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2024 Laser science, LS 2024

作者： Paul, Aditya Liu, Danxian Zeng, Tianyi Bostan, Egemen Lu, Jesse Yang, Kiyoul John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States SPINS Photonics Inc. HollisterCA United States

We experimentally demonstrate an inverse-designed mode-division multiplexer on an 800-nm-thick silicon nitride platform. The multiplexer is optimized within a 14.4 × 9.6 µm2 footprint. The measured insertion loss and crosstalk are 2.11 dB and 17.1 dB, respectively. © 2024 The Author(s). © Optica Publishing Group 2024.

关键词： Silicon nitride

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Research on sustainable development of ecological environment based on AHP-FCEM modeling 24

Research on sustainable development of ecological environmen...

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5th International Conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Zhou, Qinzhao Wang, Yanzhe Zhao, Haiyan Jia, Yizheng Su, Jiaer School of Art and Design Xi'an University of Technology Shaanxi Xi'an China National Security College People's Public Security University of China Beijing China School of Environmental and Chemical Engineering Nanchang Hangkong University Jiangxi Nanchang China Business College Southwest University Chongqing China School of Computer Science and Technology Guangzhou Institute of Applied Science and Technology Guangdong Guangzhou China

ISBN: (纸本)9798400718007

In this paper, an evaluation model of sustainable development of ecological environment is constructed by using Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method (FCEM). First, through in-depth analysis and research, the main factors affecting the sustainable development of the ecological environment were identified, and a multi-level evaluation index system was established. Then, the weights of the indicators were determined by using AHP, and the fuzzy judgment matrix was constructed by blurring the indicators through FCEM. Finally, combining the weights and the fuzzy judgment matrix, the quantitative assessment of the sustainable development status of the ecological environment was carried out, and the comprehensive evaluation score was 88.193, indicating that the Sehampa has a better ability to resist sandstorms and dust storms in Beijing. The results of the study show that the model can comprehensively and objectively reflect the development status of the ecological environment and provide a scientific basis for ecological environmental protection and sustainable development. It aims to promote the continuous improvement and sustainable development of the ecological environment, and provide strong support for realizing the goal of harmonious coexistence between human beings and nature. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Natural environment

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FGRL-Net: Fine-Grained Personalized Patient Representation Learning for Clinical Risk Prediction Based on EHRs

FGRL-Net: Fine-Grained Personalized Patient Representation L...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Chio, KaKit Zhu, Wenhao He, Lihua Zhang, Dian Yang, Xu Luo, Wuman Macao Polytechnic University Faculty of Applied Sciences S.A.R Macao China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Shenzhen University Department of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350337020

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net. © 2023 IEEE.

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Charged particles

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Ikcs: An Effective Intelligent K-Medoids Clustering Scheme Based on Granularity Rough Entropy for Social Networks

SSRN

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SSRN 2024年

作者： Chen, Linshu Chen, Panyu Chen, Yuxiang Xiong, Neal Naixue Woźniak, Marcin Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Department of Mathematics and Computer Science Northeastern State University OK United States Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44100 Poland

The k-Medoids clustering algorithm is one of the commonly used clustering methods, but it does not consider the weight of each attribute which lead to different impacts on clustering results. Granular computing characterizes given knowledge from different perspectives while considering the granularity and completeness of the given knowledge, thus determining which feature has the greatest impact on the results in clustering problems, which helps to focus on important features. Therefore, this paper proposes an effective Intelligent k-Medoids Clustering Scheme based on granularity rough entropy for social networks (IKCS). Specifically, the granularity rough entropy is firstly defined based on indistinguishable relationships and roughness. Secondly, attribute weight coefficients are introduced to define the similarity function between samples. Finally, the samples are clustered based on the similarity function mentioned above, and the original cluster centers are initialized through k remote cluster centers. The experimental results on the artificial and UCI datasets effectively prove the validity of the proposed IKCS. As a result, it achieves the average Accuracy of Clustering 5.36% higher than the PAM and 4.16% higher than the fast k-Medoids. Moreover, it exhibits excellence in both the clustering Sum of Squared Errors and Adjusted Rand Index. © 2024, The Authors. All rights reserved.

