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Software engineering and Computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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BSCP-SG: Blockchain-enabled secure communication protocols for IoT-driven smart grid systems

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Internet of Things (The Netherlands) 2025年 32卷

作者： Alrashdi, Ibrahim Tanveer, Muhammad Aldossari, Saud Alhajaj Alshammeri, Menwa Armghan, Ammar Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia School of Systems and Technology (SST) University of Management and Technology Lahore Pakistan Department of Electrical Engineering College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Wadi Alddawasir Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University Sakaka 72388 Saudi Arabia

Smart grids (SG) utilize emerging communication technologies and IoT innovations to advance power system efficiency, reliability, and sustainability. Smart meters (SM), as key components of SG, facilitate real-time monitoring and control of energy usage by supplying data to both consumers and service providers (SP). However, the open communication framework of IoT-enabled SGs introduces significant security vulnerabilities that can disrupt the smooth operation of SGs. To address these risks, secure communication protocols are crucial, ensuring confidentiality, integrity, and authentication for data transmitted between SMs and SPs, thereby safeguarding SGs from cyber-attacks and ensuring the reliability of their operations. To address security challenges, we propose a blockchain-enabled secure communication protocol for IoT-driven SGs, called BSCP-SG. The BSCP-SG protocol is developed utilizing the AEAD primitive, ECC, and SHA-256 to enable mutual authentication between SM and SP. Once authenticated, a session key (SK) is generated, enabling indecipherable communication between the SM and SP. The data obtained from the SMs is converted into secure transactions, grouped into blocks, and recorded on the blockchain by the SP, which uses a secure PBFT consensus mechanism within a peer-to-peer SP network, ensuring both data integrity and immutability. Furthermore, the resilience of SK is validated through the ROR model. The resilience and resistance of BSCP-SG against potential attacks are further confirmed through informal analysis. The proposed protocol not only strengthens security but also reduces computational and communication costs, presenting a secure and efficient solution for IoT-based SG systems. © 2025

关键词： Authentication Blockchain Consensus Security Smart grids

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Assistive technology with device for self-inflating bag in neonatology

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Research on Biomedical engineering 2025年第2期41卷

作者： Marcelino, Ana Carolina Rodovanski, Giovana Pascoali Bonetti, Karoline Panes, Caio Rabinovich de Almeida Martins, Fernando Moran, Cristiane Aparecida Araranguá Brazil Graduate Program in Neurosciences and Behavior Department of Experimental Psychology of the Institute of Psychology-University of São Paulo Sao Paulo Brazil Araranguá Brazil Computer Engineering Instituto Mauá de Tecnologia São Caetano do Sul Brazil Electronic Engineering and Computing with Emphasis on Electronic Devices and Systems at Instituto Tecnológico de Aeronáutica–ITA São Caetano do Sul Brazil Department of Physiotheraphy and at the Postgraduate Program in Rehabilitation Science of the Federal University of Santa Catarina-UFCS Ararangua Brazil

Purpose: The objectives of this study were to create a device with technological development of the self-inflating bag (SIB) equipment to measure the variables tidal volume, peak inspiratory pressure, and inspiratory flow and to evaluate the knowledge of undergraduate students in handling the SIB. To reduce the risks of mortality, length of hospital stay, and infections, SIB is a resource used to ventilate the patient through the inflation of the device, but it is not capable of measuring the ventilatory parameters. Excess ventilation and incorrect handling of the bag can cause lung injury in newborns. Therefore, measuring these parameters can prevent lung injury resulting from inadequate ventilation. Method: This is a cross-sectional and experimental study, with the development of a device SIB that allows the evaluation of variables of neonatal respiratory mechanics that were tested with an artificial lung. It was approved by the Research Ethics Committee and the research was developed in three stages: the first represents the search for scientific articles for the consensus of neonatal ventilatory variables for the construction of the SIB parameters;the second stage depicts the technological development of a device capable of monitoring such variables through the SIB. In the third stage, the content validation of the device with the handling of the SIB was carried out by students of medicine and physiotherapy courses of Brazil. Results: In this work, a device that analyzes the values of the main ventilatory parameters in neonatology was developed and to generate a safety range for the use of the device, curves with a tolerance of 10% up and down were created. All electronic components were coupled to the equipment, with a display that transmits the parameter values. The web page can be used on a cell phone, tablet, computer, or SmartTv, as long as it is connected to the "RespiratorIoT_AP" network. Among the 29 participants in the study, 8 said they were familiar w

关键词： Students

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Sustainable energy management in the AI era: a comprehensive analysis of ML and DL approaches

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Computing 2025年第6期107卷 1-64页

作者： Javed, Haseeb Eid, Fatma El-Sappagh, Shaker Abuhmed, Tamer Department of Computer Science and Engineering College of Computing and Informatics Sungkyunkwan University Suwon South Korea Technology Management Stony Brook University New York USA Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Suwon South Korea Faculty of Computer Science and Engineering Galala University Suez Egypt Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt

