Search Results (24,766)

This study investigates a polymorphic underwater vehicle designed to combine long-range cruising with stable underwater operation, reducing dependence on surface support vessels. By introducing a foldable polymorphic structure, the vehicle can switch configurations, including serial and parallel. However, underwater environments often contain obstacles, and the vehicle may collide with them during the folding process. To prevent collisions between the vehicle and surrounding obstacles during the folding process, this paper investigates the motion envelope of the vehicle and examines how motion parameters and mass distribution influence the motion envelope. In this work, the polymorphic underwater vehicle is modeled as a multibody system operating under a neutrally buoyant condition. Based on space robot modeling methodologies and the linear and angular momentum theorems, the equations of motion of the polymorphic underwater vehicle are derived and verified using the Adams software 2020. In summary, the present study establishes a clear relationship between motion parameters, mass distribution, hydrodynamic effects, and the resulting motion envelope of a polymorphic underwater vehicle. The results show that the attitude of the vehicle during the folding process is uniquely determined by the joint angles, and a larger relative speed between the outer and inner folding motions produces a more compact attitude during the folding process. Mass distribution further influences the motion envelope of the vehicle: concentrating mass toward the center of the vehicle shifts the overall motion envelope upward, whereas concentrating mass toward both ends of the vehicle shifts it downward. In addition, hydrodynamic forces introduce an upward velocity component of the vehicle in the vertical direction during the folding process, which leads to an upward shift in the overall center of mass of the vehicle. Full article

Background: Gestational diabetes mellitus (GDM) affects between 1% and 14% of pregnancies worldwide. Major risk factors include advanced maternal age, excess adiposity, family history of type 2 diabetes, and unhealthy dietary habits. In Mexico, evidence on the association between dietary patterns and GDM remains scarce, particularly in socioeconomically vulnerable populations with limited access to specialized nutrition services. This study aimed to evaluate the association between dietary patterns and the presence of GDM in pregnant women attending the outpatient obstetrics clinic of a teaching public hospital in Guadalajara, México. Methods: We conducted a case–control study including 169 pregnant women: 71 with GDM confirmed by the ADA one-step 75 g oral glucose tolerance test OGTT criteria and 98 without GDM based on a negative OGTT, recruited consecutively from the same clinic during the same period. Dietary intake was assessed using a culturally adapted and validated Food Frequency Questionnaire. Dietary patterns were identified through Principal Component Analysis, and associations were examined using logistic regression adjusted for maternal age, pregestational BMI, and family history of type 2 diabetes. Results: Women with GDM had higher maternal age, greater pregestational BMI, and more frequent family history of type 2 diabetes compared with controls. Three dietary patterns were identified: Western, Healthy, and Dairy/Refined. High adherence to the Western pattern was inversely associated with GDM (aOR = 0.36; 95% CI: 0.16–0.78; p = 0.010); however, this finding most likely reflects post-diagnosis dietary modifications rather than a protective effect, while maternal age remained the strongest risk factor (OR = 1.09; 95% CI: 1.03–1.16; p = 0.002). The Healthy pattern (aOR = 1.25; 95% CI: 0.55–2.82; p = 0.593) and the Dairy/Refined pattern (aOR = 0.80; 95% CI: 0.39–1.66; p = 0.554) were not significantly associated with GDM in the adjusted model. Conclusions: GDM was associated with older maternal age, higher pregestational BMI, and family history of T2DM. The inverse association with the Western pattern may reflect post-diagnosis dietary changes rather than a protective effect. Due to the retrospective design, causal inference is not possible, highlighting the need for longitudinal studies. Full article

