Search Results (8,039)

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv9 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables. Full article

This study focuses on analyzing progressive Type-II right censoring competing risks datasets. The latent causes of failures are assumed to follow independent generalized linear exponential distributions. The maximum likelihood and maximum product of spacing methods are employed to estimate the unknown parameters and survival indices. Furthermore, approximate confidence intervals are derived using the asymptotic normality of the maximum likelihood and the maximum product of spacing estimators. Additionally, bootstrap methods are employed to construct confidence intervals. A comprehensive simulation study is carried out to evaluate the effectiveness of these estimation approaches. Finally, real-world datasets are analyzed to illustrate the practical applicability of the proposed model. Full article

This article introduces the Index of Distributive and Developmental Outlook (IDDO), a composite indicator designed to jointly assess economic performance and income inequality across EU-27 Member States. While GDP per capita is widely used to evaluate national prosperity, and the Gini coefficient captures income distribution, their separate use often obscures the interaction between growth and equity—an essential dimension of sustainable development. To address this gap, the IDDO integrates normalized values of both indicators using arithmetic and geometric means. The study applies the IDDO to a longitudinal dataset covering the years 2005, 2014, and 2024, allowing for comparative and temporal analysis. Based on IDDO scores, countries are classified into four development types: balanced development, growth with inequality, equity with stagnation, and dual vulnerability. Results show that while some Member States, such as Luxembourg, Czechia, and Slovenia, maintain consistently high IDDO levels, others—including Bulgaria, Romania, and Latvia—exhibit persistent challenges in aligning growth with equitable outcomes. The findings underscore the need for cohesion policies that prioritize not only economic convergence but also distributive fairness. The IDDO provides a practical and adaptable tool for diagnosing development patterns, benchmarking performance, and informing policy design within the EU framework. Full article

Fibromyalgia is a complex disorder characterized by chronic widespread pain and a variety of related symptoms. Growing evidence suggests that the central and peripheral nervous systems are involved, with small fiber neuropathy playing a key role in its development. We retrospectively reviewed the medical records of 100 patients diagnosed with primary fibromyalgia. Those showing symptoms indicative of small fiber dysfunction who were treated with L-Acetyl Carnitine (LAC) and Palmitoylethanolamide (PEA) alongside standard care (SOC) were compared to matched controls who received only SOC. To ensure comparable groups, propensity score matching was used. Changes in Fibromyalgia Impact Questionnaire Revised (FIQR) scores over 12 weeks were analyzed using non-parametric tests due to the data’s non-normal distribution. After matching, 86 patients (43 in each group) were included. The group receiving LAC and PEA as add-on therapy experienced a significant median reduction in FIQR scores (−19.0 points, p < 0.001), while the SOC-only group showed no significant change. Comparisons between groups confirmed that the improvement was significantly greater in the LAC+PEA group (p < 0.001). These results suggest that adding LAC and PEA to standard care may provide meaningful symptom relief for fibromyalgia patients with suspected small fiber involvement. This supports the hypothesis that peripheral nervous system dysfunction contributes to the disease burden in this subgroup. However, further prospective controlled studies are needed to confirm these promising findings. Full article

Background/Objectives: Estrogen deficiency contributes to dyslipidemia and visceral adiposity, increasing cardiovascular risk in postmenopausal women. Sargassum fulvellum (Sf), a brown seaweed rich in bioactive compounds, possesses lipid-regulating properties that may be enhanced by lactic acid bacteria fermentation. This study aimed to evaluate the effects of fermented S. fulvellum (SfLlLm), prepared using Lactococcus lactis and Leuconostoc mesenteroides, on lipid metabolism and adipose tissue remodeling in an ovariectomized (OVX) mouse model of estrogen deficiency. Methods: Female C57BL/6 mice underwent ovariectomy and were fed an AIN-76A diet supplemented with either unfermented Sf or SfLlLm for eight weeks. Sham-operated and 17β-estradiol-treated OVX groups served as controls. Serum lipid levels—total cholesterol, triglycerides, LDL-C, and HDL-C—were assessed, and histological analysis of visceral adipose tissue was conducted to evaluate adipocyte morphology. Results: OVX-induced estrogen deficiency led to increased total cholesterol, triglycerides, and LDL-C, along with hypertrophic changes in visceral adipocytes. Supplementation with fermented Sargassum fulvellum (SfLlLm) markedly improved these parameters, reducing total cholesterol by 6.7%, triglycerides by 9.3%, and LDL-C by 52.9%, while increasing HDL-C by 17.5% compared to the OVX controls. SfLlLm also normalized visceral adipocyte size and distribution. These effects were comparable to or exceeded those of 17β-estradiol treatment. Conclusions: Fermented SfLlLm ameliorated dyslipidemia and visceral adiposity under estrogen-deficient conditions. These findings support its potential as a functional dietary intervention for managing postmenopausal lipid disorders and associated metabolic complications. Full article

Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates using environmental and material parameters. Three regression models (Linear Regression, Random Forest, and Gradient Boosting) were trained on a scientifically informed synthetic dataset incorporating established corrosion principles from ISO 9223 standards and peer-reviewed literature. The Gradient Boosting model achieved superior performance with cross-validated R² = 0.835 ± 0.024 and RMSE = 98.99 ± 16.62 μm/year, significantly outperforming the Random Forest (p < 0.001) and Linear Regression approaches. Feature importance analysis revealed the copper content (30%), exposure time (20%), and chloride deposition (15%) as primary predictors, consistent with the established principles of corrosion science. Model diagnostics demonstrated excellent predictive accuracy (R² = 0.863) with normally distributed residuals and homoscedastic variance patterns. This methodology provides a systematic framework for ML-based corrosion prediction, with significant implications for protective coating design, material selection, and infrastructure risk assessment, pending comprehensive experimental validation. Full article

Tornado outbreaks can cause substantial damage, injuries, and fatalities, highlighting the need to understand their characteristics for assessing present and future risks. However, global climate models (GCMs) lack the resolution to explicitly simulate tornado outbreaks. As an alternative, researchers examine large-scale atmospheric ingredients that approximate tornado-conducive environments. Building on this approach, we tested whether patterns of covariability between WMAXSHEAR and 500-hPa geopotential height anomalies, previously identified in ERA5 reanalysis, could approximate major U.S. May tornado outbreaks in a GCM. We developed a proxy-based methodology by systematically testing pairs of thresholds for both variables to identify the combination that best reproduced the leading pattern selected for analysis. These thresholds were then applied to simulations from the high-resolution MPI-ESM1.2-HR model to assess its ability to reproduce the original pattern. Results show that the model closely mirrored the observed tornado outbreak pattern, as indicated by a low normalized root mean square error, high spatial correlation, and similar distributions. This study demonstrates a replicable approach for approximating tornado outbreak patterns, applied here to the leading pattern, within a GCM, providing a foundation for future research on how such environments might evolve in a warming climate. Full article

The growing integration of technology in education has heightened awareness of global risks, such as the spread of disinformation. This awareness is vital for fostering the well-being of individuals, especially teenagers, by promoting critical thinking and responsible digital practices. By cultivating these skills, sustainable education empowers individuals to identify potential threats, protect themselves, and advocate for informed, positive change. As part of a European project, this study aims to analyze the current level of awareness among secondary school students (12 to 17) and their teachers. Differences between both are analyzed in how they deal with disinformation in terms of (1) perceptions, (2) feelings and practices, and (3) knowledge and management. A quantitative approach was adopted for this study, which surveyed 1186 minors and 166 teachers. The analysis was based on non-parametric statistics; the Mann–Whitney U statistic was applied as the appropriate measure for comparing independent samples (teachers and students) with a non-normal distribution (p < 0.05). The results were surprising in that they highlighted that minors were more expert than expected in their use of technology and their awareness of the risks of disinformation. These conclusions make it clear that technological tools have the potential to raise awareness of the dangers of disinformation and improve the sustainability of education. Full article

This study examines the impact of reproductive inequality on the long-term survival of Homo sapiens by comparing two reproductive models: the Pareto (power-law) distribution of unequal reproduction and the Gaussian (normal) distribution of equal reproduction. We conducted simulations to explore how genetic diversity, measured by heterozygosity, evolves over time. The results predict population crashes due to genetic bottlenecks under both models, but with large differences in timing. We refer to Pareto reproductive inequality as the Genghis Khan effect. This effect accelerates the loss of genetic diversity, increasing the species’ vulnerability to environmental stressors, resource depletion, and genetic drift, and thereby raising the risk of an earlier population collapse. Our findings showcase the importance of reproductive balance for the prolonged presence of Homo sapiens on this planet. Full article

This study investigated the influence of various annealing treatments on the microstructure and corrosion behavior of 20MnCr5 steel in a 3.5% NaCl solution. A combination of microstructural analysis, hardness testing, and electrochemical techniques was used to comprehensively characterize each condition. To enhance data interpretability, a correlation analysis was performed and visualized through a correlation diagram, enabling statistical assessment of the relationships between grain features, phase distribution, mechanical properties, and corrosion indicators. The results demonstrated that corrosion resistance in 20MnCr5 steel is not governed by a single parameter but by the interplay between grain size, morphology, and phase balance. Excessive pearlite content or coarse, irregular grains were consistently associated with higher corrosion rates and lower electrochemical stability. In contrast, a moderate phase ratio and equiaxed grain structure, achieved through normalization, resulted in better corrosion resistance, confirmed by the highest polarization resistance and lowest corrosion current density values among all samples. Although increased grain refinement improved the hardness, it did not always correlate with a better corrosion performance, especially when morphological uniformity was lacking. This highlights the importance of balancing mechanical and corrosion properties through carefully controlled thermal processing. Full article

