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Nature Reviews Electrical engineering 2025年 1-15页

作者： Wenhao Xue ( Tianfang Zhang ( Xiaonan Dong ( Xuezhi Ma ( Heng Xu ( Jiaqing Zhao ( Yuan Ma ( Department of Mechanical Engineering and Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University Hong Kong S.A.R China Department of Rehabilitation Medicine First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China Technology Innovation Center Zhe Jiang Key Laboratory of Intelligent Rehabilitation and Translational Neuroelectronics Hangzhou China Life and Health Institute China Resources Research Institute of Science and Technology Company Ltd Hong Kong S.A.R. China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong S.A.R. China A*STAR Quantum Innovation Centre (Q. InC) Agency for Science Technology and Research Singapore Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore Singapore Department of Haptic Intelligence CVTE Guangzhou Shiyuan Electronics Company Ltd Guangzhou China Institute of Nuclear and New Energy Technology Tsinghua University Beijing China Research Center for Green and Smart Manufacturing PolyU-Daya Bay Technology and Innovation Research Institute Huizhou China

Digital information has permeated all aspects of life, and diverse forms of information exert a profound influence on social interactions and cognitive perceptions. In contrast to the flourishing of digital interaction devices for sighted users, the needs of blind and partially sighted users for digital interaction devices have not been adequately addressed. Current assistive devices often cause frustration in blind and partially sighted users owing to the limited efficiency and reliability of information delivery and the high cognitive load associated with their use. The expected rise in the prevalence of blindness and visual impairment due to global population ageing drives an urgent need for assistive devices that can deliver information effectively and non-visually, and thereby overcome the challenges faced by this community. This Perspective presents three potential directions in assistive device design: multisensory learning and integration; gestural interaction control; and the synchronization of tactile feedback with large-scale visual language models. Future trends in assistive devices for use by blind and partially sighted people are also explored, focusing on metrics for text delivery efficiency and the enhancement of image content delivery. Such devices promise to greatly enrich the lives of blind and partially sighted individuals in the digital age.

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Deep Brain Neural Network Model Application Using Rabi Antenna Probes as Quantum Consciousness Sensors

SSRN

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SSRN 2022年

作者： Bunruangses, Montree Youplao, Phichai Garhwal, Anita Ray, Kanad Roy, Arup Pham, Nhat Truong Prabpal, Prakasit Yupapin, Preecha Jalil, Muhammad Arif Ali, Jalil Kaiser, Shamim Mahmud, Mufti Mallik, Saurav Zhao, Zhongming Department of Computer Engineering Faculty of Industrial Education Rajamangala University of Technology Phra Nakhon Bangkok10300 Thailand Faculty of Industrial Technology Rajamangala University of Technology Isan Sakon Nakhorn Campus Sakon Nakhon547000 Thailand Asia Metropolitan University 6 Jalan Lembah Bandar Baru Seri Alam 81750 Johor Masai Malaysia Amity University Jaipur Rajasthan India Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla San Manuel Ciudad Universitaria Av. San Claudio y Av. 18 sur Col. Puebla Pue 72570 Mexico Faubert Lab Université de Montréal MontréalH3T 1P1 Canada Division of Computational Mechatronics Institute for Computational Science Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City72915 Viet Nam Department of Electrical Technology School of Industrial Technology Institute of Vocational Education Northeastern 2 Sakhon Nakhon47000 Thailand Department of Physics Faculty of Science Unversiti Teknologi Malaysia Johor Skudai81310 Malaysia Institute of Information Technology Jahangirnagar University Dhaka Savar1342 Bangladesh Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston HoustonTX77030 United States Human Genetics Center School of Public Health The University of Texas Health Science Center at Houston HoustonTX77030 United States

