yields good predictions: Topics by Science.gov

Sample records for yields good predictions

Definition of architectural ideotypes for good yield capacity in Coffea canephora.

PubMed

Cilas, Christian; Bar-Hen, Avner; Montagnon, Christophe; Godin, Christophe

2006-03-01

Yield capacity is a target trait for selection of agronomically desirable lines; it is preferred to simple yields recorded over different harvests. Yield capacity is derived using certain architectural parameters used to measure the components of yield capacity. Observation protocols for describing architecture and yield capacity were applied to six clones of coffee trees (Coffea canephora) in a comparative trial. The observations were used to establish architectural databases, which were explored using AMAPmod, a software dedicated to the analyses of plant architecture data. The traits extracted from the database were used to identify architectural parameters for predicting the yield of the plant material studied. Architectural traits are highly heritable and some display strong genetic correlations with cumulated yield. In particular, the proportion of fruiting nodes at plagiotropic level 15 counting from the top of the tree proved to be a good predictor of yield over two fruiting cycles.
Metabolomic prediction of yield in hybrid rice.

PubMed

Xu, Shizhong; Xu, Yang; Gong, Liang; Zhang, Qifa

2016-10-01

Rice (Oryza sativa) provides a staple food source for more than 50% of the world's population. An increase in yield can significantly contribute to global food security. Hybrid breeding can potentially help to meet this goal because hybrid rice often shows a considerable increase in yield when compared with pure-bred cultivars. We recently developed a marker-guided prediction method for hybrid yield and showed a substantial increase in yield through genomic hybrid breeding. We now have transcriptomic and metabolomic data as potential resources for prediction. Using six prediction methods, including least absolute shrinkage and selection operator (LASSO), best linear unbiased prediction (BLUP), stochastic search variable selection, partial least squares, and support vector machines using the radial basis function and polynomial kernel function, we found that the predictability of hybrid yield can be further increased using these omic data. LASSO and BLUP are the most efficient methods for yield prediction. For high heritability traits, genomic data remain the most efficient predictors. When metabolomic data are used, the predictability of hybrid yield is almost doubled compared with genomic prediction. Of the 21 945 potential hybrids derived from 210 recombinant inbred lines, selection of the top 10 hybrids predicted from metabolites would lead to a ~30% increase in yield. We hypothesize that each metabolite represents a biologically built-in genetic network for yield; thus, using metabolites for prediction is equivalent to using information integrated from these hidden genetic networks for yield prediction. © 2016 The Authors The Plant Journal © 2016 John Wiley & Sons Ltd.
Predicting Great Lakes fish yields: tools and constraints

USGS Publications Warehouse

Lewis, C.A.; Schupp, D.H.; Taylor, W.W.; Collins, J.J.; Hatch, Richard W.

1987-01-01

Prediction of yield is a critical component of fisheries management. The development of sound yield prediction methodology and the application of the results of yield prediction are central to the evolution of strategies to achieve stated goals for Great Lakes fisheries and to the measurement of progress toward those goals. Despite general availability of species yield models, yield prediction for many Great Lakes fisheries has been poor due to the instability of the fish communities and the inadequacy of available data. A host of biological, institutional, and societal factors constrain both the development of sound predictions and their application to management. Improved predictive capability requires increased stability of Great Lakes fisheries through rehabilitation of well-integrated communities, improvement of data collection, data standardization and information-sharing mechanisms, and further development of the methodology for yield prediction. Most important is the creation of a better-informed public that will in turn establish the political will to do what is required.
Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1976-01-01

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.
Invited review: A commentary on predictive cheese yield formulas.

PubMed

Emmons, D B; Modler, H W

2010-12-01

Predictive cheese yield formulas have evolved from one based only on casein and fat in 1895. Refinements have included moisture and salt in cheese and whey solids as separate factors, paracasein instead of casein, and exclusion of whey solids from moisture associated with cheese protein. The General, Barbano, and Van Slyke formulas were tested critically using yield and composition of milk, whey, and cheese from 22 vats of Cheddar cheese. The General formula is based on the sum of cheese components: fat, protein, moisture, salt, whey solids free of fat and protein, as well as milk salts associated with paracasein. The testing yielded unexpected revelations. It was startling that the sum of components in cheese was <100%; the mean was 99.51% (N × 6.31). The mean predicted yield was only 99.17% as a percentage of actual yields (PY%AY); PY%AY is a useful term for comparisons of yields among vats. The PY%AY correlated positively with the sum of components (SofC) in cheese. The apparent low estimation of SofC led to the idea of adjusting upwards, for each vat, the 5 measured components in the formula by the observed SofC, as a fraction. The mean of the adjusted predicted yields as percentages of actual yields was 99.99%. The adjusted forms of the General, Barbano, and Van Slyke formulas gave predicted yields equal to the actual yields. It was apparent that unadjusted yield formulas did not accurately predict yield; however, unadjusted PY%AY can be useful as a control tool for analyses of cheese and milk. It was unexpected that total milk protein in the adjusted General formula gave the same predicted yields as casein and paracasein, indicating that casein or paracasein may not always be necessary for successful yield prediction. The use of constants for recovery of fat and protein in the adjusted General formula gave adjusted predicted yields equal to actual yields, indicating that analyses of cheese for protein and fat may not always be necessary for yield prediction
Random Forests for Global and Regional Crop Yield Predictions.

PubMed

Jeong, Jig Han; Resop, Jonathan P; Mueller, Nathaniel D; Fleisher, David H; Yun, Kyungdahm; Butler, Ethan E; Timlin, Dennis J; Shim, Kyo-Moon; Gerber, James S; Reddy, Vangimalla R; Kim, Soo-Hyung

2016-01-01

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.
Application of a GCM Ensemble Seasonal Climate Forecasts to Crop Yield Prediction in East Africa

NASA Astrophysics Data System (ADS)

Ogutu, G.; Franssen, W.; Supit, I.; Hutjes, R. W. A.

2016-12-01

We evaluated the potential use of ECMWF System-4 seasonal climate forecasts (S4) for impacts analysis over East Africa. Using the 15 member, 7 months ensemble forecasts initiated every month for 1981-2010, we tested precipitation (tp), air temperature (tas) and surface shortwave radiation (rsds) forecast skill against the WATCH forcing Data ERA-Interim (WFDEI) re-analysis and other data. We used these forecasts as input in the WOFOST crop model to predict maize yields. Forecast skill is assessed using anomaly correlation (ACC), Ranked Probability Skill Score (RPSS) and the Relative Operating Curve Skill Score (ROCSS) for MAM, JJA and OND growing seasons. Predicted maize yields (S4-yields) are verified against historical observed FAO and nationally reported (NAT) yield statistics, and yields from the same crop model forced by WFDEI (WFDEI-yields). Predictability of the climate forecasts vary with season, location and lead-time. The OND tp forecasts show skill over a larger area up to three months lead-time compared to MAM and JJA. Upper- and lower-tercile tp forecasts are 20-80% better than climatology. Good tas forecast skill is apparent with three months lead-time. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher against others. S4-forecasts captures ENSO related anomalous years with region dependent skill. Anomalous ENSO influence is also seen in simulated yields. Focussing on the main sowing dates in the northern (July), equatorial (March-April) and southern (December) regions, WFDEI-yields are lower than FAO and NAT but anomalies are comparable. Yield anomalies are predictable 3-months before sowing in most of the regions. Differences in interannual variability in the range of ±40% may be related to sensitivity of WOFOST to drought stress while the ACCs are largely positive ranging from 0.3 to 0.6. Above and below-normal yields are predictable with 2-months lead time. We evidenced a potential use of seasonal
Characterizing bias correction uncertainty in wheat yield predictions

NASA Astrophysics Data System (ADS)

Ortiz, Andrea Monica; Jones, Julie; Freckleton, Robert; Scaife, Adam

2017-04-01

Farming systems are under increased pressure due to current and future climate change, variability and extremes. Research on the impacts of climate change on crop production typically rely on the output of complex Global and Regional Climate Models, which are used as input to crop impact models. Yield predictions from these top-down approaches can have high uncertainty for several reasons, including diverse model construction and parameterization, future emissions scenarios, and inherent or response uncertainty. These uncertainties propagate down each step of the 'cascade of uncertainty' that flows from climate input to impact predictions, leading to yield predictions that may be too complex for their intended use in practical adaptation options. In addition to uncertainty from impact models, uncertainty can also stem from the intermediate steps that are used in impact studies to adjust climate model simulations to become more realistic when compared to observations, or to correct the spatial or temporal resolution of climate simulations, which are often not directly applicable as input into impact models. These important steps of bias correction or calibration also add uncertainty to final yield predictions, given the various approaches that exist to correct climate model simulations. In order to address how much uncertainty the choice of bias correction method can add to yield predictions, we use several evaluation runs from Regional Climate Models from the Coordinated Regional Downscaling Experiment over Europe (EURO-CORDEX) at different resolutions together with different bias correction methods (linear and variance scaling, power transformation, quantile-quantile mapping) as input to a statistical crop model for wheat, a staple European food crop. The objective of our work is to compare the resulting simulation-driven hindcasted wheat yields to climate observation-driven wheat yield hindcasts from the UK and Germany in order to determine ranges of yield
Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach.

PubMed

Cao, Hongliang; Xin, Ya; Yuan, Qiaoxia

2016-02-01

To predict conveniently the biochar yield from cattle manure pyrolysis, intelligent modeling approach was introduced in this research. A traditional artificial neural networks (ANN) model and a novel least squares support vector machine (LS-SVM) model were developed. For the identification and prediction evaluation of the models, a data set with 33 experimental data was used, which were obtained using a laboratory-scale fixed bed reaction system. The results demonstrated that the intelligent modeling approach is greatly convenient and effective for the prediction of the biochar yield. In particular, the novel LS-SVM model has a more satisfying predicting performance and its robustness is better than the traditional ANN model. The introduction and application of the LS-SVM modeling method gives a successful example, which is a good reference for the modeling study of cattle manure pyrolysis process, even other similar processes. Copyright © 2015 Elsevier Ltd. All rights reserved.
Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database.

PubMed

Niu, Mutian; Kebreab, Ermias; Hristov, Alexander N; Oh, Joonpyo; Arndt, Claudia; Bannink, André; Bayat, Ali R; Brito, André F; Boland, Tommy; Casper, David; Crompton, Les A; Dijkstra, Jan; Eugène, Maguy A; Garnsworthy, Phil C; Haque, Md Najmul; Hellwing, Anne L F; Huhtanen, Pekka; Kreuzer, Michael; Kuhla, Bjoern; Lund, Peter; Madsen, Jørgen; Martin, Cécile; McClelland, Shelby C; McGee, Mark; Moate, Peter J; Muetzel, Stefan; Muñoz, Camila; O'Kiely, Padraig; Peiren, Nico; Reynolds, Christopher K; Schwarm, Angela; Shingfield, Kevin J; Storlien, Tonje M; Weisbjerg, Martin R; Yáñez-Ruiz, David R; Yu, Zhongtang

2018-02-16

Enteric methane (CH 4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH 4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH 4 emission conversion factors for specific regions are required to improve CH 4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and
An image based method for crop yield prediction using remotely sensed and crop canopy data: the case of Paphos district, western Cyprus

NASA Astrophysics Data System (ADS)

Papadavid, G.; Hadjimitsis, D.

2014-08-01

Remote sensing techniques development have provided the opportunity for optimizing yields in the agricultural procedure and moreover to predict the forthcoming yield. Yield prediction plays a vital role in Agricultural Policy and provides useful data to policy makers. In this context, crop and soil parameters along with NDVI index which are valuable sources of information have been elaborated statistically to test if a) Durum wheat yield can be predicted and b) when is the actual time-window to predict the yield in the district of Paphos, where Durum wheat is the basic cultivation and supports the rural economy of the area. 15 plots cultivated with Durum wheat from the Agricultural Research Institute of Cyprus for research purposes, in the area of interest, have been under observation for three years to derive the necessary data. Statistical and remote sensing techniques were then applied to derive and map a model that can predict yield of Durum wheat in this area. Indeed the semi-empirical model developed for this purpose, with very high correlation coefficient R2=0.886, has shown in practice that can predict yields very good. Students T test has revealed that predicted values and real values of yield have no statistically significant difference. The developed model can and will be further elaborated with more parameters and applied for other crops in the near future.
Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

PubMed Central

2011-01-01

Background The robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability. Results Based on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as
Earing Prediction in Cup Drawing using the BBC2008 Yield Criterion

NASA Astrophysics Data System (ADS)

Vrh, Marko; Halilovič, Miroslav; Starman, Bojan; Štok, Boris; Comsa, Dan-Sorin; Banabic, Dorel

2011-08-01

The paper deals with constitutive modelling of highly anisotropic sheet metals. It presents FEM based earing predictions in cup drawing simulation of highly anisotropic aluminium alloys where more than four ears occur. For that purpose the BBC2008 yield criterion, which is a plane-stress yield criterion formulated in the form of a finite series, is used. Thus defined criterion can be expanded to retain more or less terms, depending on the amount of given experimental data. In order to use the model in sheet metal forming simulations we have implemented it in a general purpose finite element code ABAQUS/Explicit via VUMAT subroutine, considering alternatively eight or sixteen parameters (8p and 16p version). For the integration of the constitutive model the explicit NICE (Next Increment Corrects Error) integration scheme has been used. Due to the scheme effectiveness the CPU time consumption for a simulation is comparable to the time consumption of built-in constitutive models. Two aluminium alloys, namely AA5042-H2 and AA2090-T3, have been used for a validation of the model. For both alloys the parameters of the BBC2008 model have been identified with a developed numerical procedure, based on a minimization of the developed cost function. For both materials, the predictions of the BBC2008 model prove to be in very good agreement with the experimental results. The flexibility and the accuracy of the model together with the identification and integration procedure guarantee the applicability of the BBC2008 yield criterion in industrial applications.
Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1975-01-01

A model was developed for predicting the day 50 percent of the wheat crop is planted in North Dakota. This model incorporates location as an independent variable. The Julian date when 50 percent of the crop was planted for the nine divisions of North Dakota for seven years was regressed on the 49 variables through the step-down multiple regression procedure. This procedure begins with all of the independent variables and sequentially removes variables that are below a predetermined level of significance after each step. The prediction equation was tested on daily data. The accuracy of the model is considered satisfactory for finding the historic dates on which to initiate yield prediction model. Growth prediction models were also developed for spring wheat.
On-line prediction of yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score using the MARC beef carcass image analysis system.

PubMed

Shackelford, S D; Wheeler, T L; Koohmaraie, M

2003-01-01

The present experiment was conducted to evaluate the ability of the U.S. Meat Animal Research Center's beef carcass image analysis system to predict calculated yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score under commercial beef processing conditions. In two commercial beef-processing facilities, image analysis was conducted on 800 carcasses on the beef-grading chain immediately after the conventional USDA beef quality and yield grades were applied. Carcasses were blocked by plant and observed calculated yield grade. The carcasses were then separated, with 400 carcasses assigned to a calibration data set that was used to develop regression equations, and the remaining 400 carcasses assigned to a prediction data set used to validate the regression equations. Prediction equations, which included image analysis variables and hot carcass weight, accounted for 90, 88, 90, 88, and 76% of the variation in calculated yield grade, longissimus muscle area, preliminary yield grade, adjusted preliminary yield grade, and marbling score, respectively, in the prediction data set. In comparison, the official USDA yield grade as applied by online graders accounted for 73% of the variation in calculated yield grade. The technology described herein could be used by the beef industry to more accurately determine beef yield grades; however, this system does not provide an accurate enough prediction of marbling score to be used without USDA grader interaction for USDA quality grading.
Predicting paddlefish roe yields using an extension of the Beverton–Holt equilibrium yield-per-recruit model

USGS Publications Warehouse

Colvin, M.E.; Bettoli, Phillip William; Scholten, G.D.

2013-01-01

Equilibrium yield models predict the total biomass removed from an exploited stock; however, traditional yield models must be modified to simulate roe yields because a linear relationship between age (or length) and mature ovary weight does not typically exist. We extended the traditional Beverton-Holt equilibrium yield model to predict roe yields of Paddlefish Polyodon spathula in Kentucky Lake, Tennessee-Kentucky, as a function of varying conditional fishing mortality rates (10-70%), conditional natural mortality rates (cm; 9% and 18%), and four minimum size limits ranging from 864 to 1,016mm eye-to-fork length. These results were then compared to a biomass-based yield assessment. Analysis of roe yields indicated the potential for growth overfishing at lower exploitation rates and smaller minimum length limits than were suggested by the biomass-based assessment. Patterns of biomass and roe yields in relation to exploitation rates were similar regardless of the simulated value of cm, thus indicating that the results were insensitive to changes in cm. Our results also suggested that higher minimum length limits would increase roe yield and reduce the potential for growth overfishing and recruitment overfishing at the simulated cm values. Biomass-based equilibrium yield assessments are commonly used to assess the effects of harvest on other caviar-based fisheries; however, our analysis demonstrates that such assessments likely underestimate the probability and severity of growth overfishing when roe is targeted. Therefore, equilibrium roe yield-per-recruit models should also be considered to guide the management process for caviar-producing fish species.
Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA's-AVHRR.

PubMed

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha(-1). Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly.
Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR

PubMed Central

Huang, Jingfeng; Wang, Xiuzhen; Li, Xinxing; Tian, Hanqin; Pan, Zhuokun

2013-01-01

Grain-yield prediction using remotely sensed data have been intensively studied in wheat and maize, but such information is limited in rice, barley, oats and soybeans. The present study proposes a new framework for rice-yield prediction, which eliminates the influence of the technology development, fertilizer application, and management improvement and can be used for the development and implementation of provincial rice-yield predictions. The technique requires the collection of remotely sensed data over an adequate time frame and a corresponding record of the region's crop yields. Longer normalized-difference-vegetation-index (NDVI) time series are preferable to shorter ones for the purposes of rice-yield prediction because the well-contrasted seasons in a longer time series provide the opportunity to build regression models with a wide application range. A regression analysis of the yield versus the year indicated an annual gain in the rice yield of 50 to 128 kg ha−1. Stepwise regression models for the remotely sensed rice-yield predictions have been developed for five typical rice-growing provinces in China. The prediction models for the remotely sensed rice yield indicated that the influences of the NDVIs on the rice yield were always positive. The association between the predicted and observed rice yields was highly significant without obvious outliers from 1982 to 2004. Independent validation found that the overall relative error is approximately 5.82%, and a majority of the relative errors were less than 5% in 2005 and 2006, depending on the study area. The proposed models can be used in an operational context to predict rice yields at the provincial level in China. The methodologies described in the present paper can be applied to any crop for which a sufficient time series of NDVI data and the corresponding historical yield information are available, as long as the historical yield increases significantly. PMID:23967112
Multivariate Statistical Models for Predicting Sediment Yields from Southern California Watersheds

USGS Publications Warehouse

Gartner, Joseph E.; Cannon, Susan H.; Helsel, Dennis R.; Bandurraga, Mark

2009-01-01

Debris-retention basins in Southern California are frequently used to protect communities and infrastructure from the hazards of flooding and debris flow. Empirical models that predict sediment yields are used to determine the size of the basins. Such models have been developed using analyses of records of the amount of material removed from debris retention basins, associated rainfall amounts, measures of watershed characteristics, and wildfire extent and history. In this study we used multiple linear regression methods to develop two updated empirical models to predict sediment yields for watersheds located in Southern California. The models are based on both new and existing measures of volume of sediment removed from debris retention basins, measures of watershed morphology, and characterization of burn severity distributions for watersheds located in Ventura, Los Angeles, and San Bernardino Counties. The first model presented reflects conditions in watersheds located throughout the Transverse Ranges of Southern California and is based on volumes of sediment measured following single storm events with known rainfall conditions. The second model presented is specific to conditions in Ventura County watersheds and was developed using volumes of sediment measured following multiple storm events. To relate sediment volumes to triggering storm rainfall, a rainfall threshold was developed to identify storms likely to have caused sediment deposition. A measured volume of sediment deposited by numerous storms was parsed among the threshold-exceeding storms based on relative storm rainfall totals. The predictive strength of the two models developed here, and of previously-published models, was evaluated using a test dataset consisting of 65 volumes of sediment yields measured in Southern California. The evaluation indicated that the model developed using information from single storm events in the Transverse Ranges best predicted sediment yields for watersheds in San
Limitations of lumber-yield nomograms for predicting lumber requirements

Treesearch

Kristen Hoff

2000-01-01

Lumber yield nomograms developed during the last 30 years have limited use when predicting the volume of rough lumber required to fill a particular cutting bill. Inaccuracies occur when nomogram yields are applied to situations in which processing technologies differ from those used during data collection, and when a variety of lengths and widths are specified in the...

Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

PubMed

Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

2017-05-01

physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com
Development of predictive weather scenarios for early prediction of rice yield in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.
Atomic Oxygen Erosion Yield Predictive Tool for Spacecraft Polymers in Low Earth Orbit

NASA Technical Reports Server (NTRS)

Bank, Bruce A.; de Groh, Kim K.; Backus, Jane A.

2008-01-01

A predictive tool was developed to estimate the low Earth orbit (LEO) atomic oxygen erosion yield of polymers based on the results of the Polymer Erosion and Contamination Experiment (PEACE) Polymers experiment flown as part of the Materials International Space Station Experiment 2 (MISSE 2). The MISSE 2 PEACE experiment accurately measured the erosion yield of a wide variety of polymers and pyrolytic graphite. The 40 different materials tested were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The resulting erosion yield data was used to develop a predictive tool which utilizes chemical structure and physical properties of polymers that can be measured in ground laboratory testing to predict the in-space atomic oxygen erosion yield of a polymer. The properties include chemical structure, bonding information, density and ash content. The resulting predictive tool has a correlation coefficient of 0.914 when compared with actual MISSE 2 space data for 38 polymers and pyrolytic graphite. The intent of the predictive tool is to be able to make estimates of atomic oxygen erosion yields for new polymers without requiring expensive and time consumptive in-space testing.
Atomic Oxygen Erosion Yield Prediction for Spacecraft Polymers in Low Earth Orbit

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Backus, Jane A.; Manno, Michael V.; Waters, Deborah L.; Cameron, Kevin C.; deGroh, Kim K.

2009-01-01

The ability to predict the atomic oxygen erosion yield of polymers based on their chemistry and physical properties has been only partially successful because of a lack of reliable low Earth orbit (LEO) erosion yield data. Unfortunately, many of the early experiments did not utilize dehydrated mass loss measurements for erosion yield determination, and the resulting mass loss due to atomic oxygen exposure may have been compromised because samples were often not in consistent states of dehydration during the pre-flight and post-flight mass measurements. This is a particular problem for short duration mission exposures or low erosion yield materials. However, as a result of the retrieval of the Polymer Erosion and Contamination Experiment (PEACE) flown as part of the Materials International Space Station Experiment 2 (MISSE 2), the erosion yields of 38 polymers and pyrolytic graphite were accurately measured. The experiment was exposed to the LEO environment for 3.95 years from August 16, 2001 to July 30, 2005 and was successfully retrieved during a space walk on July 30, 2005 during Discovery s STS-114 Return to Flight mission. The 40 different materials tested (including Kapton H fluence witness samples) were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The MISSE 2 PEACE Polymers experiment used carefully dehydrated mass measurements, as well as accurate density measurements to obtain accurate erosion yield data for high-fluence (8.43 1021 atoms/sq cm). The resulting data was used to develop an erosion yield predictive tool with a correlation coefficient of 0.895 and uncertainty of +/-6.3 10(exp -25)cu cm/atom. The predictive tool utilizes the chemical structures and physical properties of polymers to predict in-space atomic oxygen erosion yields. A predictive tool concept (September 2009 version) is presented which represents an improvement over an earlier (December 2008) version.
Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.
Kill ratio calculation for in-line yield prediction

NASA Astrophysics Data System (ADS)

Lorenzo, Alfonso; Oter, David; Cruceta, Sergio; Valtuena, Juan F.; Gonzalez, Gerardo; Mata, Carlos

1999-04-01

The search for better yields in IC manufacturing calls for a smarter use of the vast amount of data that can be generated by a world class production line.In this scenario, in-line inspection processes produce thousands of wafer maps, number of defects, defect type and pictures every day. A step forward is to correlate these with the other big data- generator area: test. In this paper, we present how these data can be put together and correlated to obtain a very useful yield predicting tool. This correlation will first allow us to calculate the kill ratio, i.e. the probability for a defect of a certain size in a certain layer to kill the die. Then we will use that number to estimate the cosmetic yield that a wafer will have.
Criteria for Yielding of Dispersion-Strengthened Alloys

NASA Technical Reports Server (NTRS)

Ansell, G. S.; Lenel, F. V.

1960-01-01

A dislocation model is presented in order to account for the yield behavior of alloys with a finely dispersed second-phase. The criteria for yielding used in the model, is that appreciable yielding occurs in these alloys when the shear stress due to piled-up groups of dislocations is sufficient to fracture or plastically deform the dispersed second-phase particles, relieving the back stress on the dislocation sources. Equations derived on the basis of this model, predict that the yield stress of the alloys varies as the reciprocal square root of the mean free path between dispersed particles. Experimental data is presented for several SAP-Type alloys, precipitation-hardened alloys and steels which are in good agreement with the yield strength variation as a function of dispersion spacing predicted by this theoretical treatment.
Prediction of kharif rice yield at Kharagpur using disaggregated extended range rainfall forecasts

NASA Astrophysics Data System (ADS)

Dhekale, B. S.; Nageswararao, M. M.; Nair, Archana; Mohanty, U. C.; Swain, D. K.; Singh, K. K.; Arunbabu, T.

2017-08-01

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June-September) and sub-seasonal (July-September, August-September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985-2008) and real-time mode (2009-2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of
Vector Adaptive/Predictive Encoding Of Speech

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey; Gersho, Allen

1989-01-01

Vector adaptive/predictive technique for digital encoding of speech signals yields decoded speech of very good quality after transmission at coding rate of 9.6 kb/s and of reasonably good quality at 4.8 kb/s. Requires 3 to 4 million multiplications and additions per second. Combines advantages of adaptive/predictive coding, and code-excited linear prediction, yielding speech of high quality but requires 600 million multiplications and additions per second at encoding rate of 4.8 kb/s. Vector adaptive/predictive coding technique bridges gaps in performance and complexity between adaptive/predictive coding and code-excited linear prediction.
Loblolly Pine Growth and Yield Prediction for Managed West Gulf Plantations

Treesearch

V. Clark Baldwin; D.P. Feduccia

1987-01-01

Complete description, including tables, graphs, computer output, of a growth and yield prediction system providing volume and weight yields in stand and stock table format. An example of system use is given along with information about the computer program, COMPUTE P-LOB, that operates the system.
Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

PubMed

Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J

2018-01-22

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.
Predicted yields from selected cutting prescriptions in northern Minnesota.

Treesearch

Pamela J. Jakes; W. Brad Smith

1980-01-01

Includes predicted yields based on two sets of cutting prescriptions in northern Minnesota. Indicates that given a specific set of assumptions, average annual growing-stock removals for the decade 1977-1986 would be from 69% to 124% higher than 1976 growing-stock removals.
A nonlinear viscoelastic constitutive equation - Yield predictions in multiaxial deformations

NASA Technical Reports Server (NTRS)

Shay, R. M., Jr.; Caruthers, J. M.

1987-01-01

Yield stress predictions of a nonlinear viscoelastic constitutive equation for amorphous polymer solids have been obtained and are compared with the phenomenological von Mises yield criterion. Linear viscoelasticity theory has been extended to include finite strains and a material timescale that depends on the instantaneous temperature, volume, and pressure. Results are presented for yield and the correct temperature and strain-rate dependence in a variety of multiaxial deformations. The present nonlinear viscoelastic constitutive equation can be formulated in terms of either a Cauchy or second Piola-Kirchhoff stress tensor, and in terms of either atmospheric or hydrostatic pressure.
Evaluating high temporal and spatial resolution vegetation index for crop yield prediction

USDA-ARS?s Scientific Manuscript database

Remote sensing data have been widely used in estimating crop yield. Remote sensing derived parameters such as Vegetation Index (VI) were used either directly in building empirical models or by assimilating with crop growth models to predict crop yield. The abilities of remote sensing VI in crop yiel...
Using artificial neural network and satellite data to predict rice yield in Bangladesh

NASA Astrophysics Data System (ADS)

Akhand, Kawsar; Nizamuddin, Mohammad; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch

2015-09-01

Rice production in Bangladesh is a crucial part of the national economy and providing about 70 percent of an average citizen's total calorie intake. The demand for rice is constantly rising as the new populations are added in every year in Bangladesh. Due to the increase in population, the cultivation land decreases. In addition, Bangladesh is faced with production constraints such as drought, flooding, salinity, lack of irrigation facilities and lack of modern technology. To maintain self sufficiency in rice, Bangladesh will have to continue to expand rice production by increasing yield at a rate that is at least equal to the population growth until the demand of rice has stabilized. Accurate rice yield prediction is one of the most important challenges in managing supply and demand of rice as well as decision making processes. Artificial Neural Network (ANN) is used to construct a model to predict Aus rice yield in Bangladesh. Advanced Very High Resolution Radiometer (AVHRR)-based remote sensing satellite data vegetation health (VH) indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) are used as input variables and official statistics of Aus rice yield is used as target variable for ANN prediction model. The result obtained with ANN method is encouraging and the error of prediction is less than 10%. Therefore, prediction can play an important role in planning and storing of sufficient rice to face in any future uncertainty.
Tensile Yielding of Multi-Wall Carbon Nanotube

NASA Technical Reports Server (NTRS)

Wei, Chenyu; Cho, Kyeongjae; Srivastava, Deepak; Parks, John W. (Technical Monitor)

2002-01-01

The tensile yielding of multiwall carbon nanotubes (MWCNTs) has been studied using Molecular Dynamics simulations and a Transition State Theory based model. We find a strong dependence of the yielding on the strain rate. A critical strain rate has been predicted above/below which yielding strain of a MWCNT is larger/smaller than that of the corresponding single-wall carbon nanotubes. At experimentally feasible strain rate of 1% /hour and T = 300K, the yield strain of a MWCNT is estimated to be about 3-4 % higher than that of an equivalent SWCNT (Single Wall Carbon Nanotube), in good agreement with recent experimental observations.
Predicted Exoplanet Yields for the HabEx Mission Concept

NASA Astrophysics Data System (ADS)

Stark, Christopher; Mennesson, Bertrand; HabEx STDT

2018-01-01

The Habitable Exoplanet Imaging Mission (HabEx) is a concept for a flagship mission to directly image and characterize extrasolar planets around nearby stars and to enable a broad range of general astrophysics. The HabEx Science and Technology Definition Team (STDT) is currently studying two architectures for HabEx. Here we summarize the exoplanet science yield of Architecture A, a 4 m monolithic off-axis telescope that uses a vortex coronagraph and a 72m external starshade occulter. We summarize the instruments' capabilities, present science goals and observation strategies, and discuss astrophysical assumptions. Using a yield optimization code, we predict the yield of potentially Earth-like extrasolar planets that could be detected, characterized, and searched for signs of habitability and/or life by HabEx. We demonstrate that HabEx could also detect and characterize a wide variety of exoplanets while searching for potentially Earth-like planets.
Integrated model for predicting rice yield with climate change

NASA Astrophysics Data System (ADS)

Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa

2018-04-01

Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.
Predicting Story Goodness Performance from Cognitive Measures Following Traumatic Brain Injury

ERIC Educational Resources Information Center

Le, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-01-01

Purpose: This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Le, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. Method: One hundred…
Prediction of County-Level Corn Yields Using an Energy-Crop Growth Index.

NASA Astrophysics Data System (ADS)

Andresen, Jeffrey A.; Dale, Robert F.; Fletcher, Jerald J.; Preckel, Paul V.

1989-01-01

Weather conditions significantly affect corn yields. while weather remains as the major uncontrolled variable in crop production, an understanding of the influence of weather on yields can aid in early and accurate assessment of the impact of weather and climate on crop yields and allow for timely agricultural extension advisories to help reduce farm management costs and improve marketing, decisions. Based on data for four representative countries in Indiana from 1960 to 1984 (excluding 1970 because of the disastrous southern corn leaf blight), a model was developed to estimate corn (Zea mays L.) yields as a function of several composite soil-crop-weather variables and a technology-trend marker, applied nitrogen fertilizer (N). The model was tested by predicting corn yields for 15 other counties. A daily energy-crop growth (ECG) variable in which different weights were used for the three crop-weather variables which make up the daily ECG-solar radiation intercepted by the canopy, a temperature function, and the ratio of actual to potential evapotranspiration-performed better than when the ECG components were weighted equally. The summation of the weighted daily ECG over a relatively short period (36 days spanning silk) was found to provide the best index for predicting county average corn yield. Numerical estimation results indicate that the ratio of actual to potential evapotranspiration (ET/PET) is much more important than the other two ECG factors in estimating county average corn yield in Indiana.

Predicting ad libitum dry matter intake and yields of Jersey cows.

PubMed

Holter, J B; West, J W; McGilliard, M L; Pell, A N

1996-05-01

Two data files were used that contained weekly mean values for ad libitum DMI of lactating Jersey cows along with appropriate cow, ration, and environmental traits for predicting DMI. One data file (n = 666) was used to develop prediction equations for DMI because that file represented a number of separate experiments and contained more diversity in potential predictors, especially those related to ration, such as forage type. The other data file (n = 1613) was used primarily to verify these equations. Milk protein yield displaced 4% FCM output as a prediction variable and improved the R2 by several units but was not used in the final equations, however, for the sake of simplicity. All equations contained adjustments for the effects of heat stress, parity (1 vs. > 1), DIM > 15, BW, use of recombinant bST, and other significant independent variables. Equations were developed to predict DMI of cows fed individually or in groups and to predict daily yields of 4% FCM and milk protein; equations accounted for 0.69, 0.74, 0.81, and 0.76 of the variation in the dependent variables with standard deviations of 1.7, 1.6, 2.7, and 0.084 kg/ d, respectively. These equations should be applied to the development of software for computerized dairy ration balancing.
Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

NASA Technical Reports Server (NTRS)

Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon;

2014-01-01

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

Factors affecting the CD34+ cell yields from the second donations of healthy donors: The steady-state lymphocyte count is a good predictive factor.

PubMed

Guo, Zhi-Ping; Wang, Tao; Xu, Lan-Ping; Zhang, Xiao-Hui; Wang, Yu; Huang, Xiao-Jun; Chang, Ying-Jun

2016-12-01

A second allogeneic hematopoietic stem-cell transplantation and donor lymphocyte infusion using cells from the same donor is a therapeutic option in the case of stem-cell graft failure or disease relapse, but little is known about the factors associated with the CD34 + cell yields from second donations. One-hundred healthy donors who underwent a second mobilization treatment and peripheral blood stem-cell (PBSC) collection were studied. For both mobilization processes, 5 µg of granulocyte colony-stimulating factor per kg per day was administered. The blood counts of the donors were monitored during the processes. The second donations from the same donors provided lower apheresis yields than did the initial collections. The number of CD34 + cells collected from normal donors after a second cycle of PBSC mobilization was associated with their steady-state lymphocyte counts and the intertransplantation interval. Female sex negatively affected the CD34 + cell yields. The cutoff value for the steady-state absolute lymphocyte count was 2.055 × 10 9 /L. To harvest greater numbers of CD34 + cells from second collections, male donors and those with intervals of longer than 9 months between donations should be selected. The lymphocyte counts prior to the first donations may predict the content of CD34 + cells in the allografts prepared using the second donations. Copyright © 2016 Elsevier Ltd. All rights reserved.
Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.
Remotely sensed vegetation indices for seasonal crop yields predictions in the Czech Republic

NASA Astrophysics Data System (ADS)

Hlavinka, Petr; Semerádová, Daniela; Balek, Jan; Bohovic, Roman; Žalud, Zdeněk; Trnka, Miroslav

2015-04-01

Remotely sensed vegetation indices by satellites are valuable tool for vegetation conditions assessment also in the case of field crops. This study is based on the use of NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) aboard Terra satellite. Data available from the year 2000 were analyzed and tested for seasonal yields predictions within selected districts of the Czech Republic (Central Europe). Namely the yields of spring barley, winter wheat and oilseed winter rape during the period from 2000 to 2014 were assessed. Observed yields from 14 districts (NUTS 4) were collected and thus 210 seasons were included. Selected districts differ considerably in their soil fertility and terrain configuration and represent transect across various agroclimatic conditions (from warm and dry to relative cool and wet regions). Two approaches were tested: 1) using of composite remotely sensed data (available in 16 day time step) provided by the USGS (https://lpdaac.usgs.gov/); 2) using daily remotely sensed data in combination with originally developed smoothing method. The yields were successfully predicted based on established regression models (remotely sensed data used as independent parameter). Besides others the impact of severe drought episodes within vegetation were identified and yield reductions at district level predicted (even before harvest). As a result the periods with the best relationship between remotely sensed data and yields were identified. The impact of drought conditions as well as normal or above normal yields of field crops could be predicted by proposed method within study region up to 30 days prior to the harvest. It could be concluded that remotely sensed vegetation conditions assessment should be important part of early warning systems focused on drought. Such information should be widely available for various users (decision makers, farmers, etc.) in
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

PubMed

Ashraf, M Irfan; Meng, Fan-Rui; Bourque, Charles P-A; MacLean, David A

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2) 5-year(-1) and volume: 0.0008 m(3) 5-year(-1)). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm(2) 5-year(-1) and 0.0393 m(3) 5-year(-1) in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PubMed Central

Ashraf, M. Irfan; Meng, Fan-Rui; Bourque, Charles P.-A.; MacLean, David A.

2015-01-01

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial
Predawn respiration rates during flowering are highly predictive of yield response in Gossypium hirsutum when yield variability is water-induced.

PubMed

Snider, John L; Chastain, Daryl R; Meeks, Calvin D; Collins, Guy D; Sorensen, Ronald B; Byrd, Seth A; Perry, Calvin D

2015-07-01

Respiratory carbon evolution by leaves under abiotic stress is implicated as a major limitation to crop productivity; however, respiration rates of fully expanded leaves are positively associated with plant growth rates. Given the substantial sensitivity of plant growth to drought, it was hypothesized that predawn respiration rates (RPD) would be (1) more sensitive to drought than photosynthetic processes and (2) highly predictive of water-induced yield variability in Gossypium hirsutum. Two studies (at Tifton and Camilla Georgia) addressed these hypotheses. At Tifton, drought was imposed beginning at the onset of flowering (first flower) and continuing for three weeks (peak bloom) followed by a recovery period, and predawn water potential (ΨPD), RPD, net photosynthesis (AN) and maximum quantum yield of photosystem II (Fv/Fm) were measured throughout the study period. At Camilla, plants were exposed to five different irrigation regimes throughout the growing season, and average ΨPD and RPD were determined between first flower and peak bloom for all treatments. For both sites, fiber yield was assessed at crop maturity. The relationships between ΨPD, RPD and yield were assessed via non-linear regression. It was concluded for field-grown G. hirsutum that (1) RPD is exceptionally sensitive to progressive drought (more so than AN or Fv/Fm) and (2) average RPD from first flower to peak bloom is highly predictive of water-induced yield variability. Copyright © 2015 Elsevier GmbH. All rights reserved.
Electroencephalography Predicts Poor and Good Outcomes After Cardiac Arrest: A Two-Center Study.

PubMed

Rossetti, Andrea O; Tovar Quiroga, Diego F; Juan, Elsa; Novy, Jan; White, Roger D; Ben-Hamouda, Nawfel; Britton, Jeffrey W; Oddo, Mauro; Rabinstein, Alejandro A

2017-07-01

The prognostic role of electroencephalography during and after targeted temperature management in postcardiac arrest patients, relatively to other predictors, is incompletely known. We assessed performances of electroencephalography during and after targeted temperature management toward good and poor outcomes, along with other recognized predictors. Cohort study (April 2009 to March 2016). Two academic hospitals (Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland; Mayo Clinic, Rochester, MN). Consecutive comatose adults admitted after cardiac arrest, identified through prospective registries. All patients were managed with targeted temperature management, receiving prespecified standardized clinical, neurophysiologic (particularly, electroencephalography during and after targeted temperature management), and biochemical evaluations. We assessed electroencephalography variables (reactivity, continuity, epileptiform features, and prespecified "benign" or "highly malignant" patterns based on the American Clinical Neurophysiology Society nomenclature) and other clinical, neurophysiologic (somatosensory-evoked potential), and biochemical prognosticators. Good outcome (Cerebral Performance Categories 1 and 2) and mortality predictions at 3 months were calculated. Among 357 patients, early electroencephalography reactivity and continuity and flexor or better motor reaction had greater than 70% positive predictive value for good outcome; reactivity (80.4%; 95% CI, 75.9-84.4%) and motor response (80.1%; 95% CI, 75.6-84.1%) had highest accuracy. Early benign electroencephalography heralded good outcome in 86.2% (95% CI, 79.8-91.1%). False positive rates for mortality were less than 5% for epileptiform or nonreactive early electroencephalography, nonreactive late electroencephalography, absent somatosensory-evoked potential, absent pupillary or corneal reflexes, presence of myoclonus, and neuron-specific enolase greater than 75 µg/L; accuracy was highest for
Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...
[Prediction of the side-cut product yield of atmospheric/vacuum distillation unit by NIR crude oil rapid assay].

PubMed

Wang, Yan-Bin; Hu, Yu-Zhong; Li, Wen-Le; Zhang, Wei-Song; Zhou, Feng; Luo, Zhi

2014-10-01

In the present paper, based on the fast evaluation technique of near infrared, a method to predict the yield of atmos- pheric and vacuum line was developed, combined with H/CAMS software. Firstly, the near-infrared (NIR) spectroscopy method for rapidly determining the true boiling point of crude oil was developed. With commercially available crude oil spectroscopy da- tabase and experiments test from Guangxi Petrochemical Company, calibration model was established and a topological method was used as the calibration. The model can be employed to predict the true boiling point of crude oil. Secondly, the true boiling point based on NIR rapid assay was converted to the side-cut product yield of atmospheric/vacuum distillation unit by H/CAMS software. The predicted yield and the actual yield of distillation product for naphtha, diesel, wax and residual oil were compared in a 7-month period. The result showed that the NIR rapid crude assay can predict the side-cut product yield accurately. The near infrared analytic method for predicting yield has the advantages of fast analysis, reliable results, and being easy to online operate, and it can provide elementary data for refinery planning optimization and crude oil blending.
Plateletpheresis efficiency and mathematical correction of software-derived platelet yield prediction: A linear regression and ROC modeling approach.

PubMed

Jaime-Pérez, José Carlos; Jiménez-Castillo, Raúl Alberto; Vázquez-Hernández, Karina Elizabeth; Salazar-Riojas, Rosario; Méndez-Ramírez, Nereida; Gómez-Almaguer, David

2017-10-01

Advances in automated cell separators have improved the efficiency of plateletpheresis and the possibility of obtaining double products (DP). We assessed cell processor accuracy of predicted platelet (PLT) yields with the goal of a better prediction of DP collections. This retrospective proof-of-concept study included 302 plateletpheresis procedures performed on a Trima Accel v6.0 at the apheresis unit of a hematology department. Donor variables, software predicted yield and actual PLT yield were statistically evaluated. Software prediction was optimized by linear regression analysis and its optimal cut-off to obtain a DP assessed by receiver operating characteristic curve (ROC) modeling. Three hundred and two plateletpheresis procedures were performed; in 271 (89.7%) occasions, donors were men and in 31 (10.3%) women. Pre-donation PLT count had the best direct correlation with actual PLT yield (r = 0.486. P < .001). Means of software machine-derived values differed significantly from actual PLT yield, 4.72 × 10 11 vs.6.12 × 10 11 , respectively, (P < .001). The following equation was developed to adjust these values: actual PLT yield= 0.221 + (1.254 × theoretical platelet yield). ROC curve model showed an optimal apheresis device software prediction cut-off of 4.65 × 10 11 to obtain a DP, with a sensitivity of 82.2%, specificity of 93.3%, and an area under the curve (AUC) of 0.909. Trima Accel v6.0 software consistently underestimated PLT yields. Simple correction derived from linear regression analysis accurately corrected this underestimation and ROC analysis identified a precise cut-off to reliably predict a DP. © 2016 Wiley Periodicals, Inc.
Growth and yield predictions for upland oak stands. 10 years after initial thinning

Treesearch

Martin E. Dale; Martin E. Dale

1972-01-01

The purpose of this paper is to furnish part of the needed information, that is, quantitative estimates of growth and yield 10 years after initial thinning of upland oak stands. All estimates are computed from a system of equations. These predictions are presented here in tabular form for convenient visual inspection of growth and yield trends. The tables show growth...
Crop Yield Predictions - High Resolution Statistical Model for Intra-season Forecasts Applied to Corn in the US

NASA Astrophysics Data System (ADS)

Cai, Y.

2017-12-01

Accurately forecasting crop yields has broad implications for economic trading, food production monitoring, and global food security. However, the variation of environmental variables presents challenges to model yields accurately, especially when the lack of highly accurate measurements creates difficulties in creating models that can succeed across space and time. In 2016, we developed a sequence of machine-learning based models forecasting end-of-season corn yields for the US at both the county and national levels. We combined machine learning algorithms in a hierarchical way, and used an understanding of physiological processes in temporal feature selection, to achieve high precision in our intra-season forecasts, including in very anomalous seasons. During the live run, we predicted the national corn yield within 1.40% of the final USDA number as early as August. In the backtesting of the 2000-2015 period, our model predicts national yield within 2.69% of the actual yield on average already by mid-August. At the county level, our model predicts 77% of the variation in final yield using data through the beginning of August and improves to 80% by the beginning of October, with the percentage of counties predicted within 10% of the average yield increasing from 68% to 73%. Further, the lowest errors are in the most significant producing regions, resulting in very high precision national-level forecasts. In addition, we identify the changes of important variables throughout the season, specifically early-season land surface temperature, and mid-season land surface temperature and vegetation index. For the 2017 season, we feed 2016 data to the training set, together with additional geospatial data sources, aiming to make the current model even more precise. We will show how our 2017 US corn yield forecasts converges in time, which factors affect the yield the most, as well as present our plans for 2018 model adjustments.
The ability of video image analysis to predict lean meat yield and EUROP score of lamb carcasses.

PubMed

Einarsson, E; Eythórsdóttir, E; Smith, C R; Jónmundsson, J V

2014-07-01

A total of 862 lamb carcasses that were evaluated by both the VIAscan® and the current EUROP classification system were deboned and the actual yield was measured. Models were derived for predicting lean meat yield of the legs (Leg%), loin (Loin%) and shoulder (Shldr%) using the best VIAscan® variables selected by stepwise regression analysis of a calibration data set (n=603). The equations were tested on validation data set (n=259). The results showed that the VIAscan® predicted lean meat yield in the leg, loin and shoulder with an R 2 of 0.60, 0.31 and 0.47, respectively, whereas the current EUROP system predicted lean yield with an R 2 of 0.57, 0.32 and 0.37, respectively, for the three carcass parts. The VIAscan® also predicted the EUROP score of the trial carcasses, using a model derived from an earlier trial. The EUROP classification from VIAscan® and the current system were compared for their ability to explain the variation in lean yield of the whole carcass (LMY%) and trimmed fat (FAT%). The predicted EUROP scores from the VIAscan® explained 36% of the variation in LMY% and 60% of the variation in FAT%, compared with the current EUROP system that explained 49% and 72%, respectively. The EUROP classification obtained by the VIAscan® was tested against a panel of three expert classifiers (n=696). The VIAscan® classification agreed with 82% of conformation and 73% of the fat classes assigned by a panel of expert classifiers. It was concluded that VIAscan® provides a technology that can directly predict LMY% of lamb carcasses with more accuracy than the current EUROP classification system. The VIAscan® is also capable of classifying lamb carcasses into EUROP classes with an accuracy that fulfils minimum demands for the Icelandic sheep industry. Although the VIAscan® prediction of the Loin% is low, it is comparable to the current EUROP system, and should not hinder the adoption of the technology to estimate the yield of Icelandic lambs as it delivered
Climate Based Predictability of Oil Palm Tree Yield in Malaysia.

PubMed

Oettli, Pascal; Behera, Swadhin K; Yamagata, Toshio

2018-02-02

The influence of local conditions and remote climate modes on the interannual variability of oil palm fresh fruit bunches (FFB) total yields in Malaysia and two major regions (Peninsular Malaysia and Sabah/Sarawak) is explored. On a country scale, the state of sea-surface temperatures (SST) in the tropical Pacific Ocean during the previous boreal winter is found to influence the regional climate. When El Niño occurs in the Pacific Ocean, rainfall in Malaysia reduces but air temperature increases, generating a high level of water stress for palm trees. As a result, the yearly production of FFB becomes lower than that of a normal year since the water stress during the boreal spring has an important impact on the total annual yields of FFB. Conversely, La Niña sets favorable conditions for palm trees to produce more FFB by reducing chances of water stress risk. The region of the Leeuwin current also seems to play a secondary role through the Ningaloo Niño/ Niña in the interannual variability of FFB yields. Based on these findings, a linear model is constructed and its ability to reproduce the interannual signal is assessed. This model has shown some skills in predicting the total FFB yield.
Melon yield prediction using small unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Zhao, Tiebiao; Wang, Zhongdao; Yang, Qi; Chen, YangQuan

2017-05-01

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.
Yield variability prediction by remote sensing sensors with different spatial resolution

NASA Astrophysics Data System (ADS)

Kumhálová, Jitka; Matějková, Štěpánka

2017-04-01

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.
Spectrally-Based Assessment of Crop Seasonal Performance and Yield

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana; Borisova, Denitsa; Georgiev, Georgy

The rapid advances of space technologies concern almost all scientific areas from aeronautics to medicine, and a wide range of application fields from communications to crop yield predictions. Agricultural monitoring is among the priorities of remote sensing observations for getting timely information on crop development. Monitoring agricultural fields during the growing season plays an important role in crop health assessment and stress detection provided that reliable data is obtained. Successfully spreading is the implementation of hyperspectral data to precision farming associated with plant growth and phenology monitoring, physiological state assessment, and yield prediction. In this paper, we investigated various spectral-biophysical relationships derived from in-situ reflectance measurements. The performance of spectral data for the assessment of agricultural crops condition and yield prediction was examined. The approach comprisesd development of regression models between plant spectral and state-indicative variables such as biomass, vegetation cover fraction, leaf area index, etc., and development of yield forecasting models from single-date (growth stage) and multitemporal (seasonal) reflectance data. Verification of spectral predictions was performed through comparison with estimations from biophysical relationships between crop growth variables. The study was carried out for spring barley and winter wheat. Visible and near-infrared reflectance data was acquired through the whole growing season accompanied by detailed datasets on plant phenology and canopy structural and biochemical attributes. Empirical relationships were derived relating crop agronomic variables and yield to various spectral predictors. The study findings were tested using airborne remote sensing inputs. A good correspondence was found between predicted and actual (ground-truth) estimates
Feature Selection for Wheat Yield Prediction

NASA Astrophysics Data System (ADS)

Ruß, Georg; Kruse, Rudolf

Carrying out effective and sustainable agriculture has become an important issue in recent years. Agricultural production has to keep up with an everincreasing population by taking advantage of a field’s heterogeneity. Nowadays, modern technology such as the global positioning system (GPS) and a multitude of developed sensors enable farmers to better measure their fields’ heterogeneities. For this small-scale, precise treatment the term precision agriculture has been coined. However, the large amounts of data that are (literally) harvested during the growing season have to be analysed. In particular, the farmer is interested in knowing whether a newly developed heterogeneity sensor is potentially advantageous or not. Since the sensor data are readily available, this issue should be seen from an artificial intelligence perspective. There it can be treated as a feature selection problem. The additional task of yield prediction can be treated as a multi-dimensional regression problem. This article aims to present an approach towards solving these two practically important problems using artificial intelligence and data mining ideas and methodologies.

Slow pyrolysis polygeneration of bamboo (Phyllostachys pubescens): Product yield prediction and biochar formation mechanism.

PubMed

Wang, Huihui; Wang, Xin; Cui, Yanshan; Xue, Zhongcai; Ba, Yuxin

2018-05-11

Slow pyrolysis of bamboo was conducted at 400-600 °C and pyrolysis products were characterized with FTIR, BET, XRD, SEM, EDS and GC to establish a pyrolysis product yield prediction model and biochar formation mechanism. Pyrolysis biochar yield was predicted based on content of cellulose, hemicellulose and lignin in biomass with their carbonization index of 0.20, 0.35 and 0.45. The formation mechanism of porous structure in pyrolysis biochar was established based on its physicochemical property evolution and emission characteristics of pyrolysis gas. The main components (cellulose, hemicellulose and lignin) had different pyrolysis or chemical reaction pathways to biochar. Lignin had higher aromatic structure, which resulted higher biochar yield. It was the main biochar precursor during biomass pyrolysis. Cellulose was likely to improve porous structure of pyrolysis biochar due to its high mass loss percentage. Higher pyrolysis temperatures (600 °C) promoted inter- and intra-molecular condensation reactions and aromaticity in biochar. Copyright © 2018 Elsevier Ltd. All rights reserved.
Development and validation of equations utilizing lamb vision system output to predict lamb carcass fabrication yields.

PubMed

Cunha, B C N; Belk, K E; Scanga, J A; LeValley, S B; Tatum, J D; Smith, G C

2004-07-01

This study was performed to validate previous equations and to develop and evaluate new regression equations for predicting lamb carcass fabrication yields using outputs from a lamb vision system-hot carcass component (LVS-HCC) and the lamb vision system-chilled carcass LM imaging component (LVS-CCC). Lamb carcasses (n = 149) were selected after slaughter, imaged hot using the LVS-HCC, and chilled for 24 to 48 h at -3 to 1 degrees C. Chilled carcasses yield grades (YG) were assigned on-line by USDA graders and by expert USDA grading supervisors with unlimited time and access to the carcasses. Before fabrication, carcasses were ribbed between the 12th and 13th ribs and imaged using the LVS-CCC. Carcasses were fabricated into bone-in subprimal/primal cuts. Yields calculated included 1) saleable meat yield (SMY); 2) subprimal yield (SPY); and 3) fat yield (FY). On-line (whole-number) USDA YG accounted for 59, 58, and 64%; expert (whole-number) USDA YG explained 59, 59, and 65%; and expert (nearest-tenth) USDA YG accounted for 60, 60, and 67% of the observed variation in SMY, SPY, and FY, respectively. The best prediction equation developed in this trial using LVS-HCC output and hot carcass weight as independent variables explained 68, 62, and 74% of the variation in SMY, SPY, and FY, respectively. Addition of output from LVS-CCC improved predictive accuracy of the equations; the combined output equations explained 72 and 66% of the variability in SMY and SPY, respectively. Accuracy and repeatability of measurement of LM area made with the LVS-CCC also was assessed, and results suggested that use of LVS-CCC provided reasonably accurate (R2 = 0.59) and highly repeatable (repeatability = 0.98) measurements of LM area. Compared with USDA YG, use of the dual-component lamb vision system to predict cut yields of lamb carcasses improved accuracy and precision, suggesting that this system could have an application as an objective means for pricing carcasses in a value
Exploring the performance of the SEDD model to predict sediment yield in eucalyptus plantations. Long-term results from an experimental catchment in Southern Italy

NASA Astrophysics Data System (ADS)

Porto, P.; Cogliandro, V.; Callegari, G.

2018-01-01

In this paper, long-term sediment yield data, collected in a small (1.38 ha) Calabrian catchment (W2), reafforested with eucalyptus trees (Eucalyptus occidentalis Engl.) are used to validate the performance of the SEdiment Delivery Distributed Model (SEDD) in areas with high erosion rates. At first step, the SEDD model was calibrated using field data collected in previous field campaigns undertaken during the period 1978-1994. This first phase allowed the model calibration parameter β to be calculated using direct measurements of rainfall, runoff, and sediment output. The model was then validated in its calibrated form for an independent period (2006-2016) for which new measurements of rainfall, runoff and sediment output are also available. The analysis, carried out at event and annual scale showed good agreement between measured and predicted values of sediment yield and suggested that the SEDD model can be seen as an appropriate means of evaluating erosion risk associated with manmade plantations in marginal areas. Further work is however required to test the performance of the SEDD model as a prediction tool in different geomorphic contexts.
Predicting the apparent viscosity and yield stress of digested and secondary sludge mixtures.

PubMed

Eshtiaghi, Nicky; Markis, Flora; Zain, Dwen; Mai, Kiet Hung

2016-05-15

The legal banning of conventional sludge disposal methods such as landfill has led to a global movement towards achieving a sustainable sludge management strategy. Reusing sludge for energy production (biogas production) through the anaerobic digestion of sludge can provide a sustainable solution. However, for the optimum performance of digesters with minimal use of energy input, operating conditions must be regulated in accordance with the rheological characteristics of the sludge. If it is assumed that only secondary sludge enters the anaerobic digesters, an impact of variations to the solids concentration and volume fraction of each sludge type must be investigated to understand how the apparent viscosity and yield stress of the secondary and digested sludge mixture inside the digesters changes. In this study, five different total solids concentration of secondary and digested sludge were mixed at different digested sludge volume fractions ranging from 0 to 1. It was found that if secondary sludge was mixed with digested sludge at the same total solids concentration, the apparent viscosity and the yield stress of the mixture increased exponentially by increasing the volume fraction of digested sludge. However, if secondary sludge was added to digested sludge with a different solids concentration, the apparent viscosity and yield stress of the resulting mixed sludge was controlled by the concentrated sludge regardless of its type. Semi - empirical correlations were proposed to predict the apparent viscosity and yield stress of the mixed digested and secondary sludge. A master curve was also developed to predict the flow behaviour of sludge mixtures regardless of the total solid concentration and volume fraction of each sludge type within the studied solids concentration range of 1.4 and 7%TS. This model can be used for digesters optimization and design by predicting the rheology of sludge mixture inside digester. Copyright © 2016 Elsevier Ltd. All rights
Spectrum sensitivity, energy yield, and revenue prediction of PV and CPV modules

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

Kinsey, Geoffrey S., E-mail: Geoffrey.kinsey@ee.doe.gov

2015-09-28

Impact on module performance of spectral irradiance variation has been determined for III-V multijunctions compared against the four most common flat-plate module types (cadmium telluride, multicrystalline silicon, copper indium gallium selenide, and monocrystalline silicon. Hour-by-hour representative spectra were generated using atmospheric variables for Albuquerque, New Mexico, USA. Convolution with published values for external quantum efficiency gave the predicted current output. When combined with specifications of commercial PV modules, energy yield and revenue were predicted. This approach provides a means for optimizing PV module design based on various site-specific temporal variables.
Spectrum sensitivity, energy yield, and revenue prediction of PV and CPV modules

NASA Astrophysics Data System (ADS)

Kinsey, Geoffrey S.

2015-09-01

Impact on module performance of spectral irradiance variation has been determined for III-V multijunctions compared against the four most common flat-plate module types (cadmium telluride, multicrystalline silicon, copper indium gallium selenide, and monocrystalline silicon. Hour-by-hour representative spectra were generated using atmospheric variables for Albuquerque, New Mexico, USA. Convolution with published values for external quantum efficiency gave the predicted current output. When combined with specifications of commercial PV modules, energy yield and revenue were predicted. This approach provides a means for optimizing PV module design based on various site-specific temporal variables.
NASA Earth Science Research Results for Improved Regional Crop Yield Prediction

NASA Astrophysics Data System (ADS)

Mali, P.; O'Hara, C. G.; Shrestha, B.; Sinclair, T. R.; G de Goncalves, L. G.; Salado Navarro, L. R.

2007-12-01

National agencies such as USDA Foreign Agricultural Service (FAS), Production Estimation and Crop Assessment Division (PECAD) work specifically to analyze and generate timely crop yield estimates that help define national as well as global food policies. The USDA/FAS/PECAD utilizes a Decision Support System (DSS) called CADRE (Crop Condition and Data Retrieval Evaluation) mainly through an automated database management system that integrates various meteorological datasets, crop and soil models, and remote sensing data; providing significant contribution to the national and international crop production estimates. The "Sinclair" soybean growth model has been used inside CADRE DSS as one of the crop models. This project uses Sinclair model (a semi-mechanistic crop growth model) for its potential to be effectively used in a geo-processing environment with remote-sensing-based inputs. The main objective of this proposed work is to verify, validate and benchmark current and future NASA earth science research results for the benefit in the operational decision making process of the PECAD/CADRE DSS. For this purpose, the NASA South American Land Data Assimilation System (SALDAS) meteorological dataset is tested for its applicability as a surrogate meteorological input in the Sinclair model meteorological input requirements. Similarly, NASA sensor MODIS products is tested for its applicability in the improvement of the crop yield prediction through improving precision of planting date estimation, plant vigor and growth monitoring. The project also analyzes simulated Visible/Infrared Imager/Radiometer Suite (VIIRS, a future NASA sensor) vegetation product for its applicability in crop growth prediction to accelerate the process of transition of VIIRS research results for the operational use of USDA/FAS/PECAD DSS. The research results will help in providing improved decision making capacity to the USDA/FAS/PECAD DSS through improved vegetation growth monitoring from high
Neurophysiological prediction of neurological good and poor outcome in post-anoxic coma.

