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Improvement of uniformity in cultivation environment and crop growth rate by hybrid control of air flow devices

Journal of Central South University 2015年第12期22卷 4702-4708页

作者： BAEK Min-Seon KWON Sook-Youn LIM Jae-Hyun Department of Computer Science and Engineering Kongju National University Green Energy Technology Research Center Kongju National University

A complete control type plant factory has high efficiency in terms of cultivation area by constructing vertical multiple layered cultivation ***,it has a problem of irregular crop growth due to temperature deviation at upper and lower beds and increases in energy consumption by a prolonged cultivation *** this work,air flow rate inside a facility was improved by a hybrid control of air flow devices like air conditioning and air circulation fan with an established wireless sensor network to minimize temperature deviations between upper and lower beds and to promote crop *** performance of proposed system was verified with an experimental environment or Case A wherein air conditioning device was operated without a control algorithm and Case B wherein air conditioning and circulation fans were alternatively operated based on the hybrid control *** planting leafy vegetables under each experimental condition,crops were cultivated for 21 *** a result,Case B wherein AC(air conditioning) and ACF(air-circulation fan) were alternatively operated based on the hybrid control algorithm showed that fresh mass,number of leaves,and leaf length for the crops grown were increased by 40.6%,41.1%,and 11.1%,respectively,compared to Case A.

关键词： air flow devices hybrid control cultivation environment uniformity growth rate

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ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Jingxiong Zhu, Chenglu Zheng, Sunyi Chen, Pingyi Sun, Yuxuan Li, Honglin Yang, Lin Zhejiang University College of Computer Science and Technology Hangzhou310027 China Westlake University School of Engineering Hangzhou310024 China Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer Tianjin's Clinical Research Center for Cancer State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine Tianjin Key Laboratory of Digestive Cancer Key Laboratory of Cancer Prevention and Therapy Department of Radiology Tianjin China Westlake University Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Hangzhou310024 China Westlake Institute for Advanced Study Institute of Advanced Technology Hangzhou310024 China Muyuan Laboratory Center for Interdisciplinary Research and Innovation Henan Zhengzhou450016 China

Synthetic data generation emerges as a strategy to mitigate data scarcity in digital pathology, where complicated tissue and cellular features are correlated with cancer diagnosis. The synthesis of such visuals, however, suffers from limited inter class diversity and scarcity of cellular annotations. Current methodologies struggle with capturing the broad spectrum of pathology features, causing unpredictable objects and defected fidelity. Moreover, discrepancies in image resolution across developmental and operational phases can amplify the distribution shifts, undermining the precision of diagnosis. To address these challenges, we introduce TOpology guided PathOlogy Foundation Model (ToPoFM), a visual foundation model designed for the synthesis of high-resolution pathology images with cellular-level control. Our approach integrates a topology-informed cell arrangement generator to steer large language models for crafting synthetic cell arrangements. We correlate cell arrangement guidance with diffusion model for pathology content generation, then further implement a random sliding inference strategy, merging discrete low-resolution samplings into single high-resolution representation. Our model requires only small patches for training. The efficacy of ToPoFM is demonstrated through extensive experiments, complemented by expert validations, showing high fidelity on data synthesis. Additionally, we underscore the utility of our generated imagery as an augmentation tool, enhancing the performance of downstream tasks, including cancer subtype classification and segmentation. © 2025 IEEE.

关键词： Image segmentation

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Efficient Water Evaporation Using Black Silicon

ACS Sustainable Resource Management

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ACS Sustainable Resource Management 2025年第2期2卷 316-321页

作者： Yoshiaki Nishijima Hiroyoshi Nishijima Makoto Ohashi Tomas Katkus Saulius Juodkazis Department of Electrical and Computer Engineering Graduate School of Engineering Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan；Precursory Research for Embryonic Science and Technology (PRESTO) Japan Science and Technology Agency (JST) 7 Gobancho Chiyoda-ku Tokyo 102-0076 Japan；Institute of Advanced Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan；Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan；Research Institute for Electronic Science (RIES) Hokkaido University North 20 West 10 Kita-ku Sapporo Hokkaido 001-0020 Japan Hokkaido Obihiro South Commercial High School 36-1 West 21 South 5 Obihiroshi Hokkaido 080-2471 Japan Department of Electrical and Computer Engineering Graduate School of Engineering Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan Optical Sciences Centre and ARC Training Centre in Surface Engineering for Advanced Materials School of Science Swinburne University of Technology Hawthorn Victoria 3122 Australia Institute of Advanced Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Yokohama 240-8501 Japan；Optical Sciences Centre Swinburne University of Technology Hawthorn Victoria 3122 Australia；Tokyo Tech World Research Hub Initiative School of Materials and Chemical Technology Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan；Laser Research Center Physics Faculty Vilnius University Vilnius 01513 Lithuania

