Search Results (8,320)

The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

A rise in the water level may result in different vertical rebound levels of the ground surface, adversely affecting buildings. Ground rebound occurred in the Xi’an Yuhuazhai area from 2018 to 2019, but the soil’s deformation characteristics remain unclear. Drilling and water level data and FLAC3D 6.0 were used to simulate water level recovery. The deformation characteristics of different soil layers were examined, their future development was predicted, and the influences of various parameters on ground rebound were analyzed. The rebound amount of the hanging wall in the second confined aquifer was 38.32 mm, accounting for 61.12% of the total rebound amount. The rebound amount of the footwall in the second confined aquifer was 22.14 mm, accounting for 79.63% of the total rebound amount. The predicted maximum rebound of the upper and lower soil layers in the next 5 years was 2.8 mm and 2.6 mm, respectively, representing a vertical difference of 0.2 mm, which has no significant effect on building safety. The results provide a scientific basis for groundwater management and settlement prevention and control in Xi’an. Full article

This research has been performed to optimize the management of cultivated land fragmentation in the Henan Section of the Yellow River Basin, (“the research area”), coordinate the contradiction between increasing food demand and environmental constraints, maintain regional food security, and promote agricultural and rural modernization. The spatial and temporal evolution characteristics have been summarized by calculating the fragmentation index of the cultivated land landscape, and the driving factors explored with geographical detectors. Results show the following: (1) between 2000 and 2023, the landscape fragmentation index of cultivated land in the research region exhibited a pattern of initial decline followed by a subsequent rise. It decreased by 69.33% from 2000 to 2015 and increased by 138.42% from 2015 to 2023. Over the period from 2000 to 2023, the cultivated land landscape fragmentation index in the study area saw an overall reduction of 26.87%. (2) ”The reduction in cultivated land area and the decrease in landscape fragmentation” index accounted for 82.46% in the county unit. (3) The kernel density curve of the cultivated land landscape fragmentation index showed a unimodal distribution, but the shape was flat. The regions with a fragmentation index mainly range from 4 to 6. The regional cultivated land fragmentation distribution was more dispersed. (4) The average altitude, the distance from the Yellow River, the proportion of the construction land area and population density are the main driving factors. The combined impact of the proportion of the construction land area and population density contributes more than 46% to the cultivated land landscape fragmentation index. The interaction among various factors exerts a more pronounced effect than any individual factor alone. The intensity of the main interaction factors reaches above 0.67. The findings of this study can serve as a theoretical foundation for the sustainable utilization and development of cultivated land resources, as well as for ecological protection and construction in the Henan segment of the Yellow River Basin. Full article

Lygus pratensis is a major pest of cotton, causing serious damage to cotton production. This study designed species-specific PCR detection primers for L. pratensis, established a detection system to identify L. pratensis DNA in the intestinal contents of predatory natural enemies, and investigated the control potential of four species’ predatory natural enemies against L. pratensis. The results indicated that 826 predatory natural enemies were collected from cotton fields belonging to two classes, five orders, and twelve families. Among these, 9 species of insecta natural enemies accounted for 54.12% of the total number of predatory natural enemies collected, while 14 species of arachnida predatory natural enemies comprised 45.88%. Of the 806 natural enemies tested, 5.58% were found to be positive for L. pratensis, all of which were arachnid predators, specifically Ebrechtella tricuspidata, Xysticus ephippiatus, Hylyphantes graminicola, and Oxyopes sertatus. The predation response of these four spider species to the fourth to fifth instar nymphs and adults of L. pratensis adhered to the Holling II model. The theoretical predation (a′/Th), daily maximum predation rate (T/Th), and searching effect for the fourth to fifth instar nymphs and adults of L. pratensis of the four spider species were assessed. According to the results, the species can be ranked in terms of their predatory and searching efficiency as follows: O. sertatus > E. tricuspidata > X. ephippiatus > H. graminicola. Four species of spiders had the highest theoretical predation against L. pratensis nymphs, ranging from 23.71 to 60.86, and adults, ranging from 22.14 to 50.25. Therefore, these four spider species could be utilized for L. pratensis management. This study identified the main predatory natural enemies of L. pratensis and their pest control capabilities, providing a scientific basis for selecting and utilizing natural enemies in integrated pest management (IPM) strategies. This will help promote ecological and green pest control of L. pratensis in cotton-growing areas. Full article

