Search Results (863)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Accurate side road detection is essential for traffic management, urban planning, and vehicle navigation. However, existing research mainly focuses on road network construction, lane extraction, and intersection identification, while fine-grained side road detection remains underexplored. Therefore, this study proposes a road segment-level side road detection method based on crowdsourced trajectory data: First, considering the geometric and dynamic characteristics of trajectories, SRDet introduces a trajectory lane-change pattern recognition method based on mutation intervals to distinguish the heterogeneity of lane-change behaviors between main and side roads. Secondly, combining geometric features with spatial statistical theory, SRDet constructs multimodal features for trajectories and road segments, and proposes a potential side road segment classification model based on random forests to achieve precise detection of side road segments. Finally, based on mutation intervals and potential side road segments, SRDet utilizes density peak clustering to identify main and side road access points, completing the fitting of side roads. Experiments were conducted using 2021 Beijing trajectory data. The results show that SRDet achieves precision and recall rates of 84.6% and 86.8%, respectively. This demonstrates the superior performance of SRDet in side road detection across different areas, providing support for the precise updating of urban road navigation information. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

Background: The anterior cerebral artery (ACA), a critical component of the cerebral arterial circle, exhibits substantial morphological variability. While previous studies have explored ACA morphology using cadaveric and imaging methods, a comprehensive meta-analysis incorporating the latest evidence is lacking. Methods: Following current guidelines, a systematic review and meta-analysis were performed across four major databases, supplemented by the gray literature and targeted journal searches. Ninety-nine studies, encompassing 85,316 patients, met the inclusion criteria. Statistical analyses were conducted using R, applying random effects models to estimate pooled prevalence and morphometric parameters. Results: The pooled prevalence of typical ACA morphology was 93.75%, whereas variants were noted in 6.25% of cases. The predominant variation identified was the accessory ACA (aACA) (1.99%), followed by unilateral absence of the A1 segment (1.78%), with the latter being more frequently recognized in imaging studies (p < 0.0001). Rare variants encompassed azygos ACA (azACA) (0.22%), fenestrated ACA (fACA) (0.02%), and bihemispheric ACA (bACA) (0.02%). The mean diameter and length of the A1 segment were measured at 2.10 mm and 14.24 mm, respectively. Hypoplasia of the A1 segment (<1 mm diameter) was recorded in 3.15% of cases. The influences of imaging modality, laterality, and population distribution on prevalence estimates were minimal. No significant publication bias was detected. Conclusions: Although infrequent, variants of the ACA possess significant clinical importance attributable to their correlation with aneurysm formation and the impairment of collateral circulation. The aACA and the absence of the A1 segment emerged as the most common variations. This meta-analysis presents an updated and high-quality synthesis of ACA morphology, serving as a valuable reference for clinicians and anatomists. Full article

Background: Anastomotic leak (AL) is a serious complication after gastrectomy. It is associated with prolonged hospital stay, greater expenses, and increased risk for 90-day mortality. Currently, there is no consensus regarding the effect of AL on OS in patients with GC undergoing gastrectomy. This study was designed to investigate the effect of AL on long-term survival after gastrectomy for gastric cancer. Methods: PubMed, Embase, Scopus, Google Scholar, and Cochrane Library were queried during the search process. The literature search started in January 2025 and was updated in May 2025. The studies analyzed the impact of AL on long-term survival, with the primary outcome being long-term overall survival. Pooled effect size measures included restricted mean survival time difference (RMSTD), hazard ratio (HR), and 95% confidence intervals (CIs). Results: Ten studies (11,862 patients) were included. Overall, 338 (2.9%) patients experienced AL. The RMSTD analysis indicates that at 12, 24, 36, 48, and 60 months, patients with AL tend to live 1.1, 3.1, 5.2, 8.1, and 10.6 months shorter, respectively, compared to those who did not develop AL. All results were statistically significant with p < 0.0001. The time-dependent HRs analysis for AL versus no AL shows a higher mortality hazard in patients with AL at 12 (HR 1.32, 95% CI 1.11–1.58), 24 (HR 1.61, 95% CI 1.34–1.92), 36 (HR 1.55, 95% CI 1.27–1.91), 48 months (HR 1.22, 95% CI 1.02–1.53), and 60 months (HR 0.79, 95% CI 0.59–1.10). Conclusions: This research appears to indicate a clinical impact of AL on long-term OS after gastrectomy. Patients experiencing AL appear to have an increased risk of mortality within the initial four years of follow-up. Full article