Wind turbines are increasingly deployed at larger scales and in harsher operating environments, leading to greater structural complexity, stronger load variability, and higher maintenance demands across both drivetrain and structural components. Reported field data indicate that gearboxes and bearings account for approximately 30–40% of total turbine downtime, while blade-related failures contribute roughly 20–25% of reported failure events, primarily through fatigue, delamination, leading-edge erosion, and lightning-induced defects. In parallel, large-scale and offshore turbines show increasing susceptibility to tower fatigue cracking, corrosion-assisted degradation, and flange joint bolt-preload loss under cyclic and environmental loading. This review provides a comprehensive applied-engineering synthesis of failure mechanisms, reliability challenges, and monitoring strategies for major wind turbine components, including gearboxes, bearings, blades, towers, and flange joints. A wide range of condition monitoring, structural health monitoring (SHM), and prognostics and health management (PHM) approaches is critically examined, including vibration analysis, acoustic emission, ultrasonic inspection, infrared thermography, impedance-based sensing, electromagnetic methods, machine vision, SCADA-based diagnostics, and artificial-intelligence-assisted fault classification. The review compares these techniques in terms of detectable damage types, spatial coverage, sensitivity, deployment practicality, and limitations under real operating conditions. In addition, statistical reliability methods and data-driven models are discussed to interpret failure trends and uncertainty. Recent AI-based studies have reported fault classification accuracies exceeding 90% under controlled or semi-controlled conditions; however, their field reliability remains constrained by data imbalance, domain shift, limited labeled failure datasets, model interpretability, and insufficient validation under realistic turbine operating environments. The main contribution of this review is an integrated applied synthesis that connects drivetrain and structural failure mechanisms with measurable monitoring indicators, diagnostic technologies, AI-enabled PHM limitations, and predictive-maintenance decision needs. The paper provides practical guidance for monitoring design, early fault detection, predictive maintenance, and long-term reliability improvement in next-generation wind turbine systems. Full article

Scent marking has been discussed as an important component of communication in brown bears (Ursus arctos Linnaeus, 1758). However, the environmental factors influencing the occurrence of rub trees and their value for non-invasive genetic sampling remain poorly understood. This study examined the patterns of rub tree occurrence in the eastern High Tatra Mountains (Slovakia) at two spatial scales. At the tree scale, paired-design generalized linear mixed models showed that rub trees were more frequently recorded on large-diameter coniferous trees, indicating an association with visually prominent and chemically suitable substrates. At the landscape scale, logistic regression models revealed that the probability of rub tree occurrence increased with elevation and distance from human settlements, identifying high-elevation forests as areas of higher predicted rub tree occurrence. The best-supported model was used to produce a predictive map of rub tree occurrence across the study area. We also evaluated whether rub trees are reliable sources of biological material for non-invasive sampling. Hair collected during repeated field visits provided DNA suitable for genotyping and individual identification. Overall, the results show that rub trees exhibit non-random spatial patterns and represent effective focal points for systematic genetic sampling, linking patterns of rub tree occurrence to the spatial targeting of non-invasive genetic sampling in mountain landscapes. Full article

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

Nanostructured catalysts have become a core component of energy conversion in electrocatalysis and photocatalysis; however, successfully translating their performance from laboratory scale to industrial applications remains a long-standing challenge. This paper provides a critical assessment of the field, systematically tracing the entire development trajectory from catalyst design to practical application. We focus on five major classes of catalysts—monometallic catalysts, bimetallic/multimetallic alloy catalysts, metal compound catalysts, carbon-based composite catalysts, and single-atom catalysts—and explore synthetic strategies for achieving precise structural control, including hydrothermal/solvothermal methods, electrodeposition, template-assisted and MOF-derived syntheses, high-temperature pyrolysis, and post-treatment defect engineering. This paper delves into the mechanisms and performance descriptors governing the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), urea oxidation, photocatalytic water splitting, and CO₂ reduction. Based on the above analysis, this paper lays the mechanistic foundation for five core strategies to improve catalyst performance: morphology control, elemental doping, heterostructure and interface engineering, defect and vacancy engineering, and support modification. Furthermore, this paper provides an in-depth evaluation of the applications of these catalysts in water splitting, CO₂ valorization, fuel cells, metal–air batteries, and energy-saving electrolysis, with a particular focus on earth-abundant alternatives to precious metals. We argue that in many well-studied reactions, intrinsic activity may no longer be the primary bottleneck restricting their development; instead, the core challenge now lies in maintaining excellent catalytic performance under harsh and industrially relevant conditions, especially under high-current densities, impurity-containing feed systems, and long-term operating conditions. In response to this shift in research focus, this paper clearly identifies the key obstacles hindering the industrial application of catalysts and proposes practical directions for future research. Full article

