Search Results (36,271)

Background: Composite restorations are the standard of care for posterior teeth due to their aesthetic properties and conservative nature. However, the choice between direct and semi-direct techniques can influence clinical longevity and performance. Objectives: This study aimed to compare the clinical performance of two restorative approaches: a direct technique and the semi-direct onlay technique in terms of aesthetic quality, surface finish, wear resistance, marginal integrity, and overall clinical efficiency over a two-year period. Methods: A total of 348 composite restorations were placed in 192 patients. Each restoration was evaluated at four timepoints: baseline (T0), 6 months (T1), 1 year (T2), and 2 years (T3). Clinical performance was assessed using standardised 5-point rating scales across the five dimensions. Repeated-measures ANOVA assessed changes over time, while Wilcoxon signed-rank and Mann–Whitney U tests were used for intra- and inter-group comparisons. Results: Significant time effects were observed across all clinical parameters (p < 0.0001). The direct technique exhibited superior initial results in aesthetics and surface finish at T0 and T1 (p < 0.001), but differences diminished by T3. In contrast, the semi-direct technique demonstrated improved performance in wear resistance and marginal integrity at T2 and T3. Both techniques showed progressive deterioration, particularly in marginal adaptation. Conclusions: The direct technique offers enhanced short-term aesthetics and procedural efficiency, while the semi-direct approach provides superior long-term durability and marginal adaptation. Full article

In the field of smart agriculture, soil property data at the field scale drives the precision decision-making of agricultural inputs such as seeds and chemical fertilizers. However, soil texture has significant spatial variability at the field scale, and traditional remote sensing monitoring methods have certain data intermittency, which limits small-scale prediction research. In this study, based on the Google Earth Engine platform, soil synthetic images were generated according to different time intervals using mean compositing and median compositing modes, image bands were extracted, and spectral indices were introduced; combined with the random forest algorithm, the effects of different compositing time windows, compositing modes, and compositing data types on prediction accuracy were evaluated; and three partitioning strategies based on crop growth, soil synthetic image brightness, and soil type were adopted to conduct local partitioning regression of soil texture. The results show that: (1) The use of mean compositing of multi-year May images from 2021 to 2024 can improve prediction accuracy. When this method is combined with the “band reflectance + spectral indices” dataset, compared with other compositing methods, the R² of clay particles, silt particles, and sand particles can be increased by 8.89%, 9.50%, and 2.48% on average. (2) Compared with using only image band data, the introduction of spectral indices can significantly improve the prediction accuracy of soil texture at the field scale, and the R² of clay particles, silt particles, and sand particles is increased by 4.58%, 3.43%, and 4.59% on average, respectively. (3) Global regression is superior to local partitioning regression; however, the local partitioning regression strategy based on soil type has good accuracy performance. Under the optimal compositing method, the average R² of soil particles of each size fraction is only 1.08% lower than that of global regression, which has great application potential. This study innovatively constructs a comprehensive strategy of “moisture spectral indices + specific compositing time window + specific compositing mode + soil type partitioning”, providing a new paradigm for soil texture prediction at the field scale in Northeastern China, and lays the foundation for data-driven water and fertilizer decision-making. Full article

Background/Objectives: Low back pain (LBP) is one of the most prevalent musculoskeletal disorders globally, significantly impacting quality of life across diverse populations. Despite its association with middle-aged and older populations, evidence indicates that LBP is increasingly prevalent among younger age groups. Health science students are considered a potential risk factor for LBP; however, longitudinal studies are scarce. This study aims to determine the risk factors for LBP among health science students over a 2-year follow-up. Methods: One hundred ninety-seven of the third-class health science students (Nursing, Physiotherapy, Medical laboratory science, and Emergency Medical services) were contacted in June 2024. A self-administered modified version of the Standardized Nordic Questionnaire, and data about sedentary and physical activity behavior, as well as 1-month LBP (lasting at least 12 h and numeric rating scale > 5) and stress scores, were recorded. Results: A total of 172/197 (87.3%) respondents completed the questionnaire at the end of the 2-year follow-up. The mean age was 25 ± 3.5 (years) and body mass index (BMI) value 23.5 ± 4.3 (kg/m²). About 49% (n = 84) and 20% (n = 34) of the participants had 1-month LBP and functional disability, respectively. No significant association was found between health science programs and the presence of 1-month LBP (χ² = 0.55, p > 0.05). The logistic regression analyses found that males (OR = 0.269, p = 0.005) and a history of pain frequency (OR = 3.377, p = 0.001) had a significant association with LBP over time. Conclusions: This prospective study shows a high prevalence of 1-month LBP (48.8%) among health science students at Zefat Academic College. LBP was significantly related to sex (female) and pain frequency, but not to health science students. We believe that implementing ergonomic and educational strategies is recommended for this population. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

