Search Results (3,189)

Background: This scoping review systematically maps and synthesises contemporary literature on the biomechanics of active below-knee prosthetic devices, focusing on gait kinematics, kinetics, energy expenditure, and muscle activation. It further evaluates design advancements, including powered ankle–foot prostheses and variable impedance systems, that seek to emulate physiological ankle function and enhance mobility outcomes for transtibial amputees. Methods: This review followed the PRISMA-ScR guidelines. A comprehensive literature search was conducted on ScienceDirect, PubMed and IEEE Xplore for studies published between 2013 and 2023. Search terms were structured according to the Population, Intervention, Comparator, and Outcome (PICO) framework. From 971 identified articles, 27 peer-reviewed studies were found to meet the inclusion criteria between January 2013 and December 2023. Data were extracted on biomechanical parameters, prosthetic design characteristics, and participant demographics to identify prevailing trends and research gaps. This scoping review was registered with Research Registry under the following registration number: reviewregistry 2055. Results: The reviewed studies demonstrate that active below-knee prosthetic systems substantially improve gait symmetry and ankle joint range of motion compared with passive devices. However, compensatory trunk and pelvic movements persist, indicating that full restoration of natural gait mechanics remains incomplete. Metabolic efficiency varied considerably across studies, influenced by device design, control strategies, and user adaptation. Notably, the literature exhibits a pronounced gender imbalance, with only 10.7% female participants, and a reliance on controlled laboratory conditions, limiting ecological validity. Conclusions: Active prosthetic technologies represent a significant advancement in lower-limb rehabilitation. Nevertheless, complete biomechanical normalisation has yet to be achieved. Future research should focus on long-term, real-world evaluations using larger, more diverse cohorts and adaptive technologies such as variable impedance actuators and multi-level control systems to reduce asymmetrical loading and optimise gait efficiency. Full article

This study compared the external [movement load (ML)] and internal [rating of perceived exertion (RPE), mean and peak heart rate (HRmean, HRpeak)] loads, performance and enjoyment between time-matched (~12 min) traditional (TRAD) and small-sided game (SSG) warm-ups in youth basketball players. Using a counterbalanced crossover design, 24 male players (16.0 ± 0.1 years) performed both warm-up types after reporting fatigue (ROF) and completing an 8 min standardized pre-warm-up. Before and after each warm-up, players completed 20 m sprint and countermovement jump (CMJ) tests; enjoyment (ENJ) was assessed post-warm-up. No significant differences were found between warm-ups for ROF (p = 0.053), RPE (p = 0.259), or HRmean (p = 0.053). However, SSG induced a higher HRpeak than TRAD (p = 0.001), while ML was greater in TRAD (p < 0.001). No interaction, time effect, or typology effect emerged for performance in sprinting and change of direction, although CMJ was higher after TRAD (p = 0.047). Enjoyment did not differ significantly (p = 0.066), although with a large effect size (r = 0.612). The greater ML in TRAD may reflect more dynamic basketball actions compared with SSG, which emphasized static tasks (e.g., screening, boxing out) yet produced higher HRpeak. Coaches may consider SSG warm-ups to replicate game-specific conditions while controlling the external load and maintaining adequate physiological preparation. Full article

Background/Objectives: Physical activity alleviates symptoms and may slow Parkinson’s disease (PD) progression, yet many individuals with PD remain sedentary. Kinesiophobia, the fear of movement, may represent a significant but underexplored psychological barrier to physical activity in this population. Virtual reality (VR), already effective in phobias, may represent a promising approach to address this challenge. This review initially aimed to systematically examine exposure-based interventions in VR (E-IVR) directly targeting kinesiophobia in PD. Methods: Database searches using keywords such as “kinesiophobia,” “fear of movement,” and “VR” combined with “PD” yielded no eligible studies. Consequently, the scope was broadened to include populations with neurological or musculoskeletal conditions, and a narrative review format was adopted to synthesize the available evidence. Furthermore, relevant studies of interventions in VR applied in PD, although not specifically addressing kinesiophobia, are detailed to provide evidence of efficacy and feasibility of VR interventions in PD. Finally, directions are offered to support the creation of E-IVR targeting kinesiophobia in individuals with PD. Results: Meta-analyses in neurological and musculoskeletal populations demonstrate moderate to large reductions in kinesiophobia following VR interventions, although effects vary depending on assessment tools, degree of immersion, and exposure design. In PD, VR has been applied to rehabilitation, anxiety reduction, and quality of life enhancement. These interventions achieved high adherence (≥90%), were well tolerated, and reported no major adverse events. Conclusions: Kinesiophobia is prevalent in PD and could contribute to physical inactivity. E-IVR appears feasible, safe, and innovative for addressing kinesiophobia in people living with PD. Full article