Cyber-physical smart infrastructures integrate sensing devices, communication networks, control components, and service platforms, which makes them vulnerable to malicious activities that may evolve gradually through several attack stages. The objective of this study is to develop and evaluate a simulation-based cybersecurity framework capable of detecting a proposed novel multi-stage cyber attack and identifying its internal progression within a realistic smart infrastructure environment. To achieve this objective, a NetSim-based cyber-physical smart infrastructure was modeled to generate both normal operational traffic and staged malicious traffic. The generated traffic was captured, processed, labeled, and transformed into a stage-aware cybersecurity dataset. An artificial neural network (ANN) model was then trained and evaluated for two detection tasks: binary classification of normal versus attack traffic and multi-class classification of compromise, coordination, and execution attack stages. Twenty experimental configurations were designed to examine the model under progressively broader infrastructure contexts, including sensing, service, gateway, control, backbone, and full-span operational scenarios. The best binary testing performance was achieved in the eighteenth experimental configuration, representing a broad full-span infrastructure scenario, with 97.96% accuracy, 97.80% precision, 97.65% recall, 97.72% F1-score, and 1.06% false positive rate. For stage-aware multi-class detection, the ANN model achieved 96.97% accuracy, 96.36% macro-averaged precision, 96.20% macro-averaged recall, 96.28% macro-averaged F1-score, and 96.55% weighted F1-score. Macro-averaged metrics report the unweighted average performance across classes, while weighted F1-score accounts for class support. These results show that the proposed simulation-based framework can generate realistic attack-aware traffic data and support reliable ANN-based detection of both attack presence and attack-stage progression. Full article

Maritime search and rescue is an important component of emergency response frameworks and primarily relies on Unmanned Aerial Vehicles (UAVs) for maritime object detection. However, maritime accidents frequently occur in low-visibility environments, such as foggy or low-light conditions, which lead to low contrast, blurred object boundaries, and degraded texture representations. Most existing maritime object detection algorithms are developed for natural light scenes, and their performance deteriorates markedly when deployed directly in low-visibility environments, primarily due to reduced image quality that hinders feature extraction and semantic information aggregation. Although several studies incorporate image enhancement techniques prior to detection to improve image quality, these approaches often introduce significant additional computational overhead, limiting their practical deployment on UAV platforms. To tackle these challenges, this paper proposes a lightweight model built upon a recent YOLO framework, termed Multi-Scale Adaptive YOLO (MSA-YOLO), for maritime detection using UAVs in low-visibility environments. The proposed model systematically optimizes the backbone, neck, and detection head networks. Specifically, an improved StarNet backbone is designed by integrating Efficient Channel Attention (ECA) mechanisms and multi-scale convolutional kernels, which strengthen feature extraction capability while maintaining low computational overhead. In the neck network, a high-frequency enhanced residual block branch is inserted into the C3k2 module to capture richer detailed information, while depthwise separable convolution is utilized to further reduce computational cost. Moreover, a non-parametric attention module is incorporated into the detection head to adaptively optimize features in the classification and regression branches. Finally, a joint loss function that combines bounding box regression, classification, and distribution focal losses is utilized to improve detection accuracy and training stability. Experimental results on the constructed AFO, Zhoushan Island, and Shandong Province datasets demonstrate that, relative to YOLOv11-s, MSA-YOLO reduces model parameters and FLOPs by 52.07% and 41.36%, respectively, while achieving improvements of 1.11% and 1.33% in mAP@0.5:0.95 and mAP@0.5. These results indicate that the proposed method effectively balances computational efficiency and detection accuracy, rendering it suitable for practical maritime search and rescue applications in low-visibility environments. Full article

To support producers in selecting cultivars, this study aimed to investigate whether the strawberry yield and fruit quality differ according to cultivars and growing cycles. The treatments consisted of three cultivars (‘Albion’, ‘Monterey’, and ‘San Andreas’) over four cycles (2021/2022, 2022/2023, 2023/2024, and 2024/2025). The experiments were arranged in a randomized block design, with four replications. Attributes related to yield and fruit quality were evaluated. The interaction between cultivars and cycles influenced the horticultural potential of strawberry plants in a soilless system. The best productive performance of the three cultivars was observed in 2024/2025. In this growing cycle, ‘San Andreas’ exhibited the highest yield, ‘Monterey’ produced the highest number of fruits, and both cultivars produced fruits of superior quality. In 2022/2023, ‘Albion’ produced the sweetest and most flavorful fruits. Principal component analysis formed four groups in relation to cultivars and cycles, one of which included only cultivars from the 2024/2025 cycle. The other three groupings did not show a clear pattern of separation by cultivar and/or harvest. In conclusion, the association between cultivars and production cycles interferes with the horticultural potential of strawberry under soilless cultivation systems. Full article

