Modelling Chemical Reasoning to Predict and Invent Reactions.

PubMed

Segler, Marwin H S; Waller, Mark P

2017-05-02

The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Motivation and reasons to quit: predictive validity among adolescent smokers.

PubMed

Turner, Lindsey R; Mermelstein, Robin

2004-01-01

To examine reasons to quit among adolescents in a smoking cessation program, and whether reasons were associated with subsequent cessation. Participants were 351 adolescents. At baseline, adolescents reported motivation, reasons to quit, and stage of change for cessation. Quit status was assessed at end of treatment. Girls were more likely to endorse image and appearance reasons to quit. Cessation was more likely among adolescents with higher motivation and those wanting to quit because of friends. Different reasons to quit were associated with motivation and cessation. Baseline motivation strongly predicted cessation, suggesting the relative value of assessing global motivation.

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

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...

Using the theory of reasoned action to predict organizational misbehavior.

PubMed

Vardi, Yoav; Weitz, Ely

2002-12-01

A review of literature on organizational behavior and management on predicting work behavior indicated that most reported studies emphasize positive work outcomes, e.g., attachment, performance, and satisfaction, while job related misbehaviors have received relatively less systematic research attention. Yet, forms of employee misconduct in organizations are pervasive and quite costly for both individuals and organizations. We selected two conceptual frameworks for the present investigation: Vardi and Wiener's model of organizational misbehavior and Fishbein and Ajzen's Theory of Reasoned Action. The latter views individual behavior as intentional, a function of rationally based attitudes toward the behavior, and internalized normative pressures concerning such behavior. The former model posits that different (normative and instrumental) internal forces lead to the intention to engage in job-related misbehavior. In this paper we report a scenario based quasi-experimental study especially designed to test the utility of the Theory of Reasoned Action in predicting employee intentions to engage in self-benefitting (Type S), organization-benefitting (Type O, or damaging (Type D) organizational misbehavior. Results support the Theory of Reasoned Action in predicting negative workplace behaviors. Both attitude and subjective norm are useful in explaining organizational misbehavior. We discuss some theoretical and methodological implications for the study of misbehavior intentions in organizations.

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.

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

Short-term solar flare prediction using image-case-based reasoning

NASA Astrophysics Data System (ADS)

Liu, Jin-Fu; Li, Fei; Zhang, Huai-Peng; Yu, Da-Ren

2017-10-01

Solar flares strongly influence space weather and human activities, and their prediction is highly complex. The existing solutions such as data based approaches and model based approaches have a common shortcoming which is the lack of human engagement in the forecasting process. An image-case-based reasoning method is introduced to achieve this goal. The image case library is composed of SOHO/MDI longitudinal magnetograms, the images from which exhibit the maximum horizontal gradient, the length of the neutral line and the number of singular points that are extracted for retrieving similar image cases. Genetic optimization algorithms are employed for optimizing the weight assignment for image features and the number of similar image cases retrieved. Similar image cases and prediction results derived by majority voting for these similar image cases are output and shown to the forecaster in order to integrate his/her experience with the final prediction results. Experimental results demonstrate that the case-based reasoning approach has slightly better performance than other methods, and is more efficient with forecasts improved by humans.

Why anthropic reasoning cannot predict Lambda.

PubMed

Starkman, Glenn D; Trotta, Roberto

2006-11-17

We revisit anthropic arguments purporting to explain the measured value of the cosmological constant. We argue that different ways of assigning probabilities to candidate universes lead to totally different anthropic predictions. As an explicit example, we show that weighting different universes by the total number of possible observations leads to an extremely small probability for observing a value of Lambda equal to or greater than what we now measure. We conclude that anthropic reasoning within the framework of probability as frequency is ill-defined and that in the absence of a fundamental motivation for selecting one weighting scheme over another the anthropic principle cannot be used to explain the value of Lambda, nor, likely, any other physical parameters.

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.

[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

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.

Perceptual reasoning predicts handwriting impairments in adolescents with autism

PubMed Central

Fuentes, Christina T.; Mostofsky, Stewart H.; Bastian, Amy J.

2010-01-01

Background: We have previously shown that children with autism spectrum disorder (ASD) have specific handwriting deficits consisting of poor form, and that these deficits are predicted by their motor abilities. It is not known whether the same handwriting impairments persist into adolescence and whether they remain linked to motor deficits. Methods: A case-control study of handwriting samples from adolescents with and without ASD was performed using the Minnesota Handwriting Assessment. Samples were scored on an individual letter basis in 5 categories: legibility, form, alignment, size, and spacing. Subjects were also administered an intelligence test and the Physical and Neurological Examination for Subtle (Motor) Signs (PANESS). Results: We found that adolescents with ASD, like children, show overall worse performance on a handwriting task than do age- and intelligence-matched controls. Also comparable to children, adolescents with ASD showed motor impairments relative to controls. However, adolescents with ASD differ from children in that Perceptual Reasoning Indices were significantly predictive of handwriting performance whereas measures of motor skills were not. Conclusions: Like children with ASD, adolescents with ASD have poor handwriting quality relative to controls. Despite still demonstrating motor impairments, in adolescents perceptual reasoning is the main predictor of handwriting performance, perhaps reflecting subjects' varied abilities to learn strategies to compensate for their motor impairments. GLOSSARY ASD = autism spectrum disorder; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, 4th edition; PANESS = Physical and Neurological Examination for Subtle (Motor) Signs; PRI = Perceptual Reasoning Index; WASI = Wechsler Abbreviated Scale of Intelligence; WISC = Wechsler Intelligence Scale for Children IV. PMID:21079184

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

Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

PubMed

Dahl, Ethan; Tagler, Michael J; Hohman, Zachary P

2018-03-01

Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.

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.

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.

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.

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.

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;

Memory Self-Efficacy Predicts Responsiveness to Inductive Reasoning Training in Older Adults

PubMed Central

Jackson, Joshua J.; Hill, Patrick L.; Gao, Xuefei; Roberts, Brent W.; Stine-Morrow, Elizabeth A. L.

2012-01-01

Objectives. In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Methods. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Results. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Discussion. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood. PMID:21743037

Memory self-efficacy predicts responsiveness to inductive reasoning training in older adults.

PubMed

Payne, Brennan R; Jackson, Joshua J; Hill, Patrick L; Gao, Xuefei; Roberts, Brent W; Stine-Morrow, Elizabeth A L

2012-01-01

In the current study, we assessed the relationship between memory self-efficacy at pretest and responsiveness to inductive reasoning training in a sample of older adults. Participants completed a measure of self-efficacy assessing beliefs about memory capacity. Participants were then randomly assigned to a waitlist control group or an inductive reasoning training intervention. Latent change score models were used to examine the moderators of change in inductive reasoning. Inductive reasoning showed clear improvements in the training group compared with the control. Within the training group, initial memory capacity beliefs significantly predicted change in inductive reasoning such that those with higher levels of capacity beliefs showed greater responsiveness to the intervention. Further analyses revealed that self-efficacy had effects on how trainees allocated time to the training materials over the course of the intervention. Results indicate that self-referential beliefs about cognitive potential may be an important factor contributing to plasticity in adulthood.

Bridging the Gap between Human Judgment and Automated Reasoning in Predictive Analytics

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

Sanfilippo, Antonio P.; Riensche, Roderick M.; Unwin, Stephen D.

2010-06-07

Events occur daily that impact the health, security and sustainable growth of our society. If we are to address the challenges that emerge from these events, anticipatory reasoning has to become an everyday activity. Strong advances have been made in using integrated modeling for analysis and decision making. However, a wider impact of predictive analytics is currently hindered by the lack of systematic methods for integrating predictive inferences from computer models with human judgment. In this paper, we present a predictive analytics approach that supports anticipatory analysis and decision-making through a concerted reasoning effort that interleaves human judgment and automatedmore » inferences. We describe a systematic methodology for integrating modeling algorithms within a serious gaming environment in which role-playing by human agents provides updates to model nodes and the ensuing model outcomes in turn influence the behavior of the human players. The approach ensures a strong functional partnership between human players and computer models while maintaining a high degree of independence and greatly facilitating the connection between model and game structures.« less

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

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.

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

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.

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.

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

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...

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.

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.

Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning.

PubMed

Fusar-Poli, P; Schultze-Lutter, F

2016-02-01

Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes' theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

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.

Predicting Alcohol-Impaired Driving among Spanish Youth with the Theory of Reasoned Action.

PubMed

Espada, José P; Griffin, Kenneth W; Gonzálvez, María T; Orgilés, Mireia

2015-06-19

Alcohol consumption is a risk factor for motor vehicle accidents in young drivers. Crashes associated with alcohol consumption typically have greater severity. This study examines the prevalence of driving under the influence among Spanish youth and tests the theory of reasoned action as a model for predicting driving under the influence. Participants included 478 Spanish university students aged 17-26 years. Findings indicated that alcohol was the substance most associated with impaired driving, and was involved in more traffic crashes. Men engage in higher levels of alcohol and other drug use, and perceived less risk in drunk driving (p < .01). The study confirms that alcohol use and driving under the influence of alcohol are highly prevalent in Spanish young people, and some gender differences exist in these behaviors (p < .01). Furthermore, the study confirms the validity of theory of reasoned action as a predictive model of driving under the influence of alcohol among youth in Spain (p < .001) and can help in the design of prevention programs.

A Video-Based Measure of Preservice Teachers' Abilities to Predict Elementary Students' Scientific Reasoning

ERIC Educational Resources Information Center

Akerson, Valarie L.; Carter, Ingrid S.; Park Rogers, Meredith A.; Pongsanon, Khemmawadee

2018-01-01

In this mixed methods study, the researchers developed a video-based measure called a "Prediction Assessment" to determine preservice elementary teachers' abilities to predict students' scientific reasoning. The instrument is based on teachers' need to develop pedagogical content knowledge for teaching science. Developing a knowledge…

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.

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

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.

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.

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

Examining the Roles of Reasoning and Working Memory in Predicting Casual Game Performance across Extended Gameplay.

PubMed

Kranz, Michael B; Baniqued, Pauline L; Voss, Michelle W; Lee, Hyunkyu; Kramer, Arthur F

2017-01-01

The variety and availability of casual video games presents an exciting opportunity for applications such as cognitive training. Casual games have been associated with fluid abilities such as working memory (WM) and reasoning, but the importance of these cognitive constructs in predicting performance may change across extended gameplay and vary with game structure. The current investigation examined the relationship between cognitive abilities and casual game performance over time by analyzing first and final session performance over 4-5 weeks of game play. We focused on two groups of subjects who played different types of casual games previously shown to relate to WM and reasoning when played for a single session: (1) puzzle-based games played adaptively across sessions and (2) speeded switching games played non-adaptively across sessions. Reasoning uniquely predicted first session casual game scores for both groups and accounted for much of the relationship with WM. Furthermore, over time, WM became uniquely important for predicting casual game performance for the puzzle-based adaptive games but not for the speeded switching non-adaptive games. These results extend the burgeoning literature on cognitive abilities involved in video games by showing differential relationships of fluid abilities across different game types and extended play. More broadly, the current study illustrates the usefulness of using multiple cognitive measures in predicting performance, and provides potential directions for game-based cognitive training research.

Examining the Roles of Reasoning and Working Memory in Predicting Casual Game Performance across Extended Gameplay

PubMed Central

Kranz, Michael B.; Baniqued, Pauline L.; Voss, Michelle W.; Lee, Hyunkyu; Kramer, Arthur F.

2017-01-01

The variety and availability of casual video games presents an exciting opportunity for applications such as cognitive training. Casual games have been associated with fluid abilities such as working memory (WM) and reasoning, but the importance of these cognitive constructs in predicting performance may change across extended gameplay and vary with game structure. The current investigation examined the relationship between cognitive abilities and casual game performance over time by analyzing first and final session performance over 4–5 weeks of game play. We focused on two groups of subjects who played different types of casual games previously shown to relate to WM and reasoning when played for a single session: (1) puzzle-based games played adaptively across sessions and (2) speeded switching games played non-adaptively across sessions. Reasoning uniquely predicted first session casual game scores for both groups and accounted for much of the relationship with WM. Furthermore, over time, WM became uniquely important for predicting casual game performance for the puzzle-based adaptive games but not for the speeded switching non-adaptive games. These results extend the burgeoning literature on cognitive abilities involved in video games by showing differential relationships of fluid abilities across different game types and extended play. More broadly, the current study illustrates the usefulness of using multiple cognitive measures in predicting performance, and provides potential directions for game-based cognitive training research. PMID:28326042

Theory of Reasoned Action predicts milk consumption in women.

PubMed

Brewer, J L; Blake, A J; Rankin, S A; Douglass, L W

1999-01-01

To determine the factors influencing the consumption or avoidance of milk in women. One hundred women completed food frequency questionnaires and a milk attitudes questionnaire framed within the Theory of Reasoned Action and performed sensory evaluations of different milk samples. Differences among milk types were assessed using 2-way analysis of variance and least-significant-difference mean comparison procedures. Correlation and multiple regression analyses, and standardized partial regression coefficients, were used to determine the contribution of each component of the model in predicting behavior. Mean age of the 100 subjects was 39 years (range = 20-70 years). Milk consumption among subjects was low; 23 subjects indicated that they seldom or never drank milk. Data from the dairy frequency questionnaire showed that the primary milk for 42%, 36%, 27%, and 18% of the milk drinkers was skim, 2%, 1%, and whole, respectively (subjects could indicate more than 1 type of milk consumed). The Theory of Reasoned Action indicated that health and familiarity belief items were most associated with attitudes toward milk consumption. Skim milk had significantly lower scores for taste and texture belief items than 1%, 2%, and whole milk (P < .05), yet more subjects reported that they drank skim milk (42%) than the other milk types. Sensory evaluation demonstrated that subjects linked whole milk significantly more than skim milk (P < .05). Some people continue to consume skim milk for reasons other than beliefs about taste and texture or actual sensory preference. This study identifies important factors contributing to milk consumption such as beliefs, attitudes, and sensory evaluation, which can be used to develop a specific framework in which to examine other components of milk consumption behavior.

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.

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.

Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation

PubMed Central

Ferrer, Emilio; Cutting, Laurie

2017-01-01

Prior research points to a positive concurrent relationship between reasoning ability and both frontoparietal structural connectivity (SC) as measured by diffusion tensor imaging (Tamnes et al., 2010) and frontoparietal functional connectivity (FC) as measured by fMRI (Cocchi et al., 2014). Further, recent research demonstrates a link between reasoning ability and FC of two brain regions in particular: rostrolateral prefrontal cortex (RLPFC) and the inferior parietal lobe (IPL) (Wendelken et al., 2016). Here, we sought to investigate the concurrent and dynamic, lead–lag relationships among frontoparietal SC, FC, and reasoning ability in humans. To this end, we combined three longitudinal developmental datasets with behavioral and neuroimaging data from 523 male and female participants between 6 and 22 years of age. Cross-sectionally, reasoning ability was most strongly related to FC between RLPFC and IPL in adolescents and adults, but to frontoparietal SC in children. Longitudinal analysis revealed that RLPFC–IPL SC, but not FC, was a positive predictor of future changes in reasoning ability. Moreover, we found that RLPFC–IPL SC at one time point positively predicted future changes in RLPFC–IPL FC, whereas, in contrast, FC did not predict future changes in SC. Our results demonstrate the importance of strong white matter connectivity between RLPFC and IPL during middle childhood for the subsequent development of both robust FC and good reasoning ability. SIGNIFICANCE STATEMENT The human capacity for reasoning develops substantially during childhood and has a profound impact on achievement in school and in cognitively challenging careers. Reasoning ability depends on communication between lateral prefrontal and parietal cortices. Therefore, to understand how this capacity develops, we examined the dynamic relationships over time among white matter tracts connecting frontoparietal cortices (i.e., structural connectivity, SC), coordinated frontoparietal

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

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.

Early executive function predicts reasoning development.

PubMed

Richland, Lindsey E; Burchinal, Margaret R

2013-01-01

Analogical reasoning is a core cognitive skill that distinguishes humans from all other species and contributes to general fluid intelligence, creativity, and adaptive learning capacities. Yet its origins are not well understood. In the study reported here, we analyzed large-scale longitudinal data from the Study of Early Child Care and Youth Development to test predictors of growth in analogical-reasoning skill from third grade to adolescence. Our results suggest an integrative resolution to the theoretical debate regarding contributory factors arising from smaller-scale, cross-sectional experiments on analogy development. Children with greater executive-function skills (both composite and inhibitory control) and vocabulary knowledge in early elementary school displayed higher scores on a verbal analogies task at age 15 years, even after adjusting for key covariates. We posit that knowledge is a prerequisite to analogy performance, but strong executive-functioning resources during early childhood are related to long-term gains in fundamental reasoning skills.

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.

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.

Frontoparietal Structural Connectivity in Childhood Predicts Development of Functional Connectivity and Reasoning Ability: A Large-Scale Longitudinal Investigation.

PubMed

Wendelken, Carter; Ferrer, Emilio; Ghetti, Simona; Bailey, Stephen K; Cutting, Laurie; Bunge, Silvia A

2017-08-30

Prior research points to a positive concurrent relationship between reasoning ability and both frontoparietal structural connectivity (SC) as measured by diffusion tensor imaging (Tamnes et al., 2010) and frontoparietal functional connectivity (FC) as measured by fMRI (Cocchi et al., 2014). Further, recent research demonstrates a link between reasoning ability and FC of two brain regions in particular: rostrolateral prefrontal cortex (RLPFC) and the inferior parietal lobe (IPL) (Wendelken et al., 2016). Here, we sought to investigate the concurrent and dynamic, lead-lag relationships among frontoparietal SC, FC, and reasoning ability in humans. To this end, we combined three longitudinal developmental datasets with behavioral and neuroimaging data from 523 male and female participants between 6 and 22 years of age. Cross-sectionally, reasoning ability was most strongly related to FC between RLPFC and IPL in adolescents and adults, but to frontoparietal SC in children. Longitudinal analysis revealed that RLPFC-IPL SC, but not FC, was a positive predictor of future changes in reasoning ability. Moreover, we found that RLPFC-IPL SC at one time point positively predicted future changes in RLPFC-IPL FC, whereas, in contrast, FC did not predict future changes in SC. Our results demonstrate the importance of strong white matter connectivity between RLPFC and IPL during middle childhood for the subsequent development of both robust FC and good reasoning ability. SIGNIFICANCE STATEMENT The human capacity for reasoning develops substantially during childhood and has a profound impact on achievement in school and in cognitively challenging careers. Reasoning ability depends on communication between lateral prefrontal and parietal cortices. Therefore, to understand how this capacity develops, we examined the dynamic relationships over time among white matter tracts connecting frontoparietal cortices (i.e., structural connectivity, SC), coordinated frontoparietal activation

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.

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.

The predicting roles of reasons for living and social support on depression, anxiety and stress among young people in Malaysia.

PubMed

Amit, N; Ibrahim, N; Aga Mohd Jaladin, R; Che Din, N

2017-10-01

This research examined the predicting roles of reasons for living and social support on depression, anxiety and stress in Malaysia. This research was carried out on a sample of 263 participants (age range 12-24 years old), from Klang Valley, Selangor. The survey package comprises demographic information, a measure of reasons for living, social support, depression, anxiety and stress. To analyse the data, correlation analysis and a series of linear multiple regression analysis were carried out. Findings showed that there were low negative relationships between all subdomains and the total score of reasons for living and depression. There were also low negative relationships between domain-specific of social support (family and friends) and total social support and depression. In terms of the family alliance, self-acceptance and total score of reasons for living, they were negatively associated with anxiety, whereas family social support was negatively associated with stress. The linear regression analysis showed that only future optimism and family social support found to be the significant predictors for depression. Family alliance and total reasons for living were significant in predicting anxiety, whereas family social support was significant in predicting stress. These findings have the potential to promote awareness related to depression, anxiety, and stress among youth in Malaysia.

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.

Predicting homeowners' approval of fuel management at the wild-urban interface using the theory of reasoned action.

Treesearch

Christine A. Vogt; Greg Winter; Jeremy S. Fried

2005-01-01

Social science models are increasingly needed as a framework for explaining and predicting how members of the public respond to the natural environment and their communities. The theory of reasoned action is widely used in human dimensions research on natural resource problems and work is ongoing to increase the predictive power of models based on this theory. This...

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.

When do self-discrepancies predict negative emotions? Exploring formal operational thought and abstract reasoning skills as moderators.

PubMed

Stevens, Erin N; Holmberg, Nicole J; Lovejoy, M Christine; Pittman, Laura D

2014-01-01

Individual differences in higher-order cognitive abilities may be an important piece to understanding how and when self-discrepancies lead to negative emotions. In the current study, three measures of reasoning abilities were considered as potential moderators of the relationship between self-discrepancies and depression and anxiety symptoms. Participants (N = 162) completed measures assessing self-discrepancies, depression and anxiety symptoms, and were administered measures examining formal operational thought, and verbal and non-verbal abstract reasoning skills. Both formal operational thought and verbal abstract reasoning were significant moderators of the relationship between actual:ideal discrepancies and depressive symptoms. Discrepancies predicted depressive symptoms for individuals with higher levels of formal operational thought and verbal abstract reasoning skills, but not for those with lower levels. The discussion focuses on the need to consider advanced reasoning skills when examining self-discrepancies.

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.

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

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.

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus.

PubMed

Ali, Rahman; Hussain, Jamil; Siddiqi, Muhammad Hameed; Hussain, Maqbool; Lee, Sungyoung

2015-07-03

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body's resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient's data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies.

H2RM: A Hybrid Rough Set Reasoning Model for Prediction and Management of Diabetes Mellitus

PubMed Central

Ali, Rahman; Hussain, Jamil; Siddiqi, Muhammad Hameed; Hussain, Maqbool; Lee, Sungyoung

2015-01-01

Diabetes is a chronic disease characterized by high blood glucose level that results either from a deficiency of insulin produced by the body, or the body’s resistance to the effects of insulin. Accurate and precise reasoning and prediction models greatly help physicians to improve diagnosis, prognosis and treatment procedures of different diseases. Though numerous models have been proposed to solve issues of diagnosis and management of diabetes, they have the following drawbacks: (1) restricted one type of diabetes; (2) lack understandability and explanatory power of the techniques and decision; (3) limited either to prediction purpose or management over the structured contents; and (4) lack competence for dimensionality and vagueness of patient’s data. To overcome these issues, this paper proposes a novel hybrid rough set reasoning model (H2RM) that resolves problems of inaccurate prediction and management of type-1 diabetes mellitus (T1DM) and type-2 diabetes mellitus (T2DM). For verification of the proposed model, experimental data from fifty patients, acquired from a local hospital in semi-structured format, is used. First, the data is transformed into structured format and then used for mining prediction rules. Rough set theory (RST) based techniques and algorithms are used to mine the prediction rules. During the online execution phase of the model, these rules are used to predict T1DM and T2DM for new patients. Furthermore, the proposed model assists physicians to manage diabetes using knowledge extracted from online diabetes guidelines. Correlation-based trend analysis techniques are used to manage diabetic observations. Experimental results demonstrate that the proposed model outperforms the existing methods with 95.9% average and balanced accuracies. PMID:26151207

Yield of Echocardiogram and Predictors of Positive Yield in Pediatric Patients: A Study in an Urban, Community-Based Outpatient Pediatric Cardiology Clinic.

