The Role of Rapid Maxillary Expansion in the Management of Obstructive Sleep Apnoea: Monitoring Respiratory Parameters-A Systematic Review and Meta-Analysis.

Obstructive sleep apnoea (OSA) is a sleep-related breathing condition that involves the presence of episodic disruptions to the sleeping pattern due to partial or complete airway obstruction. There are a range of treatment options that exist to alleviate the symptoms of this condition including CPAP, mandibular advancement, and maxillary expansion techniques. This systematic review and meta-analysis of published articles aims to determine if rapid maxillary expansion ("RME") is an effective treatment option in the management of OSA, using quantitative parameters of AHI and SpO.

Comparative Evaluation of Temporomandibular Disorders and Dental Wear in Video Game Players.

: The increasing prevalence of video gaming has raised concerns about its potential impact on musculoskeletal health, particularly temporomandibular disorders (TMDs). This study aims to compare TMD symptoms, mandibular function, and dental wear between gamers and non-gamers among university students. : An observational study included 108 students aged 20 to 23 years, divided into gamers (n = 48) and non-gamers (n = 60).

Recent Advances and Future Directions in Extracorporeal Carbon Dioxide Removal.

Extracorporeal carbon dioxide removal (ECCOR) is an emerging technique designed to reduce carbon dioxide (CO) levels in venous blood while enabling lung-protective ventilation or alleviating the work of breathing. Unlike high-flow extracorporeal membrane oxygenation (ECMO), ECCOR operates at lower blood flows (0.4-1.

Displacement Measurement Based on the Missing-Order Talbot Effect.

Displacement measurement is a crucial application, with laser-based methods offering high precision and being well established in commercial settings. However, these methods often come with the drawbacks of significant size and exorbitant costs. We introduce a novel displacement measurement method that utilizes the missing-order Talbot effect.

A New Approach for Infrared Temperature Measurement Sensor Systems and Temperature Control for Domestic Induction Hobs.

The accurate measurement of cooking vessel temperatures in induction hobs is crucial for ensuring optimal cooking performance and safety. To achieve this, improvements in existing measurement methods such as thermocouples, thermistors, and infrared (IR) temperature sensors are being explored. However, traditional IR sensors are sensitive to interference from the heated glass ceramic, severely affecting accuracy.

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.

The Challenges and Opportunities for Performance Enhancement in Resonant Fiber Optic Gyroscopes.

In the last decade, substantial progress has been made to improve the performance of optical gyroscopes for inertial navigation applications in terms of critical parameters such as bias stability, scale factor stability, and angular random walk (ARW). Specifically, resonant fiber optic gyroscopes (RFOGs) have emerged as a viable alternative to widely popular interferometric fiber optic gyroscopes (IFOGs). In a conventional RFOG, a single-wavelength laser source is used to generate counter-propagating waves in a ring resonator, for which the phase difference is measured in terms of the resonant frequency shift to obtain the rotation rate.

Regeneration Filter: Enhancing Mosaic Algorithm for Near Salt & Pepper Noise Reduction.

This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution.

Vehicle Target Detection of Autonomous Driving Vehicles in Foggy Environments Based on an Improved YOLOX Network.

To address the problems that exist in the target detection of vehicle-mounted visual sensors in foggy environments, a vehicle target detection method based on an improved YOLOX network is proposed. Firstly, to address the issue of vehicle target feature loss in foggy traffic scene images, specific characteristics of fog-affected imagery are integrated into the network training process. This not only augments the training data but also improves the robustness of the network in foggy environments.

The Realization of a Three-Dimensional Temperature Measurement System with a Two-Dimensional Sensor Array and the Demonstration of the Deformation Effect of Gravity on the Heating Patterns.

Electric heaters are widely used owing to their portability, fast heating, single-focus heating, and energy efficiency advantages. Manufacturers provide customers with information on the power consumption and energy efficiency classes of heaters but do not provide any information on heating patterns. Knowing the heating pattern enables users to select the correct heater, which has a significant effect on comfort, health, energy efficiency, industrial process performance, plant growth, and climate change.

Cardiac Function and Fatigue During Exoskeleton-Assisted Sit-to-Stand Maneuver and Walking in People with Stroke with Moderate to Severe Gait Disability: A Pilot Cross-Sectional Study.

Background: Wearable powered exoskeletons could be used to provide robotic-assisted gait training (RAGT) in people with stroke (PwST) and walking disability. The study aims to compare the differences in cardiac function, fatigue, and workload during activities of daily living (ADLs), while wearing an exoskeleton.

Methods: Five PwST were recruited in this pilot cross-sectional study.

Research on Mine-Personnel Helmet Detection Based on Multi-Strategy-Improved YOLOv11.

In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management.

Digital Health Technologies for Optimising Treatment and Rehabilitation Following Surgery: Device-Based Measurement of Sling Posture and Adherence.

Background: Following shoulder surgery, controlled and protected mobilisation for an appropriate duration is crucial for appropriate recovery. However, methods for objective assessment of sling wear and use in everyday living are currently lacking. In this pilot study, we aim to determine if a sling-embedded triaxial accelerometer and/or wrist-worn sensor can be used to quantify arm posture during sling wear and adherence to sling wear.

Magnetic Induction Phase Difference for Cerebral Hemorrhage Detection.

