Search Results (24,168)

As a key piece of equipment in badminton, the surface treatment technology of rackets has garnered significant attention in the fields of material science and sports engineering. This study is the first to systematically review research on racket coatings, integrating interdisciplinary knowledge on the classification of functional coatings, their performance-enhancing principles, and their relationship with competitive levels, thereby addressing a gap in theoretical research in this field. This study focuses on four major functional coating systems: superhydrophobic coatings (to improve environmental adaptability and reduce air resistance), anti-scratch coatings (to prolong the life of the equipment), vibration-damping coatings (to optimise vibration damping performance), and strength-enhancing coatings (to safeguard structural stability). In badminton, differences in player skill levels and usage scenarios lead to variations in racket materials, which, in turn, result in different preparation processes and performance effects. The use of vibration-damping materials alleviates the impact force on the wrist, effectively preventing sports injuries caused by prolonged training; leveraging the aerodynamic properties of superhydrophobic technology enhances racket swing speed, thereby improving hitting power and accuracy. From the perspective of performance optimization, coating technology improves athletic performance in three ways: nanocomposite coatings enhance the fatigue resistance of the racket frame; customized damping layers reduce muscle activation delays; and surface energy regulation technology improves grip stability. Challenges remain in the industrial application of environmentally friendly water-based coatings and the evaluation system for coating lifespan under multi-field coupling conditions. Future research should integrate intelligent algorithms to construct a tripartite optimization system of “racket-coating-user” and utilize digital sports platforms to analyze its mechanism of influence on professional athletes’ tactical choices, providing a theoretical paradigm and technical roadmap for the targeted development of next-generation smart badminton rackets. Full article

Background/Objectives: Glidants and lubricants are commonly used pharmaceutical excipients that enhance powder flowability and reduce inter-particle friction, respectively, but they also negatively impact critical quality attributes such as tablet tensile strength and drug release rate. Quantifying these effects is essential as the pharmaceutical industry transitions from batch to continuous manufacturing. Methods: This study develops a rational-function-based modeling approach to capture the effects of lubricants and glidants on tableting. The framework automatically identifies upstream critical material attributes and process parameters, such as excipient concentration and mixing time, and describes their coupling to first and second orders. Reduced-order models were constructed to evaluate the influence of these variables on the four stages of powder compaction—die filling, compaction, unloading, and ejection—using formulations composed of 10% acetaminophen, microcrystalline cellulose, and varying small concentrations of magnesium stearate or colloidal silica. Tablets were fabricated across a wide range of relative densities by varying dosing position and turret speed. Results: The modeling approach successfully quantified the effects of lubricant and glidant mixing conditions on each compaction stage, providing mechanistic insight into how upstream conditions propagate through the tableting process and influence critical quality attributes. Conclusions: Overall, the rational-function-based framework offers a systematic approach to quantify and predict the impact of lubricants and glidants on tablet performance, thereby enhancing product and process understanding in continuous manufacturing. Full article

Prior research has proposed a basic configuration for a deep-sea mining system integrating slurry transport and wastewater discharge, and examined the operational characteristics of water-driven diaphragm pumps. Against the backdrop of commercial deep-sea polymetallic nodule exploitation, this study focuses on the technical design of seabed diaphragm pump groups and hydraulic parameter matching for a coupled slurry transport-wastewater discharge system. The solid–liquid two-phase output characteristics of the water-driven diaphragm pump were analyzed, leading to the proposal of a four-pump staggered configuration to ensure continuous particulate discharge throughout the full operating cycle. To meet commercial mining capacity requirements, the system consists of two sets (each with four pumps) operating with a phase offset to reduce fluctuations in slurry output concentration. A centralized output device was developed for the pump group, and a centralized mixing tank was designed based on analyses of outlet pipe length and positional effects. CFD-DEM simulations show that the combined effects of phased pump operation and centralized mixing tank mixing result in the slurry concentration delivered to the riser pipeline staying within ±1% of the mean for up to 57.8% of the system’s operational time. Considering the characteristics of both diaphragm and centrifugal pumps, the system is designed to output high-concentration slurry from the seabed diaphragm pumps, driven solely by wastewater, while centrifugal pumps provide lower-concentration transport by adding supplementary water from a buffer—thus reducing the risk of clogging. Under the constraints of centrifugal pump capacity, the system’s hydraulic parameters were optimized to maximize overall slurry transport efficiency while minimizing the energy consumption from wastewater discharge. The resulting configuration defines the flow rate and slurry concentration of the diaphragm pump group. Compared with conventional centrifugal pump-based transport schemes, the proposed system increases the slurry pipeline efficiency from 53.14% to 55.43% and reduces wastewater discharge-related pipeline resistance losses from 475.9 mH₂O to 361.7 mH₂O. Full article

