

2015-01-01

In June 2014, the Oklahoma State Regents of Higher Education (OSRHE) commissioned American Institutes for Research (AIR) to conduct a study to better understand both historical and future predicted trends of educator supply and demand across Oklahoma. OSRHE commissioned the study in partnership with the Oklahoma Commission for Teacher Preparation…

Threats to coastal communities of Mahanadi delta due to imminent consequences of erosion - Present and near future.

PubMed

Mukhopadhyay, Anirban; Ghosh, Pramit; Chanda, Abhra; Ghosh, Amit; Ghosh, Subhajit; Das, Shouvik; Ghosh, Tuhin; Hazra, Sugata

2018-05-11

Coastal erosion is a natural hazard which causes significant loss to properties as well as coastal habitats. Coastal districts of Mahanadi delta, one of the most populated deltas of the Indian subcontinent, are suffering from the ill effects of coastal erosion. An important amount of assets is being lost every year along with forced migration of huge portions of coastal communities due to erosion. An attempt has been made in this study to predict the future coastline of the Mahanadi Delta based on historical trends. Historical coastlines of the delta have been extracted using semi-automated Tasselled Cap technique from the LANDSAT satellite imageries of the year 1990, 1995, 2000, 2006 and 2010. Using Digital Shoreline Assessment System (DSAS) tool of USGS, the trend of the coastline has been assessed in the form of End Point Rate (EPR) and Linear Regression Rate (LRR). A hybrid methodology has been adopted using statistical (EPR) and trigonometric functions to predict the future positions of the coastlines of the years 2020, 2035 and 2050. The result showed that most of the coastline (≈65%) is facing erosion at present. The predicted outcome shows that by the end of year 2050 the erosion scenario will worsen which in turn would lead to very high erosion risk for 30% of the total coastal mouzas (small administrative blocks). This study revealed the coastal erosion trend of Mahanadi delta and based on the predicted coastlines it can be inferred that the coastal communities in near future would be facing substantial threat due to erosion particularly in areas surrounding Puri (a renowned tourist pilgrimage) and Paradwip (one of the busiest ports and harbours of the country). Copyright © 2018 Elsevier B.V. All rights reserved.

Forecasting Higher Education's Future.

ERIC Educational Resources Information Center

Boyken, Don; Buck, Tina S.; Kollie, Ellen; Przyborowski, Danielle; Rondinelli, Joseph A.; Hunter, Jeff; Hanna, Jeff

2003-01-01

Offers predictions on trends in higher education to accommodate changing needs, lower budgets, and increased enrollment. They involve campus construction, security, administration, technology, interior design, athletics, and transportation. (EV)

Four Futures for Social Studies.

ERIC Educational Resources Information Center

Morrissett, Irving

The current status of social studies education provides information for assessing and predicting future trends in social studies education. A 3-year study, "Social Studies Priorities and Needs," found that: social studies/social science educators are concerned with constructing a rationale and definition of social studies; the dominant approach to…

Perspectives on the future of the electric utility industry

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

Tonn, B.; Schaffhauser, A.

1994-04-01

This report offers perspectives on the future of the electric utility industry. These perspectives will be used in further research to assess the prospects for Integrated Resource Planning (IRP). The perspectives are developed first by examining economic, political and regulatory, societal, technological, and environmental trends that are (1) national and global in scope and (2) directly related to the electric utility industry. Major national and global trends include increasing global economic competition, increasing political and ethnic strife, rapidly changing technologies, and increasing worldwide concern about the environment. Major trends in the utility industry include increasing competition in generation; changing patternsmore » of electricity demand; increasing use of information technology to control power systems; and increasing implementation of environmental controls. Ways in which the national and global trends may directly affect the utility industry are also explored. The trends are used to construct three global and national scenarios- ``business as usual,`` ``technotopia future,`` and ``fortress state`` -and three electric utility scenarios- ``frozen in headlights,`` ``megaelectric,`` and ``discomania.`` The scenarios are designed to be thought provoking descriptions of potential futures, not predictions of the future, although three key variables are identified that will have significant impacts on which future evolves-global climate change, utility technologies, and competition. While emphasis needs to be placed on understanding the electric utility scenarios, the interactions between the two sets of scenarios is also of interest.« less

The Year 2000: Teacher Education.

ERIC Educational Resources Information Center

Van Til, William

In speculating about the future, scholar-prophets can account for future social changes (such as those induced by computer technology) by extrapolating current trends, but "systems breaks," or sudden changes in the characteristics of a system (caused by biological transformations for instance) may invalidate their predictions. With that in mind,…

Community College Roles in Teacher Education: Current Approaches and Future Possibilities.

ERIC Educational Resources Information Center

Townsend, Barbara K.; Ignash, Jan M, ED.

2003-01-01

Examines the current role of community colleges in pre-service and in-service teacher education, including the development of the associate of arts degree in teacher education, the community college baccalaureate in teacher education, and alternative certification programs. Discusses factors influencing future trends and predictions about the…

Imagining wilderness

Treesearch

Daniel Dustin; Jeff Rose; Adrienne Cachelin; Wynn Shooter; Scott Schumann

2012-01-01

The future of wilderness is open for discussion and debate. In this paper we invite readers to consider four wilderness scenarios, any one of which, or combination of which, seems possible based on current demographic, social, and cultural trends. The purpose of the paper is not so much to try to predict the future as it is to prod readers into pondering the future—a...

On the significance of future trends in flood frequencies

NASA Astrophysics Data System (ADS)

Bernhardt, M.; Schulz, K.; Wieder, O.

2015-12-01

Floods are a significant threat for alpine headwater catchments and for the forelands. The formation of significant flood events is thereby often coupled on processes occurring in the alpine zone. Rain on snow events are just one example. The prediction of flood risks or trends of flood risks is of major interest to people under direct threat, policy and decision makers as well as for insurance companies. A lot of research was and is currently done in view of detecting future trends in flood extremes or return periods. From a pure physically based point of view, there is strong evidence that those trends exist. But, the central point question is if trends in flood events or other extreme events could be detected from a statistical point of view and on the basis of the available data. This study will investigate this question on the basis of different target parameters and by using long term measurements.

Community Services and Training (01-01-00 to 01-01-25!).

ERIC Educational Resources Information Center

Andrews, Hans A.; Cavan, John

2000-01-01

Presents opinions of the present and former presidents of the National Council for Continuing Education and Training (NCCET) as to the future of community services and workforce training. Predicts future trends for NCCET, such as: (1) partnering nationally; (2) staying on the cutting edge; (3) offering accountability and convenience; (4) being…

The State of Educational Data Mining in 2009: A Review and Future Visions

ERIC Educational Resources Information Center

Baker, Ryan S. J. D.; Yacef, Kalina

2009-01-01

We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence…

Multi-decadal trends in global terrestrial evapotranspiration and its components.

PubMed

Zhang, Yongqiang; Peña-Arancibia, Jorge L; McVicar, Tim R; Chiew, Francis H S; Vaze, Jai; Liu, Changming; Lu, Xingjie; Zheng, Hongxing; Wang, Yingping; Liu, Yi Y; Miralles, Diego G; Pan, Ming

2016-01-11

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981-2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.

Multi-decadal trends in global terrestrial evapotranspiration and its components

PubMed Central

Zhang, Yongqiang; Peña-Arancibia, Jorge L.; McVicar, Tim R.; Chiew, Francis H. S.; Vaze, Jai; Liu, Changming; Lu, Xingjie; Zheng, Hongxing; Wang, Yingping; Liu, Yi Y.; Miralles, Diego G.; Pan, Ming

2016-01-01

Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle. PMID:26750505

Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015.

PubMed

Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

2016-01-01

Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. It was a cross-sectional study. All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents.

Evaluating a fish monitoring protocol using state-space hierarchical models

USGS Publications Warehouse

Russell, Robin E.; Schmetterling, David A.; Guy, Chris S.; Shepard, Bradley B.; McFarland, Robert; Skaar, Donald

2012-01-01

Using data collected from three river reaches in Montana, we evaluated our ability to detect population trends and predict fish future fish abundance. Data were collected as part of a long-term monitoring program conducted by Montana Fish, Wildlife and Parks to primarily estimate rainbow (Oncorhynchus mykiss) and brown trout (Salmo trutta) abundance in numerous rivers across Montana. We used a hierarchical Bayesian mark-recapture model to estimate fish abundance over time in each of the three river reaches. We then fit a state-space Gompertz model to estimate current trends and future fish populations. Density dependent effects were detected in 1 of the 6 fish populations. Predictions of future fish populations displayed wide credible intervals. Our simulations indicated that given the observed variation in the abundance estimates, the probability of detecting a 30% decline in fish populations over a five-year period was less than 50%. We recommend a monitoring program that is closely tied to management objectives and reflects the precision necessary to make informed management decisions.

Human Influence on Tropical Cyclone Intensity

NASA Technical Reports Server (NTRS)

Sobel, Adam H.; Camargo, Suzana J.; Hall, Timothy M.; Lee, Chia-Ying; Tippett, Michael K.; Wing, Allison A.

2016-01-01

Recent assessments agree that tropical cyclone intensity should increase as the climate warms. Less agreement exists on the detection of recent historical trends in tropical cyclone intensity.We interpret future and recent historical trends by using the theory of potential intensity, which predicts the maximum intensity achievable by a tropical cyclone in a given local environment. Although greenhouse gas-driven warming increases potential intensity, climate model simulations suggest that aerosol cooling has largely canceled that effect over the historical record. Large natural variability complicates analysis of trends, as do poleward shifts in the latitude of maximum intensity. In the absence of strong reductions in greenhouse gas emissions, future greenhouse gas forcing of potential intensity will increasingly dominate over aerosol forcing, leading to substantially larger increases in tropical cyclone intensities.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

Future Performance Trend Indicators: A Current Value Approach to Human Resources Accounting. Report III. Multivariate Predictions of Organizational Performance Across Time.

ERIC Educational Resources Information Center

Pecorella, Patricia A.; Bowers, David G.

Multiple regression in a double cross-validated design was used to predict two performance measures (total variable expense and absence rate) by multi-month period in five industrial firms. The regressions do cross-validate, and produce multiple coefficients which display both concurrent and predictive effects, peaking 18 months to two years…

Early warning model based on correlated networks in global crude oil markets

NASA Astrophysics Data System (ADS)

Yu, Jia-Wei; Xie, Wen-Jie; Jiang, Zhi-Qiang

2018-01-01

Applying network tools on predicting and warning the systemic risks provides a novel avenue to manage risks in financial markets. Here, we construct a series of global crude oil correlated networks based on the historical 57 oil prices covering a period from 1993 to 2012. Two systemic risk indicators are constructed based on the density and modularity of correlated networks. The local maximums of the risk indicators are found to have the ability to predict the trends of oil prices. In our sample periods, the indicator based on the network density sends five signals and the indicator based on the modularity index sends four signals. The four signals sent by both indicators are able to warn the drop of future oil prices and the signal only sent by the network density is followed by a huge rise of oil prices. Our results deepen the application of network measures on building early warning models of systemic risks and can be applied to predict the trends of future prices in financial markets.

Visualization and classification of physiological failure modes in ensemble hemorrhage simulation

NASA Astrophysics Data System (ADS)

Zhang, Song; Pruett, William Andrew; Hester, Robert

2015-01-01

In an emergency situation such as hemorrhage, doctors need to predict which patients need immediate treatment and care. This task is difficult because of the diverse response to hemorrhage in human population. Ensemble physiological simulations provide a means to sample a diverse range of subjects and may have a better chance of containing the correct solution. However, to reveal the patterns and trends from the ensemble simulation is a challenging task. We have developed a visualization framework for ensemble physiological simulations. The visualization helps users identify trends among ensemble members, classify ensemble member into subpopulations for analysis, and provide prediction to future events by matching a new patient's data to existing ensembles. We demonstrated the effectiveness of the visualization on simulated physiological data. The lessons learned here can be applied to clinically-collected physiological data in the future.

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend

PubMed Central

Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span. PMID:27974883

Artificial Neural Network and Genetic Algorithm Hybrid Intelligence for Predicting Thai Stock Price Index Trend.

PubMed

Inthachot, Montri; Boonjing, Veera; Intakosum, Sarun

2016-01-01

This study investigated the use of Artificial Neural Network (ANN) and Genetic Algorithm (GA) for prediction of Thailand's SET50 index trend. ANN is a widely accepted machine learning method that uses past data to predict future trend, while GA is an algorithm that can find better subsets of input variables for importing into ANN, hence enabling more accurate prediction by its efficient feature selection. The imported data were chosen technical indicators highly regarded by stock analysts, each represented by 4 input variables that were based on past time spans of 4 different lengths: 3-, 5-, 10-, and 15-day spans before the day of prediction. This import undertaking generated a big set of diverse input variables with an exponentially higher number of possible subsets that GA culled down to a manageable number of more effective ones. SET50 index data of the past 6 years, from 2009 to 2014, were used to evaluate this hybrid intelligence prediction accuracy, and the hybrid's prediction results were found to be more accurate than those made by a method using only one input variable for one fixed length of past time span.

River-ice break-up/freeze-up: a review of climatic drivers, historical trends and future predictions

NASA Astrophysics Data System (ADS)

Prowse, T. D.; Bonsal, B. R.; Duguay, C. R.; Lacroix, M. P.

2007-10-01

River ice plays a fundamental role in biological, chemical and physical processes that control freshwater regimes of the cold regions. Moreover, it can have enormous economic implications for river-based developments. All such activities and processes can be modified significantly by any changes to river-ice thickness, composition or event timing and severity. This paper briefly reviews some of the major hydraulic, mechanical and thermodynamic processes controlling river-ice events and how these are influenced by variations in climate. A regional and temporal synthesis is also made of the observed historical trends in river-ice break-up/freeze-up occurrence from the Eurasian and North American cold regions. This involves assessment of several hydroclimatic variables that have influenced past trends and variability in river-ice break-up/freeze-up dates including air-temperature indicators (e.g. seasonal temperature, 0°C isotherm dates and various degree-days) and large-scale atmospheric circulation patterns or teleconnections. Implications of future climate change on the timing and severity of river-ice events are presented and discussed in relation to the historical trends. Attention is drawn to the increasing trends towards the occurrence of mid-winter break-up events that can produce especially severe flood conditions but prove to be the most difficult type of event to model and predict.

Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015

PubMed Central

Mehmandar, Mohammadreza; Soori, Hamid; Mehrabi, Yadolah

2016-01-01

Background: Predicting the trend in traffic accidents deaths and its analysis can be a useful tool for planning and policy-making, conducting interventions appropriate with death trend, and taking the necessary actions required for controlling and preventing future occurrences. Objective: Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. Settings and Design: It was a cross-sectional study. Materials and Methods: All the information related to fatal traffic accidents available in the database of Iran Legal Medicine Organization from 2004 to the end of 2013 were used to determine the change points (multi-variable time series analysis). Using autoregressive integrated moving average (ARIMA) model, traffic accidents death rates were predicted for 2014 and 2015, and a comparison was made between this rate and the predicted value in order to determine the efficiency of the model. Results: From the results, the actual death rate in 2014 was almost similar to that recorded for this year, while in 2015 there was a decrease compared with the previous year (2014) for all the months. A maximum value of 41% was also predicted for the months of January and February, 2015. Conclusion: From the prediction and analysis of the death trends, proper application and continuous use of the intervention conducted in the previous years for road safety improvement, motor vehicle safety improvement, particularly training and culture-fostering interventions, as well as approval and execution of deterrent regulations for changing the organizational behaviors, can significantly decrease the loss caused by traffic accidents. PMID:27308255

More than just the mean: moving to a dynamic view of performance-based compensation.

PubMed

Barnes, Christopher M; Reb, Jochen; Ang, Dionysius

2012-05-01

Compensation decisions have important consequences for employees and organizations and affect factors such as retention, motivation, and recruitment. Past research has primarily focused on mean performance as a predictor of compensation, promoting the implicit assumption that alternative aspects of dynamic performance are not relevant. To address this gap in the literature, we examined the influence of dynamic performance characteristics on compensation decisions in the National Basketball Association (NBA). We predicted that, in addition to performance mean, performance trend and variability would also affect compensation decisions. Results revealed that performance mean and trend, but not variability, were significantly and positively related to changes in compensation levels of NBA players. Moreover, trend (but not mean or variability) predicted compensation when controlling for future performance, suggesting that organizations overweighted trend in their compensation decisions. Theoretical and practical implications are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Future methods in pharmacy practice research.

PubMed

Almarsdottir, A B; Babar, Z U D

2016-06-01

This article describes the current and future practice of pharmacy scenario underpinning and guiding this research and then suggests future directions and strategies for such research. First, it sets the scene by discussing the key drivers which could influence the change in pharmacy practice research. These are demographics, technology and professional standards. Second, deriving from this, it seeks to predict and forecast the future shifts in use of methodologies. Third, new research areas and availability of data impacting on future methods are discussed. These include the impact of aging information technology users on healthcare, understanding and responding to cultural and social disparities, implementing multidisciplinary initiatives to improve health care, medicines optimization and predictive risk analysis, and pharmacy as business and health care institution. Finally, implications of the trends for pharmacy practice research methods are discussed.

The Soldier-Cyborg Transformation: A Framework for Analysis of Social and Ethical Issues of Future Warfare

DTIC Science & Technology

1998-05-26

attitude about the use of chemical and biologic weapons , one must question the deterrent value of WMD. With perhaps the 19 exception of nuclear...ENHANCING, TRANSFORMING AND TRANSCENDING 1 TRENDS AND PREDICTIONS ABOUT FUTURE WARFARE 3 CHANGING DEMOGRAPHICS 8 THE BIOLOGIC SHIFT 10 STRATEGIC...without widespread loss of life. Thus, low lethality weapons and distant applications of precisely- applied force are mandatory to make future

Future trends in image coding

NASA Astrophysics Data System (ADS)

Habibi, Ali

1993-01-01

The objective of this article is to present a discussion on the future of image data compression in the next two decades. It is virtually impossible to predict with any degree of certainty the breakthroughs in theory and developments, the milestones in advancement of technology and the success of the upcoming commercial products in the market place which will be the main factors in establishing the future stage to image coding. What we propose to do, instead, is look back at the progress in image coding during the last two decades and assess the state of the art in image coding today. Then, by observing the trends in developments of theory, software, and hardware coupled with the future needs for use and dissemination of imagery data and the constraints on the bandwidth and capacity of various networks, predict the future state of image coding. What seems to be certain today is the growing need for bandwidth compression. The television is using a technology which is half a century old and is ready to be replaced by high definition television with an extremely high digital bandwidth. Smart telephones coupled with personal computers and TV monitors accommodating both printed and video data will be common in homes and businesses within the next decade. Efficient and compact digital processing modules using developing technologies will make bandwidth compressed imagery the cheap and preferred alternative in satellite and on-board applications. In view of the above needs, we expect increased activities in development of theory, software, special purpose chips and hardware for image bandwidth compression in the next two decades. The following sections summarize the future trends in these areas.

The present state of urbanisation in the South African homelands: rethinking the concepts and predicting the future.

PubMed

Graaff, J F

1987-02-01

Trends in urbanization in the South African homelands are analyzed. The need to reconsider the definition of an urban area is first established. Consideration is given to the likely impact of the abolition of migration controls on urbanization trends in South Africa as a whole, particularly as this affects migration to urban areas in South Africa outside the homelands.

Multi-decadal trends in global terrestrial evapotranspiration and its components

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

Zhang, Yongqiang; Peña-Arancibia, Jorge L.; McVicar, Tim R.

In this study, evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (E t), direct evaporation from the soil (E s) and vaporization of intercepted rainfall from vegetationmore » (E i). During this period, ET over land has increased significantly (p < 0.01), caused by increases in E t and E i, which are partially counteracted by E s decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in E t over land is about twofold of the decrease in E s. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.« less

Multi-decadal trends in global terrestrial evapotranspiration and its components

DOE PAGES

Zhang, Yongqiang; Peña-Arancibia, Jorge L.; McVicar, Tim R.; ...

2016-01-11

In this study, evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (E t), direct evaporation from the soil (E s) and vaporization of intercepted rainfall from vegetationmore » (E i). During this period, ET over land has increased significantly (p < 0.01), caused by increases in E t and E i, which are partially counteracted by E s decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in E t over land is about twofold of the decrease in E s. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.« less

Existing generating assets squeezed as new project starts slow

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

Jones, R.B.; Tiffany, E.D.

Most forecasting reports concentrate on political or regulatory events to predict future industry trends. Frequently overlooked are the more empirical performance trends of the principal power generation technologies. Solomon and Associates queried its many power plant performance databases and crunched some numbers to identify those trends. Areas of investigation included reliability, utilization (net output factor and net capacity factor) and cost (operating costs). An in-depth analysis for North America and Europe is presented in this article, by region and by regeneration technology. 4 figs., 2 tabs.

Emergency post-fire rehabilitation treatment effects on burned area ecology and long-term restoration

Treesearch

Peter R. Robichaud; Sarah A. Lewis; Robert E. Brown; Louise E. Ashmun

2009-01-01

The predicted continuation of strong drying and warming trends in the southwestern United States underlies the associated prediction of increased frequency, area, and severity of wildfires in the coming years. As a result, the management of wildfires and fire effects on public lands will continue to be a major land management priority for the foreseeable future....

Time series analysis of malaria in Afghanistan: using ARIMA models to predict future trends in incidence.

PubMed

Anwar, Mohammad Y; Lewnard, Joseph A; Parikh, Sunil; Pitzer, Virginia E

2016-11-22

Malaria remains endemic in Afghanistan. National control and prevention strategies would be greatly enhanced through a better ability to forecast future trends in disease incidence. It is, therefore, of interest to develop a predictive tool for malaria patterns based on the current passive and affordable surveillance system in this resource-limited region. This study employs data from Ministry of Public Health monthly reports from January 2005 to September 2015. Malaria incidence in Afghanistan was forecasted using autoregressive integrated moving average (ARIMA) models in order to build a predictive tool for malaria surveillance. Environmental and climate data were incorporated to assess whether they improve predictive power of models. Two models were identified, each appropriate for different time horizons. For near-term forecasts, malaria incidence can be predicted based on the number of cases in the four previous months and 12 months prior (Model 1); for longer-term prediction, malaria incidence can be predicted using the rates 1 and 12 months prior (Model 2). Next, climate and environmental variables were incorporated to assess whether the predictive power of proposed models could be improved. Enhanced vegetation index was found to have increased the predictive accuracy of longer-term forecasts. Results indicate ARIMA models can be applied to forecast malaria patterns in Afghanistan, complementing current surveillance systems. The models provide a means to better understand malaria dynamics in a resource-limited context with minimal data input, yielding forecasts that can be used for public health planning at the national level.

The future burden of obesity-related diseases in the 53 WHO European-Region countries and the impact of effective interventions: a modelling study

PubMed Central

Webber, Laura; Divajeva, Diana; Marsh, Tim; McPherson, Klim; Brown, Martin; Galea, Gauden; Breda, Joao

2014-01-01

Objective Non-communicable diseases (NCDs) are the biggest cause of death in Europe putting an unsustainable burden on already struggling health systems. Increases in obesity are a major cause of NCDs. This paper projects the future burden of coronary heart disease (CHD), stroke, type 2 diabetes and seven cancers by 2030 in 53 WHO European Region countries based on current and past body mass index (BMI) trends. It also tests the impact of obesity interventions on the future disease burden. Setting and participants Secondary data analysis of country-specific epidemiological data using a microsimulation modelling process. Interventions The effect of three hypothetical scenarios on the future burden of disease in 2030 was tested: baseline scenario, BMI trends go unchecked; intervention 1, population BMI decreases by 1%; intervention 2, BMI decreases by 5%. Primary and secondary outcome measures Quantifying the future burden of major NCDs and the impact of interventions on this future disease burden. Results By 2030 in the whole of the European region, the prevalence of diabetes, CHD and stroke and cancers was projected to reach an average of 3990, 4672 and 2046 cases/100 000, respectively. The highest prevalence of diabetes was predicted in Slovakia (10 870), CHD and stroke—in Greece (11 292) and cancers—in Finland (5615 cases/100 000). A 5% fall in population BMI was projected to significantly reduce cumulative incidence of diseases. The largest reduction in diabetes and CHD and stroke was observed in Slovakia (3054 and 3369 cases/100 000, respectively), and in cancers was predicted in Germany (331/100 000). Conclusions Modelling future disease trends is a useful tool for policymakers so that they can allocate resources effectively and implement policies to prevent NCDs. Future research will allow real policy interventions to be tested; however, better surveillance data on NCDs and their risk factors are essential for research and policy. PMID:25063459

When Waves Collide: Future Conflict

DTIC Science & Technology

1995-01-01

predictions merely guesswork, and forecasts often nothing more than co- herent fiction masquerading as fact.2 Trends and megatrends , which are linear...transportation, on-site inspection, and environmental cleanup—including radi- ological, chemical, and biological —as well as enforcement of the

Nursing education trends: future implications and predictions.

PubMed

Valiga, Theresa M Terry

2012-12-01

This article examines current trends in nursing education and proposes numerous transformations needed to ensure that programs are relevant, fully engage learners, reflect evidence-based teaching practices, and are innovative. Such program characteristics are essential if we are to graduate nurses who can practice effectively in today's complex, ambiguous, ever-changing health care environments and who are prepared to practice in and, indeed, shape tomorrow's unknown practice environments. Copyright © 2012 Elsevier Inc. All rights reserved.

Human influence on tropical cyclone intensity.

PubMed

Sobel, Adam H; Camargo, Suzana J; Hall, Timothy M; Lee, Chia-Ying; Tippett, Michael K; Wing, Allison A

2016-07-15

Recent assessments agree that tropical cyclone intensity should increase as the climate warms. Less agreement exists on the detection of recent historical trends in tropical cyclone intensity. We interpret future and recent historical trends by using the theory of potential intensity, which predicts the maximum intensity achievable by a tropical cyclone in a given local environment. Although greenhouse gas-driven warming increases potential intensity, climate model simulations suggest that aerosol cooling has largely canceled that effect over the historical record. Large natural variability complicates analysis of trends, as do poleward shifts in the latitude of maximum intensity. In the absence of strong reductions in greenhouse gas emissions, future greenhouse gas forcing of potential intensity will increasingly dominate over aerosol forcing, leading to substantially larger increases in tropical cyclone intensities. Copyright © 2016, American Association for the Advancement of Science.

Modelling uncertainties and possible future trends of precipitation and temperature for 10 sub-basins in Columbia River Basin (CRB)

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Rana, A.; Qin, Y.; Moradkhani, H.

2014-12-01

Trends and changes in future climatic parameters, such as, precipitation and temperature have been a central part of climate change studies. In the present work, we have analyzed the seasonal and yearly trends and uncertainties of prediction in all the 10 sub-basins of Columbia River Basin (CRB) for future time period of 2010-2099. The work is carried out using 2 different sets of statistically downscaled Global Climate Model (GCMs) projection datasets i.e. Bias correction and statistical downscaling (BCSD) generated at Portland State University and The Multivariate Adaptive Constructed Analogs (MACA) generated at University of Idaho. The analysis is done for with 10 GCM downscaled products each from CMIP5 daily dataset totaling to 40 different downscaled products for robust analysis. Summer, winter and yearly trend analysis is performed for all the 10 sub-basins using linear regression (significance tested by student t test) and Mann Kendall test (0.05 percent significance level), for precipitation (P), temperature maximum (Tmax) and temperature minimum (Tmin). Thereafter, all the parameters are modelled for uncertainty, across all models, in all the 10 sub-basins and across the CRB for future scenario periods. Results have indicated in varied degree of trends for all the sub-basins, mostly pointing towards a significant increase in all three climatic parameters, for all the seasons and yearly considerations. Uncertainty analysis have reveled very high change in all the parameters across models and sub-basins under consideration. Basin wide uncertainty analysis is performed to corroborate results from smaller, sub-basin scale. Similar trends and uncertainties are reported on the larger scale as well. Interestingly, both trends and uncertainties are higher during winter period than during summer, contributing to large part of the yearly change.

Trend Analysis of Betel Nut-associated Oral Cancer 
and Health Burden in China.

PubMed

Hu, Yan Jia; Chen, Jie; Zhong, Wai Sheng; Ling, Tian You; Jian, Xin Chun; Lu, Ruo Huang; Tang, Zhan Gui; Tao, Lin

To forecast the future trend of betel nut-associated oral cancer and the resulting burden on health based on historical oral cancer patient data in Hunan province, China. Oral cancer patient data in five hospitals in Changsha (the capital city of Hunan province) were collected for the past 12 years. Three methods were used to analyse the data; Microsoft Excel Forecast Sheet, Excel Trendline, and the Logistic growth model. A combination of these three methods was used to forecast the future trend of betel nut-associated oral cancer and the resulting burden on health. Betel nut-associated oral cancer cases have been increasing rapidly in the past 12  years in Changsha. As of 2016, betel nuts had caused 8,222 cases of oral cancer in Changsha and close to 25,000 cases in Hunan, resulting in about ¥5 billion in accumulated financial loss. The combined trend analysis predicts that by 2030, betel nuts will cause more than 100,000 cases of oral cancer in Changsha and more than 300,000 cases in Hunan, and more than ¥64 billion in accumulated financial loss in medical expenses. The trend analysis of oral cancer patient data predicts that the growing betel nut industry in Hunan province will cause a humanitarian catastrophe with massive loss of human life and national resources. To prevent this catastrophe, China should ban betel nuts and provide early oral cancer screening for betel nut consumers as soon as possible.

Future Cognitive Ability: US IQ Prediction until 2060 Based on NAEP.

PubMed

Rindermann, Heiner; Pichelmann, Stefan

2015-01-01

The US National Assessment of Educational Progress (NAEP) measures cognitive competences in reading and mathematics of US students (last 2012 survey N = 50,000). The long-term development based on results from 1971 to 2012 allows a prediction of future cognitive trends. For predicting US averages also demographic trends have to be considered. The largest groups' (White) average of 1978/80 was set at M = 100 and SD = 15 and was used as a benchmark. Based on two past NAEP development periods for 17-year-old students, 1978/80 to 2012 (more optimistic) and 1992 to 2012 (more pessimistic), and demographic projections from the US Census Bureau, cognitive trends until 2060 for the entire age cohort and ethnic groups were estimated. Estimated population averages for 2060 are 103 (optimistic) or 102 (pessimistic). The average rise per decade is dec = 0.76 or 0.45 IQ points. White-Black and White-Hispanic gaps are declining by half, Asian-White gaps treble. The catch-up of minorities (their faster ability growth) contributes around 2 IQ to the general rise of 3 IQ; however, their larger demographic increase reduces the general rise at about the similar amount (-1.4 IQ). Because minorities with faster ability growth also rise in their population proportion the interactive term is positive (around 1 IQ). Consequences for economic and societal development are discussed.

Future Cognitive Ability: US IQ Prediction until 2060 Based on NAEP

PubMed Central

2015-01-01

The US National Assessment of Educational Progress (NAEP) measures cognitive competences in reading and mathematics of US students (last 2012 survey N = 50,000). The long-term development based on results from 1971 to 2012 allows a prediction of future cognitive trends. For predicting US averages also demographic trends have to be considered. The largest groups’ (White) average of 1978/80 was set at M = 100 and SD = 15 and was used as a benchmark. Based on two past NAEP development periods for 17-year-old students, 1978/80 to 2012 (more optimistic) and 1992 to 2012 (more pessimistic), and demographic projections from the US Census Bureau, cognitive trends until 2060 for the entire age cohort and ethnic groups were estimated. Estimated population averages for 2060 are 103 (optimistic) or 102 (pessimistic). The average rise per decade is dec = 0.76 or 0.45 IQ points. White-Black and White-Hispanic gaps are declining by half, Asian-White gaps treble. The catch-up of minorities (their faster ability growth) contributes around 2 IQ to the general rise of 3 IQ; however, their larger demographic increase reduces the general rise at about the similar amount (-1.4 IQ). Because minorities with faster ability growth also rise in their population proportion the interactive term is positive (around 1 IQ). Consequences for economic and societal development are discussed. PMID:26460731

Advances and trends in computational structural mechanics

NASA Technical Reports Server (NTRS)

Noor, A. K.

1986-01-01

Recent developments in computational structural mechanics are reviewed with reference to computational needs for future structures technology, advances in computational models for material behavior, discrete element technology, assessment and control of numerical simulations of structural response, hybrid analysis, and techniques for large-scale optimization. Research areas in computational structural mechanics which have high potential for meeting future technological needs are identified. These include prediction and analysis of the failure of structural components made of new materials, development of computational strategies and solution methodologies for large-scale structural calculations, and assessment of reliability and adaptive improvement of response predictions.

The Orbital Debris Problem and the Challenges for Environment Remediation

NASA Technical Reports Server (NTRS)

Liou, J.-C.

2013-01-01

Orbital debris scientists from major international space agencies, including JAXA and NASA, have worked together to predict the trend of the future environment. A summary presentation was given to the United Nations in February 2013. The orbital debris population in LEO will continue to increase. Catastrophic collisions will continue to occur every 5 to 9 years center dot To limit the growth of the future debris population and to better protect future spacecraft, active debris removal, should be considered.

Predicting Liver Transplant Capacity Using Discrete Event Simulation.

PubMed

Toro-Díaz, Hector; Mayorga, Maria E; Barritt, A Sidney; Orman, Eric S; Wheeler, Stephanie B

2015-08-01

The number of liver transplants (LTs) performed in the US increased until 2006 but has since declined despite an ongoing increase in demand. This decline may be due in part to decreased donor liver quality and increasing discard of poor-quality livers. We constructed a discrete event simulation (DES) model informed by current donor characteristics to predict future LT trends through the year 2030. The data source for our model is the United Network for Organ Sharing database, which contains patient-level information on all organ transplants performed in the US. Previous analysis showed that liver discard is increasing and that discarded organs are more often from donors who are older, are obese, have diabetes, and donated after cardiac death. Given that the prevalence of these factors is increasing, the DES model quantifies the reduction in the number of LTs performed through 2030. In addition, the model estimatesthe total number of future donors needed to maintain the current volume of LTs and the effect of a hypothetical scenario of improved reperfusion technology.We also forecast the number of patients on the waiting list and compare this with the estimated number of LTs to illustrate the impact that decreased LTs will have on patients needing transplants. By altering assumptions about the future donor pool, this model can be used to develop policy interventions to prevent a further decline in this lifesaving therapy. To our knowledge, there are no similar predictive models of future LT use based on epidemiological trends. © The Author(s) 2014.

Predicting Liver Transplant Capacity Using Discrete Event Simulation

PubMed Central

Diaz, Hector Toro; Mayorga, Maria; Barritt, A. Sidney; Orman, Eric S.; Wheeler, Stephanie B.

2014-01-01

The number of liver transplants (LTs) performed in the US increased until 2006, but has since declined despite an ongoing increase in demand. This decline may be due in part to decreased donor liver quality and increasing discard of poor quality livers. We constructed a Discrete Event Simulation (DES) model informed by current donor characteristics to predict future LT trends through the year 2030. The data source for our model is the United Network for Organ Sharing database, which contains patient level information on all organ transplants performed in the US. Previous analysis showed that liver discard is increasing and that discarded organs are more often from donors who are older, obese, have diabetes, and donated after cardiac death. Given that the prevalence of these factors is increasing, the DES model quantifies the reduction in the number of LTs performed through 2030. In addition, the model estimates the total number of future donors needed to maintain the current volume of LTs, and the effect of a hypothetical scenario of improved reperfusion technology. We also forecast the number of patients on the waiting list and compare this to the estimated number of LTs to illustrate the impact that decreased LTs will have on patients needing transplants. By altering assumptions about the future donor pool, this model can be used to develop policy interventions to prevent a further decline in this life saving therapy. To our knowledge, there are no similar predictive models of future LT use based on epidemiologic trends. PMID:25391681

Future climate data from RCP 4.5 and occurrence of malaria in Korea.

PubMed

Kwak, Jaewon; Noh, Huiseong; Kim, Soojun; Singh, Vijay P; Hong, Seung Jin; Kim, Duckgil; Lee, Keonhaeng; Kang, Narae; Kim, Hung Soo

2014-10-15

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001-2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future.

Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea

PubMed Central

Kwak, Jaewon; Noh, Huiseong; Kim, Soojun; Singh, Vijay P.; Hong, Seung Jin; Kim, Duckgil; Lee, Keonhaeng; Kang, Narae; Kim, Hung Soo

2014-01-01

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future. PMID:25321875

Trends in personal income and passenger vehicle miles

DOT National Transportation Integrated Search

2007-10-01

While it is diffi cult to predict how individuals will use : increases in their real income in the future, it is clear that : historically some of that increase has been used to acquire : a household vehicle or increase the number of household : vehi...

FutureTox II: In vitro Data and In Silico Models for Predictive Toxicology

PubMed Central

Knudsen, Thomas B.; Keller, Douglas A.; Sander, Miriam; Carney, Edward W.; Doerrer, Nancy G.; Eaton, David L.; Fitzpatrick, Suzanne Compton; Hastings, Kenneth L.; Mendrick, Donna L.; Tice, Raymond R.; Watkins, Paul B.; Whelan, Maurice

2015-01-01

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. PMID:25628403

The future of housing in the United States: an econometric model of long-term predictions for the 2000 RPA timber assessment.

Treesearch

Claire A. Montgomery

2001-01-01

This report presents historical trends and future projections of forest, agricultural, and urban and other land uses for the South-Central United States. A land use share model is used to investigate the relation between the areas of land in alternative uses and economic and demographic factors influencing land use decisions. Two different versions of the empirical...

A study of current world telecommunications and a projection of the future

NASA Astrophysics Data System (ADS)

Karageorgis, Costas

1992-09-01

Telecommunications today are important factors in economic and social progress. The last decades of the 20th century and the early years of the 21st have been characterized as the Information Age. Telecommunications, the movement of information through distances, is absolutely critical to the economic and military survival of nations. This thesis is an attempt to predict the future of telecommunications, by studying and analyzing the past and present. First it examines the meaning of telecommunications today and some basics of information transmission. The current status of telecommunications is then presented, by examining the regional profiles as they are divided by the International Telecommunications Union. A number of statistical studies are given, which present a thorough picture of current world telecommunications. In an effort to predict future industry trends, the competition among the three largest telecommunications markets, U.S.A., Japan and the European Community, is also considered by looking at their present telecommunications industry, the efforts they make to improve their technology, and their plans for future investment. Finally, some major technological trends including BISDN, the use of fiber technology in the communications loop, and the use of solitons are examined. The new Metropolitan Area Network Protocol, FDDI-2 is also reviewed.

Beyond climate envelopes: effects of weather on regional population trends in butterflies.

PubMed

WallisDeVries, Michiel F; Baxter, Wendy; Van Vliet, Arnold J H

2011-10-01

Although the effects of climate change on biodiversity are increasingly evident by the shifts in species ranges across taxonomical groups, the underlying mechanisms affecting individual species are still poorly understood. The power of climate envelopes to predict future ranges has been seriously questioned in recent studies. Amongst others, an improved understanding of the effects of current weather on population trends is required. We analysed the relation between butterfly abundance and the weather experienced during the life cycle for successive years using data collected within the framework of the Dutch Butterfly Monitoring Scheme for 40 species over a 15-year period and corresponding climate data. Both average and extreme temperature and precipitation events were identified, and multiple regression was applied to explain annual changes in population indices. Significant weather effects were obtained for 39 species, with the most frequent effects associated with temperature. However, positive density-dependence suggested climatic independent trends in at least 12 species. Validation of the short-term predictions revealed a good potential for climate-based predictions of population trends in 20 species. Nevertheless, data from the warm and dry year of 2003 indicate that negative effects of climatic extremes are generally underestimated for habitat specialists in drought-susceptible habitats, whereas generalists remain unaffected. Further climatic warming is expected to influence the trends of 13 species, leading to an improvement for nine species, but a continued decline in the majority of species. Expectations from climate envelope models overestimate the positive effects of climate change in northwestern Europe. Our results underline the challenge to include population trends in predicting range shifts in response to climate change.

Comparison of mean climate trends in the Northern Hemisphere between National Centers for Environmental Prediction and two atmosphere-ocean model forced runs

NASA Astrophysics Data System (ADS)

Lucarini, Valerio; Russell, Gary L.

2002-08-01

Results are presented for two greenhouse gas experiments of the Goddard Institute for Space Studies atmosphere-ocean model (AOM). The computed trends of surface pressure; surface temperature; 850, 500, and 200 mbar geopotential heights; and related temperatures of the model for the time frame 1960-2000 are compared with those obtained from the National Centers for Enviromental Prediction (NCEP) observations. The domain of interest is the Northern Hemisphere because of the higher reliability of both the model results and the observations. A spatial correlation analysis and a mean value comparison are performed, showing good agreement in terms of statistical significance for most of the variables considered in the winter and annual means. However, the 850 mbar temperature trends do not show significant positive correlation, and the surface pressure and 850 mbar geopotential height mean trends confidence intervals do not overlap. A brief general discussion about the statistics of trend detection is presented. The accuracy that this AOM has in describing the regional and NH mean climate trends inferred from NCEP through the atmosphere suggests that it may be reliable in forecasting future climate changes.

Stated choice for transportation demand management models : using a disaggregate truth set to study predictive validity

DOT National Transportation Integrated Search

1997-01-01

Discrete choice models have expanded the ability of transportation planners to forecast future trends. Where new services or policies are proposed, the stated-choice approach can provide an objective basis for forecasts. Stated-choice models are subj...

Eurabia: Strategic Implications for the United States

DTIC Science & Technology

2010-03-01

display a currently valid OMB control number. 1. REPORT DATE 30 MAR 2010 2. REPORT TYPE 3. DATES COVERED 4. TITLE AND SUBTITLE Eurabia: Strategic...projecting current trends into the future seldom holds true in the face of demographic realities, i.e. nature gets a vote so to speak. These social welfare...portion of the population. Nevertheless, current predictions have it rising dramatically over the coming decades. Most demographers predict that by

Impact of climate change on runoff in Lake Urmia basin, Iran

NASA Astrophysics Data System (ADS)

Sanikhani, Hadi; Kisi, Ozgur; Amirataee, Babak

2018-04-01

Investigation of the impact of climate change on water resources is very necessary in dry and arid regions. In the first part of this paper, the climate model Long Ashton Research Station Weather Generator (LARS-WG) was used for downscaling climate data including rainfall, solar radiation, and minimum and maximum temperatures. Two different case studies including Aji-Chay and Mahabad-Chay River basins as sub-basins of Lake Urmia in the northwest part of Iran were considered. The results indicated that the LARS-WG successfully downscaled the climatic variables. By application of different emission scenarios (i.e., A1B, A2, and B1), an increasing trend in rainfall and a decreasing trend in temperature were predicted for both the basins over future time periods. In the second part of this paper, gene expression programming (GEP) was applied for simulating runoff of the basins in the future time periods including 2020, 2055, and 2090. The input combination including rainfall, solar radiation, and minimum and maximum temperatures in current and prior time was selected as the best input combination with highest predictive power for runoff prediction. The results showed that the peak discharge will decrease by 50 and 55.9% in 2090 comparing with the baseline period for the Aji-Chay and Mahabad-Chay basins, respectively. The results indicated that the sustainable adaptation strategies are necessary for these basins for protection of water resources in future.

Envisioning Nano Release Dynamics in a Changing World: Using Dynamic Probabilistic Modeling to Assess Future Environmental Emissions of Engineered Nanomaterials.

PubMed

Sun, Tian Yin; Mitrano, Denise M; Bornhöft, Nikolaus A; Scheringer, Martin; Hungerbühler, Konrad; Nowack, Bernd

2017-03-07

The need for an environmental risk assessment for engineered nanomaterials (ENM) necessitates the knowledge about their environmental emissions. Material flow models (MFA) have been used to provide predicted environmental emissions but most current nano-MFA models consider neither the rapid development of ENM production nor the fact that a large proportion of ENM are entering an in-use stock and are released from products over time (i.e., have a lag phase). Here we use dynamic probabilistic material flow modeling to predict scenarios of the future flows of four ENM (nano-TiO 2 , nano-ZnO, nano-Ag and CNT) to environmental compartments and to quantify their amounts in (temporary) sinks such as the in-use stock and ("final") environmental sinks such as soil and sediment. In these scenarios, we estimate likely future amounts if the use and distribution of ENM in products continues along current trends (i.e., a business-as-usual approach) and predict the effect of hypothetical trends in the market development of nanomaterials, such as the emergence of a new widely used product or the ban on certain substances, on the flows of nanomaterials to the environment in years to come. We show that depending on the scenario and the product type affected, significant changes of the flows occur over time, driven by the growth of stocks and delayed release dynamics.

Linear regression analysis of Hospital Episode Statistics predicts a large increase in demand for elective hand surgery in England.

PubMed

Bebbington, Emily; Furniss, Dominic

2015-02-01

We integrated two factors, demographic population shifts and changes in prevalence of disease, to predict future trends in demand for hand surgery in England, to facilitate workforce planning. We analysed Hospital Episode Statistics data for Dupuytren's disease, carpal tunnel syndrome, cubital tunnel syndrome, and trigger finger from 1998 to 2011. Using linear regression, we estimated trends in both diagnosis and surgery until 2030. We integrated this regression with age specific population data from the Office for National Statistics in order to estimate how this will contribute to a change in workload over time. There has been a significant increase in both absolute numbers of diagnoses and surgery for all four conditions. Combined with future population data, we calculate that the total operative burden for these four conditions will increase from 87,582 in 2011 to 170,166 (95% confidence interval 144,517-195,353) in 2030. The prevalence of these diseases in the ageing population, and increasing prevalence of predisposing factors such as obesity and diabetes, may account for the predicted increase in workload. The most cost effective treatments must be sought, which requires high quality clinical trials. Our methodology can be applied to other sub-specialties to help anticipate the need for future service provision. Copyright © 2014 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)

NASA Astrophysics Data System (ADS)

Wu, Binghui; Duan, Tingting

2017-05-01

The price of gold future is affected by many factors, which include the fluctuation of gold price and the change of trading environment. Fractal analysis can help investors gain better understandings of the price fluctuation and make reasonable investment decisions in the gold future market. After analyzing gold future price from January 2th, 2014 to April 12th, 2016 at the Shanghai Futures Exchange (SFE) in China, the conclusion is drawn that the gold future market has sustainability in each trading day, with all Hurst indexes greater than 0.5. The changing features of Hurst index indicate the sustainability of gold future market is strengthened first and weakened then. As a complicatedly nonlinear system, the gold future market can be well reflected by Elman neural network, which is capable of memorizing previous prices and particularly suited for forecasting time series in comparison with other types of neural networks. After analyzing the price trend in the gold future market, the results show that the relative error between the actual value of gold future and the predictive value of Elman neural network is smaller. This model that has a better performance in data fitting and predication, can help investors analyze and foresee the price tendency in the gold future market.

Using Text Analytics of AJPE Article Titles to Reveal Trends In Pharmacy Education Over the Past Two Decades.

PubMed

Pedrami, Farnoush; Asenso, Pamela; Devi, Sachin

2016-08-25

Objective. To identify trends in pharmacy education during last two decades using text mining. Methods. Articles published in the American Journal of Pharmaceutical Education (AJPE) in the past two decades were compiled in a database. Custom text analytics software was written using Visual Basic programming language in the Visual Basic for Applications (VBA) editor of Excel 2007. Frequency of words appearing in article titles was calculated using the custom VBA software. Data were analyzed to identify the emerging trends in pharmacy education. Results. Three educational trends emerged: active learning, interprofessional, and cultural competency. Conclusion. The text analytics program successfully identified trends in article topics and may be a useful compass to predict the future course of pharmacy education.

Recent and possible future variations in the North American Monsoon

USGS Publications Warehouse

Hoell, Andrew; Funk, Chris; Barlow, Mathew; Shukla, Shraddhanand

2016-01-01

The dynamics and recent and possible future changes of the June–September rainfall associated with the North American Monsoon (NAM) are reviewed in this chapter. Our analysis as well as previous analyses of the trend in June–September precipitation from 1948 until 2010 indicate significant precipitation increases over New Mexico and the core NAM region, and significant precipitation decreases over southwest Mexico. The trends in June–September precipitation have been forced by anomalous cyclonic circulation centered at 15°N latitude over the eastern Pacific Ocean. The anomalous cyclonic circulation is responsible for changes in the flux of moisture and the divergence of moisture flux within the core NAM region. Future climate projections using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models, as part of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5), support the observed analyses of a later shift in the monsoon season in the presence of increased greenhouse gas concentrations in the atmosphere under the RCP8.5 scenario. The CMIP5 models under the RCP8.5 scenario predict significant NAM-related rainfall decreases during June and July and predict significant NAM-related rainfall increases during September and October.

Foresight Infectious Diseases China Project--a novel approach to anticipating future trends in risk of infectious diseases in China: methodology and results from an initial application.

PubMed

Nicoll, A; Huang, J; Xie, Z

2009-07-09

The project devised a simple but novel methodology for identifying possible future trends in infectious diseases in animals and humans in China, of priority concern to the Chinese authorities. It used a model of disease drivers (social, economic, biological or environmental factors that affect disease outcomes, by changing the behaviour of diseases, sources or pathways) devised for the Foresight Programme in the United Kingdom. Nine families of drivers were adapted to Chinese circumstances and matrices were constructed to identify the likely relationship of single infectious diseases or families of diseases to the drivers. The likely future trends in those drivers in China were determined by interviews with 36 independent Chinese experts. These trends included not only potentially adverse animal and human movements but also opportunities for innovative surveillance methods, more use of hospitals, antimicrobials and vaccines. Some human behaviours and social trends were expected to increase the risk of infections (in particular sexually transmitted and healthcare-associated infections) while at the same time the experts thought the awareness of risk in the Chinese population would increase. The results suggested a number of areas where the Chinese authorities may experience difficulties in the future, such as rising numbers of healthcare-associated infections, zoonoses and other emerging diseases and sexually transmitted infections (including HIV). Not making firm predictions, this work identifies priority disease groups requiring surveillance and consideration of countermeasures as well as recommending strengthening basic surveillance and response mechanisms for unanticipatable zoonoses and other emerging disease threats.

Climate is changing, everything is flowing, stationarity is immortal

NASA Astrophysics Data System (ADS)

Koutsoyiannis, Demetris; Montanari, Alberto

2015-04-01

There is no doubt that climate is changing -- and ever has been. The environment is also changing and in the last decades, as a result of demographic change and technological advancement, environmental change has been accelerating. These affect also the hydrological processes, whose changes in connection with rapidly changing human systems have been the focus of the new scientific decade 2013-2022 of the International Association of Hydrological Sciences, entitled "Panta Rhei - Everything Flows". In view of the changing systems, it has recently suggested that, when dealing with water management and hydrological extremes, stationarity is no longer a proper assumption. Hence, it was proposed that hydrological processes should be treated as nonstationary. Two main reasons contributed to this perception. First, the climate models project a future hydroclimate that will be different from the current one. Second, as streamflow record become longer, they indicate the presence of upward or downward trends. However, till now hydroclimatic projections made in the recent past have not been verified. At the same time, evidence from quite longer records, instrumental or proxy, suggest that local trends are omnipresent but not monotonic; rather at some time upward trends turn to downward ones and vice versa. These observations suggest that improvident dismiss of stationarity and adoption of nonstationary descriptions based either on climate model outputs or observed trends may entail risks. The risks stem from the facts that the future can be different from what was deterministically projected, that deterministic projections are associated with an illusion of decreased uncertainty, as well as that nonstationary models fitted on observed data may have lower predictive capacity than simpler stationary ones. In most of the cases, what is actually needed is to revisit the concept of stationarity and try to apply it carefully, making it consistent with the presence of local trends, possibly incorporating information from deterministic predictions, whenever these prove to be reliable, and estimating the total predictive uncertainty.

Impact of climate change and seasonal trends on the fate of Arctic oil spills.

PubMed

Nordam, Tor; Dunnebier, Dorien A E; Beegle-Krause, C J; Reed, Mark; Slagstad, Dag

2017-12-01

We investigated the effects of a warmer climate, and seasonal trends, on the fate of oil spilled in the Arctic. Three well blowout scenarios, two shipping accidents and a pipeline rupture were considered. We used ensembles of numerical simulations, using the OSCAR oil spill model, with environmental data for the periods 2009-2012 and 2050-2053 (representing a warmer future) as inputs to the model. Future atmospheric forcing was based on the IPCC's A1B scenario, with the ocean data generated by the hydrodynamic model SINMOD. We found differences in "typical" outcome of a spill in a warmer future compared to the present, mainly due to a longer season of open water. We have demonstrated that ice cover is extremely important for predicting the fate of an Arctic oil spill, and find that oil spills in a warming climate will in some cases result in greater areal coverage and shoreline exposure.

Influenza forecasting with Google Flu Trends.

PubMed

Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

2013-01-01

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Trend in Obesity Prevalence in European Adult Cohort Populations during Follow-up since 1996 and Their Predictions to 2015

PubMed Central

von Ruesten, Anne; Steffen, Annika; Floegel, Anna; van der A, Daphne L.; Masala, Giovanna; Tjønneland, Anne; Halkjaer, Jytte; Palli, Domenico; Wareham, Nicholas J.; Loos, Ruth J. F.; Sørensen, Thorkild I. A.; Boeing, Heiner

2011-01-01

Objective To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations. Methods Data of 97 942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as “Diogenes cohort” in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction. Results During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R2 = 0.98), and a linear model among women (R2 = 0.99). Conclusion Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower. PMID:22102897

Progress report of the Interagency Climate Change Adaptation Task Force : recommended actions in support of a national climate change adaptation strategy

DOT National Transportation Integrated Search

2010-10-05

The scope, severity, and pace of : future climate change impacts are : difficult to predict. However, : observations and long-term scientific : trends indicate that the potential : impacts of a changing climate on : society and the environment will b...

Analysis of Patent Activity in the Field of Quantum Information Processing

NASA Astrophysics Data System (ADS)

Winiarczyk, Ryszard; Gawron, Piotr; Miszczak, Jarosław Adam; Pawela, Łukasz; Puchała, Zbigniew

2013-03-01

This paper provides an analysis of patent activity in the field of quantum information processing. Data from the PatentScope database from the years 1993-2011 was used. In order to predict the future trends in the number of filed patents time series models were used.

Environmental Scan: A Strategic Planning Document.

ERIC Educational Resources Information Center

Osborn, Frances

Information, perceptions, and predictions from a variety of sources are brought together in this document to help guide planning and decision making at Monroe Community College (MCC). The first section examines national events and trends with implications for the future of MCC, including employment projections; educational norms; data on community…

Detecting trends in academic research from a citation network using network representation learning

PubMed Central

Mori, Junichiro; Ochi, Masanao; Sakata, Ichiro

2018-01-01

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. PMID:29782521

Predictive data modeling of human type II diabetes related statistics

NASA Astrophysics Data System (ADS)

Jaenisch, Kristina L.; Jaenisch, Holger M.; Handley, James W.; Albritton, Nathaniel G.

2009-04-01

During the course of routine Type II treatment of one of the authors, it was decided to derive predictive analytical Data Models of the daily sampled vital statistics: namely weight, blood pressure, and blood sugar, to determine if the covariance among the observed variables could yield a descriptive equation based model, or better still, a predictive analytical model that could forecast the expected future trend of the variables and possibly eliminate the number of finger stickings required to montior blood sugar levels. The personal history and analysis with resulting models are presented.

Trends of Abutment-Scour Prediction Equations Applied to 144 Field Sites in South Carolina

USGS Publications Warehouse

Benedict, Stephen T.; Deshpande, Nikhil; Aziz, Nadim M.; Conrads, Paul

2006-01-01

The U.S. Geological Survey conducted a study in cooperation with the Federal Highway Administration in which predicted abutment-scour depths computed with selected predictive equations were compared with field measurements of abutment-scour depth made at 144 bridges in South Carolina. The assessment used five equations published in the Fourth Edition of 'Evaluating Scour at Bridges,' (Hydraulic Engineering Circular 18), including the original Froehlich, the modified Froehlich, the Sturm, the Maryland, and the HIRE equations. An additional unpublished equation also was assessed. Comparisons between predicted and observed scour depths are intended to illustrate general trends and order-of-magnitude differences for the prediction equations. Field measurements were taken during non-flood conditions when the hydraulic conditions that caused the scour generally are unknown. The predicted scour depths are based on hydraulic conditions associated with the 100-year flow at all sites and the flood of record for 35 sites. Comparisons showed that predicted scour depths frequently overpredict observed scour and at times were excessive. The comparison also showed that underprediction occurred, but with less frequency. The performance of these equations indicates that they are poor predictors of abutment-scour depth in South Carolina, and it is probable that poor performance will occur when the equations are applied in other geographic regions. Extensive data and graphs used to compare predicted and observed scour depths in this study were compiled into spreadsheets and are included in digital format with this report. In addition to the equation-comparison data, Water-Surface Profile Model tube-velocity data, soil-boring data, and selected abutment-scour data are included in digital format with this report. The digital database was developed as a resource for future researchers and is especially valuable for evaluating the reasonableness of future equations that may be developed.

Gloom and doom? The future of marine capture fisheries

PubMed Central

Garcia, Serge M.; Grainger, Richard J. R.

2005-01-01

Predicting global fisheries is a high-order challenge but predictions have been made and updates are needed. Past forecasts, present trends and perspectives of key parameters of the fisheries—including potential harvest, state of stocks, supply and demand, trade, fishing technology and governance—are reviewed in detail, as the basis for new forecasts and forecasting performance assessment. The future of marine capture fisheries will be conditioned by the political, social and economic evolution of the world within which they operate. Consequently, recent global scenarios for the future world are reviewed, with the emphasis on fisheries. The main driving forces (e.g. global economic development, demography, environment, public awareness, information technology, energy, ethics) including aquaculture are described. Outlooks are provided for each aspect of the fishery sector. The conclusion puts these elements in perspective and offers the authors’ personal interpretation of the possible future pathway of fisheries, the uncertainty about it and the still unanswered questions of direct relevance in shaping that future. PMID:15713587

The "wins" of change: evaluating the impact of predicted changes on case management practice.

PubMed

Stanton, Marietta P; Barnett Lammon, Carol Ann

2008-01-01

A variety of strategies were employed to identify current and future trends that would impact the practice of case management. Historical review, consultation with case management experts, literature review, and environmental scanning by practicing case managers were strategies employed to determine the impact of current and future trends on case management. The trends identified in this article have implications for case managers in a variety of settings. Case managers participating in the environmental scanning process to evaluate the impact of the identified trends on their organization included representation from acute care, home care, behavioral health, workers' compensation, and private insurance settings. The top 7 trends identified by experts in the field of case management included pay for performance, recovery audit contractors, Medicare demonstration projects, transitions of care, informatics in healthcare and case management, metrics for case management, and the impact of an aging population in case management. Practicing case managers were asked to react to these trends in terms of likelihood of occurrence in their organization and impact of these trends on their case management practice. Case management will ultimately have a higher degree of accountability for its practice if metrics to evaluate and reimbursement for case management become a reality. A multitude of performance measures exist that will be monitored and be tied to reimbursement. To ensure that agencies are accomplishing these performance measures, case management will potentially have a growing importance. Case managers perceive that these trends have a predominantly positive impact on case management.

Spatio-temporal trends of nitrogen deposition and climate effects on Sphagnum productivity in European peatlands.

