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A 3D-printed smartphone-based platform for in-situ and rapid monitoring of aquaculture pathogens using polydimethylsiloxane (PDMS) microchip with multiplex loop-mediated isothermal amplification (M-LAMP)

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Biosensors and Bioelectronics: X 2025年 25卷

作者： Li, Liyan Fu, Jing Ching Chiang, Elaine Li Yoo, Jerald Bae, Sungwoo Department of Civil and Environmental Engineering National University of Singapore Singapore Key Laboratory of Organic Compound Pollution Control Engineering School of Environmental and Chemical Engineering Shanghai University China Yong Loo Lin School of Medicine National University of Singapore Singapore Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Environmental Engineering Korea University Korea Republic of

Aquaculture pathogens pose serious risks to aquatic livestock and global food safety. Key threats in shrimp farming include white spot syndrome virus (WSSV), Vibrio parahaemolyticus (causing acute hepatopancreatic necrosis disease, AHPND), and Enterocytozoon hepatopenaei (EHP). Rapid, on-site detection is critical for early detection and outbreak prevention. In this study, we developed a portable, smartphone-based diagnostic platform utilizing multiplex loop-mediated isothermal amplification (LAMP) for simultaneous detection of multiple pathogens in a single reaction. A PDMS microchip with 30 reaction wells (5 × 6 array) and a temperature control well was fabricated for efficient multiplexing. Immobilized LAMP reagents with freeze-drying lyophilization were preloaded into wells to streamline preparation and enhance stability. The system successfully identified both waterborne indicator bacteria (E. coli, E. faecalis, Salmonella) and major shrimp pathogens (WSSV, AHPND, EHP) in samples from Penaeus vannamei, Penaeus monodon, and aquaculture water. The microchip maintained stable isothermal conditions (65.1 ± 0.6 °C), enabling visual detection via color change at low DNA concentrations (as low as 4 copies/μL). All WSSV and EHP infections in shrimp tissues and water samples were correctly identified using LAMP reaction within 35 min (excluding the DNA extraction process), demonstrating 100% positive detection rates. The smartphone interface allowed real-time imaging and result interpretation, offering a rapid, user-friendly tool for in situ pathogen monitoring. This platform represents a practical, low-cost solution for field diagnostics and improved disease management in aquaculture. © 2025 The Authors

关键词： Escherichia coli

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Current Challenges and Advancements of Aerial Thermography for Outdoor Structural Health Monitoring: A Review

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IEEE Sensors Journal 2025年

作者： Ma, Yue Zeng, Zhi Luo, Zipei Tao, Ning Deng, Lei Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Chongqing Normal University College of Computer and Information Science Chongqing401331 China Capital Normal University Department of Physics Key Lab of Terahertz Optoelectronics of the Ministry of Education Beijing100048 China

In the rapid development of outdoor structural health monitoring, infrared thermal imaging and unmanned aerial vehicles (UAVs) have emerged as transformative tools, significantly enhancing maintenance efficiency in expansive outdoor structures. This review highlights the advancements in UAV-assisted thermography, focusing on its impact on the monitoring capabilities of outdoor engineering structures. It introduces UAV-assisted thermographic methods tailored for outdoor environments, explorees current challenges influencing their efficacy, and thoroughly examines their integration with machine learning techniques, demonstrating their applications in identifying structural flaws and deformations. Furthermore, the review emphasizes the transformative potential of 3D thermographic modelling to localize and characterize defects effectively. It contributes actionable insights to optimize UAV-assisted thermographic practices. The findings underscore the importance of integrating UAVs and thermography to overcome existing limitations in outdoor structural health monitoring inspections, setting the stage for a paradigm shift in large-scale outdoor structural condition assessments. © 2001-2012 IEEE.

关键词： Thermography (imaging)

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Curiosity and Affect-Driven Cognitive Architecture for HRI

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IEEE Transactions on Affective Computing 2025年

作者： Berto, Leticia Tanevska, Ana Cirne, Azamor Costa, Paula Simoes, Alexandre Gudwin, Ricardo Rea, Francesco Colombini, Esther Sciutti, Alessandra State University of Campinas Institute of Computing Campinas Brazil Italian Institute of Technology CONTACT Unit Genoa Italy Uppsala University Department of Information Technology Uppsala Sweden Federal University of Paraiba Department of Finance and Accounting João Pessoa Brazil State University of Campinas School of Electrical and Computer Engineering Campinas Brazil São Paulo State University Department of Control and Automation Engineering Sorocaba Brazil