PubMed

Billa, Ramya Deepthi; Szpunar, Susan; Zeinali, Lida; Anne, Premchand

2018-01-01

The yield of outpatient echocardiograms varies based on the indication for the echocardiogram and the age of the patient. The purpose of this study was to determine the cumulative yield of outpatient echocardiograms by age group and reason for the test. A secondary aim was to determine the predictors of a positive echocardiogram in an outpatient cardiology clinic at a large community teaching hospital. We retrospectively reviewed the charts of 891 patients who had a first-time echocardiogram between 2011 and 2015. Positive yield was defined as echocardiographic findings that explained the reason for the echocardiogram. The overall positive yield was 8.2%. Children between birth and 3 months of age had the highest yield (34.2%), and children between 12 and 18 years of age had the lowest yield (1%). Patients with murmurs (18.1%) had the highest yield compared with patients with other signs or symptoms. By age group and reason, the highest yields were as follows: 0 to 3 months of age, murmur (39.2%); 4 to 11 months of age, >1 symptom (50%); and 1 to 5 years of age, shortness of breath (66.7%). Based on our study, the overall yield of echocardiograms in the outpatient pediatric setting is low. Age and symptoms should be considered before ordering an echocardiogram.

Age at treatment predicts reason for discontinuation of TNF antagonists: data from the BIOBADASER 2.0 registry.

PubMed

Busquets, Noemí; Tomero, Eva; Descalzo, Miguel Ángel; Ponce, Andrés; Ortiz-Santamaría, Vera; Surís, Xavier; Carmona, Loreto; Gómez-Reino, Juan J

2011-11-01

To assess the retention rate of TNF antagonists in elderly patients suffering from chronic arthropathies and to identify predictive variables of discontinuation by inefficacy or by adverse events (AEs). All patients treated with TNF antagonists in BIOBADASER 2.0, with a diagnosis of either RA or spondyloarthritis (SpA: AS and PsA) were included and classified as <65 (younger) or ≥65 years of age (older) at start of the treatment. Cumulative incidence function for discontinuation (inefficacy or AE) was estimated as being the alternative reason for a competing risk. Competing-risks regression models were used to measure the association between study groups, covariates and reason for discontinuation. A total of 4851 patients were studied; 2957 RA (2291 in the younger group and 666 in the older group) and 1894 SpA (1795 in the younger group and 99 in the older group). Retention curves were statistically differently stratified by age groups, with the SpA younger group having the largest retention rate. Competing-risks regression models showed that in the older group, AEs were the most common reason for discontinuation regardless of the diagnosis of the patient and TNF antagonist molecule, whereas in the younger group, the most common cause of discontinuation was inefficacy. In conclusion, factors predicting discontinuation of TNF antagonists due to AEs are older age and diagnosis of RA. On the other hand, younger age predicts discontinuation due to lack of efficacy.

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

Improving the yield from fermentative hydrogen production.

PubMed

Kraemer, Jeremy T; Bagley, David M

2007-05-01

Efforts to increase H(2) yields from fermentative H(2) production include heat treatment of the inoculum, dissolved gas removal, and varying the organic loading rate. Although heat treatment kills methanogens and selects for spore-forming bacteria, the available evidence indicates H(2) yields are not maximized compared to bromoethanesulfonate, iodopropane, or perchloric acid pre-treatments and spore-forming acetogens are not killed. Operational controls (low pH, short solids retention time) can replace heat treatment. Gas sparging increases H(2) yields compared to un-sparged reactors, but no relationship exists between the sparging rate and H(2) yield. Lower sparging rates may improve the H(2) yield with less energy input and product dilution. The reasons why sparging improves H(2) yields are unknown, but recent measurements of dissolved H(2) concentrations during sparging suggest the assumption of decreased inhibition of the H(2)-producing enzymes is unlikely. Significant disagreement exists over the effect of organic loading rate (OLR); some studies show relatively higher OLRs improve H(2) yield while others show the opposite. Discovering the reasons for higher H(2) yields during dissolved gas removal and changes in OLR will help improve H(2) yields.

Mental models and human reasoning

PubMed Central

Johnson-Laird, Philip N.

2010-01-01

To be rational is to be able to reason. Thirty years ago psychologists believed that human reasoning depended on formal rules of inference akin to those of a logical calculus. This hypothesis ran into difficulties, which led to an alternative view: reasoning depends on envisaging the possibilities consistent with the starting point—a perception of the world, a set of assertions, a memory, or some mixture of them. We construct mental models of each distinct possibility and derive a conclusion from them. The theory predicts systematic errors in our reasoning, and the evidence corroborates this prediction. Yet, our ability to use counterexamples to refute invalid inferences provides a foundation for rationality. On this account, reasoning is a simulation of the world fleshed out with our knowledge, not a formal rearrangement of the logical skeletons of sentences. PMID:20956326

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.

People's reasons for wanting to complete probation: Use and predictive validity in an e-health intervention.

PubMed

Spohr, Stephanie A; Taxman, Faye S; Walters, Scott T

2017-04-01

The criminal justice system tends to emphasize external contingencies (e.g., fees, jail time) to motivate offender compliance. However, people's reasons for desistance vary considerably. This study evaluated the acceptability, utility, and predictive validity of questions that ask about people's reasons for wanting to successfully complete probation. Substance-using probationers (N=113) participated in a web-based computer intervention that targeted substance use and treatment initiation. Questions around seven dimensions of reasons for completing probation were developed to provide tailored feedback during the web-based program. A principle components factor analysis found that survey items loaded onto two distinct factors. Factor one, "Tangible Loss" focused on external and present-focused reasons. Factor two, "Better Life" focused on internal and future-focused reasons. There was a significant negative association between Better Life scores and days of substance use after two months (β=-0.31, SE=0.13, p<0.05). There was a significant positive association with Better Life scores and days of treatment attendance (β=1.46, SE=0.26, p<0.001). Tangible Loss scores were no associated with substance use and treatment attendance. These findings may help to create more effective motivational tracks in e-health interventions, and may complement traditional motivation measures with an explicit focus on people's stated reasons for wanting to complete probation. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

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.

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

The Predictive Power of Fifth Graders' Learning Styles on Their Mathematical Reasoning and Spatial Ability

ERIC Educational Resources Information Center

Danisman, Sahin; Erginer, Ergin

2017-01-01

The purpose of this study was to examine fifth graders' mathematical reasoning and spatial ability, to identify a correlation with their learning styles, and to determine the predictive power of their learning styles on their mathematical learning profiles. This causal study was conducted with 97 fifth graders (60 females, 61.9% and 37 males,…

Large-scale Assessment Yields Evidence of Minimal Use of Reasoning Skills in Traditionally Taught Classes

NASA Astrophysics Data System (ADS)

Thacker, Beth

2017-01-01

Large-scale assessment data from Texas Tech University yielded evidence that most students taught traditionally in large lecture classes with online homework and predominantly multiple choice question exams, when asked to answer free-response (FR) questions, did not support their answers with logical arguments grounded in physics concepts. In addition to a lack of conceptual understanding, incorrect and partially correct answers lacked evidence of the ability to apply even lower level reasoning skills in order to solve a problem. Correct answers, however, did show evidence of at least lower level thinking skills as coded using a rubric based on Bloom's taxonomy. With the introduction of evidence-based instruction into the labs and recitations of the large courses and in a small, completely laboratory-based, hands-on course, the percentage of correct answers with correct explanations increased. The FR format, unlike other assessment formats, allowed assessment of both conceptual understanding and the application of thinking skills, clearly pointing out weaknesses not revealed by other assessment instruments, and providing data on skills beyond conceptual understanding for course and program assessment. Supported by National Institutes of Health (NIH) Challenge grant #1RC1GM090897-01.

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.

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

Examining and Predicting College Students' Reading Intentions and Behaviors: An Application of the Theory of Reasoned Action

ERIC Educational Resources Information Center

Burak, Lydia

2004-01-01

This study examined the recreational reading attitudes, intentions, and behaviors of college students. The theory of reasoned action provided the framework for the investigation and prediction of the students' intentions and behaviors. Two hundred and one students completed questionnaires developed according to the guidelines for the construction…

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

The theory of reasoned action in prediction of breast self-examination: a comparison of two studies.

PubMed

Powell-Cope, G M; Lierman, L M; Kasprzyk, D; Young, H M; Benoliel, J Q

1991-01-01

The purpose of this article is to report the application of the theory of reasoned action (TRA) to predict breast self-examination (BSE) intention and frequency in two studies with middle-aged and older women. The sample in Study 1 consisted of 93 volunteers from church groups; the second sample consisted of 175 randomly selected subscribers to a large health maintenance organization. Questionnaires to measure attitudinal and subjective normative influences relevant to BSE were developed using guidelines specified by Ajzen and Fishbein (1980). The attitudinal components predicted BSE intention in both studies and BSE frequency in Study 1. In contrast, the subjective norm contributed significantly only to the prediction of frequency in Study 1. Findings demonstrate varying degrees of success for the TRA in predicting BSE intention and behavior. Explanations for the inconsistency in the predictive ability of the TRA can be related to differences between the two studies regarding sample and design characteristics.

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.

Predicting and Explaining Intentions to Participate in Continuing Education: An Application of the Theory of Reasoned Action.

ERIC Educational Resources Information Center

Pryor, Brandt W.

1990-01-01

To test the predictive utility of the theory of reasoned action, 110 oral surgeons completed a questionnaire regarding participation in continuing education. Multiple regression analysis showed that the theory accounted for over 41 percent of variance in intention to participate. Intention appeared controlled by attitude, determined by strength of…

Applying temporal abstraction and case-based reasoning to predict approaching influenza waves.

PubMed

Schmidt, Rainer; Gierl, Lothar

2002-01-01

The goal of the TeCoMed project is to send early warnings against forthcoming waves or even epidemics of infectious diseases, especially of influenza, to interested practitioners, pharmacists etc. in the German federal state Mecklenburg-Western Pomerania. The forecast of these waves is based on written confirmations of unfitness for work of the main German health insurance company. Since influenza waves are difficult to predict because of their cyclic but not regular behaviour, statistical methods based on the computation of mean values are not helpful. Instead, we have developed a prognostic model that makes use of similar former courses. Our method combines Case-based Reasoning with Temporal Abstraction to decide whether early warning is appropriate.

Children's predictions of future perceptual experiences: Temporal reasoning and phenomenology.

PubMed

Burns, Patrick; Russell, James

2016-11-01

We investigated the development and cognitive correlates of envisioning future experiences in 3.5- to 6.5-year old children across 2 experiments, both of which involved toy trains traveling along a track. In the first, children were asked to predict the direction of train travel and color of train side, as it would be seen through an arch. Children below 5 years typically failed the task, while performance on it was associated with performance on a "before/after" comprehension task in which order-of-mention in a sentence had to be mapped to a video of 2 actions (after McCormack & Hanley, 2011). In the second train task children were asked to predict the content of a doll's visual experience at the terminal point of a train's transit, based on the tint of a doll's spectacles and the direction of travel (toward or away). Again, success under 5 years of age was very rare and performance was associated with performance on the before/after task. This time there was a strong association with mental rotation skill. We conclude that the consistent association with before/after reasoning suggests that future-envisioning depends upon certain temporal-order concepts being in place. The inconsistent association with mental rotation suggests that envisioning can be achieved phenomenologically, but that its role is only explicit when the question explicitly concerns the content of a visual field. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Motivation and Reasons to Quit: Predictive Validity among Adolescent Smokers

ERIC Educational Resources Information Center

Turner, Lindsey R.; Mermelstein, Robin

2004-01-01

Objectives : To examine reasons to quit among adolescents in a smoking cessation program, and whether reasons were associated with subsequent cessation. Methods : Participants were 351 adolescents. At baseline, adolescents reported motivation, reasons to quit, and stage of change for cessation. Quit status was assessed at end of treatment. Results…

Reasoning about Make-Believe and Hypothetical Suppositions: Towards a Theory of Belief-Contravening Reasoning

ERIC Educational Resources Information Center

Amsel, Eric; Trionfi, Gabriel; Campbell, Richard

2005-01-01

The present study explores how suppositions which conflict with accepted beliefs are represented and reasoned about. Two studies test the predictions regarding the nature and developmental changes in children's ability to represent and reason about hypothetical or make-believe suppositions which violate their everyday knowledge and beliefs. In…

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

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

Predicting Intention Perform Breast Self-Examination: Application of the Theory of Reasoned Action

PubMed Central

Dewi, Triana Kesuma; Zein, Rizqy Amelia

2017-01-01

Objective: The present study aimed to examine the applicability of the theory of reasoned action to explain intention to perform breast self-examination (BSE). Methods: A questionnaire was constructed to collect data. The hypothesis was tested in two steps. First, to assess the strength of the correlation among the constructs of theory of reasoned action (TRA), Pearson’s product moment correlations were applied. Second, multivariate relationships among the constructs were examined by performing hierarchical multiple linear regression analysis. Result: The findings supported the TRA model, explaining 45.8% of the variance in the students’ BSE intention, which was significantly correlated with attitude (r = 0.609, p = 0.000) and subjective norms (r = 0.420, p =0 .000). Conclusion: TRA could be a suitable model to predict BSE intentions. Participants who believed that doing BSE regularly is beneficial for early diagnosis of breast cancer and also believed that their significant referents think that doing BSE would significantly detect breast cancer earlier, were more likely to intend to perform BSE regularly. Therefore, the research findings supported the conclusion that promoting the importance of BSE at the community/social level would enhance individuals to perform BSE routinely. PMID:29172263

Predicting Intention Perform Breast Self-Examination: Application of the Theory of Reasoned Action

PubMed

Dewi, Triana Kesuma; Zein, Rizqy Amelia

2017-11-26

Objective: The present study aimed to examine the applicability of the theory of reasoned action to explain intention to perform breast self-examination (BSE). Methods: A questionnaire was constructed to collect data. The hypothesis was tested in two steps. First, to assess the strength of the correlation among the constructs of theory of reasoned action (TRA), Pearson’s product moment correlations were applied. Second, multivariate relationships among the constructs were examined by performing hierarchical multiple linear regression analysis. Result: The findings supported the TRA model, explaining 45.8% of the variance in the students’ BSE intention, which was significantly correlated with attitude (r = 0.609, p = 0.000) and subjective norms (r = 0.420, p =0 .000). Conclusion: TRA could be a suitable model to predict BSE intentions . Participants who believed that doing BSE regularly is beneficial for early diagnosis of breast cancer and also believed that their significant referents think that doing BSE would significantly detect breast cancer earlier, were more likely to intend to perform BSE regularly. Therefore, the research findings supported the conclusion that promoting the importance of BSE at the community/social level would enhance individuals to perform BSE routinely. Creative Commons Attribution License

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.

Health belief model and reasoned action theory in predicting water saving behaviors in yazd, iran.

PubMed

Morowatisharifabad, Mohammad Ali; Momayyezi, Mahdieh; Ghaneian, Mohammad Taghi

2012-01-01

People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter¬mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha¬viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta¬tistically positive correlation between water saving behaviors and intention. In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors.

Health Belief Model and Reasoned Action Theory in Predicting Water Saving Behaviors in Yazd, Iran

PubMed Central

Morowatisharifabad, Mohammad Ali; Momayyezi, Mahdieh; Ghaneian, Mohammad Taghi

2012-01-01

Background: People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter¬mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha¬viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. Methods: The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Results: Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta¬tistically positive correlation between water saving behaviors and intention. Conclusion: In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors. PMID:24688927

Fluid reasoning predicts future mathematics among children and adolescents

PubMed Central

Green, Chloe T.; Bunge, Silvia A.; Chiongbian, Victoria Briones; Barrow, Maia; Ferrer, Emilio

2017-01-01

The aim of this longitudinal study was to determine whether fluid reasoning (FR) plays a significant role in the acquisition of mathematics skills, above and beyond the effects of other cognitive and numerical abilities. Using a longitudinal cohort sequential design, we examined how FR measured at three assessment occasions, spaced approximately 1.5 years apart, predicted math outcomes for a group of 69 participants between ages 6 and 21 across all three assessment occasions. We used structural equation modeling (SEM) to examine the direct and indirect relations between children's prior cognitive abilities and their future math achievement. A model including age, FR, vocabulary, and spatial skills accounted for 90% of the variance in future math achievement. In this model, FR was the only significant predictor of future math achievement; neither age, vocabulary, nor spatial skills were significant predictors. Thus, FR was the only predictor of future math achievement across a wide age range that spanned primary and secondary school. These findings build on Cattell's conceptualization of FR (Cattell, 1987) as a scaffold for learning, showing that this domain-general ability supports the acquisition of rudimentary math skills as well as the ability to solve more complex mathematical problems. PMID:28152390

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

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.

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.

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

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.

What variables can influence clinical reasoning?

PubMed

Ashoorion, Vahid; Liaghatdar, Mohammad Javad; Adibi, Peyman

2012-12-01

Clinical reasoning is one of the most important competencies that a physician should achieve. Many medical schools and licensing bodies try to predict it based on some general measures such as critical thinking, personality, and emotional intelligence. This study aimed at providing a model to design the relationship between the constructs. Sixty-nine medical students participated in this study. A battery test devised that consist four parts: Clinical reasoning measures, personality NEO inventory, Bar-On EQ inventory, and California critical thinking questionnaire. All participants completed the tests. Correlation and multiple regression analysis consumed for data analysis. There is low to moderate correlations between clinical reasoning and other variables. Emotional intelligence is the only variable that contributes clinical reasoning construct (r=0.17-0.34) (R(2) chnage = 0.46, P Value = 0.000). Although, clinical reasoning can be considered as a kind of thinking, no significant correlation detected between it and other constructs. Emotional intelligence (and its subscales) is the only variable that can be used for clinical reasoning prediction.

What variables can influence clinical reasoning?

PubMed Central

Ashoorion, Vahid; Liaghatdar, Mohammad Javad; Adibi, Peyman

2012-01-01

Background: Clinical reasoning is one of the most important competencies that a physician should achieve. Many medical schools and licensing bodies try to predict it based on some general measures such as critical thinking, personality, and emotional intelligence. This study aimed at providing a model to design the relationship between the constructs. Materials and Methods: Sixty-nine medical students participated in this study. A battery test devised that consist four parts: Clinical reasoning measures, personality NEO inventory, Bar-On EQ inventory, and California critical thinking questionnaire. All participants completed the tests. Correlation and multiple regression analysis consumed for data analysis. Results: There is low to moderate correlations between clinical reasoning and other variables. Emotional intelligence is the only variable that contributes clinical reasoning construct (r=0.17-0.34) (R2 chnage = 0.46, P Value = 0.000). Conclusion: Although, clinical reasoning can be considered as a kind of thinking, no significant correlation detected between it and other constructs. Emotional intelligence (and its subscales) is the only variable that can be used for clinical reasoning prediction. PMID:23853636

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.

Logic, probability, and human reasoning.

PubMed

Johnson-Laird, P N; Khemlani, Sangeet S; Goodwin, Geoffrey P

2015-04-01

This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn - not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity. The theory of mental models solves all of these problems. It explains how people reason about probabilities and postulates that the machinery for reasoning is itself probabilistic. Recent investigations accordingly suggest a way to integrate probability and deduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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.

Structure induction in diagnostic causal reasoning.

PubMed

Meder, Björn; Mayrhofer, Ralf; Waldmann, Michael R

2014-07-01

Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences from an effect to its cause should reflect solely the empirically observed conditional probability of cause given effect. We argue against this assumption, as it neglects alternative causal structures that may have generated the sample data. Our structure induction model of diagnostic reasoning takes into account the uncertainty regarding the underlying causal structure. A key prediction of the model is that diagnostic judgments should not only reflect the empirical probability of cause given effect but should also depend on the reasoner's beliefs about the existence and strength of the link between cause and effect. We confirmed this prediction in 2 studies and showed that our theory better accounts for human judgments than alternative theories of diagnostic reasoning. Overall, our findings support the view that in diagnostic reasoning people go "beyond the information given" and use the available data to make inferences on the (unobserved) causal rather than on the (observed) data level. (c) 2014 APA, all rights reserved.

Conditional Reasoning in Schizophrenic Patients.

PubMed

Kornreich, Charles; Delle-Vigne, Dyna; Brevers, Damien; Tecco, Juan; Campanella, Salvatore; Noël, Xavier; Verbanck, Paul; Ermer, Elsa

2017-01-01

Conditional reasoning (if p then q) is used very frequently in everyday situations. Conditional reasoning is impaired in brain-lesion patients, psychopathy, alcoholism, and polydrug dependence. Many neurocognitive deficits have also been described in schizophrenia. We assessed conditional reasoning in 25 patients with schizophrenia, 25 depressive patients, and 25 controls, using the Wason selection task in three different domains: social contracts, precautionary rules, and descriptive rules. Control measures included depression, anxiety, and severity of schizophrenia measures as a Verbal Intelligence Scale. Patients with schizophrenia were significantly impaired on all conditional reasoning tasks compared to depressives and controls. However, the social contract and precautions tasks yielded better results than the descriptive tasks. Differences between groups disappeared for social contract but remained for precautions and descriptive tasks when verbal intelligence was used as a covariate. These results suggest that domain-specific reasoning mechanisms, proposed by evolutionary psychologists, are relatively resilient in the face of brain network disruptions that impair more general reasoning abilities. Nevertheless, patients with schizophrenia could encounter difficulties understanding precaution rules and social contracts in real-life situations resulting in unwise risk-taking and misunderstandings in the social world.

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).

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

Predictions of biochar production and torrefaction performance from sugarcane bagasse using interpolation and regression analysis.

