Search Results (31,260)

Background/Objectives: Dental caries is the most prevalent oral condition worldwide. Consequences of untreated dental caries (CUDC) can range from pulp damage and soft tissue ulceration due to root debris to more severe issues, such as fistulas and abscesses. Rural communities might be more vulnerable to CUDC because of lower socioeconomic status, poorer access to healthcare, and lower education levels. The objective of this study was to evaluate and compare the prevalence of CUDC in rural and urban areas in schoolchildren aged 8 to 12 years in the State of Mexico. Methods: A cross-sectional study was conducted using the PUFA index, considering the presence of pulp involvement (P), soft tissue ulcerations due to root remnants (U), fistulas (F), and abscesses (A). The independent variable was the geographic area (rural or urban), and the covariates were nutritional status, hyposalivation, having one’s own toothbrush, and having received topical fluoride in the last year. Logistic regression models were fitted, calculating odds ratios (ORs) and 95% confidence intervals (CIs). Results: The prevalence of CUDC (PUFA > 0) was 42.9% in rural areas and 25.9% in urban areas. Residing in a rural area (OR: 2.15, 95% CI 1.38–3.34, p = 0.001), hyposalivation (OR: 1.93, 95% CI 1.11–3.37, p = 0.020), and professional fluoride application (OR: 0.15, 95% CI 0.07–0.32, p < 0.001) were associated with the prevalence of CUDC. Conclusions: To prevent caries and its clinical consequences due to the lack of treatment, it is important to promote timely care seeking and access to dental care services, considering the conditions of each geographic area. Full article

The increasing frequency and intensity of wildfires pose serious threats to ecosystems, property, and human safety worldwide. Accurate semantic segmentation of wildfire images is essential for real-time fire monitoring, spread prediction, and disaster response. However, existing deep learning methods heavily rely on large volumes of pixel-level annotated data, which are difficult and costly to obtain in real-world wildfire scenarios due to complex environments and urgent time constraints. To address this challenge, we propose a semi-supervised wildfire image segmentation framework that enhances segmentation performance under limited annotation conditions by integrating multi-scale structural information fusion and pixel-level contrastive consistency learning. Specifically, a Lagrange Interpolation Module (LIM) is designed to construct structured interpolation representations between multi-scale feature maps during the decoding stage, enabling effective fusion of spatial details and semantic information, and improving the model’s ability to capture flame boundaries and complex textures. Meanwhile, a Pixel Contrast Consistency (PCC) mechanism is introduced to establish pixel-level semantic constraints between CutMix and Flip augmented views, guiding the model to learn consistent intra-class and discriminative inter-class feature representations, thereby reducing the reliance on large labeled datasets. Extensive experiments on two public wildfire image datasets, Flame and D-Fire, demonstrate that our method consistently outperforms other approaches under various annotation ratios. For example, with only half of the labeled data, our model achieves 5.0% and 6.4% mIoU improvements on the Flame and D-Fire datasets, respectively, compared to the baseline. This work provides technical support for efficient wildfire perception and response in practical applications. Full article

Coffee cherry pulp (CCP), a coffee by-product rich in pectin and phenolic compounds, serves as a valuable substrate for microbial enzyme production, improving the nutritional and antioxidant properties of poultry feed. This study evaluated the potential of Kluyveromyces marxianus ST5 to produce pectin-degrading enzymes using CCP. Under unoptimized conditions, the pectin lyase (PL) and polygalacturonase (PG) activities were 3.29 ± 0.22 and 6.32 ± 0.13 U/mL, respectively. Optimization using a central composite design (CCD) identified optimal conditions at 16.81% (w/v) CCP, 5.87% (v/v) inoculum size, pH 5.24, and 30 °C for 48 h, resulting in PL and PG activities of 9.17 ± 0.20 and 15.78 ± 0.14 U/mL, representing increases of 178.7% and 149.7% over unoptimized conditions. Fermented CCP was further evaluated using an in vitro chicken gastrointestinal digestion model. Peptide release increased by 66.2% compared with unfermented CCP. Antioxidant capacity also improved, with significant increases observed in DPPH (32.4%), ABTS (45.0%), and FRAP (42.3%) assays, along with an 11.1% increase in total phenolic content. These results demonstrate that CCP bioconversion by K. marxianus ST5 enhances digestibility and antioxidant properties, supporting its potential as a sustainable poultry feed additive and contributing to the valorization of agro-industrial waste. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

