Search Results (1,014)

Electromyography (EMG)-based sensing is an important tool for assessing neuromuscular activity and monitoring athlete development; its reliability depends on electrode placement, signal quality, and accurate identification of muscle activation periods. This study proposes an intelligent EMG sensing framework integrating preliminary electrode placement assessment, muscle activity detection, feature extraction, and regression-based progression prediction. A placement assessment indicated that positioning the electrode adjacent to the innervation zone produced the highest RMS under the tested conditions. A two-stage activity detection method based on clustering and probabilistic modeling achieved an average error of 1.5% and a temporal deviation of 19 ms. Nine time-domain EMG features extracted from the detected activity segments were used to characterize athlete progression and estimate the time required to reach a reference neuromuscular profile. Among the methods, Linear Regression provided the best fit to the data, obtaining R² = 0.987 and RMSE = 4.21 and suggesting a predominantly linear relationship between the EMG-derived features and training duration within the dataset. However, these results were obtained from only six longitudinal observation periods for a single representative athlete, with each period represented by a 90-dimensional EMG feature vector derived from the ten movement classes. Therefore, the results should be interpreted as preliminary, athlete-specific goodness-of-fit findings rather than evidence of generalizable predictive performance. Validation using larger longitudinal cohorts and independent datasets is required. The proposed framework is compatible with future IoT-enabled wearable and edge-computing architectures; however, hardware-level implementation was beyond the scope of this study. Full article

Geopolitical disruptions of natural gas supply have repeatedly forced importing countries to reorganize their electricity systems, yet it remains unclear whether such disruptions are followed by movement of structurally different economies toward comparable energy-security and sustainability outcomes. This study compares Germany, a high-income economy exposed to the 2022 curtailment of Russian pipeline gas, with Jordan, a developing import-dependent economy exposed to the repeated sabotage of the Arab Gas Pipeline after 2011, using harmonized generation-mix and carbon intensity data for Germany over 1985–2024 and Jordan over 2000–2022, supplemented by weekly German market data. The generation fuel mix concentration is measured with a Herfindahl-based Supply Concentration Index (SCI), structural change is estimated with segmented interrupted time series (ITS) regressions inferred through Newey–West heteroskedasticity- and autocorrelation-consistent standard errors, and the joint security–sustainability position of each country is summarized with a newly proposed Energy Vulnerability–Transition Index (EVTI) that combines diversification, renewable penetration, and carbon intensity performance. The results show that Jordan’s 2011 disruption was associated with a baseline estimated change in its carbon intensity trajectory from +3.32 to −12.63 gCO₂/kWh per year and with renewable growth of +2.39 percentage points per year from a near-zero base, while Germany’s 2022 disruption was associated with a temporary carbon intensity shock, visible in a coal reactivation index that peaked at 1.26 and a sixfold wholesale price increase, followed by a policy-supported return to the pre-existing decarbonization pathway. The Germany–Jordan EVTI ratio narrowed from 6.5× in 2014 to 1.8× in 2022, and this convergence is robust to alternative component weightings. The findings indicate that geopolitical gas disruptions, despite their high short-run costs, were followed in both contexts by measurable movement toward more diversified and lower-carbon electricity systems, with direct implications for Sustainable Development Goal (SDG) 7. Full article

In recent years, advances in exoskeleton technology have increased the demand for human motion classification in terms of both movement diversity and recognition accuracy, making the effective classification of more complex asymmetric movements increasingly important. This study integrated surface electromyography (sEMG) and inertial measurement unit (IMU) signals and employed mutual information (MI) and muscle symmetry features (MSF) to analyze the characteristic differences between symmetrical muscles in both legs during asymmetric movements, with the aim of improving the accuracy of asymmetric motion classification. sEMG and IMU signals were collected from six movements, including three asymmetric postures: asymmetrical stance, single-knee kneeled position, and crouching advance. The acquired signals were processed through energy envelope analysis, active segment extraction, empirical mode decomposition (EMD), feature extraction, MI extraction, and MSF extraction. The relevance based on weight feature selection (RWFS) combined with conditional mutual information (CMI) method was then applied to reduce feature dimensionality, prioritize features with significant fluctuations, and preserve key characteristics. Finally, the CNN-LSTM-Attention algorithm was used for classification. Experimental results showed that fusing sEMG and IMU signals achieved 96.30% accuracy in lower limb motion recognition. The proposed method improves asymmetric movement classification and may provide a potential basis for exoskeleton motion classification in special environments. Full article