PubMed

Granath, Gustaf; Limpens, Juul; Posch, Maximilian; Mücher, Sander; de Vries, Wim

2014-04-01

To quantify potential nitrogen (N) deposition impacts on peatland carbon (C) uptake, we explored temporal and spatial trends in N deposition and climate impacts on the production of the key peat forming functional group (Sphagnum mosses) across European peatlands for the period 1900-2050. Using a modelling approach we estimated that between 1900 and 1950 N deposition impacts remained limited irrespective of geographical position. Between 1950 and 2000 N deposition depressed production between 0 and 25% relative to 1900, particularly in temperate regions. Future scenarios indicate this trend will continue and become more pronounced with climate warming. At the European scale, the consequences for Sphagnum net C-uptake remained small relative to 1900 due to the low peatland cover in high-N areas. The predicted impacts of likely changes in N deposition on Sphagnum productivity appeared to be less than those of climate. Nevertheless, current critical loads for peatlands are likely to hold under a future climate. Copyright © 2014 Elsevier Ltd. All rights reserved.

Forecast of future aviation fuels: The model

NASA Technical Reports Server (NTRS)

Ayati, M. B.; Liu, C. Y.; English, J. M.

1981-01-01

A conceptual models of the commercial air transportation industry is developed which can be used to predict trends in economics, demand, and consumption. The methodology is based on digraph theory, which considers the interaction of variables and propagation of changes. Air transportation economics are treated by examination of major variables, their relationships, historic trends, and calculation of regression coefficients. A description of the modeling technique and a compilation of historic airline industry statistics used to determine interaction coefficients are included. Results of model validations show negligible difference between actual and projected values over the twenty-eight year period of 1959 to 1976. A limited application of the method presents forecasts of air tranportation industry demand, growth, revenue, costs, and fuel consumption to 2020 for two scenarios of future economic growth and energy consumption.

Development of a Geographic Information System (GIS) tool for the preliminary assessment of the effects of predicted sea level and tidal change on transportation infrastructure.

DOT National Transportation Integrated Search

2013-09-01

In this project, researchers from the University of Florida developed a sketch planning tool that can be used to conduct statewide and regional assessments of transportation facilities potentially vulnerable to sea level change trends. Possible futur...

Technical Processing Librarians in the 1980's: Current Trends and Future Forecasts.

ERIC Educational Resources Information Center

Kennedy, Gail

1980-01-01

This review of recent and anticipated advances in library automation technology and methodology includes a review of the effects of OCLC, MARC formatting, AACR2, and increasing costs, as well as predictions of the impact on library technical processing of networking, expansion of automation, minicomputers, specialized reference services, and…

Large-area fabrication of superhydrophobic surfaces for practical applications: an overview

PubMed Central

Xue, Chao-Hua; Jia, Shun-Tian; Zhang, Jing; Ma, Jian-Zhong

2010-01-01

This review summarizes the key topics in the field of large-area fabrication of superhydrophobic surfaces, concentrating on substrates that have been used in commercial applications. Practical approaches to superhydrophobic surface construction and hydrophobization are discussed. Applications of superhydrophobic surfaces are described and future trends in superhydrophobic surfaces are predicted. PMID:27877336

Responses of dead forest fuel moisture to climate change

Treesearch

Yongqiang Liu

2016-01-01

Forest fuel moisture is an important factor for wildland fire behavior. Predicting future wildfire trends and controlled burned conditions is essential to effective natural resource management, but the associated effects of forest fuel moisture remain uncertain. This study investigates the responses of dead forest fuel moisture to climate change in the...

A synopsis of climate change effects on groundwater recharge

NASA Astrophysics Data System (ADS)

Smerdon, Brian D.

2017-12-01

Six review articles published between 2011 and 2016 on groundwater and climate change are briefly summarized. This synopsis focuses on aspects related to predicting changes to groundwater recharge conditions, with several common conclusions between the review articles being noted. The uncertainty of distribution and trend in future precipitation from General Circulation Models (GCMs) results in varying predictions of recharge, so much so that modelling studies are often not able to predict the magnitude and direction (increase or decrease) of future recharge conditions. Evolution of modelling approaches has led to the use of multiple GCMs and hydrologic models to create an envelope of future conditions that reflects the probability distribution. The choice of hydrologic model structure and complexity, and the choice of emissions scenario, has been investigated and somewhat resolved; however, recharge results remain sensitive to downscaling methods. To overcome uncertainty and provide practical use in water management, the research community indicates that modelling at a mesoscale, somewhere between watersheds and continents, is likely ideal. Improvements are also suggested for incorporating groundwater processes within GCMs.

Making predictions skill level analysis

NASA Astrophysics Data System (ADS)

Katarína, Krišková; Marián, Kireš

2017-01-01

The current trend in the education is focused on skills that are cross-subject and have a great importance for the pupil future life. Pupils should acquire different types of skills during their education to be prepared for future careers and life in the 21st century. Physics as a subject offers many opportunities for pupils' skills development. One of the skills that are expected to be developed in physics and also in other sciences is making predictions. The prediction, in the meaning of the argument about what may happen in the future, is an integral part of the empirical cognition, in which students confront existing knowledge and experience with new, hitherto unknown and surprising phenomena. The extent of the skill is the formulation of hypotheses, which is required in the upper secondary physics education. In the contribution, the prediction skill is specified and its eventual levels are classified. Authors focus on the tools for skill level determination based on the analysis of pupils` worksheets. Worksheets are the part of the educational activities conducted within the Inquiry Science Laboratory Steelpark. Based on the formulation of pupils' prediction the pupils thinking can be seen and their understanding of the topic, as well as preconceptions and misconceptions.

Predicting consumer behavior with Web search.

PubMed

Goel, Sharad; Hofman, Jake M; Lahaie, Sébastien; Pennock, David M; Watts, Duncan J

2010-10-12

Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

Predicting consumer behavior with Web search

PubMed Central

Goel, Sharad; Hofman, Jake M.; Lahaie, Sébastien; Pennock, David M.; Watts, Duncan J.

2010-01-01

Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future. PMID:20876140

Future export of particulate and dissolved organic carbon from land to coastal zones of the Baltic Sea

NASA Astrophysics Data System (ADS)

Strååt, Kim Dahlgren; Mörth, Carl-Magnus; Undeman, Emma

2018-01-01

The Baltic Sea is a semi-enclosed brackish sea in Northern Europe with a drainage basin four times larger than the sea itself. Riverine organic carbon (Particulate Organic Carbon, POC and Dissolved Organic Carbon, DOC) dominates carbon input to the Baltic Sea and influences both land-to-sea transport of nutrients and contaminants, and hence the functioning of the coastal ecosystem. The potential impact of future climate change on loads of POC and DOC in the Baltic Sea drainage basin (BSDB) was assessed using a hydrological-biogeochemical model (CSIM). The changes in annual and seasonal concentrations and loads of both POC and DOC by the end of this century were predicted using three climate change scenarios and compared to the current state. In all scenarios, overall increasing DOC loads, but unchanged POC loads, were projected in the north. In the southern part of the BSDB, predicted DOC loads were not significantly changing over time, although POC loads decreased in all scenarios. The magnitude and significance of the trends varied with scenario but the sign (+ or -) of the projected trends for the entire simulation period never conflicted. Results were discussed in detail for the "middle" CO2 emission scenario (business as usual, a1b). On an annual and entire drainage basin scale, the total POC load was projected to decrease by ca 7% under this scenario, mainly due to reduced riverine primary production in the southern parts of the BSDB. The average total DOC load was not predicted to change significantly between years 2010 and 2100 due to counteracting decreasing and increasing trends of DOC loads to the six major sub-basins in the Baltic Sea. However, predicted seasonal total loads of POC and DOC increased significantly by ca 46% and 30% in winter and decreased by 8% and 21% in summer over time, respectively. For POC the change in winter loads was a consequence of increasing soil erosion and a shift in duration of snowfall and onset of the spring flood impacting the input of terrestrial litter, while reduced primary production mainly explained the differences predicted in summer. The simulations also showed that future changes in POC and DOC export can vary significantly across the different sub-basins of the Baltic Sea. These changes in organic carbon input may impact future coastal food web structures e.g. by influencing bacterial and phytoplankton production in coastal zones, which in turn may have consequences at higher trophic levels.

Regional sea level variability in a high-resolution global coupled climate model

NASA Astrophysics Data System (ADS)

Palko, D.; Kirtman, B. P.

2016-12-01

The prediction of trends at regional scales is essential in order to adapt to and prepare for the effects of climate change. However, GCMs are unable to make reliable predictions at regional scales. The prediction of local sea level trends is particularly critical. The main goal of this research is to utilize high-resolution (HR) (0.1° resolution in the ocean) coupled model runs of CCSM4 to analyze regional sea surface height (SSH) trends. Unlike typical, lower resolution (1.0°) GCM runs these HR runs resolve features in the ocean, like the Gulf Stream, which may have a large effect on regional sea level. We characterize the variability of regional SSH along the Atlantic coast of the US using tide gauge observations along with fixed radiative forcing runs of CCSM4 and HR interactive ensemble runs. The interactive ensemble couples an ensemble mean atmosphere with a single ocean realization. This coupling results in a 30% decrease in the strength of the Atlantic meridional overturning circulation; therefore, the HR interactive ensemble is analogous to a HR hosing experiment. By characterizing the variability in these high-resolution GCM runs and observations we seek to understand what processes influence coastal SSH along the Eastern Coast of the United States and better predict future SLR.

Forecasting turning trends in knowledge networks

NASA Astrophysics Data System (ADS)

Szántó-Várnagy, Ádám; Farkas, Illés J.

2018-10-01

A large portion of our collective human knowledge is in electronic repositories. These repositories range from "hard fact" databases (e.g., scientific publications and patents) to "soft" knowledge such as news portals. The common denominator between them all is that they can be thought of in terms of topics/keywords. The interest in these topics is constantly changing over time. Their frequency occurrence diagrams can be used for effective prediction by the most straightforward simplification. In this paper, we use these diagrams to produce simple and human-readable rules that are able to predict the future trends of the most important keywords in 5 data sets of different types. A thorough analysis of the necessary input variables and parameters and their relation to the success rate is presented, as well.

Remote Sensing and halocene Vegetation: History of Global Change

NASA Technical Reports Server (NTRS)

D'Antoni, Hector L.; Schaebitz, Frank

1995-01-01

Predictions of the future evolution of the earth's atmospheric chemistry and its impact on global circulation patterns are based on Global Climate Models (GCMs) that integrate the complex interactions of the biosphere, atmosphere and the oceans. Most of the available records of climate and environment are short-term records (from decades to a few hundred years) with convolved information of real trends and short-term fluctuations. GCMs must be tested beyond the short-term record of climate and environment to insure that predictions are based on trends and therefore are appropriate to support long term policy making. Unfortunately different parts of the world, weather stations are scattered, records extend over a period of only few years, and there are no systematic climate records for large portions of the globe.

Increase in breast cancer incidence among older women in Mumbai: 30-year trends and predictions to 2025.

PubMed

Dikshit, Rajesh P; Yeole, B B; Nagrani, Rajini; Dhillon, P; Badwe, R; Bray, Freddie

2012-08-01

Increasing trends in the incidence of breast cancer have been observed in India, including Mumbai. These have likely stemmed from an increasing adoption of lifestyle factors more akin to those commonly observed in westernized countries. Analyses of breast cancer trends and corresponding estimation of the future burden are necessary to better plan rationale cancer control programmes within the country. We used data from the population-based Mumbai Cancer Registry to study time trends in breast cancer incidence rates 1976-2005 and stratified them according to younger (25-49) and older age group (50-74). Age-period-cohort models were fitted and the net drift used as a measure of the estimated annual percentage change (EAPC). Age-period-cohort models and population projections were used to predict the age-adjusted rates and number of breast cancer cases circa 2025. Breast cancer incidence increased significantly among older women over three decades (EAPC = 1.6%; 95% CI 1.1-2.0), while lesser but significant 1% increase in incidence among younger women was observed (EAPC = 1.0; 95% CI 0.2-1.8). Non-linear period and cohort effects were observed; a trends-based model predicted a close-to-doubling of incident cases by 2025 from 1300 mean cases per annum in 2001-2005 to over 2500 cases in 2021-2025. The incidence of breast cancer has increased in Mumbai during last two to three decades, with increases greater among older women. The number of breast cancer cases is predicted to double to over 2500 cases, the vast majority affecting older women. Copyright © 2012 Elsevier Ltd. All rights reserved.

The paradox of cooling streams in a warming world: Regional climate trends do not parallel variable local trends in stream temperature in the Pacific continental United States

USGS Publications Warehouse

Arismendi, Ivan; Johnson, Sherri; Dunham, Jason B.; Haggerty, Roy; Hockman-Wert, David

2012-01-01

Temperature is a fundamentally important driver of ecosystem processes in streams. Recent warming of terrestrial climates around the globe has motivated concern about consequent increases in stream temperature. More specifically, observed trends of increasing air temperature and declining stream flow are widely believed to result in corresponding increases in stream temperature. Here, we examined the evidence for this using long-term stream temperature data from minimally and highly human-impacted sites located across the Pacific continental United States. Based on hypothesized climate impacts, we predicted that we should find warming trends in the maximum, mean and minimum temperatures, as well as increasing variability over time. These predictions were not fully realized. Warming trends were most prevalent in a small subset of locations with longer time series beginning in the 1950s. More recent series of observations (1987-2009) exhibited fewer warming trends and more cooling trends in both minimally and highly human-influenced systems. Trends in variability were much less evident, regardless of the length of time series. Based on these findings, we conclude that our perspective of climate impacts on stream temperatures is clouded considerably by a lack of long-termdata on minimally impacted streams, and biased spatio-temporal representation of existing time series. Overall our results highlight the need to develop more mechanistic, process-based understanding of linkages between climate change, other human impacts and stream temperature, and to deploy sensor networks that will provide better information on trends in stream temperatures in the future.

Road structural elements temperature trends diagnostics using sensory system of own design

NASA Astrophysics Data System (ADS)

Dudak, Juraj; Gaspar, Gabriel; Sedivy, Stefan; Pepucha, Lubomir; Florkova, Zuzana

2017-09-01

A considerable funds is spent for the roads maintenance in large areas during the winter. The road maintenance is significantly affected by the temperature change of the road structure. In remote locations may occur a situation, when it is not clear whether the sanding is actually needed because the lack of information on road conditions. In these cases, the actual road conditions are investigated by a personal inspection or by sending out a gritting vehicle. Here, however, is a risk of unnecessary trip the sanding vehicle. This situation is economically and environmentally unfavorable. The proposed system solves the problem of measuring the temperature profile of the road and the utilization of the predictive model to determine the future development trend of temperature. The system was technically designed as a set of sensors to monitor environmental values such as the temperature of the road, ambient temperature, relative air humidity, solar radiation and atmospheric pressure at the measuring point. An important part of the proposal is prediction model which based on the inputs from sensors and historical measurements can, with some probability, predict temperature trends at the measuring point. The proposed system addresses the economic and environmental aspects of winter road maintenance.

Does Demand for Breast Augmentation Reflect National Financial Trends?

PubMed

Kearney, L; Dolan, R T; Clover, A J; Kelly, E J; O'Broin, E; O'Shaughnessy, M; O'Sullivan, S T

2017-04-01

Aesthetic plastic surgery is a consumer-driven industry, subject to influence by financial forces. A changing economic environment may thus impact on the demand for surgery. The aim of this study was to explore trends in demand for bilateral breast augmentation (BBA) in consecutively presenting patients over an 11-year period and to examine if a correlation exists between these trends and changes in Gross Domestic Product (GDP), a key economic indicator. This study revealed a correlation between annual number of breast augmentation procedures performed and GDP values (r 2  = 0.34, p value = 0.059). Additionally, predicted number of BBA procedures, based on predicted GDP growth in Ireland, strongly correlated with actual number of BBA performed (r 2  = 0.93, p value = 0.000001). Predicted GDP growth can potentially forecast future demand for BBA in our cohort allowing plastic surgeons to modify their practice accordingly. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

[Phenotypic trends and breeding values for canine congenital sensorineural deafness in Dalmatian dogs].

PubMed

Blum, Meike; Distl, Ottmar

2014-01-01

In the present study, breeding values for canine congenital sensorineural deafness, the presence of blue eyes and patches have been predicted using multivariate animal models to test the reliability of the breeding values for planned matings. The dataset consisted of 6669 German Dalmatian dogs born between 1988 and 2009. Data were provided by the Dalmatian kennel clubs which are members of the German Association for Dog Breeding and Husbandry (VDH). The hearing status for all dogs was evaluated using brainstem auditory evoked potentials. The reliability using the prediction error variance of breeding values and the realized reliability of the prediction of the phenotype of future progeny born in each one year between 2006 and 2009 were used as parameters to evaluate the goodness of prediction through breeding values. All animals from the previous birth years were used for prediction of the breeding values of the progeny in each of the up-coming birth years. The breeding values based on pedigree records achieved an average reliability of 0.19 for the future 1951 progeny. The predictive accuracy (R2) for the hearing status of single future progeny was at 1.3%. Combining breeding values for littermates increased the predictive accuracy to 3.5%. Corresponding values for maternal and paternal half-sib groups were at 3.2 and 7.3%. The use of breeding values for planned matings increases the phenotypic selection response over mass selection. The breeding values of sires may be used for planned matings because reliabilities and predictive accuracies for future paternal progeny groups were highest.

Reversal of Increasing Tropical Ocean Hypoxia Trends With Sustained Climate Warming

NASA Astrophysics Data System (ADS)

Fu, Weiwei; Primeau, Francois; Keith Moore, J.; Lindsay, Keith; Randerson, James T.

2018-04-01

Dissolved oxygen (O2) is essential for the survival of marine animals. Climate change impacts on future oxygen distributions could modify species biogeography, trophic interactions, biodiversity, and biogeochemistry. The Coupled Model Intercomparison Project Phase 5 models predict a decreasing trend in marine O2 over the 21st century. Here we show that this increasing hypoxia trend reverses in the tropics after 2100 in the Community Earth System Model forced by atmospheric CO2 from the Representative Concentration Pathway 8.5 and Extended Concentration Pathway 8.5. In tropical intermediate waters between 200 and 1,000 m, the model predicts a steady decline of O2 and an expansion of oxygen minimum zones (OMZs) during the 21st century. By 2150, however, the trend reverses with oxygen concentration increasing and OMZ volume shrinking through 2300. A novel five-box model approach in conjunction with output from the full Earth system model is used to separate the contributions of biological and physical processes to the trends in tropical oxygen. The tropical O2 recovery is caused mainly by reductions in tropical biological export, coupled with a modest increase in ventilation after 2200. The time-evolving oxygen distribution impacts marine nitrogen cycling, with potentially important climate feedbacks.

FutureTox II: in vitro data and in silico models for predictive toxicology.

PubMed

Knudsen, Thomas B; Keller, Douglas A; Sander, Miriam; Carney, Edward W; Doerrer, Nancy G; Eaton, David L; Fitzpatrick, Suzanne Compton; Hastings, Kenneth L; Mendrick, Donna L; Tice, Raymond R; Watkins, Paul B; Whelan, Maurice

2015-02-01

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effect of climate change on shoreline shifts at a straight and continuous coast

NASA Astrophysics Data System (ADS)

Rajasree, B. R.; Deo, M. C.; Sheela Nair, L.

2016-12-01

The prediction of the rate of shoreline shifts as well as that of erosion and accretion over future at a given location is traditionally done on the basis of analysis of past wave data. However under the changing climate affected by global warming it is better done considering the projected wave conditions over the future. The same is demonstrated in this work with respect to a stretch of coastline at 'Udupi' along the west coast of India. The shoreline changes in the past are first determined with the help of historic satellite images. A numerical shoreline model is later run on the basis of wave simulations of past 35 years as well as future 35 years. The latter wave conditions are obtained from wind projections corresponding to a high resolution regional climate model run for a moderate pathway of global warming. Alternatively prediction of the changes over future 35 years is also made by using the soft computing tool of artificial neural network (ANN) trained with the help of past satellite images. The results indicate that the area under consideration presently undergoes considerable erosion and this process will accelerate in future. The volume of annual sediment transport will also substantially increase over the future. The alternative computations made with the help of an ANN confirmed the future rising trend of erosion, albeit at smaller rate than the numerically predicted one.

Financial Services and the Internet: What Does Cyberspace Mean for the Financial Services Industry?

ERIC Educational Resources Information Center

Birch, David; Young, Michael A.

1997-01-01

More than 30 million households own PCs and more than 20% of these use PCs to manage their finances. This article examines the Internet and financial services, consumer needs, and differentiation in service products and predicts future trends in retail financial services (cheaper niche products, cross-border selling, selling knowledge, payments,…

Late-successional forests and northern spotted owls: how effective is the Northwest Forest Plan?

Treesearch

Miles Hemstrom; Martin G. Raphael

2000-01-01

This paper describes the late-successional and old-growth forest and the northern spotted owl effectiveness monitoring plans for the Northwest Forest Plan. The effectiveness monitoring plan for late-successional and old-growth forests will track changes in forest spatial distribution, and within-stand structure and composition, and it will predict future trends.

How Does Target Know so Much about Its Customers? Utilizing Customer Analytics to Make Marketing Decisions

ERIC Educational Resources Information Center

Corrigan, Hope B.; Craciun, Georgiana; Powell, Allison M.

2014-01-01

Every time shoppers make a purchase at a store or browse a Web site, customer behavior is tracked, analyzed, and perhaps shared with other businesses. Target Corporation is a leader in analyzing vast amounts of data to identify buying patterns, improve customer satisfaction, predict future trends, select promotional strategies, and increase…

Techniques of data analysis and presentation for planners of the metropolitan environment

Treesearch

Joelee Normand

1977-01-01

Relationships between the characteristics of the physical environment of a metropolitan area and the activities of its human inhabitants can be used to predict probable future dynamic trends, both demographic and environmental. Using simple linear regression, we were able to highlight several dynamic features of the metropolitan area of Tulsa, Oklahoma. Computer movies...

The 2008-18 Job Outlook in Brief

ERIC Educational Resources Information Center

Occupational Outlook Quarterly, 2010

2010-01-01

Some occupations will fare better than others over the 2008-18 decade. Although it's impossible to predict the future, one can gain insight into job outlook by analyzing trends in population growth, technological advances, and business practices. This insight is helpful in planning a career. Every 2 years, the U.S. Bureau of Labor Statistics (BLS)…

Comparison of Mean Climate Trends in the Northern Hemisphere Between N.C.E.P. and Two Atmosphere-Ocean Model Forced Runs

NASA Technical Reports Server (NTRS)

Lucarini, Valerio; Russell, Gary L.; Hansen, James E. (Technical Monitor)

2002-01-01

Results are presented for two greenhouse gas experiments of the Goddard Institute for Space Studies Atmosphere-Ocean Model (AOM). The computed trends of surface pressure, surface temperature, 850, 500 and 200 mb geopotential heights and related temperatures of the model for the time frame 1960-2000 are compared to those obtained from the National Centers for Environmental Prediction observations. A spatial correlation analysis and mean value comparison are performed, showing good agreement. A brief general discussion about the statistics of trend detection is presented. The domain of interest is the Northern Hemisphere (NH) because of the higher reliability of both the model results and the observations. The accuracy that this AOM has in describing the observed regional and NH climate trends makes it reliable in forecasting future climate changes.

Recent trends and future of pharmaceutical packaging technology

PubMed Central

Zadbuke, Nityanand; Shahi, Sadhana; Gulecha, Bhushan; Padalkar, Abhay; Thube, Mahesh

2013-01-01

The pharmaceutical packaging market is constantly advancing and has experienced annual growth of at least five percent per annum in the past few years. The market is now reckoned to be worth over $20 billion a year. As with most other packaged goods, pharmaceuticals need reliable and speedy packaging solutions that deliver a combination of product protection, quality, tamper evidence, patient comfort and security needs. Constant innovations in the pharmaceuticals themselves such as, blow fill seal (BFS) vials, anti-counterfeit measures, plasma impulse chemical vapor deposition (PICVD) coating technology, snap off ampoules, unit dose vials, two-in-one prefilled vial design, prefilled syringes and child-resistant packs have a direct impact on the packaging. The review details several of the recent pharmaceutical packaging trends that are impacting packaging industry, and offers some predictions for the future. PMID:23833515

A Comparison of Combustor-Noise Models

NASA Technical Reports Server (NTRS)

Hultgren, Lennart S.

2012-01-01

The present status of combustor-noise prediction in the NASA Aircraft Noise Prediction Program (ANOPP)1 for current-generation (N) turbofan engines is summarized. Several semi-empirical models for turbofan combustor noise are discussed, including best methods for near-term updates to ANOPP. An alternate turbine-transmission factor2 will appear as a user selectable option in the combustor-noise module GECOR in the next release. The three-spectrum model proposed by Stone et al.3 for GE turbofan-engine combustor noise is discussed and compared with ANOPP predictions for several relevant cases. Based on the results presented herein and in their report,3 it is recommended that the application of this fully empirical combustor-noise prediction method be limited to situations involving only General-Electric turbofan engines. Long-term needs and challenges for the N+1 through N+3 time frame are discussed. Because the impact of other propulsion-noise sources continues to be reduced due to turbofan design trends, advances in noise-mitigation techniques, and expected aircraft configuration changes, the relative importance of core noise is expected to greatly increase in the future. The noise-source structure in the combustor, including the indirect one, and the effects of the propagation path through the engine and exhaust nozzle need to be better understood. In particular, the acoustic consequences of the expected trends toward smaller, highly efficient gas-generator cores and low-emission fuel-flexible combustors need to be fully investigated since future designs are quite likely to fall outside of the parameter space of existing (semi-empirical) prediction tools.

A new seasonal-deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios.

PubMed

Chen, Min; Melaas, Eli K; Gray, Josh M; Friedl, Mark A; Richardson, Andrew D

2016-11-01

A spring phenology model that combines photoperiod with accumulated heating and chilling to predict spring leaf-out dates is optimized using PhenoCam observations and coupled into the Community Land Model (CLM) 4.5. In head-to-head comparison (using satellite data from 2003 to 2013 for validation) for model grid cells over the Northern Hemisphere deciduous broadleaf forests (5.5 million km 2 ), we found that the revised model substantially outperformed the standard CLM seasonal-deciduous spring phenology submodel at both coarse (0.9 × 1.25°) and fine (1 km) scales. The revised model also does a better job of representing recent (decadal) phenological trends observed globally by MODIS, as well as long-term trends (1950-2014) in the PEP725 European phenology dataset. Moreover, forward model runs suggested a stronger advancement (up to 11 days) of spring leaf-out by the end of the 21st century for the revised model. Trends toward earlier advancement are predicted for deciduous forests across the whole Northern Hemisphere boreal and temperate deciduous forest region for the revised model, whereas the standard model predicts earlier leaf-out in colder regions, but later leaf-out in warmer regions, and no trend globally. The earlier spring leaf-out predicted by the revised model resulted in enhanced gross primary production (up to 0.6 Pg C yr -1 ) and evapotranspiration (up to 24 mm yr -1 ) when results were integrated across the study region. These results suggest that the standard seasonal-deciduous submodel in CLM should be reconsidered, otherwise substantial errors in predictions of key land-atmosphere interactions and feedbacks may result. © 2016 John Wiley & Sons Ltd.

Global Crop Yields, Climatic Trends and Technology Enhancement

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Khanbilvardi, R.; Kogan, F.

2016-12-01

During the last decades the global agricultural production has soared up and technology enhancement is still making positive contribution to yield growth. However, continuing population, water crisis, deforestation and climate change threaten the global food security. Attempts to predict food availability in the future around the world can be partly understood from the impact of changes to date. A new multilevel model for yield prediction at the country scale using climate covariates and technology trend is presented in this paper. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling and/or clustering to automatically group and reduce estimation uncertainties. El Niño Southern Oscillation (ENSO), Palmer Drought Severity Index (PDSI), Geopotential height (GPH), historical CO2 level and time-trend as a relatively reliable approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2007. Results show that these indicators can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications.

Breast Cancer Trend in Iran from 2000 to 2009 and Prediction till 2020 using a Trend Analysis Method.

PubMed

Zahmatkesh, Bibihajar; Keramat, Afsaneh; Alavi, Nasrinossadat; Khosravi, Ahmad; Kousha, Ahmad; Motlagh, Ali Ghanbari; Darman, Mahboobeh; Partovipour, Elham; Chaman, Reza

2016-01-01

Breast cancer is the most common cancer in women worldwide with a rising incidence rate in most countries. Considering the increase in life expectancy and change in lifestyle of Iranian women, this study investigated the age-adjusted trend of breast cancer incidence during 2000-2009 and predicted its incidence to 2020. The 1997 and 2006 census results were used for the projection of female population by age through the cohort-component method over the studied years. Data from the Iranian cancer registration system were used to calculate the annual incidence rate of breast cancer. The age-adjusted incidence rate was then calculated using the WHO standard population distribution. The five-year-age-specific incidence rates were also obtained for each year and future incidence was determined using the trend analysis method. Annual percentage change (APC) was calculated through the joinpoint regression method. The bias adjusted incidence rate of breast cancer increased from 16.7 per 100,000 women in 2000 to 33.6 per 100,000 women in 2009. The incidence of breast cancer had a growing trend in almost all age groups above 30 years over the studied years. In this period, the age groups of 45-65 years had the highest incidence. Investigation into the joinpoint curve showed that the curve had a steep slope with an APC of 23.4% before the first joinpoint, but became milder after this. From 2005 to 2009, the APC was calculated as 2.7%, through which the incidence of breast cancer in 2020 was predicted as 63.0 per 100,000 women. The age-adjusted incidence rate of breast cancer continues to increas in Iranian women. It is predicted that this trend will continue until 2020. Therefore, it seems necessary to prioritize the prevention, control and care for breast cancer in Iran.

Status and trends in active control technology

NASA Technical Reports Server (NTRS)

Rediess, H. A.; Szalai, K. J.

1975-01-01

The emergence of highly reliable fly-by-wire flight control systems makes it possible to consider a strong reliance on automatic control systems in the design optimization of future aircraft. This design philosophy has been referred to as the control configured vehicle approach or the application of active control technology. Several studies and flight tests sponsored by the Air Force and NASA have demonstrated the potential benefits of control configured vehicles and active control technology. The present status and trends of active control technology are reviewed and the impact it will have on aircraft designs, design techniques, and the designer is predicted.

Recently Identified Changes to the Demographics of the Current and Future Geoscience Workforce

NASA Astrophysics Data System (ADS)

Wilson, C. E.; Keane, C. M.; Houlton, H. R.

2014-12-01

The American Geosciences Institute's (AGI) Geoscience Workforce Program collects and analyzes data pertaining to the changes in the supply, demand, and training of the geoscience workforce. Much of these trends are displayed in detail in AGI's Status of the Geoscience Workforce reports. In May, AGI released the Status of the Geoscience Workforce 2014, which updates these trends since the 2011 edition of this report. These updates highlight areas of change in the education of future geoscientists from K-12 through graduate school, the transition of geoscience graduates into early-career geoscientists, the dynamics of the current geoscience workforce, and the future predictions of the changes in the availability of geoscience jobs. Some examples of these changes include the increase in the number of states that will allow a high school course of earth sciences as a credit for graduation and the increasing importance of two-year college students as a talent pool for the geosciences, with over 25% of geoscience bachelor's graduates attending a two-year college for at least a semester. The continued increase in field camp hinted that these programs are at or reaching capacity. The overall number of faculty and research staff at four-year institutions increased slightly, but the percentages of academics in tenure-track positions continued to slowly decrease since 2009. However, the percentage of female faculty rose in 2013 for all tenure-track positions. Major geoscience industries, such as petroleum and mining, have seen an influx of early-career geoscientists. Demographic trends in the various industries in the geoscience workforce forecasted a shortage of approximately 135,000 geoscientists in the next decade—a decrease from the previously predicted shortage of 150,000 geoscientists. These changes and other changes identified in the Status of the Geoscience Workforce will be addressed in this talk.

Opposing Seasonal Trends in Seawater pH and Aragonite Saturation State on the Bermuda Coral Reef Platform Reveal Complex Controls on Seawater Chemistry by Biological and Physical Processes

NASA Astrophysics Data System (ADS)

Andersson, A. J.; Bates, N. R.; dePutron, S.; Collins, A.; Neely, K.; Best, M.; Noyes, T.

2011-12-01

To accurately predict future consequences of ocean acidification on coastal environments and ecosystems, it is critical to understand present conditions and variability. As part of the Bermuda ocean acidification and coral reef investigation (BEACON), significant efforts have been dedicated to characterize the complete surface seawater carbonic-acid system at different temporal and spatial scales on the Bermuda coral reef platform to understand current levels and variability in seawater CO2 parameters, reef metabolism, and future potential changes arising from ocean acidification. A four years monthly time-series of seawater carbonic-acid parameters at eight different locations on the Bermuda coral reef platform reveals strong seasonal patterns in dissolved inorganic carbon (DIC), total alkalinity (TA), pH, pCO2, and [HCO3-], and somewhat weaker trends in [CO32-] and saturation state with respect to CaCO3 minerals. Strong spatial gradients are also observed in DIC and TA during summertime owing to reef metabolism, but no or weak spatial gradients of these parameters are observed in the wintertime. Interestingly, maximum pH-sws (~8.15) is observed during wintertime when minimum aragonite saturation state (<3.0) is observed. In contrast, minimum pH-sws (~7.95) is observed in the summertime when maximum aragonite saturation state (>3.70) is observed. The observed trends and gradients point to complex relationships and interactions between seawater chemistry, biology and physics that need to be considered in the context of ocean acidification and in making future predictions on the effects of this perturbation on coral reefs and coastal ecosystems.

FUTURE LOGISTICS AND OPERATIONAL ADAPTABILITY

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

Houck, Roger P.

2009-10-01

While we cannot predict the future, we can ascertain trends and examine them through the use of alternative futures methodologies and tools. From a logistics perspective, we know that many different futures are possible, all of which are obviously dependent on decisions we make in the present. As professional logisticians we are obligated to provide the field - our Soldiers - with our best professional opinion of what will result in success on the battlefield. Our view of the future should take history and contemporary conflict into account, but it must also consider that continuity with the past cannot bemore » taken for granted. If we are too focused on past and current experience, then our vision of the future will be limited indeed. On the one hand, the future must be explained in language that does not defy common sense. On the other hand, the pace of change is such that we must conduct qualitative and quantitative trend analyses, forecasting, and explorative scenario development in ways that allow for significant breaks - or "shocks" - that may "change the game". We will need capabilities and solutions that are constantly evolving - and improving - to match the operational tempo of a radically changing threat environment. For those who provide quartermaster services, this article will briefly examine what this means from the perspective of creating what might be termed a preferred future.« less

Future Mission Trends and their Implications for the Deep Space Network

NASA Technical Reports Server (NTRS)

Abraham, Douglas S.

2006-01-01

This viewgraph presentation discusses the direction of future missions and it's significance to the Deep Space Network. The topics include: 1) The Deep Space Network (DSN); 2) Past Missions Driving DSN Evolution; 3) The Changing Mission Paradigm; 4) Assessing Future Mission Needs; 5) Link Support Trends; 6) Downlink Rate Trends; 7) Uplink Rate Trends; 8) End-to-End Link Difficulty Trends; 9) Summary: Future Mission Trend Drivers; and 10) Conclusion: Implications for the DSN.

Long-Term Temporal Trends of Polychlorinated Biphenyls and Their Controlling Sources in China.

PubMed

Zhao, Shizhen; Breivik, Knut; Liu, Guorui; Zheng, Minghui; Jones, Kevin C; Sweetman, Andrew J

2017-03-07

Polychlorinated biphenyls (PCBs) are industrial organic contaminants identified as persistent, bioaccumulative, toxic (PBT), and subject to long-range transport (LRT) with global scale significance. This study focuses on a reconstruction and prediction for China of long-term emission trends of intentionally and unintentionally produced (UP) ∑ 7 PCBs (UP-PCBs, from the manufacture of steel, cement and sinter iron) and their re-emissions from secondary sources (e.g., soils and vegetation) using a dynamic fate model (BETR-Global). Contemporary emission estimates combined with predictions from the multimedia fate model suggest that primary sources still dominate, although unintentional sources are predicted to become a main contributor from 2035 for PCB-28. Imported e-waste is predicted to play an increasing role until 2020-2030 on a national scale due to the decline of intentionally produced (IP) emissions. Hypothetical emission scenarios suggest that China could become a potential source to neighboring regions with a net output of ∼0.4 t year -1 by around 2050. However, future emission scenarios and hence model results will be dictated by the efficiency of control measures.

Climate change impact on soil erosion in the Mandakini River Basin, North India

NASA Astrophysics Data System (ADS)

Khare, Deepak; Mondal, Arun; Kundu, Sananda; Mishra, Prabhash Kumar

2017-09-01

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

The future of satellite remote sensing: A worldwide assessment and prediction

NASA Technical Reports Server (NTRS)

Spann, G. W.

1984-01-01

A frame-work in which to assess and predict the future prospects for satellite remote sensing markets is provided. The scope of the analysis is the satellite-related market for data, equipment, and services. It encompasses both domestic and international markets and contains an examination of the various market characteristics by market segment (e.g., Federal Government, State and Local Governments, Academic Organizations, Industrial Companies, and Individuals) and primary applications areas (e.g., Geology, Forestry, Land Resource Management, Agriculture and Cartography). The forecasts are derived from an analysis of both U.S. and foreign market data. The evolution and current status of U.S. and Foreign markets to arrive at market growth rates is evaluated. Circumstances and events which are likely to affect the future market development are examined. A market growth scenario is presented that is consistent with past data sales trends and takes into account the dynamic nature of the future satellite remote sensing market. Several areas of current and future business opportunities available in this market are discussed. Specific worldwide forecasts are presented in three market sectors for the period 1980 to 1990.

Landscape context and long-term tree influences shape the dynamics of forest-meadow ecotones in mountain ecosystems

Treesearch

R.E. Haugo; C.B. Halpern; J.D. Bakker

2011-01-01

Forest-meadow ecotones are prominent and dynamic features of mountain ecosystems. Understanding how vegetation changes are shaped by long-term interactions with trees and are mediated by the physical environment is critical to predicting future trends in biological diversity across these landscapes. We examined 26 yr of vegetation change (1983-2009) across 20 forest-...

Doctoral Social Work Education: Responding to Trends in Society and the Academy

ERIC Educational Resources Information Center

Cnaan, Ram A.; Ghose, Toorjo

2018-01-01

This article is intended to forecast major environmental changes that may impact social work doctoral education and assess what should be done in anticipation of these changes. We apply an open system and future studies perspective to guide our work. We present a set of predicted societal changes that will impact social work as a profession and…

The Future of Newsprint; 1970-2000. Report R-16.

ERIC Educational Resources Information Center

Baran, Paul

A panel of 37 experts predicted trends in the use of newsprint by the year 2000. The panelists agreed that as a result of increasing education, worldwide demand for newsprint will continue to increase, with consumption exceeding 35 million short tons a year in 2000, compared with 10 million in 1950 and 22 million in 1970. The type of paper desired…

Toward Ubiquitous BCIs

NASA Astrophysics Data System (ADS)

Allison, Brendan Z.

The preceding chapters in this book described modern BCI systems. This concluding chapter instead discusses future directions. While there are some specific predictions, I mainly analyze key factors and trends relating to practical mainstream BCI development. While I note some disruptive technologies that could dramatically change BCIs, this chapter focuses mainly on realistic, incremental progress and how progress could affect user groups and ethical issues.

Skill Acquisition and Use across the Life Course: Current Trends, Future Prospects

ERIC Educational Resources Information Center

Martin, Bill

2007-01-01

People's life pathways are no longer as predictable as they were in the second half of the 20th century. It is no longer as simple as moving from school to work, probably via tertiary education, to living independently, then getting married and starting a family. Changes in how people combine education with life-course transitions will influence…

Forest cover dynamics in the Pacific Northwest west side: regional trends and predictions.

Treesearch

Ralph J. Alig; Daolan Zheng; Thomas A. Spies; Brett J. Butler

2000-01-01

The objectives of this paper were to (1) analyze recent rates of transitions among forest cover types on private timberland, (2) identify differences by ownership class, and (3) project future changes under different scenarios related to current policy issues in the Pacific Northwest. Timber harvests are the dominant class of disturbance on private timberland in...

Exploring Temporal Frameworks for Constructing Longitudinal Instance-Specific Models from Clinical Data

ERIC Educational Resources Information Center

Watt, Emily

2012-01-01

The prevalence of the EMR in biomedical research is growing, the EMR being regarded as a source of contextually rich, longitudinal data for computation and statistical/trend analysis. However, models trained with data abstracted from the EMR often (1) do not capture all features needed to accurately predict the patient's future state and to…

Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US

USGS Publications Warehouse

Evangelista, P.H.; Kumar, S.; Stohlgren, T.J.; Young, N.E.

2011-01-01

The aim of our study was to estimate forest vulnerability and potential distribution of three bark beetles (Curculionidae: Scolytinae) under current and projected climate conditions for 2020 and 2050. Our study focused on the mountain pine beetle (Dendroctonus ponderosae), western pine beetle (Dendroctonus brevicomis), and pine engraver (Ips pini). This study was conducted across eight states in the Interior West of the US covering approximately 2.2millionkm2 and encompassing about 95% of the Rocky Mountains in the contiguous US. Our analyses relied on aerial surveys of bark beetle outbreaks that occurred between 1991 and 2008. Occurrence points for each species were generated within polygons created from the aerial surveys. Current and projected climate scenarios were acquired from the WorldClim database and represented by 19 bioclimatic variables. We used Maxent modeling technique fit with occurrence points and current climate data to model potential beetle distributions and forest vulnerability. Three available climate models, each having two emission scenarios, were modeled independently and results averaged to produce two predictions for 2020 and two predictions for 2050 for each analysis. Environmental parameters defined by current climate models were then used to predict conditions under future climate scenarios, and changes in different species' ranges were calculated. Our results suggested that the potential distribution for bark beetles under current climate conditions is extensive, which coincides with infestation trends observed in the last decade. Our results predicted that suitable habitats for the mountain pine beetle and pine engraver beetle will stabilize or decrease under future climate conditions, while habitat for the western pine beetle will continue to increase over time. The greatest increase in habitat area was for the western pine beetle, where one climate model predicted a 27% increase by 2050. In contrast, the predicted habitat of the mountain pine beetle from another climate model suggested a decrease in habitat areas as great as 46% by 2050. Generally, 2020 and 2050 models that tested the three climate scenarios independently had similar trends, though one climate scenario for the western pine beetle produced contrasting results. Ranges for all three species of bark beetles shifted considerably geographically suggesting that some host species may become more vulnerable to beetle attack in the future, while others may have a reduced risk over time. ?? 2011 Elsevier B.V.

Popularity Prediction Tool for ATLAS Distributed Data Management

NASA Astrophysics Data System (ADS)

Beermann, T.; Maettig, P.; Stewart, G.; Lassnig, M.; Garonne, V.; Barisits, M.; Vigne, R.; Serfon, C.; Goossens, L.; Nairz, A.; Molfetas, A.; Atlas Collaboration

2014-06-01

This paper describes a popularity prediction tool for data-intensive data management systems, such as ATLAS distributed data management (DDM). It is fed by the DDM popularity system, which produces historical reports about ATLAS data usage, providing information about files, datasets, users and sites where data was accessed. The tool described in this contribution uses this historical information to make a prediction about the future popularity of data. It finds trends in the usage of data using a set of neural networks and a set of input parameters and predicts the number of accesses in the near term future. This information can then be used in a second step to improve the distribution of replicas at sites, taking into account the cost of creating new replicas (bandwidth and load on the storage system) compared to gain of having new ones (faster access of data for analysis). To evaluate the benefit of the redistribution a grid simulator is introduced that is able replay real workload on different data distributions. This article describes the popularity prediction method and the simulator that is used to evaluate the redistribution.

Observed SWE trends and climate analysis for Northwest Pacific North America: validation for future projection of SWE using the CRCM and VIC

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Bronaugh, D.; Rodenhuis, D.

2008-12-01

Observational databases of snow water equivalent (SWE) have been collected from Alaska, western US states and the Canadian provinces of British Columbia, Alberta, Saskatchewan, and territories of NWT, and the Yukon. These databases were initially validated to remove inconsistencies and errors in the station records, dates or the geographic co-ordinates of the station. The cleaned data was then analysed for historical (1950 to 2006) trend using emerging techniques for trend detection based on (first of the month) estimates for January to June. Analysis of SWE showed spatial variability in the count of records across the six month time period, and this study illustrated differences between Canadian and US (or the north and south) collection. Two different data sets (one gridded and one station) were then used to analyse April 1st records, for which there was the greatest spatial spread of station records for analysis with climate information. Initial results show spatial variability (in both magnitude and direction of trend) for trend results, and climate correlations and principal components indicate different drivers of change in SWE across the western US, Canada and north to Alaska. These results will be used to validate future predictions of SWE that are being undertaken using the Canadian Regional Climate Model (CRCM) and the Variable Infiltration Capacity (VIC) hydrologic model for Western Northern America (CRCM) and British Columbia (VIC).

Distribution analysis for F100(3) engine

NASA Technical Reports Server (NTRS)

Walter, W. A.; Shaw, M.

1980-01-01

The F100(3) compression system response to inlet circumferential distortion was investigated using an analytical compressor flow model. Compression system response to several types of distortion, including pressure, temperature, and combined pressure/temperature distortions, was investigated. The predicted response trends were used in planning future F100(3) distortion tests. Results show that compression system response to combined temperature and pressure distortions depends upon the relative orientation, as well as the individual amplitudes and circumferential extents of the distortions. Also the usefulness of the analytical predictions in planning engine distortion tests is indicated.

Trends and forecasts of hospital admissions for acute and chronic pancreatitis in the Netherlands.

PubMed

Spanier, Bernhard Werner Marcel; Dijkgraaf, Marcel G W; Bruno, Marco J

2008-07-01

The incidence and prevalence of acute and chronic pancreatitis have increased in Western countries. It is likely, the number of hospital admissions has increased correspondingly. To analyze the trends in hospital admissions in the Netherlands for acute and chronic pancreatitis from 1992 to 2004 and to forecast the number of admissions up to 2010. Analysis of hospital admissions for acute and chronic pancreatitis accumulated in a nationwide database. Curve fitting regression models were used to explore future trends. The number of acute pancreatitis admissions rose in 1992-2004 from 1,785 to 3,120 (74.8% increase). The overall 'annual number' of acute pancreatitis admissions increased from 11.8 to 19.2 per 100,000 person-years. The linear regression model predicted 3,205 [95% confidence intervals (CI), 3,111-3,299] and 3,537 (95% CI, 3,429-3,645) admissions for 2007 and 2010, respectively, a further increase of at least 9.9% in 2010 compared with 2004. In the 12-year time period, chronic pancreatitis admissions showed an increase of 75.4% (from 790 to 1,386). The overall 'annual number' of chronic pancreatitis admissions increased from 5.2 to 8.5 per 100,000 person-years. The cubic regression model predicted 1868 (95% CI, 1,619-2,117) and 3,173 (95% CI, 2,456-3,890) admissions for 2007 and 2010, respectively, an additional increase of 77.2% in 2010 compared with 2004. Hospital admissions for acute and chronic pancreatitis have increased substantially from 1992-2004. This trend will most likely continue for the near future and the burden and costs to the Dutch health care system will increase accordingly.

Future trends in soil cadmium concentration under current cadmium fluxes to European agricultural soils.

PubMed

Six, L; Smolders, E

2014-07-01

The gradual increase of soil cadmium concentrations in European soils during the 20th century has prompted environmental legislation to limit soil cadmium (Cd) accumulation. Mass balances (input-output) reflecting the period 1980-1995 predicted larger Cd inputs via phosphate (P) fertilizers and atmospheric deposition than outputs via crop uptake and leaching. This study updates the Cd mass balance for the agricultural top soils of EU-27+Norway (EU-27+1). Over the past 15 years, the use of P fertilizers in the EU-27+1 has decreased by 40%. The current mean atmospheric deposition of Cd in EU is 0.35 g Cd ha(-1) yr(-1), this is strikingly smaller than values used in the previous EU mass balances (~3 g Cd ha(-1) yr(-1)). Leaching of Cd was estimated with most recent data of soil solution Cd concentrations in 151 soils, which cover the range of European soil properties. No significant time trends were found in the data of net applications of Cd via manure, compost, sludge and lime, all being small sources of Cd at a large scale. Modelling of the future long-term changes in soil Cd concentrations in agricultural top soils under cereal or potato culture predicts soil Cd concentrations to decrease by 15% over the next 100 years in an average scenario, with decreasing trends in some scenarios being more prevalent than increasing trends in other scenarios. These Cd balances have reverted from the general positive balances estimated 10 or more years ago. Uncertainty analysis suggests that leaching is the most uncertain relative to other fluxes. Copyright © 2014 Elsevier B.V. All rights reserved.

Assessment for fuel consumption and exhaust emissions of China's vehicles: future trends and policy implications.

PubMed

Wu, Yingying; Zhao, Peng; Zhang, Hongwei; Wang, Yuan; Mao, Guozhu

2012-01-01

In the recent years, China's auto industry develops rapidly, thus bringing a series of burdens to society and environment. This paper uses Logistic model to simulate the future trend of China's vehicle population and finds that China's auto industry would come into high speed development time during 2020-2050. Moreover, this paper predicts vehicles' fuel consumption and exhaust emissions (CO, HC, NO(x), and PM) and quantificationally evaluates related industry policies. It can be concluded that (1) by 2020, China should develop at least 47 million medium/heavy hybrid cars to prevent the growth of vehicle fuel consumption; (2) China should take the more stringent vehicle emission standard V over 2017-2021 to hold back the growth of exhaust emissions; (3) developing new energy vehicles is the most effective measure to ease the pressure brought by auto industry.

Assessment for Fuel Consumption and Exhaust Emissions of China's Vehicles: Future Trends and Policy Implications

PubMed Central

Zhao, Peng; Zhang, Hongwei; Wang, Yuan; Mao, Guozhu

2012-01-01

In the recent years, China's auto industry develops rapidly, thus bringing a series of burdens to society and environment. This paper uses Logistic model to simulate the future trend of China's vehicle population and finds that China's auto industry would come into high speed development time during 2020–2050. Moreover, this paper predicts vehicles' fuel consumption and exhaust emissions (CO, HC, NOx, and PM) and quantificationally evaluates related industry policies. It can be concluded that (1) by 2020, China should develop at least 47 million medium/heavy hybrid cars to prevent the growth of vehicle fuel consumption; (2) China should take the more stringent vehicle emission standard V over 2017–2021 to hold back the growth of exhaust emissions; (3) developing new energy vehicles is the most effective measure to ease the pressure brought by auto industry. PMID:23365524

Forecast models for suicide: Time-series analysis with data from Italy.

PubMed

Preti, Antonio; Lentini, Gianluca

2016-01-01

The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.

Statistical approach to the analysis of olive long-term pollen season trends in southern Spain.

PubMed

García-Mozo, H; Yaezel, L; Oteros, J; Galán, C

2014-03-01

Analysis of long-term airborne pollen counts makes it possible not only to chart pollen-season trends but also to track changing patterns in flowering phenology. Changes in higher plant response over a long interval are considered among the most valuable bioindicators of climate change impact. Phenological-trend models can also provide information regarding crop production and pollen-allergen emission. The interest of this information makes essential the election of the statistical analysis for time series study. We analysed trends and variations in the olive flowering season over a 30-year period (1982-2011) in southern Europe (Córdoba, Spain), focussing on: annual Pollen Index (PI); Pollen Season Start (PSS), Peak Date (PD), Pollen Season End (PSE) and Pollen Season Duration (PSD). Apart from the traditional Linear Regression analysis, a Seasonal-Trend Decomposition procedure based on Loess (STL) and an ARIMA model were performed. Linear regression results indicated a trend toward delayed PSE and earlier PSS and PD, probably influenced by the rise in temperature. These changes are provoking longer flowering periods in the study area. The use of the STL technique provided a clearer picture of phenological behaviour. Data decomposition on pollination dynamics enabled the trend toward an alternate bearing cycle to be distinguished from the influence of other stochastic fluctuations. Results pointed to show a rising trend in pollen production. With a view toward forecasting future phenological trends, ARIMA models were constructed to predict PSD, PSS and PI until 2016. Projections displayed a better goodness of fit than those derived from linear regression. Findings suggest that olive reproductive cycle is changing considerably over the last 30years due to climate change. Further conclusions are that STL improves the effectiveness of traditional linear regression in trend analysis, and ARIMA models can provide reliable trend projections for future years taking into account the internal fluctuations in time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Recent drug approvals from the US FDA and EMEA: what the future holds.

PubMed

Pevarello, Paolo

2009-04-01

The decreased productivity of the pharmaceutical industry in terms of new medical entities approved by the US FDA and the European Medicines Agency (EMEA) on a yearly basis has long been debated. This review will analyze overall new drug applications (NDAs) approved by both the FDA and EMEA in 2007, with the aim of finding trends (also looking at the past) that can be used to predict what the future may be. After a general introduction to the regulatory terminology, NDA approvals in 2007 are divided into categories (new applications of old medicines, metabolites, enantiomers and prodrugs, biological products, natural products and small organic molecule new molecular entities) and discussed. General aspects of the NDA approvals, such as historical trends, the length of the drug-discovery process, geography, differences among therapeutic areas, and the relative role of biotech and pharma industries are also outlined. From this analysis, a perspective is gained on some aspects that will probably influence future drug approvals. The conclusion is that 2007 may represent an inflexion point, in terms of quality if not quantity of new approvals, and that the future may be brighter than previously forecast.

Using the North American Breeding Bird Survey as a tool for conservation: A critique of Bart et al. (2004)

USGS Publications Warehouse

Sauer, John R.; Link, William A.; Nichols, James D.; Royle, J. Andrew

2005-01-01

Bart et al. (2004) develop methods for predicting needed samples for estimation of long-term trends from Count survey data, and they apply these methods to the North American Breeding Bird Survey (BBS). They recommend adding approximately 40% more survey routes ill the BBS to allow for estimation of long-term (i.e., 20 year) trends for a collection of species. We critique several aspects of their analysis and suggest that their focus on long-term trends and expansion of the present survey design will provide limited benefits for conservation because it fails to either enhance the credibility of the survey or better tie the survey to regional management activities. A primary innovation claimed by Bart et al. (2004) is the incorporation of bias in estimation of study planning. We question the value of this approach, as it requires reliable estimates of range of future bias. We show that estimates of bias used by Bart et al. (2004) are speculative. Failure to obtain better estimates of this bias is likely to compromise the credibility of future analyses of the survey. We also note that the generic analysis of population trends that they provide is of questionable validity and is unlikely to be relevant for regions and species of management concern.

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

Analysis of Science and Technology Trend Based on Word Usage in Digitized Books

NASA Astrophysics Data System (ADS)

Yun, Jinhyuk; Kim, Pan-Jun; Jeong, Hawoong

2013-03-01

Throughout mankind's history, forecasting and predicting future has been a long-lasting interest to our society. Many fortune-tellers have tried to forecast the future by ``divine'' items. Sci-fi writers have also imagined what the future would look like. However most of them have been illogical and unscientific. Meanwhile, scientists have also attempted to discover future trend of science. Many researchers have used quantitative models to study how new ideas are used and spread. Besides the modeling works, in the early 21st century, the rise of data science has provided another prospect of forecasting future. However many studies have focused on very limited set of period or age, due to the limitations of dataset. Hence, many questions still remained unanswered. Fortunately, Google released a new dataset named ``Google N-Gram Dataset.'' This dataset provides us with 5 million words worth of literature dating from 1520 to 2008, and this is nearly 4% of publications ever printed. With this new time-varying dataset, we studied the spread and development of technologies by searching ``Science and Technology'' related words from 1800 to 2000. By statistical analysis, some general scaling laws were discovered. And finally, we determined factors that strongly affect the lifecycle of a word.

Physical characteristics and evolutionary trends of continental rifts

NASA Technical Reports Server (NTRS)

Ramberg, I. B.; Morgan, P.

1984-01-01

Rifts may be defined as zones beneath which the entire lithosphere has ruptured in extension. They are widespread and occur in a variety of tectonic settings, and range up to 2,600 m.y. in age. The object of this review is to highlight characteristic features of modern and ancient rifts, to emphasize differences and similarities in order to help characterize evolutionary trends, to identify physical conditions favorable for initiation as well as termination of rifting, and to provide constraints for future modeling studies of rifting. Rifts are characterized on the basis of their structural, geomorphic, magmatic and geophysical features and the diverse character of these features and their evolutionary trends through time are discussed. Mechanisms of rifting are critically examined in terms of the physical characteristics and evolutionary trends of rifts, and it is concluded that while simple models can give valuable insight into specific processes of rifting, individual rifts can rarely, if ever, be characterized by well defined trends predicted by these models. More data are required to clearly define evolutionary trends, and the models require development to incorporate the effects of lithospheric heterogeneities and complex geologic histories.

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America

PubMed Central

Falk, Donald A.; Westerling, Anthony L.; Swetnam, Thomas W.

2017-01-01

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread. PMID:29244839

A Hybrid Model for Predicting the Prevalence of Schistosomiasis in Humans of Qianjiang City, China

PubMed Central

Wang, Ying; Lu, Zhouqin; Tian, Lihong; Tan, Li; Shi, Yun; Nie, Shaofa; Liu, Li

2014-01-01

Backgrounds/Objective Schistosomiasis is still a major public health problem in China, despite the fact that the government has implemented a series of strategies to prevent and control the spread of the parasitic disease. Advanced warning and reliable forecasting can help policymakers to adjust and implement strategies more effectively, which will lead to the control and elimination of schistosomiasis. Our aim is to explore the application of a hybrid forecasting model to track the trends of the prevalence of schistosomiasis in humans, which provides a methodological basis for predicting and detecting schistosomiasis infection in endemic areas. Methods A hybrid approach combining the autoregressive integrated moving average (ARIMA) model and the nonlinear autoregressive neural network (NARNN) model to forecast the prevalence of schistosomiasis in the future four years. Forecasting performance was compared between the hybrid ARIMA-NARNN model, and the single ARIMA or the single NARNN model. Results The modelling mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model was 0.1869×10−4, 0.0029, 0.0419 with a corresponding testing error of 0.9375×10−4, 0.0081, 0.9064, respectively. These error values generated with the hybrid model were all lower than those obtained from the single ARIMA or NARNN model. The forecasting values were 0.75%, 0.80%, 0.76% and 0.77% in the future four years, which demonstrated a no-downward trend. Conclusion The hybrid model has high quality prediction accuracy in the prevalence of schistosomiasis, which provides a methodological basis for future schistosomiasis monitoring and control strategies in the study area. It is worth attempting to utilize the hybrid detection scheme in other schistosomiasis-endemic areas including other infectious diseases. PMID:25119882

Direct and indirect climate controls predict heterogeneous early-mid 21st century wildfire burned area across western and boreal North America.

