Search Results (2,455)

Ectropis grisescens Warren (Lepidoptera: Geometridae) is a destructive pest that has severely impacted major tea-growing regions in recent years; as such, it is vital to determine how climate change influences its areas of distribution. In this study, we employed a parameter-optimized maximum entropy (MaxEnt) model, integrating 170 E. grisescens occurrence records and seven selected environmental variables, to predict the pest’s current and future potential distribution in China. Parameter optimization was conducted with the ENMeval package in R, identifying the optimal feature combination as “linear—L, quadratic—Q” and the regularization multiplier as 0.5. These results indicated that the mean diurnal range (bio2), precipitation of driest month (bio14), and elevation were the key variables contributing to the suitable area for E. grisescens. Currently, the total potential suitable area for E. grisescens in China spans approximately 1.969 × 10⁶ km², covering 20.51% of the country's land area, of which 5.121 × 10⁵ km², 7.385 × 10⁵ km², and 7.185 × 10⁵ km² possess low, medium, and high suitability, respectively. Notably, the high-suitability regions are predominantly concentrated in southeastern China, encompassing the provinces and municipalities of Zhejiang, Anhui, Hunan, Jiangsu, Chongqing, Jiangxi, Guangxi, Hubei, and Sichuan. Under future climate scenarios, it is projected that the suitable habitats for this pest will undergo varying degrees of change. Specifically, under the SSP1-2.6 scenario, the suitable habitat area is estimated to increase by up to 12.21% by the 2070s. Under the SSP2-4.5 scenario, the centroid of the suitable habitat will be displaced northwest by up to 238.4 km by the 2030s. Our findings provide valuable insights into the future management of E. grisescens and will aid in mitigating its ecological and economic impacts. Full article

This paper investigates the laws governing the evolution of nanostructures on pre-patterned fused silica surfaces by energetic ion erosion. First, regular nanostructures are fabricated with the Focused Ion Beam (FIB) operating at optimized processing parameters. Then, as a function of the different ion fluences, the surface morphology evolution is studied on a surface with newly formed nanostructures. An experimental phenomenon of inter-transformation between nano-ripples and random dot-like structures is observed. In addition, the principles of the development of the nanostructures are analyzed theoretically. The simulation results fit well with the experiments. This work deeply studies the influence of the initial surface micro-morphology on the evolution of nanostructures, and is of great significance for the control of surface nanostructures generated by energetic ion sputtering. Full article

The Allen-Cahn equation is a fundamental model in materials science for describing phase separation phenomena. This paper introduces an Energy-Stabilized Scaled Deep Neural Network (ES-ScaDNN) framework to solve the Allen-Cahn equation by energy minimization. Unlike traditional numerical methods, our approach directly approximates the solution of steady-state solution the Allen-Cahn equation by minimizing the associated energy functional using a deep neural network. ES-ScaDNN incorporates two key innovations. The first is a scaling layer designed to map the network output to the physical range of the Allen-Cahn phase variable. The second is a variance-based regularization term designed to promote clear phase separation. We demonstrate the accuracy and efficiency of ES-ScaDNN through comprehensive numerical experiments in both one and two dimensions. Our results show that ReLU activation functions are particularly well-suited for one-dimensional cases, while tanh functions are more suitable for two-dimensional problems due to their superior ability to maintain solution smoothness. Furthermore, we investigate how training epochs and the interface parameter ε influence the behavior of the solution. ES-ScaDNN provides a novel, accurate, and efficient deep learning framework for solving the Allen-Cahn equation, paving the way for tackling more complex phase-field problems. Full article

