Search Results (12,841)

Traditional craft education relies heavily on hands-on practice; however, novice learners often struggle with procedural complexity, material behavior, and the tacit knowledge typically transmitted through prolonged apprenticeship. This paper presents an integrated framework that combines semantic Knowledge Graphs (KGs), real-time Finite Element Method (FEM) simulation, and high-fidelity physically based rendering (PBR) to support the teaching, understanding, and preservation of traditional crafts. Craft processes are modelled as ontologically grounded KGs that capture tools, materials, actions, decision points, and common procedural errors through an extensible representation aligned with CIDOC-CRM. These semantic structures drive an interactive FEM-based simulation that enables learners to enact craft actions in a virtual environment while receiving predictive feedback and corrective guidance derived from expert-defined execution parameters. The resulting workpiece states are visualized using PBR techniques, providing perceptually accurate cues essential for assessing surface changes, deformation patterns, and material conditions. The methodology is embedded within an eLearning ecosystem that supports the generation of structured courses, multimodal exemplars, and instructional design informed by Cognitive Load Theory. A use case involving wood and aluminum carving demonstrates the system’s ability to simulate realistic tool–material interactions and produce visually interpretable outcomes. The results indicate that coupling executable semantic knowledge modelling with physically grounded simulation offers a viable pathway toward scalable, safe, and contextually rich craft training while supporting the long-term preservation of domain expertise. Full article

In Social Internet of Vehicles (SIoV) environments, fog computing plays a crucial role in supporting real-time services by reducing the latency inherent in cloud-based architectures. However, fog nodes are typically deployed in physically exposed roadside environments and can be operated by several system operators, making them vulnerable to physical compromise and unauthorized access. Despite these threats, many existing authentication schemes assume fog nodes to be fully trusted or honest-but-curious, allowing them to decrypt transmitted data using a session key shared among vehicles, fog nodes, and cloud servers. To overcome these limitations, this paper proposes a quantum-secure pairwise key agreement scheme that establishes distinct session keys for vehicle–fog, fog–cloud, and vehicle–cloud communications. This design effectively prevents the disclosure of sensitive information even in the event of fog node compromise. Furthermore, Physical Unclonable Functions (PUFs) are employed to mitigate physical capture attacks, while lattice-based cryptography based on the Module Learning with Errors (MLWE) problem is integrated to ensure resistance against quantum computing attacks. The security of the proposed protocol is rigorously validated through formal analysis using AVISPA, BAN logic, and the Real-or-Random (RoR) model, in addition to informal security analysis. Comparative performance evaluations against related schemes demonstrate that the proposed approach achieves a balance between efficiency and security, making it well suited for practical deployment in SIoV environments. Full article

Unsaturated polyester resin (UPR)–quartz composites have become increasingly important in structural, sanitary, and architectural applications. However, their manufacturing processes still rely heavily on empirical knowledge. This review compiles recent developments in materials science, curing kinetics, and digital manufacturing, outlining a pathway toward data-driven, adaptive production of quartz-filled thermosets. The chemical and physical fundamentals of UPR polymerization are summarized, including the influence of initiator systems, filler characteristics, and thermal management on network formation. Challenges associated with highly filled formulations—such as viscosity control, dispersion, shrinkage, and exothermic peak prediction—are discussed in detail. Recent advances in digital twins (DTs) and artificial intelligence (AI) are reviewed, demonstrating how physics-based simulations, machine learning models, and hybrid mechanistic–data-driven approaches improve the prediction of rheology, curing behavior, and quality outcomes in thermoset polymer processes. A practical application example demonstrates the prediction of peak time in quartz–UPR composites using Random Forest and Gradient Boosting ensemble models. Two prediction scenarios are evaluated: Scenario A with gel time by Leave-One-Out cross-validation, and Scenario B without gel time, representing post-mixing and pre-process prediction contexts, respectively. Stratified bootstrap augmentation improves Gradient Boosting in both scenarios. Principal component analysis confirms that the curing process is governed by three independent physical dimensions: curing reactivity, thermal environment and resin thermal state. Full article