Invasions by alien plants are major global drivers of ecosystem changes and loss of biodiversity. Guangxi is an ecological barrier in southern China that is increasingly being affected by invasive alien plant species. We comprehensively reviewed the literature, compiling and analyzing the long-term changes in species composition, native range, life forms, municipal-scale patterns, and correlates of invasive alien plant richness in Guangxi at three time points (1973, 2010, and 2023). Over the 50-year period, the number of invasive alien plant species markedly increased from 31 species in 1973 to 84 in 2010 and 158 in 2023; the number of families, genera, and species increased 2.05-, 3.75-, and 5.10-fold, respectively. Species native to North America consistently dominated the invasive flora, followed by those native to Africa. The number of species native to South America and Asia increased in the records from 2010 to 2023. Annual herbaceous plants accounted for the largest proportion of invasive species throughout the study period and showed the largest absolute increase in species number. However, no substantial temporal shifts in the overall life-form composition were detected. At the municipal scale, the invasive alien plant richness exhibited pronounced spatial heterogeneity. The invasive alien plant richness was highest in Guilin and Baise in 1973, in Guilin in 2023, followed by Nanning and Baise. Correlation analyses based on 2023 data revealed a significant positive association between invasive alien plant richness and tourism intensity, whereas relationships between population size, gross domestic product, and climatic variables were weak or nonsignificant. Overall, our results document the continued expansion and the spatial differentiation of invasive alien plants in Guangxi over the 50-year period of 1973–2023. These patterns primarily reflect the accumulation in the number of recorded invasive species under a consistent classification framework and should be interpreted with caution given the potential variation in survey effort among periods and cities. The results provide a descriptive baseline for the provincial-scale monitoring, risk assessment, and management of invasive alien plants. Full article

The use of ships and boats as sea-state (SS) measurement platforms has the potential to expand ocean observations while providing actionable information for real-time operational decision-making at sea. Within the framework of the Wave Buoy Analogy (WBA), this work develops an inverse approach in which efficient simulations of wave-induced motions of an advancing vessel are used to train a neural network (NN) to predict SS parameters across a broad range of wave climates. We show that a reduced set of novel motion discriminant variables (MDVs)—computed from short time series of heave, roll, and pitch motions measured by an onboard inertial measurement unit (IMU), together with the vessel’s forward speed—provides sufficient and robust information for accurate, near-real-time SS estimation. The methodology targets small, barge-like tugboats whose operations are SS-limited and whose motions can become large and strongly nonlinear near their upper operating limits. To accurately model such responses and generate training data, an efficient nonlinear time-domain seakeeping model is developed that includes nonlinear hydrostatic and viscous damping terms and explicitly accounts for forward-speed effects. The model is experimentally validated using a scaled physical model in laboratory wave-tank tests, demonstrating the necessity of these nonlinear contributions for this class of vessels. The validated model is then used to generate large, high-fidelity datasets for NN training. When applied to independent numerically simulated motion time series, the trained NN predicts SS parameters with errors typically below 5%, with slightly larger errors for SS directionality under relatively high measurement noise. Application to experimentally measured vessel motions yields similarly small errors, confirming the robustness and practical applicability of the proposed framework. In operational settings, the trained NN can be deployed onboard a tugboat and driven by IMU measurements to provide real-time SS estimates. While results are presented for a specific vessel, the methodology is general and readily transferable to other ship geometries given appropriate hydrodynamic coefficients. Full article