Targeting the rock burst hazard induced by the hard roof key stratum during deep mining at the Mengcun Coal Mine in the Binchang mining area, this study takes the No. 403109 working face as the engineering background and systematically investigates the rockburst prevention mechanism and effectiveness of ground hydraulic fracturing through theoretical analysis, UDEC numerical simulation, and surface microseismic monitoring. The results indicate that fracturing pre-weakens the overlying key stratum, transforming its load-bearing mode from a long-beam rigid support to a segmented flexible support. This significantly reduces the cantilever length, lowers the accumulation of elastic strain energy, and enables flexible load transfer and stress redistribution in the overburden. Numerical simulations reveal that after fracturing, the breakage timing of the key stratum advances, the fragmentation size decreases, and the over-burden movement shifts from stepwise fracturing to sequential caving, with the stress concentration zone substantially narrowed. In the field, a total of 44 fracturing stages were implemented in wells MC-05L and MC-06L, creating a fracture network with an average fracture length of 317 m and an average fracture height of 55 m, achieving an effective stimulated volume ratio of 86.7%. During the mining period, microseismic events exhibited a median energy of only 868.14 J, characterized by high frequency and low energy. The average weighting interval was 13.69 m, the peak coal stress was controlled within 5.0–6.7 MPa, and the loads on roadway bolts and cables remained within safe limits. This study validates the source-control effect of ground hydraulic fracturing on working faces with strong rock burst risks in deep mining, providing a theoretical basis and engineering reference for mines with analogous conditions. Full article

Vietnam’s domestic gold price has persistently exceeded the world price by a wide margin, even as recent reforms have begun to relax the state’s historical monopoly over gold-bar production and imports. This paper asks why the gap persists, and whether it is confined to normal market conditions or extends into periods of extreme price movement. Using daily data spanning 2 January 2019 to 31 July 2026 (1856 trading days), covering the reform introduced by Decree No. 232/2025/ND-CP we decompose the domestic premium into a currency component and a pure physical-gold component, and use a copula-based framework to separately assess average price linkage and tail (extreme-event) co-movement between the domestic and world markets. Domestic gold bars traded at an average premium of 16.0% over import-parity world prices, of which 13.8 percentage points reflect the physical-gold component driven by constrained arbitrage, while currency factors account for only about 2 percentage points. The average linkage between the two markets is weak, indicating persistent segmentation, and this segmentation extends into the tails of the distribution for most of the sample. The premium itself carries substantial latent risk: a reversion to price parity would imply a one-off loss of about 9.6% of value, roughly eight to ten times the historical one-day 5% Value-at-Risk. Following the reform’s effective date, however, we find early evidence of emerging co-movement specifically in extreme upside price movements, even though the physical premium itself has not yet narrowed—consistent with a reform that has been enacted in law but remains at an early stage of operational implementation. The results indicate that administrative restrictions on the physical gold supply chain, rather than currency controls, are the principal source of Vietnam’s persistent gold-price gap, with direct implications for how the ongoing liberalization process should be sequenced. Full article

Background: Facial nerve regeneration across segmental defects remains challenging despite advances in nerve guidance conduits. Tissue engineering strategies that combine biomaterial scaffolds with bioactive and cellular components may enhance functional and structural nerve repair. This study investigated the regenerative efficacy of a cellulose-based nerve conduit augmented with Matrigel, mesenchymal stem cells (MSCs), and placental extract (PE) in a rat facial nerve gap model. Methods: A 2 mm defect was created at the main trunk of the facial nerve in adult Sprague–Dawley rats. Animals were divided into three groups: control group (Cellulose/Matrigel), group I (Cellulose/Matrigel/MSC), and group II (Cellulose/Matrigel/MSC/PE). Functional recovery was assessed at 2, 4, 6, and 8 weeks postoperatively using slow-motion vibrissa movement analysis, electrically stimulated facial nerve action potential measurements, and facial nerve blood flow by laser Doppler blood flow analysis. Morphological regeneration was evaluated using light microscopy and transmission electron microscopy. Western blotting using facial muscle was performed. Results: All groups exhibited time-dependent facial nerve regeneration. However, group II demonstrated significantly enhanced functional recovery, greater electrophysiological responses, increased nerve blood flow, and superior histological and ultrastructural regeneration compared with the other groups. These improvements were particularly prominent at 6 and 8 weeks postoperatively. Conclusions: The combination of mesenchymal stem cells and PE within a cellulose-based nerve conduit synergistically enhanced facial nerve regeneration. This tissue-engineered strategy may represent an effective approach for improving peripheral nerve repair. Full article