The plasmonic antenna probe is designed using a silver bar embedded in a modified add-drop filter within a Mach Zehnder interferometer (MZI). The Rabi antenna is formed when the space-time control has reached the two-level system oscillation, which can be applied as a quantum brain-sensingprobe. A photonic neural network is designed using brain-Rabi antenna communication, wherein the transmission is connected by the neurons. The communication signals are transferred by the electron spins (up-down) and tunable Rabi frequency. The hidden variables and deep brain signals can be obtained by the external detection. We have developed a Rabi antenna via simulation using Computer Simulation technology (CST) software. We have developed a communication device using the Optiwave program (OptiFDTD). The output signal is plotted by the Matlab program using parameters from the results of the OptiFDTD simulation. The proposed antenna resonates in the frequency range of 192THz-202THz with a maximum gain of 22.4dBi. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Moreover, an intelligent machine learning algorithm is proposed for the identification of high-quality transmission and predicting the behavior of the transmission in near future. The sensor sensitivity is calculated along withelectron spin results and applied to form the quantum-brain interconnection. Finally, human quantum consciousness, dreams and human thoughts can be retrieved and discovered. © 2022, The Authors. All rights reserved.

关键词： Antennas

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Chromatin 3D structure, phase separation and disease

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Life Medicine 2023年第2期2卷 3页

作者： Lili Fan Xinyi Liu Diana Guallar Junjun Ding Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine School of Traditional Chinese Medicine Jinan University Guangzhou 510632 China RNA Biomedical Institute Sun Yat-sen Memorial Hospital Zhongshan School of Medicine Sun Yat-sen University Guangzhou 510080 China Advanced Medical Technology Center The First Affiliated Hospital Zhongshan School of Medicine Sun Yat-sen University Guangzhou 510006 China Center for Stem Cell Biology and Tissue Engineering Key Laboratory for Stem Cells and Tissue Engineering Ministry of Education Zhongshan School of Medicine Sun Yat-sen University Guangzhou 510006 China Center for Research in Molecular Medicine and Chronic Diseases Universidade de Santiago de Compostela Santiago de Compostela 15782 Spain RNA Biomedical Institute Sun Yat-sen Memorial Hospital Zhongshan School of Medicine Sun Yat-sen University Guangzhou 510080 China Advanced Medical Technology Center The First Affiliated Hospital Zhongshan School of Medicine Sun Yat-sen University Guangzhou 510006 China Center for Stem Cell Biology and Tissue Engineering Key Laboratory for Stem Cells and Tissue Engineering Ministry of Education Zhongshan School of Medicine Sun Yat-sen University Guangzhou 510006 China Department of Histology and Embryology School of Basic Medical Sciences Guangzhou Medical University Guangzhou 511436 China West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu 610041 China

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Preface

EAI/Springer Innovations in Communication and Computing

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EAI/Springer Innovations in Communication and Computing 2021年 vii-x页

作者： Ur Rehman, Muhammad Habib Svetinovic, Davor Salah, Khaled Damiani, Ernesto Department of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom Center for Cyber-Physical Systems Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Italy

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Artificial intelligence enabled reagent-free imaging hematology analyzer

arXiv

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arXiv 2020年

作者： Shu, Xin Sansare, Sameera Jin, Di Zeng, Xiangxiang Tong, Kai-Yu Pandey, Rishikesh Zhou, Renjie Department of Biomedical Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong Department of Pharmaceutical Sciences University of Connecticut StorrsCT06269 United States Connecticut Children’s Innovation Center University of Connecticut School of Medicine FarmingtonCT06032 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States School of Information Science and Engineering Hunan University Changsha410076 China Department of Biomedical Engineering University of Connecticut StorrsCT06269 United States Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Shatin New Territories Hong Kong

Leukocyte differential test is a widely performed clinical procedure for screening infectious diseases. Existing hematology analyzers require labor-intensive work and a panel of expensive reagents. Here we report an artificial-intelligence enabled reagent-free imaging hematology analyzer (AIRFIHA) modality that can accurately classify subpopulations of leukocytes with minimal sample preparation. AIRFIHA is realized through training a two-step residual neural network using label-free images of separated leukocytes acquired from a custom-built quantitative phase microscope. We validated the performance of AIRFIHA in randomly selected test set and cross-validated it across all blood donors. AIRFIHA outperforms current methods in classification accuracy, especially in B and T lymphocytes, while preserving the natural state of cells. It also shows a promising potential in differentiating CD4 and CD8 cells. Owing to its easy operation, low cost, and strong discerning capability of complex leukocyte subpopulations, we envision AIRFIHA is clinically translatable and can also be deployed in resource-limited settings, e.g., during pandemic situations for the rapid screening of infectious diseases. © 2020, CC BY-NC-SA.

