Search Results (5,693)

Introduction: The main purpose of this study was to test a hierarchical model of motivation that integrates Achievement Goal Theory and Self-Determination Theory to explain and predict exercise adherence. Method: In total, 2180 exercisers (1020 female, 1160 male) aged between 18 and 60 years, from different gyms and health clubs, completed several scales validated in exercise settings, regarding perceived motivational climate, basic psychological need satisfaction, behavioral regulation, and exercise adherence. For the last measure, weekly computer access to a control system over a 6-month period before and after data collection was consulted. Results: Through structural equation models (SEM), it was verified that (1) task-involving climate positively predicted basic psychological needs. In turn, the satisfaction of these needs predicted autonomous motivation, which led to a positive prediction of adherence; (2) a small variation in exercise adherence was explained by the motivational model under analysis. Nevertheless, models significantly improved their analytical power when past adherence was inserted in the model increasing the explained variance in future behavior from 9.2% to 64%. Conclusions: In conclusion, autonomous motivation can predict people’s exercise adherence, and past behavior increases that predictive effect. The present study brings scientific evidence to the popular saying “tell me what you’ve done and, and I’ll predict what you’ll do”. Full article

This study analyzes the impact of geometric variations induced by the manufacturing process on the aerodynamic efficiency of an airfoil used in the design of a 3 kW wind turbine blade. For this purpose, a computational fluid dynamics (CFD) analysis was implemented, and the results were compared with those obtained using QBlade software. After blade fabrication, experimental evaluation was performed using the laser triangulation technique, enabling the reconstruction of the deformed airfoils and their comparison with the original geometry. Additional CFD simulations were carried out on the manufactured airfoil to quantify the loss of aerodynamic efficiency due to geometrical deformations. The results show that the geometric deviations significantly affect the aerodynamic coefficients, generating a decrease in the lift coefficient and an increase in the drag coefficient, which negatively impacts the airfoil aerodynamic efficiency. A 14.9% reduction in the rotor power coefficient was observed with the deformed airfoils compared to the original design. This study emphasizes the importance of quality control in wind turbine blade manufacturing processes and its impact on turbine power performance. In addition, the findings can contribute to the development of design compensation strategies to mitigate the adverse effects of geometric imperfections on the aerodynamic performance of wind turbines. Full article

The average body size (fork length) of juvenile salmonids in small streams varies across landscapes and can be influenced by stream temperature, density dependence, catchment size, and physical habitat. In this study, we compared sets of 16 mixed-effects linear models representing these four potentially influencing indicators for three species/age classes to assess the relative importance of their influences on body size. The global model containing all indicators was the most parsimonious model for juvenile coho salmon (Oncorhynchus kisutch; R²m = 0.4581, R²c = 0.5859), age-0 trout (R²m = 0.4117, R²c = 0.5968), and age-1 or older coastal cutthroat trout (O. clarkii; R²m = 0.2407, R²c = 0.5188). Contrary to expectations, salmonid density, catchment size, and physical habitat metrics contributed more to the top models for both coho salmon and age-1 or older cutthroat trout than stream temperature metrics. However, a stream temperature metric, accumulated degree days, had the only significant relationship (positive) of the indicators with body size in age-0 trout (95% CI 1.58 to 23.04). Our analysis identifies complex relationships between salmonid body size and environmental influences, such as the importance of physical habitat such as pool size and boulders. However, management or restoration actions aimed at improving or preventing anticipated declines in physical habitat such as adding instream wood or actions that may lead to increasing pool area have potential to ensure a natural range of salmonid body sizes across watersheds. Full article

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

Silicon-based microcavity quantum dot lasers are attractive candidates for on-chip light sources in photonic integrated circuits due to their small size, low power consumption, and compatibility with silicon photonic platforms. However, integrating components like quantum dot lasers and photodetectors on a single chip remains challenging due to material compatibility issues and mode field mismatch problems. In this work, we have demonstrated monolithic integration of an InAs quantum dot microdisk light emitter, waveguide, and photodetector on a silicon platform using a shared epitaxial structure. The photodetector successfully monitored variations in light emitter output power, experimentally proving the feasibility of this integrated scheme. This work represents a key step toward multifunctional integrated photonic systems. Future efforts will focus on enhancing the light emitter output power, improving waveguide efficiency, and scaling up the integration density for advanced applications in optical communication. Full article

