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Entry, Descent and Landing (EDL) Modeling and Simulation (MS) is an enabling capability for complex NASA entry missions such as MSL and Orion. MS is used in every mission phase to define mission concepts, select appropriate architectures, design EDL systems, quantify margin and risk, ensure correct system operation, and analyze data returned from the entry. In an environment where it is impossible to fully test EDL concepts on the ground prior to use, accurate MS capability is required to extrapolate ground test results to expected flight performance.

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Our long-term goal is to explore and test the potential effectiveness of low-level nutrient concentrations (nitrate, nitrite, and ammonia) as descriptors of geophysical fields and tracers of physical processes in oligotrophic coastal waters, with particular attention to adapting our laboratory sensor of these nutrients for use in an AUV. The nutrient data are to be incorporated into prognostic physical-biogeochemical models in a feedback mode.

Our long-term goal is to explore and test the potential effectiveness of low-level nutrient concentrations (nitrate, nitrite, and ammonia) as descriptors of geophysical fields and tracers of physical processes in oligotrophic coastal waters, with particular attention to adapting our laboratory sensor of these nutrients for use in an AUV. The nutrient data are to be incorporated into prognostic physical-biogeochemical models in a feedback mode.

This article is from The EPMA Journal , volume 5 . Abstract None

The large majority of inferences drawn in empirical political research follow from model-based associations (e.g. regression). Here, we articulate the benefits of predictive modeling as a complement to this approach. Predictive models aim to specify a probabilistic model that provides a good fit to testing data that were not used to estimate the model's parameters. Our goals are threefold. First, we review the central benefits of this under-utilized approach from a perspective uncommon in the existing literature: we focus on how predictive modeling can be used to complement and augment standard associational analyses. Second, we advance the state of the literature by laying out a simple set of benchmark predictive criteria. Third, we illustrate our approach through a detailed application to the prediction of interstate conflict.

The purpose of this study is to examine the relationships between achievement goal orientations and Learning Strategies. The sample of study consists of 350 public high school students (135 males and 215 females, mean age: 17 ± 0.65) from two high schools in Kerman province of Iran selected by random multistage cluster sampling method. In this study, The Achievement Goal Orientations Scale (Elliot & McGregor, 2001) and Learning Strategies Scale (Kember, Biggs, & Leung, 2004) were used. Structural equation modeling (SEM) was used to test the hypotheses. In correlation analysis, mastery goals predicted positive deep strategy and performance goals predicted positive surface strategy in a positive way. The model demonstrated fit (?[superscript 2]/df = 1.99, GFI = 0.97, AGFI = 0.97, CFI = 0.93, NFI = 0.93, RFI = 0.93, and RMSEA = 0.05). According to the results, achievement goal orientations (exception of the path from performance-approach to deep strategy) were significant determinants of learning strategies. Results were discussed in the light of literature.

An investigation of the predictive capability of a new Air Force Weapons Laboratory model for transistor and diode failure under reverse bias was initiated. A comparison with the junction capacitance damage model shows a doubled improvement at high confidence levels based on an Army-generated population of experimental damage data. (Author)

For all the difficulties engendered in its use, semiconductor device damage data are an integral part of many programs of electromagnetic pulse vulnerability assessment and hardening. Experimental damage data, which are generated only as a result of dedicated efforts, can be expected to be available for only a minor fraction of all semiconductor devices. This limited supply has spurred efforts to develop predictive damage models, in order to bypass the tedious experimental requirements for generating damage data. The predictive ability of the best of these models, the junction capacitance damage model, is investigated in detail. Central to this study is a library of experimental damage data for 46 silicon device types, comprising bipolar transistors and diodes tested at the 10-, 1-, and 0.1-micro-sec. pulse durations. These are devices from the front ends of a number of Army systems and represent radio, field wire, and cable functions with operating ranges in the direct current (dc) to microwave region. Of the 46 experimental devices comprising 68 junction types (collector-to-base and emitter-to-base junctions treated as distinct for all transistors), sufficient published manufacturers' data were available for the damage modeling of 11 junctions. These were supplemented with measured parameters for 27 junction types.