AIS trajectories in and around ports are often incomplete because of transmission interruptions, reception failures, and infrastructure outages. In this paper, we investigate whether the Data-driven AIS Trajectory Interpolation method (DAISTIN), which reconstructs missing trajectory segments using a graph derived from historical AIS observations, can be improved by retaining local directional information that is otherwise discarded when observations are sampled to form the graph. To this end, we introduce two extensions, xDAISTIN and xDAISTOUT. The originality of the proposed approach lies in extending DAISTIN by enriching each sampled graph node with neighbourhood-level directional information from nearby historical AIS observations. Specifically, local vessel headings are encoded cyclically and modelled using kernel density estimators, allowing candidate graph transitions to be evaluated using local movement probabilities rather than only the heading of the sampled point. The methods are evaluated on real-world AIS data from the Port of Antwerp by introducing synthetic gaps into held-out trajectories and measuring how well they are reconstructed by the methods. Using 36,748 moving sub-trajectories from 5488 vessel trips, we compare the proposed methods against DAISTIN, linear interpolation, and several directed, undirected, probabilistic, and shortest-path graph variants. Across graph sizes from 100 k to 180 k nodes, DAISTIN and its undirected variant reconstructed $48.48$–$84.61\%$ and $72.35$–$91.23\%$ of missing segments, respectively, compared to $88.02$–$96.42\%$ and $91.36$–$99.13\%$ for xDAISTIN and its variants, and $65.75$–$85.17\%$ and $72.12$–$97.80\%$ for xDAISTOUT and its variants. Compared with undirected DAISTIN at 180 k nodes, undirected xDAISTIN at 180 k nodes achieved significantly lower mean SPD, DFD, and HD (all Holm-adjusted, $p<0.001$), by approximately $14\%$, $43\%$, and $36\%$, respectively. These findings demonstrate that preserving neighbourhood-level directional behaviour during graph construction can substantially improve the interpolation completion rate and reconstruction accuracy in complex port environments. Full article

Understanding which segments of the economy drive aggregate stock market movements is central to risk management. This study traces how the economic drivers of the Standard & Poor’s 500 Index (S&P 500) changed across two episodes: the 2020 COVID-19 shock and the 2025 technology-led period, using eleven sector indices and nine deep learning architectures. During COVID-19, forecasting power concentrated in Consumer Discretionary, Health Care, and Industrials before reorganizing sharply around Information Technology, consistent with a disruptive break. In 2025, Information Technology and market momentum dominated throughout, with no comparable reorganization, consistent with a gradual adjustment rather than a disruptive shift. This distinction, invisible from accuracy metrics alone (Gated Recurrent Unit: Mean Absolute Percentage Error = 3.41% and 2.16%), shows that information-concentration diagnostics can complement forecast-accuracy and risk-monitoring frameworks. Full article

The Timed Up and Go (TUG) test is widely used to assess functional mobility in Parkinson’s disease (PD), although total test duration may overlook phase-specific biomechanical alterations. This cross-sectional study investigated whether phase-specific analysis of the instrumented TUG (iTUG) could identify candidate biomechanical markers characterizing differences among early-stage PD subgroups and healthy controls. Seventy-nine participants (38 PD, 41 controls) performed four iTUG trials in the OFF-medication state. Movement data were collected using synchronized inertial measurement units and optoelectronic motion capture systems. The iTUG was segmented into six phases, and temporal, spatiotemporal, variability, and multisegmental kinematic parameters were analyzed. Participants with PD at modified Hoehn and Yahr (mH&Y) stage 2 performed the iTUG more slowly than controls (p < 0.001), mainly due to impairments during walking (p = 0.012) and turning. Turning was the only phase that distinguished controls from individuals with PD at mH&Y stages 1–1.5 (p = 0.015), who also showed increased elbow asymmetry (p = 0.009). Participants with PD at mH&Y stage 2 exhibited broader differences, including increased double-support time, shorter stride length, lower walking speed, reduced frontal-plane control, and reduced trunk, elbow, hip, and ankle motion, whereas gait variability did not differ significantly between groups. Sagittal trunk ROM on the less affected side was the only variable that significantly differed between the two PD subgroups. These findings suggest that phase-specific iTUG analysis may reveal candidate biomechanical markers associated with mH&Y stage and functional mobility impairment and support sensor-based assessment for objective characterization of functional mobility in PD. Full article