关键词： Entropy

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Cerebral and muscle tissue oxygenation during exercise in healthy adults: A systematic review

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Journal of Sport and Health science 2024年第4期13卷 459-471,F0003页

作者： Diego Orcioli-Silva Victor Spiandor Beretta Paulo Cezar Rocha Santos Felipe Marroni Rasteiro Anita Brum Marostegan Rodrigo Vitorio Claudio Alexandre Gobatto Fulvia Barros Manchado-Gobatto Laboratory of Applied Sport Physiology(LAFAE) School of Applied Sciences(FCA)University of Campinas(UNICAMP)Limeira 13484-350Brazil Posture and Gait Studies Laboratory(LEPLO) Institute of BiosciencesSao Paulo State University(UNESP)Rio Claro 13506-900Brazil Physical Education Department School of Technology and SciencesSao Paulo State University(UNESP)Presidente Prudente 19060-900Brazil Department of Computer Science&Applied Mathematics Weizmann Institute of ScienceRehovot 7610001Israel Center of Advanced Technologies in Rehabilitation Sheba Medical CenterRamat Gan 5265601Israel Department of Sport Exercise and RehabilitationNorthumbria UniversityNewcastle upon Tyne NE18STUK

Background:Near-infrared spectroscopy(NIRS)technology has allowed for the measurement of cerebral and skeletal muscle oxygenation simultaneously during *** this technology has been growing and is now successfully used in laboratory and sports settings,this systematic review aimed to synthesize the evidence and enhance an integrative understanding of bloodﬂow adjustments and oxygen(O_(2))changes(i.e.,the balance between O_(2) delivery and O_(2) consumption)within the cerebral and muscle systems during ***:A systematic review was conducted using PubMed,Embase,Scopus,and Web of science databases to search for relevant studies that simultaneously investigated cerebral and muscle hemodynamic changes using the near-infrared spectroscopy system during *** review considered manuscripts written in English and available before February 9,*** step of screening involved evaluation by 2 inde-pendent authors,with disagreements resolved by a third *** Joanna Briggs Institute Critical Appraisal Checklist was used to assess the methodological quality of the ***:Twenty studies were included,of which 80%had good methodological quality,and involved 290 young or middle-aged *** types of exercises were used to assess cerebral and muscle hemodynamic changes,such as cycling(n=11),treadmill(n=1),knee extension(n=5),isometric contraction of biceps brachii(n=3),and duet swim routines(n=1).The cerebral hemodynamics anal-ysis was focused on the frontal cortex(n=20),while in the muscle,the analysis involved vastus lateralis(n=18),gastrocnemius(n=3),biceps brachii(n=5),deltoid(n=1),and intercostal muscle(n=1).Overall,muscle deoxygenation increases during exercise,reaching a plateau in voluntary exhaustion,while in the brain,oxyhemoglobin concentration increases with exercise intensity,reaching a plateau or declining at the exhaustion ***:Muscle and cerebral oxygenation respond differently to exercise,with muscle

关键词： Near-infrared spectroscopy Hemodynamic Blood flow Brain Muscle metabolism

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Evaluation of Transfer Learning with a U-Net Architectures for Kidney Segmentation 12th

Evaluation of Transfer Learning with a U-Net Architecture...

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12th International Conference on Mobile Communication and Healthcare, MobiHealth 2023

作者： Matos, Caio Eduardo Falcão do Vale, João Guilherme Araújo Ferreira, Marcos Melo Júnior, Geraldo Braz de Almeida, João Dallyson Sousa Federal Institute of Education Science and Technology of Maranhão São Luís Brazil Applied Computer Group Federal University of Maranhão São Luís Brazil Federal University of Maranhão São Luís Brazil

ISBN: (纸本)9783031606649

Kidney cancer emerges as one of the primary causes of mortality due to neoplasms on a global scale. Early detection and diagnosis of this disease often allow for more treatment options, contributing to the reduction of death rates. In this way, a correct delimitation of kidneys and renal tumor areas provides better analysis and diagnosis of suspicious lesions, contributing to treatment planning. This task is usually performed manually, making the process susceptible to fatigue (physical and visual) and distraction. Therefore, computational techniques, such as deep neural networks, are presented with great prominence as alternatives to improve segmentation precision and contribute to the early diagnosis of kidney cancer. In this work, we propose a methodology for kidney segmentation in computed tomography images by transfer learning to the U-Net network architecture. The KiTS19 dataset was used to evaluate the proposed methodology and obtained the best result for kidney segmentation of 96.0% of average Dice coefficient and average Jaccard index of 94.4%, using a pre-trained EfficentNet as an encoder for a U-Net. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Network architecture

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Multisequences with High Joint Nonlinear Complexity from Function Fields

Multisequences with High Joint Nonlinear Complexity from Fun...