PubMed

Chen, Wei-Hsin; Hsu, Hung-Jen; Kumar, Gopalakrishnan; Budzianowski, Wojciech M; Ong, Hwai Chyuan

2017-12-01

This study focuses on the biochar formation and torrefaction performance of sugarcane bagasse, and they are predicted using the bilinear interpolation (BLI), inverse distance weighting (IDW) interpolation, and regression analysis. It is found that the biomass torrefied at 275°C for 60min or at 300°C for 30min or longer is appropriate to produce biochar as alternative fuel to coal with low carbon footprint, but the energy yield from the torrefaction at 300°C is too low. From the biochar yield, enhancement factor of HHV, and energy yield, the results suggest that the three methods are all feasible for predicting the performance, especially for the enhancement factor. The power parameter of unity in the IDW method provides the best predictions and the error is below 5%. The second order in regression analysis gives a more reasonable approach than the first order, and is recommended for the predictions. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Inductive reasoning about causally transmitted properties.

PubMed

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.

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.

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

Causal beliefs about depression in different cultural groups—what do cognitive psychological theories of causal learning and reasoning predict?

PubMed Central

Hagmayer, York; Engelmann, Neele

2014-01-01

Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic literature review on causal beliefs about depression was conducted, including original, quantitative research. Thirty-six studies investigating 13 non-Western and 32 Western cultural groups were analyzed by classifying assumed causes and preferred forms of treatment into common categories. Relations between beliefs and treatment preferences were assessed. Substantial agreement between cultural groups was found with respect to the impact of observable causes. Stress was generally rated as most important. Less agreement resulted for hidden, especially supernatural causes. Causal beliefs were clearly related to treatment preferences in Western groups, while evidence was mostly lacking for non-Western groups. Overall predictions were supported, but there were considerable methodological limitations. Pointers to future research, which may combine studies on causal beliefs with experimental paradigms on causal reasoning, are given. PMID:25505432

Prediction of Protein Configurational Entropy (Popcoen).

PubMed

Goethe, Martin; Gleixner, Jan; Fita, Ignacio; Rubi, J Miguel

2018-03-13

A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

Designing Effective Supports for Causal Reasoning

ERIC Educational Resources Information Center

Jonassen, David H.; Ionas, Ioan Gelu

2008-01-01

Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the "cement of the universe" [Hume (1739/2000). Causal reasoning is required for making predictions, drawing implications and…

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...

Utility of the theory of reasoned action and theory of planned behavior for predicting Chinese adolescent smoking.

PubMed

Guo, Qian; Johnson, C Anderson; Unger, Jennifer B; Lee, Liming; Xie, Bin; Chou, Chih-Ping; Palmer, Paula H; Sun, Ping; Gallaher, Peggy; Pentz, MaryAnn

2007-05-01

One third of smokers worldwide live in China. Identifying predictors of smoking is important for prevention program development. This study explored whether the Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) predict adolescent smoking in China. Data were obtained from 14,434 middle and high school students (48.6% boys, 51.4% girls) in seven geographically varied cities in China. TRA and TPB were tested by multilevel mediation modeling, and compared by multilevel analyses and likelihood ratio tests. Perceived behavioral control was tested as a main effect in TPB and a moderation effect in TRA. The mediation effects of smoking intention were supported in both models (p<0.001). TPB accounted for significantly more variance than TRA (p<0.001). Perceived behavioral control significantly interacted with attitudes and social norms in TRA (p<0.001). Therefore, TRA and TPB are applicable to China to predict adolescent smoking. TPB is superior to TRA for the prediction and TRA can better predict smoking among students with lower than higher perceived behavioral control.

A seasonal forecast scheme for the Inner Mongolia spring drought part-II: a logical reasoning evidence-based method for spring predictions

NASA Astrophysics Data System (ADS)

Gao, Tao; Wulan, Wulan; Yu, Xiao; Yang, Zelong; Gao, Jing; Hua, Weiqi; Yang, Peng; Si, Yaobing

2018-05-01

Spring precipitation is the predominant factor that controls meteorological drought in Inner Mongolia (IM), China. This study used the anomaly percentage of spring precipitation (PAP) as a drought index to measure spring drought. A scheme for forecasting seasonal drought was designed based on evidence of spring drought occurrence and speculative reasoning methods introduced in computer artificial intelligence theory. Forecast signals with sufficient lead-time for predictions of spring drought were extracted from eight crucial areas of oceans and 500-hPa geopotential height. Using standardized values, these signals were synthesized into three examples of spring drought evidence (SDE) depending on their primary effects on three major atmospheric circulation components of spring precipitation in IM: the western Pacific subtropical high, North Polar vortex, and East Asian trough. Thresholds for the SDE were determined following numerical analyses of the influential factors. Furthermore, five logical reasoning rules for distinguishing the occurrence of SDE were designed after examining all possible combined cases. The degree of confidence in the rules was determined based on estimations of their prior probabilities. Then, an optimized logical reasoning scheme was identified for judging the possibility of spring drought. The scheme was successful in hindcast predictions of 11 of the 16 (accuracy: 68.8%) spring droughts that have occurred during 1960-2009. Moreover, the accuracy ratio for the same period was 82.0% for drought (PAP ≤ -20%) or not (PAP > -20%). Predictions for the recent 6-year period (2010-2015) demonstrated successful outcomes.

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...

The seats of reason? An imaging study of deductive and inductive reasoning.

PubMed

Goel, V; Gold, B; Kapur, S; Houle, S

1997-03-24

We carried out a neuroimaging study to test the neurophysiological predictions made by different cognitive models of reasoning. Ten normal volunteers performed deductive and inductive reasoning tasks while their regional cerebral blood flow pattern was recorded using [15O]H2O PET imaging. In the control condition subjects semantically comprehended sets of three sentences. In the deductive reasoning condition subjects determined whether the third sentence was entailed by the first two sentences. In the inductive reasoning condition subjects reported whether the third sentence was plausible given the first two sentences. The deduction condition resulted in activation of the left inferior frontal gyrus (Brodmann areas 45, 47). The induction condition resulted in activation of a large area comprised of the left medial frontal gyrus, the left cingulate gyrus, and the left superior frontal gyrus (Brodmann areas 8, 9, 24, 32). Induction was distinguished from deduction by the involvement of the medial aspect of the left superior frontal gyrus (Brodmann areas 8, 9). These results are consistent with cognitive models of reasoning that postulate different mechanisms for inductive and deductive reasoning and view deduction as a formal rule-based process.

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...

Aircraft noise-induced awakenings are more reasonably predicted from relative than from absolute sound exposure levels.

PubMed

Fidell, Sanford; Tabachnick, Barbara; Mestre, Vincent; Fidell, Linda

2013-11-01

Assessment of aircraft noise-induced sleep disturbance is problematic for several reasons. Current assessment methods are based on sparse evidence and limited understandings; predictions of awakening prevalence rates based on indoor absolute sound exposure levels (SELs) fail to account for appreciable amounts of variance in dosage-response relationships and are not freely generalizable from airport to airport; and predicted awakening rates do not differ significantly from zero over a wide range of SELs. Even in conjunction with additional predictors, such as time of night and assumed individual differences in "sensitivity to awakening," nominally SEL-based predictions of awakening rates remain of limited utility and are easily misapplied and misinterpreted. Probabilities of awakening are more closely related to SELs scaled in units of standard deviates of local distributions of aircraft SELs, than to absolute sound levels. Self-selection of residential populations for tolerance of nighttime noise and habituation to airport noise environments offer more parsimonious and useful explanations for differences in awakening rates at disparate airports than assumed individual differences in sensitivity to awakening.

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.

Cognitive Training for Children: Effects on Inductive Reasoning, Deductive Reasoning, and Mathematics Achievement in an Australian School Setting

ERIC Educational Resources Information Center

Barkl, Sophie; Porter, Amy; Ginns, Paul

2012-01-01

Inductive reasoning is a core cognitive process of fluid intelligence, predicting a variety of educational outcomes. The Cognitive Training for Children (CTC) program is an educational intervention designed to develop children's inductive reasoning skills, with previous investigations finding substantial effects of the program on both inductive…

Reasoning, Problem Solving, and Intelligence.

DTIC Science & Technology

1980-04-01

designed to test the validity of their model of response choice in analogical reason- ing. In the first experiment, they set out to demonstrate that...second experiment were somewhat consistent with the prediction. The third experiment used a concept-formation design in which subjects were required to... designed to show interrelationships between various forms of inductive reasoning. Their model fits were highly comparable to those of Rumelhart and

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.

Development of the reasons for living inventory for young adults.

PubMed

Gutierrez, Peter M; Osman, Augustine; Barrios, Francisco X; Kopper, Beverly A; Baker, Monty T; Haraburda, Cheryl M

2002-04-01

Assessment of the reliability, validity, and predictive power of a new measure, the Reasons for Living Inventory for Young Adults (RFL-YA) is described. A series of three studies was conducted at two Midwestern universities to develop initial items for this new measure, refine item selection, and demonstrate the psychometric properties of the RFL-YA. The theoretical differences between the RFL-YA and the College Student Reasons for Living Inventory (CS-RFL) are discussed. Although the two measures were not directly compared, it appears that the RFL-YA has greater specificity for exploring aspects of the protective construct and may be more parsimonious than the CS-RFL. Principal-axis factor analysis yielded a five-factor solution for the RFL-YA accounting for 61.5% of the variance. This five-factor oblique model was confirmed in the final phase of investigation. Alpha estimates for the five subscales ranged from.89 to.94. Concurrent, convergent-discriminant, and criterion validity also were demonstrated. The importance of assessing protective factors in addition to negative risk factors for suicidality is discussed. Directions for future research with the RFL-YA also are discussed. Copyright 2002 Wiley Periodicals, Inc.

Inductive reasoning 2.0.

PubMed

Hayes, Brett K; Heit, Evan

2018-05-01

Inductive reasoning entails using existing knowledge to make predictions about novel cases. The first part of this review summarizes key inductive phenomena and critically evaluates theories of induction. We highlight recent theoretical advances, with a special emphasis on the structured statistical approach, the importance of sampling assumptions in Bayesian models, and connectionist modeling. A number of new research directions in this field are identified including comparisons of inductive and deductive reasoning, the identification of common core processes in induction and memory tasks and induction involving category uncertainty. The implications of induction research for areas as diverse as complex decision-making and fear generalization are discussed. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Learning. © 2017 Wiley Periodicals, Inc.

Causal reasoning with mental models

PubMed Central

Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

Causal reasoning with mental models.

PubMed

Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results

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

Chavez, Gregory M; Key, Brian P; Zerkle, David K

2009-01-01

The security risk associated with malevolent acts such as those of terrorism are often void of the historical data required for a traditional PRA. Most information available to conduct security risk assessments for these malevolent acts is obtained from subject matter experts as subjective judgements. Qualitative reasoning approaches such as approximate reasoning and evidential reasoning are useful for modeling the predicted risk from information provided by subject matter experts. Absent from these approaches is a consistent means to compare the security risk assessment results. Associated with each predicted risk reasoning result is a quantifiable amount of information uncertainty which canmore » be measured and used to compare the results. This paper explores using entropy measures to quantify the information uncertainty associated with conflict and non-specificity in the predicted reasoning results. The measured quantities of conflict and non-specificity can ultimately be used to compare qualitative reasoning results which are important in triage studies and ultimately resource allocation. Straight forward extensions of previous entropy measures are presented here to quantify the non-specificity and conflict associated with security risk assessment results obtained from qualitative reasoning models.« less

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

Morphometry of Left Frontal and Temporal Poles Predicts Analogical Reasoning Abilities.

PubMed

Aichelburg, Clarisse; Urbanski, Marika; Thiebaut de Schotten, Michel; Humbert, Frederic; Levy, Richard; Volle, Emmanuelle

2016-03-01

Analogical reasoning is critical for making inferences and adapting to novelty. It can be studied experimentally using tasks that require creating similarities between situations or concepts, i.e., when their constituent elements share a similar organization or structure. Brain correlates of analogical reasoning have mostly been explored using functional imaging that has highlighted the involvement of the left rostrolateral prefrontal cortex (rlPFC) in healthy subjects. However, whether inter-individual variability in analogical reasoning ability in a healthy adult population is related to differences in brain architecture is unknown. We investigated this question by employing linear regression models of performance in analogy tasks and voxel-based morphometry in 54 healthy subjects. Our results revealed that the ability to reason by analogy was associated with structural variability in the left rlPFC and the anterior part of the inferolateral temporal cortex. Tractography of diffusion-weighted images suggested that these 2 regions have a different set of connections but may exchange information via the arcuate fasciculus. These results suggest that enhanced integrative and semantic abilities supported by structural variation in these areas (or their connectivity) may lead to more efficient analogical reasoning. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Risk Factors Analysis and Death Prediction in Some Life-Threatening Ailments Using Chi-Square Case-Based Reasoning (χ2 CBR) Model.

PubMed

Adeniyi, D A; Wei, Z; Yang, Y

2018-01-30

A wealth of data are available within the health care system, however, effective analysis tools for exploring the hidden patterns in these datasets are lacking. To alleviate this limitation, this paper proposes a simple but promising hybrid predictive model by suitably combining the Chi-square distance measurement with case-based reasoning technique. The study presents the realization of an automated risk calculator and death prediction in some life-threatening ailments using Chi-square case-based reasoning (χ 2 CBR) model. The proposed predictive engine is capable of reducing runtime and speeds up execution process through the use of critical χ 2 distribution value. This work also showcases the development of a novel feature selection method referred to as frequent item based rule (FIBR) method. This FIBR method is used for selecting the best feature for the proposed χ 2 CBR model at the preprocessing stage of the predictive procedures. The implementation of the proposed risk calculator is achieved through the use of an in-house developed PHP program experimented with XAMP/Apache HTTP server as hosting server. The process of data acquisition and case-based development is implemented using the MySQL application. Performance comparison between our system, the NBY, the ED-KNN, the ANN, the SVM, the Random Forest and the traditional CBR techniques shows that the quality of predictions produced by our system outperformed the baseline methods studied. The result of our experiment shows that the precision rate and predictive quality of our system in most cases are equal to or greater than 70%. Our result also shows that the proposed system executes faster than the baseline methods studied. Therefore, the proposed risk calculator is capable of providing useful, consistent, faster, accurate and efficient risk level prediction to both the patients and the physicians at any time, online and on a real-time basis.

Predicting Student Success in a Major's Introductory Biology Course via Logistic Regression Analysis of Scientific Reasoning Ability and Mathematics Scores

NASA Astrophysics Data System (ADS)

Thompson, E. David; Bowling, Bethany V.; Markle, Ross E.

2018-02-01

Studies over the last 30 years have considered various factors related to student success in introductory biology courses. While much of the available literature suggests that the best predictors of success in a college course are prior college grade point average (GPA) and class attendance, faculty often require a valuable predictor of success in those courses wherein the majority of students are in the first semester and have no previous record of college GPA or attendance. In this study, we evaluated the efficacy of the ACT Mathematics subject exam and Lawson's Classroom Test of Scientific Reasoning in predicting success in a major's introductory biology course. A logistic regression was utilized to determine the effectiveness of a combination of scientific reasoning (SR) scores and ACT math (ACT-M) scores to predict student success. In summary, we found that the model—with both SR and ACT-M as significant predictors—could be an effective predictor of student success and thus could potentially be useful in practical decision making for the course, such as directing students to support services at an early point in the semester.

The development of causal reasoning.

PubMed

Kuhn, Deanna

2012-05-01

How do inference rules for causal learning themselves change developmentally? A model of the development of causal reasoning must address this question, as well as specify the inference rules. Here, the evidence for developmental changes in processes of causal reasoning is reviewed, with the distinction made between diagnostic causal inference and causal prediction. Also addressed is the paradox of a causal reasoning literature that highlights the competencies of young children and the proneness to error among adults. WIREs Cogn Sci 2012, 3:327-335. doi: 10.1002/wcs.1160 For further resources related to this article, please visit the WIREs website. Copyright © 2012 John Wiley & Sons, Ltd.

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.

How many kinds of reasoning? Inference, probability, and natural language semantics.

PubMed

Lassiter, Daniel; Goodman, Noah D

2015-03-01

The "new paradigm" unifying deductive and inductive reasoning in a Bayesian framework (Oaksford & Chater, 2007; Over, 2009) has been claimed to be falsified by results which show sharp differences between reasoning about necessity vs. plausibility (Heit & Rotello, 2010; Rips, 2001; Rotello & Heit, 2009). We provide a probabilistic model of reasoning with modal expressions such as "necessary" and "plausible" informed by recent work in formal semantics of natural language, and show that it predicts the possibility of non-linear response patterns which have been claimed to be problematic. Our model also makes a strong monotonicity prediction, while two-dimensional theories predict the possibility of reversals in argument strength depending on the modal word chosen. Predictions were tested using a novel experimental paradigm that replicates the previously-reported response patterns with a minimal manipulation, changing only one word of the stimulus between conditions. We found a spectrum of reasoning "modes" corresponding to different modal words, and strong support for our model's monotonicity prediction. This indicates that probabilistic approaches to reasoning can account in a clear and parsimonious way for data previously argued to falsify them, as well as new, more fine-grained, data. It also illustrates the importance of careful attention to the semantics of language employed in reasoning experiments. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Reducing pressure on natural forests through high-yield forestry

Treesearch

W.T. Gladstone; F. Thomas Ledig

1990-01-01

High-yield forestry can make a valuable contribution to the conservation and sustained use of forest ecosystems. Despite the pressing reasons for conserving forest resources, population growth creates pressures for exploiting them. Unless needs for forest products, export credits, and local employment can be met by new devices, such as high-yield forestry, these...

Predicting children's sunscreen use: application of the theories of reasoned action and planned behavior.

PubMed

Martin, S C; Jacobsen, P B; Lucas, D J; Branch, K A; Ferron, J M

1999-07-01

Skin cancer remains the most common form of cancer in the United States despite the fact that most cases can be prevented by limiting sun exposure. Childhood and adolescence are periods of life during which prolonged sun exposure is particularly common. Accordingly, promoting sun-protective behaviors during these formative years can be of critical importance in preventing skin cancer. The present study applied the theories of reasoned action and planned behavior to the understanding of children's sunscreen use. Based on these theories, it was hypothesized that attitudes, subjective norms, and perceived behavioral control would be related to intentions to use sunscreen, which, in turn, would be related to actual sunscreen use. Questionnaires measuring sun-related attitudes, beliefs, perceived control, and intentions were administered to 199 fourth graders (ages 9 to 13, mean = 10.3) attending public schools in Florida. Self-report measures of sun-related behavior were administered to the same subjects 1 month later. Results of correlational analyses were consistent with study hypotheses. Higher rates of sunscreen use at follow-up were predicted by stronger intentions to use sunscreen assessed 1 month previously. In addition, stronger intentions to use sunscreen were found to be related to more favorable attitudes toward sunscreen use, stronger beliefs that peers and parents favored sunscreen use, and greater perceptions of personal control in using sunscreen. Path and multiple regression analyses identified direct and indirect relationships among study variables that partially confirmed those predicted by the theories and provided support for the use of an expanded model that included perceived behavioral control. The present study confirmed hypotheses derived from the theories of reasoned action and planned behavior regarding the relation of attitudes, subjective norms, and perceived behavioral control to sunscreen use among fourth graders. In addition to their

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.

Reasoning in believers in the paranormal.

PubMed

Lawrence, Emma; Peters, Emmanuelle

2004-11-01

Reasoning biases have been identified in deluded patients, delusion-prone individuals, and believers in the paranormal. This study examined content-specific reasoning and delusional ideation in believers in the paranormal. A total of 174 members of the Society for Psychical Research completed a delusional ideation questionnaire and a deductive reasoning task. The reasoning statements were manipulated for congruency with paranormal beliefs. As predicted, individuals who reported a strong belief in the paranormal made more errors and displayed more delusional ideation than skeptical individuals. However, no differences were found with statements that were congruent with their belief system, confirming the domain-specificity of reasoning. This reasoning bias was limited to people who reported a belief in, rather than experience of, paranormal phenomena. These results suggest that reasoning abnormalities may have a causal role in the formation of unusual beliefs. The dissociation between experiences and beliefs implies that such abnormalities operate at the evaluative, rather than the perceptual, stage of processing.

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

Dyslexia and reasoning: the importance of visual processes.

PubMed

Bacon, Alison M; Handley, Simon J

2010-08-01

Recent research has suggested that individuals with dyslexia rely on explicit visuospatial representations for syllogistic reasoning while most non-dyslexics opt for an abstract verbal strategy. This paper investigates the role of visual processes in relational reasoning amongst dyslexic reasoners. Expt 1 presents written and verbal protocol evidence to suggest that reasoners with dyslexia generate detailed representations of relational properties and use these to make a visual comparison of objects. Non-dyslexics use a linear array of objects to make a simple transitive inference. Expt 2 examined evidence for the visual-impedance effect which suggests that visual information detracts from reasoning leading to longer latencies and reduced accuracy. While non-dyslexics showed the impedance effects predicted, dyslexics showed only reduced accuracy on problems designed specifically to elicit imagery. Expt 3 presented problems with less semantically and visually rich content. The non-dyslexic group again showed impedance effects, but dyslexics did not. Furthermore, in both studies, visual memory predicted reasoning accuracy for dyslexic participants, but not for non-dyslexics, particularly on problems with highly visual content. The findings are discussed in terms of the importance of visual and semantic processes in reasoning for individuals with dyslexia, and we argue that these processes play a compensatory role, offsetting phonological and verbal memory deficits.

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

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%.

Counterfactual Reasoning: Developing a sense of “nearest possible world”

PubMed Central

Rafetseder, Eva; Cristi-Vargas, Renate; Perner, Josef

2011-01-01

We investigated at what point in development 3- to 6-year-old children begin to demonstrate counterfactual reasoning by controlling for fortuitously correct answers that result from basic conditional reasoning. Basic conditional reasoning occurs when one applies typical regularities (such as “If it doesn’t rain the street is dry”) to counterfactual questions (such as “If it had not rained, would the street be wet or dry?”) without regard to actual events (for example, if street cleaners had just been washing the street). In counterfactual reasoning, however, the conditional reasoning must be constrained by actual events (according to the “nearest possible world”). In situations when counterfactual reasoning and basic conditional reasoning would yield the same answers, even the youngest children gave mostly correct answers. However, tasks in which the two reasoning strategies resulted in different answers proved unusually difficult even for the older children. PMID:20331674

Perceived Reasons for Living at Index Hospitalization and Future Suicide Attempt

PubMed Central

Lizardi, Dana; Currier, Diane; Galfalvy, Hanga; Sher, Leo; Burke, Ainsley; Mann, John; Oquendo, Maria

2013-01-01

It is unclear why certain individuals choose not to engage in suicidal behavior. Although important, protective factors against suicidal behavior have seldom been studied. The Reasons for Living Inventory is a measure of putative protective factors that is inversely related to a history of suicide attempts, but its predictive utility remains relatively untested. This study sought to determine whether the Reasons for Living Inventory predicts future suicide attempts over a 2-year period. Depressed inpatients were assessed for reasons for living and were followed for 2 years. Follow-up interviews took place at 3 months, 1 year, and 2 years after discharge from the index hospitalization. Survival analysis indicates a high score on the Reasons for Living Inventory predicted fewer future suicide attempts within a 2-year period in women but not in men. Perceived reasons for living serve as protective factors against suicide attempt in women and not in men. PMID:17502812

The neural basis of conditional reasoning with arbitrary content.