Accurate 3D semantic perception is essential for robotic operations in multi-tier caged-pigeon houses, where dense cage-mounted facilities, repetitive layouts, local geometric similarity, and dynamic objects make static-facility segmentation difficult. To address these issues, this study proposes GSR-PointNet++, a point cloud semantic segmentation method with dynamic suppression. A Hierarchical Dynamic Suppression Front-End (HDS-Front) is introduced before static map construction to reduce dynamic interference caused by pigeon activity and human movement. Based on the processed static point clouds, a semantic segmentation dataset for multi-tier caged-pigeon houses is constructed. GSR-PointNet++ is developed on the PointNet++ backbone and incorporates a Geometric Structure-Relationship Module (GSRM), which models local normal consistency and spatial relationships to enhance feature representation in structurally repetitive scenes. Geometry-aware Prototype Consistency Learning (GPCL) is further introduced during training to improve class separability and prediction stability. Independent testing on the annotated dataset collected from the second pigeon house showed that the proposed method achieved a mean intersection over union (mIoU) of 96.29% ± 0.25% and an overall accuracy (OA) of 98.62% ± 0.13% for the four target facility classes, outperforming PointNet++ by 5.92 and 2.54 percentage points, respectively. These results indicate that the proposed method improves point cloud semantic segmentation for static facilities and has potential to support robotic perception in structured livestock environments. Full article

Accelerating the construction of an international, modern, and people-oriented city, Suzhou often faces bottlenecks in its peripheral communities. These open spaces suffer from rapid vitality decline, extensive governance, and weak resilience, which directly hinder residents’ daily interactions and community integration. Moving beyond subjective qualitative approaches, this study selects typical peripheral communities in Suzhou and employs a comprehensive methodology combining field surveys, space syntax analysis, and time-segmented spatial trajectory entropy measurement to quantitatively assess the “lack of vitality.” By quantifying spatial form, accessibility, and behavioral patterns, the study examines their coupling relationships with vitality, social interaction, and safety resilience. The results show that the spatial trajectory entropy model effectively captures the spatiotemporal movement patterns of crowds, addressing the gap in dynamic characterization left by traditional methods. Community vitality is significantly positively correlated with accessibility, interface continuity, and facility allocation. These spatial elements also play a crucial role in promoting social interaction and enhancing the sustainable use of spaces. Based on quantitative diagnosis, this paper proposes a governance framework of “digital empowerment–community co-governance–resilience enhancement,” transforming spatial weaknesses into targeted strategies for smart governance. This research provides quantitative analysis methods and localized implementation approaches for the refined governance and sustainable development of communities on the outskirts of cities like Suzhou. Full article

The currently common procedures in orthognathic surgery are widely used. Different forms of skeletal malocclusion can be treated with great outcomes in terms of bite, bone harmony, function, improved mastication, esthetics, and even decreased problems with breathing. Each type and intensity of malocclusion may affect decision-making in terms of distraction, selection of osteotomy design, or even guide clinical and surgical decisions to use a less popular osteotomy protocol. CBCT (cone-beam computed tomography) is currently a milestone in the establishment of the condition of jaw bones, bone structure, temporomandibular joints, and central relation (CR), as well as the scope of maxilla–mandibular and tooth relations. When establishing all of the following in a clinical and radiological (CBCT-based) evaluation, it becomes possible to measure the occlusal plane, height, length, and three-dimensional position of each dental segment, as well as other dentoalveolar features. In the present case, CBCT helped distinguish a localized vertical anterior mandibular dentoalveolar discrepancy from generalized mandibular malposition and supported the planning of a mandibular anterior segmental subapical osteotomy (MASSO) by mapping the osteotomy corridor in relation to the dental roots, cortical bone, periapical bone height, and neurovascular structures. Significant therapeutic complexity emerges when severe skeletal Class III malocclusion (mandibular prognathism) is compounded by transverse maxillary discrepancies and a markedly elevated mandibular anterior segment. This constellation of deformities leads to profound disturbances in the anterior facial height, a disrupted maxillomandibular interincisal relationship, and the loss of a level occlusal plane. The presented case highlights how the patient’s clinical evaluation should be carefully compared with radiological data, status of both cortices in the mandible, the height of the mandibular anterior segment, presence of gum recessions and poor bone status that may affect healing, osteotomies, and occurrence of some unwanted events. Therefore, CBCT was useful not only for diagnosis and planning, but also for understanding the biological limits of movement and the risk of postoperative periodontal complications in this uncommon reversed Köle-type MASSO. Full article

