Identifying the Primary Kinetic Factors Influencing the Anterior-Posterior Center of Mass Displacement in Barbell Squats: A Factor Regression Analysis.

Barbell squats are commonly used in strength training, but the anterior-posterior displacement of the Center of Mass (COM) may impair joint stability and increase injury risk. This study investigates the key factors influencing COM displacement during different squat modes.; Methods: This study recruited 15 male strength training enthusiasts, who performed 60% of their one-repetition maximum (1RM) in the Front Barbell Squat (FBS), High Bar Back Squat (HBBS), and Low Bar Back Squat (LBBS).

Computer-Aided Supporting Models of Customized Crack Propagation Sensors for Analysis and Prototyping.

The range of sensor technologies for structural health monitoring (SHM) systems is expanding as the need for ongoing structural monitoring increases. In such a case, damage to the monitored structure elements is detected using an integrated network of sensors operating in real-time or periodically in frequent time stamps. This paper briefly introduces a new type of sensor, called a Customized Crack Propagation Sensor (CCPS), which is an alternative for crack gauges, but with enhanced functional features and customizability.

Action Recognition in Basketball with Inertial Measurement Unit-Supported Vest.

In this study, an action recognition system was developed to identify fundamental basketball movements using a single Inertial Measurement Unit (IMU) sensor embedded in a wearable vest. This study aims to enhance basketball training by providing a high-performance, low-cost solution that minimizes discomfort for athletes. Data were collected from 21 collegiate basketball players, and movements such as dribbling, passing, shooting, layup, and standing still were recorded.

The Application of Supervised Machine Learning Algorithms for Image Alignment in Multi-Channel Imaging Systems.

This study presents a method for aligning the geometric parameters of images in multi-channel imaging systems based on the application of pre-processing methods, machine learning algorithms, and a calibration setup using an array of orderly markers at the nodes of an imaginary grid. According to the proposed method, one channel of the system is used as a reference. The images from the calibration setup in each channel determine the coordinates of the markers, and the displacements of the marker centers in the system's channels relative to the coordinates of the centers in the reference channel are then determined.

The SPICE Modeling of a Radiation Sensor Based on a MOSFET with a Dielectric HfO/SiO Double-Layer.

We report on a procedure for extracting the SPICE model parameters of a RADFET sensor with a dielectric HfO/SiO double-layer. RADFETs, traditionally fabricated as PMOS transistors with SiO, are enhanced by incorporating high-k dielectric materials such as HfO to reduce oxide thickness in modern radiation sensors. The fabrication steps of the sensor are outlined, and model parameters, including the threshold voltage and transconductance, are extracted based on experimental data.

GravelSens: A Smart Gravel Sensor for High-Resolution, Non-Destructive Monitoring of Clogging Dynamics.

Engineers, geomorphologists, and ecologists acknowledge the need for temporally and spatially resolved measurements of sediment clogging (also known as colmation) in permeable gravel-bed rivers due to its adverse impacts on water and habitat quality. In this paper, we present a novel method for non-destructive, real-time measurements of pore-scale sediment deposition and monitoring of clogging by using wire-mesh sensors (WMSs) embedded in spheres, forming a smart gravel bed (GravelSens). The measuring principle is based on one-by-one voltage excitation of transmitter electrodes, followed by simultaneous measurements of the resulting current by receiver electrodes at each crossing measuring pores.

Symmetry Breaking as a Basis for Characterization of Dielectric Materials.

This paper introduces a novel method for measuring the dielectric permittivity of materials within the microwave and millimeter wave frequency ranges. The proposed approach, classified as a guided wave transmission system, employs a periodic transmission line structure characterized by mirror/glide symmetry. The dielectric permittivity is deduced by measuring the transmission properties of such structure when presence of the dielectric material breaks the inherent symmetry of the structure and consequently introduce a stopband in propagation characteristic.

Web Real-Time Communications-Based Unmanned-Aerial-Vehicle-Borne Internet of Things and Stringent Time Sensitivity: A Case Study.

The currently observed development of time-sensitive applications also affects wireless communication with the IoT carried by UAVs. Although research on wireless low-latency networks has matured, there are still issues to solve at the transport layer. Since there is a general agreement that classical transport solutions are not able to achieve end-to-end delays in the single-digit millisecond range, in this paper, the use of WebRTC is proposed as a potential solution to this problem.

An Approach to Fall Detection Using Statistical Distributions of Thermal Signatures Obtained by a Stand-Alone Low-Resolution IR Array Sensor Device.

Infrared array sensor-based fall detection and activity recognition systems have gained momentum as promising solutions for enhancing healthcare monitoring and safety in various environments. Unlike camera-based systems, which can be privacy-intrusive, IR array sensors offer a non-invasive, reliable approach for fall detection and activity recognition while preserving privacy. This work proposes a novel method to distinguish between normal motion and fall incidents by analyzing thermal patterns captured by infrared array sensors.

A Comprehensive Analysis of a Social Intelligence Dataset and Response Tendencies Between Large Language Models (LLMs) and Humans.

In recent years, advancements in the interaction and collaboration between humans and have garnered significant attention. Social intelligence plays a crucial role in facilitating natural interactions and seamless communication between humans and Artificial Intelligence (AI). To assess AI's ability to understand human interactions and the components necessary for such comprehension, datasets like Social-IQ have been developed.