This study explores how humans and cognitive robots with different value systems and motivations understand each other's needs in free-form interactions. We developed a cognitive architecture that links sensing and perception to internal motivation and an intrinsic value system for determining actions. Inspired by young children's needs, this architecture includes three drives: learning, interaction, and recharging, each with varying dependence on the human partner. We aimed to assess how experimentally changing the importance of these drives within a fixed architecture affects interaction dynamics with human partners (acting as caregivers) and their understanding of the robot's needs. By adjusting the learning and interaction drives, we created two robot profiles: Playful, which prioritizes environmental exploration and playfulness to reduce boredom, and Social, which focuses on social interaction through touch and visual contact to increase comfort. Our findings show that changing the importance of these drives produces distinct behaviors and human perceptions. Robot behaviors matched their profiles, and participants adapted their responses accordingly. Participants identified and attributed distinct traits to each robot without knowing the specific profiles. Despite variability among human partners, the robots, especially the playful one, were generally well understood by most participants. © 2025 IEEE. All rights reserved.

关键词： Robot learning

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Robust Maximum Power Point Tracking in PV Generation System: A Hybrid ANN-Backstepping Approach With PSO-GA Optimization

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IEEE Transactions on Consumer Electronics 2025年

作者： Hussan, Umair Waheed, Ahsan Bilal, Hazrat Wang, Huaizhi Hassan, Mudasser Ullah, Inam Peng, Jianchun Hosseinzadeh, Mehdi Shenzhen University College of Mechatronics and the Control Engineering Shenzhen518060 China The University of Faisalabad Department of Electrical Engineering Faisalabad38000 Pakistan University of Tasmania School of Engineering HobartTAS7005 Australia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Macquarie University School of Computing Sydney Australia Duy Tan University School of Computer Science Da Nang Viet Nam

The growing diversity of consumer loads emphasizes the need for efficient operation of photovoltaic generation systems (PVGSs). This paper presents a novel control framework for maximum power point tracking (MPPT) in PVGSs, designed to enhance efficiency, robustness, and adaptability under dynamic environmental conditions. The proposed methodology integrates optimized Artificial Neural Network (ANN) with nonlinear backstepping controller. Hybrid particle swarm optimization (PSO) and genetic algorithm (GA) approach is used to optimize ANN, which leverages the global search capabilities of PSO and the local refinement strengths of GA to optimize the ANN’s weights and biases, ensuring accurate prediction of the PV reference voltage that is critical for MPPT. Additionally, the nonlinear backstepping controller ensures system stability and precise tracking of the predicted reference voltage by recursively constructing Lyapunov functions to guarantee asymptotic stability. The effectiveness of this control framework is evaluated through MATLAB/Simulink and validated with hardware testing. It is observed from simulation and hardware testing results, compared to existing ANN and conventional techniques, the proposed approach yielded significant improvements by achieving root mean square error of 0.103%, tracking accuracy of 99. 8%, and reduction of power loss to 0. 2%. The proposed control framework significantly enhances MPPT robustness and efficiency in dynamic environments and enables higher integration of renewable energy. This advancement supports scalable, resilient PV systems critical for sustainable energy transitions. © 1975-2011 IEEE.

关键词： Genetic algorithms

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Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms: Challenges and a Roadmap

arXiv

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arXiv 2025年

作者： Xu, Jianye Alrifaee, Bassam Betz, Johannes Mokhtarian, Armin Mittal, Archak Cai, Mengchi Mangharam, Rahul Shehata, Omar M. Elias, Catherine M. Zaech, Jan-Nico Scheffe, Patrick Jahncke, Felix Ulhas, Sangeet Sankaramangalam Arfvidsson, Kaj Munhoz Department of Computer Science RWTH Aachen University Germany Department of Aerospace Engineering University of the Bundeswehr Munich Germany Professorship of Autonomous Vehicle Systems Technical University of Munich Germany Department of Civil Engineering Indian Institute of Technology Bombay India School of Vehicle and Mobility Tsinghua University China Department of Electrical and Systems Engineering University of Pennsylvania United States Mechatronics Engineering Department Faculty of Engineering and Materials Science The German University in Cairo Egypt Department of Computer Science Engineering The German University in Cairo Egypt INSAIT Sofia University "St. Kliment Ohridski" Bulgaria School for Engineering of Matter Transport and Energy Arizona State University United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This article proposes a roadmap to address the current challenges in small-scale testbeds for Connected and Automated Vehicles (CAVs) and robot swarms. The roadmap is a joint effort of participants in the workshop "1st Workshop on Small-Scale Testbeds for Connected and Automated Vehicles and Robot Swarms," held on June 2 at the IEEE Intelligent Vehicles Symposium (IV) 2024 in Jeju, South Korea. The roadmap contains three parts: 1) enhancing accessibility and diversity, especially for underrepresented communities, 2) sharing best practices for the development and maintenance of testbeds, and 3) connecting testbeds through an abstraction layer to support collaboration. The workshop features eight invited speakers, four contributed papers [1]–[4], and a presentation of a survey paper on testbeds [5]. The survey paper provides an online comparative table of more than 25 testbeds, available at https://***/testbeds-survey. The workshop’s own website is available at https://***/iv24-workshop. Copyright © 2025, The Authors. All rights reserved.