Radio-frequency (RF) impairments such as thermal noise, phase noise, and nonlinear distortion are inherently coupled in practical multiple-input multiple-output (MIMO) transceivers, yet most existing mitigation techniques treat them independently or rely on correlation-based black-box learning models. These approaches often fail to generalize under varying operating conditions because they do not capture the underlying causal relationships among hardware impairments. This paper proposes a causal representation learning framework that jointly models and mitigates coupled RF impairments by learning disentangled latent variables aligned with their physical causal structure. A causal variational autoencoder with a structured physics-informed prior and causal regularization is developed to recover impairment-specific representations and enable targeted compensation under diverse channel conditions. The framework is evaluated in a controlled MIMO simulation environment to systematically analyze impairment interactions and mitigation performance. Experimental results show that the proposed method significantly outperforms both classical receivers and conventional learning-based approaches. In particular, the framework achieves an average BER reduction of approximately 57% compared with the classical model-based receiver and about 30% relative to correlation-based deep learning models, while also outperforming recent variational autoencoder-based MIMO detectors in robustness under unseen operating conditions. The output signal-to-noise ratio improves by up to 2.2 dB across the evaluated SNR range. Furthermore, latent representation analysis shows a substantial reduction in cross-covariance, with the disentanglement score decreasing from above 0.48 in standard variational models to approximately 0.12 using the proposed causal approach. Under unseen combinations of SNR and impairment severity, the proposed model achieves the lowest BER degradation and a robustness score of 0.86, confirming improved generalization beyond the training distribution. These results demonstrate that causal representation learning provides a principled and effective solution for modeling and mitigating coupled RF impairments in MIMO communication systems. Full article

Robotic manipulators were initially introduced to replace repetitive human labor and have since evolved to perform complex tasks in dynamic environments. In such systems, imitation learning and reinforcement learning models capable of real-time trajectory generation are widely applied. Among these approaches, imitation learning enables rapid training when high-quality datasets are available. However, it suffers from high costs associated with collecting expert demonstration data and significant performance variability depending on data quality. Recently, learning approaches utilizing large-scale datasets have been explored, but they often struggle to guarantee reliable performance in tasks requiring precise control and incur substantial computational costs for model construction, limiting their applicability as a general-purpose learning strategy. To address these limitations, this paper proposes an imitation learning framework that integrates sampling-based motion planning with a hybrid data curation strategy. The proposed method employs a sampling-based planner (e.g., RRT*) to generate diverse physically feasible trajectories, thereby reducing the cost of acquiring expert demonstration data. The generated trajectories are then curated through clustering-based grouping and rule-based filtering to select high-quality training samples from large-scale datasets. The proposed framework automatically generates physically feasible trajectories while selecting high-quality data from large trajectory pools, thereby improving training stability and reducing data-related costs. Experimental results demonstrate that the proposed method achieves an average success rate of 79.1% (95% CI: 74.3–83.2%) and produces trajectories with shorter trajectories, lower final distances, and reduced joint movements compared to conventional filtering methods. Full article

Industrial hemp (Cannabis sativa L.) is versatile and rapidly developing, offering new prospects to producers as a multipurpose crop, yet limited water availability in the Mediterranean area due to climate change makes its sustainable management challenging. Although the plant’s water requirements have been studied, a significant gap remains regarding irrigation management based on the hydraulic properties that govern water movement. The present study elucidates the role of soil hydraulic parameters in water dynamics within the rhizosphere of industrial hemp (Cannabis sativa L., ‘Felina 32’). For this purpose, a pot experiment of three irrigation treatments (100% FC, 80% FC, 60% FC; FC is the field capacity) was set up using two different soil types (clay loam CL and sandy clay loam SCL). SCL soil showed a higher C_max of about 4 cm⁻¹ compared to the C_max of 0.11 cm⁻¹ of CL soil, but dropped drastically within a narrow frame of soil moisture. CL soil resulted in about 12-fold higher diffusivity (D_max ≈ 0.23 cm² min⁻¹) within a wider range of soil moisture compared to the SCL soil (D_max ≈ 0.02 cm² min⁻¹), which facilitated water redistribution at CL, allowing the plant to maximize its water uptake, even at the lowest water input. As a result, the CL soil allowed more flexible scheduling and in contrast, SCL soil necessitated a high frequency irrigation protocol. The integration of hydraulic properties into irrigation planning revealed the potential of CL to apply water to plants efficiently across full and deficit irrigation, showing the peak performance of the irrigation water use efficiency (IWUE) (0.929 g/mm) under the 60% FC regime. The findings provide a framework for linking soil physics–agricultural hydraulics with irrigation strategies in controlled environments. Full article