PubMed

Noveck, Ira A; Goel, Vinod; Smith, Kathleen W

2004-01-01

Behavioral predictions about reasoning have usually contrasted two accounts, Mental Logic and Mental Models. Neuroimaging techniques have been providing new measures that transcend this debate. We tested a hypothesis from Goel and Dolan (2003) that predicts neural activity predominantly in a left parietal-frontal system when participants reason with arbitrary (non-meaningful) materials. In an event-related fMRI investigation, we employed propositional syllogisms, the majority of which involved conditional reasoning. While investigating conditional reasoning generally, we ultimately focused on the neural activity linked to the two valid conditional forms--Modus Ponens (If p then q; p//q) and Modus Tollens (If p then q; not-q//not-p). Consistent with Goel and Dolan (2003), we found a left lateralized parietal frontal network for both inference forms with increasing activation when reasoning becomes more challenging by way of Modus Tollens. These findings show that the previous findings with more complex Aristotlean syllogisms are robust and cast doubt upon accounts of reasoning that accord primary inferential processes uniquely to either the right hemisphere or to language areas.

On Using Homogeneous Polynomials To Design Anisotropic Yield Functions With Tension/Compression Symmetry/Assymetry

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

Soare, S.; Cazacu, O.; Yoon, J. W.

With few exceptions, non-quadratic homogeneous polynomials have received little attention as possible candidates for yield functions. One reason might be that not every such polynomial is a convex function. In this paper we show that homogeneous polynomials can be used to develop powerful anisotropic yield criteria, and that imposing simple constraints on the identification process leads, aposteriori, to the desired convexity property. It is shown that combinations of such polynomials allow for modeling yielding properties of metallic materials with any crystal structure, i.e. both cubic and hexagonal which display strength differential effects. Extensions of the proposed criteria to 3D stressmore » states are also presented. We apply these criteria to the description of the aluminum alloy AA2090T3. We prove that a sixth order orthotropic homogeneous polynomial is capable of a satisfactory description of this alloy. Next, applications to the deep drawing of a cylindrical cup are presented. The newly proposed criteria were implemented as UMAT subroutines into the commercial FE code ABAQUS. We were able to predict six ears on the AA2090T3 cup's profile. Finally, we show that a tension/compression asymmetry in yielding can have an important effect on the earing profile.« less

On Using Homogeneous Polynomials To Design Anisotropic Yield Functions With Tension/Compression Symmetry/Assymetry

NASA Astrophysics Data System (ADS)

Soare, S.; Yoon, J. W.; Cazacu, O.

2007-05-01

With few exceptions, non-quadratic homogeneous polynomials have received little attention as possible candidates for yield functions. One reason might be that not every such polynomial is a convex function. In this paper we show that homogeneous polynomials can be used to develop powerful anisotropic yield criteria, and that imposing simple constraints on the identification process leads, aposteriori, to the desired convexity property. It is shown that combinations of such polynomials allow for modeling yielding properties of metallic materials with any crystal structure, i.e. both cubic and hexagonal which display strength differential effects. Extensions of the proposed criteria to 3D stress states are also presented. We apply these criteria to the description of the aluminum alloy AA2090T3. We prove that a sixth order orthotropic homogeneous polynomial is capable of a satisfactory description of this alloy. Next, applications to the deep drawing of a cylindrical cup are presented. The newly proposed criteria were implemented as UMAT subroutines into the commercial FE code ABAQUS. We were able to predict six ears on the AA2090T3 cup's profile. Finally, we show that a tension/compression asymmetry in yielding can have an important effect on the earing profile.

[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

[Prevention of HIV transmission: application of the theory of reasoned action to the prediction of condom use].

PubMed

Diaz-loving, R; Rivera Aragon, S

1995-01-01

1203 sexually active workers in six government agencies in Mexico City participated in a study of the applicability of the theory of reasoned action to prediction of condom use for AIDS prevention. The theory of reasoned action is one of a series of models of attitudes that have had consistent success in predicting various types of intentions and behaviors, especially in the area of sexual and contraceptive behavior. The theory specifies that the intention of executing a particular behavior is determined as the function of attitude toward the behavior and a social factor termed "subjective norm", referring to the perception of social pressure supporting or opposing a particular behavior. The 1203 subjects, who ranged from low to high educational and socioeconomic status, completed self-administered questionnaires concerning their beliefs, attitudes, and intentions regarding condom use, motivation to comply with the subjective norm, and actual condom use. Various scales were constructed to measure the different components of the theory. Hierarchical regression analysis was carried out separately for men and women and for condom use with regular or occasional partners. The model explained over 20% of condom use behavior. The total explained variance was similar in all groups, but the components of the model determining the variance were different. Personal beliefs and attitudes were more important in reference to occasional sexual partners, but the subjective norm and motivation to comply with the reference group were more important with regular sexual partners. The results demonstrate the need for interventions to be adapted to gender groups and in reference to regular or occasional partners.

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.

Frontotemporal Dementia Selectively Impairs Transitive Reasoning About Familiar Spatial Environments

PubMed Central

Vartanian, Oshin; Goel, Vinod; Tierney, Michael; Huey, Edward D.; Grafman, Jordan

2010-01-01

Although patients with frontotemporal dementia (FTD) are known to exhibit a wide range of cognitive and personality difficulties, some evidence suggests that there may be a degree of selectivity in their reasoning impairments. Based on a recent review of the neuroimaging literature on reasoning, the authors hypothesized that the presence or absence of familiar content may have a selective impact on the reasoning abilities of patients with FTD. Specifically, the authors predicted that patients with frontalvariant FTD would be more impaired when reasoning about transitive arguments involving familiar spatial environments than when reasoning about identical logical arguments involving unfamiliar spatial environments. As predicted, patients with FTD were less accurate than normal controls only when the content of arguments involved familiar spatial environments. These results indicate a degree of selectivity in the cognitive deficits of this patient population and suggest that the frontal-temporal lobe system may play a necessary role in reasoning about familiar material. PMID:19702415

Causal knowledge and the development of inductive reasoning.

PubMed

Bright, Aimée K; Feeney, Aidan

2014-06-01

We explored the development of sensitivity to causal relations in children's inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey→predator) or diagnostic (predator→prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children's inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning. Copyright © 2013 Elsevier Inc. All rights reserved.

Reasons for Trying E-cigarettes and Risk of Continued Use

PubMed Central

Kong, Grace; Cavallo, Dana A.; Camenga, Deepa R.; Krishnan-Sarin, Suchitra

2016-01-01

BACKGROUND: Longitudinal research is needed to identify predictors of continued electronic cigarette (e-cigarette) use among youth. We expected that certain reasons for first trying e-cigarettes would predict continued use over time (eg, good flavors, friends use), whereas other reasons would not predict continued use (eg, curiosity). METHODS: Longitudinal surveys from middle and high school students from fall 2013 (wave 1) and spring 2014 (wave 2) were used to examine reasons for trying e-cigarettes as predictors of continued e-cigarette use over time. Ever e-cigarette users (n = 340) at wave 1 were categorized into those using or not using e-cigarettes at wave 2. Among those who continued using e-cigarettes, reasons for trying e-cigarettes were examined as predictors of use frequency, measured as the number of days using e-cigarettes in the past 30 days at wave 2. Covariates included age, sex, race, and smoking of traditional cigarettes. RESULTS: Several reasons for first trying e-cigarettes predicted continued use, including low cost, the ability to use e-cigarettes anywhere, and to quit smoking regular cigarettes. Trying e-cigarettes because of low cost also predicted more days of e-cigarette use at wave 2. Being younger or a current smoker of traditional cigarettes also predicted continued use and more frequent use over time. CONCLUSIONS: Regulatory strategies such as increasing cost or prohibiting e-cigarette use in certain places may be important for preventing continued use in youth. In addition, interventions targeting current cigarette smokers and younger students may also be needed. PMID:27503349

Reasons for Trying E-cigarettes and Risk of Continued Use.

PubMed

Bold, Krysten W; Kong, Grace; Cavallo, Dana A; Camenga, Deepa R; Krishnan-Sarin, Suchitra

2016-09-01

Longitudinal research is needed to identify predictors of continued electronic cigarette (e-cigarette) use among youth. We expected that certain reasons for first trying e-cigarettes would predict continued use over time (eg, good flavors, friends use), whereas other reasons would not predict continued use (eg, curiosity). Longitudinal surveys from middle and high school students from fall 2013 (wave 1) and spring 2014 (wave 2) were used to examine reasons for trying e-cigarettes as predictors of continued e-cigarette use over time. Ever e-cigarette users (n = 340) at wave 1 were categorized into those using or not using e-cigarettes at wave 2. Among those who continued using e-cigarettes, reasons for trying e-cigarettes were examined as predictors of use frequency, measured as the number of days using e-cigarettes in the past 30 days at wave 2. Covariates included age, sex, race, and smoking of traditional cigarettes. Several reasons for first trying e-cigarettes predicted continued use, including low cost, the ability to use e-cigarettes anywhere, and to quit smoking regular cigarettes. Trying e-cigarettes because of low cost also predicted more days of e-cigarette use at wave 2. Being younger or a current smoker of traditional cigarettes also predicted continued use and more frequent use over time. Regulatory strategies such as increasing cost or prohibiting e-cigarette use in certain places may be important for preventing continued use in youth. In addition, interventions targeting current cigarette smokers and younger students may also be needed. Copyright © 2016 by the American Academy of Pediatrics.

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 ...

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

Reason for Referral Predicts Utilization and Perceived Impact of Early Intervention Services.

PubMed

Javalkar, Karina; Litt, Jonathan S

Children participating in early intervention (EI) vary in their medical needs and degree of delay, and previous studies have shown significant differences in EI enrollment based on the reason for referral. The effect of reason for referral on service provision and family satisfaction is largely unknown. We used data from the National Early Intervention Longitudinal Study for our secondary data analysis. The main predictor was the reason for referral: a diagnosed condition, documented developmental delay, or other risk factors. Outcomes included unmet service needs, program dropout, and family satisfaction with services. The 2966 participants were mostly white (51.9%), male (60.3%), and had an annual household income at or below $50,000 (77.0%). There were 1924 referred due to diagnosis, 691 due to delay, and 351 due to other risks. Compared with the diagnosis group, children with delays were more likely (adjusted odds ratio [aOR] 1.38, 95% confidence interval [CI], 1.02-1.87) to have unmet service needs and to drop out of EI programs (aOR 1.44, 95% CI, 1.07-1.96); their families were less likely to report that services were highly individualized (aOR 0.80, 95% CI, 0.65-0.98) or had an impact on their children's development (aOR 0.77, 95% CI, 0.62-0.96). Children participating in EI because of developmental delays are more likely to have unmet service needs, drop out of services because of a reason other than ineligibility (family or child-related reason), and have lower caregiver satisfaction than those participating because of diagnosed conditions. It is important to determine reasons for these differences and their impact on developmental outcomes.

Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

PubMed Central

Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

2017-01-01

Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

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.

Reason and less

PubMed Central

Goel, Vinod

2014-01-01

We consider ourselves to be rational beings. We feel that our choices, decisions, and actions are selected from a flexible array of possibilities, based upon reasons. When we vote for a political candidate, it is because they share our views on certain critical issues. When we hire an individual for a job, it is because they are the best qualified. However, if this is true, why does an analysis of the direction of shift in the timbre of the voice of political candidates during an exchange or debate, predict the winner of American presidential elections? Why is it that while only 3% of the American population consists of white men over 6′4″ tall, 30% of the CEOs of Fortune 500 companies are white men over 6′4″ tall? These are examples of “instinctual biases” affecting or modulating rational thought processes. I argue that existing theories of reasoning cannot substantively accommodate these ubiquitous, real-world phenomena. Failure to recognize and incorporate these types of phenomena into the study of human reasoning results in a distorted understanding of rationality. The goal of this article is to draw attention to these types of phenomena and propose an “adulterated rationality” account of reasoning as a first step in trying to explain them. PMID:25191288

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.

Climatic and technological ceilings for Chinese rice stagnation based on yield gaps and yield trend pattern analysis.

PubMed

Zhang, Tianyi; Yang, Xiaoguang; Wang, Hesong; Li, Yong; Ye, Qing

2014-04-01

Climatic or technological ceilings could cause yield stagnation. Thus, identifying the principal reasons for yield stagnation within the context of the local climate and socio-economic conditions are essential for informing regional agricultural policies. In this study, we identified the climatic and technological ceilings for seven rice-production regions in China based on yield gaps and on a yield trend pattern analysis for the period 1980-2010. The results indicate that 54.9% of the counties sampled experienced yield stagnation since the 1980. The potential yield ceilings in northern and eastern China decreased to a greater extent than in other regions due to the accompanying climate effects of increases in temperature and decreases in radiation. This may be associated with yield stagnation and halt occurring in approximately 49.8-57.0% of the sampled counties in these areas. South-western China exhibited a promising scope for yield improvement, showing the greatest yield gap (30.6%), whereas the yields were stagnant in 58.4% of the sampled counties. This finding suggests that efforts to overcome the technological ceiling must be given priority so that the available exploitable yield gap can be achieved. North-eastern China, however, represents a noteworthy exception. In the north-central area of this region, climate change has increased the yield potential ceiling, and this increase has been accompanied by the most rapid increase in actual yield: 1.02 ton ha(-1) per decade. Therefore, north-eastern China shows a great potential for rice production, which is favoured by the current climate conditions and available technology level. Additional environmentally friendly economic incentives might be considered in this region. © 2013 John Wiley & Sons Ltd.

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...

Nut crop yield records show that budbreak-based chilling requirements may not reflect yield decline chill thresholds

NASA Astrophysics Data System (ADS)

Pope, Katherine S.; Dose, Volker; Da Silva, David; Brown, Patrick H.; DeJong, Theodore M.

2015-06-01

Warming winters due to climate change may critically affect temperate tree species. Insufficiently cold winters are thought to result in fewer viable flower buds and the subsequent development of fewer fruits or nuts, decreasing the yield of an orchard or fecundity of a species. The best existing approximation for a threshold of sufficient cold accumulation, the "chilling requirement" of a species or variety, has been quantified by manipulating or modeling the conditions that result in dormant bud breaking. However, the physiological processes that affect budbreak are not the same as those that determine yield. This study sought to test whether budbreak-based chilling thresholds can reasonably approximate the thresholds that affect yield, particularly regarding the potential impacts of climate change on temperate tree crop yields. County-wide yield records for almond ( Prunus dulcis), pistachio ( Pistacia vera), and walnut ( Juglans regia) in the Central Valley of California were compared with 50 years of weather records. Bayesian nonparametric function estimation was used to model yield potentials at varying amounts of chill accumulation. In almonds, average yields occurred when chill accumulation was close to the budbreak-based chilling requirement. However, in the other two crops, pistachios and walnuts, the best previous estimate of the budbreak-based chilling requirements was 19-32 % higher than the chilling accumulations associated with average or above average yields. This research indicates that physiological processes beyond requirements for budbreak should be considered when estimating chill accumulation thresholds of yield decline and potential impacts of climate change.

Alcohol and drug abusers' reasons for seeking treatment.

PubMed

Cunningham, J A; Sobell, L C; Sobell, M B; Gaskin, J

1994-01-01

Clients at two different treatment facilities were asked at assessment how influential each of 10 possible reasons were in their decision to change their alcohol or drug use. Clients at both facilities most often endorsed "weighing the pros and cons of drinking or drug use" and a "warning from spouse." Client's reasons for seeking treatment were also examined in relation to treatment compliance. Three reasons--"weighing the pros and cons," "hitting rock bottom," and experiencing a "major lifestyle change"--were predictive of treatment compliance. Clients who rated any of these reasons as influential were more likely to enter and complete treatment. Although more research is needed, knowledge of clients' reasons for seeking treatment might be useful in treatment matching.

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...

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

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*.

Individual differences and reasoning: a study on personality traits.

PubMed

Bensi, Luca; Giusberti, Fiorella; Nori, Raffaella; Gambetti, Elisa

2010-08-01

Personality can play a crucial role in how people reason and decide. Identifying individual differences related to how we actively gather information and use evidence could lead to a better comprehension and predictability of human reasoning. Recent findings have shown that some personality traits are related to similar decision-making patterns showed by people with mental disorders. We performed research with the aim to investigate delusion-proneness, obsessive-like personality, anxiety (trait and state), and reasoning styles in individuals from the general population. We introduced personality trait and state anxiety scores in a regression model to explore specific associations with: (1) amount of data-gathered prior to making a decision; and (2) the use of confirmatory and disconfirmatory evidence. Results showed that all our independent variables were positively or negatively associated with the amount of data collected in order to make simple probabilistic decisions. Anxiety and obsessiveness were the only predictors of the weight attributed to evidence in favour or against a hypothesis. Findings were discussed in relation to theoretical assumptions, predictions, and clinical implications. Personality traits can predict peculiar ways to reason and decide that, in turn, could be involved to some extent in the formation and/or maintenance of psychological disorders.

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

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

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.

Thinking before sinning: reasoning processes in hedonic consumption

PubMed Central

de Witt Huberts, Jessie; Evers, Catharine; de Ridder, Denise

2014-01-01

Whereas hedonic consumption is often labeled as impulsive, findings from self-licensing research suggest that people sometimes rely on reasons to justify hedonic consumption. Although the concept of self-licensing assumes the involvement of reasoning processes, this has not been demonstrated explicitly. Two studies investigated whether people indeed rely on reasons to allow themselves a guilty pleasure. Participants were exposed to a food temptation after which passive and active reasoning was assessed by asking participants to indicate the justifications that applied to them for indulging in that temptation (Study 1) or having them construe reasons to consume the hedonic product (Study 2). Regression analyses indicated that higher levels of temptation predicted the number of reasons employed and construed to justify consumption. By providing evidence for the involvement of reasoning processes, these findings support the assumption of self-licensing theory that temptations not only exert their influence by making us more impulsive, but can also facilitate gratification by triggering deliberative reasoning processes. PMID:25408680

On the Short Horizon of Spontaneous Iterative Reasoning in Logical Puzzles and Games

ERIC Educational Resources Information Center

Mazzocco, Ketti; Cherubini, Anna Maria; Cherubini, Paolo

2013-01-01

A reasoning strategy is iterative when the initial conclusion suggested by a set of premises is integrated into that set of premises in order to yield additional conclusions. Previous experimental studies on game theory-based strategic games (such as the beauty contest game) observed difficulty in reasoning iteratively, which has been partly…

Model fitting data from syllogistic reasoning experiments.

PubMed

Hattori, Masasi

2016-12-01

The data presented in this article are related to the research article entitled "Probabilistic representation in syllogistic reasoning: A theory to integrate mental models and heuristics" (M. Hattori, 2016) [1]. This article presents predicted data by three signature probabilistic models of syllogistic reasoning and model fitting results for each of a total of 12 experiments ( N =404) in the literature. Models are implemented in R, and their source code is also provided.

Probabilistic Reasoning and Prediction with Young Children

ERIC Educational Resources Information Center

Kinnear, Virginia; Clark, Julie

2014-01-01

This paper reports findings from a classroom based study with 5 year old children in their first term of school. A data modelling activity contextualised by a picture story book was used to present a prediction problem. A data table with numerical data values provided for three consecutive days of rubbish collection was provided, with a fourth day…

Prediction of seasonal climate-induced variations in global food production

NASA Astrophysics Data System (ADS)

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

2013-10-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important 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 found that moderate-to-marked yield loss over a substantial percentage (26-33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop--rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.

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

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.

Vehicle Integrated Prognostic Reasoner (VIPR) Final Report

NASA Technical Reports Server (NTRS)

Bharadwaj, Raj; Mylaraswamy, Dinkar; Cornhill, Dennis; Biswas, Gautam; Koutsoukos, Xenofon; Mack, Daniel

2013-01-01

A systems view is necessary to detect, diagnose, predict, and mitigate adverse events during the flight of an aircraft. While most aircraft subsystems look for simple threshold exceedances and report them to a central maintenance computer, the vehicle integrated prognostic reasoner (VIPR) proactively generates evidence and takes an active role in aircraft-level health assessment. Establishing the technical feasibility and a design trade-space for this next-generation vehicle-level reasoning system (VLRS) is the focus of our work.

Detaching reasons from aims: fair play and well-being in soccer as a function of pursuing performance-approach goals for autonomous or controlling reasons.

PubMed

Vansteenkiste, Maarten; Mouratidis, Athanasios; Lens, Willy

2010-04-01

In two cross-sectional studies we investigated whether soccer players' well-being (Study 1) and moral functioning (Studies 1 and 2) is related to performance-approach goals and to the autonomous and controlling reasons underlying their pursuit. In support of our hypotheses, we found in Study 1 that autonomous reasons were positively associated with vitality and positive affect, whereas controlling reasons were positively related to negative affect and mostly unrelated to indicators of morality. To investigate the lack of systematic association with moral outcomes, we explored in Study 2 whether performance-approach goals or their underlying reasons would yield an indirect relation to moral outcomes through their association with players' objectifying attitude-their tendency to depersonalize their opponents. Structural equation modeling showed that controlling reasons for performance-approach goals were positively associated with an objectifying attitude, which in turn was positively associated to unfair functioning. Results are discussed within the achievement goal perspective (Elliot, 2005) and self-determination theory (Deci & Ryan, 2000).

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.

