Search Results (30,169)

Background and Objectives: While the effectiveness of video modeling (VM) in teaching academic, daily living, and social skills to individuals with Autism Spectrum Disorder (ASD) is frequently investigated, studies examining the use of VM in teaching gymnastics-based motor skills are limited. This study aimed to examine the effects of VM on the acquisition and maintenance of a gymnastics-based motor skills in preschool children with ASD. Methods: The study employed a multiple-probe method across participants in a single-subject research design. Three preschool children diagnosed with mild ASD participated in this study. Baseline, intervention, and follow-up data were systematically collected and analyzed. Social validity data were obtained through semi-structured interviews with parents and special education teachers. Results: The percentage of correct responses increased throughout the VM intervention sessions, and all participants reached the proficiency criterion. Follow-up data collected after the intervention showed that the acquired skill was maintained, and the percentages of correct responses ranged from 80% to 100%. Social validity findings revealed that both teachers and parents perceived VM as an effective and feasible teaching approach for teaching motor skills to children with ASD. Conclusions: The research findings demonstrate that VM is an effective and socially valid teaching method for teaching and maintaining gymnastics-based motor skills in preschool children with ASD. These results contribute to the existing literature by demonstrating the applicability of video modeling in the context of gymnastics-based training. Full article

During the high-temperature deposition of tungsten thin films on alumina ceramic substrates, the inherent mismatch in thermal expansion coefficients frequently triggers interfacial delamination, where uncontrollable factors in stochastic surface topographies can exacerbate localized stress concentrations. To resolve these interfacial failures, the enhancement of interfacial adhesion through a deterministic surface microgroove design is identified as the general objective of the present research. Within this framework, the establishment of a robust quantitative mapping between the transverse scratching offset distances and the resultant periodic microgeometry is first pursued as a specific experimental objective. This methodological approach effectively transforms the stochastic nature of the substrate into deterministic geometric configurations. Second, a specific numerical objective is fulfilled by evaluating the interfacial stress redistribution and damage evolution utilizing refined thermomechanical coupled simulations based on the cohesive zone model. The integrated findings demonstrate that optimizing the microgroove spacing effectively governs the morphological transition and broadens stress diffusion pathways to mitigate thermal mismatch effects. Specifically, the structural optimization at a spacing of 28.8 μm facilitates an approximately 31.8% reduction in the maximum interfacial stress and a 10% decrease in the average film stress compared to the 13.6 μm spacing. Finally, this research clarifies the underlying mechanisms of stress buffering and provides a rigorous engineering methodology for the structural design of reliable high-performance ceramic–metal interfaces in extreme environments. Full article

Tribotronic machine elements achieve active control by incorporating sensing, control and actuation into engineering components that are otherwise conventionally passive. There has been a trend towards the development and use of active tribological (tribotronic) components over recent years. This paper briefly recounts the historical development of tribotronics, then presents two examples of research on components as case studies based on research by the authors to demonstrate how tribotronics can drive forward the technical capabilities of two common machine elements. In this context, this paper deals with the tribotronics of tilting-pad thrust bearings as well as active lubrication for internal combustion engine cylinder systems. The aim of the paper is to demonstrate how tribotronic technology can be applied to realise transformative reductions in energy loss by controlling friction well beyond those that could be gained by more conventional improvements in design or the use of enhanced materials, In addition to the technical discussion, this paper incorporates a short reflection the very significant financial and environmental gains that can potentially be obtained by using tribotronic components in the field. Finally, closing remarks are made regarding the more general advantages of tribotronic approaches and the potential future of this technology. Full article

Cu/Ni/Ag composite materials are widely used in the manufacturing of electrical connector contacts due to their excellent electrical conductivity and good wear resistance. During hot plugging and unplugging operations, electrical connectors inevitably generate arc discharge, leading to melting, splashing, and erosion of the contact material, which severely threaten system reliability and service life. To investigate the arc behavior of Cu/Ni/Ag composite electrical connectors during plugging and unplugging, this paper establishes a multiphysics coupling model incorporating electric field, fluid heat transfer, and laminar flow based on the COMSOL simulation software (version 6.2). The model employs a multiphysics coupling approach, incorporating electric field, fluid heat transfer, and laminar flow, to systematically simulate the formation and evolution mechanisms of the arc during plugging and unplugging. The study focuses on analyzing the effects of plugging and unplugging speed, operating voltage, and arc gap distance on the arc, exploring the temporal and spatial evolution characteristics and distribution patterns of arc temperature. The simulation results reveal that the arc temperature follows a radially decreasing gradient, with the core region exceeding 10,000 K. When the operating voltage increases to 1000 V, the arc peak temperature rises to 1.3 × 10⁴ K. As the arc gap distance increases, the arc coverage area expands, and the peak arc temperature increases by approximately 2% to 8%. As the plugging/unplugging speed is increased to 500 mm/s, the peak temperature of the arc increases from 1.19 × 10⁴ K to 1.3 × 10⁴ K. The distribution characteristics of the magnetic field are clearly correlated with the arc temperature field and the electric field intensity distribution and the current density also exhibits typical constriction characteristics. Prolonged arc duration is correlated with an upward trend in peak temperature. Further analysis indicates that the temperature distribution characteristics of the arc are constrained by the competition mechanism of energy deposition and diffusion, while the evolution characteristics of the arc are regulated by the coupling effect of electromagnetic field and mechanical work. The research results provide a theoretical basis and simulation methods for the design of arc-resistant structures in Cu/Ni/Ag composite electrical connectors. Full article

