Search Results (860)

The active constituents of essential plant oils (EOAIs), monoterpenoid carvacrol and monoterpene p-cymene, are widely distributed in many aromatic plants and their products. They differ in that carvacrol has a phenolic functional group. The numerous pharmacological effects of these two EOAIs are well known. In different doses/concentrations, they exhibit analgesic, neuroprotective, vasorelaxant, anti-inflammatory, antiviral, antibacterial and antiparasitic effects. The acute toxicity of carvacrol and p-cymene in rats and the free-living nematode Caenorhabditis elegans was investigated. Furthermore, the impact of subacute administration of these two terpenes on general health, CNS integration, i.e., motor coordination and balance of rats, as well as their effects on the movement of adult C. elegans, was also examined. The aim was to compare the effects and describe in more detail the selective toxicity of carvacrol and p-cymene. The calculated LD₅₀ value of carvacrol was 790.15 ± 1.15 mg/kg, while the LD₅₀ value of p-cymene is above 3000 mg/kg. Tested doses of carvacrol and p-cymene administered for 28 days (50, 100, and 200 mg/kg) did not exert any effect on the CNS of rats or cause any clinical disorders. LC₅₀ value of carvacrol for adult C. elegans was 184.13 ± 1.51 μM and for p-cymene 1268 ± 1.65 μM. In subacute testing, carvacrol showed negative effects on C. elegans reproduction, distance traveled, movement speed and rotational index at lower concentrations than p-cymene, indicating higher toxicity, which may be due to its phenolic structure. On the other hand, although less toxic to C. elegans, p-cymene exhibited a specific effect on worm motility, with more rolling which should be further investigated, and can be a consequence of cuticle damage or loss of orientation. Full article

Fault-induced delayed voltage recovery (FIDVR) poses a serious threat to modern power grid operation, where stalled induction motors following a fault can sustain dangerously low bus voltages and potentially trigger cascading failures. While deep reinforcement learning (DRL) has shown promise for emergency load shedding control, existing centralized DRL approaches require extensive communication infrastructure and large neural network models that are computationally prohibitive to train at scale. Fully decentralized approaches, on the other hand, lack inter-agent information sharing and coordination, often resulting in inadequate voltage recovery across area boundaries. To address these limitations, we propose a Cloud–Edge Collaborative DRL framework that combines lightweight, area-specific edge agents for local load shedding control with a supervisory cloud agent that coordinates their actions globally, achieving scalable training and system-wide voltage recovery simultaneously. Training is further accelerated through a modified Guided Surrogate-gradient-based Evolutionary Random Search (GSERS) algorithm. Validation on the IEEE 300-bus system demonstrates that the proposed framework reduces training time by approximately 90% compared to the fully centralized approach, while achieving comparable voltage recovery performance to the centralized method and approximately 80% better reward performance than the fully decentralized approach, confirming the critical benefit of the cloud-level coordination mechanism. Full article

Background: Wrist–forearm orthoses used during self-feeding may alter scapular and shoulder mechanics and increase proximal load, but this has not been quantified. Methods: Seventeen right-hand-dominant young adults performed a spoon-feeding task under free and restricted conditions. A thermoplastic wrist–forearm orthosis positioned the wrist at approximately 30° dorsiflexion at rest and was intended to constrain wrist motion during the task without rigidly immobilizing forearm pronation–supination. Three-dimensional kinematics (scapula, shoulder, trunk, and distal joints) were recorded using inertial sensors, and surface electromyography was obtained from the upper trapezius, middle deltoid, and biceps brachii. Maximum joint angles and mean %MVC over the feeding cycle were compared between conditions (α = 0.05). Results: The restriction condition resulted in a more anteriorly tilted and downwardly rotated scapular posture, greater shoulder abduction and external rotation, and increased thoracic flexion, whereas maximum distal joint angles did not differ, suggesting a functional distal constraint rather than rigid immobilization. Middle deltoid and biceps brachii activities increased significantly, with a nonsignificant trend toward higher upper trapezius activation. Conclusions: In healthy young adults, limited wrist motion and forearm rotation during spoon-feeding were associated with altered proximal coordination, including scapular, shoulder/trunk, and proximal muscle changes. Full article

