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Electrochemical hydrogen charging treatment for hydrogenated 316 L stainless steel induced hydrogen therapy for orthopedic implantation

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Surface and Coatings technology 2024年 479卷

作者： Lin, Yu-Chien Dhawan, Udesh Liu, Yen-Chen Lee, Yueh-Lien Liu, Xinke Yen, Hung-Wei Hu, Chih-Chien Chung, Ren-Jei Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan School of Materials Science and Engineering Nanyang Technological University Singapore Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow Glasgow United Kingdom Department of Engineering Science and Ocean Engineering National Taiwan University Taipei Taiwan College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhe China Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Materials Science and Engineering National Taiwan University Taipei Taiwan Bone and Joint Research Center Chang Gung Memorial Hospital Linko Taiwan Department of Orthopaedic Surgery Chang Gung Memorial Hospital Linko Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Taipei Taiwan

Post-implant inflammation is a leading factor in the failure of orthopedic implant surgery. Hydrogen therapy is viewed as a medium capable of reducing reactive oxygen species (ROS) to alleviate inflammatory responses. However, existing hydrogen therapy methods have limitations, often involving undesired inhalation, injection, or non-targeted exposure of hydrogen to the body. To address this, the concept of delivering hydrogen precisely using the implant itself has been conducted in this study. Traditional hydrogen treatments for metallic implant materials, such as 316 L stainless steel (316 L SS), can induce hydrogen embrittlement (HE), significantly restricting the potential applications of hydrogenated implants in the biomedical field. Therefore, in this study, the electrochemical cathodic hydrogen charging method was employed to diffuse a hydrogenated layer of approximately 216 ± 31 nm on the surface of 316 L SS. This renders the material minimized by HE and alters its surface properties. Through in vitro and in vivo experiments, an approximately 80 % free radical scavenging rate was achieved. Additionally, significant anti-inflammatory effects were observed in the histological analysis of Femur implantation. This demonstrates that the surface hydrogenation treatment can impart anti-inflammatory properties to metallic implant materials without compromising their inherent material properties, thereby enhancing the success rate of surgical procedures. © 2024

关键词： Hydrogen

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Contactless Breathing Waveform Detection Through RF Sensing: Radar vs. Wi-Fi Techniques 10

Contactless Breathing Waveform Detection Through RF Sensing:...

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10th IEEE International Conference on Communications and Networking, ComNet 2023

作者： Saeed, Umer Zheng, Dingchang Shah, Behzad Ali Shah, Syed Ikram Jan, Sana Ullah Ahmad, Jawad Abbasi, Qammer H. Shah, Syed Aziz Boulila, Wadii Coventry University Research Centre for Intelligent Healthcare CoventryCV1 5FB United Kingdom National University of Sciences and Technology College of Electrical and Mechanical Engineering Islamabad44000 Pakistan Edinburgh Napier University School of Computing Engineering and the Built Environment EdinburghEH10 5DT United Kingdom University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom University of Manouba RIADI Laboratory National School of Computer Sciences Manouba Tunisia

ISBN: (纸本)9798350381719

Human breathing detection plays a vital role in healthcare, safety, and various other applications. This research paper explores the use of radio-frequency (RF) sensing technologies, specifically radar and Wi-Fi, for detecting human breathing patterns. Abnormal breathing patterns can indicate respiratory or cardiovascular diseases, and early detection is crucial for timely diagnosis and treatment. Radar-based systems utilize low-power RF pulses to capture subtle chest movements associated with breathing, while software-defined radio (SDR)-based systems analyze Wi-Fi signals to detect variations caused by human chest motion. Deep learning algorithms, namely residual neural network (ResNet) and deep multilayer perceptron (DMLP), are employed to classify breathing patterns based on the collected data. ResNet attained classification accuracy up to 90% on radar-based spectrogram images data, while DMLP attained classification accuracy up to 99% on SDR-based channel state information data. The proposed approaches offer non-intrusive, remote-operable, and cost-effective solutions for breathing detection. The research demonstrates the potential of RF sensing technologies in healthcare, eldercare, sleep monitoring, and emergency response systems, paving the way for enhanced well-being and safety. © 2023 IEEE.

关键词： Software radio

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Optical Wavelength Tunability of Negative Refractive Index Transmission Line Phase Shifter

Optical Wavelength Tunability of Negative Refractive Index T...