The purpose of this 1985 status report is to describe the: 1. morphology, thickness and structure characterization of chromic acid anodized Ti-6Al-4V determined by Transmission Electron Microscopy (TEM) and Selected Area Electron Diffraction (SAED); 2. unfulled polyphenylquinoxaline (PPQ) - chromic acid anodized (CAA) Ti-6Al-4V unaged single lap bond strength data; 3. unstressed thermal aging tests in progress for PPQ - CAA Ti-6Al-4V single lap bonds, and 4. tests in progress to determine the electrical properties of CAA Ti-6Al-4V; these tests will determine the anodic oxide capacitance, dielectric constant and bulk resistivity.

A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.

A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies, and, machine learning to reconstruct discrepancy information from a large number of inverse problems into corrective model forms. We apply the methodology to turbulent flows over airfoils involving flow separation. Model augmentations are developed for the Spalart Allmaras (SA) model using adjoint-based full field inference on experimentally measured lift coefficient data. When these model forms are reconstructed using neural networks (NN) and embedded within a standard solver, we show that much improved predictions in lift can be obtained for geometries and flow conditions that were not used to train the model. The NN-augmented SA model also predicts surface pressures extremely well. Portability of this approach is demonstrated by confirming that predictive improvements are preserved when the augmentation is embedded in a different commercial finite-element solver. The broader vision is that by incorporating data that can reveal the form of the innate model discrepancy, the applicability of data-driven turbulence models can be extended to more general flows.

This work presents an original and innovative approach by combining greenhouse cooling with artificial intelligence models to ensure food security. The study is divided into two parts: Experimental and theoretical. 

This work presents an original and innovative approach by combining greenhouse cooling with artificial intelligence models to ensure food security. The study is divided into two parts: Experimental and theoretical. 

Our long-term goal is to explore and test the potential effectiveness of low-level nutrient concentrations (nitrate, nitrite, and ammonia) as descriptors of geophysical fields and tracers of physical processes in oligotrophic coastal waters, with particular attention to adapting our laboratory sensor for these nutrients for use in an AUV. The nutrient data are to be incorporated into prognostic physical-biogeochemical models in a feedback mode.

Connectome-based predictive modeling (CPM), one among many methods of brain-wide association studies (BWAS), has gained substantial popularity in the neuroimaging community to predict behavioral phenotypes from individual’s brain connectome. Using CPM, many studies have reported significant success in the prediction of general intelligence, personality traits and symptom severity in diseases like social anxiety or depression. Recently Marek et al. (2022) examined the effect sizes in multivariate brain-wide associations and observed that variances of possible prediction accuracy increased with decreasing sample size. Therefore, for a study employing a small sample size, it is much more likely to observe spuriously high predictive accuracy by chance. We would like to test this theoretical hypothesis by looking at the actual CPM literature and see whether the found effect sizes are comparable to the bootstrapped distribution that Marek et al reported. If the reported effect sizes in the CPM-literature deviates significantly from the bootstrapped distribution of effect sizes, we can further evaluate if there exist a potential inflation of prediction accuracy by published CPM studies.

Connectome-based predictive modeling (CPM), one among many methods of brain-wide association studies (BWAS), has gained substantial popularity in the neuroimaging community to predict behavioral phenotypes from individual’s brain connectome. Using CPM, many studies have reported significant success in the prediction of general intelligence, personality traits and symptom severity in diseases like social anxiety or depression. Recently Marek et al. (2022) examined the effect sizes in multivariate brain-wide associations and observed that variances of possible prediction accuracy increased with decreasing sample size. Therefore, for a study employing a small sample size, it is much more likely to observe spuriously high predictive accuracy by chance. We would like to test this theoretical hypothesis by looking at the actual CPM literature and see whether the found effect sizes are comparable to the bootstrapped distribution that Marek et al reported. If the reported effect sizes in the CPM-literature deviates significantly from the bootstrapped distribution of effect sizes, we can further evaluate if there exist a potential inflation of prediction accuracy by published CPM studies.