Background/Objectives: Fibromyalgia syndrome (FMS) is a chronic pain condition in which executive function (EF) alterations have been reported, though strikingly, relationships between simple executive functions (EFs) (updating, inhibition, and mental flexibility) and high-order ones, such as planning and problem-solving, have not been addressed yet in this population. This research aimed to firstly explore how low-level EFs play a role in planning and problem-solving performances. Methods: Thirty FMS patients and thirty healthy participants completed a series of neuropsychological tests evaluating low- and high-order EFs. Clinical and emotional symptoms were assessed with self-report questionnaires, while pain and fatigue levels were measured with numerical scales. Importantly, specific drug restrictions were accounted for. Results: Patients scored lower in most neurocognitive tests, with statistical significance noted only for visuospatial working memory (WM) and two planning and problem-solving tests. Pain, fatigue, and sleep disturbances showed important effects on most of the cognitive outcomes. Multiple regression analyses reflected that planning and problem-solving were successfully and partially predicted by updating, inhibition, and mental flexibility (though differences emerged between tasks). Conclusions: Our study confirms the presence of cognitive impairments in FMS, especially in high-order EFs, supporting patients’ complaints. Clinical symptoms play a role in FMS dyscognition but do not explain it completely. For the first time, as far as the authors know, simple EF influences on planning and problem-solving tests have been described for FMS patients. These results might help in unraveling the dysexecutive profile in FMS to design more adjusted treatment options. Full article

Random Forests are powerful machine learning models widely applied in classification and regression tasks due to their robust predictive performance. Nevertheless, traditional Random Forests face computational challenges during tree construction, particularly in high-dimensional data or on resource-constrained devices. In this paper, a novel node-splitting algorithm, BayesSplit, is proposed to accelerate decision tree construction via a Bayesian-based impurity estimation framework. BayesSplit treats impurity reduction as a Bernoulli event with Beta-conjugate priors for each split point and incorporates two main strategies. First, Dynamic Posterior Parameter Refinement updates the Beta parameters based on observed impurity reductions in batch iterations. Second, Posterior-Derived Confidence Bounding establishes statistical confidence intervals, efficiently filtering out suboptimal splits. Theoretical analysis demonstrates that BayesSplit converges to optimal splits with high probability, while experimental results show up to a 95% reduction in training time compared to baselines and maintains or exceeds generalization performance. Compared to the state-of-the-art MABSplit, BayesSplit achieves similar accuracy on classification tasks and reduces regression training time by 20–70% with lower MSEs. Furthermore, BayesSplit enhances feature importance stability by up to 40%, making it particularly suitable for deployment in computationally constrained environments. Full article

Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent connectivity. While most research focuses on single-server or hierarchical cloud-based FL, multi-server FL can alleviate the communication bottlenecks of traditional setups. To this end, we propose an edge-based, multi-server FL (MS-FL) framework that combines performance-driven aggregation at each server—including statistical weighting of peer updates and outlier mitigation—with an application layer handover protocol that preserves model updates when vehicles move between RSU coverage areas. We evaluate MS-FL on both MNIST and GTSRB benchmarks under shard- and Dirichlet-based non-IID splits, comparing it against single-server FL and a two-layer edge-plus-cloud baseline. Over multiple communication rounds, MS-FL with the Statistical Performance-Aware Aggregation method and Dynamic Weighted Averaging Aggregation achieved up to a 20-percentage-point improvement in accuracy and consistent gains in precision, recall, and F1-score (95% confidence), while matching the low latency of edge-only schemes and avoiding the extra model transfer delays of cloud-based aggregation. These results demonstrate that coordinated cooperation among servers based on model quality and seamless handovers can accelerate convergence, mitigate data heterogeneity, and deliver robust, privacy-aware learning in connected vehicle environments. Full article

