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Comparative Effectiveness of Different Exercises for Reducing Pain Intensity in Primary Dysmenorrhea: A Systematic Review and network Meta-analysis of Randomized Controlled Trials

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Sports Medicine - Open 2024年第1期10卷 63页

作者： Tsai, I-Chen Hsu, Chih-Wei Chang, Chun-Hung Lei, Wei-Te Tseng, Ping-Tao Chang, Ke-Vin Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Congenital Heart Disease Study Group Asian Society of Cardiovascular Imaging Seoul South Korea InnovaRad Inc. Taichung Taiwan Department of Psychiatry Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Institute of Clinical Medical Science China Medical University Taichung Taiwan Department of Psychiatry and Brain Disease Research Center China Medical University Hospital Taichung Taiwan An Nan Hospital China Medical University Tainan Taiwan Section of Immunology Rheumatology and Allergy Department of Pediatrics Hsinchu Mackay Memorial Hospital Hsinchu Taiwan Graduate Institute of Clinical Medical Sciences College of Medicine Chang Gung University Taoyuan Taiwan Prospect Clinic for Otorhinolaryngology and Neurology Kaohsiung Taiwan Institute of Biomedical Sciences National Sun Yat-Sen University Kaohsiung Taiwan Department of Psychology College of Medical and Health Science Asia University Taichung Taiwan Institute of Precision Medicine National Sun Yat-Sen University Kaohsiung City Taiwan Department of Physical Medicine and Rehabilitation National Taiwan University Hospital and National Taiwan University College of Medicine No. 1 Changde St. Zhongzheng Dist. Taipei City 100229 Taiwan Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Bei-Hu Branch Taipei Taiwan Center for Regional Anesthesia and Pain Medicine Taipei Medical University Wang-Fang Hospital Taipei Taiwan

Background: Studies have demonstrated that exercise can mitigate the intensity of menstrual pain in primary dysmenorrhea, but the most effective type of exercise remains unclear. The objective of this systematic review and network meta-analysis was to evaluate the effectiveness of different exercise regimens in reducing pain associated with primary dysmenorrhoea. Methods: Randomized controlled trials investigating the relationship between menstrual pain and exercise were selected from major electronic databases until February 2, 2024. The primary outcome was the effect of exercise on pain intensity measured by the mean difference on a 10-cm visual analogue scale at 4 and 8 weeks after intervention. The secondary outcome was the difference in risk of dropout at 8 weeks. The study protocol was registered as INPLASY202330050. Results: This systematic review and network meta-analysis included 29 randomized controlled trials, which involved 1808 participants with primary dysmenorrhea. Exercise interventions included relaxation exercise, strength training, aerobic activity, yoga, mixed exercise, and the Kegel maneuver. Relaxation exercise was the most effective in reducing menstrual pain in 4 weeks (− 3.56;95% confidence interval: − 5.03 to − 2.08). All exercise interventions were effective in reducing menstrual pain at 8 weeks, with reductions ranging from − 3.87 (95% CI − 5.51 to − 2.22) for relaxation exercise to − 2.75 (95% CI − 4.00 to − 1.51) for yoga, compared to the control group. Relaxation exercises were found to have a significantly lower dropout risk (− 0.11;95% CI − 0.20 to 0.02), while none of the exercise types was associated with a higher dropout risk than the control group. Conclusion: All exercise interventions were effective in reducing menstrual pain in primary dysmenorrhea after 8 weeks of intervention. However, relaxation exercise was found to be the most effective intervention at 4 and 8 weeks and had the lowest risk of dropout. Graphical Abstract:

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Deepgt: Deep Learning-Based Quantification of Nanosized Bioparticles in Bright-Field Micrographs of Gires-Tournois Biosensor