Age differences in proactive interference, working memory, and abstract reasoning

PubMed Central

Emery, Lisa; Hale, Sandra; Myerson, Joel

2008-01-01

It has been hypothesized that older adults are especially susceptible to proactive interference (PI), and that this may contribute to age differences in working memory performance. In young adults, individual differences in PI affect both working memory and reasoning ability, but the relations between PI, working memory, and reasoning in older adults have not been examined. In the current study, young, old, and very old adults performed a modified operation span task that induced several cycles of PI buildup and release, as well as two tests of abstract reasoning ability. Age differences in working memory scores increased as PI built up, consistent with the hypothesis that older adults are more susceptible to PI, but both young and older adults showed complete release from PI. Young adults' reasoning ability was best predicted by working memory performance under high-PI conditions, replicating Bunting (2006). In contrast, older adults' reasoning ability was best predicted by their working memory performance under low-PI conditions, thereby raising questions regarding the general role of susceptibility to PI in differences in higher cognitive function among older adults. PMID:18808252

Effect of dislocation pile-up on size-dependent yield strength in finite single-crystal micro-samples

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

Pan, Bo; Shibutani, Yoji, E-mail: sibutani@mech.eng.osaka-u.ac.jp; Zhang, Xu

2015-07-07

Recent research has explained that the steeply increasing yield strength in metals depends on decreasing sample size. In this work, we derive a statistical physical model of the yield strength of finite single-crystal micro-pillars that depends on single-ended dislocation pile-up inside the micro-pillars. We show that this size effect can be explained almost completely by considering the stochastic lengths of the dislocation source and the dislocation pile-up length in the single-crystal micro-pillars. The Hall–Petch-type relation holds even in a microscale single-crystal, which is characterized by its dislocation source lengths. Our quantitative conclusions suggest that the number of dislocation sources andmore » pile-ups are significant factors for the size effect. They also indicate that starvation of dislocation sources is another reason for the size effect. Moreover, we investigated the explicit relationship between the stacking fault energy and the dislocation “pile-up” effect inside the sample: materials with low stacking fault energy exhibit an obvious dislocation pile-up effect. Our proposed physical model predicts a sample strength that agrees well with experimental data, and our model can give a more precise prediction than the current single arm source model, especially for materials with low stacking fault energy.« 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.

Role of the N*(1535) in the J/{psi}{yields}p{eta}p and J/{psi}{yields}pK{sup +}{lambda} reactions

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

Geng, L. S.; Oset, E.; Zou, B. S.

2009-02-15

We study the J/{psi}{yields}p{eta}p and J/{psi}{yields}pK{sup +}{lambda} reactions with a unitary chiral approach. We find that the unitary chiral approach, which generates the N*(1535) dynamically, can describe the data reasonably well, particularly the ratio of the integrated cross sections. This study provides further support for the unitary chiral description of the N*(1535). We also discuss some subtle differences between the coupling constants determined from the unitary chiral approach and those determined from phenomenological studies.

Prefrontal and parietal activity is modulated by the rule complexity of inductive reasoning and can be predicted by a cognitive model.

PubMed

Jia, Xiuqin; Liang, Peipeng; Shi, Lin; Wang, Defeng; Li, Kuncheng

2015-01-01

In neuroimaging studies, increased task complexity can lead to increased activation in task-specific regions or to activation of additional regions. How the brain adapts to increased rule complexity during inductive reasoning remains unclear. In the current study, three types of problems were created: simple rule induction (i.e., SI, with rule complexity of 1), complex rule induction (i.e., CI, with rule complexity of 2), and perceptual control. Our findings revealed that increased activations accompany increased rule complexity in the right dorsal lateral prefrontal cortex (DLPFC) and medial posterior parietal cortex (precuneus). A cognitive model predicted both the behavioral and brain imaging results. The current findings suggest that neural activity in frontal and parietal regions is modulated by rule complexity, which may shed light on the neural mechanisms of inductive reasoning. Copyright © 2014. Published by Elsevier 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.

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.

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.

Reasoning in people with obsessive-compulsive disorder.

PubMed

Simpson, Jane; Cove, Jennifer; Fineberg, Naomi; Msetfi, Rachel M; J Ball, Linden

2007-11-01

The aim of this study was to investigate the inductive and deductive reasoning abilities of people with obsessive-compulsive disorder (OCD). Following previous research, it was predicted that people with OCD would show different abilities on inductive reasoning tasks but similar abilities to controls on deductive reasoning tasks. A two-group comparison was used with both groups matched on a range of demographic variables. Where appropriate, unmatched variables were entered into the analyses as covariates. Twenty-three people with OCD and 25 control participants were assessed on two tasks: an inductive reasoning task (the 20-questions task) and a deductive reasoning task (a syllogistic reasoning task with a content-neutral and content-emotional manipulation). While no group differences emerged on several of the parameters of the inductive reasoning task, the OCD group did differ on one, and arguably the most important, parameter by asking fewer correct direct-hypothesis questions. The syllogistic reasoning task results were analysed using both correct response and conclusion acceptance data. While no main effects of group were evident, significant interactions indicated important differences in the way the OCD group reasoned with content neutral and emotional syllogisms. It was argued that the OCD group's patterns of response on both tasks were characterized by the need for more information, states of uncertainty, and doubt and postponement of a final decision.

Model of critical diagnostic reasoning: achieving expert clinician performance.

PubMed

Harjai, Prashant Kumar; Tiwari, Ruby

2009-01-01

Diagnostic reasoning refers to the analytical processes used to determine patient health problems. While the education curriculum and health care system focus on training nurse clinicians to accurately recognize and rescue clinical situations, assessments of non-expert nurses have yielded less than satisfactory data on diagnostic competency. The contrast between the expert and non-expert nurse clinician raises the important question of how differences in thinking may contribute to a large divergence in accurate diagnostic reasoning. This article recognizes superior organization of one's knowledge base, using prototypes, and quick retrieval of pertinent information, using similarity recognition as two reasons for the expert's superior diagnostic performance. A model of critical diagnostic reasoning, using prototypes and similarity recognition, is proposed and elucidated using case studies. This model serves as a starting point toward bridging the gap between clinical data and accurate problem identification, verification, and management while providing a structure for a knowledge exchange between expert and non-expert clinicians.

Team reasoning and collective rationality: piercing the veil of obviousness.

PubMed

Colman, Andrew M; Pulford, Briony D; Rose, Jo

2008-06-01

The experiments reported in our target article provide strong evidence of collective utility maximization, and the findings suggest that team reasoning should now be included among the social value orientations used in cognitive and social psychology. Evidential decision theory offers a possible alternative explanation for our results but fails to predict intuitively compelling strategy choices in simple games with asymmetric team-reasoning outcomes. Although many of our experimental participants evidently used team reasoning, some appear to have ignored the other players' expected strategy choices and used lower-level, nonstrategic forms of reasoning. Standard payoff transformations cannot explain the experimental findings, nor team reasoning in general, without an unrealistic assumption that players invariably reason nonstrategically.

Liquid Propellant Blast Yields for Delta IV Heavy Vehicles

DTIC Science & Technology

2010-07-01

explode simultaneously, up to 1.4 million lb of liquid oxygen and liquid hydrogen (LO2/ LH2 ) may be involved and at least partially contribute to the...in the third so as to prevent them from contributing to the blast yield. Since the PYRO LO2/ LH2 yield model was originally developed using data from...that mixing interfaces between the LO2 and LH2 tanks for all three CBCs occur simultaneously, then a reasonable argument can be made for all three

Explaining the Alluring Influence of Neuroscience Information on Scientific Reasoning

ERIC Educational Resources Information Center

Rhodes, Rebecca E.; Rodriguez, Fernando; Shah, Priti

2014-01-01

Previous studies have investigated the influence of neuroscience information or images on ratings of scientific evidence quality but have yielded mixed results. We examined the influence of neuroscience information on evaluations of flawed scientific studies after taking into account individual differences in scientific reasoning skills, thinking…

Using electrical impedance to predict catheter-endocardial contact during RF cardiac ablation.

PubMed

Cao, Hong; Tungjitkusolmun, Supan; Choy, Young Bin; Tsai, Jang-Zern; Vorperian, Vicken R; Webster, John G

2002-03-01

During radio-frequency (RF) cardiac catheter ablation, there is little information to estimate the contact between the catheter tip electrode and endocardium because only the metal electrode shows up under fluoroscopy. We present a method that utilizes the electrical impedance between the catheter electrode and the dispersive electrode to predict the catheter tip electrode insertion depth into the endocardium. Since the resistivity of blood differs from the resistivity of the endocardium, the impedance increases as the catheter tip lodges deeper in the endocardium. In vitro measurements yielded the impedance-depth relations at 1, 10, 100, and 500 kHz. We predict the depth by spline curve interpolation using the obtained calibration curve. This impedance method gives reasonably accurate predicted depth. We also evaluated alternative methods, such as impedance difference and impedance ratio.

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.

[Effect of different fertilization treatments on yield and secondary metabolites of Codonopsis pilosula].

PubMed

Hu, Jia-Dong; Mao, Ge; Zhang, Zhi-Wei; Ma, Cun-de; Liang, Zong-Suo; Xia, Guang-Dong; Dong, Juan-E

2017-08-01

The research studies the effect of different fertilization treatments on yield and accumulation of secondary metabolites of Codonopsis pilosula by using single factor randomized block design, in order to ensure reasonable harvesting time and fertilization ratio, and provide the basis for standardized cultivation of C. pilosula. According to the clustering results, the nitrogen fertilizer benefitted for the improvement of root diameter and biomass of C. pilosula. The phosphate fertilizer could promote the content of C. pilosula polysaccharide. The organic fertilizers could increase the content of lobetyolin. With the time going on, C. pilosula's yield, polysaccharide and ehanol-soluble extracts increased while the content of lobetyolin decreased. According to various factors, October is a more reasonable harvest period. Organic fertilizers are more helpful to the yield and accumulation of secondary metabolites of C. pilosula. Copyright© by the Chinese Pharmaceutical Association.

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

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.

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.

Watch what happens: using a web-based multimedia platform to enhance intraoperative learning and development of clinical reasoning.

PubMed

Fingeret, Abbey L; Martinez, Rebecca H; Hsieh, Christine; Downey, Peter; Nowygrod, Roman

2016-02-01

We aim to determine whether observed operations or internet-based video review predict improved performance in the surgery clerkship. A retrospective review of students' usage of surgical videos, observed operations, evaluations, and examination scores were used to construct an exploratory principal component analysis. Multivariate regression was used to determine factors predictive of clerkship performance. Case log data for 231 students revealed a median of 25 observed cases. Students accessed the web-based video platform a median of 15 times. Principal component analysis yielded 4 factors contributing 74% of the variability with a Kaiser-Meyer-Olkin coefficient of .83. Multivariate regression predicted shelf score (P < .0001), internal clinical skills examination score (P < .0001), subjective evaluations (P < .001), and video website utilization (P < .001) but not observed cases to be significantly associated with overall performance. Utilization of a web-based operative video platform during a surgical clerkship is an independently associated with improved clinical reasoning, fund of knowledge, and overall evaluation. Thus, this modality can serve as a useful adjunct to live observation. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

NASA Technical Reports Server (NTRS)

Cornhill, Dennis; Bharadwaj, Raj; Mylaraswamy, Dinkar

2013-01-01

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

The theory of reasoned action as parallel constraint satisfaction: towards a dynamic computational model of health behavior.

PubMed

Orr, Mark G; Thrush, Roxanne; Plaut, David C

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual's pre-existing belief structure and the beliefs of others in the individual's social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics.

The Theory of Reasoned Action as Parallel Constraint Satisfaction: Towards a Dynamic Computational Model of Health Behavior

PubMed Central

Orr, Mark G.; Thrush, Roxanne; Plaut, David C.

2013-01-01

The reasoned action approach, although ubiquitous in health behavior theory (e.g., Theory of Reasoned Action/Planned Behavior), does not adequately address two key dynamical aspects of health behavior: learning and the effect of immediate social context (i.e., social influence). To remedy this, we put forth a computational implementation of the Theory of Reasoned Action (TRA) using artificial-neural networks. Our model re-conceptualized behavioral intention as arising from a dynamic constraint satisfaction mechanism among a set of beliefs. In two simulations, we show that constraint satisfaction can simultaneously incorporate the effects of past experience (via learning) with the effects of immediate social context to yield behavioral intention, i.e., intention is dynamically constructed from both an individual’s pre-existing belief structure and the beliefs of others in the individual’s social context. In a third simulation, we illustrate the predictive ability of the model with respect to empirically derived behavioral intention. As the first known computational model of health behavior, it represents a significant advance in theory towards understanding the dynamics of health behavior. Furthermore, our approach may inform the development of population-level agent-based models of health behavior that aim to incorporate psychological theory into models of population dynamics. PMID:23671603

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.

MCAT Verbal Reasoning score: less predictive of medical school performance for English language learners.

PubMed

Winegarden, Babbi; Glaser, Dale; Schwartz, Alan; Kelly, Carolyn

2012-09-01

Medical College Admission Test (MCAT) scores are widely used as part of the decision-making process for selecting candidates for admission to medical school. Applicants who learned English as a second language may be at a disadvantage when taking tests in their non-native language. Preliminary research found significant differences between English language learners (ELLs), applicants who learned English after the age of 11 years, and non-ELL examinees on the Verbal Reasoning (VR) sub-test of the MCAT. The purpose of this study was to determine if relationships between VR sub-test scores and measures of medical school performance differed between ELL and non-ELL students. Scores on the MCAT VR sub-test and student performance outcomes (grades, examination scores, and markers of distinction and difficulty) were extracted from University of California San Diego School of Medicine admissions files and the Association of American Medical Colleges database for 924 students who matriculated in 1998-2005 (graduation years 2002-2009). Regression models were fitted to determine whether MCAT VR sub-test scores predicted medical school performance similarly for ELLs and non-ELLs. For several outcomes, including pre-clerkship grades, academic distinction, US Medical Licensing Examination Step 2 Clinical Knowledge scores and two clerkship shelf examinations, ELL status significantly affects the ability of the VR score to predict performance. Higher correlations between VR score and medical school performance emerged for non-ELL students than for ELL students for each of these outcomes. The MCAT VR score should be used with discretion when assessing ELL applicants for admission to medical school. © Blackwell Publishing Ltd 2012.

Moral Bioenhancement, Freedom and Reason.

PubMed

Persson, Ingmar; Savulescu, Julian

2016-01-01

In this paper we reply to the most important objections to our advocacy of moral enhancement by biomedical means - moral bioenhancement - that John Harris advances in his new book How to be Good . These objections are to effect that such moral enhancement undercuts both moral reasoning and freedom. The latter objection is directed more specifically at what we have called the God Machine, a super-duper computer which predicts our decisions and prevents decisions to perpertrate morally atrocious acts. In reply, we argue first that effective moral bioenhancement presupposes moral reasoning rather than undermines it. Secondly, that the God Machine would leave us with extensive freedom and that the restrictions it imposes on it are morally justified by the prevention of harm to victims.

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.

Reasons for drinking as predictors of alcohol involvement one year later among HIV-infected individuals with and without hepatitis C.

PubMed

Elliott, Jennifer C; Stohl, Malka; Aharonovich, Efrat; O'Leary, Ann; Hasin, Deborah S

2016-12-01

Heavy drinking can be harmful for individuals with HIV, particularly those coinfected with hepatitis C virus (HCV). HIV patients' reasons for drinking predict short-term alcohol involvement, but whether they predict longer-term involvement is unknown. Also, it remains unknown whether these motives are differentially predictive for HIV monoinfected and HIV/HCV coinfected patients. HIV-infected heavy drinkers (n = 254) participated in a randomized trial of brief alcohol interventions, 236 (92.9%) of whom reported on baseline motives and alcohol involvement 12 months later (77.1% male, 94.9% minority, 30.6% with HCV). Greater endorsement of baseline drinking to cope with negative affect predicted greater alcohol dependence symptoms at 12 months (incident rate ratio [IRR] = 1.80, p < 0.05), while greater endorsement of baseline drinking due to social pressure predicted fewer drinks consumed at 12 months (IRR = 0.67, p < 0.05). Coping and social reasons were both predictive for HIV monoinfected patients, whereas only coping reasons were predictive for HIV/HCV coinfected patients. Drinking for coping and social reasons predict alcohol involvement 12 months later; however, social reasons may only be important for HIV monoinfected patients. Understanding patient reasons for drinking may help predict patient risk up to a year later. KEY MESSAGES Among HIV patients, drinking motives predict alcohol involvement 12 months later. For HIV monoinfected patients, drinking to cope and drinking for social reasons predict 12-month alcohol involvement. For HIV/Hepatitis C coinfected patients, coping (but not social) motives predict 12-month alcohol involvement.

The probability heuristics model of syllogistic reasoning.

PubMed

Chater, N; Oaksford, M

1999-03-01

A probability heuristic model (PHM) for syllogistic reasoning is proposed. An informational ordering over quantified statements suggests simple probability based heuristics for syllogistic reasoning. The most important is the "min-heuristic": choose the type of the least informative premise as the type of the conclusion. The rationality of this heuristic is confirmed by an analysis of the probabilistic validity of syllogistic reasoning which treats logical inference as a limiting case of probabilistic inference. A meta-analysis of past experiments reveals close fits with PHM. PHM also compares favorably with alternative accounts, including mental logics, mental models, and deduction as verbal reasoning. Crucially, PHM extends naturally to generalized quantifiers, such as Most and Few, which have not been characterized logically and are, consequently, beyond the scope of current mental logic and mental model theories. Two experiments confirm the novel predictions of PHM when generalized quantifiers are used in syllogistic arguments. PHM suggests that syllogistic reasoning performance may be determined by simple but rational informational strategies justified by probability theory rather than by logic. Copyright 1999 Academic Press.

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

Moral Reasoning and Personality Variables in Relation to Moral Behavior.

ERIC Educational Resources Information Center

Dras, Stephen R.; And Others

The relation of moral reasoning to moral behavior has been the subject of a substantial number of empirical studies; it may be more productive to employ a configuration of characteristics to predict moral behavior. To investigate the relation of moral reasoning and personality variables to moral behavior, 74 undergraduates, 30 males and 44…

Adolescents' Reported Reasons for Alcohol and Marijuana Use as Predictors of Substance Use and Problems in Adulthood*

PubMed Central

Patrick, Megan E.; Schulenberg, John E.; O'malley, Patrick M.; Johnston, Lloyd D.; Bachman, Jerald G.

2011-01-01

Objective: The aim of this study was to examine how reasons for substance use at age 18 relate to alcohol and marijuana use at ages 18 and 35 and to symptoms of alcohol use disorder and marijuana use disorder at age 35. Method: Bivariate correlation and multivariate regression analyses were conducted to examine the prediction of substance use and misuse by social/recreational, coping with negative affect, compulsive, and drug effect reasons for alcohol and marijuana use. Control variables included gender, race/ethnicity, parent education, and previous substance use (for age 35 outcomes). Results: Social/recreational, coping, and drug effect reasons for drinking predicted symptoms of alcohol use disorder 17 years later. Reasons for marijuana use were generally associated only with concurrent marijuana use; an exception was that drug effect reasons predicted marijuana use disorder at age 35. Conclusions: The long-term longitudinal predictive power of reasons for alcohol use (and, to a lesser extent, for marijuana use) suggests that adolescents' self-reported reasons, in particular those involving regulating emotions and experiences, may be early risk factors for continued use and misuse of substances into adulthood. PMID:21138717

Inductive reasoning.

PubMed

Hayes, Brett K; Heit, Evan; Swendsen, Haruka

2010-03-01

Inductive reasoning entails using existing knowledge or observations to make predictions about novel cases. We review recent findings in research on category-based induction as well as theoretical models of these results, including similarity-based models, connectionist networks, an account based on relevance theory, Bayesian models, and other mathematical models. A number of touchstone empirical phenomena that involve taxonomic similarity are described. We also examine phenomena involving more complex background knowledge about premises and conclusions of inductive arguments and the properties referenced. Earlier models are shown to give a good account of similarity-based phenomena but not knowledge-based phenomena. Recent models that aim to account for both similarity-based and knowledge-based phenomena are reviewed and evaluated. Among the most important new directions in induction research are a focus on induction with uncertain premise categories, the modeling of the relationship between inductive and deductive reasoning, and examination of the neural substrates of induction. A common theme in both the well-established and emerging lines of induction research is the need to develop well-articulated and empirically testable formal models of induction. Copyright © 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website. Copyright © 2010 John Wiley & Sons, Ltd.

Full-Waveform Envelope Templates for Low Magnitude Discrimination and Yield Estimation at Local and Regional Distances with Application to the North Korean Nuclear Tests

NASA Astrophysics Data System (ADS)

Yoo, S. H.

2017-12-01

Monitoring seismologists have successfully used seismic coda for event discrimination and yield estimation for over a decade. In practice seismologists typically analyze long-duration, S-coda signals with high signal-to-noise ratios (SNR) at regional and teleseismic distances, since the single back-scattering model reasonably predicts decay of the late coda. However, seismic monitoring requirements are shifting towards smaller, locally recorded events that exhibit low SNR and short signal lengths. To be successful at characterizing events recorded at local distances, we must utilize the direct-phase arrivals, as well as the earlier part of the coda, which is dominated by multiple forward scattering. To remedy this problem, we have developed a new hybrid method known as full-waveform envelope template matching to improve predicted envelope fits over the entire waveform and account for direct-wave and early coda complexity. We accomplish this by including a multiple forward-scattering approximation in the envelope modeling of the early coda. The new hybrid envelope templates are designed to fit local and regional full waveforms and produce low-variance amplitude estimates, which will improve yield estimation and discrimination between earthquakes and explosions. To demonstrate the new technique, we applied our full-waveform envelope template-matching method to the six known North Korean (DPRK) underground nuclear tests and four aftershock events following the September 2017 test. We successfully discriminated the event types and estimated the yield for all six nuclear tests. We also applied the same technique to the 2015 Tianjin explosions in China, and another suspected low-yield explosion at the DPRK test site on May 12, 2010. Our results show that the new full-waveform envelope template-matching method significantly improves upon longstanding single-scattering coda prediction techniques. More importantly, the new method allows monitoring seismologists to extend

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.

Shear-transformation-zone theory of yielding in athermal amorphous materials

DOE PAGES

Langer, J. S.

2015-07-22

Yielding transitions in athermal amorphous materials undergoing steady-state shear flow resemble critical phenomena. Historically, they have been described by the Herschel-Bulkley rheological formula, which implies singular behaviors at yield points. In this paper, I examine this class of phenomena using an elementary version of the thermodynamic shear-transformation-zone (STZ) theory, focusing on the role of the effective disorder temperature, and paying special attention to scaling and dimensional arguments. I find a wide variety of Herschel-Bulkley-like rheologies but, for fundamental reasons not specific to the STZ theory, conclude that the yielding transition is not truly critical. Specifically, for realistic many-body models withmore » short-range interactions, there is a correlation length that grows rapidly but ultimately saturates near the yield point.« less

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.

Recent climate variability and its impacts on soybean yields in Southern Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Danielle Barros; Rao, V. Brahmananda

2011-08-01

Recent climate variability in rainfall, temperatures (maximum and minimum), and the diurnal temperature range is studied with emphasis on its influence over soybean yields in southern Brazil, during 1969 to 2002. The results showed that the soybean ( Glycine max L. Merril) yields are more affected by changes in temperature during summer, while changes in rainfall are more important during the beginning of plantation and at its peak of development. Furthermore, soybean yields in Paraná are more sensitive to rainfall variations, while soybean yields in the Rio Grande do Sul are more sensitive to variations in temperature. Effects of interannual climatic variability on soybean yields are evaluated through three agro-meteorological models: additive Stewart, multiplicative Rao, and multiplicative Jensen. The Jensen model is able to reproduce the interannual behavior of soybean yield reasonably well.