Propagating easterly waves (EW) are analyzed here, within the dynamical environment of the Caribbean Low-Level Jet (CLLJ) using radiosondes from the Organization of Tropical East Pacific Convection (OTREC) field campaign, ERA5 reanalysis, and lightning from the World Wide Lightning Location Network (WWLLN) over $5^{\circ }$–${20}^{\circ }$ N, ${60}^{\circ }$–${100}^{\circ }$ W during 21 August–30 September 2019. Radiosondes resolve the vertical structure of the waves at San Andrés (Colombia), Limón and Santa Cruz–Guanacaste (Costa Rica), while ERA5 provides spatial–temporal continuity and vertically integrated diagnostics—namely, the vertically integrated moisture flux divergence (VIMFD) and the vertically integrated geopotential flux divergence (VIGFD). Lightning from WWLLN and precipitation from ERA5 and the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement mission (GPM IMERG) offer independent convective proxies to track disturbances. Mean profiles from radiosondes and ERA5 show strong agreement at Limón and Guanacaste and some differences at San Andrés, yet all datasets capture coherent, phase-locked anomalies in zonal wind, meridional wind, temperature, humidity, vertical velocity and vorticity used to diagnose EW–CLLJ interactions. VIMFD, VIGFD, lightning and precipitation exhibit westward-propagating cores that align with the above anomalies, indicating that organized convection is coupled to the disturbances, whereas the mean state preconditions the environment to enable wave-induced upward motion. A robust vertical adjustment of the CLLJ is documented: the core shifts from near 925 hPa over the Caribbean Sea to about 700 hPa over the Eastern Tropical Pacific ($\Delta p\sim 150$ hPa). This feature is reproduced by a 30-year ERA5 climatology, consistent with jet-exit forcing and enhanced boundary-layer coupling over land. Conditions favorable for barotropic instability using the Rayleigh–Kuo criterion, were present over most of the period. A qualitative barotropic conversion proxy, computed from the eddy momentum covariance $⟨u^{\prime }{v}^{\prime }⟩$, shows positive values in the lower troposphere at Guanacaste and in the layer 850–700 hPa at San Andrés, suggesting mean-to-eddy momentum transfer, whereas the signal at Limón is weaker. Together, these results provide a physically consistent view of EW–CLLJ interactions across the IAS; therefore, a schematic of those mechanisms is proposed here. The results highlight the need for high-resolution modeling and full energy-budget analyses. Full article

Objectives: To confirm the utility of barometric whole-body plethysmography (BWBP) as a non-invasive, clinical diagnostic test for brachycephalic obstructive airway syndrome (BOAS) in cats. Methods: Client-owned cats belonging to brachycephalic breeds were enrolled and classified into two clinical severity grades of upper airway obstruction (UAO). Brachycephalic cats with high-grade UAO severity (Brachy-H-UAO) represented those with clinically evident effects on clinical signs or physical examination findings, whereas brachycephalic cats with low-grade UAO severity (Brachy-L-UAO) represented those without clinically evident problems. A group of non-brachycephalic (NB) cats that were respiratory disease-free and with neither a history of cardiac or systemic diseases nor exposure to cigarette smoke was used as the control group. Cats were placed in the BWBP chamber, and breathing signals were obtained after an adaptation period in a quiet and silent environment. The ventilatory variables obtained were respiratory rate (RR; [bpm]), tidal and minute volume per kilogram bodyweight (MV/BW and TV/BW; [mL/kg]), inspiratory (Ti; [s]) and expiratory (Te; [s]) intervals, airway obstruction index enhanced pause (Penh), and peak inspiratory and expiratory flows per kilogram (PIF and PEF; [mL/s/kg]). Results: Forty-three client-owned cats (11 Brachy-H-UAO, 7 Brachy-L-UAO, and 25 NB) were included. Brachycephalic cats (Brachy-H-UAO: 311 mL/kg; Brachy-L-UAO: 253 mL/kg) showed significantly lower median MV/BW than NB cats (503 mL/kg) (p = 0.01). Brachy-H-UAO cats demonstrated significantly higher median PEF/PIF ratios (Brachy-H-UAO: 1.46, minimum–maximum 0.82–2.48; Brachy-L-UAO: 0.76, 0.52–1.11; NB: 0.73, 0.56–1.00) and Penh (Brachy-H-UAO: 2.37, minimum–maximum 0.57–23.82; Brachy-L-UAO: 0.57, 0.27–1.11; NB: 0.53, 0.21–0.68) than Brachy-L-UAO and NB cats (p < 0.001). No significant differences were observed among the three groups for RR, TV/BW, Ti, Te, or Te/Ti. Conclusions and Relevance: Cats affected by BOAS demonstrate impaired ventilatory function, with reduced minute ventilation and a distinctive flow pattern and parameters reflecting limited inspiratory flow and increased upper airway resistance. BWBP can serve as a useful tool to diagnose and characterize the severity of BOAS in cats. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article