PubMed

Kitzberger, Thomas; Falk, Donald A; Westerling, Anthony L; Swetnam, Thomas W

2017-01-01

Predicting wildfire under future conditions is complicated by complex interrelated drivers operating across large spatial scales. Annual area burned (AAB) is a useful index of global wildfire activity. Current and antecedent seasonal climatic conditions, and the timing of snowpack melt, have been suggested as important drivers of AAB. As climate warms, seasonal climate and snowpack co-vary in intricate ways, influencing fire at continental and sub-continental scales. We used independent records of seasonal climate and snow cover duration (last date of permanent snowpack, LDPS) and cell-based Structural Equation Models (SEM) to separate direct (climatic) and indirect (snow cover) effects on relative changes in AAB under future climatic scenarios across western and boreal North America. To isolate seasonal climate variables with the greatest effect on AAB, we ran multiple regression models of log-transformed AAB on seasonal climate variables and LDPS. We used the results of multiple regressions to project future AAB using GCM ensemble climate variables and LDPS, and validated model predictions with recent AAB trends. Direct influences of spring and winter temperatures on AAB are larger and more widespread than the indirect effect mediated by changes in LDPS in most areas. Despite significant warming trends and reductions in snow cover duration, projected responses of AAB to early-mid 21st century are heterogeneous across the continent. Changes in AAB range from strongly increasing (one order of magnitude increases in AAB) to moderately decreasing (more than halving of baseline AAB). Annual wildfire area burned in coming decades is likely to be highly geographically heterogeneous, reflecting interacting regional and seasonal climate drivers of fire occurrence and spread.

Using the Personality Assessment Inventory Antisocial and Borderline Features Scales to Predict Behavior Change.

PubMed

Penson, Brittany N; Ruchensky, Jared R; Morey, Leslie C; Edens, John F

2016-11-01

A substantial amount of research has examined the developmental trajectory of antisocial behavior and, in particular, the relationship between antisocial behavior and maladaptive personality traits. However, research typically has not controlled for previous behavior (e.g., past violence) when examining the utility of personality measures, such as self-report scales of antisocial and borderline traits, in predicting future behavior (e.g., subsequent violence). Examination of the potential interactive effects of measures of both antisocial and borderline traits also is relatively rare in longitudinal research predicting adverse outcomes. The current study utilizes a large sample of youthful offenders ( N = 1,354) from the Pathways to Desistance project to examine the separate effects of the Personality Assessment Inventory Antisocial Features (ANT) and Borderline Features (BOR) scales in predicting future offending behavior as well as trends in other negative outcomes (e.g., substance abuse, violence, employment difficulties) over a 1-year follow-up period. In addition, an ANT × BOR interaction term was created to explore the predictive effects of secondary psychopathy. ANT and BOR both explained unique variance in the prediction of various negative outcomes even after controlling for past indicators of those same behaviors during the preceding year.

Mount St. Helens Future Expected Deposition Scenario (FEDS)

DTIC Science & Technology

2011-04-14

Gradation in HEC - RAS Sediment Transport Model of the Lower Cowlitz River ...Cowlitz 1-D/2-D modeling. Will also be used to test proposed measures where appropriate. Cowlitz River Toutle to Columbia 1-D HEC - RAS Aug 2004...Sep 2008 (6 years) Calibration Model Cowlitz River Toutle to Columbia 1-D HEC - RAS Oct 2007 – Sep 2035 (28 years) Forecast to predict trends in

The past as prelude to the future for understanding 21st-Century climate effects on Rocky Mountain trout

Treesearch

Daniel J. Isaak; Clint C. Muhlfeld; Andrew S. Todd; Robert Al-Chokhachy; James Roberts; Jeffrey L. Kershner; Kurt D. Fausch; Steven W. Hostetler

2012-01-01

Bioclimatic models predict large reductions in native trout across the Rocky Mountains in the 21st century but lack details about how changes will occur. Through five case histories across the region, we explore how a changing climate has been affecting streams and the potential consequences for trout. Monitoring records show trends in temperature and hydrographs...

Cameron predicts dry tree production system as the future subsea trend

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

Not Available

1978-06-05

Dry chambers are coming into increasing use in the search for offshore oil. With Subsea Station Cameron, an oil well can be drilled on the ocean floor and then topped with a dry production chamber for shirtsleeve work at the wellhead. Components of the station are described: production Christmas tree, chamber, adapter spool, docking plate, entry hatch, flowline connection, and variable controls. (DLC)

Monitoring and predicting eutrophication of Sri Lankan inland waters using ASTER satellite data

NASA Astrophysics Data System (ADS)

Dahanayaka, D. D. G. L.; Wijeyaratne, M. J. S.; Tonooka, H.; Minato, A.; Ozawa, S.; Perera, B. D. C.

2014-10-01

This study focused on determining the past changes and predicting the future trends in eutrophication of the Bolgoda North lake, Sri Lanka using in situ Chlorophyll-a (Chl-a) measurements and Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) satellite data. This Lake is located in a mixed land use area with industries, some agricultural lands, middle income and high income housing, tourist hotels and low income housing. From March to October 2013, water samples from five sampling sites were collected once a month parallel to ASTER overpass and Chl-a, nitrate and phosphate contents of each sample were measured using standard laboratory methods. Cloud-free ASTER scenes over the lake during the 2000-2013 periods were acquired for Chl-a estimation and trend analysis. All ASTER images were atmospherically corrected using FLAASH software and in-situ Chl-a data were regressed with atmospherically corrected three ASTER VNIR band ratios of the same date. The regression equation of the band ratio and Chl-a content with the highest correlation, which was the green/red band ratio was used to develop algorithm for generation of 15-m resolution Chl-a distribution maps. According to the ASTER based Chl-a distribution maps it was evident that eutrophication of this lake has gradually increased from 2008-2011. Results also indicated that there had been significantly high eutrophic conditions throughout the year 2013 in several regions, especially in water stagnant areas and adjacent to freshwater outlets. Field observations showed that this lake is receiving various discharges from factories. Unplanned urbanization and inadequacy of proper facilities in the nearby industries for waste management have resulted in the eutrophication of the water body. If the present trends of waste disposal and unplanned urbanization continue, enormous environmental problems would be resulted in future. Results of the present study showed that information from satellite remote sensing can play a useful role in the development of time series Chl-a distribution maps. Such information is important for the future predictions, development and management of this area as well as in the conservation of this water body.

Predicting future spatial distribution of SOC across entire France

NASA Astrophysics Data System (ADS)

Meersmans, Jeroen; Van Rompaey, Anton; Quine, Tim; Martin, Manuel; Pagé, Christian; Arrouays, Dominique

2013-04-01

Soil organic carbon (SOC) is widely recognized as a key factor controlling soil quality and as a crucial and active component of the global C-cycle. Hence, there exists a growing interest in monitoring and modeling the spatial and temporal behavior of this pool. So far, a large attempt has been made to map SOC at national scales for current and/or past situations. Despite some coarse predictions, detailed spatial SOC predictions for the future are still lacking. In this study we aim to predict future spatial evolution of SOC driven by climate and land use change for France up to the year 2100. Therefore, we combined 1) an existing model, predicting SOC as a function of soil type, climate, land use and management (Meersmans et al 2012), with 2) eight different IPCC spatial explicit climate change predictions (conducted by CERFACS) and 3) Land use change scenario predictions. We created business-as-usual land use change scenarios by extrapolating observed trends and calibrating logistic regression models, incorporating a large set of physical and socio-economic factors, at the regional level in combination with a multi-objective land allocation (MOLA) procedure. The resultant detailed projections of future SOC evolution across all regions of France, allow us to identify regions that are most likely to be characterized by a significant gain or loss of SOC and the degree to which land use decisions/outcomes control the scale of loss and gain. Therefore, this methodology and resulting maps can be considered as powerful tools to aid decision making concerning appropriate soil management, in order to enlarge SOC storage possibilities and reduce soil related CO2 fluxes.

Lake Baikal isotope records of Holocene Central Asian precipitation

NASA Astrophysics Data System (ADS)

Swann, George E. A.; Mackay, Anson W.; Vologina, Elena; Jones, Matthew D.; Panizzo, Virginia N.; Leng, Melanie J.; Sloane, Hilary J.; Snelling, Andrea M.; Sturm, Michael

2018-06-01

Climate models currently provide conflicting predictions of future climate change across Central Asia. With concern over the potential for a change in water availability to impact communities and ecosystems across the region, an understanding of historical trends in precipitation is required to aid model development and assess the vulnerability of the region to future changes in the hydroclimate. Here we present a record from Lake Baikal, located in the southern Siberian region of central Asia close to the Mongolian border, which demonstrates a relationship between the oxygen isotope composition of diatom silica (δ18Odiatom) and precipitation to the region over the 20th and 21st Century. From this, we suggest that annual rates of precipitation in recent times are at their lowest for the past 10,000 years and identify significant long-term variations in precipitation throughout the early to late Holocene interval. Based on comparisons to other regional records, these trends are suggested to reflect conditions across the wider Central Asian region around Lake Baikal and highlight the potential for further changes in precipitation with future climate change.

Climate change and livestock system in mountain: Understanding from Gandaki River basin of Nepal Himalaya.

NASA Astrophysics Data System (ADS)

Dahal, P.; Shrestha, N. S.; Krakauer, N.; Lakhankar, T.; Panthi, J., Sr.; Pradhanang, S.; Jha, A. K.; Shrestha, M.; Sharma, M.

2015-12-01

In recent years climate change has emerged as a source of vulnerability for agro-livestock smallholders in Nepal where people are mostly dependent on rain-fed agriculture and livestock farming for their livelihoods. There is a need to understand and predict the potential impacts of climate change on agro-livestock farmer to develop effective mitigation and adaptation strategies. To understand dynamics of this vulnerability, we assess the farmers' perceptions of climate change, analysis of historical and future projections of climatic parameters and try to understand impact of climate change on livestock system in Gandaki River Basin of Central Nepal. During the period of 1981-2012, as reported by the mountain communities, the most serious hazards for livestock system and agriculture are the increasing trend of temperature, erratic rainfall patterns and increase in drought. Poor households without irrigated land are facing greater risks and stresses than well-off people. Analysis of historical climate data also supports the farmer perception. Result shows that there is increasing trend of temperature but no consistent trend in precipitation but a notable finding is that wet areas are getting wetter and dry areas getting drier. Besides that, there is increase in percentage of warm days and nights with decrease in the cool nights and days. The magnitude of the trend is found to be higher in high altitude. Trend of wet days has found to be increasing with decreasing in rainy days. Most areas are characterized by increases in both severity and frequency of drought and are more evident in recent years. The summers of 2004/05/06/09 and winters of 2006/08/09 were the worst widespread droughts and have a serious impact on livestock since 1981. Future projected change in temperature and precipitation obtained from downscaling the data global model by regional climate model shows that precipitation in central Nepal will change by -8% to 12% and temperature will change by 1.9 0C to 3 0C in 2031-2060 compared to the baseline period 1970-2000. Since there will be an increase in temperature and most of the area will experience decreasing rainfall we can predict that there will be increasing vulnerability on livestock system in central Nepal in future which is already facing a serious impact.

FORUM - FutureTox II: In vitro Data and In Silico Models for ...

EPA Pesticide Factsheets

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. This article reports on the outcome of FutureTox II1,2, the second in a series of Society of Toxicology (SOT) Contemporary Concepts in Toxicology (CCT) Workshops, which was attended by invitees and participants from governmental and regulatory agencies, research institutes, academ

Woody plants and the prediction of climate-change impacts on bird diversity.

PubMed

Kissling, W D; Field, R; Korntheuer, H; Heyder, U; Böhning-Gaese, K

2010-07-12

Current methods of assessing climate-induced shifts of species distributions rarely account for species interactions and usually ignore potential differences in response times of interacting taxa to climate change. Here, we used species-richness data from 1005 breeding bird and 1417 woody plant species in Kenya and employed model-averaged coefficients from regression models and median climatic forecasts assembled across 15 climate-change scenarios to predict bird species richness under climate change. Forecasts assuming an instantaneous response of woody plants and birds to climate change suggested increases in future bird species richness across most of Kenya whereas forecasts assuming strongly lagged woody plant responses to climate change indicated a reversed trend, i.e. reduced bird species richness. Uncertainties in predictions of future bird species richness were geographically structured, mainly owing to uncertainties in projected precipitation changes. We conclude that assessments of future species responses to climate change are very sensitive to current uncertainties in regional climate-change projections, and to the inclusion or not of time-lagged interacting taxa. We expect even stronger effects for more specialized plant-animal associations. Given the slow response time of woody plant distributions to climate change, current estimates of future biodiversity of many animal taxa may be both biased and too optimistic.

Development, Problems and Countermeasures of Chinese Racing Car Industry

NASA Astrophysics Data System (ADS)

Yang, J. J.

2018-05-01

In recent years, motor car racing has developed rapidly in China. However, under the background of maximum vehicle production and car ownership in China, the racing car industry has a long way compared with that of the developed countries. The paper analyzes the current situation and summarizes the problems of Chinese racing car industry with supporting documentation and review of the literature. The future trend of the development of car industry in China is discussed. On the basis of the analysis and prediction, the strategies to respond to the future racing car industry in China are presented.

Likelihood of achieving air quality targets under model uncertainties.

PubMed

Digar, Antara; Cohan, Daniel S; Cox, Dennis D; Kim, Byeong-Uk; Boylan, James W

2011-01-01

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Evaluating Google Flu Trends in Latin America: Important Lessons for the Next Phase of Digital Disease Detection.

PubMed

Pollett, Simon; Boscardin, W John; Azziz-Baumgartner, Eduardo; Tinoco, Yeny O; Soto, Giselle; Romero, Candice; Kok, Jen; Biggerstaff, Matthew; Viboud, Cecile; Rutherford, George W

2017-01-01

 Latin America has a substantial burden of influenza and rising Internet access and could benefit from real-time influenza epidemic prediction web tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most Latin American countries or in any low- to middle-income country. Our aim was to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay.  Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011-December 2014. Concurrent weekly Google-predicted influenza activity in the same countries was abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.  There were frequent GFT prediction errors, with correlation ranging from r = -0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific for influenza virus activity.  We note substantial inaccuracies with GFT-predicted influenza activity compared with FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future Internet-based biosurveillance tools. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Spatiotemporal analysis of changes in lode mining claims around the McDermitt Caldera, northern Nevada and southern Oregon

USGS Publications Warehouse

Coyan, Joshua; Zientek, Michael L.; Mihalasky, Mark J.

2017-01-01

Resource managers and agencies involved with planning for future federal land needs are required to complete an assessment of and forecast for future land use every ten years. Predicting mining activities on federal lands is difficult as current regulations do not require disclosure of exploration results. In these cases, historic mining claims may serve as a useful proxy for determining where mining-related activities may occur. We assess the utility of using a space–time cube (STC) and associated analyses to evaluate and characterize mining claim activities around the McDermitt Caldera in northern Nevada and southern Oregon. The most significant advantage of arranging the mining claim data into a STC is the ability to visualize and compare the data, which allows scientists to better understand patterns and results. Additional analyses of the STC (i.e., Trend, Emerging Hot Spot, Hot Spot, and Cluster and Outlier Analyses) provide extra insights into the data and may aid in predicting future mining claim activities.

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

The wisdom of crowds in action: Forecasting epidemic diseases with a web-based prediction market system.

PubMed

Li, Eldon Y; Tung, Chen-Yuan; Chang, Shu-Hsun

2016-08-01

The quest for an effective system capable of monitoring and predicting the trends of epidemic diseases is a critical issue for communities worldwide. With the prevalence of Internet access, more and more researchers today are using data from both search engines and social media to improve the prediction accuracy. In particular, a prediction market system (PMS) exploits the wisdom of crowds on the Internet to effectively accomplish relatively high accuracy. This study presents the architecture of a PMS and demonstrates the matching mechanism of logarithmic market scoring rules. The system was implemented to predict infectious diseases in Taiwan with the wisdom of crowds in order to improve the accuracy of epidemic forecasting. The PMS architecture contains three design components: database clusters, market engine, and Web applications. The system accumulated knowledge from 126 health professionals for 31 weeks to predict five disease indicators: the confirmed cases of dengue fever, the confirmed cases of severe and complicated influenza, the rate of enterovirus infections, the rate of influenza-like illnesses, and the confirmed cases of severe and complicated enterovirus infection. Based on the winning ratio, the PMS predicts the trends of three out of five disease indicators more accurately than does the existing system that uses the five-year average values of historical data for the same weeks. In addition, the PMS with the matching mechanism of logarithmic market scoring rules is easy to understand for health professionals and applicable to predict all the five disease indicators. The PMS architecture of this study affords organizations and individuals to implement it for various purposes in our society. The system can continuously update the data and improve prediction accuracy in monitoring and forecasting the trends of epidemic diseases. Future researchers could replicate and apply the PMS demonstrated in this study to more infectious diseases and wider geographical areas, especially the under-developed countries across Asia and Africa. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Modelling Future Cardiovascular Disease Mortality in the United States: National Trends and Racial and Ethnic Disparities

PubMed Central

Pearson-Stuttard, Jonathan; Guzman-Castillo, Maria; Penalvo, Jose L.; Rehm, Colin D.; Afshin, Ashkan; Danaei, Goodarz; Kypridemos, Chris; Gaziano, Tom; Mozaffarian, Dariush; Capewell, Simon; O’Flaherty, Martin

2016-01-01

Background Accurate forecasting of cardiovascular disease (CVD) mortality is crucial to guide policy and programming efforts. Prior forecasts have often not incorporated past trends in rates of reduction in CVD mortality. This creates uncertainties about future trends in CVD mortality and disparities. Methods and Results To forecast US CVD mortality and disparities to 2030, we developed a hierarchical Bayesian model to determine and incorporate prior age, period and cohort (APC) effects from 1979–2012, stratified by age, gender and race; which we combined with expected demographic shifts to 2030. Data sources included the National Vital Statistics System, SEER single year population estimates, and US Bureau of Statistics 2012 National Population projections. We projected coronary disease and stroke deaths to 2030, first based on constant APC effects at 2012 values, as most commonly done (conventional); and then using more rigorous projections incorporating expected trends in APC effects (trend-based). We primarily evaluated absolute mortality. The conventional model projected total coronary and stroke deaths by 2030 to increase by approximately 18% (67,000 additional coronary deaths/year) and 50% (64,000 additional stroke deaths/year). Conversely, the trend-based model projected that coronary mortality would fall by 2030 by approximately 27% (79,000 fewer deaths/year); and stroke mortality would remain unchanged (200 fewer deaths/year). Health disparities will be improved in stroke deaths, but not coronary deaths. Conclusions After accounting for prior mortality trends and expected demographic shifts, total US coronary deaths are expected to decline, while stroke mortality will remain relatively constant. Health disparities in stroke, but not coronary, deaths will be improved but not eliminated. These APC approaches offer more plausible predictions than conventional estimates. PMID:26846769

Forecast of jet engine exhaust emissions for future high altitude commercial aircraft

NASA Technical Reports Server (NTRS)

Grobman, J.; Ingebo, R. D.

1974-01-01

Projected minimum levels of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are presented. The forecasts are based on: (1) current knowledge of emission characteristics of combustors and augmentors; (2) the current status of combustion research in emission reduction technology; (3) predictable trends in combustion systems and operating conditions as required for projected engine designs that are candidates for advanced subsonic or supersonic commercial aircraft. Results are presented for cruise conditions in terms of an emission index, g pollutant/kg fuel. Two sets of engine exhaust emission predictions are presented: the first, based on an independent NASA study and the second, based on the consensus of an ad hoc committee composed of industry, university, and government representatives. The consensus forecasts are in general agreement with the NASA forecasts.

Forecast of jet engine exhaust emissions for future high altitude commercial aircraft

NASA Technical Reports Server (NTRS)

Grobman, J.; Ingebo, R. D.

1974-01-01

Projected minimum levels of engine exhaust emissions that may be practicably achievable for future commercial aircraft operating at high altitude cruise conditions are presented. The forecasts are based on: (1) current knowledge of emission characteristics of combustors and augmentors; (2) the current status of combustion research in emission reduction technology; and (3) predictable trends in combustion systems and operating conditions as required for projected engine designs that are candidates for advanced subsonic or supersonic commercial aircraft. Results are presented for cruise conditions in terms of an emission index, g pollutant/kg fuel. Two sets of engine exhaust emission predictions are presented: the first, based on an independent NASA study and the second, based on the consensus of an ad hoc committee composed of industry, university, and government representatives. The consensus forecasts are in general agreement with the NASA forecasts.

The MJO-SSW Teleconnection: Interaction Between MJO-Forced Waves and the Midlatitude Jet

NASA Astrophysics Data System (ADS)

Kang, Wanying; Tziperman, Eli

2018-05-01

The Madden-Julian Oscillation (MJO) was shown to affect both present-day sudden stratospheric warming (SSW) events in the Arctic and their future frequency under global warming scenarios, with implications to the Arctic Oscillation and midlatitude extreme weather. This work uses a dry dynamic core model to understand the dependence of SSW frequency on the amplitude and longitudinal range of the MJO, motivated by the prediction that the MJO will strengthen and broaden its longitudinal range in a warmer climate. We focus on the response of the midlatitude jets and the corresponding generated stationary waves, which are shown to dominate the response of SSW events to MJO forcing. Momentum budget analysis of a large ensemble of spinup simulations suggests that the climatological jet response is driven by the MJO-forced meridional eddy momentum transport. The results suggest that the trends in both MJO amplitude and longitudinal range are important for the prediction of the midlatitude jet response and for the prediction of SSWs in a future climate.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

Looking Back and Looking Forward: Reprising the Promise and Predicting the Future of Formation Flying and Spaceborne GPS Navigation Systems

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Dennehy, Neil

2015-01-01

A retrospective consideration of two 15-year old Guidance, Navigation and Control (GN&C) technology 'vision' predictions will be the focus of this paper. A look back analysis and critique of these late 1990s technology roadmaps out-lining the future vision, for two then nascent, but rapidly emerging, GN&C technologies will be performed. Specifically, these two GN&C technologies were: 1) multi-spacecraft formation flying and 2) the spaceborne use and exploitation of global positioning system (GPS) signals to enable formation flying. This paper reprises the promise of formation flying and spaceborne GPS as depicted in the cited 1999 and 1998 papers. It will discuss what happened to cause that promise to be mostly unfulfilled and the reasons why the envisioned formation flying dream has yet to become a reality. The recent technology trends over the past few years will then be identified and a renewed government interest in spacecraft formation flying/cluster flight will be highlighted. The authors will conclude with a reality-tempered perspective, 15 years after the initial technology roadmaps were published, predicting a promising future of spacecraft formation flying technology development over the next decade.

Predicting Node Degree Centrality with the Node Prominence Profile

PubMed Central

Yang, Yang; Dong, Yuxiao; Chawla, Nitesh V.

2014-01-01

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics. PMID:25429797

Drivers of annual to decadal streamflow variability in the lower Colorado River Basin

NASA Astrophysics Data System (ADS)

Lambeth-Beagles, R. S.; Troch, P. A.

2010-12-01

The Colorado River is the main water supply to the southwest region. As demand reaches the limit of supply in the southwest it becomes increasingly important to understand the dynamics of streamflow in the Colorado River and in particular the tributaries to the lower Colorado River. Climate change may pose an additional threat to the already-scarce water supply in the southwest. Due to the narrowing margin for error, water managers are keen on extending their ability to predict streamflow volumes on a mid-range to decadal scale. Before a predictive streamflow model can be developed, an understanding of the physical drivers of annual to decadal streamflow variability in the lower Colorado River Basin is needed. This research addresses this need by applying multiple statistical methods to identify trends, patterns and relationships present in streamflow, precipitation and temperature over the past century in four contributing watersheds to the lower Colorado River. The four watersheds selected were the Paria, Little Colorado, Virgin/Muddy, and Bill Williams. Time series data over a common period from 1906-2007 for streamflow, precipitation and temperature were used for the initial analysis. Through statistical analysis the following questions were addressed: 1) are there observable trends and patterns in these variables during the past century and 2) if there are trends or patterns, how are they related to each other? The Mann-Kendall test was used to identify trends in the three variables. Assumptions regarding autocorrelation and persistence in the data were taken into consideration. Kendall’s tau-b test was used to establish association between any found trends in the data. Initial results suggest there are two primary processes occurring. First, statistical analysis reveals significant upward trends in temperatures and downward trends in streamflow. However, there appears to be no trend in precipitation data. These trends in streamflow and temperature speak to increasing evaporation and transpiration processes. Second, annual variability in streamflow is not statistically correlated with annual temperature variability but appears to be highly correlated with annual precipitation variability. This implies that on a year-to-year basis, changes in streamflow volumes are directly affected by precipitation and not temperature. Future development of a predictive streamflow model will need to take into consideration these two processes to obtain accurate results. In order to extend predictive skill to the multi-year scale relationships between precipitation, temperature and persistent climate indices such as the Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation and El Nino/Southern Oscillation will need to be examined.

Climate Change and Tropical Total Lightning

NASA Technical Reports Server (NTRS)

Albrecht, R.; Petersen, W.; Buechler, D.; Goodman, S.; Blakeslee, R.; Christian, H.

2009-01-01

While global warming is regarded as a fact by many in the scientific community, its future impact remains a challenge to be determined and measured. The International Panel on Climate Change (IPCC) assessment report (IPCC, 2007) shows inconclusive answers on global rainfall trends and general agreement on a future drier climate with increased global warming. The relationship between temperature, humidity and convection is not linear and is strongly dependent on regional scale features, such as topography and land cover. Furthermore, the relationship between convective lightning production (thunderstorms) and temperature is even more complicated, being subjected to the cloud dynamics and microphysics. Total lightning (intracloud and cloud-to-ground) monitoring is a relatively new field of observation. Global and tropical total lightning began to be more extensively measured by satellites in the mid 90s. In this scope, the Lightning Imaging Sensor (LIS) onboard of the Tropical Rainfall Measurement Mission (TRMM) has been operational for over 11 years. Here we address total lightning trends observed by LIS from 1998 to 2008 in different temporal (annual and seasonal) and spatial (large and regional) scales. The observed 11-year trends are then associate to different predicted/hypothesized climate change scenarios.

Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms.

PubMed

Paul, Susannah; Mgbere, Osaro; Arafat, Raouf; Yang, Biru; Santos, Eunice

2017-01-01

Objective The objective was to forecast and validate prediction estimates of influenza activity in Houston, TX using four years of historical influenza-like illness (ILI) from three surveillance data capture mechanisms. Background Using novel surveillance methods and historical data to estimate future trends of influenza-like illness can lead to early detection of influenza activity increases and decreases. Anticipating surges gives public health professionals more time to prepare and increase prevention efforts. Methods Data was obtained from three surveillance systems, Flu Near You, ILINet, and hospital emergency center (EC) visits, with diverse data capture mechanisms. Autoregressive integrated moving average (ARIMA) models were fitted to data from each source for week 27 of 2012 through week 26 of 2016 and used to forecast influenza-like activity for the subsequent 10 weeks. Estimates were then compared to actual ILI percentages for the same period. Results Forecasted estimates had wide confidence intervals that crossed zero. The forecasted trend direction differed by data source, resulting in lack of consensus about future influenza activity. ILINet forecasted estimates and actual percentages had the least differences. ILINet performed best when forecasting influenza activity in Houston, TX. Conclusion Though the three forecasted estimates did not agree on the trend directions, and thus, were considered imprecise predictors of long-term ILI activity based on existing data, pooling predictions and careful interpretations may be helpful for short term intervention efforts. Further work is needed to improve forecast accuracy considering the promise forecasting holds for seasonal influenza prevention and control, and pandemic preparedness.

Statistical model selection for better prediction and discovering science mechanisms that affect reliability

DOE PAGES

Anderson-Cook, Christine M.; Morzinski, Jerome; Blecker, Kenneth D.

2015-08-19

Understanding the impact of production, environmental exposure and age characteristics on the reliability of a population is frequently based on underlying science and empirical assessment. When there is incomplete science to prescribe which inputs should be included in a model of reliability to predict future trends, statistical model/variable selection techniques can be leveraged on a stockpile or population of units to improve reliability predictions as well as suggest new mechanisms affecting reliability to explore. We describe a five-step process for exploring relationships between available summaries of age, usage and environmental exposure and reliability. The process involves first identifying potential candidatemore » inputs, then second organizing data for the analysis. Third, a variety of models with different combinations of the inputs are estimated, and fourth, flexible metrics are used to compare them. As a result, plots of the predicted relationships are examined to distill leading model contenders into a prioritized list for subject matter experts to understand and compare. The complexity of the model, quality of prediction and cost of future data collection are all factors to be considered by the subject matter experts when selecting a final model.« less

Climate Prediction Sees Future Despite Chaos: Researchers Outside NASA use NCCS Resources for Studies

NASA Technical Reports Server (NTRS)

2002-01-01

The air on this mostly sunny January day is crisp and the wind is blustery. The morning's National Weather Service 6-hour forecast had accurately predicted these conditions for the Baltimore-Washington area and the 2-3 day extended outlook was almost perfect. The previous week, the National Center for Environmental Prediction's (NCEP) 6-10 day temperature and precipitation outlook for the general trends for the' region was correct as well. However, no forecast could have predicted specific details about this day. It is 28.5 F in the sunshine bright enough for dark sunglasses, and windy enough to blow off a hat. Such details are impossible to foresee with any accuracy and are outside the scope of routine weather prediction. Equally difficult is accurately forecasting weather beyond about 2 weeks.

Live Fast, Die Young: Experimental Evidence of Population Extinction Risk due to Climate Change.

PubMed

Bestion, Elvire; Teyssier, Aimeric; Richard, Murielle; Clobert, Jean; Cote, Julien

2015-10-01

Evidence has accumulated in recent decades on the drastic impact of climate change on biodiversity. Warming temperatures have induced changes in species physiology, phenology, and have decreased body size. Such modifications can impact population dynamics and could lead to changes in life cycle and demography. More specifically, conceptual frameworks predict that global warming will severely threaten tropical ectotherms while temperate ectotherms should resist or even benefit from higher temperatures. However, experimental studies measuring the impacts of future warming trends on temperate ectotherms' life cycle and population persistence are lacking. Here we investigate the impacts of future climates on a model vertebrate ectotherm species using a large-scale warming experiment. We manipulated climatic conditions in 18 seminatural populations over two years to obtain a present climate treatment and a warm climate treatment matching IPCC predictions for future climate. Warmer temperatures caused a faster body growth, an earlier reproductive onset, and an increased voltinism, leading to a highly accelerated life cycle but also to a decrease in adult survival. A matrix population model predicts that warm climate populations in our experiment should go extinct in around 20 y. Comparing our experimental climatic conditions to conditions encountered by populations across Europe, we suggest that warming climates should threaten a significant number of populations at the southern range of the distribution. Our findings stress the importance of experimental approaches on the entire life cycle to more accurately predict population and species persistence in future climates.

Live Fast, Die Young: Experimental Evidence of Population Extinction Risk due to Climate Change

PubMed Central

Bestion, Elvire; Teyssier, Aimeric; Richard, Murielle; Clobert, Jean; Cote, Julien

2015-01-01

Evidence has accumulated in recent decades on the drastic impact of climate change on biodiversity. Warming temperatures have induced changes in species physiology, phenology, and have decreased body size. Such modifications can impact population dynamics and could lead to changes in life cycle and demography. More specifically, conceptual frameworks predict that global warming will severely threaten tropical ectotherms while temperate ectotherms should resist or even benefit from higher temperatures. However, experimental studies measuring the impacts of future warming trends on temperate ectotherms' life cycle and population persistence are lacking. Here we investigate the impacts of future climates on a model vertebrate ectotherm species using a large-scale warming experiment. We manipulated climatic conditions in 18 seminatural populations over two years to obtain a present climate treatment and a warm climate treatment matching IPCC predictions for future climate. Warmer temperatures caused a faster body growth, an earlier reproductive onset, and an increased voltinism, leading to a highly accelerated life cycle but also to a decrease in adult survival. A matrix population model predicts that warm climate populations in our experiment should go extinct in around 20 y. Comparing our experimental climatic conditions to conditions encountered by populations across Europe, we suggest that warming climates should threaten a significant number of populations at the southern range of the distribution. Our findings stress the importance of experimental approaches on the entire life cycle to more accurately predict population and species persistence in future climates. PMID:26501958

Isolating the Effects of the Warming Trend from the General Climate Change in Water Resources: California Case

NASA Astrophysics Data System (ADS)

Wang, J.; Yin, H.; Chung, F.

2008-12-01

While the population growth, the future land use change, and the desire for better environmental preservation and protection are adding up pressure on water resources management in California, California is facing an extra challenge of addressing potential climate change impacts on water supple and demand in California. The concerns on water facilities planning and flood control caused by climate change include modified precipitation patterns, changes in snow levels and runoff patterns due to increased air temperatures. Although long-term climate projections are largely uncertain, there appears to be a strong consistency in predicting the warming trend of future surface temperature, and the resulting shift in the seasonal patterns of runoff. However, projected changes in precipitation (wetting or drying), which control annual runoff, are far less certain. This paper attempts to separate the effects of warming trend from the effects of precipitation trend on water planning especially in California where reservoir operations are more sensitive to seasonal patterns of runoff than to the total annual runoff. The water resources systems planning model, CALSIM2, is used to evaluate climate change impact on water resource management in California. Rather than directly ingesting estimated streamflows from climate model projections into CALSIM2, a three step perturbation ratio method is proposed to introduce climate change impact into the planning model. Firstly, monthly perturbation ratio of projected monthly inflow to simulated historical monthly inflow is applied to observed historical monthly inflow to generate climate change inflows to major dams and reservoirs. To isolate the effects of warming trend on water resources, a further annual inflow adjustment is applied to the inflows generated in step one to preserve the volume of the observed annual inflow. To re-introduce the effects of precipitation trend on water resources, an additional inflow trend adjustment is applied to the adjusted climate change inflow. Therefore, three CALSIM2 experiments will be implemented: (1) base run with the observed historic inflow (1921 to 2003); (2) sensitivity run with the adjusted climate change inflow through annual inflow adjustment; (3) sensitivity run with the adjusted climate change inflow through annual inflow adjustment and inflow trend adjustment. To account for the variability of various climate models in projecting future climates, the uncertainty in future emission scenarios, and the difference in different projection periods, estimated inflows from 6 climate models for 2 emission scenarios (A2 and B1) and two projection periods (2030-2059 and 2070-2099) are included in the CALSIM model experiments.

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand.

PubMed

Nateghi, Roshanak; Mukherjee, Sayanti

2017-01-01

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months.

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand

PubMed Central

Nateghi, Roshanak

2017-01-01

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months. PMID:29155862

Core Noise - Increasing Importance

NASA Technical Reports Server (NTRS)

Hultgren, Lennart S.

2011-01-01

This presentation is a technical summary of and outlook for NASA-internal and NASA-sponsored external research on core (combustor and turbine) noise funded by the Fundamental Aeronautics Program Subsonic Fixed Wing (SFW) Project. Sections of the presentation cover: the SFW system-level noise metrics for the 2015, 2020, and 2025 timeframes; turbofan design trends and their aeroacoustic implications; the emerging importance of core noise and its relevance to the SFW Reduced-Perceived-Noise Technical Challenge; and the current research activities in the core-noise area, with additional details given about the development of a high-fidelity combustor-noise prediction capability as well as activities supporting the development of improved reduced-order, physics-based models for combustor-noise prediction. The need for benchmark data for validation of high-fidelity and modeling work and the value of a potential future diagnostic facility for testing of core-noise-reduction concepts are indicated. The NASA Fundamental Aeronautics Program has the principal objective of overcoming today's national challenges in air transportation. The SFW Reduced-Perceived-Noise Technical Challenge aims to develop concepts and technologies to dramatically reduce the perceived aircraft noise outside of airport boundaries. This reduction of aircraft noise is critical to enabling the anticipated large increase in future air traffic. Noise generated in the jet engine core, by sources such as the compressor, combustor, and turbine, can be a significant contribution to the overall noise signature at low-power conditions, typical of approach flight. At high engine power during takeoff, jet and fan noise have traditionally dominated over core noise. However, current design trends and expected technological advances in engine-cycle design as well as noise-reduction methods are likely to reduce non-core noise even at engine-power points higher than approach. In addition, future low-emission combustor designs could increase the combustion-noise component. The trend towards high-power-density cores also means that the noise generated in the low-pressure turbine will likely increase. Consequently, the combined result from these emerging changes will be to elevate the overall importance of turbomachinery core noise, which will need to be addressed in order to meet future noise goals.

Role of land-surface changes in arctic summer warming

USGS Publications Warehouse

Chapin, F. S.; Sturm, M.; Serreze, Mark C.; McFadden, J.P.; Key, J.R.; Lloyd, A.H.; McGuire, A.D.; Rupp, T.S.; Lynch, A.H.; Schimel, Joshua P.; Beringer, J.; Chapman, W.L.; Epstein, H.E.; Euskirchen, E.S.; Hinzman, L.D.; Jia, G.; Ping, C.-L.; Tape, K.D.; Thompson, C.D.C.; Walker, D.A.; Welker, J.M.

2005-01-01

A major challenge in predicting Earth's future climate state is to understand feedbacks that alter greenhouse-gas forcing. Here we synthesize field data from arctic Alaska, showing that terrestrial changes in summer albedo contribute substantially to recent high-latitude warming trends. Pronounced terrestrial summer warming in arctic Alaska correlates with a lengthening of the snow-free season that has increased atmospheric heating locally by about 3 watts per square meter per decade (similar in magnitude to the regional heating expected over multiple decades from a doubling of atmospheric CO2). The continuation of current trends in shrub and tree expansion could further amplify this atmospheric heating by two to seven times.

Forecasting Tunisian type 2 diabetes prevalence to 2027: validation of a simple model.

PubMed

Saidi, Olfa; O'Flaherty, Martin; Mansour, Nadia Ben; Aissi, Wafa; Lassoued, Olfa; Capewell, Simon; Critchley, Julia A; Malouche, Dhafer; Romdhane, Habiba Ben

2015-02-07

Most projections of type 2 diabetes (T2D) prevalence are simply based on demographic change (i.e. ageing). We developed a model to predict future trends in T2D prevalence in Tunisia, explicitly taking into account trends in major risk factors (obesity and smoking). This could improve assessment of policy options for prevention and health service planning. The IMPACT T2D model uses a Markov approach to integrate population, obesity and smoking trends to estimate future T2D prevalence. We developed a model for the Tunisian population from 1997 to 2027, and validated the model outputs by comparing with a subsequent T2D prevalence survey conducted in 2005. The model estimated that the prevalence of T2D among Tunisians aged over 25 years was 12.0% in 1997 (95% confidence intervals 9.6%-14.4%), increasing to 15.1% (12.5%-17.4%) in 2005. Between 1997 and 2005, observed prevalence in men increased from 13.5% to 16.1% and in women from 12.9% to 14.1%. The model forecast for a dramatic rise in prevalence by 2027 (26.6% overall, 28.6% in men and 24.7% in women). However, if obesity prevalence declined by 20% in the 10 years from 2013, and if smoking decreased by 20% over 10 years from 2009, a 3.3% reduction in T2D prevalence could be achieved in 2027 (2.5% in men and 4.1% in women). This innovative model provides a reasonably close estimate of T2D prevalence for Tunisia over the 1997-2027 period. Diabetes burden is now a significant public health challenge. Our model predicts that this burden will increase significantly in the next two decades. Tackling obesity, smoking and other T2D risk factors thus needs urgent action. Tunisian decision makers have therefore defined two strategies: obesity reduction and tobacco control. Responses will be evaluated in future population surveys.

Red blood cell use in Switzerland: trends and demographic challenges

PubMed Central

Volken, Thomas; Buser, Andreas; Castelli, Damiano; Fontana, Stefano; Frey, Beat M.; Rüsges-Wolter, Ilka; Sarraj, Amira; Sigle, Jörg; Thierbach, Jutta; Weingand, Tina; Taleghani, Behrouz Mansouri

2018-01-01

Background Several studies have raised concerns that future demand for blood products may not be met. The ageing of the general population and the fact that a large proportion of blood products is transfused to elderly patients has been identified as an important driver of blood shortages. The aim of this study was to collect, for the first time, nationally representative data regarding blood donors and transfusion recipients in order to predict the future evolution of blood donations and red blood cell (RBC) use in Switzerland between 2014 and 2035. Materials and methods Blood donor and transfusion recipient data, subdivided by the subjects’ age and gender were obtained from Regional Blood Services and nine large, acute-care hospitals in various regions of Switzerland. Generalised additive regression models and time-series models with exponential smoothing were employed to estimate trends of whole blood donations and RBC transfusions. Results The trend models employed suggested that RBC demand could equal supply by 2018 and could eventually cause an increasing shortfall of up to 77,000 RBC units by 2035. Discussion Our study highlights the need for continuous monitoring of trends of blood donations and blood transfusions in order to take proactive measures aimed at preventing blood shortages in Switzerland. Measures should be taken to improve donor retention in order to prevent a further erosion of the blood donor base. PMID:27723455

Technology and the Future of Healthcare

PubMed Central

Thimbleby, Harold

2013-01-01

Healthcare changes dramatically because of technological developments, from anesthetics and antibiotics to magnetic resonance imaging scanners and radiotherapy. Future technological innovation is going to keep transforming healthcare, yet while technologies (new drugs and treatments, new devices, new social media support for healthcare, etc) will drive innovation, human factors will remain one of the stable limitations of breakthroughs. No predictions can satisfy everybody; instead, this article explores fragments of the future to see how to think more clearly about how to get where we want to go. Significance for public health Technology drives healthcare more than any other force, and in the future it will continue to develop in dramatic ways. While we can glimpse and debate the details of future trends in healthcare, we need to be clear about the drivers so we can align with them and actively work to ensure the best outcomes for society as a whole. PMID:25170499

Hip fracture incidence 2003-2013 and projected cases until 2050 in Austria: a population-based study.

PubMed

Concin, Hans; Brozek, Wolfgang; Benedetto, Karl-Peter; Häfele, Hartmut; Kopf, Joachim; Bärenzung, Thomas; Schnetzer, Richard; Schenk, Christian; Stimpfl, Elmar; Waheed-Hutter, Ursula; Ulmer, Hanno; Rapp, Kilian; Zwettler, Elisabeth; Nagel, Gabriele

2016-12-01

Elevated hip fracture incidence is a major public health problem looming to aggravate in industrialized countries due to demographic developments. We report hip fracture incidence and expected future cases from Vorarlberg, the westernmost province of Austria, results potentially representative of Central European populations. Crude and standardized hip fracture incidence rates in Vorarlberg 2003-2013 are reported. Based on the age-specific incidence in 2013 or trends 2003-2013, we predict hip fractures till 2050. Female age-standardized hip fracture incidence decreased 2005-2013, whereas for men, the trend was rather unclear. Uncorrected forecasts indicate that by 2050, female and male cases will each have more than doubled from 2015 in all demographic core scenarios. Corrected by incidence trends before 2013, cases are expected to drop among women but rise among men. We anticipate rising hip fracture numbers in Vorarlberg within the next decades, unless prevention programs that presumably account for decreasing incidence rates, particularly among women since 2005, take further effect to counteract the predicted steady increase due to demographic changes. Concomitantly, augmented endeavors to target the male population by these programs are needed.

The influence of environment and energy macro surroundings on the development of tourism in the 21st century.

PubMed

Jovicić, Dobrica

2012-06-01

Trying to anticipate the future of tourism may be a particularly fraught task. However, this does not mean that trying to predict the future of tourism is not without value. From a business perspective, examining the future enables firms to anticipate new business conditions and develop new strategies. From a destination perspective, reflections on the future enable consideration of how to maintain or improve the qualities of a destination. The paper is focused on an analysis of the impacts of the energy and ecological macro environments on tourism trends in 21st century. Mass international tourism has thrived on the abundant and cheap supply of energy, and this may be about to change as the world moves towards 'Peak Oil'. The resultant scarcity and high price of all energy fuels will produce changes in human activities, specifically in tourism. The basis of the health of the economy is the health of the environment. Therefore issues of global environmental changes are increasingly influencing consideration of trends in tourism. In this looming transitional era tourism needs to make some dramatic changes to harmonize with the new realities of a post-energy world affected additionaly by global warming and other environmental changes.

Temporal Trends and Future Prediction of Breast Cancer Incidence Across Age Groups in Trivandrum, South India.

PubMed

Mathew, Aleyamma; George, Preethi Sara; Arjunan, Asha; Augustine, Paul; Kalavathy, Mc; Padmakumari, G; Mathew, Beela Sarah

2016-01-01

Increasing breast cancer (BC) incidence rates have been reported from India; causal factors for this increased incidence are not understood and diagnosis is mostly in advanced stages. Trivandrum exhibits the highest BC incidence rates in India. This study aimed to estimate trends in incidence by age from 2005- 2014, to predict rates through 2020 and to assess the stage at diagnosis of BC in Trivandrum. BC cases were obtained from the Population Based Cancer Registry, Trivandrum. Distribution of stage at diagnosis and incidence rates of BC [Age-specific (ASpR), crude (CR) and age-standardized (ASR)] are described and employed with a joinpoint regression model to estimate average annual percent changes (AAPC) and a Bayesian model to estimate predictive rates. BC accounts for 31% (2681/8737) of all female cancers in Trivandrum. Thirty-five percent (944/2681) are <50 years of age and only 9% present with stage I disease. Average age increased from 53 to 56.4 years (p=0.0001), CR (per 105 women) increased from 39 (ASR: 35.2) to 55.4 (ASR: 43.4), AAPC for CR was 5.0 (p=0.001) and ASR was 3.1 (p=0.001). Rates increased from 50 years. Predicted ASpR is 174 in 50-59 years, 231 in > 60 years and overall CR is 80 (ASR: 57) for 2019- 20. BC, mostly diagnosed in advanced stages, is rising rapidly in South India with large increases likely in the future; particularly among post-menopausal women. This increase might be due to aging and/or changes in lifestyle factors. Reasons for the increased incidence and late stage diagnosis need to be studied.

Hierarchical prediction of industrial water demand based on refined Laspeyres decomposition analysis.

PubMed

Shang, Yizi; Lu, Shibao; Gong, Jiaguo; Shang, Ling; Li, Xiaofei; Wei, Yongping; Shi, Hongwang

2017-12-01

A recent study decomposed the changes in industrial water use into three hierarchies (output, technology, and structure) using a refined Laspeyres decomposition model, and found monotonous and exclusive trends in the output and technology hierarchies. Based on that research, this study proposes a hierarchical prediction approach to forecast future industrial water demand. Three water demand scenarios (high, medium, and low) were then established based on potential future industrial structural adjustments, and used to predict water demand for the structural hierarchy. The predictive results of this approach were compared with results from a grey prediction model (GPM (1, 1)). The comparison shows that the results of the two approaches were basically identical, differing by less than 10%. Taking Tianjin, China, as a case, and using data from 2003-2012, this study predicts that industrial water demand will continuously increase, reaching 580 million m 3 , 776.4 million m 3 , and approximately 1.09 billion m 3 by the years 2015, 2020 and 2025 respectively. It is concluded that Tianjin will soon face another water crisis if no immediate measures are taken. This study recommends that Tianjin adjust its industrial structure with water savings as the main objective, and actively seek new sources of water to increase its supply.

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.

PubMed

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

The voluntary community health movement in India: a strengths, weaknesses, opportunities, and threats (SWOT) analysis.

PubMed

Sharma, M; Bhatia, G

1996-12-01

There has been a prolific growth of voluntary organizations in India since independence in 1947. One of the major areas of this growth has been in the field of community health. The purpose of this article is to historically trace the voluntary movement in community health in India, analyze the current status, and predict future trends of voluntary efforts. A review of the literature in the form of a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was the method of this study. Some of the key trends which emerged as the priority areas for progress and for strengthening voluntary organizations in the future were enhancing linkages between health and development; building upon collective force; greater utilization of participatory training; establishing egalitarian and effectual linkages for decision making at the international level; developing self-reliant community-based models; and the need for attaining holistic empowerment at individual, organizational, and community levels through "duty consciousness" as opposed to merely asking for rights.

Decision support systems and methods for complex networks

DOEpatents

Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

2012-02-28

Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

A circumpolar monitoring framework for polar bears

USGS Publications Warehouse

Vongraven, Dag; Aars, Jon; Amstrup, Steven C.; Atkinson, Stephen N.; Belikov, Stanislav; Born, Erik W.; DeBruyn, T.D.; Derocher, Andrew E.; Durner, George M.; Gill, Michael J.; Lunn, Nicholas J.; Obbard, Martyn E.; Omelak, Jack; Ovsyanikov, Nikita; Peacock, Elizabeth; Richardson, E.E.; Sahanatien, Vicki; Stirling, Ian; Wiig, Øystein

2012-01-01

Polar bears (Ursus maritimus) occupy remote regions that are characterized by harsh weather and limited access. Polar bear populations can only persist where temporal and spatial availability of sea ice provides adequate access to their marine mammal prey. Observed declines in sea ice availability will continue as long as greenhouse gas concentrations rise. At the same time, human intrusion and pollution levels in the Arctic are expected to increase. A circumpolar understanding of the cumulative impacts of current and future stressors is lacking, long-term trends are known from only a few subpopulations, and there is no globally coordinated effort to monitor effects of stressors. Here, we describe a framework for an integrated circumpolar monitoring plan to detect ongoing patterns, predict future trends, and identify the most vulnerable polar bear subpopulations. We recommend strategies for monitoring subpopulation abundance and trends, reproduction, survival, ecosystem change, human-caused mortality, human–bear conflict, prey availability, health, stature, distribution, behavioral change, and the effects that monitoring itself may have on polar bears. We assign monitoring intensity for each subpopulation through adaptive assessment of the quality of existing baseline data and research accessibility. A global perspective is achieved by recommending high intensity monitoring for at least one subpopulation in each of four major polar bear ecoregions. Collection of data on harvest, where it occurs, and remote sensing of habitat, should occur with the same intensity for all subpopulations. We outline how local traditional knowledge may most effectively be combined with the best scientific methods to provide comparable and complementary lines of evidence. We also outline how previously collected intensive monitoring data may be sub-sampled to guide future sampling frequencies and develop indirect estimates or indices of subpopulation status. Adoption of this framework will inform management and policy responses to changing worldwide polar bear status and trends.

Putting oxygen and temperature thresholds of marine animals in context of environmental change in coastal seas: a regional perspective for the Scotian Shelf and Gulf of St. Lawrence

NASA Astrophysics Data System (ADS)

Brennan, Catherine E.; Blanchard, Hannah; Fennel, Katja

2014-05-01

We surveyed the literature in order to compile reported oxygen, temperature, salinity and depth preferences and thresholds of important marine species found in the Gulf of St. Lawrence and the Scotian Shelf regions of the northwest North Atlantic. We determined species importance based on the existence of a commercial fishery, a threatened or at risk status, or by meeting the following criteria: bycatch, baitfish, invasive, vagrant, important for ecosystem energy transfer, and predators and prey of the above species. Using the dataset compiled for the 53 regional fishes and macroinvertebrates, we rank species (including for different lifestages) by their maximum thermal limit, as well as by the lowest oxygen concentration tolerated before negative impacts (e.g. physiological stress), 50% mortality or 100% mortality are experienced. Additionally, we compare these thresholds to observed marine deoxygenation trends at multiple sites, and observed surface warming trends. This results in an assessment of which regional species are most vulnerable to future warming and oxygen depletion, and a first-order estimate of the consequences of thermal and oxygen stress on a highly productive marine shelf. If regional multi-decadal oxygen and temperature trends continue through the 21st century, many species will lose favorable oxygen conditions, experience oxygen-stress, or disappear due to insufficient oxygen. Future warming can additionally displace vulnerable species, though we note that large natural variability in environmental conditions may amplify or dampen the effects of anthropogenic surface warming trends. This dataset may be combined with regional ocean model predictions to map future species distributions.

Concepts of English 1964: Traditions and Innovations. Report of the Annual English Conference of the Metropolitan Detroit Bureau of School Studies (14th, Ferndale, Michigan, March 12, 1964).

ERIC Educational Resources Information Center

Simon, Myron, Ed.

Traditions and emerging trends in the teaching of linguistics and composition are examined in this collection of papers. Joseph Mersand predicts a future emphasis on the subject of English as a discipline, leading to a more adequate preparation and increased supervision of English teachers and a greater stress in the classroom on written…

Legal ramifications of intellectual property

NASA Technical Reports Server (NTRS)

Kempf, Robert F.

1990-01-01

Recent government policy changes that have resulted in encouraging or requiring increased intellectual property rights of federally funded research and development activities are examined. The reasons for these changes are discussed, including considerations related to technology transfer, patent rights, copyrights, trade secrets, and computer software issues. The effect of these changes on traditional approaches to the dissemination of federally funded scientific and technical information is considered and predictions concerning future trends in intellectual property rights are given.

Legal ramifications of intellectual property

NASA Technical Reports Server (NTRS)

Kempf, Robert F.

1990-01-01

Recent government policy changes that have resulted in encouraging or requiring increased intellectual property rights of Federally funded research and development activities are examined. The reasons for these changes are discussed, including considerations related to technology transfer, patent rights, copyrights, trade secrets, and computer software issues. The effect of these changes on traditional approaches to the dissemination of Federally funded scientific and technical information is considered and predictions concerning future trends in intellectual property rights are given.

Army Accessions Research Consortium, Shaping the Future Force and Predicting It’s Success, 31 August - 3 September 2009 Hampton, Virginia

DTIC Science & Technology

2010-04-01

recruiting, childhood obesity, JROTC, Whole Soldier, Accessions Research Award, Human Dimension, Army Experience Center, MEPCOM, Mental health...2 September 2009 Opening Comments (COL Jeff Schamburg) “ Childhood Obesity in the US: Prevalence, Trends & Health Risks." (Dr. Cynthia Ogden...experts are saying.” • Claire Raines - (Generational analyst-workforce) – Young people are shaped by defining events, the media, parenting patterns

Theoretical Models for Aircraft Availability: Classical Approach to Identification of Trends, Seasonality, and System Constraints in the Development of Realized Models

DTIC Science & Technology

2004-03-01

predicting future events ( Heizer and Render , 1999). Forecasting techniques fall into two major categories, qualitative and quantitative methods...Globemaster III.” Excerpt from website. www.globalsecurity.org/military /systems/ aircraft/c-17-history.htm. 2003. Heizer , Jay, and Barry Render ...of the past data used to make the forecast ( Heizer , et. al., 1999). Explanatory forecasting models assume that the variable being forecasted

An Optimized Handover Scheme with Movement Trend Awareness for Body Sensor Networks

PubMed Central

Sun, Wen; Zhang, Zhiqiang; Ji, Lianying; Wong, Wai-Choong

2013-01-01

When a body sensor network (BSN) that is linked to the backbone via a wireless network interface moves from one coverage zone to another, a handover is required to maintain network connectivity. This paper presents an optimized handover scheme with movement trend awareness for BSNs. The proposed scheme predicts the future position of a BSN user using the movement trend extracted from the historical position, and adjusts the handover decision accordingly. Handover initiation time is optimized when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized. The proposed handover scheme is simulated in a BSN deployment area in a hospital environment in UK. Simulation results show that the proposed scheme reduces the outage probability by 22% as compared with the existing hysteresis-based handover scheme under the constraint of acceptable handover rate. PMID:23736852

The role of Internet resources in clinical oncology: promises and challenges.

PubMed

Hesse, Bradford W; Greenberg, Alexandra J; Rutten, Lila J Finney

2016-12-01

The Internet is a valuable tool that continues to revolutionize many aspects of our lives; however, the ability to disseminate diverse data across populations and nations presents both opportunities and challenges. Online resources are increasingly used in health care, providing wider access to information for patients, researchers, and clinicians. At the turn of the millennium, the National Cancer Institute (NCI) predicted that Internet-based technologies would create a revolution in communication for oncology professionals and patients with cancer. Herein, findings from the NCI's Health Information National Trends Survey are reviewed to give insight into how Internet trends related to oncology patients are evolving. Future trends are discussed, including examples of 'connected health' in oncology; the spread of mobile and ubiquitous access points to Internet-hosted information; the diffusion of devices, sensors, and apps; the spread of personal data sharing; and an evolution in how networks can support person-centred and family-centred care.

Current and Potential Future Seasonal Trends of Indoor Dwelling Temperature and Likely Health Risks in Rural Southern Africa.

PubMed

Kapwata, Thandi; Gebreslasie, Michael T; Mathee, Angela; Wright, Caradee Yael

2018-05-10

Climate change has resulted in rising temperature trends which have been associated with changes in temperature extremes globally. Attendees of Conference of the Parties (COP) 21 agreed to strive to limit the rise in global average temperatures to below 2 °C compared to industrial conditions, the target being 1.5 °C. However, current research suggests that the African region will be subjected to more intense heat extremes over a shorter time period, with projections predicting increases of 4⁻6 °C for the period 2071⁻2100, in annual average maximum temperatures for southern Africa. Increased temperatures may exacerbate existing chronic ill health conditions such as cardiovascular disease, respiratory disease, cerebrovascular disease, and diabetes-related conditions. Exposure to extreme temperatures has also been associated with mortality. This study aimed to consider the relationship between temperatures in indoor and outdoor environments in a rural residential setting in a current climate and warmer predicted future climate. Temperature and humidity measurements were collected hourly in 406 homes in summer and spring and at two-hour intervals in 98 homes in winter. Ambient temperature, humidity and windspeed were obtained from the nearest weather station. Regression models were used to identify predictors of indoor apparent temperature (AT) and to estimate future indoor AT using projected ambient temperatures. Ambient temperatures will increase by a mean of 4.6 °C for the period 2088⁻2099. Warming in winter was projected to be greater than warming in summer and spring. The number of days during which indoor AT will be categorized as potentially harmful will increase in the future. Understanding current and future heat-related health effects is key in developing an effective surveillance system. The observations of this study can be used to inform the development and implementation of policies and practices around heat and health especially in rural areas of South Africa.

Technology Directions for the 21st Century. Vol. 2

NASA Technical Reports Server (NTRS)

Crimi, Giles F.; Verheggen, Henry; Malinowski, John; Malinowski, Robert; Botta, Robert

1996-01-01

The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter.

Water temperature of streams in the Cook Inlet basin, Alaska, and implications of climate change

USGS Publications Warehouse

Kyle, Rebecca E.; Brabets, Timothy P.

2001-10-02

Water-temperature data from 32 sites in the Cook Inlet Basin, south-central Alaska, indicate various trends that depend on watershed characteristics. Basins with 25 percent or more of their area consisting of glaciers have the coldest water temperatures during the open-water season, mid-May to mid-October. Streams and rivers that drain lowlands have the warmest water temperatures. A model that uses air temperature as input to predict water temperature as output was utilized to simulate future trends in water temperature based on increased air temperatures due to climate warming. Based on the Nash-Sutcliffe coefficient, the model produced acceptable results for 27 sites. For basins with more than 25 percent glacial coverage, the model was not as accurate. Results indicate that 15 sites had a predicted water-temperature change of 3 degrees Celsius or more, a magnitude of change that is considered significant for the incidence of disease in fish populations.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets

NASA Astrophysics Data System (ADS)

Panagos, Panos; Ballabio, Cristiano; Meusburger, Katrin; Spinoni, Jonathan; Alewell, Christine; Borrelli, Pasquale

2017-05-01

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha-1 h-1 yr-1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

Towards estimates of future rainfall erosivity in Europe based on REDES and WorldClim datasets.