Background: Diabetes Mellitus (DM) represents a significant public health challenge in Pakistan, with a high prevalence exacerbated by various socio-demographic, clinical, and environmental factors. Community pharmacies offer an accessible setting for managing chronic diseases, yet the combined influence of these factors on diabetes control within Pakistani community settings remains underexplored. Objective: This study aimed to assess the impact of socio-demographic, environmental, and clinical factors on diabetes control among patients attending community pharmacies in Lahore, Pakistan. Methods: A cross-sectional study was conducted involving 321 patients with type 2 diabetes recruited from community pharmacies across three regions of Lahore. A structured questionnaire, developed based on international guidelines, was used to collect data on socio-demographic characteristics, clinical history, lifestyle behaviors, and environmental factors. Diabetes control was categorized as controlled, partially controlled, or uncontrolled. Data were analyzed using descriptive statistics, chi-square tests, and multiple logistic regression in SPSS version 26.0. Results: Key socio-demographic predictors of better diabetes control included higher education levels (AOR = 1.317–2.338, p ≤ 0.006) and non-obese status (AOR = 1.057, p = 0.006). Significant clinical and lifestyle predictors were treatment adherence (AOR = 1.287, p < 0.001), regular physical activity (AOR = 1.387, p < 0.001), healthy dietary patterns (AOR = 1.317, p < 0.001), and longer duration of diabetes (>5 years, AOR = 1.277, p = 0.008). Conversely, a family history of diabetes (AOR = 1.967, p < 0.001) and the presence of comorbidities were associated with poorer control. Rural residence showed lower odds of good diabetes control (AOR = 0.857, p = 0.001). Smoking status was also influential, with ex-smokers demonstrating better control than current smokers. Conclusions: Diabetes control is multifactorial, strongly influenced by education, residence, obesity, lifestyle behaviors, and treatment adherence. Interventions targeting modifiable risk factors through patient education, lifestyle counseling, and personalized care are essential to improve diabetes outcomes in community settings. These findings underscore the critical role of community pharmacists in providing holistic diabetes management. Full article

Digital soil mapping has become increasingly important for large-scale soil organic carbon (SOC) assessments, yet the choice of accuracy assessment method significantly influences model performance interpretation. This study investigates the impact of cross-validation fold numbers on accuracy metrics and compares cross-validation with split-sample validation approaches in national-scale SOC mapping. Five machine learning algorithms (Random Forest, Cubist, Support Vector Regression, Bayesian Regularized Neural Networks, and ensemble modeling) were evaluated to predict SOC content across France (539,661 km²) and Czechia (78,873 km²) using 2731 and 445 soil samples, respectively. Environmental covariates included satellite imagery (Sentinel-1, Sentinel-2, and MODIS), climate data (CHELSA), and topographic variables. Four cross-validation approaches (k = 2, 4, 5, 10) were utilized with 100 repetitions each and the results were compared with the existing literature using both cross-validation and split-sample methods. Ensemble models consistently produced the highest prediction accuracy and lowest variance per fold across all validation approaches. Higher fold numbers (k = 10) also produced higher accuracy estimates compared to lower folds (k = 2) and had the greatest value ranges of accuracy assessment metrics. This confirmed the observations from previous studies, in which split-sample validation reported higher R² values (0.10–0.90) compared to cross-validation studies (0.03–0.68), suggesting a strong effect of randomness in training and test data split in the split-sample approach. This suggests that k-fold cross-validation should preferably be used in reporting prediction accuracy in similar studies, with the split-sample approach being strongly affected by value properties from training and test data from particular splits used for validation. Full article

As part of Vision 2030, Saudi Arabia is transforming its healthcare system, shifting away from an unsustainable free-service model. To establish a more sustainable healthcare system, policymakers are considering introducing a National Health Insurance (NHI) program, which would require citizens to make regular financial contributions. This research explores Saudi citizens’ willingness to support and contribute financially to the proposed NHI program, as well as the key socio-economic, demographic, and health-related factors influencing their decision. This study employed a cross-sectional design, utilizing the Contingent Valuation (CV) method. Primary data were collected from 1194 respondents residing in the Qassim region of Saudi Arabia through an online survey between November 2024 and January 2025. Descriptive statistics, binomial, and multiple regression were applied to identify the factors associated with Willingness to Pay (WTP) for NHI. The study indicated that approximately half of the respondents (49.33%) support and are willing to pay for the NHI program (p < 0.01). The mean monthly contribution is estimated at 158 SAR (42.13 US$) with a median amount of 100 SAR (26.6 US$). This amount constitutes about 1.8% of the current healthcare expenditure in 2023. Factors such as being male, having a medium-sized family, and having a family member with a chronic disease increase the likelihood of WTP for NHI. Additionally, the maximum amount of respondents are willing to pay is significantly associated with gender, the presence of chronic disease in the family, obstacles to accessing healthcare, satisfaction with current healthcare services, and existing health insurance status. This study offers valuable insights into Saudi citizens’ willingness to financially contribute to the NHI program. However, its successful implementation depends on addressing cultural acceptance, building public trust, and ensuring affordability for low-income groups. Effective rollout of the NHI requires the coordinated efforts of multiple stakeholders, including government agencies, healthcare providers, employers, health insurance organizations, civil society, and regulatory bodies. Full article