There has been increasing interest in the Moon for deep space exploration missions in the last few decades. To accommodate fuel-optimal lunar landing missions, it is essential to develop a fast trajectory planning algorithm considering constrained six-degree-of-freedom (6-DOF) dynamics. On the one hand, the trajectory planning problem involves a coordination of the optimal fuel consumption and the vehicle’s position, velocity, and attitude, which requires computational efficiency. On the other hand, the initialization setup of the existing sequential convex optimization method provides the linear reference trajectory, which slows down the convergence of the iterative process. In this manuscript, an improved sequential convex programming algorithm is proposed to solve the minimum-fuel 6-DOF powered descent problem. Firstly, we suggest a trajectory planning method based on a normalized finite rotation formulation, which improves the efficiency of the computational processes. Secondly, we present an initial guess method that computes the projection-analogous gradient with respect to the terminal value, accelerating the convergence of the algorithm. The simulation results show that the proposed method improves computational efficiency, indicating the potential for future applications in autonomous landing missions. Full article

Reliable control of robotic hands using residual muscle activity is challenging due to low-amplitude myoelectric signals, susceptibility to noise, and the need for real-time actuation. This paper presents a myoelectric-controlled robotic hand capable of voluntary independent finger motion. Surface myoelectric signals from the forearm are processed via amplification, filtering, and digital analysis to enable accurate detection of muscle activity. The system achieves independent and simultaneous actuation of five fingers using a tendon-driven, servo-actuated mechanism in a lightweight ABS structure. Experimental evaluation demonstrates finger actuation delays ranging from 314 ms to 650 ms, maximum holding strengths between 1.75 N and 4.07 N, and minimum gripping distances between 22 mm and 49 mm across all five fingers, with peak motor currents remaining below 0.7 A. Results validate consistent muscle activity detection, successful execution of individual and combined finger movements, and the robustness of the proposed design. Full article

Background/Objectives: Reaction time, hand–eye coordination, spatial orientation, and attention play a key role in handball, which is characterized by high intensity as well as high cognitive and motor demands. The level of these abilities may change during the season, potentially reflecting training adaptations and increasing physical fatigue. The aim of the study was to compare the level of psychomotor abilities in professional handball players before the start of the competition round and after the end of the league season. The study included 77 handball players playing in the Polish Handball Super League (average age: 25.6 ± 5.2 years). The players were divided according to position: pivot, center, and wing. Methods: Psychomotor abilities were assessed using the Test2Drive computer system, employing tests of simple and choice reaction time, eye–hand coordination, spatial orientation, perception and attention, and movement anticipation. Results: At the end of the season, a statistically significant reduction in reaction time was observed in the choice reaction (p = 0.001), eye–hand coordination (p = 0.002), and spatial orientation tests (p = 0.003). In terms of motor skills, an increase in time was observed in the SIRT test (p = 0.003), CHORT (p = 0.005) and HECOR (p = 0.011) tests, while the time in the PUT test was shortened for both neutral (p = 0.002) and critical (p = 0.025) stimuli. Positional analysis showed that after the season, the pivot player achieved higher effectiveness in the CHORT test than the wing player (p = 0.020). Additionally, statistically significant differences were observed for correct responses in the SPANT test (p = 0.032). In terms of correct answers in the PAMT test, the pivot player had the lowest effectiveness. Conclusions: Participation in the full season of competition coincided with significant changes in the psychomotor profile of handball players, with a simultaneous improvement in reaction speed and deterioration in movement time parameters. Full article

For the first time, hybrid nanocomposites based on poly(2,5-dichloro-3,6-bis(phenylamino)-p-benzoquinone) (PCPAB) and multi-walled carbon nanotubes (MWCNTs) were obtained and the influence of the preparation method on their structure and functional properties was demonstrated. The nanocomposites were obtained both by ultrasonic mixing of PCPAB and MWCNTs, and via in situ oxidative polymerization of CPAB in the presence of MWCNTs or MWCNTs with the addition of ZnO. The formation of hybrid nanocomposites occurs due to non-covalent interaction (π-stacking) between the graphene structures of the MWCNT surface and the phenyl rings of PCPAB. It was found that during the in situ oxidative polymerization of CPAB in the presence of MWCNTs, the growth of polymer chains occurred in close proximity to the filler surface, which led to the formation of a polymer coating. ZnO particles, localized on MWCNTs, on the one hand, prevent their aggregation, and on the other hand, create additional polymerization reaction centers due to the coordination of the Zn-O bond at the H and O atoms of the monomer. An increase in the concentration of reaction centers as a result led to a 2–2.5-fold reduction in the induction polymerization period. According to SEM data, in this case, a more ordered and denser polymer layer is formed due to intermolecular complexation between the main and side chains of the growing polymer with the participation of Zn²⁺ ions formed as a result of the transformation of ZnO to ZnCl₂ in the acidic reaction medium of polymerization. The results of the study of the frequency dependences of conductivity indicate a hopping mechanism of conductivity of nanocomposites. The electrical conductivity of nanocomposites depends on their production method and the MWCNT content and varies between 0.5 and 1.1 S∙cm⁻¹, which is 6–12 times higher than the conductivity of the original polymer. Thermogravimetric analysis revealed that the nanocomposites exhibit enhanced thermal stability compared to PCPAB. The best results were shown by nanocomposites with a higher content of MWCNTs, for which the residual mass at 450 °C was 51–53%. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