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Antennas and Propagation & USNC/URSI national Radio Science Meeting

作者： Asif Bilal Abdul Quddious Atsushi Kanno Tetsuya Kawanishi Marco A. Antoniades Stavros Iezekiel EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universität Dresden Dresden Germany Nagoya Institute of Technology Aichi Japan National Institute of Information and Communications Technology Tokyo Japan Waseda University Tokyo Japan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

An optically reconfigurable phase shifter based on negative-refractive-index transmission line (NRI-TL) metamaterials is presented; the design is suitable for 5G antenna beam steering. Continuous tuning of the transmitted signal's phase is achieved by using varactor diodes for which the biasing is provided via an array of photodiodes placed on the back of the substrate. These are illuminated by a remote optical source whose wavelength can be tuned in the visible range, thus enabling indirect control of the varactor capacitance (and hence the phase shift) while also eliminating the need for a complex biasing network. Three different phases are achieved by illuminating the photodiode array with three different wavelengths (blue, green, and red). The compact size along with simplified biasing network introduced by the inclusion of the optical wavelength tunability has numerous applications in antenna and phased array technology, making the design a suitable candidate for 5G beam steering applications.

关键词： Phased arrays Varactors Beam steering Optical design Phase shifters Optical fiber networks Optical variables control

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Self-testing quantum randomness expansion on an integrated photonic chip

arXiv

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arXiv 2024年

作者： Zhang, Gong Primaatmaja, Ignatius William Chen, Yue Ng, Si Qi Ng, Hong Jie Pistoia, Marco Gong, Xiao Goh, Koon Tong Wang, Chao Lim, Charles Department of Electrical & Computer Engineering National University of Singapore Singapore Singapore Squareroot8 Technologies Pte Ltd Singapore Singapore Global Technology Applied Research JPMorganChase New YorkNY10017 United States Centre for Quantum Technologies National University of Singapore Singapore Singapore

The power of quantum random number generation is more than just the ability to create truly random numbers-it can also enable self-testing, which allows the user to verify the implementation integrity of certain critical quantum components with minimal assumptions. In this work, we develop and implement a self-testing quantum random number generator (QRNG) chipset capable of generating 15.33 Mbits of certifiable randomness in each run (an expansion rate of 5.11 × 10-4 at a repetition rate of 10 Mhz). The chip design is based on a highly loss-and-noise tolerant measurement-device-independent protocol, where random coherent states encoded using quadrature phase shift keying (QPSK) are used to self-test the quantum homodyne detection unit-well-known to be challenging to characterise in practice. Importantly, this proposal opens up the possibility to implement miniaturised self-testing QRNG devices at production scale using standard silicon photonics foundry platforms. © 2024, CC BY.

关键词： Silicon photonics

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Photonic Frequency Space Simulation of Tight-Binding Lattices Using Integrated LNOI Ring Resonators 16

Photonic Frequency Space Simulation of Tight-Binding Lattice...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Balčytis, Armandas Dinh, Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre Rmit University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We use electro-optically modulated ring cavities, integrated on thin-film lithium niobate, to model frequency dimension tight-binding lattices with versatile connectivity. Inter-mode coupling range, strength, and hopp... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonics

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RFiDARFusion: Enhancing Contactless Activity Monitoring with Radar and RFID Fusion

RFiDARFusion: Enhancing Contactless Activity Monitoring with...

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Antennas and Propagation & USNC/URSI national Radio Science Meeting

作者： Muhammad Zakir Khan Muhammad Bilal Alexander Maniangat Luke Kamran Arshad Khaled Assaleh Syed Tariq Shah Muhammad Ali Imran Qammer H. Abbasi James Watt School of Engineering University of Glasgow Glasgow UK Department of Electrical Engineering National University of Technology (NUTECH) Islamabad Pakistan Department of Clinical Sciences College of Dentistry Ajman University UAE Artificial Intelligence Research Centre Aiman University Ajman UAE Department of Electrical and Computer Engineering College of Engineering and IT Ajman University UAE School of Computer Science and Electronic Engineering University of Essex Colchester UK

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

Indoor human activity recognition (HAR) faces challenges due to the limitations of single-sensor systems in terms of privacy and cost. Our contactless RFiDARFusion system aims to reduce healthcare costs by swiftly identifying unforeseen activities through artificial intelligence and wireless communication. The system accurately discerns complex human activities by employing the LSTM-VAE fusion algorithm for multi-sensor data extraction, proving effective in long-range and non-line-of-sight environments. Two data fusion algorithms were studied across five distinct activities, significantly improving HAR accuracy by a minimum of 4.5% and 6.5% under data and feature-level fusion, respectively. The resulting RFiDARFusion system is contactless, precise, and robust, promising to enhance the quality of life for elderly patients in assisted living.

