Search Results (316)

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article

Pipeline leakage is a common issue in many pressurized pipeline systems, with significant hazards, making it a current research hotspot. To reveal the fundamental characteristics of leakage in straight pipelines and 90° elbows transporting different media and thereby predict leakage locations, this paper conducts numerical calculations of the internal flow, while also predicting the pipeline leakage location monitoring model. The study finds that under air medium conditions, the nonlinear function model demonstrates excellent prediction accuracy, with R² > 0.99 for the water₃ condition. Under water medium conditions, the model’s fitting performance gradually weakens with increasing inlet pressure, with R² dropping to 0.77. For a bent pipe, when air is used as the medium, the pressure peak at the large bend angle increases significantly under high inlet pressure. In contrast, when water is the medium, the local pressure reconstruction effect in the bent pipe exhibits a linear strengthening trend as the inlet pressure increases. Full article

This study presents the first integrated analysis of genotype–medium interactions and temporal morphogenesis profiling in sunflower regeneration. It aims to characterize genotype-specific responses, identify predictive morphological markers, and develop a scalable framework for breeding and transformation. Eighteen sunflower genotypes were evaluated to assess organogenic performance. The model genotype Ha-26-PR was used for a complementary experiment, testing varying sucrose concentrations to examine their influence on morphogenic outcomes. Hierarchical Cluster Analysis (HCA), guided by the Elbow method, identified four optimal clusters (K = 4). These aligned with three biologically meaningful categories: High Regenerators (Cluster 1), Moderate/Specific Regenerators (Clusters 2 and 3), and Non-Regenerators (Cluster 4). On S1 medium, NO-SU-12 and AS-1-PR showed superior shoot regeneration, while on R4 medium, HA-26-PR-SU and NO-SU-12 performed best. Genotypes such as NO-SU-12 and AS-1-PR consistently excelled across both media, whereas AB-OR-8 and FE-7 remained non-regenerators. Medium R4 supported superior regeneration, primarily through root formation, while S1 failed to induce roots in any genotype, highlighting the importance of hormonal composition. Although sucrose promoted callus induction, it did not trigger organogenesis. Callus was consistently present across media and time points, but its correlations with shoot and root formation were weak and temporally unstable, limiting its predictive value. Root formation at 14 days (Root 14D) emerged as a robust early predictor of organogenic success. This integration of morphological, temporal, and statistical analyses offers a genotype-tailored regeneration framework with direct applications in molecular breeding and CRISPR/Cas-based genome editing. Full article

Melasma management is challenged by heterogeneity in patient presentation, particularly among individuals with darker skin tones. This study applied k-means clustering, an unsupervised machine learning algorithm that partitions data into k distinct clusters based on feature similarity, to identify patient subgroups that could provide a hypothesis-generating framework for future precision strategies. We analysed clinical and demographic data from 150 South African women with melasma using k-means clustering. The optimal number of clusters was determined using the Elbow Method and Bayesian Information Criterion (BIC), with t-distributed stochastic neighbour embedding (t-SNE) visualization for assessment. The k-Means algorithm identified seven exploratory patient clusters explaining 52.6% of the data variability (R² = 0.526), with model evaluation metrics including BIC = 951.630 indicating optimal model fit and a Silhouette Score of 0.200 suggesting limited separation between clusters consistent with overlapping clinical phenotypes, while the Calinski-Harabasz index of 26.422 confirmed relatively well-defined clusters that were characterized by distinct profiles including “The Moderately Sun Exposed Young Women”, “Elderly Women with Long-Term Melasma”, and “Younger Women with Severe Melasma”, with key differentiators being age distribution and menopausal status, melasma severity and duration patterns, sun exposure behaviours, and quality of life impact profiles that collectively define the unique clinical characteristics of each subgroup. This study demonstrates how machine learning can identify clinically relevant patient subgroups in melasma. Aligning interventions with the characteristics of specific clusters can potentially improve treatment efficacy. Full article

Control of elbow exoskeletons using muscular signals, although promising for the rehabilitation of millions of patients, has not yet been widely commercialized due to challenges in real-time intention estimation and management of dynamic uncertainties. From a practical perspective, millions of patients with stroke, spinal cord injury, or neuromuscular disorders annually require active rehabilitation, and elbow exoskeletons with precise and safe motion intention tracking capabilities can restore functional independence, reduce muscle atrophy, and lower treatment costs. In this research, an intelligent control framework was developed for an elbow joint exoskeleton, designed with the aim of precise and safe real-time tracking of the user’s motion intention. The proposed framework consists of two main stages: (a) real-time estimation of desired joint angle (as a proxy for movement intention) from High-Density Surface Electromyography (HD-sEMG) signals using an LSTM network and (b) implementation and comparison of three PID, impedance, and sliding mode controllers. A public EMG dataset including signals from 12 healthy individuals in four isometric tasks (flexion, extension, pronation, supination) and three effort levels (10, 30, 50 percent MVC) is utilized. After comprehensive preprocessing (Butterworth filter, 50 Hz notch, removal of faulty channels) and extraction of 13 time-domain features with 99 percent overlapping windows, the LSTM network with optimal architecture (128 units, Dropout, batch normalization) is trained. The model attained an RMSE of 0.630 Nm, R² of 0.965, and a Pearson correlation of 0.985 for the full dataset, indicating a 47% improvement in R² relative to traditional statistical approaches, where EMG is converted to desired angle via joint stiffness. An assessment of 12 motion–effort combinations reveals that the sliding mode controller consistently surpassed the alternatives, achieving the minimal tracking errors (average RMSE = 0.21 Nm, R² ≈ 0.96) and showing superior resilience across all tasks and effort levels. The impedance controller demonstrates superior performance in flexion/extension (average RMSE ≈ 0.22 Nm, R² > 0.94) but experiences moderate deterioration in pronation/supination under increased loads, while the classical PID controller shows significant errors (RMSE reaching 17.24 Nm, negative R² in multiple scenarios) and so it is inappropriate for direct myoelectric control. The proposed LSTM–sliding mode hybrid architecture shows exceptional accuracy, robustness, and transparency in real-time intention monitoring, demonstrating promising performance in offline simulation, with potential for real-time clinical applications pending hardware validation for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications. Full article

Background: Immersive virtual reality (VR) has emerged as a promising tool to enhance neuroplasticity, motivation, and engagement during post-stroke motor rehabilitation. However, evidence on its feasibility and data-driven integration into clinical practice remains limited. Objective: This pilot study aimed to evaluate the feasibility, usability, and short-term motor outcomes of an immersive VR-assisted rehabilitation program using the Travee-VR system. Methods: Fourteen adults with post-stroke upper-limb paresis completed a 10-day hybrid rehabilitation program combining conventional therapy with immersive VR sessions. Feasibility and tolerability were assessed through adherence, adverse events, the System Usability Scale (SUS), and the Simulator Sickness Questionnaire (SSQ). Motor outcomes included active and passive range of motion (AROM, PROM) and a derived GAP index (PROM–AROM). Correlations between clinical changes and in-game performance metrics were explored to identify potential digital performance metrics of recovery. Results: All participants completed the program without adverse events. Usability was rated as high (mean SUS = 79 ± 11.3), and cybersickness remained mild (SSQ < 40). Significant improvements were observed in shoulder abduction (+7.3°, p < 0.01) and elbow flexion (+5.8°, p < 0.05), with moderate-to-large effect sizes. Performance gains in the Fire and Fruits games correlated with clinical improvement in shoulder AROM (ρ = 0.45, p = 0.041). Cluster analysis identified distinct responder profiles, reflecting individual variability in neuroplastic adaptation. Conclusions: The Travee-VR system proved feasible, well tolerated, and associated with measurable short-term improvements in upper-limb function. By linking clinical outcomes with real-time kinematic data, this study supports the role of immersive, feedback-driven VR as a catalyst for data-informed neuroplastic recovery. These results lay the groundwork for adaptive, clinic-to-home rehabilitation models integrating clinical and exploratory digital performance metrics. Full article

Background/Objectives: Combat trauma involving large joints is associated with a high risk of thromboinflammatory complications. Early identification of laboratory markers for hypercoagulability is essential to optimise perioperative management. This study aimed to evaluate the dynamics of inflammation and haemostasis indicators in patients with combat-related joint trauma and to identify the most informative markers for preoperative risk assessment. Methods: A total of 29 patients with combat injuries to the hip, knee, elbow, or ankle joints were examined. Blood samples were taken 1–3 days prior to surgery and again on the first postoperative day. Parameters of coagulation (e.g., PT, INR, fibrinogen, D-dimer, soluble fibrin complexes, antithrombin III), fibrinolysis, and inflammation (e.g., CRP, haptoglobin, sialic acid, ESR, LSI, LII) were analysed and compared to those of 30 healthy controls. Statistical analysis included Student’s t-test and Pearson’s correlation. Results: At baseline, patients demonstrated significant increases in inflammatory markers (CRP 64.2 ± 7.3 mg/L, ↑738.9%; haptoglobin 3.25 ± 0.4 g/L, ↑164.3%; ESR 46.8 ± 5.2 mm/h, ↑313.8%) and procoagulant activity (D-dimer 1.42 ± 0.18 µg/mL, ↑136.6%; fibrinogen 6.12 ± 0.51 g/L, ↑102.4%; soluble fibrin complexes 38.7 ± 4.9 mg/L, ↑597.3%), together with a reduction in antithrombin III activity (63.5 ± 6.2%, ↓39.5%) and prolonged fibrinolysis time (increase by 197%). Postoperatively, these abnormalities intensified, indicating a sustained thromboinflammatory response. Strong correlations were found between inflammatory and haemostatic markers. Conclusions: Combat trauma of large joints is associated with preoperative thromboinflammatory dysregulation, which is exacerbated by surgery. Monitoring specific biochemical and haematological markers—such as CRP, fibrinogen, D-dimer, and soluble fibrin complexes—may support preoperative risk assessment and postoperative monitoring strategies for hypercoagulable states in this high-risk group. These findings lay the groundwork for future prospective studies aimed at developing stratified therapeutic protocols and predictive models for thromboinflammatory complications in orthopaedic trauma care. Full article