Urban green spaces (UGS) are pivotal to urban sustainability, yet their morphology—patch size, shape, and configuration—remains insufficiently linked to institutional drivers. We investigate how land tenure strength shapes UGS morphology across 36 cities in nine countries. Using OpenStreetMap data, we delineate UGS and compute landscape metrics (AREA, PARA, SHAPE, FRAC, PAFRAC) via FRAGSTATS; we develop a composite index of land tenure strength capturing ownership, use-right duration, expropriation compensation, and government land governance capacity. Spearman’s rank correlations indicate a scale-dependent coupling: stronger tenure is significantly associated with micro-scale patterns—smaller patch areas and more complex, irregular boundaries—consistent with fragmented ownership and higher transaction costs, whereas macro-scale indicators (e.g., overall green coverage/connectivity) show weaker sensitivity. These findings clarify an institutional pathway through which property rights intensity influences the physical fabric of urban nature. Policy implications are twofold: in high-intensity contexts, flexible instruments (e.g., transferable development rights, negotiated acquisition, ecological compensation) can maintain network connectivity via embedded, fine-grain interventions; in low-intensity contexts, one-off land assembly can efficiently deliver larger, regular green cores. The results provide evidence-based guidance for aligning green infrastructure design with diverse governance regimes and advancing context-sensitive sustainability planning. Full article

Background/Objective: In the context of global aging, maintaining daily habits such as adequate nutrition and regular exercise are essential to achieve healthy aging. Therefore, the preservation of swallowing and physical functions is fundamental. Jaw-opening force, an important swallowing function, is linked to physical function. Daily health behaviors are shaped by decision-making biases, which influence decision-making. Individuals with high procrastination tendencies may be less likely to engage in health-promoting behaviors, potentially leading to functional decline. While such tendencies are associated with general health behaviors, little is known about their associations with swallowing and physical functions among older adults. The objective of this study was to examine the associations between decision-making biases and swallowing and physical functions in community-dwelling older adults. Methods: A questionnaire survey was conducted to collect basic information and assess decision-making biases. The jaw-opening force (swallowing function) and grip strength (physical function) were measured. Associations of decision-making biases with jaw-opening force and grip strength were examined using multivariable linear regression analysis. We further conducted sex-stratified sensitivity analyses. Results: This cross-sectional study targeted 107 community-dwelling older adults. There was a significant negative association between procrastination tendency and jaw-opening force (B = −0.715, p = 0.005), and grip strength (B = −1.552, p = 0.003), indicating that individuals with a propensity for procrastination had lower jaw-opening force and grip strength. Conclusions: Procrastination tendency may be used as an indicator to detect swallowing and physical functions. Moreover, incorporating this modifiable factor to promote behavior change may prevent functional decline. The study results highlight the significance of considering individuals’ decision-making biases—particularly procrastination tendency—in clinical settings. Full article