This study systematically integrates 13 core psychological predictors—place attachment, place dependence, social connection, nature connectedness, altruistic values, egoistic values, biosphere values, environmental efficacy, environmental responsibility, climate risk perception, climate change beliefs, pro-environmental behavior, and environmental self-identity—based on the Value-Belief-Norm theory, Attachment Theory, Protection Motivation Theory, and Environmental Spillover Effect Theory. Unlike previous climate action studies that predominantly focus on single theoretical frameworks, this study attempts to merge multiple theoretical perspectives to address multidimensional psychological mechanisms. These include cognitive factors (e.g., climate risk perception), emotional factors (e.g., place attachment), behavioral intentions (e.g., environmental efficacy), and identity-related factors (e.g., environmental self-identity), with the aim of filling the gap in collaborative theoretical analysis. This study employs a mixed-methods framework to predict youth climate action levels. First, a random forest classification model was developed using 17 integrated predictor variables. Model hyperparameters (mtry, ntree, min_n) were optimized via five-fold cross-validation, and generalizability was assessed using out-of-bag (OOB) error. Variable importance was ranked to identify key predictors. Second, Lasso regression was applied to perform sparse selection of highly important variables, eliminating redundancy through the L1 regularization path and yielding a parsimonious predictor set. Finally, logistic regression was used as a baseline model to compare predictive performance (AUC, accuracy) with the random forest and to interpret the direction and significance of linear effects. Model robustness was evaluated using permutation tests and stratified sampling. The findings were further interpreted within theoretical frameworks such as the Value-Belief-Norm theory and Protection Motivation Theory, shedding light on the synergistic mechanisms driving youth climate action.

Our long-term goal is to explore and test the potential effectiveness of low-level nutrient concentrations (nitrate, nitrite, and ammonia) as descriptors of geophysical fields and tracers of physical processes in oligotrophic coastal waters. To test the validity of this approach, studies are performed using the deliberately injected inert tracer sulfur hexafluoride (SF6) and the advection and dispersion patterns of this tracer are compared with the patterns observed with the naturally occurring low-level nutrient concentrations. The SF6 work includes development of sampling methods to characterize the tracer field with high spatial and temporal resolution in near real-time.

Recent years saw an increased interest in modeling and understanding the mechanisms of opinion and innovation spread through human networks. Using analysis of real-world social data, researchers are able to gain a better understanding of the dynamics of social networks and subsequently model the changes in such networks over time. We developed a social network model that both utilizes an agent-based approach with a dynamic update of opinions and connections between agents and reflects opinion propagation and structural changes over time as observed in real-world data. We validate the model using data from the Social Evolution dataset of the MIT Human Dynamics Lab describing changes in friendships and health self-perception in a targeted student population over a nine-month period. We demonstrate the effectiveness of the approach by predicting changes in both opinion spread and connectivity of the network. We also use the model to evaluate how the network parameters, such as the level of `openness' and willingness to incorporate opinions of neighboring agents, affect the outcome. The model not only provides insight into the dynamics of ever changing social networks, but also presents a tool with which one can investigate opinion propagation strategies for networks of various structures and opinion distributions.

This article is from Bioinformatics , volume 29 . Abstract Motivation: Pre-mRNA cleavage and polyadenylation are essential steps for 3′-end maturation and subsequent stability and degradation of mRNAs. This process is highly controlled by cis-regulatory elements surrounding the cleavage/polyadenylation sites (polyA sites), which are frequently constrained by sequence content and position. More than 50% of human transcripts have multiple functional polyA sites, and the specific use of alternative polyA sites (APA) results in isoforms with variable 3′-untranslated regions, thus potentially affecting gene regulation. Elucidating the regulatory mechanisms underlying differential polyA preferences in multiple cell types has been hindered both by the lack of suitable data on the precise location of cleavage sites, as well as of appropriate tests for determining APAs with significant differences across multiple libraries.Results: We applied a tailored paired-end RNA-seq protocol to specifically probe the position of polyA sites in three human adult tissue types. We specified a linear-effects regression model to identify tissue-specific biases indicating regulated APA; the significance of differences between tissue types was assessed by an appropriately designed permutation test. This combination allowed to identify highly specific subsets of APA events in the individual tissue types. Predictive models successfully classified constitutive polyA sites from a biologically relevant background (auROC = 99.6%), as well as tissue-specific regulated sets from each other. We found that the main cis-regulatory elements described for polyadenylation are a strong, and highly informative, hallmark for constitutive sites only. Tissue-specific regulated sites were found to contain other regulatory motifs, with the canonical polyadenylation signal being nearly absent at brain-specific polyA sites. Together, our results contribute to the understanding of the diversity of post-transcriptional gene regulation.Availability: Raw data are deposited on SRA, accession numbers: brain SRX208132, kidney SRX208087 and liver SRX208134. Processed datasets as well as model code are published on our website: http://www.genome.duke.edu/labs/ohler/research/UTR/Contact:[email protected]