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2022 International Conference on Electronic Information Engineering and computer science, EIECS 2022

作者： Yan, Yang Wang, Qiuyan Wu, Chenghuang Zhao, Chun'e Liu, Lin School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China School of Computer Science and Technology Tiangong University Tianjin China Provincial Key Laboratory of Applied Mathematics Putian University Fujian Putian China College of Science University of Petroleum Shandong Qingdao China Tianjin LINHAITEC Ltd Tianjin China

ISBN: (纸本)9781510663312

Multisequences over finite fields play an important role in applications that related to parallelization, such as word-based stream ciphers and pseudorandom vector generators. It is interesting to study complexity measures for multisequences. In this paper, we propose three constructions of multisequences over finite fields from rational function fields and Hermitian function fields. We also analyze the joint nonlinear complexity of these multisequences. Moreover, the length and dimension of these multisequences are flexible. © 2023 SPIE.

关键词： Rational functions

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Smart Aero Generator Monitoring System using IoT 2

Smart Aero Generator Monitoring System using IoT

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Sathyamoorthy, Malathy Vanitha, C.N. Dhanaraj, Rajesh Kumar Mon, Feslin Anish Rai, Priti Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Pune India University of Technology and Applied Sciences Faculty in Department of Information Technology Ibri Oman Gl Bajaj Institute of Management Greater Noida India

ISBN: (纸本)9798350325799

Wind turbine plays a major role in energy production. From small wind turbines to ocean wind turbines, wind turbine industry introduces the latest version with bigger flexible blades, high towers, good efficiency and low-cost repairs. Small wind turbines malfunctioning will lead to a reduction in total energy. Faults like increasing in temperature may lead to fire accidents. The excessive vibration of the turbines may cause the turbine to sustain damage. The current shock will be passed to the people from the turbine if they try to contact it. IoT sensors can help us to overcome these obstacles. The proposed system will monitor the temperature, measures the speed, smoke and motion near the windmills. If the temperature exceeds the threshold then an alert will be generated and also water will be splashed in order to overcome the fire accident. If the speed of the wind is more than the threshold then the windmill be turned off to avoid damage in the turbines. Also, if an unnecessary movement is detected after a particular distance then the gate will be closed in the proposed system. It also measures the smoke for the gases released and generates an alert to avoid further damage to human lives and the windmill. The experimental results prove that the proposed system will improve the security of the windmill and also reduces the damage that occurs in the environment. The alert will be generated within 15s of an incident which proves that this is an efficient system when compared to the existing ones. © 2023 IEEE.

关键词： Smoke

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Effect of Credit Financing on the Supply Chain for Imperfect Deteriorating Items with Carbon Emission 11

Effect of Credit Financing on the Supply Chain for Imperfect...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mittal, Mandeep Chauhan, Sudhir Kumar Rastogi, Shivangi School of Computer Science Engineering and Technology Bennett University Department of Mathematics Uttar Pradesh Greater Noida India Amity University Uttar Pradesh Aias Department of Mathematics Noida India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The sustainable inventory model is a framework designed to ensure manufacturing processes' sustainability while maintaining profitability. During manufacturing, some items may experience degradation, resulting in imperfect goods. Nevertheless, a screening process is implemented to eliminate such items before they reach the consumers. Therefore, it is critical to scrutinize items prone to deterioration thoroughly. Suppliers prioritize trade credit in payments when supplying retailers, necessitating the development of an inventory model that caters to the needs of retailers dealing with imperfect and deteriorating quality items while considering carbon emissions and trade credit as a payment. The study presents numerical illustrations and sensitivity analysis for a better understanding of the model. © 2024 IEEE.

关键词： Supply chains

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