PubMed

Panagos, Panos; Ballabio, Cristiano; Meusburger, Katrin; Spinoni, Jonathan; Alewell, Christine; Borrelli, Pasquale

2017-05-01

The policy requests to develop trends in soil erosion changes can be responded developing modelling scenarios of the two most dynamic factors in soil erosion, i.e. rainfall erosivity and land cover change. The recently developed Rainfall Erosivity Database at European Scale (REDES) and a statistical approach used to spatially interpolate rainfall erosivity data have the potential to become useful knowledge to predict future rainfall erosivity based on climate scenarios. The use of a thorough statistical modelling approach (Gaussian Process Regression), with the selection of the most appropriate covariates (monthly precipitation, temperature datasets and bioclimatic layers), allowed to predict the rainfall erosivity based on climate change scenarios. The mean rainfall erosivity for the European Union and Switzerland is projected to be 857 MJ mm ha -1  h -1  yr -1 till 2050 showing a relative increase of 18% compared to baseline data (2010). The changes are heterogeneous in the European continent depending on the future projections of most erosive months (hot period: April-September). The output results report a pan-European projection of future rainfall erosivity taking into account the uncertainties of the climatic models.

Population Trends of Central European Montane Birds Provide Evidence for Adverse Impacts of Climate Change on High-Altitude Species.

PubMed

Flousek, Jiří; Telenský, Tomáš; Hanzelka, Jan; Reif, Jiří

2015-01-01

Climate change is among the most important global threats to biodiversity and mountain areas are supposed to be under especially high pressure. Although recent modelling studies suggest considerable future range contractions of montane species accompanied with increased extinction risk, data allowing to test actual population consequences of the observed climate changes and identifying traits associated to their adverse impacts are very scarce. To fill this knowledge gap, we estimated long-term population trends of montane birds from 1984 to 2011 in a central European mountain range, the Giant Mountains (Krkonoše), where significant warming occurred over this period. We then related the population trends to several species' traits related to the climate change effects. We found that the species breeding in various habitats at higher altitudes had more negative trends than species breeding at lower altitudes. We also found that the species moved upwards as a response to warming climate, and these altitudinal range shifts were associated with more positive population trends at lower altitudes than at higher altitudes. Moreover, long-distance migrants declined more than residents or species migrating for shorter distances. Taken together, these results indicate that the climate change, besides other possible environmental changes, already influences populations of montane birds with particularly adverse impacts on high-altitude species such as water pipit (Anthus spinoletta). It is evident that the alpine species, predicted to undergo serious climatically induced range contractions due to warming climate in the future, already started moving along this trajectory.

Reactor pressure vessel embrittlement: Insights from neural network modelling

NASA Astrophysics Data System (ADS)

Mathew, J.; Parfitt, D.; Wilford, K.; Riddle, N.; Alamaniotis, M.; Chroneos, A.; Fitzpatrick, M. E.

2018-04-01

Irradiation embrittlement of steel pressure vessels is an important consideration for the operation of current and future light water nuclear reactors. In this study we employ an ensemble of artificial neural networks in order to provide predictions of the embrittlement using two literature datasets, one based on US surveillance data and the second from the IVAR experiment. We use these networks to examine trends with input variables and to assess various literature models including compositional effects and the role of flux and temperature. Overall, the networks agree with the existing literature models and we comment on their more general use in predicting irradiation embrittlement.

FORECAST MODEL FOR MODERATE EARTHQUAKES NEAR PARKFIELD, CALIFORNIA.

USGS Publications Warehouse

Stuart, William D.; Archuleta, Ralph J.; Lindh, Allan G.

1985-01-01

The paper outlines a procedure for using an earthquake instability model and repeated geodetic measurements to attempt an earthquake forecast. The procedure differs from other prediction methods, such as recognizing trends in data or assuming failure at a critical stress level, by using a self-contained instability model that simulates both preseismic and coseismic faulting in a natural way. In short, physical theory supplies a family of curves, and the field data select the member curves whose continuation into the future constitutes a prediction. Model inaccuracy and resolving power of the data determine the uncertainty of the selected curves and hence the uncertainty of the earthquake time.

Temporal and spatial variation of hydrological condition in the Ziwu River Basin of the Han River in China

NASA Astrophysics Data System (ADS)

Li, Ziyan; Liu, Dengfeng; Huang, Qiang; Bai, Tao; Zhou, Shuai; Lin, Mu

2018-06-01

The middle route of South-To-North Water Diversion in China transfers water from the Han River and Han-To-Wei Water Diversion project of Shaanxi Province will transfer water from the Ziwu River, which is a tributary of the Han River. In order to gain a better understanding of future changes in the hydrological conditions within the Ziwu River basin, a Mann-Kendall (M-K) trend analysis is coupled with a persistence analysis using the rescaled range analysis (R/S) method. The future change in the hydrological characteristics of the Ziwu River basin is obtained by analysing the change of meteorological factors. The results show that, the future precipitation and potential evaporation are seasonal, and the spatial variation is significant. The proportion of basin area where the spring, summer, autumn and winter precipitation is predicted to continue increase is 0.00, 100.00, 19.00 and 16.00 %, meanwhile, the proportion of basin area that will continue to decrease in the future respectively will be 100.00, 0.00, 81.00 and 74.00 %.The future potential evapotranspiration of the four seasons in the basin shows a decreasing trend. The future water supply situation in the spring and autumn of the Ziwu River basin will degrade, and the future water supply situation in the summer and winter will improve. In addition, the areas with the same water supply situation are relatively concentrated. The results will provide scientific basis for the planning and management of river basin water resources and socio-hydrological processes analysis.

Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes

NASA Astrophysics Data System (ADS)

Vallam, P.; Qin, X. S.

2017-10-01

Anthropogenic-driven climate change would affect the global ecosystem and is becoming a world-wide concern. Numerous studies have been undertaken to determine the future trends of meteorological variables at different scales. Despite these studies, there remains significant uncertainty in the prediction of future climates. To examine the uncertainty arising from using different schemes to downscale the meteorological variables for the future horizons, projections from different statistical downscaling schemes were examined. These schemes included statistical downscaling method (SDSM), change factor incorporated with LARS-WG, and bias corrected disaggregation (BCD) method. Global circulation models (GCMs) based on CMIP3 (HadCM3) and CMIP5 (CanESM2) were utilized to perturb the changes in the future climate. Five study sites (i.e., Alice Springs, Edmonton, Frankfurt, Miami, and Singapore) with diverse climatic conditions were chosen for examining the spatial variability of applying various statistical downscaling schemes. The study results indicated that the regions experiencing heavy precipitation intensities were most likely to demonstrate the divergence between the predictions from various statistical downscaling methods. Also, the variance computed in projecting the weather extremes indicated the uncertainty derived from selection of downscaling tools and climate models. This study could help gain an improved understanding about the features of different downscaling approaches and the overall downscaling uncertainty.

Model Projections of Future Fluvial Sediment Delivery to Major Deltas Under Environmental Change

NASA Astrophysics Data System (ADS)

Darby, S. E.; Dunn, F.; Nicholls, R. J.; Cohen, S.; Zarfl, C.

2017-12-01

Deltas are important hot spots for climate change impacts on which over half a billion people live worldwide. Most of the world's deltas are sinking as a result of natural and anthropogenic subsidence and due to eustatic sea level rise. The ability to predict rates of delta aggradation is therefore critical to assessments of the extent to which sedimentation can potentially offset sea level rise, but our ability to make such predictions is severely hindered by a lack of insight into future trends of the fluvial sediment load supplied to their deltas by feeder watersheds. To address this gap we investigate fluvial sediment fluxes under future environmental change for a selection (47) of the world's major river deltas. Specifically, we employed the numerical model WBMsed to project future variations in mean annual fluvial sediment loads under a range of environmental change scenarios that account for changes in climate, socio-economics and dam construction. Our projections indicate a clear decrease (by 34 to 41% on average, depending on the specific scenario) in future fluvial sediment supply to most of the 47 deltas. These reductions in sediment delivery are driven primarily by anthropogenic disturbances, with reservoir construction being the most influential factor globally. Our results indicate the importance of developing new management strategies for reservoir construction and operation.

Sixteen Trends...Their Profound Impact on Our Future

ERIC Educational Resources Information Center

Marx, Gary

2011-01-01

Seismic Shifts. Future Forces. Call them whatever you'd like. The Sixteen Trends revealed in this benchmark book will have a profound impact on our future. Noted futurist, educator, communicator, executive and leadership counsel, author, and international speaker Gary Marx makes the case for those trends and speculates on their implications for…

Emerging trend prediction in biomedical literature.

PubMed

Moerchen, Fabian; Fradkin, Dmitriy; Dejori, Mathaeus; Wachmann, Bernd

2008-11-06

We present a study on how to predict new emerging trends in the biomedical domain based on textual data. We thereby propose a way of anticipating the transformation of arbitrary information into ground truth knowledge by predicting the inclusion of new terms into the MeSH ontology. We also discuss the preparation of a dataset for the evaluation of emerging trend prediction algorithms that is based on PubMed abstracts and related MeSH terms. The results suggest that early prediction of emerging trends is possible.

Oncology practice trends from the national practice benchmark.

PubMed

Barr, Thomas R; Towle, Elaine L

2012-09-01

In 2011, we made predictions on the basis of data from the National Practice Benchmark (NPB) reports from 2005 through 2010. With the new 2011 data in hand, we have revised last year's predictions and projected for the next 3 years. In addition, we make some new predictions that will be tracked in future benchmarking surveys. We also outline a conceptual framework for contemplating these data based on an ecological model of the oncology delivery system. The 2011 NPB data are consistent with last year's prediction of a decrease in the operating margins necessary to sustain a community oncology practice. With the new data in, we now predict these reductions to occur more slowly than previously forecast. We note an ease to the squeeze observed in last year's trend analysis, which will allow more time for practices to adapt their business models for survival and offer the best of these practices an opportunity to invest earnings into operations to prepare for the inevitable shift away from historic payment methodology for clinical service. This year, survey respondents reported changes in business structure, first measured in the 2010 data, indicating an increase in the percentage of respondents who believe that change is coming soon, but the majority still have confidence in the viability of their existing business structure. Although oncology practices are in for a bumpy ride, things are looking less dire this year for practices participating in our survey.

Qualitative simulation of bathymetric changes due to reservoir sedimentation: A Japanese case study

PubMed Central

Dai, Wenhong; Larson, Magnus; Beebo, Qaid Naamo; Xie, Qiancheng

2017-01-01

Sediment-dynamics modeling is a useful tool for estimating a dam’s lifespan and its cost–benefit analysis. Collecting real data for sediment-dynamics analysis from conventional field survey methods is both tedious and expensive. Therefore, for most rivers, the historical record of data is either missing or not very detailed. Available data and existing tools have much potential and may be used for qualitative prediction of future bathymetric change trend. This study shows that proxy approaches may be used to increase the spatiotemporal resolution of flow data, and hypothesize the river cross-sections and sediment data. Sediment-dynamics analysis of the reach of the Tenryu River upstream of Sakuma Dam in Japan was performed to predict its future bathymetric changes using a 1D numerical model (HEC-RAS). In this case study, only annually-averaged flow data and the river’s longitudinal bed profile at 5-year intervals were available. Therefore, the other required data, including river cross-section and geometry and sediment inflow grain sizes, had to be hypothesized or assimilated indirectly. The model yielded a good qualitative agreement, with an R2 (coefficient of determination) of 0.8 for the observed and simulated bed profiles. A predictive simulation demonstrated that the useful life of the dam would end after the year 2035 (±5 years), which is in conformity with initial detailed estimates. The study indicates that a sediment-dynamic analysis can be performed even with a limited amount of data. However, such studies may only assess the qualitative trends of sediment dynamics. PMID:28384361

Qualitative simulation of bathymetric changes due to reservoir sedimentation: A Japanese case study.

PubMed

Bilal, Ahmed; Dai, Wenhong; Larson, Magnus; Beebo, Qaid Naamo; Xie, Qiancheng

2017-01-01

Sediment-dynamics modeling is a useful tool for estimating a dam's lifespan and its cost-benefit analysis. Collecting real data for sediment-dynamics analysis from conventional field survey methods is both tedious and expensive. Therefore, for most rivers, the historical record of data is either missing or not very detailed. Available data and existing tools have much potential and may be used for qualitative prediction of future bathymetric change trend. This study shows that proxy approaches may be used to increase the spatiotemporal resolution of flow data, and hypothesize the river cross-sections and sediment data. Sediment-dynamics analysis of the reach of the Tenryu River upstream of Sakuma Dam in Japan was performed to predict its future bathymetric changes using a 1D numerical model (HEC-RAS). In this case study, only annually-averaged flow data and the river's longitudinal bed profile at 5-year intervals were available. Therefore, the other required data, including river cross-section and geometry and sediment inflow grain sizes, had to be hypothesized or assimilated indirectly. The model yielded a good qualitative agreement, with an R2 (coefficient of determination) of 0.8 for the observed and simulated bed profiles. A predictive simulation demonstrated that the useful life of the dam would end after the year 2035 (±5 years), which is in conformity with initial detailed estimates. The study indicates that a sediment-dynamic analysis can be performed even with a limited amount of data. However, such studies may only assess the qualitative trends of sediment dynamics.

Lung cancer incidence trends in Uruguay 1990-2014: An age-period-cohort analysis.

PubMed

Alonso, Rafael; Piñeros, Marion; Laversanne, Mathieu; Musetti, Carina; Garau, Mariela; Barrios, Enrique; Bray, Freddie

2018-05-11

Uruguay, a country with one of the highest lung cancer rates worldwide, initiated a series of comprehensive anti-smoking measures in 2005. We assess the tobacco control policies in the context of cohort-driven lung cancer incidence trends over a 25-year period, providing baseline predictions to 2035. Using data from the National Cancer Registry of Uruguay, an age-period-cohort analysis of trends 1990-2014 was performed. The NORDPRED package was used to predict the annual number of new cases of lung cancer and incidence rates up to 2035. In men, age-standardised (world) rates declined from a peak of 165.6 in 1995 to 103.1 by 2014, translating to a 70% reduction in the risk of lung cancer in men born in 1970 relative to the early-1940s. In females, rates increased steadily from 18.3 in 1991 to 30.0 by 2014, with successive increases in risk among generations of women born 1940-1960. There is however evidence of a decline in observed rates in women born recently. Extrapolations of the trends indicate an 8% reduction in the mean number of new lung cancer cases in men by 2035, but a 69% increase in women. Despite observed and predicted reductions in lung cancer incidence in Uruguayan men, rates among women are set to continue to increase, with a large rise in the annual number of female lung cancer diagnoses expected before 2035. There are signals of a diminishing risk among recent generations of women born after 1960. The current analysis provides important baseline information in assessing the future impact of the recent tobacco control initiatives in Uruguay. Copyright © 2018 Elsevier Ltd. All rights reserved.

High School and Youth Trends

MedlinePlus

... High School and Youth Trends Monitoring the Future Survey: High School and Youth Trends Email Facebook Twitter ... December 2017 This year's Monitoring the Future (MTF) survey of drug use and attitudes among 8th, 10th, ...

Mechanistic Lake Modeling to Understand and Predict Heterogeneous Responses to Climate Warming

NASA Astrophysics Data System (ADS)

Read, J. S.; Winslow, L. A.; Rose, K. C.; Hansen, G. J.

2016-12-01

Substantial warming has been documented for of hundreds globally distributed lakes, with likely impacts on ecosystem processes. Despite a clear pattern of widespread warming, thermal responses of individual lakes to climate change are often heterogeneous, with the warming rates of neighboring lakes varying across depths and among seasons. We aggregated temperature observations and parameterized mechanistic models for 9,000 lakes in the U.S. states of Minnesota, Wisconsin, and Michigan to examine broad-scale lake warming trends and among-lake diversity. Daily lake temperature profiles and ice-cover dynamics were simulated using the General Lake Model for the contemporary period (1979-2015) using drivers from the North American Land Data Assimilation System (NLDAS-2) and for contemporary and future periods (1980-2100) using downscaled data from six global circulation models driven by the Representative Climate Pathway 8.5 scenario. For the contemporary period, modeled vs observed summer mean surface temperatures had a root mean squared error of 0.98°C with modeled warming trends similar to observed trends. Future simulations under the extreme 8.5 scenario predicted a median lake summer surface warming rate of 0.57°C/decade until mid-century, with slower rates in the later half of the 21st century (0.35°C/decade). Modeling scenarios and analysis of field data suggest that the lake-specific properties of size, water clarity, and depth are strong controls on the sensitivity of lakes to climate change. For example, a simulated 1% annual decline in water clarity was sufficient to override the effects of climate warming on whole lake water temperatures in some - but not all - study lakes. Understanding heterogeneous lake responses to climate variability can help identify lake-specific features that influence resilience to climate change.

Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions

NASA Astrophysics Data System (ADS)

Van Hooidonk, R. J.

2011-12-01

Future widespread coral bleaching and subsequent mortality has been projected with sea surface temperature (SST) data from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. These model weaknesses likely reduce the skill of coral bleaching predictions, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends and their propagation in predictions. To analyze the relative importance of various types of model errors and biases on coral reef bleaching predictive skill, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from GCMs 20th century simulations to be included in the Intergovernmental Panel on Climate Change (IPCC) 5th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate skill using an objective measure of forecast quality, the Peirce Skill Score (PSS). This methodology will identify frequency bands that are important to predicting coral bleaching and it will highlight deficiencies in these bands in models. The methodology we describe can be used to improve future climate model derived predictions of coral reef bleaching and it can be used to better characterize the errors and uncertainty in predictions.

Forecasting distributions of an aquatic invasive species (Nitellopsis obtusa) under future climate scenarios

PubMed Central

Varela, Sara; Larkin, Daniel J.; Phelps, Nicholas B. D.

2017-01-01

Starry stonewort (Nitellopsis obtusa) is an alga that has emerged as an aquatic invasive species of concern in the United States. Where established, starry stonewort can interfere with recreational uses of water bodies and potentially have ecological impacts. Incipient invasion of starry stonewort in Minnesota provides an opportunity to predict future expansion in order to target early detection and strategic management. We used ecological niche models to identify suitable areas for starry stonewort in Minnesota based on global occurrence records and present-day and future climate conditions. We assessed sensitivity of forecasts to different parameters, using four emission scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5) from five future climate models (i.e., CCSM, GISS, IPSL, MIROC, and MRI). From our niche model analyses, we found that (i) occurrences from the entire range, instead of occurrences restricted to the invaded range, provide more informed models; (ii) default settings in Maxent did not provide the best model; (iii) the model calibration area and its background samples impact model performance; (iv) model projections to future climate conditions should be restricted to analogous environments; and (v) forecasts in future climate conditions should include different future climate models and model calibration areas to better capture uncertainty in forecasts. Under present climate, the most suitable areas for starry stonewort are predicted to be found in central and southeastern Minnesota. In the future, suitable areas for starry stonewort are predicted to shift in geographic range under some future climate models and to shrink under others, with most permutations indicating a net decrease of the species’ suitable range. Our suitability maps can serve to design short-term plans for surveillance and education, while future climate models suggest a plausible reduction of starry stonewort spread in the long-term if the trends in climate warming remain. PMID:28704433

Forecasting distributions of an aquatic invasive species (Nitellopsis obtusa) under future climate scenarios.

PubMed

Romero-Alvarez, Daniel; Escobar, Luis E; Varela, Sara; Larkin, Daniel J; Phelps, Nicholas B D

2017-01-01

Starry stonewort (Nitellopsis obtusa) is an alga that has emerged as an aquatic invasive species of concern in the United States. Where established, starry stonewort can interfere with recreational uses of water bodies and potentially have ecological impacts. Incipient invasion of starry stonewort in Minnesota provides an opportunity to predict future expansion in order to target early detection and strategic management. We used ecological niche models to identify suitable areas for starry stonewort in Minnesota based on global occurrence records and present-day and future climate conditions. We assessed sensitivity of forecasts to different parameters, using four emission scenarios (i.e., RCP 2.6, RCP 4.5, RCP 6, and RCP 8.5) from five future climate models (i.e., CCSM, GISS, IPSL, MIROC, and MRI). From our niche model analyses, we found that (i) occurrences from the entire range, instead of occurrences restricted to the invaded range, provide more informed models; (ii) default settings in Maxent did not provide the best model; (iii) the model calibration area and its background samples impact model performance; (iv) model projections to future climate conditions should be restricted to analogous environments; and (v) forecasts in future climate conditions should include different future climate models and model calibration areas to better capture uncertainty in forecasts. Under present climate, the most suitable areas for starry stonewort are predicted to be found in central and southeastern Minnesota. In the future, suitable areas for starry stonewort are predicted to shift in geographic range under some future climate models and to shrink under others, with most permutations indicating a net decrease of the species' suitable range. Our suitability maps can serve to design short-term plans for surveillance and education, while future climate models suggest a plausible reduction of starry stonewort spread in the long-term if the trends in climate warming remain.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level.

PubMed

Marques-Toledo, Cecilia de Almeida; Degener, Carolin Marlen; Vinhal, Livia; Coelho, Giovanini; Meira, Wagner; Codeço, Claudia Torres; Teixeira, Mauro Martins

2017-07-01

Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.

Evaluation of the impacts of climate change on disease vectors through ecological niche modelling.

PubMed

Carvalho, B M; Rangel, E F; Vale, M M

2017-08-01

Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal 'gold standard' method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.

Predicting temporal trends in total absenteeism rates for civil service employees of a federal public health agency.

PubMed

Spears, D Ross; McNeil, Carrie; Warnock, Eli; Trapp, Jonathan; Oyinloye, Oluremi; Whitehurst, Vanessa; Decker, K C; Chapman, Sandy; Campbell, Morris; Meechan, Paul

2014-06-01

This study evaluates the predictability in temporal absences trends due to all causes (total absenteeism) among employees at a federal agency. The objective is to determine how leave trends vary within the year, and determine whether trends are predictable. Ten years of absenteeism data from an attendance system were analyzed for rates of total absence. Trends over a 10-year period followed predictable and regular patterns during a given year that correspond to major holiday periods. Temporal trends in leave among small, medium, and large facilities compared favorably with the agency as a whole. Temporal trends in total absenteeism rates for an organization can be determined using its attendance system. The ability to predict employee absenteeism rates can be extremely helpful for management in optimizing business performance and ensuring that an organization meets its mission.

Changing climate in Hungary and trends in the annual number of heat stress days

NASA Astrophysics Data System (ADS)

Solymosi, Norbert; Torma, Csaba; Kern, Anikó; Maróti-Agóts, Ákos; Barcza, Zoltán; Könyves, László; Berke, Olaf; Reiczigel, Jenő

2010-07-01

Global climate change can have serious direct effects on animal health and production through heat stress. In Hungary, the number of heat stress days per year (YNHD), i.e., days when the temperature humidity index (THI) is above a specific comfort threshold, has increased in recent years based on observed meteorological data. Between 1973 and 2008, the countrywide average increase in YNHD was 4.1% per year. Climate scenarios based on regional climate models (RCM) were used to predict possible changes in YNHD for the near future (2021-2050) relative to the reference period (1961-1990). This comparison shows that, in Hungary, the 30-year mean of YNHD is expected to increase by between 1 and 27 days, depending on the RCM used. Half of the scenarios investigated in this study predicted that, in large parts of Hungary, YNHD will increase by at least 1 week. However, the increase observed in the past, and that predicted for the near future, is spatially heterogeneous, and areas that currently have large cattle populations are expected to be affected more severely than other regions.

The Ultimate Spitzer Phase Curve Survey: Cross-Planetary Comparison of Heat-Redistribution Efficiencies

NASA Astrophysics Data System (ADS)

Fraine, Jonathan D.; Stevenson, Kevin; Bean, Jacob; Deming, Drake; Fortney, Jonathan; Kataria, Tiffany; Kempton, Eliza; Lewis, Nikole K.; Line, Michael; Morley, Caroline; Rauscher, Emily; Showman, Adam; Feng, Katherina

2018-01-01

Exoplanet phase curves provide a wealth of information about exoplanet atmospheres, including longitudinal constraints on atmospheric composition, thermal structure, and energy transport, that continue to open new doors of scientific inquiry and propel future investigations. The measured heat redistribution efficiency (or ability to transport energy from a planet's highly-irradiated dayside to its eternally-dark nightside) shows considerable variation between exoplanets. Theoretical models predict a correlation between heat redistribution efficiency and planet temperature; however, the latest results are inconsistent with current predictions from 3D atmospheric simulations. We will present preliminary results from a 660-hour Spitzer phase curve survey program that targeted six short-period extrasolar planets. By comparing short periods exoplanets over a range of equilibrium temperatures, we can begin to disentangle the effects of planetary rotation and energy budget on a planet's thermal properties. We will discuss how the measured planet temperature and rotation rate affect the heat redistribution efficiencies, examine trends in the phase curve peak offset, and discuss cloud coverage constraints. Our Spitzer observations will provide valuable information for predicting and interpreting future, JWST-era observations.

IBM Cloud Computing Powering a Smarter Planet

NASA Astrophysics Data System (ADS)

Zhu, Jinzy; Fang, Xing; Guo, Zhe; Niu, Meng Hua; Cao, Fan; Yue, Shuang; Liu, Qin Yu

With increasing need for intelligent systems supporting the world's businesses, Cloud Computing has emerged as a dominant trend to provide a dynamic infrastructure to make such intelligence possible. The article introduced how to build a smarter planet with cloud computing technology. First, it introduced why we need cloud, and the evolution of cloud technology. Secondly, it analyzed the value of cloud computing and how to apply cloud technology. Finally, it predicted the future of cloud in the smarter planet.

Global Trends and Future Warfare (Strategic Insights. Special Issue, October 2011)

DTIC Science & Technology

2011-10-01

CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ...cyberspace are constructed .33 In the early 1970s Daniel Bell in The Coming of Post- Industrial Society predicted something like this when he wrote of...ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School,Center on Contemporary Conflict,Monterey,CA,93943 8. PERFORMING ORGANIZATION REPORT

Virtual reality, robotics, and other wizardry in 21st century trauma care.

PubMed

Maniscalco-Theberge, M E; Elliott, D C

1999-12-01

The former Special Assistant to the Director on Biomedical Technology, Defense Advanced Research Projects Agency (DARPA), COL RM Satava, notes "Predicting the future trends in any profession jeopardizes the credibility of the author." Thus, we have attempted to outline current systems and prototype models in testing phases. Technologic advances will enable enhanced care of trauma patients. In the acute care setting, they also will affect the educational system in theory and practice.

Sensitivity of salmonid freshwater life history in western US streams to future climate conditions.

PubMed

Beer, W Nicholas; Anderson, James J

2013-08-01

We projected effects of mid-21st century climate on the early life growth of Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in western United States streams. Air temperature and snowpack trends projected from observed 20th century trends were used to predict future seasonal stream temperatures. Fish growth from winter to summer was projected with temperature-dependent models of egg development and juvenile growth. Based on temperature data from 115 sites, by mid-21st century, the effects of climate change are projected to be mixed. Fish in warm-region streams that are currently cooled by snow melt will grow less, and fish in suboptimally cool streams will grow more. Relative to 20th century conditions, by mid-21st century juvenile salmonids' weights are expected to be lower in the Columbia Basin and California Central Valley, but unchanged or greater in coastal and mountain streams. Because fish weight affects fish survival, the predicted changes in weight could impact population fitness depending on other factors such as density effects, food quality and quantity changes, habitat alterations, etc. The level of year-to-year variability in stream temperatures is high and our analysis suggests that identifying effects of climate change over the natural variability will be difficult except in a few streams. © 2013 John Wiley & Sons Ltd.

The trend of the multi-scale temporal variability of precipitation in Colorado River Basin

NASA Astrophysics Data System (ADS)

Jiang, P.; Yu, Z.

2011-12-01

Hydrological problems like estimation of flood and drought frequencies under future climate change are not well addressed as a result of the disability of current climate models to provide reliable prediction (especially for precipitation) shorter than 1 month. In order to assess the possible impacts that multi-scale temporal distribution of precipitation may have on the hydrological processes in Colorado River Basin (CRB), a comparative analysis of multi-scale temporal variability of precipitation as well as the trend of extreme precipitation is conducted in four regions controlled by different climate systems. Multi-scale precipitation variability including within-storm patterns and intra-annual, inter-annual and decadal variabilities will be analyzed to explore the possible trends of storm durations, inter-storm periods, average storm precipitation intensities and extremes under both long-term natural climate variability and human-induced warming. Further more, we will examine the ability of current climate models to simulate the multi-scale temporal variability and extremes of precipitation. On the basis of these analyses, a statistical downscaling method will be developed to disaggregate the future precipitation scenarios which will provide a more reliable and finer temporal scale precipitation time series for hydrological modeling. Analysis results and downscaling results will be presented.

Amphibian breeding phenology trends under climate change: predicting the past to forecast the future.

PubMed

Green, David M

2017-02-01

Global climate warming is predicted to hasten the onset of spring breeding by anuran amphibians in seasonal environments. Previous data had indicated that the breeding phenology of a population of Fowler's Toads (Anaxyrus fowleri) at their northern range limit had been progressively later in spring, contrary to generally observed trends in other species. Although these animals are known to respond to environmental temperature and the lunar cycle to commence breeding, the timing of breeding should also be influenced by the onset of overwintering animals' prior upward movement through the soil column from beneath the frost line as winter becomes spring. I used recorded weather data to identify four factors of temperature, rainfall and snowfall in late winter and early spring that correlated with the toads' eventual date of emergence aboveground. Estimated dates of spring emergence of the toads calculated using a predictive model based on these factors, as well as the illumination of the moon, were highly correlated with observed dates of emergence over 24 consecutive years. Using the model to estimate of past dates of spring breeding (i.e. retrodiction) indicated that even three decades of data were insufficient to discern any appreciable phenological trend in these toads. However, by employing weather data dating back to 1876, I detected a significant trend over 140 years towards earlier spring emergence by the toads by less than half a day/decade, while, over the same period of time, average annual air temperature and annual precipitation had both increased. Changes in the springtime breeding phenology for late-breeding species, such as Fowler's Toads, therefore may conform to expectations of earlier breeding under global warming. Improved understanding of the environmental cues that bring organisms out of winter dormancy will enable better interpretation of long-term phenological trends. © 2016 John Wiley & Sons Ltd.

Dissolved-solids sources, loads, yields, and concentrations in streams of the conterminous United States

USGS Publications Warehouse

Anning, David W.; Flynn, Marilyn E.

2014-01-01

Results from the trend analysis and from the SPARROW model indicate that, compared to monitoring stations with no trends or decreasing trends, stations with increasing trends are associated with a smaller percentage of the predicted dissolved-solids load originating from geologic sources, and a larger percentage originating from urban lands and road deicers. Conversely, compared to stations with increasing trends or no trends, stations with decreasing trends have a larger percentage of the predicted dissolved-solids load originating from geologic sources and a smaller percentage originating from urban lands and road deicers. Stations with decreasing trends also have larger percentages of predicted dissolved-solids load originating from cultivated lands and pasture lands, compared to stations with increasing trends or no trends.

Behavioral medicine: a voyage to the future.

PubMed

Keefe, Francis J

2011-04-01

This paper discusses trends and future directions in behavioral medicine. It is divided into three sections. The first briefly reviews key developments in the history of behavioral medicine. The second section highlights trends and future directions in pain research and practice as a way of illustrating future directions for behavioral medicine. Consistent with the biopsychosocial model of pain, this section focuses on trends and future directions in three key areas: biological, psychological, and social. The third section describes recent Society of Behavioral Medicine initiatives designed to address some of the key challenges facing our field as we prepare for the future.

Trends in ischemic heart disease mortality in Korea, 1985-2009: an age-period-cohort analysis.

PubMed

Lee, Hye Ah; Park, Hyesook

2012-09-01

Economic growth and development of medical technology help to improve the average life expectancy, but the western diet and rapid conversions to poor lifestyles lead an increasing risk of major chronic diseases. Coronary heart disease mortality in Korea has been on the increase, while showing a steady decline in the other industrialized countries. An age-period-cohort analysis can help understand the trends in mortality and predict the near future. We analyzed the time trends of ischemic heart disease mortality, which is on the increase, from 1985 to 2009 using an age-period-cohort model to characterize the effects of ischemic heart disease on changes in the mortality rate over time. All three effects on total ischemic heart disease mortality were statistically significant. Regarding the period effect, the mortality rate was decreased slightly in 2000 to 2004, after it had continuously increased since the late 1980s that trend was similar in both sexes. The expected age effect was noticeable, starting from the mid-60's. In addition, the age effect in women was more remarkable than that in men. Women born from the early 1900s to 1925 observed an increase in ischemic heart mortality. That cohort effect showed significance only in women. The future cohort effect might have a lasting impact on the risk of ischemic heart disease in women with the increasing elderly population, and a national prevention policy is need to establish management of high risk by considering the age-period-cohort effect.

Using Future Trends to Inform Planning/Marketing.

ERIC Educational Resources Information Center

Nichols, John V.

1995-01-01

Explores the reasons for incorporating trend analysis of librarianship into library planning and marketing. Key financial and technological issues are reviewed, and the techniques of environmental scanning and alternative scenario-building to incorporate future trends are discussed. (AEF)

Spatial heterogeneity in the timing of birch budburst in response to future climate warming in Ireland

NASA Astrophysics Data System (ADS)

Caffarra, Amelia; Zottele, Fabio; Gleeson, Emily; Donnelly, Alison

2014-05-01

In order to predict the impact of future climate warming on trees it is important to quantify the effect climate has on their development. Our understanding of the phenological response to environmental drivers has given rise to various mathematical models of the annual growth cycle of plants. These models simulate the timing of phenophases by quantifying the relationship between development and its triggers, typically temperature. In addition, other environmental variables have an important role in determining the timing of budburst. For example, photoperiod has been shown to have a strong influence on phenological events of a number of tree species, including Betula pubescens (birch). A recently developed model for birch (DORMPHOT), which integrates the effects of temperature and photoperiod on budburst, was applied to future temperature projections from a 19-member ensemble of regional climate simulations (on a 25 km grid) generated as part of the ENSEMBLES project, to simulate the timing of birch budburst in Ireland each year up to the end of the present century. Gridded temperature time series data from the climate simulations were used as input to the DORMPHOT model to simulate future budburst timing. The results showed an advancing trend in the timing of birch budburst over most regions in Ireland up to 2100. Interestingly, this trend appeared greater in the northeast of the country than in the southwest, where budburst is currently relatively early. These results could have implications for future forest planning, species distribution modeling, and the birch allergy season.

Forecasting sagebrush ecosystem components and greater sage-grouse habitat for 2050: learning from past climate patterns and Landsat imagery to predict the future

USGS Publications Warehouse

Homer, Collin G.; Xian, George Z.; Aldridge, Cameron L.; Meyer, Debra K.; Loveland, Thomas R.; O'Donnell, Michael S.

2015-01-01

Sagebrush (Artemisia spp.) ecosystems constitute the largest single North American shrub ecosystem and provide vital ecological, hydrological, biological, agricultural, and recreational ecosystem services. Disturbances have altered and reduced this ecosystem historically, but climate change may ultimately represent the greatest future risk. Improved ways to quantify, monitor, and predict climate-driven gradual change in this ecosystem is vital to its future management. We examined the annual change of Daymet precipitation (daily gridded climate data) and five remote sensing ecosystem sagebrush vegetation and soil components (bare ground, herbaceous, litter, sagebrush, and shrub) from 1984 to 2011 in southwestern Wyoming. Bare ground displayed an increasing trend in abundance over time, and herbaceous, litter, shrub, and sagebrush showed a decreasing trend. Total precipitation amounts show a downward trend during the same period. We established statistically significant correlations between each sagebrush component and historical precipitation records using a simple least squares linear regression. Using the historical relationship between sagebrush component abundance and precipitation in a linear model, we forecasted the abundance of the sagebrush components in 2050 using Intergovernmental Panel on Climate Change (IPCC) precipitation scenarios A1B and A2. Bare ground was the only component that increased under both future scenarios, with a net increase of 48.98 km2 (1.1%) across the study area under the A1B scenario and 41.15 km2 (0.9%) under the A2 scenario. The remaining components decreased under both future scenarios: litter had the highest net reductions with 49.82 km2 (4.1%) under A1B and 50.8 km2 (4.2%) under A2, and herbaceous had the smallest net reductions with 39.95 km2 (3.8%) under A1B and 40.59 km2 (3.3%) under A2. We applied the 2050 forecast sagebrush component values to contemporary (circa 2006) greater sage-grouse (Centrocercus urophasianus) habitat models to evaluate the effects of potential climate-induced habitat change. Under the 2050 IPCC A1B scenario, 11.6% of currently identified nesting habitat was lost, and 0.002% of new potential habitat was gained, with 4% of summer habitat lost and 0.039% gained. Our results demonstrate the successful ability of remote sensing based sagebrush components, when coupled with precipitation, to forecast future component response using IPCC precipitation scenarios. Our approach also enables future quantification of greater sage-grouse habitat under different precipitation scenarios, and provides additional capability to identify regional precipitation influence on sagebrush component response.

Taking Charge of Your Career Path: A Future Trend of the Workforce

ERIC Educational Resources Information Center

DiMattina, Cara; Ferris, Lisa-Anne

2013-01-01

Workplace trends portray relevant information regarding the present and future of the workforce and its members. These trends signify changes within the workplace regarding performance, retention, satisfaction and many other areas that affect the individual employee, as well as the organization and industry as a whole. Trends can be seen in…

Role of volcanic and anthropogenic aerosols in the recent global surface warming slowdown

NASA Astrophysics Data System (ADS)

Smith, Doug M.; Booth, Ben B. B.; Dunstone, Nick J.; Eade, Rosie; Hermanson, Leon; Jones, Gareth S.; Scaife, Adam A.; Sheen, Katy L.; Thompson, Vikki

2016-10-01

The rate of global mean surface temperature (GMST) warming has slowed this century despite the increasing concentrations of greenhouse gases. Climate model experiments show that this slowdown was largely driven by a negative phase of the Pacific Decadal Oscillation (PDO), with a smaller external contribution from solar variability, and volcanic and anthropogenic aerosols. The prevailing view is that this negative PDO occurred through internal variability. However, here we show that coupled models from the Fifth Coupled Model Intercomparison Project robustly simulate a negative PDO in response to anthropogenic aerosols implying a potentially important role for external human influences. The recovery from the eruption of Mount Pinatubo in 1991 also contributed to the slowdown in GMST trends. Our results suggest that a slowdown in GMST trends could have been predicted in advance, and that future reduction of anthropogenic aerosol emissions, particularly from China, would promote a positive PDO and increased GMST trends over the coming years. Furthermore, the overestimation of the magnitude of recent warming by models is substantially reduced by using detection and attribution analysis to rescale their response to external factors, especially cooling following volcanic eruptions. Improved understanding of external influences on climate is therefore crucial to constrain near-term climate predictions.

Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models

USGS Publications Warehouse

Rodhouse, Thomas J.; Ormsbee, Patricia C.; Irvine, Kathryn M.; Vierling, Lee A.; Szewczak, Joseph M.; Vierling, Kerri T.

2012-01-01

Despite its common status, M. lucifugus was only detected during ∼50% of the surveys in occupied sample units. The overall naïve estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to ∼0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (∼0.04–0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.

Potential relationships between the river discharge and the precipitation in the Jinsha River basin, China

NASA Astrophysics Data System (ADS)

Wang, Gaoxu; Zeng, Xiaofan; Zhao, Na; He, Qifang; Bai, Yiran; Zhang, Ruoyu

2018-02-01

The relationships between the river discharge and the precipitation in the Jinsha River basin are discussed in this study. In addition, the future precipitation trend from 2011-2050 and its potential influence on the river discharge are analysed by applying the CCLM-modelled precipitation. According to the observed river discharge and precipitation, the annual river discharge at the two main hydrological stations displays good correlations with the annual precipitation in the Jinsha River basin. The predicted future precipitation tends to change similarly as the change that occurred during the observation period, whereas the monthly distributions over a year could be more uneven, which is unfavourable for water resources management.

Secular trends in storm-level geomagnetic activity

USGS Publications Warehouse

Love, J.J.

2011-01-01

Analysis is made of K-index data from groups of ground-based geomagnetic observatories in Germany, Britain, and Australia, 1868.0-2009.0, solar cycles 11-23. Methods include nonparametric measures of trends and statistical significance used by the hydrological and climatological research communities. Among the three observatory groups, German K data systematically record the highest disturbance levels, followed by the British and, then, the Australian data. Signals consistently seen in K data from all three observatory groups can be reasonably interpreted as physically meaninginful: (1) geomagnetic activity has generally increased over the past 141 years. However, the detailed secular evolution of geomagnetic activity is not well characterized by either a linear trend nor, even, a monotonic trend. Therefore, simple, phenomenological extrapolations of past trends in solar and geomagnetic activity levels are unlikely to be useful for making quantitative predictions of future trends lasting longer than a solar cycle or so. (2) The well-known tendency for magnetic storms to occur during the declining phase of a sunspot-solar cycles is clearly seen for cycles 14-23; it is not, however, clearly seen for cycles 11-13. Therefore, in addition to an increase in geomagnetic activity, the nature of solar-terrestrial interaction has also apparently changed over the past 141 years. ?? Author(s) 2011.

Std trends in chengalpattu hospital.

PubMed

Krishnamurthy, V R; Ramachandran, V

1996-01-01

A retrospective data analysis was carried out to find the trends in frequency and distribution of different STDs at Chengalpattu during 1988-1994. Of the 4549 patients who attended the clinic 3621 (79.6%) were males and 928 (20.4%) were females. The commonest STD was Chancroid (24.4%) in men and Syphillis (29%) in women. Balanoposthitis (11.4%) ranked third among STDs in males. Though the STD attendance showed a declining trend, most diseases showed a constant distribution. The percentage composition of secondary and latent syphillis, Genital Warts, Genital Herpes and the Non-Venereal group showed an increased composition in recent years. Primary syphillis in females showed a definite declining trend. The HIV sero-positive detection rate was 2.06%. Of the 1116 patients screened for HIV antibody, 23 patients were detected sero-positive. Time Series Regression Analysis was used to predict the number of patients who would attend the STD clinic with various STDs in 1995 and 1996 to help in the understanding of the disease load and pattern in future, in resources management and in developing and evaluating preventive measures.

Potential health implications of water resources depletion and sewage discharges in the Republic of Macedonia.

PubMed

Hristovski, Kiril D; Pacemska-Atanasova, Tatjana; Olson, Larry W; Markovski, Jasmina; Mitev, Trajce

2016-08-01

Potential health implications of deficient sanitation infrastructure and reduced surface water flows due to climate change are examined in the case study of the Republic of Macedonia. Changes in surface water flows and wastewater discharges over the period 1955-2013 were analyzed to assess potential future surface water contamination trends. Simple model predictions indicated a decline in surface water hydrology over the last half century, which caused the surface waters in Macedonia to be frequently dominated by >50% of untreated sewage discharges. The surface water quality deterioration is further supported by an increasing trend in modeled biochemical oxygen demand trends, which correspond well with the scarce and intermittent water quality data that are available. Facilitated by the climate change trends, the increasing number of severe weather events is already triggering flooding of the sewage-dominated rivers into urban and non-urban areas. If efforts to develop a comprehensive sewage collection and treatment infrastructure are not implemented, such events have the potential to increase public health risks and cause epidemics, as in the 2015 case of a tularemia outbreak.

Analysis on the overpressure characterization with respect to depositional environment facies: Case Study in Miri Area, Baram Delta

NASA Astrophysics Data System (ADS)

Mhd Hanapiah, N.; Yusoff, W. I. Wan; Zakariah, M. N. A.

2017-10-01

Overpressure studies in oil and gas exploration and production are carried out in order to mitigate any losses that could happen while drilling. These concerns can be addressed by enhancing the understanding of overpressure characterization in the fields. This research emphasizes in determining the pore pressure trend in Miri area to assist pore pressure prediction for future hydrocarbon exploration and production. Generally, pore pressure trends are related to mechanisms that contribute to the overpressure generation. In the region predominant overpressure are disequilibrium compaction within the prodelta shales meanwhile in outer shelf overpressure generation controlled by fluid expansion in deltaic sequence of inner shelf area. The objective of this research is to analyze the pore pressure profile of wells for determining vertical trends of pore pressure for various depositional environment facies of Miri area. Integration of rock physics and pore pressure analysis and relating the trends to environment depositional environment facies within shale underlying sand interval. Analysis done shows that overpressure top is characterize by depositional environment facies within shale underlying sand interval.

Climate Trends and Farmers' Perceptions of Climate Change in Zambia.

PubMed

Mulenga, Brian P; Wineman, Ayala; Sitko, Nicholas J

2017-02-01

A number of studies use meteorological records to analyze climate trends and assess the impact of climate change on agricultural yields. While these provide quantitative evidence on climate trends and the likely effects thereof, they incorporate limited qualitative analysis of farmers' perceptions of climate change and/or variability. The present study builds on the quantitative methods used elsewhere to analyze climate trends, and in addition compares local narratives of climate change with evidence found in meteorological records in Zambia. Farmers offer remarkably consistent reports of a rainy season that is growing shorter and less predictable. For some climate parameters-notably, rising average temperature-there is a clear overlap between farmers' observations and patterns found in the meteorological records. However, the data do not support the perception that the rainy season used to begin earlier, and we generally do not detect a reported increase in the frequency of dry spells. Several explanations for these discrepancies are offered. Further, we provide policy recommendations to help farmers adapt to climate change/variability, as well as suggestions to shape future climate change policies, programs, and research in developing countries.

The Linear Predictability of Sea Level: A Benchmark

NASA Astrophysics Data System (ADS)

Sonnewald, M.; Wunsch, C.; Heimbach, P.

2016-12-01

A benchmark of linear predictive skill of global sea level is presented, complimenting more complicated model studies of future predictive skill. Sea level is of great socioeconomic interest, as most of the worlds population live by the sea. Currently, the spread in model projections suggests poor predictive skill outside the seasonal cycle. We use 20 years of data from the ECCOv4 state estimate (1992-2012), assessing the variance attributable to the seasons and the linear predictability potential of the deseasoned component of sea level. The Northern Hemisphere has large regions where the seasons make up >90% of the variance, particularly in the western boundary current regions and zonal bands along the equator. The deaseasoned sea level is more dominant in the Southern Hemisphere, particularly in the Southern Ocean. We treat the deseasoned sea level as a weakly stationary random process, whose predictability is given by the covariance structure. Fitting an ARMA(n,m) model, we choose the order using the Akaike and Bayesian Information Criteria (AIC and BIC). The AIC is more appropriate, with generally higher orders chosen and offering slightly more predictive accuracy. Monthly detrended data shows skill generally of the order of a few months, with isolated regions of twelve months or more. With the trend, the predictive skill increases, particularly in the South Pacific. We assess the annually averaged data, although our time-series is too short to assess the variability. There is some predictive skill, which is enhanced if the trend is not removed. A major caveat of our approach is that we test and train our model on the same dataset due to the short duration of available data.

Spatiotemporal patterns of evapotranspiration in response to multiple environmental factors simulated by the Community Land Model

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

Shi, Xiaoying; Mao, Jiafu; Thornton, Peter E

In this study, spatial and temporal patterns of evapotranspiration (ET) over the period of 1982-2008 are investigated and attributed to multiple environmental factors using the Community Land Model version 4 (CLM4). Our results show that CLM4 captures the spatial distribution and interannual variability of ET well when compared to observation-based estimates derived from the FLUXNET network of eddy covariance towers using the model tree ensembles (MTE) approach. We find that climate trends and variability dominate predicted variability in ET. Elevated atmospheric CO2 concentration also plays an important role in modulating the trend of predicted ET over most land areas, andmore » functions as the dominant factor controlling ET changes over North America, South America and Asia regions. Compared to the effect of climate change and CO2 concentration, the roles of other factors such as nitrogen deposition, land use change and aerosol deposition are less pronounced and regionally dependent. For example, the aerosol deposition contribution is the third-most important factor for trends of ET over Europe, while it has the smallest impact on ET trend over other regions. As ET is a dominant component of the terrestrial water cycle, our results suggest that environmental factors like elevated CO2, nitrogen and aerosol depositions, and land use and land cover change, in addition to climate, could have significant impact on future projections of water resources and water cycle dynamics at global and regional scales.« less

Canary in a coal mine: does the plastic surgery market predict the american economy?

PubMed

Wong, Wendy W; Davis, Drew G; Son, Andrew K; Camp, Matthew C; Gupta, Subhas C

2010-08-01

Economic tools have been used in the past to predict the trends in plastic surgery procedures. Since 1992, U.S. cosmetic surgery volumes have increased overall, but the exact relationship between economic downturns and procedural volumes remains elusive. If an economic predicting role can be established from plastic surgery indicators, this could prove to be a very powerful tool. A rolling 3-month revenue average of an eight-plastic surgeon practice and various economic indicators were plotted and compared. An investigation of the U.S. procedural volumes was performed from the American Society of Plastic Surgeons statistics between 1996 and 2008. The correlations of different economic variables with plastic surgery volumes were evaluated. Lastly, search term frequencies were examined from 2004 to July of 2009 to study potential patient interest in major plastic surgery procedures. The self-payment revenue of the plastic surgery group consistently proved indicative of the market trends approximately 1 month in advance. The Standard and Poor's 500, Dow Jones Industrial Average, National Association of Securities Dealers Automated Quotations, and Standard and Poor's Retail Index demonstrated a very close relationship with the income of our plastic surgery group. The frequency of Internet search terms showed a constant level of interest in the patient population despite economic downturns. The data demonstrate that examining plastic surgery revenue can be a useful tool to analyze and possibly predict trends, as it is driven by a market and shows a close correlation to many leading economic indicators. The persisting and increasing interest in plastic surgery suggests hope for a recovering and successful market in the near future.

Past and Future Trends in Automobile Sales

DOT National Transportation Integrated Search

1981-07-01

The report uses the Wharton EFA Motor Vehicle Demand Model (Mark I) and its associates data bases to discuss and analyze past and future trends in the automobile market. Part A analyzes the historical trends, generally covering the 1958-1976 period, ...

Sliding contact fracture of dental ceramics: Principles and validation

PubMed Central

Ren, Linlin; Zhang, Yu

2014-01-01

Ceramic prostheses are subject to sliding contact under normal and tangential loads. Accurate prediction of the onset of fracture at two contacting surfaces holds the key to greater long-term performance of these prostheses. In this study, building on stress analysis of Hertzian contact and considering fracture criteria for linear elastic materials, a constitutive fracture mechanics relation was developed to incorporate the critical fracture load with the contact geometry, coefficient of friction and material fracture toughness. Critical loads necessary to cause fracture under a sliding indenter were calculated from the constitutive equation, and compared with the loads predicted from elastic stress analysis in conjunction with measured critical load for frictionless normal contact—a semi-empirical approach. The major predictions of the models were calibrated with experimentally determined critical loads of current and future dental ceramics after contact with a rigid spherical slider. Experimental results conform with the trends predicted by the models. PMID:24632538

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change.

PubMed

Schut, Antonius G T; Ivits, Eva; Conijn, Jacob G; Ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982-2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17-36% of all productive areas depending on the NDVI metric used. For only 1-2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity.

Trends in hormone use and ovarian cancer incidence in US white and Australian women: implications for the future.

PubMed

Webb, Penelope M; Green, Adèle C; Jordan, Susan J

2017-05-01

To compare trends in ovarian cancer incidence in the USA and Australia in relation to changes in oral contraceptive pill (OCP) and menopausal hormone therapy (MHT) use. US cancer incidence data (1973-2013) were accessed via SEER*Stat; Australian data (1982-2012) were accessed from the Australian Institute of Health and Welfare Cancer Incidence and Mortality books. Age-period-cohort models were constructed to assess trends in ovarian cancer incidence by birth cohort and year of diagnosis. Ovarian cancer rates were increasing until the cohorts born around 1918 in the USA and 1923 in Australia who were the first to use the OCP. They then declined dramatically across subsequent cohorts such that rates for the 1968 cohort were about half those of women born 45 years earlier; however, there are early suggestions that this decline may not continue in more recent cohorts. In contrast, despite the large reduction in MHT use, there was no convincing evidence that ovarian cancer incidence rates in either country were lower after 2002 than would have been expected based on the declining trend from 1985. The major driver of ovarian cancer incidence rates appears to be the OCP. This means that when those women born since the late 1960s (who have used the OCP at high rates from an early age) reach their 60s and 70s, incidence rates are likely to stop falling and may even increase with changes in the prevalence of other factors such as tubal ligation and obesity. Forward predictions based on past trends may thus underestimate future rates and numbers of women likely to be affected.

The future of postgraduate training.

PubMed

Walsh, Kieran

2014-01-01

Improvements to postgraduate training have included newly designed postgraduate curricula, new forms of delivery of learning, more valid and reliable assessments, and more rigorous evaluation of training programmes. All these changes have been necessary and have now started to settle in. Now therefore is an appropriate time to look to the future of postgraduate training. Predicting the future is difficult in any course of life-however an examination of recent trends is often a good place to start. In this regard the recent trend to start to produce more doctors and healthcare professionals of the type that the population needs is likely to continue for some time to come. Medical education will also need to be more flexible in the future. The more flexible that training programmes are, the more likely that we will have experts that are sufficiently flexible to meet a range of different challenges throughout the rest of their careers. Medical education will also become more seamless in the future (at present there are probably too many major milestones and transitions in medical education). In the future educators will make much more use of technology enhanced learning, e-learning and simulation in postgraduate medical education. There will also be more pressure on postgraduate training programmes to offer value for money and to be able to demonstrate such value for money. Postgraduate medical education of the future will also be a more personalised and adaptive experience. It will be far more based on learners' individual needs and will be more responsive to those needs. Lastly postgraduate education will be much more closely supervised than it has been in the past. A common theme running through these changes will be patient centredness. This will mean safer training programmes that produce the type of doctors that patients and populations need.

Ecological investigations: vegetation studies, preliminary findings

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

Olgeirson, E.R.; Martin, R.B.

1978-09-01

The objective of the vegetation studies conducted on the research site is to produce a descriptive data base that can be applied to determinations of carrying capacity of the site and surrounding area. Additional information obtained about parameters that influence vegetation growth and maintenance of soil nutrients, and moisture and temperature regimes help define dynamic relationships that must be understood to effect successful revegetation and habitat rehabilitation. The descriptive vegetation baseline also provides a point of departure for design of future monitoring programs, and predictive models and strategies to be used in dealing with impact mitigation; in turn, monitoring programsmore » and predictive modeling form the bases for making distinctions between natural trends and man-induced perturbations.« less

USGS Arctic Ocean Carbon Cruise 2012: Field Activity L-01-12-AR to collect carbon data in the Arctic Ocean, August-September 2012

USGS Publications Warehouse

Robbins, Lisa L.; Wynn, Jonathan; Knorr, Paul O.; Onac, Bogdan; Lisle, John T.; McMullen, Katherine Y.; Yates, Kimberly K.; Byrne, Robert H.; Liu, Xuewu

2014-01-01

During the cruise, underway continuous and discrete water samples were collected, and discrete water samples were collected at stations to document the carbonate chemistry of the Arctic waters and quantify the saturation state of seawater with respect to calcium carbonate. These data are critical for providing baseline information in areas where no data have existed prior and will also be used to test existing models and predict future trends.

Risk identification and prediction of coal workers' pneumoconiosis in Kailuan Colliery Group in China: a historical cohort study.

PubMed

Shen, Fuhai; Yuan, Juxiang; Sun, Zhiqian; Hua, Zhengbing; Qin, Tianbang; Yao, Sanqiao; Fan, Xueyun; Chen, Weihong; Liu, Hongbo; Chen, Jie

2013-01-01

Prior to 1970, coal mining technology and prevention measures in China were poor. Mechanized coal mining equipment and advanced protection measures were continuously installed in the mines after 1970. All these improvements may have resulted in a change in the incidence of coal workers' pneumoconiosis (CWP). Therefore, it is important to identify the characteristics of CWP today and trends for the incidence of CWP in the future. A total of 17,023 coal workers from the Kailuan Colliery Group were studied. A life-table method was used to calculate the cumulative incidence rate of CWP and predict the number of new CWP patients in the future. The probability of developing CWP was estimated by a multilayer perceptron artificial neural network for each coal worker without CWP. The results showed that the cumulative incidence rates of CWP for tunneling, mining, combining, and helping workers were 31.8%, 27.5%, 24.2%, and 2.6%, respectively, during the same observation period of 40 years. It was estimated that there would be 844 new CWP cases among 16,185 coal workers without CWP within their life expectancy. There would be 273.1, 273.1, 227.6, and 69.9 new CWP patients in the next <10, 10-, 20-, and 30- years respectively in the study cohort within their life expectancy. It was identified that coal workers whose risk probabilities were over 0.2 were at high risk for CWP, and whose risk probabilities were under 0.1 were at low risk. The present and future incidence trends of CWP remain high among coal workers. We suggest that coal workers at high risk of CWP undergo a physical examination for pneumoconiosis every year, and the coal workers at low risk of CWP be examined every 5 years.

Testicular cancer incidence to rise by 25% by 2025 in Europe? Model-based predictions in 40 countries using population-based registry data.

PubMed

Le Cornet, Charlotte; Lortet-Tieulent, Joannie; Forman, David; Béranger, Rémi; Flechon, Aude; Fervers, Béatrice; Schüz, Joachim; Bray, Freddie

2014-03-01

Testicular cancer mainly affects White Caucasian populations, accounts for 1% of all male cancers, and is frequently the most common malignancy among young adult men. In light of the escalating rates of testicular cancer incidence in Europe, and in support of future planning to ensure optimal care of patients with what can be a curable disease, we predict the future burden in 40 European countries around 2025. Current observed trends were extrapolated with the NORDPRED model to estimate the future burden of testicular cancer in the context of changes in risk versus changes in demographics. Despite substantial heterogeneity in the rates, the vast majority of European countries will see an increasing burden over the next two decades. We estimate there will be 23,000 new cases of testicular cancer annually in Europe by 2025, a rise of 24% from 2005. Some of the most rapid increases in testicular cancer are observed in Croatia, Slovenia, Italy and Spain, and a transition is underway, whereby recent attenuations and declines in rates in certain high-risk countries in Northern Europe contrast with the increasing trends and escalating burden in Southern Europe. According to our estimates for 2025, around one in 100 men will be diagnosed with the disease annually in the highest risk countries of Europe (Croatia, Slovenia and Norway). Elucidating the key determinants of testicular cancer and the equitable provision of optimal care for patients across Europe are priorities given the steady rise in the number of patients by 2025, and an absence of primary prevention opportunities. None. Copyright © 2013 Elsevier Ltd. All rights reserved.

Past and future trends in cancer and biomedical research: a comparison between Egypt and the world using PubMed-indexed publications.