For training a model, the raw data have to go through various preprocessing phases like Cleaning, Missing Values Imputation, Dimension Variable reduction, and Sampling. These steps are data and problem specific and affect the accuracy of the model at a very large extent. For the current scenario, we have 2.2M records with 511 variables. This data was used in a Direct Mail Campaign of some Life Insurance Products and now we know which record had a positive response for the campaign. Rows records 2,259,747 Columns 511 Rows with positive response 2,739, i.e. Response Rate 0.1212 . The dataset is not complete, i.e. we have to take care of missing values. Farzana Naz | Imaad Shafi | Md Kamre Alam "Preprocessing of Low Response Data for Predictive Modeling" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21667.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21667/preprocessing-of-low-response-data-for-predictive-modeling/farzana-naz

Our long term fundamental goals are I) to better understand the interaction between the ocean and tropical cyclones using numerical simulation models, and II) to investigate the operational capability of coupled tropical cyclone-ocean models in predicting the ocean response and tropical cyclone movement and intensity.

As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied repeatedly and the computation can be costly. We are especially interested in the need for an accurate and efficient approach for handling these applications: (1) capturing complex nonlinear dynamics inherent in a system under consideration and (2) versatility (robustness) to encompass a range of parametric variations. In our previous paper, we proposed to use first-order Taylor expansion collected at numerous sampling points along a trajectory and assembled together via nonlinear weighting functions. The validity and performance of this approach was demonstrated for a number of problems with a vastly different input functions. In this study, we are especially interested in enhancing the method's accuracy; we extend it to include the second-orer Taylor expansion, which however requires a complicated evaluation of Hessian matrices for a system of equations, like in fluid dynamics. We propose a method to avoid these Hessian matrices, while maintaining the accuracy. Results based on the method are presented to confirm its validity.

As computational fluid dynamics techniques and tools become widely accepted for realworld practice today, it is intriguing to ask: what areas can it be utilized to its potential in the future. Some promising areas include design optimization and exploration of fluid dynamics phenomena (the concept of numerical wind tunnel), in which both have the common feature where some parameters are varied repeatedly and the computation can be costly. We are especially interested in the need for an accurate and efficient approach for handling these applications: (1) capturing complex nonlinear dynamics inherent in a system under consideration and (2) versatility (robustness) to encompass a range of parametric variations. In our previous paper, we proposed to use first-order Taylor expansion collected at numerous sampling points along a trajectory and assembled together via nonlinear weighting functions. The validity and performance of this approach was demonstrated for a number of problems with a vastly different input functions. In this study, we are especially interested in enhancing the method's accuracy; we extend it to include the second-orer Taylor expansion, which however requires a complicated evaluation of Hessian matrices for a system of equations, like in fluid dynamics. We propose a method to avoid these Hessian matrices, while maintaining the accuracy. Results based on the method are presented to confirm its validity.

Thrust 1. Experimental studies of nonequilibrium air-fuel plasma kinetics using advanced non-intrusive diagnostics * Task 2: Laminar Flow Reactor and Nanoparticle Studies at Low to Intermediate Temperatures (Radar REMPI and Filtered Rayleigh Scattering in flames) * Task 7: Fundamental studies on microwave enhanced combustion at atmospheric and higher pressures (Laser designated microwave driven ignition and microwave enhanced flame propagation) * Thrust 3. Experimental and modeling studies of fundamental nonequilibrium discharge processes * Task 10: Characterization and Modeling of Nsec Pulsed Plasma Discharges (Modeling and Radar REMPI of nonequilibrium states) * Task 11: Experimental and Modeling Study of Plasma properties using Radar REMPI (Radar REMPI measurement of electron loss mechanism and rates and local electron number density)

This work applied particle swarm heuristic optimization techniques to the problem of finding a near-optimal order in which to schedule tasks in a real-time embedded system in order to minimize cache miss rates experienced by the software. Reducing the number of cache misses is an important component of runtime execution efficiency. We demonstrated runtime reductions of 3-5% in execution time, significant for embedded systems attempting to add new capability without upgrading hardware. The expectation is that these gains can be improved further by the use of hardware with pseudo-LRU cache behavior.