Introduction: Elasmobranchs play a crucial role in ecosystem regulation, but they are highly vulnerable to rapid environmental changes, particularly those driven by anthropogenic activities. Therefore, elasmobranchs are among the most threatened vertebrate groups worldwide, with overfishing and habitat degradation representing the primary threats to their survival. To address these challenges, in situ and ex situ conservation programs are complementary approaches. Objective: The implementation of assisted reproductive technologies, still poorly developed for elasmobranchs, represents a critical component of these ex situ strategies. Focused on that aspect, the main goal of this work was to get a better understanding of the sperm cells morphologies of different Mediterranean elasmobranch species. Results: The Elasmobranchii spermatozoa possesses a long and he-lical head, an elongated midpiece, and a flagellum supplemented with additional ultrastructural components to its axoneme. The comparative analysis of sperm head morphology revealed substantial interspecific variation among the studied elasmobranchs. Head length was relatively conserved, ranging from 48.5 to 62.0 μm, whereas helical parameters showed much greater variability. S. canicula and M. mobular exhibited the most compact head morphology, characterized by short helical wavelengths, low amplitudes, and the highest numbers of helices. In contrast, the batoids R. rhinobatos, R. radula, and R. clavata displayed broader, more widely spaced helices and fewer turns. Phylogenetic patterns were partially evident, as the closely related rajids shared very similar sperm morphology, while R. rhinobatos showed a comparable batoid morphotype. However, similarities between the distantly related M. mobular and S. canicula, and differences between the scyliorhinids S. canicula and G. melastomus, suggest that ecological and reproductive factors, in addition to phylogeny, have influenced the evolution of sperm head morphology in elasmobranchs. Conclusion: Elasmobranchii species possess big spermatozoa (compared to bony fishes) with an elongated helical head and tail similar to one currently existing (but later diverged) in birds, reptiles, and amphibians, which can be considered an evolutionary ancient. Sperm head morphology varies markedly among elasmobranchs, mainly regarding helical traits rather than head length. While phylogeny explains similarities among rajids, convergent patterns in distantly related species suggest that additional ecological and reproductive factors influence sperm evolution and structural design. Full article

Aiming to develop high-performance flexible electrode materials for dielectric elastomer actuation systems applied to micro-crawling robots, this study proposes multi-dimensional filler composite electrode materials with a methyl vinyl silicone rubber matrix. Three types of conductive fillers—namely, zero-dimensional super-conductive carbon black, one-dimensional single-walled carbon nanotubes, and two-dimensional flaky micron-sized silver powder—were employed to construct a hierarchical multi-dimensional conductive network within the silicone rubber matrix via a three-stage fabrication strategy. The electrical conductivity and conductive stability of the as-prepared composite electrode materials were systematically investigated, where the intrinsic mechanisms and evolutionary laws of material electrical performance variations were analyzed. Furthermore, the effects of fillers with different dimensional morphologies on the comprehensive properties of the composites at each fabrication stage were explored, and the optimal filler dosage for each component was determined. Microstructural observations of the staged conductive network formation further verified the rationality of the stage-based functional design model. The optimized composite electrode delivers an initial electrical conductivity of 1.5 × 10⁴ S/m, with only a 14.9% conductivity attenuation under 50% tensile strain, demonstrating excellent electromechanical stability. Moreover, a prototype micro-crawling robot was fabricated using the optimized composite electrode, achieving a maximum linear crawling speed of 8 mm/s. These experimental results validate the feasibility and superiority of the proposed multi-dimensional filler composite strategy. This work provides a novel technical approach for the design and development of high-performance flexible electrode materials for flexible electronic and micro-robotic actuation applications. Full article