PubMed

Grippo, A; Carrai, R; Scarpino, M; Spalletti, M; Lanzo, G; Cossu, C; Peris, A; Valente, S; Amantini, A

2017-06-01

Investigation of the utility of association between electroencephalogram (EEG) and somatosensory-evoked potentials (SEPs) for the prediction of neurological outcome in comatose patients resuscitated after cardiac arrest (CA) treated with therapeutic hypothermia, according to different recording times after CA. Glasgow Coma Scale, EEG and SEPs performed at 12, 24 and 48-72 h after CA were assessed in 200 patients. Outcome was evaluated by Cerebral Performance Category 6 months after CA. Within 12 h after CA, grade 1 EEG predicted good outcome and bilaterally absent (BA) SEPs predicted poor outcome. Because grade 1 EEG and BA-SEPs were never found in the same patient, the recording of both EEG and SEPs allows us to correctly prognosticate a greater number of patients with respect to the use of a single test within 12 h after CA. At 48-72 h after CA, both grade 2 EEG and BA-SEPs predicted poor outcome with FPR=0.0%. When these neurophysiological patterns are both present in the same patient, they confirm and strengthen their prognostic value, but because they also occurred independently in eight patients, poor outcome is predictable in a greater number of patients. The combination of EEG/SEP findings allows prediction of good and poor outcome (within 12 h after CA) and of poor outcome (after 48-72 h). Recording of EEG and SEPs in the same patients allows always an increase in the number of cases correctly classified, and an increase of the reliability of prognostication in a single patient due to concordance of patterns. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Technical note: A mathematical function to predict daily milk yield of dairy cows in relation to the interval between milkings.

PubMed

Klopčič, M; Koops, W J; Kuipers, A

2013-09-01

The milk production of a dairy cow is characterized by lactation production, which is calculated from daily milk yields (DMY) during lactation. The DMY is calculated from one or more milkings a day collected at the farm. Various milking systems are in use today, resulting in one or many recorded milk yields a day, from which different calculations are used to determine DMY. The primary objective of this study was to develop a mathematical function that described milk production of a dairy cow in relation to the interval between 2 milkings. The function was partly based on the biology of the milk production process. This function, called the 3K-function, was able to predict milk production over an interval of 12h, so DMY was twice this estimate. No external information is needed to incorporate this function in methods to predict DMY. Application of the function on data from different milking systems showed a good fit. This function could be a universal tool to predict DMY for a variety of milking systems, and it seems especially useful for data from robotic milking systems. Further study is needed to evaluate the function under a wide range of circumstances, and to see how it can be incorporated in existing milk recording systems. A secondary objective of using the 3K-function was to compare how much DMY based on different milking systems differed from that based on a twice-a-day milking. Differences were consistent with findings in the literature. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

PubMed

Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

2017-07-01

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.
Predicting cotton yield of small field plots in a cotton breeding program using UAV imagery data

NASA Astrophysics Data System (ADS)

Maja, Joe Mari J.; Campbell, Todd; Camargo Neto, Joao; Astillo, Philip

2016-05-01

One of the major criteria used for advancing experimental lines in a breeding program is yield performance. Obtaining yield performance data requires machine picking each plot with a cotton picker, modified to weigh individual plots. Harvesting thousands of small field plots requires a great deal of time and resources. The efficiency of cotton breeding could be increased significantly while the cost could be decreased with the availability of accurate methods to predict yield performance. This work is investigating the feasibility of using an image processing technique using a commercial off-the-shelf (COTS) camera mounted on a small Unmanned Aerial Vehicle (sUAV) to collect normal RGB images in predicting cotton yield on small plot. An orthonormal image was generated from multiple images and used to process multiple, segmented plots. A Gaussian blur was used to eliminate the high frequency component of the images, which corresponds to the cotton pixels, and used image subtraction technique to generate high frequency pixel images. The cotton pixels were then separated using k-means cluster with 5 classes. Based on the current work, the calculated percentage cotton area was computed using the generated high frequency image (cotton pixels) divided by the total area of the plot. Preliminary results showed (five flights, 3 altitudes) that cotton cover on multiple pre-selected 227 sq. m. plots produce an average of 8% which translate to approximately 22.3 kgs. of cotton. The yield prediction equation generated from the test site was then use on a separate validation site and produced a prediction error of less than 10%. In summary, the results indicate that a COTS camera with an appropriate image processing technique can produce results that are comparable to expensive sensors.
(18)F-FDG uptake predicts diagnostic yield of transbronchial biopsy in peripheral lung cancer.

PubMed

Umeda, Yukihiro; Demura, Yoshiki; Anzai, Masaki; Matsuoka, Hiroki; Araya, Tomoyuki; Nishitsuji, Masaru; Nishi, Koichi; Tsuchida, Tatsuro; Sumida, Yasuyuki; Morikawa, Miwa; Ameshima, Shingo; Ishizaki, Takeshi; Kasahara, Kazuo; Ishizuka, Tamotsu

2014-07-01

Recent advances in endobronchial ultrasonography with a guide sheath (EBUS-GS) have enabled better visualization of distal airways, while virtual bronchoscopic navigation (VBN) has been shown useful as a guide to navigate the bronchoscope. However, indications for utilizing VBN and EBUS-GS are not always clear. To clarify indications for a bronchoscopic examination using VBN and EBUS-GS, we evaluated factors that predict the diagnostic yield of a transbronchial biopsy (TBB) procedure for peripheral lung cancer (PLC) lesions. We retrospectively reviewed the charts of 194 patients with 201 PLC lesions (≤3cm mean diameter), and analyzed the association of diagnostic yield of TBB with [(18)F]-fluoro-2-deoxy-d-glucose ((18)F-FDG) positron emission tomography and chest computed tomography (CT) findings. The diagnostic yield of TBB using VBN and EBUS-GS was 66.7%. High maximum standardized uptake value (SUVmax), positive bronchus sign, and ground-glass opacity component shown on CT were all significant predictors of diagnostic yield, while multivariate analysis showed only high (18)F-FDG uptake (SUVmax ≥2.8) and positive bronchus sign as significant predictors. Diagnostic yield was higher for PLC lesions with high (18)F-FDG uptake (SUVmax ≥2.8) and positive bronchus sign (84.6%) than for those with SUVmax <2.8 and negative bronchus sign (33.3%). High (18)F-FDG uptake was also correlated with tumor invasiveness. High (18)F-FDG uptake predicted the diagnostic yield of TBB using VBN and EBUS-GS for PLC lesions. (18)F-FDG uptake and bronchus sign may indicate for the accurate application of bronchoscopy with those modalities for diagnosing PLC. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Factors Predicting a Good Symptomatic Outcome After Prostate Artery Embolisation (PAE).

PubMed

Maclean, D; Harris, M; Drake, T; Maher, B; Modi, S; Dyer, J; Somani, B; Hacking, N; Bryant, T

2018-02-26

As prostate artery embolisation (PAE) becomes an established treatment for benign prostatic obstruction, factors predicting good symptomatic outcome remain unclear. Pre-embolisation prostate size as a predictor is controversial with a handful of papers coming to conflicting conclusions. We aimed to investigate if an association existed in our patient cohort between prostate size and clinical benefit, in addition to evaluating percentage volume reduction as a predictor of symptomatic outcome following PAE. Prospective follow-up of 86 PAE patients at a single institution between June 2012 and January 2016 was conducted (mean age 64.9 years, range 54-80 years). Multiple linear regression analysis was performed to assess strength of association between clinical improvement (change in IPSS) and other variables, of any statistical correlation, through Pearson's bivariate analysis. No major procedural complications were identified and clinical success was achieved in 72.1% (n = 62) at 12 months. Initial prostate size and percentage reduction were found to have a significant association with clinical improvement. Multiple linear regression analysis (r 2 = 0.48) demonstrated that percentage volume reduction at 3 months (r = 0.68, p < 0.001) had the strongest correlation with good symptomatic improvement at 12 months after adjusting for confounding factors. Both the initial prostate size and percentage volume reduction at 3 months predict good symptomatic outcome at 12 months. These findings therefore aid patient selection and counselling to achieve optimal outcomes for men undergoing prostate artery embolisation.
Influence of Yield Stress Determination in Anisotropic Hardening Model on Springback Prediction in Dual-Phase Steel

NASA Astrophysics Data System (ADS)

Lee, J.; Bong, H. J.; Ha, J.; Choi, J.; Barlat, F.; Lee, M.-G.

2018-05-01

In this study, a numerical sensitivity analysis of the springback prediction was performed using advanced strain hardening models. In particular, the springback in U-draw bending for dual-phase 780 steel sheets was investigated while focusing on the effect of the initial yield stress determined from the cyclic loading tests. The anisotropic hardening models could reproduce the flow stress behavior under the non-proportional loading condition for the considered parametric cases. However, various identification schemes for determining the yield stress of the anisotropic hardening models significantly influenced the springback prediction. The deviations from the measured springback varied from 4% to 13.5% depending on the identification method.
Predicting red meat yields in carcasses from beef-type and calf-fed Holstein steers using the United States Department of Agriculture calculated yield grade.

PubMed

Lawrence, T E; Elam, N A; Miller, M F; Brooks, J C; Hilton, G G; VanOverbeke, D L; McKeith, F K; Killefer, J; Montgomery, T H; Allen, D M; Griffin, D B; Delmore, R J; Nichols, W T; Streeter, M N; Yates, D A; Hutcheson, J P

2010-06-01

Analyses were conducted to evaluate the ability of the USDA yield grade equation to detect differences in subprimal yield of beef-type steers and calf-fed Holstein steers that had been fed zilpaterol hydrochloride (ZH; Intervet Inc., Millsboro, DE) as well as those that had not been fed ZH. Beef-type steer (n = 801) and calf-fed Holstein steer (n = 235) carcasses were fabricated into subprimal cuts and trim. Simple correlations between calculated yield grades and total red meat yields ranged from -0.56 to -0.62 for beef-type steers. Reliable correlations from calf-fed Holstein steers were unobtainable; the probability of a type I error met or exceeded 0.39. Linear models were developed for the beef-type steers to predict total red meat yield based on calculated USDA yield grade within each ZH duration. At an average calculated USDA yield grade of 2.9, beef-type steer carcasses that had not been fed ZH had an estimated 69.4% red meat yield, whereas those fed ZH had an estimated 70.7% red meat yield. These results indicate that feeding ZH increased red meat yield by 1.3% at a constant calculated yield grade. However, these data also suggest that the calculated USDA yield grade score is a poor and variable estimator (adjusted R(2) of 0.31 to 0.38) of total red meat yield of beef-type steer carcasses, regardless of ZH feeding. Moreover, no relationship existed (adjusted R(2) of 0.00 to 0.01) for calf-fed Holstein steer carcasses, suggesting the USDA yield grade is not a valid estimate of calf-fed Holstein red meat yield.
Temporal prediction modulates the evaluative processing of "good" action feedback: An electrophysiological study.

PubMed

Kimura, Kenta; Kimura, Motohiro; Iwaki, Sunao

2016-10-01

The present study aimed to investigate whether or not the evaluative processing of action feedback can be modulated by temporal prediction. For this purpose, we examined the effects of the predictability of the timing of action feedback on an ERP effect that indexed the evaluative processing of action feedback, that is, an ERP effect that has been interpreted as a feedback-related negativity (FRN) elicited by "bad" action feedback or a reward positivity (RewP) elicited by "good" action feedback. In two types of experimental blocks, the participants performed a gambling task in which they chose one of two cards and received an action feedback that indicated monetary gain or loss. In fixed blocks, the time interval between the participant's choice and the onset of the action feedback was fixed at 0, 500, or 1,000 ms in separate blocks; thus, the timing of action feedback was predictable. In mixed blocks, the time interval was randomly chosen from the same three intervals with equal probability; thus, the timing was less predictable. The results showed that the FRN/RewP was smaller in mixed than fixed blocks for the 0-ms interval trial, whereas there was no difference between the two block types for the 500-ms and 1,000-ms interval trials. Interestingly, the smaller FRN/RewP was due to the modulation of gain ERPs rather than loss ERPs. These results suggest that temporal prediction can modulate the evaluative processing of action feedback, and particularly good feedback, such as that which indicates monetary gain. © 2016 Society for Psychophysiological Research.
Online evaluation of a commercial video image analysis system (Computer Vision System) to predict beef carcass red meat yield and for augmenting the assignment of USDA yield grades. United States Department of Agriculture.

PubMed

Cannell, R C; Belk, K E; Tatum, J D; Wise, J W; Chapman, P L; Scanga, J A; Smith, G C

2002-05-01

Objective quantification of differences in wholesale cut yields of beef carcasses at plant chain speeds is important for the application of value-based marketing. This study was conducted to evaluate the ability of a commercial video image analysis system, the Computer Vision System (CVS) to 1) predict commercially fabricated beef subprimal yield and 2) augment USDA yield grading, in order to improve accuracy of grade assessment. The CVS was evaluated as a fully installed production system, operating on a full-time basis at chain speeds. Steer and heifer carcasses (n = 296) were evaluated using CVS, as well as by USDA expert and online graders, before the fabrication of carcasses into industry-standard subprimal cuts. Expert yield grade (YG), online YG, CVS estimated carcass yield, and CVS measured ribeye area in conjunction with expert grader estimates of the remaining YG factors (adjusted fat thickness, percentage of kidney-pelvic-heart fat, hot carcass weight) accounted for 67, 39, 64, and 65% of the observed variation in fabricated yields of closely trimmed subprimals. The dual component CVS predicted wholesale cut yields more accurately than current online yield grading, and, in an augmentation system, CVS ribeye measurement replaced estimated ribeye area in determination of USDA yield grade, and the accuracy of cutability prediction was improved, under packing plant conditions and speeds, to a level close to that of expert graders applying grades at a comfortable rate of speed offline.
What is the Best Model Specification and Earth Observation Product for Predicting Regional Grain Yields in Food Insecure Countries?

NASA Astrophysics Data System (ADS)

Davenport, F., IV; Harrison, L.; Shukla, S.; Husak, G. J.; Funk, C. C.

2017-12-01

We evaluate the predictive accuracy of an ensemble of empirical model specifications that use earth observation data to predict sub-national grain yields in Mexico and East Africa. Products that are actively used for seasonal drought monitoring are tested as yield predictors. Our research is driven by the fact that East Africa is a region where decisions regarding agricultural production are critical to preventing the loss of economic livelihoods and human life. Regional grain yield forecasts can be used to anticipate availability and prices of key staples, which can turn can inform decisions about targeting humanitarian response such as food aid. Our objective is to identify-for a given region, grain, and time year- what type of model and/or earth observation can most accurately predict end of season yields. We fit a set of models to county level panel data from Mexico, Kenya, Sudan, South Sudan, and Somalia. We then examine out of sample predicative accuracy using various linear and non-linear models that incorporate spatial and time varying coefficients. We compare accuracy within and across models that use predictor variables from remotely sensed measures of precipitation, temperature, soil moisture, and other land surface processes. We also examine at what point in the season a given model or product is most useful for determining predictive accuracy. Finally we compare predictive accuracy across a variety of agricultural regimes including high intensity irrigated commercial agricultural and rain fed subsistence level farms.
Predicted Cubic-foot Yields of Lumber, Sawdust, and Sawmill Residue from the Sawtimber Portions of Hardwood Trees

Treesearch

Leland F. Hanks

1977-01-01

We have presented prediction equations and tables for estimating the gross cubic-foot volume of sawtimber for hardwood trees, and cubic-foot yields of lumber, sawdust, and sawmill residue that are produced during the sawing process. Yields are presented for northern red oak, black oak, white oak, chestnut oak, red maple, sugar maple, yellow-poplar, yellow birch, paper...
Anisotropic yield function capable of predicting eight ears

NASA Astrophysics Data System (ADS)

Yoon, J. H.; Cazacu, O.

2011-08-01

Deep drawing of a cylindrical cup from a rolled sheet is one of the typical forming operations where the effect of this anisotropy is most evident. Indeed, it is well documented in the literature that the number of ears and the shape of the earing pattern correlate with the r-values profile. For the strongly textured aluminum alloy AA 5042 (Numisheet Benchmark 2011), the experimental r-value distribution has two minima between the rolling and transverse direction data provided for this show that the r-value along the transverse direction (TD) is five times larger than the value corresponding to the rolling direction. Therefore, it is expected that there are more that the earing profile has more than four ears. The main objective of this paper is to assess whether a new form of CPB06ex2 yield function (Plunkett et al. (2008)) tailored for metals with no tension-compression asymmetry is capable of predicting more than four ears for this material.

Prediction of beef carcass salable yield and trimmable fat using bioelectrical impedance analysis.

PubMed

Zollinger, B L; Farrow, R L; Lawrence, T E; Latman, N S

2010-03-01

Bioelectrical impedance technology (BIA) is capable of providing an objective method of beef carcass yield estimation with the rapidity of yield grading. Electrical resistance (Rs), reactance (Xc), impedance (I), hot carcass weight (HCW), fat thickness between the 12th and 13th ribs (FT), estimated percentage kidney, pelvic, and heart fat (KPH%), longissimus muscle area (LMA), length between electrodes (LGE) as well as three derived carcass values that included electrical volume (EVOL), reactive density (XcD), and resistive density (RsD) were determined for the carcasses of 41 commercially fed cattle. Carcasses were subsequently fabricated into salable beef products reflective of industry standards. Equations were developed to predict percentage salable carcass yield (SY%) and percentage trimmable fat (FT%). Resulting equations accounted for 81% and 84% of variation in SY% and FT%, respectively. These results indicate that BIA technology is an accurate predictor of beef carcass composition. Copyright 2009 Elsevier Ltd. All rights reserved.
Crystal plasticity assisted prediction on the yield locus evolution and forming limit curves

NASA Astrophysics Data System (ADS)

Lian, Junhe; Liu, Wenqi; Shen, Fuhui; Münstermann, Sebastian

2017-10-01

The aim of this study is to predict the plastic anisotropy evolution and its associated forming limit curves of bcc steels purely based on their microstructural features by establishing an integrated multiscale modelling approach. Crystal plasticity models are employed to describe the micro deformation mechanism and correlate the microstructure with mechanical behaviour on micro and mesoscale. Virtual laboratory is performed considering the statistical information of the microstructure, which serves as the input for the phenomenological plasticity model on the macroscale. For both scales, the microstructure evolution induced evolving features, such as the anisotropic hardening, r-value and yield locus evolution are seamlessly integrated. The predicted plasticity behaviour by the numerical simulations are compared with experiments. These evolutionary features of the material deformation behaviour are eventually considered for the prediction of formability.
Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A.

PubMed Central

Sharma, Lakesh K.; Bu, Honggang; Denton, Anne; Franzen, David W.

2015-01-01

Active-optical sensor readings from an N non-limiting area standard established within a farm field are used to predict yield in the standard. Lower yield predictions from sensor readings obtained from other parts of the field outside of the N non-limiting standard area indicate a need for supplemental N. Active-optical sensor algorithms for predicting corn (Zea mays, L.) yield to direct in-season nitrogen (N) fertilization in corn utilize red NDVI (normalized differential vegetative index). Use of red edge NDVI might improve corn yield prediction at later growth stages when corn leaves cover the inter-row space resulting in “saturation” of red NDVI readings. The purpose of this study was to determine whether the use of red edge NDVI in two active-optical sensors (GreenSeeker™ and Holland Scientific Crop Circle™) improved corn yield prediction. Nitrogen rate experiments were established at 15 sites in North Dakota (ND). Sensor readings were conducted at V6 and V12 corn. Red NDVI and red edge NDVI were similar in the relationship of readings with yield at V6. At V12, the red edge NDVI was superior to the red NDVI in most comparisons, indicating that it would be most useful in developing late-season N application algorithms. PMID:26540057
Active-Optical Sensors Using Red NDVI Compared to Red Edge NDVI for Prediction of Corn Grain Yield in North Dakota, U.S.A.

PubMed

Sharma, Lakesh K; Bu, Honggang; Denton, Anne; Franzen, David W

2015-11-02

Active-optical sensor readings from an N non-limiting area standard established within a farm field are used to predict yield in the standard. Lower yield predictions from sensor readings obtained from other parts of the field outside of the N non-limiting standard area indicate a need for supplemental N. Active-optical sensor algorithms for predicting corn (Zea mays, L.) yield to direct in-season nitrogen (N) fertilization in corn utilize red NDVI (normalized differential vegetative index). Use of red edge NDVI might improve corn yield prediction at later growth stages when corn leaves cover the inter-row space resulting in "saturation" of red NDVI readings. The purpose of this study was to determine whether the use of red edge NDVI in two active-optical sensors (GreenSeeker™ and Holland Scientific Crop Circle™) improved corn yield prediction. Nitrogen rate experiments were established at 15 sites in North Dakota (ND). Sensor readings were conducted at V6 and V12 corn. Red NDVI and red edge NDVI were similar in the relationship of readings with yield at V6. At V12, the red edge NDVI was superior to the red NDVI in most comparisons, indicating that it would be most useful in developing late-season N application algorithms.
Salience Assignment for Multiple-Instance Data and Its Application to Crop Yield Prediction

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran

2010-01-01

An algorithm was developed to generate crop yield predictions from orbital remote sensing observations, by analyzing thousands of pixels per county and the associated historical crop yield data for those counties. The algorithm determines which pixels contain which crop. Since each known yield value is associated with thousands of individual pixels, this is a multiple instance learning problem. Because individual crop growth is related to the resulting yield, this relationship has been leveraged to identify pixels that are individually related to corn, wheat, cotton, and soybean yield. Those that have the strongest relationship to a given crop s yield values are most likely to contain fields with that crop. Remote sensing time series data (a new observation every 8 days) was examined for each pixel, which contains information for that pixel s growth curve, peak greenness, and other relevant features. An alternating-projection (AP) technique was used to first estimate the "salience" of each pixel, with respect to the given target (crop yield), and then those estimates were used to build a regression model that relates input data (remote sensing observations) to the target. This is achieved by constructing an exemplar for each crop in each county that is a weighted average of all the pixels within the county; the pixels are weighted according to the salience values. The new regression model estimate then informs the next estimate of the salience values. By iterating between these two steps, the algorithm converges to a stable estimate of both the salience of each pixel and the regression model. The salience values indicate which pixels are most relevant to each crop under consideration.
Predicting yields for autotrophic and cometabolic processes

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

Andrews, G.

1995-12-31

The goal of bioprocess engineering is to state how the optimum design and control strategy for a bioprocess follow from the metabolism of the particular microorganism. A necessary step toward this goal is to show how the parameters used in quantitative descriptions of a process (e.g., yield and maintenance coefficients) are related to those describing the metabolism [e.g., Y{sub ATP}, (P/O)]. The {open_quotes}yield equation{close_quotes} approach to this problem involves dividing metabolism into the separate pathways for catabolism, anabolism, respiration, and product formation and balancing the production and consumption of reducing equivalents and ATP. The general approach, demonstrated previously for heterotrophicmore » cell growth and products of fermentation, is illustrated by three new examples: the cell yield for chemoautotrophic iron-oxidizing bacteria, the cometabolic degradation of chloroform by methanotrophic bacteria, and the theoretical yield of succinic acid from glucose.« less
Simplified combustion noise theory yielding a prediction of fluctuating pressure level

NASA Technical Reports Server (NTRS)

Huff, R. G.

1984-01-01

The first order equations for the conservation of mass and momentum in differential form are combined for an ideal gas to yield a single second order partial differential equation in one dimension and time. Small perturbation analysis is applied. A Fourier transformation is performed that results in a second order, constant coefficient, nonhomogeneous equation. The driving function is taken to be the source of combustion noise. A simplified model describing the energy addition via the combustion process gives the required source information for substitution in the driving function. This enables the particular integral solution of the nonhomogeneous equation to be found. This solution multiplied by the acoustic pressure efficiency predicts the acoustic pressure spectrum measured in turbine engine combustors. The prediction was compared with the overall sound pressure levels measured in a CF6-50 turbofan engine combustor and found to be in excellent agreement.
Simulating and Predicting Cereal Crop Yields in Ethiopia: Model Calibration and Verification

NASA Astrophysics Data System (ADS)

Yang, M.; Wang, G.; Ahmed, K. F.; Eggen, M.; Adugna, B.; Anagnostou, E. N.

2017-12-01

Agriculture in developing countries are extremely vulnerable to climate variability and changes. In East Africa, most people live in the rural areas with outdated agriculture techniques and infrastructure. Smallholder agriculture continues to play a key role in this area, and the rate of irrigation is among the lowest of the world. As a result, seasonal and inter-annual weather patterns play an important role in the spatiotemporal variability of crop yields. This study investigates how various climate variables (e.g., temperature, precipitation, sunshine) and agricultural practice (e.g., fertilization, irrigation, planting date) influence cereal crop yields using a process-based model (DSSAT) and statistical analysis, and focuses on the Blue Nile Basin of Ethiopia. The DSSAT model is driven with meteorological forcing from the ECMWF's latest reanalysis product that cover the past 35 years; the statistical model will be developed by linking the same meteorological reanalysis data with harvest data at the woreda level from the Ethiopian national dataset. Results from this study will set the stage for the development of a seasonal prediction system for weather and crop yields in Ethiopia, which will serve multiple sectors in coping with the agricultural impact of climate variability.
Yield Hardening of Electrorheological Fluids in Channel Flow

NASA Astrophysics Data System (ADS)

Helal, Ahmed; Qian, Bian; McKinley, Gareth H.; Hosoi, A. E.

2016-06-01

Electrorheological fluids offer potential for developing rapidly actuated hydraulic devices where shear forces or pressure-driven flow are present. In this study, the Bingham yield stress of electrorheological fluids with different particle volume fractions is investigated experimentally in wall-driven and pressure-driven flow modes using measurements in a parallel-plate rheometer and a microfluidic channel, respectively. A modified Krieger-Dougherty model can be used to describe the effects of the particle volume fraction on the yield stress and is in good agreement with the viscometric data. However, significant yield hardening in pressure-driven channel flow is observed and attributed to an increase and eventual saturation of the particle volume fraction in the channel. A phenomenological physical model linking the densification and consequent microstructure to the ratio of the particle aggregation time scale compared to the convective time scale is presented and used to predict the enhancement in yield stress in channel flow, enabling us to reconcile discrepancies in the literature between wall-driven and pressure-driven flows.
Cortical activity predicts good variation in human motor output.

PubMed

Babikian, Sarine; Kanso, Eva; Kutch, Jason J

2017-04-01

Human movement patterns have been shown to be particularly variable if many combinations of activity in different muscles all achieve the same task goal (i.e., are goal-equivalent). The nervous system appears to automatically vary its output among goal-equivalent combinations of muscle activity to minimize muscle fatigue or distribute tissue loading, but the neural mechanism of this "good" variation is unknown. Here we use a bimanual finger task, electroencephalography (EEG), and machine learning to determine if cortical signals can predict goal-equivalent variation in finger force output. 18 healthy participants applied left and right index finger forces to repeatedly perform a task that involved matching a total (sum of right and left) finger force. As in previous studies, we observed significantly more variability in goal-equivalent muscle activity across task repetitions compared to variability in muscle activity that would not achieve the goal: participants achieved the task in some repetitions with more right finger force and less left finger force (right > left) and in other repetitions with less right finger force and more left finger force (left > right). We found that EEG signals from the 500 milliseconds (ms) prior to each task repetition could make a significant prediction of which repetitions would have right > left and which would have left > right. We also found that cortical maps of sites contributing to the prediction contain both motor and pre-motor representation in the appropriate hemisphere. Thus, goal-equivalent variation in motor output may be implemented at a cortical level.
Optimal survey strategies and predicted planet yields for the Korean microlensing telescope network

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

Henderson, Calen B.; Gaudi, B. Scott; Skowron, Jan

2014-10-10

The Korean Microlensing Telescope Network (KMTNet) will consist of three 1.6 m telescopes each with a 4 deg{sup 2} field of view (FoV) and will be dedicated to monitoring the Galactic Bulge to detect exoplanets via gravitational microlensing. KMTNet's combination of aperture size, FoV, cadence, and longitudinal coverage will provide a unique opportunity to probe exoplanet demographics in an unbiased way. Here we present simulations that optimize the observing strategy for and predict the planetary yields of KMTNet. We find preferences for four target fields located in the central Bulge and an exposure time of t {sub exp} = 120more » s, leading to the detection of ∼2200 microlensing events per year. We estimate the planet detection rates for planets with mass and separation across the ranges 0.1 ≤ M{sub p} /M {sub ⊕} ≤ 1000 and 0.4 ≤ a/AU ≤ 16, respectively. Normalizing these rates to the cool-planet mass function of Cassan et al., we predict KMTNet will be approximately uniformly sensitive to planets with mass 5 ≤ M{sub p} /M {sub ⊕} ≤ 1000 and will detect ∼20 planets per year per dex in mass across that range. For lower-mass planets with mass 0.1 ≤ M{sub p} /M {sub ⊕} < 5, we predict KMTNet will detect ∼10 planets per year. We also compute the yields KMTNet will obtain for free-floating planets (FFPs) and predict KMTNet will detect ∼1 Earth-mass FFP per year, assuming an underlying population of one such planet per star in the Galaxy. Lastly, we investigate the dependence of these detection rates on the number of observatories, the photometric precision limit, and optimistic assumptions regarding seeing, throughput, and flux measurement uncertainties.« less
Prediction of explosive yield and other characteristics of liquid rocket propellant explosions

NASA Technical Reports Server (NTRS)

Farber, E. A.; Smith, J. H.; Watts, E. H.

1973-01-01

Work which has been done at the University of Florida in arriving at credible explosive yield values for liquid rocket propellants is presented. The results are based upon logical methods which have been well worked out theoretically and verified through experimental procedures. Three independent methods to predict explosive yield values for liquid rocket propellants are described. All three give the same end result even though they utilize different parameters and procedures. They are: (1) mathematical model; (2) seven chart approach; and (3) critical mass method. A brief description of the methods, how they were derived, how they were applied, and the results which they produced are given. The experimental work used to support and verify the above methods both in the laboratory and in the field with actually explosive mixtures are presented. The methods developed are used and their value demonstrated in analyzing real problems, among them the destruct system of the Saturn 5, and the early configurations of the space shuttle.
Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; de Los Campos, Gustavo; Alvarado, Gregorio; Suchismita, Mondal; Rutkoski, Jessica; González-Pérez, Lorena; Burgueño, Juan

2017-01-01

Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars. This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined. We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction
Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data.

PubMed

Montesinos-López, Abelardo; Montesinos-López, Osval A; Cuevas, Jaime; Mata-López, Walter A; Burgueño, Juan; Mondal, Sushismita; Huerta, Julio; Singh, Ravi; Autrique, Enrique; González-Pérez, Lorena; Crossa, José

2017-01-01

Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier
Yield prediction by analysis of multispectral scanner data

NASA Technical Reports Server (NTRS)

Colwell, J. E.; Suits, G. H.

1975-01-01

A preliminary model describing the growth and grain yield of wheat was developed. The modeled growth characteristics of the wheat crop were used to compute wheat canopy reflectance using a model of vegetation canopy reflectance. The modeled reflectance characteristics were compared with the corresponding growth characteristics and grain yield in order to infer their relationships. It appears that periodic wheat canopy reflectance characteristics potentially derivable from earth satellites will be useful in forecasting wheat grain yield.
Influence of Different Yield Loci on Failure Prediction with Damage Models

NASA Astrophysics Data System (ADS)

Heibel, S.; Nester, W.; Clausmeyer, T.; Tekkaya, A. E.

2017-09-01

Advanced high strength steels are widely used in the automotive industry to simultaneously improve crash performance and reduce the car body weight. A drawback of these multiphase steels is their sensitivity to damage effects and thus the reduction of ductility. For that reason the Forming Limit Curve is only partially suitable for this class of steels. An improvement in failure prediction can be obtained by using damage mechanics. The objective of this paper is to comparatively review the phenomenological damage model GISSMO and the Enhanced Lemaitre Damage Model. GISSMO is combined with three different yield loci, namely von Mises, Hill48 and Barlat2000 to investigate the influence of the choice of the plasticity description on damage modelling. The Enhanced Lemaitre Model is used with Hill48. An inverse parameter identification strategy for a DP1000 based on stress-strain curves and optical strain measurements of shear, uniaxial, notch and (equi-)biaxial tension tests is applied to calibrate the models. A strong dependency of fracture strains on the choice of yield locus can be observed. The identified models are validated on a cross-die cup showing ductile fracture with slight necking.
Can we Predict Quantum Yields Using Excited State Density Functional Theory for New Families of Fluorescent Dyes?

NASA Astrophysics Data System (ADS)

Kohn, Alexander W.; Lin, Zhou; Shepherd, James J.; Van Voorhis, Troy

2016-06-01

For a fluorescent dye, the quantum yield characterizes the efficiency of energy transfer from the absorbed light to the emitted fluorescence. In the screening among potential families of dyes, those with higher quantum yields are expected to have more advantages. From the perspective of theoreticians, an efficient prediction of the quantum yield using a universal excited state electronic structure theory is in demand but still challenging. The most representative examples for such excited state theory include time-dependent density functional theory (TDDFT) and restricted open-shell Kohn-Sham (ROKS). In the present study, we explore the possibility of predicting the quantum yields for conventional and new families of organic dyes using a combination of TDDFT and ROKS. We focus on radiative (kr) and nonradiative (knr) rates for the decay of the first singlet excited state (S_1) into the ground state (S_0) in accordance with Kasha's rule. M. Kasha, Discuss. Faraday Soc., 9, 14 (1950). For each dye compound, kr is calculated with the S_1-S_0 energy gap and transition dipole moment obtained using ROKS and TDDFT respectively at the relaxed S_1 geometry. Our predicted kr agrees well with the experimental value, so long as the order of energy levels is correctly predicted. Evaluation of knr is less straightforward as multiple processes are involved. Our study focuses on the S_1-T_1 intersystem crossing (ISC) and the S_1-S_0 internal conversion (IC): we investigate the properties that allow us to model the knr value using a Marcus-like expression, such as the Stokes shift, the reorganization energy, and the S_1-T_1 and S_1-S_0 energy gaps. Taking these factors into consideration, we compare our results with those obtained using the actual Marcus theory and provide explanation for discrepancy. T. Kowalczyk, T. Tsuchimochi, L. Top, P.-T. Chen, and T. Van Voorhis, J. Chem. Phys., 138, 164101 (2013). M. Kasha, Discuss. Faraday Soc., 9, 14 (1950).
e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding.

PubMed

Baudracco, J; Lopez-Villalobos, N; Holmes, C W; Comeron, E A; Macdonald, K A; Barry, T N; Friggens, N C

2012-06-01

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment
Canopy Chlorophyll Density Based Index for Estimating Nitrogen Status and Predicting Grain Yield in Rice

PubMed Central

Liu, Xiaojun; Zhang, Ke; Zhang, Zeyu; Cao, Qiang; Lv, Zunfu; Yuan, Zhaofeng; Tian, Yongchao; Cao, Weixing; Zhu, Yan

2017-01-01

Canopy chlorophyll density (Chl) has a pivotal role in diagnosing crop growth and nutrition status. The purpose of this study was to develop Chl based models for estimating N status and predicting grain yield of rice (Oryza sativa L.) with Leaf area index (LAI) and Chlorophyll concentration of the upper leaves. Six field experiments were conducted in Jiangsu Province of East China during 2007, 2008, 2009, 2013, and 2014. Different N rates were applied to generate contrasting conditions of N availability in six Japonica cultivars (9915, 27123, Wuxiangjing 14, Wuyunjing 19, Yongyou 8, and Wuyunjing 24) and two Indica cultivars (Liangyoupei 9, YLiangyou 1). The SPAD values of the four uppermost leaves and LAI were measured from tillering to flowering growth stages. Two N indicators, leaf N accumulation (LNA) and plant N accumulation (PNA) were measured. The LAI estimated by LAI-2000 and LI-3050C were compared and calibrated with a conversion equation. A linear regression analysis showed significant relationships between Chl value and N indicators, the equations were as follows: PNA = (0.092 × Chl) − 1.179 (R2 = 0.94, P < 0.001, relative root mean square error (RRMSE) = 0.196), LNA = (0.052 × Chl) − 0.269 (R2 = 0.93, P < 0.001, RRMSE = 0.185). Standardized method was used to quantity the correlation between Chl value and grain yield, normalized yield = (0.601 × normalized Chl) + 0.400 (R2 = 0.81, P < 0.001, RRMSE = 0.078). Independent experimental data also validated the use of Chl value to accurately estimate rice N status and predict grain yield. PMID:29163568
Optimization of thermo-alkaline disintegration of sewage sludge for enhanced biogas yield.

PubMed

Shehu, Muhammad Sani; Abdul Manan, Zainuddin; Alwi, Sharifah Rafidah Wan

2012-06-01

Optimization of thermo-alkaline disintegration of sewage sludge for enhanced biogas yield was carried out using response surface methodology (RSM) and Box-Behnken design of experiment. The individual linear and quadratic effects as well as the interactive effects of temperature, NaOH concentration and time on the degree of disintegration were investigated. The optimum degree of disintegration achieved was 61.45% at 88.50 °C, 2.29 M NaOH (24.23%w/w total solids) and 21 min retention time. Linear and quadratic effects of temperature are most significant in affecting the degree of disintegration. The coefficient of determination (R(2)) of 99.5% confirms that the model used in predicting the degree of disintegration process has a very good fitness with the experimental variables. The disintegrated sludge increased the biogas yield by 36%v/v compared to non-disintegrated sludge. The RSM with Box-Behnken design is an effective tool in predicting the optimum degree of disintegration of sewage sludge for increased biogas yield. Copyright © 2012 Elsevier Ltd. All rights reserved.

Predicting watershed post-fire sediment yield with the InVEST sediment retention model: Accuracy and uncertainties

USGS Publications Warehouse

Sankey, Joel B.; McVay, Jason C.; Kreitler, Jason R.; Hawbaker, Todd J.; Vaillant, Nicole; Lowe, Scott

2015-01-01

Increased sedimentation following wildland fire can negatively impact water supply and water quality. Understanding how changing fire frequency, extent, and location will affect watersheds and the ecosystem services they supply to communities is of great societal importance in the western USA and throughout the world. In this work we assess the utility of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) Sediment Retention Model to accurately characterize erosion and sedimentation of burned watersheds. InVEST was developed by the Natural Capital Project at Stanford University (Tallis et al., 2014) and is a suite of GIS-based implementations of common process models, engineered for high-end computing to allow the faster simulation of larger landscapes and incorporation into decision-making. The InVEST Sediment Retention Model is based on common soil erosion models (e.g., USLE – Universal Soil Loss Equation) and determines which areas of the landscape contribute the greatest sediment loads to a hydrological network and conversely evaluate the ecosystem service of sediment retention on a watershed basis. In this study, we evaluate the accuracy and uncertainties for InVEST predictions of increased sedimentation after fire, using measured postfire sediment yields available for many watersheds throughout the western USA from an existing, published large database. We show that the model can be parameterized in a relatively simple fashion to predict post-fire sediment yield with accuracy. Our ultimate goal is to use the model to accurately predict variability in post-fire sediment yield at a watershed scale as a function of future wildfire conditions.
The Generation, Radiation and Prediction of Supersonic Jet Noise. Volume 1

DTIC Science & Technology

1978-10-01

standard, Gaussian correlation function model can yield a good noise spectrum prediction (at 900), but the corresponding axial source distributions do not...forms for the turbulence cross-correlation function. Good agreement was obtained between measured and calculated far- field noise spectra. However, the...complementary error function profile (3.63) was found to provide a good fit to the axial velocity distribution tor a wide range of Mach numbers in the Initial
Xenon Sputter Yield Measurements for Ion Thruster Materials

NASA Technical Reports Server (NTRS)

Williams, John D.; Gardner, Michael M.; Johnson, Mark L.; Wilbur, Paul J.

2003-01-01

In this paper, we describe a technique that was used to measure total and differential sputter yields of materials important to high specific impulse ion thrusters. The heart of the technique is a quartz crystal monitor that is swept at constant radial distance from a small target region where a high current density xenon ion beam is aimed. Differential sputtering yields were generally measured over a full 180 deg arc in a plane that included the beam centerline and the normal vector to the target surface. Sputter yield results are presented for a xenon ion energy range from 0.5 to 10 keV and an angle of incidence range from 0 deg to 70 deg from the target surface normal direction for targets consisting of molybdenum, titanium, solid (Poco) graphite, and flexible graphite (grafoil). Total sputter yields are calculated using a simple integration procedure and comparisons are made to sputter yields obtained from the literature. In general, the agreement between the available data is good. As expected for heavy xenon ions, the differential and total sputter yields are found to be strong functions of angle of incidence. Significant under- and over-cosine behavior is observed at low- and high-ion energies, respectively. In addition, strong differences in differential yield behavior are observed between low-Z targets (C and Ti) and high-Z targets (Mo). Curve fits to the differential sputter yield data are provided. They should prove useful to analysts interested in predicting the erosion profiles of ion thruster components and determining where the erosion products re-deposit.
Molecular Based Temperature and Strain Rate Dependent Yield Criterion for Anisotropic Elastomeric Thin Films

NASA Technical Reports Server (NTRS)

Bosi, F.; Pellegrino, S.

2017-01-01

A molecular formulation of the onset of plasticity is proposed to assess temperature and strain rate effects in anisotropic semi-crystalline rubbery films. The presented plane stress criterion is based on the strain rate-temperature superposition principle and the cooperative theory of yielding, where some parameters are assumed to be material constants, while others are considered to depend on specific modes of deformation. An orthotropic yield function is developed for a linear low density polyethylene thin film. Uniaxial and biaxial inflation experiments were carried out to determine the yield stress of the membrane via a strain recovery method. It is shown that the 3% offset method predicts the uniaxial elastoplastic transition with good accuracy. Both the tensile yield points along the two principal directions of the film and the biaxial yield stresses are found to obey the superposition principle. The proposed yield criterion is compared against experimental measurements, showing excellent agreement over a wide range of deformation rates and temperatures.
From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

NASA Astrophysics Data System (ADS)

JÉGO, G.; Pattey, E.; Liu, J.

2013-12-01

The increase in global population coupled with new pressures to produce energy and bioproducts from agricultural land requires an increase in crop productivity. However, the influence of climate and soil variations on crop production and environmental performance is not fully understood and accounted for to define more sustainable and economical management strategies. Regional crop modeling can be a great tool for understanding the impact of climate variations on crop production, for planning grain handling and for assessing the impact of agriculture on the environment, but it is often limited by the availability of input data. The STICS ("Simulateur mulTIdisciplinaire pour les Cultures Standard") crop model, developed by INRA (France) is a functional crop model which has a built-in module to optimize several input parameters by minimizing the difference between calculated and measured output variables, such as Leaf Area Index (LAI). STICS crop model was adapted to the short growing season of the Mixedwood Plains Ecozone using field experiments results, to predict biomass and yield of soybean, spring wheat and corn. To minimize the numbers of inference required for regional applications, 'generic' cultivars rather than specific ones have been calibrated in STICS. After the calibration of several model parameters, the root mean square error (RMSE) of yield and biomass predictions ranged from 10% to 30% for the three crops. A bit more scattering was obtained for LAI (20%prediction to climate variations. Using RS data to re-initialize input parameters that are not readily available (e.g. seeding date) is considered an effective way
Weather-based forecasts of California crop yields

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

Lobell, D B; Cahill, K N; Field, C B

2005-09-26

Crop yield forecasts provide useful information to a range of users. Yields for several crops in California are currently forecast based on field surveys and farmer interviews, while for many crops official forecasts do not exist. As broad-scale crop yields are largely dependent on weather, measurements from existing meteorological stations have the potential to provide a reliable, timely, and cost-effective means to anticipate crop yields. We developed weather-based models of state-wide yields for 12 major California crops (wine grapes, lettuce, almonds, strawberries, table grapes, hay, oranges, cotton, tomatoes, walnuts, avocados, and pistachios), and tested their accuracy using cross-validation over themore » 1980-2003 period. Many crops were forecast with high accuracy, as judged by the percent of yield variation explained by the forecast, the number of yields with correctly predicted direction of yield change, or the number of yields with correctly predicted extreme yields. The most successfully modeled crop was almonds, with 81% of yield variance captured by the forecast. Predictions for most crops relied on weather measurements well before harvest time, allowing for lead times that were longer than existing procedures in many cases.« less
The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from unprocessed bovine milk samples.

PubMed

Ferragina, A; Cipolat-Gotet, C; Cecchinato, A; Bittante, G

2013-01-01

Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh unprocessed milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the milk. All individual milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm(-1). Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR=0.65). Promising results were obtained for recovered protein (1-VR=0.81), total solids (1-VR=0.86), and energy (1-VR=0.76), whereas recovered fat exhibited a low accuracy (1-VR=0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese
A Case Study of Improving Yield Prediction and Sulfur Deficiency Detection Using Optical Sensors and Relationship of Historical Potato Yield with Weather Data in Maine

PubMed Central

Sharma, Lakesh K.; Bali, Sukhwinder K.; Dwyer, James D.; Plant, Andrew B.; Bhowmik, Arnab

2017-01-01

In Maine, potato yield is consistent, 38 t·ha−1, for last 10 years except 2016 (44 t·ha−1) which confirms that increasing the yield and quality of potatoes with current fertilization practices is difficult; hence, new or improvised agronomic methods are needed to meet with producers and industry requirements. Normalized difference vegetative index (NDVI) sensors have shown promise in regulating N as an in season application; however, using late N may stretch out the maturation stage. The purpose of the research was to test Trimble GreenSeeker® (TGS) and Holland Scientific Crop Circle™ ACS-430 (HCCACS-430) wavebands to predict potato yield, before the second hilling (6–8 leaf stage). Ammonium sulfate, S containing N fertilizer, is not advised to be applied on acidic soils but accounts for 60–70% fertilizer in Maine’s acidic soils; therefore, sensors are used on sulfur deficient site to produce sensor-bound S application guidelines before recommending non-S-bearing N sources. Two study sites investigated for this research include an S deficient site and a regular spot with two kinds of soils. Six N treatments, with both calcium ammonium nitrate and ammonium nitrate, under a randomized complete block design with four replications, were applied at planting. NDVI readings from both sensors were obtained at V8 leaf stages (8 leaf per plant) before the second hilling. Both sensors predict N and S deficiencies with a strong interaction with an average coefficient of correlation (r2) ~45. However, HCCACS-430 was observed to be more virtuous than TGS. The correlation between NDVI (from both sensors) and the potato yield improved using proprietor-proxy leaf area index (PPLAI) from HCCACS-430, e.g., r2 value of TGS at Easton site improve from 48 to 60. Weather data affected marketable potato yield (MPY) significantly from south to north in Maine, especially precipitation variations that could be employed in the N recommendations at planting and in season
The good, the bad and the ugly of marine reserves for fishery yields.

PubMed

De Leo, Giulio A; Micheli, Fiorenza

2015-11-05

Marine reserves (MRs) are used worldwide as a means of conserving biodiversity and protecting depleted populations. Despite major investments in MRs, their environmental and social benefits have proven difficult to demonstrate and are still debated. Clear expectations of the possible outcomes of MR establishment are needed to guide and strengthen empirical assessments. Previous models show that reserve establishment in overcapitalized, quota-based fisheries can reduce both catch and population abundance, thereby negating fisheries and even conservation benefits. By using a stage-structured, spatially explicit stochastic model, we show that catches under quota-based fisheries that include a network of MRs can exceed maximum sustainable yield (MSY) under conventional quota management if reserves provide protection to old, large spawners that disproportionally contribute to recruitment outside the reserves. Modelling results predict that the net fishery benefit of MRs is lost when gains in fecundity of old, large individuals are small, is highest in the case of sedentary adults with high larval dispersal, and decreases with adult mobility. We also show that environmental variability may mask fishery benefits of reserve implementation and that MRs may buffer against collapse when sustainable catch quotas are exceeded owing to stock overestimation or systematic overfishing. © 2015 The Author(s).
The good, the bad and the ugly of marine reserves for fishery yields

PubMed Central

De Leo, Giulio A.; Micheli, Fiorenza

2015-01-01

Marine reserves (MRs) are used worldwide as a means of conserving biodiversity and protecting depleted populations. Despite major investments in MRs, their environmental and social benefits have proven difficult to demonstrate and are still debated. Clear expectations of the possible outcomes of MR establishment are needed to guide and strengthen empirical assessments. Previous models show that reserve establishment in overcapitalized, quota-based fisheries can reduce both catch and population abundance, thereby negating fisheries and even conservation benefits. By using a stage-structured, spatially explicit stochastic model, we show that catches under quota-based fisheries that include a network of MRs can exceed maximum sustainable yield (MSY) under conventional quota management if reserves provide protection to old, large spawners that disproportionally contribute to recruitment outside the reserves. Modelling results predict that the net fishery benefit of MRs is lost when gains in fecundity of old, large individuals are small, is highest in the case of sedentary adults with high larval dispersal, and decreases with adult mobility. We also show that environmental variability may mask fishery benefits of reserve implementation and that MRs may buffer against collapse when sustainable catch quotas are exceeded owing to stock overestimation or systematic overfishing. PMID:26460129
Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1975-01-01

The growth-environment relationships for greenhouse and field conditions are compared, and the development of growth-prediction models for spring wheat is discussed along with the development of models for predicting the date for spring wheat emergence in North Dakota.
Linking Field and Satellite Observations to Reveal Differences in Single vs. Double-Cropped Soybean Yields in Central Brazil

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.

2016-12-01

Soy-corn double cropping (DC) has been widely adopted in Central Brazil alongside single cropped (SC) soybean production. DC involves different cropping calendars, soy varieties, and may be associated with different crop yield patterns and volatility than SC. Study of the performance of the region's agriculture in a changing climate depends on tracking differences in the productivity of SC vs. DC, but has been limited by crop yield data that conflate the two systems. We predicted SC and DC yields across Central Brazil, drawing on field observations and remotely sensed data. We first modeled field yield estimates as a function of remotely sensed DC status and vegetation index (VI) metrics, and other management and biophysical factors. We then used the statistical model estimated to predict SC and DC soybean yields at each 500 m2 grid cell of Central Brazil for harvest years 2001 - 2015. The yield estimation model was constructed using 1) a repeated cross-sectional survey of soybean yields and management factors for years 2007-2015, 2) a custom agricultural land cover classification dataset which assimilates earlier datasets for the region, and 3) 500m 8-day MODIS image composites used to calculate the wide dynamic range vegetation index (WDRVI) and derivative metrics such as area under the curve for WDRVI values in critical crop development periods. A statistical yield estimation model which primarily entails WDRVI metrics, DC status, and spatial fixed effects was developed on a subset of the yield dataset. Model validation was conducted by predicting previously withheld yield records, and then assessing error and goodness-of-fit for predicted values with metrics including root mean squared error (RMSE), mean squared error (MSE), and R2. We found a statistical yield estimation model which incorporates WDRVI and DC status to be way to estimate crop yields over the region. Statistical properties of the resulting gridded yield dataset may be valuable for understanding
Predicting story goodness performance from cognitive measures following traumatic brain injury.

PubMed

Lê, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-05-01

This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Lê, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. One hundred sixty-seven individuals with TBI participated in the study. Story retellings were analyzed using the SGI protocol. Three cognitive measures--Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) Sorting Test, Wechsler Memory Scale--Third Edition (WMS-III; Wechsler, 1997) Working Memory Primary Index (WMI), and WMS-III Immediate Memory Primary Index (IMI)--were entered into a multiple linear regression model for each discourse measure. Two sets of regression analyses were performed, the first with the Sorting Test as the first predictor and the second with it as the last. The first set of regression analyses identified the Sorting Test and IMI as the only significant predictors of performance on measures of the SGI. The second set identified all measures as significant predictors when evaluating each step of the regression function. The cognitive variables predicted performance on the SGI measures, although there were differences in the amount of explained variance. The results (a) suggest that storytelling ability draws on a number of underlying skills and (b) underscore the importance of using discrete cognitive tasks rather than broad cognitive indices to investigate the cognitive substrates of discourse.
The yield and post-yield behavior of high-density polyethylene

NASA Technical Reports Server (NTRS)

Semeliss, M. A.; Wong, R.; Tuttle, M. E.

1990-01-01

An experimental and analytical evaluation was made of the yield and post-yield behavior of high-density polyethylene, a semi-crystalline thermoplastic. Polyethylene was selected for study because it is very inexpensive and readily available in the form of thin-walled tubes. Thin-walled tubular specimens were subjected to axial loads and internal pressures, such that the specimens were subjected to a known biaxial loading. A constant octahederal shear stress rate was imposed during all tests. The measured yield and post-yield behavior was compared with predictions based on both isotropic and anisotropic models. Of particular interest was whether inelastic behavior was sensitive to the hydrostatic stress level. The major achievements and conclusions reached are discussed.
Development of a CSP plant energy yield calculation tool applying predictive models to analyze plant performance sensitivities

NASA Astrophysics Data System (ADS)

Haack, Lukas; Peniche, Ricardo; Sommer, Lutz; Kather, Alfons

2017-06-01

At early project stages, the main CSP plant design parameters such as turbine capacity, solar field size, and thermal storage capacity are varied during the techno-economic optimization to determine most suitable plant configurations. In general, a typical meteorological year with at least hourly time resolution is used to analyze each plant configuration. Different software tools are available to simulate the annual energy yield. Software tools offering a thermodynamic modeling approach of the power block and the CSP thermal cycle, such as EBSILONProfessional®, allow a flexible definition of plant topologies. In EBSILON, the thermodynamic equilibrium for each time step is calculated iteratively (quasi steady state), which requires approximately 45 minutes to process one year with hourly time resolution. For better presentation of gradients, 10 min time resolution is recommended, which increases processing time by a factor of 5. Therefore, analyzing a large number of plant sensitivities, as required during the techno-economic optimization procedure, the detailed thermodynamic simulation approach becomes impracticable. Suntrace has developed an in-house CSP-Simulation tool (CSPsim), based on EBSILON and applying predictive models, to approximate the CSP plant performance for central receiver and parabolic trough technology. CSPsim significantly increases the speed of energy yield calculations by factor ≥ 35 and has automated the simulation run of all predefined design configurations in sequential order during the optimization procedure. To develop the predictive models, multiple linear regression techniques and Design of Experiment methods are applied. The annual energy yield and derived LCOE calculated by the predictive model deviates less than ±1.5 % from the thermodynamic simulation in EBSILON and effectively identifies the optimal range of main design parameters for further, more specific analysis.
Video image analysis in the Australian meat industry - precision and accuracy of predicting lean meat yield in lamb carcasses.

PubMed

Hopkins, D L; Safari, E; Thompson, J M; Smith, C R

2004-06-01

A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-MEAT sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the lean meat yield determined. The current industry model for predicting meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict lean meat yield (R(2)=0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR (R(2)=0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved (R(2)=0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures (R(2)=0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting lean meat yield automatically.
Rx for low cash yields.

PubMed

Tobe, Chris

2003-10-01

Certain strategies can offer not-for-profit hospitals potentially greater investment yields while maintaining stability and principal safety. Treasury inflation-indexed securities can offer good returns, low volatility, and inflation protection. "Enhanced cash" strategies offer liquidity and help to preserve capital. Stable value "wrappers" allow hospitals to pursue higher-yielding fixed-income securities without an increase in volatility.
Predicting the apparent viscosity and yield stress of mixtures of primary, secondary and anaerobically digested sewage sludge: Simulating anaerobic digesters.

PubMed

Markis, Flora; Baudez, Jean-Christophe; Parthasarathy, Rajarathinam; Slatter, Paul; Eshtiaghi, Nicky

2016-09-01

Predicting the flow behaviour, most notably, the apparent viscosity and yield stress of sludge mixtures inside the anaerobic digester is essential because it helps optimize the mixing system in digesters. This paper investigates the rheology of sludge mixtures as a function of digested sludge volume fraction. Sludge mixtures exhibited non-Newtonian, shear thinning, yield stress behaviour. The apparent viscosity and yield stress of sludge mixtures prepared at the same total solids concentration was influenced by the interactions within the digested sludge and increased with the volume fraction of digested sludge - highlighted using shear compliance and shear modulus of sludge mixtures. However, when a thickened primary - secondary sludge mixture was mixed with dilute digested sludge, the apparent viscosity and yield stress decreased with increasing the volume fraction of digested sludge. This was caused by the dilution effect leading to a reduction in the hydrodynamic and non-hydrodynamic interactions when dilute digested sludge was added. Correlations were developed to predict the apparent viscosity and yield stress of the mixtures as a function of the digested sludge volume fraction and total solids concentration of the mixtures. The parameters of correlations can be estimated using pH of sludge. The shear and complex modulus were also modelled and they followed an exponential relationship with increasing digested sludge volume fraction. Copyright © 2016 Elsevier Ltd. All rights reserved.
Brazilian Soybean Yields and Yield Gaps Vary with Farm Size

NASA Astrophysics Data System (ADS)

Jeffries, G. R.; Cohn, A.; Griffin, T. S.; Bragança, A.

2017-12-01

Understanding the farm size-specific characteristics of crop yields and yield gaps may help to improve yields by enabling better targeting of technical assistance and agricultural development programs. Linking remote sensing-based yield estimates with property boundaries provides a novel view of the relationship between farm size and yield structure (yield magnitude, gaps, and stability over time). A growing literature documents variations in yield gaps, but largely ignores the role of farm size as a factor shaping yield structure. Research on the inverse farm size-productivity relationship (IR) theory - that small farms are more productive than large ones all else equal - has documented that yield magnitude may vary by farm size, but has not considered other yield structure characteristics. We examined farm size - yield structure relationships for soybeans in Brazil for years 2001-2015. Using out-of-sample soybean yield predictions from a statistical model, we documented 1) gaps between the 95th percentile of attained yields and mean yields within counties and individual fields, and 2) yield stability defined as the standard deviation of time-detrended yields at given locations. We found a direct relationship between soy yields and farm size at the national level, while the strength and the sign of the relationship varied by region. Soybean yield gaps were found to be inversely related to farm size metrics, even when yields were only compared to farms of similar size. The relationship between farm size and yield stability was nonlinear, with mid-sized farms having the most stable yields. The work suggests that farm size is an important factor in understanding yield structure and that opportunities for improving soy yields in Brazil are greatest among smaller farms.
Climate change and maize yield in Iowa

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

Xu, Hong; Twine, Tracy E.; Girvetz, Evan

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21 st century as compared with late 20 th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model withmore » output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20 th century to middle and late 21 st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Lastly, our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10-20% by the end of the 21 st century.« less

Climate change and maize yield in Iowa

DOE PAGES

Xu, Hong; Twine, Tracy E.; Girvetz, Evan

2016-05-24

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21 st century as compared with late 20 th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model withmore » output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20 th century to middle and late 21 st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Lastly, our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10-20% by the end of the 21 st century.« less
Advanced model for the prediction of the neutron-rich fission product yields

NASA Astrophysics Data System (ADS)

Rubchenya, V. A.; Gorelov, D.; Jokinen, A.; Penttilä, H.; Äystö, J.

2013-12-01

The consistent models for the description of the independent fission product formation cross sections in the spontaneous fission and in the neutron and proton induced fission at the energies up to 100 MeV is developed. This model is a combination of new version of the two-component exciton model and a time-dependent statistical model for fusion-fission process with inclusion of dynamical effects for accurate calculations of nucleon composition and excitation energy of the fissioning nucleus at the scission point. For each member of the compound nucleus ensemble at the scission point, the primary fission fragment characteristics: kinetic and excitation energies and their yields are calculated using the scission-point fission model with inclusion of the nuclear shell and pairing effects, and multimodal approach. The charge distribution of the primary fragment isobaric chains was considered as a result of the frozen quantal fluctuations of the isovector nuclear matter density at the scission point with the finite neck radius. Model parameters were obtained from the comparison of the predicted independent product fission yields with the experimental results and with the neutron-rich fission product data measured with a Penning trap at the Accelerator Laboratory of the University of Jyväskylä (JYFLTRAP).
Semen cryopreservation in pubertal boys before gonadotoxic treatment and the role of endocrinologic evaluation in predicting sperm yield.