Metabolic syndrome (MetS)—characterized by dyslipidemia, hypertension, hyperglycemia, and abdominal obesity—is a common, modifiable condition that contributes to functional decline and premature mortality in older adults. The accumulation of MetS components increases the risk of cardiovascular, cerebrovascular, and renal diseases, as well as cognitive impairment and polypharmacy in aging populations. A narrative review was conducted focusing on the management of MetS in adults aged 65 and older. Sources were identified through targeted searches of PubMed and relevant guidelines, with an emphasis on literature discussing geriatric-specific considerations. The review was structured using the Institute for Healthcare Improvement’s (IHI) 4Ms Framework: What Matters, Medication, Mentation, and Mobility. Findings highlight that current MetS guidelines are often extrapolated from younger populations and insufficiently account for geriatric-specific factors such as altered pharmacokinetics, multimorbidity, and social determinants of health. The 4Ms Framework provides a comprehensive lens to adapt these guidelines, supporting individualized treatment plans that consider patient goals, cognitive status, and functional capacity. Incorporating social services and aligning interventions with socioeconomic realities can further bridge disparities in care. The 4Ms framework can help healthcare providers communicate effectively with patients, ensuring treatment plans align with evidence-based practices and the patient’s individual priorities. Treatment of MetS must be tailored to individual patient needs based on presented risk factors, severity of risks, and social determinants of health. Adjusting treatment plans in accordance with the socioeconomic status (SES) of patients will allow for systematic improvement of outcomes. Full article

The health risks of PM_2.5-bound metals highlight the need for burden assessment, metal prioritization, and key factor analysis to support effective air quality management, yet relevant studies remain limited. Shandong Province is one of the most polluted regions in northern China, providing an ideal setting for this investigation. We monitored 17 PM_2.5-bound metals for three years across Shandong, China and performed disease burden assessment based on disability-adjusted life years (DALYs). Furthermore, key influencing factors contributing to high-hazard metals were identified through explainable machine learning. The results showed that PM_2.5-bound metal concentrations were generally higher in inland areas than in coastal regions, with Ni concentrations elevated in coastal areas. K, Ca, Zn, and Mn exhibited the highest three-year average concentrations among the metals, while Cr averaged 6.12 ng/m³, significantly exceeding the recommended annual limit of 0.025 ng/m³ set by Chinese Ambient Air Quality Standards. Jinan carried the greatest burden at 4.67 DALYs per 1000 people, followed by Zibo (3.78), Weifang (2.98), and Rizhao (2.80). CKD, interstitial pneumonia, and chronic respiratory diseases account for the highest DALYs from PM_2.5-bound metals in Shandong Province. Industrial emissions are the largest contributors to the disease burden (>34%), with Cr, Cd, and Pb as the primary contributing metals requiring priority control. Fractional vegetation cover was identified as the key factor contributing to the reduction in their concentrations. These results underscore that prioritizing the regulation of industrial combustion, particularly concerning Cr, Cd, and Pb, and enhancing fractional vegetation cover could reduce disease burden and provide public health benefits. Full article

Air pollution threatens human and environmental health worldwide. A Harvard study in partnership with UK institutions found that fossil fuel pollution killed over 8 million people in 2018, accounting for 1 in 5 fatalities worldwide. Iraq, including the Kurdistan Region of Iraq, has a major environmental issue in that it ranks second worst in 2022 due to the high level of particulate matter, specifically PM_2.5. In this paper, a LoRa-based infrastructure for environmental monitoring in the Kurdistan Region has been designed and developed. The infrastructure encompasses end-node devices, an open-source network server, and an IoT platform. Two AirQNodes were prototyped and deployed to measure particulate matter values, temperature, humidity, and atmospheric pressure using manufacturer-calibrated PM sensors and combined temperature, humidity, and atmospheric sensors. An open-source network server is adopted to manage the AirQNodes and the entire network. In addition, an IoT platform has also been designed and implemented to visualize and analyze the collected data. The platform processes and stores the data, making it accessible for the public and decision-making parties. The infrastructure was tested and results validated by deploying two AirQNodes at separate locations adjacent to existing air quality monitoring stations as reference points. The findings demonstrated that the AirQNodes reliably mirrored the trends and patterns observed in the reference monitors. This research establishes a comprehensive infrastructure for monitoring air quality in the Kurdistan Region of Iraq. Furthermore, complete ownership of the system can be attained by possessing and overseeing the critical components of the infrastructure, which encompass the end devices, network server, and IoT platform. This integrated strategy is especially crucial for the Kurdistan Region of Iraq, where cost-efficiency and enduring sustainability are vital, yet such a structure is absent. Full article

