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Evaluating Energy Consumption for Routing Selection using the Multi-Routing Clustering Protocol using Timeslot Transmission in Dynamic Path Selection in Wireless Sensor Networks

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Procedia computer science 2025年 252卷 251-259页

作者： M. Prakash J. Abinesh P. Malarvizhi J. Jeba Emilyn A. Sam Thamburaj D. Vinod Kumar Department of Computer Science and Engineering Knowledge Institute of Technology Salem-637504 Tamil Nadu India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Vinayaka Mission’s College of Physiotherapy Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India Depertment of Biomedical Engineering Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India

Wireless sensor networks (WSNs) are cutting-edge technology that can be used in many fields requiring critical information. However, limited resource constraints, contextual connectivity, and lifecycle requirements drive designers to seek more efficient WSN infrastructures. Unbalanced energy utilization of sensor hubs during information steering in Wireless Sensor Networks (WSN). Accordingly, one of the principal configuration difficulties of remote sensor networks is to limit the energy utilization of sensor hubs. Therefore, many routing schemes are designed to efficiently utilize sensor nodes’ limited Energy. These schemes generally use low-power paths to transmit data. It turns out that using the same path is suboptimal given the network lifetime. To overcome this problem, a new method is introduced that uses a Multi-Route Clustering Protocol Using Timeslot Transmission (MRCP-TTDPS) with dynamic path selection. It consumes Energy on path selection. In the first step, multipath routing is configured to detect path quality, so this method uses Multipath Optimized Routing (MPRS) to send high-quality path data. The second stage uses Cluster-Based Optimal Path Selection (CORS) to establish the best energy path. The third stage develops energy consumption models. The amount of the hubs with the most noteworthy leftover Energy is chosen as the group top of each round. Every standard hub partakes in a group framed by neigh exhausting bunch heads. Every sensor hub sends identified information to the bunch head in each round. The group head sends the data to the base station. The results show that the model is better than the path model regarding path identifier energy utilization and path quality.

关键词： Wireless Sensor Networks (WSN) Multi-Route clustering Protocol using Timeslot Transmission in Dynamic path selection (MRCP-TTDPS) Energy data transfer multipath routing Quality path sensor nodes

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External validation of an RSNA 2023 Abdominal Trauma AI Challenge high performing machine learning model in the detection and grading of splenic injuries on CT

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Abdominal Radiology 2025年 1-10页

作者： Kirpalani, Anish Viel, Théo Hu, Zixuan Lin, Hui Ming Hermans, Sebastiaan Gomez, David Moreland, Robert Mathur, Shobhit Jhaveri, Aaditeya Wu, Matthew Vlachou, Paraskevi A. Tafur, Monica Sejdić, Ervin Colak, Errol Department of Medical Imaging St. Michael’s Hospital Unity Health Toronto Toronto Canada Department of Medical Imaging Temetry Faculty of Medicine University of Toronto Toronto Canada Li Ka Shing Knowledge Institute Unity Health Toronto Toronto Canada NVIDIA Paris France The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto Toronto Canada Division of General Surgery St. Michael’s Hospital Unity Health Toronto Toronto Canada Department of Surgery Temetry Faculty of Medicine University of Toronto Toronto Canada Institute for Medical Science University of Toronto Toronto Canada Institute of Health Policy Management and Evaluation University of Toronto Toronto Canada North York General Hospital Toronto Canada

Purpose: This study aims to validate the performance of an award-winning machine learning (ML) model from the Radiological Society of North America (RSNA) 2023 Abdominal Trauma AI Challenge in detecting splenic injuries on CT scans using a large, geographically and temporally distinct external dataset. Method: A single-center retrospective study was conducted using an external dataset comprising 1216 CT scans (608 positive and 608 negative for splenic injuries). The ML model, trained on the RSNA Abdominal Traumatic Injury CT (RATIC) dataset, employs a multi-component pipeline including 2D MaxVit, 2.5D CoatNet with LSTM for study-level predictions. Model performance was evaluated using sensitivity, specificity, PPV, NPV, accuracy, F1 score, and AUC. Results: The ML model achieved an AUC of 0.931 (95% CI: 0.917, 0.945) for binary classification of splenic injuries, with an accuracy of 0.849 (95% CI: 0.827, 0.868), sensitivity of 0.747 (95% CI: 0.711, 0.780), and specificity of 0.951 (95% CI: 0.930, 0.965). For high-grade splenic injuries, the model achieved an AUC of 0.950 (95% CI: 0.932, 0.968), accuracy of 0.928 (95% CI: 0.912, 0.941), sensitivity of 0.719 (95% CI: 0.643, 0.784), and specificity of 0.958 (95% CI: 0.944, 0.968). Conclusion: The ML model shows strong, reliable performance and generalizability in detecting and grading splenic injuries on CT scans. This supports its potential clinical application, particularly for quick and accurate diagnosis in splenic trauma patients, and highlights the value of RSNA AI challenges in advancing clinical research and applications in medical imaging. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： AAST Artificial intelligence Computed tomography (CT) External validation Machine learning Splenic injury Trauma