This work applied particle swarm heuristic optimization techniques to the problem of finding a near-optimal order in which to schedule tasks in a real-time embedded system in order to minimize cache miss rates experienced by the software. Reducing the number of cache misses is an important component of runtime execution efficiency. We demonstrated runtime reductions of 3-5% in execution time, significant for embedded systems attempting to add new capability without upgrading hardware. The expectation is that these gains can be improved further by the use of hardware with pseudo-LRU cache behavior.

For all the difficulties engendered in its use, semiconductor device damage data are an integral part of many programs of electromagnetic pulse vulnerability assessment and hardening. Experimental damage data, which are generated only as a result of dedicated efforts, can be expected to be available for only a minor fraction of all semiconductor devices. This limited supply has spurred efforts to develop predictive damage models, in order to bypass the tedious experimental requirements for generating damage data. The predictive ability of the best of these models, the junction capacitance damage model, is investigated in detail. Central to this study is a library of experimental damage data for 46 silicon device types, comprising bipolar transistors and diodes tested at the 10-, 1-, and 0.1-micro-sec. pulse durations. These are devices from the front ends of a number of Army systems and represent radio, field wire, and cable functions with operating ranges in the direct current (dc) to microwave region. Of the 46 experimental devices comprising 68 junction types (collector-to-base and emitter-to-base junctions treated as distinct for all transistors), sufficient published manufacturers' data were available for the damage modeling of 11 junctions. These were supplemented with measured parameters for 27 junction types.

Community colleges continue to experience growth in online courses. This growth reflects the need to increase the numbers of students who complete certificates or degrees. Retaining online students, not to mention assuring their success, is a challenge that must be addressed through practical institutional responses. By leveraging existing student information, higher education institutions can build statistical models, or learning analytics, to forecast student outcomes. This is a case study from a community college utilizing learning analytics and the development of predictive models to identify at-risk students based on dozens of key variables. (Contains 4 tables and 3 figures.)

"The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors."

PRINCIPAL OBJECTIVE: Develop experimentally validated kinetic mechanisms and modeling codes capable of predicting the impact of nonequilibrium plasmas on reactive processes, particularly on ignition,chemical energy release, and flameholding in combustors of flight vehicle engines. PRIMARY DELIVERABLES: * Extensive new experimental data sets of non-equilibrium plasma chemical energy conversion kinetics over a wide range of initial temperatures and pressures, in a variety of complementary new test facilities, specifically designed and fabricated for this program. * Detailed non-equilibrium plasma chemical energy conversion kinetic mechanisms, validated over a wide range of conditions, using data from multiple facilities. * Extensive experimental data sets on ignition delay, flameholding and laminar flame speed augmentation by nonequilibrium discharges, including nsec pulsed, DC/RF, and microwave. * High fidelity multi-dimensional plasma combustion modeling codes, validated in a series of model flows, with emphasis on the high subsonic to supersonic flow regimes.

This article is from The EPMA Journal , volume 4 . Abstract With the significant advancement of high-throughput technologies and diagnostic techniques throughout the past decades, molecular underpinnings of many disorders have been identified. However, translation of patient-specific molecular mechanisms into tailored clinical applications remains a challenging task, which requires integration of multi-dimensional molecular and clinical data into patient-centric models. This task becomes even more challenging when dealing with complex diseases such as neurodegenerative disorders. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. We argue that integrative disease modeling will be an indispensable part of any P4 medicine research and development in the near future and that it supports the shift from descriptive to causal mechanistic diagnosis and treatment of complex diseases. For each ‘P’ in predictive, preventive, personalized and participatory (P4) medicine, we demonstrate how integrative disease modeling can contribute to addressing the real-world issues in development of new predictive, preventive, personalized and participatory measures. With the increasing recognition that application of integrative systems modeling is the key to all activities in P4 medicine, we envision that translational bioinformatics in general and integrative modeling in particular will continue to open up new avenues of scientific research for current challenges in P4 medicine.