Background: Several studies have examined the prevalence of musculoskeletal pain (MSP) among university students internationally. We aimed to assess the prevalence, pattern, and potential risk factors for MSP among Majmaah University students in Saudi Arabia. Methods: A cross-sectional questionnaire was administered to students from different faculties at Majmaah University. We collected data via the validated Arabic versions of the Standardized Nordic Musculoskeletal Questionnaire, the International Physical Activity Questionnaire, and the Perceived Stress Scale. Bivariate and multivariate logistic regression analyses were performed to explore associations between MSP and demographic, ergonomic, lifestyle, and psychosocial variables. Results: A total of 257 students (n = 195, 75.9% female; n = 62, 24.1% male) were included in this study. The 12-month prevalence of MSP was 225 (87.5%), with the lower back (n = 119, 46.3%) and neck (n = 113, 44.0%) regions being the most affected. Compared with male students, female students reported a higher MSP prevalence (90.3% vs. 79.0%, p = 0.035). The multivariable model was significant (likelihood-ratio χ² = 26.042, df = 7, p < 0.001), accounted for Nagelkerke R² = 0.182 of variance, and showed good calibration (Hosmer–Lemeshow χ² = 8.505, df = 8, p = 0.386). Perceived stress was the only independent predictor of 12-month MSP (β = 0.084, adjusted OR = 1.087, 95% CI 1.034–1.143, Wald χ² = 10.732, p = 0.001), while sex, smoking, academic workload, and sleep duration were non-significant (all p > 0.127). Conclusions: MSP appears to be prevalent among Majmaah University students, with psychological stress emerging as a key independent risk factor. Preventive strategies should include stress management prioritization and ergonomic and physical activity education to support university student well-being. Full article

Climate change poses an existential threat, spurring businesses and financial markets to integrate environmental accounting and ESG (Environmental, Social, and Governance) disclosures into decision-making. This study aims to examine how environmental accounting practices and ESG reporting contribute to climate change mitigation in organizations. It seeks to highlight the significance of these tools in enhancing transparency and accountability, thereby driving more sustainable corporate behavior. By synthesizing the recent literature, the study contributes a comprehensive overview of best practices and challenges at the intersection of accounting and climate action, addressing a noted gap in consolidated knowledge. We conducted a systematic literature review (SLR) following PRISMA guidelines. A broad search (2010–2024) across Scopus, Web of Science, and Google Scholar identified 73 records, which were rigorously screened and distilled to 47 relevant peer-reviewed studies. These studies span global contexts and include both conceptual and empirical work, providing a robust dataset for analysis. Environmental accounting was found to play a pivotal role in measuring and managing corporate carbon footprints, effectively translating climate impacts into quantifiable metrics. Firms that implement rigorous carbon accounting and internalize environmental costs tend to set more precise emission reduction targets and justify mitigation investments through a cost–benefit analysis. ESG disclosure frameworks emerged as critical external tools: a high-quality climate disclosure is linked with greater stakeholder trust and even financial benefits such as lower capital costs. Leading companies aligning reports with standards like TCFD or GRI often enjoy enhanced credibility and investor confidence. However, the review also uncovered challenges, like the lack of standardized reporting, risks of greenwashing, and disparities in adoption across regions, that impede the full effectiveness of these practices. The findings underscore that while environmental accounting and ESG reporting are powerful means to drive corporate climate action, their impact depends on improving consistency, rigor, and integration. Harmonizing global reporting standards and mandating disclosures are identified as key steps to improve data comparability. Strengthening the credibility of ESG disclosures and embedding environmental metrics into core decision-making are essential to leverage accounting as a tool for climate change mitigation. The study recommends that policymakers accelerate moves toward mandatory, standardized ESG reporting and urges organizations to proactively enhance their environmental accounting systems that will support global climate objectives and further research on actual emission outcomes. Full article

