Search Results (1,413)

Introduction: River connectivity is a fundamental component of lotic ecosystems, frequently disrupted by anthropogenic barriers. Fishways are critical mitigation tools for restoring this connectivity; however, their temporal use by fish is often assumed to be restricted to specific seasonal windows (mainly spawning season). Understanding the fine-scale temporal dynamics of fishway use by native species such as the common barbel (Luciobarbus bocagei) is essential for effective, long-term river management and the conservation of Iberian freshwater biodiversity. Objective: This study aims to evaluate the daily and seasonal patterns of use of pool-type fishways by L. bocagei, in order to improve the determination of their temporal period of use and provide data-driven recommendations for their management. Methodology: The study was conducted in the middle reach of the Duero River (Burgos province). Over a multi-year period, more than 1000 individual barbels were captured, tagged with Passive Integrated Transponder (PIT) tags, and released. Movements were continuously monitored across two pool-type fishways. Telemetry detection data were analyzed to evaluate daily rhythms and seasonal use, as well as to estimate the start and end dates of the movement periods. Results: Our analysis revealed a highly marked daily pattern in fishway use, showing a clear increase during daylight hours, with more pronounced peaks at dawn and dusk. This daily activity dynamically adjusts throughout the year in response to the solar cycle. Furthermore, we successfully determined that the seasonal activity extends from March to November, a pattern that remained consistent throughout the different study years. Crucially, the telemetry data empirically demonstrated that L. bocagei utilizes fishways for a substantial portion of the year, indicating relevant non-reproductive movements. Conclusions: The extended temporal use of these fishways indicates their potential ecological functions throughout much of the year. This must be reflected in management and maintenance protocols that ensure sufficient flow conditions, cleaning, and active vigilance during most of the year. Additionally, this predictability reinforces the value of fishways as strategic biological monitoring stations for endemic populations. Full article

Oculomotor dysfunction is an eye movement disorder frequently experienced in patients with Parkinson’s disease. Many patients tend to experience visual symptoms, and this can exacerbate cognitive symptoms when visual tasks become more demanding. The purpose of this review is to characterize oculomotor dysfunction in patients with Parkinson’s disease based on the distinct types of ocular motor deficits and their corresponding impact on the patient’s symptoms, visual perception, activities of daily living, and quality-of-life. A systematic literature search was conducted to identify relevant articles. The results from the sensorimotor framework analysis are categorized in five domains: visual-sensory, visual-motor, visual-perceptual, cognitive processing, and psychosocial challenges. The findings suggest that clinical evaluation and neurorehabilitation should move beyond speed-dependent metrics, but focus on specific non-speed-dependent ocular motor deficits. By fostering interdisciplinary collaboration, healthcare professionals can take proactive steps to address the vision-related challenges faced by patients with Parkinson’s disease. Full article

Urban freight distribution is essential for supplying commercial activities, but it also increases pressure on curb space, vehicular circulation, pedestrian movement, and public space management, especially in medium-sized cities where dedicated loading and unloading infrastructure is often limited. Although recent literature emphasizes the need for data-driven urban logistics planning, empirical evidence from intermediate Latin American cities remains scarce. This study develops and applies a spatial-operational framework to characterize urban freight distribution, identify patterns of conflict and informality, estimate loading and unloading bay requirements, and prioritize intervention areas in a medium-sized city. A quantitative, observational, exploratory–descriptive, and correlational design was applied, based on 642 georeferenced loading and unloading operations recorded through a digital field survey. The analysis integrated data cleaning, descriptive and inferential statistics, logistic models, an operational sustainability risk/pressure index, DBSCAN spatial clustering, logistics pressure and sustainable transport priority indices, and a capacity model based on average daily operations. The results revealed spatial concentration of logistics activity, a predominance of light trucks, frequent use of paid parking areas and roadways, and a high presence of operational conflicts. The study provides a replicable and planning-oriented framework for prioritizing curbside management interventions for sustainable urban freight distribution in medium-sized Latin American cities. Full article

