Search Results (3,720)

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

Early rehabilitation is essential for restoring functional recovery in patients with stroke, particularly during the early phase of post-acute care (PAC), or the subacute stage. We aimed to evaluate the effectiveness of a 7-week PAC rehabilitation program in improving muscle strength, physical performance, and functional recovery. A total of 219 inpatients with stroke in the subacute stage were initially recruited from the PAC ward of a regional teaching hospital in Northern Taiwan, with 79 eligible patients—within 1 month of an acute stroke—included in the analysis. The program was delivered 5 days per week, with 3–4 sessions daily (20–30 min each, up to 120 min daily), comprising physical, occupational, and speech–language therapies. Sociodemographic data, muscle strength, physical performance (Berg Balance Scale [BBS], gait speed, and 6-minute walk test [6MWT]), and functional recovery (modified Rankin Scale [mRS], Barthel Index [BI], Instrumental Activities of Daily Living [IADL], and Fugl–Meyer assessment: sensory and upper extremity) were collected at baseline, 3 weeks, and 7 weeks. Generalized estimating equations analyzed program effectiveness. Among the 56 patients (70.9%) who completed the program, significant improvements were observed in the muscle strength of both the affected upper (B = 0.93, p < 0.001) and lower limbs (B = 0.88, p < 0.001), as well as in their corresponding unaffected limbs; in physical performance, including balance (BBS score: B = 9.70, p = 0.003) and gait speed (B = 0.23, p = 0.024); and in functional recovery, including BI (B = 19.5, p < 0.001), IADL (B = 1.48, p < 0.001), and mRS (B = −0.13, p = 0.028). These findings highlight the 7-week PAC rehabilitation program as an effective strategy during the critical recovery phase for patients with stroke. Full article

It is well-known that the necessary and sufficient condition for a connected graph to be unicyclic is that its omega invariant, a recently introduced graph invariant useful in combinatorial and topological calculations, is zero. This condition could be stated as the condition that the order and the size of the graph are equal. Using a recent result saying that the length of the unique cycle could be any integer between 1 and n − a₁ where a₁ is the number of pendant vertices in the graph, two explicit labeling algorithms are provided that attain these extremal values of the first and second Zagreb indices by means of an application of the well-known rearrangement inequality. When the cycle has the maximum length, we obtain the situation where all the pendant vertices are adjacent to the support vertices, the neighbors of the pendant vertices, which are placed only on the unique cycle. This makes it easy to calculate the second Zagreb index, as the contribution of the pendant edges to such indices is fixed, implying that we can only calculate these indices for the edges on the cycle. Full article

Aiming at the multifunctional requirements of the limited space in high-end vehicle side-door latches, a double single-loop RRUPRR mechanism driven by a single motor for both electric releasing and cinching is proposed based on the POC set. The kinematical equations of the RRURR mechanism possess 2 × 2 analytical solutions. In order to apply the current motion/force transmission performance index of the parallel mechanisms to the transmission performance analysis of the serial mechanisms, matching methods for chain-driving transference and the moving/fixed platform inversion are proposed. The solution of the performance index of a single-degree-of-freedom single-loop mechanism is equivalent to the solution of the input motion/force transmission performance index of a parallel mechanism. The overall motion/force transmission performance index of a single-loop mechanism is constructed, and the corresponding calculation procedure is defined. Chain-driving transference can be obtained through forward and inverse solutions of the RRURR mechanism. In response to the extremely high requirements for motion/force transmission performance of electric release mechanisms, the proposed overall motion/force transmission performance index is used to calculate for the input motion screw and corresponding transmission-force screw of the single-loop RRURR mechanism and obtain the overall motion/force transmission performance of the mechanism. The performance atlas of the mechanism shows that it has excellent motion/force transmission characteristics within the workspace. Using ADAMS simulation software, the driving torque required for electric releasing and cinching of a vehicle side-door latch mechanism with a single motor is analyzed. The overall motion/force transmission performance index of a single-loop mechanism can be applied to single-loop overconstrained mechanisms and non-overconstrained mechanisms. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Background and Objectives: Surgical site infections (SSIs) are a frequent complication after lower extremity fracture surgery, yet tools for individualized risk prediction remain limited. This study aimed to develop and internally validate a nomogram for individualized SSI risk prediction based on perioperative clinical parameters. Materials and Methods: This retrospective cohort study included adults who underwent lower extremity fracture surgery between 2022 and 2025 at a tertiary care center. Thirty candidate predictors were evaluated. Feature selection was performed using six strategies, and the final model was developed with logistic regression based on bootstrap inclusion frequency. Model performance was assessed by area under the curve, calibration slope, Brier score, sensitivity, and specificity. Results: Among 638 patients undergoing lower extremity fracture surgery, 76 (11.9%) developed SSIs. Of six feature selection strategies compared, bootstrap inclusion frequency identified seven predictors: red blood cell count, preoperative C-reactive protein, chronic kidney disease, operative time, chronic obstructive pulmonary disease, body mass index, and blood transfusion. The final model demonstrated an AUROC of 0.924 (95% CI, 0.876–0.973), a calibration slope of 1.03, and a Brier score of 0.0602. Sensitivity was 86.2% (95% CI, 69.4–94.5) and specificity was 89.5% (95% CI, 83.8–93.3). Chronic kidney disease (OR, 88.75; 95% CI, 5.51–1428.80) and blood transfusion (OR, 85.07; 95% CI, 11.69–619.09) were the strongest predictors of infection. Conclusions: The developed nomogram demonstrates strong predictive performance and may support personalized SSI risk assessment in patients undergoing lower extremity fracture surgery. Full article

