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Variable Length Multi-Objective Whale Optimization for Trust Aware Data Gathering in Wireless Sensor Network

IEEE Access 2025年 13卷 32100-32121页

作者： Saad, Mohammed Ayad Jaafar, Rosmina Chellappan, Kalaivani Faculty of Engineering and Built Environment Department of Electrical Electronics and System Engineering Selangor Bangi43600 Malaysia Al-Kitab University Department of Medical Instrumentations Technique Engineering Kirkuk36001 Iraq Universitas Airlangga Biomedical Engineering Study Program Faculty of Science and Technology Surabaya60115 Indonesia

Efficient data collection in wireless sensor networks (WSNs) is crucial. While traditional approaches rely on stationary data sinks, the use of mobile sinks, like unmanned aerial vehicles (UAVs), has shown promise in enhancing data-gathering capabilities. However, existing methods often overlook the importance of trust, leaving the network susceptible to malicious or faulty nodes. To address this issue, we propose a novel trust-aware data-gathering algorithm, Variable Length Multi-Objective Whale Optimization Data Gathering (VLMOWO-DG). Our algorithm simultaneously optimizes energy consumption, delay, and trust while employing mobile sinks to collect data from sensor nodes. By considering trust as a key performance metric, we improve data reliability and security. The VLMOWO-DG algorithm introduces flexibility through variable-length solution representation and application-oriented operators, allowing for efficient exploration of the solution space. Simulation results demonstrate that our proposed algorithm significantly outperforms established benchmarks, NSGA-II and NSGA-III, in terms of domination and hypervolume. This leads to a remarkable improvement of 200% in data gathering performance. © 2015 The Authors.

关键词： Data collection

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Enhancing compatibility and biodegradability of polylactic acid/biomass composites through torrefaction of forest residue

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Journal of Bioresources and Bioproducts 2025年第1期10卷 51-61页

作者： June-Ho Choi Myeong Rok Ahn Chae-Hwi Yoon Yeon-Su Lim Jong Ryeol Kim Hyolin Seong Chan-Duck Jung Sang-Mook You Jonghwa Kim Younghoon Kim Hyun Gil Cha Jae-Won Lee Hoyong Kim Center for Bio-based Chemistry Korea Research Institute of Chemical Technology(KRICT)Ulsan 44429South Korea Department of Wood Science and Engineering College of Agriculture and Life SciencesChonnam National UniversityGwangju 61186South Korea Interdisciplinary Program in IT-Bio Convergence System Chonnam National UniversityGwangju 61186South Korea

This study investigated the effects of torrefaction on forest residue(FR)and its subsequent ap-plication as a bulk-loading filler in polylactic acid(PLA)*** enhanced the chemical properties of FR,improving its compatibility with PLA,and the crystallinity increased from 24.9%to 42.5%.The process also improved the hydrophobicity of PLA/biomass composites,as demonstrated by the water contact angle of 76.1°,closely matching that of neat PLA(76.4°).With the introduction of 20%modified biomass properties after torrefaction treatment,the tensile strength of PLA/biomass composite increased from 58.7 to 62.3 ***,the addition of torrefied forest residue(TFR)accelerated biodegradation by increasing the onset of degrada-tion and inhibiting *** 90 d,the biodegradability of PLA/biomass composites reached 94.9%,which had a 6.9%increase compared to the neat PLA(88.8%).Overall,this study highlights the potential of torrefaction in enhancing both the physical properties and biodegrad-ability of PLA-based composites,contributing to a more sustainable approach to reducing plastic pollution.

关键词： Forest residue Torrefaction Bulk-loading filler Polylactic acid composite Hydrophobicity Tensile strength Biodegradability

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Unassisted photoelectrochemical CO_(2)reduction by employingⅢ-Ⅴphotoelectrode with 15%solar-to-fuel efficiency

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Carbon Energy 2025年第3期7卷 1-10页

作者： Karthik Peramaiah Purushothaman Varadhan Vinoth Ramalingam Bilawal Khan Pradip Kumar Das Hao Huang Hui-Chun Fu Xiulin Yang Vincent Tung Kuo-Wei Huang Jr-Hau He Chemistry Program Center for Renewable Energy and Storage Technologies(CREST)and Division of Physical Science and EngineeringKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Institute of Sustainability for Chemicals Energyand Environment(ISCE2)Agency for ScienceTechnologyand Research(A*STAR)Singapore School of Computing Engineering and TechnologyRobert Gordon UniversityAberdeenUK Department of Materials Science and Engineering City University of Hong KongKowloonSARHong KongChina Guangxi Key Laboratory of Low Carbon Energy Materials School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilinChina Department of Chemical System and Engineering University of TokyoTokyoJapan

Solar-driven carbon dioxide reduction reaction(CO_(2)RR)provides an oppor tunity to produce value-added chemical feedstocks and ***,achieving efficient and stable photoelectrochemical(PEC)CO_(2)RR into selec tive products is challenging owing to the difficulties associated with the optical and the electrical configuration of PEC devices and electrocatalyst ***,we construct an efficient,concentrated sunlight-driven CO_(2)RR setup consisting of InGaP/GaAs/Ge triple-junction cell as a photoanode and oxide-derived Au(Ox-Au)as a cathode to perform the unassisted PEC CO_(2)*** one-sun illumination,a maximum operating current density of 11.5 mA cm^(-2) with an impressive Faradaic efficiency(FE)of~98%is achieved for carbon monoxide(CO)production,leading to a solar-to-fuel conversion efficiency of~15%.Under concentrated intensity of 10 sun,the photoanode records a maximum current density of~124 mAcm^(-2) and maintains~60%of FE for CO *** results demonstrate crucial advancements in usingⅢ-Ⅴbased photoanodes for concentrated PEC CO_(2)RR.

