Search Results (634)

Landslide susceptibility mapping (LSM) faces persistent challenges in defining representative stable samples as conventional random selection often includes unstable areas, introducing spatial bias and compromising model accuracy. To address this, we redefine the certainty factor (CF) method—traditionally for factor weighting—as a spatial screening tool for stable zone delineation and apply it to the tectonically active upper Jinsha River (937 km², southeastern Tibetan Plateau). Our approach first generates a preliminary susceptibility map via CF, using the natural breaks method to define low- and very low-susceptibility zones (CF < 0.1) as statistically stable regions. Non-landslide samples are exclusively selected from these zones for support vector machine (SVM) modeling with five-fold cross-validation. Key results: CF-guided sampling achieves training/testing AUC of 0.924/0.920, surpassing random sampling (0.882/0.878) by 4.8% and reducing ROC standard deviation by 32%. The final map shows 88.49% of known landslides concentrated in 25.70% of high/very high-susceptibility areas, aligning with geological controls (e.g., 92% of high-susceptibility units in soft lithologies within 500 m of faults). Despite using a simpler SVM, our framework outperforms advanced models (ANN: AUC, 0.890; RF: AUC, 0.870) in the same region, proving physical heuristic sample curation supersedes algorithmic complexity. This transferable framework embeds geological prior knowledge into machine learning, offering high-precision risk zoning for disaster mitigation in data-scarce mountainous regions. Full article

Optimizing metadata indexing remains critical for enhancing distributed file system performance. The Traditional Log-Structured Merge-Trees (LSM-Trees) architecture, while effective for write-intensive operations, exhibits significant limitations when handling massive metadata workloads, particularly manifesting as suboptimal read performance and substantial indexing overhead. Although existing learned indexes perform well on read-only workloads, they struggle to support modifications such as inserts and updates effectively. This paper proposes SwiftKV, a novel metadata indexing scheme that combines LSM-Tree and learned indexes to address these issues. Firstly, SwiftKV employs a dynamic partition strategy to narrow the metadata search range. Secondly, a two-level learned index block, consisting of Greedy Piecewise Linear Regression (Greedy-PLR) and Linear Regression (LR) models, is leveraged to replace the typical Sorted String Table (SSTable) index block for faster location prediction than binary search. Thirdly, SwiftKV incorporates a load-aware construction mechanism and parallel optimization to minimize training overhead and enhance efficiency. This work bridges the gap between LSM-Trees’ write efficiency and learned indexes’ query performance, offering a scalable and high-performance solution for modern distributed file systems. This paper implements the prototype of SwiftKV based on RocksDB. The experimental results show that it narrows the memory usage of index blocks by 30.06% and reduces read latency by 1.19×~1.60× without affecting write performance. Furthermore, SwiftKV’s two-level learned index achieves a 15.13% reduction in query latency and a 44.03% reduction in memory overhead compared to a single-level model. For all YCSB workloads, SwiftKV outperforms other schemes. Full article

Solid oxide electrolysis cells (SOECs) are emerging as a promising technology for high-efficiency and environmentally friendly hydrogen production. While laboratory-scale experiments and physics-based simulations have significantly advanced SOEC research, there remains a need for faster, scalable, and cost-effective methods to predict electrochemical performance. This study explores the feasibility of using machine learning (ML) techniques to model the performance of SOECs with the material configuration LSM-YSZ/YSZ/Ni-YSZ. A dataset of 593 records (from 31 IV curves) was compiled from 12 peer-reviewed sources and used to train and evaluate four ML algorithms: SVR, ANN, XGBoost, and Random Forest. Among these, XGBoost achieved the highest accuracy, with an R² of 98.39% for cell voltage prediction and 98.10% for IV curve interpolation test under typical conditions. Extrapolation tests revealed the model’s limitations in generalizing beyond the bounds of the training data, emphasizing the importance of comprehensive data coverage. Overall, the results confirm that ML models, particularly XGBoost, can serve as accurate and efficient tools for predicting SOEC electrochemical behavior when applied with appropriate data coverage and guided by materials science concepts. Full article