In the rapidly evolving fields of materials science, catalysis, electronics, drug delivery, and environmental remediation, the development of effective substrates for molecular deposition has become increasingly crucial. Ordered mesoporous silica materials have garnered significant attention due to their unique structural properties and exceptional potential as substrates for molecular immobilization across these diverse applications. This study compares three mesoporous silica powders: MCM-41, SBA-15, and SBA-16. A multi-technique characterization approach was employed, utilizing low- and wide-angle X-ray diffraction (XRD), nitrogen physisorption, and transmission electron microscopy (TEM) to elucidate the structure–property relationships of these materials. XRD analysis confirmed the amorphous nature of silica frameworks and revealed distinct pore symmetries: a two-dimensional hexagonal (P6mm) structure for MCM-41 and SBA-15, and three-dimensional cubic (Im$\overline{3}$m) structure for SBA-16. Nitrogen sorption measurements demonstrated significant variations in textural properties, with MCM-41 exhibiting uniform cylindrical mesopores and the highest surface area, SBA-15 displaying hierarchical meso- and microporosity confirmed by NLDFT analysis, and SBA-16 showing a complex 3D interconnected cage-like structure with broad pore size distribution. TEM imaging provided direct visualization of particle morphology and internal pore architecture, enabling estimation of lattice parameters and identification of structural gradients within individual particles. The integration of these complementary techniques proved essential for comprehensive material characterization, particularly for MCM-41, where its small particle size (45–75 nm) contributed to apparent structural inconsistencies between XRD and sorption data. This integrated analytical approach provides valuable insights into the fundamental structure–property relationships governing ordered mesoporous silica materials and demonstrates the necessity of combined characterization strategies for accurate structural determination. Full article

Background: Mesenchymal stem cells (MSCs), multipotent and immune-regulatory cells derived from tissues such as bone marrow, dental pulp, and periodontal ligament, emerged as promising agents in regenerative dentistry. Their clinical applications include endodontic tissue regeneration, periodontal healing, and alveolar bone repair, addressing critical challenges in dental tissue restoration. Methods: A systematic review was conducted following PRISMA guidelines and registered in PROSPERO. We searched PubMed, Scopus, and Web of Science databases for open-access, English-language clinical trials and observational studies published from 2015 to 2025. Studies focusing on the application of MSCs in dental tissue regeneration were included based on predefined eligibility criteria. Results: Out of 2400 initial records, 13 studies met the inclusion criteria after screening and eligibility assessment. Most studies investigated MSCs derived from dental pulp and periodontal ligament for regenerating periodontal tissues and alveolar bone defects. The majority reported improved clinical outcomes; however, variations in MSC sources, delivery methods, sample sizes, and follow-up periods introduced methodological heterogeneity. Conclusions: MSCs show significant potential in enhancing bone and periodontal regeneration in dental practice. Nonetheless, the current evidence is limited by small sample sizes, short follow-up, and inconsistent methodologies. Future large-scale, standardized clinical trials are required to validate MSC-based regenerative therapies and optimize treatment protocols. Full article

This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article

The Shenhu sea area is rich in unconsolidated hydrate reserves, but the formation mineral particles are small, the rock cementation is weak, and the coupling mechanism of hydrate phase change, fluid seepage, and formation deformation is complex, resulting in unclear productivity change law under depressurization exploitation. Therefore, a thermal–fluid–solid–chemical coupling model for natural gas hydrate depressurization exploitation in the Shenhu sea area was constructed to analyze the variation law of reservoir parameters and productivity. The results show that within 0–30 days, rapid near-well pressure drop (13.83→9.8 MPa, 36.37%) drives peak gas production (25,000 m³/d) via hydrate dissociation, with porosity (0.41→0.52) and permeability (75→100 mD) increasing. Within 30–60 days, slower pressure decline (9.8→8.6 MPa, 12.24%) and fines migration cause permeability fluctuations (120→90 mD), reducing gas production to 20,000 m³/d. Within 60–120 days, pressure stabilizes (~7.6 MPa) with residual hydrate saturation < 0.1, leading to stable low permeability (60 mD) and gas production (15,000 m³/d), with cumulative production reaching 2.2 × 10⁶ m³. This study clarifies that productivity is governed by coupled “pressure-driven dissociation–heat limitation–fines migration” mechanisms, providing key insights for optimizing depressurization strategies (e.g., timed heat supplementation, anti-clogging measures) to enhance commercial viability of unconsolidated hydrate reservoirs. Full article

Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines. Full article

Alterations in rice qualities during ageing are related to changes in starch molecular structures. However, if and how storage temperature determines starch structure–function relations remain unknown. This study applied four storage temperatures to investigate the effects of ageing on starch structure–function relations. A small but significant variation was observed for starch chain lengths, and this variation depended on both rice varieties and storage temperatures. Rice grains aged at higher temperatures had much higher peak (~25% larger) and setback viscosities (~50% larger) compared to those stored at lower temperatures. The digestion rate constant was lowered (~10%) most significantly at 40 °C. However, the maximum starch digested percentage increased after ageing. All rice varieties showed the lowest hardness at 4 °C and the highest hardness at 40 °C (~20% larger) after ageing. The changes in starch molecular structures were consistent with altered rice properties according to the established structure–property correlations. These results could improve our understanding of the complex rice ageing process. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for road infrastructure inspection and monitoring. However, challenges such as scale variation, complex background interference, and the scarcity of annotated UAV datasets limit the performance of traditional segmentation models. To address these challenges, this study proposes UAV-YOLOv12, a multi-scale segmentation model specifically designed for UAV-based road imagery analysis. The proposed model builds on the YOLOv12 architecture by adding two key modules. It uses a Selective Kernel Network (SKNet) to adjust receptive fields dynamically and a Partial Convolution (PConv) module to improve spatial focus and robustness in occluded regions. These enhancements help the model better detect small and irregular road features in complex aerial scenes. Experimental results on a custom UAV dataset collected from national highways in Wuxi, China, show that UAV-YOLOv12 achieves F1-scores of 0.902 for highways (road-H) and 0.825 for paths (road-P), outperforming the original YOLOv12 by 5% and 3.2%, respectively. Inference speed is maintained at 11.1 ms per image, supporting near real-time performance. Moreover, comparative evaluations with U-Net show that UAV-YOLOv12 improves by 7.1% and 9.5%. The model also exhibits strong generalization ability, achieving F1-scores above 0.87 on public datasets such as VHR-10 and the Drone Vehicle dataset. These results demonstrate that the proposed UAV-YOLOv12 can achieve high accuracy and robustness in diverse road environments and object scales. Full article