The proliferation of Internet of Things (IoT) devices in financial markets has created distributed ecosystems where cryptocurrency exchanges, trading platforms, and market data providers operate as autonomous edge nodes generating massive volumes of sensitive financial data. Collaborative machine learning across these distributed financial IoT nodes faces fundamental challenges: institutions possess valuable proprietary data but cannot share it directly due to competitive concerns, regulatory constraints, and trust management requirements in decentralized networks. This study presents a privacy-preserving federated learning framework tailored for distributed financial IoT systems, combining differential privacy with Shamir secret sharing to enable secure collaborative intelligence across blockchain-based cryptocurrency trading networks. We implement per-layer gradient clipping and Rényi differential privacy composition to minimize utility loss while maintaining formal privacy guarantees in edge computing scenarios. Using 5.6 million orderbook observations from 11 cryptocurrency pairs collected across distributed exchange nodes, we evaluate three data partitioning strategies simulating realistic heterogeneity patterns in financial IoT deployments. Our experiments reveal that federated edge learning imposes 9–15 percentage point accuracy degradation compared to centralized cloud processing, driven primarily by data distribution heterogeneity across autonomous nodes. Critically, adding differential privacy (ε = 3.0) and cryptographic secret sharing increases this degradation by less than 0.3 percentage points when mechanisms are calibrated appropriately for edge devices. The framework achieves 62–66.5% direction accuracy on cryptocurrency price movements, with confidence-based execution generating 71–137 basis points average profit per trade. These results demonstrate the practical viability of privacy-preserving collaborative intelligence for distributed financial IoT while identifying that the federated optimization gap dominates privacy mechanism costs. Our findings offer architectural insights for designing trustworthy distributed systems in blockchain-enabled financial IoT ecosystems. Full article

Background/Objectives: Differential learning (DL) amplifies natural fluctuations in movement execution and, in its more extreme forms, facilitates repetition-free training with minimal external feedback. While increasingly recognized in the field of skill acquisition, its application in anterior cruciate ligament (ACL) rehabilitation remains underexplored. Methods: This study examined the application of DL in the rehabilitation of an 18-year-old trained basketball player following left-ACL reconstruction. The athlete completed a 42-week rehabilitation program in which DL principles were incorporated throughout the pre-operative, early, mid-, and late phases, culminating in return to sport. Training included differential mobility work, motor control, plyometric exercises, and sport-specific drills. Functional recovery was evaluated using single-leg hop tests, change-of-direction tasks, and sprint performance, while self-reported knee function was monitored via the International Knee Documentation Committee (IKDC) questionnaire. Results: Results indicated substantial improvements in both functional performance and psychological readiness. The IKDC score increased from 13.8% at baseline to 95.4% postoperatively, reaching the normal functional range. An ACL-RSI score of 85.2%, and inter-limb asymmetries were reduced to below 10%. Strength, agility, and sprint performance exceeded pre-injury levels. Conclusions: DL again shows potential as an effective approach to facilitating recovery and return to sport after ACL reconstruction, but larger controlled studies are needed for validation. Full article

This paper presents a new approach to the dimensional synthesis of a robotic limb mechanism for a wheel-legged robot. The proposed kinematic structure enables independent control of wheel motions relative to the robot platform, allowing each drive to perform a distinct movement. The selection of the mechanism’s common dimensions simplifies platform levelling to a single-drive actuation. The hybrid limb design, which combines features of driving and walking systems, enhances platform stability on uneven terrain and is suitable for rescue, exploration, and inspection robots. The synthesis method defines the desired trajectory of the wheel centre and applies a genetic algorithm to determine mechanism dimensions that reproduce this motion. The stochastic optimisation process yields multiple feasible solutions, enabling the introduction of additional design criteria for optimal configuration selection. Analytical kinematic relations were developed for workspace and trajectory evaluation, solving both direct and inverse kinematic problems. The results confirm the effectiveness of evolutionary optimisation in synthesising complex kinematic mechanisms. The proposed approach can be adapted to other mobile robot structures. Future work will address dynamic modelling, adaptive control for real-time platform levelling, and comparative studies with other synthesis methods. Full article

