Search Results (14,208)

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

E-textiles, a type of textile integrated with conductive sensors, allows users to freely utilize any area of the body in a convenient and comfortable manner. Thus, interactions with e-textiles are attracting more and more attention, especially for text input. However, the functional aging of e-textiles affects the characteristics and even the quality of the captured signal, presenting serious challenges for character recognition. This paper focuses on studying the behavior of e-textile functional aging and alleviating its impact on text input with an unsupervised domain adaptation technique, named A²TEXT (aging-aware e-textile-based text input). We first designed a deep kernel-based two-sample test method to validate the impact of functional aging on handwriting with an e-textile input. Based on that, we introduced a so-called Gabor domain adaptation technique, which adopts a novel Gabor orientation filter in feature extraction under an adversarial domain adaptation framework. We demonstrated superior performance compared to traditional models in four different transfer tasks, validating the effectiveness of our work. Full article

The Child-Focused Injury Risk Screening Tool (ChildFIRST) assesses movement competence in children and currently uses a dichotomous scoring scale, which, while simple and practical, may lack the precision needed for nuanced movement skill analysis. This study compared the inter- and intra-rater reliability of the ChildFIRST when scored using a dichotomous scale versus a seven-point Likert scale. Fourteen trained raters evaluated video recordings of eight children performing ten standardized movement tasks using both scales across two sessions. Reliability was assessed using intraclass correlation coefficients (ICCs). The dichotomous scale demonstrated moderate to excellent inter-rater reliability (ICC = 0.50–1.00) and good to excellent intra-rater reliability (ICC = 0.75–1.00). The seven-point scale showed similar inter-rater reliability but generally lower intra-rater reliability (ICC = 0.50–1.00). In addition, raters preferred the dichotomous scale in terms of practicality (91.6%), feasibility (75%), and overall usability (66.6%). These findings suggest that while both scales provide comparable inter-rater agreement, the dichotomous format offers greater consistency across repeated ratings and is more favorably received by users. The dichotomous scoring system is therefore recommended for continued use in field-based screening and future applications of the ChildFIRST. Full article

Purpose: Despite significant attention being paid to compression garments (CG) in the sports field, there remains ongoing debate regarding their actual effectiveness in enhancing athletic performance and expediting post-exercise recovery. This article examines their various aspects, with a focus on CG design and the materials they are made of, aiming to analyze the importance of personalized compression strategies based on individual anthropometric measurements and non-linear compression designs. Methods: Using anthropometric analysis of 40 healthy participants, this study examines the morphological characteristics of the lower limb and their implications for CG design. Results: Measurements of limb length and circumferences revealed complex interactions among anatomical variables, emphasizing the need for customized and adaptable device design. Finite element simulations clarified the challenges in achieving uniform pressure gradients along the lower limb, highlighting the limitations of one-piece devices and suggesting tailored segmented designs for individual limb segments. Conclusion: The results demonstrate that one-piece devices often fail to provide optimal compression due to non-linear variations in limb dimensions. Conversely, segmented devices, particularly those with bilinear progression, exhibited superior performance in applying targeted compression across different limb segments. This more detailed approach to customization could significantly contribute to optimizing outcomes and user comfort. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from digital twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R²) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model’s robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems. Full article

This paper introduces and empirically evaluates Aurea EDEN, a novel 3D visualization library for e-commerce customer journey analytics. Traditional 2D dashboards often fragment performance data from the structural context of the conversion funnel, increasing analysts’ cognitive load. To address this, Aurea EDEN provides a method for programmatically generating 3D funnel diagrams where Key Performance Indicators (KPIs) are encoded directly as geometric properties. This approach is validated through two empirical studies: a comparative user study and a technical performance benchmark. The user study demonstrates that, for common analytical tasks, the 3D visualization significantly reduces task completion time and perceived cognitive workload (measured via NASA-TLX) while improving usability (measured via SUS) compared to a traditional 2D dashboard baseline. The benchmark confirms the library’s scalability for complex, real-world scenarios. These findings indicate that integrating KPIs as geometric attributes in a 3D model offers a more effective and efficient alternative for analyzing e-commerce funnels, providing a tangible contribution to the fields of information visualization and e-commerce analytics. Full article

Radio spectrum planning has become increasingly important, since the radio spectrum is a scarce resource. Moreover, the utilization of millimeter wave (mmWave) frequencies with fifth-generation (5G) standards has made radio planning more compelling. Considering their different strengths and weaknesses, it is essential to know when mmWave frequencies should be selected in radio planning. In this paper, an approach with edge learning is developed to provide smart spectrum recommendations on which frequency bands should be used for a region. Using the proposed approach, radio spectrum planning can be carried out more efficiently, especially for the frequency ranges of mmWave communications. The proposed approach is designed with a distributed structure, based on awareness of the environment and ambient intelligence. This approach can be performed for each transmission point considering the environment information of the related coverage area. As a result, radio spectrum planning can be conducted for an entire region with the proposed system. The results show that this study both enhances overall user satisfaction and provides reasonable recommendations to operators in the transition to mmWave usage. Thus, the developed approach can be utilized for 5G and beyond communications. Specifically, this methodology is based on applying supervised ML algorithms to a synthetically generated dataset, and the best model achieves around 80% classification accuracy, demonstrating the feasibility of the approach. These quantitative results confirm its practicality and provide a concrete baseline for future studies. Full article