Collagen peptides derived from fish skin may be limited in food applications due to undesirable flavors. To investigate the effects of Maillard reaction modification on their physicochemical and flavor properties, collagen peptides from tilapia skin were prepared via enzymatic hydrolysis, followed by the Maillard reaction with four reducing sugars (xylose, ribose, glucose and glucosamine) through a combined procedure involving simultaneous enzyme inactivation and Maillard reaction at 100 °C. The resultant Maillard reaction products (MRPs) were characterized by analyzing free amino groups, peptide size distribution and color difference, while the reaction progression was monitored using UV absorption and fluorescence spectroscopy. The flavor profile of MRPs was analyzed through quantitative descriptive sensory evaluation and GC-MS coupled with principal component analysis. Among the four reducing sugars tested, glucosamine-induced Maillard reaction products exhibited the most pronounced physicochemical and sensory improvements. Specifically, glucosamine-MRPs showed the greatest reduction in free amino groups (0.69 μmol/L) and a notable decrease in high-molecular-weight peptides (3.31%), accompanied by an increase in low-molecular-weight fractions. Colorimetric analysis revealed a marked color change (ΔE = 31.78), and spectral analysis further confirmed intensified UV absorbance and fluorescence intensity in the glucosamine group, indicating advanced reaction progression. Sensory evaluation demonstrated a significant reduction in bitterness and enhancement of umami and saltiness. Moreover, GC-MS analysis revealed that the glucosamine-treated group exhibited the most favorable volatile profile, characterized by an increase in aromatic compounds and a substantial decrease in undesirable odorants. This study provides a theoretical basis for controlling the undesirable flavor of collagen peptides through low-extent Maillard reactions by different reducing sugars. Full article

The Voith Schneider Propeller (VSP) operates with blades undergoing an approximately sinusoidal periodic motion along a circular path. Hydrodynamically, the continuous significant variation in the angle of attack between the blades and incoming flow, together with additional inertial effects caused by accelerated rotation, complicates the computation and measurement of hydrodynamic performance. To investigate the unsteady hydrodynamic behavior resulting from this coupled motion, a numerical model incorporating adaptive mesh refinement was developed to simulate VSP performance. Based on insights into the interaction between blade motion and hydrodynamics, an experimental platform was designed using servo motors to achieve precise synchronized blade control, enabling mutual validation between numerical simulations and transient hydrodynamic measurements. Results demonstrate that the coupled blade motion induces nonlinear variations in hydrodynamic forces. Rotational power loss limits VSP efficiency, and a negative thrust regime occurs at high advance coefficients. Rapid blade flipping leads to flow separation, identified as the primary cause of nonlinear lateral forces. The consistency between numerical and experimental results provides reliable data supporting theoretical studies. These findings offer valuable insights for optimizing motion control strategies in cycloidal propeller applications. Full article

To address the tool calibration challenge in robotic systems equipped with grinding tools, this paper proposes a high-precision method utilizing a stylus assembly and the Particle Swarm Optimization (PSO) algorithm. A global optimization strategy is implemented, which simultaneously identifies and compensates for coupled error sources, including the robot’s kinematic (DH) parameters, the tool coordinate frame (TCF), and the stylus tip’s spatial position. This approach transforms the complex calibration task into a constrained, high-dimensional optimization problem. The experimental results demonstrate the method’s effectiveness, reducing the final calibration Root Mean Square Error (RMSE) to below 0.1 mm. Validation through a practical grinding experiment confirmed a significant improvement in machining accuracy, with the workpiece’s axis deviation from the ideal model decreasing from 1.477° to 0.326°, and the maximum contour error being reduced from 1.4 mm to under 0.3 mm. This study provides a robust, low-cost technical solution for tool calibration in complex industrial applications. Full article