The increasing aging population and the rise in sports injuries have led to greater demand for elbow function rehabilitation and daily assistance. To address the limitations of traditional rigid rehabilitation aids and existing flexible assistive systems, this paper designs a wearable elbow-assist robot that arranges pneumatic muscles based on the distribution of human elbow muscles. By integrating bionic design, experimental research, and mathematical modeling, the proposed approach determines the optimal scheme through comparative experiments on material structures and provides supporting data, while the mathematical model describes the force characteristics of the pneumatic muscles. Final experiments verify that the system can effectively assist elbow movement and significantly enhance flexion torque. Full article

Background/Objectives: Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. Methods: Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. Results: When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R² = 0.56, p = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R² = 0.56, p = 0.01). Conclusions: Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies. Full article

Erosion wear represents a significant issue in piping systems across energy and chemical industries, particularly in elbows. This study develops a prediction model for erosion wear based on tangential and normal impact energy for elbow tubes fabricated from zinc oxide-modified bidirectional E-glass fiber-reinforced epoxy resin composites (ZnO-BE-GFRP). Using a combined CFD-DEM approach, the wear characteristics under gas–solid two-phase flow conditions were systematically investigated. The model quantifies the contributions of tangential and normal impact energy to material removal through the specific energy for cutting wear (e_t) and the specific energy for deformation wear (e_n), with key parameters calibrated against experimental data from ZnO-BE-GFRP. This study shows that the increase in gas velocity significantly intensifies wear, and the wear area extends towards the middle of the elbow as the gas velocity increases. The 40–45° area of the elbow is a high-risk wear zone due to the concentration of particle kinetic energy and high-frequency collisions. The particle size distribution has a significant impact on wear: as the degree of particle dispersion increases, the wear on the elbow extrados decreases. Full article

Background: Disasters generate abrupt surges in humanitarian demand, requiring response strategies that balance operational performance with vulnerability considerations. This study examines how temporary Points of Distribution (PODs) can be planned and activated to support timely and equitable resource distribution after a high-magnitude earthquake. Methods: A two-stage framework is proposed. First, a modular p-median model identifies POD locations and allocates modular capacity to minimize population-weighted distance under capacity constraints; travel-distance percentiles guide the selection of p. Second, a SMART-based multi-criteria model ranks facilities using operational metrics and vulnerability indicators, including seismic and economic conditions and the presence of at-risk groups. Results: Evaluation of p values from 3 to 30 shows substantial reductions in travel distances as PODs increase, with an elbow at $p=12$, where 50% of the residents are within 500 m, 75% within 675 m, and 95% within 1200 m. The SMART analysis forms three priority clusters: facilities 24 and 9 as highest priority; 23, 4, 12, and 22 as medium priority; and the remaining sites as lower priority. Sensitivity analysis shows that rankings are responsive to vulnerability weights, although clusters remain stable. Conclusions: The framework integrates optimization and multi-criteria decision analysis without increasing model complexity, enabling meaningful decision-maker involvement throughout the modeling process. Full article

Background/Objectives: Elbow fractures are the most common injuries in children and are frequently evaluated with plain radiographs in the acute setting. As dedicated pediatric radiology services are not widely available, diagnosis of fractures could be delayed. Since 2023, ChatGPT-4 has offered image analysis capabilities, which has untapped potential for radiographic analysis. This study represents the first evaluation of ChatGPT-4o, a multimodal large language model, in interpreting pediatric elbow radiographs for fracture detection, thereby demonstrating its potential as a generalist AI tool distinct from domain-specific pediatric models. Methods: A curated set of 200 pediatric elbow radiographs (100 normal, 100 abnormal with at least one fracture site, 105 right elbow, and 95 left elbow radiographs) acquired between October 2023 and March 2024 at a tertiary pediatric hospital were analyzed in this case–control study. Each anonymized radiograph was evaluated by ChatGPT-4o via a standardized prompt. ChatGPT-4o’s prediction outputs (fracture vs. no fracture) were subsequently compared against verified radiology reports (ground-truth). Diagnostic performance metrics such as sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score were calculated. Results: ChatGPT-4o achieved an overall accuracy of 85% in detecting elbow fractures on pediatric radiographs, with a sensitivity of 87% and specificity of 82%. PPVs and NPVs were 83% and 86%, respectively. The F1 score was 0.85. ChatGPT-4o correctly identified the fracture site in 68 (78%) of the 87 studies in which it had detected fractures accurately. Cohen’s kappa coefficient was 0.69, indicating substantial agreement with actual diagnoses. Conclusions: This study highlights the utility and potential applications of ChatGPT-4o as a valuable point-of-care tool in aiding the detection of pediatric elbow fractures in emergency settings, particularly where specialist access is limited. Full article

