Search Results (190,431)

Tan spot of wheat, caused by the fungus Pyrenophora tritici-repentis (Ptr), is one of the most destructive foliar diseases of wheat worldwide and in Kazakhstan. Expansion of wheat plantings, the adoption of no-till methods, and the use of ineffective fungicides contribute to the accumulation of inoculum and the spread of the pathogen. Despite the important role of fungicides in plant protection, data on the susceptibility of Ptr populations in Kazakhstan are lacking. This study, for the first time, assessed the susceptibility of Ptr isolates from various regions of Kazakhstan to QoI and DMI fungicides. A predominance of genotypes associated with ToxA (82.9%) was found, with a limited distribution of ToxB (7.9%). Propiconazole demonstrated the highest efficacy, inhibiting mycelial growth by an average of 70.85%, followed by pyraclostrobin (69.04%), while azoxystrobin demonstrated lower efficacy (41.47%). Molecular analysis revealed the widespread prevalence of the G143A mutation in the cytochrome b gene, associated with resistance to the QoI fungicide. These results indicate the emergence of strobilurin resistance in Ptr populations in Kazakhstan and highlight the need for regular monitoring of fungicide susceptibility and the development of effective resistance management strategies. Full article

L. angustifolia is a perennial shrub native to the Mediterranean region with multiple medicinal properties. In this study, we report on the chemical composition of L. angustifolia essential oil (LEO), its antibacterial, antibiofilm, and antioxidant activities against ten clinical isolates. Chemical constituents of LEO were identified using Gas Chromatography-Mass Spectrometry (GC–MS). Its antibacterial activity was evaluated in vitro against Gram-positive and Gram-negative bacteria using disk diffusion and broth microdilution methods. A growth inhibition assay was performed to determine the bacterial growth spectrophotometrically. The antibiofilm activity was assessed using a Crystal Violet assay. Finally, the activities of oxidative stress indicators, including Superoxide dismutase (SOD) and Catalase (CAT), were evaluated. GC–MS findings of the essential oil revealed the predominance of Linalool as the major compound. Antimicrobial tests demonstrated activity against Acetobacter aceti, Acinetobacter baumannii, Enterococcus faecium, Escherichia coli, Methicillin-resistant Staphylococcus aureus, Proteus vulgaris, Klebsiella pneumonia, Staphylococcus aureus, Staphylococcus haemolyticus and Stenotrophomonas maltophilia. Furthermore, LEO modulated bacterial growth over time, inhibited biofilm formation and eradicated pre-formed ones. Additionally, LEO significantly decreased the activities of the antioxidant enzymes SOD and CAT. Our findings demonstrated the therapeutic potential of LEO against pathogenic strains and broad antibacterial efficacy. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

As the core driving force behind the new wave of technological revolution and industrial transformation, digital–green synergy (DGS) has become a crucial pathway of low-carbon development in the tourism industry. On the basis of panel data from 54 coastal cities in China from 2011 to 2023, this study employs baseline regression models, moderation effect models, threshold effect models, and spatial spillover effect models to empirically examine the impact mechanisms of DGS on tourism carbon emission efficiency (TCEE), and its spatial spillover effects. The results indicate that (1) DGS can effectively enhance TCEE. (2) Environmental regulation (ER) and tourism industry agglomeration (TIA) play positive moderating roles in the relationship between DGS and TCEE. (3) The effect of DGS on TCEE exhibits nonlinearity, with a double-threshold characteristic, which leads to leap-like changes. (4) DGS has spatial spillover effects on TCEE, facilitating coordinated emission reductions across regions. (5) The results of the heterogeneity analysis indicate that the promoting effect of DGS on TCEE is more pronounced in the southern marine economic circles and economically advanced regions. The present study offers theoretical evidence and policy insights for promoting the deep integration of digitalization and greening development and for achieving high-quality development of the tourism industry in Chinese coastal regions. Full article

