Search Results (1,189)

Crack-templated thin films, inspired by naturally occurring patterns such as leaf venation, spider webs, and the networked structure of dried egg white, represent a paradigm shift in the design of functional materials. Traditionally, cracks in coatings are seen as defects to be avoided due to their potential to compromise mechanical integrity and performance. However, in this context, cracks are deliberately induced and meticulously controlled to serve as templates for versatile applications. This review explores the latest advances in preparation techniques, including solvent evaporation and thermal stress induction, with a focus on the interplay between material properties (e.g., polymers and ceramics) and process parameters (e.g., drying rates and temperature, layer thickness, substrate interactions) that govern crack behavior. The resulting crack patterns offer tunable features, such as density, width, shape, and orientation, which can be harnessed for applications in semitransparent electrodes, flexible sensors, and wearable and energy storage devices. Our study aims to navigate the advancements in crack engineering in the last 10 years and underscores its importance as a purposeful and versatile strategy for next-generation thin-film technologies, offering a novel and affordable approach to transforming perceived defects into assets for cutting-edge thin-film technologies. Full article

The increasing use of large glass surfaces in modern architecture requires robust adhesive solutions that balance aesthetic appeal with structural resilience, particularly in timber–glass applications. This study examines the influence of primer treatments on the shear performance of timber–glass adhesive joints, employing a combination of experimental testing and simulation techniques. Double-lap shear tests with epoxy adhesives assess the impact of various surface treatments on joint stiffness, shear stress distribution, and deformation. Additionally, a finite element model is developed to simulate joint behavior, evaluate failure modes, and analyze displacement patterns. Results indicate that primer applications notably enhance structural integrity by reducing displacement and increasing joint stability, thereby supporting more durable timber–glass assemblies. These findings offer valuable insights for advancing adhesive technologies in architectural components, enabling a closer alignment between structural performance and design innovation in timber–glass systems. Full article

The high prevalence of conditions leading to foot drop highlights the need for devices that restore functionality, enabling patients to regain a natural gait pattern. There is a demand for a portable, lightweight, low-cost, and efficient active ankle-foot orthosis. In this work, we present the prototype of a new design that was simulated in a previous contribution, with a test bench evaluation of the low-level control. The dynamical behavior of a cam suspension interaction with a proportional–integral–derivative controller system for transmission is evaluated. The proposed active orthosis includes a novel cam-based actuator, designed to intervene at the dorsiflexion stage of gait, without influencing the plantar flexion. This design is aimed at specific lower limb ailments that cause a need for assistance only in raising the foot, and it leverages a commercial servomotor to achieve ankle angle tracking. System identification was performed using a test bench, with three degrees of freedom to emulate tibial motion during gait. Response evaluations of the device showed low values for the integral time squared error, peak overshoot, and settling time for step inputs, with and without additional periodic perturbations. The root mean squared error of the device while tracking an ankle angle signal varied from 0.1 to 6.5 degrees, depending on the speed of the changes. Full article

Background/Objectives: Recent research suggests that video games may serve as cognitive training tools to enhance visual working memory (VWM) capacity. However, the effectiveness of game-based cognitive training remains debated, and the underlying neural mechanisms, as well as the relationship between training efficacy and game design factors, are unclear. This study aimed to evaluate the impact of video game training on VWM capacity and explore its neural correlates. Methods: Two groups underwent 56 daily 20 min training sessions with two distinct video games targeting different cognitive skills: a reaction-time training game and a VWM-specific training game. Behavioral assessments included accuracy, hit response times, correct rejection response times, and Cowan’s K values. Neural correlates were measured through Negative Slow Wave (NSW) activity using EEG. Decision tree classification analyses were applied to NSW data across sessions and set sizes to identify patterns linked to VWM capacity. Results: Preliminary results are that both groups showed improvements in behavioral measures (accuracy, response times, and Cowan’s K values). NSW analyses revealed a main effect of set size in both groups, and classification results indicated that NSW patterns differed between groups, across sessions, and set sizes, supporting the relationship between NSW and VWM capacity. Conclusions: These findings contribute to understanding NSW as a neurophysiological correlate of VWM capacity, demonstrating its plasticity through video game training. Simple video games could effectively enhance behavioral and neural aspects of VWM, encouraging their potential as accessible cognitive training tools. Full article

