Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.

Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.

Optimal Position and Orientation of an Ossicular Accelerometer for Human Auditory Prostheses.

In this study, a method for determining the optimal location and orientation of an implantable piezoelectric accelerometer on the short process of the incus is presented. The accelerometer is intended to be used as a replacement for an external microphone to enable totally implantable auditory prostheses. The optimal orientation of the sensor and the best attachment point are determined based on two criteria-maximum pressure sensitivity sum and minimum loudness level sum.

Characterization of an Inorganic Powder-Based Scintillation Detector Under a UHDR Electron Beam.

(1) Background: Ultra-high dose rate (UHDR) radiation therapy needs a reliable dosimetry solution and scintillation detectors are promising candidates. In this study, we characterized an inorganic powder-based scintillation detector under a 9 MeV UHDR electron beam. (2) Methods: A mixture of ZnS:Ag powder and optic glue was coupled to an 8 m Eska GH-4001-P polymethyl methacrylate (PMMA) optical fiber.

Smart Driving Technology for Non-Invasive Detection of Age-Related Cognitive Decline.

Alzheimer's disease (AD) and Alzheimer's Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI.

Geohazard Identification in Underground Mines: A Mobile App.

Mining is a critical industry that provides essential minerals and resources for modern society. Despite its benefits, the industry is also recognized as one of the most dangerous occupations, with geotechnical hazards being a primary concern. This study introduces the hazard recognition in underground mines application (HUMApp), a mobile application developed to enhance safety within underground mines by efficiently identifying geotechnical hazards, specifically focusing on roof falls.

Numerical Investigation of a Microfluidic Biosensor Based on I-Shaped Micropillar Array Electrodes.

Micropillar array electrodes offer several advantages, such as enhanced mass transport, lower detection limits, and the potential for miniaturization, making them instrumental in the design and fabrication of electrochemical biosensors. The performance of these biosensors is influenced by electrode geometry, including parameters like shape and height, which affect surface area and overall sensitivity. In this study, we designed a microfluidic electrochemical biosensor featuring micropillar array electrodes, modeled in COMSOL Multiphysics.

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.

A Lyapunov Optimization-Based Approach to Autonomous Vehicle Local Path Planning.

Autonomous vehicles (AVs) offer significant potential to improve safety, reduce emissions, and increase comfort, drawing substantial attention from both research and industry. A critical challenge in achieving SAE Level 5 autonomy, full automation, is path planning. Ongoing efforts in academia and industry are focused on optimizing AV path planning, reducing computational complexity, and enhancing safety.

Using Heart Rate and Behaviors to Predict Effective Intervention Strategies for Children on the Autism Spectrum: Validation of a Technology-Based Intervention.

Many children on the autism spectrum engage in challenging behaviors, like aggression, due to difficulties communicating and regulating their stress. Identifying effective intervention strategies is often subjective and time-consuming. Utilizing unobservable internal physiological data to predict strategy effectiveness may help simplify this process for teachers and parents.

A Time-Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen.

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process.

Detection and Quantification of DNA by Fluorophore-Induced Plasmonic Current: A Novel Sensing Approach.

We report on the detection and quantification of aqueous DNA by a fluorophore-induced plasmonic current (FIPC) sensing method. FIPC is a mechanism described by our group in the literature where a fluorophore in close proximity to a plasmonically active metal nanoparticle film (MNF) is able to couple with it, when in an excited state. This coupling produces enhanced fluorescent intensity from the fluorophore-MNF complex, and if conditions are met, a current is generated in the film that is intrinsically linked to the properties of the fluorophore in the complex.

Domain Adversarial Convolutional Neural Network Improves the Accuracy and Generalizability of Wearable Sleep Assessment Technology.

Wearable accelerometers are widely used as an ecologically valid and scalable solution for long-term at-home sleep monitoring in both clinical research and care. In this study, we applied a deep learning domain adversarial convolutional neural network (DACNN) model to this task and demonstrated that this new model outperformed existing sleep algorithms in classifying sleep-wake and estimating sleep outcomes based on wrist-worn accelerometry. This model generalized well to another dataset based on different wearable devices and activity counts, achieving an accuracy of 80.

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.