Objectives: Spinal tumors often require complex surgical treatments, in particular en bloc vertebrectomy, and a pre-operative kinematics and functional assessment of the spine could be advised to improve surgical outcomes and patients’ quality of life. However, established protocols for stereophotogrammetry-based gait analysis are not available. This study aims at evaluating the feasibility of an experimental protocol for multi-segment trunk and spine kinematics in patients scheduled for en bloc vertebrectomy. Methods: Ten patients and ten age- and sex-matched healthy controls underwent full-body gait analysis using an established marker-set able to track the lower limbs, pelvis, thorax, shoulders, and five spine line segments. The participants completed static double leg standing, barefoot level walking, and elementary trunk movements. A preliminary within-session repeatability assessment in two individuals (one representative patient and one healthy participant) was performed (root mean square error smaller than 10% of ROM; intra-class correlation coefficients > 0.8). Results: Compared with controls, patients affected by spinal tumors showed slower gait, shorter stride length, smaller ROM at the pelvis and thorax, and reduced frontal-plane mobility between spine line segments, but physiological lower-limb joint kinematics. During elementary movements, patients exhibited reduced trunk ROM in the anatomical plane of motion, particularly at a central spine segment, and smaller pelvic mobility in axial rotations. Conclusions: The results of this study support the feasibility of the proposed protocol for gait analysis in the rare clinical population of patients affected by spinal tumors and provide descriptive functional information that may complement conventional pre-operative clinical and imaging assessments. Full article

The aim of the study is to compare changes in physical fitness in children aged 7–10 training in two martial arts: Korean International Taekwon-Do Federation (ITF) Taekwon-do and Japanese Kyokushin Karate. A group of 163 children (51.5% boys) aged 7–10 years (median-7.7 years (Q1-7.4, Q3-8.1)) was examined. The study participants were divided into: Training group (T = 55), group TT (n = 19, Taekwon-do), group TK (n = 36, Kyokushin Karate) and the control group (C), consisting of 108 children. The study was multi-stage: Stage I—an initial screening for group selection, height, weight, and segmental body composition, as well as physical fitness using selected EUROFIT tests. In Stage II, children from groups TT and TK completed a 12-week training programme, while group C did not change their physical activity routines. After the program was completed, all groups (TT, TK, C) repeated the tests from Stage I. In the group of children training Taekwon-do, positive changes were observed in the range of fitness tests, including flexibility, speed of hand movements and abdominal strength; the differences are statistically significant. In the group of children training Karate, positive, statistically insignificant changes were observed only in functional strength. Introducing martial arts elements into physical education classes can have a positive impact on improving the physical condition of children aged 7–10. Full article

High-risk landslide extraction from remote sensing imagery is a fundamental task for geological disaster prevention, emergency response, and land-use planning in mountainous regions. Although deep-learning semantic segmentation has substantially advanced landslide detection from optical imagery, existing methods still suffer from three critical limitations: (i) Interferometric Synthetic Aperture Radar (InSAR) deformation data are treated as auxiliary channels and dominated by optical features during fusion; (ii) predicted masks exhibit fragmented boundaries and incomplete delineation due to the absence of deformation continuity constraints reflecting the physical coherence of slope movements; and (iii) heavy Transformer backbones hinder practical deployment over large areas. To address these issues, we propose a Deformation-Integrity-Guided Symmetric Fusion Network (DIGSFNet) for high-risk landslide extraction from InSAR and optical imagery. The framework consists of three components: a Symmetric Deformation-Aware Encoder (SDAE) that treats InSAR and optical modalities as equal information sources through modality-aware adapters and dynamic sparse cross-modal fusion; a Deformation Integrity Prior Decoder (DIPD) that imposes deformation continuity and boundary-gradient consistency as physical priors to enforce mask completeness and boundary accuracy; and a Lightweight Deployable Student Network (LDSN) obtained via cross-modal knowledge distillation and INT8 quantization for efficient inference. Experiments on the Nanning High-Hazard Landslide Segmentation (Nanning-HHLS) dataset and the public HAEFNet benchmark covering the Qinghai–Tibet–Sichuan landslide-prone regions show that the full DIGSFNet achieves state-of-the-art extraction accuracy, reaching 83.57% and 78.92% mIoU on the two datasets and surpassing the strongest competing method by 2.63 and 3.68 percentage points with a Recall of 91.48% on Nanning-HHLS, while its distilled lightweight student retains 79.24% mIoU at 218 frames per second after INT8 quantization, enabling efficient large-area operational deployment. Full article