PubMed

Zeeneldin, Ahmed Abdelmabood; Taha, Fatma Mohamed; Moneer, Manar

2012-07-10

PubMed is a free web literature search service that contains almost 21 millions of abstracts and publications with almost 5 million user queries daily. The purposes of the study were to compare trends in PubMed-indexed cancer and biomedical publications from Egypt to that of the world and to predict future publication volumes. The PubMed was searched for the biomedical publications between 1991 and 2010 (publications dates). Affiliation was then limited to Egypt. Further limitation was applied to cancer, human and animal publications. Poisson regression model was used for prediction of future number of publications between 2011 and 2020. Cancer publications contributed 23% to biomedical publications both for Egypt and the world. Egyptian biomedical and cancer publications contributed about 0.13% to their world counterparts. This contribution was more than doubled over the study period. Egyptian and world's publications increased from year to year with rapid rise starting the year 2003. Egyptian as well as world's human cancer publications showed the highest increases. Egyptian publications had some peculiarities; they showed some drop at the years 1994 and 2002 and apart from the decline in the animal: human ratio with time, all Egyptian publications in the period 1991-2000 were significantly more than those in 2001-2010 (P < 0.05 for all). By 2020, Egyptian biomedical and cancer publications will increase by 158.7% and 280% relative to 2010 to constitute 0.34% and 0.17% of total PubMed publications, respectively. The Egyptian contribution to world's biomedical and cancer publications needs significant improvements through research strategic planning, setting national research priorities, adequate funding and researchers' training.

Past and future trends in cancer and biomedical research: a comparison between Egypt and the World using PubMed-indexed publications

PubMed Central

2012-01-01

Background PubMed is a free web literature search service that contains almost 21 millions of abstracts and publications with almost 5 million user queries daily. The purposes of the study were to compare trends in PubMed-indexed cancer and biomedical publications from Egypt to that of the world and to predict future publication volumes. Methods The PubMed was searched for the biomedical publications between 1991 and 2010 (publications dates). Affiliation was then limited to Egypt. Further limitation was applied to cancer, human and animal publications. Poisson regression model was used for prediction of future number of publications between 2011 and 2020. Results Cancer publications contributed 23% to biomedical publications both for Egypt and the world. Egyptian biomedical and cancer publications contributed about 0.13% to their world counterparts. This contribution was more than doubled over the study period. Egyptian and world’s publications increased from year to year with rapid rise starting the year 2003. Egyptian as well as world’s human cancer publications showed the highest increases. Egyptian publications had some peculiarities; they showed some drop at the years 1994 and 2002 and apart from the decline in the animal: human ratio with time, all Egyptian publications in the period 1991-2000 were significantly more than those in 2001-2010 (P < 0.05 for all). By 2020, Egyptian biomedical and cancer publications will increase by 158.7% and 280% relative to 2010 to constitute 0.34% and 0.17% of total PubMed publications, respectively. Conclusions The Egyptian contribution to world’s biomedical and cancer publications needs significant improvements through research strategic planning, setting national research priorities, adequate funding and researchers’ training. PMID:22780908

Late Holocene droughts in the Fertile Crescent recorded in a speleothem from northern Iraq

NASA Astrophysics Data System (ADS)

Flohr, Pascal; Fleitmann, Dominik; Zorita, Eduardo; Sadekov, Aleksey; Cheng, Hai; Bosomworth, Matt; Edwards, Lawrence; Matthews, Wendy; Matthews, Roger

2017-02-01

Droughts have had large impacts on past and present societies. High-resolution paleoclimate data are essential to place recent droughts in a meaningful historical context and to predict regional future changes with greater accuracy. Such records, however, are very scarce in the Middle East in general, and the Fertile Crescent in particular. Here we present a 2400 year long speleothem-based multiproxy record from Gejkar Cave in northern Iraq. Oxygen and carbon isotopes and magnesium are faithful recorders of effective moisture. The new Gejkar record not only shows that droughts in 1998-2000 and 2007-2010, which have been argued to be a contributing factor to Syrian civil war, were extreme compared to the current mean climate, but they were also superimposed on a long-term aridification trend that already started around or before 950 C.E. (Common Era). This long-term trend is not captured by tree ring records and climate models, emphasizing the importance of using various paleoclimate proxy data to evaluate and improve climate models and to correctly inform policy makers about future hydroclimatic changes in this drought-prone region.

Novelty and Foreseeing Research Trends: The Case of Astrophysics and Astronomy

NASA Astrophysics Data System (ADS)

Varga, Attila

2018-05-01

Metrics based on reference lists of research articles or on keywords have been used to predict citation impact. The concept behind such metrics is that original ideas stem from the reconfiguration of the structure of past knowledge, and therefore atypical combinations in the reference lists, keywords, or classification codes indicate future high-impact research. The current paper serves as an introduction to this line of research for astronomers and also addresses some of the methodological questions in this field of innovation studies. It is still not clear if the choice of particular indexes, such as references to journals, articles, or specific bibliometric classification codes affects the relationship between atypical combinations and citation impact. To understand more aspects of the innovation process, a new metric has been devised to measure to what extent researchers are able to anticipate the changing combinatorial trends of the future. Results show that the variant of the latter anticipation scores that is based on paper combinations is a good predictor of the future citation impact of scholarly works. The study also shows that the effects of tested indexes vary with the aggregation levels that were used to construct them. A detailed analysis of combinatorial novelty in the field reveals that certain sub-fields of astronomy and astrophysics have different roles in the reconfiguration of past knowledge.

Insensitivity of evapotranspiration to seasonal rainfall distribution directs climate change impacts at water yield

NASA Astrophysics Data System (ADS)

Montaldo, N.; Oren, R.

2017-12-01

Over the past century, climate change is affecting precipitation regimes across the world. In the Mediterranean regions there is a persistent trend of precipitation and runoff decreases, generating a desertification process. Given the past winter precipitation shifts, the impacts on evapotranspiration (ET) need to be carefully evaluated, and the compelling question is what will be the impact of future climate change scenarios (predicting changes of precipitation and vapor pressure deficit, VPD) on evapotranspiration and water yield? Looking for the key elements of the climate change that are impacting annual ET, we investigate main climate conditions (e.g. precipitation and VPD) and basin physiographic properties contributing to annual ET. We propose a simplified model for annual ET predictions that accounts for the strong meteo seasonality typical of Mediterranean climates, using the steady state assumption of the basin water balance at mean annual scale. We investigate the Sardinia case study because the position of the island of Sardinia in the center of the western Mediterranean Sea basin and its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. Sardinian runoff decreased drastically over the 1975-2010 period, with mean yearly runoff reduced by more than 40% compared to the previous 1922-1974 period, and most yearly runoff in the Sardinian basins (70% on average) is produced by winter precipitation due to the seasonality typical of the Mediterranean climate regime. The use of our proposed model allows to predict future ET and water yield using future climate scenarios. We use the future climate scenarios predicted by Global climate models (GCM) in the Fifth Assessment report of the Intergovernmental Panel on Climate Change (IPCC), and we select most reliable models testing the past GCM predictions with historical data. Contrasting shifts of precipitation (both positive and negative) are predicted in the future scenarios by GCMs but these changes will produce significant changes (level of significance > 90%) only in runoff and not in ET. Surprisingly, we show that ET is insensitive to intra-annual rainfall distribution changes, and is insensitive to VPD scenario changes.

Future irrigation expansion outweigh groundwater recharge gains from climate change in semi-arid India.

PubMed

Sishodia, Rajendra P; Shukla, Sanjay; Wani, Suhas P; Graham, Wendy D; Jones, James W

2018-09-01

Simultaneous effects of future climate and irrigation intensification on surface and groundwater systems are not well understood. Efforts are needed to understand the future groundwater availability and associated surface flows under business-as-usual management to formulate policy changes to improve water sustainability. We combine measurements with integrated modeling (MIKE SHE/MIKE11) to evaluate the effects of future climate (2040-2069), with and without irrigation expansion, on water levels and flows in an agricultural watershed in low-storage crystalline aquifer region of south India. Demand and supply management changes, including improved efficiency of irrigation water as well as energy uses, were evaluated. Increased future rainfall (7-43%, from 5 Global Climate Models) with no further expansion of irrigation wells increased the groundwater recharge (10-55%); however, most of the recharge moved out of watershed as increased baseflow (17-154%) with a small increase in net recharge (+0.2mm/year). When increased rainfall was considered with projected increase in irrigation withdrawals, both hydrologic extremes of well drying and flooding were predicted. A 100-year flow event was predicted to be a 5-year event in the future. If irrigation expansion follows the historical trends, earlier and more frequent well drying, a source of farmers' distress in India, was predicted to worsen in the future despite the recharge gains from increased rainfall. Storage and use of excess flows, improved irrigation efficiency with flood to drip conversion in 25% of irrigated area, and reduced energy subsidy (free electricity for 3.5h compared to 7h/day; $1 billion savings) provided sufficient water savings to support future expansion in irrigated areas while mitigating well drying as well as flooding. Reductions in energy subsidy to fund the implementation of economically desirable (high benefit-cost ratio) demand (drip irrigation) and supply (water capture and storage) management was recommended to achieve a sustainable food-water-energy nexus in semi-arid regions. Copyright © 2018 Elsevier B.V. All rights reserved.

Contrasting effects of climate change on rabbit populations through reproduction.

PubMed

Tablado, Zulima; Revilla, Eloy

2012-01-01

Climate change is affecting many physical and biological processes worldwide. Anticipating its effects at the level of populations and species is imperative, especially for organisms of conservation or management concern. Previous studies have focused on estimating future species distributions and extinction probabilities directly from current climatic conditions within their geographical ranges. However, relationships between climate and population parameters may be so complex that to make these high-level predictions we need first to understand the underlying biological processes driving population size, as well as their individual response to climatic alterations. Therefore, the objective of this study is to investigate the influence that climate change may have on species population dynamics through altering breeding season. We used a mechanistic model based on drivers of rabbit reproductive physiology together with demographic simulations to show how future climate-driven changes in breeding season result in contrasting rabbit population trends across Europe. In the Iberian Peninsula, where rabbits are a native species of high ecological and economic value, breeding seasons will shorten and become more variable leading to population declines, higher extinction risk, and lower resilience to perturbations. Whereas towards north-eastern countries, rabbit numbers are expected to increase through longer and more stable reproductive periods, which augment the probability of new rabbit invasions in those areas. Our study reveals the type of mechanisms through which climate will cause alterations at the species level and emphasizes the need to focus on them in order to better foresee large-scale complex population trends. This is especially important in species like the European rabbit whose future responses may aggravate even further its dual keystone/pest problematic. Moreover, this approach allows us to predict not only distribution shifts but also future population status and growth, and to identify the demographic parameters on which to focus to mitigate global change effects.

Contrasting Effects of Climate Change on Rabbit Populations through Reproduction

PubMed Central

Tablado, Zulima; Revilla, Eloy

2012-01-01

Background Climate change is affecting many physical and biological processes worldwide. Anticipating its effects at the level of populations and species is imperative, especially for organisms of conservation or management concern. Previous studies have focused on estimating future species distributions and extinction probabilities directly from current climatic conditions within their geographical ranges. However, relationships between climate and population parameters may be so complex that to make these high-level predictions we need first to understand the underlying biological processes driving population size, as well as their individual response to climatic alterations. Therefore, the objective of this study is to investigate the influence that climate change may have on species population dynamics through altering breeding season. Methodology/Principal Findings We used a mechanistic model based on drivers of rabbit reproductive physiology together with demographic simulations to show how future climate-driven changes in breeding season result in contrasting rabbit population trends across Europe. In the Iberian Peninsula, where rabbits are a native species of high ecological and economic value, breeding seasons will shorten and become more variable leading to population declines, higher extinction risk, and lower resilience to perturbations. Whereas towards north-eastern countries, rabbit numbers are expected to increase through longer and more stable reproductive periods, which augment the probability of new rabbit invasions in those areas. Conclusions/Significance Our study reveals the type of mechanisms through which climate will cause alterations at the species level and emphasizes the need to focus on them in order to better foresee large-scale complex population trends. This is especially important in species like the European rabbit whose future responses may aggravate even further its dual keystone/pest problematic. Moreover, this approach allows us to predict not only distribution shifts but also future population status and growth, and to identify the demographic parameters on which to focus to mitigate global change effects. PMID:23152836

The past is the key to the future

USGS Publications Warehouse

Doe, B.R.

1983-01-01

A new major frontier of geological research, which was initiated in the 1970's, involves predicting future geologic trends or events through study of the present and past, rather than trying to understand the past, often using what one knows about the present. Like most scientific frontiers, this one began from practical considerations-environmental concerns. The lack of formal recognition of this frontier results from fragmentation among many Federal agencies and highly focused mission-oriented programs (e.g., earthquake prediction, CO2, nuclear-energy safety, etc.). Most programs aim to predict only the next 50-100 years, but much longer periods of the past need to be studied to do this. Nuclear-waste disposal has sometimes been considered in terms of the next million years, a period of time permitting significant and broad geologic changes. Decreasing public interest in environmental concerns relegates many questions from the realm of applied research back to that of basic research. Most of these questions are so fascinating, however, that the frontier is still worth pursuing. Such questions include whether a phenomenon will or will not take place and the rates at which it can develop (e.g., how fast do rifts form, how fast can a caldera event begin, and how quickly can a glacial maximum arrive?). Common elements of all studies include the historic record, trends in the Quaternary, analogues in various periods of the geologic time scale, and allowance for phenomena never experienced before. Other examples of studies include the Cretaceous as a period of a climatic extreme, an especially interesting time period; establishing the amount of paleocloudiness, a particularly challenging and important research area; acid rain as a possible new phenomenon. Geochemistry has much to contribute to this frontier science. ?? 1983.

And then the internet happened: Thoughts on the future of concept mapping.

PubMed

McLinden, Daniel

2017-02-01

Over 25 years ago, in the late twentieth century, concept mapping emerged as a mixed method approach to inquiry that enables a group of people to conceptualize their thinking about a specific topic. Since then, the application of concept mapping has spread widely and an easy prediction for the future is that this trend is likely to continue; a more important and greater challenge is to think about the ways in which concept mapping may and should evolve. Discussed here are thoughts about the future of concept mapping including some predictions of likely directions and suggestions for new possibilities. Thoughts on the future are grounded in concept mapping applications that have emerged and gained ground in recent years; these include exploring wicked problems in communities and integrating concept mapping with other methods of inquiry. Thoughts on the future are also grounded in the social and cultural milieu in which we find ourselves at this time. The influence of social media and internet technologies has led to the emergence peer production and crowdsourcing as approaches to co-create information, knowledge, products and services. These tactics may create fertile ground for the further spread of concept mapping. This same collaborative milieu has produced the open software movement which in turn, offers opportunities to enhancing the methodology of concept mapping. Copyright © 2016 Elsevier Ltd. All rights reserved.

Oncology Practice Trends From the National Practice Benchmark

PubMed Central

Barr, Thomas R.; Towle, Elaine L.

2012-01-01

In 2011, we made predictions on the basis of data from the National Practice Benchmark (NPB) reports from 2005 through 2010. With the new 2011 data in hand, we have revised last year's predictions and projected for the next 3 years. In addition, we make some new predictions that will be tracked in future benchmarking surveys. We also outline a conceptual framework for contemplating these data based on an ecological model of the oncology delivery system. The 2011 NPB data are consistent with last year's prediction of a decrease in the operating margins necessary to sustain a community oncology practice. With the new data in, we now predict these reductions to occur more slowly than previously forecast. We note an ease to the squeeze observed in last year's trend analysis, which will allow more time for practices to adapt their business models for survival and offer the best of these practices an opportunity to invest earnings into operations to prepare for the inevitable shift away from historic payment methodology for clinical service. This year, survey respondents reported changes in business structure, first measured in the 2010 data, indicating an increase in the percentage of respondents who believe that change is coming soon, but the majority still have confidence in the viability of their existing business structure. Although oncology practices are in for a bumpy ride, things are looking less dire this year for practices participating in our survey. PMID:23277766

Dynamic Forecasting of Zika Epidemics Using Google Trends

PubMed Central

Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks. PMID:28060809

Modelling analysis and prediction of women javelin throw results in the years 1946 – 2013

PubMed Central

Grycmann, P; Maszczyk, A; Socha, T; Wilk, M; Zając, T; Przednowek, K

2015-01-01

The main goals of our study of the women’s javelin throw were twofold:. first, to analyse the dynamics of female javelin throw results variability as a function of time (time period 1946-2014), second, to create a predictive model of the results during the upcoming 4 years. The study material consisted of databases covering the female track and field events obtained from the International Association of Athletics Federations. Prior to predicting the magnitude of results change dynamics in the time to follow, the adjustment of trend function to empirical data was tested using the coefficients of convergence. Phase II of the investigation consisted of the construction of predictive models. The greatest decreases in result indexes were noted in 2000 (9.4%), 2005-2006 (8.7%) and 2009 (7.4%). The trend increase was only noted in the years 2006-2008. In general, until 1998 the mean result improved by 54.6% (100% - results of 1946) whereas from 1999 through 2011 the result only increased by 1.3%. Based on data and results variability analysis it might be presumed that, in the nearest future (2015-2018), results variability will increase by approximately 9.7%. Percent improvement of javelin throw distance calculated on the basis of the 1999 raw input data is 1.4% (end of 2014). PMID:28479665

Dynamic Forecasting of Zika Epidemics Using Google Trends.

PubMed

Teng, Yue; Bi, Dehua; Xie, Guigang; Jin, Yuan; Huang, Yong; Lin, Baihan; An, Xiaoping; Feng, Dan; Tong, Yigang

2017-01-01

We developed a dynamic forecasting model for Zika virus (ZIKV), based on real-time online search data from Google Trends (GTs). It was designed to provide Zika virus disease (ZVD) surveillance and detection for Health Departments, and predictive numbers of infection cases, which would allow them sufficient time to implement interventions. In this study, we found a strong correlation between Zika-related GTs and the cumulative numbers of reported cases (confirmed, suspected and total cases; p<0.001). Then, we used the correlation data from Zika-related online search in GTs and ZIKV epidemics between 12 February and 20 October 2016 to construct an autoregressive integrated moving average (ARIMA) model (0, 1, 3) for the dynamic estimation of ZIKV outbreaks. The forecasting results indicated that the predicted data by ARIMA model, which used the online search data as the external regressor to enhance the forecasting model and assist the historical epidemic data in improving the quality of the predictions, are quite similar to the actual data during ZIKV epidemic early November 2016. Integer-valued autoregression provides a useful base predictive model for ZVD cases. This is enhanced by the incorporation of GTs data, confirming the prognostic utility of search query based surveillance. This accessible and flexible dynamic forecast model could be used in the monitoring of ZVD to provide advanced warning of future ZIKV outbreaks.

Resting-state functional connectivity predicts longitudinal pain symptom change in urologic chronic pelvic pain syndrome: a MAPP network study.

PubMed

Kutch, Jason J; Labus, Jennifer S; Harris, Richard E; Martucci, Katherine T; Farmer, Melissa A; Fenske, Sonja; Fling, Connor; Ichesco, Eric; Peltier, Scott; Petre, Bogdan; Guo, Wensheng; Hou, Xiaoling; Stephens, Alisa J; Mullins, Chris; Clauw, Daniel J; Mackey, Sean C; Apkarian, A Vania; Landis, J Richard; Mayer, Emeran A

2017-06-01

Chronic pain symptoms often change over time, even in individuals who have had symptoms for years. Studying biological factors that predict trends in symptom change in chronic pain may uncover novel pathophysiological mechanisms and potential therapeutic targets. In this study, we investigated whether brain functional connectivity measures obtained from resting-state functional magnetic resonance imaging at baseline can predict longitudinal symptom change (3, 6, and 12 months after scan) in urologic chronic pelvic pain syndrome. We studied 52 individuals with urologic chronic pelvic pain syndrome (34 women, 18 men) who had baseline neuroimaging followed by symptom tracking every 2 weeks for 1 year as part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network study. We found that brain functional connectivity can make a significant prediction of short-term (3 month) pain reduction with 73.1% accuracy (69.2% sensitivity and 75.0% precision). In addition, we found that the brain regions with greatest contribution to the classification were preferentially aligned with the left frontoparietal network. Resting-state functional magnetic resonance imaging measures seemed to be less informative about 6- or 12-month symptom change. Our study provides the first evidence that future trends in symptom change in patients in a state of chronic pain may be linked to functional connectivity within specific brain networks.

Research on the Direct Carbon Emission Forecast of CHINA'S Provincial Residents Based on Neural Network

NASA Astrophysics Data System (ADS)

Zhang, T.; Zhou, B.; Zhou, S.; Yan, W.

2018-04-01

Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It's particularly important to make accurate predictions of carbon emissions based on current carbon emissions. This paper accounted out the direct consumption of carbon emissions data from 1995 to 2014 about 30 provinces (the data of Tibet, Hong Kong, Macao and Taiwan is missing) and the whole of China. And it selected the optimal models from BP, RBF and Elman neural network for direct carbon emission prediction, what aim was to select the optimal prediction method and explore the possibility of reaching the peak of residents direct carbon emissions of China in 2030. Research shows that: 1) Residents' direct carbon emissions per capita of all provinces showed an upward trend in 20 years. 2) The accuracy of the prediction results by Elman neural network model is higher than others and more suitable for carbon emission data projections. 3) With the situation of residents' direct carbon emissions free development, the direct carbon emissions will show a fast to slow upward trend in the next few years and began to flatten after 2020, and the direct carbon emissions of per capita will reach the peak in 2032. This is also confirmed that China is expected to reach its peak in carbon emissions by 2030 in theory.

Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions

NASA Astrophysics Data System (ADS)

van Hooidonk, R.; Huber, M.

2012-03-01

Future widespread coral bleaching and subsequent mortality has been projected using sea surface temperature (SST) data derived from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. Such weaknesses most likely reduce the accuracy of predicting coral bleaching, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends, and their propagation in predictions. To analyze the relative importance of various types of model errors and biases in predicting coral bleaching, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from 24 GCMs 20th century simulations included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate accuracy using an objective measure of forecast quality, the Peirce skill score (PSS). Major findings are that: (1) predictions are most sensitive to the seasonal cycle and inter-annual variability in the ENSO 24-60 months frequency band and (2) because models tend to understate the seasonal cycle at reef locations, they systematically underestimate future bleaching. The methodology we describe can be used to improve the accuracy of bleaching predictions by characterizing the errors and uncertainties involved in the predictions.

Sustainability of winter tourism in a changing climate over Kashmir Himalaya.

PubMed

Dar, Reyaz Ahmad; Rashid, Irfan; Romshoo, Shakil Ahmad; Marazi, Asif

2014-04-01

Mountain areas are sensitive to climate change. Implications of climate change can be seen in less snow, receding glaciers, increasing temperatures, and decreasing precipitation. Climate change is also a severe threat to snow-related winter sports such as skiing, snowboarding, and cross-country skiing. The change in climate will put further pressure on the sensitive environment of high mountains. Therefore, in this study, an attempt has been made to know the impact of climate change on the snow precipitation, water resources, and winter tourism in the two famous tourist resorts of the Kashmir Valley. Our findings show that winters are getting prolonged with little snow falls on account of climate change. The average minimum and maximum temperatures are showing statistically significant increasing trends for winter months. The precipitation is showing decreasing trends in both the regions. A considerable area in these regions remains under the snow and glacier cover throughout the year especially during the winter and spring seasons. However, time series analysis of LandSat MODIS images using Normalized Difference Snow Index shows a decreasing trend in snow cover in both the regions from past few years. Similarly, the stream discharge, comprising predominantly of snow- and glacier-melt, is showing a statistically significant declining trend despite the melting of these glaciers. The predicted futuristic trends of temperature from Predicting Regional Climates for Impact Studies regional climate model are showing an increase which may enhance snow-melting in the near future posing a serious threat to the sustainability of winter tourism in the region. Hence, it becomes essential to monitor the changes in temperature and snow cover depletion in these basins in order to evaluate their effect on the winter tourism and water resources in the region.

Mai Population

PubMed

Sripan, Patumrat; Sriplung, Hutcha; Pongnikorn, Donsuk; Virani, Shama; Bilheem, Surichai; Chaisaengkhaum, Udomlak; Maneesai, Puttachart; Waisri, Narate; Hanpragopsuk, Chirapong; Tansiri, Panrada; Khamsan, Varunee; Poungsombat, Malisa; Mawoot, Aumnart; Chitapanarux, Imjai

2017-05-01

Objectives: This study was conducted to determine incidence trends of female breast cancer according to age groups and to predict future change in Chiang Mai women through 2028. Method: Data were collected from all hospitals in Chiang Mai in northern Thailand, from 1989 through 2013, and used to investigate effects of age, year of diagnosis (period) and year of birth (cohort) on female breast cancer incidences using an age-period-cohort model. This model features geometric cut trends to predict change by young (<40 years), middle-aged (40-59) and elderly (≥60) age groups. Result: Of 5, 417 female breast cancer patients with a median age of 50 years (interquartile range: 43 to 59 years), 15%, 61% and 24% were young, middle-aged and elderly, respectively. Seventy nine percent of cancer cases in this study were detected at advanced stage. The trend in stage classification showed an increase in percentage of early stage and a decrease in metastatic cancers. Linear trends for cohort and period were not found in young females but were observed in middle-aged and elderly groups. Age-standardized rates (ASR) can be expected to remain stable around 6.8 per 100,000 women-years in young females. In the other age groups, the ASR trends were calculated to increase and reach peaks in 2024 of 120.2 and 138.2 per 100,000 women-years, respectively. Conclusion: Cohort effects or generation-specific effects, such as life style factors and the year of diagnosis (period) might have impacted on increased incidence in women aged over 40 years but not those under 40 years. A budget should be provided for treatment facilities and strategies to detect early stage cancers. The cost effectiveness of screening measures i.e. mammographic screening may need to be reconsidered for women age over 40 years. Creative Commons Attribution License

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

PubMed

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol; Jang, Huisu

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.

Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets

PubMed Central

Pyo, Sujin; Lee, Jaewook; Cha, Mincheol

2017-01-01

The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction. PMID:29136004

The Art and Science of Long-Range Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Hathaway, David H.; Wilson, Robert M.

2006-01-01

Long-range space weather forecasts are akin to seasonal forecasts of terrestrial weather. We don t expect to forecast individual events but we do hope to forecast the underlying level of activity important for satellite operations and mission pl&g. Forecasting space weather conditions years or decades into the future has traditionally been based on empirical models of the solar cycle. Models for the shape of the cycle as a function of its amplitude become reliable once the amplitude is well determined - usually two to three years after minimum. Forecasting the amplitude of a cycle well before that time has been more of an art than a science - usually based on cycle statistics and trends. Recent developments in dynamo theory -the theory explaining the generation of the Sun s magnetic field and the solar activity cycle - have now produced models with predictive capabilities. Testing these models with historical sunspot cycle data indicates that these predictions may be highly reliable one, or even two, cycles into the future.

Industry trends point toward bright future

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

Kliewer, G.

Examination of the trends for the past 5 yr shows increasing soundness in the international petroleum industry. U.S. figures alone have a mixed complexion, with imports, exports, crude prices, and demand rising steadily. U.S. crude oil production, however, increased through 1970, but leveled off last year. Decreases occurred during 1971 in the number of rigs working, gas producers, and wildcats drilled. Trends in Free World drilling and production (outside the U.S. and Canada) have been upward and are expected to continue expanding generally through the year 2000. World petroleum demand, the key to this activity, has risen steadily, and allmore » forecasts predict increases of around 3.5% a yr for the next 15 yr. Included in this outlook is a booming natural gas use, which comes as a result of worldwide emphasis on clean-burning, nonpolluting fuels. Competition among energy sources may not materialize. Instead, a mix of all available energy sources will be needed to meet the demand. Tabular data provide complete details.« less

Time trends in age at onset of anorexia nervosa and bulimia nervosa.

PubMed

Favaro, Angela; Caregaro, Lorenza; Tenconi, Elena; Bosello, Romina; Santonastaso, Paolo

2009-12-01

This study aims to explore the time trends in age at onset of anorexia nervosa and bulimia nervosa. The sample was composed of 1,666 anorexia nervosa subjects and 793 bulimia nervosa subjects (according to DSM-IV criteria) without previous anorexia nervosa consecutively referred to our outpatient unit in the period between 1985 and 2008. Time trends in illness onset were analyzed according to the year of birth of subjects. In both anorexia nervosa and bulimia nervosa, age at onset showed a significant decrease according to year of birth. A regression model showed a significant independent effect of socioeconomic status, age at menarche, and number of siblings in predicting age at onset lower than 16 years. Age at onset of anorexia nervosa and bulimia nervosa is decreasing in younger generations. The implications of our findings in terms of long-term outcome remain to be understood. Biologic and sociocultural factors explaining this phenomenon need to be explored in future studies. Copyright 2009 Physicians Postgraduate Press, Inc.

Dynamic modeling of Tampa Bay urban development using parallel computing

USGS Publications Warehouse

Xian, G.; Crane, M.; Steinwand, D.

2005-01-01

Urban land use and land cover has changed significantly in the environs of Tampa Bay, Florida, over the past 50 years. Extensive urbanization has created substantial change to the region's landscape and ecosystems. This paper uses a dynamic urban-growth model, SLEUTH, which applies six geospatial data themes (slope, land use, exclusion, urban extent, transportation, hillside), to study the process of urbanization and associated land use and land cover change in the Tampa Bay area. To reduce processing time and complete the modeling process within an acceptable period, the model is recoded and ported to a Beowulf cluster. The parallel-processing computer system accomplishes the massive amount of computation the modeling simulation requires. SLEUTH calibration process for the Tampa Bay urban growth simulation spends only 10 h CPU time. The model predicts future land use/cover change trends for Tampa Bay from 1992 to 2025. Urban extent is predicted to double in the Tampa Bay watershed between 1992 and 2025. Results show an upward trend of urbanization at the expense of a decline of 58% and 80% in agriculture and forested lands, respectively.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Response of non-point source pollutant loads to climate change in the Shitoukoumen reservoir catchment.

PubMed

Zhang, Lei; Lu, Wenxi; An, Yonglei; Li, Di; Gong, Lei

2012-01-01

The impacts of climate change on streamflow and non-point source pollutant loads in the Shitoukoumen reservoir catchment are predicted by combining a general circulation model (HadCM3) with the Soil and Water Assessment Tool (SWAT) hydrological model. A statistical downscaling model was used to generate future local scenarios of meteorological variables such as temperature and precipitation. Then, the downscaled meteorological variables were used as input to the SWAT hydrological model calibrated and validated with observations, and the corresponding changes of future streamflow and non-point source pollutant loads in Shitoukoumen reservoir catchment were simulated and analyzed. Results show that daily temperature increases in three future periods (2010-2039, 2040-2069, and 2070-2099) relative to a baseline of 1961-1990, and the rate of increase is 0.63°C per decade. Annual precipitation also shows an apparent increase of 11 mm per decade. The calibration and validation results showed that the SWAT model was able to simulate well the streamflow and non-point source pollutant loads, with a coefficient of determination of 0.7 and a Nash-Sutcliffe efficiency of about 0.7 for both the calibration and validation periods. The future climate change has a significant impact on streamflow and non-point source pollutant loads. The annual streamflow shows a fluctuating upward trend from 2010 to 2099, with an increase rate of 1.1 m(3) s(-1) per decade, and a significant upward trend in summer, with an increase rate of 1.32 m(3) s(-1) per decade. The increase in summer contributes the most to the increase of annual load compared with other seasons. The annual NH (4) (+) -N load into Shitoukoumen reservoir shows a significant downward trend with a decrease rate of 40.6 t per decade. The annual TP load shows an insignificant increasing trend, and its change rate is 3.77 t per decade. The results of this analysis provide a scientific basis for effective support of decision makers and strategies of adaptation to climate change.

Predicted trends in the supply and demand of veterinarians in Japan.

PubMed

Kimura, S; Shinkawa, S; Mago, J; Yamamoto, M; Sakai, M; Sugisaki, T; Karaki, H; Sugiura, K

2008-12-01

Currently in Japan, there are 32,000 active veterinarians, mainly engaged in small and large animal practice and public animal health and public health services. In the face of the notable increase in recent years in the proportion of female students enrolled in veterinary schools and in the number of households with companion animals, a model was developed to predict the supply and demand of veterinarians toward 2040 in Japan. Surveys were conducted on sampled households and veterinarians to estimate input variables used in the supply and demand model. From this data it is predicted that there might be somewhere between a shortage of 1,000 to an over-supply of 3,700 veterinarians engaged in small animal practice in 2040. This, however, will depend on possible changes in the number of visits made to veterinarians by small animal owners and the efficiency of practices in the future. The model also predicts that there will be a shortage of around 1,100 veterinarians in large animal practice in 2040. Considering the many assumptions made to estimate the input variables used in the model, the results of this study do not provide definitive conclusions, but provide a base for discussions on what will be needed in the veterinary profession in the future.

Concepts and tools for predictive modeling of microbial dynamics.

PubMed

Bernaerts, Kristel; Dens, Els; Vereecken, Karen; Geeraerd, Annemie H; Standaert, Arnout R; Devlieghere, Frank; Debevere, Johan; Van Impe, Jan F

2004-09-01

Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.

Global elimination of leprosy by 2020: are we on track?

PubMed

Blok, David J; De Vlas, Sake J; Richardus, Jan Hendrik

2015-10-22

Every year more than 200,000 new leprosy cases are registered globally. This number has been fairly stable over the past 8 years. WHO has set a target to interrupt the transmission of leprosy globally by 2020. The aim of this study is to investigate whether this target, interpreted as global elimination, is feasible given the current control strategy. We focus on the three most important endemic countries, India, Brazil and Indonesia, which together account for more than 80 % of all newly registered leprosy cases. We used the existing individual-based model SIMCOLEP to predict future trends of leprosy incidence given the current control strategy in each country. SIMCOLEP simulates the spread of M. leprae in a population that is structured in households. Current control consists of passive and active case detection, and multidrug therapy (MDT). Predictions of leprosy incidence were made for each country as well as for one high-endemic region within each country: Chhattisgarh (India), Pará State (Brazil) and Madura (Indonesia). Data for model quantification came from: National Leprosy Elimination Program (India), SINAN database (Brazil), and Netherlands Leprosy Relief (Indonesia). Our projections of future leprosy incidence all show a downward trend. In 2020, the country-level leprosy incidence has decreased to 6.2, 6.1 and 3.3 per 100,000 in India, Brazil and Indonesia, respectively, meeting the elimination target of less than 10 per 100,000. However, elimination may not be achieved in time for the high-endemic regions. The leprosy incidence in 2020 is predicted to be 16.2, 21.1 and 19.3 per 100,000 in Chhattisgarh, Pará and Madura, respectively, and the target may only be achieved in another 5 to 10 years. Our predictions show that although country-level elimination is reached by 2020, leprosy is likely to remain a problem in the high-endemic regions (i.e. states, districts and provinces with multimillion populations), which account for most of the cases in a country.

Shifts in frog size and phenology: Testing predictions of climate change on a widespread anuran using data from prior to rapid climate warming.

PubMed

Sheridan, Jennifer A; Caruso, Nicholas M; Apodaca, Joseph J; Rissler, Leslie J

2018-01-01

Changes in body size and breeding phenology have been identified as two major ecological consequences of climate change, yet it remains unclear whether climate acts directly or indirectly on these variables. To better understand the relationship between climate and ecological changes, it is necessary to determine environmental predictors of both size and phenology using data from prior to the onset of rapid climate warming, and then to examine spatially explicit changes in climate, size, and phenology, not just general spatial and temporal trends. We used 100 years of natural history collection data for the wood frog, Lithobates sylvaticus with a range >9 million km 2 , and spatially explicit environmental data to determine the best predictors of size and phenology prior to rapid climate warming (1901-1960). We then tested how closely size and phenology changes predicted by those environmental variables reflected actual changes from 1961 to 2000. Size, phenology, and climate all changed as expected (smaller, earlier, and warmer, respectively) at broad spatial scales across the entire study range. However, while spatially explicit changes in climate variables accurately predicted changes in phenology, they did not accurately predict size changes during recent climate change (1961-2000), contrary to expectations from numerous recent studies. Our results suggest that changes in climate are directly linked to observed phenological shifts. However, the mechanisms driving observed body size changes are yet to be determined, given the less straightforward relationship between size and climate factors examined in this study. We recommend that caution be used in "space-for-time" studies where measures of a species' traits at lower latitudes or elevations are considered representative of those under future projected climate conditions. Future studies should aim to determine mechanisms driving trends in phenology and body size, as well as the impact of climate on population density, which may influence body size.

The Contribution of Soils to North America's Current and Future Climate

NASA Astrophysics Data System (ADS)

Mayes, M. A.; Reed, S.; Thornton, P. E.; Lajtha, K.; Bailey, V. L.; Shrestha, G.; Jastrow, J. D.; Torn, M. S.

2015-12-01

This presentation will cover key aspects of the terrestrial soil carbon cycle in North America and the US for the upcoming State of the Carbon Cycle Report (SOCCRII). SOCCRII seeks to summarize how natural processes and human interactions affect the global carbon cycle, how socio-economic trends affect greenhouse gas concentrations in the atmosphere, and how ecosystems are influenced by and respond to greenhouse gas emissions, management decisions, and concomitant climate effects. Here, we will summarize the contemporary understanding of carbon stocks, fluxes, and drivers in the soil ecosystem compartment. We will highlight recent advances in modeling the magnitude of soil carbon stocks and fluxes, as well as the importance of remaining uncertainties in predicting soil carbon cycling and its relationship with climate. Attention will be given to the role of uncertainties in predicting future fluxes from soils, and how those uncertainties vary by region and ecosystem. We will also address how climate feedbacks and management decisions can enhance or minimize future climatic effects based on current understanding and observations, and will highlight select research needs to improve our understanding of the balance of carbon in soils in North America.

Language and hope in schizophrenia-spectrum disorders.

PubMed

Bonfils, Kelsey A; Luther, Lauren; Firmin, Ruth L; Lysaker, Paul H; Minor, Kyle S; Salyers, Michelle P

2016-11-30

Hope is integral to recovery for those with schizophrenia. Considering recent advancements in the examination of clients' lexical qualities, we were interested in how clients' words reflect hope. Using computerized lexical analysis, we examined social, emotion, and future words' relations to hope and its pathways and agency components. Forty-five clients provided detailed narratives about their life and mental illness. Transcripts were analyzed using the Linguistic Inquiry and Word Count program (LIWC), which assigns words to categories (e.g., "anxiety") based on a pre-existing dictionary. Correlations and linear multiple regression were used to examine relationships between lexical qualities and hope. Hope and its subcomponents had significant or trending bivariate correlations in expected directions with several emotion-related word categories (anger and sadness) but were not associated with expected categories such as social words, positive emotions, optimism, achievement, and future words. In linear multiple regressions, no LIWC variable significantly predicted hope agency, but anger words significantly predicted both total hope and hope pathways. Our findings indicate lexical analysis tools can be used to investigate recovery-oriented concepts such as hope, and results may inform clinical practice. Future research should aim to replicate our findings in larger samples. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Impacts of climate change on the trends of extreme rainfall indices and values of maximum precipitation at Olimpiyat Station, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Nigussie, Tewodros Assefa; Altunkaynak, Abdusselam

2018-03-01

In this study, extreme rainfall indices of Olimpiyat Station were determined from reference period (1971-2000) and future period (2070-2099) daily rainfall data projected using the HadGEM2-ES and GFDL-ESM2M global circulation models (GCMs) and downscaled by the RegCM4.3.4 regional model under the Representative Concentration Pathway RCP4.5 and RCP8.5 scenarios. The Mann-Kendall (MK) trend statistics was used to detect trends in the indices of each group, and the nonparametric Wilcoxon signed ranks test was employed to identify the presence of differences among the values of the rainfall indices of the three groups. Moreover, the peaks-over-threshold (POT) method was used to undertake frequency analysis and estimate the maximum 24-h rainfall values of various return periods. The results of the M-K-based trend analyses showed that there are insignificant increasing trends in most of the extreme rainfall indices. However, based on the Wilcoxon signed ranks test, the values of the extreme rainfall indices determined for the future period, particularly under RCP8.5, were found to be significantly different from the corresponding values determined for the reference period. The maximum 24-h rainfall amounts of the 50-year return period of the future period under RCP4.5 of the HadGEM2-ES and GFDL-ESM2M GCMs were found to be larger (by 5.85%) than the corresponding value of the reference period by 5.85 and 21.43%, respectively. The results also showed that the maximum 24-h rainfall amount under RCP8.5 of both the HadGEM2-ES and GFDL-ESM2M GCMs was found to be greater (34.33 and 12.18%, respectively, for the 50-year return period) than the reference period values. This may increase the risk of flooding in Ayamama Watershed, and thus, studying the effects of the predicted amount of rainfall under the RCP8.5 scenario on the flooding risk of Ayamama Watershed and devising management strategies are recommended to enhance the design and implementation of adaptation measures.

Trends in continental temperature and humidity directly linked to ocean warming.

PubMed

Byrne, Michael P; O'Gorman, Paul A

2018-05-08

In recent decades, the land surface has warmed substantially more than the ocean surface, and relative humidity has fallen over land. Amplified warming and declining relative humidity over land are also dominant features of future climate projections, with implications for climate-change impacts. An emerging body of research has shown how constraints from atmospheric dynamics and moisture budgets are important for projected future land-ocean contrasts, but these ideas have not been used to investigate temperature and humidity records over recent decades. Here we show how both the temperature and humidity changes observed over land between 1979 and 2016 are linked to warming over neighboring oceans. A simple analytical theory, based on atmospheric dynamics and moisture transport, predicts equal changes in moist static energy over land and ocean and equal fractional changes in specific humidity over land and ocean. The theory is shown to be consistent with the observed trends in land temperature and humidity given the warming over ocean. Amplified land warming is needed for the increase in moist static energy over drier land to match that over ocean, and land relative humidity decreases because land specific humidity is linked via moisture transport to the weaker warming over ocean. However, there is considerable variability about the best-fit trend in land relative humidity that requires further investigation and which may be related to factors such as changes in atmospheric circulations and land-surface properties.

Putting Temperature and Oxygen Thresholds of Marine Animals in Context of Environmental Change: A Regional Perspective for the Scotian Shelf and Gulf of St. Lawrence

PubMed Central

2016-01-01

We conducted a literature review of reported temperature, salinity, pH, depth and oxygen preferences and thresholds of important marine species found in the Gulf of St. Lawrence and Scotian Shelf region. We classified 54 identified fishes and macroinvertebrates as important either because they support a commercial fishery, have threatened or at risk status, or meet one of the following criteria: bycatch, baitfish, invasive, vagrant, important for ecosystem energy transfer, or predators or prey of the above species. The compiled data allow an assessment of species-level impacts including physiological stress and mortality given predictions of future ocean physical and biogeochemical conditions. If an observed, multi-decadal oxygen trend on the central Scotian Shelf continues, a number of species will lose favorable oxygen conditions, experience oxygen-stress, or disappear due to insufficient oxygen in the coming half-century. Projected regional trends and natural variability are both large, and natural variability will act to alternately amplify and dampen anthropogenic changes. When estimates of variability are included with the trend, species encounter unfavourable oxygen conditions decades sooner. Finally, temperature and oxygen thresholds of adult Atlantic wolffish (Anarhichas lupus) and adult Atlantic cod (Gadus morhua) are assessed in the context of a potential future scenario derived from high-resolution ocean models for the central Scotian Shelf. PMID:27997536

Implications of sedimentological studies for environmental pollution assessment and management: Examples from fluvial systems in North Queensland and Western Australia

NASA Astrophysics Data System (ADS)

Eyre, Bradley; McConchie, David

1993-05-01

Sedimentology is of increasing importance in environmental research, particularly environmental pollution studies, where past trends in environmental processes need to be combined with data on present conditions to predict likely future changes—the past and present as a key to the future. Two examples are used to illustrate the role of sedimentology in assessing the influence of major processes on the transport, accumulation, deposition and modification of contaminants in fluvial/estuarine systems and in developing environmental management plans. Example 1 shows that when assessing nutrient behaviour in fluvial/estuarine depositional settings, it is important to examine the partitioning of phosphorus between grain size fractions to evaluate the sedimentological processes which control the dispersion and trapping of these contaminants. Example 2 shows that in studies of anthropogenic metal inputs to modern depositional settings, lateral and stratigraphic trends in sediment texture and mineralogy should be examined, in addition to trends in metal loads and evaluation of the prevailing physical, chemical and biological processes that may influence metal mobility and dispersion. Clearly, basic sedimentological data should form part of any assessment of potentially contaminated sites and part of investigations into the dispersion and trapping of contaminants in fluvial systems. These data are also required for rational environmental management to ensure that planning decisions are compatible with natural environmental constraints.

Putting Temperature and Oxygen Thresholds of Marine Animals in Context of Environmental Change: A Regional Perspective for the Scotian Shelf and Gulf of St. Lawrence.

PubMed

Brennan, Catherine E; Blanchard, Hannah; Fennel, Katja

2016-01-01

We conducted a literature review of reported temperature, salinity, pH, depth and oxygen preferences and thresholds of important marine species found in the Gulf of St. Lawrence and Scotian Shelf region. We classified 54 identified fishes and macroinvertebrates as important either because they support a commercial fishery, have threatened or at risk status, or meet one of the following criteria: bycatch, baitfish, invasive, vagrant, important for ecosystem energy transfer, or predators or prey of the above species. The compiled data allow an assessment of species-level impacts including physiological stress and mortality given predictions of future ocean physical and biogeochemical conditions. If an observed, multi-decadal oxygen trend on the central Scotian Shelf continues, a number of species will lose favorable oxygen conditions, experience oxygen-stress, or disappear due to insufficient oxygen in the coming half-century. Projected regional trends and natural variability are both large, and natural variability will act to alternately amplify and dampen anthropogenic changes. When estimates of variability are included with the trend, species encounter unfavourable oxygen conditions decades sooner. Finally, temperature and oxygen thresholds of adult Atlantic wolffish (Anarhichas lupus) and adult Atlantic cod (Gadus morhua) are assessed in the context of a potential future scenario derived from high-resolution ocean models for the central Scotian Shelf.

Projection of future runoff change using climate elasticity method derived from Budyko framework in major basins across China

NASA Astrophysics Data System (ADS)

Xing, Wanqiu; Wang, Weiguang; Zou, Shan; Deng, Chao

2018-03-01

This study established a climate elasticity method based on Budyko hypothesis and enhanced it by selecting the most effective Budyko-type formula to strengthen the runoff change prediction reliability. The spatiotemporal variations in hydrologic variables (i.e., runoff, precipitation and potential evaporation) during historical period were revealed first and the climate elasticities of runoff were investigated. The proposed climate elasticity method was also applied to project the spatiotemporal variations in future runoff and its key influencing factors in 35 watersheds across China. Wherein, the future climate series were retrieved by consulting the historical series, informed by four global climate models (GCMs) under representative concentration pathways from phase five of the Coupled Model Intercomparison Project. Wang-Tang equation was selected as the optimal Budyko-type equation for its best ability in reproducing the runoff change (with a coefficient of determination and mean absolute error of 0.998 and 1.36 mm, respectively). Observed runoff presents significant decreasing trends in the northern and increasing trends in the southern regions of China, and generally its change is identified to be more sensitive to climatic variables in Hai River Basin and lower Yellow River Basin. Compared to the runoff during the reference period, positive change rates in the north and negative change rates in the south of China in the mid-21st century can be practically generalized from the majority of GCMs projections. This maybe resulted from the increasing precipitation, especially in parts of northern basins. Meanwhile, GCMs project a consistently upward trend in potential evaporation although significant decreasing trends occur in the majority of catchments for the historical period. The results indicate that climate change will possibly bring some changes to the water resources over China in the mid-21st century and some countermeasures of water resources planning and management should be taken.

The 'wired' universe of organic chemistry.

PubMed

Grzybowski, Bartosz A; Bishop, Kyle J M; Kowalczyk, Bartlomiej; Wilmer, Christopher E

2009-04-01

The millions of reactions performed and compounds synthesized by organic chemists over the past two centuries connect to form a network larger than the metabolic networks of higher organisms and rivalling the complexity of the World Wide Web. Despite its apparent randomness, the network of chemistry has a well-defined, modular architecture. The network evolves in time according to trends that have not changed since the inception of the discipline, and thus project into chemistry's future. Analysis of organic chemistry using the tools of network theory enables the identification of most 'central' organic molecules, and for the prediction of which and how many molecules will be made in the future. Statistical analyses based on network connectivity are useful in optimizing parallel syntheses, in estimating chemical reactivity, and more.

Determining the Current and Future Health of Low-Latitude Andean Glaciers Using an Equilibrium Line Altitude Model and Hypsometric Data from the Randolph Glacier Inventory

NASA Astrophysics Data System (ADS)

Malone, A.; MacAyeal, D. R.

2015-12-01

Mountain glaciers have been described as the water towers of world, and for many populations in the low-latitude South American Andes, glacial runoff is vital for agricultural, industrial, and basic water needs. Previous studies of low-latitude Andean glaciers suggest a precarious future due to contemporary warming. These studies have looked at trends in freezing level heights or observations of contemporary retreat. However, regional-scale understanding of low-latitude glacial responses to present and future climate change is limited, in part due to incomplete information about the extent and elevation distribution of low-latitude glaciers. The recently published Randolph Glacier Inventory (RGI) (5.0) provides the necessary information about the size and elevation distribution of low-latitude glaciers to begin such studies. We determine the contemporary equilibrium line altitudes (ELAs) for low-latitude Andean glaciers in the RGI, using a numerical energy balance ablation model driven with reanalysis and gridded data products. Contemporary ELAs tend to fall around the peak of the elevation histogram, with an exception being the southern-most outer tropical glaciers whose modeled ELAs tend to be higher than the elevation histogram for that region (see below figure). Also, we use the linear tends in temperature and precipitation from the contemporary climatology to extrapolate 21stcentury climate forcings. Modeled ELAs by the middle on the century are universally predicted to rise, with outer tropical ELAs rising more than the inner tropical glaciers. These trends continue through the end of the century. Finally, we explore how climate variables and parameters in our numerical model may vary for different warming scenarios from United Nation's IPCC AR5 report. We quantify the impacts of these changes on ELAs for various climate change trajectories. These results support previous work on the precarious future of low latitude Andean glaciers, while providing a richer understanding of the glacial impacts of contemporary and future warming. Also, this work provides analysis of processes and feedbacks between different climate variables important to glacier mass balances in a warming world, improving predictions for the fate of low-latitude Andean glaciers.

Climate Change, Extreme Weather Events, and Fungal Disease Emergence and Spread

NASA Technical Reports Server (NTRS)

Tucker, Compton J.; Yager, Karina; Anyamba, Assaf; Linthicum, Kenneth J.

2011-01-01

Empirical evidence from multiple sources show the Earth has been warming since the late 19th century. More recently, evidence for this warming trend is strongly supported by satellite data since the late 1970s from the cryosphere, atmosphere, oceans, and land that confirms increasing temperature trends and their consequences (e.g., reduced Arctic sea ice, rising sea level, ice sheet mass loss, etc.). At the same time, satellite observations of the Sun show remarkably stable solar cycles since the late 1970s, when direct observations of the Sun's total solar irradiance began. Numerical simulation models, driven in part by assimilated satellite data, suggest that future-warming trends will lead to not only a warmer planet, but also a wetter and drier climate depending upon location in a fashion consistent with large-scale atmospheric processes. Continued global warming poses new opportunities for the emergence and spread of fungal disease, as climate systems change at regional and global scales, and as animal and plant species move into new niches. Our contribution to this proceedings is organized thus: First, we review empirical evidence for a warming Earth. Second, we show the Sun is not responsible for the observed warming. Third, we review numerical simulation modeling results that project these trends into the future, describing the projected abiotic environment of our planet in the next 40 to 50 years. Fourth, we illustrate how Rift Valley fever outbreaks have been linked to climate, enabling a better understanding of the dynamics of these diseases, and how this has led to the development of an operational predictive outbreak model for this disease in Africa. Fifth, We project how this experience may be applicable to predicting outbreaks of fungal pathogens in a warming world. Lastly, we describe an example of changing species ranges due to climate change, resulting from recent warming in the Andes and associated glacier melt that has enabled amphibians to colonize higher elevation lakes, only to be followed shortly by the emergence of fungal disease in the new habitats.

Expected changes in future agro-climatological conditions in Northeast Thailand and their differences between general circulation models

NASA Astrophysics Data System (ADS)

Masaki, Yoshimitsu; Ishigooka, Yasushi; Kuwagata, Tsuneo; Goto, Shinkichi; Sawano, Shinji; Hasegawa, Toshihiro

2011-12-01

We have studied future changes in the atmospheric and hydrological environments in Northeast Thailand from the viewpoint of risk assessment of future cultural environments in crop fields. To obtain robust and reliable estimation for future climate, ten general circulation models under three warming scenarios, B1, A1B, and A2, were used in this study. The obtained change trends show that daily maximum air temperature and precipitation will increase by 2.6°C and 4.0%, respectively, whereas soil moisture will decrease by c.a. 1% point in volumetric water content at the end of this century under the A1B scenario. Seasonal contrasts in precipitation will intensify: precipitation increases in the rainy season and precipitation decreases in the dry season. Soil moisture will slightly decrease almost throughout the year. Despite a homogeneous increase in the air temperature over Northeast Thailand, a future decrease in soil water content will show a geographically inhomogeneous distribution: Soil will experience a relative larger decrease in wetness at a shallow depth on the Khorat plateau than in the surrounding mountainous area, reflecting vegetation cover and soil texture. The predicted increase in air temperature is relatively consistent between general circulation models. In contrast, relatively large intermodel differences in precipitation, especially in long-term trends, produce unwanted bias errors in the estimation of other hydrological elements, such as soil moisture and evaporation, and cause uncertainties in projection of the agro-climatological environment. Offline hydrological simulation with a wide precipitation range is one strategy to compensate for such uncertainties and to obtain reliable risk assessment of future cultural conditions in rainfed paddy fields in Northeast Thailand.

2017 Annual Meeting of the American College of Radiology-Moreton Lecture: Forecasting the Futures of Radiology.

PubMed

Bauer, Jeffrey C

2017-12-01

The traditional forces of change in health care are no longer working as they did in the past. Political gridlock has destroyed Washington's ability to create sensible policy for reforming the medical marketplace, creating chaos for providers. Fortunately, chaos creates opportunity. The idea of creating one's future has never been more relevant and necessary. Predicting-the science of extrapolating future values from historical data-is not a valid method for looking ahead when causal relationships that explained change in the past are themselves being redefined (the current situation). Forecasting-the art of estimating probabilities of possibilities-is the appropriate method for anticipating futures when causality is being redefined. With its focus on identifying a range of possibilities, forecasting identifies many different outcomes that are simultaneously possible for radiology. Health care's new climate is being shaped by four defining trends: 1) revolution in biological science that is shifting caregivers' mission from acute care to disease management; 2) proliferation of information and communications technologies that are transforming the way care is delivered; 3) end of economic growth in the medical marketplace that is reshaping demand for care; and 4) political dysfunction that forces caregivers and their business partners to develop successful futures on their own. Radiology 3.0 is nicely aligned with the transformational trends. Staying focused on implementing Radiology 3.0 is supported as the optimal strategy for creating the profession's futures. Diagnostic convergence, establishing a unified diagnostic science with laboratory medicine, is proposed as a complementary initiative. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

USGS Publications Warehouse

van Mantgem, P.J.; Stephenson, N.L.

2005-01-01

1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.

Fluvial fluxes from the Magdalena River into Cartagena Bay, Caribbean Colombia: Trends, future scenarios, and connections with upstream human impacts

NASA Astrophysics Data System (ADS)

Restrepo, Juan D.; Escobar, Rogger; Tosic, Marko

2018-02-01

Fluxes of continental runoff and sediments as well as downstream deposition of eroded soils have severely altered the structure and function of fluvial and deltaic-estuarine ecosystems. The Magdalena River, the main contributor of continental fluxes into the Caribbean Sea, delivers important amounts of water and sediments into Cartagena Bay, a major estuarine system in northern Colombia. Until now, trends in fluvial fluxes into the bay, as well as the relationship between these tendencies in fluvial inputs and associated upstream changes in the Magdalena catchment, have not been studied. Here we explore the interannual trends of water discharge and sediment load flowing from the Magdalena River-Canal del Dique system into Cartagena Bay during the last three decades, forecast future scenarios of fluxes into the bay, and discuss possible connections between observed trends in fluvial inputs and trends in human intervention in the Magdalena River basin. Significant upward trends in annual runoff and sediment load during the mid-1980s, 1990s, and post-2000 are observed in the Magdalena and in the Canal del Dique flowing into Cartagena Bay. During the last decade, Magdalena streamflow and sediment load experienced increases of 24% and 33%, respectively, compared to the pre-2000 year period. Meanwhile, the Canal del Dique witnessed increases in water discharge and sediment load of 28% and 48%, respectively. During 26 y of monitoring, the Canal del Dique has discharged 177 Mt of sediment to the coastal zone, of which 52 Mt was discharged into Cartagena Bay. Currently, the Canal drains 6.5% and transports 5.1% of the Magdalena water discharge and sediment load. By 2020, water discharge and sediment flux from the Canal del Dique flowing to the coastal zone will witness increments of 164% and 260%, respectively. Consequently, sediment fluxes into Cartagena Bay will witness increments as high as 8.2 Mt y- 1 or 317%. Further analyses of upstream sediment load series for 21 tributary systems of the main Magdalena during the 2005-2010 period reveal that six tributaries, representing 55% of the analyzed Magdalena basin area, have witnessed increasing trends in sediment load, raising the river's sediment load by 44 Mt y- 1. Overall, trends in sediment load of the Magdalena and the Canal del Dique during the last three decades are in close agreement with the observed trends in human induced upstream erosion. The last decade has witnessed even stronger increments in fluvial fluxes to Cartagena Bay. Our results emphasize the importance of the catchment-coast linkage in order to predict future changes of fluvial fluxes into Caribbean estuarine systems.

Escalating burden of breast cancer in southern Thailand: analysis of 1990-2010 incidence and prediction of future trends.

PubMed

Virani, Shama; Sriplung, Hutcha; Rozek, Laura S; Meza, Rafael

2014-06-01

Thailand is undergoing an epidemiologic transition, with decreasing incidence of infectious diseases and increasing rates of chronic conditions, including cancer. Breast cancer has the highest incidence rates among females both in the southern region Thailand and throughout Thailand. However, there is a lack of research on the epidemiology of this and other cancers. Here we use cancer incidence data from the Songkhla Cancer Registry to characterize and analyze the incidence of breast cancer in Southern Thailand. We use joinpoint analysis, age-period-cohort models and nordpred analysis to investigate the incidence of breast cancer in Southern Thailand from 1990 to 2010 and project future trends from 2010 to 2029. We found that age-adjusted breast cancer incidence rates in Southern Thailand increased by almost 300% from 1990 to 2010 going from 10.0 to 27.8 cases per 100,000 person-years. Both period and cohort effects played a role in shaping the increase in incidence. Three distinct incidence projection methods consistently suggested that incidence rates will continue to increase in the future with incidence for women age 50 and above increasing at a higher rate than for women below 50. To date, this is the first study to examine Thai breast cancer incidence from a regional registry. This study provides a basis for future planning strategies in breast cancer prevention and to guide hypotheses for population-based epidemiologic research in Thailand. Copyright © 2014 Elsevier Ltd. All rights reserved.

Reducing unnecessary lab testing in the ICU with artificial intelligence.