Industrial emissions of large amounts of CO₂ have seriously affected human health, making it imperative to reduce atmospheric CO₂ concentrations. However, carbon capture technologies such as chemical absorption and membrane separation are still limited by high regenerative energy costs, corrosion, and low efficiency in diluting flue gas. Within this technological landscape, physical adsorption separation technology, due to its advantages such as a wide operating temperature range, low equipment corrosivity, and low regeneration energy consumption, has gradually become a research hotspot in carbon capture technology. The core of physical adsorption lies in finding high-quality adsorbents. Metal–organic frameworks (MOFs), with their ultra-high specific surface area, tunable pore structure, and abundant functionalization sites, are considered highly promising next-generation CO₂ adsorbent materials. This review summarizes strategies for modifying MOFs to improve CO₂ adsorption performance, focusing on aperture adjustment, doped metal ions, functional group doping, and computational screening. Performance enhancements are mechanism-dependent rather than simply additive. Moderate aperture adjustment and defect engineering can improve gas selectivity and CO₂ capture capacity, while excessively narrow pores sacrifice available pore volume and gas diffusion. Doped metal ions, particularly in MOF-74 and related materials, can enhance CO₂ capture capacity while controlling framework integrity and dopant composition. Functional group Doping remains an effective method for capturing low-partial-pressure CO₂. Computational screening is shifting from ranking based on single adsorption capacity to a comprehensive consideration that includes humidity tolerance, stability, and regenerability. Overall, under industrial conditions, modified MOFs should be evaluated by balancing affinity, selectivity, capacity, stability, and energy efficiency. This review provides guidance for the rational design of MOF-based carbon capture adsorbents. Full article

To systematically investigate the combined effects of moisture content, confining pressure, and loading rate on the mechanical properties of weakly cemented soft rock, this study focuses on the Jurassic coal measures from the Hoxtolgay coalfield in Xinjiang. A series of uniaxial and triaxial compression tests were conducted under varying moisture states, loading velocities, and confining pressures. Complementary X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brazilian splitting tests were performed to analyze the microstructural evolution and tensile failure characteristics. The experimental results demonstrate that moisture content acts as the primary governing factor for mechanical degradation; increased hydration promotes clay mineral swelling and attenuates inter-granular cementation, leading to a continuous reduction in both compressive and tensile strengths, as well as the elastic modulus. Conversely, confining pressure consistently enhances these macroscopic mechanical parameters by restricting lateral deformation. While the loading rate alters the mechanical response, its impact is secondary compared to the definitive effects of moisture and stress constraints. Furthermore, by utilizing established stress–strain-based indices, the study quantitatively evaluates the brittleness characteristics, confirming that hydration fundamentally drives the rock mass from a brittle state toward ductility. This research elucidates the coupled degradation mechanisms of highly sensitive soft rock, providing a theoretical foundation for stability design and risk assessment in underground geotechnical engineering. Full article

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article

Children with Autism Spectrum Disorder (ASD) frequently exhibit marked impairments in nonverbal communication, particularly in eye contact, which serves as a foundational element for social interaction and relational development. This study evaluated the effectiveness of an early intervention program utilizing interactive games supported by Augmented Reality (AR) technology to enhance eye contact behaviors, specifically initiation and maintenance, in children with autism. Using a multiple baseline across participants single-case experimental design, four boys (aged 5–7 years) diagnosed with ASD participated in an 8-week intervention at a specialized center in Saudi Arabia. The intervention featured tablet-based, gamified AR tasks incorporating real-time visual feedback, graduated difficulty levels, and reinforcement mechanisms designed to elicit social gaze and sustained eye contact. Eye contact duration and frequency were measured during structured social interactions via systematic direct observation. The results demonstrated significant improvements across all participants, with the mean duration of eye contact increasing from a baseline of 2.0 s to 5.8 s post-intervention. Visual analysis revealed robust treatment effects, further supported by substantial Tau-U effect sizes (range = 0.89–0.96; M = 0.93). Follow-up data collected three weeks post-intervention confirmed the maintenance of gains for three of the four participants. These findings suggest that AR-based interventions provide an effective and culturally responsive approach for enhancing specific nonverbal communication behaviors among children with autism in Middle Eastern contexts. Implications for clinical practice and directions for future research are discussed. Full article