关键词： Testbeds

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UAV-Based Real-Time Air Quality Monitoring and Prediction Using Machine Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Hsu, Huan-Chia Tarekegn, Getaneh Berie Getnet, Mastewal Endeshaw Yu, Kuo-Pin Tai, Li-Chia NYCU Department of Electrical and Computer Engineering Hsinchu30010 Taiwan NYCU Institute of Electrical and Control Engineering Hsinchu30010 Taiwan NYCU Institute of Environmental and Occupational Health Sciences Taipei112304 Taiwan NYCU Department of International Health Program Taipei112304 Taiwan

Globally, air pollution has become a major problem due to the growth of the human population and rapid urbanization, posing a serious threat to the environment and human health. Currently, most monitoring systems are based on fixed monitoring stations and are inefficient due to slow response time. An accurate and real-time air quality monitoring and prediction (AQMP) system is required to prevent health problems caused by air pollution. With the swift advancement of Internet of Things (IoT) technologies such as low-cost sensors and advanced wireless communication systems capable of sensing their environment, connecting with other systems, and interacting with users. To overcome the problems of existing systems, this paper aims to develop an unmanned aerial vehicle (UAV)-based real-time AQMP system by integrating artificial intelligence and data science technologies. The proposed UAV is equipped with lightweight air quality sensors including particulate matter (i.e., PM2.5 and PM10), carbon monoxide (CO), carbon dioxide (CO2), and ozone (O3), and meteorological sensors. The sensors are mounted on the UAV via the microcontroller, and the sensors reading data is transmitted to the web server via a message queueing telemetry transport (MQTT) protocol using a 5G mobile network module. Practical experiments were conducted in Hsinchu City and the industrial area of Yunlin County to collect real-time air pollutant data. Then, we employed a hybrid of attention mechanism, convolutional neural network (CNN) and gated recurrent unit (GRU) algorithms to predict future air quality levels. Experimental results show that the proposed system improves prediction performance compared with baseline time series algorithms. © 2014 IEEE.

关键词： Air quality

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Seamless Graph Task Scheduling over Dynamic Vehicular Clouds: A Hybrid Methodology for Integrating Pilot and Instantaneous Decisions

arXiv

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arXiv 2025年

作者： Guo, Bingshuo Liwang, Minghui Xia, Xiaoyu Li, Li Jiao, Zhenzhen Hosseinalipour, Seyyedali Wang, Xianbin School of Informatics Xiamen University Fujian China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering Tongji University Shanghai China iF-Labs Beijing Teleinfo Technology Co. Ltd. CAICT China Department of Electrical Engineering University at Buffalo-SUNY NY United States Department of Electrical and Computer Engineering Western University ON Canada

Vehicular clouds (VCs) play a crucial role in the Internet-of-Vehicles (IoV) ecosystem by securing essential computing resources for a wide range of tasks. This paPertackles the intricacies of resource provisioning in dynamic VCs for computation-intensive tasks, represented by undirected graphs for parallel processing over multiple vehicles. We model the dynamics of VCs by considering multiple factors, including varying communication quality among vehicles, fluctuating computing capabilities of vehicles, uncertain contact duration among vehicles, and dynamic data exchange costs between vehicles. Our primary goal is to obtain feasible assignments between task components and nearby vehicles, called templates, in a timely manner with minimized task completion time and data exchange overhead. To achieve this, we propose a hybrid graph task scheduling (P-HTS) methodology that combines offline and online decision-making modes. For the offline mode, we introduce an approach called risk-aware pilot isomorphic subgraph searching (RA-PilotISS), which predicts feasible solutions for task scheduling in advance based on historical information. Then, for the online mode, we propose time-efficient instantaneous isomorphic subgraph searching (TE-InstaISS), serving as a backup approach for quickly identifying new optimal scheduling template when the one identified by RA-PilotISS becomes invalid due to changing conditions. Through comprehensive experiments, we demonstrate the superiority of our proposed hybrid mechanism compared to state-of-the-art methods in terms of various evaluative metrics, e.g., time efficiency such as the delay caused by seeking for possible templates and task completion time, as well as cost function, upon considering different VC scales and graph task topologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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An approach to stock price prediction based on improved EEMD and attention-enhanced BiLSTM

