Search Results (1,427)

Occupational exposure to respirable coal mine dust remains a significant health risk, especially for underground workers. Rapid dust monitoring methods are sought to support timely identification of hazards and corrective actions. Recent research has investigated how optical light microscopy (OLM) with automated image processing might meet this need. In laboratory studies, this approach has been demonstrated to classify particles into three primary classes—coal, silicates and carbonates. If the same is achievable in the field, results could support both hazard monitoring and dust source apportionment. The objective of the current study is to evaluate the performance of OLM with image processing to classify real coal mine dust particles, employing scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX) as a reference method. The results highlight two possible challenges for field implementation. First, particle agglomeration can effectively yield mixed particles that are difficult to classify, so integration of a dispersion method into the dust collection or sample preparation should be considered. Second, optical differences can exist between dust particles used for classification model development (i.e., typically generated in the lab from high-purity materials) versus real mine dust, so our results demonstrate the necessity of site-specific model calibration. Full article

Background/Objectives: Mothers expecting a second child experience the parenting of multiple children for the first time, differing from first-time motherhood. This highlights the need for childbirth preparation education tailored to families expecting a second child. Parental self-efficacy influences maternal mental health, child development, and parent–child interactions. This non-randomized pilot exploratory study aimed to examine the association between childbirth preparation education for families expecting a second child and maternal parental self-efficacy at 1-month postpartum, focusing on a family-based, single-session program actively involving firstborn children. Methods: The intervention group (n = 18) received childbirth preparation education during pregnancy and completed questionnaires and semi-structured interviews at 1-month postpartum. The control group (n = 34) completed questionnaires only at 1-month postpartum. Questionnaires included the Parenting Sense of Competence Scale, Rosenberg Self-Esteem Scale, Maternal Attachment Inventory, Edinburgh Postnatal Depression Scale, and demographic information. Semi-structured interviews explored participants’ experiences and feelings after attending the childbirth preparation class. Results: Compared to the control group, the intervention group had higher Parenting Sense of Competence Scale scores; mothers in the intervention group reported smoother family-wide adaptation to life with a second child, greater confidence in child-rearing, recognition of the firstborn’s growth into an older sibling, and effective use of hands-on experiences from the class. Conclusions: Childbirth preparation education for families expecting a second child may be associated with higher maternal parental self-efficacy at 1-month postpartum. This association may reflect collective family preparation and adjustment supporting adaptation to life with a second child. Full article

Inflammatory bowel disease (IBD) includes Crohn’s disease (CD) and ulcerative colitis (UC). The accurate classification of IBD from colonoscopy images is critical for diagnosis and treatment. However, the lack of labeled data poses a major challenge for developing deep learning-based IBD classification approaches. Recently, pseudo-labeling-based semi-supervised learning methods offer a promising solution in leveraging both labeled and unlabeled data to improve classification performance. Nevertheless, due to significant intra-class variability and the subtle inter-class differences in IBD colonoscopy images, pseudo-labels are often inaccurate, which results in confirmation bias and suboptimal performance. To address this challenge, a Subclass-Aware Contrastive Semi-Supervised Learning method, referred to as SACSSL, is proposed for accurate IBD classification by integrating a subclass-aware contrastive module into a pseudo-labeling-based semi-supervised framework, e.g., FixMatch. Specifically, unlabeled samples are first partitioned into confident and uncertain samples according to the confidence of pseudo-labels. An instance-level contrastive loss is then applied to uncertain samples, aiming to mitigate confirmation bias. Furthermore, intra-class heterogeneity is captured by introducing a set of prototypes for each subclass and assigning confident samples to these prototypes to form fine-grained subclasses, and supervised contrastive loss is applied to promote intra-subclass clustering, thereby enhancing inter-class separability while preserving intra-class diversity. Our method is evaluated on two datasets, i.e., an in-house collected Daping dataset for IBD classification and a publicly available LIMUC dataset for UC severity grading. On both datasets, our method achieves state-of-the-art performance under the semi-supervised setting. Specifically, with only 20% labeled data, the proposed method reaches an overall accuracy of 93.2% and an F1-score of 80.1% on the Daping dataset, which is close to the fully supervised upper bound (94.0% accuracy and 80.8% F1-score), and it achieves an overall accuracy of 76.4% and an F1-score of 68.9% on the LIMUC dataset. Comprehensive experimental results demonstrate the effectiveness of our method for semi-supervised colonoscopy image classification. Full article