This study aims to examine the improvement of algebra problem-solving ability and metacognitive awareness among junior high school students through the use of visualization based on a deep learning approach. The research employed a quantitative method with a quasi-experimental design, specifically a pretest–posttest control group design. The population consisted of all students from public schools in Tangerang City, Indonesia. The sample comprised seventh-grade students studying algebra. A purposive sampling technique was used to determine the experimental and control groups, with a total sample size of 51 students. The instruments included an algebra problem-solving ability test consisting of nine essay questions and a metacognitive awareness questionnaire with 52 items. Data were collected using these two instruments, with a pretest administered before the intervention and a posttest administered afterward. Data analysis was conducted using a prerequisite test, continued with independent sample t-tests, nonparametric tests, ANCOVA, and multiple linear regression. The results based on statistics indicated a significant improvement in students’ algebra problem-solving ability with a large effect. Nevertheless, the absolute increase in problem-solving scores in the experimental group is very small (N-gain mean = 0.02). Additionally, metacognitive awareness was not found to be a significant predictor of problem-solving ability; instead, initial ability (pretest) emerged as the strongest predictor. Only understanding the problem has a moderate effect; planning strategies has a small effect, and otherwise there is no effect. In conclusion, the use of visualization-based worked examples with a deep learning approach has a statistically significant effect, but its impact on improving students’ abilities should be interpreted with caution. So the practical effects of the intervention are limited; however, metacognitive awareness is not the main predictor in algebra problem-solving ability. Full article

Many physics educators seek to improve their courses but feel constrained by traditional post-secondary structures and norms. Instructors often perceive a false tension between fostering inclusive learning environments and maintaining the rigor central to the discipline. The Effective Practices for Physics Programs (EP3) Guide synthesizes decades of research-based recommendations for improving physics education. However, it offers limited guidance on how to integrate these diverse recommendations into a coherent, course-level approach—a responsibility that falls to individual instructors, whose graduate training prepared them primarily as researchers rather than as educators. This paper begins by motivating and introducing the Practicing Professionalism Framework (PPF), a course design framework developed in alignment with EP3 recommendations that encourages development of professional skills in a way that connects students’ interests and values to the broader physics community. We present the PPF in sufficient detail to enable motivated faculty to adopt and adapt it as a research-informed tool for aligning their course design with both their professional values and instructional goals. Next we present the PPF implemented in two very different instructional contexts, demonstrating how the PPF can offer a structured pathway for making courses more inclusive while preserving disciplinary rigor. We conclude with observations across the two case studies. Full article

This paper reviews recent work on dynamic virtual power plants (DVPPs) using an Energy–Information–Market framework. It addresses the important problem of how DVPPs can support low-inertia power system operation and feeder-level stability under high renewable penetration. First, system-level studies on low-inertia operation and frequency control are used to frame quantitative requirements on rate of change of frequency, nadir, and quasi-steady-state limits. Second, energy-layer models are surveyed, including participation-factor-based DVPP controllers, grid-forming architectures, model-free frequency regulation, and robust frequency-constrained scheduling for allocating virtual inertia and fast frequency response (FFR) across distributed energy resource fleets. Third, information-layer and market-layer models are reviewed, covering stochastic and robust bidding, distribution locational marginal price-based clearing, peer-to-peer and community markets, privacy-preserving coordination, and emerging governance and cybersecurity schemes for DVPP participation. Across these strands, much of the literature remains centred on steady-state active and reactive power dispatch, with dynamic security enforced as constraints rather than formulated as verifiable and tradable services. This review identifies gaps in dynamic metrics and benchmarks, forecasting of available inertia and FFR capacity, market-physics co-design, multi-aggregator interaction, and experimentally validated DVPP implementations. These findings suggest that DVPPs can “sell stability” at the feeder level only through co-designed control, information, and market mechanisms and outline a research roadmap for this purpose. Full article

