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arXiv 2025年

作者： Jung, Woochan Arnault, Ethan G. Huang, Bevin Park, Jinho Jang, Seong Watanabe, Kenji Taniguchi, Takashi Englund, Dirk Fong, Kin Chung Lee, Gil-Ho Physics Department Pohang University of Science and Technology Pohang37673 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Department of Physics Northeastern University BostonMA02115 United States Quantum Materials and Sensing Institute BurlingtonMA01803 United States

The thermal response of proximity Josephson junctions (JJs) is governed by the temperature (T)- dependent occupation of Andreev bound states (ABS), making them promising candidates for sensitive thermal detection. In this study, we systematically engineer ABS to enhance the thermal sensitivity of critical current (lc) of proximity JJs, quantified as |dlc/dT/| for threshold readout scheme and |dlc/***−1| for inductive readout scheme. Using a gate-tunable graphene-based JJ platform, we explore the impact of key parameters—including channel length, transparency, carrier density, and superconducting material—on the thermal response. Our results reveal that the proximity-induced superconducting gap plays a crucial role in optimizing thermal sensitivity. Notably, we see a maximum |dlc/***−1| value of 0.6 K−1 at low temperatures with titanium-based graphene JJs. By demonstrating a systematic approach to engineering ABS in proximity JJs, this work establishes a versatile framework for optimizing thermal sensors and advancing the study of ABS-mediated transport. © 2025, CC BY.

关键词： Superconducting materials

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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A Smote-Based Churn Prediction System Using Machine Learning Techniques

A Smote-Based Churn Prediction System Using Machine Learning...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Akinyemi Omololu Akinrotimi Roseline Oluwaseun Ogundokun Modinat Abolore Mabayoje Rafiat Ajibade Oyekunle Marion Olubunmi Adebiyi Department of Mathematics Sciences(Computer Science Program) Augustine University Ilara-Epe Epe Nigeria Department of Computer Science Landmark University Omu-Aran Nigeria Department of Multimedia Engineering Kaunas University of Technology Lithuania SDG 4. Quality Education Landmark University Omu Aran Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Information and Communication University of Ilorin Ilorin Nigeria Department of Computer Science Landmark University Omu-Aran Omu Aran Nigeria

The financial backbone of every telecommunications company is strictly made up of the number of customers patronizing the organization. Due to the high level of competition amongst existing telecommunication companies, customers sometimes leave, dropping the services of a particular one for the other. Predicting which subscribers may want to leave a telecommunications company and providing solutions to keep them from doing so, are the main objectives of customer churn prediction. Churn prediction helps classify consumers who are likely to move from one business to another. The ability to foresee churners before they leave has been increasingly useful in recent years, especially in light of increased competition among communications carriers. To compute churners for telecommunications providers, the study described in this paper used oversampling techniques to balance churn data and applied the dimensionality reduction technique to discover optimal features with a strong predictive ability for detecting would-be churners and non-churners. The model used Logistic Regression and the Naïve Bayes Classification Algorithm to implement comparative classification strategies. The performance levels of these strategies were compared using a Telecommunications Customer Churn data set, as well as performance metrics such as accuracy, sensitivity, and specificity. The Naïve Bayes Algorithm proves more efficient than its counterpart giving higher positive rate detection and a lower negative rate detection.

关键词： Measurement Companies Sensitivity and specificity Prediction algorithms Data models Telecommunications Numerical models

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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Suboptimal Safety-Critical Control for Continuous Systems Using Prediction-Correction Online Optimization

arXiv

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arXiv 2022年

作者： Wang, Shengbo Wen, Shiping Yang, Yin Cao, Yuting Shi, Kaibo Huang, Tingwen The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar School of Information Science and Engineering Chengdu University Chengdu611040 China Science Program Texas A & M University at Qatar Doha23874 Qatar

This paper presents an innovative efficient safety-critical control scheme for nonlinear systems by combining techniques of control barrier function (CBF) and online time-varying optimization. The idea lies in that when directly calling the complete optimization solvers used in the CBF method, such as CBF-based quadratic programming (CBF-QP), is computationally inefficient for complex tasks, the suboptimal solutions obtained from online learning techniques can be an alternatively reliable choice for ensuring both efficiency and control performance. By using the barrier-based interior point method, the original optimization problem with CBF constraints is reduced to an unconstrained one with approximate optimality. Then, Newton- and gradient-based continuous dynamics are introduced to generate alternative cheap solutions while ensuring safety. By further considering the lag effect of online tracking, a prediction term is added to the dynamics. In this way, the online cheap solutions are proven to exponentially converge to the time-varying suboptimal solutions of the interior point method. Furthermore, the safety criteria are established, and the robustness of the designed algorithms is analyzed theoretically. Finally, the effectiveness is illustrated by conducting two experiments on obstacle avoidance and anti-swing tasks. © 2022, CC BY-NC-ND.

关键词： Quadratic programming

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Cyber-physical infrastructures for advancing pyrolysis conversion process: A case study of biochar production

Cyber-physical infrastructures for advancing pyrolysis conve...