Vegetation segmentation in Very High-Resolution (VHR) satellite imagery has become an essential task for ecological monitoring, supporting diverse applications such as large-scale vegetation conservation and detailed segmentation of small local areas. In particular, multi-class vegetation segmentation, which distinguishes various vegetation types beyond simple binary segmentation of vegetation and non-vegetation, enables detailed analysis of subtle ecosystem changes and has gained increasing importance. However, the annotation of VHR satellite imagery requires extensive time and effort, resulting in a lack of datasets for vegetation segmentation, especially those including multi-class annotations. To address this limitation, this study proposes MultiVeg, a deep learning dataset based on VHR satellite imagery for detailed multi-class vegetation segmentation. MultiVeg includes preprocessed 0.5 m resolution images collected by the KOMPSAT-3 and 3A satellites from 2014 to 2023, covering diverse environments such as urban, agricultural, and forest regions. Each image was carefully annotated by experts into three semantic classes, which are Background, Tree, and Low Vegetation, and validated through a structured quality check process. To verify the effectiveness of MultiVeg, seven representative semantic segmentation models, including convolutional neural network and Transformer-based architectures, were trained and comparatively analyzed. The results demonstrated consistent segmentation performance across all classes, confirming that MultiVeg is a high-quality and reliable dataset for deep learning-based multi-class vegetation segmentation research using VHR satellite imagery. The MultiVeg will be publicly available through GitHub (release v1.0), serving as a valuable resource for advancing deep leaning-based vegetation segmentation research in the remote sensing field. Full article

The incretin hormones glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) play central roles in metabolic and cardiovascular regulation. GLP-1 receptor agonists (GLP-1RAs) are established therapies for type 2 diabetes mellitus (T2DM) and obesity because of their insulinotropic effects, weight reduction, and proven cardiovascular benefit in trial level. In contrast, GIP was historically overlooked due to reduced β-cell responsiveness in T2DM. The development of dual GIP/GLP-1 receptor agonists has reshaped this view. Tirzepatide, the first-in-class co-agonist, an antidiabetic medication to treat type 2 diabetes and for weight loss, provides superior glycemic control and weight loss compared with selective GLP-1RAs in clinical trials, demonstrating synergistic actions between the two incretin pathways. This review summarizes key physiology, pathophysiology, and therapeutic evidence in incretin biology. We describe secretion patterns, receptor distributions, and distinct actions of GIP and GLP-1, as well as alterations in incretin signaling in T2DM and obesity. Cardiovascular protective mechanisms are outlined, including improvements in lipid metabolism, reductions in blood pressure, enhanced endothelial nitric oxide activity, suppression of macrophage inflammation, decreased foam-cell formation, and stabilization of atherosclerotic plaques. At the therapeutic level, emerging directions—such as dual and triple agonists—and unresolved questions regarding long-term vascular effects of GIP and the potential for genotype-guided incretin therapy are also discussed. Collectively, these findings highlight an emerging shift toward integrated incretin-axis modulation as a therapeutic strategy for metabolic and cardiovascular disease. Full article

This study aims to investigate hydrogeochemical characteristics and groundwater quality in the Bashang Area in Chengde and to discuss factors controlling the groundwater quality. A total of 91 groundwater samples were collected and a fuzzy synthetic evaluation (FSE) method was used for assessing groundwater quality. Results show the groundwater chemistry in the study area is predominantly characterized by HCO₃⁻-Ca type waters. Rock weathering processes dominate the hydrogeochemical processes within the study area, while also being influenced by evaporation and concentration effects. The results of the fuzzy evaluation indicate that 94.5% of groundwater samples are of good quality and suitable for drinking (Classes I, II, and III), while 5.5% are of poor quality and unsuitable for drinking (Class IV). Among these, bedrock fissure water exhibited superior quality. Within clastic rock pore water, elevated levels of NO₃⁻ and F⁻ ions were observed in certain localized areas. The exceedance of NO₃⁻ concentrations stems from agricultural expansion, where the application of nitrogen fertilizers constitutes the primary driver of local nitrate pollution. Excessive F⁻ levels correlate with the region’s indigenous geological background. Fluoride-bearing minerals such as fluorite and biotite are widely distributed throughout the study area. Intensive evaporation concentrates groundwater, while the region’s slow groundwater flow facilitates the accumulation and enrichment of F⁻ within aquifers. Full article