(1) Background: trays are surgery-specific sets of required materials and medical devices, assembled in consultation between manufacturer and user, and provided in a sterile package. (2) Methods: in a high-volume urological center performing 11,920 operations/procedures annually (2023), we prospectively evaluated the effect of trays compared with the standard approach in a comparative study of 64 operations conducted between 29 October and 30 November 2024. The primary endpoints were the amount of operating room (OR) waste (volume/cm³, weight/g) and setup time (minutes). The secondary endpoint was the workflow assessment by nursing staff, rated on a numerical score (0–10) across seven relevant domains. (3) Results: for endourological procedures, setup time was reduced by 35%, operating room (OR) waste by 34%, and waste volume by 19.0%. Workflow was positively rated with a mean score of 9.75/10. For major open procedures, setup time was reduced by 43%, waste weight by 24.8%, and waste volume by 32%. Workflow was positively rated with a mean score of 8.9/10. (4) Conclusions: Trays have a sustainable and significant impact on reducing OR waste, save nursing staff preparation time, and facilitate improved workflow in the operating room. Full article

With the rapid growth of Artificial Intelligence of Things (AIoT), identity management and trusted communication have become critical for system security and reliability. Continuous AI learning and large-scale device connectivity introduce challenges such as permission drift, cross-domain access, and fine-grained API calls. Traditional identity management often fails to balance privacy protection with efficiency, leading to risks of data leakage and misuse. To address these issues, this paper proposes a blockchain-based self-sovereign identity (SSI) management mechanism for AIoT. By integrating SSI with a zero-trust framework, it achieves decentralized identity storage and continuous verification, effectively preventing unauthorized access and misuse of identity data. The mechanism employs selective disclosure (SD) technology, allowing users to submit only necessary attributes, thereby ensuring user control over self-sovereign identity information and guaranteeing the privacy and integrity of undisclosed attributes. This significantly reduces verification overhead. Additionally, this paper designs a context-aware dynamic permission management that generates minimal permission sets in real time based on device requirements and environmental changes. Combined with the zero-trust principles of continuous verification and least privilege, it enhances secure interactions while maintaining flexibility. Performance experiments demonstrate that, compared with conventional approaches, the proposed zero-trust architecture-based SSI management mechanism better mitigates the risk of sensitive attribute leakage, improves identity verification efficiency under SD, and enhances the responsiveness of dynamic permission management, providing robust support for secure and efficient AIoT operations. Full article

The rapid evolution of human–technology interaction necessitates continuous sensorimotor adaptation to new digital interfaces and tasks. Mobile typing, defined as text entry on smartphone touchscreens, offers a compelling example of this process, requiring users to adapt fine motor control and coordination to a constrained virtual environment. Aligned with the embodied cognition framework, understanding these digital sensorimotor experiences is crucial. A key theoretical question is whether these skills primarily involve adaptation of existing motor patterns or necessitate de novo learning, a distinction particularly relevant across generations with differing early sensorimotor experiences. This narrative review synthesizes current understanding of the sensorimotor aspects of smartphone engagement and typing skill evaluation methods. It examines touchscreen competence, skill acquisition, diverse strategies employed, and the influence of interface constraints on motor performance, while also detailing various sophisticated performance metrics and analyzing different data collection methodologies. Research highlights that analyzing typing behaviors and their underlying neural correlates increasingly serves as a potential source of behavioral biomarkers. However, while notable progress has been made, the field is still developing, requiring stronger methodological foundations and crucial standardization of metrics and protocols to fully capture and understand the dynamic sensorimotor processes involved in digital interactions. Nevertheless, mobile typing emerges as a compelling model for advancing our understanding of human sensorimotor learning and cognitive function, offering a rich, ecologically valid platform for investigating human-world interaction. Full article