Surface functionalization is key for tuning the electronic and magnetic properties essential in spintronics, yet its impact on chromium-based MXenes (Cr₃C₂T₂) is not fully understood. Using spin-polarized DFT+U, this study investigates how O, F, and OH groups modify the magnetic state, electronic structure, and Curie temperature. Functionalization dramatically changes magnetism: O termination gives ferromagnetism, while F and OH yield ferrimagnetism. Our results show surface functionalization effectively adjusts the Curie temperature, critical for spintronic materials. The electronic character is highly functional group dependent: pristine Cr₃C₂ is half-metallic, Cr₃C₂O₂ metallic, and Cr₃C₂F₂/Cr₃C₂(OH)₂ semiconducting with narrow gaps. Structures with dynamic stability are analyzed through phonon spectroscopy. These findings provide fundamental insights into controlling MXene properties via surface functionalization, guiding the design of next-generation spintronic materials. Full article

This study demonstrates how Structural Topic Modeling (STM) can be used to analyze qualitative student comments in conjunction with quantitative student evaluation of teaching (SET) scores, providing a scalable framework for interpreting student evaluations of teaching. Drawing on 286,203 open-ended comments collected over fourteen years at a large U.S. research university, we identify eleven latent topics that characterize how students describe instructional experiences. Unlike traditional topic modeling methods, STM allows us to examine how topic prevalence varies with course and instructor attributes, including instructor gender, course discipline, enrollment size, and numeric SET scores. To illustrate the utility of the model, we show that topic prevalence aligns with SET ratings in expected ways and that students associate specific teaching attributes with instructor gender, though the effects are relatively small. Importantly, the direction and strength of topic–SET correlations are consistent across male and female instructors, suggesting shared student perceptions of effective teaching practices. Our findings underscore the potential of STM to contextualize qualitative feedback, support fairer teaching evaluations, inform institutional decision-making, and examine the relationship between qualitative student comments and numeric SET ratings. Full article

In relay-assisted Industrial Internet of Things (IIoT) systems with ultra-reliable low-latency communication (uRLLC) requirements, finite blocklength coding imposes stringent resource constraints. In this work, the packet error probability (PEP) and blocklength allocation across two-hop links are jointly optimized to minimize total blocklength (resource consumption) while satisfying reliability, latency, and throughput requirements. The original multi-variable problem is decomposed into two tractable subproblems. In the first subproblem, for a fixed total blocklength, the achievable rate is maximized. A near-optimal PEP is first derived via theoretical analysis. Subsequently, theoretical analysis proves that blocklength must be optimized to equalize the achievable rates between the two hops to maximize system performance. Consequently, the closed-form solution to optimal blocklength allocation is derived. In the second subproblem, the total blocklength is minimized via a bisection search method. Simulation results show that by adopting near-optimal PEPs, our approach reduces computation time by two orders of magnitude while limiting the achievable rate loss to within 1% compared to the exhaustive search method. At peak rates, the hop with superior channel conditions requires fewer resources. Compared with three baseline algorithms, the proposed algorithm achieves average resource savings of 21.40%, 14.03%, and 17.18%, respectively. Full article