Magnetic induction phase shift is a promising technology for the detection of cerebral hemorrhage, owing to its nonradioactive, noninvasive, and real-time detection properties. To enhance the detection sensitivity and linearity, a zero-flow sensor was proposed. The uniform primary magnetic field and its counteraction were achieved.

A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.

In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory and descriptive approach is used in the analysis. Computational tools such as the biblioshiny application for R-Bibliometrix and VOSViewer are employed to generate data on years, sources, authors, affiliation, country, documents, co-author, co-citation, and co-occurrence.

Analyzing the Impact of Various Jump Load Intensities on Countermovement Jump Metrics: A Comparison of Average, Peak, and Peak-to-Average Ratios in Force-Based Metrics.

The purpose was to create a systematic approach for analyzing data to improve predictive models for fatigue and neuromuscular performance in volleyball, with potential applications in other sports. The study aimed to assess whether average, peak, or peak-to-average ratios of countermovement jump (CMJ) force plate metrics exhibit stronger correlations and determine which metric most effectively predicts performance. Data were obtained from nine division I female volleyball athletes over a season, recording daily jump loads (total jumps, jump counts >38.

Predicting Sensory and Affective Tactile Perception from Physical Parameters Obtained by Using a Biomimetic Multimodal Tactile Sensor.

Tactile perception plays a crucial role in the perception of products and consumer preferences. This perception process is structured in hierarchical layers comprising a sensory layer (soft and smooth) and an affective layer (comfort and luxury). In this study, we attempted to predict the evaluation score of sensory and affective tactile perceptions of materials using a biomimetic multimodal tactile sensor that mimics the active touch behavior of humans and measures physical parameters such as force, vibration, and temperature.

Progressive Bounded Error Piecewise Linear Approximation with Resolution Reduction for Time Series Data Compression.

Today, huge amounts of time series data are sensed continuously by AIoT devices, transmitted to edge nodes, and to data centers. It costs a lot of energy to transmit these data, store them, and process them. Data compression technologies are commonly used to reduce the data size and thus save energy.

New Torsional Surface Elastic Waves in Cylindrical Metamaterial Waveguides for Sensing Applications.

In this paper, we demonstrate that torsional surface elastic waves can propagate along the curved surface of a metamaterial elastic rod (cylinder) embedded in a conventional elastic medium. The crucial parameter of the metamaterial rod is its elastic compliance s44(1)ω, which varies as a function of frequency ω analogously to the dielectric function εω in Drude's model of metals. As a consequence, the elastic compliance s44(1)ω can take negative values s44(1)ω<0 as a function of frequency ω.

Accuracy of Rhythm Diagnostic Systems' MultiSense in Detection of Arterial Oxygen Saturation and Respiratory Rate During Hypoxia in Humans: Effects of Skin Color and Device Localization.

The continuous monitoring of oxygen saturation (SpO) and respiratory rates (RRs) are major clinical issues in many cardio-respiratory diseases and have been of tremendous importance during the COVID-19 pandemic. The early detection of hypoxemia was crucial since it precedes significant complications, and SpO follow-up allowed early hospital discharge in patients needing oxygen therapy. Nevertheless, fingertip devices showed some practical limitations.

Room Temperature NO-Sensing Properties of N-Doped ZnO Nanoparticles Activated by UV-Vis Light.

Zinc oxide nanoparticles (ZnO NPs) with varying levels of nitrogen (N) doping were synthesized using a straightforward sol-gel approach. The morphology and microstructure of the N-doped ZnO NPs were examined through techniques such as SEM, XRD, photoluminescence, and Raman spectroscopy. The characterization revealed visible changes in the morphology and microstructure resulting from the incorporation of nitrogen into the ZnO lattice.

Time-Series Forecasting of PM and PM Concentrations Based on the Integration of Surveillance Images.

Accurate and timely air quality forecasting is crucial for mitigating pollution-related hazards and protecting public health. Recently, there has been a growing interest in integrating visual data for air quality prediction. However, some limitations remain in existing literature, such as their focus on coarse-grained classification, single-moment estimation, or reliance on indirect and unintuitive information from visual images.

Improved Deep Support Vector Data Description Model Using Feature Patching for Industrial Anomaly Detection.

In industrial contexts, anomaly detection is crucial for ensuring quality control and maintaining operational efficiency in manufacturing processes. Leveraging high-level features extracted from ImageNet-trained networks and the robust capabilities of the Deep Support Vector Data Description (SVDD) model for anomaly detection, this paper proposes an improved Deep SVDD model, termed Feature-Patching SVDD (FPSVDD), designed for unsupervised anomaly detection in industrial applications. This model integrates a feature-patching technique with the Deep SVDD framework.

Fault Detection and Diagnosis in Industry 4.0: A Review on Challenges and Opportunities.

Integrating Machine Learning (ML) in industrial settings has become a cornerstone of Industry 4.0, aiming to enhance production system reliability and efficiency through Real-Time Fault Detection and Diagnosis (RT-FDD). This paper conducts a comprehensive literature review of ML-based RT-FDD.

Exploring Task-Related EEG for Cross-Subject Early Alzheimer's Disease Susceptibility Prediction in Middle-Aged Adults Using Multitaper Spectral Analysis.

The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT).