Search Results (8,815)

Accurate urban expansion mapping in dryland environments is essential for sustainable planning, infrastructure management, and heritage-sensitive development, yet it remains methodologically challenging because built-up surfaces often exhibit strong spectral similarity to bright bare soils. This study comparatively evaluates three widely used urban mapping approaches in Diriyah, Saudi Arabia, a rapidly transforming heritage district of high relevance to Saudi Vision 2030: the Global Human Settlement Layer (GHSL), the Normalized Difference Built-up Index (NDBI), and unsupervised k-means classification. Built-up extent was mapped for 2015, 2020, and 2025, and method performance was assessed using 150 stratified reference points interpreted from high-resolution imagery. The results reveal substantial quantitative differences among methods. GHSL produced the most conservative estimates of urban extent (2.80, 4.94, and 5.31 km²), while NDBI and unsupervised classification generated much larger and less realistic built-up areas due to spectral confusion with bright bare soil. Accuracy assessment confirmed the superiority of GHSL, which achieved the highest overall accuracy (0.88) and Kappa coefficient (0.83), compared with NDBI (0.53; 0.41) and unsupervised classification (0.61; 0.50). To support integrative interpretation, the study also developed a Hybrid Built-up Detection Model (HBDM), which combines the three outputs into a continuous urban intensity layer that helps distinguish persistent urban cores from uncertain transition zones. The findings demonstrate that conservative global built-up products provide a more reliable baseline than index-based or unsupervised methods in bright-soil dryland settings. More broadly, the study offers practical methodological guidance for urban monitoring and sustainable land management in desert cities undergoing rapid transformation under large-scale development agendas such as Saudi Vision 2030. Full article

Flood hazard mapping remains challenging in regions with limited hydrological and topographic data, despite increasing flood risk driven by climate change and land-use dynamics. This study aims to demonstrate that preliminary flood inundation maps can be developed under data-scarce conditions by integrating limited field observations with publicly available datasets and simplified hydrodynamic modeling. The Khlong Wat watershed in southern Thailand, where flood hazard maps had not previously existed despite recurrent flood events, was used as a case study. Flood simulations were conducted using the HEC-RAS model with a simplified terrain representation to approximate river bathymetry, acknowledging uncertainties in channel geometry. Hydrodynamic results show a systematic increase in flood extent and depth with increasing flood recurrence intervals, with inundated areas expanding from 1.43 km² for a 10-year flood to 4.02 km² and 5.97 km² for 100- and 500-year events, respectively. Agricultural land is consistently the most affected category, accounting for more than two-thirds of the flooded area across all scenarios, with rubber plantations being the dominant land use. Urban exposure increases with flood magnitude, although most buildings remain affected by shallow inundation below 0.5 m. The results confirm that meaningful flood hazard assessments can be achieved in data-limited regions and provide a transferable framework to support flood risk management and spatial planning in similar environments. Full article

This study presents the development of a Python-based flood-susceptibility risk-mapping tool, implemented in Jupyter Notebook, applied to Rwanda. A Flood Susceptibility Index (FSI) was developed by integrating 20 causal factors associated with flood occurrences, including topographic, hydrological, geological, and anthropogenic variables. Logistic regression, and Variance Inflation Factor were implemented in Python using libraries such as Numpy, Arcpy, traceback, scipy, Pandas, Seaborn, and statsmodel to assign weights to each factor, and to address multicollinearity. The model was validated against flood extent data derived from Sentinel-1 satellite imagery for the major historical flood event that occurred from 2014 to 2024, ensuring spatial consistency and predictive reliability. To project future flood susceptibility for 2030, precipitation data from the Institut Pierre Simon Laplace Coupled Model, version 5A, Medium Resolution (IPSL-CM5A-MR) climate model under the Representative Concentration Pathway 8.5 (RCP 8.5) scenario were utilized. The resulting FSI was classified into five susceptibility levels, from very low to very high, and visualized using Python’s geospatial and plotting tools within Jupyter Notebook in ArcGIS Pro 3.5. It indicates that areas with high amounts of rainfall, and proximity to wetlands and rivers reveal the highest flood risk. The automated and reproducible approach offered by Python enhances transparency and scalability, providing a decision-support tool for disaster risk reduction and climate adaptation planning in Rwanda. Full article

Produced-water (PW) management in the Permian Basin faces tightening injection constraints, induced seismicity concerns, and volatile saltwater disposal (SWD) costs. At the same time, chemistry-rich PW contains dissolved constituents (e.g., Li, B, and Sr) that may be valorized if SWD recovery performance and market conditions support favorable techno-economics. Here, we develop an integrated decision-support framework that couples (i) chemistry-informed surrogate models for unit process performance (recovery, effluent quality, and energy/chemical intensity) with (ii) a network-based allocation model that routes PW from sources through pretreatment, optional treatment and mineral-recovery modules (e.g., desalination and direct lithium extraction), and end-use nodes (beneficial reuse, hydraulic fracturing reuse, mineral recovery/valorization, or Class II disposal). This is a screening-level demonstration using publicly available chemistry percentiles and representative pilot-reported performance windows; it is not a site-specific facility design or a bankable TEA for a particular operator. The optimization is posed as a tri-objective problem—to maximize expected net present value, minimize SWD, and minimize an injection-risk indicator R—subject to mass balance, capacity, quality, and regulatory constraints. Uncertainty in commodity prices, recovery fractions, and operating costs is propagated via Monte Carlo scenario sampling, yielding PARETO-efficient portfolios that quantify trade-offs between profitability and risk mitigation. Using the PW chemistry percentiles reported by the Texas Produced Water Consortium for the Delaware and Midland Basins, we derive screening-level break-even lithium concentrations and illustrate how lithium-carbonate-equivalent price and recovery govern the extent to which mineral revenue can offset SWD expenditures. Comparative brine benchmarks (Smackover Formation and Salton Sea geothermal systems) contextualize the Permian’s generally lower-Li PW and highlight transferability of the workflow across brine types. The proposed framework provides a transparent, extensible basis for design matrix planning under evolving injection limits, enabling risk-aware PW management strategies that reduce disposal dependence while improving water resilience. Full article

Background: The growing use of digital health technologies in diabetes care offers new opportunities for self-management and clinical monitoring. However, there remains significant variability in the extent to which individuals engage with these digital tools. Understanding the psychosocial and clinical factors associated with the use of digital health technologies is crucial for developing targeted implementation strategies. Objectives: The aim of this study was to assess the use of digital health technologies among adults with diabetes and to explore their relationship with psychosocial factors—especially technology acceptance and self-efficacy—as well as certain clinical characteristics, including diabetes-related stress, age, and disease duration. Methods: We conducted a cross-sectional study involving 304 adults with diabetes. Digital engagement was measured using the Digital Adherence and Use Questionnaire (DAUQ), a 7-item self-report instrument (Cronbach’s α = 0.89), from which a composite Digital Engagement Score was calculated (range 1–5) to indicate the level of technology-related self-management behaviors. Participants were descriptively categorized into low- and high-engagement groups. Engagement patterns were also analyzed by diabetes type to understand structural differences in technology exposure. Relationships between psychosocial variables and the outcome were examined using correlation analyses. Since engagement among participants with type 1 diabetes (T1D) showed limited variability, multivariable regression analyses were performed on participants with type 2 diabetes (T2D) using beta regression, with linear regression as a sensitivity analysis. An exploratory beta regression was also conducted for T1D. Results: Overall, 35.5% of participants were classified as having high digital engagement. High engagement was observed in more than 90% of participants with T1D, compared to 4.1% of those with T2D. Median engagement scores differed significantly between low- and high-engagement groups (median [Q1–Q3]: 1.71 [1.71–2.39] vs. 3.86 [3.86–4.43]). Highly engaged participants reported much higher levels of openness to technology (median [Q1–Q3]: 5.00 [1.00–5.00] vs. 1.00 [1.00–1.00], p < 0.001) and self-efficacy (median [Q1–Q3]: 3.00 [3.00–3.00] vs. 5.00 [5.00–5.00], p < 0.001). In T1D, multivariable beta regression analyses showed that age was independently associated with digital engagement, with each 10-year increase corresponding to a decrease in engagement (β = −0.147, 95% CI −0.219 to −0.075, p < 0.001). Diabetes duration and psychosocial variables were not independently associated with engagement in the multivariable model. In contrast, among participants with T2D, insulin treatment emerged as the strongest independent predictor of engagement (β = 0.996, 95% CI 0.859–1.134, p < 0.001), and diabetes-related stress emerged as an independent predictor of engagement (β = 0.069, 95% CI 0.006–0.132, p = 0.033). Technology acceptance was positively associated with engagement (β = 0.694, 95% CI 0.350–1.037, p < 0.001), whereas higher self-efficacy was independently associated with lower engagement intensity (β = −0.366, 95% CI −0.608 to −0.124, p = 0.003). Age and diabetes duration were not independently associated with engagement after adjustment. Conclusions: Digital engagement appears to function as a structurally embedded component of self-management in T1D, with limited variability and largely independent of psychosocial modulation. In T2D, engagement is predominantly driven by treatment characteristics (insulin treatment), psychosocial dynamics (stress, technology acceptance), with higher self-efficacy associated with reduced reliance on digital tools. These findings suggest distinct behavioral mechanisms underlying digital health utilization across diabetes types and support the need for tailored implementation strategies. Full article

Climate change is expected to modify precipitation patterns and increase flood hazard in urban areas, potentially affecting critical infrastructures such as wastewater treatment plants (WWTPs), often located in flood-prone zones. This study assesses the impacts of climate-driven changes in extreme rainfall on flood hazard, pedestrian safety, and tangible physical damage at WWTPs in the Metropolitan Area of Barcelona, Spain. Twenty-four future flood scenarios are defined using CMIP6-based downscaled climate projections (SSP126 and SSP585), two time horizons (2041–2070 and 2071–2100), and different climate model percentiles. Climate Change Coefficients derived from updated Intensity–Duration–Frequency curves are applied to hydrodynamic simulations to evaluate flooded and high-hazard areas for plant workers, as well as direct economic damage at the Montcada i Reixac WWTP, used as a case study. Results indicate limited changes under SSP126, while SSP585 leads to systematic increases in hazard extent and damage, particularly for long-term projections (2071–2100) and extreme percentiles (90th). A large dispersion among climate models is also observed, especially for extraordinary flood events. Finally, a site-specific nature-based adaptation measure targeting frequent floods is proposed, demonstrating the potential of integrated assessments to support sustainable adaptation planning and to reduce the Expected Annual Damage in future climate conditions by 93%. Full article

Research using latent profile analysis (LPA) has yielded inconsistent results regarding the number of personality profiles among athletes, the specific configuration of the Big Five traits, and their interpretation. This study seeks to explore personality types by excluding additional variables from the LPA model, aiming to assess how well personality profiles are universal (independent of gender and cultural context) and can predict academic achievement in student athletes. A cross-sectional study was conducted using a paper-and-pencil questionnaire among 424 student athletes from two universities in Poland and Ukraine. The average age of participants was 20 years old (M = 20.01; SD = 2.48), 62% were male, 53% lived in Poland, and 58% studied Sports Sciences vs. 42% Physical Education. The Mini-International Personality Item Pool (Mini-IPIP) was used to assess the Big Five personality traits, and grade point average (GPA) was used to measure students’ academic achievements in the last semester. The LPA identified four personality profiles: (1) Restrained Neurotic (Profile 1, 32%), Open Extravert (Profile 2, 42%), Competitive Neurotic (Profile 3, 17%), and Cooperative Perfectionist (Profile 4, 8%). Profiles 1, 3, and 4 showed similarly low levels of emotional stability, extraversion, and intellect but differed significantly in agreeableness and conscientiousness. Gender and country differences across athletes representing specific profiles were also noted. Profile 2 showed the strongest link with academic achievement. Hierarchical multiple linear regression showed that LPA profiles explained only 2% of GPA variance, compared to Big Five personality traits (9%) and demographic variables, such as sex, country, and study major (8%), which were also included in the following steps in the regression model, explaining only 9% and 8%, respectively. Most student athletes (52%) with personality profiles 1 (Restrained Neurotic), 3 (Competitive Neurotic), and 4 (Cooperative Perfectionist) may require psychological training to better cope with negative emotions and stress arising in competitive and academic settings. Profile 2 (Open Extravert) seems to be the most adaptive and potentially successful personality type. Personality types are, at least to some extent, related to gender and country of residence. More cross-cultural research is required to further verify the types of athletic personalities. Full article

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems. Full article

Recent anatomical and morphometric studies indicate that the sella turcica is a structurally informative region and a distinctive anatomical formation that can exhibit shape variation among individuals. The aim of this study was to evaluate, in three dimensions, the extent to which sella turcica morphology differs among three sheep breeds (Akkaraman, Morkaraman, Zom) and between sexes. A total of 102 specimens were examined. All skulls were CT-scanned specifically for this study; the sella turcica region was reconstructed as a three-dimensional model, and 12 anatomical landmarks were manually digitized for each specimen. The findings showed that sella turcica size differed among breeds, with the Zom group exhibiting the largest sella turcica size. In contrast, no clear size difference was observed between females and males. Shape assessment also revealed differences among breeds, largely driven by the separation of Zom from Akkaraman and Morkaraman, whereas no distinct sex-related shape pattern was detected. Importantly, the breed-related shape differences persisted after accounting for size effects. Overall, these results suggest that the sella turcica carries a breed-associated morphological signal in sheep, while showing no pronounced sexual differentiation in the present sample. Full article

Social media platforms have become primary communication channels for technical European universities. However, the extent to which global platform algorithms homogenize individual preferences across cultures remains underexplored. Although the current literature offers insights into the topic, none of the works consider the cross-national and multimodal nature of the phenomenon. In the current paper, we introduce the Media Information Processing System (MIPS), a privacy-preserving multimodal deep learning (DL) framework that incorporates large language models (LLMs), computer vision (CV), and knowledge graphs. We analyze data from 15,520 public videos shared by 2359 followers of six top technical universities from Romania, Germany, Italy, and Russia. The results of the study suggest that the degree of homogeneity of the followers’ interest profiles is markedly high. Statistical profiling of the data indicates that the interest profiles of the followers from different countries are positively correlated with a high degree of strength (mean Pearson r = 0.96; p > 0.90). Consensus clustering of the data reveals the existence of stable clusters of themes with high stability scores (>0.75), such as “Human Interaction Dynamics”. The results of the study contradict the traditional theory of regional cultural differentiation. Instead, the results suggest the existence of a new “digital student persona” that is characteristic of the academic lifestyle of students from different countries. Full article

The mechanical behavior, the necking process and the geometry of the neck in rectangular cross-section single-crystal copper specimens under macroscopic uniaxial large-strain tensile conditions were numerically simulated and analyzed using the classical Chaboche combined hardening model and the crystal plasticity constitutive model including the effect of back stress. The simulation results show that, although the classical Chaboche model can simulate the load–displacement curve during the tensile process, it cannot simulate the geometric shape change in the cross-section of the single-crystal copper specimen during the necking process. However, simulation using the crystal plasticity model can not only accurately simulate the macroscopic load–displacement mechanical curves of specimens with different crystal orientations (considering eight off-axis states) but also successfully displays the complex necking morphologies, consistent with experimental observations in the literature for various orientations. The research indicates that the classical Chaboche model lacks the ability to describe the deformation characteristics of single-crystal copper specimens; meanwhile, the crystal plasticity model has a significant advantage in simulating the necking process and characteristics of single-crystal materials under slip mechanisms and can effectively capture the differences in necking morphology caused by the crystal orientation, revealing, to a certain extent, the plastic deformation mechanism in single-crystal metallic materials. Full article

Background: Elevated prolactin levels are associated with disturbances in female sexual function. While long-term therapy with dopamine agonists has been shown to improve these disturbances, the therapeutic benefits appear to be reduced in the presence of vitamin D deficiency or insufficiency. Therefore, the present study aimed to examine whether vitamin D status modulates the effects of metformin—a medication with less pronounced prolactin-lowering properties—on sexual function and depressive symptoms. Methods: The study cohort comprised three groups of reproductive-age women with drug-induced hyperprolactinemia and prediabetes, matched for age, glycated hemoglobin, and prolactin concentrations. Group I included 25 women with normal vitamin D status who were not receiving vitamin D supplementation. Group II consisted of 25 women with vitamin D deficiency or insufficiency that was adequately corrected through supplementation, while group III included 25 women with untreated vitamin D deficiency or insufficiency. All participants received metformin throughout the six-month study period. Female sexual function and depressive symptoms were assessed before and after metformin therapy using the Female Sexual Function Index (FSFI) and the Beck Depression Inventory-II (BDI-II), respectively. Additional outcome measures included plasma 25-hydroxyvitamin D, fasting plasma glucose, glycated hemoglobin (HbA_1c), the homeostatic model assessment of insulin resistance (HOMA-IR), prolactin, gonadotropins, and sex hormones. Results: Improvements in glucose homeostasis were observed across all groups; however, these changes were more pronounced in groups I and II than in group III. Reductions in prolactin concentrations (total and monomeric), accompanied by increases in gonadotropins, estradiol, and testosterone, were observed exclusively in women with normal vitamin D status. In groups I and II, metformin therapy resulted in significant improvements in total FSFI scores as well as in all individual domain scores. In contrast, in group III, the effects of metformin were limited to increases in the domain scores for lubrication and sexual satisfaction. Improvements in sexual function were positively associated with baseline 25-hydroxyvitamin D levels, reductions in prolactin concentrations, and, to a lesser extent, treatment-related changes in HbA_1c and increases in testosterone. A treatment-induced reduction in total BDI-II scores was observed only among women with normal vitamin D status. Conclusions: Low vitamin D status diminishes the beneficial effects of metformin on sexual function and depressive symptoms in reproductive-age women with iatrogenic hyperprolactinemia. Full article