PubMed

van Casteren, Niels J; Dohle, Gert R; Romijn, Johanens C; de Muinck Keizer-Schrama, Sabine M P F; Weber, Robertus F A; van den Heuvel-Eibrink, Marry M

2008-10-01

To evaluate the feasibility of semen cryopreservation in pubertal boys before they receive gonadotoxic therapy and to identify which pretreatment parameters might predict successful cryopreservation. Retrospective data analysis. Tertiary fertility center, academic children's hospital. Between 1995 and 2005, 80 boys (median age 16.6 years, range 13.7-18.9 years) consulted the outpatient clinic of andrology for semen cryopreservation before a potentially gonadotoxic treatment. We assessed the pretreatment semen parameters, hormone levels, and patients' characteristics. Measurement of the number of adolescents able to cryopreserve semen. Thirteen boys were unable to produce semen by masturbation. In 53 boys semen quality was adequate for cryopreservation. In 14 patients semen analysis did not show motile spermatozoa, and therefore semen cryopreservation could not be performed. Although inhibin B showed a strong correlation with sperm count, no significant difference was found in serum T, inhibin B, LH, and FSH levels in the patients with or without successful sperm yield. Moreover, median age was not different between patients with and without a successful sperm yield. Semen cryopreservation in boys is a feasible method to preserve spermatozoa before gonadotoxic therapy is started and should be offered to all pubertal boys despite their young age. Serum hormone levels do not predict sperm yield.
Global Agriculture Yields and Conflict under Future Climate

NASA Astrophysics Data System (ADS)

Rising, J.; Cane, M. A.

2013-12-01

Aspects of climate have been shown to correlate significantly with conflict. We investigate a possible pathway for these effects through changes in agriculture yields, as predicted by field crop models (FAO's AquaCrop and DSSAT). Using satellite and station weather data, and surveyed data for soil and management, we simulate major crop yields across all countries between 1961 and 2008, and compare these to FAO and USDA reported yields. Correlations vary by country and by crop, from approximately .8 to -.5. Some of this range in crop model performance is explained by crop varieties, data quality, and other natural, economic, and political features. We also quantify the ability of AquaCrop and DSSAT to simulate yields under past cycles of ENSO as a proxy for their performance under changes in climate. We then describe two statistical models which relate crop yields to conflict events from the UCDP/PRIO Armed Conflict dataset. The first relates several preceding years of predicted yields of the major grain in each country to any conflict involving that country. The second uses the GREG ethnic group maps to identify differences in predicted yields between neighboring regions. By using variation in predicted yields to explain conflict, rather than actual yields, we can identify the exogenous effects of weather on conflict. Finally, we apply precipitation and temperature time-series under IPCC's A1B scenario to the statistical models. This allows us to estimate the scale of the impact of future yields on future conflict. Centroids of the major growing regions for each country's primary crop, based on USDA FAS consumption. Correlations between simulated yields and reported yields, for AquaCrop and DSSAT, under the assumption that no irrigation, fertilization, or pest control is used. Reported yields are the average of FAO yields and USDA FAS yields, where both are available.
Simulating the effects of climate and agricultural management practices on global crop yield

NASA Astrophysics Data System (ADS)

Deryng, D.; Sacks, W. J.; Barford, C. C.; Ramankutty, N.

2011-06-01

Climate change is expected to significantly impact global food production, and it is important to understand the potential geographic distribution of yield losses and the means to alleviate them. This study presents a new global crop model, PEGASUS 1.0 (Predicting Ecosystem Goods And Services Using Scenarios) that integrates, in addition to climate, the effect of planting dates and cultivar choices, irrigation, and fertilizer application on crop yield for maize, soybean, and spring wheat. PEGASUS combines carbon dynamics for crops with a surface energy and soil water balance model. It also benefits from the recent development of a suite of global data sets and analyses that serve as model inputs or as calibration data. These include data on crop planting and harvesting dates, crop-specific irrigated areas, a global analysis of yield gaps, and harvested area and yield of major crops. Model results for present-day climate and farm management compare reasonably well with global data. Simulated planting and harvesting dates are within the range of crop calendar observations in more than 75% of the total crop-harvested areas. Correlation of simulated and observed crop yields indicates a weighted coefficient of determination, with the weighting based on crop-harvested area, of 0.81 for maize, 0.66 for soybean, and 0.45 for spring wheat. We found that changes in temperature and precipitation as predicted by global climate models for the 2050s lead to a global yield reduction if planting and harvesting dates remain unchanged. However, adapting planting dates and cultivar choices increases yield in temperate regions and avoids 7-18% of global losses.
Efficacy of /sup 67/Ga-scintigraphy in predicting the diagnostic yield of transbronchial lung biopsy in pulmonary sarcoidosis

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

Ackart, R.S.; Munzel, T.L.; Rodriguez, J.J.

1982-07-01

Nineteen consecutive patients with clinically suspected sarcoidosis underwent /sup 67/Ga-scintigraphy prior to transbronchial lung biopsy (TBLB) to determine if /sup 67/Ga uptake in lung parenchyma would increase the diagnostic yield of the biopsy procedure. Biopsies were obtained from the areas showing parenchymal uptake on the /sup 67/Ga scan in 13 of the 19 patients. In the six patients not demonstrating uptake of /sup 67/Ga in the lung parenchyma, biopsies were obtained at random from the right lower lobe. There was no correlation between /sup 67/Ga uptake in hilar nodes or pulmonary parenchyma tissue and the diagnostic yield from TBLB. Researchersmore » conclude that /sup 67/Ga scanning is neither efficacious nor cost-effective in predicting the diagnostic yield of TBLB in sarcoidosis.« less
Modeling water yield response to forest cover changes in northern Minnesota

Treesearch

S.C. Bernath; E.S. Verry; K.N. Brooks; P.F. Ffolliott

1982-01-01

A water yield model (TIMWAT) has been developed to predict changes in water yield following changes in forest cover in northern Minnesota. Two versions of the model exist; one predicts changes in water yield as a function of gross precipitation and time after clearcutting. The second version predicts changes in water yield due to changes in above-ground biomass...
Yield performance and stability of CMS-based triticale hybrids.

PubMed

Mühleisen, Jonathan; Piepho, Hans-Peter; Maurer, Hans Peter; Reif, Jochen Christoph

2015-02-01

CMS-based triticale hybrids showed only marginal midparent heterosis for grain yield and lower dynamic yield stability compared to inbred lines. Hybrids of triticale (×Triticosecale Wittmack) are expected to possess outstanding yield performance and increased dynamic yield stability. The objectives of the present study were to (1) examine the optimum choice of the biometrical model to compare yield stability of hybrids versus lines, (2) investigate whether hybrids exhibit a more pronounced grain yield performance and yield stability, and (3) study optimal strategies to predict yield stability of hybrids. Thirteen female and seven male parental lines and their 91 factorial hybrids as well as 30 commercial lines were evaluated for grain yield in up to 20 environments. Hybrids were produced using a cytoplasmic male sterility (CMS)-inducing cytoplasm that originated from Triticumtimopheevii Zhuk. We found that the choice of the biometrical model can cause contrasting results and concluded that a group-by-environment interaction term should be added to the model when estimating stability variance of hybrids and lines. midparent heterosis for grain yield was on average 3 % with a range from -15.0 to 11.5 %. No hybrid outperformed the best inbred line. Hybrids had, on average, lower dynamic yield stability compared to the inbred lines. Grain yield performance of hybrids could be predicted based on midparent values and general combining ability (GCA)-predicted values. In contrast, stability variance of hybrids could be predicted only based on GCA-predicted values. We speculated that negative effects of the used CMS cytoplasm might be the reason for the low performance and yield stability of the hybrids. For this purpose a detailed study on the reasons for the drawback of the currently existing CMS system in triticale is urgently required comprising also the search of potentially alternative hybridization systems.
Polyelectrolyte scaling laws for microgel yielding near jamming.

PubMed

Bhattacharjee, Tapomoy; Kabb, Christopher P; O'Bryan, Christopher S; Urueña, Juan M; Sumerlin, Brent S; Sawyer, W Gregory; Angelini, Thomas E

2018-02-28

Micro-scale hydrogel particles, known as microgels, are used in industry to control the rheology of numerous different products, and are also used in experimental research to study the origins of jamming and glassy behavior in soft-sphere model systems. At the macro-scale, the rheological behaviour of densely packed microgels has been thoroughly characterized; at the particle-scale, careful investigations of jamming, yielding, and glassy-dynamics have been performed through experiment, theory, and simulation. However, at low packing fractions near jamming, the connection between microgel yielding phenomena and the physics of their constituent polymer chains has not been made. Here we investigate whether basic polymer physics scaling laws predict macroscopic yielding behaviours in packed microgels. We measure the yield stress and cross-over shear-rate in several different anionic microgel systems prepared at packing fractions just above the jamming transition, and show that our data can be predicted from classic polyelectrolyte physics scaling laws. We find that diffusive relaxations of microgel deformation during particle re-arrangements can predict the shear-rate at which microgels yield, and the elastic stress associated with these particle deformations predict the yield stress.
Predicting yields from Appalachian red oak logs and lumber

Treesearch

Daniel E. Dunmire

1971-01-01

One utilization problem is in pinpointing how to efficiently and effectively recover usable parts from logs, bolts, and lumber. Yields, which are output divided by input, provide a key to managers who make processing decisions. Research results are applied to indicate yields of graded lumber and dimension stock from graded Appalachian red oak (group) logs. How to...
Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

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

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less
Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield

USDA-ARS?s Scientific Manuscript database

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat (Triticum aestivum L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect s...
Earthquake prediction; new studies yield promising results

USGS Publications Warehouse

Robinson, R.

1974-01-01

On Agust 3, 1973, a small earthquake (magnitude 2.5) occurred near Blue Mountain Lake in the Adirondack region of northern New York State. This seemingly unimportant event was of great significance, however, because it was predicted. Seismologsits at the Lamont-Doherty geologcal Observatory of Columbia University accurately foretold the time, place, and magnitude of the event. Their prediction was based on certain pre-earthquake processes that are best explained by a hypothesis known as "dilatancy," a concept that has injected new life and direction into the science of earthquake prediction. Although much mroe reserach must be accomplished before we can expect to predict potentially damaging earthquakes with any degree of consistency, results such as this indicate that we are on a promising road.
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.
Yield of undamaged slash pine stands in South Florida

Treesearch

O. Gordon Langdon

1961-01-01

Predictions of future timber yields are necessary for formulating management plans and for comparing timber growing with alternative land uses. One useful tool for making these predictions is a set of yield tables.
Sources of nitrate yields in the Mississippi River Basin.

PubMed

David, Mark B; Drinkwater, Laurie E; McIsaac, Gregory F

2010-01-01

Riverine nitrate N in the Mississippi River leads to hypoxia in the Gulf of Mexico. Several recent modeling studies estimated major N inputs and suggested source areas that could be targeted for conservation programs. We conducted a similar analysis with more recent and extensive data that demonstrates the importance of hydrology in controlling the percentage of net N inputs (NNI) exported by rivers. The average fraction of annual riverine nitrate N export/NNI ranged from 0.05 for the lower Mississippi subbasin to 0.3 for the upper Mississippi River basin and as high as 1.4 (4.2 in a wet year) for the Embarras River watershed, a mostly tile-drained basin. Intensive corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] watersheds on Mollisols had low NNI values and when combined with riverine N losses suggest a net depletion of soil organic N. We used county-level data to develop a nonlinear model ofN inputs and landscape factors that were related to winter-spring riverine nitrate yields for 153 watersheds within the basin. We found that river runoff times fertilizer N input was the major predictive term, explaining 76% of the variation in the model. Fertilizer inputs were highly correlated with fraction of land area in row crops. Tile drainage explained 17% of the spatial variation in winter-spring nitrate yield, whereas human consumption of N (i.e., sewage effluent) accounted for 7%. Net N inputs were not a good predictor of riverine nitrate N yields, nor were other N balances. We used this model to predict the expected nitrate N yield from each county in the Mississippi River basin; the greatest nitrate N yields corresponded to the highly productive, tile-drained cornbelt from southwest Minnesota across Iowa, Illinois, Indiana, and Ohio. This analysis can be used to guide decisions about where efforts to reduce nitrate N losses can be most effectively targeted to improve local water quality and reduce export to the Gulf of Mexico.
A reexamination of the use of simple concepts for predicting the shape and location of detached shock waves

NASA Technical Reports Server (NTRS)

Love, Eugene S

1957-01-01

A reexamination has been made of the use of simple concepts for predicting the shape and location of detached shock waves. The results show that simple concepts and modifications of existing methods can yield good predictions for many nose shapes and for a wide range of Mach numbers.
Validation of the Unthinned Loblolly Pine Plantation Yield Model-USLYCOWG

Treesearch

V. Clark Baldwin; D.P. Feduccia

1982-01-01

Yield and stand structure predictions from an unthinned loblolly pine plantation yield prediction system (USLYCOWG computer program) were compared with observations from 80 unthinned loblolly pine plots. Overall, the predicted estimates were reasonable when compared to observed values, but predictions based on input data at or near the system's limits may be in...
Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in Rheumatoid Arthritis.

PubMed

Nguyen, Minh Vu Chuong; Baillet, Athan; Romand, Xavier; Trocmé, Candice; Courtier, Anaïs; Marotte, Hubert; Thomas, Thierry; Soubrier, Martin; Miossec, Pierre; Tébib, Jacques; Grange, Laurent; Toussaint, Bertrand; Lequerré, Thierry; Vittecoq, Olivier; Gaudin, Philippe

2018-06-06

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients. Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi. A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR-) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually. A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment. Copyright © 2018. Published by Elsevier Masson SAS.
Evaluation of different gridded rainfall datasets for rainfed wheat yield prediction in an arid environment

NASA Astrophysics Data System (ADS)

Lashkari, A.; Salehnia, N.; Asadi, S.; Paymard, P.; Zare, H.; Bannayan, M.

2018-05-01

The accuracy of daily output of satellite and reanalysis data is quite crucial for crop yield prediction. This study has evaluated the performance of APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation), PERSIANN (Rainfall Estimation from Remotely Sensed Information using Artificial Neural Networks), TRMM (Tropical Rainfall Measuring Mission), and AgMERRA (The Modern-Era Retrospective Analysis for Research and Applications) precipitation products to apply as input data for CSM-CERES-Wheat crop growth simulation model to predict rainfed wheat yield. Daily precipitation output from various sources for 7 years (2000-2007) was obtained and compared with corresponding ground-observed precipitation data for 16 ground stations across the northeast of Iran. Comparisons of ground-observed daily precipitation with corresponding data recorded by different sources of datasets showed a root mean square error (RMSE) of less than 3.5 for all data. AgMERRA and APHRODITE showed the highest correlation (0.68 and 0.87) and index of agreement (d) values (0.79 and 0.89) with ground-observed data. When daily precipitation data were aggregated over periods of 10 days, the RMSE values, r, and d values increased (30, 0.8, and 0.7) for AgMERRA, APHRODITE, PERSIANN, and TRMM precipitation data sources. The simulations of rainfed wheat leaf area index (LAI) and dry matter using various precipitation data, coupled with solar radiation and temperature data from observed ones, illustrated typical LAI and dry matter shape across all stations. The average values of LAImax were 0.78, 0.77, 0.74, 0.70, and 0.69 using PERSIANN, AgMERRA, ground-observed precipitation data, APHRODITE, and TRMM. Rainfed wheat grain yield simulated by using AgMERRA and APHRODITE daily precipitation data was highly correlated (r 2 ≥ 70) with those simulated using observed precipitation data. Therefore, gridded data have high potential to be used to supply lack of data and

Does the Dark Triad of Personality Predict Corrupt Intention? The Mediating Role of Belief in Good Luck

PubMed Central

Zhao, Huanhuan; Zhang, Heyun; Xu, Yan

2016-01-01

The current study is the first attempt to examine the association between the Dark Triad of personality (i.e., Machiavellianism, narcissism, and psychopathy) and corruption through a mediator—belief in good luck. Based on Ajzen's theory of planned behavior, we assumed that individuals with Dark Triad would be more likely to engage in corruption as a result of belief in good luck. In Study 1, a set of hypothetical scenarios was used to assess the bribe-offering intention and the corresponding belief in good luck. Results indicated that while the Dark Triad of personality positively predicted bribe-offering intention, it was mediated by the belief in good luck in gain-seeking. In Study 2, we presented participants with some hypothetical scenarios of bribe-taking and the corresponding belief in good luck. Findings revealed that the Dark Triad of personality was positively related to bribe-taking intention; the relationship between narcissism and bribe-taking intention, and that between psychopathy and bribe-taking intention was mediated by the belief in good luck in penalty-avoidance. However, this belief in good luck did not mediate the relationship between Machiavellianism and bribe-taking intention. These results hold while controlling for demographic variables, dispositional optimism, and self-efficacy. Taken together, this study extended previous research by providing evidence that belief in good luck may be one of the reasons explaining why people with Dark Triad are more likely to engage in corruption regardless of the potential outcomes. Theoretical and practical implications were discussed. PMID:27199841
Does the Dark Triad of Personality Predict Corrupt Intention? The Mediating Role of Belief in Good Luck.

PubMed

Zhao, Huanhuan; Zhang, Heyun; Xu, Yan

2016-01-01

The current study is the first attempt to examine the association between the Dark Triad of personality (i.e., Machiavellianism, narcissism, and psychopathy) and corruption through a mediator-belief in good luck. Based on Ajzen's theory of planned behavior, we assumed that individuals with Dark Triad would be more likely to engage in corruption as a result of belief in good luck. In Study 1, a set of hypothetical scenarios was used to assess the bribe-offering intention and the corresponding belief in good luck. Results indicated that while the Dark Triad of personality positively predicted bribe-offering intention, it was mediated by the belief in good luck in gain-seeking. In Study 2, we presented participants with some hypothetical scenarios of bribe-taking and the corresponding belief in good luck. Findings revealed that the Dark Triad of personality was positively related to bribe-taking intention; the relationship between narcissism and bribe-taking intention, and that between psychopathy and bribe-taking intention was mediated by the belief in good luck in penalty-avoidance. However, this belief in good luck did not mediate the relationship between Machiavellianism and bribe-taking intention. These results hold while controlling for demographic variables, dispositional optimism, and self-efficacy. Taken together, this study extended previous research by providing evidence that belief in good luck may be one of the reasons explaining why people with Dark Triad are more likely to engage in corruption regardless of the potential outcomes. Theoretical and practical implications were discussed.
Do good actions inspire good actions in others?

PubMed

Capraro, Valerio; Marcelletti, Alessandra

2014-12-12

Actions such as sharing food and cooperating to reach a common goal have played a fundamental role in the evolution of human societies. Despite the importance of such good actions, little is known about if and how they can spread from person to person to person. For instance, does being recipient of an altruistic act increase your probability of being cooperative with a third party? We have conducted an experiment on Amazon Mechanical Turk to test this mechanism using economic games. We have measured willingness to be cooperative through a standard Prisoner's dilemma and willingness to act altruistically using a binary Dictator game. In the baseline treatments, the endowments needed to play were given by the experimenters, as usual; in the control treatments, they came from a good action made by someone else. Across four different comparisons and a total of 572 subjects, we have never found a significant increase of cooperation or altruism when the endowment came from a good action. We conclude that good actions do not necessarily inspire good actions in others. While this is consistent with the theoretical prediction, it challenges the majority of other experimental studies.
ROI on yield data analysis systems through a business process management strategy

NASA Astrophysics Data System (ADS)

Rehani, Manu; Strader, Nathan; Hanson, Jeff

2005-05-01

The overriding motivation for yield engineering is profitability. This is achieved through application of yield management. The first application is to continually reduce waste in the form of yield loss. New products, new technologies and the dynamic state of the process and equipment keep introducing new ways to cause yield loss. In response, the yield management efforts have to continually come up with new solutions to minimize it. The second application of yield engineering is to aid in accurate product pricing. This is achieved through predicting future results of the yield engineering effort. The more accurate the yield prediction, the more accurate the wafer start volume, the more accurate the wafer pricing. Another aspect of yield prediction pertains to gauging the impact of a yield problem and predicting how long that will last. The ability to predict such impacts again feeds into wafer start calculations and wafer pricing. The question then is that if the stakes on yield management are so high why is it that most yield management efforts are run like science and engineering projects and less like manufacturing? In the eighties manufacturing put the theory of constraints1 into practice and put a premium on stability and predictability in manufacturing activities, why can't the same be done for yield management activities? This line of introspection led us to define and implement a business process to manage the yield engineering activities. We analyzed the best known methods (BKM) and deployed a workflow tool to make them the standard operating procedure (SOP) for yield managment. We present a case study in deploying a Business Process Management solution for Semiconductor Yield Engineering in a high-mix ASIC environment. We will present a description of the situation prior to deployment, a window into the development process and a valuation of the benefits.
Empirical yield tables for spruce-fir cut-over lands in the Northeast

Treesearch

Marinus Westveld

1953-01-01

Predicting future timber yields is an unavoidable task for the forest manager who is interested in growing timber as a long-term investment. He must predict yields as a basis for formulating management plans and policies. And he must predict yields from lands that differ greatly in productivity.
Modeling of yield surface evolution in uniaxial and biaxial loading conditions using a prestrained large scale specimen

NASA Astrophysics Data System (ADS)

Zaman, Shakil Bin; Barlat, Frédéric; Kim, Jin Hwan

2018-05-01

Large-scale advanced high strength steel (AHSS) sheet specimens were deformed in uniaxial tension, using a novel grip system mounted on a MTS universal tension machine. After pre-strain, they were used as a pre-strained material to examine the anisotropic response in the biaxial tension tests with various load ratios, and orthogonal tension tests at 45° and 90° from the pre-strain axis. The flow curve and the instantaneous r-value of the pre-strained steel in each of the aforementioned uniaxial testing conditions were also measured and compared with those of the undeformed steel. Furthermore, an exhaustive analysis of the yield surface was also conducted and the results, prior and post-prestrain were represented and compared. The homogeneous anisotropic hardening (HAH) model [1] was employed to predict the behavior of the pre-strained material. It was found that the HAH-predicted flow curves after non-linear strain path change and the yield loci after uniaxial pre-strain were in good agreement with the experiments, while the r-value evolution after strain path change was qualitatively well predicted.
Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.
Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

PubMed Central

Arnould, Valérie M. R.; Reding, Romain; Bormann, Jeanne; Gengler, Nicolas; Soyeurt, Hélène

2015-01-01

Simple Summary Reducing the frequency of milk recording decreases the costs of official milk recording. However, this approach can negatively affect the accuracy of predicting daily yields. Equations to predict daily yield from morning or evening data were developed in this study for fatty milk components from traits recorded easily by milk recording organizations. The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields were estimated from the morning (evening) milkings. The simplicity of the proposed models which do not include the milking interval should facilitate their use by breeding and milk recording organizations. Abstract Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for
[Climate change impacts on yield of Cordyceps sinensis and research on yield prediction model of C. sinensis].

PubMed

Zhu, Shou-Dong; Huang, Lu-Qi; Guo, Lan-Ping; Ma, Xing-Tian; Hao, Qing-Xiu; Le, Zhi-Yong; Zhang, Xiao-Bo; Yang, Guang; Zhang, Yan; Chen, Mei-Lan

2017-04-01

Cordyceps sinensis is a Chinese unique precious herbal material, its genuine producing areas covering Naqu, Changdu in Qinghai Tibet Plateau, Yushu in Qinghai province and other regions. In recent 10 years, C. sinensis resources is decreasing as a result of the blindly and excessively perennial dug. How to rationally protect, develop and utilize of the valuable resources of C. sinensis has been referred to an important field of research on C. sinensis. The ecological environment and climate change trend of Qinghai Tibet plateau happens prior to other regions, which means that the distribution and evolution of C. sinensis are more obvious and intense than those of the other populations. Based on RS (remote sensing)/GIS(geographic information system) technology, this paper utilized the relationship between the snowline elevation, the average temperature, precipitation and sunshine hours in harvest period (April and may) of C. sinensis and the actual production of C. sinensis to establish a weighted geometric mean model. The model's prediction accuracy can reach 82.16% at least in forecasting C. sinensis year yield in Naqu area in every early June. This study can provide basic datum and information for supporting the C. sinensis industry healthful, sustainable development. Copyright© by the Chinese Pharmaceutical Association.
An Improvement of the Anisotropy and Formability Predictions of Aluminum Alloy Sheets

NASA Astrophysics Data System (ADS)

Banabic, D.; Comsa, D. S.; Jurco, P.; Wagner, S.; Vos, M.

2004-06-01

The paper presents an yield criterion for orthotropic sheet metals and its implementation in a theoretical model in order to calculate the Forming Limit Curves. The proposed yield criterion has been validated for two aluminum alloys: AA3103-0 and AA5182-0, respectively. The biaxial tensile test of cross specimens has been used for the determination of the experimental yield locus. The new yield criterion has been implemented in the Marciniak-Kuczynski model for the calculus of limit strains. The calculated Forming Limit Curves have been compared with the experimental ones, determined by frictionless test: bulge test, plane strain test and uniaxial tensile test. The predicted Forming Limit Curves using the new yield criterion are in good agreement with the experimental ones.
Good Practices in Free-energy Calculations

NASA Technical Reports Server (NTRS)

Pohorille, Andrew; Jarzynski, Christopher; Chipot, Christopher

2013-01-01

As access to computational resources continues to increase, free-energy calculations have emerged as a powerful tool that can play a predictive role in drug design. Yet, in a number of instances, the reliability of these calculations can be improved significantly if a number of precepts, or good practices are followed. For the most part, the theory upon which these good practices rely has been known for many years, but often overlooked, or simply ignored. In other cases, the theoretical developments are too recent for their potential to be fully grasped and merged into popular platforms for the computation of free-energy differences. The current best practices for carrying out free-energy calculations will be reviewed demonstrating that, at little to no additional cost, free-energy estimates could be markedly improved and bounded by meaningful error estimates. In energy perturbation and nonequilibrium work methods, monitoring the probability distributions that underlie the transformation between the states of interest, performing the calculation bidirectionally, stratifying the reaction pathway and choosing the most appropriate paradigms and algorithms for transforming between states offer significant gains in both accuracy and precision. In thermodynamic integration and probability distribution (histogramming) methods, properly designed adaptive techniques yield nearly uniform sampling of the relevant degrees of freedom and, by doing so, could markedly improve efficiency and accuracy of free energy calculations without incurring any additional computational expense.
Effects of User Puff Topography, Device Voltage, and Liquid Nicotine Concentration on Electronic Cigarette Nicotine Yield: Measurements and Model Predictions

PubMed Central

Talih, Soha; Balhas, Zainab; Eissenberg, Thomas; Salman, Rola; Karaoghlanian, Nareg; El Hellani, Ahmad; Baalbaki, Rima; Saliba, Najat

2015-01-01

Introduction: Some electronic cigarette (ECIG) users attain tobacco cigarette–like plasma nicotine concentrations while others do not. Understanding the factors that influence ECIG aerosol nicotine delivery is relevant to regulation, including product labeling and abuse liability. These factors may include user puff topography, ECIG liquid composition, and ECIG design features. This study addresses how these factors can influence ECIG nicotine yield. Methods: Aerosols were machine generated with 1 type of ECIG cartridge (V4L CoolCart) using 5 distinct puff profiles representing a tobacco cigarette smoker (2-s puff duration, 33-ml/s puff velocity), a slow average ECIG user (4 s, 17 ml/s), a fast average user (4 s, 33 ml/s), a slow extreme user (8 s, 17 ml/s), and a fast extreme user (8 s, 33 ml/s). Output voltage (3.3–5.2 V or 3.0–7.5 W) and e-liquid nicotine concentration (18–36 mg/ml labeled concentration) were varied. A theoretical model was also developed to simulate the ECIG aerosol production process and to provide insight into the empirical observations. Results: Nicotine yields from 15 puffs varied by more than 50-fold across conditions. Experienced ECIG user profiles (longer puffs) resulted in higher nicotine yields relative to the tobacco smoker (shorter puffs). Puff velocity had no effect on nicotine yield. Higher nicotine concentration and higher voltages resulted in higher nicotine yields. These results were predicted well by the theoretical model (R 2 = 0.99). Conclusions: Depending on puff conditions and product features, 15 puffs from an ECIG can provide far less or far more nicotine than a single tobacco cigarette. ECIG emissions can be predicted using physical principles, with knowledge of puff topography and a few ECIG device design parameters. PMID:25187061
Comparison of statistical models for analyzing wheat yield time series.

PubMed

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha⁻¹ year⁻¹ in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.
Comparison of Statistical Models for Analyzing Wheat Yield Time Series

PubMed Central

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha−1 year−1 in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale. PMID:24205280
Genotyping by sequencing for genomic prediction in a soybean breeding population.

PubMed

Jarquín, Diego; Kocak, Kyle; Posadas, Luis; Hyma, Katie; Jedlicka, Joseph; Graef, George; Lorenz, Aaron

2014-08-29

Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations. Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller. Using GBS for genomic prediction in soybean holds good potential to
Growth and Yield Predictions for Thinned and Unthinned Slash Pine Plantations on Cutover Sites in the West Gulf Region

Treesearch

Stanley J. Zarnoch; Donald P. Feduccia; V. Clark Baldwin; Tommy R. Dell

1991-01-01

A-growth and yield model has been developed for slash pine plantations on problem-free cutover sites in the west gulf region. The model was based on the moment-percentile method using the Weibull distribution for tree diameters. This technique was applied to untbinned and thinned stand projections and, subsequently, to the prediction of residual stands immediately...
Using remote sensing satellite data and artificial neural network for prediction of potato yield in Bangladesh

NASA Astrophysics Data System (ADS)

Akhand, Kawsar; Nizamuddin, Mohammad; Roytman, Leonid; Kogan, Felix

2016-09-01

Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world's top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture's contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.
Getting Good Results from Survey Research: Part III

ERIC Educational Resources Information Center

McNamara, James F.

2004-01-01

This article is the third contribution to a research methods series dedicated to getting good results from survey research. In this series, "good results" is a stenographic term used to define surveys that yield accurate and meaningful information that decision makers can use with confidence when conducting program evaluation and policy assessment…
Effect of Evolutionary Anisotropy on Earing Prediction in Cylindrical Cup Drawing

NASA Astrophysics Data System (ADS)

Choi, H. J.; Lee, K. J.; Choi, Y.; Bae, G.; Ahn, D.-C.; Lee, M.-G.

2017-05-01

The formability of sheet metals is associated with their planar anisotropy, and finite element simulations have been applied to the sheet metal-forming process by describing the anisotropic behaviors using yield functions and hardening models. In this study, the evaluation of anisotropic constitutive models was performed based on the non-uniform height profile or earing in circular cylindrical cup drawing. Two yield functions, a quadratic Hill1948 and a non-quadratic Yld2000-2d model, were used under non-associated and associated flow rules, respectively, to simultaneously capture directional differences in yield stress and r value. The effect of the evolution of anisotropy on the earing prediction was also investigated by employing simplified equivalent plastic strain rate-dependent anisotropic coefficients. The computational results were in good agreement with experiments when the proper choice of the yield function and flow rule, which predicts the planar anisotropy, was made. Moreover, the accuracy of the earing profile could be significantly enhanced if the evolution of anisotropy between uniaxial and biaxial stress states was additionally considered.
Application of an airfoil stall flutter computer prediction program to a three-dimensional wing: Prediction versus experiment. M.S. Thesis

NASA Technical Reports Server (NTRS)

Muffoletto, A. J.

1982-01-01

An aerodynamic computer code, capable of predicting unsteady and C sub m values for an airfoil undergoing dynamic stall, is used to predict the amplitudes and frequencies of a wing undergoing torsional stall flutter. The code, developed at United Technologies Research Corporation (UTRC), is an empirical prediction method designed to yield unsteady values of normal force and moment, given the airfoil's static coefficient characteristics and the unsteady aerodynamic values, alpha, A and B. In this experiment, conducted in the PSU 4' x 5' subsonic wind tunnel, the wing's elastic axis, torsional spring constant and initial angle of attack are varied, and the oscillation amplitudes and frequencies of the wing, while undergoing torsional stall flutter, are recorded. These experimental values show only fair comparisons with the predicted responses. Predictions tend to be good at low velocities and rather poor at higher velocities.

[Predicting the impact of climate change in the next 40 years on the yield of maize in China].

PubMed

Ma, Yu-ping; Sun, Lin-li; E, You-hao; Wu, Wei

2015-01-01

Climate change will significantly affect agricultural production in China. The combination of the integral regression model and the latest climate projection may well assess the impact of future climate change on crop yield. In this paper, the correlation model of maize yield and meteorological factors was firstly established for different provinces in China by using the integral regression method, then the impact of climate change in the next 40 years on China's maize production was evaluated combined the latest climate prediction with the reason be ing analyzed. The results showed that if the current speeds of maize variety improvement and science and technology development were constant, maize yield in China would be mainly in an increasing trend of reduction with time in the next 40 years in a range generally within 5%. Under A2 climate change scenario, the region with the most reduction of maize yield would be the Northeast except during 2021-2030, and the reduction would be generally in the range of 2.3%-4.2%. Maize yield reduction would be also high in the Northwest, Southwest and middle and lower reaches of Yangtze River after 2031. Under B2 scenario, the reduction of 5.3% in the Northeast in 2031-2040 would be the greatest across all regions. Other regions with considerable maize yield reduction would be mainly in the Northwest and the Southwest. Reduction in maize yield in North China would be small, generally within 2%, under any scenarios, and that in South China would be almost unchanged. The reduction of maize yield in most regions would be greater under A2 scenario than under B2 scenario except for the period of 2021-2030. The effect of the ten day precipitation on maize yield in northern China would be almost positive. However, the effect of ten day average temperature on yield of maize in all regions would be generally negative. The main reason of maize yield reduction was temperature increase in most provinces but precipitation decrease in a few
[Nitrogen status diagnosis and yield prediction of spring maize after green manure incorporation by using a digital camera].

PubMed

Bai, Jin-Shun; Cao, Wei-Dong; Xiong, Jing; Zeng, Nao-Hua; Shimizu, Katshyoshi; Rui, Yu-Kui

2013-12-01

In order to explore the feasibility of using the image processing technology to diagnose the nitrogen status and to predict the maize yield, a field experiment with different nitrogen rates with green manure incorporation was conducted. Maize canopy digital images over a range of growth stages were captured by digital camera. Maize nitrogen status and the relationships between image color indices derived by digital camera for maize at different growth stages and maize nitrogen status indicators were analyzed. These digital camera sourced image color indices at different growth stages for maize were also regressed with maize grain yield at maturity. The results showed that the plant nitrogen status for maize was improved by green manure application. The leaf chlorophyll content (SPAD value), aboveground biomass and nitrogen uptake for green manure treatments at different maize growth stages were all higher than that for chemical fertilization treatments. The correlations between spectral indices with plant nitrogen indicators for maize affected by green manure application were weaker than that affected by chemical fertilization. And the correlation coefficients for green manure application were ranged with the maize growth stages changes. The best spectral indices for diagnosis of plant nitrogen status after green manure incorporation were normalized blue value (B/(R+G+B)) at 12-leaf (V12) stage and normalized red value (R/(R+G+B)) at grain-filling (R4) stage individually. The coefficients of determination based on linear regression were 0. 45 and 0. 46 for B/(R+G+B) at V12 stage and R/(R+G+B) at R4 stage respectively, acting as a predictor of maize yield response to nitrogen affected by green manure incorporation. Our findings suggested that digital image technique could be a potential tool for in-season prediction of the nitrogen status and grain yield for maize after green manure incorporation when the suitable growth stages and spectral indices for diagnosis
Analysis of the trade-off between high crop yield and low yield instability at the global scale

NASA Astrophysics Data System (ADS)

Ben-Ari, Tamara; Makowski, David

2016-10-01

Yield dynamics of major crops species vary remarkably among continents. Worldwide distribution of cropland influences both the expected levels and the interannual variability of global yields. An expansion of cultivated land in the most productive areas could theoretically increase global production, but also increase global yield instability if the most productive regions are characterized by high interannual yield variability. In this letter, we use portfolio analysis to quantify the tradeoff between the expected values and the interannual variance of global yield. We compute optimal frontiers for four crop species i.e., maize, rice, soybean and wheat and show how the distribution of cropland among large world regions can be optimized to either increase expected global crop production or decrease its interannual variability. We also show that a preferential allocation of cropland in the most productive regions can increase global expected yield at the expense of yield stability. Theoretically, optimizing the distribution of a small fraction of total cultivated areas can help find a good compromise between low instability and high crop yields at the global scale.
Negative impacts of climate change on cereal yields: statistical evidence from France

NASA Astrophysics Data System (ADS)

Gammans, Matthew; Mérel, Pierre; Ortiz-Bobea, Ariel

2017-05-01

In several world regions, climate change is predicted to negatively affect crop productivity. The recent statistical yield literature emphasizes the importance of flexibly accounting for the distribution of growing-season temperature to better represent the effects of warming on crop yields. We estimate a flexible statistical yield model using a long panel from France to investigate the impacts of temperature and precipitation changes on wheat and barley yields. Winter varieties appear sensitive to extreme cold after planting. All yields respond negatively to an increase in spring-summer temperatures and are a decreasing function of precipitation about historical precipitation levels. Crop yields are predicted to be negatively affected by climate change under a wide range of climate models and emissions scenarios. Under warming scenario RCP8.5 and holding growing areas and technology constant, our model ensemble predicts a 21.0% decline in winter wheat yield, a 17.3% decline in winter barley yield, and a 33.6% decline in spring barley yield by the end of the century. Uncertainty from climate projections dominates uncertainty from the statistical model. Finally, our model predicts that continuing technology trends would counterbalance most of the effects of climate change.
Real-time yield estimation based on deep learning

NASA Astrophysics Data System (ADS)

Rahnemoonfar, Maryam; Sheppard, Clay

2017-05-01

Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.
Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, C. C., Jr.

1988-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 to + or - 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was developed to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Strengths of specimens containing crack-like slits were calculated from predicted failing strains using uniaxial stress-strain curves. Predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only + or - 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.
Use of Direct and Indirect Estimates of Crown Dimensions to Predict One Seed Juniper Woody Biomass Yield for Alternative Energy Uses

USDA-ARS?s Scientific Manuscript database

Throughout the western United States there is increased interest in utilizing woodland biomass as an alternative energy source. We conducted a pilot study to predict one seed juniper (Juniperus monosperma) chip yield from tree-crown dimensions measured on the ground or derived from Very Large Scale ...
Life history trait analysis of the entomopathogenic nematode Steinernema feltiae provides the basis for prediction of dauer juvenile yields in monoxenic liquid culture.

PubMed

Addis, Temesgen; Teshome, Asmamaw; Strauch, Olaf; Ehlers, Ralf-Udo

2016-05-01

Entomopathogenic nematodes (Steinernema spp.) are used in integrated pest management to control insect pests in cryptic environments. The nematodes are mass produced in monoxenic liquid culture with their symbiotic bacteria Xenorhabdus spp. For a better understanding of nematode population dynamics, the life history traits (LHTs) of the entomopathogenic nematode Steinernema feltiae were assessed at 25 °C by observing single pairs of male and female nematodes using a hanging drop technique. To investigate the influence of different food supplies on nematode reproduction, the LHTs were assessed with a daily supply of 5 ×, 10 × and 20 × 10(9) cells ml(-1) of the nematode's bacterial symbiont Xenorhabdus bovienii in semi-solid nematode growth gelrite (NGG) medium. Increasing bacterial density had a significant positive influence on the average number of offspring produced, which ranged from 359 to 813 per female. The intrinsic rate of natural increase r m, which ranges from 1.10 to 1.19 day(-1), was neither influenced by the bacterial density, nor was the mean generation time T (5.12-5.25 days) and population doubling time (PDT) (0.64-0.59 days). The average lifespan of reproductive females, which ranged from 6.7 to 7.3 days, was positively correlated with bacterial density. A positive correlation between female body volume and bacterial density was recorded (R = 0.67) as well as a significant positive correlation between female body size and offspring production (R = 0.89) in hanging drops. Whether these data can be used to predict nematode yields in liquid culture was tested. The total female body volume calculated as the average female body volume × total number of parental females per millilitre 3 days after nematode inoculation was positively correlated (R = 0.72) with nematode yields. The total female body volume on process day 3 is thus a good indicator for the estimation of nematode yield at the end of the process (12-15 days post dauer
A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

PubMed Central

Vergara-Díaz, Omar; Zaman-Allah, Mainassara A.; Masuka, Benhildah; Hornero, Alberto; Zarco-Tejada, Pablo; Prasanna, Boddupalli M.; Cairns, Jill E.; Araus, José L.

2016-01-01

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R2~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization. PMID:27242867
Effects of user puff topography, device voltage, and liquid nicotine concentration on electronic cigarette nicotine yield: measurements and model predictions.

PubMed

Talih, Soha; Balhas, Zainab; Eissenberg, Thomas; Salman, Rola; Karaoghlanian, Nareg; El Hellani, Ahmad; Baalbaki, Rima; Saliba, Najat; Shihadeh, Alan

2015-02-01

Some electronic cigarette (ECIG) users attain tobacco cigarette-like plasma nicotine concentrations while others do not. Understanding the factors that influence ECIG aerosol nicotine delivery is relevant to regulation, including product labeling and abuse liability. These factors may include user puff topography, ECIG liquid composition, and ECIG design features. This study addresses how these factors can influence ECIG nicotine yield. Aerosols were machine generated with 1 type of ECIG cartridge (V4L CoolCart) using 5 distinct puff profiles representing a tobacco cigarette smoker (2-s puff duration, 33-ml/s puff velocity), a slow average ECIG user (4 s, 17 ml/s), a fast average user (4 s, 33 ml/s), a slow extreme user (8 s, 17 ml/s), and a fast extreme user (8 s, 33 ml/s). Output voltage (3.3-5.2 V or 3.0-7.5 W) and e-liquid nicotine concentration (18-36 mg/ml labeled concentration) were varied. A theoretical model was also developed to simulate the ECIG aerosol production process and to provide insight into the empirical observations. Nicotine yields from 15 puffs varied by more than 50-fold across conditions. Experienced ECIG user profiles (longer puffs) resulted in higher nicotine yields relative to the tobacco smoker (shorter puffs). Puff velocity had no effect on nicotine yield. Higher nicotine concentration and higher voltages resulted in higher nicotine yields. These results were predicted well by the theoretical model (R (2) = 0.99). Depending on puff conditions and product features, 15 puffs from an ECIG can provide far less or far more nicotine than a single tobacco cigarette. ECIG emissions can be predicted using physical principles, with knowledge of puff topography and a few ECIG device design parameters. © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Grapevine canopy reflectance and yield

NASA Technical Reports Server (NTRS)

Minden, K. A.; Philipson, W. R.

1982-01-01

Field spectroradiometric and airborne multispectral scanner data were applied in a study of Concord grapevines. Spectroradiometric measurements of 18 experimental vines were collected on three dates during one growing season. Spectral reflectance, determined at 30 intervals from 0.4 to 1.1 microns, was correlated with vine yield, pruning weight, clusters/vine, and nitrogen input. One date of airborne multispectral scanner data (11 channels) was collected over commercial vineyards, and the average radiance values for eight vineyard sections were correlated with the corresponding average yields. Although some correlations were significant, they were inadequate for developing a reliable yield prediction model.
Influence of yield surface curvature on the macroscopic yielding and ductile failure of isotropic porous plastic materials

NASA Astrophysics Data System (ADS)

Dæhli, Lars Edvard Bryhni; Morin, David; Børvik, Tore; Hopperstad, Odd Sture

2017-10-01

Numerical unit cell models of an approximative representative volume element for a porous ductile solid are utilized to investigate differences in the mechanical response between a quadratic and a non-quadratic matrix yield surface. A Hershey equivalent stress measure with two distinct values of the yield surface exponent is employed as the matrix description. Results from the unit cell calculations are further used to calibrate a heuristic extension of the Gurson model which incorporates effects of the third deviatoric stress invariant. An assessment of the porous plasticity model reveals its ability to describe the unit cell response to some extent, however underestimating the effect of the Lode parameter for the lower triaxiality ratios imposed in this study when compared to unit cell simulations. Ductile failure predictions by means of finite element simulations using a unit cell model that resembles an imperfection band are then conducted to examine how the non-quadratic matrix yield surface influences the failure strain as compared to the quadratic matrix yield surface. Further, strain localization predictions based on bifurcation analyses and imperfection band analyses are undertaken using the calibrated porous plasticity model. These simulations are then compared to the unit cell calculations in order to elucidate the differences between the various modelling strategies. The current study reveals that strain localization analyses using an imperfection band model and a spatially discretized unit cell are in reasonable agreement, while the bifurcation analyses predict higher strain levels at localization. Imperfection band analyses are finally used to calculate failure loci for the quadratic and the non-quadratic matrix yield surface under a wide range of loading conditions. The underlying matrix yield surface is demonstrated to have a pronounced influence on the onset of strain localization.
Hydrostatic Stress Effect On the Yield Behavior of Inconel 100

NASA Technical Reports Server (NTRS)

Allen, Phillip A.; Wilson, Christopher D.

2002-01-01

Classical metal plasticity theory assumes that hydrostatic stress has no effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of notched geometries. New experiments and nonlinear finite element analyses (FEA) of Inconel 100 (IN 100) equal-arm bend and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions was performed. In all test cases, the von Mises constitutive model, which is independent of hydrostatic pressure, overestimated the load for a given displacement or strain. Considering the failure displacements or strains, the Drucker-Prager FEMs predicted loads that were 3% to 5% lower than the von Mises values. For the failure loads, the Drucker Prager FEMs predicted strains that were 20% to 35% greater than the von Mises values. The Drucker-Prager yield function seems to more accurately predict the overall specimen response of geometries with significant internal hydrostatic stress influence.
Good Models Gone Bad: Quantifying and Predicting Parameter-Induced Climate Model Simulation Failures

NASA Astrophysics Data System (ADS)

Lucas, D. D.; Klein, R.; Tannahill, J.; Brandon, S.; Covey, C. C.; Domyancic, D.; Ivanova, D. P.

2012-12-01

Simulations using IPCC-class climate models are subject to fail or crash for a variety of reasons. Statistical analysis of the failures can yield useful insights to better understand and improve the models. During the course of uncertainty quantification (UQ) ensemble simulations to assess the effects of ocean model parameter uncertainties on climate simulations, we experienced a series of simulation failures of the Parallel Ocean Program (POP2). About 8.5% of our POP2 runs failed for numerical reasons at certain combinations of parameter values. We apply support vector machine (SVM) classification from the fields of pattern recognition and machine learning to quantify and predict the probability of failure as a function of the values of 18 POP2 parameters. The SVM classifiers readily predict POP2 failures in an independent validation ensemble, and are subsequently used to determine the causes of the failures via a global sensitivity analysis. Four parameters related to ocean mixing and viscosity are identified as the major sources of POP2 failures. Our method can be used to improve the robustness of complex scientific models to parameter perturbations and to better steer UQ ensembles. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).
Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India

NASA Astrophysics Data System (ADS)

Jayakumar, M.; Rajavel, M.; Surendran, U.

2016-12-01

A study on the variability of coffee yield of both Coffea arabica and Coffea canephora as influenced by climate parameters (rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH)) was undertaken at Regional Coffee Research Station, Chundale, Wayanad, Kerala State, India. The result on the coffee yield data of 30 years (1980 to 2009) revealed that the yield of coffee is fluctuating with the variations in climatic parameters. Among the species, productivity was higher for C. canephora coffee than C. arabica in most of the years. Maximum yield of C. canephora (2040 kg ha-1) was recorded in 2003-2004 and there was declining trend of yield noticed in the recent years. Similarly, the maximum yield of C. arabica (1745 kg ha-1) was recorded in 1988-1989 and decreased yield was noticed in the subsequent years till 1997-1998 due to year to year variability in climate. The highest correlation coefficient was found between the yield of C. arabica coffee and maximum temperature during January (0.7) and between C. arabica coffee yield and RH during July (0.4). Yield of C. canephora coffee had highest correlation with maximum temperature, RH and rainfall during February. Statistical regression model between selected climatic parameters and yield of C. arabica and C. canephora coffee was developed to forecast the yield of coffee in Wayanad district in Kerala. The model was validated for years 2010, 2011, and 2012 with the coffee yield data obtained during the years and the prediction was found to be good.
Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.
Stand-yield prediction for managed Ocala sand pine

Treesearch

D.L. Rockwood; B. Yang; K.W. Outcalt

1997-01-01

Sand pine is a very important species in Florida, producing significant quantities of fiber. The purpose of this study was to develop the site index and stand-level growth and yield equations managers need to make informed decisions. Data were collected from 35 seeded plots of Ocala sand pine covering a range of site indexes, ages, and densities in 1982-83. These plots...
Guidelines for Estimating Cone and Seed Yields of Southern Pines

Treesearch

James P. Barnett

1999-01-01

Our ability to predict cone and seed yields of southern pines (Pinus spp.) prior to collection is important when scheduling and allocating resources. Many managers have enough historical data to predict their orchards' yield; but such data are generally unavailable for some species and for collections outside of orchards. Guidelines are...
Revealing how network structure affects accuracy of link prediction

NASA Astrophysics Data System (ADS)

Yang, Jin-Xuan; Zhang, Xiao-Dong

2017-08-01

Link prediction plays an important role in network reconstruction and network evolution. The network structure affects the accuracy of link prediction, which is an interesting problem. In this paper we use common neighbors and the Gini coefficient to reveal the relation between them, which can provide a good reference for the choice of a suitable link prediction algorithm according to the network structure. Moreover, the statistical analysis reveals correlation between the common neighbors index, Gini coefficient index and other indices to describe the network structure, such as Laplacian eigenvalues, clustering coefficient, degree heterogeneity, and assortativity of network. Furthermore, a new method to predict missing links is proposed. The experimental results show that the proposed algorithm yields better prediction accuracy and robustness to the network structure than existing currently used methods for a variety of real-world networks.
Satellite-based mapping of field-scale stress indicators for crop yield forecasting: an application over Mead, NE

NASA Astrophysics Data System (ADS)

Yang, Y.; Anderson, M. C.; Gao, F.; Wardlow, B.; Hain, C.; Otkin, J.; Sun, L.; Dulaney, W.

2017-12-01

In agricultural regions, water is one of the most widely limiting factors of crop performance and production. Evapotranspiration (ET) describes crop water use through transpiration and water lost through direct soil evaporation, which makes it a good indicator of soil moisture availability and vegetation health and thus has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) describes temporal anomalies in a normalized evapotranspiration metric (fRET) as derived from satellite remote sensing and has demonstrated capacity to explain regional yield variability in water limited crop growing regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded. In this study we generated maps of ET, fRET, and ESI at high spatiotemporal resolution (30-m pixels, daily timesteps) using a multi-sensor data fusion method, integrating information from satellite platforms with good temporal coverage and other platforms that provide field-scale spatial detail. The study was conducted over the period 2010-2014, covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of corn yield are investigated at both the field and county level to assess the value of ESI as a yield forecasting tool. To examine the role of phenology in ESI-yield correlations, annual input fRET timeseries were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). Results demonstrate that mapping of fRET and ESI at 30-m has the advantage of being able to resolve different crop types with varying phenology. The study also suggests that incorporating phenological information significantly improves yield-correlations by accounting for effects of phenology such as variable planting date and emergence date. The yield-ESI relationship in this study well captures the inter-annual variability of yields

Time series models of environmental exposures: Good predictions or good understanding.

PubMed

Barnett, Adrian G; Stephen, Dimity; Huang, Cunrui; Wolkewitz, Martin

2017-04-01

Time series data are popular in environmental epidemiology as they make use of the natural experiment of how changes in exposure over time might impact on disease. Many published time series papers have used parameter-heavy models that fully explained the second order patterns in disease to give residuals that have no short-term autocorrelation or seasonality. This is often achieved by including predictors of past disease counts (autoregression) or seasonal splines with many degrees of freedom. These approaches give great residuals, but add little to our understanding of cause and effect. We argue that modelling approaches should rely more on good epidemiology and less on statistical tests. This includes thinking about causal pathways, making potential confounders explicit, fitting a limited number of models, and not over-fitting at the cost of under-estimating the true association between exposure and disease. Copyright © 2017 Elsevier Inc. All rights reserved.
Predicting Reactive Intermediate Quantum Yields from Dissolved Organic Matter Photolysis Using Optical Properties and Antioxidant Capacity.

PubMed

Mckay, Garrett; Huang, Wenxi; Romera-Castillo, Cristina; Crouch, Jenna E; Rosario-Ortiz, Fernando L; Jaffé, Rudolf

2017-05-16

The antioxidant capacity and formation of photochemically produced reactive intermediates (RI) was studied for water samples collected from the Florida Everglades with different spatial (marsh versus estuarine) and temporal (wet versus dry season) characteristics. Measured RI included triplet excited states of dissolved organic matter ( 3 DOM*), singlet oxygen ( 1 O 2 ), and the hydroxyl radical ( • OH). Single and multiple linear regression modeling were performed using a broad range of extrinsic (to predict RI formation rates, R RI ) and intrinsic (to predict RI quantum yields, Φ RI ) parameters. Multiple linear regression models consistently led to better predictions of R RI and Φ RI for our data set but poor prediction of Φ RI for a previously published data set,1 probably because the predictors are intercorrelated (Pearson's r > 0.5). Single linear regression models were built with data compiled from previously published studies (n ≈ 120) in which E2:E3, S, and Φ RI values were measured, which revealed a high degree of similarity between RI-optical property relationships across DOM samples of diverse sources. This study reveals that • OH formation is, in general, decoupled from 3 DOM* and 1 O 2 formation, providing supporting evidence that 3 DOM* is not a • OH precursor. Finally, Φ RI for 1 O 2 and 3 DOM* correlated negatively with antioxidant activity (a surrogate for electron donating capacity) for the collected samples, which is consistent with intramolecular oxidation of DOM moieties by 3 DOM*.
Plasticity Tool for Predicting Shear Nonlinearity of Unidirectional Laminates Under Multiaxial Loading

NASA Technical Reports Server (NTRS)

Wang, John T.; Bomarito, Geoffrey F.

2016-01-01

This study implements a plasticity tool to predict the nonlinear shear behavior of unidirectional composite laminates under multiaxial loadings, with an intent to further develop the tool for use in composite progressive damage analysis. The steps for developing the plasticity tool include establishing a general quadratic yield function, deriving the incremental elasto-plastic stress-strain relations using the yield function with associated flow rule, and integrating the elasto-plastic stress-strain relations with a modified Euler method and a substepping scheme. Micromechanics analyses are performed to obtain normal and shear stress-strain curves that are used in determining the plasticity parameters of the yield function. By analyzing a micromechanics model, a virtual testing approach is used to replace costly experimental tests for obtaining stress-strain responses of composites under various loadings. The predicted elastic moduli and Poisson's ratios are in good agreement with experimental data. The substepping scheme for integrating the elasto-plastic stress-strain relations is suitable for working with displacement-based finite element codes. An illustration problem is solved to show that the plasticity tool can predict the nonlinear shear behavior for a unidirectional laminate subjected to multiaxial loadings.
Predicting Radiotherapy Necessity in Tongue Cancer Using Lymph Node Yield.

PubMed

Feng, Zhien; Xu, Qiao Shi; Qin, Li Zheng; Li, Hua; Han, Zhengxue

2017-05-01

In patients with head and neck cancer and a single metastatic lymph node (pN1), the value of lymph node yield (LNY) remains controversial in determining the prognosis and identifying patients who require radiotherapy. This study evaluated the role of LNY in predicting the adequacy of neck dissection, need for adjuvant radiotherapy, and survival in patients with pN1 oral tongue squamous cell carcinoma. The authors implemented a retrospective cohort study. The predictor variable was LNY. The outcome variables were 5-year disease-specific survival and the need for adjuvant radiotherapy. Other study variables were age, gender, tumor stage, pathologic grade, growth pattern, tobacco and alcohol habits, and time frame. Descriptive and bivariate statistics were computed, and a P value less than .05 was considered statistically significant. The sample was chosen from among 2,792 patients who were histopathologically diagnosed as having oral squamous cell carcinoma and underwent surgical treatment from June 1996 through December 2012. One hundred forty-one patients treated at the Department of Oral and Maxillofacial-Head and Neck Oncology of the Beijing Stomatological Hospital (Beijing, China) were screened for the study. Receiver operating characteristics curve analysis identified that a cutoff (LNY, 20; area under the curve, 0.708; 95% confidence interval, 0.625-0.781; sensitivity and specificity, 64.94 and 70.31%, respectively; P = .0001) could best discriminate patients into 2 groups according to need for adjuvant radiotherapy. Interestingly, subgroup analyses showed that patients who underwent adjuvant radiotherapy had notably better 5-year disease-specific survival than those who did not undergo radiotherapy if the LNY was smaller than 20 (58.0 vs 21.0%; P = .021). However, there was no significant association for 5-year disease-specific survival between the low and high LNY groups (49.2 vs 58.7%; P = .363). An LNY smaller than 20 at levels I to III predicted a
Fission yield measurements at IGISOL

NASA Astrophysics Data System (ADS)

Lantz, M.; Al-Adili, A.; Gorelov, D.; Jokinen, A.; Kolhinen, V. S.; Mattera, A.; Moore, I.; Penttilä, H.; Pomp, S.; Prokofiev, A. V.; Rakopoulos, V.; Rinta-Antila, S.; Simutkin, V.; Solders, A.

2016-06-01

The fission product yields are an important characteristic of the fission process. In fundamental physics, knowledge of the yield distributions is needed to better understand the fission process. For nuclear energy applications good knowledge of neutroninduced fission-product yields is important for the safe and efficient operation of nuclear power plants. With the Ion Guide Isotope Separator On-Line (IGISOL) technique, products of nuclear reactions are stopped in a buffer gas and then extracted and separated by mass. Thanks to the high resolving power of the JYFLTRAP Penning trap, at University of Jyväskylä, fission products can be isobarically separated, making it possible to measure relative independent fission yields. In some cases it is even possible to resolve isomeric states from the ground state, permitting measurements of isomeric yield ratios. So far the reactions U(p,f) and Th(p,f) have been studied using the IGISOL-JYFLTRAP facility. Recently, a neutron converter target has been developed utilizing the Be(p,xn) reaction. We here present the IGISOL-technique for fission yield measurements and some of the results from the measurements on proton induced fission. We also present the development of the neutron converter target, the characterization of the neutron field and the first tests with neutron-induced fission.
Compression of freestanding gold nanostructures: from stochastic yield to predictable flow

NASA Astrophysics Data System (ADS)

Mook, W. M.; Niederberger, C.; Bechelany, M.; Philippe, L.; Michler, J.

2010-02-01

Characterizing the mechanical response of isolated nanostructures is vitally important to fields such as microelectromechanical systems (MEMS) where the behaviour of nanoscale contacts can in large part determine system reliability and lifetime. To address this challenge directly, single crystal gold nanodots are compressed inside a high resolution scanning electron microscope (SEM) using a nanoindenter equipped with a flat punch tip. These structures load elastically, and then yield in a stochastic manner, at loads ranging from 16 to 110 µN, which is up to five times higher than the load necessary for flow after yield. Yielding is immediately followed by displacement bursts equivalent to 1-50% of the initial height, depending on the yield point. During the largest displacement bursts, strain energy within the structure is released while new surface area is created in the form of localized slip bands, which are evident in both the SEM movies and still-images. A first order estimate of the apparent energy release rate, in terms of fracture mechanics concepts, for bursts representing 5-50% of the structure's initial height is on the order of 10-100 J m-2, which is approximately two orders of magnitude lower than bulk values. Once this initial strain burst during yielding has occurred, the structures flow in a ductile way. The implications of this behaviour, which is analogous to a brittle to ductile transition, are discussed with respect to mechanical reliability at the micro- and nanoscales.
Predictive Effects of Good Self-Control and Poor Regulation on Alcohol-Related Outcomes: Do Protective Behavioral Strategies Mediate?

PubMed Central

Pearson, Matthew R.; Kite, Benjamin A.; Henson, James M.