Voice pathology diagnosis is essential for the timely detection and management of voice disorders, which can significantly impact an individual’s quality of life. This study employed logistic regression to evaluate the predictive power of variables that include age, severity, loudness, breathiness, pitch, roughness, strain, and gender on a binary diagnosis outcome (Yes/No). The analysis was performed on the Perceptual Voice Qualities Database (PVQD), a comprehensive dataset containing voice samples with perceptual ratings. Two widely used voice quality assessment tools, CAPE-V (Consensus Auditory-Perceptual Evaluation of Voice) and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain), were employed to annotate voice qualities, ensuring systematic and clinically relevant perceptual evaluations. The model revealed that age (odds ratio: 1.033, p < 0.001), loudness (odds ratio: 1.071, p = 0.005), and gender (male) (odds ratio: 1.904, p = 0.043) were statistically significant predictors of voice pathology. In contrast, severity and voice quality-related features like breathiness, pitch, roughness, and strain did not show statistical significance, suggesting their limited predictive contributions within this model. While the results provide valuable insights, the study underscores notable limitations of logistic regression. The model assumes a linear relationship between the independent variables and the log odds of the outcome, which restricts its ability to capture complex, non-linear patterns within the data. Additionally, logistic regression does not inherently account for interactions between predictors or feature dependencies, potentially limiting its performance in more intricate datasets. Furthermore, a fixed classification threshold (0.5) may lead to misclassification, particularly in datasets with imbalanced classes or skewed predictor distributions. These findings highlight that although logistic regression serves as a useful tool for identifying significant predictors, its results are dataset-dependent and cannot be generalized across diverse populations. Future research should validate these findings using heterogeneous datasets and employ advanced machine learning techniques to address the limitations of logistic regression. Integrating non-linear models or feature interaction analyses may enhance diagnostic accuracy, ensuring more reliable and robust voice pathology predictions. Full article

As bike-sharing systems become increasingly integral to sustainable urban mobility, understanding their economic viability requires moving beyond conventional linear models to capture complex operational dynamics. This study develops an interpretable analytical framework to uncover non-linear relationships governing bike-sharing economic performance in Xi’an, China, utilizing one-month operational data across 202 Transportation Analysis Zones (TAZs). Combining spatial analysis with explainable machine learning (XGBoost–SHAP), we systematically examine how operational factors and built environment characteristics interact to influence economic outcomes, achieving superior predictive performance (R² = 0.847) compared to baseline linear regression models (R² = 0.652). The SHAP-based interpretation reveals three key findings: (1) bike-sharing performance exhibits pronounced spatial heterogeneity that correlates strongly with urban functional patterns), with commercial districts and transit-adjacent areas demonstrating consistently higher economic returns. (2) Gradual positive relationships emerge across multiple factors—including bike supply density (maximum SHAP contribution +1.0), commercial POI distribution, and transit accessibility—with performance showing consistent but moderate improvements rather than dramatic threshold effects. (3) Significant interaction effects are quantified between key factors, with bike supply density and commercial POI density exhibiting strong synergistic relationships (interaction values 1.5–2.0), particularly in areas combining high commercial activity with good transit connectivity. The findings challenge simplistic linear assumptions in bike-sharing management while providing quantitative evidence for spatially differentiated strategies that account for moderate threshold behaviors and factor synergies. Cross-validation results (5-fold, R² = 0.89 ± 0.018) confirm model robustness, while comprehensive performance metrics demonstrate substantial improvements over traditional approaches (35.1% RMSE reduction, 36.6% MAE improvement). The proposed framework offers urban planners a data-driven tool for evidence-based decision-making in sustainable mobility systems, with broader methodological applicability for similar urban contexts. Full article