Background/Objectives: Cervical cancer remains a significant global health issue, with high incidence and mortality rates, particularly in Eastern Europe. Despite the availability of vaccines against human papillomavirus (HPV), regular screening remains crucial for prevention. Testing for HPV, alone or combined with cytology, has become an alternative to traditional methods. However, since many HPV infections are transient, additional tests are needed to identify high-risk cases. Methods: This study aims to generate detailed statistical data specific to the Bulgarian population, reinforcing the necessity of incorporating updated European methodologies and algorithms for the prophylaxis and prevention of cervical carcinoma. Results: By evaluating epidemiological trends, risk factors, and the effectiveness of current preventive measures, this research seeks to provide a strong foundation for enhancing cervical cancer screening and early detection programs. This method improves triage by identifying women who require further evaluation, ensuring timely referrals for colposcopy or biopsy. Conclusions: While liquid-based cytology (LBC) and HPV genotyping improve detection, the introduction of p16/Ki-67 dual staining has enhanced risk stratification, offering higher sensitivity and specificity for detecting high-grade lesions. These advancements are improving cervical cancer screening and patient outcomes. Full article

The rising share of wind generation in power systems, driven by the need to decarbonise the energy sector, is changing the relationship between wind speed and electricity prices. In the case of Poland, this relationship has not been thoroughly investigated, particularly in the aftermath of the restrictive legal changes introduced in 2016, which halted numerous onshore wind investments. Studying this relationship remains necessary to understand the broader market effects of wind speed on electricity prices, especially considering evolving policies and growing interest in renewable energy integration. In this context, this paper analyses wind speed, wind generation, and other relevant datasets in relation to electricity prices using multiple statistical methods, including correlation analysis, regression modelling, and artificial neural networks. The results show that wind speed is a significant factor in setting electricity prices (with a correlation coefficient reaching up to −0.7). The findings indicate that not only is it important to include wind speed as an electricity price indicator, but it is also worth investing in wind generation, since higher wind output can be translated into lower electricity prices. This study contributes to a better understanding of how natural variability in renewable resources translates into electricity market outcomes under policy-constrained conditions. Its innovative aspect lies in combining statistical and machine learning techniques to quantify the influence of wind speed on electricity prices, using updated data from a period of regulatory stagnation. Full article

Rental accommodations near European university campuses keep rising in price, while listings remain scattered and opaque. This paper proposes a solution that overcomes these issues by integrating real-time open listing ingestion, zone-level geospatial enrichment, and a reinforcement-learning recommender into one streamlined analysis pipeline. On demand, the system updates price statistics for most districts in Timișoara and returns five budget-safe offers in a short amount of time. By combining adaptive ranking with new spatial metrics, it significantly cuts search time and removes irrelevant offers in pilot trials. Moreover, this implementation is fully open-data, open-source, and free, designed specifically for students to ensure accessibility, transparency, and cost efficiency. Full article

The evaluation of climate change effects on airport infrastructures is important to maintain safety and flexibility in air travel operations. Airports are particularly vulnerable to extreme weather events and temperature fluctuations, which can disrupt operations, compromise passenger safety, and cause economic losses. Issues such as flooded runways and the disruption of power supplies highlight the need for strong adaptation strategies. The study focuses on the application of the high-resolution regional model COSMO-CLM to assess climate change impacts on Roma Fiumicino airport (Italy) under the IPCC RCP8.5 scenario. The complex topography of Italy requires fine-scale simulation to catch localized climate dynamics. By employing advanced statistical methods, such as fractal analysis, this research aims to increase an understanding of climate change and improve the model prediction capability. The findings provide valuable insights for designing resilient airport infrastructures and updating operational protocols in view of evolving climate risks. A consistent increase in daily temperatures is projected, along with a modest positive trend in annual precipitation. The use of advanced statistical methods revealed insights into the fractal dimensions and frequency components of climate variables, showing an increasing complexity and variability of future climatic patterns. Full article