2016-01-01

In the present study, we examined whether use of protective behavioral strategies mediated the relationship between self-control constructs and alcohol-related outcomes. According to the two-mode model of self-control, good self-control (planfulness; measured with Future Time Perspective, Problem Solving, and Self-Reinforcement) and poor regulation (impulsivity; measured with Present Time Perspective, Poor Delay of Gratification, Distractibility) are theorized to be relatively independent constructs rather than opposite ends of a single continuum. The analytic sample consisted of 278 college student drinkers (68% women) who responded to a battery of surveys at a single time point. Using a structural equation model based on the two-mode model of self-control, we found that good self-control predicted increased use of three types of protective behavioral strategies (Manner of Drinking, Limiting/Stopping Drinking, and Serious Harm Reduction). Poor regulation was unrelated to use of protective behavioral strategies, but had direct effects on alcohol use and alcohol problems. Further, protective behavioral strategies mediated the relationship between good self-control and alcohol use. The clinical implications of these findings are discussed. PMID:22663345
Posterior Predictive Bayesian Phylogenetic Model Selection

PubMed Central

Lewis, Paul O.; Xie, Wangang; Chen, Ming-Hui; Fan, Yu; Kuo, Lynn

2014-01-01

We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand–Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (GGp) and goodness-of-fit (GGg), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML) which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data. [Bayesian; conditional predictive ordinate; CPO; L-measure; LPML; model selection; phylogenetics; posterior predictive.] PMID:24193892
Neutrino flux predictions for the NuMI beam

NASA Astrophysics Data System (ADS)

Aliaga, L.; Kordosky, M.; Golan, T.; Altinok, O.; Bellantoni, L.; Bercellie, A.; Betancourt, M.; Bravar, A.; Budd, H.; Carneiro, M. F.; Dytman, S.; Díaz, G. A.; Endress, E.; Felix, J.; Fields, L.; Fine, R.; Gago, A. M.; Galindo, R.; Gallagher, H.; Gran, R.; Harris, D. A.; Higuera, A.; Hurtado, K.; Kiveni, M.; Kleykamp, J.; Le, T.; Maher, E.; Manly, S.; Mann, W. A.; Marshall, C. M.; Martinez Caicedo, D. A.; McFarland, K. S.; McGivern, C. L.; McGowan, A. M.; Messerly, B.; Miller, J.; Mislivec, A.; Morfín, J. G.; Mousseau, J.; Naples, D.; Nelson, J. K.; Norrick, A.; Nuruzzaman, Paolone, V.; Park, J.; Patrick, C. E.; Perdue, G. N.; Ransome, R. D.; Ray, H.; Ren, L.; Rimal, D.; Rodrigues, P. A.; Ruterbories, D.; Schellman, H.; Solano Salinas, C. J.; Sánchez Falero, S.; Tice, B. G.; Valencia, E.; Walton, T.; Wolcott, J.; Wospakrik, M.; Zhang, D.; MinerνA Collaboration

2016-11-01

Knowledge of the neutrino flux produced by the Neutrinos at the Main Injector (NuMI) beamline is essential to the neutrino oscillation and neutrino interaction measurements of the MINERvA, MINOS + , NOvA and MicroBooNE experiments at Fermi National Accelerator Laboratory. We have produced a flux prediction which uses all available and relevant hadron production data, incorporating measurements of particle production off of thin targets as well as measurements of particle yields from a spare NuMI target exposed to a 120 GeV proton beam. The result is the most precise flux prediction achieved for a neutrino beam in the one to tens of GeV energy region. We have also compared the prediction to in situ measurements of the neutrino flux and find good agreement.
Kinetically accessible yield (KAY) for redirection of metabolism to produce exo-metabolites

DOE PAGES

Lafontaine Rivera, Jimmy G.; Theisen, Matthew K.; Chen, Po-Wei; ...

2017-04-05

The product formation yield (product formed per unit substrate consumed) is often the most important performance indicator in metabolic engineering. Until now, the actual yield cannot be predicted, but it can be bounded by its maximum theoretical value. The maximum theoretical yield is calculated by considering the stoichiometry of the pathways and cofactor regeneration involved. Here in this paper we found that in many cases, dynamic stability becomes an issue when excessive pathway flux is drawn to a product. This constraint reduces the yield and renders the maximal theoretical yield too loose to be predictive. We propose a more realisticmore » quantity, defined as the kinetically accessible yield (KAY) to predict the maximum accessible yield for a given flux alteration. KAY is either determined by the point of instability, beyond which steady states become unstable and disappear, or a local maximum before becoming unstable. Thus, KAY is the maximum flux that can be redirected for a given metabolic engineering strategy without losing stability. Strictly speaking, calculation of KAY requires complete kinetic information. With limited or no kinetic information, an Ensemble Modeling strategy can be used to determine a range of likely values for KAY, including an average prediction. We first apply the KAY concept with a toy model to demonstrate the principle of kinetic limitations on yield. We then used a full-scale E. coli model (193 reactions, 153 metabolites) and this approach was successful in E. coli for predicting production of isobutanol: the calculated KAY values are consistent with experimental data for three genotypes previously published.« less
Relationship between soybean yield/quality and soil quality in a major soybean-producing area based on a 2D-QSAR model

NASA Astrophysics Data System (ADS)

Gao, Ming; Li, Shiwei

2017-05-01

Based on experimental data of the soybean yield and quality from 30 sampling points, a quantitative structure-activity relationship model (2D-QSAR) was established using the soil quality (elements, pH, organic matter content and cation exchange capacity) as independent variables and soybean yield or quality as the dependent variable, with SPSS software. During the modeling, the full data set (30 and 14 compounds) was divided into a training set (24 and 11 compounds) for model generation and a test set (6 and 3 compounds) for model validation. The R2 values of the resulting models and data were 0.826 and 0.808 for soybean yield and quality, respectively, and all regression coefficients were significant (P < 0.05). The correlation coefficient R2pred of observed values and predicted values of the soybean yield and soybean quality in the test set were 0.961 and 0.956, respectively, indicating that the models had a good predictive ability. Moreover, the Mo, Se, K, N and organic matter contents and the cation exchange capacity of soil had a positive effect on soybean production, and the B, Mo, Se, K and N contents and cation exchange coefficient had a positive effect on soybean quality. The results are instructive for enhancing soils to improve the yield and quality of soybean, and this method can also be used to study other crops or regions, providing a theoretical basis to improving the yield and quality of crops.
Dual-component video image analysis system (VIASCAN) as a predictor of beef carcass red meat yield percentage and for augmenting application of USDA yield grades.

PubMed

Cannell, R C; Tatum, J D; Belk, K E; Wise, J W; Clayton, R P; Smith, G C

1999-11-01

An improved ability to quantify differences in the fabrication yields of beef carcasses would facilitate the application of value-based marketing. This study was conducted to evaluate the ability of the Dual-Component Australian VIASCAN to 1) predict fabricated beef subprimal yields as a percentage of carcass weight at each of three fat-trim levels and 2) augment USDA yield grading, thereby improving accuracy of grade placement. Steer and heifer carcasses (n = 240) were evaluated using VIASCAN, as well as by USDA expert and online graders, before fabrication of carcasses to each of three fat-trim levels. Expert yield grade (YG), online YG, VIASCAN estimates, and VIASCAN estimated ribeye area used to augment actual and expert grader estimates of the remaining YG factors (adjusted fat thickness, percentage of kidney-pelvic-heart fat, and hot carcass weight), respectively, 1) accounted for 51, 37, 46, and 55% of the variation in fabricated yields of commodity-trimmed subprimals, 2) accounted for 74, 54, 66, and 75% of the variation in fabricated yields of closely trimmed subprimals, and 3) accounted for 74, 54, 71, and 75% of the variation in fabricated yields of very closely trimmed subprimals. The VIASCAN system predicted fabrication yields more accurately than current online yield grading and, when certain VIASCAN-measured traits were combined with some USDA yield grade factors in an augmentation system, the accuracy of cutability prediction was improved, at packing plant line speeds, to a level matching that of expert graders applying grades at a comfortable rate.
Microscopic predictions of fission yields based on the time dependent GCM formalism

NASA Astrophysics Data System (ADS)

Regnier, D.; Dubray, N.; Schunck, N.; Verrière, M.

2016-03-01

Accurate knowledge of fission fragment yields is an essential ingredient of numerous applications ranging from the formation of elements in the r-process to fuel cycle optimization in nuclear energy. The need for a predictive theory applicable where no data is available, together with the variety of potential applications, is an incentive to develop a fully microscopic approach to fission dynamics. One of the most promising theoretical frameworks is the time-dependent generator coordinate method (TDGCM) applied under the Gaussian overlap approximation (GOA). Previous studies reported promising results by numerically solving the TDGCM+GOA equation with a finite difference technique. However, the computational cost of this method makes it difficult to properly control numerical errors. In addition, it prevents one from performing calculations with more than two collective variables. To overcome these limitations, we developed the new code FELIX-1.0 that solves the TDGCM+GOA equation based on the Galerkin finite element method. In this article, we briefly illustrate the capabilities of the solver FELIX-1.0, in particular its validation for n+239Pu low energy induced fission. This work is the result of a collaboration between CEA,DAM,DIF and LLNL on nuclear fission theory.
Impacts of variability in cellulosic biomass yields on energy security.

PubMed

Mullins, Kimberley A; Matthews, H Scott; Griffin, W Michael; Anex, Robert

2014-07-01

The practice of modeling biomass yields on the basis of deterministic point values aggregated over space and time obscures important risks associated with large-scale biofuel use, particularly risks related to drought-induced yield reductions that may become increasingly frequent under a changing climate. Using switchgrass as a case study, this work quantifies the variability in expected yields over time and space through switchgrass growth modeling under historical and simulated future weather. The predicted switchgrass yields across the United States range from about 12 to 19 Mg/ha, and the 80% confidence intervals range from 20 to 60% of the mean. Average yields are predicted to decrease with increased temperatures and weather variability induced by climate change. Feedstock yield variability needs to be a central part of modeling to ensure that policy makers acknowledge risks to energy supplies and develop strategies or contingency plans that mitigate those risks.
A Priori Estimation of Organic Reaction Yields

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

Emami, Fateme S.; Vahid, Amir; Wylie, Elizabeth K.

2015-07-21

A thermodynamically guided calculation of free energies of substrate and product molecules allows for the estimation of the yields of organic reactions. The non-ideality of the system and the solvent effects are taken into account through the activity coefficients calculated at the molecular level by perturbed-chain statistical associating fluid theory (PC-SAFT). The model is iteratively trained using a diverse set of reactions with yields that have been reported previously. This trained model can then estimate a priori the yields of reactions not included in the training set with an accuracy of ca. ±15 %. This ability has the potential tomore » translate into significant economic savings through the selection and then execution of only those reactions that can proceed in good yields.« less
Normalized Rotational Multiple Yield Surface Framework (NRMYSF) stress-strain curve prediction method based on small strain triaxial test data on undisturbed Auckland residual clay soils

NASA Astrophysics Data System (ADS)

Noor, M. J. Md; Ibrahim, A.; Rahman, A. S. A.

2018-04-01

Small strain triaxial test measurement is considered to be significantly accurate compared to the external strain measurement using conventional method due to systematic errors normally associated with the test. Three submersible miniature linear variable differential transducer (LVDT) mounted on yokes which clamped directly onto the soil sample at equally 120° from the others. The device setup using 0.4 N resolution load cell and 16 bit AD converter was capable of consistently resolving displacement of less than 1µm and measuring axial strains ranging from less than 0.001% to 2.5%. Further analysis of small strain local measurement data was performed using new Normalized Multiple Yield Surface Framework (NRMYSF) method and compared with existing Rotational Multiple Yield Surface Framework (RMYSF) prediction method. The prediction of shear strength based on combined intrinsic curvilinear shear strength envelope using small strain triaxial test data confirmed the significant improvement and reliability of the measurement and analysis methods. Moreover, the NRMYSF method shows an excellent data prediction and significant improvement toward more reliable prediction of soil strength that can reduce the cost and time of experimental laboratory test.
Yield surface evolution for columnar ice

NASA Astrophysics Data System (ADS)

Zhou, Zhiwei; Ma, Wei; Zhang, Shujuan; Mu, Yanhu; Zhao, Shunpin; Li, Guoyu

A series of triaxial compression tests, which has capable of measuring the volumetric strain of the sample, were conducted on columnar ice. A new testing approach of probing the experimental yield surface was performed from a single sample in order to investigate yield and hardening behaviors of the columnar ice under complex stress states. Based on the characteristic of the volumetric strain, a new method of defined the multiaxial yield strengths of the columnar ice is proposed. The experimental yield surface remains elliptical shape in the stress space of effective stress versus mean stress. The effect of temperature, loading rate and loading path in the initial yield surface and deformation properties of the columnar ice were also studied. Subsequent yield surfaces of the columnar ice have been explored by using uniaxial and hydrostatic paths. The evolution of the subsequent yield surface exhibits significant path-dependent characteristics. The multiaxial hardening law of the columnar ice was established experimentally. A phenomenological yield criterion was presented for multiaxial yield and hardening behaviors of the columnar ice. The comparisons between the theoretical and measured results indicate that this current model is capable of giving a reasonable prediction for the multiaxial yield and post-yield properties of the columnar ice subjected to different temperature, loading rate and path conditions.
Predicting the shock compression response of heterogeneous powder mixtures

NASA Astrophysics Data System (ADS)

Fredenburg, D. A.; Thadhani, N. N.

2013-06-01

A model framework for predicting the dynamic shock-compression response of heterogeneous powder mixtures using readily obtained measurements from quasi-static tests is presented. Low-strain-rate compression data are first analyzed to determine the region of the bulk response over which particle rearrangement does not contribute to compaction. This region is then fit to determine the densification modulus of the mixture, σD, an newly defined parameter describing the resistance of the mixture to yielding. The measured densification modulus, reflective of the diverse yielding phenomena that occur at the meso-scale, is implemented into a rate-independent formulation of the P-α model, which is combined with an isobaric equation of state to predict the low and high stress dynamic compression response of heterogeneous powder mixtures. The framework is applied to two metal + metal-oxide (thermite) powder mixtures, and good agreement between the model and experiment is obtained for all mixtures at stresses near and above those required to reach full density. At lower stresses, rate-dependencies of the constituents, and specifically those of the matrix constituent, determine the ability of the model to predict the measured response in the incomplete compaction regime.
Communication: Theoretical prediction of free-energy landscapes for complex self-assembly

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

Jacobs, William M.; Reinhardt, Aleks; Frenkel, Daan

2015-01-14

We present a technique for calculating free-energy profiles for the nucleation of multicomponent structures that contain as many species as building blocks. We find that a key factor is the topology of the graph describing the connectivity of the target assembly. By considering the designed interactions separately from weaker, incidental interactions, our approach yields predictions for the equilibrium yield and nucleation barriers. These predictions are in good agreement with corresponding Monte Carlo simulations. We show that a few fundamental properties of the connectivity graph determine the most prominent features of the assembly thermodynamics. Surprisingly, we find that polydispersity in themore » strengths of the designed interactions stabilizes intermediate structures and can be used to sculpt the free-energy landscape for self-assembly. Finally, we demonstrate that weak incidental interactions can preclude assembly at equilibrium due to the combinatorial possibilities for incorrect association.« less
Simulating large-scale crop yield by using perturbed-parameter ensemble method

NASA Astrophysics Data System (ADS)

Iizumi, T.; Yokozawa, M.; Sakurai, G.; Nishimori, M.

2010-12-01

Toshichika Iizumi, Masayuki Yokozawa, Gen Sakurai, Motoki Nishimori Agro-Meteorology Division, National Institute for Agro-Environmental Sciences, Japan Abstract One of concerning issues of food security under changing climate is to predict the inter-annual variation of crop production induced by climate extremes and modulated climate. To secure food supply for growing world population, methodology that can accurately predict crop yield on a large scale is needed. However, for developing a process-based large-scale crop model with a scale of general circulation models (GCMs), 100 km in latitude and longitude, researchers encounter the difficulties in spatial heterogeneity of available information on crop production such as cultivated cultivars and management. This study proposed an ensemble-based simulation method that uses a process-based crop model and systematic parameter perturbation procedure, taking maize in U.S., China, and Brazil as examples. The crop model was developed modifying the fundamental structure of the Soil and Water Assessment Tool (SWAT) to incorporate the effect of heat stress on yield. We called the new model PRYSBI: the Process-based Regional-scale Yield Simulator with Bayesian Inference. The posterior probability density function (PDF) of 17 parameters, which represents the crop- and grid-specific features of the crop and its uncertainty under given data, was estimated by the Bayesian inversion analysis. We then take 1500 ensemble members of simulated yield values based on the parameter sets sampled from the posterior PDF to describe yearly changes of the yield, i.e. perturbed-parameter ensemble method. The ensemble median for 27 years (1980-2006) was compared with the data aggregated from the county yield. On a country scale, the ensemble median of the simulated yield showed a good correspondence with the reported yield: the Pearson’s correlation coefficient is over 0.6 for all countries. In contrast, on a grid scale, the correspondence

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.
Neutrino flux predictions for the NuMI beam

DOE PAGES

Aliaga, L.; Kordosky, M.; Golan, T.; ...

2016-11-29

Knowledge of the neutrino flux produced by the Neutrinos at the Main Injector (NuMI) beamline is essential to the neutrino oscillation and neutrino interaction measurements of the MINERvA, MINOS+, NOvA and MicroBooNE experiments at Fermi National Accelerator Laboratory. We have produced a flux prediction which uses all available and relevant hadron production data, incorporating measurements of particle production off of thin targets as well as measurements of particle yields from a spare NuMI target exposed to a 120 GeV proton beam. The result is the most precise flux prediction achieved for a neutrino beam in the one to tens of GeVmore » energy region. Lastly, we have also compared the prediction to in situ measurements of the neutrino flux and find good agreement.« less
Individualized relapse prediction: Personality measures and striatal and insular activity during reward-processing robustly predict relapse.

PubMed

Gowin, Joshua L; Ball, Tali M; Wittmann, Marc; Tapert, Susan F; Paulus, Martin P

2015-07-01

Nearly half of individuals with substance use disorders relapse in the year after treatment. A diagnostic tool to help clinicians make decisions regarding treatment does not exist for psychiatric conditions. Identifying individuals with high risk for relapse to substance use following abstinence has profound clinical consequences. This study aimed to develop neuroimaging as a robust tool to predict relapse. 68 methamphetamine-dependent adults (15 female) were recruited from 28-day inpatient treatment. During treatment, participants completed a functional MRI scan that examined brain activation during reward processing. Patients were followed 1 year later to assess abstinence. We examined brain activation during reward processing between relapsing and abstaining individuals and employed three random forest prediction models (clinical and personality measures, neuroimaging measures, a combined model) to generate predictions for each participant regarding their relapse likelihood. 18 individuals relapsed. There were significant group by reward-size interactions for neural activation in the left insula and right striatum for rewards. Abstaining individuals showed increased activation for large, risky relative to small, safe rewards, whereas relapsing individuals failed to show differential activation between reward types. All three random forest models yielded good test characteristics such that a positive test for relapse yielded a likelihood ratio 2.63, whereas a negative test had a likelihood ratio of 0.48. These findings suggest that neuroimaging can be developed in combination with other measures as an instrument to predict relapse, advancing tools providers can use to make decisions about individualized treatment of substance use disorders. Published by Elsevier Ireland Ltd.
Prediction of Precipitation Strengthening in the Commercial Mg Alloy AZ91 Using Dislocation Dynamics

DOE PAGES

Aagesen, L. K.; Miao, J.; Allison, J. E.; ...

2018-03-05

In this paper, dislocation dynamics simulations were used to predict the strengthening of a commercial magnesium alloy, AZ91, due to β-Mg 17Al 12 formed in the continuous precipitation mode. The precipitate distributions used in simulations were determined based on experimental characterization of the sizes, shapes, and number densities of the precipitates for 10-hour aging and 50-hour aging. For dislocations gliding on the basal plane, which is expected to be the dominant contributor to plastic deformation at room temperature, the critical resolved shear stress to bypass the precipitate distribution was 3.5 MPa for the 10-hour aged sample and 16.0 MPa formore » the 50-hour aged sample. The simulation results were compared to an analytical model of strengthening in this alloy, and the analytical model was found to predict critical resolved shear stresses that were approximately 30 pct lower. A model for the total yield strength was developed and compared with experiment for the 50-hour aged sample. Finally, the predicted yield strength, which included the precipitate strengthening contribution from the DD simulations, was 132.0 MPa, in good agreement with the measured yield strength of 141 MPa.« less
Prediction of Precipitation Strengthening in the Commercial Mg Alloy AZ91 Using Dislocation Dynamics

NASA Astrophysics Data System (ADS)

Aagesen, L. K.; Miao, J.; Allison, J. E.; Aubry, S.; Arsenlis, A.

2018-03-01

Dislocation dynamics simulations were used to predict the strengthening of a commercial magnesium alloy, AZ91, due to β-Mg17Al12 formed in the continuous precipitation mode. The precipitate distributions used in simulations were determined based on experimental characterization of the sizes, shapes, and number densities of the precipitates for 10-hour aging and 50-hour aging. For dislocations gliding on the basal plane, which is expected to be the dominant contributor to plastic deformation at room temperature, the critical resolved shear stress to bypass the precipitate distribution was 3.5 MPa for the 10-hour aged sample and 16.0 MPa for the 50-hour aged sample. The simulation results were compared to an analytical model of strengthening in this alloy, and the analytical model was found to predict critical resolved shear stresses that were approximately 30 pct lower. A model for the total yield strength was developed and compared with experiment for the 50-hour aged sample. The predicted yield strength, which included the precipitate strengthening contribution from the DD simulations, was 132.0 MPa, in good agreement with the measured yield strength of 141 MPa.
Prediction of Precipitation Strengthening in the Commercial Mg Alloy AZ91 Using Dislocation Dynamics

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

Aagesen, L. K.; Miao, J.; Allison, J. E.

In this paper, dislocation dynamics simulations were used to predict the strengthening of a commercial magnesium alloy, AZ91, due to β-Mg 17Al 12 formed in the continuous precipitation mode. The precipitate distributions used in simulations were determined based on experimental characterization of the sizes, shapes, and number densities of the precipitates for 10-hour aging and 50-hour aging. For dislocations gliding on the basal plane, which is expected to be the dominant contributor to plastic deformation at room temperature, the critical resolved shear stress to bypass the precipitate distribution was 3.5 MPa for the 10-hour aged sample and 16.0 MPa formore » the 50-hour aged sample. The simulation results were compared to an analytical model of strengthening in this alloy, and the analytical model was found to predict critical resolved shear stresses that were approximately 30 pct lower. A model for the total yield strength was developed and compared with experiment for the 50-hour aged sample. Finally, the predicted yield strength, which included the precipitate strengthening contribution from the DD simulations, was 132.0 MPa, in good agreement with the measured yield strength of 141 MPa.« less
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.
MULTI-KEV X-RAY YIELDS FROM HIGH-Z GAS TARGETS FIELDED AT OMEGA

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

Kane, J O; Fournier, K B; May, M J

2010-11-04

The authors report on modeling of x-ray yield from gas-filled targets shot at the OMEGA laser facility. The OMEGA targets were 1.8 mm long, 1.95 mm in diameter Be cans filled with either a 50:50 Ar:Xe mixture, pure Ar, pure Kr or pure Xe at {approx} 1 atm. The OMEGA experiments heated the gas with 20 kJ of 3{omega} ({approx} 350 nm) laser energy delivered in a 1 ns square pulse. the emitted x-ray flux was monitored with the x-ray diode based DANTE instruments in the sub-keV range. Two-dimensional x-ray images (for energies 3-5 keV) of the targets were recordedmore » with gated x-ray detectors. The x-ray spectra were recorded with the HENWAY crystal spectrometer at OMEGA. Predictions are 2D r-z cylindrical with DCA NLTE atomic physics. Models generally: (1) underpredict the Xe L-shell yields; (2) overpredict the Ar K-shell yields; (3) correctly predict the Xe thermal yields; and (4) greatly underpredict the Ar thermal yields. However, there are spreads within the data, e.g. the DMX Ar K-shell yields are correctly predicted. The predicted thermal yields show strong angular dependence.« less
Monitoring Crop Yield in USA Using a Satellite-Based Climate-Variability Impact Index

NASA Technical Reports Server (NTRS)

Zhang, Ping; Anderson, Bruce; Tan, Bin; Barlow, Mathew; Myneni, Ranga

2011-01-01

A quantitative index is applied to monitor crop growth and predict agricultural yield in continental USA. The Climate-Variability Impact Index (CVII), defined as the monthly contribution to overall anomalies in growth during a given year, is derived from 1-km MODIS Leaf Area Index. The growing-season integrated CVII can provide an estimate of the fractional change in overall growth during a given year. In turn these estimates can provide fine-scale and aggregated information on yield for various crops. Trained from historical records of crop production, a statistical model is used to produce crop yield during the growing season based upon the strong positive relationship between crop yield and the CVII. By examining the model prediction as a function of time, it is possible to determine when the in-season predictive capability plateaus and which months provide the greatest predictive capacity.
Strain intensity factor approach for predicting the strength of continuously reinforced metal matrix composites

NASA Technical Reports Server (NTRS)

Poe, Clarence C., Jr.

1989-01-01

A method was previously developed to predict the fracture toughness (stress intensity factor at failure) of composites in terms of the elastic constants and the tensile failing strain of the fibers. The method was applied to boron/aluminum composites made with various proportions of 0 deg and +/- 45 deg plies. Predicted values of fracture toughness were in gross error because widespread yielding of the aluminum matrix made the compliance very nonlinear. An alternate method was develolped to predict the strain intensity factor at failure rather than the stress intensity factor because the singular strain field was not affected by yielding as much as the stress field. Far-field strains at failure were calculated from the strain intensity factor, and then strengths were calculated from the far-field strains using uniaxial stress-strain curves. The predicted strengths were in good agreement with experimental values, even for the very nonlinear laminates that contained only +/- 45 deg plies. This approach should be valid for other metal matrix composites that have continuous fibers.
Use of vegetation health data for estimation of aus rice yield in bangladesh.

PubMed

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991-2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8-13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.
Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh

PubMed Central

Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch

2009-01-01

Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991–2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March–April (weeks 8–13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost. PMID:22574057
Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis.

PubMed

Piepho, H P

1994-11-01

Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.
21 CFR 211.103 - Calculation of yield.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 4 2013-04-01 2013-04-01 false Calculation of yield. 211.103 Section 211.103 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL CURRENT GOOD MANUFACTURING PRACTICE FOR FINISHED PHARMACEUTICALS Production and Process Controls...
21 CFR 211.103 - Calculation of yield.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 4 2012-04-01 2012-04-01 false Calculation of yield. 211.103 Section 211.103 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL CURRENT GOOD MANUFACTURING PRACTICE FOR FINISHED PHARMACEUTICALS Production and Process Controls...
21 CFR 211.103 - Calculation of yield.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 4 2014-04-01 2014-04-01 false Calculation of yield. 211.103 Section 211.103 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) DRUGS: GENERAL CURRENT GOOD MANUFACTURING PRACTICE FOR FINISHED PHARMACEUTICALS Production and Process Controls...
Evidence for Ni-56 yields Co-56 yields Fe-56 decay in type Ia supernovae

NASA Technical Reports Server (NTRS)

Kuchner, Marc J.; Kirshner, Robert P.; Pinto, Philip A.; Leibundgut, Bruno

1994-01-01

In the prevailing picture of Type Ia supernovae (SN Ia), their explosive burning produces Ni-56, and the radioactive decay chain Ni-56 yields Co-56 yields Fe-56 powers the subsequent emission. We test a central feature of this theory by measuring the relative strengths of a (Co III) emission feature near 5900 A and a (Fe III) emission feature near 4700 A. We measure 38 spectra from 13 SN Ia ranging from 48 to 310 days after maximum light. When we compare the observations with a simple multilevel calculation, we find that the observed Fe/Co flux ratio evolves as expected when the Fe-56/Co-56 abundance ratio follows from Ni-56 yields Co-56 yields Fe-56 decay. From this agreement, we conclude that the cobalt and iron atoms we observe through SN Ia emission lines are produced by the radioactive decay of Ni-56, just as predicted by a wide range of models for SN Ia explosions.
Development of a European Ensemble System for Seasonal Prediction: Application to crop yield

NASA Astrophysics Data System (ADS)

Terres, J. M.; Cantelaube, P.

2003-04-01

Western European agriculture is highly intensive and the weather is the main source of uncertainty for crop yield assessment and for crop management. In the current system, at the time when a crop yield forecast is issued, the weather conditions leading up to harvest time are unknown and are therefore a major source of uncertainty. The use of seasonal weather forecast would bring additional information for the remaining crop season and has valuable benefit for improving the management of agricultural markets and environmentally sustainable farm practices. An innovative method for supplying seasonal forecast information to crop simulation models has been developed in the frame of the EU funded research project DEMETER. It consists in running a crop model on each individual member of the seasonal hindcasts to derive a probability distribution of crop yield. Preliminary results of cumulative probability function of wheat yield provides information on both the yield anomaly and the reliability of the forecast. Based on the spread of the probability distribution, the end-user can directly quantify the benefits and risks of taking weather-sensitive decisions.
Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance

PubMed Central

Cao, Xueren; Luo, Yong; Zhou, Yilin; Fan, Jieru; Xu, Xiangming; West, Jonathan S.; Duan, Xiayu; Cheng, Dengfa

2015-01-01

To determine the influence of plant density and powdery mildew infection of winter wheat and to predict grain yield, hyperspectral canopy reflectance of winter wheat was measured for two plant densities at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 in the 2009–2010 and 2010–2011 seasons. Reflectance in near infrared (NIR) regions was significantly correlated with disease index at GS 10.5.3, 10.5.4, and 11.1 at two plant densities in both seasons. For the two plant densities, the area of the red edge peak (Σdr 680–760 nm), difference vegetation index (DVI), and triangular vegetation index (TVI) were significantly correlated negatively with disease index at three GSs in two seasons. Compared with other parameters Σdr 680–760 nm was the most sensitive parameter for detecting powdery mildew. Linear regression models relating mildew severity to Σdr 680–760 nm were constructed at three GSs in two seasons for the two plant densities, demonstrating no significant difference in the slope estimates between the two plant densities at three GSs. Σdr 680–760 nm was correlated with grain yield at three GSs in two seasons. The accuracies of partial least square regression (PLSR) models were consistently higher than those of models based on Σdr 680760 nm for disease index and grain yield. PLSR can, therefore, provide more accurate estimation of disease index of wheat powdery mildew and grain yield using canopy reflectance. PMID:25815468
Modeling and prediction of extraction profile for microwave-assisted extraction based on absorbed microwave energy.

PubMed

Chan, Chung-Hung; Yusoff, Rozita; Ngoh, Gek-Cheng

2013-09-01

A modeling technique based on absorbed microwave energy was proposed to model microwave-assisted extraction (MAE) of antioxidant compounds from cocoa (Theobroma cacao L.) leaves. By adapting suitable extraction model at the basis of microwave energy absorbed during extraction, the model can be developed to predict extraction profile of MAE at various microwave irradiation power (100-600 W) and solvent loading (100-300 ml). Verification with experimental data confirmed that the prediction was accurate in capturing the extraction profile of MAE (R-square value greater than 0.87). Besides, the predicted yields from the model showed good agreement with the experimental results with less than 10% deviation observed. Furthermore, suitable extraction times to ensure high extraction yield at various MAE conditions can be estimated based on absorbed microwave energy. The estimation is feasible as more than 85% of active compounds can be extracted when compared with the conventional extraction technique. Copyright © 2013 Elsevier Ltd. All rights reserved.

Seeing is believing I: The use of thermal sensing from satellite imagery to predict crop yield

NASA Astrophysics Data System (ADS)

B, Potgieter A.; D, Rodriguez; B, Power; J, Mclean; P, Davis

2014-02-01

90m for thermal) satellite platforms. Results showed that spatial variations in crop yield were related to a satellite derived canopy stress index (CSIsat) and a moisture stress index (MSIsat). A weather station level canopy stress index (CSIws) calculated at midday was correlated to the CSIsat at late morning. In addition, a strong linear relationship was observed between EVI and LST at point scale throughout the crop growth period. Differences were smallest at anthesis when the canopy closure was highest. This suggests that LST imagery data around flowering could be used to calculate crop stress over large areas of the crop. The harvested yield was related (R2 = 0.67) to CSIsat using a fix date across all fields. This relationship improved (R2 = 0.92) using both indices from all five dates across all fields during the crop growth period. Here we successfully showed that satellite derived crop attributes (CSIsat and MSIsat) can account for most of the variability in final crop yield and that they can be used to predict crop yield at field scales. Applications of these results could enhance the ability of producers to hedge their financial on -farm crop production losses due to in-season water stress by taking crop insurance. This is likely to further improve their adaptive capacity and thus strengthening the long-term viability of the industry domestically and elsewhere.
Assessment of cluster yield components by image analysis.

PubMed

Diago, Maria P; Tardaguila, Javier; Aleixos, Nuria; Millan, Borja; Prats-Montalban, Jose M; Cubero, Sergio; Blasco, Jose

2015-04-01

Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R(2) between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry.
A Comparison of Machine Learning Approaches for Corn Yield Estimation

NASA Astrophysics Data System (ADS)

Kim, N.; Lee, Y. W.

2017-12-01

Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.
Dynamic Predictions of Crop Yield and Irrigation in Sub-Saharan Africa Due to Climate Change Impacts

NASA Astrophysics Data System (ADS)

Foster-Wittig, T.

2012-12-01

The highest damages from climate change are predicted to be in the agricultural sector in sub-Saharan Africa. Agriculture is predicted to be especially vulnerable in this region because of its current state of high temperature and low precipitation and because it is usually rain-fed or relies on relatively basic technologies which therefore limit its ability to sustain in increased poor climatic conditions [1]. The goal of this research is to quantify the vulnerability of this ecosystem by projecting future changes in agriculture due to IPCC predicted climate change impacts on precipitation and temperature. This research will provide a better understanding of the relationship between precipitation and rain-fed agriculture in savannas. In order to quantify the effects of climate change on agriculture, the impacts of climate change are modeled through the use of a land surface vegetation dynamics model previously developed combined with a crop model [2,4]. In this project, it will be used to model yield for point cropland locations within sub-Saharan Africa between Kenya and Botswana with a range of annual rainfall. With this model, future projections are developed for what can be anticipated for the crop yield based on two precipitation climate change scenarios; (1) decreased depth and (2) decreased frequency as well as temperature change scenarios; (3) only temperature increased, (4) temperature increase dand decreased precipitation depth, and (5) temperature increased and decreased precipitation frequency. Therefore, this will allow conclusions to be drawn about how mean precipitation and a changing climate effect food security in sub-Saharan Africa. As an additional analysis, irrigation is added to the model as it is thought to be the solution to protect food security by maximizing on the potential of food production. In water-limited areas such as Sub-Saharan Africa, it is important to consider water efficient irrigation techniques such as demand-based micro
Yield stress in amorphous solids: A mode-coupling-theory analysis

NASA Astrophysics Data System (ADS)

Ikeda, Atsushi; Berthier, Ludovic

2013-11-01

The yield stress is a defining feature of amorphous materials which is difficult to analyze theoretically, because it stems from the strongly nonlinear response of an arrested solid to an applied deformation. Mode-coupling theory predicts the flow curves of materials undergoing a glass transition and thus offers predictions for the yield stress of amorphous solids. We use this approach to analyze several classes of disordered solids, using simple models of hard-sphere glasses, soft glasses, and metallic glasses for which the mode-coupling predictions can be directly compared to the outcome of numerical measurements. The theory correctly describes the emergence of a yield stress of entropic nature in hard-sphere glasses, and its rapid growth as density approaches random close packing at qualitative level. By contrast, the emergence of solid behavior in soft and metallic glasses, which originates from direct particle interactions is not well described by the theory. We show that similar shortcomings arise in the description of the caging dynamics of the glass phase at rest. We discuss the range of applicability of mode-coupling theory to understand the yield stress and nonlinear rheology of amorphous materials.
A novel method for structure-based prediction of ion channel conductance properties.

PubMed Central

Smart, O S; Breed, J; Smith, G R; Sansom, M S

1997-01-01

A rapid and easy-to-use method of predicting the conductance of an ion channel from its three-dimensional structure is presented. The method combines the pore dimensions of the channel as measured in the HOLE program with an Ohmic model of conductance. An empirically based correction factor is then applied. The method yielded good results for six experimental channel structures (none of which were included in the training set) with predictions accurate to within an average factor of 1.62 to the true values. The predictive r2 was equal to 0.90, which is indicative of a good predictive ability. The procedure is used to validate model structures of alamethicin and phospholamban. Two genuine predictions for the conductance of channels with known structure but without reported conductances are given. A modification of the procedure that calculates the expected results for the effect of the addition of nonelectrolyte polymers on conductance is set out. Results for a cholera toxin B-subunit crystal structure agree well with the measured values. The difficulty in interpreting such studies is discussed, with the conclusion that measurements on channels of known structure are required. Images FIGURE 1 FIGURE 3 FIGURE 4 FIGURE 6 FIGURE 10 PMID:9138559
Prediction of the Dynamic Yield Strength of Metals Using Two Structural-Temporal Parameters

NASA Astrophysics Data System (ADS)

Selyutina, N. S.; Petrov, Yu. V.

2018-02-01

The behavior of the yield strength of steel and a number of aluminum alloys is investigated in a wide range of strain rates, based on the incubation time criterion of yield and the empirical models of Johnson-Cook and Cowper-Symonds. In this paper, expressions for the parameters of the empirical models are derived through the characteristics of the incubation time criterion; a satisfactory agreement of these data and experimental results is obtained. The parameters of the empirical models can depend on some strain rate. The independence of the characteristics of the incubation time criterion of yield from the loading history and their connection with the structural and temporal features of the plastic deformation process give advantage of the approach based on the concept of incubation time with respect to empirical models and an effective and convenient equation for determining the yield strength in a wider range of strain rates.
Changes in the yield of chlorophyll a from dissolved available inorganic nitrogen after an enrichment event—applications for predicting eutrophication in coastal waters

NASA Astrophysics Data System (ADS)

Edwards, V. R.; Tett, P.; Jones, K. J.

2003-11-01

An understanding of the dynamic relationship between nitrogen supply and the formation of phytoplankton biomass is important in predicting and avoiding marine eutrophication. This relationship can be expressed as the short-term yield q of chlorophyll from dissolved available inorganic nitrogen (DAIN), the sum of nitrate, nitrite and ammonium. This paper communicates the results of a continuous culture nitrate enrichment experiment undertaken to investigate the cumulative yield of chlorophyll from DAIN ( q). The purposes of the study were: to acquire a better understanding of the relationship between chlorophyll formation and DAIN; to obtain values that could be used in models for predicting eutrophication. The results of a time series experiment carried out using microplankton (all organisms <200 μm in size) indicate that the parameter q does not have a single value but is affected by the ecophysiological response of phytoplankton to changing nutrient status after an enrichment event. It is also dependent on changes in the allocation of nitrogen between autotrophs and heterotrophs. The value of yield obtained at the height of the bloom can be represented by q (max) (2.35 μg chl (μmol N) -1). The post-bloom, steady state value of q can be represented by qeq (0.95 μg chl (μmol N) -1). The microcosm steady state yield was not significantly different from the median value obtained from synoptic studies of Scottish west coast waters. It is proposed that qeq is the most appropriate value for assessing the general potential for eutrophication resulting from continuous nutrient enrichment into coastal waters. It is further proposed that q (max) be used for cases of sporadic enrichment and where a short burst of unrestricted growth may be detrimental.
The Z {yields} cc-bar {yields} {gamma}{gamma}*, Z {yields} bb-bar {yields} {gamma}{gamma}* triangle diagrams and the Z {yields} {gamma}{psi}, Z {yields} {gamma}Y decays

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

Achasov, N. N., E-mail: achasov@math.nsc.ru

2011-03-15

The approach to the Z {yields} {gamma}{psi} and Z {yields} {gamma}Y decay study is presented in detail, based on the sum rules for the Z {yields} cc-bar {yields} {gamma}{gamma}* and Z {yields} bb-bar {yields} {gamma}{gamma}* amplitudes and their derivatives. The branching ratios of the Z {yields} {gamma}{psi} and Z {yields} {gamma}Y decays are calculated for different hypotheses on saturation of the sum rules. The lower bounds of {Sigma}{sub {psi}} BR(Z {yields} {gamma}{psi}) = 1.95 Multiplication-Sign 10{sup -7} and {Sigma}{sub {upsilon}} BR(Z {yields} {gamma}Y) = 7.23 Multiplication-Sign 10{sup -7} are found. Deviations from the lower bounds are discussed, including the possibilitymore » of BR(Z {yields} {gamma}J/{psi}(1S)) {approx} BR(Z {yields} {gamma}Y(1S)) {approx} 10{sup -6}, that could be probably measured in LHC. The angular distributions in the Z {yields} {gamma}{psi} and Z {yields} {gamma}Y decays are also calculated.« less
Prediction-Oriented Marker Selection (PROMISE): With Application to High-Dimensional Regression.

PubMed

Kim, Soyeon; Baladandayuthapani, Veerabhadran; Lee, J Jack

2017-06-01

In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on an individual patient's biomarker/genomic profile. Two goals are to choose important biomarkers that accurately predict treatment outcomes and to cull unimportant biomarkers to reduce the cost of biological and clinical verifications. These goals are challenging due to the high dimensionality of genomic data. Variable selection methods based on penalized regression (e.g., the lasso and elastic net) have yielded promising results. However, selecting the right amount of penalization is critical to simultaneously achieving these two goals. Standard approaches based on cross-validation (CV) typically provide high prediction accuracy with high true positive rates but at the cost of too many false positives. Alternatively, stability selection (SS) controls the number of false positives, but at the cost of yielding too few true positives. To circumvent these issues, we propose prediction-oriented marker selection (PROMISE), which combines SS with CV to conflate the advantages of both methods. Our application of PROMISE with the lasso and elastic net in data analysis shows that, compared to CV, PROMISE produces sparse solutions, few false positives, and small type I + type II error, and maintains good prediction accuracy, with a marginal decrease in the true positive rates. Compared to SS, PROMISE offers better prediction accuracy and true positive rates. In summary, PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize false positives and maximize prediction accuracy.
Spectral behavior of wheat yield variety trials

NASA Technical Reports Server (NTRS)

Hatfield, J. L.

1981-01-01

Little variation between varieties is seen at jointing, but the variability is found to increase during grain filling and decline again at maturity. No relationship is found between spectral response and yield, and when yields are segregated into various classes the spectral response is the same. Spring and winter nurseries are found to separate during the reproductive stage because of differences in dates of heading and maturity, but they exhibit similar spectral responses. The transformed normalized difference is at a minimum after the maximum grain weight occurs and the leaves begin to brown and fall off. These data of 100% ground cover demonstrate that it is not possible to predict grain yield from only spectral data. This, however, may not apply when reduced yields are caused by less-than-full ground cover
Why significant variables aren't automatically good predictors.

PubMed

Lo, Adeline; Chernoff, Herman; Zheng, Tian; Lo, Shaw-Hwa

2015-11-10

Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%.
When Did You Last Predict a Good Idea?

ERIC Educational Resources Information Center

Penaluna, Kathryn; Penaluna, Andrew; Jones, Colin; Matlay, Harry

2014-01-01

It has been noted elsewhere that an idea is acknowledged to be creative if it is novel, or surprising and adaptive. So how does that fit with education's desire to measure student performance against fixed, consistent and predicted learning outcomes? This study explores practical measures and theoretical constructs that address the dearth of…
Predicted harvest time effects on switchgrass moisture content, nutrient concentration, yield, and profitability

USDA-ARS?s Scientific Manuscript database

Production costs change with harvest date of switchgrass (Panicum virgatum L.) as a result of nutrient recycling and changes in yield of this perennial crop. This study examines the range of cost of production from an early, yield-maximizing harvest date to a late winter harvest date at low moisture...
Optimal prediction of the number of unseen species

PubMed Central

Orlitsky, Alon; Suresh, Ananda Theertha; Wu, Yihong

2016-01-01

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher et al. [Fisher RA, Corbet AS, Williams CB (1943) J Animal Ecol 12(1):42−58], uses n samples to predict the number U of hitherto unseen species that would be observed if t⋅n new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted. In seminal works, Good and Toulmin [Good I, Toulmin G (1956) Biometrika 43(102):45−63] constructed an intriguing estimator that predicts U for all t≤1. Subsequently, Efron and Thisted [Efron B, Thisted R (1976) Biometrika 63(3):435−447] proposed a modification that empirically predicts U even for some t>1, but without provable guarantees. We derive a class of estimators that provably predict U all of the way up to t∝log⁡n. We also show that this range is the best possible and that the estimator’s mean-square error is near optimal for any t. Our approach yields a provable guarantee for the Efron−Thisted estimator and, in addition, a variant with stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators are simple, linear, computationally efficient, and scalable to massive datasets. Their performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product. PMID:27830649
Optimal prediction of the number of unseen species.

PubMed

Orlitsky, Alon; Suresh, Ananda Theertha; Wu, Yihong

2016-11-22

Estimating the number of unseen species is an important problem in many scientific endeavors. Its most popular formulation, introduced by Fisher et al. [Fisher RA, Corbet AS, Williams CB (1943) J Animal Ecol 12(1):42-58], uses n samples to predict the number U of hitherto unseen species that would be observed if [Formula: see text] new samples were collected. Of considerable interest is the largest ratio t between the number of new and existing samples for which U can be accurately predicted. In seminal works, Good and Toulmin [Good I, Toulmin G (1956) Biometrika 43(102):45-63] constructed an intriguing estimator that predicts U for all [Formula: see text] Subsequently, Efron and Thisted [Efron B, Thisted R (1976) Biometrika 63(3):435-447] proposed a modification that empirically predicts U even for some [Formula: see text], but without provable guarantees. We derive a class of estimators that provably predict U all of the way up to [Formula: see text] We also show that this range is the best possible and that the estimator's mean-square error is near optimal for any t Our approach yields a provable guarantee for the Efron-Thisted estimator and, in addition, a variant with stronger theoretical and experimental performance than existing methodologies on a variety of synthetic and real datasets. The estimators are simple, linear, computationally efficient, and scalable to massive datasets. Their performance guarantees hold uniformly for all distributions, and apply to all four standard sampling models commonly used across various scientific disciplines: multinomial, Poisson, hypergeometric, and Bernoulli product.
How good are the Garvey-Kelson predictions of nuclear masses?

NASA Astrophysics Data System (ADS)

Morales, Irving O.; López Vieyra, J. C.; Hirsch, J. G.; Frank, A.

2009-09-01

The Garvey-Kelson relations are used in an iterative process to predict nuclear masses in the neighborhood of nuclei with measured masses. Average errors in the predicted masses for the first three iteration shells are smaller than those obtained with the best nuclear mass models. Their quality is comparable with the Audi-Wapstra extrapolations, offering a simple and reproducible procedure for short range mass predictions. A systematic study of the way the error grows as a function of the iteration and the distance to the known masses region, shows that a correlation exists between the error and the residual neutron-proton interaction, produced mainly by the implicit assumption that V varies smoothly along the nuclear landscape.
Airborne monitoring of crop canopy temperatures for irrigation scheduling and yield prediction

NASA Technical Reports Server (NTRS)

Millard, J. P.; Jackson, R. D.; Goettelman, R. C.; Reginato, R. J.; Idso, S. B.; Lapado, R. L.

1977-01-01

Airborne and ground measurements were made on April 1 and 29, 1976, over a USDA test site consisting mostly of wheat in various stages of water stress, but also including alfalfa and bare soil. These measurements were made to evaluate the feasibility of measuring crop temperatures from aircraft so that a parameter termed stress degree day, SDD, could be computed. Ground studies have shown that SDD is a valuable indicator of a crop's water needs, and that it can be related to irrigation scheduling and yield. The aircraft measurement program required predawn and afternoon flights coincident with minimum and maximum crop temperatures. Airborne measurements were made with an infrared line scanner and with color IR photography. The scanner data were registered, subtracted, and color-coded to yield pseudo-colored temperature-difference images. Pseudo-colored images reading directly in daily SDD increments were also produced. These maps enable a user to assess plant water status and thus determine irrigation needs and crop yield potentials.
Life Satisfaction among Highly Achieving Students in Hong Kong: Do Gratitude and the "Good-Enough Mindset" Add to the Contribution of Perfectionism in Prediction?

ERIC Educational Resources Information Center

Chan, David W.

2012-01-01

This study investigated whether gratitude and the "good-enough mindset" added to the contribution of perfectionism in predicting life satisfaction in 245 Chinese highly achieving students in Hong Kong. Participants completed self-report questionnaires that included scales on life satisfaction, positive and negative perfectionism…
Combined application of Sentinel2A data and growth modelling for novel monitoring and prediction of pasture yields

NASA Astrophysics Data System (ADS)

Verhoef, A.; Punalekar, S.; Quaife, T. L.; Humphries, D.; Reynolds, C.

2017-12-01

Currently, 30% of the world's land area is covered by permanent pasture. Grazing ruminants convert forage materials into milk and meat for human consumption; ruminant production is a key agricultural enterprise. Management of pasture farms (nutrient and herbi-/pesticides application, grazing rotations) is often suboptimal. Furthermore, adverse weather can have negative effects on pasture growth and quality. Near real-time herbage monitoring and prediction could help improve farm profitability. While the use of remote sensing (RS) in the context of arable crop growth prediction is becoming more established, the same is not true for pasture. However, recently launched Sentinel satellites offer real opportunities to exploit high spatio-temporal resolution datasets for effective monitoring of pastures, as well as crops. A perennial grazed ryegrass field in the Southwest of the UK was monitored regularly using field hyperspectral spectro-radiometers. Simultaneously, leaf area index (LAI) was measured using a ceptometer, and yield was measured, indirectly using a `plate meter' and directly by destructive sampling. Two sets of spectral data were used to retrieve LAI with the PROSAIL radiative transfer model: (i) Sentinel-2A bands convolved from field spectral data, (ii) actual Sentinel-2A image pixels for the sampling plots. Retrieved LAI was compared against field observations. LAI estimates were assimilated in a bespoke growth model (including grazing and management), driven by weather data, for calibration of sensitive parameters using a 4D-Var scheme, to obtain pasture biomass. The developed approach was used to study a pasture farm in the South of the UK, for which a large number of Sentinel-2A images were available throughout 2016-17. Retrieved LAI compared well with in-situ LAI, and significantly improved yield estimates. The calibrated model parameters compared well with literature values. The model, guided by satellite data and general information on farm

Ethiopian Wheat Yield and Yield Gap Estimation: A Spatial Small Area Integrated Data Approach

NASA Astrophysics Data System (ADS)

Mann, M.; Warner, J.

2015-12-01

Despite the collection of routine annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has been undertaken in predicting developing nation's agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011-2013 Meher crop seasons aggregated to the woreda administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. The model also identifies specific contributors to wheat yields that include farm management techniques (eg. area planted, improved seed, fertilizer, irrigation), weather (eg. rainfall), water availability (vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their potential wheat output per hectare given their altitude, weather conditions, terrain, and plant health. At the median, Amhara, Oromiya, SNNP, and Tigray produce 48.6, 51.5, 49.7, and 61.3% of their local attainable yields, respectively. This research has a broad range of applications, especially from a public policy perspective: identifying causes of yield fluctuations, remotely evaluating larger agricultural intervention packages, and analyzing relative yield potential. Overall, the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels.
Measurement and prediction of model-rotor flow fields

NASA Technical Reports Server (NTRS)

Owen, F. K.; Tauber, M. E.

1985-01-01

This paper shows that a laser velocimeter can be used to measure accurately the three-component velocities induced by a model rotor at transonic tip speeds. The measurements, which were made at Mach numbers from 0.85 to 0.95 and at zero advance ratio, yielded high-resolution, orthogonal velocity values. The measured velocities were used to check the ability of the ROT22 full-potential rotor code to predict accurately the transonic flow field in the crucial region around and beyond the tip of a high-speed rotor blade. The good agreement between the calculated and measured velocities established the code's ability to predict the off-blade flow field at transonic tip speeds. This supplements previous comparisons in which surface pressures were shown to be well predicted on two different tips at advance ratios to 0.45, especially at the critical 90 deg azimuthal blade position. These results demonstrate that the ROT22 code can be used with confidence to predict the important tip-region flow field, including the occurrence, strength, and location of shock waves causing high drag and noise.
Good soldiers and good actors: prosocial and impression management motives as interactive predictors of affiliative citizenship behaviors.

PubMed

Grant, Adam M; Mayer, David M

2009-07-01

Researchers have discovered inconsistent relationships between prosocial motives and citizenship behaviors. We draw on impression management theory to propose that impression management motives strengthen the association between prosocial motives and affiliative citizenship by encouraging employees to express citizenship in ways that both "do good" and "look good." We report 2 studies that examine the interactions of prosocial and impression management motives as predictors of affiliative citizenship using multisource data from 2 different field samples. Across the 2 studies, we find positive interactions between prosocial and impression management motives as predictors of affiliative citizenship behaviors directed toward other people (helping and courtesy) and the organization (initiative). Study 2 also shows that only prosocial motives predict voice-a challenging citizenship behavior. Our results suggest that employees who are both good soldiers and good actors are most likely to emerge as good citizens in promoting the status quo.
Predicting equilibrium states with Reynolds stress closures in channel flow and homogeneous shear flow

NASA Technical Reports Server (NTRS)

Abid, R.; Speziale, C. G.

1993-01-01

Turbulent channel flow and homogeneous shear flow have served as basic building block flows for the testing and calibration of Reynolds stress models. A direct theoretical connection is made between homogeneous shear flow in equilibrium and the log-layer of fully-developed turbulent channel flow. It is shown that if a second-order closure model is calibrated to yield good equilibrium values for homogeneous shear flow it will also yield good results for the log-layer of channel flow provided that the Rotta coefficient is not too far removed from one. Most of the commonly used second-order closure models introduce an ad hoc wall reflection term in order to mask deficient predictions for the log-layer of channel flow that arise either from an inaccurate calibration of homogeneous shear flow or from the use of a Rotta coefficient that is too large. Illustrative model calculations are presented to demonstrate this point which has important implications for turbulence modeling.
Predicting equilibrium states with Reynolds stress closures in channel flow and homogeneous shear flow

NASA Technical Reports Server (NTRS)

Abid, R.; Speziale, C. G.

1992-01-01

Turbulent channel flow and homogeneous shear flow have served as basic building block flows for the testing and calibration of Reynolds stress models. A direct theoretical connection is made between homogeneous shear flow in equilibrium and the log-layer of fully-developed turbulent channel flow. It is shown that if a second-order closure model is calibrated to yield good equilibrium values for homogeneous shear flow it will also yield good results for the log-layer of channel flow provided that the Rotta coefficient is not too far removed from one. Most of the commonly used second-order closure models introduce an ad hoc wall reflection term in order to mask deficient predictions for the log-layer of channel flow that arise either from an inaccurate calibration of homogeneous shear flow or from the use of a Rotta coefficient that is too large. Illustrative model calculations are presented to demonstrate this point which has important implications for turbulence modeling.
The Parity of the Neutral Pion and the Decay pi{sup 0} Yields 2e{sup +} + 2e{sup -}

DOE R&D Accomplishments Database

Samios, N. P.; Plano, R.; Prodell, A.; Schwartz, M.; Steinberger, J.

1962-01-01

Two hundred and six electronic decays of the pi{sup 0}, pi{sup 0} yields e{sup +} + e{sup -} + e{sup +} + e{sup -}, were observed in a hydrogen bubble chamber. The decay distributions of the electron pairs and the total rate for this process are shown to be in good agreement with theory. An examination of correlations of the e{sup +}e{sup -} pair decay planes on the basis of electrodynamic predictions is in agreement with the hypothesis that the pi{sup 0} is pseudoscalar, but disagrees for scalar pions by 3.6 standard deviations. (auth)
Simulation of fatigue crack growth under large scale yielding conditions

NASA Astrophysics Data System (ADS)

Schweizer, Christoph; Seifert, Thomas; Riedel, Hermann

2010-07-01

A simple mechanism based model for fatigue crack growth assumes a linear correlation between the cyclic crack-tip opening displacement (ΔCTOD) and the crack growth increment (da/dN). The objective of this work is to compare analytical estimates of ΔCTOD with results of numerical calculations under large scale yielding conditions and to verify the physical basis of the model by comparing the predicted and the measured evolution of the crack length in a 10%-chromium-steel. The material is described by a rate independent cyclic plasticity model with power-law hardening and Masing behavior. During the tension-going part of the cycle, nodes at the crack-tip are released such that the crack growth increment corresponds approximately to the crack-tip opening. The finite element analysis performed in ABAQUS is continued for so many cycles until a stabilized value of ΔCTOD is reached. The analytical model contains an interpolation formula for the J-integral, which is generalized to account for cyclic loading and crack closure. Both simulated and estimated ΔCTOD are reasonably consistent. The predicted crack length evolution is found to be in good agreement with the behavior of microcracks observed in a 10%-chromium steel.
Gender, body mass index, and PPARγ polymorphism are good indicators in hyperuricemia prediction for Han Chinese.

PubMed

Lee, Ming-Fen; Liou, Tsan-Hon; Wang, Weu; Pan, Wen-Harn; Lee, Wei-Jei; Hsu, Chung-Tan; Wu, Suh-Fen; Chen, Hsin-Hung

2013-01-01

Hyperuricemia is closely associated with obesity and metabolic abnormalities, which is also an independent risk factor for cardiovascular diseases. The PPARγ gene, which is linked to obesity and metabolic abnormalities in Han Chinese, might be considered a top candidate gene that is involved in hyperuricemia. This study recruited 457 participants, aged 20-40 years old, to investigate the associations of the PPARγ gene and metabolic parameters with hyperuricemia. Three tag-single nucleotide polymorphisms, rs2292101, rs4684846, and rs1822825, of the PPARγ gene were selected to explore their association with hyperuricemia. Risk genotypes on rs1822825 of the PPARγ gene exhibited statistical significance with hyperuricemia (odds ratio: 1.9; 95% confidence interval: 1.05-3.57). Although gender, body mass index (BMI), serum total cholesterol concentration, or protein intake per day were statistically associated with hyperuricemia, the combination of BMI, gender, and rs1822825, rather than that of age, serum lipid profile, blood pressure, and protein intake per day, satisfied the predictability for hyperuricemia (sensitivity: 69.3%; specificity: 83.7%) in Taiwan-born obese Han Chinese. BMI, gender, and the rs1822825 polymorphism in the PPARγ gene appeared good biomarkers in hyperuricemia; therefore, these powerful indicators may be included in the prediction of hyperuricemia to increase the accuracy of the analysis.
Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments

PubMed Central

Truong, Sandra K.; McCormick, Ryan F.; Mullet, John E.

2017-01-01

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum's long duration of vegetative growth increased water capture and biomass yield by ~30% compared to short season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by development
Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments.

PubMed

Truong, Sandra K; McCormick, Ryan F; Mullet, John E

2017-01-01

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum's long duration of vegetative growth increased water capture and biomass yield by ~30% compared to short season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by development
Bioenergy sorghum crop model predicts VPD-limited transpiration traits enhance biomass yield in water-limited environments

DOE PAGES

Truong, Sandra K.; McCormick, Ryan F.; Mullet, John E.

2017-03-21

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum’s long duration of vegetative growth increased water capture and biomass yield by ~30% compared to shortmore » season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by
Bioenergy sorghum crop model predicts VPD-limited transpiration traits enhance biomass yield in water-limited environments

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

Truong, Sandra K.; McCormick, Ryan F.; Mullet, John E.

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum’s long duration of vegetative growth increased water capture and biomass yield by ~30% compared to shortmore » season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by
A nonlinear dynamical system approach for the yielding behaviour of a viscoplastic material.

PubMed

Burghelea, Teodor; Moyers-Gonzalez, Miguel; Sainudiin, Raazesh

2017-03-08

A nonlinear dynamical system model that approximates a microscopic Gibbs field model for the yielding of a viscoplastic material subjected to varying external stresses recently reported in R. Sainudiin, M. Moyers-Gonzalez and T. Burghelea, Soft Matter, 2015, 11(27), 5531-5545 is presented. The predictions of the model are in fair agreement with microscopic simulations and are in very good agreement with the micro-structural semi-empirical model reported in A. M. V. Putz and T. I. Burghelea, Rheol. Acta, 2009, 48, 673-689. With only two internal parameters, the nonlinear dynamical system model captures several key features of the solid-fluid transition observed in experiments: the effect of the interactions between microscopic constituents on the yield point, the abruptness of solid-fluid transition and the emergence of a hysteresis of the micro-structural states upon increasing/decreasing external forces. The scaling behaviour of the magnitude of the hysteresis with the degree of the steadiness of the flow is consistent with previous experimental observations. Finally, the practical usefulness of the approach is demonstrated by fitting a rheological data set measured with an elasto-viscoplastic material.
Role of the N*(1535) resonance and the {pi}{sup -}p{yields}KY amplitudes in the OZI forbidden {pi}N{yields}{phi}N reaction

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

Doering, M.; Oset, E.; Zou, B. S.