The 2030 Bilingual Policy was introduced in Taiwan to strengthen citizens’ English communication skills, especially among young people, and to enhance their global competitiveness. Within Taiwan’s educational context, several challenges have emerged. In response, researchers have examined the key factors contributing to effective bilingual education, including policy implementation, bilingual instruction models, teacher professional development, and the availability of teaching resources. Despite their important role in bilingual instruction, bilingual professional learning community (BPLC) remains an underexplored topic of discussion. In particular, how BPLC can support subject teachers with limited English proficiency, a common phenomenon in Taiwan, is a question that needs further investigation. Thus, this study aims to implement a sustained BPLC to examine its functional role and influence on bilingual instructional transformation among teachers with limited English proficiency. The study draws on classroom observation notes, BPLC discussions, and interview data collected from fall 2024 to summer 2025. The findings show that regular classroom observations and bilingual professional conversation meetings benefit teachers across various areas, including classroom management, lesson planning, and language development. The BPLC provides real-time feedback and long-term developmental guidance, thereby facilitating more effective bilingual instruction. To conclude, this study contributes to the understanding of the dynamic relationship between bilingual instruction and BPLC. It also offers insights into how BPLC can support teachers with limited English proficiency in various ways. Full article

Despite existing biosecurity frameworks, there is limited empirical evidence on how US swine producers’ beliefs, behaviors, and risk perceptions influence enhanced biosecurity implementation. We conducted an online survey among US swine producers to understand their biosecurity beliefs, behaviors, and practices. We used descriptive, unsupervised machine learning, and Factor Analysis for Mixed Data (FAMD). Of fifty-four respondents, 48.1% reported implementing some biosecurity measures, and 72.2% valued having enhanced biosecurity protocols. Majority (53.7%) considered their veterinarian’s biosecurity opinion most important, and 37% were not concerned about African swine fever. Almost all (90.7%) felt confident they could contain an outbreak on their farms. However, none practiced enhanced biosecurity. The cluster analysis identified four distinct producer profiles (K = 4). Cluster A had young, inexperienced producers operating breeding facilities, with moderate biosecurity adoption. Cluster B included young, small-farm producers with variable biosecurity practices and low mortality rates. Cluster C comprised farms with moderate experience, higher mortality rates, and the lowest biosecurity adoption. Cluster D was composed of older, experienced, educated producers with the highest biosecurity standards and lowest mortality rates. FAMD revealed clustering along human capital and resource availability dimensions. Regular biosecurity assessments, tailored recommendations, and training would improve biosecurity in the swine industry. Full article

Background/Objectives: Korean medicine healthcare (KMHC), a form of traditional medicine including acupuncture and herbal medicine, is widely utilized by patients with intervertebral disc disease (IVDD). With the increasing use of real-world evidence (RWE) in the medical field, this study aims to derive RWE on KMHC utilization and its associated factors in patients with IVDD. Methods: Data from 495 individuals who received outpatient healthcare for IVDD regardless of the purpose such as treatment, examination, rehabilitation, monitoring, or prescription were analyzed using the 2022 Korea Health Panel Survey (KHPS). Multinomial logistic regression analyses were performed to identify factors associated with healthcare use for IVDD. Regression models were constructed by sequentially adding predisposing, enabling, and need factors following Andersen’s behavioral model. All statistical analyses accounted for the complex survey design of the KHPS using survey sampling weights. Results: Individuals aged 45–59 years were less likely to use both KMHC and conventional medicine healthcare (CMHC) for IVDD compared to those aged 19–44 years (adjusted odds ratio [95% confidence interval], 0.28 [0.09, 0.89]). People with disabilities showed lower utilization of both KMHC and CMHC for IVDD compared to those without disabilities (0.27 [0.09, 0.81]). Individuals who were employed (2.37 [1.06, 5.3]) or perceived their health status as fair (3.05 [1.17, 8]) or poor/very poor (6.13 [2.04, 18.45]) were more inclined to use both KMHC and CMHC for IVDD. Individuals who engaged in regular physical activities (2.65 [1.19, 5.9]) or had shoulder joint diseases (3.71 [1.22, 11.29]) or other spine-related diseases (2.63 [1.16, 5.96]) were more inclined to use KMHC-only for IVDD. Conclusions: This study identified significant demographic and health-related factors influencing KMHC utilization for IVDD. These findings emphasize the need for tailored healthcare policies regarding KMHC for IVDD for effective resource distribution. Full article