For all the difficulties engendered in its use, semiconductor device damage data are an integral part of many programs of electromagnetic pulse vulnerability assessment and hardening. Experimental damage data, which are generated only as a result of dedicated efforts, can be expected to be available for only a minor fraction of all semiconductor devices. This limited supply has spurred efforts to develop predictive damage models, in order to bypass the tedious experimental requirements for generating damage data. The predictive ability of the best of these models, the junction capacitance damage model, is investigated in detail. Central to this study is a library of experimental damage data for 46 silicon device types, comprising bipolar transistors and diodes tested at the 10-, 1-, and 0.1-micro-sec. pulse durations. These are devices from the front ends of a number of Army systems and represent radio, field wire, and cable functions with operating ranges in the direct current (dc) to microwave region. Of the 46 experimental devices comprising 68 junction types (collector-to-base and emitter-to-base junctions treated as distinct for all transistors), sufficient published manufacturers' data were available for the damage modeling of 11 junctions. These were supplemented with measured parameters for 27 junction types.

The assessment presented in this article is centred on investigating the interaction of turbidity, total suspended solids and total dissolved solids interaction within the water bearing aquifer of Obite to Oboburu communities of Ogba/ Egbema/ Ndoni local government area of Rivers State, Nigeria. Experimental and modeled turbidity, total suspended solids and total dissolved solids investigated are within recommended standard of World Health Organization revealing the reliability of model equation in predicting groundwater parameters distribution upon influence of time, recharge, flow rate. Ottos C. G | Isaac E. O"Modeling of Predictive interaction of Water Parameters in Groundwater" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11292.pdf http://www.ijtsrd.com/engineering/civil-engineering/11292/modeling-of-predictive-interaction-of-water-parameters-in-groundwater/ottos-c-g

This technical report, produced in partnership by the Council for Advancement and Support of Education (CASE) and SPSS Inc., explores the promise of data mining alumni records at educational institutions. Working with individual alumni records from The Johns Hopkins Zanvyl Krieger School of Arts and Sciences, a predictive regression model is developed based on commonly-collected variables and a ten-step model-building and testing process. The resulting model's wider applicability is tested successfully on datasets from nine other educational institutions in the United States, Canada, and Europe. Analysis reveals four distinct patterns of giving by alumni. Fundraisers will benefit from this work by using the model to generate predictive scores identifying prospects in their own alumni databases, likely to make a major gift as well as appreciating their own institutions' pattern of giving when making strategic fundraising decisions. A table detailing the aggregated study results is appended. [This report was produced jointly with SPSS Inc.]

Assessing potential patient predictors (e.g. gender, level of ocular inflammation) for treatment success in the FAST uveitis trial.

This study develops a hybrid predictive and optimization model for surface mount device (SMD) design, addressing the extended design cycle times in the semiconductor industry caused by high computational demands. Challenges are tackled effectively through integration of convolutional neural network (CNN) for high-accuracy predictions and simulated annealing (SA) algorithm for optimization of SMD physical parameters. CNN model that trained on Monte Carlo simulation (MCS) data, achieved a predictive accuracy of 99.91% in forecasting SMD design errors. Concurrently, SA algorithm refined design parameters and substantially reducing error rates to nearly zero after 800 iterations. Our results indicate that combining predictive modeling with an optimization algorithm significantly enhances SMD design efficiency, providing a robust tool for mitigating time-to-market risks in semiconductor manufacturing.

The long-term goal of the proposed research is to develop a modeling architecture that sets the stage for future capabilities that are beyond the current generation of weather prediction models such as the Weather Research Forecast (WRF) model.