2008-08-15

We study the {pi}N{yields}{phi}N reaction close to the {phi}N threshold within the chiral unitary approach, by combining the {pi}{sup -}p{yields}K{sup +}{sigma}{sup -},{pi}{sup -}p{yields}K{sup 0}{sigma}{sup 0}, and {pi}{sup -}p{yields}K{sup 0}{lambda} amplitudes with the coupling of {phi} to the K components of the final states of these reactions via quantum loops. We obtain good agreement with experiment when the dominant {pi}{sup -}p{yields}K{sup 0}{lambda} amplitude is constrained with its experimental cross section. We also evaluate the coupling of N*(1535) to {phi}N and find a moderate coupling as a consequence of partial cancellation of the large KY components of N*(1535). We also show thatmore » the N*(1535) pole approximation is too small to reproduce the measured cross section for the {pi}{sup -}N{yields}{phi}N reaction.« less
Measurements of {Gamma}(Z{sup O} {yields} b{bar b})/{Gamma}(Z{sup O} {yields} hadrons) using the SLD

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

Neal, H.A. Jr. II

1995-07-01

The quantity R{sub b} = {Gamma}(Z{sup o} {yields}b{bar b})/{Gamma}(Z{sup o} {yields} hadrons) is a sensitive measure of corrections to the Zbb vertex. The precision necessary to observe the top quark mass dependent corrections is close to being achieved. LEP is already observing a 1.8{sigma} deviation from the Standard Model prediction. Knowledge of the top quark mass combined with the observation of deviations from the Standard Model prediction would indicate new physics. Models which include charged Higgs or light SUSY particles yield predictions for R{sub b} appreciably different from the Standard Model. In this thesis two independent methods are used tomore » measure R{sub b}. One uses a general event tag which determines R{sub b} from the rate at which events are tagged as Z{sup o} {yields} b{bar b} in data and the estimated rates at which various flavors of events are tagged from the Monte Carlo. The second method reduces the reliance on the Monte Carlo by separately tagging each hemisphere as containing a b-decay. The rates of single hemisphere tagged events and both hemisphere tagged events are used to determine the tagging efficiency for b-quarks directly from the data thus eliminating the main sources of systematic error present in the event tag. Both measurements take advantage of the unique environment provided by the SLAC Linear Collider (SLC) and the SLAC Large Detector (SLD). From the event tag a result of R{sub b} = 0.230{plus_minus}0.004{sub statistical}{plus_minus}0.013{sub systematic} is obtained. The higher precision hemisphere tag result obtained is R{sub b} = 0.218{plus_minus}0.004{sub statistical}{plus_minus}0.004{sub systematic}{plus_minus}0.003{sub Rc}.« less
ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

PubMed

Comeau, Stephen R; Gatchell, David W; Vajda, Sandor; Camacho, Carlos J

2004-01-01

Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu
Predicting Performance in Higher Education Using Proximal Predictors.

PubMed

Niessen, A Susan M; Meijer, Rob R; Tendeiro, Jorge N

2016-01-01

We studied the validity of two methods for predicting academic performance and student-program fit that were proximal to important study criteria. Applicants to an undergraduate psychology program participated in a selection procedure containing a trial-studying test based on a work sample approach, and specific skills tests in English and math. Test scores were used to predict academic achievement and progress after the first year, achievement in specific course types, enrollment, and dropout after the first year. All tests showed positive significant correlations with the criteria. The trial-studying test was consistently the best predictor in the admission procedure. We found no significant differences between the predictive validity of the trial-studying test and prior educational performance, and substantial shared explained variance between the two predictors. Only applicants with lower trial-studying scores were significantly less likely to enroll in the program. In conclusion, the trial-studying test yielded predictive validities similar to that of prior educational performance and possibly enabled self-selection. In admissions aimed at student-program fit, or in admissions in which past educational performance is difficult to use, a trial-studying test is a good instrument to predict academic performance.
Development of a computer method for predicting lumber cutting yields.

Treesearch

Daniel E. Dunmire; George H. Englerth

1967-01-01

A system of locating defects in a board by intersecting coordinate points was developed and a computer program devised that used these points to locate all possible clear areas in the board. The computer determined the yields by placing any given size or sizes of cuttings in these clear areas, and furthermore stated the type, location, and number of saw cuts. The...
Sound absorption by suspensions of nonspherical particles: Measurements compared with predictions using various particle sizing techniques

NASA Astrophysics Data System (ADS)

Richards, Simon D.; Leighton, Timothy G.; Brown, Niven R.

2003-10-01

Knowledge of the particle size distribution is required in order to predict ultrasonic absorption in polydisperse particulate suspensions. This paper shows that the method used to measure the particle size distribution can lead to important differences in the predicted absorption. A reverberation technique developed for measuring ultrasonic absorption by suspended particles is used to measure the absorption in suspensions of nonspherical particles. Two types of particulates are studied: (i) kaolin (china clay) particles which are platelike in form; and (ii) calcium carbonate particles which are more granular. Results are compared to theoretical predictions of visco-inertial absorption by suspensions of spherical particles. The particle size distributions, which are required for these predictions, are measured by laser diffraction, gravitational sedimentation and centrifugal sedimentation, all of which assume spherical particles. For a given sample, each sizing technique yields a different size distribution, leading to differences in the predicted absorption. The particle size distributions obtained by gravitational and centrifugal sedimentation are reinterpreted to yield a representative size distribution of oblate spheroids, and predictions for absorption by these spheroids are compared with the measurements. Good agreement between theory and measurement for the flat kaolin particles is obtained, demonstrating that these particles can be adequately represented by oblate spheroids.
Encapsulation Processing and Manufacturing Yield Analysis

NASA Technical Reports Server (NTRS)

Willis, P. B.

1984-01-01

The development of encapsulation processing and a manufacturing productivity analysis for photovoltaic cells are discussed. The goals were: (1) to understand the relationships between both formulation variables and process variables; (2) to define conditions required for optimum performance; (3) to predict manufacturing yield; and (4) to provide documentation to industry.

Encapsulation processing and manufacturing yield analysis

NASA Astrophysics Data System (ADS)

Willis, P. B.

1984-10-01

The development of encapsulation processing and a manufacturing productivity analysis for photovoltaic cells are discussed. The goals were: (1) to understand the relationships between both formulation variables and process variables; (2) to define conditions required for optimum performance; (3) to predict manufacturing yield; and (4) to provide documentation to industry.
Global growth and stability of agricultural yield decrease with pollinator dependence

PubMed Central

Garibaldi, Lucas A.; Aizen, Marcelo A.; Klein, Alexandra M.; Cunningham, Saul A.; Harder, Lawrence D.

2011-01-01

Human welfare depends on the amount and stability of agricultural production, as determined by crop yield and cultivated area. Yield increases asymptotically with the resources provided by farmers’ inputs and environmentally sensitive ecosystem services. Declining yield growth with increased inputs prompts conversion of more land to cultivation, but at the risk of eroding ecosystem services. To explore the interdependence of agricultural production and its stability on ecosystem services, we present and test a general graphical model, based on Jensen's inequality, of yield–resource relations and consider implications for land conversion. For the case of animal pollination as a resource influencing crop yield, this model predicts that incomplete and variable pollen delivery reduces yield mean and stability (inverse of variability) more for crops with greater dependence on pollinators. Data collected by the Food and Agriculture Organization of the United Nations during 1961–2008 support these predictions. Specifically, crops with greater pollinator dependence had lower mean and stability in relative yield and yield growth, despite global yield increases for most crops. Lower yield growth was compensated by increased land cultivation to enhance production of pollinator-dependent crops. Area stability also decreased with pollinator dependence, as it correlated positively with yield stability among crops. These results reveal that pollen limitation hinders yield growth of pollinator-dependent crops, decreasing temporal stability of global agricultural production, while promoting compensatory land conversion to agriculture. Although we examined crop pollination, our model applies to other ecosystem services for which the benefits to human welfare decelerate as the maximum is approached. PMID:21422295
Stocking, growth, and yield of oak stands

Treesearch

Samuel F. Gingrich

1971-01-01

An appraisal of stocking in even-aged upland oak stands is a prerequisite for determining the cultural needs of a given stand. Most oak stands have sufficient stocking to utilize the site, but are deficient in high-quality trees. Thinning such stands offers a good opportunity to upgrade the relative quality of the growing stock and enhance the growth and yield...
Prediction of retail beef yield and fat content from live animal and carcass measurements in Nellore cattle.

PubMed

Sakamoto, L S; Mercadante, M E Z; Bonilha, S F M; Branco, R H; Bonilha, E F M; Magnani, E

2014-11-01

Data from 156 Nellore males were used to develop equations for the prediction of retail beef yield and carcass fat content, expressed as kilograms and as a percentage, from live animal and carcass measurements. Longissimus muscle area and backfat and rump fat thickness were measured by ultrasound up to 5 d before slaughter and fasted live weight was determined 1 d before slaughter. The same traits were obtained after slaughter. The carcass edible portion (CEP in kg and CEP% in percentage; n = 116) was calculated by the sum of the edible portions of primal cuts: hindquarter, forequarter, and spare ribs. Trimmable fat from the carcass boning process, with the standardization of about 3 mm of fat on retail beef, was considered to be representative of carcass fat content. Most of the variation in CEP was explained by fasted live weight or carcass weight (R(2) of 0.92 and 0.96); the same occurred for CEP% (R(2) of 0.15 and 0.13), and for CEP, the inclusion of LM area and fat thickness reduced the equation bias (lower value of Mallow's Cp statistics). For trimmable fat, most variation could be explained by weight or rump fat thickness. In general, the equations developed from live animal measurements showed a predictive power similar to the equations using carcass measurements. In all cases, the traits expressed as kilograms were better predicted (R(2) of 0.39 to 0.96) than traits expressed as a percentage (R(2) of 0.08 to 0.42).
Predicting watershed sediment yields after wildland fire with the InVEST sediment retention model at large geographic extent in the western USA: accuracy and uncertainties

NASA Astrophysics Data System (ADS)

Sankey, J. B.; Kreitler, J.; McVay, J.; Hawbaker, T. J.; Vaillant, N.; Lowe, S. E.

2014-12-01

Wildland fire is a primary threat to watersheds that can impact water supply through increased sedimentation, water quality decline, and change the timing and amount of runoff leading to increased risk from flood and sediment natural hazards. It is of great societal importance in the western USA and throughout the world to improve understanding of how changing fire frequency, extent, and location, in conjunction with fuel treatments will affect watersheds and the ecosystem services they supply to communities. In this work we assess the utility of the InVEST Sediment Retention Model to accurately characterize vulnerability of burned watersheds to erosion and sedimentation. The InVEST tools are GIS-based implementations of common process models, engineered for high-end computing to allow the faster simulation of larger landscapes and incorporation into decision-making. The InVEST Sediment Retention Model is based on common soil erosion models (e.g., RUSLE -Revised Universal Soil Loss Equation) and determines which areas of the landscape contribute the greatest sediment loads to a hydrological network and conversely evaluate the ecosystem service of sediment retention on a watershed basis. We evaluate the accuracy and uncertainties for InVEST predictions of increased sedimentation after fire, using measured post-fire sedimentation rates available for many watersheds in different rainfall regimes throughout the western USA from an existing, large USGS database of post-fire sediment yield [synthesized in Moody J, Martin D (2009) Synthesis of sediment yields after wildland fire in different rainfall regimes in the western United States. International Journal of Wildland Fire 18: 96-115]. The ultimate goal of this work is to calibrate and implement the model to accurately predict variability in post-fire sediment yield as a function of future landscape heterogeneity predicted by wildfire simulations, and future landscape fuel treatment scenarios, within watersheds.
Predicting lignin depolymerization yields from quantifiable properties using fractionated biorefinery lignins

USDA-ARS?s Scientific Manuscript database

Lignin depolymerization to aromatic monomers with high yields and selectivity is essential for the economic feasibility of many lignin-valorization strategies within integrated biorefining processes. Importantly, the quality and properties of the lignin source play an essential role in impacting the...
An evaluation of the lamb vision system as a predictor of lamb carcass red meat yield percentage.

PubMed

Brady, A S; Belk, K E; LeValley, S B; Dalsted, N L; Scanga, J A; Tatum, J D; Smith, G C

2003-06-01

An objective method for predicting red meat yield in lamb carcasses is needed to accurately assess true carcass value. This study was performed to evaluate the ability of the lamb vision system (LVS; Research Management Systems USA, Fort Collins, CO) to predict fabrication yields of lamb carcasses. Lamb carcasses (n = 246) were evaluated using LVS and hot carcass weight (HCW), as well as by USDA expert and on-line graders, before fabrication of carcass sides to either bone-in or boneless cuts. On-line whole number, expert whole-number, and expert nearest-tenth USDA yield grades and LVS + HCW estimates accounted for 53, 52, 58, and 60%, respectively, of the observed variability in boneless, saleable meat yields, and accounted for 56, 57, 62, and 62%, respectively, of the variation in bone-in, saleable meat yields. The LVS + HCW system predicted 77, 65, 70, and 87% of the variation in weights of boneless shoulders, racks, loins, and legs, respectively, and 85, 72, 75, and 86% of the variation in weights of bone-in shoulders, racks, loins, and legs, respectively. Addition of longissimus muscle area (REA), adjusted fat thickness (AFT), or both REA and AFT to LVS + HCW models resulted in improved prediction of boneless saleable meat yields by 5, 3, and 5 percentage points, respectively. Bone-in, saleable meat yield estimations were improved in predictive accuracy by 7.7, 6.6, and 10.1 percentage points, and in precision, when REA alone, AFT alone, or both REA and AFT, respectively, were added to the LVS + HCW output models. Use of LVS + HCW to predict boneless red meat yields of lamb carcasses was more accurate than use of current on-line whole-number, expert whole-number, or expert nearest-tenth USDA yield grades. Thus, LVS + HCW output, when used alone or in combination with AFT and/or REA, improved on-line estimation of boneless cut yields from lamb carcasses. The ability of LVS + HCW to predict yields of wholesale cuts suggests that LVS could be used as an objective
Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.
Prediction of foal carcass composition and wholesale cut yields by using video image analysis.

PubMed

Lorenzo, J M; Guedes, C M; Agregán, R; Sarriés, M V; Franco, D; Silva, S R

2018-01-01

This work represents the first contribution for the application of the video image analysis (VIA) technology in predicting lean meat and fat composition in the equine species. Images of left sides of the carcass (n=42) were captured from the dorsal, lateral and medial views using a high-resolution digital camera. A total of 41 measurements (angles, lengths, widths and areas) were obtained by VIA. The variation of percentage of lean meat obtained from the forequarter (FQ) and hindquarter (HQ) carcass ranged between 5.86% and 7.83%. However, the percentage of fat (FAT) obtained from the FQ and HQ carcass presented a higher variation (CV between 41.34% and 44.58%). By combining different measurements and using prediction models with cold carcass weight (CCW) and VIA measurement the coefficient of determination (k-fold-R 2) were 0.458 and 0.532 for FQ and HQ, respectively. On the other hand, employing the most comprehensive model (CCW plus all VIA measurements), the k-fold-R 2 increased from 0.494 to 0.887 and 0.513 to 0.878 with respect to the simplest model (only with CCW), while precision increased with the reduction in the root mean square error (2.958 to 0.947 and 1.841 to 0.787) for the hindquarter fat and lean percentage, respectively. With CCW plus VIA measurements is possible to explain the wholesale value cuts yield variation (k-fold-R 2 between 0.533 and 0.889). Overall, the VIA technology performed in the present study could be considered as an accurate method to assess the horse carcass composition which could have a role in breeding programmes and research studies to assist in the development of a value-based marketing system for horse carcass.
Hydrostatic Stress Effect on the Yield Behavior of Inconel 100

NASA Technical Reports Server (NTRS)

Allen, Phillip A.; Wilson, Christopher D.

2003-01-01

Classical metal plasticity theory assumes that hydrostatic stress has negligible effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of various geometries. Fatigue tests and nonlinear finite element analyses (FEA) of Inconel 100 (IN100) equal-arm bend specimens and new monotonic tests and nonlinear finite element analyses of IN100 smooth tension, smooth compression, and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions were performed. A new FEA constitutive model was developed that incorporates a pressure-dependent yield function with combined multilinear kinematic and multilinear isotropic hardening using the ABAQUS user subroutine (UMAT) utility. In all monotonic tensile test cases, the von Mises constitutive model, overestimated the load for a given displacement or strain. Considering the failure displacements or strains for the DENT specimen, the Drucker-Prager FEM s predicted loads that were approximately 3% lower than the von Mises values. For the failure loads, the Drucker Prager FEM s predicted strains that were up to 35% greater than the von Mises values. Both the Drucker-Prager model and the von Mises model performed equally-well in simulating the equal-arm bend fatigue test.
Development of demi-span equations for predicting height among the Malaysian elderly.

PubMed

Ngoh, H J; Sakinah, H; Harsa Amylia, M S

2012-08-01

This study aimed to develop demi-span equations for predicting height in the Malaysian elderly and to explore the applicability of previous published demi-span equations derived from adult populations to the elderly. A cross-sectional study was conducted on Malaysian elderly aged 60 years and older. Subjects were residents of eight shelter homes in Peninsular Malaysia; 204 men and 124 women of Malay, Chinese and Indian ethnicity were included. Measurements of weight, height and demi-span were obtained using standard procedures. Statistical analyses were performed using SPSS version 18.0. The demi-span equations obtained were as follows: Men: Height (cm) = 67.51 + (1.29 x demi-span) - (0.12 x age) + 4.13; Women: Height (cm) = 67.51 + (1.29 x demi-span) - (0.12 x age). Height predicted from these new equations demonstrated good agreement with measured height and no significant differences were found between the mean values of predicted and measured heights in either gender (p>0.05). However, the heights predicted from previous published adult-derived demi-span equations failed to yield good agreement with the measured height of the elderly; significant over-estimation and underestimation of heights tended to occur (p>0.05). The new demi-span equations allow prediction of height with sufficient accuracy in the Malaysian elderly. However, further validation on other elderly samples is needed. Also, we recommend caution when using adult-derived demi-span equations to predict height in elderly people.
Direct and indirect predictions of enteric methane daily production, yield, and intensity per unit of milk and cheese, from fatty acids and milk Fourier-transform infrared spectra.

PubMed

Bittante, Giovanni; Cipolat-Gotet, Claudio

2018-05-23

Mitigating the dairy chain's contribution to climate change requires cheap, rapid methods of predicting enteric CH 4 emissions (EME) of dairy cows in the field. Such methods may also be useful for genetically improving cows to reduce EME. Our objective was to evaluate different procedures for predicting EME traits from infrared spectra of milk samples taken at routine milk recording of cows. As a reference method, we used EME traits estimated from published equations developed from a meta-analysis of data from respiration chambers through analysis of various fatty acids in milk fat by gas chromatography (FA GC ). We analyzed individual milk samples of 1,150 Brown Swiss cows from 85 farms operating different dairy systems (from very traditional to modern), and obtained the cheese yields of individual model cheeses from these samples. We also obtained Fourier-transform infrared absorbance spectra on 1,060 wavelengths (5,000 to 930 waves/cm) from the same samples. Five reference enteric CH 4 traits were calculated: CH 4 yield (CH 4 /DMI, g/kg) per unit of dry matter intake (DMI), and CH 4 intensity (CH 4 /CM, g/kg) per unit of corrected milk (CM) from the FA GC profiles; CH 4 intensity per unit of fresh cheese (CH 4 /CY CURD , g/kg) and cheese solids (CH 4 /CY SOLIDS , g/kg) from individual cheese yields (CY); and daily CH 4 production (dCH 4 , g/d). Direct infrared (IR) calibrations were obtained by BayesB modeling; the determination coefficients of cross-validation varied from 0.36 for dCH 4 to 0.57 for CH 4 /CM, and were similar to the coefficient of determination values of the equations based on FA GC used as the reference method (0.47 for CH 4 /DMI and 0.54 for CH 4 /CM). The models allowed us to select the most informative wavelengths for each EME trait and to infer the milk chemical features underlying the predictions. Aside from the 5 direct infrared prediction calibrations, we tested another 8 indirect prediction models. Using IR-predicted informative fatty
Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy.

PubMed

Bittante, G; Ferragina, A; Cipolat-Gotet, C; Cecchinato, A

2014-10-01

Cheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerød, Denmark) over the spectral range, from 5,000 to 900 wavenumber × cm(-1). The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole
Linking crop yield anomalies to large-scale atmospheric circulation in Europe.

PubMed

Ceglar, Andrej; Turco, Marco; Toreti, Andrea; Doblas-Reyes, Francisco J

2017-06-15

Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used: North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.
Partitioning potential fish yields from the Great Lakes

USGS Publications Warehouse

Loftus, D.H.; Olver, C.H.; Brown, Edward H.; Colby, P.J.; Hartman, Wilbur L.; Schupp, D.H.

1987-01-01

We proposed and implemented procedures for partitioning future fish yields from the Great Lakes into taxonomic components. These projections are intended as guidelines for Great Lakes resource managers and scientists. Attainment of projected yields depends on restoration of stable fish communities containing some large piscivores that will use prey efficiently, continuation of control of the sea lamprey (Petromyzon marinus), and restoration of high-quality fish habitat. Because Great Lakes fish communities were harmonic before their collapse, we used their historic yield properties as part of the basis for projecting potential yields of rehabilitated communities. This use is qualified, however, because of possible inaccuracies in the wholly commercial yield data, the presence now of greatly expanded sport fisheries that affect yield composition and magnitude, and some possibly irreversible changes since the 1950s in the various fish communities themselves. We predict that total yields from Lakes Superior, Huron, and Ontario will be increased through rehabilitation, while those from Lakes Michigan and Erie will decline. Salmonines and coregonines will dominate future yields from the upper lakes. The Lake Erie fishery will continue to yield mostly rainbow smelt (Osmerus mordax), but the relative importance of percids, especially of walleye (Stizostedion vitreum vitreum) will increase. In Lake Ontario, yields of salmonines will be increased. Managers will have to apply the most rigorous management strictures to major predator species.
Development of buoyant currents in yield stress fluids

NASA Astrophysics Data System (ADS)

Rossi, P.; Karimfazli, I.

2017-11-01

Infinitesimal perturbations are known to decay in a motionless yield stress fluid. We present experimental evidence to reveal other mechanisms promoting free advection from a motionless background state. Development of natural convection in a cavity with differentially heated side-walls is investigated as a benchmark. Velocity and temperature fields are measured using particle image velocimetry/thermometry. We examine time evolution of the flow, compare experimental findings with theoretical predictions and comment on the striking features brought about by the yield stress.
An Electrophysiological Index of Perceptual Goodness

PubMed Central

Makin, Alexis D.J.; Wright, Damien; Rampone, Giulia; Palumbo, Letizia; Guest, Martin; Sheehan, Rhiannon; Cleaver, Helen; Bertamini, Marco

2016-01-01

A traditional line of work starting with the Gestalt school has shown that patterns vary in strength and salience; a difference in “Perceptual goodness.” The Holographic weight of evidence model quantifies goodness of visual regularities. The key formula states that W = E/N, where E is number of holographic identities in a pattern and N is number of elements. We tested whether W predicts the amplitude of the neural response to regularity in an extrastriate symmetry-sensitive network. We recorded an Event Related Potential (ERP) generated by symmetry called the Sustained Posterior Negativity (SPN). First, we reanalyzed the published work and found that W explained most variance in SPN amplitude. Then in four new studies, we confirmed specific predictions of the holographic model regarding 1) the differential effects of numerosity on reflection and repetition, 2) the similarity between reflection and Glass patterns, 3) multiple symmetries, and 4) symmetry and anti-symmetry. In all cases, the holographic approach predicted SPN amplitude remarkably well; particularly in an early window around 300–400 ms post stimulus onset. Although the holographic model was not conceived as a model of neural processing, it captures many details of the brain response to symmetry. PMID:27702812
Fresh and Stored Pollen From Slash and Loblolly Pines Compared For Seed Yields

Treesearch

John F. Kraus; Davie L. Hunt

1970-01-01

Seed yields showed no consistent differences between fresh and stored pollen from 8 years of controlled pollination on slash pine and 4 years on loblolly pine. Collection of male strobili at the proper stage of pollen maturity was an important factor in obtaining good seed yields from stored pollen. Criteria are described which were useful in determining when to...
Improving adaptation to drought stress in white pea bean (Phaseolus vulgaris L): genotypic effects on grain yield, yield components and pod harvest index

USDA-ARS?s Scientific Manuscript database

Common bean (Phaseolus vulgaris L.) is the most important food legume crop in Africa and Latin America where rainfall pattern is unpredictable. The objectives were to identify better yielding common bean lines with good canning quality under drought, and to identify traits that could be used as sele...
Estimated winter wheat yield from crop growth predicted by LANDSAT

NASA Technical Reports Server (NTRS)

Kanemasu, E. T.

1977-01-01

An evapotranspiration and growth model for winter wheat is reported. The inputs are daily solar radiation, maximum temperature, minimum temperature, precipitation/irrigation and leaf area index. The meteorological data were obtained from National Weather Service while LAI was obtained from LANDSAT multispectral scanner. The output provides daily estimates of potential evapotranspiration, transpiration, evaporation, soil moisture (50 cm depth), percentage depletion, net photosynthesis and dry matter production. Winter wheat yields are correlated with transpiration and dry matter accumulation.

Yield estimation of corn with multispectral data and the potential of using imaging spectrometers

NASA Astrophysics Data System (ADS)

Bach, Heike

1997-05-01

In the frame of the special yield estimation, a regular procedure conducted for the European Union to more accurately estimate agricultural yield, a project was conducted for the state minister for Rural Environment, Food and Forestry of Baden-Wuerttemberg, Germany) to test remote sensing data with advanced yield formation models for accuracy and timelines of yield estimation of corn. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on 4 LANDSAT-derived estimates and daily meteorological data the grain yield of corn stands was determined for 1995. The modeled yield was compared with results independently gathered within the special yield estimation for 23 test fields in the Upper Rhine Valley. The agrement between LANDSAT-based estimates and Special Yield Estimation shows a relative error of 2.3 percent. The comparison of the results for single fields shows, that six weeks before harvest the grain yield of single corn fields was estimated with a mean relative accuracy of 13 percent using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results or yield prediction with remote sensing.
Yield of bedrock wells in the Nashoba terrane, central and eastern Massachusetts

USGS Publications Warehouse

DeSimone, Leslie A.; Barbaro, Jeffrey R.

2012-01-01

The yield of bedrock wells in the fractured-bedrock aquifers of the Nashoba terrane and surrounding area, central and eastern Massachusetts, was investigated with analyses of existing data. Reported well yield was compiled for 7,287 wells from Massachusetts Department of Environmental Protection and U.S. Geological Survey databases. Yield of these wells ranged from 0.04 to 625 gallons per minute. In a comparison with data from 103 supply wells, yield and specific capacity from aquifer tests were well correlated, indicating that reported well yield was a reasonable measure of aquifer characteristics in the study area. Statistically significant relations were determined between well yield and a number of cultural and hydrogeologic factors. Cultural variables included intended water use, well depth, year of construction, and method of yield measurement. Bedrock geology, topography, surficial geology, and proximity to surface waters were statistically significant hydrogeologic factors. Yield of wells was higher in areas of granites, mafic intrusive rocks, and amphibolites than in areas of schists and gneisses or pelitic rocks; higher in valleys and low-slope areas than on hills, ridges, or high slopes; higher in areas overlain by stratified glacial deposits than in areas overlain by till; and higher in close proximity to streams, ponds, and wetlands than at greater distances from these surface-water features. Proximity to mapped faults and to lineaments from aerial photographs also were related to well yield by some measures in three quadrangles in the study area. Although the statistical significance of these relations was high, their predictive power was low, and these relations explained little of the variability in the well-yield data. Similar results were determined from a multivariate regression analysis. Multivariate regression models for the Nashoba terrane and for a three-quadrangle subarea included, as significant variables, many of the cultural and
Relationships between primary production and crop yields in semi-arid and arid irrigated agro-ecosystems

NASA Astrophysics Data System (ADS)

Jaafar, H. H.; Ahmad, F. A.

2015-04-01

In semi-arid areas within the MENA region, food security problems are the main problematic imposed. Remote sensing can be a promising too early diagnose food shortages and further prevent the population from famine risks. This study is aimed at examining the possibility of forecasting yield before harvest from remotely sensed MODIS-derived Enhanced Vegetation Index (EVI), Net photosynthesis (net PSN), and Gross Primary Production (GPP) in semi-arid and arid irrigated agro-ecosystems within the conflict affected country of Syria. Relationships between summer yield and remotely sensed indices were derived and analyzed. Simple regression spatially-based models were developed to predict summer crop production. The validation of these models was tested during conflict years. A significant correlation (p<0.05) was found between summer crop yield and EVI, GPP and net PSN. Results indicate the efficiency of remotely sensed-based models in predicting summer yield, mostly for cotton yields and vegetables. Cumulative summer EVI-based model can predict summer crop yield during crisis period, with deviation less than 20% where vegetables are the major yield. This approach prompts to an early assessment of food shortages and lead to a real time management and decision making, especially in periods of crisis such as wars and drought.
Development of an aerobic capacity prediction model from one-mile run/walk performance in adolescents aged 13-16 years.

PubMed

Burns, Ryan D; Hannon, James C; Brusseau, Timothy A; Eisenman, Patricia A; Shultz, Barry B; Saint-Maurice, Pedro F; Welk, Gregory J; Mahar, Matthew T

2016-01-01

A popular algorithm to predict VO2Peak from the one-mile run/walk test (1MRW) includes body mass index (BMI), which manifests practical issues in school settings. The purpose of this study was to develop an aerobic capacity model from 1MRW in adolescents independent of BMI. Cardiorespiratory endurance data were collected on 90 adolescents aged 13-16 years. The 1MRW was administered on an outside track and a laboratory VO2Peak test was conducted using a maximal treadmill protocol. Multiple linear regression was employed to develop the prediction model. Results yielded the following algorithm: VO2Peak = 7.34 × (1MRW speed in m s(-1)) + 0.23 × (age × sex) + 17.75. The New Model displayed a multiple correlation and prediction error of R = 0.81, standard error of the estimate = 4.78 ml kg(-1) · min(-1), with measured VO2Peak and good criterion-referenced (CR) agreement into FITNESSGRAM's Healthy Fitness Zone (Kappa = 0.62; percentage agreement = 84.4%; Φ = 0.62). The New Model was validated using k-fold cross-validation and showed homoscedastic residuals across the range of predicted scores. The omission of BMI did not compromise accuracy of the model. In conclusion, the New Model displayed good predictive accuracy and good CR agreement with measured VO2Peak in adolescents aged 13-16 years.
Effect of Anisotropic Yield Function Evolution on Estimation of Forming Limit Diagram

NASA Astrophysics Data System (ADS)

Bandyopadhyay, K.; Basak, S.; Choi, H. J.; Panda, S. K.; Lee, M. G.

2017-09-01

In case of theoretical prediction of the FLD, the variations in yield stress and R-values along different material directions, were long been implemented to enhance the accuracy. Although influences of different yield models and hardening laws on formability were well addressed, anisotropic evolution of yield loci under monotonic loading with different deformation modes is yet to be explored. In the present study, Marciniak-Kuckzinsky (M-K) model was modified to incorporate the change in the shape of the initial yield function with evolution due to anisotropic hardening. Swift’s hardening law along with two different anisotropic yield criteria, namely Hill48 and Yld2000-2d were implemented in the model. The Hill48 yield model was applied with non-associated flow rule to comprehend the effect of variations in both yield stress and R-values. The numerically estimated FLDs were validated after comparing with FLD evaluated through experiments. A low carbon steel was selected, and hemispherical punch stretching test was performed for FLD evaluation. Additionally, the numerically estimated FLDs were incorporated in FE simulations to predict limiting dome heights for validation purpose. Other formability performances like strain distributions over the deformed cup surface were validated with experimental results.
Growth and Yield of Appalachian Mixed Hardwoods After Thinning

Treesearch

Wade C. Harrison; Harold E. Burkhart; Thomas E. Burk; Donald E. Beckand

1986-01-01

G-RAT (Growth of Hardwoods After Thinning) is a system of computer programs used to predict growth and yield of Appalachian mixed hardwoods after thinning. Given a tree list or stand table, along with inputs of stand age, site index, and stand basal area before thinning, G-RAT software uses species-specific individual tree equations to predict tree basal area...
Study of the strong {sigma}{sub c}{yields}{lambda}{sub c}{pi},{sigma}{sub c}*{yields}{lambda}{sub c}{pi} and {xi}{sub c}*{yields}{xi}{sub c}{pi} decays in a nonrelativistic quark model

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

Albertus, C.; Nieves, J.; Hernandez, E.

We present results for the strong widths corresponding to the {sigma}{sub c}{yields}{lambda}{sub c}{pi}, {sigma}{sub c}*{yields}{lambda}{sub c}{pi} and {xi}{sub c}*{yields}{xi}{sub c}{pi} decays. The calculations have been done in a nonrelativistic constituent quark model with wave functions that take advantage of the constraints imposed by heavy quark symmetry. Partial conservation of axial current hypothesis allows us to determine the strong vertices from an analysis of the axial current matrix elements. Our results {gamma}({sigma}{sub c}{sup ++}{yields}{lambda}{sub c}{sup +}{pi}{sup +})=2.41{+-}0.07{+-}0.02 MeV, {gamma}({sigma}{sub c}{sup +}{yields}{lambda}{sub c}{sup +}{pi}{sup 0})=2.79{+-}0.08{+-}0.02 MeV, {gamma}({sigma}{sub c}{sup 0}{yields}{lambda}{sub c}{sup +}{pi}{sup -})=2.37{+-}0.07{+-}0.02 MeV, {gamma}({sigma}{sub c}*{sup ++}{yields}{lambda}{sub c}{sup +}{pi}{sup +})=17.52{+-}0.74{+-}0.12 MeV, {gamma}({sigma}{sub c}*{supmore » +}{yields}{lambda}{sub c}{sup +}{pi}{sup 0})=17.31{+-}0.73{+-}0.12 MeV, {gamma}({sigma}{sub c}*{sup 0}{yields}{lambda}{sub c}{sup +}{pi}{sup -})=16.90{+-}0.71{+-}0.12 MeV, {gamma}({xi}{sub c}*{sup +}{yields}{xi}{sub c}{sup 0}{pi}{sup +}+{xi}{sub c}{sup +}{pi}{sup 0})=3.18{+-}0.10{+-}0.01 MeV, and {gamma}({xi}{sub c}*{sup 0}{yields}{xi}{sub c}{sup +}{pi}{sup -}+{xi}{sub c}{sup 0}{pi}{sup 0})=3.03{+-}0.10{+-}0.01 MeV are in good agreement with experimental determinations.« less
Prediction of random-regression coefficient for daily milk yield after 305 days in milk by using the regression-coefficient estimates from the first 305 days.

PubMed

Yamazaki, Takeshi; Takeda, Hisato; Hagiya, Koichi; Yamaguchi, Satoshi; Sasaki, Osamu

2018-03-13

Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a random regression model. We analyzed test-day milk records from 85690 Holstein cows in their first lactations and 131727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. The first-order Legendre polynomials were practical covariates of random regression for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.
Growth, yield and compositional characteristics of Jerusalem artichoke as it relates to biomass production

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

Stauffer, M.D.; Chubey, B.B.; Dorrell, D.G.

1980-01-01

Jerusalem artichoke (Helianthus tuberosus L.) has shown excellent potential as a carbohydrate-rich crop. Initial investigations determined inulin and tuber yields; however, when additional studies showed that good quality pulp remained after inulin extraction and high forage yields per hectare were obtainable, the scope of investigation was broadened to assess utilization of the total plant. Plant growth, yield and compositional characteristics of Jerusalem artichoke as they relate to biomass production will be reported.
A new UK fission yield evaluation UKFY3.7

NASA Astrophysics Data System (ADS)

Mills, Robert William

2017-09-01

The JEFF neutron induced and spontaneous fission product yield evaluation is currently unchanged from JEFF-3.1.1, also known by its UK designation UKFY3.6A. It is based upon experimental data combined with empirically fitted mass, charge and isomeric state models which are then adjusted within the experimental and model uncertainties to conform to the physical constraints of the fission process. A new evaluation has been prepared for JEFF, called UKFY3.7, that incorporates new experimental data and replaces the current empirical models (multi-Gaussian fits of mass distribution and Wahl Zp model for charge distribution combined with parameter extrapolation), with predictions from GEF. The GEF model has the advantage that one set of parameters allows the prediction of many different fissioning nuclides at different excitation energies unlike previous models where each fissioning nuclide at a specific excitation energy had to be fitted individually to the relevant experimental data. The new UKFY3.7 evaluation, submitted for testing as part of JEFF-3.3, is described alongside initial results of testing. In addition, initial ideas for future developments allowing inclusion of new measurements types and changing from any neutron spectrum type to true neutron energy dependence are discussed. Also, a method is proposed to propagate uncertainties of fission product yields based upon the experimental data that underlies the fission yield evaluation. The covariance terms being determined from the evaluated cumulative and independent yields combined with the experimental uncertainties on the cumulative yield measurements.
Application of Grey Model GM(1, 1) to Ultra Short-Term Predictions of Universal Time

NASA Astrophysics Data System (ADS)

Lei, Yu; Guo, Min; Zhao, Danning; Cai, Hongbing; Hu, Dandan

2016-03-01

A mathematical model known as one-order one-variable grey differential equation model GM(1, 1) has been herein employed successfully for the ultra short-term (<10days) predictions of universal time (UT1-UTC). The results of predictions are analyzed and compared with those obtained by other methods. It is shown that the accuracy of the predictions is comparable with that obtained by other prediction methods. The proposed method is able to yield an exact prediction even though only a few observations are provided. Hence it is very valuable in the case of a small size dataset since traditional methods, e.g., least-squares (LS) extrapolation, require longer data span to make a good forecast. In addition, these results can be obtained without making any assumption about an original dataset, and thus is of high reliability. Another advantage is that the developed method is easy to use. All these reveal a great potential of the GM(1, 1) model for UT1-UTC predictions.
Experimental evidence for an absorbing phase transition underlying yielding of a soft glass

NASA Astrophysics Data System (ADS)

Nagamanasa, K. Hima; Gokhale, Shreyas; Sood, A. K.; Ganapathy, Rajesh

2014-03-01

A characteristic feature of solids ranging from foams to atomic crystals is the existence of a yield point, which marks the threshold stress beyond which a material undergoes plastic deformation. In hard materials, it is well-known that local yield events occur collectively in the form of intermittent avalanches. The avalanche size distributions exhibit power-law scaling indicating the presence of self-organized criticality. These observations led to predictions of a non-equilibrium phase transition at the yield point. By contrast, for soft solids like gels and dense suspensions, no such predictions exist. In the present work, by combining particle scale imaging with bulk rheology, we provide a direct evidence for a non-equilibrium phase transition governing yielding of an archetypal soft solid - a colloidal glass. The order parameter and the relaxation time exponents revealed that yielding is an absorbing phase transition that belongs to the conserved directed percolation universality class. We also identified a growing length scale associated with clusters of particles with high Debye-Waller factor. Our findings highlight the importance of correlations between local yield events and may well stimulate the development of a unified description of yielding of soft solids.
Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application.

PubMed

Mahjourimajd, Saba; Taylor, Julian; Sznajder, Beata; Timmins, Andy; Shahinnia, Fahimeh; Rengel, Zed; Khabaz-Saberi, Hossein; Kuchel, Haydn; Okamoto, Mamoru; Langridge, Peter

2016-01-01

Nitrogen (N) is a major nutrient needed to attain optimal grain yield (GY) in all environments. Nitrogen fertilisers represent a significant production cost, in both monetary and environmental terms. Developing genotypes capable of taking up N early during development while limiting biomass production after establishment and showing high N-use efficiency (NUE) would be economically beneficial. Genetic variation in NUE has been shown previously. Here we describe the genetic characterisation of NUE and identify genetic loci underlying N response under different N fertiliser regimes in a bread wheat population of doubled-haploid lines derived from a cross between two Australian genotypes (RAC875 × Kukri) bred for a similar production environment. NUE field trials were carried out at four sites in South Australia and two in Western Australia across three seasons. There was genotype-by-environment-by-treatment interaction across the sites and also good transgressive segregation for yield under different N supply in the population. We detected some significant Quantitative Trait Loci (QTL) associated with NUE and N response at different rates of N application across the sites and years. It was also possible to identify lines showing positive N response based on the rankings of their Best Linear Unbiased Predictions (BLUPs) within a trial. Dissecting the complexity of the N effect on yield through QTL analysis is a key step towards elucidating the molecular and physiological basis of NUE in wheat.
Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application

PubMed Central

Mahjourimajd, Saba; Taylor, Julian; Sznajder, Beata; Timmins, Andy; Shahinnia, Fahimeh; Rengel, Zed; Khabaz-Saberi, Hossein; Kuchel, Haydn; Okamoto, Mamoru

2016-01-01

Nitrogen (N) is a major nutrient needed to attain optimal grain yield (GY) in all environments. Nitrogen fertilisers represent a significant production cost, in both monetary and environmental terms. Developing genotypes capable of taking up N early during development while limiting biomass production after establishment and showing high N-use efficiency (NUE) would be economically beneficial. Genetic variation in NUE has been shown previously. Here we describe the genetic characterisation of NUE and identify genetic loci underlying N response under different N fertiliser regimes in a bread wheat population of doubled-haploid lines derived from a cross between two Australian genotypes (RAC875 × Kukri) bred for a similar production environment. NUE field trials were carried out at four sites in South Australia and two in Western Australia across three seasons. There was genotype-by-environment-by-treatment interaction across the sites and also good transgressive segregation for yield under different N supply in the population. We detected some significant Quantitative Trait Loci (QTL) associated with NUE and N response at different rates of N application across the sites and years. It was also possible to identify lines showing positive N response based on the rankings of their Best Linear Unbiased Predictions (BLUPs) within a trial. Dissecting the complexity of the N effect on yield through QTL analysis is a key step towards elucidating the molecular and physiological basis of NUE in wheat. PMID:27459317
Anaerobic digestion of spring and winter wheat: Comparison of net energy yields.

PubMed

Rincón, Bárbara; Heaven, Sonia; Salter, Andrew M; Banks, Charles J

2016-10-14

Anaerobic digestion of wheat was investigated under batch conditions. The article compares the potential net energy yield between a winter wheat (sown in the autumn) and a spring wheat (sown in the spring) grown in the same year and harvested at the same growth stage in the same farm. The spring wheat had a slightly higher biochemical methane potential and required lower energy inputs in cultivation, but produced a lower dry biomass yield per hectare, which resulted in winter wheat providing the best overall net energy yield. The difference was small; both varieties gave a good net energy yield. Spring sowing may also offer the opportunity for growing an additional over-winter catch crop for spring harvest, thus increasing the overall biomass yield per hectare, with both crops being potential digester feedstocks.
A Growth and Yield Model for Thinned Stands of Yellow-Poplar

Treesearch

Bruce R. Knoebel; Harold E. Burkhart; Donald E. Beck

1986-01-01

Simultaneous growth and yield equations were developed for predicting basal area growth and cubic-foot volume growth and yield in thinned stands of yellow-poplar. A joint loss function involving both volume and basal area was used to estimate the coefficients in the system of equations. The estimates obtained were analytically compatible, invariant for projection...
CROMAX : a crosscut-first computer simulation program to determine cutting yield

Treesearch

Pamela J. Giese; Jeanne D. Danielson

1983-01-01

CROMAX simulates crosscut-first, then rip operations as commonly practiced in furniture manufacture. This program calculates cutting yields from individual boards based on board size and defect location. Such information can be useful in predicting yield from various grades and grade mixes thereby allowing for better management decisions in the rough mill. The computer...
A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.
Statistical validation of predictive TRANSP simulations of baseline discharges in preparation for extrapolation to JET D-T

NASA Astrophysics Data System (ADS)

Kim, Hyun-Tae; Romanelli, M.; Yuan, X.; Kaye, S.; Sips, A. C. C.; Frassinetti, L.; Buchanan, J.; Contributors, JET

2017-06-01

This paper presents for the first time a statistical validation of predictive TRANSP simulations of plasma temperature using two transport models, GLF23 and TGLF, over a database of 80 baseline H-mode discharges in JET-ILW. While the accuracy of the predicted T e with TRANSP-GLF23 is affected by plasma collisionality, the dependency of predictions on collisionality is less significant when using TRANSP-TGLF, indicating that the latter model has a broader applicability across plasma regimes. TRANSP-TGLF also shows a good matching of predicted T i with experimental measurements allowing for a more accurate prediction of the neutron yields. The impact of input data and assumptions prescribed in the simulations are also investigated in this paper. The statistical validation and the assessment of uncertainty level in predictive TRANSP simulations for JET-ILW-DD will constitute the basis for the extrapolation to JET-ILW-DT experiments.
Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier.

PubMed

Pandey, Daya Shankar; Pan, Indranil; Das, Saptarshi; Leahy, James J; Kwapinski, Witold

2015-03-01

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of geoengineering on crop yields

NASA Astrophysics Data System (ADS)

Pongratz, J.; Lobell, D. B.; Cao, L.; Caldeira, K.

2011-12-01

The potential of "solar radiation management" (SRM) to reduce future climate change and associated risks has been receiving significant attention in scientific and policy circles. SRM schemes aim to reduce global warming despite increasing atmospheric CO2 concentrations by diminishing the amount of solar insolation absorbed by the Earth, for example, by injecting scattering aerosols into the atmosphere. Climate models predict that SRM could fully compensate warming at the global mean in a high-CO2 world. While reduction of global warming may offset a part of the predicted negative effects of future climate change on crop yields, SRM schemes are expected to alter regional climate and to have substantial effects on climate variables other than temperature, such as precipitation. It has therefore been warned that, overall, SRM may pose a risk to food security. Assessments of benefits and risks of geoengineering are imperative, yet such assessments are only beginning to emerge; in particular, effects on global food security have not previously been assessed. Here, for the first time, we combine climate model simulations with models of crop yield responses to climate to assess large-scale changes in yields and food production under SRM. In most crop-growing regions, we find that yield losses caused by climate changes are substantially reduced under SRM as compared with a non-geoengineered doubling of atmospheric CO2. Substantial yield losses with SRM are only found for rice in high latitudes, where the limits of low temperatures are no longer alleviated. At the same time, the beneficial effect of CO2-fertilization on plant productivity remains active. Overall therefore, SRM in our models causes global crop yields to increase. We estimate the direct effects of climate and CO2 changes on crop production, and do not quantify effects of market dynamics and management changes. We note, however, that an SRM deployment would be unlikely to maintain the economic status quo, as
Graphical user interface for yield and dose estimations for cyclotron-produced technetium

NASA Astrophysics Data System (ADS)

Hou, X.; Vuckovic, M.; Buckley, K.; Bénard, F.; Schaffer, P.; Ruth, T.; Celler, A.

2014-07-01

The cyclotron-based 100Mo(p,2n)99mTc reaction has been proposed as an alternative method for solving the shortage of 99mTc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with 99mTc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced 99mTc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.
Graphical user interface for yield and dose estimations for cyclotron-produced technetium.

PubMed

Hou, X; Vuckovic, M; Buckley, K; Bénard, F; Schaffer, P; Ruth, T; Celler, A

2014-07-07

The cyclotron-based (100)Mo(p,2n)(99m)Tc reaction has been proposed as an alternative method for solving the shortage of (99m)Tc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with (99m)Tc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced (99m)Tc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.
BioSTAR, a New Biomass and Yield Modeling Software

NASA Astrophysics Data System (ADS)

Kappas, M.; Degener, J.; Bauboeck, R.

2013-12-01

, sorghum, sunflower and, sugar beet. Calibrations for rye grass, cup plant, poplar and willow still need to be performed. A Comparison of simulated and observed biomass yields for sites in Lower Saxony has rendered good results with errors (RMSE) ranging from below 10% (winter wheat, n= 102) and 18.6 % (sunflower, n=8) (Bauböck, unpublished). Because simulations can be made with limited soil data (soil type or texture class) and a limited climate data set (smallest set can be either monthly means of precipitation, temperature and, radiation or precipitation, temperature and, humidity) and the software is capable of processing large datasets, the model appears to be a promising tool for mid or large scale biomass and yield predictions. Up to now the model has only been used for yield predictions with current state climate and climate change scenarios in Lower Saxony, but comparisons with output data of the model AquaCrop (Steduto, et al., 2009) have shown good performance in arid and semi-arid climates (Bauböck, 2013).
Growth and yield of quaking aspen in North-central Minnesota.

Treesearch

Bryce E. Schlaegel

1971-01-01

Summaries of total and merchantable stand data from 34 permanent sample plots were used to derive equations for predicting present and future stand volumes. Equations are presented for predicting total cubic-foot volume, ratio of merchantable volume to total volume, and future stand diameter, heights, and basal area. Yield tables are given for total stand volume and...
A new yield and failure theory for composite materials under static and dynamic loading

DOE PAGES

Daniel, Isaac M.; Daniel, Sam M.; Fenner, Joel S.

2017-09-12

In order to facilitate and accelerate the process of introducing, evaluating and adopting new material systems, it is important to develop/establish comprehensive and effective procedures of characterization, modeling and failure prediction of composite structures based on the properties of the constituent materials, e. g., fibers, matrix, and the single ply or lamina. A new yield/failure theory is proposed for predicting lamina yielding and failure under multi-axial states of stress including strain rate effects. It is based on the equivalent stress concept derived from energy principles and is expressed in terms of a single criterion. It is presented in the formmore » of master yield and failure envelopes incorporating strain rate effects. The theory can be further adapted and extended to the prediction of in situ first ply yielding and failure (FPY and FPF) and progressive damage of multi-directional laminates under static and dynamic loadings. The significance of this theory is that it allows for rapid screening of new composite materials without extensive testing and offers easily implemented design tools.« less
A new yield and failure theory for composite materials under static and dynamic loading

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

Daniel, Isaac M.; Daniel, Sam M.; Fenner, Joel S.

In order to facilitate and accelerate the process of introducing, evaluating and adopting new material systems, it is important to develop/establish comprehensive and effective procedures of characterization, modeling and failure prediction of composite structures based on the properties of the constituent materials, e. g., fibers, matrix, and the single ply or lamina. A new yield/failure theory is proposed for predicting lamina yielding and failure under multi-axial states of stress including strain rate effects. It is based on the equivalent stress concept derived from energy principles and is expressed in terms of a single criterion. It is presented in the formmore » of master yield and failure envelopes incorporating strain rate effects. The theory can be further adapted and extended to the prediction of in situ first ply yielding and failure (FPY and FPF) and progressive damage of multi-directional laminates under static and dynamic loadings. The significance of this theory is that it allows for rapid screening of new composite materials without extensive testing and offers easily implemented design tools.« less
Assessment of climate change impact on yield of major crops in the Banas River Basin, India.

PubMed

Dubey, Swatantra Kumar; Sharma, Devesh

2018-09-01

Crop growth models like AquaCrop are useful in understanding the impact of climate change on crop production considering the various projections from global circulation models and regional climate models. The present study aims to assess the climate change impact on yield of major crops in the Banas River Basin i.e., wheat, barley and maize. Banas basin is part of the semi-arid region of Rajasthan state in India. AquaCrop model is used to calculate the yield of all the three crops for a historical period of 30years (1981-2010) and then compared with observed yield data. Root Mean Square Error (RMSE) values are calculated to assess the model accuracy in prediction of yield. Further, the calibrated model is used to predict the possible impacts of climate change and CO 2 concentration on crop yield using CORDEX-SA climate projections of three driving climate models (CNRM-CM5, CCSM4 and MPI-ESM-LR) for two different scenarios (RCP4.5 and RCP8.5) for the future period 2021-2050. RMSE values of simulated yield with respect to observed yield of wheat, barley and maize are 11.99, 16.15 and 19.13, respectively. It is predicted that crop yield of all three crops will increase under the climate change conditions for future period (2021-2050). Copyright © 2018 Elsevier B.V. All rights reserved.
Stocking, growth, and yield of birch stands

Treesearch

Dale S. Solomon; William B. Leak

1969-01-01

Intensive forest management depends heavily upon our ability to measure, control, and predict the growth, yield, or general development of timber stands, regardless of whether the management goal is for timber, aesthetics, recreation, water, or wildlife. A large amount of mensurational data about birch stands has been developed in recent years or synthesized from...
Mathematical functions for predicting growth and yield of black walnut plantations in the Central States.

Treesearch

Raymond S. Ferrell; Allen L. Lundgren

1976-01-01

Mathematical functions were fitted to unpublished summaries of yield data collected and reported by L.F. Kellogg in 1940 for unmanaged black walnut plantations in the Central States. They cover a wide range of conditions and provide the best data base available for simulating growth and yield in plantations.
Regional crop yield forecasting: a probabilistic approach

NASA Astrophysics Data System (ADS)

de Wit, A.; van Diepen, K.; Boogaard, H.

2009-04-01

Information on the outlook on yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies with a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, and for commodity traders. Process-based mechanistic crop models are an important tool for providing such information, because they can integrate the effect of crop management, weather and soil on crop growth. When properly integrated in a yield forecasting system, the aggregated model output can be used to predict crop yield and production at regional, national and continental scales. Nevertheless, given the scales at which these models operate, the results are subject to large uncertainties due to poorly known weather conditions and crop management. Current yield forecasting systems are generally deterministic in nature and provide no information about the uncertainty bounds on their output. To improve on this situation we present an ensemble-based approach where uncertainty bounds can be derived from the dispersion of results in the ensemble. The probabilistic information provided by this ensemble-based system can be used to quantify uncertainties (risk) on regional crop yield forecasts and can therefore be an important support to quantitative risk analysis in a decision making process.
Quantifying effectiveness of failure prediction and response in HPC systems : methodology and example.

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

Mayo, Jackson R.; Chen, Frank Xiaoxiao; Pebay, Philippe Pierre

2010-06-01

Effective failure prediction and mitigation strategies in high-performance computing systems could provide huge gains in resilience of tightly coupled large-scale scientific codes. These gains would come from prediction-directed process migration and resource servicing, intelligent resource allocation, and checkpointing driven by failure predictors rather than at regular intervals based on nominal mean time to failure. Given probabilistic associations of outlier behavior in hardware-related metrics with eventual failure in hardware, system software, and/or applications, this paper explores approaches for quantifying the effects of prediction and mitigation strategies and demonstrates these using actual production system data. We describe context-relevant methodologies for determining themore » accuracy and cost-benefit of predictors. While many research studies have quantified the expected impact of growing system size, and the associated shortened mean time to failure (MTTF), on application performance in large-scale high-performance computing (HPC) platforms, there has been little if any work to quantify the possible gains from predicting system resource failures with significant but imperfect accuracy. This possibly stems from HPC system complexity and the fact that, to date, no one has established any good predictors of failure in these systems. Our work in the OVIS project aims to discover these predictors via a variety of data collection techniques and statistical analysis methods that yield probabilistic predictions. The question then is, 'How good or useful are these predictions?' We investigate methods for answering this question in a general setting, and illustrate them using a specific failure predictor discovered on a production system at Sandia.« less
Discrete Spring Model for Predicting Delamination Growth in Z-Fiber Reinforced DCB Specimens

NASA Technical Reports Server (NTRS)

Ratcliffe, James G.; OBrien, T. Kevin

2004-01-01

Beam theory analysis was applied to predict delamination growth in Double Cantilever Beam (DCB) specimens reinforced in the thickness direction with pultruded pins, known as Z-fibers. The specimen arms were modeled as cantilever beams supported by discrete springs, which were included to represent the pins. A bi-linear, irreversible damage law was used to represent Z-fiber damage, the parameters of which were obtained from previous experiments. Closed-form solutions were developed for specimen compliance and displacements corresponding to Z-fiber row locations. A solution strategy was formulated to predict delamination growth, in which the parent laminate mode I critical strain energy release rate was used as the criterion for delamination growth. The solution procedure was coded into FORTRAN 90, giving a dedicated software tool for performing the delamination prediction. Comparison of analysis results with previous analysis and experiment showed good agreement, yielding an initial verification for the analytical procedure.
Discrete Spring Model for Predicting Delamination Growth in Z-Fiber Reinforced DCB Specimens

NASA Technical Reports Server (NTRS)

Ratcliffe, James G.; O'Brien, T. Kevin

2004-01-01

Beam theory analysis was applied to predict delamination growth in DCB specimens reinforced in the thickness direction with pultruded pins, known as Z-fibers. The specimen arms were modeled as cantilever beams supported by discrete springs, which were included to represent the pins. A bi-linear, irreversible damage law was used to represent Z-fiber damage, the parameters of which were obtained from previous experiments. Closed-form solutions were developed for specimen compliance and displacements corresponding to Z-fiber row locations. A solution strategy was formulated to predict delamination growth, in which the parent laminate mode I fracture toughness was used as the criterion for delamination growth. The solution procedure was coded into FORTRAN 90, giving a dedicated software tool for performing the delamination prediction. Comparison of analysis results with previous analysis and experiment showed good agreement, yielding an initial verification for the analytical procedure.
Effect of warming temperatures on US wheat yields.

PubMed

Tack, Jesse; Barkley, Andrew; Nalley, Lawton Lanier

2015-06-02

Climate change is expected to increase future temperatures, potentially resulting in reduced crop production in many key production regions. Research quantifying the complex relationship between weather variables and wheat yields is rapidly growing, and recent advances have used a variety of model specifications that differ in how temperature data are included in the statistical yield equation. A unique data set that combines Kansas wheat variety field trial outcomes for 1985-2013 with location-specific weather data is used to analyze the effect of weather on wheat yield using regression analysis. Our results indicate that the effect of temperature exposure varies across the September-May growing season. The largest drivers of yield loss are freezing temperatures in the Fall and extreme heat events in the Spring. We also find that the overall effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Our analysis indicates that there exists a tradeoff between average (mean) yield and ability to resist extreme heat across varieties. More-recently released varieties are less able to resist heat than older lines. Our results also indicate that warming effects would be partially offset by increased rainfall in the Spring. Finally, we find that the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts on yields.
Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation

PubMed Central

Puntel, Laila A.; Sawyer, John E.; Barker, Daniel W.; Dietzel, Ranae; Poffenbarger, Hanna; Castellano, Michael J.; Moore, Kenneth J.; Thorburn, Peter; Archontoulis, Sotirios V.

2016-01-01

Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR’s were within the historical N rate error range (40–50 kg N ha-1). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward
Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

PubMed

Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Dietzel, Ranae; Poffenbarger, Hanna; Castellano, Michael J; Moore, Kenneth J; Thorburn, Peter; Archontoulis, Sotirios V

2016-01-01

Improved prediction of optimal N fertilizer rates for corn ( Zea mays L. ) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean ( Glycine max L. ) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha -1 ) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha -1 ). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward
Does Parsonnet scoring model predict mortality following adult cardiac surgery in India?

PubMed

Srilata, Moningi; Padhy, Narmada; Padmaja, Durga; Gopinath, Ramachandran

2015-01-01

To validate the Parsonnet scoring model to predict mortality following adult cardiac surgery in Indian scenario. A total of 889 consecutive patients undergoing adult cardiac surgery between January 2010 and April 2011 were included in the study. The Parsonnet score was determined for each patient and its predictive ability for in-hospital mortality was evaluated. The validation of Parsonnet score was performed for the total data and separately for the sub-groups coronary artery bypass grafting (CABG), valve surgery and combined procedures (CABG with valve surgery). The model calibration was performed using Hosmer-Lemeshow goodness of fit test and receiver operating characteristics (ROC) analysis for discrimination. Independent predictors of mortality were assessed from the variables used in the Parsonnet score by multivariate regression analysis. The overall mortality was 6.3% (56 patients), 7.1% (34 patients) for CABG, 4.3% (16 patients) for valve surgery and 16.2% (6 patients) for combined procedures. The Hosmer-Lemeshow statistic was <0.05 for the total data and also within the sub-groups suggesting that the predicted outcome using Parsonnet score did not match the observed outcome. The area under the ROC curve for the total data was 0.699 (95% confidence interval 0.62-0.77) and when tested separately, it was 0.73 (0.64-0.81) for CABG, 0.79 (0.63-0.92) for valve surgery (good discriminatory ability) and only 0.55 (0.26-0.83) for combined procedures. The independent predictors of mortality determined for the total data were low ejection fraction (odds ratio [OR] - 1.7), preoperative intra-aortic balloon pump (OR - 10.7), combined procedures (OR - 5.1), dialysis dependency (OR - 23.4), and re-operation (OR - 9.4). The Parsonnet score yielded a good predictive value for valve surgeries, moderate predictive value for the total data and for CABG and poor predictive value for combined procedures.
Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

NASA Astrophysics Data System (ADS)

Suarez, L. A.; Apan, A.; Werth, J.

2016-10-01

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.
K+-nucleus scattering using K {yields} {mu}{nu} decays as a normalization check

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

Michael, R.; Hicks, K.; Bart, S.