The Ghris watershed in southern Morocco is a significant ecological and agricultural area. However, due to the current impacts of climate change, farming activities, and pollution, data on its quality and biological importance need to be updated. Therefore, this study aimed to evaluate the physico-chemical and biological quality of surface water in the Ghris River. The Water Quality Index (WQI) and the Iberian Biological Monitoring Working Group (IBMWP) index were used to assess water quality along four sampling sites in 2024. The collected data were analyzed with descriptive and multivariate statistics. In total, 424 benthic macroinvertebrates belonging to seven orders were identified in the surface waters of the Ghris basin. These microfauna were significantly variable among the studied sites (p < 0.05). Station S4 is significantly rich in species, including seven orders and nine families of macroinvertebrates, followed by Station S2, with seven orders and eight families. Stations S3 and S1 showed less species diversity, with three orders and one family, respectively. The Insecta comprised 95.9% of the abundance, while the Crustacea constituted just 4.1%. The physico-chemical parameters significantly surpassed (p < 0.05) the specified norms of surface water in Morocco. This indicates a decline in the water quality of the studied sites. The findings of the principal component analysis (PCA) demonstrate that the top two axes explain 87% of the cumulative variation in the data. Stations 2 and 3 are closely associated with high concentrations of pollutants, notably Cl⁻, SO₄²⁻, NO₃⁻, and K⁺ ions. Dissolved oxygen (DO) showed a slight correlation with S2 and S3, while S4 was characterized by high COD and PO₄ concentrations, low levels of mineral components (except Cl⁻), and average temperature conditions. Bioindication scores for macroinvertebrate groups ranging from 1 to 10 enabled the assessment of pollution’s influence on aquatic biodiversity. The IBMWP biotic index indicated discrepancies in water quality across the sites. This study gives the first insight and updated data on the biological and chemical quality of surface water in the Ghris River and the entire aquatic ecosystem in southeast Morocco. These data are proposed as a reference for North African and Southern European rivers. However, more investigations are needed to evaluate the impacts of farming, mining, and urbanization on the surface and ground waters in the study zone. Similarly, it is vital to carry out additional research in arid and semi-arid zones since there is a paucity of understanding regarding taxonomic and functional diversity, as well as the physico-chemical factors impacting water quality. Full article

In the context of accelerating digitalization, this study investigates how electronic commerce performance is influenced by Web 2.0 instruments in the 27 EU member states. Analyzing literature reviews and performing our own bibliometric review, we identified a gap related to the measurable economic results of e-commerce. The scope of this study was to analyze the relationship between Web 2.0 tools and the level of turnover generated by e-commerce, applying robust econometric models based on panel data regression with random effects and fixed effects (Arellano–Bond). The results highlight that the online paid advertisement and social media usage variables have significant, positive effects on e-commerce performance, confirming the first and second hypotheses. “Use the enterprise’s blog or microblogs” and “use of multimedia content sharing websites” do not influence enterprises’ total turnover from e-commerce sales to a valid and statistically significant extent. Thus, the third and fourth hypotheses are not confirmed by the results of the research conducted, possibly due to limited innovation and platform ownership in Europe. This study makes a notable empirical and methodological contribution, embedding digital sustainability in the analysis, which implies that the findings can be used for updating e-commerce policies. Full article

As a classical statistical method, multiple regression is widely used for forecasting tasks in power, medicine, finance, and other fields. The rise of machine learning has led to the adoption of neural networks, particularly Long Short-Term Memory (LSTM) models, for handling complex forecasting problems, owing to their strong ability to capture temporal dependencies in sequential data. Nevertheless, the performance of LSTM models is highly sensitive to hyperparameter configuration. Traditional manual tuning methods suffer from inefficiency, excessive reliance on expert experience, and poor generalization. Aiming to address the challenges of complex hyperparameter spaces and the limitations of manual adjustment, an enhanced sparrow search algorithm (ISSA) with adaptive parameter configuration was developed for LSTM-based multivariate regression frameworks, where systematic optimization of hidden layer dimensionality, learning rate scheduling, and iterative training thresholds enhances its model generalization capability. In terms of SSA improvement, first, the population is initialized by the reverse learning strategy to increase the diversity of the population. Second, the mechanism for updating the positions of producer sparrows is improved, and different update formulas are selected based on the sizes of random numbers to avoid convergence to the origin and improve search flexibility. Then, the step factor is dynamically adjusted to improve the accuracy of the solution. To improve the algorithm’s global search capability and escape local optima, the sparrow search algorithm’s position update mechanism integrates Lévy flight for detection and early warning. Experimental evaluations using benchmark functions from the CEC2005 test set demonstrated that the ISSA outperforms PSO, the SSA, and other algorithms in optimization performance. Further validation with power load and real estate datasets revealed that the ISSA-LSTM model achieves superior prediction accuracy compared to existing approaches, achieving an RMSE of 83.102 and an $R^2$ of 0.550 during electric load forecasting and an RMSE of 18.822 and an $R^2$ of 0.522 during real estate price prediction. Future research will explore the integration of the ISSA with alternative neural architectures such as GRUs and Transformers to assess its flexibility and effectiveness across different sequence modeling paradigms. Full article