(1) Background: Self-medication, defined as the use of medications without professional supervision, is a common practice that presents both potential benefits and significant risks. This study analyzes the prevalence, patterns, and determinants of self-medication among health professionals in Spain. (2) Methods: A cross-sectional descriptive design was employed with 438 health professionals, predominantly women (81.1%), with a median age of 42 years. The majority of the healthcare workers were nurses (45%). (3) Results: The results revealed a high prevalence of self-medication (59.4%). Analgesics and anti-inflammatory drugs were the most commonly used. Age and professional knowledge emerged as significant factors influencing this behavior. The main reasons for self-medication included the mildness of symptoms, easy access to medications, and previous successful experiences. Digital sources, especially websites, were the most consulted. Confidence in artificial intelligence tools as a clinical resource was moderate, with 18% of participants consulting AI tools, a rate comparable to the 19.5% for scientific databases. Logistic regression analysis identified age, knowledge of recommended doses, and perceived efficacy as significant predictors, while concern about risks acted as a protective factor. (4) Conclusions: This study highlights the need for educational interventions aimed at promoting responsible self-medication practices and mitigating associated risks among healthcare professionals. Full article

The detection of security vulnerabilities in software design patterns and antipatterns is crucial for maintaining robust and maintainable systems, particularly in dynamic Continuous Integration/Continuous Deployment (CI/CD) environments. Traditional static analysis tools, while effective for identifying isolated issues, often lack contextual awareness, leading to missed vulnerabilities and high rates of false positives. This paper introduces a novel framework leveraging Large Language Models (LLMs) to detect and mitigate security risks in design patterns and antipatterns. By analyzing relationships and behavioral dynamics in code, LLMs provide a nuanced, context-aware approach to identifying issues such as unauthorized state changes, insecure communication, and improper data handling. The proposed framework integrates key security heuristics—such as the principles of least privilege and input validation—to enhance LLM performance. An evaluation of the framework demonstrates its potential to outperform traditional tools in terms of accuracy and efficiency, enabling the proactive detection and remediation of vulnerabilities in real time. This study contributes to the field of software engineering by offering an innovative methodology for securing software systems using LLMs, promoting both academic research and practical application in industry settings. Full article

Understanding the relationship between muscle activation and deformation is essential for analyzing arm movement dynamics in both daily activities and clinical settings. Accurate characterization of this relationship impacts rehabilitation strategies, prosthetic development, and athletic training by providing deeper insights into muscle functions. However, direct analysis of raw neuromuscular and biomechanical signals remains limited due to their complex interplay. Traditional research implicitly applied this relationship without exploring the intricacies of the muscle behavior. In contrast, in this study, we explored the relationship between neuromuscular and biomechanical signals via a motion classification task based on a proposed deep learning approach, which was designed to classify arm motions separately using muscle activation patterns from surface electromyography (sEMG) and muscle thickness deformation measured by A-mode ultrasound. The classification results were directly compared through the chi-square analysis. In our experiment, six participants performed a specified arm lifting motion, creating a general motion dataset for the study. Our findings investigated the correlation between muscle activation and deformation patterns, offering special insights into muscle contraction dynamics, and potentially enhancing applications in rehabilitation and prosthetics in the future. Full article

This study investigates the impact of sand-induced ballast fouling on railway track performance, focusing on track stiffness (modulus), settlement, and overall degradation. The research utilized an 18-cubic-foot ballast box designed to replicate real-world track conditions under controlled laboratory settings. A key focus was quantifying voids within clean ballast to establish baseline characteristics, which provided a foundation for evaluating the effects of sand fouling. Two distinct test series were conducted to comprehensively analyze track behavior. The first series investigated pre-existing fouling by thoroughly mixing sand into the ballast to achieve uniform fouling levels. The second series simulated natural fouling processes by progressively adding sand from the top of the ballast layer, mimicking real-world conditions such as those in sandy environments. These methodologies allowed for detailed analysis of changes in track stiffness, deflection, and settlement under varying fouling levels. The findings demonstrate a direct correlation between increasing sand fouling levels and heightened track stiffness and settlement. Dynamic load testing revealed that as void spaces were filled with sand, the track’s flexibility and drainage capacity was significantly compromised, leading to accelerated degradation of track geometry. Settlement patterns and deflection data provided critical insights into how fouling adversely affects track performance. These results contribute significantly to understanding the broader implications of sand-induced fouling on track degradation, offering valuable insights for railway maintenance and design improvements. By integrating void analysis, test series data, and load-deflection relationships, this study provides actionable recommendations for enhancing railway infrastructure resilience and optimizing maintenance strategies in sandy terrains. Full article