The predictive validity of computer simulation modeling of the operator's mental workload and situational awareness (SA) during a target acquisition mission was assessed in the present study. In Phase 1, twelve participants completed a series of target acquisition trials in a laboratory flight simulator and provided subjective ratings of workload (using the Subjective Workload Assessment Technique (SWAT)) and SA (using the Situational Awareness Rating Technique (SART)). In Phase 2 computer models of the laboratory task were constructed using the Micro Saint modeling tool. The visual, auditory, kinesthetic, cognitive, and psychomotor components of the workload associated with each task were estimated and used to obtain the measures of average and peak workload. The results from the lab data versus the Micro Saint data were similar but not identical, indicating the computer models were partially, but not completely valid predictors of mental workload and SA. The computer modeling appeared to be a more effective predictor of SA rather than mental workload.

The Air Force has expressed a need for an alternative method for evaluating the toxicity of Chemicals. Computational chemistry and Quantitative Structure-Activity Relationships (QSAR) offer a possible approach to the computer assisted assessment of toxicity. The objective of this study request was to evaluate software products that have the capability of estimating toxicological end points. The need for this type of capability at the Toxic Hazards Research Unit (THRU) is necessitated by the vast number of chemical substances that have no toxicological data. The toxicity of some of these compounds can be addressed through OSAR where a data base of structurally related compounds is available for comparison. The data base approach operates by correlating structural descriptors of an unknown with the descriptors of toxicologically characterized compounds contained in the data base. Another approach used by some software products is a ruled-based system which has the ability to apply expert criteria to a compound. The rule-based, or expert system, applies a hierarchy of criteria to evaluate a toxicological end point

The Air Force has expressed a need for an alternative method for evaluating the toxicity of Chemicals. Computational chemistry and Quantitative Structure-Activity Relationships (QSAR) offer a possible approach to the computer assisted assessment of toxicity. The objective of this study request was to evaluate software products that have the capability of estimating toxicological end points. The need for this type of capability at the Toxic Hazards Research Unit (THRU) is necessitated by the vast number of chemical substances that have no toxicological data. The toxicity of some of these compounds can be addressed through OSAR where a data base of structurally related compounds is available for comparison. The data base approach operates by correlating structural descriptors of an unknown with the descriptors of toxicologically characterized compounds contained in the data base. Another approach used by some software products is a ruled-based system which has the ability to apply expert criteria to a compound. The rule-based, or expert system, applies a hierarchy of criteria to evaluate a toxicological end point

The hydrothermal durability of adhesive bonds is currently an active area of research. This work focuses on titanium/epoxy bonding using metal alkoxide primers as adhesive promoters. Pike showed that sec-butyl aluminum alkoxide enhances the durability of PAA (phosphoric acid anodized) and FPL (Forest Products Lab) etched 2024 aluminum bonded with the wedge test using epoxy adhesives. No variation in bond performance was observed when the cure temperature of the alkoxide film was varied. Hydrolysis of metal alkoxides occurs readily in the presence of moisture as indicated by the equation. Titanium alkoxide hydrolysis is complex because it involves simultaneous hydrolysis and polymerization. These reactions form polycondensates dependent on the water/alkoxide ratio, hydroylsis medium, temperature, and the nature of the alkyl group. Aluminum alkoxides hydrolyze to monohydroxides initially and later convert to trihydroxides. In hot water, a crystalline boehmite is formed, whereas an amorphous hydroxide is formed in cold water. Both hydroxides will convert to aluminum oxide at 500 C. This report encompasses characterization and adhesive bonding studies of three metal alkoxide primers: tetra n-butyl titanate, TNBT; tetra isopropyl titanate, TIPT; and sec-butyl aluminum alkoxide, E-8385. The films have been characterized by XPS (x-ray photoelectron spectroscopy), STEM (scanning transmission electron microscopy), and FTIR (Fourier transform infrared spectroscopy). Adhesive bonding included the wedge test and the stress durability test to determine time to failure and locus of failure. It was conclude that E-8385 promotes the durability of P/F pretreated adherends, whereas the titanates provide no improvement.