关键词： photoelectrochemical oxide derived Au concentrated sunlight selec tive products III V photoelectrode pec devices CO reduction solar fuel efficiency

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Altitude Hold Controls on Multi-Rotor sUAV with Barometer Effects

Altitude Hold Controls on Multi-Rotor sUAV with Barometer Ef...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Jirayuth Jantawong Prasongsuk Songsree Warinthorn Kirathihiran Faculty of Engineering and Technology Technology Program in Electrical Technology and Automatic Control System Siam Technology College Bangkok Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This paper presents an accurate altitude estimation on a small multirotor UAV. That is crucial for the stability and performance of UAV, particularly in altitude hold control. Barometric pressure sensors are widely used to estimate altitude based on atmospheric pressure, but their readings are highly susceptible to environmental fluctuations, temperature variations, and dynamic airflow effects. To mitigate these altitude errors, this study focuses on enhanced flight control installation techniques, including the physical protection of barometric sensors inside enclosed, well-ventilated compartments to shield them from wind disturbances and rapid pressure changes. The proposed mitigation strategy is validated through ground testing and flight testing, where altitude hold performance is assessed under various environmental conditions. The results confirm that enclosing the barometric sensor within a protective, ventilated chamber significantly reduces altitude estimation errors, leading to more stable altitude hold control with reduced vertical oscillations. These findings highlight the importance of integrated sensor protection in UAV flight control system design and provide a practical framework for improving altitude estimation reliability in both consumer and industrial UAV applications.

关键词： Temperature sensors Wind Technological innovation Fluctuations Autonomous aerial vehicles Barometers Protection Thermal stability Aerospace control Testing

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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Computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Occurrence, migration and environmental effects of neglected BTEX in the coastal multiple environments

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Journal of Hazardous Materials 2025年 494卷 138555页

作者： Qiao, Hao Wang, Jian Qiao, Wen-Zhu Liu, Zhen-Fei Zhou, Li-Min Zhang, Hong-Hai Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Marine Chemistry Theory and Technology Ministry of Education Ocean University of China Qingdao266100 China Laoshan National Laboratory Science and Technology Innovation Program Project Qingdao266237 China College of Chemistry and Chemical Engineering Ocean University of China Qingdao266100 China

Benzene, toluene, ethylbenzene, and o-, m-, p-xylene (BTEX) are toxic and hazardous pollutants with critical impacts on ecosystem health and air quality. Here, the spatial distributions, interfacial exchange fluxes and environmental effects of BTEX were investigated in multiple environmental mediums in the East China Sea. The average concentrations of ∑BTEX in the surface (bottom) seawater, atmosphere and sediment were 235 ± 168 (152 ± 98.3) ng L−1, 600 ± 408 pptv and 0.76 ± 0.29 μg kg−1, respectively, generally decreasing from inshore to offshore regions. Furthermore, BTEX in seawater served as sources for those in atmosphere and sediment, with interfacial exchange fluxes of ∑BTEX estimated at 734 ± 748 and 1.80 ± 1.56 g km−2 d−1, respectively. Ocean currents, air masses properties and accumulation of long-term terrestrial inputs were identified as key factors controlling BTEX distributions across the different mediums. Additionally, the ozone and secondary organic aerosol formations potentials of atmospheric ∑BTEX were 0.134 ± 0.093 and 14.3 ± 11.5 μg m−3, respectively. This study addresses a critical gap in data on BTEX contaminations in coastal environments, improves understanding of their interfacial exchange processes, underscores the necessity of continuous monitoring and provided a valuable basis for environmental impact assessments. © 2025 Elsevier B.V.

关键词： Air quality

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Reducing Errors and Powers in Lpddr for Dnn Inference: A Compression and Iecc-Based Approach

SSRN

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SSRN 2025年

作者： Hong, Jae-Youn Jang, Je-Woo Cho, Sung-Hyuk Kong, Youngbae Kim, Sungkyu Kang, Youngjung Ko, Jaehyung Chung, Jaeyong Yang, Joon-Sung Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Memory Division Samsung Electronics Hwaseong Korea Republic of Department of System Semiconductor Engineering Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of