Combustion instability is one of the prominent and unavoidable problems in the design of high-performance propulsion systems. This study investigates the heat release rate (HRR) responses in a triple-nozzle swirling nonpremixed combustor under various thermoacoustic self-excited instability modes. Dynamic pressure sensors and high-speed imaging were employed to capture the pressure oscillations within the combustion chamber and the characteristics of flame dynamics, respectively. The results reveal nonlinear bifurcations in the self-excited thermoacoustic instabilities at different equivalence ratios. Significant differences in flame dynamics were observed across the instability modes. In lower frequency modes, the fluctuations in flame length contribute to the driving force of thermoacoustic instability. In relatively high-frequency modes, HRR fluctuations are dominated by the rolling up and convective processes of wrinkles on the flame surface. Alternating regions of gain and damping are observed on the flame surface. At even higher frequencies, both aforementioned HRR fluctuation patterns are simultaneously observed. These findings provide a deeper understanding of the complex interactions between flame dynamics and thermoacoustic instabilities, offering new insights into the design and optimization of nonpremixed combustion systems. The study underscores the importance of considering the spatial and temporal variations in flame behavior to effectively predict and control thermoacoustic instabilities. Full article

Background: Children with attention deficit hyperactivity disorder (ADHD) frequently encounter eating problems. However, qualitative research on the eating problems of these children and the strategies employed by their parents to manage these issues remains limited. This study aimed to explore the eating problems of school-age children with ADHD and the coping strategies used by parents in urban settings like Hong Kong. Methods: A descriptive qualitative design was utilized, employing semi-structured focus group interviews. Purposive sampling was used to recruit 12 parents who voluntarily participated in five focus group sessions. The interviews were conducted in Cantonese, audio-recorded, and transcribed verbatim. Data were analyzed using template thematic analysis to identify key themes and subthemes. Results: Two major themes emerged: challenges affecting ADHD children’s eating behaviors and parental coping strategies in Hong Kong. Children’s eating difficulties stemmed from ADHD-specific behaviors, compounded by Hong Kong’s demanding educational system and urban environmental constraints. In response, parents developed multifaceted coping approaches, ranging from dietary modifications and behavioral management strategies to healthcare resource utilization, while adapting their urban lifestyle to accommodate their children’s needs. Conclusions: Children with ADHD face eating challenges that intersect with Hong Kong’s sociocultural environment, where educational pressure, limited living spaces, and parents’ work schedules influence their eating patterns. Parents adopt integrated Eastern–Western approaches, supported by Hong Kong’s comprehensive healthcare resources spanning professional networks and community programs. Evidence-based dietary guidelines are essential to address ADHD-related nutritional misconceptions. Full article

A novel sandwich structure of a fractal tree-like lattice (SSFL) is proposed. The geometry characteristics were constructed based on the fractal tree-like patterns found in many biological structures, such as giant water lilies and dragon blood trees. The compressive performance of the proposed structures with different fractal orders was experimentally and numerically investigated. The experimental samples were made by 3D printing technology. Axial compression tests were conducted to study the compressive performance and failure mode of the SSFLs. The results indicated that the new structure was good at multiple bearing and energy absorption. The finite element method (FEM) was performed to investigate the influence of geometry parameters on the compression behaviors of the SSFLs. The findings of this study provide an effective guide for using the fractal method to design lattice structures with a high bearing capacity. Full article

