The effectiveness of digital technology-based Otago Exercise Program on balance ability, muscle strength and fall efficacy in the elderly: a systematic review and meta-analysis.

Objective: To explore the impact of the digital implementation of the Otago Exercise Program (OEP) on balance ability (static and dynamic), muscle strength, and fall efficacy in elderly people; and analyze different potential influencing factors in subgroups to find the most suitable training plan.

Methods: EBSCO, PubMed, Web of Science, and China Knowledge Network databases (core) were searched up to August 1, 2023. Experimental studies of implementing OEP based on digital technology to improve outcomes related to falls in the elderly were included.

CDPMF-DDA: contrastive deep probabilistic matrix factorization for drug-disease association prediction.

The process of new drug development is complex, whereas drug-disease association (DDA) prediction aims to identify new therapeutic uses for existing medications. However, existing graph contrastive learning approaches typically rely on single-view contrastive learning, which struggle to fully capture drug-disease relationships. Subsequently, we introduce a novel multi-view contrastive learning framework, named CDPMF-DDA, which enhances the model's ability to capture drug-disease associations by incorporating diverse information representations from different views.

In situ structural-functional synchronous dissection of dynamic neuromuscular system via an integrated multimodal wearable patch.

Neuromuscular abnormality is the leading cause of disability in adults. Understanding the complex interplay between muscle structure and function is crucial for effective treatment and rehabilitation. However, the substantial deformation of muscles during movement (up to 40%) poses challenges for accurate assessment.

Disorder-order transition-induced unusual bandgap bowing effect of tin-lead mixed perovskites.

Owing to the predominant merit of tunable bandgaps, tin-lead mixed perovskites have shown great potentials in realizing near-infrared optoelectronics and are receiving increasing attention. However, despite the merit, there is still a lack of fundamental understanding of the bandgap variation as a function of Sn/Pb ratio, mainly because the films are easy to segregate in terms of both composition and phase. Here, we report a fully stoichiometric synthesis of monocrystalline FAPbSnI nanocrystals as well as their atomic-scale imaging.

Large stroke radially oriented MXene composite fiber tensile artificial muscles.

Actuation is normally dramatically enhanced by introducing so much yarn fiber twist that the fiber becomes fully coiled. In contrast, we found that usefully high muscle strokes and contractile work capacities can be obtained for non-twisted MXene (TiCT) fibers comprising MXene nanosheets that are stacked in the fiber direction. The MXene fiber artificial muscles are called MFAMs.

Mechanical Twisting-Induced Enhancement of Second-Order Optical Nonlinearity in a Flexible Molecular Crystal.

Flexible molecular crystals are essential for advancing smart materials, providing unique functionality and adaptability for applications in next-generation electronics, pharmaceuticals, and energy storage. However, the optical applications of flexible molecular crystals have been largely restricted to linear optics, with nonlinear optical (NLO) properties rarely explored. Herein, we report on the application of mechanical twisting of flexible molecular crystals for second-order nonlinear optics.

Using Large Language Models to Assist Antimicrobial Resistance Policy Development: Integrating the Environment into Health Protection Planning.

Increasing antimicrobial resistance (AMR) poses a substantial threat to global health and economies, which has led many countries and regions to develop AMR National Action Plans (NAPs). However, inadequate logistical capacity, funding, and essential information can hinder NAP policymaking, especially in low-to-middle-income countries (LMICs). Therefore, major gaps exist between aspirations and actions, such as fully operationalized environmental AMR surveillance programs in NAPs.

Methylome analysis in long-lived men deciphers DNA methylation modifications associated with male longevity in humans.

Men, despite having a lower likelihood of longevity compared to women, generally exhibit better health status when they achieve longevity. The role of DNA methylation in this paradox remains unclear. We performed whole-genome bisulfite sequencing on long-lived men (LLMs), long-lived women (LLWs), younger men (YMs) and younger women (YWs) to explore specific methylation characteristics in LLMs.

Soybean2035: A decadal vision for soybean functional genomics and breeding.

Soybean, the fourth most important crop in the world, uniquely serves as a source of both plant oil and plant protein for the world's food and animal feed. Although soybean production has increased approximately 13-fold over the past 60 years, the continually growing global population necessitates further increases in soybean production. In the past, especially in the last decade, significant progress has been made in both functional genomics and molecular breeding.

An Investigation into High-Accuracy and Energy-Efficient Novel Capacitive MEMS for Tire Pressure Sensor Application.

Tire pressure monitoring systems (TPMSs) are essential for maintaining driving safety by continuously monitoring critical tire parameters, such as pressure and temperature, in real time during vehicle operation. Among these parameters, tire pressure is the most significant, necessitating the use of highly precise, cost-effective, and energy-efficient sensing technologies. With the rapid advancements in micro-electro-mechanical system (MEMS) technology, modern automotive sensing and monitoring systems increasingly rely on MEMS sensors due to their compact size, low cost, and low power consumption.

Age of Information Analysis for Multi-Priority Queue and Non-Orthoganal Multiple Access (NOMA)-Enabled Cellular Vehicle-to-Everything in Internet of Vehicles.

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions.

Robust Automatic Modulation Classification via a Lightweight Temporal Hybrid Neural Network.

In the rapidly developing field of wireless communications, the precise classification of modulated signals is essential for optimizing spectrum utilization and improving communication quality. However, existing networks face challenges in robustness against signals containing phase shift keying and computational efficiency. This paper introduces TCN-GRU, a lightweight model that combines the advantages of multiscale feature extraction of the temporal convolutional network (TCN) and global sequence modeling of gated recurrent unit (GRU).