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UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

arXiv

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arXiv 2025年

作者： Zheng, Xiaoya Sun, Geng Li, Jiahui Wang, Jiacheng Wu, Qingqing Niyato, Dusit Jamalipour, Abbas College of Computer Science d Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and ta Science Nanyang Technological University 639798 Singapore Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Computer Engiering The University of Sydney SydneyNSW2006 Australia

The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the construct

关键词： Unmanned aerial vehicles (UAV)

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Recent developments in the thermal radiative flow of dusty Ellis trihybrid nanofluid with activation energy using Hamilton-Crosser thermal conductivity model

International Journal of Thermofluids

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International Journal of Thermofluids 2025年 27卷

作者： Okasha, Mostafa Mohamed Abbas, Munawar Akgül, Ali Formanova, Shoira Ibrahim, Talib K. Hassani, Murad Khan Department of Mechanical Engineering College of Engineering Northern Border University Arar Saudi Arabia Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai602105 India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkapı Istanbul34010 Turkey Department of Chemistry and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Petroleum Engineering College of Engineering Knowledge University Erbil 44001 Iraq Department of Petroleum Engineering Al-Kitab University Altun Kupri Iraq Ghazni University Department of Mathematics Afghanistan Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey

This study scrutinizes the characteristics of activation energy on Darcy Forchheimer radiative flow of dusty Ellis trihybrid nanofluid over a Riga plate when dust and nanoparticles are present. The goal of the present work is to use the Hamilton-Crosser thermal conductivity model to scrutinize the heat transmission for the Darcy Forchheimer flow of dusty Ellis trihybrid nanofluid. The flow is impacted by heat source with the properties of Marangoni convection. The base fluid, propylene glycol (C3H8O2), is mixed with Ag, TiO2 and Al2O3 nanoparticles. The model is applicable to sophisticated heat transfer systems, including solar energy harvesting and electronic device cooling technologies. Additionally, it finds application in thermal management of industrial processes using nanofluids and aerospace engineering. Using the shooting technique, the numerical results of the governing equations are obtained (RKF-45th). The impacts on dimensionless physical quantities of interest of geometrical and physical properties relevant to this study are analysed using the required tables and figures. The results demonstrated that the Ellis fluid parameter raised the heat transmission, mass transmission rate, and velocity profiles. As the chemical reaction parameter upsurges, the concentration distributions decrease. © 2025 The Author(s)

关键词： Activation energy

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Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

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computer Systems science & engineering 2022年第10期43卷 77-86页

作者： V.Kumar N.Jayapandian P.Balasubramanie Department of Computer Science and Engineering Knowledge Institute of TechnologySalemIndia Department of Computer Science and Engineering Christ UniversityBangaloreIndia Department of Computer Science and Engineering Kongu Engineering CollegePerunduraiIndia

Through Wireless Sensor Networks(WSN)formation,industrial and academic communities have seen remarkable development in recent *** of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating *** most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering *** this method,new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each *** of effective energy use and the ability to decide the best method of attachments are *** Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high.A newer version of Gravity Analysis Algorithm(GAA)is used to solve this *** proposed new approach GAA is introduced to improve network lifetime,increase system energy efficiency and end delay *** results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA *** proposed method provides a set of data collection and increased throughput in wireless sensor networks.