1995-04-01

Elastic scattering of 720 and 620 MeV/c positive kaons from targets of {sup 12}C and {sup 6}Li has been measured up to laboratory angles of 42{degrees}. Since the magnitude of the cross sections is sensitive to nuclear medium effects, the K{yields}{mu}{nu} decay mode has been used to check the normalization. GEANT has been used to mimic the kaon decays over a path length of 12cm, with a correlated beam structure matching the experimental kaon beam. The corresponding muon distribution has been passed thru Monte Carlo simulations of the moby dick spectrometer. The results are compared with the experimental number ofmore » decay muons with good agreement. These results also agree with the normalization found using p-p elastic scattering. The normalized K{sup +} elastic data are compared to recent optical model predictions based on both Klein-Gordon and KDP equations in the impulse approximation.« less

A hierarchical spatial model for well yield in complex aquifers

NASA Astrophysics Data System (ADS)

Montgomery, J.; O'sullivan, F.

2017-12-01

Efficiently siting and managing groundwater wells requires reliable estimates of the amount of water that can be produced, or the well yield. This can be challenging to predict in highly complex, heterogeneous fractured aquifers due to the uncertainty around local hydraulic properties. Promising statistical approaches have been advanced in recent years. For instance, kriging and multivariate regression analysis have been applied to well test data with limited but encouraging levels of prediction accuracy. Additionally, some analytical solutions to diffusion in homogeneous porous media have been used to infer "effective" properties consistent with observed flow rates or drawdown. However, this is an under-specified inverse problem with substantial and irreducible uncertainty. We describe a flexible machine learning approach capable of combining diverse datasets with constraining physical and geostatistical models for improved well yield prediction accuracy and uncertainty quantification. Our approach can be implemented within a hierarchical Bayesian framework using Markov Chain Monte Carlo, which allows for additional sources of information to be incorporated in priors to further constrain and improve predictions and reduce the model order. We demonstrate the usefulness of this approach using data from over 7,000 wells in a fractured bedrock aquifer.
Reflexion on linear regression trip production modelling method for ensuring good model quality

NASA Astrophysics Data System (ADS)

Suprayitno, Hitapriya; Ratnasari, Vita

2017-11-01

Transport Modelling is important. For certain cases, the conventional model still has to be used, in which having a good trip production model is capital. A good model can only be obtained from a good sample. Two of the basic principles of a good sampling is having a sample capable to represent the population characteristics and capable to produce an acceptable error at a certain confidence level. It seems that this principle is not yet quite understood and used in trip production modeling. Therefore, investigating the Trip Production Modelling practice in Indonesia and try to formulate a better modeling method for ensuring the Model Quality is necessary. This research result is presented as follows. Statistics knows a method to calculate span of prediction value at a certain confidence level for linear regression, which is called Confidence Interval of Predicted Value. The common modeling practice uses R2 as the principal quality measure, the sampling practice varies and not always conform to the sampling principles. An experiment indicates that small sample is already capable to give excellent R2 value and sample composition can significantly change the model. Hence, good R2 value, in fact, does not always mean good model quality. These lead to three basic ideas for ensuring good model quality, i.e. reformulating quality measure, calculation procedure, and sampling method. A quality measure is defined as having a good R2 value and a good Confidence Interval of Predicted Value. Calculation procedure must incorporate statistical calculation method and appropriate statistical tests needed. A good sampling method must incorporate random well distributed stratified sampling with a certain minimum number of samples. These three ideas need to be more developed and tested.
A comparison of two adaptive multivariate analysis methods (PLSR and ANN) for winter wheat yield forecasting using Landsat-8 OLI images

NASA Astrophysics Data System (ADS)

Chen, Pengfei; Jing, Qi

2017-02-01

An assumption that the non-linear method is more reasonable than the linear method when canopy reflectance is used to establish the yield prediction model was proposed and tested in this study. For this purpose, partial least squares regression (PLSR) and artificial neural networks (ANN), represented linear and non-linear analysis method, were applied and compared for wheat yield prediction. Multi-period Landsat-8 OLI images were collected at two different wheat growth stages, and a field campaign was conducted to obtain grain yields at selected sampling sites in 2014. The field data were divided into a calibration database and a testing database. Using calibration data, a cross-validation concept was introduced for the PLSR and ANN model construction to prevent over-fitting. All models were tested using the test data. The ANN yield-prediction model produced R2, RMSE and RMSE% values of 0.61, 979 kg ha-1, and 10.38%, respectively, in the testing phase, performing better than the PLSR yield-prediction model, which produced R2, RMSE, and RMSE% values of 0.39, 1211 kg ha-1, and 12.84%, respectively. Non-linear method was suggested as a better method for yield prediction.
An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

NASA Astrophysics Data System (ADS)

Shao, Yang; Campbell, James B.; Taff, Gregory N.; Zheng, Baojuan

2015-06-01

The Midwestern United States is one of the world's most important corn-producing regions. Monitoring and forecasting of corn yields in this intensive agricultural region are important activities to support food security, commodity markets, bioenergy industries, and formation of national policies. This study aims to develop forecasting models that have the capability to provide mid-season prediction of county-level corn yields for the entire Midwestern United States. We used multi-temporal MODIS NDVI (normalized difference vegetation index) 16-day composite data as the primary input, with digital elevation model (DEM) and parameter-elevation relationships on independent slopes model (PRISM) climate data as additional inputs. The DEM and PRISM data, along with three types of cropland masks were tested and compared to evaluate their impacts on model predictive accuracy. Our results suggested that the use of general cropland masks (e.g., summer crop or cultivated crops) generated similar results compared with use of an annual corn-specific mask. Leave-one-year-out cross-validation resulted in an average R2 of 0.75 and RMSE value of 1.10 t/ha. Using a DEM as an additional model input slightly improved performance, while inclusion of PRISM climate data appeared not to be important for our regional corn-yield model. Furthermore, our model has potential for real-time/early prediction. Our corn yield esitmates are available as early as late July, which is an improvement upon previous corn-yield prediction models. In addition to annual corn yield forecasting, we examined model uncertainties through spatial and temporal analysis of the model's predictive error distribution. The magnitude of predictive error (by county) appears to be associated with the spatial patterns of corn fields in the study area.
A GIS modeling method applied to predicting forest songbird habitat

USGS Publications Warehouse

Dettmers, Randy; Bart, Jonathan

1999-01-01

We have developed an approach for using a??presencea?? data to construct habitat models. Presence data are those that indicate locations where the target organism is observed to occur, but that cannot be used to define locations where the organism does not occur. Surveys of highly mobile vertebrates often yield these kinds of data. Models developed through our approach yield predictions of the amount and the spatial distribution of good-quality habitat for the target species. This approach was developed primarily for use in a GIS context; thus, the models are spatially explicit and have the potential to be applied over large areas. Our method consists of two primary steps. In the first step, we identify an optimal range of values for each habitat variable to be used as a predictor in the model. To find these ranges, we employ the concept of maximizing the difference between cumulative distribution functions of (1) the values of a habitat variable at the observed presence locations of the target organism, and (2) the values of that habitat variable for all locations across a study area. In the second step, multivariate models of good habitat are constructed by combining these ranges of values, using the Boolean operators a??anda?? and a??or.a?? We use an approach similar to forward stepwise regression to select the best overall model. We demonstrate the use of this method by developing species-specific habitat models for nine forest-breeding songbirds (e.g., Cerulean Warbler, Scarlet Tanager, Wood Thrush) studied in southern Ohio. These models are based on speciesa?? microhabitat preferences for moisture and vegetation characteristics that can be predicted primarily through the use of abiotic variables. We use slope, land surface morphology, land surface curvature, water flow accumulation downhill, and an integrated moisture index, in conjunction with a land-cover classification that identifies forest/nonforest, to develop these models. The performance of these
Whole season compared to growth-stage resolved temperature trends: implications for US maize yield

NASA Astrophysics Data System (ADS)

Butler, E. E.; Mueller, N. D.; Huybers, P. J.

2014-12-01

The effect of temperature on maize yield has generally been considered using a single value for the entire growing season. We compare the effect of temperature trends on yield between two distinct models: a single temperature sensitivity for the whole season and a variable sensitivity across four distinct agronomic development stages. The more resolved variable-sensitivity model indicates roughly a factor of two greater influence of temperature on yield than that implied by the single-sensitivity model. The largest discrepancies occur in silking, which is demonstrated to be the most sensitive stage in the variable-sensitivity model. For instance, whereas median yields are observed to be only 53% of typical values during the hottest 1% of silking-stage temperatures, the single-sensitivity model over predicts median yields of 68% whereas the variable-sensitivity model more correctly predicts median yields of 61%. That the variable sensitivity model is also not capable of capturing the full extent of yield losses suggests that further refinement to represent the non-linear response would be useful. Results from the variable sensitivity model also indicate that management decisions regarding planting times, which have generally shifted toward earlier dates, have led to greater yield benefit than that implied by the single-sensitivity model. Together, the variation of both temperature trends and yield variability within growing stages calls for closer attention to how changes in management interact with changes in climate to ultimately affect yields.
Pure human urine is a good fertiliser for cucumbers.

PubMed

Heinonen-Tanski, Helvi; Sjöblom, Annalena; Fabritius, Helena; Karinen, Päivi

2007-01-01

Human urine obtained from separating toilets was tested as a fertiliser for cultivation of outdoor cucumber (Cucumis sativus L.) in a Nordic climate. The urine used contained high amounts of nitrogen with some phosphorus and potassium, but numbers of enteric microorganisms were low even though urine had not been preserved before sampling. The cucumber yield after urine fertilisation was similar or slightly better than the yield obtained from control rows fertilised with commercial mineral fertiliser. None of the cucumbers contained any enteric microorganisms (coliforms, enterococci, coliphages and clostridia). In the taste assessment, 11 out of 20 persons could recognise which cucumber of three cucumbers was different but they did not prefer one over the other cucumber samples, since all of them were assessed as equally good.
Assessment of different gridded weather data for soybean yield simulations in Brazil

NASA Astrophysics Data System (ADS)

Battisti, R.; Bender, F. D.; Sentelhas, P. C.

2018-01-01

A high-density, well-distributed, and consistent historical weather data series is of major importance for agricultural planning and climatic risk evaluation. A possible option for regions where weather station network is irregular is the use of gridded weather data (GWD), which can be downloaded online from different sources. Based on that, the aim of this study was to assess the suitability of two GWD, AgMERRA and XAVIER, by comparing them with measured weather data (MWD) for estimating soybean yield in Brazil. The GWD and MWD were obtained for 24 locations across Brazil, considering the period between 1980 and 2010. These data were used to estimate soybean yield with DSSAT-CROPGRO-Soybean model. The comparison of MWD with GWD resulted in a good agreement between climate variables, except for solar radiation. The crop simulations with GWD and MWD resulted in a good agreement for vegetative and reproductive phases. Soybean potential yield (Yp) simulated with AgMERRA and XAVIER had a high correlation (r > 0.88) when compared to the estimates with MWD, with the RMSE of about 400 kg ha-1. For attainable yield (Ya), estimates with XAVIER resulted in a RMSE of 700 kg ha-1 against 864 kg ha-1 from AgMERRA, both compared to the simulations using MWD. Even with these differences in Ya simulations, both GWD can be considered suitable for simulating soybean growth, development, and yield in Brazil; however, with XAVIER GWD presenting a better performance for weather and crop variables assessed.
AMMI adjustment for statistical analysis of an international wheat yield trial.

PubMed

Crossa, J; Fox, P N; Pfeiffer, W H; Rajaram, S; Gauch, H G

1991-01-01

Multilocation trials are important for the CIMMYT Bread Wheat Program in producing high-yielding, adapted lines for a wide range of environments. This study investigated procedures for improving predictive success of a yield trial, grouping environments and genotypes into homogeneous subsets, and determining the yield stability of 18 CIMMYT bread wheats evaluated at 25 locations. Additive Main effects and Multiplicative Interaction (AMMI) analysis gave more precise estimates of genotypic yields within locations than means across replicates. This precision facilitated formation by cluster analysis of more cohesive groups of genotypes and locations for biological interpretation of interactions than occurred with unadjusted means. Locations were clustered into two subsets for which genotypes with positive interactions manifested in high, stable yields were identified. The analyses highlighted superior selections with both broad and specific adaptation.
Soil Moisture as an Estimator for Crop Yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Meyer, Volker; Samaniego, Luis; Thober, Stephan

2015-04-01

Annual crop yield depends on various factors such as soil properties, management decisions, and meteorological conditions. Unfavorable weather conditions, e.g. droughts, have the potential to drastically diminish crop yield in rain-fed agriculture. For example, the drought in 2003 caused direct losses of 1.5 billion EUR only in Germany. Predicting crop yields allows to mitigate negative effects of weather extremes which are assumed to occur more often in the future due to climate change. A standard approach in economics is to predict the impact of climate change on agriculture as a function of temperature and precipitation. This approach has been developed further using concepts like growing degree days. Other econometric models use nonlinear functions of heat or vapor pressure deficit. However, none of these approaches uses soil moisture to predict crop yield. We hypothesize that soil moisture is a better indicator to explain stress on plant growth than estimations based on precipitation and temperature. This is the case because the latter variables do not explicitly account for the available water content in the root zone, which is the primary source of water supply for plant growth. In this study, a reduced form panel approach is applied to estimate a multivariate econometric production function for the years 1999 to 2010. Annual crop yield data of various crops on the administrative district level serve as depending variables. The explanatory variable of major interest is the Soil Moisture Index (SMI), which quantifies anomalies in root zone soil moisture. The SMI is computed by the mesoscale Hydrological Model (mHM, www.ufz.de/mhm). The index represents the monthly soil water quantile at a 4 km2 grid resolution covering entire Germany. A reduced model approach is suitable because the SMI is the result of a stochastic weather process and therefore can be considered exogenous. For the ease of interpretation a linear functionality is preferred. Meteorological
Evaluation of Video Image Analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions.

PubMed

Rius-Vilarrasa, E; Bünger, L; Maltin, C; Matthews, K R; Roehe, R

2009-05-01

The Meat and Livestock Commission's (MLC) EUROP classification based scheme and Video Image Analysis (VIA) system were compared in their ability to predict weights of primal carcass joints. A total of 443 commercial lamb carcasses under 12 months of age and mixed gender were selected by their cold carcass weight (CCW), conformation and fat scores. Lamb carcasses were classified for conformation and fatness, scanned by the VIA system and dissected into primal joints of leg, chump, loin, breast and shoulder. After adjustment for CCW, the estimation of primal joints using MLC EUROP scores showed high coefficients of determination (R(2)) in the range of 0.82-0.99. The use of VIA always resulted in equal or higher R(2). The precision measured as root mean square error (RMSE) was 27% (leg), 13% (chump), 1% (loin), 11% (breast), 5% (shoulders) and 13% (total primals) higher using VIA than MLC carcass information. Adjustment for slaughter day and gender effects indicated that estimations of primal joints using MLC EUROP scores were more sensitive to these factors than using VIA. This was consistent with an increase in stability of the prediction model of 28%, 11%, 2%, 12%, 6% and 14% for leg, chump, loin, breast and shoulder and total primals, respectively, using VIA compared to MLC EUROP scores. Consequently, VIA was capable of improving the prediction of primal meat yields compared to the current MLC EUROP carcass classification scheme used in the UK abattoirs.
Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819
The kinetic origin of delayed yielding in metallic glasses

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

Ye, Y. F.; Liu, X. D.; Wang, S.

2016-06-20

Recent experiments showed that irreversible structural change or plasticity could occur in metallic glasses (MGs) even within the apparent elastic limit after a sufficiently long waiting time. To explain this phenomenon, a stochastic shear transformation model is developed based on a unified rate theory to predict delayed yielding in MGs, which is validated afterwards through extensive atomistic simulations carried out on different MGs. On a fundamental level, an analytic framework is established in this work that links time, stress, and temperature altogether into a general yielding criterion for MGs.
The effect of soil moisture anomalies on maize yield in Germany

NASA Astrophysics Data System (ADS)

Peichl, Michael; Thober, Stephan; Meyer, Volker; Samaniego, Luis

2018-03-01

Crop models routinely use meteorological variations to estimate crop yield. Soil moisture, however, is the primary source of water for plant growth. The aim of this study is to investigate the intraseasonal predictability of soil moisture to estimate silage maize yield in Germany. We also evaluate how approaches considering soil moisture perform compare to those using only meteorological variables. Silage maize is one of the most widely cultivated crops in Germany because it is used as a main biomass supplier for energy production in the course of the German Energiewende (energy transition). Reduced form fixed effect panel models are employed to investigate the relationships in this study. These models are estimated for each month of the growing season to gain insights into the time-varying effects of soil moisture and meteorological variables. Temperature, precipitation, and potential evapotranspiration are used as meteorological variables. Soil moisture is transformed into anomalies which provide a measure for the interannual variation within each month. The main result of this study is that soil moisture anomalies have predictive skills which vary in magnitude and direction depending on the month. For instance, dry soil moisture anomalies in August and September reduce silage maize yield more than 10 %, other factors being equal. In contrast, dry anomalies in May increase crop yield up to 7 % because absolute soil water content is higher in May compared to August due to its seasonality. With respect to the meteorological terms, models using both temperature and precipitation have higher predictability than models using only one meteorological variable. Also, models employing only temperature exhibit elevated effects.
Concept analysis of good death in long term care residents.

PubMed

Krishnan, Preetha

2017-01-02

The purpose of this concept analysis paper is to delineate the meaning of good death in long term care (LTC) settings and examine its implications for nursing. The Walker and Avant (2011) method was chosen for this analysis. An in depth literature review identifies uses of the concept and determines the defining attributes of the good death. This paper also illustrates case presentations, antecedents, consequences, empirical referents and implications for clinical practice to clarify the concept of 'good death' in this population. In LTC, death is experienced frequently and is considered the ultimate outcome for most admissions. Much of the existing research on end-of-life care has focused on community dwelling cancer patients whose death trajectory is predictable and who may remain cognitively intact until actively dying. In contrast, the LTC population is older and more likely to suffer from dementia and experience chronic illness for long periods prior to death, and they follow a less predictable death trajectory. In this century, death became the province of older people and the assurance of a good death became the responsibility of those caring for them.
Multimodel predictive system for carbon dioxide solubility in saline formation waters.

PubMed

Wang, Zan; Small, Mitchell J; Karamalidis, Athanasios K

2013-02-05

The prediction of carbon dioxide solubility in brine at conditions relevant to carbon sequestration (i.e., high temperature, pressure, and salt concentration (T-P-X)) is crucial when this technology is applied. Eleven mathematical models for predicting CO(2) solubility in brine are compared and considered for inclusion in a multimodel predictive system. Model goodness of fit is evaluated over the temperature range 304-433 K, pressure range 74-500 bar, and salt concentration range 0-7 m (NaCl equivalent), using 173 published CO(2) solubility measurements, particularly selected for those conditions. The performance of each model is assessed using various statistical methods, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Different models emerge as best fits for different subranges of the input conditions. A classification tree is generated using machine learning methods to predict the best-performing model under different T-P-X subranges, allowing development of a multimodel predictive system (MMoPS) that selects and applies the model expected to yield the most accurate CO(2) solubility prediction. Statistical analysis of the MMoPS predictions, including a stratified 5-fold cross validation, shows that MMoPS outperforms each individual model and increases the overall accuracy of CO(2) solubility prediction across the range of T-P-X conditions likely to be encountered in carbon sequestration applications.
Yield modeling of acoustic charge transport transversal filters

NASA Technical Reports Server (NTRS)

Kenney, J. S.; May, G. S.; Hunt, W. D.

1995-01-01

This paper presents a yield model for acoustic charge transport transversal filters. This model differs from previous IC yield models in that it does not assume that individual failures of the nondestructive sensing taps necessarily cause a device failure. A redundancy in the number of taps included in the design is explained. Poisson statistics are used to describe the tap failures, weighted over a uniform defect density distribution. A representative design example is presented. The minimum number of taps needed to realize the filter is calculated, and tap weights for various numbers of redundant taps are calculated. The critical area for device failure is calculated for each level of redundancy. Yield is predicted for a range of defect densities and redundancies. To verify the model, a Monte Carlo simulation is performed on an equivalent circuit model of the device. The results of the yield model are then compared to the Monte Carlo simulation. Better than 95% agreement was obtained for the Poisson model with redundant taps ranging from 30% to 150% over the minimum.
Rainfall prediction with backpropagation method

NASA Astrophysics Data System (ADS)

Wahyuni, E. G.; Fauzan, L. M. F.; Abriyani, F.; Muchlis, N. F.; Ulfa, M.

2018-03-01

Rainfall is an important factor in many fields, such as aviation and agriculture. Although it has been assisted by technology but the accuracy can not reach 100% and there is still the possibility of error. Though current rainfall prediction information is needed in various fields, such as agriculture and aviation fields. In the field of agriculture, to obtain abundant and quality yields, farmers are very dependent on weather conditions, especially rainfall. Rainfall is one of the factors that affect the safety of aircraft. To overcome the problems above, then it’s required a system that can accurately predict rainfall. In predicting rainfall, artificial neural network modeling is applied in this research. The method used in modeling this artificial neural network is backpropagation method. Backpropagation methods can result in better performance in repetitive exercises. This means that the weight of the ANN interconnection can approach the weight it should be. Another advantage of this method is the ability in the learning process adaptively and multilayer owned on this method there is a process of weight changes so as to minimize error (fault tolerance). Therefore, this method can guarantee good system resilience and consistently work well. The network is designed using 4 input variables, namely air temperature, air humidity, wind speed, and sunshine duration and 3 output variables ie low rainfall, medium rainfall, and high rainfall. Based on the research that has been done, the network can be used properly, as evidenced by the results of the prediction of the system precipitation is the same as the results of manual calculations.
Optimal design criteria - prediction vs. parameter estimation

NASA Astrophysics Data System (ADS)

Waldl, Helmut

2014-05-01

G-optimality is a popular design criterion for optimal prediction, it tries to minimize the kriging variance over the whole design region. A G-optimal design minimizes the maximum variance of all predicted values. If we use kriging methods for prediction it is self-evident to use the kriging variance as a measure of uncertainty for the estimates. Though the computation of the kriging variance and even more the computation of the empirical kriging variance is computationally very costly and finding the maximum kriging variance in high-dimensional regions can be time demanding such that we cannot really find the G-optimal design with nowadays available computer equipment in practice. We cannot always avoid this problem by using space-filling designs because small designs that minimize the empirical kriging variance are often non-space-filling. D-optimality is the design criterion related to parameter estimation. A D-optimal design maximizes the determinant of the information matrix of the estimates. D-optimality in terms of trend parameter estimation and D-optimality in terms of covariance parameter estimation yield basically different designs. The Pareto frontier of these two competing determinant criteria corresponds with designs that perform well under both criteria. Under certain conditions searching the G-optimal design on the above Pareto frontier yields almost as good results as searching the G-optimal design in the whole design region. In doing so the maximum of the empirical kriging variance has to be computed only a few times though. The method is demonstrated by means of a computer simulation experiment based on data provided by the Belgian institute Management Unit of the North Sea Mathematical Models (MUMM) that describe the evolution of inorganic and organic carbon and nutrients, phytoplankton, bacteria and zooplankton in the Southern Bight of the North Sea.
Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-02-01

Multiresolution analysis techniques including continuous wavelet transform, empirical mode decomposition, and variational mode decomposition are tested in the context of interest rate next-day variation prediction. In particular, multiresolution analysis techniques are used to decompose interest rate actual variation and feedforward neural network for training and prediction. Particle swarm optimization technique is adopted to optimize its initial weights. For comparison purpose, autoregressive moving average model, random walk process and the naive model are used as main reference models. In order to show the feasibility of the presented hybrid models that combine multiresolution analysis techniques and feedforward neural network optimized by particle swarm optimization, we used a set of six illustrative interest rates; including Moody's seasoned Aaa corporate bond yield, Moody's seasoned Baa corporate bond yield, 3-Month, 6-Month and 1-Year treasury bills, and effective federal fund rate. The forecasting results show that all multiresolution-based prediction systems outperform the conventional reference models on the criteria of mean absolute error, mean absolute deviation, and root mean-squared error. Therefore, it is advantageous to adopt hybrid multiresolution techniques and soft computing models to forecast interest rate daily variations as they provide good forecasting performance.

Scalar resonances in a unitary {pi}{pi} S-wave model for D{sup +}{yields}{pi}{sup +}{pi}{sup -}{pi}{sup +}

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

Boito, D. R.; Instituto de Fisica, Universidade de Sao Paulo, C.P. 66318, 05315-970, Sao Paulo, SP; Dedonder, J.-P.

We propose a model for D{sup +}{yields}{pi}{sup +}{pi}{sup -}{pi}{sup +} decays following experimental results which indicate that the two-pion interaction in the S wave is dominated by the scalar resonances f{sub 0}(600)/{sigma} and f{sub 0}(980). The weak decay amplitude for D{sup +}{yields}R{pi}{sup +}, where R is a resonance that subsequently decays into {pi}{sup +}{pi}{sup -}, is constructed in a factorization approach. In the S wave, we implement the strong decay R{yields}{pi}{sup +}{pi}{sup -} by means of a scalar form factor. This provides a unitary description of the pion-pion interaction in the entire kinematically allowed mass range m{sub {pi}}{sub {pi}}{sup 2}more » from threshold to about 3 GeV{sup 2}. In order to reproduce the experimental Dalitz plot for D{sup +}{yields}{pi}{sup +}{pi}{sup -}{pi}{sup +}, we include contributions beyond the S wave. For the P wave, dominated by the {rho}(770){sup 0}, we use a Breit-Wigner description. Higher waves are accounted for by using the usual isobar prescription for the f{sub 2}(1270) and {rho}(1450){sup 0}. The major achievement is a good reproduction of the experimental m{sub {pi}}{sub {pi}}{sup 2} distribution, and of the partial as well as the total D{sup +}{yields}{pi}{sup +}{pi}{sup -}{pi}{sup +} branching ratios. Our values are generally smaller than the experimental ones. We discuss this shortcoming and, as a by-product, we predict a value for the poorly known D{yields}{sigma} transition form factor at q{sup 2}=m{sub {pi}}{sup 2}.« less
Role of the N*(1535) in pp{yields}pp{phi} and {pi}{sup -}p{yields}n{phi} reactions

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

Xie Jujun; Graduate University of Chinese Academy of Sciences, Beijing 100049; Zou Bingsong

2008-01-15

The near-threshold {phi}-meson production in proton-proton and {pi}{sup -}p collisions is studied with the assumption that the production mechanism is due to the sub-N{phi}-threshold N*(1535) resonance. The {pi}{sup 0}-,{eta}-, and {rho}{sup 0}-meson exchanges for proton-proton collisions are considered. It is shown that the contribution to the pp{yields}pp{phi} reaction from the t-channel {pi}{sup 0}-meson exchange is dominant. With a significant N*(1535)N{phi} coupling [g{sub N*(1535)N{phi}}{sup 2}/4{pi}=0.13], both pp{yields}pp{phi} and {pi}{sup -}p{yields}n{phi} data are very well reproduced. The significant coupling of the N*(1535) resonance to N{phi} is compatible with previous indications of a large ss component in the quark wave function of themore » N*(1535) resonance and may be the real origin of the significant enhancement of the {phi} production over the naive OZI-rule predictions.« less
Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model

NASA Astrophysics Data System (ADS)

Bach, Heike

1998-07-01

In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Württemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.
Validation of finite element and boundary element methods for predicting structural vibration and radiated noise

NASA Technical Reports Server (NTRS)

Seybert, A. F.; Wu, X. F.; Oswald, Fred B.

1992-01-01

Analytical and experimental validation of methods to predict structural vibration and radiated noise are presented. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and boundary element method (BEM) models of the apparatus were used to predict the noise radiated from the box. The FEM was used to predict the vibration, and the surface vibration was used as input to the BEM to predict the sound intensity and sound power. Vibration predicted by the FEM model was validated by experimental modal analysis. Noise predicted by the BEM was validated by sound intensity measurements. Three types of results are presented for the total radiated sound power: (1) sound power predicted by the BEM modeling using vibration data measured on the surface of the box; (2) sound power predicted by the FEM/BEM model; and (3) sound power measured by a sound intensity scan. The sound power predicted from the BEM model using measured vibration data yields an excellent prediction of radiated noise. The sound power predicted by the combined FEM/BEM model also gives a good prediction of radiated noise except for a shift of the natural frequencies that are due to limitations in the FEM model.
Clinical outcomes and predictive factors related to good outcomes in plasma exchange in severe attack of NMOSD and long extensive transverse myelitis: Case series and review of the literature.

PubMed

Aungsumart, Saharat; Apiwattanakul, Metha

2017-04-01

To investigate the predictive factors associated with good outcomes of plasma exchange in severe attacks through neuromyelitis optica spectrum disorder (NMOSD) and long extensive transverse myelitis (LETM). In addition, to review the literature of predictive factors associated with the good outcomes of plasma exchange in central nervous system inflammatory demyelinating diseases (CNS IDDs). Retrospective study in 27 episodes of severe acute attacks myelitis and optic neuritis in 24 patients, including 20 patients with NMOSD seropositive, 1 patient with NMOSD seronegative and 3 patients with LETM. Plasma exchange was performed, reflecting poor responses to high-dose intravenous methylprednisolone (IVMP) therapy. The outcomes of the present study were the functional outcome improvements at 6 months after plasma exchange. The predictive factors of good outcomes after plasma exchange were determined in this cohort, and additional factors reported in the literature were reviewed. Plasma exchange was performed in 16 spinal cord attacks and 11 attacks of optic neuritis. Twenty patients were female (83%). The median age of the patients at the time of plasma exchange was 41 years old. The median disease duration was 0.6 years. The AQP4-IgG status was positive in 20 patients (83%). Plasma exchange following IVMP therapy led to a significant improvement in 81% of the cases after 6 months of follow up. A baseline Expanded Disability Status Scale (EDSS) score ≤6 before the attack was associated with significant improvement at 6 months (p=0.02, OR 58.33, 95%CI 1.92-1770). In addition, we reviewed the evidence for factors associated with good outcomes of plasma exchange in CNS IDDs, classified according to pre-plasma exchange, post-plasma exchange, and radiological features. Plasma exchange following IVMP therapy is effective as a treatment for patients experiencing a severe attack of NMOSD or LETM. The factors associated with good outcomes after plasma exchange in CNS IDDs are
Yield of illicit indoor cannabis cultivation in the Netherlands.

PubMed

Toonen, Marcel; Ribot, Simon; Thissen, Jac

2006-09-01

To obtain a reliable estimation on the yield of illicit indoor cannabis cultivation in The Netherlands, cannabis plants confiscated by the police were used to determine the yield of dried female flower buds. The developmental stage of flower buds of the seized plants was described on a scale from 1 to 10 where the value of 10 indicates a fully developed flower bud ready for harvesting. Using eight additional characteristics describing the grow room and cultivation parameters, regression analysis with subset selection was carried out to develop two models for the yield of indoor cannabis cultivation. The median Dutch illicit grow room consists of 259 cannabis plants, has a plant density of 15 plants/m(2), and 510 W of growth lamps per m(2). For the median Dutch grow room, the predicted yield of female flower buds at the harvestable developmental stage (stage 10) was 33.7 g/plant or 505 g/m(2).
Improving yield and performance in ZnO thin-film transistors made using selective area deposition.

PubMed

Nelson, Shelby F; Ellinger, Carolyn R; Levy, David H

2015-02-04

We describe improvements in both yield and performance for thin-film transistors (TFTs) fabricated by spatial atomic layer deposition (SALD). These improvements are shown to be critical in forming high-quality devices using selective area deposition (SAD) as the patterning method. Selective area deposition occurs when the precursors for the deposition are prevented from reacting with some areas of the substrate surface. Controlling individual layer quality and the interfaces between layers is essential for obtaining good-quality thin-film transistors and capacitors. The integrity of the gate insulator layer is particularly critical, and we describe a method for forming a multilayer dielectric using an oxygen plasma treatment between layers that improves crossover yield. We also describe a method to achieve improved mobility at the important interface between the semiconductor and the gate insulator by, conversely, avoiding oxygen plasma treatment. Integration of the best designs results in wide design flexibility, transistors with mobility above 15 cm(2)/(V s), and good yield of circuits.
Minimum number of measurements for evaluating soursop (Annona muricata L.) yield.

PubMed

Sánchez, C F B; Teodoro, P E; Londoño, S; Silva, L A; Peixoto, L A; Bhering, L L

2017-05-31

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.
Possible changes to arable crop yields by 2050

PubMed Central

Jaggard, Keith W.; Qi, Aiming; Ober, Eric S.

2010-01-01

By 2050, the world population is likely to be 9.1 billion, the CO2 concentration 550 ppm, the ozone concentration 60 ppb and the climate warmer by ca 2°C. In these conditions, what contribution can increased crop yield make to feeding the world? CO2 enrichment is likely to increase yields of most crops by approximately 13 per cent but leave yields of C4 crops unchanged. It will tend to reduce water consumption by all crops, but this effect will be approximately cancelled out by the effect of the increased temperature on evaporation rates. In many places increased temperature will provide opportunities to manipulate agronomy to improve crop performance. Ozone concentration increases will decrease yields by 5 per cent or more. Plant breeders will probably be able to increase yields considerably in the CO2-enriched environment of the future, and most weeds and airborne pests and diseases should remain controllable, so long as policy changes do not remove too many types of crop-protection chemicals. However, soil-borne pathogens are likely to be an increasing problem when warmer weather will increase their multiplication rates; control is likely to need a transgenic approach to breeding for resistance. There is a large gap between achievable yields and those delivered by farmers, even in the most efficient agricultural systems. A gap is inevitable, but there are large differences between farmers, even between those who have used the same resources. If this gap is closed and accompanied by improvements in potential yields then there is a good prospect that crop production will increase by approximately 50 per cent or more by 2050 without extra land. However, the demands for land to produce bio-energy have not been factored into these calculations. PMID:20713388
Possible changes to arable crop yields by 2050.

PubMed

Jaggard, Keith W; Qi, Aiming; Ober, Eric S

2010-09-27

By 2050, the world population is likely to be 9.1 billion, the CO(2) concentration 550 ppm, the ozone concentration 60 ppb and the climate warmer by ca 2 degrees C. In these conditions, what contribution can increased crop yield make to feeding the world? CO(2) enrichment is likely to increase yields of most crops by approximately 13 per cent but leave yields of C4 crops unchanged. It will tend to reduce water consumption by all crops, but this effect will be approximately cancelled out by the effect of the increased temperature on evaporation rates. In many places increased temperature will provide opportunities to manipulate agronomy to improve crop performance. Ozone concentration increases will decrease yields by 5 per cent or more. Plant breeders will probably be able to increase yields considerably in the CO(2)-enriched environment of the future, and most weeds and airborne pests and diseases should remain controllable, so long as policy changes do not remove too many types of crop-protection chemicals. However, soil-borne pathogens are likely to be an increasing problem when warmer weather will increase their multiplication rates; control is likely to need a transgenic approach to breeding for resistance. There is a large gap between achievable yields and those delivered by farmers, even in the most efficient agricultural systems. A gap is inevitable, but there are large differences between farmers, even between those who have used the same resources. If this gap is closed and accompanied by improvements in potential yields then there is a good prospect that crop production will increase by approximately 50 per cent or more by 2050 without extra land. However, the demands for land to produce bio-energy have not been factored into these calculations.
Maximum credibly yield for deuteriuim-filled double shell imaging targets meeting requirements for yield bin Category A

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

Wilson, Douglas Carl; Loomis, Eric Nicholas

2017-08-17

We are anticipating our first NIF double shell shot using an aluminum ablator and a glass inner shell filled with deuterium shown in figure 1. The expected yield is between a few 10 10 to a few 10 11 dd neutrons. The maximum credible yield is 5e+13. This memo describes why, and what would be expected with variations on the target. This memo evaluates the maximum credible yield for deuterium filled double shell capsule targets with an aluminum ablator shell and a glass inner shell in yield Category A (< 10 14 neutrons). It also pertains to fills of gasmore » diluted with hydrogen, helium ( 3He or 4He), or any other fuel except tritium. This memo does not apply to lower z ablator dopants, such as beryllium, as this would increase the ablation efficiency. This evaluation is for 5.75 scale hohlraum targets of either gold or uranium with helium gas fills with density between 0 and 1.6 mg/cc. It could be extended to other hohlraum sizes and shapes with slight modifications. At present only laser pulse energies up to 1.5 MJ were considered with a single step laser pulse of arbitrary shape. Since yield decreases with laser energy for this target, the memo could be extended to higher laser energies if desired. These maximum laser parameters of pulses addressed here are near the edge of NIF’s capability, and constitute the operating envelope for experiments covered by this memo. We have not considered multiple step pulses, would probably create no advantages in performance, and are not planned for double shell capsules. The main target variables are summarized in Table 1 and explained in detail in the memo. Predicted neutron yields are based on 1D and 2D clean simulations.« less
An analysis of yield stability in a conservation agriculture system

USDA-ARS?s Scientific Manuscript database

Climate models predict increasing growing-season weather variability, with negative consequences for crop production. Maintaining agricultural productivity despite variability in weather (i.e., crop yield stability) will be critical to meeting growing global demand. Conservation agriculture is an ...
Meta-analyses of oil yield in Cuphea PSR23

USDA-ARS?s Scientific Manuscript database

Oil content and composition of Cuphea seed are of special economic value as raw materials for industrial and food applications. The inherent unpredictability in determining and predicting Cuphea’s oil yield is attributed, in part, to the indeterminate growth habit and the persistence of the domestic...
Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

NASA Astrophysics Data System (ADS)

Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

2018-03-01

During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. 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 to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.
Diagnostic yield and predictive factors of findings in small-bowel capsule endoscopy in the setting of iron-deficiency anemia.

PubMed

Olano, Carolina; Pazos, Ximena; Avendaño, Karla; Calleri, Alfonso; Ketzoian, Carlos

2018-06-01

Despite the widespread use of small-bowel capsule endoscopy (CE), there is still limited data on its utility and effectiveness for the diagnosis and management of patients with iron-deficiency anemia (IDA). To assess the diagnostic yield of CE and the factors predicting positive findings in patients with IDA. Patients with unexplained IDA and negative upper and lower endoscopy were included. A positive diagnostic yield was considered when CE diagnosed one or more lesions that could explain the IDA. Sex, age, NSAID consumption, blood transfusion requirement, and ferritin and hemoglobin levels were recorded. In total, 120 CE were included (mean age 58.5 years; F/M 82:38). Mean hemoglobin levels were 9 g/dL and mean ferritin levels were 15.7 ng/mL. Positive findings were present in 50 % of patients. The most frequent was angiodysplasia (45 %). Despite several baseline variables being significantly associated with positive findings, using a logistic regression model, it was verified that male sex (OR 3.93; 95 %CI 1.57 - 9.86), age (OR 1.03; 95 %CI 1.00 - 1.06), and hemoglobin levels (OR 0.73; 95 %CI 0.57 - 0.94) were the variables having an independent effect on the probability of obtaining positive findings. Age older than 50 years (OR 14.05; 95 %CI 1.69 - 116.23) and male sex (OR 3.63; 95 %CI 1.29 - 10.17) were the variables which increased the risk of diagnosing angiodysplasia. CE is a useful technique in patients with IDA. To improve its yield, it is necessary to select patients carefully. Male sex, older age, and low hemoglobin levels were associated with a risk of positive finding in this group of patients. The risk of diagnosing angiodysplasia increased with male sex and older age.
Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

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

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less
Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

DOE PAGES

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.; ...

2017-06-03

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less
Genome-enabled prediction models for yield related traits in chickpea

USDA-ARS?s Scientific Manuscript database

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped...
Simulating the effect of vegetation cover on the sediment yield of mediterranean catchments using SHETRAN

NASA Astrophysics Data System (ADS)

Lukey, B. T.; Sheffield, J.; Bathurst, J. C.; Lavabre, J.; Mathys, N.; Martin, C.

1995-08-01

The sediment yield of two catchments in southern France was modelled using the newly developed sediment code of SHETRAN. A fire in August 1990 denuded the Rimbaud catchment, providing an opportunity to study the effect of vegetation cover on sediment yield by running the model for both pre-and post-fire cases. Model output is in the form of upper and lower bounds on sediment discharge, reflecting the uncertainty in the erodibility of the soil. The results are encouraging since measured sediment discharge falls largely between the predicted bounds, and simulated sediment yield is dramatically lower for the catchment before the fire which matches observation. SHETRAN is also applied to the Laval catchment, which is subject to Badlands gulley erosion. Again using the principle of generating upper and lower bounds on sediment discharge, the model is shown to be capable of predicting the bulk sediment discharge over periods of months. To simulate the effect of reforestation, the model is run with vegetation cover equivalent to a neighbouring fully forested basin. The results obtained indicate that SHETRAN provides a powerful tool for predicting the impact of environmental change and land management on sediment yield.
Nanoscale Roughness of Natural Fault Surfaces Controlled by Scale-Dependent Yield Strength

NASA Astrophysics Data System (ADS)

Thom, C. A.; Brodsky, E. E.; Carpick, R. W.; Pharr, G. M.; Oliver, W. C.; Goldsby, D. L.

2017-09-01

Many natural fault surfaces exhibit remarkably similar scale-dependent roughness, which may reflect the scale-dependent yield strength of rocks. Using atomic force microscopy (AFM), we show that a sample of the Corona Heights Fault exhibits isotropic surface roughness well-described by a power law, with a Hurst exponent of 0.75 +/- 0.05 at all wavelengths from 60 nm to 10 μm. The roughness data and a recently proposed theoretical framework predict that yield strength varies with length scale as λ-0.25+/-0.05. Nanoindentation tests on the Corona Heights sample and another fault sample whose topography was previously measured with AFM (the Yair Fault) reveal a scale-dependent yield stress with power-law exponents of -0.12 +/- 0.06 and -0.18 +/- 0.08, respectively. These values are within one to two standard deviations of the predicted value, and provide experimental evidence that fault roughness is controlled by intrinsic material properties, which produces a characteristic surface geometry.

Crystal structure of minoxidil at low temperature and polymorph prediction.

PubMed

Martín-Islán, Africa P; Martín-Ramos, Daniel; Sainz-Díaz, C Ignacio

2008-02-01

An experimental and theoretical investigation on crystal forms of the popular and ubiquitous pharmaceutical Minoxidil is presented here. A new crystallization method is presented for Minoxidil (6-(1-piperidinyl)-2,4-pyrimidinediamide 3-oxide) in ethanol-poly(ethylene glycol), yielding crystals with good quality. The crystal structure is determined at low temperature, with a final R value of 0.035, corresponding to space group P2(1) (monoclinic) with cell dimensions a = 9.357(1) A, b = 8.231(1) A, c = 12.931(2) A, and beta = 90.353(4) degrees . Theoretical calculations of the molecular structure of Minoxidil are set forward using empirical force fields and quantum-mechanical methods. A theoretical prediction for Minoxidil crystal structure shows many possible polymorphs. The predicted crystal structures are compared with X-ray experimental data obtained in our laboratory, and the experimental crystal form is found to be one of the lowest energy polymorphs.
Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials.

PubMed

Michel, Sebastian; Ametz, Christian; Gungor, Huseyin; Akgöl, Batuhan; Epure, Doru; Grausgruber, Heinrich; Löschenberger, Franziska; Buerstmayr, Hermann

2017-02-01

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials. The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.
A logging residue "yield" table for Appalachian hardwoods

Treesearch

A. Jeff Martin

1976-01-01

An equation for predicting logging-residue volume per acre for Appalachian hardwoods was developed from data collected on 20 timber sales in national forests in West Virginia and Virginia. The independent variables of type-of-cut, products removed, basal area per acre, and stand age explained 95 percent of the variation in residue volume per acre. A "yield"...
Yield gaps and yield relationships in US soybean production systems

USDA-ARS?s Scientific Manuscript database

The magnitude of yield gaps (YG) (potential yield – farmer yield) provides some indication of the prospects for increasing crop yield to meet the food demands of future populations. Quantile regression analysis was applied to county soybean [Glycine max (L.) Merrill] yields (1971 – 2011) from Kentuc...
Evaluation of the CEAS model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The CEAS yield model is based upon multiple regression analysis at the CRD and state levels. For the historical time series, yield is regressed on a set of variables derived from monthly mean temperature and monthly precipitation. Technological trend is represented by piecewise linear and/or quadriatic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-79) demonstrated that biases are small and performance as indicated by the root mean square errors are acceptable for intended application, however, model response for individual years particularly unusual years, is not very reliable and shows some large errors. The model is objective, adequate, timely, simple and not costly. It considers scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.
S100A8+ stroma cells predict a good prognosis and inhibit aggressiveness in colorectal carcinoma.

PubMed

Li, Si; Xu, Fangying; Li, Hui; Zhang, Jing; Zhong, Anjing; Huang, Bin; Lai, Maode

2017-01-01

Gene microarray and bioinformatic analysis showed that S100A8 was more abundant in the stroma surrounding tumor buddings (TBs) than in the stroma surrounding primary tumor cells in colorectal carcinomas. Here, S100A8 + cells in 419 colorectal carcinoma samples were stained by immunohistochemistry and counted using Image-pro plus 6.0. TBs were also counted and biomarkers associated with the epithelial-mesenchymal transition and apoptosis were assessed by immunohistochemistry. We evaluated the association between S100A8 + cells and clinico-pathological variables as well as survival. Migration and invasion as well as biomarkers of the epithelial-mesenchymal transition and apoptosis were tested in CRC cells, treated with graded concentrations of recombinant human S100A8 protein. We found that the density of S100A8 + cells in the tumor invasive front (S100A8 + TIF ) clearly distinguished patients with 5-y survival from those who did not survive ( p = 0.01). The S100A8 + -associated tumor budding (SATB) index determined by the S100A8 + TIF and TB was an independent predictor of overall survival ( p = 0.001) other than the S100A8 + TIF or TB alone. Migration and invasion properties of CRC cells were inhibited by recombinant human S100A8 treatment. The particular S100A8 + cells in the stroma were associated with important biomarkers of the epithelial-mesenchymal transition (E-cadherin and SNAIL) and apoptosis (BCL2). In conclusion, S100A8 + cells in the stroma predict a good prognosis in colorectal carcinoma. An index combining S100A8 + cells and TB independently predicts survival. Recombinant human S100A8 inhibited CRC cell migration and invasion, which was involved in epithelial-mesenchymal transition (E-cadherin and SNAIL) and apoptosis (BCL2).
S100A8+ stroma cells predict a good prognosis and inhibit aggressiveness in colorectal carcinoma

PubMed Central

Li, Si; Xu, Fangying; Li, Hui; Zhang, Jing; Zhong, Anjing; Huang, Bin; Lai, Maode

2017-01-01

ABSTRACT Gene microarray and bioinformatic analysis showed that S100A8 was more abundant in the stroma surrounding tumor buddings (TBs) than in the stroma surrounding primary tumor cells in colorectal carcinomas. Here, S100A8+ cells in 419 colorectal carcinoma samples were stained by immunohistochemistry and counted using Image-pro plus 6.0. TBs were also counted and biomarkers associated with the epithelial–mesenchymal transition and apoptosis were assessed by immunohistochemistry. We evaluated the association between S100A8+ cells and clinico-pathological variables as well as survival. Migration and invasion as well as biomarkers of the epithelial–mesenchymal transition and apoptosis were tested in CRC cells, treated with graded concentrations of recombinant human S100A8 protein. We found that the density of S100A8+ cells in the tumor invasive front (S100A8+TIF) clearly distinguished patients with 5-y survival from those who did not survive (p = 0.01). The S100A8+-associated tumor budding (SATB) index determined by the S100A8+TIF and TB was an independent predictor of overall survival (p = 0.001) other than the S100A8+TIF or TB alone. Migration and invasion properties of CRC cells were inhibited by recombinant human S100A8 treatment. The particular S100A8+ cells in the stroma were associated with important biomarkers of the epithelial–mesenchymal transition (E-cadherin and SNAIL) and apoptosis (BCL2). In conclusion, S100A8+ cells in the stroma predict a good prognosis in colorectal carcinoma. An index combining S100A8+ cells and TB independently predicts survival. Recombinant human S100A8 inhibited CRC cell migration and invasion, which was involved in epithelial–mesenchymal transition (E-cadherin and SNAIL) and apoptosis (BCL2). PMID:28197382
Asymmetric Yield Function Based on the Stress Invariants for Pressure Sensitive Metals

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

Jeong Wahn Yoon; Yanshan Lou; Jong Hun Yoon

A general asymmetric yield function is proposed with dependence on the stress invariants for pressure sensitive metals. The pressure sensitivity of the proposed yield function is consistent with the experimental result of Spitzig and Richmond (1984) for steel and aluminum alloys while the asymmetry of the third invariant is preserved to model strength differential (SD) effect of pressure insensitive materials. The proposed yield function is transformed in the space of the stress triaxaility, the von Mises stress and the normalized invariant to theoretically investigate the possible reason of the SD effect. The proposed plasticity model is further extended to characterizemore » the anisotropic behavior of metals both in tension and compression. The extension of the yield function is realized by introducing two distinct fourth-order linear transformation tensors of the stress tensor for the second and third invariants, respectively. The extended yield function reasonably models the evolution of yield surfaces for a zirconium clock-rolled plate during in-plane and through-thickness compression reported by Plunkett et al. (2007). The extended yield function is also applied to describe the orthotropic behavior of a face-centered cubic metal of AA 2008-T4 and two hexagonal close-packed metals of high-purity-titanium and AZ31 magnesium alloy. The orthotropic behavior predicted by the generalized model is compared with experimental results of these metals. The comparison validates that the proposed yield function provides sufficient predictability on SD effect and anisotropic behavior both in tension and compression. When it is necessary to consider r-value anisotropy, the proposed function is efficient to be used with nonassociated flow plasticity by introducing a separate plastic potential for the consideration of r-values as shown in Stoughton & Yoon (2004, 2009).« less
Hyperextended Cosmological Perturbation Theory: Predicting Nonlinear Clustering Amplitudes

NASA Astrophysics Data System (ADS)

Scoccimarro, Román; Frieman, Joshua A.

1999-07-01

We consider the long-standing problem of predicting the hierarchical clustering amplitudes Sp in the strongly nonlinear regime of gravitational evolution. N-body results for the nonlinear evolution of the bispectrum (the Fourier transform of the three-point density correlation function) suggest a physically motivated Ansatz that yields the strongly nonlinear behavior of the skewness, S3, starting from leading-order perturbation theory. When generalized to higher order (p>3) polyspectra or correlation functions, this Ansatz leads to a good description of nonlinear amplitudes in the strongly nonlinear regime for both scale-free and cold dark matter models. Furthermore, these results allow us to provide a general fitting formula for the nonlinear evolution of the bispectrum that interpolates between the weakly and strongly nonlinear regimes, analogous to previous expressions for the power spectrum.
The yield of DNA double strand breaks determined after exclusion of those forming from heat-labile lesions predicts tumor cell radiosensitivity to killing.

PubMed

Cheng, Yanlei; Li, Fanghua; Mladenov, Emil; Iliakis, George

2015-09-01

The radiosensitivity to killing of tumor cells and in-field normal tissue are key determinants of radiotherapy response. In vitro radiosensitivity of tumor- and normal-tissue-derived cells often predicts radiation response, but high determination cost in time and resources compromise utility as routine response-predictor. Efforts to use induction or repair of DNA double-strand-breaks (DSBs) as surrogate-predictors of cell radiosensitivity to killing have met with limited success. Here, we re-visit this issue encouraged by our recent observations that ionizing radiation (IR) induces not only promptly-forming DSBs (prDSBs), but also DSBs developing after irradiation from the conversion to breaks of thermally-labile sugar-lesions (tlDSBs). We employ pulsed-field gel-electrophoresis and flow-cytometry protocols to measure total DSBs (tDSB=prDSB+tlDSBs) and prDSBs, as well as γH2AX and parameters of chromatin structure. We report a fully unexpected and in many ways unprecedented correlation between yield of prDSBs and radiosensitivity to killing in a battery of ten tumor cell lines that is not matched by yields of tDSBs or γH2AX, and cannot be explained by simple parameters of chromatin structure. We propose the introduction of prDSBs-yield as a novel and powerful surrogate-predictor of cell radiosensitivity to killing with potential for clinical application. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Predicting apparent singlet oxygen quantum yields of dissolved black carbon and humic substances using spectroscopic indices.

PubMed

Du, Ziyan; He, Yingsheng; Fan, Jianing; Fu, Heyun; Zheng, Shourong; Xu, Zhaoyi; Qu, Xiaolei; Kong, Ao; Zhu, Dongqiang

2018-03-01

Dissolved black carbon (DBC) is ubiquitous in aquatic systems, being an important subgroup of the dissolved organic matter (DOM) pool. Nevertheless, its aquatic photoactivity remains largely unknown. In this study, a range of spectroscopic indices of DBC and humic substance (HS) samples were determined using UV-Vis spectroscopy, fluorescence spectroscopy, and proton nuclear magnetic resonance. DBC can be readily differentiated from HS using spectroscopic indices. It has lower average molecular weight, but higher aromaticity and lignin content. The apparent singlet oxygen quantum yield (Φ singlet oxygen ) of DBC under simulated sunlight varies from 3.46% to 6.13%, significantly higher than HS, 1.26%-3.57%, suggesting that DBC is the more photoactive component in the DOM pool. Despite drastically different formation processes and structural properties, the Φ singlet oxygen of DBC and HS can be well predicted by the same simple linear regression models using optical indices including spectral slope coefficient (S 275-295 ) and absorbance ratio (E 2 /E 3 ) which are proxies for the abundance of singlet oxygen sensitizers and for the significance of intramolecular charge transfer interactions. The regression models can be potentially used to assess the photoactivity of DOM at large scales with in situ water spectrophotometry or satellite remote sensing. Copyright © 2017 Elsevier Ltd. All rights reserved.
Influence of biochemical composition during hydrothermal liquefaction of algae on product yields and fuel properties.

PubMed

Shakya, Rajdeep; Adhikari, Sushil; Mahadevan, Ravishankar; Shanmugam, Saravanan R; Nam, Hyungseok; Hassan, El Barbary; Dempster, Thomas A

2017-11-01

Hydrothermal liquefaction (HTL) of nine algae species were performed at two reaction temperatures (280 and 320°C) to compare the effect of their biomass composition on product yields and properties. Results obtained after HTL indicate large variations in terms of bio-oil yields and its properties. The maximum bio-oil yield (66wt%) was obtained at 320°C with a high lipid containing algae Nannochloropsis. The higher heating value of bio-oils ranged from 31 to 36MJ/kg and around 50% of the bio-oils was in the vacuum gas oil range while high lipid containing algae Nannochloropsis contained a significant portion (33-42%) in the diesel range. A predictive relationship between bio-oil yields and biochemical compositions was developed and showed a broad agreement between predictive and experimental yields. The aqueous phases obtained had high amount of TOC (12-43g/L), COD (35-160g/L), TN (1-18g/L), ammonium (0.34-12g/L) and phosphate (0.7-12g/L). Copyright © 2017 Elsevier Ltd. All rights reserved.
Simulating effects of microtopography on wetland specific yield and hydroperiod

USGS Publications Warehouse

Summer, David M.; Wang, Xixi

2011-01-01

Specific yield and hydroperiod have proven to be useful parameters in hydrologic analysis of wetlands. Specific yield is a critical parameter to quantitatively relate hydrologic fluxes (e.g., rainfall, evapotranspiration, and runoff) and water level changes. Hydroperiod measures the temporal variability and frequency of land-surface inundation. Conventionally, hydrologic analyses used these concepts without considering the effects of land surface microtopography and assumed a smoothly-varying land surface. However, these microtopographic effects could result in small-scale variations in land surface inundation and water depth above or below the land surface, which in turn affect ecologic and hydrologic processes of wetlands. The objective of this chapter is to develop a physically-based approach for estimating specific yield and hydroperiod that enables the consideration of microtopographic features of wetlands, and to illustrate the approach at sites in the Florida Everglades. The results indicate that the physically-based approach can better capture the variations of specific yield with water level, in particular when the water level falls between the minimum and maximum land surface elevations. The suggested approach for hydroperiod computation predicted that the wetlands might be completely dry or completely wet much less frequently than suggested by the conventional approach neglecting microtopography. One reasonable generalization may be that the hydroperiod approaches presented in this chapter can be a more accurate prediction tool for water resources management to meet the specific hydroperiod threshold as required by a species of plant or animal of interest.
Modeling the impact of bubbling bed hydrodynamics on tar yield and its fluctuations during biomass fast pyrolysis

DOE PAGES

Xiong, Qingang; Ramirez, Emilio; Pannala, Sreekanth; ...

2015-10-09

The impact of bubbling bed hydrodynamics on temporal variations in the exit tar yield for biomass fast pyrolysis was investigated using computational simulations of an experimental laboratory-scale reactor. A multi-fluid computational fluid dynamics model was employed to simulate the differential conservation equations in the reactor, and this was combined with a multi-component, multi-step pyrolysis kinetics scheme for biomass to account for chemical reactions. The predicted mean tar yields at the reactor exit appear to match corresponding experimental observations. Parametric studies predicted that increasing the fluidization velocity should improve the mean tar yield but increase its temporal variations. Increases in themore » mean tar yield coincide with reducing the diameter of sand particles or increasing the initial sand bed height. However, trends in tar yield variability are more complex than the trends in mean yield. The standard deviation in tar yield reaches a maximum with changes in sand particle size. As a result, the standard deviation in tar yield increases with the increases in initial bed height in freely bubbling state, while reaches a maximum in slugging state.« less
Diagnostic yield and safety of closed needle pleural biopsy in exudative pleural effusion.

PubMed

Rajawat, Govind Singh; Batra, Supreet; Takhar, Rajendra Prasad; Rathi, Lalit; Bhandari, Chand; Gupta, Manohar Lal

2017-01-01

Closed pleural biopsy was previously considered a procedure of choice in cases of undiagnosed pleural effusion with good efficacy. Currently, the closed pleural biopsy has been replaced by thoracoscopic biopsy but not easily available in resource-limited setups. The objective of this study was to analyze the diagnostic yield and safety of closed needle pleural biopsy in exudative pleural effusion and assessment of patients' characteristics with the yield of pleural biopsy. This was a cross-sectional study. This study was conducted at Institute of Respiratory Diseases, SMS Medical College, Jaipur, a tertiary care center of West India. A total of 250 cases of pleural effusion were evaluated with complete pleural fluid biochemical, microbiological, and cytological examination. Out of these 250 patients, 59 were excluded from the study as the diagnosis could be established on initial pleural fluid examination. The remaining (191) patients were considered for closed pleural biopsy with Abrams pleural biopsy needle. The main outcome measure was diagnostic yield in the form of confirming diagnosis. Out of the 191 patients with exudative lymphocytic pleural effusion, 123 (64.40%) were diagnosed on the first pleural biopsy. Among the remaining 68 patients, 22 patients had repeat pleural biopsy with a diagnostic yield of 59.9%. The overall pleural biopsy could establish the diagnosis in 136 (71.20%) patients with pleural effusion. The most common diagnosis on pleural biopsy was malignancy followed by tuberculosis. Closed pleural biopsy provides diagnostic yield nearly comparative to thoracoscopy in properly selected patients of pleural effusions. In view of good yield, low cost, easy availability, and very low complication rate, it should be used routinely in all cases of undiagnosed exudative lymphocytic pleural effusion. There was no comparison with a similar group undergoing thoracoscopic pleural biopsy.
Optimizing rice yields while minimizing yield-scaled global warming potential.

PubMed

Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A

2014-05-01

To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. © 2013 John Wiley & Sons Ltd.
Impact of simulated heat waves on soybean physiology and yield

USDA-ARS?s Scientific Manuscript database

With increases in mean global temperatures and associated climate change, extreme temperature events are predicted to increase in both intensity and frequency. Despite the clearly documented negative public health impacts of heat waves, the impact on physiology and yields of key agricultural species...
Phosphorus, zinc, and boron influence yield components in Earliglow strawberry

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

May, G.M.; Pritts, M.P.

1993-01-01

The main effects and interactions of soil-applied P, B, and Zn on yield and its components were examined in the field at two pH levels with Earliglow' strawberries (Fragaria ananassa Duch.). Applied nutrients had significant effects on several yield components, but responses depended on the levels of other nutrients or the soil pH. At a soil pH of 5.5, yield responded linearly to B and quadratically to P. At pH 6.5, P interacted with B and Zn. Fruit count per inflorescence was the yield component most strongly associated with yield, followed by individual fruit weight. However, these two yield componentsmore » responded differently to soil-applied nutrients. Foliar nutrient levels generally did not increase with the amount of applied nutrient, but often an applied nutrient had a strong effect on the level of another nutrient. Leaf nutrient levels were often correlated with fruit levels, but foliar and fruit levels at harvest were not related to reproductive performance. The study identifies some of the problems inherent in using foliar nutrient levels to predict a yield response and demonstrates how plant responses to single nutrients depend on soil chemistry and the presence of other nutrients.« less
Mechanochemical Synthesis of Pharmaceutical Cocrystal Suspensions via Hot Melt Extrusion: Enhancing Cocrystal Yield.