This paper analyzes volatility forecasting in the Spanish electricity spot market over the period 2021–2025, characterized by uncertainty, frequent price jumps, and the increasing occurrence of zero and negative prices. To accommodate these features, electricity prices are shifted to ensure well-defined log-returns, and predictable intraday and seasonal patterns are removed using the Ullrich demeaning procedure. Daily realized volatility measures are constructed from high-frequency data, including jump-robust and noise-robust estimators such as Median Realized Volatility and Realized Kernel. A broad set of volatility models, comprising GARCH-type specifications and multiple extensions of the Heterogeneous Autoregressive (HAR) framework, is evaluated using a coherent out-of-sample forecasting procedure. Model comparison is conducted through the Model Confidence Set methodology based on the QLIKE loss function, which identifies a Superior Set of Models with equal predictive ability. Conditional diagnostics, including Out-of-Sample $R_{\mathrm{OOS}}^2$ measures and Mincer–Zarnowitz regressions, are subsequently used to characterize forecast accuracy, unbiasedness, and efficiency. The empirical results show that all GARCH models are systematically excluded from the superior set, while HAR-type specifications based on realized volatility dominate. Within this set, a HAR model incorporating Median Realized Volatility, jump components, and day-of-the-week effects delivers the strongest economic performance, achieving an Out-of-Sample $R_{\mathrm{OOS} }^2$ close to 0.5 with unbiased forecasts. Overall, the findings highlight the importance of long-memory dynamics, discontinuous price movements, and residual weekly seasonality for volatility forecasting in modern electricity markets. Full article

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article

Knee motion is a key biomarker in chronic musculoskeletal diseases, yet conventional in-lab optical motion capture falls short of identifying how knee motion continuously impacts joint health outside the lab. Inertial measurement unit (IMU) provides a clinically attractive approach for continuous real-world motion tracking. Our goal was to establish a clinically practical, minimum-setup pipeline for leg-worn IMUs to estimate knee flexion and determine its concurrent validity to optical motion capture during various knee movements. We recorded thigh and shank-worn IMU data with concurrent marker-based and markerless optical motion capture on 10 healthy adults, who performed 10 common movements including walking, running, and stair navigation. We combined IMU functional alignment with data fusion to estimate knee flexion during each movement and compared IMU-based estimate against both motion capture systems using Pearson correlation (R_xy) and root-mean-square difference (RMSD). IMU-estimated knee flexion strongly correlated with motion capture (R_xy ≥ 0.9). RMSDs were smaller for slower movements like walking (RMSD = 4.4–6.0°) while larger during faster movements like running (RMSD = 5.4–9.4°). Wearable IMUs track knee flexion with comparable results to motion capture during daily activities typical to older adults, highlighting their potential for continuous patient monitoring. Our simple pipeline makes IMU-based knee motion tracking more practical and compatible with clinical research. Future research should seek IMU-wearing best practices to secure clinically meaningful data on real-world knee mobility. Full article

Essential tremor (ET) is among the most prevalent movement disorders, causing significant impairment in manual dexterity and daily functioning. Although ET affects individuals across the lifespan, exercise intervention research has focused almost exclusively on older adults, leaving young adults, for whom early intervention may prevent long-term neuromuscular deterioration, critically underrepresented. Furthermore, the effects of strength–endurance oriented exercise combined with task-specific motor activities remain insufficiently explored in any ET population. This quasi-experimental pre-test–post-test study investigated the effects of a 6-week progressive strength–endurance and task-specific exercise program on tremor severity, manual dexterity, and upper extremity functional performance in young adult males with ET (n = 18; mean age: 22.6 ± 4.1 years). The 24-session intervention (four sessions/week) combined proximal upper extremity strength–endurance exercises with seven ADL-specific fine motor tasks. Tremor severity was assessed using the Fahn–Tolosa–Marin Tremor Rating Scale (FTMTRS), manual dexterity using the Nine-Hole Peg Test (NHPT), and upper extremity stability using the Closed Kinetic Chain Upper Extremity Stability Test (CKCUEST). The Wilcoxon signed-rank test was used for within-group comparisons, with rank biserial correlation (r) and Cohen’s d reported as effect size indices. Significant pre-to-post improvements were observed across nearly all outcome measures, with medium-to-large effect sizes. Spiral drawing performance improved significantly in five of six conditions (r = 0.47–0.62), with the exception of the Spiral left–B task (p = 0.083). Postural tremor, NHPT (both hands), and CKCUEST also showed significant improvements (r = 0.47–0.73). A composite tremor score, integrating all tremor sub-scores, demonstrated a 14.1% overall reduction (p = 0.001, r = 0.83), providing strong evidence of program-wide effectiveness. Session adherence was 95.8%. To our knowledge, this is one of the first studies to show that a structured strength–endurance and task-specific exercise program was associated with reductions in tremor severity and improvements in upper extremity function, specifically in young adults with ET. These findings support the clinical utility of exercise as a non-pharmacological intervention in this underserved population and highlight the importance of early, targeted intervention during young adulthood. Full article