Background: Glioblastoma (GBM) is an aggressive brain tumor, with surgery being an integral part of treatment. Aggressive resections improve clinical outcomes but need to be balanced against potential functional impairment. Neuropsychological (NP) testing is an important tool neurosurgeons use to assess cognitive functioning. Importantly, associations between NP test scores and imaging biomarkers could enable a testable baseline by which to track patient outcomes over time and aid in presurgical counseling. Methods: We identified 44 patients diagnosed with primary GBM and who had detailed NP testing and presurgical imaging. Regression models for NP indices were created with tumor size, hemisphere, and lobar location as predictors. Lesion–symptom mapping (LSM) analyses were used to identify more detailed structure–function relationships. Results: Larger tumor volumes predicted worse attention, immediate memory, language, visuospatial, and overall NP performance (p < 0.05 for all). Left hemisphere involvement predicted worse attention, language, and immediate memory NP performance (p < 0.01 for all). Only visuospatial testing had lobar location significantly associated with worse scores (occipital lobe; p < 0.05). The LSM analyses identified areas around the left sagittal stratum as significantly associated with language performance (p < 0.05), with no other structure–function relationships being identified. Conclusions: These findings support the growing evidence that outside of a small number of truly critical regions, high-grade gliomas impair cognition generally, likely due to progressive tumor infiltration-associated neuroplasticity of complex parallel and interconnected networks. To investigate this, future studies should incorporate larger cohort sizes and should examine the relationship of glioma-induced network-level perturbations on cognitive decline. Full article

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed in this study. The optimization process includes three components. First, a method for calculating the actual equivalent antenna height is introduced, utilizing high-precision remote sensing satellite imagery to obtain communication path profiles. This method accounts for the antenna’s physical length, vehicular height, and local terrain characteristics, thereby providing an accurate representation of the antenna’s effective height within its operational environment. Second, an equivalent substitution method for ground loss is developed, utilizing surface information derived from high-precision remote sensing satellite images. This method integrates ground loss directly into the Egli model’s calculation process, eliminating the need for separate computations and simplifying the model. Third, leveraging the Egli model as a foundation, the least squares method (LSM) is employed to fit the relief height, ensuring the model meets the requirements for ultra-short wave communication distances under line-of-sight (LOS) conditions and enhances suitability for real-world vehicular communication systems. Finally, the validity and accuracy of the optimization model are verified by comparing the measured data with the theoretical calculated values. Compared with the Egli model, the Egli model with additional correction factors, and the measured data, the average error of the optimized model is reduced by 8.98%, 2.09%, and the average error is 0.45%. Full article

Methane (CH₄) emissions from the wetlands of the Tibetan Plateau (TP) remain poorly quantified, particularly regarding their historical dynamics, spatial heterogeneity, and response to climate change. This study provides the high-resolution, observation-driven reconstruction of TP wetland CH₄ emissions over the past four decades, integrating a machine learning model with 108 flux measurements from 67 sites. This unique combination of field-based data and fine-scale mapping enables unprecedented accuracy in quantifying both emission intensity and long-term trends. We show that current TP wetlands emit 5.87 ± 1.43 g CH₄ m⁻² yr⁻¹, totaling 97.3 Gg CH₄ yr⁻¹, equivalent to 7.8% of East Asia’s annual wetland emissions. Despite a climate-driven increase in per-unit-area CH₄ fluxes, a 19.8% (8432.9 km²) loss of wetland area since the 1980s has reduced total emissions by 15%, counteracting the enhancement from warming and moisture increases. Our comparative analysis demonstrates that existing land surface models (LSMs) substantially underestimate TP wetland CH₄ emissions, largely due to the inadequate representation of TP wetlands and their dynamics. Projections under future climate scenarios indicate a potential 8.5–21.2% increase in emissions by 2100, underscoring the importance of integrating high-quality, region-specific observational datasets into Earth system models. By bridging the gap between field observations and large-scale modeling, this work advances understanding of alpine wetland–climate feedback, and provides a robust foundation for improving regional carbon budget assessments in one of the most climate-sensitive regions on Earth. Full article