PubMed

Li, Shu; Yu, Tao; Tian, Yiwei; Lagan, Colette; Jones, David S; Andrews, Gavin P

2017-11-22

Pharmaceutical cocrystals have attracted increasing attention over the past decade as an alternative way to modify the physicochemical properties and hence improve the bioavailability of a drug, without sacrificing thermodynamic stability. Our previous work has demonstrated the viability of in-situ formation of ibuprofen/isonicotinamide cocrystal suspensions within a matrix carrier via a single-step hot-melt extrusion (HME) process. The key aim of the current work is to establish optimised processing conditions to improve cocrystal yield within extruded matrices. The solubility of each individual cocrystal component in the matrix carrier was estimated using two different methods, calculation of Hansen solubility parameters, and Flory-Huggins solution theory using melting point depression measurement, respectively. The latter was found to be more relevant to extrusion cocrystallisation because of the ability to predict miscibility across a range of temperatures. The predictions obtained from the F-H phase diagrams were verified using ternary extrusion processing. Temperatures that promote solubilisation of the parent reagents during processing, and precipitation of the newly formed cocrystal were found to be the most suitable in generating high cocrystal yields. The incorporation of intensive mixing/kneading elements to the screw configuration was also shown to significantly improve the cocrystal yield when utilising a matrix platform. This work has shown that intensive mixing in combination with appropriate temperature selection, can significantly improve the cocrystal yield within a stable and low viscosity carrier during HME processing. Most importantly, this work reports, for the very first time in the literature, the use of the F-H phase diagrams to predict the most appropriate HME processing window to drive higher cocrystal yield.
Absolute 1* quantum yields for the ICN A state by diode laser gain versus absorption spectroscopy

NASA Technical Reports Server (NTRS)

Hess, Wayne P.; Leone, Stephen R.

1987-01-01

Absolute I* quantum yields were measured as a function of wavelength for room temperature photodissociation of the ICN A state continuum. The temperature yields are obtained by the technique of time-resolved diode laser gain-versus-absorption spectroscopy. Quantum yields are evaluated at seven wavelengths from 248 to 284 nm. The yield at 266 nm is 66.0 +/- 2% and it falls off to 53.4 +/- 2% and 44.0 +/- 4% at 284 and 248 respectively. The latter values are significantly higher than those obtained by previous workers using infrared fluorescence. Estimates of I* quantum yields obtained from analysis of CN photofragment rotational distributions, as discussed by other workers, are in good agreement with the I* yields. The results are considered in conjunction with recent theoretical and experimental work on the CN rotational distributions and with previous I* yield results.

Single-step methods for predicting orbital motion considering its periodic components

NASA Astrophysics Data System (ADS)

Lavrov, K. N.

1989-01-01

Modern numerical methods for integration of ordinary differential equations can provide accurate and universal solutions to celestial mechanics problems. The implicit single sequence algorithms of Everhart and multiple step computational schemes using a priori information on periodic components can be combined to construct implicit single sequence algorithms which combine their advantages. The construction and analysis of the properties of such algorithms are studied, utilizing trigonometric approximation of the solutions of differential equations containing periodic components. The algorithms require 10 percent more machine memory than the Everhart algorithms, but are twice as fast, and yield short term predictions valid for five to ten orbits with good accuracy and five to six times faster than algorithms using other methods.
Impacts of El Nino Southern Oscillation on the Global Yields of Major Crops

NASA Technical Reports Server (NTRS)

Iizumi, Toshichika; Luo, Jing-Jia; Challinor, Andrew J.; Sakurai, Gen; Yokozawa, Masayuki; Sakuma, Hirofumi; Brown, Molly Elizabeth; Yamagata, Toshio

2014-01-01

The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Nino/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Nino likely improves the global-mean soybean yield by 2.15.4 but appears to change the yields of maize, rice and wheat by -4.3 to +0.8. The global-mean yields of all four crops during La Nina years tend to be below normal (-4.5 to 0.0).Our findings highlight the importance of ENSO to global crop production.
A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

PubMed

Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

2017-04-01

The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEI G90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our
Effect of environment and variety on the relationships of wheat grain physical and chemical characteristics with ethanol yield.

PubMed

Awole, Kedija D; Kettlewell, Peter S; Hare, Martin C; Agu, Reginald C; Brosnan, James M; Bringhurst, Thomas A

2012-02-01

Following the Renewable Transport Fuel Obligation (RTFO), there is an increasing demand for wheat grain for liquid biofuel in the UK. In order to enhance productivity of the bioethanol industry, good quality wheat must be used. A total of 84 grain samples comprising 14 varieties collected from 11 sites in two harvest years were analysed for a range of grain quality parameters and ethanol yield (EY). The grain quality parameters studied were starch and protein concentration, specific weight, grain density, packing efficiency, thousand-grain weight (TGW), grain length, width, length/width ratio and hardness index. Regression analysis was used to establish the relationships between grain quality parameters and EY. Apart from grain length and density, all grain parameters had significant relationships with EY. In the order of importance, protein concentration, TGW, packing efficiency and specific weight showed good relationships with EY. All other parameters, including starch concentration, showed a poor correlation with EY. EY and the relationship with the grain parameters were affected more by environment than by variety. Some sites gave consistently higher EY than others. When site and variety were considered with TGW and protein, a good prediction of EY could be made (variance accounted for = 87%). Combining TGW and protein concentration could be a better indicator of EY than the current practice of specific weight and protein. Copyright © 2011 Society of Chemical Industry.
Bounded rationality in volunteering public goods games.

PubMed

Xu, Zhaojin; Wang, Zhen; Zhang, Lianzhong

2010-05-07

It is one of the fundamental problems in biology and social sciences how to maintain high levels of cooperation among selfish individuals. Theorists present an effective mechanism promoting cooperation by allowing for voluntary participation in public goods games. But Nash's theory predicts that no one can do better or worse than loners (players unwilling to join the public goods game) in the long run, and that the frequency of participants is independent of loners' payoff. In this paper, we introduce a degree of rationality and investigate the model by means of an approximate best response dynamics. Our research shows that the payoffs of the loners have a significant effect in anonymous voluntary public goods games by this introduction and that the dynamics will drive the system to a fixed point, which is different from the Nash equilibrium. In addition, we also qualitatively explain the existing experimental results. Copyright (c) 2010 Elsevier Ltd. All rights reserved.
Rainmakers: why bad weather means good productivity.

PubMed

Lee, Jooa Julia; Gino, Francesca; Staats, Bradley R

2014-05-01

People believe that weather conditions influence their everyday work life, but to date, little is known about how weather affects individual productivity. Contrary to conventional wisdom, we predict and find that bad weather increases individual productivity and that it does so by eliminating potential cognitive distractions resulting from good weather. When the weather is bad, individuals appear to focus more on their work than on alternate outdoor activities. We investigate the proposed relationship between worse weather and higher productivity through 4 studies: (a) field data on employees' productivity from a bank in Japan, (b) 2 studies from an online labor market in the United States, and (c) a laboratory experiment. Our findings suggest that worker productivity is higher on bad-, rather than good-, weather days and that cognitive distractions associated with good weather may explain the relationship. We discuss the theoretical and practical implications of our research. (c) 2014 APA, all rights reserved.
Anomalous yield reduction in direct-drive DT implosions due to 3He addition

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

Herrmann, Hans W; Langenbrunner, James R; Mack, Joseph M

2008-01-01

Glass capsules were imploded in direct drive on the OMEGA laser [T. R. Boehly et aI., Opt. Commun. 133, 495, 1997] to look for anomalous degradation in deuterium/tritium (DT) yield (i.e., beyond what is predicted) and changes in reaction history with {sup 3}He addition. Such anomalies have previously been reported for D/{sup 3}He plasmas, but had not yet been investigated for DT/{sup 3}He. Anomalies such as these provide fertile ground for furthering our physics understanding of ICF implosions and capsule performance. A relatively short laser pulse (600 ps) was used to provide some degree of temporal separation between shock andmore » compression yield components for analysis. Anomalous degradation in the compression component of yield was observed, consistent with the 'factor of two' degradation previously reported by MIT at a 50% {sup 3}He atom fraction in D{sub 2} using plastic capsules [Rygg et aI., Phys. Plasmas 13, 052702 (2006)]. However, clean calculations (i.e., no fuel-shell mixing) predict the shock component of yield quite well, contrary to the result reported by MIT, but consistent with LANL results in D{sub 2}/{sup 3}He [Wilson, et aI., lml Phys: Conf Series 112, 022015 (2008)]. X-ray imaging suggests less-than-predicted compression ofcapsules containing {sup 3}He. Leading candidate explanations are poorly understood Equation-of-State (EOS) for gas mixtures, and unanticipated particle pressure variation with increasing {sup 3}He addition.« less
Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level

NASA Technical Reports Server (NTRS)

Rajbhandaril, Narayan; Crosson, William; Tsegaye, Teferi; Coleman, Tommy; Liu, Yaping; Soman, Vishwas

1998-01-01

Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.
Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

NASA Astrophysics Data System (ADS)

Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

2017-04-01

Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed
Testing actinide fission yield treatment in CINDER90 for use in MCNP6 burnup calculations

DOE PAGES

Fensin, Michael Lorne; Umbel, Marissa

2015-09-18

Most of the development of the MCNPX/6 burnup capability focused on features that were applied to the Boltzman transport or used to prepare coefficients for use in CINDER90, with little change to CINDER90 or the CINDER90 data. Though a scheme exists for best solving the coupled Boltzman and Bateman equations, the most significant approximation is that the employed nuclear data are correct and complete. Thus, the CINDER90 library file contains 60 different actinide fission yields encompassing 36 fissionable actinides (thermal, fast, high energy and spontaneous fission). Fission reaction data exists for more than 60 actinides and as a result, fissionmore » yield data must be approximated for actinides that do not possess fission yield information. Several types of approximations are used for estimating fission yields for actinides which do not possess explicit fission yield data. The objective of this study is to test whether or not certain approximations of fission yield selection have any impact on predictability of major actinides and fission products. Further we assess which other fission products, available in MCNP6 Tier 3, result in the largest difference in production. Because the CINDER90 library file is in ASCII format and therefore easily amendable, we assess reasons for choosing, as well as compare actinide and major fission product prediction for the H. B. Robinson benchmark for, three separate fission yield selection methods: (1) the current CINDER90 library file method (Base); (2) the element method (Element); and (3) the isobar method (Isobar). Results show that the three methods tested result in similar prediction of major actinides, Tc-99 and Cs-137; however, certain fission products resulted in significantly different production depending on the method of choice.« less
Prediction of winter wheat high yield from remote sensing based model: application in United States and Ukraine

NASA Astrophysics Data System (ADS)

Franch, B.; Vermote, E.; Roger, J. C.; Skakun, S.; Becker-Reshef, I.; Justice, C. O.

2017-12-01

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season and the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data. These methods were applied to MODIS CMG data in Ukraine, the US and China with errors around 10%. However, the NDVI is saturated for yield values higher than 4 MT/ha. As a consequence, the model had to be re-calibrated in each country and the validation of the national yields showed low correlation coefficients. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national yield of winter wheat in the United States and Ukraine from 2001 to 2016.
APEX (Aqueous Photochemistry of Environmentally occurring Xenobiotics): a free software tool to predict the kinetics of photochemical processes in surface waters.

PubMed

Bodrato, Marco; Vione, Davide

2014-04-01

The APEX software predicts the photochemical transformation kinetics of xenobiotics in surface waters as a function of: photoreactivity parameters (direct photolysis quantum yield and second-order reaction rate constants with transient species, namely ˙OH, CO₃(-)˙, (1)O₂ and the triplet states of chromophoric dissolved organic matter, (3)CDOM*), water chemistry (nitrate, nitrite, bicarbonate, carbonate, bromide and dissolved organic carbon, DOC), and water depth (more specifically, the optical path length of sunlight in water). It applies to well-mixed surface water layers, including the epilimnion of stratified lakes, and the output data are average values over the considered water column. Based on intermediate formation yields from the parent compound via the different photochemical pathways, the software can also predict intermediate formation kinetics and overall yield. APEX is based on a photochemical model that has been validated against available field data of pollutant phototransformation, with good agreement between model predictions and field results. The APEX software makes allowance for different levels of knowledge of a photochemical system. For instance, the absorption spectrum of surface water can be used if known, or otherwise it can be modelled from the values of DOC. Also the direct photolysis quantum yield can be entered as a detailed wavelength trend, as a single value (constant or average), or it can be defined as a variable if unknown. APEX is based on the free software Octave. Additional applications are provided within APEX to assess the σ-level uncertainty of the results and the seasonal trend of photochemical processes.
Bird Communities and Biomass Yields in Potential Bioenergy Grasslands

PubMed Central

Blank, Peter J.; Sample, David W.; Williams, Carol L.; Turner, Monica G.

2014-01-01

Demand for bioenergy is increasing, but the ecological consequences of bioenergy crop production on working lands remain unresolved. Corn is currently a dominant bioenergy crop, but perennial grasslands could produce renewable bioenergy resources and enhance biodiversity. Grassland bird populations have declined in recent decades and may particularly benefit from perennial grasslands grown for bioenergy. We asked how breeding bird community assemblages, vegetation characteristics, and biomass yields varied among three types of potential bioenergy grassland fields (grass monocultures, grass-dominated fields, and forb-dominated fields), and assessed tradeoffs between grassland biomass production and bird habitat. We also compared the bird communities in grassland fields to nearby cornfields. Cornfields had few birds compared to perennial grassland fields. Ten bird Species of Greatest Conservation Need (SGCN) were observed in perennial grassland fields. Bird species richness and total bird density increased with forb cover and were greater in forb-dominated fields than grass monocultures. SGCN density declined with increasing vertical vegetation density, indicating that tall, dense grassland fields managed for maximum biomass yield would be of lesser value to imperiled grassland bird species. The proportion of grassland habitat within 1 km of study sites was positively associated with bird species richness and the density of total birds and SGCNs, suggesting that grassland bioenergy fields may be more beneficial for grassland birds if they are established near other grassland parcels. Predicted total bird density peaked below maximum biomass yields and predicted SGCN density was negatively related to biomass yields. Our results indicate that perennial grassland fields could produce bioenergy feedstocks while providing bird habitat. Bioenergy grasslands promote agricultural multifunctionality and conservation of biodiversity in working landscapes. PMID:25299593
Plausible rice yield losses under future climate warming.

PubMed

Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Huang, Yao; Ciais, Philippe; Elliott, Joshua; Huang, Mengtian; Janssens, Ivan A; Li, Tao; Lian, Xu; Liu, Yongwen; Müller, Christoph; Peng, Shushi; Wang, Tao; Zeng, Zhenzhong; Peñuelas, Josep

2016-12-19

Rice is the staple food for more than 50% of the world's population 1-3 . Reliable prediction of changes in rice yield is thus central for maintaining global food security. This is an extraordinary challenge. Here, we compare the sensitivity of rice yield to temperature increase derived from field warming experiments and three modelling approaches: statistical models, local crop models and global gridded crop models. Field warming experiments produce a substantial rice yield loss under warming, with an average temperature sensitivity of -5.2 ± 1.4% K -1 . Local crop models give a similar sensitivity (-6.3 ± 0.4% K -1 ), but statistical and global gridded crop models both suggest less negative impacts of warming on yields (-0.8 ± 0.3% and -2.4 ± 3.7% K -1 , respectively). Using data from field warming experiments, we further propose a conditional probability approach to constrain the large range of global gridded crop model results for the future yield changes in response to warming by the end of the century (from -1.3% to -9.3% K -1 ). The constraint implies a more negative response to warming (-8.3 ± 1.4% K -1 ) and reduces the spread of the model ensemble by 33%. This yield reduction exceeds that estimated by the International Food Policy Research Institute assessment (-4.2 to -6.4% K -1 ) (ref. 4). Our study suggests that without CO 2 fertilization, effective adaptation and genetic improvement, severe rice yield losses are plausible under intensive climate warming scenarios.
Normal stress-only myocardial single photon emission computed tomography predicts good outcome in patients with coronary artery stenoses between 40 and 70.

PubMed

Jiang, Zhixin; Liu, Yangqing; Xin, Chaofan; Zhou, Yanli; Wang, Cheng; Zhao, Zhongqiang; Li, Chunxiang; Li, Dianfu

2016-09-01

Normal stress myocardial single photon emission computed tomography (SPECT) usually indicates good physiologic function of all coronary lesions, and also indicates a good outcome. We hypothesize that it can still predict good outcome in patients with coronary stenoses between 40 and 70%. A group of patients who underwent stress myocardial SPECT after coronary angiography were consecutively recruited in our center. Patients were eligible if they had one or more coronary stenoses between 40 and 70%. Patients with coronary stenoses greater than 50% diameter of left main or greater than 70% diameter of nonleft main epicardial vessels, and left ventricular ejection fraction less than 50% were excluded. The outcome was defined as major adverse events, including cardiac death, nonfatal myocardial infarction, and revascularization. Patients' survival curves were constructed accorded to the method of Kaplan and Meier and compared using the log-rank test. A study cohort of 77 patients was enrolled. According to the summed stress score, 43 patients were assigned to the perfusion defect group and 34 patients were assigned to the perfusion normal group. The follow-up duration was 6.4±0.3 years. In the perfusion normal group, only one of 34 (2.9%) patients developed major adverse events. In the perfusion defect group, six of 43 (14%) developed major adverse events, P-value of 0.041. It is safe to defer a percutaneous coronary intervention in patients with coronary stenoses between 40 and 70% and normal stress myocardial SPECT.
Which response format reveals the truth about donations to a public good?

Treesearch

Thomas C. Brown; Patricia A. Champ; Richard C. Bishop; Daniel W. McCollum

1996-01-01

Seceral contingent valuation studies hace found that the open-ended format yields lower estimates of willingness to pay (WTP) than does the closed-ended, or dichotomous choice, format. In this study, WTP for a public encironmental good was estimated under four conditions: actual payment in response to open-ended and closed-ended requests, and hypothetical payment in...
Towards predicting the encoding capability of MR fingerprinting sequences.

PubMed

Sommer, K; Amthor, T; Doneva, M; Koken, P; Meineke, J; Börnert, P

2017-09-01

Sequence optimization and appropriate sequence selection is still an unmet need in magnetic resonance fingerprinting (MRF). The main challenge in MRF sequence design is the lack of an appropriate measure of the sequence's encoding capability. To find such a measure, three different candidates for judging the encoding capability have been investigated: local and global dot-product-based measures judging dictionary entry similarity as well as a Monte Carlo method that evaluates the noise propagation properties of an MRF sequence. Consistency of these measures for different sequence lengths as well as the capability to predict actual sequence performance in both phantom and in vivo measurements was analyzed. While the dot-product-based measures yielded inconsistent results for different sequence lengths, the Monte Carlo method was in a good agreement with phantom experiments. In particular, the Monte Carlo method could accurately predict the performance of different flip angle patterns in actual measurements. The proposed Monte Carlo method provides an appropriate measure of MRF sequence encoding capability and may be used for sequence optimization. Copyright © 2017 Elsevier Inc. All rights reserved.
Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.
Variation of yield loci in finite element analysis by considering texture evolution for AA5042 aluminum sheets

NASA Astrophysics Data System (ADS)

Yoon, Jonghun; Kim, Kyungjin; Yoon, Jeong Whan

2013-12-01

Yield function has various material parameters that describe how materials respond plastically in given conditions. However, a significant number of mechanical tests are required to identify the many material parameters for yield function. In this study, an effective method using crystal plasticity through a virtual experiment is introduced to develop the anisotropic yield function for AA5042. The crystal plasticity approach was used to predict the anisotropic response of the material in order to consider a number of stress or strain modes that would not otherwise be evident through mechanical testing. A rate-independent crystal plasticity model based on a smooth single crystal yield surface, which removes the innate ambiguity problem within the rate-independent model and Taylor model for polycrystalline deformation behavior were employed to predict the material's response in the balanced biaxial stress, pure shear, and plane strain states to identify the parameters for the anisotropic yield function of AA5042.
Mammography performance in Oman: Review of factors influencing cancer yield and positive predictive value.

PubMed

Taif, Sawsan; Tufail, Fatma; Alnuaimi, Ahmed Sameer

2016-06-01

The aim of this study is to assess mammography performance in Oman by estimating the breast cancer rate and the positive predictive value (PPV) with the influence of some variables. This cross-sectional study was conducted on mammograms done in one of the three main breast imaging centers in Oman between January 2008 and July 2012. Diagnostic and screening groups were identified and assessed separately. Rate of abnormal mammograms, rate of breast cancer and the PPV were estimated according to Breast Imaging Reporting and Data System (BIRADS) score, presence of breast lump and patient's age. Total of 653 mammograms were included, 254 diagnostic and 399 screening. Abnormal mammograms (BIRADS 4 and 5) form 31.9% of the diagnostic examinations compared with 6.8% of screening examinations. Breast cancer was present in 17.9% of the diagnostic compared with 1.0% of the screening group. The PPV of BIRADS 5 was 94.1%, and for BIRADS 4 was 37.1 and 26.7% for diagnostic and screening studies. Overall PPV for abnormal mammograms was 65.2% in the diagnostic and 26.7% in the screening group. Mammography PPV shows positive association with age (P = 0.039) while presence of breast lump has no significant effect on the PPV (P = 0.38). BIRADS 5 score was found to have a high cancer yield making it a strong predictor of cancer. Different results were obtained in the diagnostic compared with screening mammography with higher rates of abnormal mammograms and breast cancer. Mammography performance should be better in the older women. © 2014 Wiley Publishing Asia Pty Ltd.

Specific yield: compilation of specific yields for various materials

USGS Publications Warehouse

Johnson, A.I.

1967-01-01

Specific yield is defined as the ratio of (1) the volume of water that a saturated rock or soil will yield by gravity to (2) the total volume of the rock or soft. Specific yield is usually expressed as a percentage. The value is not definitive, because the quantity of water that will drain by gravity depends on variables such as duration of drainage, temperature, mineral composition of the water, and various physical characteristics of the rock or soil under consideration. Values of specific yields nevertheless offer a convenient means by which hydrologists can estimate the water-yielding capacities of earth materials and, as such, are very useful in hydrologic studies. The present report consists mostly of direct or modified quotations from many selected reports that present and evaluate methods for determining specific yield, limitations of those methods, and results of the determinations made on a wide variety of rock and soil materials. Although no particular values are recommended in this report, a table summarizes values of specific yield, and their averages, determined for 10 rock textures. The following is an abstract of the table. [Table
Yield and yield gaps in central U.S. corn production systems

USDA-ARS?s Scientific Manuscript database

The magnitude of yield gaps (YG) (potential yield – farmer yield) provides some indication of the prospects for increasing crop yield. Quantile regression analysis was applied to county maize (Zea mays L.) yields (1972 – 2011) from Kentucky, Iowa and Nebraska (irrigated) (total of 115 counties) to e...
Accuracy statistics in predicting Independent Activities of Daily Living (IADL) capacity with comprehensive and brief neuropsychological test batteries.

PubMed

Karzmark, Peter; Deutsch, Gayle K

2018-01-01

This investigation was designed to determine the predictive accuracy of a comprehensive neuropsychological and brief neuropsychological test battery with regard to the capacity to perform instrumental activities of daily living (IADLs). Accuracy statistics that included measures of sensitivity, specificity, positive and negative predicted power and positive likelihood ratio were calculated for both types of batteries. The sample was drawn from a general neurological group of adults (n = 117) that included a number of older participants (age >55; n = 38). Standardized neuropsychological assessments were administered to all participants and were comprised of the Halstead Reitan Battery and portions of the Wechsler Adult Intelligence Scale-III. A comprehensive test battery yielded a moderate increase over base-rate in predictive accuracy that generalized to older individuals. There was only limited support for using a brief battery, for although sensitivity was high, specificity was low. We found that a comprehensive neuropsychological test battery provided good classification accuracy for predicting IADL capacity.
High-biomass C4 grasses-Filling the yield gap.

PubMed

Mullet, John E

2017-08-01

A significant increase in agricultural productivity will be required by 2050 to meet the needs of an expanding and rapidly developing world population, without allocating more land and water resources to agriculture, and despite slowing rates of grain yield improvement. This review examines the proposition that high-biomass C 4 grasses could help fill the yield gap. High-biomass C 4 grasses exhibit high yield due to C 4 photosynthesis, long growth duration, and efficient capture and utilization of light, water, and nutrients. These C 4 grasses exhibit high levels of drought tolerance during their long vegetative growth phase ideal for crops grown in water-limited regions of agricultural production. The stems of some high-biomass C 4 grasses can accumulate high levels of non-structural carbohydrates that could be engineered to enhance biomass yield and utility as feedstocks for animals and biofuels production. The regulatory pathway that delays flowering of high-biomass C 4 grasses in long days has been elucidated enabling production and deployment of hybrids. Crop and landscape-scale modeling predict that utilization of high-biomass C 4 grass crops on land and in regions where water resources limit grain crop yield could increase agricultural productivity. Copyright © 2017 Elsevier B.V. All rights reserved.
A complete genetic linkage map and QTL analyses for bast fibre quality traits, yield and yield components in jute (Corchorus olitorius L.).

PubMed

Topdar, N; Kundu, A; Sinha, M K; Sarkar, D; Das, M; Banerjee, S; Kar, C S; Satya, P; Balyan, H S; Mahapatra, B S; Gupta, P K

2013-01-01

We report the first complete microsatellite genetic map of jute (Corchorus olitorius L.; 2n = 2x = 14) using an F6 recombinant inbred population. Of the 403 microsatellite markers screened, 82 were mapped on the seven linkage groups (LGs) that covered a total genetic distance of 799.9 cM, with an average marker interval of 10.7 cM. LG5 had the longest and LG7 the shortest genetic lengths, whereas LG1 had the maximum and LG7 the minimum number of markers. Segregation distortion of microsatellite loci was high (61%), with the majority of them (76%) skewed towards the female parent. Genomewide non-parametric single-marker analysis in combination with multiple quantitative trait loci (QTL)-models (MQM) mapping detected 26 definitive QTLs for bast fibre quality, yield and yield-related traits. These were unevenly distributed on six LGs, as colocalized clusters, at genomic sectors marked by 15 microsatellite loci. LG1 was the QTL-richest map sector, with the densest colocalized clusters of QTLs governing fibre yield, yield-related traits and tensile strength. Expectedly, favorable QTLs were derived from the desirable parents, except for nearly all of those of fibre fineness, which might be due to the creation of new gene combinations. Our results will be a good starting point for further genome analyses in jute.
Mortality prediction using TRISS methodology in the Spanish ICU Trauma Registry (RETRAUCI).

PubMed

Chico-Fernández, M; Llompart-Pou, J A; Sánchez-Casado, M; Alberdi-Odriozola, F; Guerrero-López, F; Mayor-García, M D; Egea-Guerrero, J J; Fernández-Ortega, J F; Bueno-González, A; González-Robledo, J; Servià-Goixart, L; Roldán-Ramírez, J; Ballesteros-Sanz, M Á; Tejerina-Alvarez, E; Pino-Sánchez, F I; Homar-Ramírez, J

2016-10-01

To validate Trauma and Injury Severity Score (TRISS) methodology as an auditing tool in the Spanish ICU Trauma Registry (RETRAUCI). A prospective, multicenter registry evaluation was carried out. Thirteen Spanish Intensive Care Units (ICUs). Individuals with traumatic disease and available data admitted to the participating ICUs. Predicted mortality using TRISS methodology was compared with that observed in the pilot phase of the RETRAUCI from November 2012 to January 2015. Discrimination was evaluated using receiver operating characteristic (ROC) curves and the corresponding areas under the curves (AUCs) (95% CI), with calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. A value of p<0.05 was considered significant. Predicted and observed mortality. A total of 1405 patients were analyzed. The observed mortality rate was 18% (253 patients), while the predicted mortality rate was 16.9%. The area under the ROC curve was 0.889 (95% CI: 0.867-0.911). Patients with blunt trauma (n=1305) had an area under the ROC curve of 0.887 (95% CI: 0.864-0.910), and those with penetrating trauma (n=100) presented an area under the curve of 0.919 (95% CI: 0.859-0.979). In the global sample, the HL test yielded a value of 25.38 (p=0.001): 27.35 (p<0.0001) in blunt trauma and 5.91 (p=0.658) in penetrating trauma. TRISS methodology underestimated mortality in patients with low predicted mortality and overestimated mortality in patients with high predicted mortality. TRISS methodology in the evaluation of severe trauma in Spanish ICUs showed good discrimination, with inadequate calibration - particularly in blunt trauma. Copyright © 2015 Elsevier España, S.L.U. y SEMICYUC. All rights reserved.
Machine characterization and benchmark performance prediction

NASA Technical Reports Server (NTRS)

Saavedra-Barrera, Rafael H.

1988-01-01

From runs of standard benchmarks or benchmark suites, it is not possible to characterize the machine nor to predict the run time of other benchmarks which have not been run. A new approach to benchmarking and machine characterization is reported. The creation and use of a machine analyzer is described, which measures the performance of a given machine on FORTRAN source language constructs. The machine analyzer yields a set of parameters which characterize the machine and spotlight its strong and weak points. Also described is a program analyzer, which analyzes FORTRAN programs and determines the frequency of execution of each of the same set of source language operations. It is then shown that by combining a machine characterization and a program characterization, we are able to predict with good accuracy the run time of a given benchmark on a given machine. Characterizations are provided for the Cray-X-MP/48, Cyber 205, IBM 3090/200, Amdahl 5840, Convex C-1, VAX 8600, VAX 11/785, VAX 11/780, SUN 3/50, and IBM RT-PC/125, and for the following benchmark programs or suites: Los Alamos (BMK8A1), Baskett, Linpack, Livermore Loops, Madelbrot Set, NAS Kernels, Shell Sort, Smith, Whetstone and Sieve of Erathostenes.
A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method.

PubMed

Zou, Kelly H; Resnic, Frederic S; Talos, Ion-Florin; Goldberg-Zimring, Daniel; Bhagwat, Jui G; Haker, Steven J; Kikinis, Ron; Jolesz, Ferenc A; Ohno-Machado, Lucila

2005-10-01

Medical classification accuracy studies often yield continuous data based on predictive models for treatment outcomes. A popular method for evaluating the performance of diagnostic tests is the receiver operating characteristic (ROC) curve analysis. The main objective was to develop a global statistical hypothesis test for assessing the goodness-of-fit (GOF) for parametric ROC curves via the bootstrap. A simple log (or logit) and a more flexible Box-Cox normality transformations were applied to untransformed or transformed data from two clinical studies to predict complications following percutaneous coronary interventions (PCIs) and for image-guided neurosurgical resection results predicted by tumor volume, respectively. We compared a non-parametric with a parametric binormal estimate of the underlying ROC curve. To construct such a GOF test, we used the non-parametric and parametric areas under the curve (AUCs) as the metrics, with a resulting p value reported. In the interventional cardiology example, logit and Box-Cox transformations of the predictive probabilities led to satisfactory AUCs (AUC=0.888; p=0.78, and AUC=0.888; p=0.73, respectively), while in the brain tumor resection example, log and Box-Cox transformations of the tumor size also led to satisfactory AUCs (AUC=0.898; p=0.61, and AUC=0.899; p=0.42, respectively). In contrast, significant departures from GOF were observed without applying any transformation prior to assuming a binormal model (AUC=0.766; p=0.004, and AUC=0.831; p=0.03), respectively. In both studies the p values suggested that transformations were important to consider before applying any binormal model to estimate the AUC. Our analyses also demonstrated and confirmed the predictive values of different classifiers for determining the interventional complications following PCIs and resection outcomes in image-guided neurosurgery.
Large Area Crop Inventory Experiment (LACIE). Feasibility of assessing crop condition and yield from LANDSAT data

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. Yield modelling for crop production estimation derived a means of predicting the within-a-year yield and the year-to-year variability of yield over some fixed or randomly located unit of area. Preliminary studies indicated that the requirements for interpreting LANDSAT data for yield may be sufficiently similar to those of signature extension that it is feasible to investigate the automated estimation of production. The concept of an advanced yield model consisting of both spectral and meteorological components was endorsed. Rationale for using meteorological parameters originated from known between season and near harvest dynamics in crop environmental-condition-yield relationships.
Assessing the phototransformation of diclofenac, clofibric acid and naproxen in surface waters: Model predictions and comparison with field data.

PubMed

Avetta, Paola; Fabbri, Debora; Minella, Marco; Brigante, Marcello; Maurino, Valter; Minero, Claudio; Pazzi, Marco; Vione, Davide

2016-11-15

Phototransformation is important for the fate in surface waters of the pharmaceuticals diclofenac (DIC) and naproxen (NAP) and for clofibric acid (CLO), a metabolite of the drug clofibrate. The goal of this paper is to provide an overview of the prevailing photochemical processes, which these compounds undergo in the different conditions found in freshwater environments. The modelled photochemical half-life times of NAP and DIC range from a few days to some months, depending on water conditions (chemistry and depth) and on the season. The model indicates that direct photolysis is the dominant degradation pathway of DIC and NAP in sunlit surface waters, and potentially toxic cyclic amides were detected as intermediates of DIC direct phototransformation. With modelled half-life times in the month-year range, CLO is predicted to be more photostable than DIC or NAP and to be degraded mainly by reaction with the • OH radical and with the triplet states of chromophoric dissolved organic matter ( 3 CDOM*). The CLO intermediates arising from these processes and detected in this study (hydroquinone and 4-chlorophenol) are, respectively, a chronic toxicant to aquatic organisms and a possible carcinogen for humans. Hydroquinone is formed with only ∼5% yield upon CLO triplet-sensitised transformation, but it is highly toxic for algae and crustaceans. In contrast, the formation yield of 4-chlorophenol reaches ∼50% upon triplet sensitisation and ∼10% by · OH reaction. The comparison of model predictions with field data from a previous study yielded a very good agreement in the case of DIC and, when using 4-carboxybenzophenone as proxy for triplet sensitisation by CDOM, a good agreement was found for CLO as well. In the case of NAP, the comparison with field data suggests that its direct photolysis quantum yield approaches or even falls below the lower range of literature values. Copyright © 2016 Elsevier Ltd. All rights reserved.
PREDICTS

NASA Technical Reports Server (NTRS)

Zhou, Hanying

2007-01-01

PREDICTS is a computer program that predicts the frequencies, as functions of time, of signals to be received by a radio science receiver in this case, a special-purpose digital receiver dedicated to analysis of signals received by an antenna in NASA s Deep Space Network (DSN). Unlike other software used in the DSN, PREDICTS does not use interpolation early in the calculations; as a consequence, PREDICTS is more precise and more stable. The precision afforded by the other DSN software is sufficient for telemetry; the greater precision afforded by PREDICTS is needed for radio-science experiments. In addition to frequencies as a function of time, PREDICTS yields the rates of change and interpolation coefficients for the frequencies and the beginning and ending times of reception, transmission, and occultation. PREDICTS is applicable to S-, X-, and Ka-band signals and can accommodate the following link configurations: (1) one-way (spacecraft to ground), (2) two-way (from a ground station to a spacecraft to the same ground station), and (3) three-way (from a ground transmitting station to a spacecraft to a different ground receiving station).
Stand-level growth and yield component models for red oak-sweetgum forests on Mid-South minor stream bottoms

Treesearch

Emily B. Schultz; J. Clint Iles; Thomas G. Matney; Andrew W. Ezell; James S. Meadows; Theodor D. Leininger; al. et.

2010-01-01

Greater emphasis is being placed on Southern bottomland hardwood management, but relatively few growth and yield prediction systems exist that are based on sufficient measurements. We present the aggregate stand-level expected yield and structural component equations for a red oak (Quercus section Lobatae)-sweetgum (Liquidambar styraciflua L.) growth and yield model....
Persistency of milk yield in Indonesian Holstein cows

NASA Astrophysics Data System (ADS)

Widyas, N.; Putra, F. Y.; Nugroho, T.; Pramono, A.; Susilowati, A.; Sutarno; Prastowo, S.

2018-03-01

Milk yield is an important trait in dairy industry; thus, information regarding this phenotype is essential to measure the productivity of a farm. Total milk yield in one lactation period was often predicted using information from samples collected within certain time intervals. The rate of change of milk production between two-time intervals is defined as persistency. This article aims to estimate the persistency of milk yield between lactation 1, 2 and 3 in Indonesian Friesian Holstein (IFH) cows. Data was collected from Limpakuwus stable, Baturraden Dairy Cattle Breeding Centre, Central Java Indonesia. Records were obtained from cows which started lactating on 2013 until the end of third lactation around the beginning of 2016. Milk yield from the first (L1), second (L2) and third (L3) lactations of 21 cows were recorded in kilograms. Samples were collected in 30 days basis interval started from the 10th day of lactation up to the 10th month. In this population, the cows first calving was around February – April 2013; while the second and third calving occurred all over the relevant year. The mean of milk yield for L1, L2 and L3 were 17.77±3.70, 16.09±5.17 and 13.73±4.02 Kg respectively. The peak of milk yields was achieved at the second month of the lactation for L1, L2 and L3. The persistency from the second to the tenth test days were 97, 93 and 94% for L1, L2 and L3, respectively. Milk yield persistency is representing ability of cow in maintain milk production after peak during lactation period. The more persistent shows better performance of dairy cattle as well as farm management. For that, persistency value could be used as valuable information in evaluating the management in Indonesian dairy farms.
Predicted cubic-foot yields of sawmill products for black cherry trees

Treesearch

Leland F. Hanks

1980-01-01

Equations and tables for estimating the cubic-foot volumes of lumber, sawdust, and sawmill residue for black cherry trees are presented. Also included are cubic-foot and board-foot predictions for the sawlog portion of the trees.
Modeling the yield potential of dryland canola under current and future climates in California

NASA Astrophysics Data System (ADS)

George, N.; Kaffka, S.; Beeck, C.; Bucaram, S.; Zhang, J.

2012-12-01

Models predict that the climate of California will become hotter, drier and more variable under future climate change scenarios. This will lead to both increased irrigation demand and reduced irrigation water availability. In addition, it is predicted that most common Californian crops will suffer a concomitant decline in productivity. To remain productive and economically viable, future agricultural systems will need to have greater water use efficiency, tolerance of high temperatures, and tolerance of more erratic temperature and rainfall patterns. Canola (Brassica napus) is the third most important oilseed globally, supporting large and well-established agricultural industries in Canada, Europe and Australia. It is an agronomically useful and economically valuable crop, with multiple end markets, that can be grown in California as a dryland winter rotation with little to no irrigation demand. This gives canola great potential as a new crop for Californian farmers both now and as the climate changes. Given practical and financial limitations it is not always possible to immediately or widely evaluate a crop in a new region. Crop production models are therefore valuable tools for assessing the potential of new crops, better targeting further field research, and refining research questions. APSIM is a modular modeling framework developed by the Agricultural Production Systems Research Unit in Australia, it combines biophysical and management modules to simulate cropping systems. This study was undertaken to examine the yield potential of Australian canola varieties having different water requirements and maturity classes in California using APSIM. The objective of the work was to identify the agricultural regions of California most ideally suited to the production of Australian cultivars of canola and to simulate the production of canola in these regions to estimate yield-potential. This will establish whether the introduction and in-field evaluation of better
Drought mitigation in perennial crops by fertilization and adjustments of regional yield models for future climate variability

NASA Astrophysics Data System (ADS)

Kantola, I. B.; Blanc-Betes, E.; Gomez-Casanovas, N.; Masters, M. D.; Bernacchi, C.; DeLucia, E. H.

2017-12-01

Increased variability and intensity of precipitation in the Midwest agricultural belt due to climate change is a major concern. The success of perennial bioenergy crops in replacing maize for bioethanol production is dependent on sustained yields that exceed maize, and the marketing of perennial crops often emphasizes the resilience of perennial agriculture to climate stressors. Land conversion from maize for bioethanol to Miscanthus x giganteus (miscanthus) increases yields and annual evapotranspiration rates (ET). However, establishment of miscanthus also increases biome water use efficiency (the ratio between net ecosystem productivity after harvest and ET), due to greater belowground biomass in miscanthus than in maize or soybean. In 2012, a widespread drought reduced the yield of 5-year-old miscanthus plots in central Illinois by 36% compared to the previous two years. Eddy covariance data indicated continued soil water deficit during the hydrologically-normal growing season in 2013 and miscanthus yield failed to rebound as expected, lagging behind pre-drought yields by an average of 53% over the next three years. In early 2014, nitrogen fertilizer was applied to half of mature (7-year-old) miscanthus plots in an effort to improve yields. In plots with annual post-emergence application of 60 kg ha-1 of urea, peak biomass was 29% greater than unfertilized miscanthus in 2014, and 113% greater in 2015, achieving statistically similar yields to the pre-drought average. Regional-scale models of perennial crop productivity use 30-year climate averages that are inadequate for predicting long-term effects of short-term extremes on perennial crops. Modeled predictions of perennial crop productivity incorporating repeated extreme weather events, observed crop response, and the use of management practices to mitigate water deficit demonstrate divergent effects on predicted yields.
Evolutionary agroecology: individual fitness and population yield in wheat (Triticum aestivum).

PubMed

Weiner, Jacob; Du, Yan-Lei; Zhang, Cong; Qin, Xiao-Liang; Li, Feng-Min

2017-09-01

Although the importance of group selection in nature is highly controversial, several researchers have argued that plant breeding for agriculture should be based on group selection, because the goal in agriculture is to optimize population production, not individual fitness. A core hypothesis behind this claim is that crop genotypes with the highest individual fitness in a mixture of genotypes will not produce the highest population yield, because fitness is often increased by "selfish" behaviors, which reduce population performance. We tested this hypothesis by growing 35 cultivars of spring wheat (Triticum aestivum L.) in mixtures and monocultures, and analyzing the relationship between population yield in monoculture and individual yield in mixture. The relationship was unimodal, as predicted. The highest-yielding populations were from cultivars that had intermediate fitness, and these produced, on average, 35% higher yields than cultivars with the highest fitness. It is unlikely that plant breeding or genetic engineering can improve traits that natural selection has been optimizing for millions of years, but there is unutilized potential in traits that increase crop yield by decreasing individual fitness. © 2017 by the Ecological Society of America.
Perceptual and Acoustic Analyses of Good Voice Quality in Male Radio Performers.

PubMed

Warhurst, Samantha; Madill, Catherine; McCabe, Patricia; Ternström, Sten; Yiu, Edwin; Heard, Robert

2017-03-01

Good voice quality is an asset to professional voice users, including radio performers. We examined whether (1) voices could be reliably categorized as good for the radio and (2) these categories could be predicted using acoustic measures. Male radio performers (n = 24) and age-matched male controls performed "The Rainbow Passage" as if presenting on the radio. Voice samples were rated using a three-stage paired-comparison paradigm by 51 naive listeners and perceptual categories were identified (Study 1), and then analyzed for fundamental frequency, long-term average spectrum, cepstral peak prominence, and pause or spoken-phrase duration (Study 2). Study 1: Good inter-judge reliability was found for perceptual judgments of the best 15 voices (good for radio category, 14/15 = radio performers), but agreement on the remaining 33 voices (unranked category) was poor. Study 2: Discriminant function analyses showed that the SD standard deviation of sounded portion duration, equivalent sound level, and smoothed cepstral peak prominence predicted membership of categories with moderate accuracy (R 2 = 0.328). Radio performers are heterogeneous for voice quality; good voice quality was judged reliably in only 14 out of 24 radio performers. Current acoustic analyses detected some of the relevant signal properties that were salient in these judgments. More refined perceptual analysis and the use of other perceptual methods might provide more information on the complex nature of judging good voices. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.
Runaway sexual selection leads to good genes.

PubMed

Chandler, Christopher H; Ofria, Charles; Dworkin, Ian

2013-01-01

Mate choice and sexual displays are widespread in nature, but their evolutionary benefits remain controversial. Theory predicts these traits can be favored by runaway sexual selection, in which preference and display reinforce one another due to genetic correlation; or by good genes benefits, in which mate choice is advantageous because extreme displays indicate a well-adapted genotype. However, these hypotheses are not mutually exclusive, and the adaptive benefits underlying mate choice can themselves evolve. In particular, examining how and why sexual displays become indicators of good genes is challenging in natural systems. Here, we use experimental evolution in "digital organisms" to demonstrate the origins of condition-dependent indicator displays following their spread due to a runaway process. Surprisingly, handicap-like costs are not necessary for displays to become indicators of male viability. Instead, a pleiotropic genetic architecture underlies both displays and viability. Runaway sexual selection and good genes benefits should thus be viewed as interacting mechanisms that reinforce one another. © 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.
Improving the yield and quality of DNA isolated from white-rot fungi.

PubMed

Kuhad, R C; Kapoor, R K; Lal, R

2004-01-01

A new simple method used to eliminate polysaccharides that cause problems during DNA isolation was established for 6 different white-rot fungi using 1% hexadecyltrimethylammonium bromide (CTAB) as wash buffer and followed by centrifugation. Variation in the DNA yield and quality was ascertained using precipitating agents, detergents and cell-wall-hydrolyzing chitinase. Considerable amount of exopolysaccharides from fungal biomass was removed with the use of 1% CTAB wash buffer followed by centrifugation. The DNA varied in terms of yield and quality. For the DNA extraction use of 2% SDS in extraction buffer worked best for Pycnoporus cinnabarinus, Cyathus bulleri, Cyathus striatus and Cyathus stercoreus, while 2% CTAB worked best for Phanerochaete chrysosporium and Pleurotus ostreatus. Elimination of phenol and use of absolute ethanol for precipitating DNA resulted in good yield and quality of DNA. This DNA was amenable to restriction endonuclease digestion.

SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS

PubMed Central

Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo

2009-01-01

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450
Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

2001-10-01

- The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.
Classical and Bayesian Seismic Yield Estimation: The 1998 Indian and Pakistani Tests

NASA Astrophysics Data System (ADS)

Shumway, R. H.

The nuclear tests in May, 1998, in India and Pakistan have stimulated a renewed interest in yield estimation, based on limited data from uncalibrated test sites. We study here the problem of estimating yields using classical and Bayesian methods developed by Shumway (1992), utilizing calibration data from the Semipalatinsk test site and measured magnitudes for the 1998 Indian and Pakistani tests given by Murphy (1998). Calibration is done using multivariate classical or Bayesian linear regression, depending on the availability of measured magnitude-yield data and prior information. Confidence intervals for the classical approach are derived applying an extension of Fieller's method suggested by Brown (1982). In the case where prior information is available, the posterior predictive magnitude densities are inverted to give posterior intervals for yield. Intervals obtained using the joint distribution of magnitudes are comparable to the single-magnitude estimates produced by Murphy (1998) and reinforce the conclusion that the announced yields of the Indian and Pakistani tests were too high.
Fission yield calculation using toy model based on Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Jubaidah, Kurniadi, Rizal

2015-09-01

Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. There are five Gaussian parameters used in this research. They are scission point of the two curves (Rc), mean of left curve (μL) and mean of right curve (μR), deviation of left curve (σL) and deviation of right curve (σR). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90yield is in about 135
Measurement of fission yields and isomeric yield ratios at IGISOL

NASA Astrophysics Data System (ADS)

Pomp, Stephan; Mattera, Andrea; Rakopoulos, Vasileios; Al-Adili, Ali; Lantz, Mattias; Solders, Andreas; Jansson, Kaj; Prokofiev, Alexander V.; Eronen, Tommi; Gorelov, Dimitri; Jokinen, Ari; Kankainen, Anu; Moore, Iain D.; Penttilä, Heikki; Rinta-Antila, Sami

2018-03-01

Data on fission yields and isomeric yield ratios (IYR) are tools to study the fission process, in particular the generation of angular momentum. We use the IGISOL facility with the Penning trap JYFLTRAP in Jyväskylä, Finland, for such measurements on 232Th and natU targets. Previously published fission yield data from IGISOL concern the 232Th(p,f) and 238U(p,f) reactions at 25 and 50 MeV. Recently, a neutron source, using the Be(p,n) reaction, has been developed, installed and tested. We summarize the results for (p,f) focusing on the first measurement of IYR by direct ion counting. We also present first results for IYR and relative yields for Sn and Sb isotopes in the 128-133 mass range from natU(n,f) based on γ-spectrometry. We find a staggering behaviour in the cumulative yields for Sn and a shift in the independent fission yields for Sb as compared to current evaluations. Plans for the future experimental program on fission yields and IYR measurements are discussed.
Dynamic Yielding and Spall Behavior of Commercially Pure Grade 4 Titanium

NASA Astrophysics Data System (ADS)

Thadhani, Naresh; Whelchel, R. L.; Sanders, Tom; Mehkote, D. S.; Iyer, K. A.; Georgia Instiutute of Technology Collaboration; Johns Hopkins University, Applied Physics Labortaory Collaboration

2015-06-01

The dynamic yielding and fracture (spalling) of commercially pure (grade 4) titanium are investigated using symmetric plate impact experiments over a peak stress range of 5.6 GPa to 12.5 GPa, using the 80-mm single-stage gas-gun. VISAR rear free surface velocity profiles display both a Hugoniot elastic limit (HEL) and a velocity pullback, which are indicative of dynamic compressive yielding and tensile fracture (spalling), respectively. The HEL values appear to show a slight decrease with peak stress from 2.2 GPa to 2.0 GPa along with a corresponding increase in twinning observed in recovered impacted samples. The spall strength on the other hand increases with peak stress from a value of 3.3 GPa to 3.8 GPa and shows a good power law fit with the decompression strain rate. The differing responses in dynamic yield and fracture behavior suggest that void nucleation may be the dominant mechanism affecting the spall strength of grade 4 titanium.
A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Kujirai, Toshihiro

2014-10-01

A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.
Correlation between molecular secondary ion yield and cluster ion sputtering for samples with different stopping powers

NASA Astrophysics Data System (ADS)

Heile, A.; Muhmann, C.; Lipinsky, D.; Arlinghaus, H. F.

2012-07-01

In static SIMS, the secondary ion yield, defined as detected ions per primary ion, can be increased by altering several primary ion parameters. For many years, no quantitative predictions could be made for the secondary ion yield enhancement of molecular ions. For thick samples of organic compounds, a power dependency of the secondary ion yield on the sputtering yield was shown. For this article, samples with thick molecular layers and (sub-)monolayers composed of various molecules were prepared on inorganic substrates such as silicon, silver, and gold, and subsequently analyzed. For primary ion bombardment, monoatomic (Ne+, Ar+, Ga+, Kr+, Xe+, Bi+) as well as polyatomic (Bin+, Bin++) primary ions were used within an energy range of 10-50 keV. The power dependency was found to hold true for the different samples; however, the exponent decreased with increasing stopping power. Based on these findings, a rule of thumb is proposed for the prediction of the lower limit of the secondary ion yield enhancement as a function of the primary ion species. Additionally, effects caused by the variation of the energy deposition are discussed, including the degree of molecular fragmentation and the non-linear increase of the secondary ion yield when polyatomic primary ions are used.
Optimizing Dense Plasma Focus Neutron Yields With Fast Gas Jets

NASA Astrophysics Data System (ADS)

McMahon, Matthew; Stein, Elizabeth; Higginson, Drew; Kueny, Christopher; Link, Anthony; Schmidt, Andrea

2017-10-01

We report a study using the particle-in-cell code LSP to perform fully kinetic simulations modeling dense plasma focus (DPF) devices with high density gas jets on axis. The high-density jets are modeled in the large-eddy Navier-Stokes code CharlesX, which is suitable for modeling both sub-sonic and supersonic gas flow. The gas pattern, which is essentially static on z-pinch time scales, is imported from CharlesX to LSP for neutron yield predictions. Fast gas puffs allow for more mass on axis while maintaining the optimal pressure for the DPF. As the density of a subsonic jet increases relative to the background fill, we find the neutron yield increases, as does the variability in the neutron yield. Introducing perturbations in the jet density via super-sonic flow (also known as Mach diamonds) allow for consistent seeding of the m =0 instability leading to more consistent ion acceleration and higher neutron yields with less variability. Jets with higher on axis density are found to have the greatest yield. The optimal jet configuration and the necessary jet conditions for increasing neutron yield and reducing yield variability are explored. Simulations of realistic jet profiles are performed and compared to the ideal scenario. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and supported by the Laboratory Directed Research and Development Program (15-ERD-034) at LLNL.
OAKSIM: An individual-tree growth and yield simulator for managed, even-aged, upland oak stands

Treesearch

Donald E. Hilt; Donald E. Hilt

1985-01-01

OAKSIM is an individual-tree growth and yield simulator for managed, even-aged, upland oak stands. Growth and yield projections for various thinning alternatives can be made with OAKSIM for a period of up to 50 years. Simulator components include an individual-tree diameter growth model, a mortality model, height prediction equations, bark ratio equations, a taper-...
Slope Controls Grain Yield and Climatic Yield in Mountainous Yunnan province, China

NASA Astrophysics Data System (ADS)

Duan, X.; Rong, L.; Gu, Z.; Feng, D.

2017-12-01

Mountainous regions are increasingly vulnerable to food insecurity because of limited arable land, growing population pressure, and climate change. Development of sustainable mountain agriculture will require an increased understanding of the effects of environmental factors on grain and climatic yields. The objective of this study was to explore the relationships between actual grain yield, climatic yield, and environmental factors in a mountainous region in China. We collected data on the average grain yield per unit area in 119 counties in Yunnan province from 1985 to 2012, and chose 17 environmental factors for the same period. Our results showed that actual grain yield ranged from 1.43 to 6.92 t·ha-1, and the climatic yield ranged from -0.15 to -0.01 t·ha-1. Lower climatic yield but higher grain yield was generally found in central areas and at lower slopes and elevations in the western and southwestern counties of Yunnan province. Higher climatic yield but lower grain yield were found in northwestern parts of Yunnan province on steep slopes. Annual precipation and temperature had a weak influence on the climatic yield. Slope explained 44.62 and 26.29% of the variation in grain yield and climatic yield. The effects of topography on grain and climatic yields were greater than climatic factors. Slope was the most important environmental variable for the variability in climatic and grain yields in the mountainous Yunnan province due to the highly heterogeneous topographic conditions. Conversion of slopes to terraces in areas with higher climatic yields is an effective way to maintain grain production in response to climate variability. Additionally, soil amendments and soil and water conservation measures should be considered to maintain soil fertility and aid in sustainable development in central areas, and in counties at lower slopes and elevations in western and southwestern Yunnan province.
A link prediction approach to cancer drug sensitivity prediction.

PubMed

Turki, Turki; Wei, Zhi

2017-10-03

Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.
Parameterizing the Supernova Engine and Its Effect on Remnants and Basic Yields

NASA Astrophysics Data System (ADS)

Fryer, Chris L.; Andrews, Sydney; Even, Wesley; Heger, Alex; Safi-Harb, Samar

2018-03-01

Core-collapse supernova science is now entering an era in which engine models are beginning to make both qualitative and, in some cases, quantitative predictions. Although the evidence in support of the convective engine for core-collapse supernova continues to grow, it is difficult to place quantitative constraints on this engine. Some studies have made specific predictions for the remnant distribution from the convective engine, but the results differ between different groups. Here we use a broad parameterization for the supernova engine to understand the differences between distinct studies. With this broader set of models, we place error bars on the remnant mass and basic yields from the uncertainties in the explosive engine. We find that, even with only three progenitors and a narrow range of explosion energies, we can produce a wide range of remnant masses and nucleosynthetic yields.
Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

NASA Astrophysics Data System (ADS)

Zhang, Xiaoyang; Zhang, Qingyuan

2016-04-01

Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05° (∼5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers.
A Remote Sensing-Derived Corn Yield Assessment Model

NASA Astrophysics Data System (ADS)

Shrestha, Ranjay Man

be further associated with the actual yield. Utilizing satellite remote sensing products, such as daily NDVI derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m pixel size, the crop yield estimation can be performed at a very fine spatial resolution. Therefore, this study examined the potential of these daily NDVI products within agricultural studies and crop yield assessments. In this study, a regression-based approach was proposed to estimate the annual corn yield through changes in MODIS daily NDVI time series. The relationship between daily NDVI and corn yield was well defined and established, and as changes in corn phenology and yield were directly reflected by the changes in NDVI within the growing season, these two entities were combined to develop a relational model. The model was trained using 15 years (2000-2014) of historical NDVI and county-level corn yield data for four major corn producing states: Kansas, Nebraska, Iowa, and Indiana, representing four climatic regions as South, West North Central, East North Central, and Central, respectively, within the U.S. Corn Belt area. The model's goodness of fit was well defined with a high coefficient of determination (R2>0.81). Similarly, using 2015 yield data for validation, 92% of average accuracy signified the performance of the model in estimating corn yield at county level. Besides providing the county-level corn yield estimations, the derived model was also accurate enough to estimate the yield at finer spatial resolution (field level). The model's assessment accuracy was evaluated using the randomly selected field level corn yield within the study area for 2014, 2015, and 2016. A total of over 120 plot level corn yield were used for validation, and the overall average accuracy was 87%, which statistically justified the model's capability to estimate plot-level corn yield. Additionally, the proposed model was applied to the impact estimation by examining the changes in corn yield
Growth and Yield Predictions for Thinned Stands of Even-aged Natural Longleaf Pine

Treesearch

Robert M. Farrar

1979-01-01

This paper presents a system of equations and resulting tables that can predict stand volumes for thinned natural longleaf pine. The system can predict current and future total stand volume in cubic feet and merchantable stand volume in cubic feet, cords, and board feet. The system also provides for estimating dry-weight production of wood. The system uses input data...
Geomorphic considerations for erosion prediction

USGS Publications Warehouse

Osterkamp, W.R.; Toy, T.J.

1997-01-01

Current soil-erosion prediction technology addresses processes of rainsplash, overland-flow sediment transport, and rill erosion in small watersheds. The effects of factors determining sediment yield from larger-scale drainage basins, in which sediment movement is controlled by the combined small-scale processes and a complex set of channel and other basin-scale sediment-delivery processes, such as soil creep, bioturbation, and accelerated erosion due to denudation of vegetation, have been poorly evaluated. General suggestions are provided for the development of erosion-prediction technology at the geomorphic or drainage-basin scale based on the separation of sediment-yield data for channel and geomorphic processes from those of field-scale soil loss. An emerging technology must consider: (1) the effects on sediment yield of climate, geology and soils, topography, biotic interactions with other soil processes, and land-use practices; (2) all processes of sediment delivery to a channel system; and (3) the general tendency in most drainage basins for progressively greater sediment storage in the downstream direction.
Study of B{yields}{lambda}{sub c}{lambda}{sub c} and B{yields}{lambda}{sub c}{lambda}{sub c}K

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

Cheng, H.-Y.; Hsiao, Y.-K.; Chua, C.-K.

2009-06-01

We study the doubly charmful two-body and three-body baryonic B decays B{yields}{lambda}{sub c}{sup +}{lambda}{sub c}{sup -} and B{yields}{lambda}{sub c}{sup +}{lambda}{sub c}{sup -}K. As pointed out before, a naive estimate of the branching ratio O(10{sup -8}) for the latter decay is too small by 3 to 4 orders of magnitude compared to experiment. Previously, it has been shown that a large enhancement for the {lambda}{sub c}{sup +}{lambda}{sub c}{sup -}K production can occur due to a charmoniumlike resonance (e.g. X(4630) discovered by Belle) with a mass near the {lambda}{sub c}{lambda}{sub c} threshold. Motivated by the BABAR's observation of a resonance in themore » {lambda}{sub c}K system with a mass of order 2930 MeV, we study in this work the contribution to B{yields}{lambda}{sub c}{sup +}{lambda}{sub c}{sup -}K from the intermediate state {xi}{sub c}(2980) which is postulated to be a first positive-parity excited D-wave charmed baryon state. Assuming that a soft qq quark pair is produced through the {sigma} and {pi} meson exchanges in the configuration for B{yields}{xi}{sub c}(2980){lambda}{sub c} and {lambda}{sub c}{lambda}{sub c}, it is found that branching ratios of B{yields}{lambda}{sub c}{sup +}{lambda}{sub c}{sup -}K and B{yields}{lambda}{sub c}{sup +}{lambda}{sub c}{sup -} are of order 3.5x10{sup -4} and 5x10{sup -5}, respectively, in agreement with experiment except that the prediction for the {lambda}{sub c}{lambda}{sub c}K{sup -} is slightly smaller. In conjunction with our previous analysis, we conclude that the enormously large rate of B{yields}{lambda}{sub c}{sup +}{lambda}{sub c}{sup -}K arises from the resonances {xi}{sub c}(2980) and X(4630)« less
Recalibration and predictive reliability of a solute-transport model of an irrigated stream-aquifer system

USGS Publications Warehouse

Person, M.; Konikow, Leonard F.