PubMed

Cismondi, F; Celi, L A; Fialho, A S; Vieira, S M; Reti, S R; Sousa, J M C; Finkelstein, S N

2013-05-01

To reduce unnecessary lab testing by predicting when a proposed future lab test is likely to contribute information gain and thereby influence clinical management in patients with gastrointestinal bleeding. Recent studies have demonstrated that frequent laboratory testing does not necessarily relate to better outcomes. Data preprocessing, feature selection, and classification were performed and an artificial intelligence tool, fuzzy modeling, was used to identify lab tests that do not contribute an information gain. There were 11 input variables in total. Ten of these were derived from bedside monitor trends heart rate, oxygen saturation, respiratory rate, temperature, blood pressure, and urine collections, as well as infusion products and transfusions. The final input variable was a previous value from one of the eight lab tests being predicted: calcium, PTT, hematocrit, fibrinogen, lactate, platelets, INR and hemoglobin. The outcome for each test was a binary framework defining whether a test result contributed information gain or not. Predictive modeling was applied to recognize unnecessary lab tests in a real world ICU database extract comprising 746 patients with gastrointestinal bleeding. Classification accuracy of necessary and unnecessary lab tests of greater than 80% was achieved for all eight lab tests. Sensitivity and specificity were satisfactory for all the outcomes. An average reduction of 50% of the lab tests was obtained. This is an improvement from previously reported similar studies with average performance 37% by [1-3]. Reducing frequent lab testing and the potential clinical and financial implications are an important issue in intensive care. In this work we present an artificial intelligence method to predict the benefit of proposed future laboratory tests. Using ICU data from 746 patients with gastrointestinal bleeding, and eleven measurements, we demonstrate high accuracy in predicting the likely information to be gained from proposed future lab testing for eight common GI related lab tests. Future work will explore applications of this approach to a range of underlying medical conditions and laboratory tests. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Reducing unnecessary lab testing in the ICU with artificial intelligence

PubMed Central

Cismondi, F.; Celi, L.A.; Fialho, A.S.; Vieira, S.M.; Reti, S.R.; Sousa, J.M.C.; Finkelstein, S.N.

2017-01-01

Objectives To reduce unnecessary lab testing by predicting when a proposed future lab test is likely to contribute information gain and thereby influence clinical management in patients with gastrointestinal bleeding. Recent studies have demonstrated that frequent laboratory testing does not necessarily relate to better outcomes. Design Data preprocessing, feature selection, and classification were performed and an artificial intelligence tool, fuzzy modeling, was used to identify lab tests that do not contribute an information gain. There were 11 input variables in total. Ten of these were derived from bedside monitor trends heart rate, oxygen saturation, respiratory rate, temperature, blood pressure, and urine collections, as well as infusion products and transfusions. The final input variable was a previous value from one of the eight lab tests being predicted: calcium, PTT, hematocrit, fibrinogen, lactate, platelets, INR and hemoglobin. The outcome for each test was a binary framework defining whether a test result contributed information gain or not. Patients Predictive modeling was applied to recognize unnecessary lab tests in a real world ICU database extract comprising 746 patients with gastrointestinal bleeding. Main results Classification accuracy of necessary and unnecessary lab tests of greater than 80% was achieved for all eight lab tests. Sensitivity and specificity were satisfactory for all the outcomes. An average reduction of 50% of the lab tests was obtained. This is an improvement from previously reported similar studies with average performance 37% by [1–3]. Conclusions Reducing frequent lab testing and the potential clinical and financial implications are an important issue in intensive care. In this work we present an artificial intelligence method to predict the benefit of proposed future laboratory tests. Using ICU data from 746 patients with gastrointestinal bleeding, and eleven measurements, we demonstrate high accuracy in predicting the likely information to be gained from proposed future lab testing for eight common GI related lab tests. Future work will explore applications of this approach to a range of underlying medical conditions and laboratory tests. PMID:23273628

Trends in Female Breast Cancer by Age Group in the Chiang Mai Population

PubMed Central

Sripan, Patumrat; Sriplung, Hutcha; Pongnikorn, Donsuk; Virani, Shama; Bilheem, Surichai; Chaisaengkhaum, Udomlak; Maneesai, Puttachart; Waisri, Narate; Hanpragopsuk, Chirapong; Tansiri, Panrada; Khamsan, Varunee; Poungsombat, Malisa; Mawoot, Aumnart; Chitapanarux, Imjai

2017-01-01

Objectives: This study was conducted to determine incidence trends of female breast cancer according to age groups and to predict future change in Chiang Mai women through 2028. Method: Data were collected from all hospitals in Chiang Mai in northern Thailand, from 1989 through 2013, and used to investigate effects of age, year of diagnosis (period) and year of birth (cohort) on female breast cancer incidences using an age-period-cohort model. This model features geometric cut trends to predict change by young (<40 years), middle-aged (40-59) and elderly (≥60) age groups. Result: Of 5, 417 female breast cancer patients with a median age of 50 years (interquartile range: 43 to 59 years), 15%, 61% and 24% were young, middle-aged and elderly, respectively. Seventy nine percent of cancer cases in this study were detected at advanced stage. The trend in stage classification showed an increase in percentage of early stage and a decrease in metastatic cancers. Linear trends for cohort and period were not found in young females but were observed in middle-aged and elderly groups. Age-standardized rates (ASR) can be expected to remain stable around 6.8 per 100,000 women-years in young females. In the other age groups, the ASR trends were calculated to increase and reach peaks in 2024 of 120.2 and 138.2 per 100,000 women-years, respectively. Conclusion: Cohort effects or generation-specific effects, such as life style factors and the year of diagnosis (period) might have impacted on increased incidence in women aged over 40 years but not those under 40 years. A budget should be provided for treatment facilities and strategies to detect early stage cancers. The cost effectiveness of screening measures i.e. mammographic screening may need to be reconsidered for women age over 40 years. PMID:28612595

Assessment of the present and future offshore wind power potential: a case study in a target territory of the Baltic Sea near the Latvian coast.

PubMed

Lizuma, Lita; Avotniece, Zanita; Rupainis, Sergejs; Teilans, Artis

2013-01-01

Offshore wind energy development promises to be a significant domestic renewable energy source in Latvia. The reliable prediction of present and future wind resources at offshore sites is crucial for planning and selecting the location for wind farms. The overall goal of this paper is the assessment of offshore wind power potential in a target territory of the Baltic Sea near the Latvian coast as well as the identification of a trend in the future wind energy potential for the study territory. The regional climate model CLM and High Resolution Limited Area Model (Hirlam) simulations were used to obtain the wind climatology data for the study area. The results indicated that offshore wind energy is promising for expanding the national electricity generation and will continue to be a stable resource for electricity generation in the region over the 21st century.

Comparison of future and base precipitation anomalies by SimCLIM statistical projection through ensemble approach in Pakistan

NASA Astrophysics Data System (ADS)

Amin, Asad; Nasim, Wajid; Mubeen, Muhammad; Kazmi, Dildar Hussain; Lin, Zhaohui; Wahid, Abdul; Sultana, Syeda Refat; Gibbs, Jim; Fahad, Shah

2017-09-01

Unpredictable precipitation trends have largely influenced by climate change which prolonged droughts or floods in South Asia. Statistical analysis of monthly, seasonal, and annual precipitation trend carried out for different temporal (1996-2015 and 2041-2060) and spatial scale (39 meteorological stations) in Pakistan. Statistical downscaling model (SimCLIM) was used for future precipitation projection (2041-2060) and analyzed by statistical approach. Ensemble approach combined with representative concentration pathways (RCPs) at medium level used for future projections. The magnitude and slop of trends were derived by applying Mann-Kendal and Sen's slop statistical approaches. Geo-statistical application used to generate precipitation trend maps. Comparison of base and projected precipitation by statistical analysis represented by maps and graphical visualization which facilitate to detect trends. Results of this study projects that precipitation trend was increasing more than 70% of weather stations for February, March, April, August, and September represented as base years. Precipitation trend was decreased in February to April but increase in July to October in projected years. Highest decreasing trend was reported in January for base years which was also decreased in projected years. Greater variation in precipitation trends for projected and base years was reported in February to April. Variations in projected precipitation trend for Punjab and Baluchistan highly accredited in March and April. Seasonal analysis shows large variation in winter, which shows increasing trend for more than 30% of weather stations and this increased trend approaches 40% for projected precipitation. High risk was reported in base year pre-monsoon season where 90% of weather station shows increasing trend but in projected years this trend decreased up to 33%. Finally, the annual precipitation trend has increased for more than 90% of meteorological stations in base (1996-2015) which has decreased for projected year (2041-2060) up to 76%. These result revealed that overall precipitation trend is decreasing in future year which may prolonged the drought in 14% of weather stations under study.

Seven Future Trends in the Workplace.

ERIC Educational Resources Information Center

Satterlee, Brian; Craig, Will

In the future, seven trends--already firmly established--will become dominant in the world of work. Those trends are as follows: (1) middle management positions will continue to be cut; (2) project teams will be assembled for a single purpose; (3) more women will have executive leadership roles; (4) organizations will continue to increase their…

Toward a More Equitable Future: The Trends and Challenges Facing America's Latino Children

ERIC Educational Resources Information Center

Foxen, Patricia; Mather, Mark

2016-01-01

Rapid demographic change is transforming the landscape of America in exciting and challenging ways. This report, an update of the 2010 publication "America's Future: Latino Child Well-Being in Numbers and Trends," provides a comprehensive overview of national and state-level trends in the characteristics and well-being of Hispanic…

Performance of univariate forecasting on seasonal diseases: the case of tuberculosis.

PubMed

Permanasari, Adhistya Erna; Rambli, Dayang Rohaya Awang; Dominic, P Dhanapal Durai

2011-01-01

The annual disease incident worldwide is desirable to be predicted for taking appropriate policy to prevent disease outbreak. This chapter considers the performance of different forecasting method to predict the future number of disease incidence, especially for seasonal disease. Six forecasting methods, namely linear regression, moving average, decomposition, Holt-Winter's, ARIMA, and artificial neural network (ANN), were used for disease forecasting on tuberculosis monthly data. The model derived met the requirement of time series with seasonality pattern and downward trend. The forecasting performance was compared using similar error measure in the base of the last 5 years forecast result. The findings indicate that ARIMA model was the most appropriate model since it obtained the less relatively error than the other model.

Assessing delivery practices of mothers over time and over space in Uganda, 2003-2012.

PubMed

Sprague, Daniel A; Jeffery, Caroline; Crossland, Nadine; House, Thomas; Roberts, Gareth O; Vargas, William; Ouma, Joseph; Lwanga, Stephen K; Valadez, Joseph J

2016-01-01

It is well known that safe delivery in a health facility reduces the risks of maternal and infant mortality resulting from perinatal complications. What is less understood are the factors associated with safe delivery practices. We investigate factors influencing health facility delivery practices while adjusting for multiple other factors simultaneously, spatial heterogeneity, and trends over time. We fitted a logistic regression model to Lot Quality Assurance Sampling (LQAS) data from Uganda in a framework that considered individual-level covariates, geographical features, and variations over five time points. We accounted for all two-covariate interactions and all three-covariate interactions for which two of the covariates already had a significant interaction, were able to quantify uncertainty in outputs using computationally intensive cluster bootstrap methods, and displayed outputs using a geographical information system. Finally, we investigated what information could be predicted about districts at future time-points, before the next LQAS survey is carried out. To do this, we applied the model to project a confidence interval for the district level coverage of health facility delivery at future time points, by using the lower and upper end values of known demographics to construct a confidence range for the prediction and define priority groups. We show that ease of access, maternal age and education are strongly associated with delivery in a health facility; after accounting for this, there remains a significant trend towards greater uptake over time. We use this model together with known demographics to formulate a nascent early warning system that identifies candidate districts expected to have low prevalence of facility-based delivery in the immediate future. Our results support the hypothesis that increased development, particularly related to education and access to health facilities, will act to increase facility-based deliveries, a factor associated with reducing perinatal associated mortality. We provide a statistical method for using inexpensive and routinely collected monitoring and evaluation data to answer complex epidemiology and public health questions in a resource-poor setting. We produced a model based on this data that explained the spatial distribution of facility-based delivery in Uganda. Finally, we used this model to make a prediction about the future priority of districts that was validated by monitoring and evaluation data collected in the next year.

Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level

PubMed Central

Degener, Carolin Marlen; Vinhal, Livia; Coelho, Giovanini; Meira, Wagner; Codeço, Claudia Torres; Teixeira, Mauro Martins

2017-01-01

Background Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. Methodology / Principal findings In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to ‘nowcast’, i.e. estimate disease numbers in the same week, but also ‘forecast’ disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. Conclusions Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity. PMID:28719659

About the Federal Energy Management Program

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

Richard Kidd

2009-04-23

Richard Kidd, Program Manager for the Federal Energy Management Program (FEMP), presents a discussion on FEMP direction and its future role, federal funding trends, future financing trends, and Earth Day observations.

Effective Mitigation and Adaptation Strategies for Public Health Impacts of Heatwaves for Brookline, MA

NASA Astrophysics Data System (ADS)

Jalalzadeh Fard, B.; Hassanzadeh, H.; Bhatia, U.; Ganguly, A. R.

2016-12-01

Studies on urban areas show a significant increase in frequency and intensity of heatwaves over the past decades, and predict the same trend for future. Since heatwaves have been responsible for a large number of life losses, urgent adaptation and mitigation strategies are required in the policy and decision making level for a sustainable urban planning. The Sustainability and Data Sciences Laboratory at Northeastern University, under the aegis of Thriving Earth Exchange of AGU, is working with the town of Brookline to understand the potential public health impacts of anticipated heatwaves. We consider the most important social and physical factors to obtain vulnerability and exposure parameters for each census block group of the town. Utilizing remote sensing data, we locate Urban Heat Islands (UHIs) during a recent heatwave event, as the hazard parameter. We then create priority risk map using the risk framework. Our analyses show spatial correlations between the UHIs and social factors such as poverty, and physical factors such as land cover variations. Furthermore, we investigate the future heatwave frequency and intensity increases by analyzing the climate models predictions. For future changes of UHIs, land cover changes are investigated using available predictive data. Also, socioeconomic predictions are carried out to complete the futuristic models of heatwave risks. Considering plausible scenarios for Brookline, we develop different risk maps based on the vulnerability, exposure and hazard parameters. Eventually, we suggest guidelines for Heatwave Action Plans for prioritizing effective mitigation and adaptation strategies in urban planning for the town of Brookline.

[Concepts of development of the neurosurgical operative environment in the 21st century].

PubMed

Apuzzo, M; Liu, C

2002-01-01

The operative environment has to a large extent defines the "state of the art and science" of neurosurgery, which is now undergoing rapid reinvention. In order to remain current, each neurosurgeon should periodically reconsider their personal operative environment and its functional design with reference to modernity of practice as currently defined. Historical trends and their analysis offer predictive guides for development of such settings with an eye toward the future. Examination of technical developments in decade timeframes defines the progress in capability and need. Progressive minimalism of manipulation and the presence of operative definition with increasing precision are evident, with concurrent miniaturization of attendant computerized support systems, sensors, robotic interfaces, and imaging devices. These trends and developments offer the opportunity for simplificity of setting design with higher functionality as the desired endpoint.

Evolution of optical fibre cabling components at CERN: Performance and technology trends analysis

NASA Astrophysics Data System (ADS)

Shoaie, Mohammad Amin; Meroli, Stefano; Machado, Simao; Ricci, Daniel

2018-05-01

CERN optical fibre infrastructure has been growing constantly over the past decade due to ever increasing connectivity demands. The provisioning plan and fibre installation of this vast laboratory is performed by Fibre Optics and Cabling Section at Engineering Department. In this paper we analyze the procurement data for essential fibre cabling components during a five-year interval to extract the existing trends and anticipate future directions. The analysis predicts high contribution of LC connector and an increasing usage of multi-fibre connectors. It is foreseen that single-mode fibres become the main fibre type for mid and long-range installations while air blowing would be the major installation technique. Performance assessment of various connectors shows that the expanded beam ferrule is favored for emerging on-board optical interconnections thanks to its scalable density and stable return-loss.

Genealogical Trees of Scientific Papers

PubMed Central

Waumans, Michaël Charles; Bersini, Hugues

2016-01-01

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field. PMID:26954677

Development of time-trend model for analysing and predicting case pattern of dog bite injury induced rabies-like-illness in Liberia, 2014-2017.

PubMed

Jomah, N D; Ojo, J F; Odigie, E A; Olugasa, B O

2014-12-01

The post-civil war records of dog bite injuries (DBI) and rabies-like-illness (RLI) among humans in Liberia is a vital epidemiological resource for developing a predictive model to guide the allocation of resources towards human rabies control. Whereas DBI and RLI are high, they are largely under-reported. The objective of this study was to develop a time model of the case-pattern and apply it to derive predictors of time-trend point distribution of DBI-RLI cases. A retrospective 6 years data of DBI distribution among humans countrywide were converted to quarterly series using a transformation technique of Minimizing Squared First Difference statistic. The generated dataset was used to train a time-trend model of the DBI-RLI syndrome in Liberia. An additive detenninistic time-trend model was selected due to its performance compared to multiplication model of trend and seasonal movement. Parameter predictors were run on least square method to predict DBI cases for a prospective 4 years period, covering 2014-2017. The two-stage predictive model of DBI case-pattern between 2014 and 2017 was characterised by a uniform upward trend within Liberia's coastal and hinterland Counties over the forecast period. This paper describes a translational application of the time-trend distribution pattern of DBI epidemics, 2008-2013 reported in Liberia, on which a predictive model was developed. A computationally feasible two-stage time-trend permutation approach is proposed to estimate the time-trend parameters and conduct predictive inference on DBI-RLI in Liberia.

Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks.

PubMed

Saad, E W; Prokhorov, D V; Wunsch, D C

1998-01-01

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.

Spatiotemporal mathematical modelling of mutations of the dhps gene in African Plasmodium falciparum.

PubMed

Flegg, Jennifer A; Patil, Anand P; Venkatesan, Meera; Roper, Cally; Naidoo, Inbarani; Hay, Simon I; Sibley, Carol Hopkins; Guerin, Philippe J

2013-07-17

Plasmodium falciparum has repeatedly evolved resistance to first-line anti-malarial drugs, thwarting efforts to control and eliminate the disease and in some period of time this contributed largely to an increase in mortality. Here a mathematical model was developed to map the spatiotemporal trends in the distribution of mutations in the P. falciparum dihydropteroate synthetase (dhps) gene that confer resistance to the anti-malarial sulphadoxine, and are a useful marker for the combination of alleles in dhfr and dhps that is highly correlated with resistance to sulphadoxine-pyrimethamine (SP). The aim of this study was to present a proof of concept for spatiotemporal modelling of trends in anti-malarial drug resistance that can be applied to monitor trends in resistance to components of artemisinin combination therapy (ACT) or other anti-malarials, as they emerge or spread. Prevalence measurements of single nucleotide polymorphisms in three codon positions of the dihydropteroate synthetase (dhps) gene from published studies of dhps mutations across Africa were used. A model-based geostatistics approach was adopted to create predictive surfaces of the dhps540E mutation over the spatial domain of sub-Saharan Africa from 1990-2010. The statistical model was implemented within a Bayesian framework and hence quantified the associated uncertainty of the prediction of the prevalence of the dhps540E mutation in sub-Saharan Africa. The maps presented visualize the changing prevalence of the dhps540E mutation in sub-Saharan Africa. These allow prediction of space-time trends in the parasite resistance to SP, and provide probability distributions of resistance prevalence in places where no data are available as well as insight on the spread of resistance in a way that the data alone do not allow. The results of this work will be extended to design optimal sampling strategies for the future molecular surveillance of resistance, providing a proof of concept for similar techniques to design optimal strategies to monitor resistance to ACT.

Informing management on the future structure of hospital care: an extrapolation of trends in demand and costs in lung diseases.

PubMed

Vogl, Matthias; Leidl, Reiner

2016-05-01

The planning of health care management benefits from understanding future trends in demand and costs. In the case of lung diseases in the national German hospital market, we therefore analyze the current structure of care, and forecast future trends in key process indicators. We use standardized, patient-level, activity-based costing from a national cost calculation data set of respiratory cases, representing 11.9-14.1 % of all cases in the major diagnostic category "respiratory system" from 2006 to 2012. To forecast hospital admissions, length of stay (LOS), and costs, the best adjusted models out of possible autoregressive integrated moving average models and exponential smoothing models are used. The number of cases is predicted to increase substantially, from 1.1 million in 2006 to 1.5 million in 2018 (+2.7 % each year). LOS is expected to decrease from 7.9 to 6.1 days, and overall costs to increase from 2.7 to 4.5 billion euros (+4.3 % each year). Except for lung cancer (-2.3 % each year), costs for all respiratory disease areas increase: surgical interventions +9.2 % each year, COPD +3.9 %, bronchitis and asthma +1.7 %, infections +2.0 %, respiratory failure +2.6 %, and other diagnoses +8.5 % each year. The share of costs of surgical interventions in all costs of respiratory cases increases from 17.8 % in 2006 to 30.8 % in 2018. Overall costs are expected to increase particularly because of an increasing share of expensive surgical interventions and rare diseases, and because of higher intensive care, operating room, and diagnostics and therapy costs.

Range-wide patterns of greater sage-grouse persistence

USGS Publications Warehouse

Aldridge, Cameron L.; Nielsen, Scott E.; Beyer, Hawthorne L.; Boyce, Mark S.; Connelly, John W.; Knick, Steven T.; Schroeder, Michael A.

2008-01-01

Aim: Greater sage-grouse (Centrocercus urophasianus), a shrub-steppe obligate species of western North America, currently occupies only half its historical range. Here we examine how broad-scale, long-term trends in landscape condition have affected range contraction. Location: Sagebrush biome of the western USA. Methods: Logistic regression was used to assess persistence and extirpation of greater sage-grouse range based on landscape conditions measured by human population (density and population change), vegetation (percentage of sagebrush habitat), roads (density of and distance to roads), agriculture (cropland, farmland and cattle density), climate (number of severe and extreme droughts) and range periphery. Model predictions were used to identify areas where future extirpations can be expected, while also explaining possible causes of past extirpations. Results: Greater sage-grouse persistence and extirpation were significantly related to sagebrush habitat, cultivated cropland, human population density in 1950, prevalence of severe droughts and historical range periphery. Extirpation of sage-grouse was most likely in areas having at least four persons per square kilometre in 1950, 25% cultivated cropland in 2002 or the presence of three or more severe droughts per decade. In contrast, persistence of sage-grouse was expected when at least 30 km from historical range edge and in habitats containing at least 25% sagebrush cover within 30 km. Extirpation was most often explained (35%) by the combined effects of peripherality (within 30 km of range edge) and lack of sagebrush cover (less than 25% within 30 km). Based on patterns of prior extirpation and model predictions, we predict that 29% of remaining range may be at risk. Main Conclusions: Spatial patterns in greater sage-grouse range contraction can be explained by widely available landscape variables that describe patterns of remaining sagebrush habitat and loss due to cultivation, climatic trends, human population growth and peripherality of populations. However, future range loss may relate less to historical mechanisms and more to recent changes in land use and habitat condition, including energy developments and invasions by non-native species such as cheatgrass (Bromus tectorum) and West Nile virus. In conjunction with local measures of population performance, landscape-scale predictions of future range loss may be useful for prioritizing management and protection. Our results suggest that initial conservation efforts should focus on maintaining large expanses of sagebrush habitat, enhancing quality of existing habitats, and increasing habitat connectivity.

Teacher Education Futures: Today's Trends, Tomorrow's Expectations

ERIC Educational Resources Information Center

Aubusson, Peter; Schuck, Sandy

2013-01-01

Education is facing significant political and contextual challenges that will impact its future. This study employs a Delphi methodology to investigate teacher educators' views of current trends and their consequences for teacher education futures. Interviews were conducted with a sample of expert teacher educators drawn from eight countries. This…

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks.

PubMed

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.

Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

PubMed Central

Manikandan, Narayanan; Subha, Srinivasan

2016-01-01

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used. PMID:26881271

System and method for pre-cooling of buildings

DOEpatents

Springer, David A.; Rainer, Leo I.

2011-08-09

A method for nighttime pre-cooling of a building comprising inputting one or more user settings, lowering the indoor temperature reading of the building during nighttime by operating an outside air ventilation system followed, if necessary, by a vapor compression cooling system. The method provides for nighttime pre-cooling of a building that maintains indoor temperatures within a comfort range based on the user input settings, calculated operational settings, and predictions of indoor and outdoor temperature trends for a future period of time such as the next day.

Information system in transition: The Hungarian Scene

NASA Technical Reports Server (NTRS)

Stubnya, Gyorgy; Herman, Akos Robert

1994-01-01

Recent changes in political and economical conditions in eastern European countries are influencing the function and activities of the Hungarian Library and Information network. The National Technical Information Center and Library (OMIKK) is an active participant in this process of transition. In the first part of this paper, the general transformations of Hungarian libraries and information centers are analyzed and some predictions for future trends are presented. The second part is a short summary of the activities of OMIKK and its present and prospective role in the development of national information policy.

The atmospheric boundary layer — advances in knowledge and application

NASA Astrophysics Data System (ADS)

Garratt, J. R.; Hess, G. D.; Physick, W. L.; Bougeault, P.

1996-02-01

We summarise major activities and advances in boundary-layer knowledge in the 25 years since 1970, with emphasis on the application of this knowledge to surface and boundary-layer parametrisation schemes in numerical models of the atmosphere. Progress in three areas is discussed: (i) the mesoscale modelling of selected phenomena; (ii) numerical weather prediction; and (iii) climate simulations. Future trends are identified, including the incorporation into models of advanced cloud schemes and interactive canopy schemes, and the nesting of high resolution boundary-layer schemes in global climate models.

Micro-Scale Avionics Thermal Management

NASA Technical Reports Server (NTRS)

Moran, Matthew E.

2001-01-01

Trends in the thermal management of avionics and commercial ground-based microelectronics are converging, and facing the same dilemma: a shortfall in technology to meet near-term maximum junction temperature and package power projections. Micro-scale devices hold the key to significant advances in thermal management, particularly micro-refrigerators/coolers that can drive cooling temperatures below ambient. A microelectromechanical system (MEMS) Stirling cooler is currently under development at the NASA Glenn Research Center to meet this challenge with predicted efficiencies that are an order of magnitude better than current and future thermoelectric coolers.

CD-ROM-aided Databases

NASA Astrophysics Data System (ADS)

Masuyama, Keiichi

CD-ROM has rapidly evolved as a new information medium with large capacity, In the U.S. it is predicted that it will become two hundred billion yen market in three years, and thus CD-ROM is strategic target of database industry. Here in Japan the movement toward its commercialization has been active since this year. Shall CD-ROM bussiness ever conquer information market as an on-disk database or electronic publication? Referring to some cases of the applications in the U.S. the author views marketability and the future trend of this new optical disk medium.

Understanding the Seasonal Greenness Trends and Controls in South Asia Using Satellite Based Observations

NASA Astrophysics Data System (ADS)

Sarmah, S.; Jia, G.; Zhang, A.; Singha, M.

2017-12-01

South Asia (SA) is one of the most remarkable regions in changing vegetation greenness along with its major expansion of agricultural activity, especially irrigated farming. However, SA is predicted to be a vulnerable agricultural regions to future climate changes. The influence of monsoon climate on the seasonal trends and anomalies of vegetation greenness are not well understood in the region which can provide valuable information about climate-ecosystem interaction. This study analyzed the spatio-temporal patterns of seasonal vegetation trends and variability using satellite vegetation indices (VI) including AVHRR Normalized Difference Vegetation Index (NDVI) (1982-2013) and MODIS Enhanced Vegetation Index (EVI) (2000-2013) in summer monsoon (SM) (June-Sept) and winter monsoon (WM) (Dec-Apr) seasons among irrigated cropland (IC), rainfed cropland (RC) and natural vegetation (NV). Seasonal VI variations with climatic factors (precipitation and temperature) and LULC changes have been investigated to identify the forcings behind the vegetation trends and variability. We found that major greening occurred in the last three decades due to the increase in IC productivity noticeably in WM, however, recent (2000-2013) greening trends were lower than the previous decades (1982-1999) in both the IC and RC indicating the stresses on them. The browning trends, mainly concentrated in NV areas were prominent during WM and rigorous since 2000, confirmed from the moderate resolution EVI and LULC datasets. Winter time maximal temperature had been increasing tremendously whereas precipitation trend was not significant over SA. Both the climate variability and LULC changes had integrated effects on the vegetation changes in NV areas specifically in hilly regions. However, LULC impact was intensified since 2000, mostly in north east India. This study also revealed a distinct seasonal variation in spatial distribution of correlation between VI's and climate anomalies over SA. Concluding, so far SA has managed to get a decent productivity over croplands due to the advanced cultivation techniques which likely to be at risk under future warming climate. Also NV areas of SA are in constant threat from the anthropogenic activities and climate changes.

Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends

PubMed Central

Teodoro, Douglas; Lovis, Christian

2013-01-01

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends. PMID:23637796

Challenges for the New Century: Trends That Will Influence Kentucky's Future.

ERIC Educational Resources Information Center

Smith-Mello, Michal; Childress, Michael T.; Watts, Amy; Watkins, John F.

Trends that will influence Kentucky's future were examined along with policy options for responding to those trends. According to the analysis, the effects of the new economy on Kentucky has been mixed. Although many Kentuckians have benefited from the new economy, the state's least educated and poorest residents are falling further behind the…

Constrains on the South Atlantic Anomaly from Réunion Island

NASA Astrophysics Data System (ADS)

Béguin, A.; de Groot, L. V.

2017-12-01

The South Atlantic Anomaly (SAA) is a region where the geomagnetic field intensity is about half as strong as would be expected from the current geomagnetic dipole moment that arises from geomagnetic field models. Those field models predict a westward movement of the SAA and predicts its origin East of Africa around 1500 AD. The onset and evolution of the SAA, however, are poorly constrained due to a lack of full-vector paleomagnetic data from Africa and the Indian Ocean for the past centuries. Here we present a full-vector paleosecular variation (PSV) curve for Réunion Island (21°S, 55°E) located East the African continent, in the region that currently shows the fastest increase in geomagnetic field strength in contrast to the average global decay. We sampled 27 sites covering the last 700 years, and subjected them to a directional and multi-method paleointensity study. The obtained directional records reveal shallower inclinations and less variation in the declination compared to current geomagnetic field model predictions. Scrutinizing the IZZI-Thellier, Multispecimen, and calibrated pseudo-Thellier results produces a coherent paleointensity record. The predicted intensity trend from the geomagnetic field models generally agrees with the trend in our data, however, the high paleointensities are higher than the models predict, and the low paleointensities are lower than the models. This illustrates the inevitable smoothing inherent to geomagnetic field modelling. We will discuss the constraints on the onset of the SAA that arise from the new full-vector PSV curve for Réunion that we present and the implications for the past and future evolution of this geomagnetic phenomenon.

Arthroplasty Utilization in the United States is Predicted by Age-Specific Population Groups.

PubMed

Bashinskaya, Bronislava; Zimmerman, Ryan M; Walcott, Brian P; Antoci, Valentin

2012-01-01

Osteoarthritis is a common indication for hip and knee arthroplasty. An accurate assessment of current trends in healthcare utilization as they relate to arthroplasty may predict the needs of a growing elderly population in the United States. First, incidence data was queried from the United States Nationwide Inpatient Sample from 1993 to 2009. Patients undergoing total knee and hip arthroplasty were identified. Then, the United States Census Bureau was queried for population data from the same study period as well as to provide future projections. Arthroplasty followed linear regression models with the population group >64 years in both hip and knee groups. Projections for procedure incidence in the year 2050 based on these models were calculated to be 1,859,553 cases (hip) and 4,174,554 cases (knee). The need for hip and knee arthroplasty is expected to grow significantly in the upcoming years, given population growth predictions.

Effect of accuracy of wind power prediction on power system operator

NASA Technical Reports Server (NTRS)

Schlueter, R. A.; Sigari, G.; Costi, T.

1985-01-01

This research project proposed a modified unit commitment that schedules connection and disconnection of generating units in response to load. A modified generation control is also proposed that controls steam units under automatic generation control, fast responding diesels, gas turbines and hydro units under a feedforward control, and wind turbine array output under a closed loop array control. This modified generation control and unit commitment require prediction of trend wind power variation one hour ahead and the prediction of error in this trend wind power prediction one half hour ahead. An improved meter for predicting trend wind speed variation is developed. Methods for accurately simulating the wind array power from a limited number of wind speed prediction records was developed. Finally, two methods for predicting the error in the trend wind power prediction were developed. This research provides a foundation for testing and evaluating the modified unit commitment and generation control that was developed to maintain operating reliability at a greatly reduced overall production cost for utilities with wind generation capacity.

Future Mission Trends and their Implications for the Deep Space Network

NASA Technical Reports Server (NTRS)

Abraham, Douglas S.

2006-01-01

Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.

A Perspective of the future of nuclear medicine training and certification

PubMed Central

Arevalo-Perez, Julio; Paris, Manuel; Graham, Michael M.; Osborne, Joseph R.

2016-01-01

Nuclear Medicine has evolved from a medical subspecialty using quite basic tests to one using elaborate methods to image organ physiology and has truly become “Molecular Imaging”. Concurrently, there has also been a timely debate about who has to be responsible for keeping pace with all of the components of the developmental cycle; imaging, radiopharmaceuticals and instrumentation. Since the foundation of the ABNM, the practice of Nuclear Medicine and the process toward certification have undergone major revisions. At present, the debate is focused on the inevitable future convergence of Radiology and Nuclear Medicine. The potential for further cooperation or fusion of the American Board of Radiology (ABR) and the American Board of Nuclear Medicine (ABNM) is likely to bring about a new path for Nuclear Medicine and Molecular Imaging training. If the merger is done carefully, respecting the strengths of both partners equally, there is an excellent potential to create a hybrid Nuclear Medicine – Radiology specialty that combines Physiology and Molecular Biology with detailed anatomic imaging that will sustain the innovation that has been central to nuclear medicine residency and practice. Herein, we also introduce a few basic trends in imaging utilization in the United States. These trends do not predict future utilization, but highlight the need for an appropriately credentialed practitioner to interpret these examinations and provide value to the healthcare system. PMID:26687859

Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries.

PubMed

McMeekin, T A

2007-09-01

Predictive microbiology is considered in the context of the conference theme "chance, innovation and challenge", together with the impact of quantitative approaches on food microbiology, generally. The contents of four prominent texts on predictive microbiology are analysed and the major contributions of two meat microbiologists, Drs. T.A. Roberts and C.O. Gill, to the early development of predictive microbiology are highlighted. These provide a segue into R&D trends in predictive microbiology, including the Refrigeration Index, an example of science-based, outcome-focussed food safety regulation. Rapid advances in technologies and systems for application of predictive models are indicated and measures to judge the impact of predictive microbiology are suggested in terms of research outputs and outcomes. The penultimate section considers the future of predictive microbiology and advances that will become possible when data on population responses are combined with data derived from physiological and molecular studies in a systems biology approach. Whilst the emphasis is on science and technology for food safety management, it is suggested that decreases in foodborne illness will also arise from minimising human error by changing the food safety culture.

Clearance Prediction Methodology Needs Fundamental Improvement: Trends Common to Rat and Human Hepatocytes/Microsomes and Implications for Experimental Methodology.

PubMed

Wood, F L; Houston, J B; Hallifax, D

2017-11-01

Although prediction of clearance using hepatocytes and liver microsomes has long played a decisive role in drug discovery, it is widely acknowledged that reliably accurate prediction is not yet achievable despite the predominance of hepatically cleared drugs. Physiologically mechanistic methodology tends to underpredict clearance by several fold, and empirical correction of this bias is confounded by imprecision across drugs. Understanding the causes of prediction uncertainty has been slow, possibly reflecting poor resolution of variables associated with donor source and experimental methods, particularly for the human situation. It has been reported that among published human hepatocyte predictions there was a tendency for underprediction to increase with increasing in vivo intrinsic clearance, suggesting an inherent limitation using this particular system. This implied an artifactual rate limitation in vitro, although preparative effects on cell stability and performance were not yet resolved from assay design limitations. Here, to resolve these issues further, we present an up-to-date and comprehensive examination of predictions from published rat as well as human studies (where n = 128 and 101 hepatocytes and n = 71 and 83 microsomes, respectively) to assess system performance more independently. We report a clear trend of increasing underprediction with increasing in vivo intrinsic clearance, which is similar both between species and between in vitro systems. Hence, prior concerns arising specifically from human in vitro systems may be unfounded and the focus of investigation in the future should be to minimize the potential in vitro assay limitations common to whole cells and subcellular fractions. Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

Numerical Prediction of Chevron Nozzle Noise Reduction using Wind-MGBK Methodology

NASA Technical Reports Server (NTRS)

Engblom, W.A.; Bridges, J.; Khavarant, A.

2005-01-01

Numerical predictions for single-stream chevron nozzle flow performance and farfield noise production are presented. Reynolds Averaged Navier Stokes (RANS) solutions, produced via the WIND flow solver, are provided as input to the MGBK code for prediction of farfield noise distributions. This methodology is applied to a set of sensitivity cases involving varying degrees of chevron inward bend angle relative to the core flow, for both cold and hot exhaust conditions. The sensitivity study results illustrate the effect of increased chevron bend angle and exhaust temperature on enhancement of fine-scale mixing, initiation of core breakdown, nozzle performance, and noise reduction. Direct comparisons with experimental data, including stagnation pressure and temperature rake data, PIV turbulent kinetic energy fields, and 90 degree observer farfield microphone data are provided. Although some deficiencies in the numerical predictions are evident, the correct farfield noise spectra trends are captured by the WIND-MGBK method, including the noise reduction benefit of chevrons. Implications of these results to future chevron design efforts are addressed.

Early Shear Failure Prediction in Incremental Sheet Forming Process Using FEM and ANN

NASA Astrophysics Data System (ADS)

Moayedfar, Majid; Hanaei, Hengameh; Majdi Rani, Ahmad; Musa, Mohd Azam Bin; Sadegh Momeni, Mohammad

2018-03-01

The application of incremental sheet forming process as a rapid forming technique is rising in variety of industries such as aerospace, automotive and biomechanical purposes. However, the sheet failure is a big challenge in this process which leads wasting lots of materials. Hence, this study tried to propose a method to predict the early sheet failure in this process using mathematical solution. For the feasibility of the study, design of experiment with the respond surface method is employed to extract a set of experiments data for the simulation. The significant forming parameters were recognized and their integration was used for prediction system. Then, the results were inserted to the artificial neural network as input parameters to predict a vast range of applicable parameters avoiding sheet failure in ISF. The value of accuracy R2 ∼0.93 was obtained and the maximum sheet stretch in the depth of 25mm were recorded. The figures generate from the trend of interaction between effective parameters were provided for future studies.

Prediction of Early Childhood Caries via Spatial-Temporal Variations of Oral Microbiota.

PubMed

Teng, Fei; Yang, Fang; Huang, Shi; Bo, Cunpei; Xu, Zhenjiang Zech; Amir, Amnon; Knight, Rob; Ling, Junqi; Xu, Jian

2015-09-09

Microbiota-based prediction of chronic infections is promising yet not well established. Early childhood caries (ECC) is the most common infection in children. Here we simultaneously tracked microbiota development at plaque and saliva in 50 4-year-old preschoolers for 2 years; children either stayed healthy, transitioned into cariogenesis, or experienced caries exacerbation. Caries onset delayed microbiota development, which is otherwise correlated with aging in healthy children. Both plaque and saliva microbiota are more correlated with changes in ECC severity (dmfs) during onset than progression. By distinguishing between aging- and disease-associated taxa and exploiting the distinct microbiota dynamics between onset and progression, we developed a model, Microbial Indicators of Caries, to diagnose ECC from healthy samples with 70% accuracy and predict, with 81% accuracy, future ECC onsets for samples clinically perceived as healthy. Thus, caries onset in apparently healthy teeth can be predicted using microbiota, when appropriately de-trended for age. Copyright © 2015 Elsevier Inc. All rights reserved.

Alternative Scenarios of the American Future: 1980-2000.

ERIC Educational Resources Information Center

Glover, Robert

This report is a summary of the findings of the societal trends survey completed at the National Forum on Learning and The American Future, which focused on factors influencing the future of adult learning. The survey questionnaire and results consist of 120 societal trend statements organized into sixteen different content areas: demography;…

Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change

PubMed Central

Schut, Antonius G. T.; Ivits, Eva; Conijn, Jacob G.; ten Brink, Ben; Fensholt, Rasmus

2015-01-01

Detailed understanding of a possible decoupling between climatic drivers of plant productivity and the response of ecosystems vegetation is required. We compared trends in six NDVI metrics (1982–2010) derived from the GIMMS3g dataset with modelled biomass productivity and assessed uncertainty in trend estimates. Annual total biomass weight (TBW) was calculated with the LINPAC model. Trends were determined using a simple linear regression, a Thiel-Sen medium slope and a piecewise regression (PWR) with two segments. Values of NDVI metrics were related to Net Primary Production (MODIS-NPP) and TBW per biome and land-use type. The simple linear and Thiel-Sen trends did not differ much whereas PWR increased the fraction of explained variation, depending on the NDVI metric considered. A positive trend in TBW indicating more favorable climatic conditions was found for 24% of pixels on land, and for 5% a negative trend. A decoupled trend, indicating positive TBW trends and monotonic negative or segmented and negative NDVI trends, was observed for 17–36% of all productive areas depending on the NDVI metric used. For only 1–2% of all pixels in productive areas, a diverging and greening trend was found despite a strong negative trend in TBW. The choice of NDVI metric used strongly affected outcomes on regional scales and differences in the fraction of explained variation in MODIS-NPP between biomes were large, and a combination of NDVI metrics is recommended for global studies. We have found an increasing difference between trends in climatic drivers and observed NDVI for large parts of the globe. Our findings suggest that future scenarios must consider impacts of constraints on plant growth such as extremes in weather and nutrient availability to predict changes in NPP and CO2 sequestration capacity. PMID:26466347

An acute post-rape intervention to prevent substance use and abuse.

PubMed

Acierno, Ron; Resnick, Heidi S; Flood, Amanda; Holmes, Melisa

2003-12-01

The trauma of rape is routinely associated with extreme acute distress. Such peri-event anxiety increases risk of developing psychopathology and substance use or abuse post-rape, with the degree of initial distress positively predicting future problems. Unfortunately, the nature of post-rape forensic evidence collection procedures may exacerbate initial distress, thereby potentiating post-rape negative emotional sequelae. Consequently, substance use may increase in an effort to ameliorate this distress. To address this, a two-part video intervention was developed for use in acute post-rape time frames to (a) minimize anxiety during forensic rape examinations, thereby reducing risk of future emotional problems, and (b) prevent increased post-rape substance use and abuse. Pilot study data with 124 rape victims indicated that the low-cost, easily administered intervention was effective in reducing risk of marijuana abuse at 6 weeks. Nonstatistically significant trends also were evident for reduced marijuana use. Trends were also noted in favor of the intervention in the subgroup of women who were actively using substances pre-rape (among pre-rape alcohol users, 28% viewers vs. 43% nonviewers met criteria for post-rape alcohol abuse; among pre-rape marijuana users, the rates of post-marijuana use were 17% vs. 43%).

Contract Training: Progress and Policy Issues.

ERIC Educational Resources Information Center

Deegan, William L.; Drisko, Ronald

1985-01-01

Provides results of a national survey of community college contract training programs, including data on the extent of the colleges' involvement, centralization/decentralization of contract training, problems and benefits, and future trends. Discusses future policy trends. (HB)

Positron Computed Tomography: Current State, Clinical Results and Future Trends

DOE R&D Accomplishments Database

Schelbert, H. R.; Phelps, M. E.; Kuhl, D. E.

1980-09-01

An overview is presented of positron computed tomography: its advantages over single photon emission tomography, its use in metabolic studies of the heart and chemical investigation of the brain, and future trends. (ACR)

Predictors of Final Specialty Choice by Internal Medicine Residents

PubMed Central

Diehl, Andrew K; Kumar, Vineeta; Gateley, Ann; Appleby, Jane L; O'Keefe, Mary E

2006-01-01

BACKGROUND Sociodemographic factors and personality attributes predict career decisions in medical students. Determinants of internal medicine residents' specialty choices have received little attention. OBJECTIVE To identify factors that predict the clinical practice of residents following their training. DESIGN Prospective cohort study. PARTICIPANTS Two hundred and four categorical residents from 2 university-based residency programs. MEASUREMENTS Sociodemographic and personality inventories performed during residency, and actual careers 4 to 9 years later. RESULTS International medical school graduates (IMGs) were less likely to practice general medicine than U.S. graduates (33.3% vs 70.6%, P<.001). Residents with higher loan indebtedness more often became generalists (P = .001). A corresponding trend favoring general internal medicine was observed among those who perceived General Internists to have lower potential incomes (69.0% vs 53.3%, P = .08). There was a trend for generalists to have lower scores on scales measuring authoritarianism, negative orientation to psychological problems, and Machiavellianism (0.05

Planning intensive care unit design using computer simulation modeling: optimizing integration of clinical, operational, and architectural requirements.

PubMed

OʼHara, Susan

2014-01-01

Nurses have increasingly been regarded as critical members of the planning team as architects recognize their knowledge and value. But the nurses' role as knowledge experts can be expanded to leading efforts to integrate the clinical, operational, and architectural expertise through simulation modeling. Simulation modeling allows for the optimal merge of multifactorial data to understand the current state of the intensive care unit and predict future states. Nurses can champion the simulation modeling process and reap the benefits of a cost-effective way to test new designs, processes, staffing models, and future programming trends prior to implementation. Simulation modeling is an evidence-based planning approach, a standard, for integrating the sciences with real client data, to offer solutions for improving patient care.

Optical data communication: fundamentals and future directions

NASA Astrophysics Data System (ADS)

DeCusatis, Casimer M.

1998-12-01

An overview of optical data communications is provided, beginning with a brief history and discussion of the unique requirements that distinguish this subfield from related areas such as telecommunications. Each of the major datacom standards is then discussed, including the physical layer specification, distances and data rates, fiber and connector types, data frame structures, and network considerations. These standards can be categorized by their prevailing applications, either storage [Enterprise System Connection, Fiber Channel Connection, and Fiber Channel], coupling (Fiber Channel), or networking [Fiber Distributed Data Interface, Gigabit Ethernet, and asynchronous transfer mode/synchronous optical network]. We also present some emerging technologies and their applications, including parallel optical interconnects, plastic optical fiber, wavelength multiplexing, and free- space optical links. We conclude with some cost/performance trade-offs and predictions of future bandwidth trends.

Climate change induces demographic resistance to disease in novel coral assemblages

PubMed Central

Yakob, Laith; Mumby, Peter J.

2011-01-01

Climate change is reshaping biological communities and has already generated novel ecosystems. The functioning of novel ecosystems could depart markedly from that of existing systems and therefore obscure the impacts of climate change. We illustrate this possibility for coral reefs, which are at the forefront of climatic stress. Disease has been a principal cause of reef degradation and is expected to worsen with increased future thermal stress. However, using a field-tested epizoological model, we show that high population turnover within novel ecosystems enhances coral resistance to epizootics. Thus, disease could become a less important driver of change in the future. We emphasize the need to move away from projections based on historic trends toward predictions that account for novel behavior of ecosystems under climate change. PMID:21245326

Future trends in the health care economy.

PubMed

Kajander, J; Samuels, M

1996-01-01

Most articles on the future of health care are by professionals involved in the delivery of health care services. This article is unique in that trends are examined from the perspective of the public and purchasers of care. The authors focus on 12 trends that are or will be affecting the industry, and on the sometimes unintended consequences and new conflicts that may develop.

Detecting Emergence of Acidification and Warming as Stressors for Coral Reef Regions using Earth System Models

NASA Astrophysics Data System (ADS)

Menendez, A. T.

2015-12-01

Coral reef ecosystems rely on complex interactions between biological, biogeochemical, and physical processes to ensure their survival and resilience. However, both human interaction and anthropogenic climate change have negatively impacted the prosperity of these regions, resulting in a crucial need to understand and predict the future of important biogeochemical and physical stressors. Contemporary changes to these relationships and environmental conditions in coral reef ecosystems are a mixture of anthropogenic contributions and natural variability (e.g. ENSO) of the climate system. To better quantify the uncertainty in future projections, it is exceedingly necessary to differentiate between these two contributors. In this study we look at acidification and warming stressors in the Galapagos, Coral Triangle, and Hawaiian islands regions. We use a suite of hindcast simulations (a 30-member large initial condition ensemble) done with an Earth Systems Model (GFDL-ESM2M) in order quantify the degree to which natural variability alters the emergence time-scales of anthropogenically-induced changes to ecosystem drivers such as pH, ΩArag, and SST. A comparison of output from a suit of CMIP5 models will be used to evaluate model uncertainty for the same regions. Simulated trends and variability in these ecosystem drivers were then compared to observed trends over the three Pacific regions. Evidently the models and observed trends proved invaluable for testing the hypothesis addressing the presence of a temporal hierarchy between emergence, defined by a signal-to-noise ratio, of acidification stressors and temperature as a stressor. Furthermore, challenges in deconvolving anthropogenic and natural contributions to stressor trends will be discussed for each of the three sites.

Chance, choice, and the future of reproduction.

PubMed

Miller, W B

1983-11-01

The evolution of reproduction has been characterized by the development of complex biological and behavioral mechanisms that serve to regulate chance events. Human reproduction has been characterized by the increasing importance of individual choice. Some contemporary manifestations of this broad trend are the high incidence of contraceptive and "proceptive" behavior among couples in Western, industrialized nations. The former behavior willingly attempts to prevent conception while the latter actively attempts to induce conception (such as concentrating intercourse around the time of ovulation). Both patterns of behavior indicate that a choice is being made. A 3-year study of 1000 women revealed proceptive behavior as the most important factor predicting occurance of conception among married couples in the United States. The general strategeis people follow while making childbearing decisions: termination, sequencing, and pre-planning form a continuum following the historical trend toward greater reproductive control. In the terminating strategy, a couple makes no decision about child bearing until the number of children they have become enough or too much. In the sequencing strategy, decisions to have children are made 1 child at a time until a satisfactory limit is reached. In the pre-planning strategy, a plan is worked out ahead of time and is subsequently carried out. As new reproductive technology is introduced and as progressive change is made in society's reproductive related values and beliefs, choice will continue to dominate chance as the highly likely trend for the future of reproduction. Surrogate maternity is just 1 example of this trend. However, these new options, which culminate in the theory and practice of "progensis," (still in its infancy), as well as offering a rich opportunity, can also incur psychological burdens on a couple. Thus, as with any kind of freedom, these developments will require care, caution and responsibility.

Common mental disorder and obesity: insight from four repeat measures over 19 years: prospective Whitehall II cohort study.

PubMed

Kivimäki, Mika; Lawlor, Debbie A; Singh-Manoux, Archana; Batty, G David; Ferrie, Jane E; Shipley, Martin J; Nabi, Hermann; Sabia, Séverine; Marmot, Michael G; Jokela, Markus

2009-10-06

To examine potential reciprocal associations between common mental disorders and obesity, and to assess whether dose-response relations exist. Prospective cohort study with four measures of common mental disorders and obesity over 19 years (Whitehall II study). Civil service departments in London. 4363 adults (28% female, mean age 44 years at baseline). Common mental disorder defined as general health questionnaire "caseness;" overweight and obesity based on Word Health Organization definitions. In models adjusted for age, sex, and body mass index at baseline, odds ratios for obesity at the fourth screening were 1.33 (95% confidence interval 1.00 to 1.77), 1.64 (1.13 to 2.36), and 2.01 (1.21 to 3.34) for participants with common mental disorder at one, two, or three preceding screenings compared with people free from common mental disorder (P for trend<0.001). The corresponding mean differences in body mass index at the most recent screening were 0.20, 0.31, and 0.50 (P for trend<0.001). These associations remained after adjustment for baseline characteristics related to mental health and exclusion of participants who were obese at baseline. In addition, obesity predicted future risk of common mental disorder, again with evidence of a dose-response relation (P for trend=0.02, multivariable model). However, this association was lost when people with common mental disorder at baseline were excluded (P for trend=0.33). These findings suggest that in British adults the direction of association between common mental disorders and obesity is from common mental disorder to increased future risk of obesity. This association is cumulative such that people with chronic or repeat episodes of common mental disorder are particularly at risk of weight gain.

Evaluation of procedures for prediction of unconventional gas in the presence of geologic trends

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.

2009-01-01

This study extends the application of local spatial nonparametric prediction models to the estimation of recoverable gas volumes in continuous-type gas plays to regimes where there is a single geologic trend. A transformation is presented, originally proposed by Tomczak, that offsets the distortions caused by the trend. This article reports on numerical experiments that compare predictive and classification performance of the local nonparametric prediction models based on the transformation with models based on Euclidean distance. The transformation offers improvement in average root mean square error when the trend is not severely misspecified. Because of the local nature of the models, even those based on Euclidean distance in the presence of trends are reasonably robust. The tests based on other model performance metrics such as prediction error associated with the high-grade tracts and the ability of the models to identify sites with the largest gas volumes also demonstrate the robustness of both local modeling approaches. ?? International Association for Mathematical Geology 2009.

The Importance of Biotic vs. Abiotic Drivers of Local Plant Community Composition Along Regional Bioclimatic Gradients

PubMed Central

Klanderud, Kari; Vandvik, Vigdis; Goldberg, Deborah

2015-01-01

We assessed if the relative importance of biotic and abiotic factors for plant community composition differs along environmental gradients and between functional groups, and asked which implications this may have in a warmer and wetter future. The study location is a unique grid of sites spanning regional-scale temperature and precipitation gradients in boreal and alpine grasslands in southern Norway. Within each site we sampled vegetation and associated biotic and abiotic factors, and combined broad- and fine-scale ordination analyses to assess the relative explanatory power of these factors for species composition. Although the community responses to biotic and abiotic factors did not consistently change as predicted along the bioclimatic gradients, abiotic variables tended to explain a larger proportion of the variation in species composition towards colder sites, whereas biotic variables explained more towards warmer sites, supporting the stress gradient hypothesis. Significant interactions with precipitation suggest that biotic variables explained more towards wetter climates in the sub alpine and boreal sites, but more towards drier climates in the colder alpine. Thus, we predict that biotic interactions may become more important in alpine and boreal grasslands in a warmer future, although more winter precipitation may counteract this trend in oceanic alpine climates. Our results show that both local and regional scales analyses are needed to disentangle the local vegetation-environment relationships and their regional-scale drivers, and biotic interactions and precipitation must be included when predicting future species assemblages. PMID:26091266

The Importance of Biotic vs. Abiotic Drivers of Local Plant Community Composition Along Regional Bioclimatic Gradients.

PubMed

Klanderud, Kari; Vandvik, Vigdis; Goldberg, Deborah

2015-01-01

We assessed if the relative importance of biotic and abiotic factors for plant community composition differs along environmental gradients and between functional groups, and asked which implications this may have in a warmer and wetter future. The study location is a unique grid of sites spanning regional-scale temperature and precipitation gradients in boreal and alpine grasslands in southern Norway. Within each site we sampled vegetation and associated biotic and abiotic factors, and combined broad- and fine-scale ordination analyses to assess the relative explanatory power of these factors for species composition. Although the community responses to biotic and abiotic factors did not consistently change as predicted along the bioclimatic gradients, abiotic variables tended to explain a larger proportion of the variation in species composition towards colder sites, whereas biotic variables explained more towards warmer sites, supporting the stress gradient hypothesis. Significant interactions with precipitation suggest that biotic variables explained more towards wetter climates in the sub alpine and boreal sites, but more towards drier climates in the colder alpine. Thus, we predict that biotic interactions may become more important in alpine and boreal grasslands in a warmer future, although more winter precipitation may counteract this trend in oceanic alpine climates. Our results show that both local and regional scales analyses are needed to disentangle the local vegetation-environment relationships and their regional-scale drivers, and biotic interactions and precipitation must be included when predicting future species assemblages.

The MICORE review of historical changes in storminess in Europe

NASA Astrophysics Data System (ADS)

Ciavola, P.

2009-12-01

The main objective of the European-funded MICORE project is to develop and demonstrate on-line tools for the reliable prediction of storm impacts on coastlines and to develop and enhance existing civil protection strategies. The magnitude and frequency of storms was analyzed at 9 diverse Europe sites in order to determine storm trends over a period spanning between 30 and 100 years. Meteorological and marine data available at national and European level were included in the analysis. Here the aim was to improve understanding of coastal responses to changes in storminess and only event above a locally defined storm threshold were considered. This overcame the problems associated with the integration and comparison of information from widely dispersed geographical locations in Europe. The storm duration analysis performed for France (Aquitaine and Mediterranean), Italy (Northern Adriatic), Portugal (West Coast), Spain (Catalonia) and UK (Eastern Irish Sea) did not find any statistically significant change during the studied period. Similarly, no significant trends were observed for the Bulgarian and southern Portugal sites. The Polish site was the exception, showing a slight increase in storminess over the period studied. No clear trends in storm intensity were found for Italy-Northern Adriatic (waves and winds), Portugal-West Coast, and UK - Eastern Irish Sea. Similarly, Belgium, the Netherlands and Spain-Atlantic Andalusia (waves) did not detect any statistically significant trends. However, data from the Bulgarian and southern Portuguese coastlines indicated a slightly decreasing storminess trend. In contrast, a slight increase in storm frequency was observed in France-Aquitaine and Mediterranean (from the 1970s till 1990s), Italy-Northern Adriatic (only surges), Poland (significant both for surges and waves) and Spain-Andalusia (significant for wind). Results from the coastal regions in this study therefore support the conclusion that there are no significant trends detected in the magnitude or frequency of storms in Europe during the study period. The study provided some evidence that storminess variability is much higher than the observed trends at the time-scales used in this work (i.e. more than 3 decades). It was, however, not possible to observe any clear association between storminess changes and changes in the global climate. This does not imply that global climate change consequences will not have an influence on European storminess and on storminess impacts in the future. However, for the existing and available data sets at a European level, those impacts have not been detected in this study It is important to note also that although no clear trends in storminess emerge from the present study, this result does not necessarily imply that longer-term trends are absent. The study so far did not considered changes in the occurrence of “clusters” of events, i.e. the occurrence of several medium-energy events over a short time-scale. With respect to coastal evolution such storm ‘sequencing’ can have a major role in depleting beach sediments and decreasing resilience of dune ridges. Understanding and predicting this impact using innovative approaches will form the next area of work in the MICORE project and will provide a basis for the development of future coastal storm warning systems.

Northern Eurasian Heat Waves and Droughts

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Wang, Hailan; Koster, Randal; Suarez, Max; Groisman, Pavel

2013-01-01

This article reviews our understanding of the characteristics and causes of northern Eurasian summertime heat waves and droughts. Additional insights into the nature of temperature and precipitation variability in Eurasia on monthly to decadal time scales and into the causes and predictability of the most extreme events are gained from the latest generation of reanalyses and from supplemental simulations with the NASA GEOS-5 AGCM. Key new results are: 1) the identification of the important role of summertime stationary Rossby waves in the development of the leading patterns of monthly Eurasian surface temperature and precipitation variability (including the development of extreme events such as the 2010 Russian heat wave), 2) an assessment of the mean temperature and precipitation changes that have occurred over northern Eurasia in the last three decades and their connections to decadal variability and global trends in SST, and 3) the quantification (via a case study) of the predictability of the most extreme simulated heat wave/drought events, with some focus on the role of soil moisture in the development and maintenance of such events. A literature survey indicates a general consensus that the future holds an enhanced probability of heat waves across northern Eurasia, while there is less agreement regarding future drought, reflecting a greater uncertainty in soil moisture and precipitation projections. Substantial uncertainties remain in our understanding of heat waves and drought, including the nature of the interactions between the short-term atmospheric variability associated with such extremes and the longer-term variability and trends associated with soil moisture feedbacks, SST anomalies, and an overall warming world.