This study investigates how targeted attacks can compromise the reliability and applications of large language models (LLMs) in educational assessment, highlighting security vulnerabilities that are frequently underestimated in current AI-supported learning environments. As LLMs and other AI tools are increasingly being integrated into grading, providing feedback, and supporting the evaluation workflow, educators are adopting them for their potential to increase efficiency and scalability. However, this rapid adoption also introduces new risks. An unexplored threat is prompt injection, whereby a student acting as an attacker embeds malicious instructions within seemingly regular assignment submissions to influence the model’s behaviour and obtain a more favourable evaluation. To the best of our knowledge, this is the first systematic comparative study to investigate the vulnerability of popular LLMs within a real-world educational context. We analyse a significant representative scenario involving prompt injection in exam assessment to highlight how easily such manipulations can bypass the teacher’s oversight and distort results, thereby disrupting the entire evaluation process. By modelling the structure and behavioural patterns of LLMs under attack, we aim to clarify the underlying mechanisms and expose their limitations when used in educational settings. Full article

We investigate a variation of the classical voter model where the set of influencing agents depends on an individual’s current opinion. The initial population is made up of a random sample of equally sized sub-populations for each state, and two types of interactions are considered: (i) direct neighbors and (ii) second neighbors (friends of direct neighbors, excluding the direct neighbors themselves). The neighborhood size, reflecting regular network connectivity, remains constant across all agents. Our findings show that varying the interaction range introduces asymmetries that affect the probability of consensus and convergence time. At low connectivity, direct neighbor interactions dominate, leading to consensus. As connectivity increases, the probability of either state reaching consensus becomes equal, reflecting symmetric dynamics. This asymmetric effect on the probability of consensus is shown to be independent of network topology in small-world and scale-free networks. Asymmetry also influences convergence time: while symmetric cases display decreasing times with increased connectivity, asymmetric cases show an almost linear increase. Unlike the probability of reaching consensus, the impact of asymmetry on convergence time depends on the network topology. The introduction of stubborn agents further magnifies these effects, especially when they favor the less dominant state, significantly lengthening the time to consensus. We conclude by discussing the implications of these findings for decision-making processes and political campaigns in human populations. Full article

Flood frequency analysis is essential for designing resilient hydraulic infrastructure, but traditional stationary models fail to capture the influence of climate variability and land-use change. This study applies a bivariate logistic model with non-stationary marginals to eight gauging stations in Sinaloa, Mexico, each with over 30 years of maximum discharge records. We compared stationary and non-stationary Gumbel and Generalized Extreme Value (GEV) distributions, along with their bivariate combinations. Results show that the non-stationary bivariate GEV–Gumbel distribution provided the best overall performance according to AIC. Importantly, GEV and Gumbel marginals captured site-specific differences: GEV was most suitable for sites with highly variable extremes, while Gumbel offered a robust fit for more regular records. At station 10086, where a significant increasing trend was detected by the Mann–Kendall and Spearman tests, the stationary GEV estimated a 50-year return flow of 772.66 m³/s, while the non-stationary model projected 861.00 m³/s for 2075. Under stationary assumptions, this discharge would be underestimated, occurring every ~30 years by 2075. These findings demonstrate that ignoring non-stationarity leads to systematic underestimation of design floods, while non-stationary bivariate models provide more reliable, policy-relevant estimates for climate adaptation and infrastructure safety. Full article

The solar terminator, due to its unique characteristics, is a remarkable source of atmospheric disturbances. Due to its regularity and constancy, dependent solely on geometric factors, it can serve as a test source of disturbances, which can be used to test the response of the medium through which it passes and determine its state. However, our knowledge of the atmospheric phenomena generated by the terminator is far from complete. One clear indication of the terminator’s influence is geomagnetic disturbances manifested in the vertical and eastward components of the magnetic field measured at magnetic observatories. To determine the sources of geomagnetic disturbances from the solar terminator, which can be identified by the strict phase correlation of these disturbances with the moments of terminator passage, ionospheric irregularities arising during terminator passage were studied. Ionospheric irregularities extending along the boundary of the morning solar terminator were detected in total electron content data, based on measurements by GNSS receivers. Assumptions are made about the possible parameters of the ionospheric current structure that creates variations in the magnetic field associated with the passage of the solar terminator. Full article