1986-01-01

A solute-transport model of an irrigated stream-aquifer system was recalibrated because of discrepancies between prior predictions of ground-water salinity trends during 1971-1982 and the observed outcome in February 1982. The original model was calibrated with a 1-year record of data collected during 1971-1972 in an 18-km reach of the Arkansas River Valley in southeastern Colorado. The model is improved by incorporating additional hydrologic processes (salt transport through the unsaturated zone) and through reexamination of the reliability of some input data (regression relationship used to estimate salinity from specific conductance data). Extended simulations using the recalibrated model are made to investigate the usefulness of the model for predicting long-term trends of salinity and water levels within the study area. Predicted ground-water levels during 1971-1982 are in good agreement with the observed, indicating that the original 1971-1972 study period was sufficient to calibrate the flow model. However, long-term simulations using the recalibrated model based on recycling the 1971-1972 data alone yield an average ground-water salinity for 1982 that is too low by about 10%. Simulations that incorporate observed surface-water salinity variations yield better results, in that the calculated average ground-water salinity for 1982 is within 3% of the observed value. Statistical analysis of temporal salinity variations of the applied surface water indicates that at least a 4-year sampling period is needed to accurately calibrate the transport model. ?? 1986.
When bad stress goes good: increased threat reactivity predicts improved category learning performance.

PubMed

Ell, Shawn W; Cosley, Brandon; McCoy, Shannon K

2011-02-01

The way in which we respond to everyday stressors can have a profound impact on cognitive functioning. Maladaptive stress responses in particular are generally associated with impaired cognitive performance. We argue, however, that the cognitive system mediating task performance is also a critical determinant of the stress-cognition relationship. Consistent with this prediction, we observed that stress reactivity consistent with a maladaptive, threat response differentially predicted performance on two categorization tasks. Increased threat reactivity predicted enhanced performance on an information-integration task (i.e., learning is thought to depend upon a procedural-based memory system), and a (nonsignificant) trend for impaired performance on a rule-based task (i.e., learning is thought to depend upon a hypothesis-testing system). These data suggest that it is critical to consider both variability in the stress response and variability in the cognitive system mediating task performance in order to fully understand the stress-cognition relationship.

Absolute I(asterisk) quantum yields for the ICN A state by diode laser gain-vs-absorption spectroscopy

NASA Technical Reports Server (NTRS)

Hess, Wayne P.; Leone, Stephen R.

1987-01-01

Absolute I(asterisk) quantum yields have been measured as a function of wavelength for room temperature photodissociation of the ICN A state continuum. The yields are obtained by the technique of time-resolved diode laser gain-vs-absorption spectroscopy. Quantum yields are evaluated at seven wavelengths from 248 to 284 nm. The yield at 266 nm is 66.0 + or - 2 percent and it falls off to 53.4 + or - 2 percent and 44.0 + or - 4 percent at 284 and 248 nm, respectively. The latter values are significantly higher than those obtained by previous workers using infrared fluorescence. Estimates of I(asterisk) quantum yields obtained from analysis of CN photofragment rotational distributions, as discussed by other workers, are in good agreement with the I(asterisk) yields reported here. The results are considered in conjunction with recent theoretical and experimental work on the CN rotational distributions and with previous I(asterisk) quantum yield results.
Predicting hydration free energies of amphetamine-type stimulants with a customized molecular model

NASA Astrophysics Data System (ADS)

Li, Jipeng; Fu, Jia; Huang, Xing; Lu, Diannan; Wu, Jianzhong

2016-09-01

Amphetamine-type stimulants (ATS) are a group of incitation and psychedelic drugs affecting the central nervous system. Physicochemical data for these compounds are essential for understanding the stimulating mechanism, for assessing their environmental impacts, and for developing new drug detection methods. However, experimental data are scarce due to tight regulation of such illicit drugs, yet conventional methods to estimate their properties are often unreliable. Here we introduce a tailor-made multiscale procedure for predicting the hydration free energies and the solvation structures of ATS molecules by a combination of first principles calculations and the classical density functional theory. We demonstrate that the multiscale procedure performs well for a training set with similar molecular characteristics and yields good agreement with a testing set not used in the training. The theoretical predictions serve as a benchmark for the missing experimental data and, importantly, provide microscopic insights into manipulating the hydrophobicity of ATS compounds by chemical modifications.
Cellular biophysics during freezing of rat and mouse sperm predicts post-thaw motility.

PubMed

Hagiwara, Mie; Choi, Jeung Hwan; Devireddy, Ramachandra V; Roberts, Kenneth P; Wolkers, Willem F; Makhlouf, Antoine; Bischof, John C

2009-10-01

Though cryopreservation of mouse sperm yields good survival and motility after thawing, cryopreservation of rat sperm remains a challenge. This study was designed to evaluate the biophysics (membrane permeability) of rat in comparison to mouse to better understand the cooling rate response that contributes to cryopreservation success or failure in these two sperm types. In order to extract subzero membrane hydraulic permeability in the presence of ice, a differential scanning calorimeter (DSC) method was used. By analyzing rat and mouse sperm frozen at 5 degrees C/min and 20 degrees C/min, heat release signatures characteristic of each sperm type were obtained and correlated to cellular dehydration. The dehydration response was then fit to a model of cellular water transport (dehydration) by adjusting cell-specific biophysical (membrane hydraulic permeability) parameters L(pg) and E(Lp). A "combined fit" (to 5 degrees C/min and 20 degrees C/min data) for rat sperm in Biggers-Whitten-Whittingham media yielded L(pg) = 0.007 microm min(-1) atm(-1) and E(Lp) = 17.8 kcal/mol, and in egg yolk cryopreservation media yielded L(pg) = 0.005 microm min(-1) atm(-1) and E(Lp) = 14.3 kcal/mol. These parameters, especially the activation energy, were found to be lower than previously published parameters for mouse sperm. In addition, the biophysical responses in mouse and rat sperm were shown to depend on the constituents of the cryopreservation media, in particular egg yolk and glycerol. Using these parameters, optimal cooling rates for cryopreservation were predicted for each sperm based on a criteria of 5%-15% normalized cell water at -30 degrees C during freezing in cryopreservation media. These predicted rates range from 53 degrees C/min to 70 degrees C/min and from 28 degrees C/min to 36 degrees C/min in rat and mouse, respectively. These predictions were validated by comparison to experimentally determined cryopreservation outcomes, in this case based on motility. Maximum
On the non-uniqueness of sediment yield

NASA Astrophysics Data System (ADS)

Kim, J.; Ivanov, V. Y.; Katopodes, N.

2012-12-01

Estimation of sediment yield at the catchment scale plays an important role for optimal design of hydraulic structures, such as bridges, culverts, reservoirs, and detention basins, as well as making informed decisions in environmental management. Many experimental studies focused on obtaining flow and sediment data in search of unique relationships between runoff (specifically, volume and peak) and sediment characteristics. These relationships were employed to predict sediment yield from flow information. However, despite the same flow volume, the actual sediment yield produced by river basins can vary significantly depending on several conditions: (i) the catchment size, (ii) land use, topography, and soil type, (iii) climatic variations or characteristics , and (iv) initial conditions of soil moisture and soil surface . Additionally, shield formation by relatively larger particles can be one of the possible controllers of erosion and net sediment transport. Smaller particles have low settling velocities and tend to move far from their original position of detachment. Conversely, larger particles can settle quickly near their original locations. Eventually, such particles can form a shield on soil bed and protect underlying soil from rainfall detachment and runoff entrainment. The shield formation and temporal development can be influenced by rainfall intensity, frequency, and volume. Rainfall influences the generation of runoff leading to different conditions of flow depth and velocity that can perturb intact soil into a loose condition. In this study, we numerically investigate the effects of precipitation patterns on the generation of sediment yield. In particular, we address reasons of non-uniqueness of basin sediment yield for the same runoff volume as well as causes of unsteady phenomena in erosion processes under steady state flow conditions. For numerical simulations, the two-dimensional Hairsine-Rose model coupled with a fully distributed hydrology and
Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2018-06-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.
Fission yield calculation using toy model based on Monte Carlo simulation

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

Jubaidah, E-mail: jubaidah@student.itb.ac.id; Physics Department, Faculty of Mathematics and Natural Science – State University of Medan. Jl. Willem Iskandar Pasar V Medan Estate – North Sumatera, Indonesia 20221; Kurniadi, Rizal, E-mail: rijalk@fi.itb.ac.id

2015-09-30

Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. Theremore » are five Gaussian parameters used in this research. They are scission point of the two curves (R{sub c}), mean of left curve (μ{sub L}) and mean of right curve (μ{sub R}), deviation of left curve (σ{sub L}) and deviation of right curve (σ{sub R}). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90« less
Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2017-12-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.
Theory of nonlinear elasticity, stress-induced relaxation, and dynamic yielding in dense fluids of hard nonspherical colloids

NASA Astrophysics Data System (ADS)

Zhang, Rui; Schweizer, Kenneth S.

2012-04-01

We generalize the microscopic naïve mode coupling and nonlinear Langevin equation theories of the coupled translation-rotation dynamics of dense suspensions of uniaxial colloids to treat the effect of applied stress on shear elasticity, cooperative cage escape, structural relaxation, and dynamic and static yielding. The key concept is a stress-dependent dynamic free energy surface that quantifies the center-of-mass force and torque on a moving colloid. The consequences of variable particle aspect ratio and volume fraction, and the role of plastic versus double glasses, are established in the context of dense, glass-forming suspensions of hard-core dicolloids. For low aspect ratios, the theory provides a microscopic basis for the recently observed phenomenon of double yielding as a consequence of stress-driven sequential unlocking of caging constraints via reduction of the distinct entropic barriers associated with the rotational and translational degrees of freedom. The existence, and breadth in volume fraction, of the double yielding phenomena is predicted to generally depend on both the degree of particle anisotropy and experimental probing frequency, and as a consequence typically occurs only over a window of (high) volume fractions where there is strong decoupling of rotational and translational activated relaxation. At high enough concentrations, a return to single yielding is predicted. For large aspect ratio dicolloids, rotation and translation are always strongly coupled in the activated barrier hopping event, and hence for all stresses only a single yielding process is predicted.
Potential for Improved Crop Yield Prediction Through Assimilation of Satellite-Derived Soil Moisture Data

USDA-ARS?s Scientific Manuscript database

Crop yield estimates have a strong impact on dealing with food shortages and on market demand and supply; these estimates are critical for decision-making processes by the U.S. Government, policy makers, stakeholders, etc. Most of the decision making is based on forecasts provided by the U.S. Depart...
Sorghum production under future climate in the Southwestern USA: model projections of yield, greenhouse gas emissions and soil C fluxes

NASA Astrophysics Data System (ADS)

Duval, B.; Ghimire, R.; Hartman, M. D.; Marsalis, M.

2016-12-01

Large tracts of semi-arid land in the Southwestern USA are relatively less important for food production than the US Corn Belt, and represent a promising area for expansion of biofuel/bioproduct crops. However, high temperatures, low available water and high solar radiation in the SW represent a challenge to suitable feedstock development, and future climate change scenarios predict that portions of the SW will experience increased temperature and temporal shifts in precipitation distribution. Sorghum (Sorghum bicolor) is a valuable forage crop with promise as a biofuel feedstock, given its high biomass under semi-arid conditions, relatively lower N fertilizer requirements compared to corn, and salinity tolerance. To evaluate the environmental impact of expanded sorghum cultivation under future climate in the SW USA, we used the DayCent model in concert with a suite of downscaled future weather projections to predict biogeochemical consequences (greenhouse gas flux and impacts on soil carbon) of sorghum cultivation in New Mexico. The model showed good correspondence with yield data from field trials including both dryland and irrigated sorghum (measured vs. modeled; r2 = 0.75). Simulation experiments tested the effect of dryland production versus irrigation, low N versus high N inputs and delayed fertilizer application. Nitrogen application timing and irrigation impacted yield and N2O emissions less than N rate and climate. Across N and irrigation treatments, future climate simulations resulted in 6% increased yield and 20% lower N2O emissions compared to current climate. Soil C pools declined under future climate. The greatest declines in soil C were from low N input sorghum simulations, regardless of irrigation (>20% declines in SOM in both cases), and requires further evaluation to determine if changing future climate is driving these declines, or if they are a function of prolonged sorghum-fallow rotations in the model. The relatively small gain in yield for
Quantum Yields in Mixed-Conifer Forests and Ponderosa Pine Plantations

NASA Astrophysics Data System (ADS)

Wei, L.; Marshall, J. D.; Zhang, J.

2008-12-01

Most process-based physiological models require canopy quantum yield of photosynthesis as a starting point to simulate carbon sequestration and subsequently gross primary production (GPP). The quantum yield is a measure of photosynthetic efficiency expressed in moles of CO2 assimilated per mole of photons absorbed; the process is influenced by environmental factors. In the summer 2008, we measured quantum yields on both sun and shade leaves for four conifer species at five sites within Mica Creek Experimental Watershed (MCEW) in northern Idaho and one conifer species at three sites in northern California. The MCEW forest is typical of mixed conifer stands dominated by grand fir (Abies grandis (Douglas ex D. Don) Lindl.). In northern California, the three sites with contrasting site qualities are ponderosa pine (Pinus ponderosa C. Lawson var. ponderosa) plantations that were experimentally treated with vegetation control, fertilization, and a combination of both. We found that quantum yields in MCEW ranged from ~0.045 to ~0.075 mol CO2 per mol incident photon. However, there were no significant differences between canopy positions, or among sites or tree species. In northern California, the mean value of quantum yield of three sites was 0.051 mol CO2/mol incident photon. No significant difference in quantum yield was found between canopy positions, or among treatments or sites. The results suggest that these conifer species maintain relatively consistent quantum yield in both MCEW and northern California. This consistency simplifies the use of a process-based model to accurately predict forest productivity in these areas.
A viable method to predict acoustic streaming in presence of cavitation.

PubMed

Louisnard, O

2017-03-01

The steady liquid flow observed under ultrasonic emitters generating acoustic cavitation can be successfully predicted by a standard turbulent flow calculation. The flow is driven by the classical averaged volumetric force density calculated from the acoustic field, but the inertial term in Navier-Stokes equations must be kept, and a turbulent solution must be sought. The acoustic field must be computed with a realistic model, properly accounting for dissipation by the cavitation bubbles [Louisnard, Ultrason. Sonochem., 19, (2012) 56-65]. Comparison with 20kHz experiments, involving the combination of acoustic streaming and a perpendicular forced flow in a duct, shows reasonably good agreement. Moreover, the persistence of the cavitation effects on the wall facing the emitter, in spite of the deflection of the streaming jet, is correctly reproduced by the model. It is also shown that predictions based either on linear acoustics with the correct turbulent solution, or with Louisnard's model with Eckart-Nyborg's theory yields unrealistic results. Copyright Â© 2016 Elsevier B.V. All rights reserved.
Good Education, the Good Teacher, and a Practical Art of Living a Good Life: A Catholic Perspective

ERIC Educational Resources Information Center

Hermans, Chris

2017-01-01

What is good education? We value education for reasons connected to the good provided by education in society. This good is connected to be the pedagogical aim of education. This article distinguishes five criteria for good education based on the concept of "Bildung". Next, these five criteria are used to develop the idea of the good…
PREDICTING CLIMATE-INDUCED RANGE SHIFTS FOR MAMMALS: HOW GOOD ARE THE MODELS?

EPA Science Inventory

In order to manage wildlife and conserve biodiversity, it is critical that we understand the potential impacts of climate change on species distributions. Several different approaches to predicting climate-induced geographic range shifts have been proposed to address this proble...
Low LET radiolysis escape yields for reducing radicals and H2 in pressurized high temperature water

NASA Astrophysics Data System (ADS)

Sterniczuk, Marcin; Yakabuskie, Pamela A.; Wren, J. Clara; Jacob, Jasmine A.; Bartels, David M.

2016-04-01

Low Linear Energy Transfer (LET) radiolysis escape yields (G values) are reported for the sum (G(radH)+G(e-)aq) and for G(H2) in subcritical water up to 350 °C. The scavenger system 1-10 mM acetate/0.001 M hydroxide/0.00048 M N2O was used with simultaneous mass spectroscopic detection of H2 and N2 product. Temperature-dependent measurements were carried out with 2.5 MeV electrons from a van de Graaff accelerator, while room temperature calibration measurements were done with a 60Co gamma source. The concentrations and dose range were carefully chosen so that initial spur chemistry is not perturbed and the N2 product yield corresponds to those reducing radicals that escape recombination in pure water. In comparison with a recent review recommendation of Elliot and Bartels (AECL report 153-127160-450-001, 2009), the measured reducing radical yield is seven percent smaller at room temperature but in fairly good agreement above 150 °C. The H2 escape yield is in good agreement throughout the temperature range with several previous studies that used much larger radical scavenging rates. Previous analysis of earlier high temperature measurements of Gesc(radOH) is shown to be flawed, although the actual G values may be nearly correct. The methodology used in the present report greatly reduces the range of possible error and puts the high temperature escape yields for low-LET radiation on a much firmer quantitative foundation than was previously available.
Yield and failure criteria for composite materials under static and dynamic loading

DOE PAGES

Daniel, Isaac M.

2015-12-23

To facilitate and accelerate the process of introducing, evaluating and adopting of new material systems, it is important to develop/establish comprehensive and effective procedures of characterization, modeling and failure prediction of structural laminates based on the properties of the constituent materials, e. g., fibers, matrix, and the single ply or lamina. A new failure theory, the Northwestern (NU-Daniel) theory, has been proposed for predicting lamina yielding and failure under multi-axial states of stress including strain rate effects. It is primarily applicable to matrix-dominated interfiber/interlaminar failures. It is based on micromechanical failure mechanisms but is expressed in terms of easily measuredmore » macroscopic lamina stiffness and strength properties. It is presented in the form of a master failure envelope incorporating strain rate effects. The theory was further adapted and extended to the prediction of in situ first ply yielding and failure (FPY and FPF) and progressive failure of multi-directional laminates under static and dynamic loadings. The significance of this theory is that it allows for rapid screening of new composite materials without very extensive testing and offers easily implemented design tools.« less
A Joyful Heart is Good Medicine: Positive Affect Predicts Memory Complaints.

PubMed

Lee, Pai-Lin

2016-08-01

Positive affect (PA) systematically improves cognitive performance on a wide range of cognitive tasks, but the link between PA and subjective memory complaints (SMCs) is unclear. The aim of this study was to investigate the associations between PA (level and change) and SMCs over a 10-year span. Current data included participants who completed all measures in the Midlife in the US Study (N = 2,214; age range: 50-84 years; mean: 62.81; standard deviation [SD]: 8.98). The level (mean of Time 1 and Time 2) and change (Time 2 minus Time 1) of PA was examined longitudinally to determine if PA predicts SMCs. The long-term level and change of PA predicted SMCs. No age and education differences were found for the effects of PA (PA × age and PA × education) on SMCs. Additional comparison analysis found high PA (+1 SD) differs from low PA (-1 SD) on age, financial condition and depression, and physical activity. This study provides longitudinal evidence that further supports PA is associated with a key cognitive aging outcome, SMCs. Effective cognitive-health programs may need to pay more attention to PA intervention. Copyright © 2016 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.
Spectroscopy characterization and quantum yield determination of quantum dots

NASA Astrophysics Data System (ADS)

Contreras Ortiz, S. N.; Mejía Ospino, E.; Cabanzo, R.

2016-02-01

In this paper we show the characterization of two kinds of quantum dots: hydrophilic and hydrophobic, with core and core/shell respectively, using spectroscopy techniques such as UV-Vis, fluorescence and Raman. We determined the quantum yield in the quantum dots using the quinine sulphate as standard. This salt is commonly used because of its quantum yield (56%) and stability. For the CdTe excitation, we used a wavelength of 549nm and for the CdSe/ZnS excitation a wavelength of 527nm. The results show that CdSe/ZnS (49%) has better fluorescence, better quantum dots, and confirm the fluorescence result. The quantum dots have shown a good fluorescence performance, so this property will be used to replace dyes, with the advantage that quantum dots are less toxic than some dyes like the rhodamine. In addition, in this work we show different techniques to find the quantum dots emission: fluorescence spectrum, synchronous spectrum and Raman spectrum.
Prediction of Seasonal Climate-induced Variations in Global Food Production

NASA Technical Reports Server (NTRS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-01-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.
Modeling the effects of ozone on soybean growth and yield.

PubMed

Kobayashi, K; Miller, J E; Flagler, R B; Heck, W W

1990-01-01

A simple mechanistic model was developed based on an existing growth model in order to address the mechanisms of the effects of ozone on growth and yield of soybean [Glycine max. (L.) Merr. 'Davis'] and interacting effects of other environmental stresses. The model simulates daily growth of soybean plants using environmental data including shortwave radiation, temperature, precipitation, irrigation and ozone concentration. Leaf growth, dry matter accumulation, water budget, nitrogen input and seed growth linked to senescence and abscission of leaves are described in the model. The effects of ozone are modeled as reduced photosynthate production and accelerated senescence. The model was applied to the open-top chamber experiments in which soybean plants were exposed to ozone under two levels of soil moisture regimes. After calibrating the model to the growth data and seed yield, goodness-of-fit of the model was tested. The model fitted well for top dry weight in the vegetative growth phase and also at maturity. The effect of ozone on seen yield was also described satisfactorily by the model. The simulation showed apparent interaction between the effect of ozone and soil moisture stress on the seed yield. The model revealed that further work is needed concerning the effect of ozone on the senescence process and the consequences of alteration of canopy microclimate by the open-top chambers.

Tile prediction schemes for wide area motion imagery maps in GIS

NASA Astrophysics Data System (ADS)

Michael, Chris J.; Lin, Bruce Y.

2017-11-01

Wide-area surveillance, traffic monitoring, and emergency management are just several of many applications benefiting from the incorporation of Wide-Area Motion Imagery (WAMI) maps into geographic information systems. Though the use of motion imagery as a GIS base map via the Web Map Service (WMS) standard is not a new concept, effectively streaming imagery is particularly challenging due to its large scale and the multidimensionally interactive nature of clients that use WMS. Ineffective streaming from a server to one or more clients can unnecessarily overwhelm network bandwidth and cause frustratingly large amounts of latency in visualization to the user. Seamlessly streaming WAMI through GIS requires good prediction to accurately guess the tiles of the video that will be traversed in the near future. In this study, we present an experimental framework for such prediction schemes by presenting a stochastic interaction model that represents a human user's interaction with a GIS video map. We then propose several algorithms by which the tiles of the stream may be predicted. Results collected both within the experimental framework and using human analyst trajectories show that, though each algorithm thrives under certain constraints, the novel Markovian algorithm yields the best results overall. Furthermore, we make the argument that the proposed experimental framework is sufficient for the study of these prediction schemes.
Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

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

Leow, Shijie; Witter, John R.; Vardon, Derek R.

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and othermore » conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model
Prediction of microalgae hydrothermal liquefaction products from feedstock biochemical composition

DOE PAGES

Leow, Shijie; Witter, John R.; Vardon, Derek R.; ...

2015-05-11

Hydrothermal liquefaction (HTL) uses water under elevated temperatures and pressures (200–350 °C, 5–20 MPa) to convert biomass into liquid “biocrude” oil. Despite extensive reports on factors influencing microalgae cell composition during cultivation and separate reports on HTL products linked to cell composition, the field still lacks a quantitative model to predict HTL conversion product yield and qualities from feedstock biochemical composition; the tailoring of microalgae feedstock for downstream conversion is a unique and critical aspect of microalgae biofuels that must be leveraged upon for optimization of the whole process. This study developed predictive relationships for HTL biocrude yield and othermore » conversion product characteristics based on HTL of Nannochloropsis oculata batches harvested with a wide range of compositions (23–59% dw lipids, 58–17% dw proteins, 12–22% dw carbohydrates) and a defatted batch (0% dw lipids, 75% dw proteins, 19% dw carbohydrates). HTL biocrude yield (33–68% dw) and carbon distribution (49–83%) increased in proportion to the fatty acid (FA) content. A component additivity model (predicting biocrude yield from lipid, protein, and carbohydrates) was more accurate predicting literature yields for diverse microalgae species than previous additivity models derived from model compounds. FA profiling of the biocrude product showed strong links to the initial feedstock FA profile of the lipid component, demonstrating that HTL acts as a water-based extraction process for FAs; the remainder non-FA structural components could be represented using the defatted batch. These findings were used to introduce a new FA-based model that predicts biocrude oil yields along with other critical parameters, and is capable of adjusting for the wide variations in HTL methodology and microalgae species through the defatted batch. Lastly, the FA model was linked to an upstream cultivation model (Phototrophic Process Model
Estimating sugarcane yield potential using an in-season determination of normalized difference vegetative index.

PubMed

Lofton, Josh; Tubana, Brenda S; Kanke, Yumiko; Teboh, Jasper; Viator, Howard; Dalen, Marilyn

2012-01-01

Estimating crop yield using remote sensing techniques has proven to be successful. However, sugarcane possesses unique characteristics; such as, a multi-year cropping cycle and plant height-limiting for midseason fertilizer application timing. Our study objective was to determine if sugarcane yield potential could be estimated using an in-season estimation of normalized difference vegetative index (NDVI). Sensor readings were taken using the GreenSeeker® handheld sensor from 2008 to 2011 in St. Gabriel and Jeanerette, LA, USA. In-season estimates of yield (INSEY) values were calculated by dividing NDVI by thermal variables. Optimum timing for estimating sugarcane yield was between 601-750 GDD. In-season estimated yield values improved the yield potential (YP) model compared to using NDVI. Generally, INSEY value showed a positive exponential relationship with yield (r(2) values 0.48 and 0.42 for cane tonnage and sugar yield, respectively). When models were separated based on canopy structure there was an increase the strength of the relationship for the erectophile varieties (r(2) 0.53 and 0.47 for cane tonnage and sugar yield, respectively); however, the model for planophile varieties weakened slightly. Results of this study indicate using an INSEY value for predicting sugarcane yield shows potential of being a valuable management tool for sugarcane producers in Louisiana.
Climate Change Impacts on Sediment Yield in Headwaters of a High-latitude Region in China

NASA Astrophysics Data System (ADS)

Zhou, Y.; Xu, Y. J.; Wang, J., , Dr; Weihua, X.; Huang, Y.

2017-12-01

Climate change is expected to have strongest effects in higher latitude regions. Despite intensive research on possible hydrological responses to global warming in these regions, our knowledge of climate change on surface erosion and sediment yield in high-latitude headwaters is limited. In this study, we used the Soil and Water Assessment Tool (SWAT) to predict future runoff and sediment yield from the headwaters of a high-latitude river basin in China's far northeast. The SWAT model was first calibrated with historical discharge records and the model parameterization achieved satisfactory validation. The calibrated model was then applied to two greenhouse gas concentration trajectories, RCP4.5 and RCP8.5, for the period from 2020 to 2050 to estimate future runoff. Sediment yields for this period were predicted using a discharge-sediment load rating curve developed from field measurements in the past nine years. Our preliminary results show an increasing trend of sediment yield under both climate change scenarios, and that the increase is more pronounced in the summer and autumn months. Changes in precipitation and temperature seem to exert variable impacts on runoff and sediment yield at interannual and seasonal scales in these headwaters. These findings imply that the current river basin management in the region needs to be reviewed and improved in order to be effective under a changing climate.
New measurements on isobaric fission product yields and mean kinetic energy for 241Pu thermal neutron-induced fission

NASA Astrophysics Data System (ADS)

Julien-Laferrière, Sylvain; Kessedjian, Grégoire; Serot, Olivier; Chebboubi, Abdelaziz; Bernard, David; Blanc, Aurélien; Köster, Ulli; Litaize, Olivier; Materna, Thomas; Meplan, Olivier; Rapala, Michal; Sage, Christophe

2018-03-01

Nuclear fission yields data measurements for thermal neutron induced fission of 241Pu have been carried out at the Institut Laue Langevin (ILL) in Grenoble, using the Lohengrin mass spectrometer. Mass, isotopic and isomeric yields have been extracted for the last measurements. A focus is given in this document to the mass yield results which are obtained for almost the entire heavy peak and most of the light high yields masses, along with the covariance matrix. The mean kinetic energy as a function of the fission product mass has also been extracted from the measurements. The total mean kinetic energy pre and post neutron emission have been assessed and compared to other works showing a rather good agreement.
Cow-specific diet digestibility predictions based on near-infrared reflectance spectroscopy scans of faecal samples.

PubMed

Mehtiö, T; Rinne, M; Nyholm, L; Mäntysaari, P; Sairanen, A; Mäntysaari, E A; Pitkänen, T; Lidauer, M H

2016-04-01

This study was designed to obtain information on prediction of diet digestibility from near-infrared reflectance spectroscopy (NIRS) scans of faecal spot samples from dairy cows at different stages of lactation and to develop a faecal sampling protocol. NIRS was used to predict diet organic matter digestibility (OMD) and indigestible neutral detergent fibre content (iNDF) from faecal samples, and dry matter digestibility (DMD) using iNDF in feed and faecal samples as an internal marker. Acid-insoluble ash (AIA) as an internal digestibility marker was used as a reference method to evaluate the reliability of NIRS predictions. Feed and composite faecal samples were collected from 44 cows at approximately 50, 150 and 250 days in milk (DIM). The estimated standard deviation for cow-specific organic matter digestibility analysed by AIA was 12.3 g/kg, which is small considering that the average was 724 g/kg. The phenotypic correlation between direct faecal OMD prediction by NIRS and OMD by AIA over the lactation was 0.51. The low repeatability and small variability estimates for direct OMD predictions by NIRS were not accurate enough to quantify small differences in OMD between cows. In contrast to OMD, the repeatability estimates for DMD by iNDF and especially for direct faecal iNDF predictions were 0.32 and 0.46, respectively, indicating that developing of NIRS predictions for cow-specific digestibility is possible. A data subset of 20 cows with daily individual faecal samples was used to develop an on-farm sampling protocol. Based on the assessment of correlations between individual sample combinations and composite samples as well as repeatability estimates for individual sample combinations, we found that collecting up to three individual samples yields a representative composite sample. Collection of samples from all the cows of a herd every third month might be a good choice, because it would yield a better accuracy. © 2015 Blackwell Verlag GmbH.
Fission fragment yields from heavy-ion-induced reactions measured with a fragment separator

NASA Astrophysics Data System (ADS)

Tarasov, O. B.; Delaune, O.; Farget, F.; Morrissey, D. J.; Amthor, A. M.; Bastin, B.; Bazin, D.; Blank, B.; Cacéres, L.; Chbihi, A.; Fernández-Dominguez, B.; Grévy, S.; Kamalou, O.; Lukyanov, S. M.; Mittig, W.; Pereira, J.; Perrot, L.; Saint-Laurent, M.-G.; Savajols, H.; Sherrill, B. M.; Stodel, C.; Thomas, J. C.; Villari, A. C.

2018-04-01

The systematic study of fission fragment yields under different initial conditions has provided valuable experimental data for benchmarking models of fission product yields. Nuclear reactions using inverse kinematics coupled to the use of a high-resolution spectrometer with good fragment identification are shown here to be a powerful tool to measure the inclusive isotopic yields of fission fragments. In-flight fusion-fission was used in this work to produce secondary beams of neutron-rich isotopes in the collisions of a 238U beam at 24 MeV/u with 9Be and 12C targets at GANIL using the LISE3 fragment separator. Unique identification of the A, Z, and atomic charge state, q, of fission products was attained with the Δ E- TKE-B ρ- ToF measurement technique. Mass, and atomic number distributions are reported for the two reactions. The results show the importance of different reaction mechanisms in the two cases. The optimal target material for higher yields of neutron-rich high- Z isotopes produced in fusion-fission reactions as a function of projectile energy is discussed.
Comparison of the Results of MISSE 6 Atomic Oxygen Erosion Yields of Layered Kapton H Films with Monte Carlo Computational Predictions

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Groh, Kim De; Kneubel, Christian A.

2014-01-01

A space experiment flown as part of the Materials International Space Station Experiment 6B (MISSE 6B) was designed to compare the atomic oxygen erosion yield (Ey) of layers of Kapton H polyimide with no spacers between layers with that of layers of Kapton H with spacers between layers. The results were compared to a solid Kapton H (DuPont, Wilmington, DE) sample. Monte Carlo computational modeling was performed to optimize atomic oxygen interaction parameter values to match the results of both the MISSE 6B multilayer experiment and the undercut erosion profile from a crack defect in an aluminized Kapton H sample flown on the Long Duration Exposure Facility (LDEF). The Monte Carlo modeling produced credible agreement with space results of increased Ey for all samples with spacers as well as predicting the space-observed enhancement in erosion near the edges of samples due to scattering from the beveled edges of the sample holders.
REML/BLUP and sequential path analysis in estimating genotypic values and interrelationships among simple maize grain yield-related traits.

PubMed

Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q

2017-03-22

Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε < 0.3). The number of kernels per ear (NKE) and thousand-kernel weight (TKW) are the traits with the largest direct effects on grain yield (r = 0.66 and 0.73, respectively). The high accuracy of selection (0.86 and 0.89) associated with the high heritability of the average (0.732 and 0.794) for NKE and TKW, respectively, indicated good reliability and prospects of success in the indirect selection of hybrids with high-yield potential through these traits. The negative direct effect of NKE on TKW (r = -0.856), however, must be considered. The joint use of mixed models and sequential path analysis is effective in the evaluation of maize-breeding trials.
Incorporating uncertainty into the ranking of SPARROW model nutrient yields from Mississippi/Atchafalaya River basin watersheds

USGS Publications Warehouse

Robertson, Dale M.; Schwarz, Gregory E.; Saad, David A.; Alexander, Richard B.

2009-01-01

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from
[Yield of starch extraction from plantain (Musa paradisiaca). Pilot plant study].

PubMed

Flores-Gorosquera, Emigdia; García-Suárez, Francisco J; Flores-Huicochea, Emmanuel; Núñez-Santiago, María C; González-Soto, Rosalia A; Bello-Pérez, Luis A

2004-01-01

In México, the banana (Musa paradisiaca) is cooked (boiling or deep frying) before being eaten, but the consumption is not very popular and a big quantity of the product is lost after harvesting. The unripe plantain has a high level of starch and due to this the use of banana can be diversified as raw material for starch isolation. The objective of this work was to study the starch yield at pilot plant scale. Experiments at laboratory scale were carried out using the pulp with citric acid to 0,3 % (antioxidant), in order to evaluate the different unitary operations of the process. The starch yield, based on starch presence in the pulp that can be isolated, were between 76 and 86 %, and the values at pilot plant scale were between 63 and 71 %, in different lots of banana fruit. Starch yield values were similar among the diverse lots, showing that the process is reproducible. The lower values of starch recovery at pilot plant scale are due to the loss during sieving operations; however, the amount of starch recovery is good.
Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.
Do quality indicators for general practice teaching practices predict good outcomes for students?

PubMed

Bartlett, Maggie; Potts, Jessica; McKinley, Bob

2016-07-01

Keele medical students spend 113 days in general practices over our five-year programme. We collect practice data thought to indicate good quality teaching. We explored the relationships between these data and two outcomes for students; Objective Structured Clinical Examination (OSCE) scores and feedback regarding the placements. Though both are surrogate markers of good teaching, they are widely used. We collated practice and outcome data for one academic year. Two separate statistical analyses were carried out: (1) to determine how much of the variation seen in the OSCE scores was due to the effect of the practice and how much to the individual student. (2) to identify practice characteristics with a relationship to student feedback scores. (1) OSCE performance: 268 students in 90 practices: six quality indicators independently influenced the OSCE score, though without linear relationships and not to statistical significance. (2) Student satisfaction: 144 students in 69 practices: student feedback scores are not influenced by practice characteristics. The relationships between the quality indicators we collect for practices and outcomes for students are not clear. It may be that neither the quality indicators nor the outcome measures are reliable enough to inform decisions about practices' suitability for teaching.
Estimating daily fat yield from a single milking on test day for herds with a robotic milking system.

PubMed

Peeters, R; Galesloot, P J B

2002-03-01

The objective of this study was to estimate the daily fat yield and fat percentage from one sampled milking per cow per test day in an automatic milking system herd, when the milking times and milk yields of all individual milkings are recorded by the automatic milking system. Multiple regression models were used to estimate the 24-h fat percentage when only one milking is sampled for components and milk yields and milking times are known for all milkings in the 24-h period before the sampled milking. In total, 10,697 cow test day records, from 595 herd tests at 91 Dutch herds milked with an automatic milking system, were used. The best model to predict 24-h fat percentage included fat percentage, protein percentage, milk yield and milking interval of the sampled milking, milk yield, and milking interval of the preceding milking, and the interaction between milking interval and the ratio of fat and protein percentage of the sampled milking. This model gave a standard deviation of the prediction error (SE) for 24-h fat percentage of 0.321 and a correlation between the predicted and actual 24-h fat percentage of 0.910. For the 24-h fat yield, we found SE = 90 g and correlation = 0.967. This precision is slightly better than that of present a.m.-p.m. testing schemes. Extra attention must be paid to correctly matching the sample jars and the milkings. Furthermore, milkings with an interval of less than 4 h must be excluded from sampling as well as milkings that are interrupted or that follow an interrupted milking. Under these restrictions (correct matching, interval of at least 4 h, and no interrupted milking), one sampled milking suffices to get a satisfactory estimate for the test-day fat yield.
Framework for making better predictions by directly estimating variables’ predictivity

PubMed Central

Chernoff, Herman; Lo, Shaw-Hwa

2016-01-01

We propose approaching prediction from a framework grounded in the theoretical correct prediction rate of a variable set as a parameter of interest. This framework allows us to define a measure of predictivity that enables assessing variable sets for, preferably high, predictivity. We first define the prediction rate for a variable set and consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data, due to its inflated bias for moderate sample size and its sensitivity to noisy useless variables. We demonstrate that the I-score of the PR method of VS yields a relatively unbiased estimate of a parameter that is not sensitive to noisy variables and is a lower bound to the parameter of interest. Thus, the PR method using the I-score provides an effective approach to selecting highly predictive variables. We offer simulations and an application of the I-score on real data to demonstrate the statistic’s predictive performance on sample data. We conjecture that using the partition retention and I-score can aid in finding variable sets with promising prediction rates; however, further research in the avenue of sample-based measures of predictivity is much desired. PMID:27911830
Reliable yields of public water-supply wells in the fractured-rock aquifers of central Maryland, USA

NASA Astrophysics Data System (ADS)

Hammond, Patrick A.

2018-02-01

Most studies of fractured-rock aquifers are about analytical models used for evaluating aquifer tests or numerical methods for describing groundwater flow, but there have been few investigations on how to estimate the reliable long-term drought yields of individual hard-rock wells. During the drought period of 1998 to 2002, many municipal water suppliers in the Piedmont/Blue Ridge areas of central Maryland (USA) had to institute water restrictions due to declining well yields. Previous estimates of the yields of those wells were commonly based on extrapolating drawdowns, measured during short-term single-well hydraulic pumping tests, to the first primary water-bearing fracture in a well. The extrapolations were often made from pseudo-equilibrium phases, frequently resulting in substantially over-estimated well yields. The methods developed in the present study to predict yields consist of extrapolating drawdown data from infinite acting radial flow periods or by fitting type curves of other conceptual models to the data, using diagnostic plots, inverse analysis and derivative analysis. Available drawdowns were determined by the positions of transition zones in crystalline rocks or thin-bedded consolidated sandstone/limestone layers (reservoir rocks). Aquifer dewatering effects were detected by type-curve matching of step-test data or by breaks in the drawdown curves constructed from hydraulic tests. Operational data were then used to confirm the predicted yields and compared to regional groundwater levels to determine seasonal variations in well yields. Such well yield estimates are needed by hydrogeologists and water engineers for the engineering design of water systems, but should be verified by the collection of long-term monitoring data.
Hauser-Feshbach fission fragment de-excitation with calculated macroscopic-microscopic mass yields

NASA Astrophysics Data System (ADS)

Jaffke, Patrick; Möller, Peter; Talou, Patrick; Sierk, Arnold J.

2018-03-01

The Hauser-Feshbach statistical model is applied to the de-excitation of primary fission fragments using input mass yields calculated with macroscopic-microscopic models of the potential energy surface. We test the sensitivity of the prompt fission observables to the input mass yields for two important reactions, 235U(nth,f ) and 239Pu(nth,f ) , for which good experimental data exist. General traits of the mass yields, such as the location of the peaks and their widths, can impact both the prompt neutron and γ -ray multiplicities, as well as their spectra. Specifically, we use several mass yields to determine a linear correlation between the calculated prompt neutron multiplicity ν ¯ and the average heavy-fragment mass 〈Ah〉 of the input mass yields ∂ ν ¯/∂ 〈Ah〉 =±0.1 (n /f ) /u . The mass peak width influences the correlation between the total kinetic energy of the fission fragments and the total number of prompt neutrons emitted, ν¯T(TKE ) . Typical biases on prompt particle observables from using calculated mass yields instead of experimental ones are δ ν ¯=4 % for the average prompt neutron multiplicity, δ M ¯γ=1 % for the average prompt γ -ray multiplicity, δ ɛ¯nLAB=1 % for the average outgoing neutron energy, δ ɛ¯γ=1 % for the average γ -ray energy, and δ 〈TKE 〉=0.4 % for the average total kinetic energy of the fission fragments.
A temperature match based optimization method for daily load prediction considering DLC effect

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

Yu, Z.

This paper presents a unique optimization method for short term load forecasting. The new method is based on the optimal template temperature match between the future and past temperatures. The optimal error reduction technique is a new concept introduced in this paper. Two case studies show that for hourly load forecasting, this method can yield results as good as the rather complicated Box-Jenkins Transfer Function method, and better than the Box-Jenkins method; for peak load prediction, this method is comparable in accuracy to the neural network method with back propagation, and can produce more accurate results than the multi-linear regressionmore » method. The DLC effect on system load is also considered in this method.« less
Ecosystem Science: measuring, mapping and predicting the production of nature’s goods and services

EPA Science Inventory

Our existence, let alone our well-being, depends on “goods and services” produced by ecosystems (food, purification of water and air, outdoor recreation, etc.). Humans have the power to enhance, protect, or degrade nature’s capacity to provide these ecosystem s...

Humor Ability Reveals Intelligence, Predicts Mating Success, and Is Higher in Males

ERIC Educational Resources Information Center

Greengross, Gil; Miller, Geoffrey

2011-01-01

A good sense of humor is sexually attractive, perhaps because it reveals intelligence, creativity, and other "good genes" or "good parent" traits. If so, intelligence should predict humor production ability, which in turn should predict mating success. In this study, 400 university students (200 men and 200 women) completed…
Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

NASA Technical Reports Server (NTRS)

Ashrafi, S.

1991-01-01

K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.
[Factors predictive of good motivation to quit smoking among Moroccan smokers attending a lung disease outpatient clinic in 2008].

PubMed

Bouaïti, E; Mzouri, M; Sbaï-Idrissi, K; Razine, R; Kassouati, J; Lamrabet, M; Hassouni, F; Ouaaline, M; Benbrahim, N Fikri

2010-02-01

Motivations for cessation of smoking should be studied to determine which factors have an impact. Educational messages can then be developed to help smokers become more successful in adopting healthy behavior. The objective of our work was to determine the factors influencing the quality of motivation for smoking cessation among patients attending a lung disease clinic. Between March and June 2008, patients attending the outpatient clinical of the Moulay Youssef Hospital Department of Pneumology in Rabat were studied. Data on the smoking status and motivation to stop smoking (Richmond's test) were collected using a standardized questionnaire. A logistic regression model was developed to analyze the quality of their motivation to quit smoking. The median age for smoking the first cigarette was low (<20 years); pharmacological dependence on nicotine was low (Fagerström score<8 in 71.8%). More than a third of patients (36.6%) had already intended to cease smoking. According to the Richmond test, only 46.0% were well motivated (score>or=8). At multivariate analysis, factors predictive of a good motivation to quit smoking were a previous attempt to stop smoking (OR=5.4 [2.5-11.7]), severe disease (OR=3.7 [1.6-8.2]). Beginning the tobacco addiction before the age of 18 years was predictive of poor motivation (OR=2.7 [1.4-5.3]). Our investigation provides evidence in favor of searching for different factors which might affect motivation to stop smoking among patients seeking care in a lung disease clinic. Lung specialists, who manage the large majority of these patients should be particularly active in this area.
Delay, probability, and social discounting in a public goods game.

PubMed

Jones, Bryan A; Rachlin, Howard

2009-01-01

A human social discount function measures the value to a person of a reward to another person at a given social distance. Just as delay discounting is a hyperbolic function of delay, and probability discounting is a hyperbolic function of odds-against, social discounting is a hyperbolic function of social distance. Experiment 1 obtained individual social, delay, and probability discount functions for a hypothetical $75 reward; participants also indicated how much of an initial $100 endowment they would contribute to a common investment in a public good. Steepness of discounting correlated, across participants, among all three discount dimensions. However, only social and probability discounting were correlated with the public-good contribution; high public-good contributors were more altruistic and also less risk averse than low contributors. Experiment 2 obtained social discount functions with hypothetical $75 rewards and delay discount functions with hypothetical $1,000 rewards, as well as public-good contributions. The results replicated those of Experiment 1; steepness of the two forms of discounting correlated with each other across participants but only social discounting correlated with the public-good contribution. Most participants in Experiment 2 predicted that the average contribution would be lower than their own contribution.
An adapted yield criterion for the evolution of subsequent yield surfaces

NASA Astrophysics Data System (ADS)

Küsters, N.; Brosius, A.

2017-09-01

In numerical analysis of sheet metal forming processes, the anisotropic material behaviour is often modelled with isotropic work hardening and an average Lankford coefficient. In contrast, experimental observations show an evolution of the Lankford coefficients, which can be associated with a yield surface change due to kinematic and distortional hardening. Commonly, extensive efforts are carried out to describe these phenomena. In this paper an isotropic material model based on the Yld2000-2d criterion is adapted with an evolving yield exponent in order to change the yield surface shape. The yield exponent is linked to the accumulative plastic strain. This change has the effect of a rotating yield surface normal. As the normal is directly related to the Lankford coefficient, the change can be used to model the evolution of the Lankford coefficient during yielding. The paper will focus on the numerical implementation of the adapted material model for the FE-code LS-Dyna, mpi-version R7.1.2-d. A recently introduced identification scheme [1] is used to obtain the parameters for the evolving yield surface and will be briefly described for the proposed model. The suitability for numerical analysis will be discussed for deep drawing processes in general. Efforts for material characterization and modelling will be compared to other common yield surface descriptions. Besides experimental efforts and achieved accuracy, the potential of flexibility in material models and the risk of ambiguity during identification are of major interest in this paper.
An Introduction to Goodness of Fit for PMU Parameter Estimation

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

Riepnieks, Artis; Kirkham, Harold

2017-10-01

New results of measurements of phasor-like signals are presented based on our previous work on the topic. In this document an improved estimation method is described. The algorithm (which is realized in MATLAB software) is discussed. We examine the effect of noisy and distorted signals on the Goodness of Fit metric. The estimation method is shown to be performing very well with clean data and with a measurement window as short as a half a cycle and as few as 5 samples per cycle. The Goodness of Fit decreases predictably with added phase noise, and seems to be acceptable evenmore » with visible distortion in the signal. While the exact results we obtain are specific to our method of estimation, the Goodness of Fit method could be implemented in any phasor measurement unit.« less
Environmental and genetic factors affecting milk yield and quality in three Italian sheep breeds.

PubMed

Selvaggi, Maria; D'Alessandro, Angela Gabriella; Dario, Cataldo

2017-02-01

The aims of the study described in the Research Communication were to determine the level of influence of some environmental factors on milk yield and quality traits, including lactose, and lactation length in ewes belonging to three different Italian breeds and to estimate the heritability for the same traits. A total of 2138 lactation records obtained from 535 ewes belonging to three different Italian breeds (Comisana, Leccese, and Sarda) were used. Breed significantly affected all of the considered traits. Moreover, year of lambing affected milk yield and lactation length without influence on milk quality traits. Parity affected significantly only the milk yield, whereas type of birth showed its effect on milk yield, fat, protein, and lactose yield. On the whole, the presently reported heritability estimates are within the range of those already obtained in other dairy breeds by other authors, with values for lactation length being very low in all the investigated populations. Considering the heritability estimates for lactose content and yield, to the best of our knowledge, there is a lack of information on these parameters in ovine species and this is the first report on heritability of lactose content and yield in dairy sheep breeds. Our results suggest that genetic variability for milk traits other than lactation length is adequate for selection indicating a good response to selection in these breeds.
Search for the Higgs Boson in the H{yields}WW{yields}l{nu}jj Decay Channel in pp Collisions at {radical}(s)=7 TeV with the ATLAS Detector

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

Aad, G.; Ahles, F.; Beckingham, M.

2011-12-02

A search for a Higgs boson has been performed in the H{yields}WW{yields}l{nu}jj channel in 1.04 fb{sup -1} of pp collision data at {radical}(s)=7 TeV recorded with the ATLAS detector at the Large Hadron Collider. No significant excess of events is observed over the expected background and limits on the Higgs boson production cross section are derived for a Higgs boson mass in the range 240 GeVyields}WW production is 3.1 pb, or 2.7 times the standard modelmore » prediction.« less
Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future

NASA Technical Reports Server (NTRS)

Karunaratne, A. S.; Walker, S.; Ruane, A. C.

2015-01-01

Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop-water productivity model) were evaluated for baseline (1980-2009) and mid-century climates (2040-2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to 31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.
The relationship between ischaemia-modified albumin and good coronary collateral circulation.

PubMed

Gök, Murat; Kundi, Harun; Kızıltunç, Emrullah; Topcuoglu, Canan; Ornek, Ender

2018-01-01

It is important to determine the grade of the coronary collateral circulation (CCC) in patients with stable coronary artery disease. In this study, we aimed to investigate the relationship between the ischaemia-modified albumin (IMA) level and good CCC. A total of 95 patients with coronary angiography and at least one epicardial coronary artery obstruction were included in the study. The Rentrop classification was used with CCC grading, where 0 and 1 were defined as poor collateral, and 2 and 3 were defined as good collateral. The IMA level of the patients was measured using an enzyme-linked immunosorbent assay (ELISA). The receiver-operating characteristic curve was used to show the sensitivity and specificity of IMA levels and the optimal cut-off value for predicting good CCC. The multiple logistic regression analysis revealed that the IMA level in the good CCC group was higher (p < 0.045). Conversely, the high-sensitivity C-reactive protein level was lower in the good CCC group (p < 0.023). We found an IMA cut-off value (4.7 ng/mL) that indicated good CCC level, and this shows good CCC with 70.2% sensitivity and 60.3% specificity. The IMA level could serve as a simple and useful predictor of well-developed CCC.
Use of a machine learning framework to predict substance use disorder treatment success.

PubMed

Acion, Laura; Kelmansky, Diana; van der Laan, Mark; Sahker, Ethan; Jones, DeShauna; Arndt, Stephan

2017-01-01

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.
Use of a machine learning framework to predict substance use disorder treatment success

PubMed Central

Kelmansky, Diana; van der Laan, Mark; Sahker, Ethan; Jones, DeShauna; Arndt, Stephan

2017-01-01

There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed. PMID:28394905
Increased yield stability of field-grown winter barley (Hordeum vulgare L.) varietal mixtures through ecological processes

PubMed Central

Creissen, Henry E.; Jorgensen, Tove H.; Brown, James K.M.

2016-01-01

Crop variety mixtures have the potential to increase yield stability in highly variable and unpredictable environments, yet knowledge of the specific mechanisms underlying enhanced yield stability has been limited. Ecological processes in genetically diverse crops were investigated by conducting field trials with winter barley varieties (Hordeum vulgare), grown as monocultures or as three-way mixtures in fungicide treated and untreated plots at three sites. Mixtures achieved yields comparable to the best performing monocultures whilst enhancing yield stability despite being subject to multiple predicted and unpredicted abiotic and biotic stresses including brown rust (Puccinia hordei) and lodging. There was compensation through competitive release because the most competitive variety overyielded in mixtures thereby compensating for less competitive varieties. Facilitation was also identified as an important ecological process within mixtures by reducing lodging. This study indicates that crop varietal mixtures have the capacity to stabilise productivity even when environmental conditions and stresses are not predicted in advance. Varietal mixtures provide a means of increasing crop genetic diversity without the need for extensive breeding efforts. They may confer enhanced resilience to environmental stresses and thus be a desirable component of future cropping systems for sustainable arable farming. PMID:27375312
Relationships between milk culture results and milk yield in Norwegian dairy cattle.

PubMed

Reksen, O; Sølverød, L; Østerås, O

2007-10-01

Associations between test-day milk yield and positive milk cultures for Staphylococcus aureus, Streptococcus spp., and other mastitis pathogens or a negative milk culture for mastitis pathogens were assessed in quarter milk samples from randomly sampled cows selected without regard to current or previous udder health status. Staphylococcus aureus was dichotomized according to sparse (< or =1,500 cfu/mL of milk) or rich (>1,500 cfu/mL of milk) growth of the bacteria. Quarter milk samples were obtained on 1 to 4 occasions from 2,740 cows in 354 Norwegian dairy herds, resulting in a total of 3,430 samplings. Measures of test-day milk yield were obtained monthly and related to 3,547 microbiological diagnoses at the cow level. Mixed model linear regression models incorporating an autoregressive covariance structure accounting for repeated test-day milk yields within cow and random effects at the herd and sample level were used to quantify the effect of positive milk cultures on test-day milk yields. Identical models were run separately for first-parity, second-parity, and third-parity or older cows. Fixed effects were days in milk, the natural logarithm of days in milk, sparse and rich growth of Staph. aureus (1/0), Streptococcus spp. (1/0), other mastitis pathogens (1/0), calving season, time of test-day milk yields relative to time of microbiological diagnosis (test day relative to time of diagnosis), and the interaction terms between microbiological diagnosis and test day relative to time of diagnosis. The models were run with the logarithmically transformed composite milk somatic cell count excluded and included. Rich growth of Staph. aureus was associated with decreased production levels in first-parity cows. An interaction between rich growth of Staph. aureus and test day relative to time of diagnosis also predicted a decline in milk production in third-parity or older cows. Interaction between sparse growth of Staph. aureus and test day relative to time of
An equation for the prediction of human skin permeability of neutral molecules, ions and ionic species.

PubMed

Zhang, Keda; Abraham, Michael H; Liu, Xiangli

2017-04-15

Experimental values of permeability coefficients, as log K p , of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37°C for the effect of temperature. The values of log K p for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log K p values have been regressed against Abraham solute descriptors to yield a correlation equation with R 2 =0.866 and SD=0.432 log units. The equation can provide valid predictions for log K p of neutral molecules, ions and ionic species, with predictive R 2 =0.858 and predictive SD=0.445 log units calculated by the leave-one-out statistics. The predicted log K p values for Na + and Et 4 N + are in good agreement with the observed values. We calculated the values of log K p of ketoprofen as a function of the pH of the donor solution, and found that log K p markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log K p was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log K p . Copyright © 2017 Elsevier B.V. All rights reserved.
Acid soil infertility effects on peanut yields and yield components

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

Blamey, F.P.C.

1983-01-01

The interpretation of soil amelioration experiments with peanuts is made difficult by the unpredictibility of the crop and by the many factors altered when ameliorating acid soils. The present study was conducted to investigate the effects of lime and gypsum applications on peanut kernel yield via the three first order yield components, pods per ha, kernels per pod, and kernel mass. On an acid medium sandy loam soil (typic Plinthustult), liming resulted in a highly significant kernel yield increase of 117% whereas gypsum applications were of no significant benefit. As indicated by path coefficient analysis, an increase in the numbermore » of pods per ha was markedly more important in increasing yield than an increase in either the number of kernels per pod or kernel mass. Furthermore, exch. Al was found to be particularly detrimental to pod number. It was postulated that poor peanut yields resulting from acid soil infertility were mainly due to the depressive effect of exch. Al on pod number. Exch. Ca appeared to play a secondary role by ameliorating the adverse effects of exch. Al.« less
Mapping Crop Yield and Sow Date Using High Resolution Imagery

NASA Astrophysics Data System (ADS)

Royal, K.

2015-12-01

Keitasha Royal, Meha Jain, Ph.D., David Lobell, Ph.D Mapping Crop Yield and Sow Date Using High Resolution ImageryThe use of satellite imagery in agriculture is becoming increasingly more significant and valuable. Due to the emergence of new satellites, such as Skybox, these satellites provide higher resolution imagery (e.g 1m) therefore improving the ability to map smallholder agriculture. For the smallholder farm dominated area of northern India, Skybox high-resolution satellite imagery can aid in understanding how to improve farm yields. In particular, we are interested in mapping winter wheat in India, as this region produces approximately 80% of the country's wheat crop, which is important given that wheat is a staple crop that provides approximately 20% of household calories. In northeast India, the combination of increased heat stress, limited irrigation access, and the difficulty for farmers to access advanced farming technologies results in farmers only producing about 50% of their potential crop yield. The use of satellite imagery can aid in understanding wheat yields through time and help identify ways to increase crop yields in the wheat belt of India. To translate Skybox satellite data into meaningful information about wheat fields, we examine vegetation indices, such as the normalized difference vegetation index (NDVI), to measure the "greenness" of plants to help determine the health of the crops. We test our ability to predict crop characteristics, like sow date and yield, using vegetation indices of 59 fields for which we have field data in Bihar, India.
Modeling runoff and sediment yield from a terraced watershed using WEPP

Treesearch

Mary Carla McCullough; Dean E. Eisenhauer; Michael G. Dosskey

2008-01-01

The watershed version of WEPP (Water Erosion Prediction Project) was used to estimate 50-year runoff and sediment yields for a 291 ha watershed in eastern Nebraska that is 90% terraced and which has no historical gage data. The watershed has a complex matrix of elements, including terraced and non-terraced subwatersheds, multiple combinations of soils and land...
The Role of Climate Covariability on Crop Yields in the Conterminous United States

DOE PAGES

Leng, Guoyong; Zhang, Xuesong; Huang, Maoyi; ...

2016-09-12

The covariability of temperature (T), precipitation (P) and radiation (R) is an important aspect in understanding the climate influence on crop yields. Here in this paper, we analyze county-level corn and soybean yields and observed climate for the period 1983–2012 to understand how growing-season (June, July and August) mean T, P and R influence crop yields jointly and in isolation across the CONterminous United States (CONUS). Results show that nationally averaged corn and soybean yields exhibit large interannual variability of 21% and 22%, of which 35% and 32% can be significantly explained by T and P, respectively. By including R,more » an additional of 5% in variability can be explained for both crops. Using partial regression analyses, we find that studies that ignore the covariability among T, P, and R can substantially overestimate the sensitivity of crop yields to a single climate factor at the county scale. Further analyses indicate large spatial variation in the relative contributions of different climate variables to the variability of historical corn and soybean yields. Finally, the structure of the dominant climate factors did not change substantially over 1983–2012, confirming the robustness of the findings, which have important implications for crop yield prediction and crop model validations.« less
A simplified approach to predict performance degradation of a solid oxide fuel cell anode

NASA Astrophysics Data System (ADS)

Khan, Muhammad Zubair; Mehran, Muhammad Taqi; Song, Rak-Hyun; Lee, Jong-Won; Lee, Seung-Bok; Lim, Tak-Hyoung

2018-07-01

The agglomeration of nickel (Ni) particles in a Ni-cermet anode is a significant degradation phenomenon for solid oxide fuel cells (SOFCs). This work aims to predict the performance degradation of SOFCs due to Ni grain growth by using a simplified approach. Accelerated aging of Ni-scandia stabilized zirconia (SSZ) as an SOFC anode is carried out at 900 °C and subsequent microstructural evolution is investigated every 100 h up to 1000 h using scanning electron microscopy (SEM). The resulting morphological changes are quantified using a two-dimensional image analysis technique that yields the particle size, phase proportion, and triple phase boundary (TPB) point distribution. The electrochemical properties of an anode-supported SOFC are characterized using electrochemical impedance spectroscopy (EIS). The changes of particle size and TPB length in the anode as a function of time are in excellent agreement with the power-law coarsening model. This model is further combined with an electrochemical model to predict the changes in the anode polarization resistance. The predicted polarization resistances are in good agreement with the experimentally obtained values. This model for prediction of anode lifetime provides deep insight into the time-dependent Ni agglomeration behavior and its impact on the electrochemical performance degradation of the SOFC anode.

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