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Self-reported physical functioning,cardiometabolic health conditions, and healthcare utilization patterns in Million Veteran program enrollees with Traumatic Brain Injury Screening and Evaluation program data

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Military Medical Research 2023年第5期10卷 587-598页

作者： Alexandra L.Clark Makenna B.McGill Erin D.Ozturk David M.Schnyer Catherine Chanfreau‑Cofnier Victoria C.Merritt Research Service VA San Diego Healthcare System(VASDHS)San DiegoCA92161USA Department of Psychology The University of Texas at AustinAustinTX 78712USA San Diego State University/University of CaliforniaSan Diego Joint Doctoral Program San DiegoCA 92120USA VA Informaticsand Computing Infrastructure(VINCI) VA Salt Lake City Health Care SystemSalt Lake CityUT 84148USA Department of Psychiatry School of MedicineUniversity of California San DiegoLa JollaCA 29093USA Center of Excel‑lence for Stress and Mental Health VASDHSSan Diego 92161USA

Background Examining the health outcomes of veterans who have completed the United States Veterans Health Administration’s(VHA’s)Traumatic Brain Injury(TBI)Screening and Evaluation program may aid in the refinement and improvement of clinical care initiatives within the *** study compared self-reported physical functioning,cardiometabolic health conditions,and health care utilization patterns in Million Veteran program enrollees with TBI Screening and Evaluation program data(collected between 2007 and 2019),with the goal of enhancing understanding of potentially modifiable health conditions in this *** In this observational cohort study,veterans(n=16,452)were grouped based on the diagnostic outcome of the TBI Screening and Evaluation program:1)negative TBI screen(Screen^(-));2)positive TBI screen but no confirmed TBI diagnosis[Screen^(+)/Comprehensive TBI Evaluation(CTBIE)^(-)];or 3)positive TBI screen and confirmed TBI diagnosis(Screen^(+)/CTBIE^(+)).Chi-square tests and analysis of covariance were used to explore group differences in physical functioning,cardiometabolic health conditions,and health care utilization patterns,and logistic regressions were used to examine predictors of Screen^(+/-)and CTBIE^(+/-)group *** The results showed that veterans in the Screen^(+)/CTBIE^(-)and Screen^(+)/CTBIE^(+)groups generally reported poorer levels of physical functioning(P’s<0.001,np2=0.02 to 0.03),higher rates of cardiometabolic health conditions(P’s<0.001,φ=0.14 to 0.52),and increased health care utilization(P’s<0.001,φ=0.14 to>0.5)compared with the Screen-group;however,health outcomes were generally comparable between the Screen^(+)/CTBIE^(-)and Screen^(+)/CTBIE^(+)***-up analyses confirmed that while physical functioning,hypertension,stroke,healthcare utilization,and prescription medication use reliably distinguished between the Screen-and Screen^(+)groups(P’s<0.02,OR’s 0.78 to 3.38),only physical functioning distinguished bet

关键词： Traumatic brain injury(TBI)screen Comprehensive TBI Evaluation(CTBIE) Health outcomes Cardiometabolichealth Veterans Million Veteran program(MVP)

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Hybrid CNN-LSTM and Domain Modeling in Climate-Energy Analysis for a Smart Environment

Hybrid CNN-LSTM and Domain Modeling in Climate-Energy Analys...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Sarahana Shrestha Brian Buckley Aparna S. Varde Daniel Cwynar Environmental Science & Mangement PhD Program Montclair State University Montclair NJ USA Clean Energy & Sustainability Analytics Center Montclair State University Montclair NJ USA School of Computing Montclair State University Montclair NJ USA

This study aims to model the effect of local climate on energy generation in power plants in the United States. It considers climate change as well as the ever-growing electricity demand, contributing to greenhouse gas emissions. We propose an integration of AI-based and domain-specific paradigms via a hybrid approach of convolutional neural networks (CNN) & long short-term memory (LSTM) coupled with environmental models. Erstwhile studies focus mainly on specific energy sources, e.g. solar/wind power. There is a need for more comprehensive examination, e.g. temperature effects across various energy generation types at the macro level of states and micro level of power plants. This forms the major focus and novelty of our study. Fathoming the factors driving temperature-energy correlations, and assessing power plant vulnerability to changing climate patterns, are both essential to promote energy infrastructure resilience and sustainability. Our study offers novel insights into the complex interplay of temperature, energy generation, sustainability and related aspects to enable enhanced macro & micro level decision-making in energy sector. It therefore makes contributions to smart environment goals for smart cities and a smart planet.