关键词： Artificial intelligence

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Analysis of Transient Processes in Stable Scalar Linear Discrete-Time Systems

Analysis of Transient Processes in Stable Scalar Linear Disc...

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作者： Pavel SHCHERBAKOV Ming YUCHI Institute of Control Sciences RAS Federal Research Center "Computer Science and Control " ISA RAS Huazhong University of Science and Technology Department of Biomedical Engineering

We consider stable linear scalar systems described by homogenous difference equations with nonzero initial conditions. We show that, at finite time instants, solutions can deviate far from the equilibrium. For classes of root locations and initial conditions, closed-form estimates are obtained for the magnitude of deviations and the instant of the maximum deviation. Simple conditions on the presence of deviations are formulated and the issues are discussed of how typical are these effects.

关键词： Linear discrete-time systems Stability Nonzero initial conditions Peaks Deviations

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Evaluation of Disinfection Methods for Autonomous Mobile Robots Used in Hospital Logistics in Emergency departments

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The Journal of hospital infection 2025年

作者： Przemysław Kardas Filip Bielec Małgorzata Brauncajs Paweł Lewek Dariusz Timler Ewelina Łojewska Marcello Chiurazzi Neri Niccolò Dei Gastone Ciuti Raquel Juliá Ros Víctor Solaz Estevan Alessia Maccaro Leandro Pecchia Beatriz Merino Alejandro Medrano Thomas Penzel Giuseppe Fico Department of Family Medicine Medical University of Lodz Lodz 90419 Poland. Department of Microbiology and Laboratory Medical Immunology Medical University of Lodz Lodz 90419 Poland. Department of Emergency and Disaster Medicine Medical University of Lodz Lodz 90419 Poland. Office for Research Strategies and Development Medical University of Lodz Lodz 90419 Poland. BioRobotics Institute & Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa 56127 Italy. Robotnik Automation S.L. Paterna Valencia 46980 Spain. Department of Social Sciences University of Naples Federico II Applied Biomedical Signal Processing Intelligent eHealth Laboratory School of Engineering University of Warwick Coventry CV4 7AL UK. Applied Biomedical Signal Processing Intelligent eHealth Laboratory School of Engineering University of Warwick Coventry CV4 7AL UK Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome 00128 Italy. Life Supporting Technologies Departamento de Tecnología Fotónica y Bioingeniería Universidad Politècnica de Madrid Madrid Spain. Interdisciplinary Center of Sleep Medicine Charité University Medicine Berlin Berlin Germany.

BACKGROUND:Autonomous Mobile Robots (AMRs) have been increasingly used in hospital logistics, particularly in high-risk areas, such as Emergency departments (EDs), to streamline operations, reduce staff fatigue, and minimize infection risks. However, their effective disinfection remains a critical concern, especially when it comes to solutions aimed at preventing spread of multi-drug-resistant organisms. OBJECTIVE:This study evaluated the microbiological cleanliness and effectiveness of various disinfection methods for AMRs in real-life hospital logistics settings, with a particular focus on their application in EDs. METHODS:The HOSBOT, an AMR designed for hospital logistics, was deployed in a tertiary hospital for two weeks and validated for transport of biological samples. Microbiological contamination was assessed at multiple robot sites before and after disinfection, using two methods, i.e. manual wiping with a standard disinfectant and non-contact fumigation with low temperature vaporized hydrogen peroxide. Contamination levels were evaluated using quantitative and qualitative microbiological techniques, and a threshold of <2.5 colony-forming units/cm for critical environments, recommended by the centers for Disease Control and Prevention (CDC), was considered as proof of success. RESULTS:Bacterial contamination exceeded thresholds at all sites. Both disinfection methods significantly decreased contamination. Manual wiping reduced bacterial counts below thresholds and eradicated fungal growth, while fumigation was effective for bacterial but not fungal contamination. Fumigation also failed to meet CDC cleanliness standards in hard-to-reach areas. CONCLUSIONS:Both manual wiping and fumigation effectively reduced bacterial contamination, however, wiping showed better results in fungal eradication. Improvements to fumigation methods are necessary, such as application of higher disinfectant concentrations or alternative chemicals. The aforementioned findings not on