Objective: This study aimed to investigate whether the subjective assessments of strong and weak muscles in the Functional Myodiagnosis muscle test (FMD-MT) can be objectively and reproducibly verified using physically measurable parameters. Additionally, we sought to evaluate the reliability of the manual muscle test in order to reinforce the scientific evidence supporting this accepted, yet not widely adopted, complementary medicine method. Methods: In a crossover observational study, three experienced medical practitioners conducted the FMD-MT of the rectus femoris muscle on 24 healthy participants using a specially designed therapy bench, with all measurements recorded via an oscillogram. The study investigated the force–time integral, joint angle change, additional force load, mean force turning point 1, as well as the interrater reliability and validity of both examiner assessments and instrumental analyses for the two muscle reaction variants: strong and weak. Results: A significant difference between the response pattern of strong and weak muscles was identified for the force–time integral (p = 0.005), the change in joint angle (p < 0.001), and the additional force load (p = 0.001). No difference between strong and weak muscles could be detected regarding the force turning point 1 (p = 0.972). The examiners demonstrated 100% accuracy in identifying weak muscle reactions as weak, and 99.2% accuracy in identifying strong muscle reactions as strong (p = 0.316). The overall intra-class correlation coefficient was 0.984. The oscillogram correctly visualized weak muscle reactions in weak muscles with an accuracy of 81.7%, and strong muscle reactions in strong muscles with an accuracy of 86.7% (p = 0.289). Conclusions: The Functional Myodiagnosis muscle test (FMD-MT) enables a clear and objective differentiation between strong and weak muscles, with statistically significant differences observed in the force–time integral, additional force load, and joint angle changes. Under rigorously standardized testing conditions, the FMD-MT of the rectus femoris muscle demonstrates a validity rate of 99.6% and an excellent reliability (ICC 0.984). Consequently, the FMD muscle test proves to be a reliable, reproducible, and objective diagnostic method. Trial registration: German Register of Clinical Studies U1111-1212-6622. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-enhanced machine learning framework that integrates SHAP with advanced ensemble methods for interpretable financialization prediction. The methodology simultaneously addresses high-dimensional feature selection using 40 independent variables (19 CSR-related and 21 financialization-related), multicollinearity issues, and model interpretability requirements. Using a comprehensive dataset of 25,642 observations from 3776 Chinese A-share companies (2011–2022), we implement nine optimized machine learning algorithms with hyperparameter tuning via the Hippopotamus Optimization algorithm and five-fold cross-validation. XGBoost demonstrates superior performance with 99.34% explained variance, achieving an RMSE of 0.082 and R² of 0.299. SHAP analysis reveals non-linear U-shaped relationships between key predictors and financialization outcomes, with critical thresholds at approximately 10 for CSR_SocR, 1.5 for CSR_S, and 5 for CSR_CV. SOE status, EPU, ownership concentration, firm size, and housing prices emerge as the most influential predictors. Notable shifts in factor importance occur during the COVID-19 pandemic period (2020–2022). This work contributes a scalable, interpretable machine learning architecture for high-dimensional financial prediction problems, with applications in risk assessment, portfolio optimization, and regulatory monitoring systems. Full article

Tea polyphenol oxidase (CsPPO) is a crucial enzyme involved in the production of tea and tea products. However, the recombinant expression of CsPPO in microorganisms is often hindered by challenges such as inclusion body formation and extremely low enzyme activity. In this study, the CsPPO gene (1800 bp) from Camellia sinensis cv. Yihongzao was cloned and 14.5% of its codons were optimized for Pichia pastoris expression. Compared to pre-optimization, codon optimization significantly enhanced CsPPO production in P. pastoris KM71, yielding a 42.89-fold increase in enzyme activity (1286.67 U/mL). The optimal temperature and pH for recombinant CsPPO were determined to be 40 °C and 5.5, respectively. This study demonstrates that codon optimization effectively improves the expression of plant-derived enzymes such as CsPPO in eukaryotic expression systems. Future research should explore the long-term stability of recombinant CsPPO and its potential applications in tea fermentation processes and functional food development. Full article