Due to their wide application, there is a large amount of residual antibiotics in our environment and food, raising concerns about health risks to children. In this study, 302 primary-school students in Hainan Province, China, were recruited to collect urine samples and questionnaires. The internal exposure levels of sixteen antibiotics and three metabolites in urine were determined by high-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS), and the contents of DNA oxidative damage markers, 8-hydroxy-2′-deoxyguanosine (8-OHdG) and lipid peroxidation marker malondialdehyde (MDA), were also measured. Antibiotics and their metabolites were frequently detected, with a total concentration of < LOD-4.58 × 10³ ng/mL. Binary logistic regression analysis revealed that the detection frequency of DFs of antibiotics was associated with animal-derived foods, such as red meat with fluoroquinolones (FQs) (OR = 76.4, 95% CI 1.68–3479), poultry with norfloxacin (NFX) (OR = 6.56, 95% CI 1.08–39.9), and aquatic products with ciprofloxacin (CIP) (OR = 3.96, 95% CI 1.32–11.9). Cumulative risk assessment based on microbial effects showed a hazard index of 3.5 for children, mainly driven by azithromycin (45.6%), oxytetracycline (18.1%), and CIP (33.9%). Multiple linear regression indicated that lipid peroxidation was significantly associated with high quantiles of three antibiotic classes, while DNA oxidation was positively correlated with all antibiotic classes except FQs. These findings indicate that children in Hainan are widely exposed to antibiotics. Although the exposure levels are generally low, chronic low-dose antibiotic exposure may contribute to disease development and oxidative stress damage. Full article

Concrete materials are vulnerable to various sorts of structural defects. Reliable measurement and quantification of concrete defects are crucial for ensuring safety and effective maintenance. Deep learning is commonly utilized to detect and classify concrete defects efficiently. However, most available studies do not study multi-class defect identification. This study aims to develop a multi-class concrete defect detection framework to enhance concrete classification accuracy while enabling reliable defect localization. To achieve this, a new image-based non-destructive measurement dataset comprising 2029 images of concrete defects, categorized into five categories, has been compiled. For defect identification, the DenseNet201 model is modified by adding a guided semantic–spatial fusion module with a squeeze-and-excitation architecture, which enhances feature representation and introduces attention mechanisms to the model, enabling it to detect and track defect regions. Experiments are conducted on the collected dataset, and various scenarios and comparisons are performed to verify the proposed model. Results reveal the superiority of the proposed architecture with an accuracy enhancement of 5.6% compared to the original DenseNet201. A graphical user interface is also designed to integrate the trained model into a practical measurement instrument, enabling users to interact with the backend model and detect various defects from intact cases. Full article

This study explores the potential of Precision Agriculture (PA) to optimize top-dressing nitrogen (N) fertilization in rainfed barley under drought conditions in Central Catalonia (Spain). Efficient N management is critical in Mediterranean dryland winter cereal systems, where water scarcity and environmental regulations limit fertilization strategies. Two plots (2.93 ha and 1.80 ha) were zoned using soil apparent electrical conductivity (ECa) and elevation data obtained with the VERIS 3100 ECa soil surveyor. An on-farm experimental design tested four N dose rates (0 kg N/ha, 32 kg N/ha, 64 kg N/ha, and 96 kg N/ha) across two management zones per plot. Yield data were collected using a combine harvester equipped with a yield monitor and were mapped using geostatistical methods. A linear model (ANOVA) was used to analyze barley yield (kg/ha at 13% moisture), with nitrogen rate and soil zone (management class) as explanatory factors. Results showed low average yields (~1200 kg/ha–1300 kg/ha) due to severe water stress during the 2022–2023 season. Non-fertilized plots (N0) and those receiving moderate (N64) or high fertilization (N96) achieved the best performance, with the latter likely enhancing crop N uptake during the post-stress recovery period. In contrast, low fertilization (N32) proved less effective. Marginal return analysis supported variable-rate N application only in one plot, whereas under drought conditions, a no-fertilization strategy proved more suitable in the other. Ultimately, additional trials conducted under more favourable climatic scenarios are necessary to assess and validate the effectiveness of Precision Agriculture-based fertilization strategies in rainfed barley. Full article

Anomaly Detection (AD) in telecommunication networks is critical for maintaining service reliability and performance. However, operational networks present significant challenges: high-dimensional Key Performance Indicator (KPI) data collected from thousands of network elements must be processed in near real time to enable timely responses. This paper presents an unsupervised approach leveraging Mahalanobis Distance (MD) to identify network anomalies. The MD model offers a scalable solution that capitalizes on multivariate relationships among KPIs without requiring labeled data. Our methodology incorporates preprocessing steps to adjust KPI ratios, normalize feature distributions, and account for contextual factors like sample size. Aggregated anomaly scores are calculated across hierarchical network levels—cells, sectors, and sites—to localize issues effectively. Through experimental evaluations, the MD approach demonstrates consistent performance across datasets of varying sizes, achieving competitive Area Under the Receiver Operating Characteristic Curve (AUC) values while significantly reducing computational overhead compared to baseline AD methods: Isolation Forest (IF), Local Outlier Factor (LOF) and One-Class Support Vector Machines (SVM). Case studies illustrate the model’s practical application, pinpointing the Random Access Channel (RACH) success rate as a key anomaly contributor. The analysis highlights the importance of dimensionality reduction and tailored KPI adjustments in enhancing detection accuracy. This unsupervised framework empowers telecom operators to proactively identify and address network issues, optimizing their troubleshooting workflows. By focusing on interpretable metrics and efficient computation, the proposed approach bridges the gap between AD and actionable insights, offering a practical tool for improving network reliability and user experience. Full article