In the tropical climates of Southeast Asia, the growing reliance on air conditioning (AC) for space cooling not only increases household energy consumption but may also diminish the role of culturally rooted adaptive behaviors such as floor-sitting. This study aims to explore the interaction between climatic factors, including regional and indoor climates, and thermally adaptive behaviors in Indonesian apartments, with a focus on floor-sitting. First, a large-scale questionnaire was conducted to analyze these interactions among different regional climates. Second, in-depth indoor climate measurements and a point-in-time questionnaire were conducted among the residents in the hotter regions. In the hotter regions like Jabodetabek (Jakarta metropolitan area) and Surabaya, floor-sitting was primarily conducted without using AC, often alongside fans in low-rise housing. In the cooler region of Bandung, floor-sitting was a common adaptive behavior with window openings in both high-rise and low-rise buildings. The in-depth measurement showed that low-rise buildings using higher thermal mass materials maintained stable indoor conditions for both air and floor temperatures even in the hotter region. The respondents could obtain coolness and remain thermally comfortable through a floor-sitting posture without using AC, especially when air and floor temperatures were both less than 31 °C. These results demonstrated that floor-sitting is a vital behavior that adapts to regional and indoor climatic conditions in the tropics while achieving thermal comfort and relying less on AC devices. Full article

Background/Objectives: Public healthcare workers face significant occupational stress during crisis situations, yet research on this particular population remains limited compared to other healthcare workers. The aim of this study was to investigate the impact of the COVID-19 pandemic on distress levels and the sense of coherence among public health workers by integrating psychological assessments with physiological markers of stress to identify protective factors against pandemic-related occupational stress. Methods: This longitudinal study was conducted at the Teaching Public Health Institute of Split and Dalmatia County from July 2021 to February 2022 at two time points: the latency phase (between COVID-19 waves) and hyperarousal phase (during an active wave). Fifty-four public health workers participated in the study. There were three questionnaires assessing psychological distress: Kessler Psychological Distress Scale, Impact of Events Scale—Revised and Sense of Coherence Scale-29. Salivary and blood samples were collected at both time points to measure cortisol levels, cortisol awakening response, and interleukin-6 concentrations. Results: The cortisol area under the curve with respect to ground (AUCg) was significantly elevated during the stress phase compared to the latency phase (234.8 vs. 201.8; p = 0.023), indicating heightened physiological stress responses. Epidemiologists demonstrated significantly lower sense of coherence scores compared to non-epidemiologists (117.9 ± 9.1 vs. 125.6 ± 10.5; p = 0.029). A lower sense of coherence was significantly associated with higher psychological distress and post-traumatic stress symptoms. Multiple linear regression analysis revealed that sense of coherence and interleukin-6 levels were significant independent predictors of cortisol changes. Conclusions: The findings demonstrate that public health workers experience measurable physiological stress responses during pandemic peaks, with sense of coherence emerging as a protective psychological factor. Interventions targeting sense of coherence and organizational support may possibly enhance resilience and reduce mental health morbidity in this vulnerable workforce during crisis situations. Full article

This study developed and evaluated an adaptive e-learning environment for selected number theory topics using a mixed-methods research design, conducted over an eleven-month period across secondary and early tertiary education contexts. The evaluation focused on three primary outcome domains: (1) learning-related outcomes (problem-solving accuracy and task success rate), (2) learner engagement and activity indicators (daily logins and tasks completed per day), and (3) system usability, assessed according to Jakob Nielsen’s usability dimensions. Quantitative data were collected through student and teacher questionnaires (N = 264 students; N = 52 teachers) and large-scale logfile analytics comprising more than 825,000 recorded system interactions. Qualitative feedback from students and teachers complemented the quantitative analyses. The results indicate statistically significant increases in learner activity, task completion rates, and problem-solving success following the introduction of the adaptive system, as demonstrated by inferential statistical analyses with confidence intervals. Post-use evaluations further indicated high levels of learner motivation and self-confidence, along with positive perceptions of system usability. Teachers evaluated the system positively in terms of learnability, efficiency, and instructional integration. Logfile analyses also revealed sustained growth in daily engagement and task success over time. Overall, the findings suggest that adaptive e-learning environments can effectively support engagement, usability, and learning-related performance in number theory education, although further research is required to examine the sustainability of learning-related outcomes over extended periods and to further refine error-handling mechanisms. Full article

