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IEEE Conference on Nanotechnology

作者： Hanrui Li Dayanand Kumar Nazek El-Atab Smart Advanced Memory Devices and Applications (SAMA) labs Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering (CEMSE) Division KAUST Thuwal Saudi Arabia

As a novel 2D material, MoS 2 has shown excellent electrical properties and resistive switching characteristics to work as a switching layer for non-volatile memory. In this work, we drop cast the MoS 2 solution to prepare the thin film and deposit an interfacial layer of Al 2 O 3 . We demonstrate the proposed memristive device with Cu/Al 2 O 3 /MoS 2 /Pt structure to work as an artificial synapse. The device shows a steady resistive switching behavior with the SET and RESET voltages of 1.3 V and -0.5 V, respectively. We further demonstrate the synapse behavior via a Hopfield Neural Network (HNN) and achieve image recognition and reconstruction with a high accuracy of 96% after 15 training epochs.

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Design of a Framework Based on MCDM and Data Analytics for Improving the Inventory Management of Supplies for Clinical Studies: A Case Study in a Research Center of a High Complexity Clinic 26th

Design of a Framework Based on MCDM and Data Analytics for I...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Jiménez-Delgado, Genett Isabel Aslan, Imran Hernández-Palma, Hugo Orozco Bohorquez, Mario Acosta Ortega, Felipe Plazas-Alvarado, Jonny Jiménez-Coronado, Angélica Roncallo-Pichon, Alberto Morales-Espinosa, Roberto Department of Industrial Engineering Institución Universitaria de Barranquilla IUB Barranquilla Colombia Faculty of Health Sciences Bingöl University Bingöl12000 Turkey Faculty of Engineering Industrial Engineering Program Corporación Universitaria Iberoamericana IBERO Bogotá Colombia Department of Computer Science and Electronics Universidad de la Costa CUC Barranquilla Colombia Faculty of Economic Accounting and Administrative Sciences Business Administration Program Fundación Universitaria de Popayan Popayan Colombia Bogotá Colombia Department of Business Transformation - Business Administration Program Corporación Unificada Nacional de Educación Superior – CUN Bogotá Colombia Faculty of Economic Administrative and Accounting Sciences. Accounting Program Corporación Universitaria Americana Barranquilla Colombia International Business Program Fundación Universitaria CEIPA Barranquilla Colombia

ISBN: (纸本)9783031768088

Inventory management in the health sector, specifically in the pharmaceutical service, represents a high percentage of logistics costs, putting pressure on health institutions to optimize inventory management to guarantee the availability of supplies. On the other hand, different regulations worldwide establish the adoption of mechanisms and policies for adequate inventory management, a situation that in low- and middle-income countries is restricted by resource limitations and the low implementation of robust techniques and methodologies. Different applications of models for inventory management have been identified in the literature, such as optimization models, Lean tools, and multicriteria decision-making methods. However, integrated approaches to optimize demand management, ordering, and controlling pharmaceutical services supplies are still under development. Therefore, the present study proposes a three-phase hybrid approach based on MCDM techniques and data analytics to improve inventory management of the pharmaceutical service in a health research center. The first stage consisted of characterizing the process to identify aspects for improvement. In the second stage, a multicriteria ABC classification model based on F-AHP and TOPSIS was applied to classify laboratory supplies for three selected clinical studies. Furthermore, the appropriate forecasting method was chosen for each clinical study, and a combined model (P model and FEFO model) was applied to establish the reorder point. Then, strategies, mechanisms, and policies were proposed to improve inventory management and redesign the process flow. As the main results, a multicriteria ABC method was obtained to classify laboratory supplies, taking into account Rotation (GW = 0.512), Criticality (GW = 0.286), and Availability (GW = 0.203). On the other hand, through data analysis and regression models, the exponential smoothing model with α = 0.10 was identified as the most convenient forecasting model, as

关键词： Decision making

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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computer science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Boby Siswanto Haryono Soeparno Nesti F. Sianipar Widodo Budiharto Computer Science Department BINUS Graduate Program- Computer Science Bina Nusantara University Jakarta Indonesia Food Technology Department Engineering Faculty Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short (<165 cm), and age=middle (45-60 years) are more likely to be affected by cardiovascular disease. The variable gender=woman with height=short indicates a 76.07% probability of developing cardiovascular disease.