Research on a Lightweight Arrhythmia Classification Model Based on Knowledge Distillation for Wearable Single-Lead ECG Monitoring Systems.

Arrhythmias are among the diseases with high mortality rates worldwide, causing millions of deaths each year. This underscores the importance of real-time electrocardiogram (ECG) monitoring for timely heart disease diagnosis and intervention. Deep learning models, trained on ECG signals across twelve or more leads, are the predominant approach for automated arrhythmia detection in the AI-assisted medical field.

Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels.

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions.

Damage and Failure Monitoring of Aerospace Insulation Layers Based on Embedded Fiber Bragg Grating Sensors.

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aviation thermal insulation layers due to their high strength-to-weight ratio and excellent high-temperature performance. However, challenges remain regarding their structural integrity and durability under extreme conditions. This study first employed finite element simulation to model the damage evolution of CFRP laminated plates under axial tensile loads and their thermal decomposition behavior in high-temperature environments, providing a theoretical reference.

Application of Fuzzy Adaptive Impedance Control Based on Backstepping Method for PAM Elbow Exoskeleton in Rehabilitation.

In this study, a fuzzy adaptive impedance control method integrating the backstepping control for the PAM elbow exoskeleton was developed to facilitate robot-assisted rehabilitation tasks. The proposed method uses fuzzy logic to adjust impedance parameters, thereby optimizing user adaptability and reducing interactive torque, which are major limitations of traditional impedance control methods. Furthermore, a repetitive learning algorithm and an adaptive control strategy were incorporated to improve the performance of position accuracy, addressing the time-varying uncertainties and nonlinear disturbances inherent in the exoskeleton.

Editorial for the Special Issue on the Latest Advancements in Semiconductor Materials, Devices and Systems.

The field of semiconductor research is experiencing a paradigm shift as the boundaries of Moore's Law are being approached [...

Range Expansion Technology for Ring MEMS Gyroscopes Based on Drive Voltage Modulation.

This paper proposes a method to control the sensitivity of a ring MEMS gyroscope by adjusting the driving control voltage via MEMS. The aim is to explore the relationship between the range of the ring MEMS gyroscope and the driving control voltage, establishing a mathematical model that correlates driving control voltage with sensitivity. By applying different driving voltages to the same gyroscope, the study evaluates the performance and range of the gyroscope.

A High-Precision Temperature Compensation Method for TMR Weak Current Sensors Based on FPGA.

Tunnel magnetoresistance (TMR) sensors, known for their high sensitivity, efficiency, and compact size, are ideal for detecting weak currents, particularly leakage currents in smart grids. However, temperature variations can negatively impact their accuracy. This work investigates the effects of temperature variations on measurement accuracy.

Nonlinear Thermomechanical Low-Velocity Impact Behaviors of Geometrically Imperfect GRC Beams.

This paper studies the thermomechanical low-velocity impact behaviors of geometrically imperfect nanoplatelet-reinforced composite (GRC) beams considering the von Kármán nonlinear geometric relationship. The graphene nanoplatelets (GPLs) are assumed to have a functionally graded (FG) distribution in the matrix beam along its thickness, following the X-pattern. The Halpin-Tsai model and the rule of mixture are employed to predict the effective Young modulus and other material properties.

Corrosion Behavior of Copper Foil on PCB Substrates Under Atmospheric Environment in Sichuan-Tibet Region of China.

Copper foil is widely used in electronic components and devices. This study investigates the corrosion behavior of copper foil on printed circuit boards exposed for one year in a closed atmospheric environment across 22 different sites in the Sichuan-Tibet region. Through electrochemical, SEM/EDS, and XRD analyses, the corrosion behavior of copper foil material across the five selected sites (Meishan, Mangkang, Luding, Batang, and Panzhihua) and the influence of environmental factors were discussed.

HSF-IBI: A Universal Framework for Extracting Inter-Beat Interval from Heterogeneous Unobtrusive Sensors.

Heartbeat inter-beat interval (IBI) extraction is a crucial technology for unobtrusive vital sign monitoring, yet its precision and robustness remain challenging. A promising approach is fusing heartbeat signals from different types of unobtrusive sensors. This paper introduces HSF-IBI, a novel and universal framework for unobtrusive IBI extraction using heterogeneous sensor fusion.

Liquid Metal-Based Dual-Response Pressure Sensor for Dual-Modality Sensing and Robotic Object Recognition.

Characterized by their high sensitivity and flexible deformation, flexible pressure sensors have been extensively applied in various fields such as wearable electronics, health monitoring, soft robotics, and human-computer interaction. In this research, we developed a dual-response pressure sensor (DRPS) designed to identify object materials. By integrating the operating principles of capacitive and resistive sensors and employing microstructured dielectric layers, we enhanced the sensitivity and detection range of the pressure sensor.

Non-Contact Stable Arterial Pulse Measurement Using mmWave Array Radar.

Pulse signals can serve as important indicators of one's cardiovascular condition. However, capturing signals with stable morphology using radar under varying measurement periods remains a significant challenge. This paper reports a non-contact arterial pulse measurement method based on mmWave radar, with stable signals achieved through a range-angle focusing algorithm.

Quantitative Prediction of Protein Content in Corn Kernel Based on Near-Infrared Spectroscopy.

Rapid and accurate detection of protein content is essential for ensuring the quality of maize. Near-infrared spectroscopy (NIR) technology faces limitations due to surface effects and sample homogeneity issues when measuring the protein content of whole maize grains. Focusing on maize grain powder can significantly improve the quality of data and the accuracy of model predictions.