Atmospheric warming results in increase in temperatures for the mean, the coldest, and the hottest day of the year, season, or month. Global warming leads to a large increase in the atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. Using the GISS-E2.1 climate model, we present the future changes in the coldest and hottest daily temperatures as well as in extreme precipitation indices (under four main Shared Socioeconomic Pathways (SSPs)). The increase in the wet-day precipitation ranges between 6% and 15% per 1 °C global surface temperature warming. Scaling of the 95th percentile versus the total precipitation showed that the sensitivity for the extreme precipitation to the warming is about 10 times stronger than that for the mean total precipitation. For six precipitation extreme indices (Total Precipitation, R95p, RX5day, R10mm, SDII, and CDD), the histograms of probability density functions become flatter, with reduced peaks and increased spread for the global mean compared to the historical period of 1850–2014. The mean values shift to the right end (toward larger precipitation and intensity). The higher the GHG emission of the SSP scenario, the more significant the increase in the index change. We found an intensification of precipitation over the globe but large uncertainties remained regionally and at different scales, especially for extremes. Over land, there is a strong increase in precipitation for the wettest day in all seasons over the mid and high latitudes of the Northern Hemisphere. There is an enlargement of the drying patterns in the subtropics including over large regions around Mediterranean, southern Africa, and western Eurasia. For the continental averages, the reduction in total precipitation was found for South America, Europe, Africa, and Australia, and there is an increase in total precipitation over North America, Asia, and the continental Russian Arctic. Over the continental Russian Arctic, there is an increase in all precipitation extremes and a consistent decrease in CDD for all SSP scenarios, with the maximum increase of more than 90% for R95p and R10 mm observed under SSP5–8.5. 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

Snails at hydrothermal vents rely on symbiotic bacteria for nutrition; however, the specifics of these associations in adapting to such extreme environments remain underexplored. This study investigated the community structure and metabolic potential of bacteria associated with two Indian Ocean vent snails, Chrysomallon squamiferum and Gigantopelta aegis. Using microscopic, phylogenetic, and metagenomic analyses, this study examines bacterial communities inhabiting the foot and gland tissues of these snails. G. aegis exhibited exceptionally low bacterial diversity (Shannon index 0.14–0.18), primarily Gammaproteobacteria (99.9%), including chemosynthetic sulfur-oxidizing Chromatiales using Calvin–Benson–Bassham cycle and methane-oxidizing Methylococcales in the glands. C. squamiferum hosted significantly more diverse symbionts (Shannon indices 1.32–4.60). Its black variety scales were dominated by Campylobacterota (67.01–80.98%), such as Sulfurovum, which perform sulfur/hydrogen oxidation via the reductive tricarboxylic acid cycle, with both Campylobacterota and Gammaproteobacteria prevalent in the glands. The white-scaled variety of C. squamiferum had less Campylobacterota but a higher diversity of heterotrophic bacteria, including Delta-/Alpha-Proteobacteria, Bacteroidetes, and Firmicutes (classified as Desulfobacterota, Pseudomomonadota, Bacteroidota, and Bacillota in GTDB taxonomy). In C. squamiferum, Gammaproteobacteria, including Chromatiales, Thiotrichales, and a novel order “Endothiobacterales,” were chemosynthetic, capable of oxidizing sulfur, hydrogen, or iron, and utilizing the Calvin–Benson–Bassham cycle for carbon fixation. Heterotrophic Delta- and Alpha-Proteobacteria, Bacteroidetes, and Firmicutes potentially utilize organic matter from protein, starch, collagen, amino acids, thereby contributing to the holobiont community and host nutrition accessibility. The results indicate that host species and intra-species variation, rather than the immediate habitat, might shape the symbiotic microbial communities, crucial for the snails’ adaptation to vent ecosystems. Full article