关键词： Wireless communication Privacy Costs Accuracy Radar Radar antennas Assisted living

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Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

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computers and Electrical engineering 2024年 116卷

作者： Hadabi, Abdalla Qu, Zheng Elhabob, Rashad Kumar, Sachin Yeh, Kuo-Hui Kumari, Saru Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611756 China Computer Science and Information Technology Karary University Khartoum Sudan Department of Computer Science and Engineering Galgotias College of Engineering and Technology Noida Greater201310 India Department of Information Management National Dong Hwa University Hualien974301 Taiwan Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140401 India

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT. © 2024

关键词： Access control

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Effect of Depression Severity on Emotion Context Insensitivity Revealed by Facial Activities Analysis

Effect of Depression Severity on Emotion Context Insensitivi...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Zou, Bochao Wang, Yingxue Liu, Rui Lyu, Xiangwen Feng, Lei Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China National Engineering Laboratory for Risk Perception and Prevention Beijing China Beijing Anding Hospital of Capital Medical University National Clinical Research Centre of Mental Disorders Beijing China

ISBN: (纸本)9781665401265

Major Depression Disorder (MDD) is defined as a mood condition. The Emotion Context Insensitivity (ECI) hypothesis of depression argues that the reaction sensitivity of depressed people to the changes of emotional context is attenuated. This paper designs an experiment under this hypothesis to more effectively distinguish facial cues for different levels of depression severity. By adopting publicly validated video stimuli for emotion elicitation, statistical analysis was performed to reveal significant facial features for depression severity assessment. The results found that the high severity group showed a decrease in specific action units and an increase in others, and made more intense facial movements for negative stimuli than that for positive and neutral stimuli. Hopefully, this study could benefit the interpretation of emotional functioning in depression and provide a reference for the evaluation of depression severity based on facial characteristics. © 2021 IEEE.

关键词： action units depression severity emotion context insensitivity facial movements

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Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework

arXiv

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arXiv 2024年

作者： Yang, Zhengwei Li, Yuke Sun, Qiang Fernando, Basura Huang, Heng Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Key Laboratory of Multimedia and Network Communication Engineering China Centre for Frontier AI Research Agency for Science Technology and Research Singapore University of Maryland College Park United States University of Toronto Canada MBZUAI United Arab Emirates

Most existing studies on few-shot learning focus on unimodal settings, where models are trained to generalize to unseen data using a limited amount of labeled examples from a single modality. However, real-world data are inherently multi-modal, and such unimodal approaches limit the practical applications of few-shot learning. To bridge this gap, this paper introduces the Cross-modal Few-Shot Learning (CFSL) task, which aims to recognize instances across multiple modalities while relying on scarce labeled data. This task presents unique challenges compared to classical few-shot learning arising from the distinct visual attributes and structural disparities inherent to each modality. To tackle these challenges, we propose a Generative Transfer Learning (GTL) framework by simulating how humans abstract and generalize concepts. Specifically, the GTL jointly estimates the latent shared concept across modalities and the in-modality disturbance through a generative structure. Establishing the relationship between latent concepts and visual content among abundant unimodal data enables GTL to effectively transfer knowledge from unimodal to novel multimodal data, as humans did. Comprehensive experiments demonstrate that the GTL achieves state-of-the-art performance across seven multi-modal datasets across RGB-Sketch, RGB-Infrared, and RGB-Depth. © 2024, CC BY-NC-ND.

关键词： Zero-shot learning

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Multi-Scale Accent Modeling and Disentangling for Multi-Speaker Multi-Accent Text-to-Speech Synthesis

arXiv

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arXiv 2024年

作者： Zhou, Xuehao Zhang, Mingyang Zhou, Yi Wu, Zhizheng Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Hong Kong Generative AI Research and Development Centre The Hong Kong University of Science and Technology Hong Kong Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

Generating speech across different accents while preserving speaker identity is crucial for various real-world applications. However, accurately and independently modeling both speaker and accent characteristics in text-to-speech (TTS) systems is challenging due to the complex variations of accents and the inherent entanglement between speaker and accent identities. In this paper, we propose a novel approach for multispeaker multi-accent TTS synthesis that aims to synthesize speech for multiple speakers, each with various accents. Our approach employs a multi-scale accent modeling strategy to address accent variations on different levels. Specifically, we introduce both global (utterance level) and local (phoneme level) accent modeling to capture overall accent characteristics within an utterance and fine-grained accent variations across phonemes, respectively. To enable independent control of speakers and accents, we use the speaker embedding to represent speaker identity and achieve speaker-independent accent control through speaker disentanglement within the multi-scale accent modeling. Additionally, we present a local accent prediction model that enables our system to generate accented speech directly from phoneme inputs. We conduct extensive experiments on an English accented speech corpus. Experimental results demonstrate that our proposed system outperforms baseline systems in terms of speech quality and accent rendering for generating multispeaker multi-accent speech. Ablation studies further validate the effectiveness of different components in our proposed system. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum entanglement

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