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ASME 2020 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2020

作者： Hersh, Benjamin Mohajeri, Amir Mirkouei, Amin Xian, Min Department of Mechanical Engineering University of Idaho ID United States Industrial Technology Program University of Idaho ID United States Industrial Technology Program Department of Mechanical Engineering University of Idaho ID United States Department of Computer Science University of Idaho ID United States

ISBN: (纸本)9780791883983

Biomass-based products (bioproducts) have been introduced as a means to address food-energy-water nexus challenges. However, the existing approaches have not been integrated to convert biomass into market-competitive bioproducts (e.g., biochar and bio-oil). Pretreatment and conversion processes represent the most substantial portion of the total bioproduct cost. This study proposes a portable conversion process for high-quality biochar and bio-oil production, using mixed reactors and advanced cyber-physical infrastructures to promote cross-cutting technological and commercialization opportunities. The proposed portable process converts biomass to bioproducts near the collection sites, which can address collection, staging, and logistics challenges. The fundamental novelty of this study lies in utilizing cyber-physical technologies for advancing pre-/postconversion processes and crossing the boundaries to meet the market needs. A case study for biochar production from various biomass feedstocks is used to demonstrate and verify the application of the conversion process and the cyber-based advances. The results indicate that the integrated cyber-physical conversion pathway can increase process yield and quality. It is also found that biomass properties and conversion process configurations play a crucial role in determining the process yield and biochar quality in terms of the physical-chemical structure. Copyright © 2020 ASME.

关键词： Biomass

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k-mer-based approaches to bridging pangenomics and population genetics

arXiv

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arXiv 2024年

作者： Roberts, Miles D. Davis, Olivia Josephs, Emily B. Williamson, Robert J. Genetics and Genome Sciences Program Michigan State University East LansingMI48824 United States Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN47803 United States Department of Biology and Biomedical Engineering Rose-Hulman Institute of Technology Terre HauteIN47803 United States Department of Plant Biology Michigan State University East LansingMI48824 United States Ecology Evolution and Behavior Program Michigan State University East LansingMI48824 United States Plant Resilience Institute Michigan State University East LansingMI48824 United States

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes is challenging, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a crucial stepping stone to bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π = 0.025 (R2 = 0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π = 0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers. © 2024, CC BY.

关键词： Genome

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The Pairing Analysis Improvement of Magnetized Structure in Electromagnetic Bulging Process in Case of Tube with Field Shaper

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Journal of Electromagnetic Analysis and Applications 2024年第4期16卷 43-67页

作者： Blaise Kevine Lenz Soronga Zhanqian Zhan Jing Liu Jonas Asamoah Mohamed Bangali Bangura Matala Makiese N’kodia Kambani Chrionie Md Jobayer Hossain Yongdong Chen Program Debugging and Design Department Guangdong Lyric Robot Automation Co. Ltd Huizhou China Electrical and Automation Department China Three Gorges University Yichang China Electronic Department Power Electronic Research Center University of Kent Giles Ln Canterbury UK Program Debugging Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Electrical and Computer Engineering Department South China University of Technology Guangzhou China World Standardization Certification and Testing Group (Shenzhen) Co. Ltd. Shenzhen China Electrical Engineering-Power System Mapon University Kindu Democratic Republic of Congo Department of Economy Managment School of Management Computer and Telecomunication Rabbat Marocco Materials Visual Calibration Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Program Design Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China

In the current practical science, the accuracy in the formability of metal alloys being the goal when using electromagnetic forming (EMF) technology, which is a high-speed processing technology that uses Lorentz forces to achieve plastic deformation of sheet metal;according to the previous analysis, the results have shown that in most cases, the Lorentz force acting on the workpiece (metal) is not uniform, there are uneven axial deformations of the metal plates which prevent the rapid advancement of today’s technology. In this article, we presented some advanced analyzes which will lead us to improve the technical solution for the problems of non-uniform axial deformations of the metals in the traditional tube electromagnetic forming technology (EMF). A field shaper is used as a practical forming tool to influence the magnetic field and magnetic pressure distribution, thereby improving the forming ability and result during the electromagnetic forming (EMF) process and we see that induced eddy current control is realized by changing the structural parameters of the magnetic field shaper;which improves the strength and controllability of the magnetic force that acts on the workpiece;thereby a greater radial magnetic pressure can be achieved with field shaper than the case without it;the field shaper regulates the electromagnetic force, the distribution of the magnetic pressure decreases, and the uniform force area of the tube increases which effectively enhances the uniform range of the pipe electromagnetic bulging and the electromagnetic induction coupling between the coil and the metallic workpiece is generally required to produce the Lorentz forces. Using COMSOL Multiphysics® simulation software helped us to accurately represent the real world, simulating multiple physical effects that happened in this model during the process.

关键词： Electromagnetic Forming Field Shaper Lorentz Force Magnetic Pressure Distribution

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

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Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

TechRxiv

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TechRxiv 2023年

作者： Petrenko, Sasha Hier, Daniel B. Obafemi-Ajayi, Tayo Bone, Mary Timpson, Erik J. Speight, Michael Marsh, William E. Wunsch, Donald C. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Department of Neurology and Rehabilitation University of Illinois at Chicago ChicagoIL United States University of Southeastern Norway Kongsberg Norway Engineering Program Missouri State University SpringfieldMO United States Honeywell Federal Manufacturing & Technologies Kansas CityMO United States

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multicategorical symbolic data. This article proposes Symbolic Tree Adaptive Resonance Theory (START), with additional supervised, Dual-Vigilance (DV-START), and Distributed Dual-Vigilance (DDV-START) formulations, for the clustering of multicategorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot-Marie-Tooth disease using genomic, phenotypic, and proteomic data. © 2023, CC BY.

关键词： Ontology

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