Improving real-time efficiency of case-based reasoning for medical diagnosis.

PubMed

Park, Yoon-Joo

2014-01-01

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. Some previous researches overcome this problem by clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new case-based reasoning method called the Clustering-Merging CBR (CM-CBR) which produces similar level of predictive performances than the conventional CBR with spending significantly less computational cost.

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

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

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

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

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.

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

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing.

PubMed

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This

Perception of Risk and Terrorism-Related Behavior Change: Dual Influences of Probabilistic Reasoning and Reality Testing

PubMed Central

Denovan, Andrew; Dagnall, Neil; Drinkwater, Kenneth; Parker, Andrew; Clough, Peter

2017-01-01

The present study assessed the degree to which probabilistic reasoning performance and thinking style influenced perception of risk and self-reported levels of terrorism-related behavior change. A sample of 263 respondents, recruited via convenience sampling, completed a series of measures comprising probabilistic reasoning tasks (perception of randomness, base rate, probability, and conjunction fallacy), the Reality Testing subscale of the Inventory of Personality Organization (IPO-RT), the Domain-Specific Risk-Taking Scale, and a terrorism-related behavior change scale. Structural equation modeling examined three progressive models. Firstly, the Independence Model assumed that probabilistic reasoning, perception of risk and reality testing independently predicted terrorism-related behavior change. Secondly, the Mediation Model supposed that probabilistic reasoning and reality testing correlated, and indirectly predicted terrorism-related behavior change through perception of risk. Lastly, the Dual-Influence Model proposed that probabilistic reasoning indirectly predicted terrorism-related behavior change via perception of risk, independent of reality testing. Results indicated that performance on probabilistic reasoning tasks most strongly predicted perception of risk, and preference for an intuitive thinking style (measured by the IPO-RT) best explained terrorism-related behavior change. The combination of perception of risk with probabilistic reasoning ability in the Dual-Influence Model enhanced the predictive power of the analytical-rational route, with conjunction fallacy having a significant indirect effect on terrorism-related behavior change via perception of risk. The Dual-Influence Model possessed superior fit and reported similar predictive relations between intuitive-experiential and analytical-rational routes and terrorism-related behavior change. The discussion critically examines these findings in relation to dual-processing frameworks. This

Bayesian processing of context-dependent text: reasons for appointments can improve detection of influenza.

PubMed

Alemi, Farrokh; Torii, Manabu; Atherton, Martin J; Pattie, David C; Cox, Kenneth L

2012-01-01

This article aims to examine whether words listed in reasons for appointments could effectively predict laboratory-verified influenza cases in syndromic surveillance systems. Data were collected from the Armed Forces Health Longitudinal Technological Application medical record system. We used 2 algorithms to combine the impact of words within reasons for appointments: Dependent (DBSt) and Independent (IBSt) Bayesian System. We used receiver operating characteristic curves to compare the accuracy of these 2 methods of processing reasons for appointments against current and previous lists of diagnoses used in the Department of Defense's syndromic surveillance system. We examined 13,096 cases, where the results of influenza tests were available. Each reason for an appointment had an average of 3.5 words (standard deviation = 2.2 words). There was no difference in performance of the 2 algorithms. The area under the curve for IBSt was 0.58 and for DBSt was 0.56. The difference was not statistically significant (McNemar statistic = 0.0054; P = 0.07). These data suggest that reasons for appointments can improve the accuracy of lists of diagnoses in predicting laboratory-verified influenza cases. This study recommends further exploration of the DBSt algorithm and reasons for appointments in predicting likely influenza cases.

Gender differences in condom use prediction with Theory of Reasoned Action and Planned Behaviour: the role of self-efficacy and control.

PubMed

Muñoz-Silva, A; Sánchez-García, M; Nunes, C; Martins, A

2007-10-01

There is much evidence that demonstrates that programs and interventions based on the theoretical models of the Theory of Reasoned Action (TRA) and the Theory of Planned Behaviour (TPB) have been effective in the prevention of the sexual transmission of HIV. The objective of this work is to compare the effectiveness of both models in the prediction of condom use, distinguishing two components inside the variable Perceived Behavioural Control of the TPB model: self-efficacy and control. The perspective of gender differences is also added. The study was carried out in a sample of 601 Portuguese and Spanish university students. The results show that the females have a higher average in all the TPB variables than males, except in the frequency of condom use: females request the use of condoms less frequently than males. On the other hand, for both females and males the TPB model predicts better condom-use intention than the TRA. However there are no differences between the two models in relation to the prediction of condom-use behaviour. For prediction of intention, the most outstanding variable among females is attitude, while among males they are subjective norm and self-efficacy. Finally, we analyze the implications of these data from a theoretical and practical point of view.

Reasoning from an incompatibility: False dilemma fallacies and content effects.

PubMed

Brisson, Janie; Markovits, Henry; Robert, Serge; Schaeken, Walter

2018-03-23

In the present studies, we investigated inferences from an incompatibility statement. Starting with two propositions that cannot be true at the same time, these inferences consist of deducing the falsity of one from the truth of the other or deducing the truth of one from the falsity of the other. Inferences of this latter form are relevant to human reasoning since they are the formal equivalent of a discourse manipulation called the false dilemma fallacy, often used in politics and advertising in order to force a choice between two selected options. Based on research on content-related variability in conditional reasoning, we predicted that content would have an impact on how reasoners treat incompatibility inferences. Like conditional inferences, they present two invalid forms for which the logical response is one of uncertainty. We predicted that participants would endorse a smaller proportion of the invalid incompatibility inferences when more counterexamples are available. In Study 1, we found the predicted pattern using causal premises translated into incompatibility statements with many and few counterexamples. In Study 2A, we replicated the content effects found in Study 1, but with premises for which the incompatibility statement is a non-causal relation between classes. These results suggest that the tendency to fall into the false dilemma fallacy is modulated by the background knowledge of the reasoner. They also provide additional evidence on the link between semantic information retrieval and deduction.

Heuristic Reasoning in Chemistry: Making decisions about acid strength

NASA Astrophysics Data System (ADS)

McClary, LaKeisha; Talanquer, Vicente

2011-07-01

The characterization of students' reasoning strategies is of central importance in the development of instructional strategies that foster meaningful learning. In particular, the identification of shortcut reasoning procedures (heuristics) used by students to reduce cognitive load can help us devise strategies to facilitate the development of more analytical ways of thinking. The central goal of this qualitative study was thus to investigate heuristic reasoning as used by organic chemistry college students, focusing our attention on their ability to predict the relative acid strength of chemical compounds represented using explicit composition and structural features (i.e., structural formulas). Our results indicated that many study participants relied heavily on one or more of the following heuristics to make most of their decisions: reduction, representativeness, and lexicographic. Despite having visual access to reach structural information about the substances included in each ranking task, many students relied on isolated composition features to make their decisions. However, the specific characteristics of the tasks seemed to trigger heuristic reasoning in different ways. Although the use of heuristics allowed students to simplify some components of the ranking tasks and generate correct responses, it often led them astray. Very few study participants predicted the correct trends based on scientifically acceptable arguments. Our results suggest the need for instructional interventions that explicitly develop college chemistry students' abilities to monitor their thinking and evaluate the effectiveness of analytical versus heuristic reasoning strategies in different contexts.

Atomic Oxygen Erosion Yield Dependence Upon Texture Development in Polymers

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Loftus, Ryan J.; Miller, Sharon K.

2016-01-01

The atomic oxygen erosion yield (volume of a polymer that is lost due to oxidation per incident atom) of polymers is typically assumed to be reasonably constant with increasing fluence. However polymers containing ash or inorganic pigments, tend to have erosion yields that decrease with fluence due to an increasing presence of protective particles on the polymer surface. This paper investigates two additional possible causes for erosion yields of polymers that are dependent upon atomic oxygen. These are the development of surface texture which can cause the erosion yield to change with fluence due to changes in the aspect ratio of the surface texture that develops and polymer specific atomic oxygen interaction parameters. The surface texture development under directed hyperthermal attack produces higher aspect ratio surface texture than isotropic thermal energy atomic oxygen attack. The fluence dependence of erosion yields is documented for low Kapton H (DuPont, Wilmington, DE) effective fluences for a variety of polymers under directed hyperthermal and isotropic thermal energy attack.

Calculation of K-shell fluorescence yields for low-Z elements

NASA Astrophysics Data System (ADS)

Nekkab, M.; Kahoul, A.; Deghfel, B.; Aylikci, N. Küp; Aylikçi, V.

2015-03-01

The analytical methods based on X-ray fluorescence are advantageous for practical applications in a variety of fields including atomic physics, X-ray fluorescence surface chemical analysis and medical research and so the accurate fluorescence yields (ωK) are required for these applications. In this contribution we report a new parameters for calculation of K-shell fluorescence yields (ωK) of elements in the range of 11≤Z≤30. The experimental data are interpolated by using the famous analytical function (ωk/(1 -ωk)) 1 /q (were q=3, 3.5 and 4) vs Z to deduce the empirical K-shell fluorescence yields. A comparison is made between the results of the procedures followed here and those theoretical and other semi-empirical fluorescence yield values. Reasonable agreement was typically obtained between our result and other works.

Reasoning and dyslexia: is visual memory a compensatory resource?

PubMed

Bacon, Alison M; Handley, Simon J

2014-11-01

Effective reasoning is fundamental to problem solving and achievement in education and employment. Protocol studies have previously suggested that people with dyslexia use reasoning strategies based on visual mental representations, whereas non-dyslexics use abstract verbal strategies. This research presents converging evidence from experimental and individual differences perspectives. In Experiment 1, dyslexic and non-dyslexic participants were similarly accurate on reasoning problems, but scores on a measure of visual memory ability only predicted reasoning accuracy for dyslexics. In Experiment 2, a secondary task loaded visual memory resources during concurrent reasoning. Dyslexics were significantly less accurate when reasoning under conditions of high memory load and showed reduced ability to subsequently recall the visual stimuli, suggesting that the memory and reasoning tasks were competing for the same visual cognitive resource. The results are consistent with an explanation based on limitations in the verbal and executive components of working memory in dyslexia and the use of compensatory visual strategies for reasoning. There are implications for cognitive activities that do not readily support visual thinking, whether in education, employment or less formal everyday settings. Copyright © 2014 John Wiley & Sons, Ltd.

Why stop self-injuring? Development of the reasons to stop self-injury questionnaire.

PubMed

Turner, Brianna J; Chapman, Alexander L; Gratz, Kim L

2014-01-01

We developed a measure of reasons to refrain from nonsuicidal self-injury (NSSI), the Reasons to Stop Self-Injury Questionnaire (RSSIQ), and examined how such reasons are associated with vulnerability versus resiliency for NSSI. Following qualitative item generation, we explored the factor structure, reliability, and convergent validity of the RSSIQ in 218 self-injuring undergraduates. In Study 2, we confirmed the hierarchical factor structure in 146 self-injuring individuals. In Study 3, we examined the incremental predictive validity of the RSSIQ. These studies resulted in a 40-item inventory with nine subscales and two higher-order factors. Resiliency-related reasons to stop NSSI were associated with greater hopefulness, social support, and adaptive coping, and prospectively protected against NSSI 3 months later, while vulnerability-related reasons were associated with greater psychopathology and dysfunctional coping, and predicted more chronic and severe NSSI. These studies, and the RSSIQ, can enhance the assessment and treatment of NSSI by clarifying motivations to stop NSSI.

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

Self-Reported Reasons for Smoking: Predicting Abstinence and Implications for Smoking Cessation Treatments Among Those With a Psychotic Disorder.

PubMed

Clark, Vanessa; Baker, Amanda; Lewin, Terry; Richmond, Robyn; Kay-Lambkin, Frances; Filia, Sacha; Castle, David; Williams, Jill; Todd, Juanita

2017-01-01

People living with a psychotic illness have higher rates of cigarette smoking and face unique barriers to quitting compared to the general population. We examined whether self-reported reasons for smoking are useful predictors of successful quit attempts among people with psychosis. As part of a randomized controlled trial addressing smoking and cardiovascular disease risk behaviors among people with psychosis, self-reported reasons for smoking were assessed at baseline (n = 235), 15 weeks (n = 151), and 12 months (n = 139). Three factors from the Reasons for Smoking Questionnaire (Coping, Physiological, and Stimulation/Activation) were entered into a model to predict short- and long-term abstinence. The relationship between these factors and mental health symptoms were also assessed. Participants scoring higher on the Stimulation/Activation factor (control of weight, enjoyment, concentration, and "peps me up") at baseline were just less than half as likely to be abstinent at 15 weeks. Female participants were five times more likely to abstinent at 15 weeks, and those with a higher global functioning at baseline were 5% more likely to be abstinent. There was a positive correlation between changes over time in the Stimulation/Activation factor from baseline to 12-month follow-up and the Brief Psychiatric Rating Scale total score at 12-month follow-up. This indicates that increasingly higher endorsement of the factor was associated with more psychological symptoms. There was also a negative correlation between the change over time in the Stimulation/Activation factor and global functioning at 12 months, indicating that increasingly higher endorsement of the factor led to lower global assessment of functioning. The Stimulation/Activation factor may be particularly important to assess and address among smokers with psychosis. It is recommended that further research use the Reasons for Smoking Questionnaire among smokers with psychosis as a clinical tool to identify

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...

An entropy and viscosity corrected potential method for rotor performance prediction

NASA Technical Reports Server (NTRS)

Bridgeman, John O.; Strawn, Roger C.; Caradonna, Francis X.

1988-01-01

An unsteady Full-Potential Rotor code (FPR) has been enhanced with modifications directed at improving its drag prediction capability. The shock generated entropy has been included to provide solutions comparable to the Euler equations. A weakly interacted integral boundary layer has also been coupled to FPR in order to estimate skin-friction drag. Pressure distributions, shock positions, and drag comparisons are made with various data sets derived from two-dimensional airfoil, hovering, and advancing high speed rotor tests. In all these comparisons, the effect of the nonisentropic modification improves (i.e., weakens) the shock strength and wave drag. In addition, the boundary layer method yields reasonable estimates of skin-friction drag. Airfoil drag and hover torque data comparisons are excellent, as are predicted shock strength and positions for a high speed advancing rotor.

The effect of iconicity of visual displays on statistical reasoning: evidence in favor of the null hypothesis.

PubMed

Sirota, Miroslav; Kostovičová, Lenka; Juanchich, Marie

2014-08-01

Knowing which properties of visual displays facilitate statistical reasoning bears practical and theoretical implications. Therefore, we studied the effect of one property of visual diplays - iconicity (i.e., the resemblance of a visual sign to its referent) - on Bayesian reasoning. Two main accounts of statistical reasoning predict different effect of iconicity on Bayesian reasoning. The ecological-rationality account predicts a positive iconicity effect, because more highly iconic signs resemble more individuated objects, which tap better into an evolutionary-designed frequency-coding mechanism that, in turn, facilitates Bayesian reasoning. The nested-sets account predicts a null iconicity effect, because iconicity does not affect the salience of a nested-sets structure-the factor facilitating Bayesian reasoning processed by a general reasoning mechanism. In two well-powered experiments (N = 577), we found no support for a positive iconicity effect across different iconicity levels that were manipulated in different visual displays (meta-analytical overall effect: log OR = -0.13, 95% CI [-0.53, 0.28]). A Bayes factor analysis provided strong evidence in favor of the null hypothesis-the null iconicity effect. Thus, these findings corroborate the nested-sets rather than the ecological-rationality account of statistical reasoning.

Pursuing Improvement in Clinical Reasoning: The Integrated Clinical Education Theory.

PubMed

Jessee, Mary Ann

2018-01-01

The link between clinical education and development of clinical reasoning is not well supported by one theoretical perspective. Learning to reason during clinical education may be best achieved in a supportive sociocultural context of nursing practice that maximizes reasoning opportunities and facilitates discourse and meaningful feedback. Prelicensure clinical education seldom incorporates these critical components and thus may fail to directly promote clinical reasoning skill. Theoretical frameworks supporting the development of clinical reasoning during clinical education were evaluated. Analysis of strengths and gaps in each framework's support of clinical reasoning development was conducted. Commensurability of philosophical underpinnings was confirmed, and complex relationships among key concepts were elucidated. Six key concepts and three tenets comprise an explanatory predictive theory-the integrated clinical education theory (ICET). ICET provides critical theoretical support for inquiry and action to promote clinical education that improves development of clinical reasoning skill. [J Nurs Educ. 2018;57(1):7-13.]. Copyright 2018, SLACK Incorporated.

Reasons for limiting drinking in an HIV primary care sample

PubMed Central

Elliott, Jennifer C.; Aharonovich, Efrat; Hasin, Deborah

2015-01-01

BACKGROUND Heavy drinking among individuals with HIV is associated with major health concerns (liver disease, medication nonadherence, immune functioning), but little is known about cognitive-motivational factors involved in alcohol consumption in this population, particularly reasons for limiting drinking. METHODS Urban HIV primary care patients (N=254; 78.0% male; 94.5% African American or Hispanic) in a randomized trial of brief drinking-reduction interventions reported on reasons for limiting drinking, alcohol consumption, and alcohol dependence symptoms prior to intervention. RESULTS Exploratory factor analysis indicated three main domains of reasons for limiting drinking: social reasons (e.g., responsibility to family), lifestyle reasons (e.g., religious/moral reasons), and impairment concerns (e.g., hangovers). These factors evidenced good internal consistency (αs=0.76–0.86). Higher scores on social reasons for limiting drinking were associated with lower typical quantity, maximum quantity, and binge frequency (ps<0.01), and higher scores on lifestyle reasons were associated with lower maximum quantity, binge frequency, and intoxication frequency (ps<0.01). In contrast, higher scores on impairment concerns were associated with more frequent drinking and intoxication, and higher risk of alcohol dependence (ps<0.05), likely because dependent drinkers are more familiar with alcohol-induced impairment. CONCLUSIONS The current study is the first to explore reasons for limiting drinking among individuals with HIV, and how these reasons relate to alcohol involvement. This study yields a scale that can be used to assess reasons for limiting drinking among HIV-positive drinkers, and provides information that can be used to enhance interventions with this population. Discussing social and lifestyle reasons for limiting drinking among less extreme drinkers may support and validate these patients’ efforts to limit engagement in heavy drinking; discussion of

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.

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.

Making Online Learning Personal: Evolution, Evidentiary Reasoning, and Self-Regulation in an Online Curriculum

NASA Astrophysics Data System (ADS)

Marsteller, Robert B.

An online curriculum about biological evolution was designed according to the Promoting Evidentiary Reasoning and Self-regulation Online (PERSON) theoretical framework. PERSON is an attempt to develop online science instruction focused on supporting evidentiary reasoning and self-regulation. An efficacy study was conducted with 80 suburban high school biology students using a design-based research approach to develop a curriculum to promote biological evolution understandings, evidentiary reasoning, and self-regulation. Data sources and instruments included (1) the Biological Evolution Assessment Measurement (BEAM); (2) the modified Motivated Strategies for Learning Questionnaire (MSLQ); (3) discussion forum posts; (4) formative assessments of evidence based reasoning; (5) Prediction, Monitoring, and Reflection forms (PMR); (6) the Online Instruction Questionnaire; and (7) field notes. Findings revealed that BEAM posttest scores were significantly greater than pretest scores for items designed to measure biological evolution content knowledge and evidentiary reasoning. Students tracked in a lower level biology course showed improvement in biological evolution understandings and evidentiary reasoning. It was found that performance on daily evidentiary reasoning tasks strongly predicted BEAM posttest scores. However, findings revealed that students did not meet local standards for performance on items designed to measure evidentiary reasoning. Students expressed a variety of opinions about their learning experiences with the online curriculum. Some students expressed a definite preference for traditional learning environments, while others expressed a definite preference for online learning. Self-regulatory ability did not significantly predict BEAM gain scores. Further, self-regulatory ability was not demonstrably improved as a result of this intervention. Implications for designing science instruction in asynchronous online learning environments to support

Ethical Reasoning Instruction in Non-Ethics Business Courses: A Non-Intrusive Approach

ERIC Educational Resources Information Center

Wilhelm, William J.

2010-01-01

This article discusses four confirmatory studies designed to corroborate findings from prior developmental research which yielded statistically significant improvements in student moral reasoning when specific instructional strategies and content materials were utilized in non-ethics business courses by instructors not formally trained in business…

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.

Intrusion-based reasoning and depression: cross-sectional and prospective relationships.

PubMed

Berle, David; Moulds, Michelle L

2014-01-01

Intrusion-based reasoning refers to the tendency to form interpretations about oneself or a situation based on the occurrence of a negative intrusive autobiographical memory. Intrusion-based reasoning characterises post-traumatic stress disorder, but has not yet been investigated in depression. We report two studies that aimed to investigate this. In Study 1 both high (n = 42) and low (n = 28) dysphoric participants demonstrated intrusion-based reasoning. High-dysphoric individuals engaged in self-referent intrusion-based reasoning to a greater extent than did low-dysphoric participants. In Study 2 there were no significant differences in intrusion-based reasoning between currently depressed (n = 27) and non-depressed (n = 51) participants, and intrusion-based reasoning did not predict depressive symptoms at 6-month follow-up. Interestingly, previously (n = 26) but not currently (n = 27) depressed participants engaged in intrusion-based reasoning to a greater extent than never-depressed participants (n = 25), indicating the possibility that intrusion-based reasoning may serve as a "scar" from previous episodes. The implications of these findings are discussed.

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

Racial Identity and Reasons for Living in African American Female Suicide Attempters

PubMed Central

Street, Jalika C.; Taha, Farah; Jones, Ashley D.; Jones, Kamilah A.; Carr, Erika; Woods, Amanda; Woodall, Staci; Kaslow, Nadine J.

