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Optical Manipulation and Structured Materials Conference 2023

作者： Budlayan, Marco Laurence M. Palangyos, Dina C. Patricio, Jonathan N. Arco, Susan D. Guerrero, Raphael A. Department of Physics Ateneo de Manila University Loyola Heights Quezon City1108 Philippines Mathematics and Natural Sciences Department College of Computing Pangasinan State University Pangasinan Urdaneta Philippines Materials Science and Engineering Program College of Science University of the Philippines Diliman Diliman Quezon City1101 Philippines Synthetic Organic Chemistry Laboratory Institute of Chemistry College of Science University of the Philippines Diliman Diliman Quezon City1101 Philippines

ISBN: (纸本)9781510663398

Surface features of natural materials such as cuticles, shells, and leaves continue to be used as models for creating sophisticated infrastructure, landscape, and microdevice designs. Optical microscopy is the easiest and most accessible tool for examining such surface features and micropatterns. This method, however, fails to probe opaque surfaces like thick skin and leaves since it needs enough light to penetrate the sample. In this work, we demonstrate the feasibility of probing the natural structures of a superhydrophobic leaf using simple optical microscopy by replicating the leaf surface on an elastomer via soft lithography. Optical images of the replica revealed intricate details of features present on the leaf, including the stoma, cuticle boundary, and trichome. Contact angle measurements were also conducted to investigate the wettability of the real leaves and their replicas. Finally, we utilized the fabricated replicas as optical gratings that were observed to generate various diffraction patterns with fluctuating intensities. Our work offers a new perspective on alternative optical microscopy strategies and optical beam tuning using biomimetic, low-cost materials. © 2023 SPIE.

关键词： Contact angle

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A Multi-objective Approach to the Protein Structure Prediction Problem using the Biased Random-Key Genetic Algorithm

A Multi-objective Approach to the Protein Structure Predicti...

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Congress on Evolutionary Computation

作者： Felipe Marchi Rafael Stubs Parpinelli Graduate Program in Applied Computing Santa Catarina State University Joinville Santa Catarina Brazil

Proteins are base molecules present in live organisms. The study of their structures and functions is of considerable importance for many application fields, particularly for the pharmaceutical area. This paper presents a multi-objective model to the Protein Structure Prediction problem, using three objectives: energy score, secondary structure information, and contact maps information. A BRKGA method is used as a global optimizer and adapted to work with multiple objective problems. Also, the MUFOLD-CL clustering method is applied to select a single predicted structure. Experiments were carried out to analyze the proposed model performance using state-of-the-art ab initio algorithms for comparison. Results obtained indicate that the proposed approach is competitive in terms of RMSD and GDT metrics.

关键词： Proteins Measurement Adaptation models Evolutionary computation Predictive models Prediction algorithms Organisms

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A Fitness-Based Migration Policy for Biased Random-Key Genetic Algorithms 26th

A Fitness-Based Migration Policy for Biased Random-Key Gene...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Boiani, Mateus Parpinelli, Rafael Stubs Dorn, Márcio Institute of Informatics Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil Graduate Program in Applied Computing Santa Catarina State University Santa Catarina Joinville Brazil Center for Biotechnology Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil National Institute of Science and Technology - Forensic Science Rio Grande do Sul Porto Alegre Brazil

ISBN: (纸本)9783031302282

Population diversity management is crucial for the quality of solutions in Evolutionary Algorithms. Many techniques require assistance to handle diverse problem characteristics and may prematurely converge in local optima. Maintaining diversity enables the algorithm to search the space and produce better results effectively. Parallel models are a common approach to preserving diversity;however, design decisions affect optimization process characteristics. For example, the Island model’s migration policy affects convergence speed. This study proposes and evaluates a fitness-based migration policy for the Biased Random-Key Genetic Algorithm (BRKGA) and compares it to two traditional strategies. The results in continuous search spaces demonstrate that the proposed policy can enhance BRKGA optimization with appropriate parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Evaluation of Enrichment in Ontology-based Knowledge Management System

Evaluation of Enrichment in Ontology-based Knowledge Managem...

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International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)

作者： Ni Kadek Dessy Hariyanti Linawati Linawati I Made Oka Widyantara Nyoman Putra Sastra Anang Kukuh Adisusilo I Wayan Budi Sentana I Dewa Made Bayu Atmaja Darmawan Study Program of Doctoral Engineering Science Universitas Udayana Indonesia Electrical Engineering Department Universitas Udayana Indonesia Engineering Department Universitas Wijaya Kusuma Surabaya Surabaya Indonesia School Of Computing Macquarie University Sydney New South Wales

Ontology is an organization of knowledge that can represent knowledge in a structured manner. An ontology-based Knowledge Management System is a system that combines elements of knowledge management with the application of ontology as a knowledge base. Meanwhile, knowledge is dynamic and continues to develop all the time, giving rise to new knowledge. Therefore, the ontology must be updated regularly through ontology enrichment to meet adequate knowledge requirements. Ontology enrichment is carried out to ensure that the ontology remains relevant and responsive to developments in its knowledge domain. However, there are no standard stages in implementing enrichment in ontology-based Knowledge Management systems. Therefore, this research conducted a literature review to determine when and how to apply enrichment in ontology development. Data was obtained from related journals in 2018 - 2023. The method used in this research is the Systematic Literature Review method to systematically identify, review, evaluate, and interpret all available research in the area of interest with specific relevant research questions. The research results show that the ontology enrichment procedure generally includes preprocessing stages, relation extraction, and enrichment processes. Ontology enrichment is generally applied during the development of a new ontology as part of the ontology development cycle and on existing ontologies, which are a separate stage from the initial ontology development cycle. Based on these findings, the researcher provides recommendations for researchers who want to use or develop new ontology enrichment methods.

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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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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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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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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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