关键词： Autonomous Mobile Robots Bacterial contamination Decontamination Disinfection Emergency department Fungal contamination Hospital logistics Intensive care unit Microbiological cleanliness robotics

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Research progress on the biomedical uses of graphene and its derivatives

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Carbon 2021年 183卷 1013-1013页

作者： Yang Liu Jing Ding Qi-qi Wang Mei-ling Wen Ting-ting Tang Yong Liu Rong Yuan Yong-feng Li Mei-wen An Institute of Biomedical Engineering College of Biomedical Engineering Taiyuan University of Technology Shanxi Key Laboratory of Material Strength & Structural Impact Taiyuan Shanxi 030024 China Research Center for Nanobiomaterials & Regenerative Medicine College of Biomedical Engineering Taiyuan University of Technology Taiyuan Shanxi 030024 China Department of Nuclear Medicine First Hospital of Shanxi Medical University Taiyuan Shanxi 030001 China Dermatology Department Shanxi Bethune Hospital Shanxi Academy of Medical Sciences Taiyuan 030032 China North District of Peking University Third Hospital UItrasonic Diagnosis Deparment Beijing 100089 China

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet

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Computers in biology and medicine 2025年 193卷 110459页

作者： Md Shaheenur Islam Sumon Muhammad E H Chowdhury Enamul Hoque Bhuiyan Md Sohanur Rahman Muntakim Mahmud Khan Israa Al-Hashimi Adam Mushtak Sohaib Bassam Zoghoul Department of Electrical Engineering Qatar University Doha 2713 Qatar. Electronic address: sumon3455.ms@***. Department of Electrical Engineering Qatar University Doha 2713 Qatar. Electronic address: mchowdhury@qu.edu.qa. Center for Magnetic Resonance Research University of Illinois Chicago IL 60612 USA. Electronic address: hoque.phy@***. Department of Electrical and Electronics Engineering University of Dhaka Dhaka Bangladesh. Electronic address: mdsohanur2087@***. Department of Biomedical Physics & Technology University of Dhaka Dhaka Bangladesh. Electronic address: isitakib@***. Department of Radiology Hamad Medical Corporation Doha Qatar. Electronic address: ialhashimi@hamad.qa. Department of Radiology Hamad Medical Corporation Doha Qatar. Electronic address: adamrads94@***. Department of Radiology Hamad Medical Corporation Doha Qatar. Electronic address: sohaibzoghoul@***.

Digital pathology relies on the morphological architecture of prostate glands to recognize cancerous tissue. Prostate cancer (PCa) originates in walnut shaped prostate gland in the male reproductive system. Deep learning (DL) pipelines can assist in identifying these regions with advanced segmentation techniques which are effective in diagnosing and treating prostate diseases. This facilitates early detection, targeted biopsy, and accurate treatment planning, ensuring consistent, reproducible results while minimizing human error. Automated segmentation techniques trained on MRI datasets can aid in monitoring disease progression which leads to clinical support by developing patient-specific models for personalized medicine. In this study, we present multiclass segmentation models designed to localize the prostate gland and its zonal regions-specifically the peripheral zone (PZ), transition zone (TZ), and the whole gland-by combining EfficientNetB4 encoders with Self-organized Operational Neural Network (Self-ONN)-based decoders. Traditional convolutional neural networks (CNNs) rely on linear neuron models, which limit their ability to capture the complex dynamics of biological neural systems. In contrast, Operational Neural Networks (ONNs), particularly Self-ONNs, address this limitation by incorporating nonlinear and adaptive operations at the neuron level. We evaluated various encoder-decoder configurations and identified that the combination of an EfficientNet-based encoder with a Self-ONN-based decoder yielded the best performance. To further enhance segmentation accuracy, we employed the STAPLE method to ensemble the top three performing models. Our approach was tested on the large-scale, recently updated PI-CAI Challenge dataset using 5-fold cross-validation, achieving Dice scores of 95.33 % for the whole gland and 92.32 % for the combined PZ and TZ regions. These advanced segmentation techniques significantly improve the quality of PCa diagnosis and treatment,

关键词： Deep learning MRI Peripheral zone Self-ONN Semantic segmentation Transitional zone

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