Two-handed dynamic gesture recognition represents a fundamental component of sign language interpretation involving the modeling of temporal dependencies and inter-hand coordination. In this task, a major challenge is modeling asymmetric motion patterns, as well as bidirectional and long-range temporal dependencies. Most existing frameworks rely on early fusion strategies that merge joints, keypoints or landmarks from both hands in early processing stages, primarily to reduce model complexity and enforce a unified representation. In this work, a novel dual-stream BiLSTM–Transformer model architecture is proposed for two-handed dynamic sign language recognition, where parallel encoders process the trajectories of each hand independently. To capture spatial and temporal dependencies for each hand, an attention-based cross-hand fusion mechanism is employed, with hand landmarks extracted by the MediaPipe Hands framework as a preprocessing step to enable real-time CPU-based inference. Experimental evaluation conducted on custom Romanian Sign Language dynamic gesture datasets indicates that the proposed dual-stream-based system outperforms single-handed baselines, achieving improvements in high recognition accuracy for asymmetric gestures and consistent performance gains for synchronized two-handed gestures. The proposed architecture represents an efficient and lightweight solution suitable for real-time sign language recognition and interpretation. Full article

Background/Objectives: Successful interception of moving targets requires combining predictive control, which anticipates future target states, and reactive control, which compensates for ongoing sensory discrepancies. How these components evolve over time and are distributed across gaze and manual behavior remains unclear. We aimed to explore the time-resolved dynamics of predictive control during continuous interception and to dissociate eye and hand contributions. Methods: Human participants intercepted a moving target in a two-dimensional arena using a joystick while eye movements were recorded. Target speed was systematically varied, and visual information was selectively reduced by occluding either the target or the user-controlled cursor. Predictive control was assessed using two complementary metrics: a geometric strategy index capturing moment-to-moment spatial lead or lag relative to target motion, applied separately to gaze and manual trajectories, and root mean square error (RMSE) computed relative to current and forward-shifted target positions to quantify predictive alignment. Results: Successful interception was characterized by structured, speed-dependent transitions between predictive and reactive control rather than a fixed strategy. Predictive alignment emerged early and was dynamically reweighted as temporal constraints increased. Gaze and manual behavior showed complementary but partially dissociable predictive signatures. Occluding the target decreased predictive alignment, whereas occluding the user-controlled cursor had comparatively minor effects, indicating strong reliance on internal state estimation rather than continuous visual feedback of the effector. Conclusions: Predictive and reactive control are continuously and dynamically reweighted during interception. Their interaction unfolds within single trials and depends on target dynamics and sensory availability. These findings provide quantitative evidence for time-resolved coordination between anticipatory and feedback-driven control mechanisms in goal-directed behavior. Full article

This study investigates the impact of board attributes (board size, board independence, gender diversity, and nationality diversity) on corporate board diligence through employing panel data of listed firms in Muscat Securities Market from 2014 to 2024. Through the application of multiple regression analysis, the paper determines predictors for board diligence and offers an agency theory-based and resource dependence theory-based perspective on this construct. The findings reveal positive relations between board independence and board diligence, which suggests that the independent director has monitoring function. On the other hand, board size and nationality diversity are negatively related to diligence levels indicating a lack of coordination and communication. However, board gender diversity does not seem statistically related to board diligence. Several robustness tests, such as lagged independent variables, fixed industry effects, alternative estimation techniques, and instrumental variable approach, support the validity of our findings. This research helps investors and policymakers to better understand the extent to which board structure is related to meeting activity and director engagement in emerging markets. The study contributes to the literature on board diligence in emerging markets and evidence the impact of gender and nationality diversity on corporate board performance in Oman. Full article