This study investigated differences in energy-expenditure (EE) modeling between badminton players of varying competitive levels during aerobic training. It evaluated the impact of sensor quantity and sample size on prediction model accuracy and generalizability, providing evidence for personalized training-load monitoring. Fifty badminton players (25 elite, 25 enthusiasts) performed treadmill running, cycling, rope skipping, and stair walking. Data were collected using accelerometers (waist, wrists, ankles), a heart rate monitor, and indirect calorimetry (criterion EE). Multiple machine learning models (Linear Regression, Bayesian Ridge Regression, Random Forest, Gradient Boosting) were employed to develop EE prediction models. Performance was assessed using R², mean absolute percentage error (MAPE), and root mean square error (RMSE), with further evaluation via the Triple-E framework (Effectiveness, Efficiency, Extension). Elite athletes demonstrated stable, coordinated movement patterns, achieving the best values for R² and the smallest errors using minimal core sensors (typically dominant side). Enthusiasts required multi-site sensors to compensate for greater execution variability. Increasing sensors beyond three yielded no performance gains; optimal configurations involved 2–3 core accelerometers combined with heart rate data. Expanding sample size significantly enhanced model stability and generalizability (e.g., running task R² increased from 0.49 (N = 20) to 0.95 (N = 40)). Triple-E evaluation indicated that strategic sensor minimization coupled with sufficient sample size maximized predictive performance while reducing computational cost and deployment burden. Competitive level significantly influences EE modeling requirements. Elite athletes are suited to a “low-sensor, small-sample” scenario, whereas enthusiasts necessitate a “multi-sensor, large-sample” strategy. Full article

Under the framework of the “dual carbon” goals, promoting the coordinated development of carbon emission efficiency, carbon sink capacity, and high-quality growth has become a critical issue for regional sustainability. Using panel data from 2006 to 2021, this study systematically investigates the three-dimensional coupling coordination among carbon emission efficiency, carbon sink capacity, and high-quality development in the Greater Chang-Zhu-Tan urban agglomeration. The spatiotemporal evolution, spatial correlation characteristics, and influencing factors of the coupling coordination were also explored. The results indicate that the coupling coordination system exhibits an evolutionary trend of overall stability with localized differentiation. The overall coupling degree remains in the “running-in” stage, while the coordination level is still in a marginally coordinated state. Spatially, the pattern has shifted from “northern leadership” to “multi-polar support,” with Yueyang achieving intermediate coordination, four cities including Changde reaching primary coordination, and three cities including Loudi remaining imbalanced. Spatial correlation has weakened from significant to insignificant, with Xiangtan showing a “low–low” cluster and Hengyang displaying a “high–low” cluster. The evolution of hot and cold spots has moved from marked differentiation to a more balanced distribution, as reflected by the disappearance of cold spots. The empirical analysis confirms a three-dimensional coupling mechanism: ecologically rich regions attain high coordination through carbon sink synergies; economically advanced areas achieve decoupling through innovation-driven development; while traditional industrial cities, despite facing the “green paradox,” demonstrate potential for leapfrog progress through transformation. Among the influencing factors, industrial structure upgrading emerged as the primary driver of spatial differentiation, though with a negative impact. Government support also exhibited a negative effect, whereas the interaction between environmental regulation and both government support and economic development was found to be significant. Full article

To address the prevalent issues of oscillation and overshoot in high-voltage circuit breaker motor direct-drive mechanisms under classical PI control, this paper proposes an optimized PI speed loop with active damping characteristics. By first establishing a detailed kinematic and dynamic model of the mechanism, we reveal the inherent coupling between tracking performance, disturbance immunity, and the damping ratio within the classical PI speed loop. Our novel method introduces a speed feedback channel at the output of the PI controller to synthesize equivalent viscous damping, thereby enhancing system stability without compromising responsiveness. Through rigorous simulation and experimental validation, the proposed controller’s effectiveness is demonstrated. Compared with the traditional PI controller, the ADPI method reduces the velocity overshoot to only 5.76% in the startup phase, and the maximum velocity tracking error of the velocity is only 18.62% and the cumulative position tracking error is only 0.632 rad under the actual working condition, which is a reduction of 42.7% in the positional error relative to the traditional PI method. The controller also exhibits low sensitivity to changes in the system’s equivalent rotational inertia. This work provides a low-complexity and easy-to-implement speed loop performance enhancement scheme, ideally suited for the short-duration, high-dynamic-load conditions of high-voltage circuit breaker applications. Full article

The integration of AI-driven optimization into Electronic Design Automation (EDA) enables smarter and more adaptive circuit design, where symmetry and asymmetry play key roles in balancing performance, robustness, and manufacturability. This work presents a model-driven optimization methodology for sizing low-phase-noise LC voltage-controlled oscillators (VCOs) at 5 GHz, targeting Wi-Fi, 5G, and automotive radar applications. The approach uses the EKV transistor model for analytical CMOS device characterization and applies a diverse set of metaheuristic algorithms for both single-objective (phase noise minimization) and multi-objective (joint phase noise and power) optimization. A central focus is on how symmetry—embedded in the complementary cross-coupled LC-VCO topology—and asymmetry—introduced by parasitics, mismatch, and layout constraints—affect optimization outcomes. The methodology implicitly captures these effects during simulation-based optimization, enabling design-space exploration that is both symmetry-aware and robust to unavoidable asymmetries. Implemented in CMOS 180 nm technology, the approach delivers designs with improved phase noise and power efficiency while ensuring manufacturability. Yield analysis confirms that integrating symmetry considerations into metaheuristic-based optimization enhances performance predictability and resilience to process variations, offering a scalable, AI-aligned solution for high-performance analog circuit design within EDA workflows. Full article