Architects and interior design (AID) practitioners have a professional responsibility to advocate and design for minority occupants, yet it is not always possible to consult with all future users due to commercial project constraints. In lieu of occupant engagement, this paper asks what self-directed inquiry might guide more inclusive strategic decision-making in AID practice? Taking a systems perspective, a novel framework for interpreting the occupant–building system is proposed. By deductively extending Shove, Panzar and Watson’s existing Social Practices Theory (SPT) operationalisation, their omission of space is remedied through integrating Reckwitz’s affective spaces of social practices. The framework changes the unit of analysis from the physical by describing occupancy as a social practice with three elements: material, the physical assemblage including human bodies and space; competences, the rules and habits of using the space; and meanings of space for occupant cohorts. The revised theory elevates the social to equal status of material, thus reinforcing their reciprocal relationship and making this explicit for AID practice. The framework is proposed as an interpretive sensemaking tool for AID practitioners to identify different spatial occupations beyond stereotypical expectations. It also offers a framework for AID practitioners to critically reflect on their agency in stabilising or evolving the spatialisation of culture. Three interpretations are demonstrated for contemporary Australian multicultural and inclusion scenarios. It is argued that this theory offers a framework for practice to enable strategic inclusive outcomes in projects with or without user consultation. Furthermore, in addressing the social practices of the built environment, this organising framework offers broader and holistic future built environment research and education. Full article

Climate change is a critical global challenge, and the building sector accounts for nearly 30% of global greenhouse gas (GHG) emissions, remaining a key target for mitigation. Indoor environments contribute significantly to GHG emissions, primarily through heating, cooling, lighting, and occupant-driven energy use. Indoor mapping, serving as the foundation for Digital Twins (DTs), provides a spatiotemporal framework that integrates sensor data with Building Information Modelling (BIM), Geographic Information Systems (GIS), and Internet of Things (IoT) to support energy-efficient, low-carbon building operations. This review examined the role of indoor mapping in understanding, modelling, and reducing GHG emissions in buildings. It synthesized current advancements in indoor spatial data acquisition, ranging from Light Detection And Ranging (LiDAR) and Simultaneous Localization and Mapping (SLAM) to deep learning-based floor plan extraction, and evaluated their contribution to improved indoor environmental analysis. The review highlighted emerging techniques, challenges, and gaps, particularly the limited integration of physical indoor spaces with virtual layers representing assets, occupants, and equipment. Addressing this gap requires embedding spatial modelling as an intermediate analytical layer that structures and contextualizes sensor data to support spatiotemporal decision-making. Overall, this review demonstrated that indoor mapping plays a critical role in transforming spatial information into actionable insights, enabling more accurate energy modelling, enhanced real-time building management, and stronger data-driven strategies for GHG mitigation in the built environment. Full article

The study evaluated the drinking water quality of Rouxville (RX) in Mohokare Local Municipality in the Free State, using chemical, physical, and microbiological parameters in comparison with South African National Standard 241 (SANS 241:2015). Drinking water samples were collected monthly from five sample sites, including the water treatment plant (WTP) and four end-user points, over a period of three years (2021–2023). Microbiological parameters revealed persistent non-compliance, with total coliforms and Escherichia coli (E. coli) frequently exceeding recommended limits by SANS 241 at multiple sites. The highest total coliform concentration of 201 CFU was recorded at the Rouxville Water Treatment Plant during the third year (2023) of sampling, while E. coli reached a maximum of 11 CFU at an end-user point, indicating the presence of possible pathogens in the water system. Colour exceeded the recommended limit (15 Pt-Co mg/L) at all sampling sites, with the highest value of 133 Pt-Co mg/L recorded at Rolelethunya Library. Chemical parameters mostly complied with SANS 241 limits, elevated values of total alkalinity and aluminium were observed at certain sites, particularly during the third year (2023) of sampling. The Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) was also used to determine the overall water quality of the sample sites. The findings revealed that several sample sites had non-compliant parameters. The CCME-WQI revealed that the drinking water quality of Rouxville was either in the marginal or fair category, indicating that the water quality may be occasionally or frequently threatened, posing public health risks. These findings highlight the urgent need to ensure regular maintenance of WTP and ensuring continuous microbial monitoring. Full article