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ETHICS-2023 Session D1 - Open Forum: Building a Technology Ethics Community

ETHICS-2023 Session D1 - Open Forum: Building a Technology E...

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IEEE International Symposium on Ethics in Science, Technology and Engineering

作者： Thomas Creely Marc Cheong Heather Love Ketra Schmitt Ethics and Emerging Military Technology Graduate Program U.S. Naval War College School of Computing and Information Systems & Centre for AI and Digital Ethics (CAIDE) University of Melbourne English Language and Literature University of Waterloo Concordia University

This session was an open forum where audience members were invited to participate in discussions of a number of themes with relevance to ethics and technology and to future conferences in the IEEE ETHICS series. The discussions took place in small groups, with groups reporting back to the full cohort for collaborative brainstorming.

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Quantum Kernel-Based Long Short-term Memory

Quantum Kernel-Based Long Short-term Memory

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yu-Chao Hsu Tai-Yu Li Kuan-Cheng Chen National Center for High-Performance Computing NARlabs Hsinchu Taiwan Cross College Elite Program National Cheng Kung University Tainan Taiwan Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The integration of quantum computing into classical machine learning architectures has emerged as a promising approach to enhance model efficiency and computational capacity. In this work, we introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network, which utilizes quantum kernel functions within the classical LSTM framework to capture complex, non-linear patterns in sequential data. By embedding input data into a high-dimensional quantum feature space, the QK-LSTM model reduces the reliance on large parameter sets, achieving effective compression while maintaining accuracy in sequence modeling tasks. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness, making it suitable for deployment in edge computing environments and resource-limited quantum devices (especially in the NISQ era). Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters, underscoring its potential to advance quantum machine learning applications in natural language processing and other domains requiring efficient temporal data processing.

关键词： Performance evaluation Quantum computing Computational modeling Computer architecture Signal processing Minimization Data models Natural language processing Speech processing Long short term memory

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Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning based Information Bottleneck

arXiv

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arXiv 2024年

作者： Wang, Dedi Tiwary, Pratyush Biophysics Program and Institute for Physical Science and Technology University of Maryland College Park20742 United States Department of Chemistry and Biochemistry Institute for Physical Science and Technology University of Maryland College Park20742 United States University of Maryland Institute for Health Computing Bethesda20852 United States

The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional collective variable (CV) space and then partitioning it into bins. The efficacy of WE simulations heavily depends on the selection of CVs and binning schemes. The recently proposed State Predictive Information Bottleneck (SPIB) method has emerged as a promising tool for automatically constructing CVs from data and guiding enhanced sampling through an iterative manner. In this work, we advance this data-driven pipeline by incorporating prior expert knowledge. Our hybrid approach combines SPIB-learned CVs to enhance sampling in explored regions with expert-based CVs to guide exploration in regions of interest, synergizing the strengths of both methods. Through benchmarking on alanine dipeptide and chignoin systems, we demonstrate that our hybrid approach effectively guides WE simulations to sample states of interest, and reduces run-to-run variances. Moreover, our integration of the SPIB model also enhances the analysis and interpretation of WE simulation data by effectively identifying metastable states and pathways, and offering direct visualization of dynamics. © 2024, CC BY.

关键词： Deep learning

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Predicting Abundance of Fish Species Populations in Manila Bay, Philippines Based on Ensemble Learning Approach

Predicting Abundance of Fish Species Populations in Manila B...