A 6.7 μW Low-Noise, Compact PLL with an Input MEMS-Based Reference Oscillator Featuring a High-Resolution Dead/Blind Zone-Free PFD.

This article reports a 110.2 MHz ultra-low-power phase-locked loop (PLL) for MEMS timing/frequency reference oscillator applications. It utilizes a 6.

Exploring the Utility of the Muse Headset for Capturing the N400: Dependability and Single-Trial Analysis.

Consumer-grade EEG devices, such as the InteraXon Muse 2 headband, present a promising opportunity to enhance the accessibility and inclusivity of neuroscience research. However, their effectiveness in capturing language-related ERP components, such as the N400, remains underexplored. This study thus aimed to investigate the feasibility of using the Muse 2 to measure the N400 effect in a semantic relatedness judgment task.

Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems.

Detecting anomalies in distributed systems through log analysis remains challenging due to the complex temporal dependencies between log events, the diverse manifestation of system states, and the intricate causal relationships across distributed components. This paper introduces a TLAN (Temporal Logical Attention Network), a novel deep learning framework that integrates temporal sequence modeling with logical dependency analysis for robust anomaly detection in distributed system logs. Our approach makes three key contributions: (1) a temporal logical attention mechanism that explicitly models both time-series patterns and logical dependencies between log events across distributed components, (2) a multi-scale feature extraction module that captures system behaviors at different temporal granularities while preserving causal relationships, and (3) an adaptive threshold strategy that dynamically adjusts detection sensitivity based on system load and component interactions.

A Rapidly Tunable Laser System for Measurements of NH at 597 nm Behind Reflected Shock Waves.

Distributed feedback lasers, which feature rapid wavelength tunability, are not presently available in the yellow and orange spectral regions, impeding spectroscopic studies of short-lived species that absorb light in this range. To meet this need, a rapidly tunable laser system was constructed, characterized, and demonstrated for measurements of the NH radical at 597.4 nm.

Edge-Cloud Synergy for AI-Enhanced Sensor Network Data: A Real-Time Predictive Maintenance Framework.

Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud.

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.

Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep-Wake Stage Classification.

Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep's impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming.

Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization.

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent's sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional 'olfaction-only' OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities.

Vedolizumab Clearance as a Surrogate Marker for Remission in Inflammatory Bowel Disease Patients: Insights from Real-World Pharmacokinetics.

Vedolizumab (VDZ) is approved in the treatment of patients with moderate to severe ulcerative colitis (UC) or Crohn's disease (CD). VDZ exhibits considerable variability in its pharmacokinetic (PK) profile, and its exposure-response relationship is not yet fully understood. The aim was to investigate the variability in VDZ trough levels and PK parameters, to assess the relationship between VDZ PK and biochemical response, as well as clinical and endoscopic outcomes.

Twin Screw Melt Granulation of Simvastatin: Drug Solubility and Dissolution Rate Enhancement Using Polymer Blends.

This study evaluates the efficacy of twin screw melt granulation (TSMG), and hot-melt extrusion (HME) techniques in enhancing the solubility and dissolution of simvastatin (SIM), a poorly water-soluble drug with low bioavailability. Additionally, the study explores the impact of binary polymer blends on the drug's miscibility, solubility, and in vitro release profile. SIM was processed with various polymeric combinations at a 30% / drug load, and a 1:1 ratio of binary polymer blends, including Soluplus (SOP), Kollidon K12 (K12), Kollidon VA64 (KVA), and Kollicoat IR (KIR).

Early-Stage IM Treatment with the Host-Derived Immunostimulant CPDI-02 Increases Curative Protection of Healthy Outbred Mice Against Subcutaneous Infection with Community-Acquired Methicillin-Resistant USA300.

: Community-acquired methicillin-resistant (CA-MRSA) greatly complicates the treatment of skin and soft tissue infections (SSTI). It was previously found that subcutaneous (SQ) treatment with the mononuclear phagocyte (MP)-selective activator complements peptide-derived immunostimulant-02 (CPDI-02; formerly EP67) and increases prophylaxis of outbred CD-1 mice against SQ infection with CA-MRSA. Here, we determined if treatment with CPDI-02 also increases curative protection.