Background/Objectives: Natural calcium carbonate materials such as Rügen chalk have a long history of use in balneology and rehabilitation, particularly for musculoskeletal disorders, yet their application remains largely confined to traditional, labour-intensive forms such as powders, suspensions, and packs, which limit usability and broader clinical translation. This study aimed to develop an alginate-based solid foam incorporating Rügen chalk and to evaluate how key formulation components influence its structural, mechanical, and thermal properties relevant for therapeutic use. Methods: Alginate–chalk foams were prepared by mechanical mixing of a sodium alginate–Rügen chalk paste with an amino acid-based surfactant, while in situ CO₂ generation from D–glucono–δ–lactone (GDL) induced calcium-mediated alginate gelation and foam stabilization. A central composite design with response surface methodology was used to assess the effects of alginate, chalk, and Perlastan^®–GDL content on foam pH, overrun, firmness, springiness, pore volume, sphericity, pore density, specific internal surface area, and heat-loss time. Foam microstructure was characterized by optical microscopy and microcomputed tomography (µCT), and the thermal conductivity and cooling behaviour of the selected formulation were compared with therapeutic peat. Results: Stable, elastic solid foams with a three-dimensional porous architecture were obtained across the investigated composition range. Foam overrun (30.8–57.1%) was primarily governed by sodium alginate and Rügen chalk concentrations, while firmness (7.4–15.2 N) increased predominantly with alginate content, and springiness remained high (70–78%), indicating good elastic recovery. Response surface modelling and ANOVA confirmed sodium alginate as the dominant factor influencing both mechanical and structural properties, with statistically significant effects on overrun, firmness, springiness, heat loss, porosity, and specific internal surface. µCT analysis revealed that all foam formulations were predominantly composed of fine, closed-cell pores, with over 96% of pores having volumes below 0.5 mm³ and a consistent median pore volume of 0.02 mm³. Structural differences between formulations were governed primarily by pore number and spatial distribution rather than pore size. Strong correlations were identified between µCT-derived parameters, particularly between specific internal surface, porosity, and pore density, confirming that internal architecture is controlled by pore population rather than individual pore dimensions. Thermal analysis demonstrated that the optimized formulation exhibited thermal conductivity comparable to therapeutic peat and maintained clinically relevant temperatures (35–45 °C) for more than one hour. Based on predefined performance criteria (overrun ≥ 50%, firmness ≤ 10 N, heat loss ≥ 120 s), formulation 7 was identified as optimal, combining favourable mechanical properties, structural uniformity and thermal retention. Conclusions: Alginate-based solid foams incorporating Rügen chalk constitute a feasible and tunable platform that combines efficient mineral loading, elastic porosity, and effective heat retention, offering a practical and modern alternative to conventional mineral-based therapeutic applications in balneology and rehabilitation. Full article

Background/Objectives: Motor impairments, including reduced muscular strength and coordination, are commonly reported in children with autism spectrum disorder (ASD) and may negatively affect functional mobility and participation in daily activities. Despite increasing recognition of these challenges, structured resistance training programs for children with ASD remain limited. This study aimed to examine the effects of a 12-week resistance training program on muscular strength and functional performance in children aged 9–11 years with mild ASD. Methods: A selected-group repeated-measures design was employed. Twenty-eight children with specialist-confirmed mild ASD were allocated to an exercise (n = 14) or control group (n = 14) using a strength-matched allocation procedure. The intervention followed established exercise guidelines for youth. Assessments were conducted at baseline, week 6, and week 12 and included handgrip strength, vertical jump height, and 10-m walk time. Non-parametric Friedman tests assessed changes over time, followed by Durbin–Conover post hoc comparisons where appropriate. Effect sizes (r) were calculated. Results: No significant overall time effect was observed for handgrip strength, although a between-group difference favoring the exercise group was observed at week 6. Vertical jump height demonstrated a significant effect over time, with improvements observed in the exercise group from baseline to week 6 and a between-group difference at week 6. Walking time improved significantly across the study period, with improvements observed in both the exercise and control groups. Conclusions: These findings suggest that structured resistance training is a feasible intervention that may support improvements in physical function in children with mild ASD. Resistance training may therefore represent a useful component of exercise programs aimed at improving functional mobility and participation in children with developmental conditions. Full article

Decarbonising the built environment has increased the importance of prefabricated construction, yet its cost and resource efficiency are still constrained by fragmented supply chain collaboration. This study examines how lifecycle supply chain collaboration affects cost control performance in prefabricated construction. Based on supply chain management theory and expert consultation, a conceptual model was developed and tested through structural equation modelling using 517 valid responses from stakeholders in China’s prefabricated construction supply chain. The results show that management factors across all four project phases (decision and design, component production, transportation, and construction and installation) significantly improve cost control performance, with design standardisation, production scheduling, transport logistics, quality assurance, and workforce proficiency as key drivers. Process coordination exerts a significant mediating effect, while environmental factors significantly moderate the relationships. In practical terms, the findings indicate that stakeholders should prioritise design standardisation at the early stage, strengthen coordination across production, transport, and installation activities, and enhance quality control and workforce training to reduce avoidable cost overruns and resource waste. Beyond their theoretical contribution to research on supply chain collaboration in prefabricated construction, these results offer concrete direction for practitioners seeking to improve cost efficiency and make better use of resources within industrialised building systems. Full article

In response to the growing challenge of electronic waste, we developed an open-source pick-and-remove system for recovering electronic components for reuse. This platform is based on a repurposed 3-D printer chassis, an infrared pre-heater, and a hot-air soldering station. The system is designed for easy adoption in academic laboratories and FabLabs. A literature review highlights the potential of electronic components recovery, while preliminary tests demonstrate high recovery rates and minimal environmental impact of the proposed system. This work aims to support the transition to a circular electronics economy by making component recovery accessible and providing additional value to electronic waste. Full article