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International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Sherrlyn M. Rasdas Arnel C. Fajardo Jomel S. Limbago Graduate Program Technological Institute of the Philippines Manila Philippines College of Computing Science Information and Communication Technology -CCSICT Isabela State University-Cauayan Philippines Fisheries and Aquatic Sciences Department (FASD) Cavite State University Naic Cavite Philippines

Manila Bay is a significant contributor to the Philippines' fish production, but its resources have been depleted due to overfishing, pollution, and damage, leading to a decline in fish catch and a shift towards less valuable species. Conventional approaches to fisheries stock assessment impose constraints on our comprehension of fish population dynamics. These limitations can be overcome through the utilization of machine learning techniques, which enable the forecasting and modeling of fisheries populations with improved accuracy and understanding. In this study, the commercial fisheries populations data collected from 2018 to 2021 in Manila Bay were used to predict the abundance of species fisheries production data using the K-NN - MLP - Logistic Regression (KNMLPR) model based on the majority voting ensemble approach. Analysis revealed that it is possible to combine the strengths of multiple models and improve overall predictive performance. The results also suggest that the k-nearest neighbors and logistic regression models have the best performance in predicting fish species population dynamics, while the neural network model shows slightly lower accuracy. This study provides valuable insights for fishery management and policymaking to support sustainable fishing practices in the region. Further research could focus on exploring additional machine learning algorithms and incorporating environmental factors to improve the prediction accuracy of the model.

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ICD-10 - ORPHA: An Interactive Complex Network Model for Brazilian Rare Diseases

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Procedia Computer Science 2024年 239卷 634-642页

作者： Mariane Barros Neiva Bibiana Mello de Oliveira Amanda Maria Schmidt Victória Machado Scheibe Júlia Cordeiro Milke Mariana Lopes dos Santos Diego Bettiol Yamada Márcio Eloi Colombo Filho Giovane Thomazini Soares Yasmin de Araújo Ribeiro Odemir Martinez Bruno Têmis Maria Félix Domingos Alves RARAS Network group Scientific Computing Group University of São Paulo São Carlos SP Brazil Ribeirão Preto Faculty of Medicine University of São Paulo São Paulo Brazil Medical Genetics Service Porto Alegre Clinical Hospital Porto Alegre RS Brazil Graduate Program in Genetics and Molecular Biology Federal University of Rio Grande do Sul Porto Alegre RS Brazil Graduate Program in Medicine: Medical Sciences Federal University of Rio Grande do Sul Porto Alegre RS Brazil

A disease is considered rare if it has a low prevalence. It is estimated that around 400 million people worldwide have a rare disease, including 15 million in Brazil. Consequently, it became a public health priority for the World Health Organization and the Brazilian Health Ministry. In 2014, the Brazilian government launched a national policy regarding the care for rare patients’, the Ordinance nº199. The national politic defines guidelines, procedures, and descriptions of rare disease codes to provide access and diagnosis in the public health system to reduce mortality and improve patient’s quality of life. Diseases are identified according to the International Classification of Diseases 10th Revision, a widely used terminology in this context. However, there are also different terminologies to codify a rare disease, such as the ORPHAcode provided by Orphanet. This paper proposes a complex network model using the terminologies’ relationship to show that the International Classification of Diseases 10th Revision may be generic for diagnosing rare Brazilian patients. Moreover, there is no perfect nomenclature to define rare diseases, but each context has a better application. So, mapping the relationship between each terminology is fundamental for creating consistent semantic relationships in biomedical ontologies, providing a functional environment for carrying out tasks involving more than one terminology.