2013-01-01

The current study investigated the association between racial identity and reasons for living in African American women who have attempted suicide. Particular attention was paid to the relation between two elements of racial identity (private regard, racial centrality) and reasons for living, an alternative assessment of suicidal risk. While private regard refers to an individual’s beliefs about the African American race, racial centrality describes the importance an individual places on his or her racial identity. The sample included 82 low-income African American women, ages 18–64, who reported a suicide attempt in the past 12 months. Participants, recruited from a large, urban public hospital located in the Southeast, completed the Reasons for Living Inventory and the Multidimensional Inventory of Black Identity, which included the private regard and racial centrality subscales. Results indicated that, as predicted, higher private regard was associated with more reasons for living. Contrary to expectations, racial centrality was not correlated with reasons for living nor was there an interaction between private regard and racial centrality indicating that racial centrality did not function as a moderator in predicting participants’ reasons for living scores. Implications for culturally competent clinical interventions that target bolstering private regard are discussed. PMID:22866689

Prediction and typicality in multiverse cosmology

NASA Astrophysics Data System (ADS)

Azhar, Feraz

2014-02-01

In the absence of a fundamental theory that precisely predicts values for observable parameters, anthropic reasoning attempts to constrain probability distributions over those parameters in order to facilitate the extraction of testable predictions. The utility of this approach has been vigorously debated of late, particularly in light of theories that claim we live in a multiverse, where parameters may take differing values in regions lying outside our observable horizon. Within this cosmological framework, we investigate the efficacy of top-down anthropic reasoning based on the weak anthropic principle. We argue contrary to recent claims that it is not clear one can either dispense with notions of typicality altogether or presume typicality, in comparing resulting probability distributions with observations. We show in a concrete, top-down setting related to dark matter, that assumptions about typicality can dramatically affect predictions, thereby providing a guide to how errors in reasoning regarding typicality translate to errors in the assessment of predictive power. We conjecture that this dependence on typicality is an integral feature of anthropic reasoning in broader cosmological contexts, and argue in favour of the explicit inclusion of measures of typicality in schemes invoking anthropic reasoning, with a view to extracting predictions from multiverse scenarios.

Sharp predictions from eternal inflation patches in D-brane inflation

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

Hertog, Thomas; Janssen, Oliver, E-mail: thomas.hertog@fys.kuleuven.be, E-mail: opj202@nyu.edu

We numerically generate the six-dimensional landscape of D3-brane inflation and identify patches of eternal inflation near sufficiently flat inflection points of the potential. We show that reasonable measures that select patches of eternal inflation in the landscape yield sharp predictions for the spectral properties of primordial perturbations on observable scales. These include a scalar tilt of .936, a running of the scalar tilt −.00103, undetectably small tensors and non-Gaussianity, and no observable spatial curvature. Our results explicitly demonstrate that precision cosmology probes the combination of the statistical properties of the string landscape and the measure implied by the universe's quantummore » state.« 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

Stochastic Quantitative Reasoning for Autonomous Mission Planning

DTIC Science & Technology

2014-04-09

points. Figure 4: Linear interpolation Table 1: Wind speed prediction information (ID:0-2 for Albany, ID:3-5 for Pittston, and ID:6-8 for JFK Airport ID...Pittston, and JFK Airport in Table 1, how can we estimate a reasonable wind speed for the current location at the current time? Figure 5: Example

26 CFR 1.148-2 - General arbitrage yield restriction rules.

Code of Federal Regulations, 2010 CFR

2010-04-01

... gross proceeds of an issue that qualifies as a reasonably required reserve or replacement fund may not... indirect investment of the gross proceeds of an issue in higher yielding investments causes the bonds of... reserve or replacement fund described in paragraph (f) of this section, or as part of a minor portion...

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...

Resting state morphology predicts the effect of theta burst stimulation in false belief reasoning.

PubMed

Hartwright, Charlotte E; Hardwick, Robert M; Apperly, Ian A; Hansen, Peter C

2016-10-01

When required to represent a perspective that conflicts with one's own, functional magnetic resonance imaging (fMRI) suggests that the right ventrolateral prefrontal cortex (rvlPFC) supports the inhibition of that conflicting self-perspective. The present task dissociated inhibition of self-perspective from other executive control processes by contrasting belief reasoning-a cognitive state where the presence of conflicting perspectives was manipulated-with a conative desire state wherein no systematic conflict existed. Linear modeling was used to examine the effect of continuous theta burst stimulation (cTBS) to rvlPFC on participants' reaction times in belief and desire reasoning. It was anticipated that cTBS applied to rvlPFC would affect belief but not desire reasoning, by modulating activity in the Ventral Attention System (VAS). We further anticipated that this effect would be mediated by functional connectivity within this network, which was identified using resting state fMRI and an unbiased model-free approach. Simple reaction-time analysis failed to detect an effect of cTBS. However, by additionally modeling individual measures from within the stimulated network, the hypothesized effect of cTBS to belief (but, importantly, not desire) reasoning was demonstrated. Structural morphology within the stimulated region, rvlPFC, and right temporoparietal junction were demonstrated to underlie this effect. These data provide evidence that inconsistencies found with cTBS can be mediated by the composition of the functional network that is being stimulated. We suggest that the common claim that this network constitutes the VAS explains the effect of cTBS to this network on false belief reasoning. Hum Brain Mapp 37:3502-3514, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Top ten models constrained by b {yields} s{gamma}

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

Hewett, J.L.

1994-12-01

The radiative decay b {yields} s{gamma} is examined in the Standard Model and in nine classes of models which contain physics beyond the Standard Model. The constraints which may be placed on these models from the recent results of the CLEO Collaboration on both inclusive and exclusive radiative B decays is summarized. Reasonable bounds are found for the parameters in some cases.

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

Expectations in Counterfactual and Theory of Mind Reasoning

ERIC Educational Resources Information Center

Ferguson, Heather J.; Scheepers, Christoph; Sanford, Anthony J.

2010-01-01

During language comprehension, information about the world is exchanged and processed. Two essential ingredients of everyday cognition that are employed during language comprehension are the ability to reason counterfactually, and the ability to understand and predict other peoples' behaviour by attributing independent mental states to them…

Incorporating time and spatial-temporal reasoning into situation management

NASA Astrophysics Data System (ADS)

Jakobson, Gabriel

2010-04-01

Spatio-temporal reasoning plays a significant role in situation management that is performed by intelligent agents (human or machine) by affecting how the situations are recognized, interpreted, acted upon or predicted. Many definitions and formalisms for the notion of spatio-temporal reasoning have emerged in various research fields including psychology, economics and computer science (computational linguistics, data management, control theory, artificial intelligence and others). In this paper we examine the role of spatio-temporal reasoning in situation management, particularly how to resolve situations that are described by using spatio-temporal relations among events and situations. We discuss a model for describing context sensitive temporal relations and show have the model can be extended for spatial relations.

Relevance and Reason Relations.

PubMed

Skovgaard-Olsen, Niels; Singmann, Henrik; Klauer, Karl Christoph

2017-05-01

This paper examines precursors and consequents of perceived relevance of a proposition A for a proposition C. In Experiment 1, we test Spohn's (2012) assumption that ∆P = P(C|A) - P(C|~A) is a good predictor of ratings of perceived relevance and reason relations, and we examine whether it is a better predictor than the difference measure (P(C|A) - P(C)). In Experiment 2, we examine the effects of relevance on probabilistic coherence in Cruz, Baratgin, Oaksford, and Over's (2015) uncertain "and-to-if" inferences. The results suggest that ∆P predicts perceived relevance and reason relations better than the difference measure and that participants are either less probabilistically coherent in "and-to-if" inferences than initially assumed or that they do not follow P(if A, then C) = P(C|A) ("the Equation"). Results are discussed in light of recent results suggesting that the Equation may not hold under conditions of irrelevance or negative relevance. Copyright © 2016 Cognitive Science Society, Inc.

The influence of power and reason on young Maya children's endorsement of testimony.

PubMed

Castelain, Thomas; Bernard, Stéphane; Van der Henst, Jean-Baptiste; Mercier, Hugo

2016-11-01

Two important parenting strategies are to impose one's power and to use reasoning. The effect of these strategies on children's evaluation of testimony has received very little attention. Using the epistemic vigilance framework, we predict that when the reasoning cue is strong enough it should overcome the power cue. We test this prediction in a population for which anthropological data suggest that power is the prominent strategy while reasoning is rarely relied on in the interactions with children. In Experiment 1, 4- to 6-year-old children from a traditional Maya population are shown to endorse the testimony supported by a strong argument over that supported by a weak argument. In Experiment 2, the same participants are shown to follow the testimony of a dominant over that of a subordinate. The participants are then shown to endorse the testimony of a subordinate who provides a strong argument over that of a dominant who provides either a weak argument (Experiment 3) or no argument (Experiment 4). Thus, when the power and reasoning cues conflict, reasoning completely trumps power. © 2015 John Wiley & Sons Ltd.

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).

Relations between Inductive Reasoning and Deductive Reasoning

ERIC Educational Resources Information Center

Heit, Evan; Rotello, Caren M.

2010-01-01

One of the most important open questions in reasoning research is how inductive reasoning and deductive reasoning are related. In an effort to address this question, we applied methods and concepts from memory research. We used 2 experiments to examine the effects of logical validity and premise-conclusion similarity on evaluation of arguments.…

A decision network account of reasoning about other people's choices

PubMed Central

Jern, Alan; Kemp, Charles

2015-01-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. PMID:26010559

Belief Reasoning and Emotion Understanding in Balanced Bilingual and Language-Dominant Mexican American Young Children.

PubMed

Weimer, Amy A; Gasquoine, Philip G

2016-01-01

Belief reasoning and emotion understanding were measured among 102 Mexican American bilingual children ranging from 4 to 7 years old. All children were tested in English and Spanish after ensuring minimum comprehension in each language. Belief reasoning was assessed using 2 false and 1 true belief tasks. Emotion understanding was measured using subtests from the Test for Emotion Comprehension. The influence of family background variables of yearly income, parental education level, and number of siblings on combined Spanish and English vocabulary, belief reasoning, and emotion understanding was assessed by regression analyses. Age and emotion understanding predicted belief reasoning. Vocabulary and belief reasoning predicted emotion understanding. When the sample was divided into language-dominant and balanced bilingual groups on the basis of language proficiency difference scores, there were no significant differences on belief reasoning or emotion understanding. Language groups were demographically similar with regard to child age, parental educational level, and family income. Results suggest Mexican American language-dominant and balanced bilinguals develop belief reasoning and emotion understanding similarly.

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.

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts

NASA Astrophysics Data System (ADS)

Balasubramanian, A.; Shamsuddin, R.; Prabhakaran, B.; Sawant, A.

2017-03-01

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%) (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable

Predictive modeling of respiratory tumor motion for real-time prediction of baseline shifts.

PubMed

Balasubramanian, A; Shamsuddin, R; Prabhakaran, B; Sawant, A

2017-03-07

Baseline shifts in respiratory patterns can result in significant spatiotemporal changes in patient anatomy (compared to that captured during simulation), in turn, causing geometric and dosimetric errors in the administration of thoracic and abdominal radiotherapy. We propose predictive modeling of the tumor motion trajectories for predicting a baseline shift ahead of its occurrence. The key idea is to use the features of the tumor motion trajectory over a 1 min window, and predict the occurrence of a baseline shift in the 5 s that immediately follow (lookahead window). In this study, we explored a preliminary trend-based analysis with multi-class annotations as well as a more focused binary classification analysis. In both analyses, a number of different inter-fraction and intra-fraction training strategies were studied, both offline as well as online, along with data sufficiency and skew compensation for class imbalances. The performance of different training strategies were compared across multiple machine learning classification algorithms, including nearest neighbor, Naïve Bayes, linear discriminant and ensemble Adaboost. The prediction performance is evaluated using metrics such as accuracy, precision, recall and the area under the curve (AUC) for repeater operating characteristics curve. The key results of the trend-based analysis indicate that (i) intra-fraction training strategies achieve highest prediction accuracies (90.5-91.4%); (ii) the predictive modeling yields lowest accuracies (50-60%) when the training data does not include any information from the test patient; (iii) the prediction latencies are as low as a few hundred milliseconds, and thus conducive for real-time prediction. The binary classification performance is promising, indicated by high AUCs (0.96-0.98). It also confirms the utility of prior data from previous patients, and also the necessity of training the classifier on some initial data from the new patient for reasonable

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

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.

Academic Achievement in Physics-Chemistry: The Predictive Effect of Attitudes and Reasoning Abilities.

PubMed

Vilia, Paulo N; Candeias, Adelinda A; Neto, António S; Franco, Maria Da Glória S; Melo, Madalena

2017-01-01

Science education plays a critical role as political priority due to its fundamental importance in engaging students to pursue technological careers considered essential in modern societies, in order to face scientific development challenges. High-level achievement on science education and positive attitudes toward science constitutes a crucial challenge for formal education. Several studies indicate close relationships between students' attitudes, cognitive abilities, and academic achievement. The main purpose of this study is to analyze the impact of student's attitudes toward the school discipline of Physics and Chemistry and their reasoning abilities on academic achievement on that school subject, among Portuguese 9th grade students using the data collected during the Project Academic Performance and Development: a longitudinal study on the effects of school transitions in Portuguese students (PTDC/CPE-CED/104884/2008). The participants were 470 students (267 girls - 56.8% and 203 boys - 43.2%), aged 14-16 years old (μ = 14.3 ± 0.58). The attitude data were collected using the Attitude toward Physics-Chemistry Questionnaire (ATPCQ) and, the Reasoning Test Battery (RTB) was used to assess the students reasoning abilities. Achievement was measured using the students' quarterly (9-week) grades in the physics and chemistry subject. The relationships between the attitude dimensions toward Physics-chemistry and the reasoning dimensions and achievement in each of the three school terms were assessed by multiple regression stepwise analyses and standardized regression coefficients (β), calculated with IBM SPSS Statistics 21 software. Both variables studied proved to be significant predictor variables of school achievement. The models obtained from the use of both variables were always stronger accounting for higher proportions of student's grade variations. The results show that ATPCQ and RTB had a significantly positive relationship with student's achievement in

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.

A Reasoned Action Approach to Health Promotion

PubMed Central

Fishbein, Martin

2008-01-01

This article describes the integrative model of behavioral prediction (IM), the latest formulation of a reasoned action approach. The IM attempts to identify a limited set of variables that can account for a considerable proportion of the variance in any given behavior. More specifically, consistent with the original theory of reasoned action, the IM assumes that intentions are the immediate antecedents of behavior, but in addition, the IM recognizes that environmental factors and skills and abilities can moderate the intention-behavior relationship. Similar to the theory of planned behavior, the IM also assumes that intentions are a function of attitudes, perceived normative pressure and self-efficacy, but it views perceived normative pressure as a function of descriptive as well as of injunctive (i.e., subjective) norms. After describing the theory and addressing some of the criticisms directed at a reasoned action approach, the paper illustrates how the theory can be applied to understanding and changing health related behaviors. PMID:19015289

A reasoned action approach to health promotion.

PubMed

Fishbein, Martin

2008-01-01

This article describes the integrative model of behavioral prediction (IM), the latest formulation of a reasoned action approach. The IM attempts to identify a limited set of variables that can account for a considerable proportion of the variance in any given behavior. More specifically, consistent with the original theory of reasoned action, the IM assumes that intentions are the immediate antecedents of behavior, but in addition, the IM recognizes that environmental factors and skills and abilities can moderate the intention-behavior relationship. Similar to the theory of planned behavior, the IM also assumes that intentions are a function of attitudes, perceived normative pressure and self-efficacy, but it views perceived normative pressure as a function of descriptive as well as of injunctive (i.e., subjective) norms. After describing the theory and addressing some of the criticisms directed at a reasoned action approach, the paper illustrates how the theory can be applied to understanding and changing health related behaviors.

Reasons for limiting drinking in an HIV primary care sample.

PubMed

Elliott, Jennifer C; Aharonovich, Efrat; Hasin, Deborah S

2014-06-01

Heavy drinking among individuals with HIV is associated with major health concerns (liver disease, medication nonadherence, immune functioning), but little is known about cognitive-motivational factors involved in alcohol consumption in this population, particularly reasons for limiting drinking. Urban HIV primary care patients (N = 254; 78.0% male; 94.5% African American or Hispanic) in a randomized trial of brief drinking-reduction interventions reported on reasons for limiting drinking, alcohol consumption, and alcohol dependence symptoms prior to intervention. Exploratory factor analysis indicated 3 main domains of reasons for limiting drinking: social reasons (e.g., responsibility to family), lifestyle reasons (e.g., religious/moral reasons), and impairment concerns (e.g., hangovers). These factors evidenced good internal consistency (αs = 0.76 to 0.86). Higher scores on social reasons for limiting drinking were associated with lower typical quantity, maximum quantity, and binge frequency (ps < 0.01), and higher scores on lifestyle reasons were associated with lower maximum quantity, binge frequency, and intoxication frequency (ps < 0.01). In contrast, higher scores on impairment concerns were associated with more frequent drinking and intoxication, and higher risk of alcohol dependence (ps < 0.05), likely because dependent drinkers are more familiar with alcohol-induced impairment. The current study is the first to explore reasons for limiting drinking among individuals with HIV and how these reasons relate to alcohol involvement. This study yields a scale that can be used to assess reasons for limiting drinking among HIV-positive drinkers and provides information that can be used to enhance interventions with this population. Discussing social and lifestyle reasons for limiting drinking among less extreme drinkers may support and validate these patients' efforts to limit engagement in heavy drinking; discussion of impairment reasons for limiting drinking

Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

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

Greitzer, Frank L.; Frincke, Deborah A.

2010-09-01

The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data tomore » yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.« less

Reasoning, biases and dual processes: The lasting impact of Wason (1960).

PubMed

Evans, Jonathan St B T

2016-10-01

Wason (1960) published a relatively short experimental paper, in which he introduced the 2-4-6 problem as a test of inductive reasoning. This paper became one of the most highly cited to be published in the Quarterly Journal of Experimental Psychology and is significant for a number of reasons. First, the 2-4-6 task itself was ingenious and yielded evidence of error and bias in the intelligent participants who attempted it. Research on the 2-4-6 problem continues to the present day. More importantly, it was Wason's first paper on reasoning and one which made strong claims for bias and irrationality in a period dominated by rationalist writers like Piaget. It set in motion the study of cognitive biases in thinking and reasoning, well before the start of Tversky and Kahneman's famous heuristics and biases research programme. I also show here something for which Wason has received insufficient credit. It was Wason's work on this task and his later studies of his four card selection task that led to the first development of the dual process theory of reasoning which is so dominant in the current literature on the topic more than half a century later.

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

On the prediction of free turbulent jets with swirl using a quadratic pressure-strain model

NASA Technical Reports Server (NTRS)

Younis, Bassam A.; Gatski, Thomas B.; Speziale, Charles G.

1994-01-01

Data from free turbulent jets both with and without swirl are used to assess the performance of the pressure-strain model of Speziale, Sarkar and Gatski which is quadratic in the Reynolds stresses. Comparative predictions are also obtained with the two versions of the Launder, Reece and Rodi model which are linear in the same terms. All models are used as part of a complete second-order closure based on the solution of differential transport equations for each non-zero component of the Reynolds stress tensor together with an equation for the scalar energy dissipation rate. For non-swirling jets, the quadratic model underestimates the measured spreading rate of the plane jet but yields a better prediction for the axisymmetric case without resolving the plane jet/round jet anomaly. For the swirling axisymmetric jet, the same model accurately reproduces the effects of swirl on both the mean flow and the turbulence structure in sharp contrast with the linear models which yield results that are in serious error. The reasons for these differences are discussed.

Reasoning in Young Children: Fantasy and Information Retrieval.

ERIC Educational Resources Information Center

Markovits, Henry; And Others

1996-01-01

A model of conditional reasoning predicted that children under 12 would respond correctly to questions of uncertain logical form if premises and context enabled them to access counterexamples from memory, and that children's performance with uncertain logical forms would decrease when empirically true premises are presented in a fantasy context.…

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

The engine of thought is a hybrid: roles of associative and structured knowledge in reasoning.

PubMed

Bright, Aimée K; Feeney, Aidan

2014-12-01

Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful. PsycINFO Database Record (c) 2014 APA, all rights reserved.

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.

Students' Understanding of Genetics Concepts: The Effect of Reasoning Ability and Learning Approaches

ERIC Educational Resources Information Center

Kiliç, Didem; Saglam, Necdet

2014-01-01

Students tend to learn genetics by rote and may not realise the interrelationships in daily life. Because reasoning abilities are necessary to construct relationships between concepts and rote learning impedes the students' sound understanding, it was predicted that having high level of formal reasoning and adopting meaningful learning orientation…

Nurses' intention to leave: critically analyse the theory of reasoned action and organizational commitment model.

PubMed

Liou, Shwu-Ru

2009-01-01

To systematically analyse the Organizational Commitment model and Theory of Reasoned Action and determine concepts that can better explain nurses' intention to leave their job. The Organizational Commitment model and Theory of Reasoned Action have been proposed and applied to understand intention to leave and turnover behaviour, which are major contributors to nursing shortage. However, the appropriateness of applying these two models in nursing was not analysed. Three main criteria of a useful model were used for the analysis: consistency in the use of concepts, testability and predictability. Both theories use concepts consistently. Concepts in the Theory of Reasoned Action are defined broadly whereas they are operationally defined in the Organizational Commitment model. Predictability of the Theory of Reasoned Action is questionable whereas the Organizational Commitment model can be applied to predict intention to leave. A model was proposed based on this analysis. Organizational commitment, intention to leave, work experiences, job characteristics and personal characteristics can be concepts for predicting nurses' intention to leave. Nursing managers may consider nurses' personal characteristics and experiences to increase their organizational commitment and enhance their intention to stay. Empirical studies are needed to test and cross-validate the re-synthesized model for nurses' intention to leave their job.

Relations between inductive reasoning and deductive reasoning.

PubMed

Heit, Evan; Rotello, Caren M

2010-05-01

One of the most important open questions in reasoning research is how inductive reasoning and deductive reasoning are related. In an effort to address this question, we applied methods and concepts from memory research. We used 2 experiments to examine the effects of logical validity and premise-conclusion similarity on evaluation of arguments. Experiment 1 showed 2 dissociations: For a common set of arguments, deduction judgments were more affected by validity, and induction judgments were more affected by similarity. Moreover, Experiment 2 showed that fast deduction judgments were like induction judgments-in terms of being more influenced by similarity and less influenced by validity, compared with slow deduction judgments. These novel results pose challenges for a 1-process account of reasoning and are interpreted in terms of a 2-process account of reasoning, which was implemented as a multidimensional signal detection model and applied to receiver operating characteristic data. PsycINFO Database Record (c) 2010 APA, all rights reserved.

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.

A decision network account of reasoning about other people's choices.

PubMed

Jern, Alan; Kemp, Charles

2015-09-01

The ability to predict and reason about other people's choices is fundamental to social interaction. We propose that people reason about other people's choices using mental models that are similar to decision networks. Decision networks are extensions of Bayesian networks that incorporate the idea that choices are made in order to achieve goals. In our first experiment, we explore how people predict the choices of others. Our remaining three experiments explore how people infer the goals and knowledge of others by observing the choices that they make. We show that decision networks account for our data better than alternative computational accounts that do not incorporate the notion of goal-directed choice or that do not rely on probabilistic inference. Copyright © 2015 Elsevier B.V. All rights reserved.

Theory of mind broad and narrow: reasoning about social exchange engages ToM areas, precautionary reasoning does not.