This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability. Full article

Stack effects in high-rise buildings cause noise, drafts, and elevator door malfunctions during cold weather yet remain difficult to control. Because vertical shafts couple pressures between floors, local fixes at a single lobby can unintentionally disturb the pressure field elsewhere. To analyze these interactions, we developed a measurement-calibrated CONTAM multizone model of a 43-story office building and evaluated representative local countermeasures. Under base winter conditions, the pressure difference across the problematic first-floor high-rise elevator doors is 56 Pa, driving approximately 1300 CMH of airflow through the door line. First-floor depressurization reduces this to 34 Pa (about 30% lower airflow) but simultaneously increases the pressure at the main entrance doors from 19 to 39 Pa. Additional first-floor partitions slightly reduce pressures on upper high-rise floors, whereas opening exterior windows in the high-rise zone increases shaft airflow by 7.7% and further amplifies elevator door pressures. We show that neutral pressure level (NPL) shifts into vertical shafts are a key mechanism limiting the effectiveness of purely local interventions. These results demonstrate that effective countermeasures must be designed at the whole-building scale, jointly controlling pressure redistribution and neutral-pressure-level movement while directing unavoidable pressure transfer toward the exterior envelope and away from sensitive interior spaces. Full article

Background/Objectives: The pericapsular nerve group (PENG) block is increasingly used as part of multimodal analgesia for total hip arthroplasty (THA). However, standard high-volume local anesthetic regimens may impair motor function. The addition of perineural dexamethasone could allow for volume reduction while maintaining analgesic efficacy and minimizing quadriceps weakness. This study evaluated whether adding dexamethasone to a low-volume PENG block prolongs analgesia, reduces opioid consumption, maintains pain control, and preserves motor function compared to a standard-volume PENG block. Materials and Methods: In this randomized controlled trial (NCT06470334), 60 adult patients undergoing THA via the direct superior approach received either a standard-volume PENG block with 20 mL of 0.2% ropivacaine (PENG group) or a low-volume block with 10 mL of 0.2% ropivacaine plus 4 mg of perineural dexamethasone (PENG + DEX group). The primary outcome was time to first rescue opioid. Secondary outcomes included total 48-h opioid consumption (oral morphine equivalents), pain scores (numeric rating scale, NRS) at rest and during movement, and quadriceps muscle strength at predefined postoperative intervals. Results: The PENG + DEX group demonstrated a significantly longer time to first opioid administration (15.0 ± 1.5 h vs. 9.1 ± 1.7 h; p < 0.0001) and reduced total opioid consumption within 48 h (2.3 ± 3.1 mEQ vs. 5.0 ± 4.4 mEQ; p = 0.0120). Pain scores were similar at 4 h but significantly lower in the PENG + DEX group at 8, 12, and 24 h postoperatively (all p < 0.01). Quadriceps strength was fully preserved in both groups at all assessed timepoints (p > 0.9999). Conclusions: The addition of perineural dexamethasone to a low-volume PENG block provides longer-lasting analgesia and reduces opioid requirements without compromising pain control or quadriceps function. This approach may improve the safety and efficacy of regional anesthesia in THA. Full article

Movement artifact direction and magnitude are critical parameters in noise detection and image analysis, especially for single-frame images where temporal information is unavailable. This paper introduces the Movement Artifact Direction Estimation (MADE) algorithm, a signal processing-based approach that performs 3D geometric analysis to estimate both the direction (in degrees) and weighted quantity (in pixels) of movement artifacts. Motivated by computational challenges in medical image quality assessment systems such as LUIAS, this work investigates directional multiplicative noise characterization using controlled experimental conditions with optical camera imaging. The MADE algorithm operates on multi-directional quantification outputs from a preprocessing pipeline—MAPE, ROPE, and MAQ. The methodology is designed for computational efficiency and instantaneous processing, providing interpretable outputs. Experimental results using precision-controlled apparatus demonstrate robust estimation of movement artifact direction and magnitude across a range of image shapes and velocities, with principal outputs aligning closely to ground truth parameters. The proposed MADE algorithm offers a methodological proof of concept for movement artifact analysis in single-frame images, emphasizing both directional accuracy and quantitative assessment under controlled imaging conditions. Full article