Investigation of Sliding Mode Control in the Nonlinear Modeling of Cordless Jigsaws.

The aim of this paper was to reduce the current spikes in battery-powered saw motors by designing and implementing a sliding mode model-following adaptive controller. The proposed controller reduces overcurrent consumption, improves system energy efficiency, and effectively maximizes battery runtime, especially under high-load conditions. By applying nonlinear compensation techniques, the controller can ensure smooth motor operation, reduce mechanical stress, and prolong tool life.

Developing a Health Support System to Promote Care for the Elderly.

In light of the demographic shift towards an aging population, there is an increasing prevalence of dementia among the elderly. The negative impact on mental health is preventing individuals from taking proper care of themselves. For individuals requiring hospital care, those receiving home care, or as a precaution for a specific individual, it is advantageous to utilize monitoring equipment to track their biological parameters on an ongoing basis.

A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.

Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities.

Incorporating Multimodal Directional Interpersonal Synchrony into Empathetic Response Generation.

This study investigates how interpersonal (speaker-partner) synchrony contributes to empathetic response generation in communication scenarios. To perform this investigation, we propose a model that incorporates multimodal directional (positive and negative) interpersonal synchrony, operationalized using the cosine similarity measure, into empathetic response generation. We evaluate how incorporating specific synchrony affects the generated responses at the language and empathy levels.

Polarization-Insensitive, High-Efficiency Metasurface with Wide Reception Angle for Energy Harvesting Applications.

This research presents an innovative polarization-insensitive metasurface (MS) harvester designed for energy harvesting applications at 5 GHz, capable of operating efficiently over wide reception angles. The proposed MS features a novel wheel-shaped resonator array whose symmetrical structure ensures insensitivity to the polarization of incident electromagnetic (EM) waves, enabling efficient energy absorption and minimizing reflections. Unlike conventional designs, the metasurface achieves near-unity harvesting efficiency, exceeds 94% under normal incidence, and maintains superior performance across various incident angles for TE and TM polarizations.

Incremental Structure from Motion for Small-Scale Scenes Based on Auxiliary Calibration.

Scarce feature points are a critical limitation affecting the accuracy and stability of incremental structure from motion (SfM) in small-scale scenes. In this paper, we propose an incremental SfM method for small-scale scenes, combined with an auxiliary calibration plate. This approach increases the number of feature points in sparse regions, and we randomly generate feature points within those areas.

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers.

Evaluating the Performance Characteristics of Pressure Monitoring Systems.

Measuring interface pressure is currently used in a variety of settings, e.g., automotive or clinical, to evaluate pressure distribution at support surface interfaces.

Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears.

Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.

Research on End-Effector Position Error Compensation of Industrial Robotic Arm Based on ECOA-BP.

Industrial robotic arms are often subject to significant end-effector pose deviations from the target position due to the combined effects of nonlinear deformations such as link flexibility, joint compliance, and end-effector load. To address this issue, a study was conducted on the analysis and compensation of end-position errors in a six-degree-of-freedom robotic arm. The kinematic model of the robotic arm was established using the Denavit-Hartenberg (DH) parameter method, and a rigid-flexible coupled virtual prototype model was developed using ANSYS and ADAMS.

DynamicVLN: Incorporating Dynamics into Vision-and-Language Navigation Scenarios.

Traditional Vision-and-Language Navigation (VLN) tasks require an agent to navigate static environments using natural language instructions. However, real-world road conditions such as vehicle movements, traffic signal fluctuations, pedestrian activity, and weather variations are dynamic and continually changing. These factors significantly impact an agent's decision-making ability, underscoring the limitations of current VLN models, which do not accurately reflect the complexities of real-world navigation.

Reusable Biosensor for Easy RNA Detection from Unfiltered Saliva.

Biosensors are transforming point-of-care diagnostics by simplifying the detection process and enabling rapid, accurate testing. This study introduces a novel, reusable biosensor designed for direct viral RNA detection from unfiltered saliva, targeting SARS-CoV-2. Unlike conventional methods requiring filtration, our biosensor leverages a unique electrode design that prevents interference from saliva debris, allowing precise measurements.

Structural Fatigue Life Monitoring with Piezoelectric-Based Sensors: Fundamentals, Current Advances, and Future Directions.

Structural fatigue can lead to catastrophic failures in various engineering applications and must be properly monitored and effectively managed. This paper provides a state-of-the-art review of recent developments in structural fatigue monitoring using piezoelectric-based sensors. Compared to alternative sensing technologies, piezoelectric sensors offer distinct advantages, including compact size, lightweight design, low cost, flexible formats, and high sensitivity to dynamic loads.

Performance Evaluation of Deep Learning Image Classification Modules in the MUN-ABSAI Ice Risk Management Architecture.

The retreat of Arctic sea ice has opened new maritime routes, offering faster shipping opportunities; however, these routes present significant navigational challenges due to the harsh ice conditions. To address these challenges, this paper proposes a deep learning-based Arctic ice risk management architecture with multiple modules, including ice classification, risk assessment, ice floe tracking, and ice load calculations. A comprehensive dataset of 15,000 ice images was created using public sources and contributions from the Canadian Coast Guard, and it was used to support the development and evaluation of the system.

Spatial Characterization of Woody Species Diversity in Tropical Savannas Using GEDI and Optical Data.

Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species.