Understanding how trust in artificial intelligence evolves is crucial for predicting human behavior in AI-enabled environments. While existing research focuses on initial acceptance factors, the temporal dynamics of AI trust remain poorly understood. This study develops a temporal trust dynamics framework proposing three phases: formation through accuracy cues, single-error shock, and post-error repair through explanations. Two experiments in financial advisory contexts tested this framework. Study 1 (N = 189) compared human versus algorithmic advisors, while Study 2 (N = 294) traced trust trajectories across three rounds, manipulating accuracy and post-error explanations. Results demonstrate three temporal patterns. First, participants initially favored algorithmic advisors, supporting “algorithmic appreciation.” Second, single advisory errors resulted in substantial trust decline (η² = 0.141), demonstrating acute sensitivity to performance failures. Third, post-error explanations significantly facilitated trust recovery, with evidence of enhancement beyond baseline. Financial literacy moderated these patterns, with higher-expertise users showing sharper decline after errors and stronger recovery following explanations. These findings reveal that AI trust follows predictable temporal patterns distinct from interpersonal trust, exhibiting heightened error sensitivity yet remaining amenable to repair through well-designed explanatory interventions. They offer theoretical integration of appreciation and aversion phenomena and practical guidance for designing inclusive AI systems. Full article

Virtual networks have emerged as a promising solution for enabling diverse users to efficiently share bandwidth resources over optical network infrastructures. Despite the invention of various schemes aimed at ensuring secure isolation among virtual networks, the security of data transfer in virtual networks remains a challenging problem. To address this challenge, the concept of evolving traditional optical networks into key programmable optical networks (KPONs) has been proposed. Inspired by this, this paper delves into the establishment of secure virtual networks over KPONs, in which the information-theoretically secure keys can be supplied for ensuring the information-theoretic security of data transfer within virtual networks. A layered architecture for secure virtual network provisioning over KPONs is proposed, which leverages software-defined networking to realize the programmable control of optical-layer resources. With this architecture, a heuristic algorithm, i.e., the key adaptation-based secure virtual network provisioning (KA-SVNP) algorithm, is designed to dynamically allocate key resources based on the adaption between the key supply and key demand. To evaluate the proposed solutions, an emulation testbed is established, achieving millisecond latencies for secure virtual network establishment and deletion. Moreover, numerical simulations indicate that the designed KA-SVNP algorithm performs superior to the benchmark algorithm in terms of the success probability of secure virtual network requests. Full article

With the global expansion of hydrogen infrastructure, the safe and efficient transportation of hydrogen is becoming more important. In this study, several technical factors, including material degradation, pressure variations, and monitoring effectiveness, that influence hydrogen transportation using pipelines are examined using system dynamics. The results show that hydrogen embrittlement, which is the result of microstructural trapping and limited diffusion in certain steels, can have a profound effect on pipeline integrity. Material incompatibility and pressure fluctuations deepen fatigue damage and leakage risk. Moreover, pipeline monitoring inefficiency, combined with hydrogen’s high flammability and diffusivity, can raise serious safety issues. An 80% decrease in monitoring efficiency will result in a 52% reduction in the total hydrogen provided to the end users. On the other hand, technical risks such as pressure fluctuations and material weakening from hydrogen embrittlement also affect overall system performance. It is essential to understand that real-time detection using hydrogen monitoring is particularly important and will lower the risk of leakage. It is crucial to know where hydrogen is lost and how it impacts transport efficiency. The model offers practical insights for developing stronger and more reliable hydrogen transport systems, thereby supporting the transition to a low-carbon energy future. Full article

Background: currently, there is a growing trend toward multifunctional cosmetics, which combine several active ingredients in a single product to enhance efficacy and user convenience. As ingredients may influence one another, it is important to study the behavior of mixing multiple compounds in complex formulations, especially regarding their interaction with the skin. Piceatannol, for instance, is a naturally occurring stilbene recognized for its in vitro potent antioxidant, anti-inflammatory, and anti-aging activities, making it a promising candidate for dermocosmetic use in suncare. But despite its beneficial biological activities, its cutaneous permeation remains poorly understood, particularly when delivered from complex formulations containing multiple ingredients. Objectives: in this sense, this study aimed to evaluate the in vitro skin diffusion profile of piceatannol from a passion fruit seed extract (P_ext) incorporated into a topical base (B_em) or an organic sunscreen emulsion (O_em), with or without a spilanthol-rich Acmella oleracea extract (J_ext) used as a natural permeation enhancer. Methods: due to ethical and variability issues with human and animal skins, the Strat-M™ synthetic membrane was chosen as a standardized model for the in vitro skin permeation assays. Piceatannol localization within membrane layers was examined by confocal Raman microscopy (CRM), while compound identification in donor and receptor compartments was performed via UHPLC-DAD. Results: piceatannol from B_em was detected up to 140 µm from the Strat-M™ surface and exceeded 180 µm in depth when J_ext and organic sunscreens were included in the formulation. Notably, formulations containing J_ext and those based on O_em promoted enhanced accumulation in both the stratum corneum and deeper skin layers, suggesting an improved delivery potential in lipid-rich vehicles. Conclusions: even though some instability issues were observed, piceatannol penetration into Strat-M™ from the proposed formulations was confirmed, and the results provide a foundation for further research on its topical delivery, supporting the rational development of formulations capable of harnessing its demonstrated biological properties. Full article