This study investigates retailers’ strategic choices between short video marketing (SVM) and live streaming marketing (LSM) in the social media era, with a focus on the synergistic effects and decision-making mechanisms of these two digital marketing models. Using game theory, we construct a game analysis model to analyze retailers’ optimal selection among three marketing strategies: S (sole implementation of SVM), L (sole implementation of LSM), and H (integration of both SVM and LSM). The findings reveal that retailers should make different strategic choices based on the different stages of development. In the early market entry phase, characterized by both a low mixed commission rate and a low slotting fee, the H strategy emerges as the optimal choice. As the market enters its growth phase, retailers should shift to the L strategy, driven by “influencer LSM”. When the market enters a mature stage, retailers should be more inclined to adopt the S strategy or the L strategy dominated by “merchant self-LSM”. These findings provide new theoretical insights into the dynamic selection mechanisms of digital marketing strategies while offering practical decision-making guidance for retailers in allocating marketing resources across different development stages. The conclusions have direct implications for optimizing corporate marketing mix strategies. Full article

Background/Objectives: The growing prevalence of metabolic-associated steatotic liver disease (MASLD)/metabolic-associated steatohepatitis (MASH) is forecasted to be over 55% by 2040, representing a significant driver of cirrhosis and highlighting demand for effective therapeutic interventions. The therapeutic landscape is evolving with agents, like glucagon-like peptide-1 receptor agonists (GLP-1 RAs), under active investigation. A common concern across emerging therapies is potentially precipitating decompensation in patients with existing cirrhosis, necessitating careful consideration in this population. Case Presentation: A 46 y.o. female with obesity and cirrhosis from MASH and alcohol who underwent a deceased-donor liver transplant developed steatohepatitis within a year post-transplant after gaining 36 kg. Transient elastography revealed controlled attenuation parameter (CAP) 400 dB/m (S3 steatosis) and liver stiffness measurement (LSM) 61.2 kPa (advanced fibrosis). Follow-up biopsy confirmed severe steatohepatitis (NAS 7/8) and advanced fibrosis (F3), attributed to metabolic dysfunction without evidence of alcohol recurrence. She decompensated with ascites and varices, leading to transplant re-enlistment at MELD-Na 29. Despite two years of intensive lifestyle modification, losing 17 kg, and recompensation, her follow-up elastography showed persistent steatosis (S3) and advanced fibrosis (F4). Subsequent allograft biopsy revealed progression to cirrhosis (F4) with ongoing steatohepatitis (NAS 3/8). Tirzepatide was initiated for the development of type 2 diabetes, attributed to steroids used for immunosuppression. After 2 years on tirzepatide, she lost 43.1 kg. Shockingly, her follow-up elastography demonstrated fibrosis regression with LSM 5.5 kPa (F1) and steatohepatitis resolution with CAP 204 dB/m (S0). Follow-up liver biopsy confirmed fibrosis regression to F2-F3 and steatohepatitis resolution (NAS 1/8). Conclusions: This case challenges the widely accepted dogma that liver MASH cirrhosis is irreversible. Using multiple liver fibrosis monitoring modalities, cirrhosis reversal was demonstrated and attributed to mechanisms of GLP-1/GIP RA therapy. This study suggests that GLP-1/GIP RA may be safe in cirrhosis and may result in fibrosis regression. Full article

Landslides are among the most frequent and dangerous natural hazards, posing serious threats to life and infrastructure. To mitigate their impacts, landslide susceptibility mapping (LSM) plays a crucial role by identifying areas prone to future landslide occurrences. This study aimed to assess how the choice of feature selection methods influences the performance of LSM models based on the eXtreme Gradient Boosting (XGBoost) algorithm when an extended set of input variables is used. Two study areas located in Southern Poland, called Biały Dunajec and Rożnów, were selected for analysis. These regions differ in terrain, elevation, and environmental characteristics and are situated approximately 65 km apart. Three widely used feature selection techniques were applied: the Pearson correlation coefficient (PCC), symmetrical uncertainty (SU), and analysis of variance (ANOVA). For each method, XGBoost models were trained and evaluated using multiple performance metrics, including the area under the curve (AUC), overall accuracy, precision, recall, and F1-score. The highest AUC values were achieved using the PCC method: 0.985 for Biały Dunajec and 0.983 for Rożnów. The best overall performance (accuracy of 0.93, recall of 0.94, and F1-score of 0.79) was obtained for the Rożnów case study using PCC features. These findings highlight that, when a comprehensive set of input variables is used, the exclusion of less informative features has little effect on model accuracy, as their information is largely preserved within the retained features. Full article