This study aimed to evaluate solid-state fermentation (SSF) as a sustainable approach for the simultaneous detoxification of olive pomace (OP) and the production of industrially relevant enzymes. OP, a semisolid byproduct of olive oil extraction, is rich in lignocellulose and phenolic compounds, which limit its direct reuse due to phytotoxicity. A native strain of Aspergillus sp., isolated from OP, was employed as the biological agent, while grape pomace (GP) was added as a co-substrate to enhance substrate structure. Fermentations were conducted at two scales, Petri dishes (20 g) and a fixed-bed bioreactor (FBR, 2 kg), under controlled conditions (25 °C, 7 days). Key parameters monitored included dry and wet weight loss, pH, color, phenolic content, and enzymatic activity. Significant reductions in color and polyphenol content were achieved, reaching 68% in Petri dishes and 88.1% in the FBR, respectively. In the FBR, simultaneous monitoring of dry and wet weight loss enabled the estimation of fungal biotransformation, revealing a hysteresis phenomenon not previously reported in SSF studies. Enzymes such as xylanase, endopolygalacturonase, cellulase, and tannase exhibited peak activities between 150 and 180 h, with maximum values of 424.6 U·g⁻¹, 153.6 U·g⁻¹, 67.43 U·g⁻¹, and 6.72 U·g⁻¹, respectively. The experimental data for weight loss, enzyme production, and phenolic reduction were accurately described by logistic and first-order models. These findings demonstrate the high metabolic efficiency of the fungal isolate under SSF conditions and support the feasibility of scaling up this process. The proposed strategy offers a low-cost and sustainable solution for OP valorization, aligning with circular economy principles by transforming agro-industrial residues into valuable bioproducts. Full article

Information is the primary driver of progress in today’s world, especially given the vast amounts of data available for extracting meaningful knowledge. The motivation for addressing the problem of forensic analysis—specifically the validity of decision making in multi-criteria contexts—stems from its limited coverage in the existing literature. Methodologically, machine learning and ensemble models represent key trends in this domain. Datasets used for such purposes can be either real or synthetic, with synthetic data becoming particularly valuable when real data is unavailable, in line with the growing use of publicly available Internet data. The integration of these two premises forms the central challenge addressed in this paper. The proposed solution is a three-layer ensemble model: the first layer employs multi-criteria decision-making methods; the second layer implements multiple machine learning algorithms through an optimized asymmetric procedure; and the third layer applies a voting mechanism for final decision making. The model is applied and evaluated through a case study analyzing the U.S. Army’s decision to replace the Colt 1911 pistol with the Beretta 92. The results demonstrate superior performance compared to state-of-the-art models, offering a promising approach to forensic decision analysis, especially in data-scarce environments. Full article

This study proposes an improved YOLO11n-seg instance segmentation model to address the limitations of existing models in accurately identifying mature blueberries in complex greenhouse environments. Current methods often lack sufficient accuracy when dealing with complex scenarios, such as fruit occlusion, lighting variations, and target overlap. To overcome these challenges, we developed a novel approach that integrates a Spatial–Channel Adaptive (SCA) attention mechanism and a Dual Attention Balancing (DAB) module. The SCA mechanism dynamically adjusts the receptive field through deformable convolutions and fuses multi-scale color features. This enhances the model’s ability to recognize occluded targets and improves its adaptability to variations in lighting. The DAB module combines channel–spatial attention and structural reparameterization techniques. This optimizes the YOLO11n structure and effectively suppresses background interference. Consequently, the model’s accuracy in recognizing fruit contours improves. Additionally, we introduce Normalized Wasserstein Distance (NWD) to replace the traditional intersection over union (IoU) metric and address bias issues that arise in dense small object matching. Experimental results demonstrate that the improved model significantly improves target detection accuracy, recall rate, and mAP@0.5, achieving increases of 1.8%, 1.5%, and 0.5%, respectively, over the baseline model. On our self-built greenhouse blueberry dataset, the mask segmentation accuracy, recall rate, and mAP@0.5 increased by 0.8%, 1.2%, and 0.1%, respectively. In tests across six complex scenarios, the improved model demonstrated greater robustness than mainstream models such as YOLOv8n-seg, YOLOv8n-seg-p6, and YOLOv9c-seg, especially in scenes with dense occlusions. The improvement in mAP@0.5 and F1 scores validates the effectiveness of combining attention mechanisms and multiple metric optimizations, for instance, segmentation tasks in complex agricultural scenes. Full article

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50–95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application. Full article