KRAS is the most frequently mutated oncogene in cancer. Its activating mutations are associated with aggressive tumor behavior and resistance to certain therapies, including anti-EGFR treatments in colorectal cancer. In particular, the KRAS G12C mutation, which accounts for approximately 3–4% of colorectal cancers (CRCs) and 12–14% of non-small cell lung cancers (NSCLCs), involves a cysteine substitution at codon 12. This has provided the opportunity to develop selective covalent inhibitors that trap the mutant protein in its inactive state. The first targeted therapies for KRAS G12C-mutant cancers comprise sotorasib and adagrasib, both of which have been authorized for use in patients with previously treated NSCLC and CRC. Nevertheless, despite the evidence of clinical activity for this class of agents, primary and acquired resistance, dose optimization, and toxicity management remain significant open challenges. In this review, we summarize recent advances in KRASG12C tumor biology and pharmacological targeting. We also provide additional insights to guide future efforts to overcome the limitations of the current approaches and implement the treatment of KRASG12C-mutant cancers. Full article

Although trap-neuter-return (TNR) is a popular method for managing free-roaming domestic cat populations, a common criticism is that sterilization fails to mitigate the public health risks posed by free-roaming cats. One of these risks is the environmental contamination of Toxoplasma gondii, a parasite that can be spread in the feces of actively infected felids (both domestic and wild). In healthy humans, toxoplasmosis tends to be mild or asymptomatic; however, the disease can have severe consequences (e.g., for pregnant women) and even be fatal in immunocompromised individuals. Previous research has examined the extent to which free-roaming domestic cats might contaminate sites frequented by young children (e.g., schools and parks). However, the model used included several assumptions that are not reflective of sterilized cats in an urban setting (e.g., smaller home range). By properly accounting for several key factors (e.g., reproductive status, home range), our modeling revealed considerably lower rates of potential incursions by sterilized free-roaming cats than those reported previously. More importantly, our results show that sterilization contributes to a considerable reduction in the risk of environmental contamination; TNR therefore appears to be a valuable harm reduction strategy in mitigating the risks of T. gondii infection. Full article

The emergence and dissemination of extended-spectrum beta-lactamase (ESBL)-producing bacteria pose a major public health threat, necessitating a One Health approach to addressing this threat. Thus, the diversity, ESBL production, and potential public health implications of Gram-negative bacteria recovered from man-made lakes and surrounding lettuce in Yamoussoukro, Côte d’Ivoire were assessed in this study. Also, the lakes’ physicochemical parameters were assessed and correlated with bacteria community using Pearson correlation. A total of 68 Gram-negative bacterial isolates were recovered from the samples and identified via 16S rDNA gene sequencing. Phylogenetic analysis suggested multiple genus-/species-level variations within the isolates. Escherichia coli was the most prevalent in lake water (39.5%), while Acinetobacter was the dominant genus in lettuce (30%). E. coli isolates showed high resistance to ampicillin (90.9%), cefepime (72.7%), cefotaxime (68.2%), and aztreonam (63.6%). Moreover, ESBL production was confirmed in E. coli isolates (22.05%), predominantly mediated by the bla_CTX-M gene. Multidrug-resistant phenotypes were widespread, yielding similar multiple antibiotic resistance index (MARI) values in water (0.27–0.63) and lettuce (0.27–0.81). These data indicate high environmental contamination, which unfortunately is not being taken into account by lettuce producers according to an interview. Statistical analyses showed a significant relationship between bacterial diversity and lakes’ physicochemical parameters, including dissolved oxygen, pH, and turbidity. The basic education level of farmers, the prevalence of ESBL-producing E. coli, and the high prevalence of MDR Gram-negative bacteria in both environmental and crop sources in Yamoussoukro underscore the need for both integrated surveillance and management strategies to mitigate potential microbial public health risks within a One Health framework. Full article

Adjusting the insulin dose in patients with diabetes is crucial for maintaining optimal blood glucose levels. Currently, the insulin dose adjustment of patients registered at the Diabetes Clinic of Kocaeli University Hospital is followed manually using clinical forms, which is time-consuming and often fails to adequately account for individual variations. In this study, a rule-based insulin dose adjustment algorithm was developed based on clinical guidelines. The algorithm analyzes blood glucose levels measured at specific times over the past three days to determine the necessary insulin dose adjustments. The algorithm is implemented as part of the measurement module in a mobile application, the so-called AIDCARE application, which provides an artificial intelligence-enabled self-management tool for diabetic patients. Full article