A novel series of Schiff bases (3a–3k), incorporating tranexamic acid (TXA) and phenol-derived aldehydes via imine linkers, was synthesized and structurally characterized. The antimicrobial activity of the compounds was evaluated against a range of clinically and environmentally relevant bacterial and fungal strains. Among them, derivatives 3i and 3k, bearing bromine and chlorine substituents on the phenol ring, exhibited the most potent antimicrobial effects, particularly against Penicillium italicum and Proteus mirabilis (MIC as low as 0.014 mg/mL). To elucidate the underlying mechanism of action, in silico molecular docking studies were conducted, revealing strong binding affinities of 3i and 3k toward fungal sterol 14α-demethylase (CYP51B), with predicted binding energies surpassing those of the reference antifungal ketoconazole. Additionally, UV-Vis and fluorescence spectroscopy assays demonstrated good stability of compound 3k in PBS and its effective binding to human serum albumin (HSA), respectively. ADMET and ProTox-II predictions further supported the drug-likeness, low toxicity (Class 4), and favorable pharmacokinetic profile of compound 3k. Collectively, these findings highlight TXA–phenol imine derivatives as promising scaffolds for the development of next-generation antimicrobial agents, particularly targeting resistant fungal pathogens. Full article

Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particularly for classifying herbaceous vegetation in inter-row areas. In this study, we introduce a novel approach to classify the groundcover into one of nine categories, in order to simplify this task. Using UAV images to train a convolutional neural network through a deep learning methodology, this study evaluates the effectiveness of different backbone structures applied to a UNet network for the classification of pixels into nine classes of groundcover: vine canopy, bare soil, and seven distinct cover crop community types. Our results demonstrate that the UNet model, especially when using an EfficientNetB0 backbone, significantly improves classification performance, achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%. Although this study demonstrates the potential of integrating remote sensing and deep learning for vineyard biodiversity monitoring, its applicability is limited by the small image coverage, as data were collected from a single vineyard and only one drone flight. Future work will focus on expanding the model’s applicability to a broader range of vineyard systems, soil types, and geographic regions, as well as testing its performance on lower-resolution multispectral imagery to reduce data acquisition costs and time, enabling large-scale and cost-effective monitoring. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Accurate recognition of complex human activities from wearable sensors plays a critical role in sports analytics and human performance monitoring. However, the high dimensionality and redundancy of raw inertial data can hinder model performance and interpretability. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (MRMR) and Regularized Neighborhood Component Analysis (RNCA) to improve classification accuracy while reducing computational complexity. Multi-sensor inertial data were collected from field hockey players performing six activity types. Time- and frequency-domain features were extracted from four body-mounted inertial measurement units (IMUs), resulting in 432 initial features. MRMR, combined with Pearson correlation filtering (|ρ| > 0.7), eliminated redundant features, and RNCA further refined the subset by learning supervised feature weights. The final model achieved a test accuracy of 92.82% and F1-score of 86.91% using only 83 features, surpassing the MRMR-only configuration and slightly outperforming the full feature set. This performance was supported by reduced training time, improved confusion matrix profiles, and enhanced class separability in PCA and t-SNE visualizations. These results demonstrate the effectiveness of the proposed two-stage feature selection method in optimizing classification performance while enhancing model efficiency and interpretability for real-time human activity recognition systems. Full article

Metabolic syndrome, a collective term for lipid and carbohydrate disorders in the organism, is the primary cause of type 2 diabetes mellitus development and its associated systemic side effects. The current approach for the medical treatment of this condition usually requires multiple medications, targeting multiple pathophysiological pathways. A promising drug class in that regard is the dual PPARα/γ agonists, which impact both lipid and carbohydrate metabolism, yet to this day the vast majority of them have not passed the clinical trials, due to potential toxicity risks. In the present study we synthesized and tested a series of monoterpene-substituted (S)-2-ethoxy-3-(4-(4-hydroxyphenethoxy)phenyl)propanoic acids as potentially effective and safe novel dual PPARα/γ agonists. In vitro studies showed that nearly all of the tested compounds were sufficiently active towards both PPARα and PPARγ. All compounds were tested in vivo, using C57BL/6 A^y/_a mice with T2DM symptoms, in order to evaluate their impact on carbohydrate and lipid metabolism. The most promising of them was found to be compound 5h, containing a cumin fragment, which showed pronounced hypoglycemic activity by boosting tissue insulin sensitivity and hypolipidemic effects manifested by reductions in fat tissue mass and blood triglyceride levels, while simultaneously displaying a relatively safe profile. Full article