Recent trends in psychiatry residency workforce with special reference to international medical graduates.

PubMed

Rao, Nyapati R

2003-01-01

This study examines trends in the supply, distribution, and demographics of psychiatry residents during the 1990s. It evaluates the extent to which the predicted downsizing of psychiatry residency training programs actually occurred and how it affected training programs of different sizes and locations. Data for this study were obtained from the American Medical Association's (AMA) Annual Survey of Graduate Medical Education (GME) Programs, the AMA GME directory, and the APA Graduate Medical Census. The study compares the roles played by international medical graduates (IMGs) in contrast to U.S. medical graduates (USMGs) in these trends. There was a significant decline in the number of residents during the years studied. The median training program size also decreased. International medical graduates found broad acceptance in training programs of all locations and sizes, including medical school based programs. Implications of the findings are discussed regarding the impact of current graduate medical education (GME) and immigration policies on future workforce patterns. The field will have to decide whether it can afford anymore residency downsizing in light of emerging evidence of a shortage of psychiatrists.

Fertility transition in Bangladesh: trends and determinants.

PubMed

Kabir, M; Uddin, M M

1987-12-01

The poor quality of data on Bangladesh fertility hampers any analysis of the country's recent demographic trends. In general, however, it appears that total fertility remained stable between the 1960s and 1975, and then fell by about 12% in the 1975-85 period. The change in fertility appears attributable to an increase in the contraceptive prevalence rate and a decline in the proportion married in the younger age groups. Bangladesh is, according to Bongaart's classification, in the second phase of demographic transition during which contraceptive use is modest and breastfeeding exerts an important curb on fertility. However, there is evidence of a latent demand for family planning to space births; improved contraceptive practice is the factor most likely to bring about large fertility reductions in the years ahead. Contraceptive prevalence is estimated to have increased from 8% of currently married women in 1975 to 25% in 1985 and there have been steady increased in method effectiveness. Other determinants, such as spouse separation, postpartum abstinence, abortion, and sterility must also be considered in predicting future fertility trends in Bangladesh.

The pharmacist Aggregate Demand Index to explain changing pharmacist demand over a ten-year period.

PubMed

Knapp, Katherine K; Shah, Bijal M; Barnett, Mitchell J

2010-12-15

To describe Aggregate Demand Index (ADI) trends from 1999-2010; to compare ADI time trends to concurrent data for US unemployment levels, US entry-level pharmacy graduates, and US retail prescription growth rate; and to determine which variables were significant predictors of ADI. Annual ADI data (dependent variable) were analyzed against annual unemployment rates, annual number of pharmacy graduates, and annual prescription growth rate (independent variables). ADI data trended toward lower demand levels for pharmacists since late 2006, paralleling the US economic downturn. National ADI data were most highly correlated with unemployment (p < 0.001), then graduates (p < 0.006), then prescription growth rate (p < 0.093). A hierarchical model with the 3 variables was significant (p = 0.019), but only unemployment was a significant ADI predictor. Unemployment and ADI also were significantly related at the regional, division, and state levels. The ADI is strongly linked to US unemployment rates. The relationship suggests that an improving economy might coincide with increased pharmacist demand. Predictable increases in future graduates and other factors support revisiting the modeling process as new data accumulate.

Identification of the effective water availability from streamflows in the Zerafshan river basin, Central Asia

NASA Astrophysics Data System (ADS)

Olsson, Oliver; Gassmann, Matthias; Wegerich, Kai; Bauer, Melanie

2010-09-01

SummaryQuantitative estimates of the hydrologic effects of climate change are essential for understanding and solving potential transboundary water conflicts in the Zerafshan river basin, Central Asia. This paper introduces an identification of runoff generation processes and a detection of changes in hydrological regimes supporting Mann-Kendall trend analysis for streamflows. By this, the effective available and future water resources are identified for the Zerafshan. The results for the subbasins in the upper Zerafshan and for the reference station at the upper catchment outlet indicate that glacier melt is the most significant component of river runoff. The Mann-Kendall trend analysis confirms the regime analysis with the shift in the seasonality of the discharge. Furthermore, the results of the Kendall-Theil Robust Line for predicted long-term discharge trends show a decreasing annual discharge. The experience gained during this study emphasizes the fact that the summer flood, urgently required for the large irrigation projects downstream in Uzbekistan, is reduced and more water will be available in spring. Additionally, following the estimation of future discharges in 50 and 100 years the hydrological changes are affecting the seasonal water availability for irrigation. This analysis highlighted that water availability is decreasing and the timing of availability is changing. Hence, there will be more competition between upstream Tajikistan and downstream Uzbekistan. Planned projects within the basin might have to be reconsidered and the changed scenario of water availability needs to be properly taken into account for long-term basin scale water management.

World Trends and Alternative Futures. Open Grants Papers No. 1.

ERIC Educational Resources Information Center

McHale, John; Cordell, Magda

We are now at a stage in human global development in which the continuous review and assessment of the long-range future implications of our past and present actions becomes crucially important for the survival of human society. This report includes a synoptic view of world trends and alternative futures. The first and major portion of the…

Future trends in society and technology: implications for wilderness research and management

Treesearch

George H. Stankey

2000-01-01

Judging the impact of social and technological trends on the future of wilderness is complex. Declining public trust, growing demands for scrutiny, a need to recognize the link between biophysical and socioeconomic systems, and the need for criteria to select among alternative futures challenge us. A burgeoning global population will increase resource impacts, but more...

Bayesian Analysis for Inference of an Emerging Epidemic: Citrus Canker in Urban Landscapes

PubMed Central

Neri, Franco M.; Cook, Alex R.; Gibson, Gavin J.; Gottwald, Tim R.; Gilligan, Christopher A.

2014-01-01

Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated? How soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of spread of the disease. A rich and complex epidemic behaviour is revealed. The spatial scale of spread is approximately constant over time and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive). In contrast, the rate of infection is characterised by strong monthly fluctuations that we associate with extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatiotemporal description of the epidemic and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a fundamental step in future modelling of weather-driven epidemics. PMID:24762851

Linear Regression Quantile Mapping (RQM) - A new approach to bias correction with consistent quantile trends

NASA Astrophysics Data System (ADS)

Passow, Christian; Donner, Reik

2017-04-01

Quantile mapping (QM) is an established concept that allows to correct systematic biases in multiple quantiles of the distribution of a climatic observable. It shows remarkable results in correcting biases in historical simulations through observational data and outperforms simpler correction methods which relate only to the mean or variance. Since it has been shown that bias correction of future predictions or scenario runs with basic QM can result in misleading trends in the projection, adjusted, trend preserving, versions of QM were introduced in the form of detrended quantile mapping (DQM) and quantile delta mapping (QDM) (Cannon, 2015, 2016). Still, all previous versions and applications of QM based bias correction rely on the assumption of time-independent quantiles over the investigated period, which can be misleading in the context of a changing climate. Here, we propose a novel combination of linear quantile regression (QR) with the classical QM method to introduce a consistent, time-dependent and trend preserving approach of bias correction for historical and future projections. Since QR is a regression method, it is possible to estimate quantiles in the same resolution as the given data and include trends or other dependencies. We demonstrate the performance of the new method of linear regression quantile mapping (RQM) in correcting biases of temperature and precipitation products from historical runs (1959 - 2005) of the COSMO model in climate mode (CCLM) from the Euro-CORDEX ensemble relative to gridded E-OBS data of the same spatial and temporal resolution. A thorough comparison with established bias correction methods highlights the strengths and potential weaknesses of the new RQM approach. References: A.J. Cannon, S.R. Sorbie, T.Q. Murdock: Bias Correction of GCM Precipitation by Quantile Mapping - How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6038, 2015 A.J. Cannon: Multivariate Bias Correction of Climate Model Outputs - Matching Marginal Distributions and Inter-variable Dependence Structure. Journal of Climate, 29, 7045, 2016

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

A multi-model framework for simulating wildlife population response to land-use and climate change

USGS Publications Warehouse

McRae, B.H.; Schumaker, N.H.; McKane, R.B.; Busing, R.T.; Solomon, A.M.; Burdick, C.A.

2008-01-01

Reliable assessments of how human activities will affect wildlife populations are essential for making scientifically defensible resource management decisions. A principle challenge of predicting effects of proposed management, development, or conservation actions is the need to incorporate multiple biotic and abiotic factors, including land-use and climate change, that interact to affect wildlife habitat and populations through time. Here we demonstrate how models of land-use, climate change, and other dynamic factors can be integrated into a coherent framework for predicting wildlife population trends. Our framework starts with land-use and climate change models developed for a region of interest. Vegetation changes through time under alternative future scenarios are predicted using an individual-based plant community model. These predictions are combined with spatially explicit animal habitat models to map changes in the distribution and quality of wildlife habitat expected under the various scenarios. Animal population responses to habitat changes and other factors are then projected using a flexible, individual-based animal population model. As an example application, we simulated animal population trends under three future land-use scenarios and four climate change scenarios in the Cascade Range of western Oregon. We chose two birds with contrasting habitat preferences for our simulations: winter wrens (Troglodytes troglodytes), which are most abundant in mature conifer forests, and song sparrows (Melospiza melodia), which prefer more open, shrubby habitats. We used climate and land-use predictions from previously published studies, as well as previously published predictions of vegetation responses using FORCLIM, an individual-based forest dynamics simulator. Vegetation predictions were integrated with other factors in PATCH, a spatially explicit, individual-based animal population simulator. Through incorporating effects of landscape history and limited dispersal, our framework predicted population changes that typically exceeded those expected based on changes in mean habitat suitability alone. Although land-use had greater impacts on habitat quality than did climate change in our simulations, we found that small changes in vital rates resulting from climate change or other stressors can have large consequences for population trajectories. The ability to integrate bottom-up demographic processes like these with top-down constraints imposed by climate and land-use in a dynamic modeling environment is a key advantage of our approach. The resulting framework should allow researchers to synthesize existing empirical evidence, and to explore complex interactions that are difficult or impossible to capture through piecemeal modeling approaches. ?? 2008 Elsevier B.V.

Foreign currency rate forecasting using neural networks

NASA Astrophysics Data System (ADS)

Pandya, Abhijit S.; Kondo, Tadashi; Talati, Amit; Jayadevappa, Suryaprasad

2000-03-01

Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

Intensity of prehistoric tropical cyclones

NASA Astrophysics Data System (ADS)

Nott, Jonathan F.

2003-04-01

Prediction of future tropical cyclone climate scenarios requires identification of quasi-periodicities at a variety of temporal scales. Extension of records to identify trends at century and millennial scales is important, but to date the emerging field of paleotempestology has been hindered by the lack of a suitable methodology to discern the intensity of prehistoric storms. Here a technique to quantify the central pressure of prehistoric tropical cyclones is presented in detail and demonstrated for the tropical southwest Pacific region. The importance of extending records to century time scales is highlighted for northeast Australia, where a virtual absence of category 5 cyclones during the 20th century stands in contrast to an active period of severe cyclogenesis during the previous century. Several land crossing storms during the 19th century achieved central pressures lower than that ever recorded historically and close to the theoretical thermodynamic limit of storms for the region. This technique can be applied to all tropical and subtropical regions globally and will assist in obtaining more realistic predictions for future storm scenarios with implications for insurance premiums, urban and infrastructural design, and emergency planning.

The dynamics of learning about a climate threshold

NASA Astrophysics Data System (ADS)

Keller, Klaus; McInerney, David

2008-02-01

Anthropogenic greenhouse gas emissions may trigger threshold responses of the climate system. One relevant example of such a potential threshold response is a shutdown of the North Atlantic meridional overturning circulation (MOC). Numerous studies have analyzed the problem of early MOC change detection (i.e., detection before the forcing has committed the system to a threshold response). Here we analyze the early MOC prediction problem. To this end, we virtually deploy an MOC observation system into a simple model that mimics potential future MOC responses and analyze the timing of confident detection and prediction. Our analysis suggests that a confident prediction of a potential threshold response can require century time scales, considerably longer that the time required for confident detection. The signal enabling early prediction of an approaching MOC threshold in our model study is associated with the rate at which the MOC intensity decreases for a given forcing. A faster MOC weakening implies a higher MOC sensitivity to forcing. An MOC sensitivity exceeding a critical level results in a threshold response. Determining whether an observed MOC trend in our model differs in a statistically significant way from an unforced scenario (the detection problem) imposes lower requirements on an observation system than the determination whether the MOC will shut down in the future (the prediction problem). As a result, the virtual observation systems designed in our model for early detection of MOC changes might well fail at the task of early and confident prediction. Transferring this conclusion to the real world requires a considerably refined MOC model, as well as a more complete consideration of relevant observational constraints.

A real-time method to predict social media popularity

NASA Astrophysics Data System (ADS)

Chen, Xiao; Lu, Zhe-Ming

How to predict the future popularity of a message or video on online social media (OSM) has long been an attractive problem for researchers. Although many difficulties are still ahead, recent studies suggest that temporal and topological features of early adopters generally play a very important role. However, with the increase of the adopters, the feature space will grow explosively. How to select the most effective features is still an open issue. In this work, we investigate several feature extraction methods over the Twitter platform and find that most predictive power concentrates on the second half of the propagation period, and that not only a model trained on one platform generalizes well to others as previous works observed, but also a model trained on one dataset performs well on predicting the popularity for other datasets with different number of observed early adopters. According to these findings, at least for the best features by far, the data used to extract features can be halved without loss of evident accuracy and we provide a way to roughly predict the growth trend of a social-media item in real-time.

International Geomagnetic Reference Field: the 12th generation

NASA Astrophysics Data System (ADS)

Thébault, Erwan; Finlay, Christopher C.; Beggan, Ciarán D.; Alken, Patrick; Aubert, Julien; Barrois, Olivier; Bertrand, Francois; Bondar, Tatiana; Boness, Axel; Brocco, Laura; Canet, Elisabeth; Chambodut, Aude; Chulliat, Arnaud; Coïsson, Pierdavide; Civet, François; Du, Aimin; Fournier, Alexandre; Fratter, Isabelle; Gillet, Nicolas; Hamilton, Brian; Hamoudi, Mohamed; Hulot, Gauthier; Jager, Thomas; Korte, Monika; Kuang, Weijia; Lalanne, Xavier; Langlais, Benoit; Léger, Jean-Michel; Lesur, Vincent; Lowes, Frank J.; Macmillan, Susan; Mandea, Mioara; Manoj, Chandrasekharan; Maus, Stefan; Olsen, Nils; Petrov, Valeriy; Ridley, Victoria; Rother, Martin; Sabaka, Terence J.; Saturnino, Diana; Schachtschneider, Reyko; Sirol, Olivier; Tangborn, Andrew; Thomson, Alan; Tøffner-Clausen, Lars; Vigneron, Pierre; Wardinski, Ingo; Zvereva, Tatiana

2015-05-01

The 12th generation of the International Geomagnetic Reference Field (IGRF) was adopted in December 2014 by the Working Group V-MOD appointed by the International Association of Geomagnetism and Aeronomy (IAGA). It updates the previous IGRF generation with a definitive main field model for epoch 2010.0, a main field model for epoch 2015.0, and a linear annual predictive secular variation model for 2015.0-2020.0. Here, we present the equations defining the IGRF model, provide the spherical harmonic coefficients, and provide maps of the magnetic declination, inclination, and total intensity for epoch 2015.0 and their predicted rates of change for 2015.0-2020.0. We also update the magnetic pole positions and discuss briefly the latest changes and possible future trends of the Earth's magnetic field.

Environmental Impact of Megacities - Results from CityZen

NASA Astrophysics Data System (ADS)

Gauss, M.

2012-04-01

Megacities have increasingly important impacts on air quality and climate change on different spatial scales, owing to their high population densities and concentrated emission sources. The EU FP7 project CityZen (Megacity - Zoom for the Environment) ended in 2011 and was, together with its sister project MEGAPOLI, part of a major research effort within FP7 on megacities in Europe and worldwide. The project mainly focused on air pollution trends in large cities and emission hotspots, climate-chemistry couplings, future projections, and emission mitigation options. Both observational and modeling tools have been extensively used. This paper reviews some of the main results from CityZen regarding present air pollution in and around megacities, future scenarios and mitigation options to reduce air pollution and/or climate change, and the main policy messages from the project. The different observed trends over European and Asian hotspots during the last 10 to 15 years are shown. Results of source attribution of pollutants, which have been measured and calculated in and around the different selected hot spots in CityZen will be discussed. Another important question to be addressed is the extent to which climate change will affect air quality and the effectiveness of air quality legislation. Although projected emission reductions are a major determinate influencing the predictions of future air pollution, model results suggest that climate change has to be taken into account when devising future air quality legislation. This paper will also summarize some important policy messages in terms of ozone, particles and the observational needs that have been put forward as conclusions from the project.

Sensitivity to Factors Underlying the Hiatus

NASA Technical Reports Server (NTRS)

Marvel, Kate; Schmidt, Gavin A.; Tsigaridis, Kostas; Cook, Benjamin I.

2015-01-01

Recent trends in global mean surface air temperature fall outside the 90 range predicted by models using the CMIP5 forcings and scenarios; this recent period of muted warming is dubbed the hiatus. The hiatus has attracted broad attention in both the popular press and the scientific literature, primarily because of its perceived implications for understanding long-term trends. Many hypotheses have been offered to explain the warming slowdown during the hiatus, and comprehensive studies of this period across multiple variables and spatial scales will likely improve our understanding of the physical mechanisms driving global temperature change and variability.We argue, however, that decadal temperature trends by themselves are unlikely to constrain future trajectories of global mean temperature and that the hiatus does not significantly revise our understanding of overall climate sensitivity. Instead, we demonstrate that, because of the poorly constrained nature of the hiatus, model-observation disagreements over this period may be resolvable via uncertainties in the observations, modeled internal variability, forcing estimates, or (more likely) some combination of all three factors. We define the hiatus interval as 1998-2012, endpoints judiciously chosen to minimize observed warming by including the large 1998 El Nio event and excluding 2014, an exceptionally warm year. Such choices are fundamentally subjective and cannot be considered random, so any probabilistic statements regarding the likelihood of this occurring need to be made carefully. Using this definition, the observed global temperature trend estimates from four datasets fall outside the 5-95 interval predicted by the CMIP5 models. Here we explore some of the plausible explanations for this discrepancy, and show that no unique explanation is likely to fully account for the hiatus.

Indicators of AEI applied to the Delaware Estuary.

PubMed

Barnthouse, Lawrence W; Heimbuch, Douglas G; Anthony, Vaughn C; Hilborn, Ray W; Myers, Ransom A

2002-05-18

We evaluated the impacts of entrainment and impingement at the Salem Generating Station on fish populations and communities in the Delaware Estuary. In the absence of an agreed-upon regulatory definition of "adverse environmental impact" (AEI), we developed three independent benchmarks of AEI based on observed or predicted changes that could threaten the sustainability of a population or the integrity of a community. Our benchmarks of AEI included: (1) disruption of the balanced indigenous community of fish in the vicinity of Salem (the "BIC" analysis); (2) a continued downward trend in the abundance of one or more susceptible fish species (the "Trends" analysis); and (3) occurrence of entrainment/impingement mortality sufficient, in combination with fishing mortality, to jeopardize the future sustainability of one or more populations (the "Stock Jeopardy" analysis). The BIC analysis utilized nearly 30 years of species presence/absence data collected in the immediate vicinity of Salem. The Trends analysis examined three independent data sets that document trends in the abundance of juvenile fish throughout the estuary over the past 20 years. The Stock Jeopardy analysis used two different assessment models to quantify potential long-term impacts of entrainment and impingement on susceptible fish populations. For one of these models, the compensatory capacities of the modeled species were quantified through meta-analysis of spawner-recruit data available for several hundred fish stocks. All three analyses indicated that the fish populations and communities of the Delaware Estuary are healthy and show no evidence of an adverse impact due to Salem. Although the specific models and analyses used at Salem are not applicable to every facility, we believe that a weight of evidence approach that evaluates multiple benchmarks of AEI using both retrospective and predictive methods is the best approach for assessing entrainment and impingement impacts at existing facilities.

What goes up must . . . Keep going up? Cultural differences in cognitive styles influence evaluations of dynamic performance.

PubMed

Ferris, D Lance; Reb, Jochen; Lian, Huiwen; Sim, Samantha; Ang, Dionysius

2018-03-01

Past research on dynamic workplace performance evaluation has taken as axiomatic that temporal performance trends produce naïve extrapolation effects on performance ratings. That is, we naïvely assume that an individual whose performance has trended upward over time will continue to improve, and rate that individual more positively than an individual whose performance has trended downward over time-even if, on average, the 2 individuals have performed at an equivalent level. However, we argue that such naïve extrapolation effects are more pronounced in Western countries than Eastern countries, owing to Eastern countries having a more holistic cognitive style. To test our hypotheses, we examined the effect of performance trend on expectations of future performance and ratings of past performance across 2 studies: Study 1 compares the magnitude of naïve extrapolation effects among Singaporeans primed with either a more or less holistic cognitive style, and Study 2 examines holistic cognitive style as a mediating mechanism accounting for differences in the magnitude of naïve extrapolation effects between American and Chinese raters. Across both studies, we found support for our predictions that dynamic performance trends have less impact on the ratings of more holistic thinkers. Implications for the dynamic performance and naïve extrapolation literatures are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The impacts of emission trends of POPs on human concentration dynamics: Lessons learned from a longitudinal study in Norway (1979-2007).

PubMed

Nøst, Therese Haugdahl; Sandanger, Torkjel Manning; Nieboer, Evert; Odland, Jon Øyvind; Breivik, Knut

2017-06-01

In this short communication, our focus is on the relationship between human concentrations of select persistent organic pollutants (POPs) and environmental emissions. It is based on a longitudinal study (1979-2007) conducted in Norway. Our aim was to extract general insights from observed and predicted temporal trends in human concentrations of 49 POPs to assist in the design and interpretation of future monitoring studies. Despite considerable decline for polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) since 1986, the sum of the targeted POPs increased from 1979 until 2001, with per- and polyfluorinated alkyl substances (PFASs) dominating recent blood burden measurements. Specifically, the time trends in serum concentrations of POPs, exemplified by PCB-153, 1,1'-(2,2,2-Trichloroethane-1,1-diyl)bis(4-chlorobenzene) (DDT) and perfluorooctane sulfonic acid (PFOS), resembled the trends in available data on their emissions, production or use. These observations suggest that interpretations of human biomonitoring data on persistent compounds must consider historic emissions, which likely vary spatially across the globe. Based on the different temporal trends observed across POP groups, it is evident that generalizations regarding temporal aspects have limitations. The discussion herein underscores the importance of understanding temporal variations in environmental emissions when designing and interpreting human biomonitoring studies. Copyright © 2017 Elsevier GmbH. All rights reserved.

Decomposition of CO2 Emission Factors in Baoding

NASA Astrophysics Data System (ADS)

Li, Wei; Wang, xuyang; Zhang, Hongzhi

2018-01-01

Baoding, as one of the first “five provinces and eight cities” low carbon pilot cities, undertakes an important task and mission. The urgent task is to explore a peak route and emission reduction path suitable for Baoding’s own development, so as to provide reference for the construction of low-carbon pilot cities. At present, the carbon emissions of Baoding city and its subordinate districts and counties are not clear, and the carbon emissions, change trends and emission characteristics of various industries have not been systematically studied. This lead researcherscan not carry out further attribution analysis, the prediction of future emissions trends and put forward specific measures to reduce emissions are impossible.If the government can not accurately and comprehensively understand the problems faced in the construction and development of low-carbon cities, it is difficult to fundamentally put forward effective emission reduction policies and measures.

Foreign medical graduates in the 1980s: trends in specialization.

PubMed Central

Mick, S S; Worobey, J L

1984-01-01

Secondary analysis of data collected by the American Medical Association and the Graduate Medical Education National Advisory Committee (GMENAC) suggests that measures to diminish the flow of alien Foreign Medical Graduates (FMGs) into the United States have been less effective than planned. Declining trends in the proportion of FMG house officers in the mid- to late-1970s have recently stabilized around 19 per cent. There has also been a dramatic increase in the number of US citizen Foreign Medical Graduates ( USFMGs ) in house officer positions. A pattern of alien FMG and USFMG house officer specialization correlates with specialties designated by the GMENAC as shortage areas by 1990 (r = -.49, p less than .05). Despite the GMENAC prediction of a surplus of physicians by 1990, differential selection of alien FMGs and USFMGs into shortage specialties may assure their substantial future presence in the US health care system. PMID:6742255

Noiseonomics: the relationship between ambient noise levels in the sea and global economic trends.

PubMed

Frisk, George V

2012-01-01

In recent years, the topic of noise in the sea and its effects on marine mammals has attracted considerable attention from both the scientific community and the general public. Since marine mammals rely heavily on acoustics as a primary means of communicating, navigating, and foraging in the ocean, any change in their acoustic environment may have an impact on their behavior. Specifically, a growing body of literature suggests that low-frequency, ambient noise levels in the open ocean increased approximately 3.3 dB per decade during the period 1950-2007. Here we show that this increase can be attributed primarily to commercial shipping activity, which in turn, can be linked to global economic growth. As a corollary, we conclude that ambient noise levels can be directly related to global economic conditions. We provide experimental evidence supporting this theory and discuss its implications for predicting future noise levels based on global economic trends.

The Northern Forest Futures Project: examining past, present, and future trends affecting forests in and around the central hardwood forest region

Treesearch

Stephen Shifley

2013-01-01

Th e Northern Forest Futures Project is intended to be a window on tomorrow's forests, revealing how today's trends and choices can change the future landscape of the Northeast and Midwest. Th e research is focused on the 20 states bounded by Maine, Maryland, Missouri and Minnesota—the most heavily forested and most densely populated quadrant of the...

Digital shoreline analysis system-based change detection along the highly eroding Krishna-Godavari delta front

NASA Astrophysics Data System (ADS)

Kallepalli, Akhil; Kakani, Nageswara Rao; James, David B.; Richardson, Mark A.

2017-07-01

Coastal regions are highly vulnerable to rising sea levels due to global warming. Previous Intergovernmental Panel on Climate Change (2013) predictions of 26 to 82 cm global sea level rise are now considered conservative. Subsequent investigations predict much higher levels which would displace 10% of the world's population living less than 10 m above sea level. Remote sensing and GIS technologies form the mainstay of models on coastal retreat and inundation to future sea-level rise. This study estimates the varying trends along the Krishna-Godavari (K-G) delta region. The rate of shoreline shift along the 330-km long K-G delta coast was estimated using satellite images between 1977 and 2008. With reference to a selected baseline from along an inland position, end point rate and net shoreline movement were calculated using a GIS-based digital shoreline analysis system. The results indicated a net loss of about 42.1 km2 area during this 31-year period, which is in agreement with previous literature. Considering the nature of landforms and EPR, the future hazard line (or coastline) is predicted for the area; the predication indicates a net erosion of about 57.6 km2 along the K-G delta coast by 2050 AD.

Assessment of the climate change impacts on fecal coliform contamination in a tidal estuarine system.

PubMed

Liu, Wen-Cheng; Chan, Wen-Ting

2015-12-01

Climate change is one of the key factors affecting the future microbiological water quality in rivers and tidal estuaries. A coupled 3D hydrodynamic and fecal coliform transport model was developed and applied to the Danshuei River estuarine system for predicting the influences of climate change on microbiological water quality. The hydrodynamic and fecal coliform model was validated using observational salinity and fecal coliform distributions. According to the analyses of the statistical error, predictions of the salinity and the fecal coliform concentration from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict the fecal coliform contamination as a result of climate change, including the change of freshwater discharge and the sea level rise. We found that the reduction of freshwater discharge under climate change scenarios resulted in an increase in the fecal coliform concentration. The sea level rise would decrease fecal coliform distributions because both the water level and the water volume increased. A reduction in freshwater discharge has a negative impact on the fecal coliform concentration, whereas a rising sea level has a positive influence on the fecal coliform contamination. An appropriate strategy for the effective microbiological management in tidal estuaries is required to reveal the persistent trends of climate in the future.

Ecological transition predictably associated with gene degeneration.

PubMed

Wessinger, Carolyn A; Rausher, Mark D

2015-02-01

Gene degeneration or loss can significantly contribute to phenotypic diversification, but may generate genetic constraints on future evolutionary trajectories, potentially restricting phenotypic reversal. Such constraints may manifest as directional evolutionary trends when parallel phenotypic shifts consistently involve gene degeneration or loss. Here, we demonstrate that widespread parallel evolution in Penstemon from blue to red flowers predictably involves the functional inactivation and degeneration of the enzyme flavonoid 3',5'-hydroxylase (F3'5'H), an anthocyanin pathway enzyme required for the production of blue floral pigments. Other types of genetic mutations do not consistently accompany this phenotypic shift. This pattern may be driven by the relatively large mutational target size of degenerative mutations to this locus and the apparent lack of associated pleiotropic effects. The consistent degeneration of F3'5'H may provide a mechanistic explanation for the observed asymmetry in the direction of flower color evolution in Penstemon: Blue to red transitions are common, but reverse transitions have not been observed. Although phenotypic shifts in this system are likely driven by natural selection, internal constraints may generate predictable genetic outcomes and may restrict future evolutionary trajectories. © The Author 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Physiological plasticity increases resilience of ectothermic animals to climate change

NASA Astrophysics Data System (ADS)

Seebacher, Frank; White, Craig R.; Franklin, Craig E.

2015-01-01

Understanding how climate change affects natural populations remains one of the greatest challenges for ecology and management of natural resources. Animals can remodel their physiology to compensate for the effects of temperature variation, and this physiological plasticity, or acclimation, can confer resilience to climate change. The current lack of a comprehensive analysis of the capacity for physiological plasticity across taxonomic groups and geographic regions, however, constrains predictions of the impacts of climate change. Here, we assembled the largest database to date to establish the current state of knowledge of physiological plasticity in ectothermic animals. We show that acclimation decreases the sensitivity to temperature and climate change of freshwater and marine animals, but less so in terrestrial animals. Animals from more stable environments have greater capacity for acclimation, and there is a significant trend showing that the capacity for thermal acclimation increases with decreasing latitude. Despite the capacity for acclimation, climate change over the past 20 years has already resulted in increased physiological rates of up to 20%, and we predict further future increases under climate change. The generality of these predictions is limited, however, because much of the world is drastically undersampled in the literature, and these undersampled regions are the areas of greatest need for future research efforts.

Estimation of Future Return Levels for Heavy Rainfall in the Iberian Peninsula: Comparison of Methodologies

NASA Astrophysics Data System (ADS)

Parey, S.

2014-12-01

F. J. Acero1, S. Parey2, T.T.H. Hoang2, D. Dacunha-Castelle31Dpto. Física, Universidad de Extremadura, Avda. de Elvas s/n, 06006, Badajoz 2EDF/R&D, 6 quai Watier, 78401 Chatou Cedex, France 3Laboratoire de Mathématiques, Université Paris 11, Orsay, France Trends can already be detected in daily rainfall amount in the Iberian Peninsula (IP), and this will have an impact on the extreme levels. In this study, we compare different ways to estimate future return levels for heavy rainfall, based on the statistical extreme value theory. Both Peaks over Threshold (POT) and block maxima with the Generalized Extreme Value (GEV) distribution will be used and their results compared when linear trends are assumed in the parameters: threshold and scale parameter for POT and location and scale parameter for GEV. But rainfall over the IP is a special variable in that a large number of the values are 0. Thus, the impact of taking this into account is discussed too. Another approach is then tested, based on the evolutions of the mean and variance obtained from the time series of rainy days only, and of the number of rainy days. A statistical test, similar to that designed for temperature in Parey et al. 2013, is used to assess if the trends in extremes can be considered as mostly due to these evolutions when considering only rainy days. The results show that it is mainly the case: the extremes of the residuals, after removing the trends in mean and standard deviation, cannot be differentiated from those of a stationary process. Thus, the future return levels can be estimated from the stationary return level of these residuals and an estimation of the future mean and standard deviation. Moreover, an estimation of the future number of rainy days is used to retrieve the return levels for all days. All of these comparisons are made for an ensemble of high quality rainfall time series observed in the Iberian Peninsula over the period 1961-2010, from which we want to estimate a 20-year return level expected in 2020. The evolutions and the impact of the different approaches will be discussed for 3 seasons: fall, spring and winter. Parey S., Hoang T.T.H., Dacunha-Castelle D.: The importance of mean and variance in predicting changes in temperature extremes, Journal of Geophysical Research: Atmospheres, Vol. 118, 1-12, 2013.

A positive relationship between spring temperature and productivity in 20 songbird species in the boreal zone.

PubMed

Meller, Kalle; Piha, Markus; Vähätalo, Anssi V; Lehikoinen, Aleksi

2018-03-01

Anthropogenic climate warming has already affected the population dynamics of numerous species and is predicted to do so also in the future. To predict the effects of climate change, it is important to know whether productivity is linked to temperature, and whether species' traits affect responses to climate change. To address these objectives, we analysed monitoring data from the Finnish constant effort site ringing scheme collected in 1987-2013 for 20 common songbird species together with climatic data. Warm spring temperature had a positive linear relationship with productivity across the community of 20 species independent of species' traits (realized thermal niche or migration behaviour), suggesting that even the warmest spring temperatures remained below the thermal optimum for reproduction, possibly due to our boreal study area being closer to the cold edge of all study species' distributions. The result also suggests a lack of mismatch between the timing of breeding and peak availability of invertebrate food of the study species. Productivity was positively related to annual growth rates in long-distance migrants, but not in short-distance migrants. Across the 27-year study period, temporal trends in productivity were mostly absent. The population sizes of species with colder thermal niches had decreasing trends, which were not related to temperature responses or temporal trends in productivity. The positive connection between spring temperature and productivity suggests that climate warming has potential to increase the productivity in bird species in the boreal zone, at least in the short term.

Climate Patterns and Trends of Tree-Mortality in the Southwestern United States

NASA Astrophysics Data System (ADS)

Yi, C.; Mu, G.; Hendrey, G. R.; Vicente-Serrano, S.

2016-12-01

Evidence suggests a world-wide increase in tree mortality associated with climate change in regions subjected to prolonged drought. This is particularly evident in the Southwestern USA (SWUSA) where trees are dying at an accelerating and alarming rate where we investigated climate patterns and trends over the past century in combination with abundant tree-ring data, and thresholds of tree-mortality. In this drought-prone region we found a strong correlation between annual tree-ring width and the corresponding annual average temperature and amount of precipitation. A standardized precipitation-evapotranspiration index (SPEI) was a robust predictor of annual tree growth. At a SPEI of -1.6, tree-ring width was found to be zero. We hypothesize that this is a tipping point for tree-ring mortality. This is confirmed in that approximately 225 million trees died in SWUSA in 2002 when SPEI fell below this tipping point. An analysis of future trends in SPEI based on four GHG concentration scenarios of the IPCC predicts that in coming decades, the conifer forest in SWUSA is expected to be lost entirely due to the prolonged drought there, as the SPEI is predicted to pass the tipping point. It can be anticipated that as the area impacted by prolonged drought increases with SPEI falling below -1.6 tree mortality will become a regional or semi-continental phenomenon. Acknowledgement:This research was supported by PSC-CUNY award (PSC-CUNY-ENHC-68849-0046) and the CUNY Collaborative Incentive Research Grant (CUNY-CIRG-80209-08 22).

Risk Identification and Prediction of Coal Workers’ Pneumoconiosis in Kailuan Colliery Group in China: A Historical Cohort Study

PubMed Central

Shen, Fuhai; Yuan, Juxiang; Sun, Zhiqian; Hua, Zhengbing; Qin, Tianbang; Yao, Sanqiao; Fan, Xueyun; Chen, Weihong; Liu, Hongbo; Chen, Jie

2013-01-01

Background Prior to 1970, coal mining technology and prevention measures in China were poor. Mechanized coal mining equipment and advanced protection measures were continuously installed in the mines after 1970. All these improvements may have resulted in a change in the incidence of coal workers’ pneumoconiosis (CWP). Therefore, it is important to identify the characteristics of CWP today and trends for the incidence of CWP in the future. Methodology/Principal Findings A total of 17,023 coal workers from the Kailuan Colliery Group were studied. A life-table method was used to calculate the cumulative incidence rate of CWP and predict the number of new CWP patients in the future. The probability of developing CWP was estimated by a multilayer perceptron artificial neural network for each coal worker without CWP. The results showed that the cumulative incidence rates of CWP for tunneling, mining, combining, and helping workers were 31.8%, 27.5%, 24.2%, and 2.6%, respectively, during the same observation period of 40 years. It was estimated that there would be 844 new CWP cases among 16,185 coal workers without CWP within their life expectancy. There would be 273.1, 273.1, 227.6, and 69.9 new CWP patients in the next <10, 10-, 20-, and 30- years respectively in the study cohort within their life expectancy. It was identified that coal workers whose risk probabilities were over 0.2 were at high risk for CWP, and whose risk probabilities were under 0.1 were at low risk. Conclusion/Significance The present and future incidence trends of CWP remain high among coal workers. We suggest that coal workers at high risk of CWP undergo a physical examination for pneumoconiosis every year, and the coal workers at low risk of CWP be examined every 5 years. PMID:24376519

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Impacts of past and future climate change on wind energy resources in the United States

NASA Astrophysics Data System (ADS)

McCaa, J. R.; Wood, A.; Eichelberger, S.; Westrick, K.

2009-12-01

The links between climate change and trends in wind energy resources have important potential implications for the wind energy industry, and have received significant attention in recent studies. We have conducted two studies that provide insights into the potential for climate change to affect future wind power production. In one experiment, we projected changes in power capacity for a hypothetical wind farm located near Kennewick, Washington, due to greenhouse gas-induced climate change, estimated using a set of regional climate model simulations. Our results show that the annual wind farm power capacity is projected to decrease 1.3% by 2050. In a wider study focusing on wind speed instead of power, we analyzed projected changes in wind speed from 14 different climate simulations that were performed in support of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Our results show that the predicted ensemble mean changes in annual mean wind speeds are expected to be modest. However, seasonal changes and changes predicted by individual models are large enough to affect the profitability of existing and future wind projects. The majority of the model simulations reveal that near-surface wind speed values are expected to shift poleward in response to the IPCC A2 emission scenario, particularly during the winter season. In the United States, most models agree that the mean annual wind speed values will increase in a region extending from the Great Lakes southward across the Midwest and into Texas. Decreased values, though, are predicted across most of the western United States. However, these predicted changes have a strong seasonal dependence, with wind speed increases over most of the United States during the winter and decreases over the northern United States during the summer.

Designing optimized multi-species monitoring networks to detect range shifts driven by climate change: a case study with bats in the North of Portugal.

PubMed

Amorim, Francisco; Carvalho, Sílvia B; Honrado, João; Rebelo, Hugo

2014-01-01

Here we develop a framework to design multi-species monitoring networks using species distribution models and conservation planning tools to optimize the location of monitoring stations to detect potential range shifts driven by climate change. For this study, we focused on seven bat species in Northern Portugal (Western Europe). Maximum entropy modelling was used to predict the likely occurrence of those species under present and future climatic conditions. By comparing present and future predicted distributions, we identified areas where each species is likely to gain, lose or maintain suitable climatic space. We then used a decision support tool (the Marxan software) to design three optimized monitoring networks considering: a) changes in species likely occurrence, b) species conservation status, and c) level of volunteer commitment. For present climatic conditions, species distribution models revealed that areas suitable for most species occur in the north-eastern part of the region. However, areas predicted to become climatically suitable in the future shifted towards west. The three simulated monitoring networks, adaptable for an unpredictable volunteer commitment, included 28, 54 and 110 sampling locations respectively, distributed across the study area and covering the potential full range of conditions where species range shifts may occur. Our results show that our framework outperforms the traditional approach that only considers current species ranges, in allocating monitoring stations distributed across different categories of predicted shifts in species distributions. This study presents a straightforward framework to design monitoring schemes aimed specifically at testing hypotheses about where and when species ranges may shift with climatic changes, while also ensuring surveillance of general population trends.

Frailty Versus Stopping Elderly Accidents, Deaths and Injuries Initiative Fall Risk Score: Ability to Predict Future Falls.

PubMed

Crow, Rebecca S; Lohman, Matthew C; Pidgeon, Dawna; Bruce, Martha L; Bartels, Stephen J; Batsis, John A

2018-03-01

To compare the ability of frailty status to predict fall risk with that of community fall risk screening tools. Analysis of cross-sectional and longitudinal data from NHATS. National Health and Aging Trend Study (NHATS) 2011-2015. Individuals aged 65 and older (N = 7,392). Fall risk was defined according to the Stopping Elderly Accidents, Deaths and Injuries (STEADI) initiative. Frailty was defined as exhaustion, weight loss, low activity, slow gait speed, and weak grip strength. Robust was defined as meeting 0 criteria, prefrailty as 1 or 2 criteria, and frailty as 3 or more criteria. Falls were self-reported and ascertained using NHATS subsequent rounds (2012-2015). We compared the ability of frailty to predict future falls with that of STEADI score, adjusting for age, race, sex, education, comorbidities, hearing and vision impairment, and disability. Of the 7,392 participants (58.5% female), there 3,545 (48.0%) were classified as being at low risk of falling, 2,966 (40.1%) as being at moderate risk, and 881 (11.9%) as being at high risk. The adjusted risk of falling over the 4 subsequent years was 2.5 times as great for the moderate-risk group (hazard ratio (HR) = 2.50, 95% confidence interval (CI) = 2.16-2.89) and almost 4 times as great (HR = 3.79, 95% CI = 2.76-5.21) for the high-risk group as for the low-risk group. Risk of falling was greater for those who were prefrail (HR = 1.34, 95% CI 1.16-1.55) and frail (HR = 1.20, 95% CI = 0.94-1.54) than for those who were robust. STEADI score is a strong predictor of future falls. Addition of frailty status does not improve the ability of the STEADI measure to predict future falls. © 2018, Copyright the Authors Journal compilation © 2018, The American Geriatrics Society.

Driving the Landscape

NASA Astrophysics Data System (ADS)

Haff, P. K.

2012-12-01

Technological modification of the earth's surface (e.g., agriculture, urbanization) is an old story in human history, but what about the future? The future of landscape in an accelerating technological world, beyond a relatively short time horizon, lies hidden behind an impenetrable veil of complexity. Sufficiently complex dynamics generates not only the trajectory of a variable of interest (e.g., vegetation cover) but also the environment in which that variable evolves (e.g., background climate). There is no way to anticipate what variables will define that environment—the dynamics creates its own variables. We are always open to surprise by a change of conditions we thought or assumed were fixed or by the appearance of new phenomena of whose possible existence we had been unaware or thought unlikely. This is especially true under the influence of technology, where novelty is the rule. Lack of direct long-term predictability of landscape change does not, however, mean we cannot say anything about its future. The presence of persistence (finite time scales) in a system means that prediction by a calibrated numerical model should be good for a limited period of time barring bad luck or faulty implementation. Short-term prediction, despite its limitations, provides an option for dealing with the longer-term future. If a computer-controlled car tries to drive itself from New York to Los Angeles, no conceivable (or possible) stand-alone software can be constructed to predict a priori the space-time trajectory of the vehicle. Yet the drive is normally completed easily by most drivers. The trip is successfully completed because each in a series of very short (linear) steps can be "corrected" on the fly by the driver, who takes her cues from the environment to keep the car on the road and headed toward its destination. This metaphor differs in a fundamental way from the usual notion of predicting geomorphic change, because it involves a goal—to reach a desired destination—whereas the natural evolution of landscape has no such goal. Goals will become an essential feature of landscape prediction. The presence of a goal potentially increases our ability to predict, provided it is possible to use feedback (i.e., management) to nudge the system back in the "right" direction when it starts to stray. Under a regime of accelerating technology the closest we can get to predicting the longer term future of landscape is adaptive management, which at large scale is really geoengineer the system. The goal presumably would be to maintain a condition conducive to human well-being, for example to maintain a suitable fraction of global arable land. A successful "prediction" would be to stay within an envelope of states consistent with that goal. We cannot say, however, in what specific state the landscape will be at any time beyond the near future; this will depend on the future sequence of management decisions, which are, like the system they are managing, unpredictable, except shortly before they are implemented. The landscape of the future will thus likely be the result of a series of quick fixes to previous trends in landscape change. Similar comments apply to the prediction, or management, of climate. There is of course no guarantee that it will be possible to stay within the desired envelope of well-being.

Future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment

Treesearch

USDA Forest Service.

2012-01-01

The Forest and Rangeland Renewable Resources Planning Act of 1974 (RPA) mandates a periodic assessment of the conditions and trends of the Nation's renewable resources on forests and rangelands. The RPA Assessment includes projections of resource conditions and trends 50 years into the future. The 2010 RPA Assessment used a set of future scenarios to provide a...

Identifying and Exploring Future Trends Impacting on Academic Libraries: A Mixed Methodology Using Journal Content Analysis, Focus Groups, and Trend Reports

ERIC Educational Resources Information Center

Gwyer, Roisin

2015-01-01

This article compares three sources of information about academic libraries to consider what the future could hold and the skills needed to deliver effective services within that future. The starting point is the contents of "New Review of Academic Librarianship" (formerly "British Journal of Academic Librarianship") from 1986,…

The Future of the Campus: Architecture and Master Planning Trends

ERIC Educational Resources Information Center

Coulson, Jonathan; Roberts, Paul; Taylor, Isabelle

2015-01-01

The article discusses current and likely future trends within the architecture and master planning of university campuses. It argues that higher education administrators must maximise the value of the campus to create physical environments that enhance the student experience.

Modelling obesity trends in Australia: unravelling the past and predicting the future.

PubMed

Hayes, A J; Lung, T W C; Bauman, A; Howard, K

2017-01-01

Modelling is increasingly being used to predict the epidemiology of obesity progression and its consequences. The aims of this study were: (a) to present and validate a model for prediction of obesity among Australian adults and (b) to use the model to project the prevalence of obesity and severe obesity by 2025. Individual level simulation combined with survey estimation techniques to model changing population body mass index (BMI) distribution over time. The model input population was derived from a nationally representative survey in 1995, representing over 12 million adults. Simulations were run for 30 years. The model was validated retrospectively and then used to predict obesity and severe obesity by 2025 among different aged cohorts and at a whole population level. The changing BMI distribution over time was well predicted by the model and projected prevalence of weight status groups agreed with population level data in 2008, 2012 and 2014.The model predicts more growth in obesity among younger than older adult cohorts. Projections at a whole population level, were that healthy weight will decline, overweight will remain steady, but obesity and severe obesity prevalence will continue to increase beyond 2016. Adult obesity prevalence was projected to increase from 19% in 1995 to 35% by 2025. Severe obesity (BMI>35), which was only around 5% in 1995, was projected to be 13% by 2025, two to three times the 1995 levels. The projected rise in obesity severe obesity will have more substantial cost and healthcare system implications than in previous decades. Having a robust epidemiological model is key to predicting these long-term costs and health outcomes into the future.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh.

PubMed

Rahman, M Tauhid Ur; Tabassum, Faheemah; Rasheduzzaman, Md; Saba, Humayra; Sarkar, Lina; Ferdous, Jannatul; Uddin, Syed Zia; Zahedul Islam, A Z M

2017-10-17

Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy cultivation to shrimp farming were related to each other. Around 31.3% of the total respondents stated that at least one of their family members had migrated. Climate-driven southwestern coastal people usually migrate from the vulnerable rural areas towards the nearest relatively safe city due to adverse effects of natural disasters. To control the unplanned development and reduce the internal migration in Assasuni and other coastal areas, a comprehensive land use management plan was suggested that would accommodate the diversified uses of coastal lands and eventually lessen the threats to the life and livelihood of the local people.

Importance of ensembles in projecting regional climate trends

NASA Astrophysics Data System (ADS)

Arritt, Raymond; Daniel, Ariele; Groisman, Pavel

2016-04-01

We have performed an ensemble of simulations using RegCM4 to examine the ability to reproduce observed trends in precipitation intensity and to project future changes through the 21st century for the central United States. We created a matrix of simulations over the CORDEX North America domain for 1950-2099 by driving the regional model with two different global models (HadGEM2-ES and GFDL-ESM2M, both for RCP8.5), by performing simulations at both 50 km and 25 km grid spacing, and by using three different convective parameterizations. The result is a set of 12 simulations (two GCMs by two resolutions by three convective parameterizations) that can be used to systematically evaluate the influence of simulation design on predicted precipitation. The two global models were selected to bracket the range of climate sensitivity in the CMIP5 models: HadGEM2-ES has the highest ECS of the CMIP5 models, while GFDL-ESM2M has one of the lowestt. Our evaluation metrics differ from many other RCM studies in that we focus on the skill of the models in reproducing past trends rather than the mean climate state. Trends in frequency of extreme precipitation (defined as amounts exceeding 76.2 mm/day) for most simulations are similar to the observed trend but with notable variations depending on RegCM4 configuration and on the driving GCM. There are complex interactions among resolution, choice of convective parameterization, and the driving GCM that carry over into the future climate projections. We also note that biases in the current climate do not correspond to biases in trends. As an example of these points the Emanuel scheme is consistently "wet" (positive bias in precipitation) yet it produced the smallest precipitation increase of the three convective parameterizations when used in simulations driven by HadGEM2-ES. However, it produced the largest increase when driven by GFDL-ESM2M. These findings reiterate that ensembles using multiple RCM configurations and driving GCMs are essential for projecting regional climate change, even when a single RCM is used. This research was sponsored by the U.S. Department of Agriculture National Institute of Food and Agriculture.

Land use in Maine: determinants of past trends and projections of future changes.

Treesearch

Andrew J. Plantinga; Thomas Mauldlin; Ralph J. Alig

1999-01-01

About 90 percent of the land in Maine is in forests. We analyzed past land use trends in Maine and developed projections of future land use. Since the 1950s, the area of forest in Maine has increased by almost 400,000 acres; however, the trends differ among ownerships, as the area of nonindustrial private timberland declined by 800,000 acres since 1950, while private...

Application of genetic algorithm to land use optimization for non-point source pollution control based on CLUE-S and SWAT

NASA Astrophysics Data System (ADS)

Wang, Qingrui; Liu, Ruimin; Men, Cong; Guo, Lijia

2018-05-01

The genetic algorithm (GA) was combined with the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) model to obtain an optimized land use pattern for controlling non-point source (NPS) pollution. The performance of the combination was evaluated. The effect of the optimized land use pattern on the NPS pollution control was estimated by the Soil and Water Assessment Tool (SWAT) model and an assistant map was drawn to support the land use plan for the future. The Xiangxi River watershed was selected as the study area. Two scenarios were used to simulate the land use change. Under the historical trend scenario (Markov chain prediction), the forest area decreased by 2035.06 ha, and was mainly converted into paddy and dryland area. In contrast, under the optimized scenario (genetic algorithm (GA) prediction), up to 3370 ha of dryland area was converted into forest area. Spatially, the conversion of paddy and dryland into forest occurred mainly in the northwest and southeast of the watershed, where the slope land occupied a large proportion. The organic and inorganic phosphorus loads decreased by 3.6% and 3.7%, respectively, in the optimized scenario compared to those in the historical trend scenario. GA showed a better performance in optimized land use prediction. A comparison of the land use patterns in 2010 under the real situation and in 2020 under the optimized situation showed that Shennongjia and Shuiyuesi should convert 1201.76 ha and 1115.33 ha of dryland into forest areas, respectively, which represented the greatest changes in all regions in the watershed. The results of this study indicated that GA and the CLUE-S model can be used to optimize the land use patterns in the future and that SWAT can be used to evaluate the effect of land use optimization on non-point source pollution control. These methods may provide support for land use plan of an area.

Trends in capacity utilization for therapeutic monoclonal antibody production.

PubMed

Langer, Eric S

2009-01-01

The administration of high doses of therapeutic antibodies requires large-scale, efficient, cost effective manufacturing processes. An understanding of how the industry is using its available production capacity is important for production planning, and facility expansion analysis. Inaccurate production planning for therapeutic antibodies can have serious financial ramifications. In the recent 5(th) Annual Report and Survey of Biopharmaceutical Manufacturing Capacity and Production, 434 qualified respondents from 39 countries were asked to indicate, among other manufacturing issues, their current trends and future predictions with respect to the production capacity utilization of monoclonal antibodies in mammalian cell culture systems. While overall production of monoclonals has expanded dramatically since 2003, the average capacity utilization for mammalian cell culture systems, has decreased each year since 2003. Biomanufacturers aggressively attempt to avoid unanticipated high production demands that can create a capacity crunch. We summarize trends associated with capacity utilization and capacity constraints which indicate that biopharmaceutical manufacturers are doing a better job planning for capacity. The results have been a smoothing of capacity use shifts and an improved ability to forecast capacity and outsourcing needs. Despite these data, today, the instability and financial constraints caused by the current global economic crisis are likely to create unforeseen shifts in our capacity utilization and capacity expansion trends. These shifts will need to be measured in subsequent studies.

Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI.

PubMed

Liu, Shiliang; Cheng, Fangyan; Dong, Shikui; Zhao, Haidi; Hou, Xiaoyun; Wu, Xue

2017-06-23

Spatiotemporal dynamics of aboveground biomass (AGB) is a fundamental problem for grassland environmental management on the Qinghai-Tibet Plateau (QTP). Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data can feasibly be used to estimate AGB at large scales, and their precise validation is necessary to utilize them effectively. In our study, the clip-harvest method was used at 64 plots in QTP grasslands to obtain actual AGB values, and a handheld hyperspectral spectrometer was used to calculate field-measured NDVI to validate MODIS NDVI. Based on the models between NDVI and AGB, AGB dynamics trends during 2000-2012 were analyzed. The results showed that the AGB in QTP grasslands increased during the study period, with 70% of the grasslands undergoing increases mainly in the Qinghai Province. Also, the meadow showed a larger increasing trend than steppe. Future AGB dynamic trends were also investigated using a combined analysis of the slope values and the Hurst exponent. The results showed high sustainability of AGB dynamics trends after the study period. Predictions indicate 60% of the steppe and meadow grasslands would continue to increase in AGB, while 25% of the grasslands would remain in degradation, with most of them distributing in Tibet.

Hair cortisol concentrations and cortisol stress reactivity predict PTSD symptom increase after trauma exposure during military deployment.

PubMed

Steudte-Schmiedgen, Susann; Stalder, Tobias; Schönfeld, Sabine; Wittchen, Hans-Ulrich; Trautmann, Sebastian; Alexander, Nina; Miller, Robert; Kirschbaum, Clemens

2015-09-01

Previous evidence on endocrine risk markers for posttraumatic stress disorder (PTSD) has been inconclusive. Here, we report results of the first prospective study to investigate whether long-term hair cortisol levels and experimentally-induced cortisol stress reactivity are predictive of the development of PTSD symptomatology in response to trauma during military deployment. Male soldiers were examined before deployment to Afghanistan and at a 12-month post-deployment follow-up using dimensional measures for psychopathological symptoms. The predictive value of baseline (i) hair cortisol concentrations (HCC, N=90) and (ii) salivary cortisol stress reactivity (measured by the Trier Social Stress Test, N=80) for the development of PTSD symptomatology after being exposed to new-onset traumatic events was analyzed. Baseline cortisol activity significantly predicted PTSD symptom change from baseline to follow-up upon trauma exposure. Specifically, our results consistently revealed that lower HCC and lower cortisol stress reactivity were predictive of a greater increase in PTSD symptomatology in soldiers who had experienced new-onset traumatic events (explaining 5% and 10.3% of variance, respectively). Longitudinal analyses revealed an increase in HCC from baseline to follow-up and a trend for a negative relationship between HCC changes and the number of new-onset traumatic events. Additional pre-deployment analyses revealed that trauma history was reflected in lower HCC (at trend level) and that HCC were negatively related to stressful load. Our data indicate that attenuated cortisol secretion is a risk marker for subsequent development of PTSD symptomatology upon trauma exposure. Future studies are needed to confirm our findings in other samples. Copyright © 2015 Elsevier Ltd. All rights reserved.

Market Efficiency and the Risks and Returns of Dynamic Trading Strategies with Commodity Futures

NASA Astrophysics Data System (ADS)

Switzer, Lorne N.; Jiang, Hui

This paper investigates relationships between profits from dynamic trading strategies, risk premium, convenience yields, and net hedging pressures for commodity futures. As a market efficiency study, it crosses a number of disciplines, including traditional finance, behavioral finance, and behavioral psychology. The term structure of oil, gold, copper and soybeans futures markets contains predictive power for the corresponding term premium. However, only oil futures and soybean futures lead their spot premium. Significant momentum profits are identified in both outright futures and spread trading strategies when the spot premium and the term premium are used to form winner and loser portfolios. Profits from active strategies based on winner and loser portfolios are conditioned on market structure and net hedging pressure effects. Dynamic trading strategies based on contracts with extreme backwardation, extreme contango, and extreme hedging pressures are also tested. On average, spread trading outperforms outright futures trading in capturing the term structure risk and hedging pressure risk. For such strategies, long-short the long-term spread offers the greatest and most significant return and it offers the only exploitable trading profits built on the past hedging pressure. The existence of profits from active trading strategies based on winners is consistent with behavioral finance and behavioral psychology models in which market participants irrationally overreact to information and trends.

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan.

PubMed

Chen, Brian K; Jalal, Hawre; Hashimoto, Hideki; Suen, Sze-Chuan; Eggleston, Karen; Hurley, Michael; Schoemaker, Lena; Bhattacharya, Jay

2016-12-01

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan's future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model - the Japanese Future Elderly Model (FEM) - for Japan. We use the model to forecast disability and health for Japan's future elderly. Our simulation suggests that by 2040, over 27 percent of Japan's elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan's future.

Future Watch: Our Schools in the 21st Century.

ERIC Educational Resources Information Center

Montgomery, Judith K.; Herer, Gilbert R.

1994-01-01

This article reviews major social, technological, economic, and political trends in the United States and relates this larger perspective to the practices of speech language pathologists and audiologists in the schools. Implications of these trends for alternative futures are drawn. (Author/DB)

Virtual Universities: Current Models and Future Trends.

ERIC Educational Resources Information Center

Guri-Rosenblit, Sarah

2001-01-01

Describes current models of distance education (single-mode distance teaching universities, dual- and mixed-mode universities, extension services, consortia-type ventures, and new technology-based universities), including their merits and problems. Discusses future trends in potential student constituencies, faculty roles, forms of knowledge…

Past and Future Trends in Light Truck Sales.

DOT National Transportation Integrated Search

1981-08-01

This report uses the Wharton EFA Motor Vehicle Demand Model (Mark II) and its associated databases to discuss and analyze past and future trends in the Light Duty Truck market. The dynamic historical growth in this market and its implications for ene...

Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology

NASA Astrophysics Data System (ADS)

Shen, Mingxi; Chen, Jie; Zhuan, Meijia; Chen, Hua; Xu, Chong-Yu; Xiong, Lihua

2018-01-01

Uncertainty estimation of climate change impacts on hydrology has received much attention in the research community. The choice of a global climate model (GCM) is usually considered as the largest contributor to the uncertainty of climate change impacts. The temporal variation of GCM uncertainty needs to be investigated for making long-term decisions to deal with climate change. Accordingly, this study investigated the temporal variation (mainly long-term) of uncertainty related to the choice of a GCM in predicting climate change impacts on hydrology by using multi-GCMs over multiple continuous future periods. Specifically, twenty CMIP5 GCMs under RCP4.5 and RCP8.5 emission scenarios were adapted to adequately represent this uncertainty envelope, fifty-one 30-year future periods moving from 2021 to 2100 with 1-year interval were produced to express the temporal variation. Future climatic and hydrological regimes over all future periods were compared to those in the reference period (1971-2000) using a set of metrics, including mean and extremes. The periodicity of climatic and hydrological changes and their uncertainty were analyzed using wavelet analysis, while the trend was analyzed using Mann-Kendall trend test and regression analysis. The results showed that both future climate change (precipitation and temperature) and hydrological response predicted by the twenty GCMs were highly uncertain, and the uncertainty increased significantly over time. For example, the change of mean annual precipitation increased from 1.4% in 2021-2050 to 6.5% in 2071-2100 for RCP4.5 in terms of the median value of multi-models, but the projected uncertainty reached 21.7% in 2021-2050 and 25.1% in 2071-2100 for RCP4.5. The uncertainty under a high emission scenario (RCP8.5) was much larger than that under a relatively low emission scenario (RCP4.5). Almost all climatic and hydrological regimes and their uncertainty did not show significant periodicity at the P = .05 significance level, but their temporal variation could be well modeled by using the fourth-order polynomial. Overall, this study further emphasized the importance of using multiple GCMs for studying climate change impacts on hydrology. Furthermore, the temporal variation of uncertainty sourced from GCMs should be given more attention.

Trends in technology, trade and consumption likely to impact on microbial food safety.

PubMed

Quested, T E; Cook, P E; Gorris, L G M; Cole, M B

2010-05-30

Current and potential future trends in technology, consumption and trade of food that may impact on food-borne disease are analysed and the key driving factors identified focusing on the European Union and, to a lesser extent, accounting for the United States and global issues. Understanding of factors is developed using system-based methods and their impact is discussed in relation to current events and predictions of future trends. These factors come from a wide range of spheres relevant to food and include political, economic, social, technological, regulatory and environmental drivers. The degree of certainty in assessing the impact of important driving factors is considered in relation to food-borne disease. The most important factors driving an increase in the burden of food-borne disease in the next few decades were found to be the anticipated doubling of the global demand for food and of the international trade in food next to a significantly increased consumption of certain high-value food commodities such as meat and poultry and fresh produce. A less important factor potentially increasing the food-borne disease burden would be the increased demand for convenience foods. Factors that may contribute to a reduction in the food-borne disease burden were identified as the ability of governments around the world to take effective regulatory measures as well as the development and use of new food safety technologies and detection methods. The most important factor in reducing the burden of food-borne disease was identified as our ability to first detect and investigate a food safety issue and then to develop effective control measures. Given the global scale of impact on food safety that current and potentially future trends have, either by potentially increasing or decreasing the food-borne disease burden, it is concluded that a key role is fulfilled by intergovernmental organisations and by international standard setting bodies in coordinating the establishment and rolling-out of effective measures that, on balance, help ensure long-term consumer protection and fair international trade. Copyright 2010 Elsevier B.V. All rights reserved.

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

NASA Astrophysics Data System (ADS)

Lei, Yaguo; Li, Naipeng; Guo, Liang; Li, Ningbo; Yan, Tao; Lin, Jing

2018-05-01

Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction. Over recent years, a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process, i.e., RUL prediction. However, there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap, this paper provides a review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction. First, in data acquisition, several prognostic datasets widely used in academic literature are introduced systematically. Then, commonly used HI construction approaches and metrics are discussed. After that, the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards, the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally, the paper provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

Proactive supply chain performance management with predictive analytics.

PubMed

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

Future hotspots of terrestrial mammal loss

PubMed Central

Visconti, Piero; Pressey, Robert L.; Giorgini, Daniele; Maiorano, Luigi; Bakkenes, Michel; Boitani, Luigi; Alkemade, Rob; Falcucci, Alessandra; Chiozza, Federica; Rondinini, Carlo

2011-01-01

Current levels of endangerment and historical trends of species and habitats are the main criteria used to direct conservation efforts globally. Estimates of future declines, which might indicate different priorities than past declines, have been limited by the lack of appropriate data and models. Given that much of conservation is about anticipating and responding to future threats, our inability to look forward at a global scale has been a major constraint on effective action. Here, we assess the geography and extent of projected future changes in suitable habitat for terrestrial mammals within their present ranges. We used a global earth-system model, IMAGE, coupled with fine-scale habitat suitability models and parametrized according to four global scenarios of human development. We identified the most affected countries by 2050 for each scenario, assuming that no additional conservation actions other than those described in the scenarios take place. We found that, with some exceptions, most of the countries with the largest predicted losses of suitable habitat for mammals are in Africa and the Americas. African and North American countries were also predicted to host the most species with large proportional global declines. Most of the countries we identified as future hotspots of terrestrial mammal loss have little or no overlap with the present global conservation priorities, thus confirming the need for forward-looking analyses in conservation priority setting. The expected growth in human populations and consumption in hotspots of future mammal loss mean that local conservation actions such as protected areas might not be sufficient to mitigate losses. Other policies, directed towards the root causes of biodiversity loss, are required, both in Africa and other parts of the world. PMID:21844048

Assessment of CORDEX-South Asia experiments for monsoonal precipitation over Himalayan region for future climate

NASA Astrophysics Data System (ADS)

Choudhary, A.; Dimri, A. P.

2018-04-01

Precipitation is one of the important climatic indicators in the global climate system. Probable changes in monsoonal (June, July, August and September; hereafter JJAS) mean precipitation in the Himalayan region for three different greenhouse gas emission scenarios (i.e. representative concentration pathways or RCPs) and two future time slices (near and far) are estimated from a set of regional climate simulations performed under Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) project. For each of the CORDEX-SA simulations and their ensemble, projections of near future (2020-2049) and far future (2070-2099) precipitation climatology with respect to corresponding present climate (1970-2005) over Himalayan region are presented. The variability existing over each of the future time slices is compared with the present climate variability to determine the future changes in inter annual fluctuations of monsoonal mean precipitation. The long-term (1970-2099) trend (mm/day/year) of monsoonal mean precipitation spatially distributed as well as averaged over Himalayan region is analyzed to detect any change across twenty-first century as well as to assess model uncertainty in simulating the precipitation changes over this period. The altitudinal distribution of difference in trend of future precipitation from present climate existing over each of the time slices is also studied to understand any elevation dependency of change in precipitation pattern. Except for a part of the Hindu-Kush area in western Himalayan region which shows drier condition, the CORDEX-SA experiments project in general wetter/drier conditions in near future for western/eastern Himalayan region, a scenario which gets further intensified in far future. Although, a gradually increasing precipitation trend is seen throughout the twenty-first century in carbon intensive scenarios, the distribution of trend with elevation presents a very complex picture with lower elevations showing a greater trend in far-future under RCP8.5 when compared with higher elevations.

Evaluating earthquake hazards in the Los Angeles region; an earth-science perspective

USGS Publications Warehouse

Ziony, Joseph I.

1985-01-01

Potentially destructive earthquakes are inevitable in the Los Angeles region of California, but hazards prediction can provide a basis for reducing damage and loss. This volume identifies the principal geologically controlled earthquake hazards of the region (surface faulting, strong shaking, ground failure, and tsunamis), summarizes methods for characterizing their extent and severity, and suggests opportunities for their reduction. Two systems of active faults generate earthquakes in the Los Angeles region: northwest-trending, chiefly horizontal-slip faults, such as the San Andreas, and west-trending, chiefly vertical-slip faults, such as those of the Transverse Ranges. Faults in these two systems have produced more than 40 damaging earthquakes since 1800. Ninety-five faults have slipped in late Quaternary time (approximately the past 750,000 yr) and are judged capable of generating future moderate to large earthquakes and displacing the ground surface. Average rates of late Quaternary slip or separation along these faults provide an index of their relative activity. The San Andreas and San Jacinto faults have slip rates measured in tens of millimeters per year, but most other faults have rates of about 1 mm/yr or less. Intermediate rates of as much as 6 mm/yr characterize a belt of Transverse Ranges faults that extends from near Santa Barbara to near San Bernardino. The dimensions of late Quaternary faults provide a basis for estimating the maximum sizes of likely future earthquakes in the Los Angeles region: moment magnitude .(M) 8 for the San Andreas, M 7 for the other northwest-trending elements of that fault system, and M 7.5 for the Transverse Ranges faults. Geologic and seismologic evidence along these faults, however, suggests that, for planning and designing noncritical facilities, appropriate sizes would be M 8 for the San Andreas, M 7 for the San Jacinto, M 6.5 for other northwest-trending faults, and M 6.5 to 7 for the Transverse Ranges faults. The geologic and seismologic record indicates that parts of the San Andreas and San Jacinto faults have generated major earthquakes having recurrence intervals of several tens to a few hundred years. In contrast, the geologic evidence at points along other active faults suggests recurrence intervals measured in many hundreds to several thousands of years. The distribution and character of late Quaternary surface faulting permit estimation of the likely location, style, and amount of future surface displacements. An extensive body of geologic and geotechnical information is used to evaluate areal differences in future levels of shaking. Bedrock and alluvial deposits are differentiated according to the physical properties that control shaking response; maps of these properties are prepared by analyzing existing geologic and soils maps, the geomorphology of surficial units, and. geotechnical data obtained from boreholes. The shear-wave velocities of near-surface geologic units must be estimated for some methods of evaluating shaking potential. Regional-scale maps of highly generalized shearwave velocity groups, based on the age and texture of exposed geologic units and on a simple two-dimensional model of Quaternary sediment distribution, provide a first approximation of the areal variability in shaking response. More accurate depictions of near-surface shear-wave velocity useful for predicting ground-motion parameters take into account the thickness of the Quaternary deposits, vertical variations in sediment .type, and the correlation of shear-wave velocity with standard penetration resistance of different sediments. A map of the upper Santa Ana River basin showing shear-wave velocities to depths equal to one-quarter wavelength of a 1-s shear wave demonstrates the three-dimensional mapping procedure. Four methods for predicting the distribution and strength of shaking from future earthquakes are presented. These techniques use different measures of strong-motion

Assessment of the Present and Future Offshore Wind Power Potential: A Case Study in a Target Territory of the Baltic Sea Near the Latvian Coast

PubMed Central

Teilans, Artis

2013-01-01

Offshore wind energy development promises to be a significant domestic renewable energy source in Latvia. The reliable prediction of present and future wind resources at offshore sites is crucial for planning and selecting the location for wind farms. The overall goal of this paper is the assessment of offshore wind power potential in a target territory of the Baltic Sea near the Latvian coast as well as the identification of a trend in the future wind energy potential for the study territory. The regional climate model CLM and High Resolution Limited Area Model (Hirlam) simulations were used to obtain the wind climatology data for the study area. The results indicated that offshore wind energy is promising for expanding the national electricity generation and will continue to be a stable resource for electricity generation in the region over the 21st century. PMID:23983619

The future of diabetes education: expanded opportunities and roles for diabetes educators.

PubMed

Martin, Annette Lenzi; Lipman, Ruth D

2013-01-01

The purpose of the article is to explore challenges and opportunities associated with the state of practice for diabetes education and diabetes educators. Observations, assumptions, predictions, and recommendations based on a literature review and the 2011 workforce study and workforce summit held by the American Association of Diabetes Educators (AADE) are presented. Demand for diabetes educators is projected to increase. The employer base will broaden beyond traditional outpatient venues and extend into industry, retail pharmacy clinics, and community-based organizations. Increasing roles in management, quality assurance, and technology interface design are possible for diabetes educators. Challenges limiting diabetes education such as poor understanding of what diabetes educators do and underutilization of diabetes education continue to need redress. Increasing utilization of diabetes education and insight about health care trends can allow diabetes educators to thrive in the workplace of the future. Diabetes educators are urged to promote the evidence concerning the benefits of diabetes education, to work to increase physician referrals, and to acquire needed competencies for the workplace of the future.

Modeling the future evolution of the virtual water trade network: A combination of network and gravity models

NASA Astrophysics Data System (ADS)

Sartori, Martina; Schiavo, Stefano; Fracasso, Andrea; Riccaboni, Massimo

2017-12-01

The paper investigates how the topological features of the virtual water (VW) network and the size of the associated VW flows are likely to change over time, under different socio-economic and climate scenarios. We combine two alternative models of network formation -a stochastic and a fitness model, used to describe the structure of VW flows- with a gravity model of trade to predict the intensity of each bilateral flow. This combined approach is superior to existing methodologies in its ability to replicate the observed features of VW trade. The insights from the models are used to forecast future VW flows in 2020 and 2050, under different climatic scenarios, and compare them with future water availability. Results suggest that the current trend of VW exports is not sustainable for all countries. Moreover, our approach highlights that some VW importers might be exposed to "imported water stress" as they rely heavily on imports from countries whose water use is unsustainable.

Innovations in cardiac transplantation.

PubMed

Hasan, Reema; Ela, Ashraf Abou El; Goldstein, Daniel

2017-03-16

As the number of people living with heart failure continues to grow, future treatments will focus on efficient donor organ donation and ensuring safe and durable outcomes. This review will focus on organ procurement, graft surveillance and emerging therapies. Preliminary studies into donation after cardiac death have indicated that this may be an effective means to increase the donor pool. Novel preservation techniques that include ex-vivo perfusion to improve donor metabolic stabilization prior to implantation may also expand the donor pool. Biomarkers, including circulating-free DNA, are emerging that could replace the endomyocardial biopsy for acute graft rejection, but we lack a risk predictive biomarker in heart transplantation. Novel immune suppressants are being investigated. Emerging therapeutics to reduce the development of chronic allograft vasculopathy are yet to be found. This review highlights the most recent studies and future possible therapies that will improve outcomes in cardiac transplantation. Larger clinical trials are currently taking place and will be needed in the future to develop and sustain current trends toward better survival rates with cardiac transplantation.

Automating Trend Analysis for Spacecraft Constellations

NASA Technical Reports Server (NTRS)

Davis, George; Cooter, Miranda; Updike, Clark; Carey, Everett; Mackey, Jennifer; Rykowski, Timothy; Powers, Edward I. (Technical Monitor)

2001-01-01

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

U.S. Natural Gas Markets: Recent Trends and Prospects for the Future

EIA Publications

2001-01-01

The purpose of this study is to examine recent trends and prospects for the future of the U.S. natural gas market. Natural gas prices rose dramatically in 2000 and remained high through the first part of 2001, raising concerns about the future of natural gas prices and potential for natural gas to fuel the growth of the U.S. economy.

Global Survey on Future Trends in Human Spaceflight: the Implications for Space Tourism

NASA Astrophysics Data System (ADS)

Gurtuna, O.; Garneau, S.

2002-01-01

With the much-publicized first ever space tourist flight, of Dennis Tito, and the announcement of the second space tourist flight to take place in April 2002, it is clear that an alternative motivation for human spaceflight has emerged. Human spaceflight is no longer only about meeting the priorities of national governments and space agencies, but is also about the tangible possibility of ordinary people seeing the Earth from a previously exclusive vantage point. It is imperative that major space players look beyond the existing human spaceflight rationale to identify some of the major driving forces behind space tourism, including the evolving market potential and developments in enabling technologies. In order to determine the influence of these forces on the future of commercial human spaceflight, the responses of a Futuraspace survey on future trends in human spaceflight are analyzed and presented. The motivation of this study is to identify sought-after space destinations, explore the expected trends in enabling technologies, and understand the future role of emerging space players. The survey will reflect the opinions of respondents from around the world including North America, Europe (including Russia) and Asia. The profiles of targeted respondents from space industry, government and academia are high-level executives/managers, senior researchers, as well as former and current astronauts. The survey instrument is a questionnaire which is validated by a pilot study. The sampling method is non-probabilistic, targeting as many space experts as possible who fit our intended respondent profile. Descriptive and comparative statistical analysis methods are implemented to investigate both global and regional perceptions of future commercial trends in human spaceflight. This study is not intended to be a formal market study of the potential viability of the space tourism market. Instead, the focus is on the future trends of human spaceflight, by drawing on the knowledge and vision of a pool of space experts from many countries, representing the multidisciplinary and international nature of human spaceflight. A comprehensive look into the future can be achieved which surpasses our individual perceptions of future trends and which will complement existing and future space tourism market studies.

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.

Field of Psychiatry: Current Trends and Future Directions: An Indian Perspective.

PubMed

Dave, Kishore P

2016-01-01

Attempting to predict future is dangerous. This is particularly true in medical science where change is a result of chance discoveries. Currently, practicing psychiatrists are aware of deficiencies in psychiatric practice. However, we have a number of genuine reasons for optimism and excitement. Genetics, novel treatment approaches, new investigative techniques, large-scale treatment trials, and research in general medicine and neurology will give better insights in psychiatric disorders and its management. Psychiatric services in rural India can be reached by telemedicine. There are some threat perceptions which require solving and remedying. Subspecialties in psychiatry are the need of the hour. There is also a requirement for common practice guidelines. Mental Health Care Bill, 2013, requires suitable amendments before it is passed in the Indian Parliament. Research in psychiatry is yet to be developed as adequate resources are not available.

Energy resources - cornucopia or empty barrel?

USGS Publications Warehouse

McCabe, P.J.

1998-01-01

Over the last 25 yr, considerable debate has continued about the future supply of fossil fuel. On one side are those who believe we are rapidly depleting resources and that the resulting shortages will have a profound impact on society. On the other side are those who see no impending crisis because long-term trends are for cheaper prices despite rising production. The concepts of resources and reserves have historically created considerable misunderstanding in the minds of many nongeologists. Hubbert-type predictions of energy production assume that there is a finite supply of energy that is measurable; however, estimates of resources and reserves are inventories of the amounts of a fossil fuel perceived to be available over some future period of time. As those resources/reserves are depleted over time, additional amounts of fossil fuels are inventoried. Throughout most of this century, for example, crude oil reserves in the United States have represented a 10-14-yr supply. For the last 50 yr, resource crude oil estimates have represented about a 60-70-yr supply for the United States. Division of reserve or resource estimates by current or projected annual consumption therefore is circular in reasoning and can lead to highly erroneous conclusions. Production histories of fossil fuels are driven more by demand than by the geologic abundance of the resource. Examination of some energy resources with well-documented histories leads to two conceptual models that relate production to price. The closed-market model assumes that there is only one source of energy available. Although the price initially may fall because of economies of scale long term, prices rise as the energy source is depleted and it becomes progressively more expensive to extract. By contrast, the open-market model assumes that there is a variety of available energy sources and that competition among them leads to long-term stable or falling prices. At the moment, the United States and the world approximate the open-market model, but in the long run the supply of fossil fuel is finite, and prices inevitably will rise unless alternate energy sources substitute for fossil energy supplies; however, there appears little reason to suspect that long-term price trends will rise significantly over the next few decades.Over the last 25 years, considerable debate has continued about the future supply of fossil fuel. On one side are those who believe that resources are rapidly depleting and that the resulting shortages will have a profound impact on society. On the other side are those who see no impending crisis because longterm trends are for cheaper prices despite rising production. This paper examines historic trends and clarify the foundations on which one may build one's predictions.

2011 Souris River flood—Will it happen again?

USGS Publications Warehouse

Nustad, Rochelle A.; Kolars, Kelsey A.; Vecchia, Aldo V.; Ryberg, Karen R.

2016-09-29

The Souris River Basin is a 61,000 square kilometer basin in the provinces of Saskatchewan and Manitoba and the state of North Dakota. Record setting rains in May and June of 2011 led to record flooding with peak annual streamflow values (762 cubic meters per second [m3/s]) more than twice that of any previously recorded peak streamflow and more than five times the estimated 100 year postregulation streamflow (142 m3/s) at the U.S. Geological Survey (USGS) streamflow-gaging station above Minot, North Dakota. Upstream from Minot, N. Dak., the Souris River is regulated by three reservoirs in Saskatchewan (Rafferty, Boundary, and Alameda) and Lake Darling in North Dakota. During the 2011 flood, the city of Minot, N. Dak., experienced devastating damages with more than 4,000 homes flooded and 11,000 evacuated. As a result, the Souris River Basin Task Force recommended the U.S. Geological Survey (in cooperation with the North Dakota State Water Commission) develop a model for estimating the probabilities of future flooding and drought. The model that was developed took on four parts: (1) looking at past climate, (2) predicting future climate, (3) developing a streamflow model in response to certain climatic variables, and (4) combining future climate estimates with the streamflow model to predict future streamflow events. By taking into consideration historical climate record and trends in basin response to various climatic conditions, it was determined flood risk will remain high in the Souris River Basin until the wet climate state ends.

COMPUTER SUPPORT SYSTEMS FOR ESTIMATING CHEMICAL TOXICITY: PRESENT CAPABILITIES AND FUTURE TRENDS

EPA Science Inventory

Computer Support Systems for Estimating Chemical Toxicity: Present Capabilities and Future Trends

A wide variety of computer-based artificial intelligence (AI) and decision support systems exist currently to aid in the assessment of toxicity for environmental chemicals. T...

An outlook for cargo aircraft of the future. [assessment of the future of air cargo by analyzing statistics and trends

NASA Technical Reports Server (NTRS)

Nicks, O. W.; Whitehead, A. H., Jr.; Alford, W. J., Jr.

1975-01-01

An assessment is provided of the future of air cargo by analyzing air cargo statistics and trends, by noting air cargo system problems and inefficiencies, by analyzing characteristics of air-eligible commodities, and by showing the promise of new technology for future cargo aircraft with significant improvements in costs and efficiency. NASA's proposed program is reviewed which would sponsor the research needed to provide for development of advanced designs by 1985.

An inferential and descriptive statistical examination of the relationship between cumulative work metrics and injury in Major League Baseball pitchers.

PubMed

Karakolis, Thomas; Bhan, Shivam; Crotin, Ryan L

2013-08-01

In Major League Baseball (MLB), games pitched, total innings pitched, total pitches thrown, innings pitched per game, and pitches thrown per game are used to measure cumulative work. Often, pitchers are allocated limits, based on pitches thrown per game and total innings pitched in a season, in an attempt to prevent future injuries. To date, the efficacy in predicting injuries from these cumulative work metrics remains in question. It was hypothesized that the cumulative work metrics would be a significant predictor for future injury in MLB pitchers. Correlations between cumulative work for pitchers during 2002-07 and injury days in the following seasons were examined using regression analyses to test this hypothesis. Each metric was then "binned" into smaller cohorts to examine trends in the associated risk of injury for each cohort. During the study time period, 27% of pitchers were injured after a season in which they pitched. Although some interesting trends were noticed during the binning process, based on the regression analyses, it was found that no cumulative work metric was a significant predictor for future injury. It was concluded that management of a pitcher's playing schedule based on these cumulative work metrics alone could not be an effective means of preventing injury. These findings indicate that an integrated approach to injury prevention is required. This approach will likely involve advanced cumulative work metrics and biomechanical assessment.

Real-time reservoir operation considering non-stationary inflow prediction

NASA Astrophysics Data System (ADS)

Zhao, J.; Xu, W.; Cai, X.; Wang, Z.

2011-12-01

Stationarity of inflow has been a basic assumption for reservoir operation rule design, which is now facing challenges due to climate change and human interferences. This paper proposes a modeling framework to incorporate non-stationary inflow prediction for optimizing the hedging operation rule of large reservoirs with multiple-year flow regulation capacity. A multi-stage optimization model is formulated and a solution algorithm based on the optimality conditions is developed to incorporate non-stationary annual inflow prediction through a rolling, dynamic framework that updates the prediction from period to period and adopt the updated prediction in reservoir operation decision. The prediction model is ARIMA(4,1,0), in which parameter 4 stands for the order of autoregressive, 1 represents a linear trend, and 0 is the order of moving average. The modeling framework and solution algorithm is applied to the Miyun reservoir in China, determining a yearly operating schedule during the period from 1996 to 2009, during which there was a significant declining trend of reservoir inflow. Different operation policy scenarios are modeled, including standard operation policy (SOP, matching the current demand as much as possible), hedging rule (i.e., leaving a certain amount of water for future to avoid large risk of water deficit) with forecast from ARIMA (HR-1), hedging (HR) with perfect forecast (HR-2 ). Compared to the results of these scenarios to that of the actual reservoir operation (AO), the utility of the reservoir operation under HR-1 is 3.0% lower than HR-2, but 3.7% higher than the AO and 14.4% higher than SOP. Note that the utility under AO is 10.3% higher than that under SOP, which shows that a certain level of hedging under some inflow prediction or forecast was used in the real-world operation. Moreover, the impacts of discount rate and forecast uncertainty level on the operation will be discussed.

A first European scale multimedia fate modelling of BDE-209 from 1970 to 2020.

PubMed

Earnshaw, Mark R; Jones, Kevin C; Sweetman, Andy J

2015-01-01

The European Variant Berkeley Trent (EVn-BETR) multimedia fugacity model is used to test the validity of previously derived emission estimates and predict environmental concentrations of the main decabromodiphenyl ether congener, BDE-209. The results are presented here and compared with measured environmental data from the literature. Future multimedia concentration trends are predicted using three emission scenarios (Low, Realistic and High) in the dynamic unsteady state mode covering the period 1970-2020. The spatial and temporal distributions of emissions are evaluated. It is predicted that BDE-209 atmospheric concentrations peaked in 2004 and will decline to negligible levels by 2025. Freshwater concentrations should have peaked in 2011, one year after the emissions peak with sediment concentrations peaking in 2013. Predicted atmospheric concentrations are in good agreement with measured data for the Realistic (best estimate of emissions) and High (worst case scenario) emission scenarios. The Low emission scenario consistently underestimates measured data. The German unilateral ban on the use of DecaBDE in the textile industry is simulated in an additional scenario, the effects of which are mainly observed within Germany with only a small effect on the surrounding areas. Overall, the EVn-BTER model predicts atmospheric concentrations reasonably well, within a factor of 5 and 1.2 for the Realistic and High emission scenarios respectively, providing partial validation for the original emission estimate. Total mean MEC:PEC shows the High emission scenario predicts the best fit between air, freshwater and sediment data. An alternative spatial distribution of emissions is tested, based on higher consumption in EBFRIP member states, resulting in improved agreement between MECs and PECs in comparison with the Uniform spatial distribution based on population density. Despite good agreement between modelled and measured point data, more long-term monitoring datasets are needed to compare predicted trends in concentration to determine the rate of change of POPs within the environment. Copyright © 2014 Elsevier Ltd. All rights reserved.

NASA trend analysis procedures

NASA Technical Reports Server (NTRS)

1993-01-01

This publication is primarily intended for use by NASA personnel engaged in managing or implementing trend analysis programs. 'Trend analysis' refers to the observation of current activity in the context of the past in order to infer the expected level of future activity. NASA trend analysis was divided into 5 categories: problem, performance, supportability, programmatic, and reliability. Problem trend analysis uncovers multiple occurrences of historical hardware or software problems or failures in order to focus future corrective action. Performance trend analysis observes changing levels of real-time or historical flight vehicle performance parameters such as temperatures, pressures, and flow rates as compared to specification or 'safe' limits. Supportability trend analysis assesses the adequacy of the spaceflight logistics system; example indicators are repair-turn-around time and parts stockage levels. Programmatic trend analysis uses quantitative indicators to evaluate the 'health' of NASA programs of all types. Finally, reliability trend analysis attempts to evaluate the growth of system reliability based on a decreasing rate of occurrence of hardware problems over time. Procedures for conducting all five types of trend analysis are provided in this publication, prepared through the joint efforts of the NASA Trend Analysis Working Group.

Analysis of growth trend changes for 51 temperate tree species using Korea national forest inventory data

NASA Astrophysics Data System (ADS)

Park, M.; Moon, M.; Park, J.; Cho, S.; Kim, H. S.

2016-12-01

Individual tree growth rates can be affected by various factors such as species, soil fertility, stand development stage, disturbance, and climate etc. To estimate the effect of changes in tree growth rate on the structure and functionality of forest ecosystem in the future, we analyzed the change of species-specific growth trends using the fifth Korea national forest inventory data, which was collected from 2006 to 2010. The ring samples of average tree were collected from nationwide inventory plots and the total number of individual tree ring series was 69,128 covering 185 tree species. Among those, fifty one species with more than 100 tree ring series were used for our analysis. For growth-trend analysis, standardized regional curves of individual species growth were generated from three forest zone in South Korea; subarctic, cool temperate, warm temperate forest zone. Then individual tree ring series was indexed by dividing the growth of the tree by expected growth from standardized regional curves. Then the ratio of all tree ring series were aligned by year and the Spearman's correlation coefficient of each species was calculated. The results show that most of species had increasing growth rates as forests developed after Korean war. For the last thirty years, 67.3% of species including Quercus spp. and Zelkova serrata had positive growth trends, on the other hand, 11.5% of species including Pinus spp. showed negative growth trends probably due to the changes in successional stages in Korean forests and climate change. These trends also vary with climate zone and species. For examples, Pinus densiflora, which showed negative growth trend overall, had steep negative growth trends in boreal and temperate zone, whereas it showed no specific trend in sub-tropical climate zone. Our trend analysis on 51 temperate tree species growth will be essential to predict the temperate forests species change for the this century.

Analysis and prediction of rainfall trends over Bangladesh using Mann-Kendall, Spearman's rho tests and ARIMA model

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid

2017-08-01

In this study, 60-year monthly rainfall data of Bangladesh were analysed to detect trends. Modified Mann-Kendall, Spearman's rho tests and Sen's slope estimators were applied to find the long-term annual, dry season and monthly trends. Sequential Mann-Kendall analysis was applied to detect the potential trend turning points. Spatial variations of the trends were examined using inverse distance weighting (IDW) interpolation. AutoRegressive integrated moving average (ARIMA) model was used for the country mean rainfall and for other two stations data which depicted the highest and the lowest trend in the Mann-Kendall and Spearman's rho tests. Results showed that there is no significant trend in annual rainfall pattern except increasing trends for Cox's Bazar, Khulna, Satkhira and decreasing trend for Srimagal areas. For the dry season, only Bogra area represented significant decreasing trend. Long-term monthly trends demonstrated a mixed pattern; both negative and positive changes were found from February to September. Comilla area showed a significant decreasing trend for consecutive 3 months while Rangpur and Khulna stations confirmed the significant rising trends for three different months in month-wise trends analysis. Rangpur station data gave a maximum increasing trend in April whereas a maximum decreasing trend was found in August for Comilla station. ARIMA models predict +3.26, +8.6 and -2.30 mm rainfall per year for the country, Cox's Bazar and Srimangal areas, respectively. However, all the test results and predictions revealed a good agreement among them in the study.

Are there trends towards drier hydrological conditions in Central America?

NASA Astrophysics Data System (ADS)

Hidalgo, H. G.

2013-12-01

A summary of hydrological projections at the end of the century from 30 General Circulation Models (GCMs) is presented; and several hydrometeorological parameters are analyzed to validate if there are hydroclimatological trends during the observational period (1982-2005) consistent with the GCMs results. At the end of the century the median of 30 GCM simulations projects a drier future for Tegucigalpa and San Jose, with a marked increment in evapotranspiration in the first half of the rainy season along with reductions of soil moisture. With respect to the observations (1982-2005): 1) the Normalized Difference Vegetation Index showed negative trends in the North Pacific coast of Costa Rica, the border of Honduras and Nicaragua, and especially in southern Mexico (except the Yucatan Peninsula). Positive trends were found in the several parts of Central America, 2) the Palmer Drought Severity Index showed strong and consistent trends from Nicaragua to the North of Central America and southern Mexico (not including Yucatan), consistent with the direction of GCM projections; 3) negative precipitation trends in satellite data were found in Nicaragua, with strong trends in its Caribbean coast; 4) NCEP/NCAR Reanalysis precipitation showed strong negative trends in northern Central America, the Central Valley, the Dry Pacific of Costa Rica and the South-Pacific coast of Nicaragua, all consistent with the direction of GCM projections; and 5) station data showed no significant trends however, and 6) Reanalysis' temperature showed positive trends in southern Mexico (not including Yucatan) and negative trends in El Salvador. It can be concluded that several trends in drought indexes and precipitation are consistent with the future projected by the GCMs; that is, with some exceptions some of the trends were validated towards a drier future for the region, especially in the northern part.

Influence of Wave Energetics on Nearshore Storms and Adjacent Shoreline Morphology

NASA Astrophysics Data System (ADS)

Wadman, H. M.; McNinch, J. E.; Hanson, J.

2008-12-01

Large-scale climatic forcings (such as NAO and ENSO) are known to induce fluctuations in regional storm frequency and intensity. Morphology-based studies have traditionally focused on individual storms and their influence on the nearshore coastal wave regime and shoreline response. Few studies have attempted to link long-term observed changes in shoreline position, beach, and nearshore morphology with large-scale climatic forcings that influence regional storm patterns. In order to predict the response of coastlines to future sea level rise and climate change, we need to understand how changes in the frequency of storms affecting nearshore regions (nearshore storms) may influence trends in shoreline position and nearshore morphology. Nearly 30 years of wave data (deep and shallow) collected off of Duck, NC are examined for trends in storm frequency and/or intensity. Changes in shoreline position and shoreface elevation, as observed from monthly beach transects over the same period, are also investigated in light of the observed trends in hydrodynamic forcings. Our preliminary analysis was unable to identify any consistent linear trends (increases or decreases) in frequency or intensity over the ~30-year time period in either the offshore wave heights or the nearshore storm record. These data might suggest that previous observations of recent increases in storm intensity and frequency, speculated to be due to climate change, might be spatially limited. Future analyses will partition the contributions from individual wind sea and swell events in order to better identify long-term trends in wave energetics from the various wave generation regions in the Atlantic. At this location, offshore wave height and the nearshore storm record are dominated by seasonal fluctuations and a strong interdecadal- to decadal periodicity. Previous research in Duck, NC has suggested that changes in shoreline position and shoreface elevations are related both to seasonal trends as well as "storm groupiness". Our analyses support these findings, but also identify interdecadal- to decadal trends in the nearshore morphology. Despite these fluctuations, the overall position of the shoreline and elevation of the shoreface shows little net change over the 30 years investigated. We hypothesize that the interdecadal- to decadal periodicity in the morphology is driven largely by the influences of large-scale climatic forcings on the nearshore wave regime as reflected in the storm record. We also explore the relationship between morphological periodicity, storm and wave height periodicity, and climatic fluctuations.

Longwave emission trends over Africa and implications for Atlantic hurricanes

NASA Astrophysics Data System (ADS)

Zhang, Lei; Rechtman, Thomas; Karnauskas, Kristopher B.; Li, Laifang; Donnelly, Jeffrey P.; Kossin, James P.

2017-09-01

The latitudinal gradient of outgoing longwave radiation (OLR) over Africa is a skillful and physically based predictor of seasonal Atlantic hurricane activity. The African OLR gradient is observed to have strengthened during the satellite era, as predicted by state-of-the-art global climate models (GCMs) in response to greenhouse gas forcing. Prior to the satellite era and the U.S. and European clean air acts, the African OLR gradient weakened due to aerosol forcing of the opposite sign. GCMs predict a continuation of the increasing OLR gradient in response to greenhouse gas forcing. Assuming a steady linear relationship between African easterly waves and tropical cyclogenesis, this result suggests a future increase in Atlantic tropical cyclone frequency by 10% (20%) at the end of the 21st century under the RCP 4.5 (8.5) forcing scenario.

Trump and the GOP agenda: implications for retirement policy.

PubMed

Madland, David; Rowell, Alex

2018-04-11

Policymakers need to act to protect Americans' retirement security. A significant portion of Americans are at risk of not being able to maintain their standard of living in retirement and research suggests that this percentage is likely to grow. This commentary provides background on the current state of American retirement, highlights recent efforts to reform retirement policy, and predicts what to expect under President Donald Trump. Retirement has not been a major focus of national policymakers in recent years. Early actions during the Trump administration to undo Obama administration policies may make it more difficult for individuals to save for retirement. While it is impossible to predict the future with any certainty, long standing trends and recent political developments suggest that major action will not be taken during the Trump presidency to boost retirement security.

Development of a time-trend model for analyzing and predicting case-pattern of Lassa fever epidemics in Liberia, 2013-2017.

PubMed

Olugasa, Babasola O; Odigie, Eugene A; Lawani, Mike; Ojo, Johnson F

2015-01-01

The objective was to develop a case-pattern model for Lassa fever (LF) among humans and derive predictors of time-trend point distribution of LF cases in Liberia in view of the prevailing under-reporting and public health challenge posed by the disease in the country. A retrospective 5 years data of LF distribution countrywide among humans were used to train a time-trend model of the disease in Liberia. A time-trend quadratic model was selected due to its goodness-of-fit (R2 = 0.89, and P < 0.05) and best performance compared to linear and exponential models. Parameter predictors were run on least square method to predict LF cases for a prospective 5 years period, covering 2013-2017. The two-stage predictive model of LF case-pattern between 2013 and 2017 was characterized by a prospective decline within the South-coast County of Grand Bassa over the forecast period and an upward case-trend within the Northern County of Nimba. Case specific exponential increase was predicted for the first 2 years (2013-2014) with a geometric increase over the next 3 years (2015-2017) in Nimba County. This paper describes a translational application of the space-time distribution pattern of LF epidemics, 2008-2012 reported in Liberia, on which a predictive model was developed. We proposed a computationally feasible two-stage space-time permutation approach to estimate the time-trend parameters and conduct predictive inference on LF in Liberia.

Stratospheric processes: Observations and interpretation

NASA Technical Reports Server (NTRS)

Brune, William H.; Cox, R. Anthony; Turco, Richard; Brasseur, Guy P.; Matthews, W. Andrew; Zhou, Xiuji; Douglass, Anne; Zander, Rudi J.; Prendez, Margarita; Rodriguez, Jose M.

1991-01-01

Explaining the observed ozone trends discussed in an earlier update and predicting future trends requires an understanding of the stratospheric processes that affect ozone. Stratospheric processes occur on both large and small spatial scales and over both long and short periods of time. Because these diverse processes interact with each other, only in rare cases can individual processes be studied by direct observation. Generally the cause and effect relationships for ozone changes were established by comparisons between observations and model simulations. Increasingly, these comparisons rely on the developing, observed relationships among trace gases and dynamical quantities to initialize and constrain the simulations. The goal of this discussion of stratospheric processes is to describe the causes for the observed ozone trends as they are currently understood. At present, we understand with considerable confidence the stratospheric processes responsible for the Antarctic ozone hole but are only beginning to understand the causes of the ozone trends at middle latitudes. Even though the causes of the ozone trends at middle latitudes were not clearly determined, it is likely that they, just as those over Antarctica, involved chlorine and bromine chemistry that was enhanced by heterogeneous processes. This discussion generally presents only an update of the observations that have occurred for stratospheric processes since the last assessment (World Meteorological Organization (WMO), 1990), and is not a complete review of all the new information about stratospheric processes. It begins with an update of the previous assessment of polar stratospheres (WMO, 1990), followed by a discussion on the possible causes for the ozone trends at middle latitudes and on the effects of bromine and of volcanoes.

Development of A Dust Climate Indicator for the US National Climate Assessment

NASA Astrophysics Data System (ADS)

Tong, D.; Wang, J. X. L.; Gill, T. E.; Van Pelt, S.; Kim, D.

2016-12-01

Dust activity is a relatively simple but practical indicator to document the response of dryland ecosystems to climate change, making it an integral part of the National Climate Assessment (NCA). We present here a multi-agency collaboration that aims at developing a suite of dust climate indicators to document and monitor the long-term variability and trend of dust storm activity in the western United States. Recent dust observations have revealed rapid intensification of dust storm activity in the western United States. This trend is also closely correlated with a rapid increase in dust deposition in rainwater and "valley fever" hospitalization in southwestern states. It remains unclear, however, if such a trend, when enhanced by predicted warming and rainfall oscillation in the Southwest, will result in irreversible environmental development such as desertification or even another "Dust Bowl". Based on continuous ground aerosol monitoring, we have reconstructed a long-term dust storm climatology in the western United States. We report here direct evidence of rapid intensification of dust storm activity over US deserts in the past decades (1990 to 2013), in contrast to the decreasing trends in Asia and Africa. The US trend is spatially and temporally correlated with incidences of valley fever, an infectious disease caused by soil-dwelling fungus that has increased eight-fold in the past decade. We further investigate the linkage between dust variations and possible climate drivers and find that the regional dust trends are likely driven by large-scale variations of sea surface temperature in the Pacific Ocean, with the strongest correlation with the Pacific Decadal Oscillation (PDO). Future study will explore the link between the temporal and spatial trends of increase in dustiness and vegetation change in southwestern semi-arid and arid ecosystems.

Prediction-Market-Based Quantification of Climate Change Consensus and Uncertainty

NASA Astrophysics Data System (ADS)

Boslough, M.

2012-12-01

Intrade is an online trading exchange that includes climate prediction markets. One such family of contracts can be described as "Global temperature anomaly for 2012 to be greater than x °C or more," where the figure x ranges in increments of .05 from .30 to 1.10 (relative to the 1951-1980 base period), based on data published by NASA GISS. Each market will settle at 10.00 if the published global temperature anomaly for 2012 is equal to or greater than x, and will otherwise settle at 0.00. Similar contracts will be available for 2013. Global warming hypotheses can be cast as probabilistic predictions for future temperatures. The first modern such climate prediction is that of Broecker (1975), whose temperatures are easily separable from his CO2 growth scenario—which he overestimated—by interpolating his table of temperature as a function of CO2 concentration and projecting the current trend into the near future. For the current concentration of 395 ppm, Broecker's equilibrium temperature anomaly prediction relative to pre-industrial is 1.05 °C, or about 0.75 °C relative to the GISS base period. His neglect of lag in response to the changes in radiative forcing was partially compensated by his low sensitivity of 2.4 °C, leading to a slight overestimate. Simple linear extrapolation of the current trend since 1975 yields an estimate of .65 ± .09 °C (net warming of .95 °C) for anthropogenic global warming with a normal distribution of random natural variability. To evaluate an extreme case, we can estimate the prediction Broecker would have made if he had used the Lindzen & Choi (2009) climate sensitivity of 0.5 °C. The net post-industrial warming by 2012 would have been 0.21 °C, for an expected change of -0.09 from the GISS base period. This is the temperature to which the Earth would be expected to revert if the observed warming since the 19th century was merely due to random natural variability that coincidentally mimicked Broecker's anthropogenic change prediction for the past 36 years. Assertions made outside the scientific literature can also be cast into predictions for 2012 temperatures, for example Carter's (2006) argument for a lack of warming since 1998 can be extrapolated to a 2012 value of 0.56 °C (net warming of .86 °C), and Easterbrook's (2010) claim of global cooling can be extrapolated to a 2012 value of .42 °C (net warming of .72 °C). All contracts in the current market ensembles are consistent with net warming from pre-industrial temperatures. They are also capable of distinguishing the level of acceptance of the various global warming hypotheses, even by their respective proponents. Moreover, they can be used as a market-based consensus estimate of future warming and climate variability that is weighted according to level of risk taken on by those providing the estimates, while filtering out the opinions of individuals unwilling to accept any financial risk associated with being wrong.

The past as prelude to the future for understanding 21st-century climate effects on Rocky Mountain Trout

USGS Publications Warehouse

Isaak, Daniel J.; Muhlfeld, Clint C.; Todd, Andrew S.; Al-chokhachy, Robert; Roberts, James; Kershner, Jeffrey L.; Fausch, Kurt D.; Hostetler, Steven W.

2012-01-01

Bioclimatic models predict large reductions in native trout across the Rocky Mountains in the 21st century but lack details about how changes will occur. Through five case histories across the region, we explore how a changing climate has been affecting streams and the potential consequences for trout. Monitoring records show trends in temperature and hydrographs consistent with a warming climate in recent decades. Biological implications include upstream shifts in thermal habitats, risk of egg scour, increased wildfire disturbances, and declining summer habitat volumes. The importance of these factors depends on the context, but temperature increases are most relevant where population boundaries are mediated by thermal constraints. Summer flow declines and wildfires will be important where trout populations are fragmented and constrained to small refugia. A critical information gap is evidence documenting how populations are adjusting to long-term habitat trends, so biological monitoring is a priority. Biological, temperature, and discharge data from monitoring networks could be used to develop accurate vulnerability assessments that provide information regarding where conservation actions would best improve population resilience. Even with better information, future uncertainties will remain large due to unknowns regarding Earth's ultimate warming trajectory and how effects translate across scales. Maintaining or increasing the size of habitats could provide a buffer against these uncertainties.

Spatio-Temporal Change Modeling of Lulc: a Semantic Kriging Approach

NASA Astrophysics Data System (ADS)

Bhattacharjee, S.; Ghosh, S. K.

2015-07-01

Spatio-temporal land-use/ land-cover (LULC) change modeling is important to forecast the future LULC distribution, which may facilitate natural resource management, urban planning, etc. The spatio-temporal change in LULC trend often exhibits non-linear behavior, due to various dynamic factors, such as, human intervention (e.g., urbanization), environmental factors, etc. Hence, proper forecasting of LULC distribution should involve the study and trend modeling of historical data. Existing literatures have reported that the meteorological attributes (e.g., NDVI, LST, MSI), are semantically related to the terrain. Being influenced by the terrestrial dynamics, the temporal changes of these attributes depend on the LULC properties. Hence, incorporating meteorological knowledge into the temporal prediction process may help in developing an accurate forecasting model. This work attempts to study the change in inter-annual LULC pattern and the distribution of different meteorological attributes of a region in Kolkata (a metropolitan city in India) during the years 2000-2010 and forecast the future spread of LULC using semantic kriging (SemK) approach. A new variant of time-series SemK is proposed, namely Rev-SemKts to capture the multivariate semantic associations between different attributes. From empirical analysis, it may be observed that the augmentation of semantic knowledge in spatio-temporal modeling of meteorological attributes facilitate more precise forecasting of LULC pattern.

How can hydrological modeling help to understand process dynamics in sparsely gauged tropical regions - case study Mata Âtlantica, Brazil

NASA Astrophysics Data System (ADS)

Künne, Annika; Penedo, Santiago; Schuler, Azeneth; Bardy Prado, Rachel; Kralisch, Sven; Flügel, Wolfgang-Albert

2015-04-01

To ensure long-term water security for domestic, agricultural and industrial use in the emerging country of Brazil with fast-growing markets and technologies, understanding of catchment hydrology is essential. Yet, hydrological analysis, high resolution temporal and spatial monitoring and reliable meteo-hydrological data are insufficient to fully understand hydrological processes in the region and to predict future trends. Physically based hydrological modeling can help to expose uncertainties of measured data, predict future trends and contribute to physical understanding about the watershed. The Brazilian Atlantic rainforest (Mata Atlântica) is one of the world's biodiversity hotspots. After the Portuguese colonization, its original expansion of 1.5 million km² was reduced to only 7% of the former area. Due to forest fragmentation, overexploitation and soil degradation, pressure on water resources in the region has significantly increased. Climatically, the region possesses distinctive wet and dry periods. While extreme precipitation events in the rainy season cause floods and landslides, dry periods can lead to water shortages, especially in the agricultural and domestic supply sectors. To ensure both, the protection of the remnants of Atlantic rainforest biome as well as water supply, a hydrological understanding of this sparsely gauged region is essential. We will present hydrological models of two meso- to large-scale catchments (Rio Macacu and Rio Dois Rios) within the Mata Âtlantica in the state of Rio de Janeiro. The results show how physically based models can contribute to hydrological system understanding within the region and answer what-if scenarios, supporting regional planners and decision makers in integrated water resources management.

Which climate change path are we following? Bad news from Scots pine

PubMed Central

D’Andrea, Ettore; Rezaie, Negar; Cammarano, Mario; Matteucci, Giorgio

2017-01-01

Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated. PMID:29252985

Which climate change path are we following? Bad news from Scots pine.

PubMed

Bombi, Pierluigi; D'Andrea, Ettore; Rezaie, Negar; Cammarano, Mario; Matteucci, Giorgio

2017-01-01

Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated.

Rosin-Rammler Distributions in ANSYS Fluent

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

Dunham, Ryan Q.

In Health Physics monitoring, particles need to be collected and tracked. One method is to predict the motion of potential health hazards with computer models. Particles released from various sources within a glove box can become a respirable health hazard if released into the area surrounding a glove box. The goal of modeling the aerosols in a glove box is to reduce the hazards associated with a leak in the glove box system. ANSYS Fluent provides a number of tools for modeling this type of environment. Particles can be released using injections into the flow path with turbulent properties. Themore » models of particle tracks can then be used to predict paths and concentrations of particles within the flow. An attempt to understand and predict the handling of data by Fluent was made, and results iteratively tracked. Trends in data were studied to comprehend the final results. The purpose of the study was to allow a better understanding of the operation of Fluent for aerosol modeling for future application in many fields.« less

Topics in Chemical Instrumentation: An Introduction to Supercritical Fluid Chromatography--Part 2. Applications and Future Trends.

ERIC Educational Resources Information Center

Palmieri, Margo D.

1989-01-01

Discussed are selected application and future trends in supercritical fluid chromatography (SFC). The greatest application for SFC involves those analytes that are difficult to separate using GC or LC methods. Optimum conditions for SFC are examined. Provided are several example chromatograms. (MVL)

Health Policy and the Economy: Guessing about the Future.

ERIC Educational Resources Information Center

Helms, Robert B.

1989-01-01

This paper looks at demographic and financial trends that can have an effect on the health care sector, the government reliance on projections of budget expenditures and the current budget deficit, and trends in health care expenditures and effects on the future of Social Security and Medicare. (MLW)

Financial Management of Libraries: Past Trends and Future Prospects.

ERIC Educational Resources Information Center

Roberts, Stephen A.

2003-01-01

The financial environment within library and information services is reviewed and a structure for financial management is presented based on funding source and level of commercial activity. Objectives for financial management of library and information services is developed and reviewed in light of future trends and stakeholder perspectives.…

Libraries and the Chief Information Officer: Implications and Trends.

ERIC Educational Resources Information Center

Woodsworth, Anne

1988-01-01

Describes the roles and responsibilities of Chief Information Officers (CIOs) in research universities and presents five models of the position. Future trends and needs for management of converging information technologies are then discussed with attention to implications for libraries. Qualifications of the CIO and the future outlook of the…

Future Trends in the Kinesiology Sciences

ERIC Educational Resources Information Center

Knudson, Duane

2016-01-01

Kinesiology emerged from its preventative medicine and education roots to establish itself as a recognized field of inquiry with numerous sub-disciplines. This article presents four trends in modern science that will likely influence the future of kinesiology sciences. Will recent increases in greater scientific specialization be overcome by the…

Global Simultaneous Estimation of Present-Day Surface Mass Trend and GIA Using Multi-Sensor Geodetic Data Combination

NASA Astrophysics Data System (ADS)

Wu, X.; Heflin, M. B.; Schotman, H.; Vermeersen, B. L.; Dong, D.; Gross, R. S.; Ivins, E. R.; Moore, A. W.; Owen, S. E.

2009-12-01

Separating geodetic signatures of present-day surface mass trend and Glacial Isostatic Adjustment (GIA) requires multi-data types of different physical characteristics. We take a kinematic approach to the global simultaneous estimation problem. Three sets of global spherical harmonic coefficients from degree 1 to 60 of the present-day surface mass trend, vertical and horizontal GIA induced surface velocity fields, as well as rotation vectors of 15 major tectonic plates are solved for. The estimation is carried out using GRACE geoid trend, 3-dimensional velocities measured at 664 SLR/VLBI/GPS sites, the data-assimilated JPL ECCO ocean model. The ICE-5G/IJ05 (VM2) predictions are used as a priori GIA mean model. An a priori covariance matrix is constructed in the spherical harmonic domain for the GIA model by propagating the covariance matrices of random and geographically correlated ice thickness errors and upper/lower mantle viscosity errors so that the resulting magnitude and geographic pattern of the geoid uncertainties roughly reflect the difference between two recent GIA models. Unprecedented high-precision results are achieved. For example, geocenter velocities due to present-day surface mass trend and due to GIA are both determined to uncertainties of better than 0.1 mm/yr without using direct geodetic geocenter information. Information content of the data sets, future improvements, and benefits from new data will also be explored in the global inverse framework.

Effects of climate warming on net primary productivity in China during 1961-2010.

PubMed

Gu, Fengxue; Zhang, Yuandong; Huang, Mei; Tao, Bo; Guo, Rui; Yan, Changrong

2017-09-01

The response of ecosystems to different magnitudes of climate warming and corresponding precipitation changes during the last few decades may provide an important reference for predicting the magnitude and trajectory of net primary productivity (NPP) in the future. In this study, a process-based ecosystem model, Carbon Exchange between Vegetation, Soil and Atmosphere (CEVSA), was used to investigate the response of NPP to warming at both national and subregional scales during 1961-2010. The results suggest that a 1.3°C increase in temperature stimulated the positive changing trend in NPP at national scale during the past 50 years. Regardless of the magnitude of temperature increase, warming enhanced the increase in NPP; however, the positive trend of NPP decreased when warming exceeded 2°C. The largest increase in NPP was found in regions where temperature increased by 1-2°C, and this rate of increase also contributed the most to the total increase in NPP in China's terrestrial ecosystems. Decreasing precipitation depressed the positive trend in NPP that was stimulated by warming. In northern China, warming depressed the increasing trend of NPP and warming that was accompanied by decreasing precipitation led to negative changing trends in NPP in large parts of northern China, especially when warming exceeded 2°C. However, warming stimulated the increase in NPP until warming was greater than 2°C, and decreased precipitation helped to increase the NPP in southern China.

Impacts of Climate Change on Water Requirements of Dry Season Boro Rice: Recent Trends and Future Scenarios

NASA Astrophysics Data System (ADS)

Acharjee, T. K.; Ludwig, F.; Halsema, G. V.; Hellegers, P.; Supit, I.

2017-12-01

The North-West part of Bangladesh is vulnerable to the impacts of climate change, because of dry season water shortage and high water demand for rice cultivation. A study was carried out to understand the impacts of recent climate change (1980-2013) and future consequences (for 2050s and 2080s) on water requirements of Boro rice. The reference crop evapotranspiration (ETo), potential crop water requirement (∑ETC), effective rainfall (ER), potential irrigation requirement for crop evapotranspiration (∑ETC-ER) and net irrigation requirement of Boro rice were estimated in CropWat using observed daily climate data for recent trends and statistically downscaled and bias corrected GCM outputs (five models and two RCPs) for future scenarios. ETo showed a significant decreasing recent trends due to increasing relative humidity and decreasing wind speed and sun shine hours instead of an increase in temperature. However, the strong future increase in temperature will lead to an insignificant increase in ETo. ∑ETC showed a decreasing recent trend and will further decrease in the future because of shortened duration of Boro growth stages as crop's phenological response to increased temperature. The variations in trends of ∑ETC-ER found among different districts, are mainly linked to the variations in trends of changes in effective rainfall. During last three decades, the net irrigation requirement has decreased by 11% at an average rate of -4.4 mm/year, instead of a decreasing effective rainfall, mainly because of high rate of decrease of crop evapotranspiration (-5.9 mm/year). In future, although daily water requirement will increase, the total net irrigation requirement of Boro rice will decrease by 1.6% in 2050s and 7.4% in 2080s for RCP 8.5 scenario on an average for five models and four districts compared to the base period (1980-2013). High variations in projected changes in rainfall bring high uncertainty for future water requirements estimation. Therefore, a warming climate will not directly increase the water demand for crop agriculture in North-West Bangladesh but will make the future agricultural water management more complex by bringing more variations and uncertainty in the system.

An innovative approach to predict technology evolution for the desoldering of printed circuit boards: A perspective from China and America.

PubMed

Wang, Chen; Zhao, Wu; Wang, Jie; Chen, Ling; Luo, Chun-Jing

2016-06-01

The printed circuit boards basis of electronic equipment have seen a rapid growth in recent years and played a significant role in modern life. Nowadays, the fact that electronic devices upgrade quickly necessitates a proper management of waste printed circuit boards. Non-destructive desoldering of waste printed circuit boards becomes the first and the most crucial step towards recycling electronic components. Owing to the diversity of materials and components, the separation process is difficult, which results in complex and expensive recovery of precious materials and electronic components from waste printed circuit boards. To cope with this problem, we proposed an innovative approach integrating Theory of Inventive Problem Solving (TRIZ) evolution theory and technology maturity mapping system to forecast the evolution trends of desoldering technology of waste printed circuit boards. This approach can be applied to analyse the technology evolution, as well as desoldering technology evolution, then research and development strategy and evolution laws can be recommended. As an example, the maturity of desoldering technology is analysed with a technology maturity mapping system model. What is more, desoldering methods in different stages are analysed and compared. According to the analysis, the technological evolution trends are predicted to be 'the law of energy conductivity' and 'increasing the degree of idealisation'. And the potential technology and evolutionary state of waste printed circuit boards are predicted, offering reference for future waste printed circuit boards recycling. © The Author(s) 2016.

Long terms trends in CD4+ cell counts, CD8+ cell counts, and the CD4+ : CD8+ ratio

PubMed Central

Hughes, Rachael A.; May, Margaret T.; Tilling, Kate; Taylor, Ninon; Wittkop, Linda; Reiss, Peter; Gill, John; Schommers, Philipp; Costagliola, Dominique; Guest, Jodie L.; Lima, Viviane D.; d’Arminio Monforte, Antonella; Smith, Colette; Cavassini, Matthias; Saag, Michael; Castilho, Jessica L.; Sterne, Jonathan A.C.

2018-01-01

Objective: Model trajectories of CD4+ and CD8+ cell counts after starting combination antiretroviral therapy (ART) and use the model to predict trends in these counts and the CD4+ : CD8+ ratio. Design: Cohort study of antiretroviral-naïve HIV-positive adults who started ART after 1997 (ART Cohort Collaboration) with more than 6 months of follow-up data. Methods: We jointly estimated CD4+ and CD8+ cell count trends and their correlation using a bivariate random effects model, with linear splines describing their population trends, and predicted the CD4+ : CD8+ ratio trend from this model. We assessed whether CD4+ and CD8+ cell count trends and the CD4+ : CD8+ ratio trend varied according to CD4+ cell count at start of ART (baseline), and, whether these trends differed in patients with and without virological failure more than 6 months after starting ART. Results: A total of 39 979 patients were included (median follow-up was 53 months). Among patients with baseline CD4+ cell count at least 50 cells/μl, predicted mean CD8+ cell counts continued to decrease between 3 and 15 years post-ART, partly driving increases in the predicted mean CD4+ : CD8+ ratio. During 15 years of follow-up, normalization of the predicted mean CD4+ : CD8+ ratio (to >1) was only observed among patients with baseline CD4+ cell count at least 200 cells/μl. A higher baseline CD4+ cell count predicted a shorter time to normalization. Conclusion: Declines in CD8+ cell count and increases in CD4+ : CD8+ ratio occurred up to 15 years after starting ART. The likelihood of normalization of the CD4+ : CD8+ ratio is strongly related to baseline CD4+ cell count. PMID:29851663