The black silicon (b-Si)─plasma-etched nanostructured surface of crystalline Si─was used as a light absorber due to its extremely low reflectivity (<1%) at the visible-to-near-IR spectral range. Flat Fresnel lenses of 1.47 and 0.36 m2were used to focus light onto b-Si, which was a photothermal absorber. Experiments to distill water were carried out from October 2021 to April 2022 in Obihiro (42°55′N 143°12′E), Hokkaido, Northern Japan. The same b-Si has biocidal properties (antibactericidal, antiviral, and antifungal) due to mechanical nanoscale needles, which complement the water purification when gray water sources are used in applications.

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Ultra-Sensitive Optical Fiber Spr Biosensor for Precise Prostate Cancer Diagnosis by Label-Free Detection of Mirna-21

SSRN

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SSRN 2025年

作者： Wang, Jiale Deng, Minghua Niu, Kunpeng Wang, Chuanquan Wang, Ning Li, Jinhua Xia, Binyun Ding, Liyun Yang, Huan Chen, Xiaohan Zhu, Jiayi National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China School of Computer and Information Engineering Hubei Normal University Huangshi China Hubei Optical Fundamental Research Center Wuhan China Collaborative Innovation Center for Advanced Organic Chemical Materials Co-constructed by the Province and Ministry Key Laboratory for the Green Preparation and Application of Functional Materials Ministry of Education Hubei Key Laboratory of Polymer Materials School of Materials Science and Engineering Hubei University Wuhan430062 China School of Physical Science and Technology Lanzhou University Lanzhou730000 China Department of Urology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430030 China

Efficient and rapid identification of miRNA-21 may be conducive to the early diagnosis of prostate cancer. In this work, a label-free and highly sensitive optical fiber Surface Plasmon Resonance (SPR) biosensor was developed for detecting the miRNA-21, which was fabricated simply by coating a layer of Au film and AuNPs onto a multi-mode-single-mode-multi-mode (MSM) optical fiber, and then biofunctionalized with ssDNA enabling the quantification of miRNA-21. The working principle involves initiating the change of the effective refractive index (RI) around the optical fiber's surface, which was caused by nucleic acid hybridization between the ssDNA and the miRNA-21. As a result, the interaction would result in a noticeable shift of the SPR wavelengths in the transmission spectra. In the experiment, a substantial enhancement was exhibited in the RI sensitivity of the optical fiber sensor due to the surface plasmon resonance (SPR) coupling effect of AuNPs and the Au film, measuring approximately 3021.48 nm/RIU. Excitingly, the biosensors, which have a simple composition, were experimentally verified to have an ultra-low limit of detection (LOD) of 10-20 M, significantly lower than current detection methods. And trace miRNA-21 was enabled for detection without RNA amplification. The SPR sensors were also experimentally proved with excellent linearity, specificity and stability. When employed to detect the serum samples, the results indicated that the proposed sensors could brilliantly distinguish clinical patients with and without prostate cancer. Compared to the commonly used PSA quantification in clinical practice, the method developed in this work has higher detection accuracy. The proposed sensing platform processes the merits of simple configuration as well as low cost and may be applied for the precise diagnosis of prostate cancer. © 2025, The Authors. All rights reserved.

关键词： Surface plasmon resonance

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Mobility-aware Decentralized Federated Learning for Autonomous Underwater Vehicles

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IEEE Transactions on Wireless Communications 2025年

作者： He, Hongyi Du, Jun Jiang, Chunxiao Wang, Jintao Song, Jian Han, Zhu Tsinghua University Department of Electronic Engineering Beijing100084 China Tsinghua University Department of Electronic Engineering State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology and State Key laboratory of Space Network and Communication Beijing100084 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments. © 2002-2012 IEEE.