This study comprehensively analyzes the application of innovative deep learning (DL) and machine learning (ML) techniques in smart energy management systems (EMSs), with an emphasis on load forecasting, demand response, and the development of smart energy sectors. The application of various ML and DL models were examined in over 200 studies from 2014 to 2024 in an electrical network's EMS to highlight the key benefits and advances made by each technology for the sustainable management systems in energy sector. The findings emphasize DL and ML models’ enhanced precision and predictive capabilities in load forecasting, their efficacy in enabling efficient demand response mechanisms, and their significance in supporting the development of smart energy sectors. Furthermore, recommendations are made based on the survey results to assist in incorporating these techniques into EMS frameworks, such as investment in data infrastructure, model training and validation, and collaboration between researchers, industry experts, and policymakers. The study also discusses the limitations identified in the literature, such as limited real-world implementations, challenges regarding quality and data availability, and the need for enhanced ML and DL model interpretability. Addressing these limitations can assist in increasing the application and efficacy of ML and DL techniques in EMSs, enabling a more efficient and sustainable energy landscape. Finally, this study facilitates researchers' exploration of ML and DL in energy management, highlighting relevant limitations, strengths, and alternative approaches associated with sustainable energy management. It also indicates potential future research directions for further investigation.

关键词： Power management (telecommunication)

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IoT-based Efficient Waste management on University Campus : Enhancing Monitoring and Optimization through Machine Learning

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Procedia Computer Science 2025年 258卷 4157-4168页

作者： Sumathi M. Hanumanthappa J. K.S. Santhosh Kumar S.P. Shiva Prakash Meghana J. Department of Studies in Computer Science Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka 570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka 570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)’s flawless

关键词： Artificial Intelligence Smart Parking: Smart city Machine Learning Smart Applications

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Comparison of multiple plant sensors aimed at early detection of drought stress in the greenhouse

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Agricultural Water management 2025年 315卷

作者： Dutta, Satadal van den Berg, Tom Koning, Mariska de Vaate, Inger Bij Bieling, Thijs J. Kaiser, Elias Verbiest, Gerard J. Fan, Qinwen van Klink, Alex Steeneken, Peter G. ten Veldhuis, Marie-Claire Department of Precision and Micro-systems Engineering Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Netherlands Plense Technologies B.V Netherlands Integrated Devices and Systems Faculty of Electrical Engineering Mathematics and Computer Sciences University of Twente Netherlands Department of Water Management Faculty of Civil Engineering and Geosciences Delft University of Technology Netherlands Horticulture and Product Physiology Department of Plant Sciences Wageningen University and Research Netherlands Department of Microelectronics Delft University of Technology Netherlands Delphy Improvement Centre Bleiswijk Netherlands

With a growing world population and mounting pressure on natural resources, the need for efficient, sustainable food production is becoming increasingly urgent. A promising route towards improving agricultural productivity is to expand the use of sensors to monitor plants and their environment with high accuracy and temporal resolution. Data generated by such sensors is useful for optimizing irrigation, nutrition and illumination in the context of autonomous greenhouses, while allowing mitigation of plant stress due to pests, diseases and extreme climate conditions. We simultaneously tested ten different types of sensors for monitoring early signs of drought stress in mature, high-wire tomato plants grown in rockwool. Sensors ranged from high-density climate sensors to novel sensors for monitoring plant-specific parameters like acoustic emissions, stomatal dynamics, sap flow and stem diameter. Withholding water for two days led to a quick and complete depletion of water in the rockwool slabs, and strongly affected whole-plant transpiration, resulting in strong changes in: acoustic emissions, stomatal pore area, stomatal conductance, and stem diameter, all of which were found to be significant indicators of early drought stress. This work demonstrates the usefulness of these sensors in a greenhouse environment and provides a comparison between measured parameters in magnitude and onset time, aimed at the early detection of drought stress. Our study aims to facilitate sensor selection and implementation in precision agriculture. © 2025 The Authors

关键词： Fruits

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Heterogeneity of the Dominant Causes of Performance Loss in End-of-Life Cathodes and Their Consequences for Direct Recycling

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Advanced Energy Materials 2025年第0期

作者： Popeil, Melissa Usseglio-Viretta, Francois L.E. Pu, Xiaofei Gasper, Paul Dutta, Nikita Wang, Evelyna Allen, Eva Mangum, John S. Sunderlin, Nathaniel Fink, Kae Allen, Jeffery M. Weddle, Peter J. DeCaluwe, Steven C. Finegan, Donal P. Mines/NREL Advanced Energy Systems Graduate Program Colorado School of Mines GoldenCO80401 United States National Renewable Energy Laboratory GoldenCO80401 United States Argonne National Laboratory LemontIL60439 United States Department of Mechanical Engineering Colorado School of Mines GoldenCO80401 United States