In recent months, some media outlets have been launching artificial intelligence-based chatbots that serve as assistants to users in their search, selection and consumption of content. This research analyses two such examples: Vera, a conversational assistant launched by the Spanish newspaper El País, and the model used by the Colombian newspaper El Espectador, which operates on the WhatsApp platform. Both chatbots share the same approach: they are tools designed for users to interact with newspaper content. This interaction takes place through natural language conversations: the technology understands ‘users’ questions or requests and provides answers based on the content hosted in the newspapers. This changes the way media content is explored. We are moving from a paradigm centred on search engines and keywords to one in which conversation determines the discovery of content. The research analyses the results of these two pioneering experiences in the Spanish-language media. The aim is to understand the extent to which they are changing the relationship with content and how they are affecting the media. Full article

In the early stages of the COVID-19 pandemic, uncertainty around the extent of SARS-CoV-2 spread hampered policymakers’ understanding of the epidemic’s extent. Mathematical models, which proved vital for aiding decision-making, relied primarily on reported cases that were unreliable due to significant underdetection and underreporting. While serological data was used to improve understanding of the epidemiology, it can be costly and difficult to implement without bias. To counter these issues, we integrated serological data from 7229 remnant serum samples collected in 15 Maryland emergency departments (EDs) in Maryland between August and December 2020 into a Bayesian modeling approach to derive an estimate of the incidence of infection and the case fatality rate during the pandemic’s initial wave. We estimated that 5.2% (95% CI, 3.7–7.2%) of the population of Maryland had been infected by late fall 2020. The inferred reporting rate that was estimated started low (<10% in March 2020) and increased to 32% (95% HDI = 26–41%) by the fall, while the estimated infection fatality rate was likely initially higher but fell to 0.51% (95% HDI = 0.43–0.68%) after 1 September 2020. These results demonstrate how existing ED infrastructure can be leveraged to generate less biased, more accurate estimates of the true prevalence of a disease, improving the ability to make decisions and allocate resources under uncertainty. Full article

Reservoirs are critical infrastructure for water and energy security, and require accurate and timely monitoring of reservoir water extent to make informed decisions. Optical remote sensing provides frequent, large-area observations; however, automated water extraction is often complicated by dam operation and surface heterogeneity, which increase spectral variability. Supervised methods, though widely used, generally require manual labels and often perform poorly when transferred across sensors and regions, limiting operational deployment. In this paper, we develop a geo-spectral feature-guided Self-Supervised Water Detection (SWD) framework, an automated algorithm designed for multi-source optical imagery. SWD consists of two stages: pixel-level classification and object-level refinement. Initially, SWD integrates spatial priors with spectral features to automatically derive high-confidence samples, which are then utilized to parameterize Gaussian mixture model to represent multimodal spectral distribution throughout the image. Furthermore, superpixel-constrained region growing is applied to refine shoreline and ensure object-level consistency. We validated SWD across 36 test cases comprising three sensors, six reservoirs, and two hydrological conditions. Compared with Random Forest and U-Net, SWD achieved the best performance. Specifically, (1) in cross-scale tests, SWD achieved high consistency with IoU ≥ 0.774; (2) in cross-region transfers, SWD maintained stable generalization (SD: 0.010); and (3) in hydrological response assessments, SWD captured water-level fluctuations with minimal bias variation (ΔRE < 1%). In addition, SWD framework is computationally efficient, with processing times of 0.49–1.29 s/Mpx on a standard CPU. This study demonstrates that SWD effectively addresses spectral variability and surface complexity in reservoir water area detection across multi-source optical imagery. It operates without manual labels or model training, enabling automated, large-scale and multi-temporal reservoir water monitoring. Full article