关键词： Rare Disease Complex Network Biomedical Ontology Graph Modeling

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To Trust or Not to Trust: Evolutionary Dynamics of an Asymmetric N-player Trust Game

arXiv

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arXiv 2023年

作者： Soo Lim, Ik. Masuda, Naoki School of Computing and Mathematical Sciences University of Greenwich LondonSE10 9LS United Kingdom Department of Mathematics and Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States

Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a sequential interaction between two players, an investor and a trustee. It requires at least two strategies or options for an investor (e.g. to trust versus not to trust a trustee). According to the evolutionary game theory, the antisocial strategies (e.g. not to trust) evolve such that the investor and trustee end up with lower payoffs than those that they would get with the prosocial strategies (e.g. to trust). A generalisation of the TG to a multiplayer (i.e. more than two players) TG was recently proposed. However, its outcomes hinge upon two assumptions that various real situations may substantially deviate from: (i) investors are forced to trust trustees and (ii) investors can turn into trustees by imitation and vice versa. We propose an asymmetric multiplayer TG that allows investors not to trust and prohibits the imitation between players of different roles;instead, investors learn from other investors and the same for trustees. We show that the evolutionary game dynamics of the proposed TG qualitatively depends on the nonlinearity of the payoff function and the amount of incentives collected from and distributed to players through an institution. We also show that incentives given to trustees can be useful and sufficient to cost-effectively promote trust and trustworthiness among self-interested players. © 2023, CC BY.

关键词： Dynamics

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Prediction of Bankruptcy among Middle 40 Per Cent (M40) in Malaysia using Support Vector Machine

Prediction of Bankruptcy among Middle 40 Per Cent (M40) in M...

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Student Conference on Research and Development (SCOReD)

作者： Nurbaity Sabri Aqilah Nadiah Md Sahiq Hazwa Hanim Mohamed Hamzah Hartayuni Sain Nur Nabilah Abu Mangshor Anis Amilah Shari College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Melaka Kampus Jasin Melaka Malaysia Faculty of Business and Management Universiti Teknologi MARA Cawangan Melaka Kampus Bandaraya Melaka Malaysia Statistics Study Program Mathematics and Natural Science Faculty Tadulako University Palu Indonesia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The Middle 40 Per Cent (M40) demographic comprises a substantial proportion of Malaysia’s consumer base, comprising the median 40% of income earners. This group is a big part of the country’s customer base. The rising cost of living, flat wage growth, and rising family debt make this group especially vulnerable to financial instability. Given these economic pressures, predicting the possibility of bankruptcy in this population is becoming increasingly crucial to allow for early intervention and preventive actions. Support Vector Machines (SVM) are recognized for their efficacy in classification tasks, particularly with small or imbalanced datasets. SVM prediction will be utilized using 14694 samples of the Debt Management program (DMP) dataset from 2016 to 2020. Twenty-three (23) features will be implemented on the dataset and Synthetic Minority Oversampling Technique (SMOTE) to improve the imbalanced datasets. The experiment demonstrates that the SVM classifier obtained 91.1% accuracy when implementing PUK kernels. It suggests that SVM is a plausible alternative model for predicting personal bankruptcy in the M40 group. To anticipate personal bankruptcy for M40, additional prediction models will be evaluated in the future.

关键词： Support vector machines Machine learning algorithms Neural networks Predictive models Prediction algorithms Data models Vectors Bankruptcy Research and development Random forests

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THE RAINBOW CONNECTION NUMBER OF THE COMMUTING GRAPH OF A FINITE NONABELIAN GROUP

arXiv

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arXiv 2023年

作者： Umbara, Rian Febrian Salman, A.N.M. Putri, Pritta Etriana Doctoral Program in Mathematics Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Bandung Indonesia Combinatorial Mathematics Research Group Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Indonesia School of Computing Telkom University Bandung Indonesia

A path in an edge-colored graph is called a rainbow path if every two distinct edges of the path have different colors. A graph whose every pair of vertices are linked by a rainbow path is called a rainbow-connected graph. The rainbow connection number of a graph is the minimum number of colors that are needed to color the edges of the graph such that the graph is rainbow-connected. In this paper, we determine the rainbow connection number of the commuting graph of a finite non-abelian group, which is a graph whose vertex set is a non-abelian group of finite order and two distinct elements of the group is adjacent in the graph if they commute. We also show that the rainbow connection number of the commuting graph of a finite group is related to the number of maximal abelian subgroups or the number of involutions that do not commute with any other non-identity element of the group. © 2023, CC BY.

关键词： Color

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