Background: Peyronie’s disease (PD) is a chronic inflammatory condition that affects the penile albuginea. Oxidative stress (OS) plays a crucial role in the development of the disease, prompting us to investigate OS levels at the site of the disease and in peripheral blood. This article presents our second study in which the OS was evaluated by calculating the OS index (OSI) in blood samples taken directly from the penile corpora cavernosa of patients with PD. Our innovative diagnostic method, which focuses on the analysis of oxidative stress (OS) in the corpora cavernosa of the penis, allows us to accurately identify the “chemical” signals (OS levels) of the pathology in the area where it is present. Methods: Our study included 102 PD patients from our Peyronie’s care center and 100 control cases. To conduct a comprehensive OS analysis, we measured both the total oxidant status (TOS) and total antioxidant status (TAS) and calculated the oxidative stress index (OSI) as OSI = TOS/TAS × 100. Blood samples were collected from the penis and a vein in the upper extremity, and OS was measured using d-ROMs and PATs (FRAS kit). Results: Pearson’s analyses revealed a significant statistical correlation between penile OSI values and PD plaque volumes (p = 0.003), while no correlation was found between systemic OSI values and plaque volumes (p = 0.356). Penile OSI values decreased significantly after PD plaque removal (p < 0.0001). A comparison of penile OSI values in PD patients (post plaque removal) and the control group showed no significant differences (p = 0.418). Conclusions: The lack of correlation between systemic OSI values and Peyronie’s plaque volume suggests that direct sampling from the site of the disease is preferable for OS studies. Conducting a penile OSI study could provide a precise oxidative marker dependent on plaque volume. In addition, the penile OSI study can biochemically monitor the therapeutic result, alongside penile ultrasound imaging. Full article

Thermal resilience is critical for building safety in cold climates during heating outages. This study presents full-scale experimental data from two residential buildings in Poland, tested during the winter of 2024–2025 under both typical and extreme outdoor conditions. The buildings—a lightweight timber-frame structure and a mediumweight masonry structure with ground coupling—were exposed to multi-day heating blackouts, and their thermal responses were monitored at a high temporal resolution. Several resilience indicators were used, including the resistance time (RT), degree of disruption (DoD), and hours of safety threshold (HST). Additionally, two time-based metrics—the time to threshold (T_x) and temperature at X-hours (T(t_x))—were introduced to improve classification in long-duration scenarios. The weighted unmet thermal performance (WUMTP) index was also implemented and validated using experimental data. The results show that thermal mass and ground coupling significantly improved passive resilience, enabling the mediumweight building to maintain temperatures above 15 °C for over 60 h without heating. This study provides new empirical evidence of passive survivability in blackout conditions and supports the development of time-sensitive assessment tools for cold climates. The findings may inform future updates to building codes and retrofit guidelines. Full article