Reproducible experimental models of localized dermal–hypodermal fibrosis are essential for standardized investigation of regenerative interventions. Variability in bleomycin dosing, anatomical targeting, and assessment strategies limits cross-study comparability. This study describes a methodological framework for standardized induction of early dermal–hypodermal remodeling in a rat model followed by autologous subcutaneous tissue grafting and multimodal longitudinal evaluation. Female Wistar rats underwent subcutaneous bleomycin administration at 1 mg/kg/day for three consecutive days. Clinical documentation, high-frequency ultrasonography with fixed imaging parameters, and sequential biopsies from a predefined thoracic anatomical site were performed at baseline, intermediate reassessment, and final evaluation. Autologous subcutaneous tissue grafting was conducted at Day 17 after study initiation. The protocol enabled controlled induction of early structural remodeling and consistent longitudinal documentation of dermal–hypodermal thickness, echogenicity changes, and histological architecture within a standardized anatomical region. This protocol development study establishes a reproducible and spatially consistent experimental platform integrating imaging and histological assessment, facilitating future hypothesis-driven investigations of fibrotic remodeling and regenerative strategies. Full article

Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development. Full article

Background: Parkinson’s disease and dementia are major neurodegenerative disorders that substantially contribute to disability, dependency, and mortality worldwide. Although prior CDC WONDER studies have separately evaluated Parkinson’s disease and dementia mortality trends, fewer analyses have examined national mortality patterns in which both conditions are recorded on death certificates simultaneously over extended time periods. Methods: We analyzed U.S. death certificates from 1999 through 2025 using the CDC WONDER Multiple Cause of Death database, identifying deaths among adults aged ≥45 years in which both Parkinson’s disease (ICD-10 G20) and dementia-related codes (F01, F03, G30, G31) were recorded anywhere on the certificate. This operational definition captures co-recorded diagnoses and does not identify clinically confirmed Parkinson’s disease dementia. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. standard population, a method that controls for shifts in population age structure over time and allows valid temporal comparisons independent of absolute population growth. Joinpoint regression was used to quantify trends. Sensitivity analyses excluded 2025 provisional data and the COVID-19 period (1999–2019). Results: A total of 337,721 deaths were identified. Overall AAMR increased from 5.75 (95% CI: 5.60–5.90) in 1999 to 11.15 (95% CI: 10.98–11.32) in 2025 (AAPC: 2.07; p = 0.002). A sharp transient increase occurred in 2020, attributable to pandemic-related factors including disproportionate COVID-19 mortality among older adults with neurodegenerative conditions, care disruptions, and changes in death-certificate coding practices. Following this pandemic-era peak, AAMRs declined significantly through 2025 and should be interpreted cautiously given provisional data. Males (AAPC: 2.14), non-Hispanic White individuals (AAPC: 2.29), the Midwest region (AAPC: 2.65), and non-metropolitan areas carried the highest mortality burden. Mortality was greatest among adults aged ≥85 years. Conclusion: Population-level death rates involving co-recorded Parkinson’s disease and dementia demonstrated significant temporal changes over the study period, with marked demographic and geographic disparities. These findings reflect death-certificate surveillance data and cannot establish clinical co-occurrence, causal relationships, or individual disease risk. Full article

Monitoring biodiversity in coastal and marine ecosystems is essential for supporting conservation strategies, sustaining ecosystem services, and meeting policy commitments at multiple scales, including the European Union’s Habitats Directive, Sustainable Development Goal 14 (SDG 14, Life Below Water), and the Kunming–Montreal Global Biodiversity Framework (GBF). However, many benthic habitats remain insufficiently mapped or monitored due to the spatial, temporal, and logistical limitations of traditional field-based approaches. Optical Remote Sensing (ORS), based on the use of optical sensors to retrieve spectral information from shallow-water environments, has emerged as a powerful tool for mapping and monitoring these ecosystems. This study presents a systematic review aimed at providing a comprehensive synthesis of above-water ORS applications for benthic biodiversity and habitat monitoring over the period 2014–2023. A total of 179 peer-reviewed studies were analyzed to identify temporal trends, geographic patterns, target ecosystems, and methodological workflows. The review considered observation platforms including satellite, airborne, unmanned aerial vehicles (UAVs), and field spectrometry systems, together with key preprocessing procedures required for reliable benthic detection, such as atmospheric correction, water column correction, and sunglint removal, alongside validation using independent measurements. The analysis reveals a rapid expansion of ORS applications, with a strong geographic concentration in tropical and subtropical regions. Studies focusing on specific benthic groups predominantly target coral reefs and seagrass ecosystems, although many adopt integrative benthic habitat classifications that incorporate multiple benthic components at the habitat level. However, significant limitations persist, including inconsistent preprocessing workflows, limited reporting transparency, and the underrepresentation of several ecologically important taxa (e.g., annelids, mollusks, echinoderms). Despite these challenges, ORS has become a cornerstone of large-scale and repeatable coastal monitoring. By analyzing methodological practices, ecological targets, and geographic biases, this review provides a critical foundation for improving the robustness, scalability, and global applicability of ORS in benthic habitat mapping, biodiversity monitoring, and ecosystem-based management. Full article