The high cost of wind turbine maintenance has intensified the need for reliable fault detection and condition monitoring methods. While Supervisory Control and Data Acquisition (SCADA) systems provide valuable operational data, the raw signals often contain noise, outliers, and missing or redundant entries, which can compromise analysis accuracy. This study presents a novel cluster-based outlier removal approach for SCADA data preprocessing, featuring a unique flexibility to include or exclude negative power values—a factor rarely investigated but potentially critical for fault detection performance. The method applies the K-Means++ unsupervised clustering algorithm to group data points along the wind speed–power curve. The number of clusters is determined heuristically using the elbow method, while outliers are identified through Mahalanobis distance with thresholds derived from Chebyshev’s inequality theorem. The approach was validated using SCADA data from a wind farm in Portugal and further assessed with a CUSUM test-based structural change detection method to study how preprocessing choices—outlier thresholds (5% vs. 1%) and inclusion/exclusion of negative power values—affect early fault identification. Results demonstrate reliable fault detection up to 14 days before failure, retaining over 99% of the original dataset. This work provides key insights into preprocessing impacts on model reliability and offers an open-source Python implementation for reproducibility. Full article

Upper limb disabilities resulting from stroke affect millions worldwide, yet current rehabilitation systems face limitations in portability, cost-effectiveness, and multi-joint integration. This study presents a cable-driven parallel exoskeleton integrating elbow, wrist, and finger assistance into a single portable device. The design strategically separates actuation components, housing all motors in a backpack unit, while limb-mounted modules serve as cable routing guides, achieving seven degrees of freedom within practical constraints of portability (1.2–1.5 kg) and cost-effectiveness (3D-printed components). The device incorporates seven servo motors controlled via Arduino with IMU feedback and PID algorithms. Kinematic and dynamic analyses informed mechanical design, while ARMAX system identification enabled controller optimization achieving 87.96% model fit. Experimental validation with eight healthy participants performing four upper limb exercises demonstrated consistent trends toward reduced activation in four monitored agonist muscles with exoskeleton assistance (21.3% average reduction, p = 0.087), with moderate effect sizes for proximal muscles (Cohen’s d = 0.70–0.79) and significant reductions in brachioradialis during radial/ulnar deviation (23.4%, p = 0.045). These findings provide preliminary evidence of the device’s potential to reduce muscular effort during assisted movements, warranting further clinical validation with patient populations. Full article

Background/Objectives: Hajj is a physically demanding mass gathering that presents distinct health risks, particularly for older adults and individuals with comorbidities. Falls are a major cause of injury in such environments; however, limited data exist on their prevalence and determinants during Hajj. This study aimed to (1) estimate the prevalence of falls among adult pilgrims during the Hajj pilgrimage in Saudi Arabia and (2) identify key demographic, behavioural/clinical, and musculoskeletal predictors of fall risk, stratified by age group. Methods: A cross-sectional survey was conducted among 1429 adult pilgrims. Data were collected at major pilgrimage sites in Mecca during the Hajj season. Variables included age, sex, body mass index, smoking status, hypertension, diabetes, physical exhaustion, and musculoskeletal pain. Bivariate chi-square tests and multivariable regression analyses were performed. Age-stratified models were developed for younger adults (≤29 years) and older adults (≥50 years) to account for physiological differences. Results: The overall fall prevalence was 13.6%, with significantly higher rates among older adults (21%) than younger adults (10.5%). In the full sample, independent predictors of falls included older age, obesity, hypertension, diabetes, physical exhaustion, and musculoskeletal pain in the upper arm, elbow, and hip/pelvis. In age-specific models, obesity, physical exhaustion, and upper arm pain predicted falls among younger adults, while obesity, hypertension, physical exhaustion, and hip/pelvis pain were significant among older adults. Conclusions: Falls during Hajj result from a multifactorial interplay of age, comorbidities, fatigue, and site-specific musculoskeletal pain. These findings support the development of targeted, age-specific fall prevention strategies in mass gathering contexts. Full article