SSRN

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SSRN 2023年

作者： Kang, Jiwon Yoo, Young Jin Park, Jin-Hwi Ko, Joo Hwan Kim, Seungtaek Stanciu, Stefan G. Stenmark, Harald A. Lee, Jinah Mahmud, Abdullah Al Jeon, Hae-Gon Song, Young Min School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States AI Graduate School Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Zoonotic Virus Laboratory Institut Pasteur Korea Seongnam13488 Korea Republic of Center for Microscopy-Microanalysis and Information Processing Politehnica University of Bucharest Bucharest060042 Romania Department of Molecular Cell Biology Institute for Cancer Research Oslo University Hospital Oslo0379 Norway Anti-Viral Research Center Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of

Rapid and decentralized quantification of viral load profiles in infected patients is vital for assessing clinical severity and tailoring appropriate therapeutic strategies. Although microscopic imaging offers potential for label-free and amplification-free quantitative diagnostics, the small size (~100 nm in diameter) and low refractive index (~1.5) of bioparticles present challenges in achieving accurate estimations, consequently increasing the limit of detection (LoD). In this study, we present a novel synergistic biosensing approach, DeepGT, combining Gires-Tournois (GT) sensing platforms with deep learning algorithms to enhance nanoscale bioparticle counting accuracy. The GT sensing platform serves as a photonic resonator, increasing bioparticle visibility in bright-field microscopy and maximizing chromatic contrast. By employing a back-end with a dilated convolutional neural network architecture, DeepGT effectively refines artifacts and color deviations, significantly improving particle estimation accuracy (MAE ~ 2.37 across 1,596 images) compared to rule-based algorithms (MAE ~ 13.47). Notably, the enhanced accuracy in detecting invisible particles (e.g., two- or three-particles) enables an LoD of 138 pg ml-1, facilitating a dynamic linear correlation at low viral concentration ranges within the clinical spectrum of infection, from asymptomatic to severe cases. Leveraging transfer learning, DeepGT, which relies on a chromatometry-based strategy instead of a spatial resolution approach, exhibits exceptional precision when analyzing particles of diverse dimensions smaller than the microscopy system’s minimum diffraction limit in visible light ( © 2023, The Authors. All rights reserved.

关键词： Biosensors

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Intelligent Reflecting Surface for MIMO VLC: Joint Design of Surface Configuration and Transceiver Signal Processing

arXiv

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arXiv 2022年

作者： Sun, Shiyuan Yang, Fang Song, Jian Zhang, Rui Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen518057 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

With the capability of reconfiguring the wireless electromagnetic environment, intelligent reflecting surface (IRS) is a new paradigm for designing future wireless communication systems. In this paper, we consider optical IRS for improving the performance of visible light communication (VLC) under a multiple-input and multiple-output (MIMO) setting. Specifically, we focus on the downlink communication of an indoor MIMO VLC system and aim to minimize the mean square error (MSE) of demodulated signals at the receiver. To this end, the MIMO channel gain of the IRS-aided VLC is first derived under the point source assumption, based on which the MSE minimization problem is then formulated subject to the emission power constraints. Next, we propose an alternating optimization algorithm, which decomposes the original problem into three subproblems, to iteratively optimize the IRS configuration, the precoding and detection matrices for minimizing the MSE. Moreover, theoretical analysis on the performance of the proposed algorithm in high and low signal-to-noise ratio (SNR) regimes is provided, revealing that the joint optimization process can be simplified in such special cases, and the algorithm’s convergence property and computational complexity are also discussed. Finally, numerical results show that IRS-aided schemes significantly reduce the MSE as compared to their counterparts without IRS, and the proposed algorithm outperforms other baseline schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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Performance Analysis of LEO Satellite-Based IoT networks in the Presence of Interference

arXiv

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arXiv 2022年

作者： Dwivedi, Ayush Kumar Chaudhari, Sachin Varshney, Neeraj Varshney, Pramod K. The Signal Processing and Communication Research Center International Institute of Information Technology Hyderabad500032 India The Wireless Networks Division National Institute of Standards and Technology GaithersburgMD20899 United States The Department of Electrical Engineering and Computer Science Syracuse University SyracuseNY13244 United States

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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Janus faces of dipolar sources in directional near-field coupling with an oriented misalignment

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Physical Review A 2024年第3期109卷 033505-033505页