In recent years, multi-view learning has received extensive research interest. Most existing multi-view learning methods often rely on well-annotated data to improve decision accuracy. However, noisy labels are ubiquitous in multi-view data due to imperfect annotations. Although some methods have achieved promising performance using robust-loss designs and implicit regularization, they fail to explicitly model the reliability of the supervision signal and fail to dynamically correct noisy labels during training. Clearly, this largely constrains their performance ceiling. To deal with this problem, we propose an Error-Entropy-Guided Distillation network (EGD) for noisy multi-view classification. In this framework, we first design an Error-Entropy (EE) metric to explicitly evaluate the reliability of sample-wise supervision, which serves as the basis for identifying and filtering noisy labels. On this basis, we adopt the distillation paradigm based on Error-Entropy (EE). The teacher model provides the student with soft label distributions that are less affected by noisy labels in the early training stage. To further mitigate noise memorization and accumulated confirmation bias, we propose a periodic memory-clearing strategy and supervision signal update strategy to prevent the teacher from error memorization and accumulating confirmation bias. Meanwhile, the student model learns from the soft supervision of the teacher to capture structured inter-class relationships. Additionally, a consistency module is employed to enhance the consistency of the student across multiple views. Extensive experiments on five benchmark datasets demonstrate that EGD consistently outperforms state-of-the-art multi-view learning methods under various noise levels. Full article

The growing interest in the role of the gut microbiome in athletic performance has led to the application of various sequencing technologies in this field. This review critically examines the sequencing methodologies used in microbiome studies on physical performance and sport, comparing their advantages, limitations, and applicability. In particular, the focus is on 16S metabarcoding and shotgun metagenomics, evaluating how these methodological approaches influence the interpretation of results in sports contexts. Close attention is directed toward technical challenges, methodological biases, and future perspectives, including emerging technologies and multi-omics approaches. This review aims to bridge the gap between methodological rigor and sports-specific applicability, providing evidence-based methodological guidance to support researchers in designing robust athlete microbiome studies and translating sequencing-derived findings into concrete applications for performance and sports health. Full article

Due to their aesthetic qualities and versatile applications, medicinal and aromatic plants are an important component of landscape systems. The diversity of color, shape, and texture observed in the vegetative and reproductive organs of these plants contributes to visual composition, while their medicinal and aromatic properties enhance their ecological and socio-cultural significance. However, many taxa are underrepresented in landscape planning applications. This study examined the diversity of medicinal and aromatic plant taxa identified at the Iğdır University Şehit Bülent Yurtseven Campus in Iğdır Province, Turkey, using a descriptive approach. Plant taxa were evaluated based on their families, life forms, leaf characteristics, flowering periods, and medicinal and aromatic properties. Multivariate analyses were conducted to examine phenological similarities among the taxa. A total of 98 plant taxa were identified; 66 taxa possess only medicinal properties, one taxon possesses only aromatic properties, and 31 taxa possess both. These findings reveal that the campus is home to a wide variety of medicinal and aromatic plant taxa, with characteristics relevant to planting layout and species selection. Consequently, this study provides a descriptive foundation for further research on how such taxa can be incorporated into campus planting designs and green space planning. Full article

In an era of rapid digital transformation, brand activism has emerged as a prominent strategy through which organizations seek to signal social and environmental commitment while engaging increasingly sceptical and digitally empowered consumers. Within this context, perceived authenticity has become a critical evaluative mechanism shaping how digital brand activism is interpreted and whether it contributes to sustainable consumption and trust-based market outcomes. This study examines how perceived authenticity in digital brand activism is associated with consumer trust, attitudes toward socially engaged brands, and behavioral intentions that support sustainable consumption. Grounded in attribution theory and the authentic brand activism framework, the study adopts a quantitative, cross-sectional design based on an online survey of 240 consumers. The findings indicate that perceived authenticity is strongly associated with higher levels of consumer trust and more favorable attitudes toward digitally activist brands, reinforcing authenticity as a key trust-building mechanism in digital environments. Trust and attitudes are, in turn, positively associated with behavioral intentions such as purchasing, recommending, and willingness to pay a premium for sustainable products. However, behavioral intentions are weaker than trust and attitudinal evaluations, providing evidence of a persistent attitude–behavior gap that limits the translation of positive digital evaluations into concrete sustainable consumption outcomes. Exploratory results further suggest that the association between perceived authenticity of brand and behavioral intentions operates primarily through trust and attitudes rather than through a strong direct relationship. By clarifying these indirect pathways, the study advances attribution-based explanations of digital brand activism and contributes to research on smart innovation and digital sustainability by highlighting the role of authenticity in trust-based market outcomes. It also underscores the importance of authentic, data-informed digital strategies for fostering consumer trust, aligning brand activism with ESG principles, and supporting sustainable growth in digitally empowered markets. Full article