In ultrasonic nondestructive testing, maintaining the ultrasonic sensor in normal contact with curved surfaces is pivotal for acquiring valid defect signals. Replacing manual operation with a robotic arm ensures stable signal collection, while stable and fast trajectory planning for complex curved-surface tracking remains a key challenge. This research investigates gesture-driven robotic trajectory planning and impact optimization via the particle swarm optimization (PSO) algorithm in the robot joint space for rapid and smooth movement. Gesture trajectories are acquired via a Leap Motion device, with unified mapping established through spatial transformations among gesture, simulation, and experimental robot spaces. PSO is utilized to optimize trajectories, enhancing accuracy and controllability. Median filtering is applied to trajectory coordinate data to suppress errors from hand tremor and sensor limitations, followed by introducing a surface normal offset to generate pose matrices at each trajectory point. Systematic comparison of interpolation methods (polynomial, cubic spline, circular, cubic B-spline) reveals that cubic B-spline interpolation achieves the shortest execution time under angular acceleration constraints. The results show that PSO optimizes point-to-point trajectories based on 5-5-5 polynomial interpolation, with impact force and execution time as objectives, yielding the optimal trajectory with minimal time under acceleration constraints. This research provides valuable methodological references for robotic manipulator trajectory planning and optimization in complex curved-surface ultrasonic testing. Full article

With the continuous advancement of the construction of new power systems, the coordinated development of source-grid-load-storage has become imperative. This paper proposes a coordinated dispatch strategy for source-grid-load-storage in active distribution networks oriented toward zero-carbon goals. First, this paper introduces the concepts of the green electricity index and zero-carbon pathway constraints. Building upon this foundation, a coordinated dispatch model for source-grid-load-storage in active distribution networks is constructed, aiming for optimal economic performance while considering equipment and system operational constraints. On the other hand, this paper employs Information Gap Decision Theory (IGDT) to construct uncertainty sets for renewable energy output and load demand, proposing a comprehensive deviation coefficient calculation method. This approach reduces the conservativeness of dispatch decisions while ensuring their robustness. Considering the nonlinear characteristics of the model, an improved sparrow search algorithm is adopted to enhance solution efficiency. Finally, validation using the IEEE-33 node test system demonstrates the effectiveness and feasibility of the proposed method. Full article

Addressing the challenges of large-scale gesture recognition models, high computational complexity, and inefficient deployment on embedded devices, this study designs and implements a visual dynamic gesture recognition system based on a lightweight CNN-LSTM-DSA model. The system captures user hand images via a camera, extracts 21 keypoint 3D coordinates using MediaPipe, and employs a lightweight hybrid model to perform spatial and temporal feature modeling on keypoint sequences, achieving high-precision recognition of complex dynamic gestures. In static gesture recognition, the system determines the gesture state through joint angle calculation and a sliding window smoothing algorithm, ensuring smooth mapping of the servo motor angles and stability of the robotic hand’s movements. In dynamic gesture recognition, the system models the key point time series based on the CNN-LSTM-DSA hybrid model, enabling accurate classification and reproduction of gesture actions. Experimental results show that the proposed system demonstrates good robustness under various lighting and background conditions, with a static gesture recognition accuracy of up to 96%, dynamic gesture recognition accuracy of 90.19%, and an overall response delay of less than 300 ms. Full article

This paper offers a hybrid forecasting approach that merges a local-level state space Kalman filter with a Long-Short-Term Memory (LSTM) neural network to assess the downside risk of the Botswana Pula versus the US Dollar (BWP/USD). Inspired by the inability of conventional econometric models to capture complex latent structural shifts and nonlinear patterns, our architecure uses a bottom-up hierarchical methodology in which the smoothed level component of the exchange rate is isolated by the Kalman filter and subsequently fed into the LSTM architecture. Three key indicators for assessing downside risk—Maximum Drawdown (MDD), Conditional Drawdown-at-Risk (CDaR), and Downside Deviation—are used to assess model performance across various time-frames (7, 30, 90, 180, and 240 days). As confirmed by Kupiec and Christoffersen’s backtesting processes, the findings show a high degree of alignment between projected and actual values, with negligible downside deviation bias and robust calibration. Moreover, global economic and geopolitical shocks, such as the COVID-19 pandemic, the Russia–Ukraine conflict, and the 2015–2016 Shanghai Stock Exchange crash, are important factors that influence exchange rate volatility, according to explainable artificial intelligence techniques, particularly SHAP (SHapley Additive exPlanations) analysis. Downside risk is also greatly increased by regional currency links, especially the impact of the ZAR/BWP exchange rate. On the other hand, domestic temporal variables, such as week, quarter, and month, have very little impact. These results emphasise how Botswana’s currency rate is structurally vulnerable to external shocks and how crucial it is to include both global and regional considerations in risk analysis. The research concludes that the accuracy and transparency of projections for exchange rate risk significantly improve when practical filtering is combined with deep learning and explainable AI. To improve macroeconomic resilience and guide successful financial risk management plans in emerging market environments, policymakers are advised to employ AI-driven forecasting techniques, enhance regional monetary coordination, and set up real-set learning systems. Full article