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Expert Systems with Applications 2025年 284卷

作者： Liu, Zhinzhong Su, Zhaohui Shang, Ling Sun, Hongxiang Zhao, Bin School of Computer and Control Engineering Yantai University Yantai264005 China Digital Smart Creative Department Henan Culture and Tourism Investment Group Co. Ltd Luoyang471026 China

Accurate stock price prediction is a urgently needed technology in economic investment. However, stock prices are influenced by various factors and display non-linear and non-stationary characteristics, which make accurately predicting stock prices a critical challenge. To tackle the above issue, we propose a method for stock price prediction based on improved ensemble empirical mode decomposition (EEMD) and attention-enhanced BiLSTM (AEBiLSTM), referred to as SPPEA. First, to reduce the nonlinearity of the stock prices and improve the prediction accuracy, our method first divides the original stock price series into subsequences with the same time scale using a sliding window. These sub-sequences are then decomposed into multiple intrinsic mode Function (IMF) components with the EEMD method which is improved by cubic spline interpolation. Then, the decomposed IMF components are input into AEBiLSTM to capture the dependencies of stock price data and the features of important points of the stock prices, and then the prediction results of each IMF component are obtained with AEBiLSTM. Finally, the prediction results of each component are integrated to obtain the final prediction results. Extensive experiments have been conducted on three stock datasets: BYD, China Unicom and Ping An Bank. Experimental results prove that our proposed method has better performance compared with the state-of-the-art methods. © 2025 Elsevier Ltd

关键词： Cost estimating

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Integral control of the proximal gradient method for unbiased sparse optimization

arXiv

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arXiv 2025年

作者： Cerone, V. Fosson, S.M. Re, A. Regruto, D. Department of Control and Computer Engineering Politecnico di Torino Italy

Proximal gradient methods are popular in sparse optimization as they are straightforward to implement. Nevertheless, they achieve biased solutions, requiring many iterations to converge. This work addresses these issues through a suitable feedback control of the algorithm’s hyperparameter. Specifically, by designing an integral control that does not substantially impact the computational complexity, we can reach an unbiased solution in a reasonable number of iterations. In the paper, we develop and analyze the convergence of the proposed approach for strongly-convex problems. Moreover, numerical simulations validate and extend the theoretical results to the non-strongly convex framework. © 2025, CC BY-NC-SA.

关键词： Mathematical programming

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ConnectomeAE: Multimodal brain connectome-based dual-branch autoencoder and its application in the diagnosis of brain diseases

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computer Methods and Programs in Biomedicine 2025年 267卷 108801-108801页

作者： Zheng, Qiang Nan, Pengzhi Cui, Yongchao Li, Lin School of Computer and Control Engineering Yantai University Yantai264005 China Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Kuala Lumpur53300 Malaysia Department of Radiology Yantaishan Hospital Affiliated to Binzhou Medical University Yantai264003 China

Background and Objective: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patients, ignoring important information about radiomics features such as shape and texture of individual brain regions in structural images. To this end, this study proposed a novel deep learning approach to integrate multimodal brain connectome information and regional radiomics features for brain disease diagnosis. Methods: A dual-branch autoencoder (ConnectomeAE) based on multimodal brain connectomes was proposed for brain disease diagnosis. Specifically, a matrix of radiomics feature extracted from structural magnetic resonance image (MRI) was used as Rad_AE branch inputs for learning important brain region features. Functional brain network built from functional MRI image was used as inputs to Cycle_AE for capturing brain disease-related connections. By separately learning node features and connection features from multimodal brain networks, the method demonstrates strong adaptability in diagnosing different brain diseases. Results: ConnectomeAE was validated on two publicly available datasets. The experimental results show that ConnectomeAE achieved excellent diagnostic performance with an accuracy of 70.7 % for autism spectrum disorder and 90.5 % for Alzheimer's disease. A comparison of training time with other methods indicated that ConnectomeAE exhibits simplicity and efficiency suitable for clinical applications. Furthermore, the interpretability analysis of the model aligned with previous studies, further supporting the biological basis of ConnectomeAE. Conclusions: ConnectomeAE could effectively leverage the complementary information between multimodal brain connectomes for brain disease diagnosis. By separately learning radiomic node features and connectivity features, ConnectomeAE demonstrated good adaptability t

关键词： Magnetic resonance imaging

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