Recycling Li-ion batteries from electric vehicles is critical for reducing costs and supporting the development of a domestic battery supply chain. Direct recycling of cathodes, like LiNixMnyCozO2 (NMC), is attractive due to its low cost, energy use, and emissions compared to traditional recycling techniques. However, a comprehensive understanding of the active material properties at end-of-life is needed to guide direct recycling processes and the performance-dependent reuse applications. Here, NMC material from an end-of-life commercial pouch cell is characterized and bench-marked against pristine non-cycled counterparts with respect to capacity, impedance, crystallography, morphology, and microstructure to identify major degradation modes and understand variability in the end-of-life material. The spatial heterogeneity of each property throughout the cell is also quantified. While the degraded material demonstrated similar capacity as the pristine, its impedance and rate capability are severely diminished. Furthermore, samples from the periphery of the electrode layers showed more severe performance loss compared to samples extracted from central regions. The dominant culprit of performance loss is the material microstructure, where the magnitude of particle cracking showed the strongest correlation to the impedance components that are most unfavorably impacted. This work suggests severe cracks in cathode active materials are the primary challenge that direct recycling methods must overcome. © 2025 The Author(s). Advanced Energy Materials published by Wiley-VCH GmbH.

关键词： battery NMC recycling

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Dramatic reduction in thermal conductivity by high-density dislocations in SrTiO3

arXiv

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arXiv 2025年

作者： Ding, Jinxue Zhang, Jiawen Dong, Jinfeng Higuchi, Kimitaka Nakamura, Atsutomo Lu, Wenjun Sun, Bo Fang, Xufei Institute for Applied Materials Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China School of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore Institute of Materials and Systems for Sustainability Nagoya University Aichi Nagoya464-8601 Japan Graduate School of Engineering Science Osaka University Toyonaka Osaka560−8531 Japan Tsinghua SIGS and Guangdong Provincial Key Laboratory of Thermal Management Engineering & Materials Tsinghua University Shenzhen518055 China

Decreasing thermal conductivity is important for designing efficient thermoelectric devices. Traditional engineering strategies have focused on point defects and interface design. Recently, dislocations as line defects have emerged as a new tool for regulating thermal conductivity. In ceramics-based thermoelectric materials, the key challenge lies in achieving sufficiently high-density dislocations to effectively scatter phonons, as the typical dislocation density in ceramics after bulk deformation is constrained to ~1012 m−2. In this work, we adopted the mechanical imprinting method and achieved a dislocation density of ~1015 m−2 in single-crystal SrTiO3, which is known for its room-temperature plasticity and acts as a promising material for thermoelectric applications. Using the time-domain thermoreflectance (TDTR) method, we measured a ~50% reduction in the thermal conductivity over a broad temperature range (80−400 K) with the engineered high-density dislocations. These results suggest that tuning dislocations could offer a new path to minimizing thermal conductivity for engineering thermoelectric materials. © 2025, CC BY.

关键词： Single crystals

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SPATIAL DISTRIBUTION CHARACTERISTICS OF NITROGEN AND PHOSPHORUS NUTRIENTS IN HIGH-ALTITUDE INLAND RIVER CASCADE RESERVOIRS

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Environment Protection engineering 2025年第1期51卷 75-92页

作者： Wang, Yu Tian, Miao Wang, Hao Luo, Tianfeng Li, Neng'An Yuan, Xingpeng Xin, Yaling Zhang, Ying College of Energy and Power Engineering Lanzhou University of Technology Gansu Lanzhou730000 China Key Laboratory of Ecohydrology of Inland River Basin Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences Gansu Lanzhou730000 China Gansu Provincial Key Laboratory of Biomass Energy and Solar Energy Synergistic Supply Systems Gansu Lanzhou730050 China Water Conservancy Project Construction Cost and Fee Management Center Gansu Provincial Water Conservancy Department Lanzhou730046 China Gansu Provincial Water Environment Monitoring Centre Lanzhou730000 China

The spatial distribution characteristics of nitrogen and phosphorus nutrients in cascade reservoirs in high-altitude regions are crucial for water quality management. Water samples were collected from 20 typical section points in the middle and upper reaches of the Heihe River. The research focused on the nutrient concentrations in the surface, middle, and bottom layers of the cascade reservoirs. Nutrient distribution characteristics in the reservoirs were analyzed using both longitudinal and vertical sampling. The results showed significant depth-dependent variations in nitrogen and phosphorus concentrations, influenced by hydrodynamic processes, sediment dynamics, and nutrient cycling. The overall trend for NO2− -N, TP, and TN concentrations at the selected sampling points was consistent, with bottom-layer concentrations being significantly higher than those in the surface layer. NH4+ -N concentration was highest in the middle layer and lowest in the bottom layer, while the CODMn was highest in the surface water and lowest in the middle layer. The primary factors affecting these spatial distribution characteristics include the types and quantities of microorganisms and biological communities at different water depths, water movement and convection, sedimentation rates of suspended particles and organic matter, as well as human activities. © 2025 Wroclaw University of Science and Technology. All rights reserved.

关键词： Reservoirs (water)

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