On 20 April 2024, an extreme rainfall event occurred in Jiangwan Town Shaoguan City, Guangdong Province, China, where a historic 24 h precipitation of 206 mm was recorded. This triggered extensive landslides that destroyed residential buildings, severed roads, and drew significant societal attention. Rapid acquisition of landslide inventories, distribution patterns, and key controlling factors is critical for post-disaster emergency response and reconstruction. Based on high-resolution Planet satellite imagery, landslide areas in Jiangwan Town were automatically extracted using the Normalized Difference Vegetation Index (NDVI) differential method, and a detailed landslide inventory was compiled. Combined with terrain, rainfall, and geological environmental factors, the spatial distribution and causes of landslides were analyzed. Results indicate that the extreme rainfall induced 1426 landslides with a total area of 4.56 km², predominantly small-to-medium scale. Landslides exhibited pronounced clustering and linear distribution along river valleys in a NE–SW orientation. Spatial analysis revealed concentrations on slopes between 200–300 m elevation with gradients of 20–30°. Four machine learning models—Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were employed to assess landslide susceptibility mapping (LSM) accuracy. RF and XGBoost demonstrated superior performance, identifying high-susceptibility zones primarily on valley-side slopes in Jiangwan Town. Shapley Additive Explanations (SHAP) value analysis quantified key drivers, highlighting elevation, rainfall intensity, profile curvature, and topographic wetness index as dominant controlling factors. This study provides an effective methodology and data support for rapid rainfall-induced landslide identification and deep learning-based susceptibility assessment. Full article

This paper presents a stochastic analysis of a two-unit cold standby system incorporating imperfect switching mechanisms. Each unit operates in one of three states: normal, partial failure, or total failure. Employing Markov processes, the study evaluates system reliability by examining the mean time to failure (MTTF) and steady-state availability metrics. Failure and repair times are assumed to follow exponential distributions, while the switching mechanism is modeled as either perfect or imperfect. The results highlight the significant influence of switching reliability on both MTTF and system availability. This analysis is crucial for optimizing the performance of complex systems, such as thermal power plants, where continuous and reliable operation is imperative. The study also aligns with recent research trends emphasizing the integration of preventive maintenance and advanced reliability modeling approaches to enhance overall system resilience. Full article

The Offset Elliptical Normal (OEN) mixture model represents the seasonal–diurnal surface wind vector for wind engineering design applications. This study upgrades the parameterization of OEN by accounting for changes in format of the global database of surface observations, improving performance by eliminating manual supervision and extending the scope of the model to include skewness. The previous coordinate transformation of binned speed and direction, used to evaluate the joint probability distributions of the wind vector, is replaced by direct kernel density estimation. The slow process of sequentially adding additional components is replaced by initializing all components together using fuzzy clustering. The supervised process of sequencing each mixture component through time is replaced by a fully automated unsupervised process using pattern matching. Previously reported departures from normal in the tails of the fuzzy-demodulated OEN orthogonal vectors are investigated by directly fitting the bivariate skew generalized t distribution, showing that the small observed skew is likely real but that the observed kurtosis is an artefact of the demodulation process, leading to a new Offset Skew Normal mixture model. The supplied open-source R scripts fully automate parametrization for locations in the NCEI Integrated Surface Hourly global database of wind observations. Full article

This paper aims to investigate a Nash equilibrium (NE)-seeking approach for the aggregative game problem of second-order multi-agent systems (MAS) with uncontrollable malicious players, which may cause the decisions of global players to become uncontrollable, thereby hindering the ability of normal players to reach the NE. To mitigate the influence of malicious players on the system, a malicious player detection and disconnection (MPDD) algorithm is proposed, based on the fixed-time convergence method. Subsequently, a predefined-time distributed NE-seeking algorithm is presented, utilizing a time-varying, time-based generator (TBG) and state-feedback scheme, ensuring that all normal players complete the game problem within the predefined time. The convergence properties of the algorithms are analyzed using Lyapunov stability theory. Theoretically, the aggregative game problem with malicious players can be solved using the proposed algorithms within any user-defined time. Finally, a numerical simulation of electricity market bidding verifies the effectiveness of the proposed algorithm. Full article