Heatwaves intensified by climate change increasingly threaten urban populations, especially the elderly. However, most existing studies have concentrated on short-term or single-scale analyses, lacking a comprehensive understanding of how land cover changes and urbanization affect the vulnerability of the elderly to extreme heat. This study aims to investigate the spatiotemporal distribution patterns of heat-related health risks among the elderly in Nanchang City and to identify their key driving factors within the context of rapid urbanization. This study employs Crichton’s risk triangle framework to the heat-related health risks for the elderly in Nanchang, China, from 2002 to 2020 by integrating meteorological records, land surface temperature, land cover data, and socioeconomic indicators. The model captures the spatiotemporal dynamics of heat hazards, exposure, and vulnerability and identifies the key drivers shaping these patterns. The results show that the heat health risk index has increased significantly over time, with notably higher levels in the urban core compared to those in suburban areas. A 1% rise in impervious surface area corresponds to a 0.31–1.19 increase in the risk index, while a 1% increase in green space leads to a 0.21–1.39 reduction. Vulnerability is particularly high in economically disadvantaged, medically under-served peripheral zones. These findings highlight the need to optimize the spatial distribution of urban green space and control the expansion of impervious surfaces to mitigate urban heat risks. In high-vulnerability areas, improving infrastructure, expanding medical resources, and establishing targeted heat health monitoring and early warning systems are essential to protecting elderly populations. Overall, this study provides a comprehensive framework for assessing urban heat health risks and offers actionable insights into enhancing climate resilience and health risk management in rapidly urbanizing regions. Full article

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered. Full article

Ecological restoration in the cold and high-altitude mining areas of the Qinghai–Tibet Plateau is faced with dual challenges of extreme environments and insufficient microbial adaptability. This study aimed to screen local microbial resources with both extreme environmental adaptability and plant-growth-promoting functions. Local fungi (DK; F18-3) and commercially available bacteria (B0) were used as materials to explore their regulatory mechanisms for plant growth, soil physicochemical factors, microbial communities, and metabolic profiles in the field. Compared to bacterial treatments, local fungi treatments exhibited stronger ecological restoration efficacy. In addition, the DK and F18-3 strains, respectively, increased shoot and root biomass by 23.43% and 195.58% and significantly enhanced soil nutrient content and enzyme activity. Microbiome analysis further implied that, compared with the CK, DK treatment could significantly improve the α-diversity of fungi in the rhizosphere soil (the Shannon index increased by 14.27%) and increased the amount of unique bacterial genera in the rhizosphere soil of plants, totaling fourteen genera. Meanwhile, this aggregated the most biomarkers and beneficial microorganisms and strengthened the interactions among beneficial microorganisms. After DK treatment, twenty of the positively accumulated differential metabolites (DMs) in the plant rhizosphere were highly positively associated with six plant traits such as shoot length and root length, as well as beneficial microorganisms (e.g., Apodus and Pseudogymnoascus), but two DMs were highly negatively related to plant pathogenic fungi (including Cistella and Alternaria). Specifically, DK mainly inhibited the growth of pathogenic fungi through regulating the accumulation of D-(+)-Malic acid and Gamma-Aminobutyric acid (Cistella and Alternaria decreased by 84.20% and 58.53%, respectively). In contrast, the F18-3 strain mainly exerted its antibacterial effect by enriching Acidovorax genus microorganisms. This study verified the core role of local fungi in the restoration of mining areas in the Qinghai–Tibet Plateau and provided a new direction for the development of microbial agents for ecological restoration in the Qinghai–Tibet Plateau. Full article

Desert plant communities play a vital role in sustaining the stability of arid ecosystems; however, they demonstrate limited resilience to environmental changes. A critical aspect of understanding community assembly mechanisms is determining whether soil texture heterogeneity affects vegetation diversity in arid deserts, especially under conditions of extreme water scarcity and restricted nutrient availability. This study systematically examined the relationships between plant diversity and soil physicochemical properties across four soil texture types—sand, sandy loam, loamy sand, and silty loam—by selecting four representative desert systems in the Hami region of Xinjiang, China. The objective was to elucidate the mechanisms through which soil texture may impact desert plant species diversity. The findings revealed that silty loam exhibited distinct characteristics in comparison to the other three sandy soil types. Despite its higher nutrient content, silty loam demonstrated the lowest vegetation diversity. The Shannon–Wiener index (H′), Simpson dominance index (C), Margalef richness index (D), and Pielou evenness index (Jsw) for silty loam were all lower compared to those for sand, sandy loam, and loamy sand. However, silty loam exhibited higher values in electrical conductivity (EC), urease activity (SUR), and nutrient content, including soil organic matter (SOM), ammonium nitrogen (NH⁴⁺-N), and available potassium (AK), than the other three soil textures. This study underscores the significant regulatory influence of soil texture on plant diversity in arid environments, offering new insights and practical foundations for the conservation and management of desert ecosystems. Full article