Disabilities of the lower extremities significantly affect a person’s ability to perform activities of daily living. Many people have been affected by this type of disability due to birth disease or injury from accidents, strokes or even old age. The technical aids used in this type of disability are very basic, and rehabilitation is mainly performed by therapists. Rehabilitation consists of repetitions of exercises with normal movements that must be performed for prolonged periods of time. On the other hand, therapists, having to support the weight of the patient, tend to get injured. This paper introduces the design and modeling a robotic device intended to assist the therapist in the rehabilitation of sit-to-stand (STS) and walking movements, focusing primarily on the technical aspects of the system. The robot is designed to safely support the user’s weight and guide the user with appropriate movements according to the usual biomechanics of STS. This paper presents the solution of the inverse kinematic modeling of both the position and velocity of the robot mechanism, as well as the dynamic analysis. A series of simulations is conducted to evaluate the performance of the proposed mechanical architecture during the STS task, providing quantitative information on the system dynamics and the interaction forces between the user and the robot. The mathematical model was employed in the design of a prototype intended for children aged 8–12 years, capable of supporting up to 50 kg and providing a vertical motion range of 20–90 cm. The main structural elements of the robot, its control architecture, and its operation during the execution of the STS task are described. Finally, the conclusions of this work are discussed, and future work derived from this research is outlined. Full article

With the development of human–computer interaction technology, non-contact interaction based on gaze tracking and facial movements has become a research hotspot. Traditional mouse-and-keyboard methods pose challenges for people with disabilities or limited hand movements, while existing gaze-tracking systems often rely on expensive hardware or lack sufficient accuracy. This paper designs and implements a real-time system using ordinary cameras, achieving natural, efficient interaction via multimodal input combination. The system uses an improved MobileNetV2 backbone to construct GazeTrackNet for gaze estimation. It adopts MediaPipe Face Mesh to detect facial landmarks. Meanwhile, it applies geometric feature analysis, including eye aspect ratio and mouth aspect ratio, to identify actions such as blinking and mouth opening. It adopts a hybrid control strategy that combines gaze jumping and head fine-tuning, using mouth state as the main control switch. Key contributions include a lightweight gaze-tracking algorithm that enables stable and efficient gaze detection on consumer-grade hardware, a multimodal interaction strategy based on facial movement that improves system stability and ease of use, and a complete prototype system that achieves real-time performance on standard laptops. Experimental results show an average gaze average angle error of 3.0°, 97% eye state recognition accuracy, and end-to-end latency below 70 ms. The system can satisfy the requirements of daily desktop interaction under normal indoor lighting, and shows potential for future barrier-free interaction applications after further validation with target users. Existing gaze-tracking methods either suffer from low precision on lightweight devices or bring heavy computational overhead. Common facial recognition approaches also face frequent false trigger interference. Compared with them, our scheme achieves balanced accuracy and real-time performance via an attention-enhanced structure, and the designed dual anti-shake mechanism effectively suppresses misjudgment, delivering a more stable hands-free interaction experience. Full article

Visual fatigue is a serious issue among Chinese secondary school students owing to prolonged daily exposure (8–10 h) to visual display terminals (VDTs) in widely equipped multimedia classrooms. To mitigate such effects, this exploratory study identifies promising lighting parameters by evaluating the influence of blackboard reflection coefficients, the ratio of desktop illumination to blackboard illumination, and correlated color temperature (CCT) in a simulated multimedia classroom environment. Thirteen participants performed visual tasks (Landolt ring visual acuity tests and Anfimov’s Chart Task) under various conditions. Visual fatigue scale (VFS-10), index of mental capacity (IMC), and eye movement parameters (EMP) were used to assess visual fatigue and efficiency. Results suggest that higher blackboard reflection coefficients improved efficiency and reduced fatigue. Increased blackboard illumination alleviated fatigue at constant CCT, whereas changes in desktop illumination showed no significant effect. The highest efficiency among the tested CCT values was observed at 4700 K, while visual fatigue was minimized at 4000 K. The findings provide preliminary practical applications for minimizing visual fatigue and improving performance efficiency in secondary school multimedia classroom environments equipped with VDTs. Full article

This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index $\overline{\alpha }=1.667$ across all constituents, substantially below the Gaussian benchmark of $\alpha =2.0$; (ii) Hurst exponents clustering below 0.5 ($\overline{H}=0.417$), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes. Full article

A neighborhood’s built environment can challenge the mobility of older mobility-challenged adults (due to knee osteoarthritis and falls), reducing their participation and quality of life. The Built Environment in Falls and arthrITis (BE-FIT) study aims to understand the neighborhood influence on the mobility, participation, and psychosocial health of older adults with knee osteoarthritis and/or falls. BE-FIT comprises four work packages (WPs). WP1 quantitatively explores relationships among environmental-, social-, and person-related factors and participation outcomes of its intended population. WP2 employs qualitative methods to comprehend the relationships among WP1’s variables. Via a combination of wearable sensor technology and qualitative geospatial methods, WP3 aims to characterize its population’s movement behavior, mobility, functional activity of daily living, and lived experiences of residing in a mature neighborhood. Finally, WP4 engages crucial stakeholders to co-develop evidence-based recommendations to inform public health, urban planning, and aging policies and implementation. BE-FIT could benefit societies with rising incidence of knee osteoarthritis and falls by improving neighborhoods and lives of older mobility-challenged residents. Full article