Physical studies of the last decade indicate that multi-junction solar cell has higher energy conversion efficiency than single-junction cells. However, choosing the type of material, modeling and finding the parameters of this type of cell has always been one of the leading challenges in this topic. Most of the proposed models are assumed to have predetermined electrical parameters of the cell or regardless of the effect of the tunnel junction, which reduces of the system. This paper discusses the modeling of solar cell triplejunction InGap/GaAs /Ge, taking into account the effect of a tunnel junction and finds the parameters of each subcellular deal. Also, predictive control will be used to control the active and reactive power of the single-phase inverter. The use of this method eliminates the need for the modulation module and the phase-lock loop (PLL). And simplifies the control algorithm for its digital implementation. The proposed cost function of this paper will be such that in addition to controlling the power of the inverter, the closed loop stability will be guaranteed based on the Lyapunov theory. Finally, the performance of the system using the software MATLAB/SIMULINK simulation will be evaluated.

For all the difficulties engendered in its use, semiconductor device damage data are an integral part of many programs of electromagnetic pulse vulnerability assessment and hardening. Experimental damage data, which are generated only as a result of dedicated efforts, can be expected to be available for only a minor fraction of all semiconductor devices. This limited supply has spurred efforts to develop predictive damage models, in order to bypass the tedious experimental requirements for generating damage data. The predictive ability of the best of these models, the junction capacitance damage model, is investigated in detail. Central to this study is a library of experimental damage data for 46 silicon device types, comprising bipolar transistors and diodes tested at the 10-, 1-, and 0.1-micro-sec. pulse durations. These are devices from the front ends of a number of Army systems and represent radio, field wire, and cable functions with operating ranges in the direct current (dc) to microwave region. Of the 46 experimental devices comprising 68 junction types (collector-to-base and emitter-to-base junctions treated as distinct for all transistors), sufficient published manufacturers' data were available for the damage modeling of 11 junctions. These were supplemented with measured parameters for 27 junction types.

For all the difficulties engendered in its use, semiconductor device damage data are an integral part of many programs of electromagnetic pulse vulnerability assessment and hardening. Experimental damage data, which are generated only as a result of dedicated efforts, can be expected to be available for only a minor fraction of all semiconductor devices. This limited supply has spurred efforts to develop predictive damage models, in order to bypass the tedious experimental requirements for generating damage data. The predictive ability of the best of these models, the junction capacitance damage model, is investigated in detail. Central to this study is a library of experimental damage data for 46 silicon device types, comprising bipolar transistors and diodes tested at the 10-, 1-, and 0.1-micro-sec. pulse durations. These are devices from the front ends of a number of Army systems and represent radio, field wire, and cable functions with operating ranges in the direct current (dc) to microwave region. Of the 46 experimental devices comprising 68 junction types (collector-to-base and emitter-to-base junctions treated as distinct for all transistors), sufficient published manufacturers' data were available for the damage modeling of 11 junctions. These were supplemented with measured parameters for 27 junction types.

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The coding of data on 2,565 individual human altitude chamber tests is reported as part of a selection procedure designed to eliminate individuals who are highly susceptible to decompression sickness, individual aircrew members were exposed to the pressure equivalent of 37,000 feet and observed for one hour. Many entries refer to subjects who have been tested two or three times. This data contains a substantial body of statistical information important to the understanding of the mechanisms of altitude decompression sickness and for the computation of improved high altitude operating procedures. Appropriate computer formats and encoding procedures were developed and all 2,565 entries have been converted to these formats and stored on magnetic tape. A gas loading file was produced.

An investigation of the predictive capability of a new Air Force Weapons Laboratory model for transistor and diode failure under reverse bias was initiated. A comparison with the junction capacitance damage model shows a doubled improvement at high confidence levels based on an Army-generated population of experimental damage data. (Author)

The integration of predictive modeling and artificial intelligence (AI) in public health represents a paradigm shift from reactive healthcare strategies to proactive, data-driven decision-making. This paper examines the foundational principles of predictive modeling, its evolution through AI techniques, and the diverse range of statistical, machine learning, and hybrid models applied within the public health sector. Drawing upon lessons from recent health crises, including the COVID-19 pandemic, the study highlights how AI enhances predictive capabilities in outbreak forecasting, resource allocation, and personalized medicine. Despite the transformative potential, challenges remain, ranging from data quality and interpretability to ethical and equity concerns. Through a synthesis of case studies, modeling methodologies, and future outlooks, this paper underscores the critical need for interdisciplinary collaboration, robust data infrastructures, and ethically grounded AI deployment to fully realize the benefits of predictive modeling in advancing global public health outcomes.