In this study, we investigate the learning opportunities offered in the enactment of a finger pattern activity with numbers exceeding 10 that shows how smaller units can be composed into larger units. Research on early arithmetic learning shows the importance of students understanding numbers as composed units and making use of arithmetic strategies that are based on unitizing rather than single-unit counting. The Many Hands activity was enacted in an intervention program focusing on 6-year-olds’ learning of structuring numbers and number relations during one school year, conducted in collaboration with teachers. The activity, with numbers exceeding 10, was enacted at the end of the program. Video observations of 19 teaching episodes in which the activity was used were analyzed using the variation theory of learning. The analysis focused on identifying which aspects of numbers were made visible for students to discern and how finger patterns became a tool for structuring numbers and number relations. Five aspects were made visible in the enactments of the Many Hands activity: (i) small numbers as composed units; (ii) units within units; (iii) units within units and new, larger units; (iv) relationships between units in the number system; and (v) place value. In 12 of the 19 episodes, the teacher or the students used their fingers to show and see the structure of numbers in relation to the identified aspects. Full article

Recent advances in precision manufacturing and high-end equipment technologies have imposed ever more stringent requirements on the accuracy, real-time performance, and lightweight design of online steel strip surface defect detection systems. To reconcile the persistent trade-off between detection precision and inference efficiency in complex industrial environments, this study proposes StripSurface–YOLO, a novel real-time defect detection framework built upon YOLOv8n. The core architecture integrates an Efficient Cross-Stage Local Perception module (ResGSCSP), which synergistically combines GSConv lightweight convolutions with a one-shot aggregation strategy, thereby markedly reducing both model parameters and computational complexity. To further enhance multi-scale feature representation, this study introduces an Efficient Multi-Scale Attention (EMA) mechanism at the feature-fusion stage, enabling the network to more effectively attend to critical defect regions. Moreover, conventional nearest-neighbor upsampling is replaced by DySample, which produces deeper, high-resolution feature maps enriched with semantic content, improving both inference speed and fusion quality. To heighten sensitivity to small-scale and low-contrast defects, the model adopts Focal Loss, dynamically adjusting to sample difficulty. Extensive evaluations on the NEU-DET dataset demonstrate that StripSurface–YOLO reduces FLOPs by 11.6% and parameter count by 7.4% relative to the baseline YOLOv8n, while achieving respective improvements of 1.4%, 3.1%, 4.1%, and 3.0% in precision, recall, mAP₅₀, and mAP_50:95. Under adverse conditions—including contrast variations, brightness fluctuations, and Gaussian noise—SteelSurface-YOLO outperforms the baseline model, delivering improvements of 5.0% in mAP₅₀ and 4.7% in mAP_50:95, attesting to the model’s robust interference resistance. These findings underscore the potential of StripSurface–YOLO to meet the rigorous performance demands of real-time surface defect detection in the metal forging industry. Full article

Evapotranspiration (ET) crucially regulates water storage dynamics and is an essential component of the terrestrial water cycle. Understanding ET dynamics is fundamental for sustainable water resource management, particularly in regions facing increasing drought risks under climate change. In regions like southwestern China, where extreme drought events are prevalent due to complex terrain and climate warming, ET becomes a key factor in understanding water availability and drought dynamics. Using the SWAT model, this study investigates ET dynamics and influencing factors in the Jizi Basin, Yunnan Province, a small basin with over 71% forest coverage. The model calibration and validation results demonstrated a high degree of consistency with observed discharge data and ERA5, confirming its reliability. The results show that the annual average ET in the Jizi Basin is 573.96 mm, with significant seasonal variations. ET in summer typically ranges from 70 to 100 mm/month, while in winter, it drops to around 20 mm/month. Spring ET exhibits the highest variability, coinciding with the occurrence of extreme hydrological events such as droughts. The monthly anomalies of ET effectively reproduce the spring and early summer 2019 drought event. Notably, ET variation exhibits significant uncertainty under scenarios of +1 °C temperature and −20% precipitation. Furthermore, although land use changes had relatively small effects on overall ET, they played crucial roles in promoting groundwater recharge through enhanced percolation, especially forest cover. The study highlights that, in addition to climate and land use, soil moisture and groundwater conditions are vital in modulating ET and drought occurrence. The findings offer insights into the hydrological processes of small forested basins in southwestern China and provide important support for sustainable water resource management and effective climate adaptation strategies, particularly in the context of increasing drought vulnerability. Full article