关键词： computer science Correlation Itemsets Cardiovascular diseases Data mining Information technology

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Neurocognitive assessment under various human-robot teaming environments

Neurocognitive assessment under various human-robot teaming ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Anna L. Packy Jayesh Jayahankar Arya Teymourlouei Joshua Stone Hyuk Oh Garrett E. Katz James A. Reggia Rodolphe J. Gentili Neuroscience and Cognitive Science Program University of Maryland College Park USA Department of Computer Science University of Maryland College Park USA Department of Electrical & Computer Engineering University of Maryland College Park USA Department of Kinesiology University of Maryland College Park USA Department of Electrical Engineering & Computer Science Syracuse University Syracuse USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Human-robot teaming has become increasingly important with the advent of intelligent machines. Prior efforts suggest that performance, mental workload, and trust are critical elements of human-robot dynamics that can be altered by the robot’s behavior. Most prior human-robot teaming studies used behavioral analyses, but a limited number used neural markers, without the use of physical robots and complex tasks. Here we combine behavioral and EEG cortical dynamics to examine cognitive-motor processes when individuals complete a complex task under various team environments with a robot. The results revealed that altering the robot quality affected both behavioral and EEG dynamics. Task completion with an experienced robot led to greater team performance and human trust along with lower mental workload compared to an inexperienced teammate or when individuals performed alone. EEG changes suggest that different attentional processes were engaged when humans worked with the robot and performed alone, and that visual processing was more prominent when teaming with an inexperienced teammate. This work can inform human cognitive-motor processes and the design of robotic controllers in human-robot teams.

关键词： Visualization Process control Humanoid robots Collaboration Human factors Control systems Electroencephalography Behavioral sciences engineering in medicine and biology Robots

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Tuning Zinc-Copper Dual-Ion Electrolytes to Extend the Cycle Life of Zinc Ion Capacitors

Tuning Zinc-Copper Dual-Ion Electrolytes to Extend the Cycle...

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International Flexible Electronics Technology Conference (IFETC)

作者： Jinwook Jung Chanho Shin Tse Nga Ng Program in Materials Science and Engineering University of California San Diego La Jolla USA Department of Electrical and Computer Engineering University of California San Diego La Jolla USA

ISBN: (数字)9798331529468

ISBN: (纸本)9798331529475

We investigated the effect of copper ion concentration in zinc-copper dual-ion electrolytes to suppress dendrites and extend the cycle life of zinc ion capacitors. The devices were characterized in terms of changes in microstructure and cycling stability. The nuclei size was decreased in the optimal copper ion concentration to promote lateral deposition and avoid vertical dendrites. The device exhibited stable cycling performance with a capacitance retention of 95% after 10,000 redox cycles, compared to the device with single zinc ion electrolyte which short circuited at around 1,250 redox cycles.

关键词： Performance evaluation Capacitors Redox Ions Electrolytes Dendrites (neurons) Copper Microstructure Zinc Tuning

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach 6

Initial Study of Oil Palm Crop Condition Detection Through D...

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6th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2024

作者： Nugraha, Winata Ermatita Stiawan, Deris Universitas Sriwijaya Faculty of Computer Science Doctoral Program Faculty of Engineering Palembang30139 Indonesia Universitas Sriwijaya Department of Information System Palembang30139 Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang30139 Indonesia

ISBN: (纸本)9798331533243

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2] © 2024 IEEE.

关键词： Photomapping

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An algorithm for conditional-fault local diagnosis of multiprocessor systems under the MM⁎ model

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Theoretical computer science 2024年 987卷

作者： Lv, Yali Lin, Cheng-Kuan Hsu, D. Frank Fan, Jianxi School of Information Technology Henan University of Chinese Medicine Zhengzhou China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology NDU Taoyuan Taiwan Department of Computer and Information Science Fordham University New York United States School of Computer Science and Technology Soochow University Suzhou China

Diagnosis is a crucial subject for maintaining the reliability of multiprocessor systems. Under the MM⁎ diagnosis model, Sengupta and Dahbura proposed a polynomial-time algorithm with time complexity O(N5) to diagnose a system with N processors. In this paper, we propose a (α,β)-trees combination S(u,X,α,β) and give an algorithm to identify the fault or fault-free status of each processor for conditional local diagnosis under the MM⁎ model. According to our results, a connected network with a (α,β)-trees combination S(u,X,α,β) for a node u is conditionally locally (α+2β−3)-diagnosable at node u and the time complexity of our algorithm to diagnose u is O(α2β+αβ2). As an application, we show that our algorithm can identify the status of each node of n-dimensional star graph Sn if the faulty node number does not exceed 3n−8. Compared with existing algorithms, our algorithm allows more faulty nodes in a multiprocessor system. © 2024 Elsevier B.V.