Background: Upper extremity deficits in unilateral cerebral palsy (UCP) severely restrict daily autonomy. Although movement-based priming is known to stimulate neuroplasticity, the distal transfer of lower extremity (LE) training to augment paretic upper limb (UL) function remains largely uninvestigated. This randomized controlled trial evaluated whether a 6-week LE cross-training (CT) priming regimen enhances UL functional restoration in pediatric UCP. Methods: Thirty-six children (6–8 years) were randomized to a conventional physical therapy cohort (n = 18) or an experimental CT cohort (n = 18). The CT group performed high-resistance contractions utilizing the non-paretic LE immediately preceding standard therapy. Blinded evaluations quantified Handgrip Strength (HGS) via dynamometry, grasping proficiency via the Peabody Developmental Motor Scales (PDMS-2), and gross dexterity via the Box and Block Test (BBT) pre and post intervention. Results: Analysis indicated a robust, near-significant between-group effect (Wilks’ Λ = 0.775, p = 0.057). While both cohorts achieved substantial internal improvements, the CT participants displayed superior developmental trajectories across all domains, notably in grasping age equivalence (34.28 ± 6.33 vs. 25.78 ± 3.26 months) and HGS (3.89 ± 0.79 vs. 3.03 ± 0.53 kg). Conclusions: LE cross-training priming may be a feasible adjunct, but it did not demonstrate statistically significant additional UL benefit versus standard rehabilitation in this sample. Therefore, these results should be interpreted as exploratory and hypothesis-generating. This potential cross-segmental transfer may theoretically operate via interhemispheric facilitation, warranting further investigation in larger, adequately powered trials. Full article

Background/Objectives: Parkinson’s disease (PD) is a major neurodegenerative disorder with no cure. The goal is to develop an active immunotherapy targeting aggregated alpha synuclein (aSyn), the root cause of PD. TRB-001 is the lead candidate of a novel class of vaccines. It is a peptide/protein conjugate coupled to sugar residues, which is used to target and activate antigen-presenting cells, and addresses aSyn. Methods: A 33-year-old male, diagnosed with PD seven years previously, with a Hoehn & Yahr stage of 1, taking Levodopa/Benserazide (100/25 mg, 6× per day), Rotigotine (8 mg) and Rasagiline (1 mg), amounting to a Levodopa equivalent daily dose (LEDD) of 940 mg, also complicated by impulse control disorder, requested experimental therapy. He received a total of four TRB-001 administrations (weeks 0, 4, 8 and 34) following informed consent. The workup included safety, immunological and clinical parameters. Results: Vaccinations were well tolerated. They induced a high-titer aSyn-specific antibody (Ab) response. Titer increase was associated with a reduction in aSyn plasma levels, suggesting target engagement. The Ab titer and the reduction in aSyn plasma levels were both long-lived. The boost elicited a recall-type Ab titer increase and triggered avidity maturation (factor 7.8). Abs demonstrated a high degree of selectivity for aggregated aSyn (factor 30). Moreover, they were found to preferentially react with tissue from PD brain lysates. The Movement Disorder Society-Sponsored Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) score for the patient remained essentially stable throughout the observation period of 53 weeks. At the time of the boost, the symptomatic PD therapy was simplified to Levodopa/Carbidopa/Entacapone 100/25/200 mg four times a day, amounting to an LEDD of 532 mg. This put an end to the symptoms of the impulse control disorder. Conclusions: Results obtained suggest that this new class of vaccines may yield Ab responses comparable in magnitude and target avidity to the therapeutic setting of monoclonal Abs. TRB-001 is currently being translated to a randomized, placebo-controlled Phase 1B study. Full article

Research Purpose: This study investigates the role of digital investor behavior, measured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Daily data from 2020 to 2025 were collected, covering both crisis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frameworks, with Max Drawdown employed as a complementary risk metric to capture extreme market movements. Findings: Digital investor attention shows a predictive association with volatility, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predictive performance. The EGARCH model successfully captured the asymmetric impact of negative shocks (γ < 0, p < 0.05), while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress, achieving an R² of 95.36%. Scientific value: This study positions digital attention as a complementary variable that improves forecasting, moving beyond conventional price-based models in volatility modeling; by integrating Google Trends with technical analysis, the research introduces a hybrid forecasting framework that can be adapted to other emerging markets. Practical Implications: The findings offer practical value for policymakers and investors. Regulators can use digital attention measures as early warning signals to anticipate volatility, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools. Full article