关键词： Autonomous underwater vehicles

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Identification and quantification of muscular cocontraction for ankle rehabilitation through variational mode decomposition in surface electromyography

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Scientific Reports 2025年第1期15卷 1-14页

作者： Yasmeen, Sania Waris, Asim Amin, Faisal Iqbal, Javaid Gilani, Syed Omer Khan, Muhammad Jawad Hazzazi, Fawwaz Imran, Ahmed Shah, Umer Hameed Ijaz, Muhammad Adeel Department of Biomedical Engineering and Sciences School of Mechanical and Manufacturing Engineering National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical Computer and Biomedical Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Department of Biomedical Engineering & Artificial Intelligence Research Center College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Biomedical Engineering and Sciences School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad Pakistan

The ankle joint plays important role in performing fundamental activities such as walking and other essential daily tasks. Ankle stabilization and muscle co-contraction are crucial for rehabilitating gait abnormalities, as impaired ankle function disrupts gait, causes pain and inflammation, and hampers recovery. Accurate assessment of muscle co-contraction is crucial for developing effective non-pharmacological interventions. This paper introduces a novel approach using Variational Mode Decomposition (VMD) combined with scalogram visualization technique to analyze surface electromyographic (sEMG) signals from antagonist muscles of the lower limb and assesses muscular co-contraction using the coscalogram function. The present study compares VMD with the Continuous Wavelet Transform (CWT) approach and shows that VMD outperforms CWT in terms of both SNR and RMSE. On average, the increase in SNR in case of VMD (−17.65 ± 8.1dB to 2.98 ± 2.2dB) was greater than that of CWT (−17.65 ± 3.7dB to 1.34 ± 1.5dB). Similarly, the reduction in RMSE with VMD (0.023 ± 0.0029 to 0.017 ± 0.0015) surpassed that achieved with CWT (0.023 ± 0.0027 to 0.020 ± 0.0025). This enhanced accuracy in identifying co-contraction events has the potential to significantly improve clinical assessment and rehabilitation strategies for patients with ankle joint pathologies. To further validate VMD’s effectiveness, we quantitatively assessed co-contraction events by comparing mean peak amplitudes identified using VMD and CWT. Our analysis, which revealed that VMD consistently captured stronger co-contraction events (higher mean peak amplitudes), supports VMD’s superiority in accurately identifying and quantifying ankle muscle co-contraction. These results have significant implications for clinical practice, offering the potential for more precise assessments of ankle joint function and the development of more targeted and effective rehabilitation interventions. © The Author(s) 2025.

关键词： Ankle rehabilitation Co-contraction Continuous wavelet transform Scalogram sEMG Variational mode decomposition

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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Low-Complexity Joint Radar-Communication Beamforming: From Optimization to Deep Unfolding

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IEEE Journal on Selected Topics in Signal Processing 2025年

作者： Zhang, Jianjun Masouros, Christos Liu, Fan Huang, Yongming Swindlehurst, A. Lee Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China University College London Department of Electronic & Electrical Engineering LondonWC1E7JE United Kingdom Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen518055 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University of California at Irvine Center for Pervasive Communications and Computing IrvineCA92697 United States

By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) systems have been envisioned as a key technology for the future wireless networks. However, advanced signal processing algorithms for DFRC, which can achieve better performance, tradeoff or other design goals, often suffer from prohibitive computational complexity. This motivates us to design low-complexity joint radar sensing and communication beamforming algorithms in this paper, so as to achieve better energy efficiency, communication-sensing tradeoff, and so on. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference so as to improve the energy efficiency. To address the formulated problem, we tailor highly parallelizable iterative optimization algorithms that are shown to converge to stationary (or locally optimal) points. To achieve better performance, we propose efficient recursive optimizations that monotonically improve the performance metric of interest. Simulation results indicate that the proposed iterative algorithms outperform the previous approaches. Finally, to further reduce the complexity, we employ deep unfolding to design efficient learning-based algorithms. Besides parallelizability, the learning-based algorithms also enjoy appealing advantages of scalability in the number of served users, the number of transmit antennas and the length of the radar pulse. © 2007-2012 IEEE.

关键词： Radar interference

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