Background/Objectives: As a noninvasive indicator of liver fibrosis and stiffness, liver stiffness measurement (LSM) has also shown significant value in differentiating focal liver lesions (FLLs). This study aimed to assess the characteristics of LSM values across different liver lesions and explore their value in differential diagnosis. Methods: A total of 8817 individuals with FLLs were assessed using liver stiffness measurements (LSMs). We evaluated the LSM characteristics across different FLL categories and further compared these values within subgroups based on their alpha-fetoprotein (AFP) and hepatitis B surface antigen (HBsAg). The LSM was visualized graphically. We compared two logistic regression models (with the LSM and without the LSM) in a cohort of 2271 patients who were both AFP-normal (<20 ng/mL) and HBsAg-negative. The differentiation value of the LSM was quantified by comparing the models’ area under the curves (AUCs) and through decision curve analysis (DCA). Results: The LSM showed significant differences (p < 0.001) among malignant lesions, benign lesions, and cirrhotic nodules (CN). Among benign lesions, only focal nodular hyperplasia (FNH) and simple hepatic cysts (SHC) showed a significant difference (p < 0.05). Among malignant lesions, significant differences in the LSM were observed between all pairs (p < 0.001) except between hepatocellular carcinoma (HCC) and combined hepatocellular-cholangiocarcinoma (cHCC-CC). Patients with elevated AFP levels exhibited significantly higher LSM across most lesion types. HBsAg-positive patients also showed significantly increased LSM in all five lesion types, except for CN and cHCC-CC. The full model (with LSM) for differentiating primary malignant lesions from benign ones was built using six variables. The AUCs of the full model were 0.897 and 0.896 in the training and validation sets, significantly outperforming the comparison model (AUC: 0.882, p = 0.0002; 0.879, p = 0.017). Conclusions: The LSM can provide additional information on focal liver lesions. Full article

Background/Objectives: Cystic fibrosis-associated liver disease (CFLD) is a significant complication in individuals with cystic fibrosis (CF), contributing to morbidity and mortality, with no universally accepted, reliable, non-invasive diagnostic tool for early detection. Current diagnostic methods, including liver biopsy and imaging, remain resource-intensive and invasive. Non-invasive biomarkers like the Fibrosis-4 (FIB-4) index have shown promise in diagnosing liver fibrosis in various chronic liver diseases. This study explores the potential of the FIB-4 index to predict CFLD in an adult CF population and assesses its correlation with transient elastography (TE) as a potential diagnostic tool. The aim of this study is to evaluate the diagnostic performance of the FIB-4 index for CFLD in adults with CF and investigate its relationship with TE-based liver stiffness measurements (LSM). Methods: The study was conducted in a regional cystic fibrosis unit, including 261 adult CF patients. FIB-4 scores were calculated using an online tool (mdcalc.com) based on patient age, aspartate aminotransferase (AST), alanine aminotransferase (ALT), and platelet count. In parallel, 29 patients underwent liver stiffness measurement using TE (Fibroscan^®). Statistical analyses included non-parametric tests for group comparisons and Pearson’s correlation to assess the relationship between FIB-4 scores and TE results. Results: The mean FIB-4 score in patients diagnosed with CFLD was higher (0.99 ± 0.83) compared to those without CFLD (0.64 ± 0.38), although the difference was not statistically significant (p > 0.05). TE results for CFLD patients (5.9 kPa) also did not show a significant difference compared to non-CFLD patients (4.2 ± 1.6 kPa, p > 0.05). However, a positive correlation (r = 0.401, p = 0.031) was found between FIB-4 scores and TE-based LSM, suggesting a potential complementary diagnostic role. Conclusions: The FIB-4 index, while not sufficient as a standalone diagnostic tool for CFLD in adults with CF, demonstrates potential when used in conjunction with other diagnostic methods like TE. This study introduces a novel approach for integrating non-invasive diagnostic markers in CF care, offering a pathway for future clinical practice. The combination of FIB-4 and TE could serve as an accessible, cost-effective alternative to invasive diagnostic techniques, improving early diagnosis and management of CFLD in the CF population. Additionally, future research should explore the integration of these tools with emerging biomarkers and clinical features to refine diagnostic algorithms for CFLD, potentially reducing reliance on liver biopsies and improving patient outcomes. Full article