关键词： WSNs Gravity analysis algorithm-GAA life time delay clustering algorithms-LTDCA end to end delay energy efficient

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Supply Chain Sustainability in Disruption: SC Intelligence, Customer knowledge Transfer, and the Mediating Role of IS/IT

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IEEE engineering Management Review 2025年

作者： Ratna, Silvia Saide, Saide Rater, Salya Svyazhanovna, Baizakova Saule Herzavina, Herzavina Faculty of Information Technology Department of Informatics Engineering and Department of Information Systems Banjarmasin Indonesia State Islamic University of Sultan Syarif Kasim Riau Universitas Islam Negeri Sultan Syarif Kasim Riau-UIN SUSKA Riau Department of Information Systems Faculty of Science and Technology Indonesia PRO Knowledge Indonesia Pekanbaru Indonesia Universitas Pahlawan Tuanku Tambusai Indonesia Lembaga Enreach UIN Suska Riau Indonesia Politeknik Aceh Selatan Department of Informatics Engineering Aceh Indonesia Information Communication Technologies Robotics in Arkalyk Pedagogical Institute Master of Natural Sciences Educational Program for the Informatics Computer Science Kazakhstan Universitas Pahlawan Tuanku Tambusai Department of Informatics Engineering Indonesia National Taiwan University of Science and Technology Taiwan

knowledge resource and information system/technology (IS/IT) capability have been considered to improve firm performance, however there is still a gap regarding the sustainability of supply chain to face and recover from disruption (e.g., COVID-19). Questionnaire-based surveys was adopted and received feedback from sixty (160) Indonesian companies or about 28.07% of response rate. © 1973-2011 IEEE.

关键词： customer knowledge transfer disruption information system/technology (IS/IT) responding knowledge management resource-based view (RBV) Supply chain (SC) sustainability supply chain knowledge transfer

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Formulation of the independence number of one-order graph associated with a linear code

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND computer scienceS 1996年第9期E79A卷 1453-1455页

作者： Shi, JJ Watanabe, Y Department Knowledge Engineering and Computer Science Doshisha University Kyotofu 610-03 Japan

The independence number of one-order graph associated with a linear code is formulated. The formulation is represented by the parameters of the generator matrix.

关键词： independence number uniquely decodable code

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A Quantized Kernel Least Mean Square Scheme with Entropy-Guided Learning for Intelligent Data Analysis

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China Communications 2017年第7期14卷 127-136页

作者： Xiong Luo Jing Deng Ji Liu Weiping Wang Xiaojuan Ban Jenq-Haur Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Department of Computer Science and Information Engineering Taipei University of Technology

Quantized kernel least mean square(QKLMS) algorithm is an effective nonlinear adaptive online learning algorithm with good performance in constraining the growth of network size through the use of quantization for input space. It can serve as a powerful tool to perform complex computing for network service and application. With the purpose of compressing the input to further improve learning performance, this article proposes a novel QKLMS with entropy-guided learning, called EQ-KLMS. Under the consecutive square entropy learning framework, the basic idea of entropy-guided learning technique is to measure the uncertainty of the input vectors used for QKLMS, and delete those data with larger uncertainty, which are insignificant or easy to cause learning errors. Then, the dataset is compressed. Consequently, by using square entropy, the learning performance of proposed EQ-KLMS is improved with high precision and low computational cost. The proposed EQ-KLMS is validated using a weather-related dataset, and the results demonstrate the desirable performance of our scheme.

关键词： quantized kernel least mean square （QKLMS） consecutive square entropy data analysis

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Multi-objective cloud load-balancing with hybrid optimization

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International Journal of computers and Applications 2023年第10期45卷 611-625页

作者： Geeta, Koppula Kamakshi Prasad, V. Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Basar India Department of Computer Science and Engineering JNTUH College of Engineering Hyderabad India