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly affects the scientific basis of decision-making for rocky desertification control and ecological conservation. This study selected the Beipanjiang River Basin in Guizhou Province, a typical karst region, as the study area. The study selected the SOS, LOS, OM, and EOS indices from the 2001–2020 MODIS MCD12Q2 phenological dataset, combined with topographic zoning data. This study developed a sample spatial optimization scheme for complex karst terrain by integrating Spearman’s correlation analysis, SKATER spatially constrained clustering, statistical tests, adaptive stratified sampling, and Random Forest classification. The scheme was designed to test a phenology–landform joint stratification strategy for spatial sample allocation. The results indicate that (1) the study area was divided into six phenological pattern subregions, with significant spatial differentiation observed among them; (2) the “phenology–landform joint stratification + dual-weighted sample allocation” method was associated with improved sample representativeness and greater internal homogeneity within sample strata under the current experimental setting; and (3) compared to simple random sampling, the remote sensing phenological pattern-driven spatial optimization scheme improved overall accuracy from 71.33% to 77.55% and increased the Kappa coefficient from 0.43 to 0.62. These results suggest that, under the current study-area, sample-size, and validation settings, the phenology–landform joint stratification and dual-weighted allocation scheme can improve the spatial organization of training samples and classification performance over complex karst terrain, although weakly vegetated or bare classes remain difficult to separate. Full article

To improve the reliability and path quality of three-dimensional UAV path planning in complex mountainous environments, this paper proposes an Adaptive Elite Differential Gold Rush Optimizer (AEDGRO). The main novelty of AEDGRO lies in the coordinated integration of three enhancement mechanisms into the original Gold Rush Optimizer: chaotic good-point initialization for improving initial population coverage, adaptive elite differential mining for strengthening exploitation around promising regions, and stagnation-aware Gaussian–Cauchy mutation for escaping local optima. A UAV path-planning model is constructed by considering path length, altitude fluctuation, trajectory smoothness, terrain collision avoidance, threat-region avoidance, and UAV safety clearance. The experimental results on the IEEE CEC2017 benchmark suite show that AEDGRO obtains the best Friedman average ranking of 1.63, outperforming the original GRO with a ranking of 4.80. In the UAV path-planning experiments, AEDGRO achieves the lowest mean fitness value of 235.69 and the smallest standard deviation of 7.55, indicating better path quality and stronger robustness than the compared algorithms. The generated trajectories are smoother and can effectively avoid mountainous terrain and threat regions. These results demonstrate that AEDGRO has clear advantages in global optimization accuracy, convergence stability, and UAV path-planning applicability. Full article