PubMed

Ermer, Elsa; Guerin, Scott A; Cosmides, Leda; Tooby, John; Miller, Michael B

2006-01-01

Baron-Cohen (1995) proposed that the theory of mind (ToM) inference system evolved to promote strategic social interaction. Social exchange--a form of co-operation for mutual benefit--involves strategic social interaction and requires ToM inferences about the contents of other individuals' mental states, especially their desires, goals, and intentions. There are behavioral and neuropsychological dissociations between reasoning about social exchange and reasoning about equivalent problems tapping other, more general content domains. It has therefore been proposed that social exchange behavior is regulated by social contract algorithms: a domain-specific inference system that is functionally specialized for reasoning about social exchange. We report an fMRI study using the Wason selection task that provides further support for this hypothesis. Precautionary rules share so many properties with social exchange rules--they are conditional, deontic, and involve subjective utilities--that most reasoning theories claim they are processed by the same neurocomputational machinery. Nevertheless, neuroimaging shows that reasoning about social exchange activates brain areas not activated by reasoning about precautionary rules, and vice versa. As predicted, neural correlates of ToM (anterior and posterior temporal cortex) were activated when subjects interpreted social exchange rules, but not precautionary rules (where ToM inferences are unnecessary). We argue that the interaction between ToM and social contract algorithms can be reciprocal: social contract algorithms requires ToM inferences, but their functional logic also allows ToM inferences to be made. By considering interactions between ToM in the narrower sense (belief-desire reasoning) and all the social inference systems that create the logic of human social interaction--ones that enable as well as use inferences about the content of mental states--a broader conception of ToM may emerge: a computational model embodying

To Reason or Not to Reason: Is Autobiographical Reasoning Always Beneficial?

ERIC Educational Resources Information Center

McLean, Kate C.; Mansfield, Cade D.

2011-01-01

Autobiographical reasoning has been found to be a critical process in identity development; however, the authors suggest that existing research shows that such reasoning may not always be critical to another important outcome: well-being. The authors describe characteristics of people such as personality and age, contexts such as conversations,…

Application of two direct runoff prediction methods in Puerto Rico

USGS Publications Warehouse

Sepulveda, N.

1997-01-01

Two methods for predicting direct runoff from rainfall data were applied to several basins and the resulting hydrographs compared to measured values. The first method uses a geomorphology-based unit hydrograph to predict direct runoff through its convolution with the excess rainfall hyetograph. The second method shows how the resulting hydraulic routing flow equation from a kinematic wave approximation is solved using a spectral method based on the matrix representation of the spatial derivative with Chebyshev collocation and a fourth-order Runge-Kutta time discretization scheme. The calibrated Green-Ampt (GA) infiltration parameters are obtained by minimizing the sum, over several rainfall events, of absolute differences between the total excess rainfall volume computed from the GA equations and the total direct runoff volume computed from a hydrograph separation technique. The improvement made in predicting direct runoff using a geomorphology-based unit hydrograph with the ephemeral and perennial stream network instead of the strictly perennial stream network is negligible. The hydraulic routing scheme presented here is highly accurate in predicting the magnitude and time of the hydrograph peak although the much faster unit hydrograph method also yields reasonable results.

Optimization of 6LiF:ZnS(Ag) scintillator light yield using GEANT4

NASA Astrophysics Data System (ADS)

Yehuda-Zada, Y.; Pritchard, K.; Ziegler, J. B.; Cooksey, C.; Siebein, K.; Jackson, M.; Hurlbut, C.; Kadmon, Y.; Cohen, Y.; Ibberson, R. M.; Majkrzak, C. F.; Maliszewskyj, N. C.; Orion, I.; Osovizky, A.

2018-06-01

A new cold neutron detector has been developed at the NIST Center for Neutron Research (NCNR) for the CANDoR (Chromatic Analysis Neutron Diffractometer or Reflectometer) project. Geometric and performance constraints dictate that this detector be exceptionally thin (∼ 2 mm). For this reason, the design of the detector consists of a 6LiF:ZnS(Ag) scintillator with embedded wavelength shifting (WLS) fibers. We used the GEANT4 package to simulate neutron capture and light transport in the detector to optimize the composition and arrangement of materials to satisfy the competing requirements of high neutron capture probability and light production and transport. In the process, we have developed a method for predicting light collection and total neutron detection efficiency for different detector configurations. The simulation was performed by adjusting crucial parameters such as the scintillator stoichiometry, light yield, component grain size, WLS fiber geometry, and reflectors at the outside edges of the scintillator volume. Three different detector configurations were fabricated and their test results were correlated with the simulations. Through this correlation we have managed to find a common photon threshold for the different detector configurations which was then used to simulate and predict the efficiencies for many other detector configurations. New detectors that have been fabricated based on simulation results yielding the desired sensitivity of 90% for 3.27 meV (5 Å) cold neutrons. The simulation has proven to be a useful tool by dramatically reducing the development period and the required number of detector prototypes. It can be used to test new designs with different thicknesses and different target neutron energies.

Average M shell fluorescence yields for elements with 70≤Z≤92

NASA Astrophysics Data System (ADS)

Kahoul, A.; Deghfel, B.; Aylikci, V.; Aylikci, N. K.; Nekkab, M.

2015-03-01

The theoretical, experimental and analytical methods for the calculation of average M-shell fluorescence yield (ω¯M ) of different elements are very important because of the large number of their applications in various areas of physical chemistry and medical research. In this paper, the bulk of the average M-shell fluorescence yield measurements reported in the literature, covering the period 1955 to 2005 are interpolated by using an analytical function to deduce the empirical average M-shell fluorescence yield in the atomic range of 70≤Z≤92. The results were compared with the theoretical and fitted values reported by other authors. Reasonable agreement was typically obtained between our result and other works.

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.

A Quantum Probability Model of Causal Reasoning

PubMed Central

Trueblood, Jennifer S.; Busemeyer, Jerome R.

2012-01-01

People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747

Emotional reasoning processes and dysphoric mood: cross-sectional and prospective relationships.

PubMed

Berle, David; Moulds, Michelle L

2013-01-01

Emotional reasoning refers to the use of subjective emotions, rather than objective evidence, to form conclusions about oneself and the world. Emotional reasoning appears to characterise anxiety disorders. We aimed to determine whether elevated levels of emotional reasoning also characterise dysphoria. In Study 1, low dysphoric (BDI-II≤4; n = 28) and high dysphoric (BDI-II ≥14; n = 42) university students were administered an emotional reasoning task relevant for dysphoria. In Study 2, a larger university sample were administered the same task, with additional self-referent ratings, and were followed up 8 weeks later. In Study 1, both the low and high dysphoric participants demonstrated emotional reasoning and there were no significant differences in scores on the emotional reasoning task between the low and high dysphoric groups. In Study 2, self-referent emotional reasoning interpretations showed small-sized positive correlations with depression symptoms. Emotional reasoning tendencies were stable across an 8-week interval although not predictive of subsequent depressive symptoms. Further, anxiety symptoms were independently associated with emotional reasoning and emotional reasoning was not associated with anxiety sensitivity, alexithymia, or deductive reasoning tendencies. The implications of these findings are discussed, including the possibility that while all individuals may engage in emotional reasoning, self-referent emotional reasoning may be associated with increased levels of depressive symptoms.

Emotional Reasoning Processes and Dysphoric Mood: Cross-Sectional and Prospective Relationships

PubMed Central

Berle, David; Moulds, Michelle L.

2013-01-01

Emotional reasoning refers to the use of subjective emotions, rather than objective evidence, to form conclusions about oneself and the world [1]. Emotional reasoning appears to characterise anxiety disorders. We aimed to determine whether elevated levels of emotional reasoning also characterise dysphoria. In Study 1, low dysphoric (BDI-II≤4; n = 28) and high dysphoric (BDI-II ≥14; n = 42) university students were administered an emotional reasoning task relevant for dysphoria. In Study 2, a larger university sample were administered the same task, with additional self-referent ratings, and were followed up 8 weeks later. In Study 1, both the low and high dysphoric participants demonstrated emotional reasoning and there were no significant differences in scores on the emotional reasoning task between the low and high dysphoric groups. In Study 2, self-referent emotional reasoning interpretations showed small-sized positive correlations with depression symptoms. Emotional reasoning tendencies were stable across an 8-week interval although not predictive of subsequent depressive symptoms. Further, anxiety symptoms were independently associated with emotional reasoning and emotional reasoning was not associated with anxiety sensitivity, alexithymia, or deductive reasoning tendencies. The implications of these findings are discussed, including the possibility that while all individuals may engage in emotional reasoning, self-referent emotional reasoning may be associated with increased levels of depressive symptoms. PMID:23826276

Clinical reasoning of Filipino physical therapists: Experiences in a developing nation.

PubMed

Rotor, Esmerita R; Capio, Catherine M

2018-03-01

Clinical reasoning is essential for physical therapists to engage in the process of client care, and has been known to contribute to professional development. The literature on clinical reasoning and experiences have been based on studies from Western and developed nations, from which multiple influencing factors have been found. A developing nation, the Philippines, has distinct social, economic, political, and cultural circumstances. Using a phenomenological approach, this study explored experiences of Filipino physical therapists on clinical reasoning. Ten therapists working in three settings: 1) hospital; 2) outpatient clinic; and 3) home health were interviewed. Major findings were: a prescription-based referral system limited clinical reasoning; procedural reasoning was a commonly experienced strategy while diagnostic and predictive reasoning were limited; factors that influenced clinical reasoning included practice setting and the professional relationship with the referring physician. Physical therapists' responses suggested a lack of autonomy in practice that appeared to stifle clinical reasoning. Based on our findings, we recommend that the current regulations governing PT practice in the Philippines may be updated, and encourage educators to strengthen teaching approaches and strategies that support clinical reasoning. These recommendations are consistent with the global trend toward autonomous practice.

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.

The Relationship between Deductive Reasoning Ability, Test Anxiety, and Standardized Test Scores in a Latino Sample

ERIC Educational Resources Information Center

Rich, John D., Jr.; Fullard, William; Overton, Willis

2011-01-01

One Hundred and Twelve Latino students from Philadelphia participated in this study, which examined the development of deductive reasoning across adolescence, and the relation of reasoning to test anxiety and standardized test scores. As predicted, 11th and ninth graders demonstrated significantly more advanced reasoning than seventh graders.…

Multivariate models for prediction of rheological characteristics of filamentous fermentation broth from the size distribution.

PubMed

Petersen, Nanna; Stocks, Stuart; Gernaey, Krist V

2008-05-01

The main purpose of this article is to demonstrate that principal component analysis (PCA) and partial least squares regression (PLSR) can be used to extract information from particle size distribution data and predict rheological properties. Samples from commercially relevant Aspergillus oryzae fermentations conducted in 550 L pilot scale tanks were characterized with respect to particle size distribution, biomass concentration, and rheological properties. The rheological properties were described using the Herschel-Bulkley model. Estimation of all three parameters in the Herschel-Bulkley model (yield stress (tau(y)), consistency index (K), and flow behavior index (n)) resulted in a large standard deviation of the parameter estimates. The flow behavior index was not found to be correlated with any of the other measured variables and previous studies have suggested a constant value of the flow behavior index in filamentous fermentations. It was therefore chosen to fix this parameter to the average value thereby decreasing the standard deviation of the estimates of the remaining rheological parameters significantly. Using a PLSR model, a reasonable prediction of apparent viscosity (micro(app)), yield stress (tau(y)), and consistency index (K), could be made from the size distributions, biomass concentration, and process information. This provides a predictive method with a high predictive power for the rheology of fermentation broth, and with the advantages over previous models that tau(y) and K can be predicted as well as micro(app). Validation on an independent test set yielded a root mean square error of 1.21 Pa for tau(y), 0.209 Pa s(n) for K, and 0.0288 Pa s for micro(app), corresponding to R(2) = 0.95, R(2) = 0.94, and R(2) = 0.95 respectively. Copyright 2007 Wiley Periodicals, Inc.

The emotional reasoning heuristic in children.

PubMed

Muris, P; Merckelbach, H; van Spauwen, I

2003-03-01

A previous study by Arntz, Rauner, and Van den Hout (1995; Behaviour Research and Therapy, 33, 917-925) has shown that adult anxiety patients tend to infer danger not only on the basis of objective danger information, but also on the basis of anxiety response information. The current study examined whether this so-called emotional reasoning phenomenon also occurs in children. Normal primary school children (N = 101) first completed scales tapping anxiety disorders symptoms, anxiety sensitivity, and trait anxiety. Next, they were asked to rate danger levels of scripts in which objective danger versus objective safety and anxiety response versus no anxiety response were systematically varied. Evidence was found for a general emotional reasoning effect. That is, children's danger ratings were not only a function of objective danger information, but also, in the case of objective safety scripts, by anxiety response information. This emotional reasoning effect was predicted by levels of anxiety sensitivity and trait anxiety. More specifically, high levels of anxiety sensitivity and trait anxiety were accompanied by a greater tendency to use anxiety-response information as an heuristic for assessing dangerousness of safety scripts. Implications of these findings are briefly discussed.

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.

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...

Strategic reasoning and bargaining in catastrophic climate change games

NASA Astrophysics Data System (ADS)

Verendel, Vilhelm; Johansson, Daniel J. A.; Lindgren, Kristian

2016-03-01

Two decades of international negotiations show that agreeing on emission levels for climate change mitigation is a hard challenge. However, if early warning signals were to show an upcoming tipping point with catastrophic damage, theory and experiments suggest this could simplify collective action to reduce greenhouse gas emissions. At the actual threshold, no country would have a free-ride incentive to increase emissions over the tipping point, but it remains for countries to negotiate their emission levels to reach these agreements. We model agents bargaining for emission levels using strategic reasoning to predict emission bids by others and ask how this affects the possibility of reaching agreements that avoid catastrophic damage. It is known that policy elites often use a higher degree of strategic reasoning, and in our model this increases the risk for climate catastrophe. Moreover, some forms of higher strategic reasoning make agreements to reduce greenhouse gases unstable. We use empirically informed levels of strategic reasoning when simulating the model.

Academic Achievement in Physics-Chemistry: The Predictive Effect of Attitudes and Reasoning Abilities

PubMed Central

Vilia, Paulo N.; Candeias, Adelinda A.; Neto, António S.; Franco, Maria Da Glória S.; Melo, Madalena

2017-01-01

Science education plays a critical role as political priority due to its fundamental importance in engaging students to pursue technological careers considered essential in modern societies, in order to face scientific development challenges. High-level achievement on science education and positive attitudes toward science constitutes a crucial challenge for formal education. Several studies indicate close relationships between students’ attitudes, cognitive abilities, and academic achievement. The main purpose of this study is to analyze the impact of student’s attitudes toward the school discipline of Physics and Chemistry and their reasoning abilities on academic achievement on that school subject, among Portuguese 9th grade students using the data collected during the Project Academic Performance and Development: a longitudinal study on the effects of school transitions in Portuguese students (PTDC/CPE-CED/104884/2008). The participants were 470 students (267 girls – 56.8% and 203 boys – 43.2%), aged 14–16 years old (μ = 14.3 ± 0.58). The attitude data were collected using the Attitude toward Physics-Chemistry Questionnaire (ATPCQ) and, the Reasoning Test Battery (RTB) was used to assess the students reasoning abilities. Achievement was measured using the students’ quarterly (9-week) grades in the physics and chemistry subject. The relationships between the attitude dimensions toward Physics-chemistry and the reasoning dimensions and achievement in each of the three school terms were assessed by multiple regression stepwise analyses and standardized regression coefficients (β), calculated with IBM SPSS Statistics 21 software. Both variables studied proved to be significant predictor variables of school achievement. The models obtained from the use of both variables were always stronger accounting for higher proportions of student’s grade variations. The results show that ATPCQ and RTB had a significantly positive relationship with student�

Using Relational Reasoning Strategies to Help Improve Clinical Reasoning Practice.

PubMed

Dumas, Denis; Torre, Dario M; Durning, Steven J

2018-05-01

Clinical reasoning-the steps up to and including establishing a diagnosis and/or therapy-is a fundamentally important mental process for physicians. Unfortunately, mounting evidence suggests that errors in clinical reasoning lead to substantial problems for medical professionals and patients alike, including suboptimal care, malpractice claims, and rising health care costs. For this reason, cognitive strategies by which clinical reasoning may be improved-and that many expert clinicians are already using-are highly relevant for all medical professionals, educators, and learners.In this Perspective, the authors introduce one group of cognitive strategies-termed relational reasoning strategies-that have been empirically shown, through limited educational and psychological research, to improve the accuracy of learners' reasoning both within and outside of the medical disciplines. The authors contend that relational reasoning strategies may help clinicians to be metacognitive about their own clinical reasoning; such strategies may also be particularly well suited for explicitly organizing clinical reasoning instruction for learners. Because the particular curricular efforts that may improve the relational reasoning of medical students are not known at this point, the authors describe the nature of previous research on relational reasoning strategies to encourage the future design, implementation, and evaluation of instructional interventions for relational reasoning within the medical education literature. The authors also call for continued research on using relational reasoning strategies and their role in clinical practice and medical education, with the long-term goal of improving diagnostic accuracy.

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.

Words of Reason.

ERIC Educational Resources Information Center

Beer, Francis A.

1994-01-01

Examines the word "reason" as it is used in political discourse. Argues that "reason"'s plasticity and flexibility help it to stimulate and evoke variable mental images and responses in different settings and situations. Notes that the example of reason of state shows "reason"'s rhetorical power and privilege, its…

Developing Students' Reasoning about Samples and Sampling in the Context of Informal Inferences

ERIC Educational Resources Information Center

Meletiou-Mavrotheris, Maria; Paparistodemou, Efi

2015-01-01

The expanding use of data in modern society for prediction and decision-making makes it a priority for mathematics instruction to help students build sound foundations of inferential reasoning at a young age. This study contributes to the emerging research literature on the early development of informal inferential reasoning through the conduct of…

Understanding Nature-Related Behaviors among Children through a Theory of Reasoned Action Approach

ERIC Educational Resources Information Center

Gotch, Chad; Hall, Troy

2004-01-01

The Theory of Reasoned Action has proven to be a valuable tool for predicting and understanding behavior and, as such, provides a potentially important basis for environmental education program design. This study used a Theory of Reasoned Action approach to examine a unique type of behavior (nature-related activities) and a unique population…

An experimental investigation of emotional reasoning processes in depression.

PubMed

Berle, David; Moulds, Michelle L

2013-09-01

Cognitive models of depression emphasize how distorted thoughts and interpretations contribute to low mood. Emotional reasoning is considered to be one such interpretative style. We used an experimental procedure to determine whether elevated levels of emotional reasoning characterize depression. Participants who were currently experiencing a major depressive episode (n = 27) were compared with those who were non-depressed (n = 25 who had never been depressed and n = 26 previously but not currently depressed) on an emotional reasoning task. Although there were some trends for depressed participants to show greater levels of emotional reasoning relative to non-depressed participants, none of these differences attained significance. Interestingly, previously depressed participants engaged in more non-self-referent emotional reasoning than never-depressed participants. Emotional reasoning does not appear to characterize mild to moderate levels of depression. The lack of significant differences in emotional reasoning between currently depressed and non-depressed participants may have been a consequence of the fact that participants in our currently depressed group were, for the most part, only mildly depressed. Non-self-referent emotional reasoning may nevertheless be a risk factor for subsequent depressive episodes, or else serve as a 'cognitive scar' from previous episodes. In contrast with the predictions of cognitive models of depression, emotional reasoning tendencies may not be especially prominent in currently depressed individuals. Depressed individuals vary greatly in the degree to which they engage in emotional reasoning. Individuals with remitted depression may show elevated of levels non-self-referent emotional reasoning compared with those who have never had a depressive episode, that is, rely on their emotions when forming interpretations about situations. Our findings require replication using alternative indices of emotional reasoning. Our currently

Symbolic Processing Combined with Model-Based Reasoning

NASA Technical Reports Server (NTRS)

James, Mark

2009-01-01

A computer program for the detection of present and prediction of future discrete states of a complex, real-time engineering system utilizes a combination of symbolic processing and numerical model-based reasoning. One of the biggest weaknesses of a purely symbolic approach is that it enables prediction of only future discrete states while missing all unmodeled states or leading to incorrect identification of an unmodeled state as a modeled one. A purely numerical approach is based on a combination of statistical methods and mathematical models of the applicable physics and necessitates development of a complete model to the level of fidelity required for prediction. In addition, a purely numerical approach does not afford the ability to qualify its results without some form of symbolic processing. The present software implements numerical algorithms to detect unmodeled events and symbolic algorithms to predict expected behavior, correlate the expected behavior with the unmodeled events, and interpret the results in order to predict future discrete states. The approach embodied in this software differs from that of the BEAM methodology (aspects of which have been discussed in several prior NASA Tech Briefs articles), which provides for prediction of future measurements in the continuous-data domain.

Estimating tar and nicotine exposure: human smoking versus machine generated smoke yields.

PubMed

St Charles, F K; Kabbani, A A; Borgerding, M F

2010-02-01

Determine human smoked (HS) cigarette yields of tar and nicotine for smokers using their own brand in their everyday environment. A robust, filter analysis method was used to estimate the tar and nicotine yields for 784 subjects. Seventeen brands were chosen to represent a wide range of styles: 85 and 100 mm lengths; menthol and non-menthol; 17, 23, and 25 mm circumference; with tar yields [Federal Trade Commission (FTC) method] ranging from 1 to 18 mg. Tar bands chosen corresponded to yields of 1-3 mg, 4-6 mg, 7-12 mg, and 13+ mg. A significant difference (p<0.0001) in HS yields of tar and nicotine between tar bands was found. Machine-smoked yields were reasonable predictors of the HS yields for groups of subjects, but the relationship was neither exact nor linear. Neither the FTC, the Massachusetts (MA) nor the Canadian Intensive (CI) machine-smoking methods accurately reflect the HS yields across all brands. The FTC method was closest for the 7-12 mg and 13+ mg products and the MA method was closest for the 1-3mg products. The HS yields for the 4-6 mg products were approximately midway between the FTC and the MA yields. HS nicotine yields corresponded well with published urinary and plasma nicotine biomarker studies. 2009 Elsevier Inc. All rights reserved.

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.

Logic and Belief across the Lifespan: The Rise and Fall of Belief Inhibition during Syllogistic Reasoning

ERIC Educational Resources Information Center

De Neys, Wim; Van Gelder, Elke

2009-01-01

Popular reasoning theories postulate that the ability to inhibit inappropriate beliefs lies at the heart of the human reasoning engine. Given that people's inhibitory capacities are known to rise and fall across the lifespan, we predicted that people's deductive reasoning performance would show similar curvilinear age trends. A group of children…

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.