This paper investigates the determinants and transmission channels underlying the synchronization between financial and business cycles across European Union (EU) member states. For the empirical approach, we combine frequency-domain filtering techniques with spillover index analysis to track cross-country macro-financial interlinkages. We measure financial cycle correlations and spillovers in terms of common exposures to trade linkages, overlapping systemic risk episodes, and bilateral financial claims. An important finding is that financial and business cycles tend to move together, largely due to shared macro-financial conditions and systemic stress episodes. While the data reveal strong co-movement between these cycles, the analysis does not imply a specific direction of causality. In particular, it remains possible that shifts in financial conditions can amplify or even precede business-cycle fluctuations, as seen during major crises. The focus of this study is, therefore, on the interdependence and synchronization of these cycles rather than on causal sequencing. The analysis combines complementary filtering and variance-decomposition methods to quantify the interdependencies shaping EU financial stability, providing a basis for enhanced macroprudential policy coordination. The policy implications for macroprudential authorities entail taking into account cross-border effects and spillovers when implementing instruments for taming the financial cycle. Full article

This study investigates the effects of a structured motor intervention program on the development of lower limb strength in junior athletes practicing Greco-Roman wrestling. Recognizing the crucial role of explosive strength in performing technical and decisive actions during combat, the research introduces a progressive, applied training protocol tailored to the neuromotor development of children aged 10 to 12 years (control group: M = 11.14, SD = 1.10; experimental group: M = 11.07, SD = 0.83). Conducted over 17 months, the study involved two groups of 14 registered wrestlers each from School Sports Club No. 5 in Bucharest. The experimental group participated in a complementary motor training program emphasizing plyometric drills, bodyweight strength exercises, and wrestling-specific movements, while the control group continued with the standard training routine. The intervention’s impact was evaluated using the OptoJump Next system, a biomechanical analysis tool measuring key indicators of explosive strength—jump height, ground contact time, flight time, and reactive strength index (RSI)—through the single-leg counter-movement jump (CMJ) test. Comparative analysis of pre- and post-intervention results showed significant improvements in neuromotor performance among athletes in the experimental group, confirming the effectiveness of the proposed methodology. This research thus provides a reproducible, evidence-based intervention model with direct applicability in optimizing the training of young Greco-Roman wrestlers. Full article

Entity resolution in real-world datasets remains a persistent challenge, particularly for identifying households and detecting co-residence patterns within noisy and incomplete data. While Large Language Models (LLMs) show promise, monolithic approaches often suffer from limited scalability and interpretability. This study introduces a multi-agent Retrieval-Augmented Generation (RAG) framework that decomposes household entity resolution into coordinated, task-specialized agents implemented using LangGraph. The system includes four agents responsible for direct matching, transitive linkage, household clustering, and residential movement detection, combining rule-based preprocessing with LLM-guided reasoning. Evaluation on synthetic S12PX dataset segments containing 200–300 records demonstrates 94.3% accuracy on name variation matching and a 61% reduction in API calls compared to single-LLM baselines, while maintaining transparent and traceable decision processes. These results indicate that coordinated multi-agent specialization improves efficiency and interpretability, providing a structured and extensible approach for entity resolution in census, healthcare, and other administrative data domains. Full article

Quotations play a central role in shaping narrative perspective, as they guide readers’ adoption and shifting of character and narrator viewpoints. While direct speech (DS) is often assumed to enhance vividness and emotional engagement, its cognitive demands relative to free direct speech (FDS) and free indirect speech (FIS) remain unclear, particularly in Chinese classical literature. Using eye-tracking, we investigated how Chinese readers process DS, FDS, and FIS in the Four Great Classical Novels, manipulating perspective congruency through address terms versus proper names. The results revealed two key findings. First, DS consistently incurred longer fixation times than FIS, demonstrating its higher processing cost. Second, congruency effects emerged robustly across all quotation types (including FIS) in later measures, suggesting that in the specific context of classical Chinese novels, FIS does not exhibit the dual-voice effect proposed in narrative theory for this particular manipulation. Complementary analyses with large language models (LLMs) further showed that DS yielded higher surprisal and entropy than both FDS and FIS, indicating greater contextual unpredictability. By integrating human eye-movement evidence with computational modeling, this study provides evidence about the cognitive processing of DS in Chinese classical texts and raises questions about the universality of dual-voice processing in FIS across different languages and text types. Full article

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas. Full article