In recent years, there has been rapid growth in aesthetic medicine and an increase in the number of minimally invasive procedures aimed at improving appearance. With the increasing number of procedures performed, the incidence of post-operative complications is also rising, and high-frequency ultrasound (HFUS) is increasingly being used to assess these complications. The article presents the case of a 52-year-old woman who reported for an HFUS examination several months after non-surgical nose correction with hyaluronic acid (HA) and implantation of polydioxanone (PDO) lifting threads. The patient experienced post-treatment complications in the form of erythema, oedema and pain, followed by blanching and bruising of the skin. Hyaluronidase and prednisone were used for treatment. Four months after the procedure, the patient returned for another HFUS examination because, despite the disappearance of most symptoms, uneven purple-blue discoloration of the skin on the nose and a subjective feeling of cold persisted. At the time of the HFUS examination, the discoloration was barely visible. The grey-scale HFUS examination revealed foci corresponding to HA deposits and PDO threads located in close proximity to the dorsal artery of the nose. A Doppler examination revealed blood flow disturbances in this artery, which may indicate compression by the threads and be the likely cause of the patient’s complaints. High-frequency ultrasound has proven to be a useful diagnostic method for assessing such complications. Due to its safety, non-invasiveness and high reliability, HFUS has the potential to become a common diagnostic tool in aesthetic medicine practice. Full article

Understanding the fluid dynamics of fluidized beds loaded with biomass pellets is of significant value for the design of wood waste gasifiers. In the present study, cylindrical wood pellets are loaded into a lab-scale cold gasifier unit at 2.5 vol% and 7.5 vol% concentrations and studied at superficial air velocities of 0.25, 0.282, and 0.344 m/s (corresponding to 80, 90, and 110 SCFM). Measurements of bubbles, sand particles, and biomass pellets are taken at a 45 cm height from the distributor plate, and at 9, 12, 15, 18, and 21 cm radial positions from the column wall by employing the CREC-GS-Optiprobes, a valuable integrated fiber optic-laser tool system. A new data processing methodology is established using laser signals that are reflected from the outer surface of aluminum-foil-wrapped cylindrical wood pellets. In addition, a new algorithm is implemented to distinguish pellet-reflected signals from those of bubbles and emulsion-phase particles. On this basis, for the first time, a Phenomenological Probabilistic Predictive Model (PPPM), is considered to predict Bubble Axial Chords (BACs) and Bubble Rise Velocities (BRVs), in a sand fluidized bed loaded with biomass pellets. This is accomplished within a set band of values accounting for three standard deviations from their means or including 85.9% of the bubbles measured. Thus, it is demonstrated that the PPPM is adequate to establish the constrained random motion of bubbles in sand fluidized beds, under the influence of uniformly distributed biomass pellets. It is anticipated that the findings of the present study will be of significant value for the design of sand biomass gasifiers of different scales. Full article

Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, which integrates local–global spatiotemporal learning with graph neural networks. The framework first extracts multi-scale temporal–frequency features using a multi-scale feature extraction module. A local graph feature extraction module then models intrinsic spatial relationships through graph convolutions, while a global graph attention module adaptively captures inter-sensor dependencies by emphasizing structurally informative nodes. A benchmark dataset generated from a scaled bridge model under progressive damage states is used to evaluate the proposed method. Extensive experiments demonstrate that LGSTA-GNN outperforms multiple graph neural network variants and conventional deep learning techniques, achieving superior accuracy, precision, recall, and F1-score. The confusion matrix and t-SNE visualization further verify its enhanced discriminative capability and robustness. Ablation studies confirm the contribution of each module, highlighting the effectiveness of global attention in identifying subtle structural deterioration. Overall, LGSTA-GNN provides an effective and interpretable solution for intelligent bridge damage detection, with strong potential for practical structural health monitoring and real-time safety assessment. Full article