The development and execution of prospective inner and outer space missions require focusing on the use of many small space vehicles operating in swarms with multiple informational, navigational, and mission-oriented interactions among themselves. Such missions involve providing communication and surveillance services, facilitating distributed material production in space, and conducting research expeditions to explore the resources and environments of new worlds. The cornerstone technology for operating distributed space systems is propulsion. Among a range of propulsion technologies—from using pressurized cold gases to implementing laser beams to generate thrust—certain methods stand out for application in small spacecraft. This paper provides a summary of space-operated propulsion, emphasizing the reasons for the more frequent adoption of one technology over another. The discussion on propulsion trends is complemented by examining the physical, engineering, production, operational, and societal rationale behind these choices. The findings reinforce the trend toward transitioning to fully electric satellites. This review serves as a means for reevaluating global propulsion trends and guiding the future development of inner and outer space propulsion-assisted economies effectively. Full article

The increasing adoption of high-aspect-ratio wings to improve aerodynamic efficiency introduces significant structural flexibility, necessitating the integration of aeroelastic considerations into the earliest design stages. While critical, existing frameworks often lack the multi-fidelity modeling capabilities and automated workflows required to bridge conceptual design and high-fidelity verification. This paper presents the Flexible Aero-Structural Toolbox (FAST), a modular framework supporting both beam and shell structural modeling and integrated with MSC NASTRAN for industry-standard aeroelastic simulation. The toolbox’s capabilities are demonstrated through modal, flutter, and static aeroelastic analyses across three distinct configurations: the P-FLEX UAV, the Ventus sailplane, and an A320-like transport aircraft, including its hydrogen-powered derivative. Results show that FAST accurately captures the aeroelastic characteristics of high-aspect-ratio wings and effectively predicts loads for large-scale flexible airframes. Notably, analysis of the hydrogen configuration reveals a significant 25% increase in wing bending moments for the “dry” wing condition compared to standard kerosene configurations. Furthermore, the tool’s ability to model unconventional mass distributions, such as cryogenic fuel tanks, highlights its adaptability for disruptive aircraft technologies. The study concludes that FAST provides a versatile, physics-based decision-making environment that significantly improves efficiency in the aeroelastic analysis process without compromising simulation fidelity. Full article

Background: Adolescence represents a critical period for the adoption of lifestyle behaviors that may influence physical health, emotional well-being, and health-related behaviors later in life. However, limited evidence exists regarding the combined association of dietary habits and physical activity with motivation toward physical education, particularly among adolescent girls from different residential environments. Objective: This study aimed to examine the relationship between adherence to the Mediterranean diet, physical activity levels, and motivation toward physical education among adolescent girls from urban and rural settings. Methods: A cross-sectional study was carried out involving girls aged 12 to 14 years (n = 217; N_Urban = 108 and N_Rural = 109). Adherence to the Mediterranean diet, physical activity levels, and motivational dimensions toward PE were assessed using validated questionnaires. Differences between groups were analyzed using analysis of variance (ANOVA), and an analysis of covariance (ANCOVA) was performed controlling for physical activity levels. Effect sizes were calculated using partial eta squared (η²p). Results: Significant differences were observed in intrinsic motivation, identified regulation, introjected regulation, and amotivation according to adherence to the Mediterranean diet (p < 0.05), with small to moderate effect sizes (η²p = 0.029–0.040). Post hoc analyses indicated that girls with optimal adherence to the Mediterranean diet exhibited higher intrinsic motivation toward PE compared with those with low adherence. The ANCOVA revealed that higher physical activity levels were significantly associated with greater intrinsic motivation, particularly among girls from urban environments. No significant differences were found between urban and rural environments in overall physical activity levels or dietary adherence. Conclusions: Greater adherence to the Mediterranean diet and higher levels of physical activity are associated with more self-determined motivational profiles toward physical education in adolescent girls. These findings highlight the importance of integrated school-based interventions that promote healthy eating and active lifestyles to enhance motivation and engagement in PE among adolescent girls. Full article