As ultra-high resolution liquid crystal displays (LCDs) advance, crosstalk has become a critical challenge due to the reduced spacing of electronic circuits and increased signal frequencies. In particular, vertical crosstalk (V-CT) in vertical-alignment LCDs arises mainly from fringing electric fields generated by data lines, along with secondary contributions from data line–pixel coupling effect, thin-film transistor leakage, and other factors. To resolve V-CT, we propose a memory-efficient compensation algorithm implemented on a field-programmable gate array as a customized timing controller. The proposed algorithm achieves compensation accuracy within 2% while significantly reducing memory requirements. A conventional 7680 × 4320 pixel LCD panel requires approximately 796 MB of memory for compensation data, whereas our method reduces this to only 0.37 MB—a nearly 2000-fold reduction—by referencing only preceding pixel information. This approach enables cost-effective implementation, faster processing, and enhanced image quality. Overall, the proposed method provides a practical and scalable solution for resolving V-CT in 8K LCD panels, establishing a new benchmark for high-resolution display technologies. Full article

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method’s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions. Full article

This study investigates the sensitivity of an urban parameterization scheme of the Weather Research and Forecasting model (WRF). The model sensitivity is tested during the period April–May 2020 over the greater Paris region. The parent domain covers Europe with a 12 km horizontal resolution, with a nested one covering the greater Paris region with a 3 km horizontal resolution. A multi-layer urban scheme called Building Effect Parameterization coupled with the Building Energy Model (BEP-BEM) was applied in two simulations: (1) BEP-BEM Paris, with urban options tailored for the Paris region, which were derived from Earth Observation data, and (2) BEP-BEM Europe, which uses an updated urban parameter table with an estimated average profile for European cities. These two simulations were compared with observations and a WRF simulation using a simple urban parameterization (BULK approach). BULK and multi-layer urban scheme experiments present a similar general error for April, underestimating temperature, while the BEP-BEM runs overestimate temperature for May. The simulation with the advanced tailored urban parameterization over Paris appears to have the best overall performance in this 2-month period. Full article

With the integration of a large number of flexible distributed resources, microgrids have become an important form for supporting the coordinated operation of power sources, grids, loads, and energy storage. The flexibility provided by the point of common coupling is also a crucial regulating resource in power systems. However, due to the complex network constraints within microgrids, such as voltage security and branch capacity limitations, the flexibility of distributed resources cannot be fully reflected at the point of common coupling. Moreover, the flexibility that can be provided externally by different network reconfiguration strategies shows significant differences. Therefore, this paper focuses on optimizing reconfiguration strategies to enhance grid-connected flexibility. Firstly, the representation methods of grid-connected power flexibility and voltage regulation flexibility based on aggregation are introduced. Next, a two-stage robust optimization model aimed at maximizing grid-connected power flexibility is constructed, which comprehensively considers the aggregation of distributed resource flexibility and reconfiguration constraints. The objective is to maximize the grid-connected power flexibility of the small-micro parks. In the first stage of the model, the topology of the small-micro parks is optimized, and the maximum flexibility of all distributed resources is aggregated at the PCC. In the second stage, the feasibility of the solution for the PCC flexible operation range obtained in the first stage is verified. Subsequently, based on strong duality theory and using the column-and-constraint generation algorithm, the model is effectively solved. Case studies show that the proposed method can fully exploit the flexibility of distributed resources through reconfiguration, thereby significantly enhancing the power flexibility and voltage support capability of the small-micro parks network at the PCC. Full article

This study introduces a new class of coupled differential systems described by fractal–fractional Caputo derivatives with both constant and state-dependent delays. In contrast to traditional delay differential equations, the proposed framework integrates memory effects and geometric complexity while capturing adaptive feedback delays that vary with the system’s state. Such a formulation provides a closer representation of biological and physical processes in which delays are not fixed but evolve dynamically. Sufficient conditions for the existence and uniqueness of solutions are established using fixed-point theory, while the stability of the solution is investigated via the Hyers–Ulam (HU) stability approach. To demonstrate applicability, the approach is applied to two illustrative examples, including a predator–prey interaction model. The findings advance the theory of fractional-order systems with mixed delays and offer a rigorous foundation for developing realistic, application-driven dynamical models. Full article