The paper investigates the application of advanced machine learning (ML) methodologies, with a particular emphasis on state-of-the-art deep learning models, to predict financial market dynamics and maximize profitability through algorithmic trading strategies. The study compares the predictive capabilities and behavioral characteristics of traditional machine learning approaches, such as logistic regression and support vector machines, with those of highly sophisticated deep learning architectures, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs). The findings underscore the fundamental distinctions between these methodologies, with deeply trained models exhibiting markedly different predictive behaviors and performance, particularly in capturing complex temporal patterns within financial data. A cornerstone of the paper is the introduction and rigorous analysis of a framework to evaluate models, by means of the SAFE framework (Sustainability, Accuracy, Fairness, and Explainability). The framework is designed to address the opacity of black-box ML models by systematically evaluating their behavior across a set of critical dimensions. It also demonstrates how models’ predictive outputs align with the observed data, thereby reinforcing their reliability and robustness. The paper leverages historical stock price data from International Business Machines Corporation (IBM). The dataset is partitioned into a training phase during which the models are calibrated, and a validation phase, used to evaluate the predictive performance of the generated trading signals. The study addresses two primary machine learning tasks: regression and classification. Classical models are utilized for classification tasks, with their outputs directly interpreted as trading signals, while advanced deep learning models are employed for regression, with predictions of future stock prices further processed into actionable trading strategies. To evaluate the effectiveness of each strategy, rigorous backtesting is conducted, incorporating visual representations such as equity curves to assess profitability and key risk metrics like maximum drawdown for risk management. Supplementary performance indicators, including hit rates and the incidence of false positions, are analyzed alongside the equity curves to provide a holistic assessment of each model’s performance. This comprehensive evaluation not only highlights the superiority of cutting-edge deep learning models in predicting financial market trends but also demonstrates the pivotal role of the SAFE framework in ensuring that machine learning models remain trustworthy, interpretable, and aligned with ethical considerations. Full article

Young children typically engage in cognitive control reactively in response to specific situations, rather than proactively preparing for them. The developmental change from reactive to proactive control seems to happen gradually across early development and ultimately results in a qualitatively different behavior pattern. However, existing evidence is mainly based on cross-sectional designs. Thus, this study adopted a longitudinal design to examine the transition from reactive control to proactive control in preschoolers. Sixty preschoolers aged 4 (n = 31) and 5 (n = 29) were recruited and required to complete two cognitive control tasks (i.e., an AX-Continuous Performance Test and a Cued Task-Switching task) twice within a five-month interval. The results showed that the children improved their cognitive control skills across both tasks, demonstrating a predominantly reactive control pattern during the time interval. This improvement reflects an age-related gradual change, which is a preparation for evolving into a qualitatively different behavioral pattern over time. These findings provide longitudinal evidence for the developmental change from reactive to proactive control in early childhood strategies. Full article

The integration of photovoltaic (PV) systems into tensioned membrane structures presents a significant advancement for sustainable applications in the built environment. However, a critical technical challenge remains in the substantial strains induced by external loads, which can compromise both PV efficiency and the structural integrity of the membrane. Current design methodologies prioritize stress, deflection, and ponding analysis of tensioned membranes. Strain behavior of whole structures, a key factor for ensuring long-term performance and compatibility of PV-integrated membranes, has been largely overlooked. This study addresses this gap by examining the whole membrane structure designed for PV integration, with the aim of optimizing the membrane to provide suitable conditions for efficient energy transfer while minimizing membrane strains. For this purpose, it provides a comprehensive strain analysis for full-scale hyperbolic paraboloid (hypar) membrane structures under various design parameters and external loads. Employing the Finite Element Method (FEM) via Sofistik software, the research examines the relationship between load type, geometry, material properties, and patterning direction of membranes to understand their performance under operational conditions. The findings reveal that strain behavior in tensioned membrane structures is strictly influenced by these parameters. Wind loads generate significantly higher strain values compared to snow loads, with positive strains nearly doubling and negative strains tripling in some configurations. Larger structure sizes and increased curvature amplify strain magnitudes, particularly in parallel patterning, whereas diagonal patterning consistently reduces strain levels. High tensile-strength materials and optimized prestress further reduce strains, although edge type has minimal influence. By systematically analyzing these aspects, this study provides practical design guidelines for enhancing the structural and operational efficiency of PV-integrated tensioned membrane structures in the built environment. Full article

This study explores the mechanisms underlying the enhanced anti-wear and thermal fatigue performance of laser-treated bionic brake drums, aiming to extend their service life and improve design quality. Bionic brake drums treated with laser patterns—point, stripe, and grid—were tested with semi-metallic, non-asbestos organic (NAO), and ceramic brake pads. A mechanical model was developed to analyze wear performance, and bench tests were conducted to assess wear patterns. Thermal fatigue tests examined the impact of thermal cycling on the treated drums’ wear behavior. The results reveal that laser-treated bionic brake drums significantly outperformed untreated ones in both wear resistance and thermal fatigue. Among the treatments, the grid pattern showed the best wear performance, and thermal fatigue life was improved by 27% for the striped pattern and 38% for the grid pattern. The study concludes that laser treatment effectively enhances both wear resistance and thermal fatigue performance in bionic brake drums, especially for the grid pattern, offering valuable insights for future brake drum design. Full article