作者： Chenxu Bian Yuhan Zhong Xuhuinan Chen Tony Low Hongsheng Chen Baile Zhang Xiao Lin State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China and International Joint Innovation Center The Electromagnetics Academy at Zhejiang University Zhejiang University Haining 314400 China Department of Electrical and Computer Engineering University of Minnesota Minneapolis Minnesota 55455 USA Key Lab of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University Zhejiang University Jinhua 321099 China and Shaoxing Institute of Zhejiang University Zhejiang University Shaoxing 312000 China Division of Physics and Applied Physics School of Physical and Mathematical Sciences and Centre for Disruptive Photonic Technologies Nanyang Technological University Singapore 637371 Singapore

Directional near-field coupling can enable many applications, such as directional routing, on-chip information processing, and chiral quantum optics. This directional near-field coupling is generally face dependent, since the induced near-field radiation pattern is dependent on which face (e.g., the upper or lower face) of the dipolar source is facing toward the outcoupler. In other words, the dipolar sources in the directional near-field coupling intrinsically have two faces (e.g., Janus faces), whose corresponding near-field coupling strength and coupling directionality might be distinct. Here, we outline a general physical framework to control these Janus faces in the directional near-field coupling and find that they can have the feature of an oriented misalignment. To be specific, the upper and lower radiation patterns of excited surface waves can have the same shape but are misaligned with a twist angle. For example, the twist angle can be any acute angle if the source is composed of a Huygens dipole and an electric dipole, and it can be any obtuse angle if the source is constructed by a circular electric dipole and a magnetic dipole.

关键词： Classical optics Light-matter interaction Metamaterials Near-field optics Photonics Plasmonics

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Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Spatial-Temporal Graph information

arXiv

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arXiv 2021年

作者： Li, Jinhao Zhang, Ruichang Wang, Hao Liu, Zhi Lai, Hongyang Zhang, Yanru The School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China The Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

Renewable energy resources (RERs) have been increasingly integrated into large-scale power systems. Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement learning (DRL)based strategy leveraging spatial-temporal (ST) graphical information of power systems, to dynamically search for the optimal power flow (OPF), of power systems with a high uptake of RERs. We formulate the OPF problem as a multi-objective optimization problem considering generation cost, voltage fluctuation, and transmission loss, and employ deep deterministic policy gradient (DDPG) to learn an optimal strategy for OPF. Moreover, given that the nodes in power systems are self-correlated and interrelated in temporal and spatial views, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for extracting ST graphical correlations and features, aiming to provide graphical knowledge of power systems for its sequential DDPG in our DRL algorithm to more effectively solve OPF. We validate our algorithm on modified IEEE 33, 69, and 118-bus radial distribution systems and demonstrate that our algorithm outperforms other benchmark algorithms. Our experimental results also reveal that our MG-ASTGCN can significantly accelerate DDPG’s training process and performance in solving OPF. The proposed DRL-based strategy also improves power systems’ robustness in the presence of node faults, especially for large-scale power systems. © 2021, CC BY.

关键词： Renewable energy resources

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AiCareNeonates:Artificial intelligence powered adaptation of transfer learning models to classify neonate's sleep-wake states for pediatricians in the loop

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Digital Communications and networks 2024年

作者： Muhammad Awais Hemant Ghayvat Rebakah Geddam Lewis Nkenyereye Kapal Dev Center for Intelligent Medical Electronics School of Information Science and Technology at Fudan University Shanghai China Computer Science Department Faculty of Technology Linnaeus University Växjö 351 95 Sweden Computer Science Department Institute of Technology Nirma University Ahmedabad 382481 Gujarat India Department of Computer and Information Security Sejong University South Korea ADAPT Centre and Department of Computer Science Munster Technological University Bishopstown Cork T12 P928 Ireland Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Centre for Research Impact & Outcome Institute of Engineering and Technology Chitkara University Rajpura Punjab India