This work develops a methodology for operating Battery Energy Storage Systems (BESSs) in distribution networks, connected in parallel with a medium- and small-scale photovoltaic Distributed Generator (PMGD), focusing on a real project located in the O’Higgins region of Chile. The objective is to increase energy sales by the PMGD while ensuring compliance with operational constraints related to the grid, PMGD, and BESSs, and optimizing renewable energy use. A real distribution network from Compañía General de Electricidad (CGE) comprising 627 nodes was simplified into a validated three-node, two-line equivalent model to reduce computational complexity while maintaining accuracy. A mathematical model was designed to maximize economic benefits through optimal energy dispatch, considering solar generation variability, demand curves, and seasonal energy sales and purchasing prices. An energy management system was proposed based on a master–slave methodology composed of Particle Swarm Optimization (PSO) and an hourly power flow using the successive approximation method. Advanced optimization techniques such as Monte Carlo (MC) and the Genetic Algorithm (GAP) were employed as comparison methods, supported by a statistical analysis evaluating the best and average solutions, repeatability, and processing times to select the most effective optimization approach. Results demonstrate that BESS integration efficiently manages solar generation surpluses, injecting energy during peak demand and high-price periods to maximize revenue, alleviate grid congestion, and improve operational stability, with PSO proving particularly efficient. This work underscores the potential of BESS in PMGD to support a more sustainable and efficient energy matrix in Chile, despite regulatory and technical challenges that warrant further investigation. Full article

The vibration monitoring of scaled models in wind tunnels is critical for aerodynamic testing and structural safety. The abrupt onset of flutter or other aeroelastic instabilities poses a significant risk, necessitating the development of real-time, model-agnostic monitoring systems. This paper proposes a novel, generalized health indicator (HI) based on an improved Fisher Discriminant Analysis (FDA) framework for vibration state classification. The core innovation lies in reformulating the FDA objective function to distinguish between stable and dangerous vibration states, rather than tracking degradation trends. To ensure cross-model applicability, a frequency-wise standardization technique is introduced, normalizing spectral amplitudes based on the statistics of a model’s stable state. Furthermore, a dual-mode entropic regularization term is incorporated into the optimization process. This term balances the dispersion of weights across frequency bands (promoting generalizability and avoiding overfitting to specific frequencies) with the concentration of weights on the most informative resonance frequencies (enhancing the sensitivity to dangerous states). The optimal frequency weights are obtained by solving a regularized generalized eigenvalue problem, and the resulting HI is the weighted sum of the standardized frequency amplitudes. The method is validated using simulated spectral data and flight data from a wind tunnel test, demonstrating a superior performance in the early detection of dangerous vibrations and the clear interpretability of critical frequency bands. Comparisons with traditional sparse measures and machine-learning methods highlight the proposed method’s advantages in trendability, robustness, and unique capability for cross-model adaptation. Full article

Porous photocatalysts from agricultural waste, i.e., apricot and peach shell, with titanium dioxide were prepared by a carbonaceous method, the adsorption and photocatalytic degradation and its kinetics about methylene blue (MB) were studied systematically. The properties of the prepared composite sorbents were characterized using Brunauer–Emmett–Teller, surface area, scanning electron microscopy, and energy dispersive spectroscopy analyses. Several key factors, including radiation, pH, temperature, initial MB concentration, contact time, and sorbent dosage, as well as photocatalytic activity were investigated. All the waste-TiO₂ adsorbents showed improved adsorption and photodegradation performance compared to commercial charchoal-TiO₂. The produced materials presented high specific surface areas especially those derived from apricot shell-TiO₂ with a combination of type I and IV adsorption isotherms with a hysteresis loop indicating micro and mesopore structures. In addition, under UV radiation, the composite sorbents exhibited greater MB removal efficiency than non-radiated composite sorbents. The examined conditions have shown the best MB adsorption results at pH greater than 7.5, temperature 30 °C, contact time 120 min, initial concentration 0.5 mg/L MB, and sorbent dosage equal to 2.0 g/L C/MB. The total removal rate of MB is 98.5%, while the respective amount of commercial charcoal-TiO₂ is equal to 75.0%. The kinetic model that best describes the experimental data of MB degradation from the photocatalytic materials is the pseudo-second order model. In summary, this work highlights the effectiveness and feasibility of transforming agricultural waste into carbonaceous composite sorbent for the removal of cationic dyes from wastewater. Future work will involve scaling up the synthesis of the catalyst and evaluating its performance using bed reactors for industrial processes. Full article