In this study, the cloud computing platform is equipped with a hybrid multi-objective meta-heuristic optimization-based load balancing model. Physical Machine (PM) allocates a specific virtual machine (VM) depending on multiple criteria, such as the amount of memory used, migration expenses, power usage, and the load balancing settings, upon receiving a request to handle a cloud user's duties (‘Response time, Turnaround time, and Server load’). Additionally, the optimal virtual machine (VM) is chosen for efficient load balancing by utilizing the recently proposed hybrid optimization approach. The Cat and Mouse-Based Optimizer (CMBO) and Standard Dingo Optimizer (DXO) are conceptually blended together to get the proposed hybridization method known as Dingo Customized Cat mouse Optimization (DCCO). The developed method achieves the lowest server load in cloud environment 1 is 33.3%, 40%, 42.3%, 40.2%, 36.8%, 42.5%, 50%, 40.2%, 39.2% improved over MOA, ABC, CSO, SSO, SSA, ACSO, SMO, CMBO, BOA, DOX, and FF-PSO, respectively. Finally, the projected DCCO model has been evaluated in terms of makespan, memory usage, migration cost, response time, usage of power server load, turnaround time, throughput, and convergence. ABBREVIATION: CDC, cloud data center;CMODLB, Clustering-based Multiple Objective Dynamic Load Balancing As A Load Balancing;CSP, Cloud service providers;CSSA, Chaotic Squirrel Search Algorithm;DA, Dragonfly Algorithm;ED, Euclidean Distance;EDA-GA, Estimation Of Distribution Algorithm And GA;FF, FireFly algorithm;GA, Genetic Algorithm;HHO, Harris Hawk Optimization;IaaS, Infrastructure-as-a-Service;MGWO, Modified Mean Grey Wolf Optimization Algorithm;MMHHO, Mantaray modified multi-objective Harris Hawk optimization;MRFO, Manta Ray Forging Optimization;PaaS, Platform-as-a-Service;PM, Physical Machine;PSO, Particle Swarm Optimization;SaaS, Software-as-a-Service;SAW, Sample additive weighting;SLA-LB, Service Level Agreement-Based Load Balancing;TBTS, Threshold-Bas

关键词： Electric power utilization

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AMPCliff: Quantitative definition and benchmarking of activity cliffs in antimicrobial peptides

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Journal of Advanced Research 2025年

作者： Li, Kewei Wu, Yuqian Li, Yinheng Guo, Yutong Kong, Yanwen Wang, Yan Liang, Yiyang Fan, Yusi Huang, Lan Zhang, Ruochi Zhou, Fengfeng College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Software Jilin University Jilin Changchun 130012 China School of Life Sciences Jilin University Jilin Changchun 130012 China Department of Computer Science Columbia University 116th and Broadway New York City 10027 NY United States School of Artificial Intelligence Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Jilin Changchun 130012 China School of Biology and Engineering Guizhou Medical University Guizhou Guiyang 550025 China School of Computer Engineering Changchun University of Engineering Jilin Changchun 130103 China Changchun Wenli High School Jilin Changchun 130062 China

Introduction: Activity cliff (AC) is a phenomenon that a pair of similar molecules differ by a small structural alternation but exhibit a large difference in their biochemical activities. This phenomenon affects various tasks ranging from virtual screening to lead optimization in drug development. The AC of small molecules has been extensively investigated but limited knowledge is accumulated about the AC phenomenon in pharmaceutical peptides with canonical amino acids. Objectives: This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids. Methods: This study establishes a benchmark dataset of paired AMPs in Staphylococcus aureus from the publicly available AMP dataset GRAMPA, and conducts a rigorous procedure to evaluate various AMP AC prediction models, including nine machine learning, four deep learning algorithms, four masked language models, and four generative language models. Results: A comprehensive analysis of the existing AMP dataset reveals a significant prevalence of AC within AMPs. AMPCliff quantifies the activities of AMPs by the metric minimum inhibitory concentration (MIC), and defines 0.9 as the minimum threshold for the normalized BLOSUM62 similarity score between a pair of aligned peptides with at least two-fold MIC changes. Our analysis reveals that these models are capable of detecting AMP AC events and the pre-trained protein language model ESM2 demonstrates superior performance across the evaluations. The predictive performance of AMP activity cliffs remains to be further improved, considering that ESM2 with 33 layers only achieves the Spearman correlation coefficient 0.4669 for the regression task of the −log(MIC) values on the benchmark dataset. Conclusion: Our findings highlight limitations in current deep learning-based representation models. To more accurately capture the properties of antimicrobial peptides (AMPs), it is ess

关键词： Activity cliff (AC) AMPCliff Antimicrobial peptide (AMP) Benchmark Minimum inhibitory concentration (MIC)

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