Monitoring neonatal sleep patterns using artificial intelligence through an innovative transfer learning approach integrating video and Electroencephalogram (EEG) data. Leveraging non-intrusive, camera-based technology, this method offers healthcare professionals an effective tool to evaluate and understand sleep quality in newborns. Such monitoring is essential for pediatricians, providing critical insights into the health and development of infants. Automated sleep/wake staging tools aid healthcare professionals in analyzing infant sleep patterns. Among these methods, camera-based approaches have gained prominence due to their non-intrusive and user-friendly characteristics, making them applicable for home monitoring. This study introduces a novel transfer learning technique for classifying neonatal sleep/wake stages. Our approach utilizes multiple color palettes, such as thermal, amber, gray-scale, high contrast, hot metal, and red-blue, captured through a Fluke® (TiX 580) camera system. Continuous monitoring of neonatal sleep is essential for pediatricians to assess neonatal sleep quality comprehensively. Automated sleep/wake staging tools assist healthcare professionals in evaluating infant sleep patterns. Camera-based approaches have garnered significant attention among the various methods due to their non-intrusive and user-friendly nature, making them suitable for home use. In this paper, we propose a novel transfer learning approach for classifying neonatal sleep/wake staging using a combination of multiple color palettes, including thermal, amber, gray-scale, high contrast, hot metal, and red-blue, recorded through a Fluke® (TiX 580) camera. The proposed method leverages the retraining of the last fully connected layer of well-established deep neural networks such as Visual Geometry Group 16 and 19, Alex Net, Inception-V3, Res Net-18, Res Net-50, and GoogLeNet to perform accurate sleep and wake stage classification. To enhance the precision of our approach

关键词： Deep Convolutional Neural network Neonatal sleep classification Transfer learning Neonatal Red-Green-Blue (RGB) face database Quadrupole exciton Polariton

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Full attention bidirectional deep learning structure for single channel speech enhancement

arXiv

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arXiv 2021年

作者： Yan, Yu-Zi Zhang, Wei-Qiang Johnson, Michael T. Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Electrical and Computer Engineering College of Engineering University of Kentucky LexingtonKY40506-0046 United States

As the cornerstone of other important technologies, such as speech recognition and speech synthesis, speech enhancement is a critical area in audio signal processing. In this paper, a new deep learning structure for speech enhancement is demonstrated. The model introduces a "full" attention mechanism to a bidirectional sequence-to-sequence method to make use of latent information after each focal frame. This is an extension of the previous attention-based RNN method. The proposed bidirectional attention-based architecture achieves better performance in terms of speech quality (PESQ), compared with OMLSA, CNN-LSTM, T-GSA and the unidirectional attention-based LSTM baseline. © 2021, CC BY.

关键词： Long short-term memory

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Higher-Order Attribute-Enhancing Heterogeneous Graph Neural networks

arXiv

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arXiv 2021年

作者： Li, Jianxin Peng, Hao Cao, Yuwei Dou, Yingtong Zhang, Hekai Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China the Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States School of Information Science and Engineering Yanshan University Qinhuangdao066004 China the Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis tasks such as node classification and node clustering. However, most methods ignore the heterogeneity in real-world graphs. Methods designed for heterogeneous graphs, on the other hand, fail to learn complex semantic representations because they only use meta-paths instead of meta-graphs. Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors. We propose a novel Higher-order Attribute-Enhancing (HAE) framework that enhances node embedding in a layer-by-layer manner. Under the HAE framework, we propose a Higher-order Attribute-Enhancing Graph Neural network (HAEGNN) for heterogeneous network representation learning. HAEGNN simultaneously incorporates meta-paths and meta-graphs for rich, heterogeneous semantics, and leverages the self-attention mechanism to explore content-based nodes’ interactions. The unique higher-order architecture of HAEGNN allows examining the first-order as well as higher-order neighborhoods. Moreover, HAEGNN shows good explainability as it learns the importances of different meta-paths and meta-graphs. HAEGNN is also memory-efficient, for it avoids per meta-path based matrix calculation. Experimental results not only show HAEGNN’s superior performance against the state-of-the-art methods in node classification, node clustering, and visualization, but also demonstrate its superiorities in terms of memory efficiency and explainability. © 2021, CC BY.

关键词： Graph neural networks

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