关键词： Computational complexity

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Characteristic Mode Analysis of Nanostructures using Hydrodynamic Volume Integral Equation

Characteristic Mode Analysis of Nanostructures using Hydrody...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Meruyert Khamitova Ran Zhao Doolos Aibek uulu Hakan Bagci Electrical and Computer Engineering (ECE) Program Computer Electrical and Mathematical Science and Engineering (CEMSE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Kingdom of Saudi Arabia School of Electronic Science and Engineering University of Electronic Science and Technology of China (UESTC) Chengdu Sichuan China Department of Mathematics and Natural Sciences University of Central Asia (UCA) Naryn Kyrgyzstan

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

The characteristic mode analysis (CMA) is formulated and implemented for the hydrodynamic volume integral equation (HDVIE) that is used to mathematically model electromagnetic field interactions and conduction current dynamics on nanoantennas and nanoscatterers. The proposed method produces excitation-independent characteristic hydrodynamic currents and the corresponding modal significance curves, providing useful information that can be used to optimize the performance of a nanoantenna. Numerical results demonstrate the reliability and the applicability of the proposed approach.

关键词： Solid modeling Nanoantennas Integral equations Hydrodynamics Mathematical models Characteristic mode analysis Numerical models

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Momentum-Based Distributed Economic Dispatch of Smart Grids in Time-Varying Directed Networks

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IEEE Transactions on Network science and engineering 2025年

作者： Zhang, Keke Lu, Qingguo Zhou, Qixing Li, Huaqing Xia, Dawen Huang, Tingwen Chongqing University College of Computer Science China Southwest University College of Electronic and Information Engineering China Kaili University College of Microelectronics and Artificial Intelligence College of Big Data Engineering China Texas A&M University at Qatar Science Program Doha23874 Qatar

In this paper, we investigate the economic dispatch problem of smart grids in time-varying directed networks. The EDP essentially revolves around optimizing the distribution of generation power amongst multiple generators, with the aim of fulfilling load demands at the lowest possible total generation cost, while strictly conforming to the constraints imposed by the local generation capacity. For a faster resolution of the EDP, we propose an efficient distributed accelerated economic dispatch algorithm that incorporates a unified momentum acceleration strategy into the push-pull deviation tracking approach. The involved acceleration strategy under specific momentum parameters includes two well-known acceleration strategies, i.e., the heavy-ball and Nesterov acceleration strategies, which is more flexible and provides additional improvements in convergence. We present rigorously theoretical proof of linear convergence to the optimal dispatch with explicit bounds for step size and momentum parameters. Finally, to verify the effectiveness of our algorithm and the correctness of the theoretical analysis, we conduct simulations of diverse EDP studies in smart grids. © 2013 IEEE.

关键词： Smart power grids

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Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models 25

Debiasing Classifiers by Amplifying Bias with Latent Diffusi...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Ko, Donggeun Lee, Dongjun Park, Namjun Shim, Wonkyeong Kim, Jaekwang AiM Future Seoul Korea Republic of Maum AI Gyeonggi-do Korea Republic of Department of Computer Science and Engineering Convergence Program for Social Innovation Sungkyunkwan University Suwon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of School of Convergence Convergence Program for Social Innovation Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400706295

Neural networks struggle with image classification when biases are learned and misleads correlations, affecting their generalization and performance. Previous methods require attribute labels (e.g. background, color) or utilizes Generative Adversarial Networks (GANs) to mitigate biases. We introduce DiffuBias, a novel pipeline for text-to-image generation that generates bias-conflict samples, without any training. By utilizing pretrained diffusion and image captioning models, DiffuBias generates, bias-conflict samples using the top-K losses from a biased classifier (fB) to debias the classifier. This method not only debiases effectively but also boosts classifier generalization capabilities. Our comprehensive experimental evaluations demonstrate that DiffuBias achieves state-of-the-art performance on benchmark datasets. Copyright © 2025 held by the owner/author(s).

关键词： Generative adversarial networks

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