Regarding the distortion correction problem of large field of view wide-angle cameras commonly used in railway visual inspection systems, this paper proposes a novel online calibration method for non-specially made cooperative calibration objects. Based on the radial distortion divisor model, first, the spatial coordinates of natural spatial landmark points are constructed according to the known track gauge value between two parallel rails and the spacing value between sleepers. By using the image coordinate relationships corresponding to these spatial coordinates, the coordinates of the distortion center point are solved according to the radial distortion fundamental matrix. Then, a constraint equation is constructed based on the collinear constraint of vanishing points in railway images, and the Levenberg–Marquardt algorithm is used to found the radial distortion coefficients. Moreover, the distortion coefficients and the coordinates of the distortion center are re-optimized according to the least squares method (LSM) between points and the fitted straight line. Finally, based on the above, the distortion correction is carried out for the distorted railway images captured by the camera. The experimental results show that the above method can efficiently and accurately perform online distortion correction for large field of view wide-angle cameras used in railway inspection without the participation of specially made cooperative calibration objects. The whole method is simple and easy to implement, with high correction accuracy, and is suitable for the rapid distortion correction of camera images in railway online visual inspection. 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

Porcine epidemic diarrhea virus (PEDV) infection induces severe, often fatal, watery diarrhea and vomiting in neonatal piglets, characterized by profound dehydration, villus atrophy, and catastrophic mortality rates approaching 100% in unprotected herds. This study developed a composite probiotic from Min-pig-derived Lactobacillus crispatus LCM233, Ligilactobacillus salivarius LSM231, and Lactiplantibacillus plantarum LPM239, which exhibited synergistic growth, potent acid/bile salt tolerance, and broad-spectrum antimicrobial activity against pathogens. In vitro, the probiotic combination disrupted pathogen ultrastructure and inhibited PEDV replication in IPI-2I cells. In vivo, PEDV-infected piglets administered with the multi-strain probiotic exhibited decreased viral loads in anal and nasal swabs, as well as in intestinal tissues. This intervention was associated with the alleviation of diarrhea symptoms and improved weight gain. Furthermore, the multi-strain probiotic facilitated the repair of intestinal villi and tight junctions, increased the number of goblet cells, downregulated pro-inflammatory cytokines, enhanced the expression of barrier proteins, and upregulated antiviral interferon-stimulated genes. These findings demonstrate that the multi-strain probiotic mitigates PEDV-induced damage by restoring intestinal barrier homeostasis and modulating immune responses, providing a novel strategy for controlling PEDV infections. Full article

This study examines diurnal surface temperature dynamics across major Moroccan cities during the growing season and explores the interaction between urban and vegetated surfaces. We also introduce the Urban Thermal Impact Ratio (UTIR), a novel metric designed to quantify urban thermal comfort as a function of the surface urban heat island (SUHI) intensity. The analysis is based on outputs from a land surface model (LSM) for the year 2010, integrating high-resolution Landsat and MODIS data to characterize land cover and biophysical parameters across twelve land cover types. Our findings reveal moderate urban–vegetation temperature differences in coastal cities like Tangier (1.8 °C) and Rabat (1.0 °C), where winter vegetation remains active. In inland areas, urban morphology plays a more dominant role: Fes, with a 20% impervious surface area (ISA), exhibits a smaller SUHI than Meknes (5% ISA), due to higher urban heating in the latter. The Atlantic desert city of Dakhla shows a distinct pattern, with a nighttime SUHI of 2.1 °C and a daytime urban cooling of −0.7 °C, driven by irrigated parks and lawns enhancing evapotranspiration and shading. At the regional scale, summer UTIR values remain below one in Tangier-Tetouan-Al Hoceima, Rabat-Sale-Kenitra, and Casablanca-Settat, suggesting that urban conditions generally stay within thermal comfort thresholds. In contrast, higher UTIR values in Marrakech-Safi, Beni Mellal-Khénifra, and Guelmim-Oued Noun indicate elevated heat discomfort. At the city scale, the UTIR in Tangier, Rabat, and Casablanca demonstrates a clear diurnal pattern: it emerges around 11:00 a.m., peaks at 1:00 p.m., and fades by 3:00 p.m. This study highlights the critical role of vegetation in regulating urban surface temperatures and modulating urban–rural thermal contrasts. The UTIR provides a practical, scalable indicator of urban heat stress, particularly valuable in data-scarce settings. These findings carry significant implications for climate-resilient urban planning, optimized energy use, and the design of public health early warning systems in the context of climate change. Full article