Dairy goat farming is a niche but relevant livestock system in alpine regions, yet its economic viability and environmental performance remain poorly quantified. This study assessed the relationship between profitability and environmental impacts in dairy goat farms in South Tyrol (Northern Italy). Data were collected from ten alpine dairy goat farms through on-farm interviews and accounting records and exploratorily analyzed using full-cost accounting and life cycle assessment (LCA). Given the small and purposive sample, all findings should be interpreted as preliminary and hypothesis-generating rather than statistically representative. Environmental impacts were evaluated from cradle to farm gate using two functional units: 1 kg of fat- and protein-corrected milk (FPCM) and 1 ha of agricultural land. Farm income per kg FPCM was highly variable, ranging from −€1.10 to €2.50, and depended strongly on herd size and subsidies. Average global warming potential was 2.96 ± 1.18 kg CO₂ eq per kg FPCM, but farm rankings changed when impacts were expressed per hectare. Pearson correlation and linear regression analyses showed a significant positive relationship between income and greenhouse gas emissions (r = 0.80, p < 0.05), indicating a trade-off between economic and environmental performance. Enteric methane and energy use were the main contributors to climate impacts. Improving productivity per animal rather than expanding herd size appears to be the most promising strategy to enhance profitability while limiting environmental burdens. Full article

During the start stage of ship welding, obtaining the three-dimensional coordinates of welding target points is affected by confined installation space, surface reflection, and deployment constraints. This paper proposes a low-complexity point-wise three-dimensional localization method based on two-dimensional visual planar guidance and one-dimensional point-laser distance constraints. A direct computation model of the laser incident point in the robot base coordinate system is established from the tool center point pose, the extrinsic parameters of the point-laser module, and real-time ranging data, enabling target-point coordinate estimation without dense three-dimensional reconstruction. A dual-stage stabilization strategy is introduced by combining ranging-level filtering, spatial coordinate smoothing, and outlier suppression. Image error-based visual closed-loop alignment is further used as a pre-measurement step to ensure that the point laser acts on the target region. Experimental results show that, after workplane-level extrinsic correction, independent validation points achieve a mean three-dimensional Euclidean error of 1.54 mm with a standard deviation of 0.28 mm. The average planar error in closed-loop alignment experiments is 1.124 mm. Passive binocular depth measurement on the current platform still yields an RMSE of 6.16 mm after linear correction. A simulated fillet-weld task verifies the feasibility of the complete perception-to-execution workflow. The proposed method provides a low-complexity coordinate acquisition route for discrete welding target points before arc ignition. Full article

Background: Cataract diagnosis and management pose a significant global health challenge, contributing to 17 million cases of blindness and over 83 million cases of vision impairment worldwide in 2020. This issue is particularly acute in regions lacking adequate ophthalmological services, where a shortage of eye care clinicians and specialized equipment like slit-lamp cameras leads to late diagnoses. To address this accessibility gap, we developed a computer-assisted cataract grading system using smartphone-acquired external eye photographs. This approach utilizes image processing and deep learning on a standard, hardware-free smartphone, offering a low-cost and portable alternative to traditional equipment. Methods: The study introduces a new advanced algorithm to stratify cataract severity into three distinct stages: normal, pre-mature, and mature. The methodology was developed using a combined dataset of 799 images sourced from the Cataract v01 Computer Vision Project and the Indian Institute of Technology, Delhi. A key step is isolating the iris and lens using a region of interest (ROI) extraction procedure powered by the open-source MediaPipe framework. Key to the algorithm’s efficacy is the use of transfer learning, adapting four customized ResNet architectures (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) to address medical image analysis intricacies. These models were fine-tuned with specific modifications, including dropout layers and the Adam optimizer, for analyzing the digital periocular images. Results: Evaluation of the models shows varied performance across the various architectures when classifying cataract stages. While the simpler ResNet-18 model exhibited the lowest performance, the deeper models showed significant improvement. The ResNet-50 architecture achieved the highest accuracy of 94%. This model also demonstrated excellent precision (94%), recall (95%), and an F1-score of 95% in multi-class classification, outperforming the other tested models. Its depth enables precise cataract classification, positioning it as a robust and reliable tool for potential medical diagnostic deployment. Conclusions: Deep learning-based analysis of smartphone-acquired external eye images demonstrated feasibility for cataract detection in this study. This method could be a scalable and easy-to-use addition to screening, especially in places where resources are limited. Further work is needed to expand the dataset and to validate the algorithm against established clinical grading systems before broader clinical implementation. Full article