In modern edge systems, the demand for data processing for complex DNN tasks, is rapidly increasing. To address this, various compression schemes have been proposed to enable on-device AI while meeting the strictpower and storage constraints. However, despite these advancements, the compatibility of the compression methods with edge device memory(LPDDR), has not been thoroughly investigated. LPDDR operates at low voltage and faces reliability challenges, which is particularly concerning for applications where accuracy is critical, such as ADAS or medical devices. To address these concerns, ECC engine, known as IECC, is employed within each LPDDR bank. While IECC improves reliability, it also incurs performance penalties due to Read-Modify-Write(RMW) operations and parity storage overheads. This paper introduces RELIA, a DNN weight compression scheme with three-stage protection, aimed at enabling power-efficient and reliable DNN operations in mobile environments. RELIA reduces the operation granularity of the IECC engine from SEC(136,128) to SEC(72,64), eliminating RMW overhead. Additionally, it proposes SEC-FOEC(72,64) scheme that can correct 99.97% of LPDDR errors. To mitigate the added storage overhead, compression scheme based on DNN weight characteristics is introduced. Experimental results show RELIA reduces power, cycle, energy, and storage by 17.35%, 13.2%, 32.7%, and 23.6%, respectively, while offering superior reliability. © 2025, The Authors. All rights reserved.

关键词： Biomedical equipment

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Saturable intercalation doping enables scalable and selective graphene gas sensors on plastic

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Chemical engineering Journal 2025年 515卷

作者： Kwon, Bitnuri Lee, Jung Hun Jo, Sae Byeok Hwang, Jinhyun Lee, Donggeun Cha, Jonghyun Lim, Soomook Suk, Ji Won Lee, Wi Hyoung Department of Organic and Nano System Engineering Konkuk University Seoul05029 Cuba Department of Materials Science and Engineering and the Materials Research Center Northwestern University 2220 Campus Drive EvanstonIL60208-3108 United States School of Chemical Engineering Sungkyunkwan University Suwon16419 Cuba Sungkyunkwan University Suwon16419 Cuba Advanced Materials Program Department of Materials Science and Engineering Konkuk University Seoul05029 Cuba School of Mechanical Engineering Sungkyunkwan University Suwon16419 Cuba Department of Smart-Fab. Technology Sungkyunkwan University Suwon16419 Cuba Department of Materials Science and Engineering Konkuk University Seoul05029 Cuba

Developing highly sensitive and selective gas sensors for nitrogen dioxide (NO2) detection remains crucial for environmental monitoring and public health protection. Graphene-based chemiresistive sensors show promise due to their exceptional electronic properties and high surface area, which enhance gas adsorption. However, they face persistent challenges in selectivity, stability, and sensitivity characteristics. This study presents a facile one-step molecular intercalation doping approach that can effectively overcome these limitations. By intercalating carefully selected dopant molecules between graphene and flexible substrates during the wet-transfer process, we achieve uniform doping distribution while maintaining intrinsic carrier mobility. The resulting sensors demonstrate remarkable NO2 detection capabilities with a limit of detection of 1 ppb at room temperature. Importantly, the intercalated devices exhibit exceptional stability over extended periods of time and show minimal cross-sensitivity to common interfering gases. Moreover, the sensors retain their sensing characteristics under mechanical deformation, including fully transparent gas sensors with a dogbone structure, thereby enabling seamless integration into wearable monitoring systems. Our scalable fabrication process and comprehensive understanding of the intercalation doping mechanism provide a practical pathway for the development of reliable, flexible gas sensors capable of addressing real-world environmental and healthcare challenges. © 2025 Elsevier B.V.

关键词： pH sensors

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Survival Analysis of Kasturi Tobacco Plants in Jember District using the Stratified Cox and Extended Cox Models

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MethodsX 2025年

作者： Angga Iryanto Pratama Dwi Rantini Ratih Ardiati Ningrum Arip Ramadan Fazidah Othman Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

关键词： Productive Agriculture Cox Proportional Hazard Stratified Cox Extended Cox Kasturi Tobacco

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Drivetrain simulation using variational autoencoders

arXiv

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arXiv 2025年

作者： Sharma, Pallavi Urrea-Quintero, Jorge-Humberto Bogdan, Bogdan Ciotec, Adrian-Dumitru Vasilie, Laura Wessels, Henning Skull, Matteo Porsche Engineering Etzelstraße 1 Bietigheim-Bissingen74321 Germany Graduate Program Computational Sciences in Engineering Technische Universität Braunschweig Braunschweig Germany Fraunhofer Institute for Energy Economics and Energy System Technology Joseph-Beuys-Straße 8 Kassel34117 Germany Institute of Applied Mechanics Division Data-Driven Modeling of Mechanical Systems Technische Universität Braunschweig Pockelsstraße 3 Braunschweig38106 Germany

This work proposes variational autoencoders (VAEs) to predict a vehicle’s jerk from a given torque demand, addressing the limitations of sparse real-world datasets. Specifically, we implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios. The VAEs are trained on experimental data collected from two variants of a fully electric SUV, which differ in maximum torque delivery and drivetrain configuration. New meaningful jerk signals are generated within an engineering context through the interpretation of the VAE’s latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs. We show that VAEs bypass the need for exhaustive manual system parametrization while maintaining physical plausibility by conditioning data generation on specific inputs. © 2025, CC BY.

关键词： Signal encoding

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