Accurate identification of concrete surfaces on roadways is critical for the advancement of autonomous navigation systems and the effective monitoring of transportation infrastructure. Nevertheless, the inherently heterogeneous texture of concrete, in conjunction with environmental variables such as lighting fluctuations and surface degradation, continues to impede precise surface segmentation. To address these challenges, a novel framework has been developed that integrates Fuzzy Topological Entropy (FTE) with Multiscale Laplacian Structural Dissimilarity (MLSD) for the robust delineation of concrete regions in road imagery. Within this framework, FTE is employed to model uncertainty and spatial ambiguity through a continuous fuzzy membership function, thereby capturing the nuanced transitions between concrete and non-concrete domains. Concurrently, MLSD is utilised to quantify multiscale structural irregularities by leveraging Laplacian-based texture dissimilarity, enhancing sensitivity to surface roughness and material inconsistencies. These complementary components are embedded within a unified energy functional, the minimisation of which is conducted via an iterative optimisation strategy that avoids the need for extensive training datasets or prior scene annotations. The proposed methodology demonstrates strong resilience across a variety of environmental conditions, including shadows, glare, occlusions, and physical wear. Superior performance is observed particularly in complex or degraded urban settings, where conventional segmentation models often fail. Owing to its non-parametric nature and computational efficiency, the approach is well-suited for real-time deployment in autonomous vehicle systems, smart city infrastructure, and road condition assessment platforms. By facilitating reliable and scalable surface segmentation without reliance on deep learning architectures or exhaustive manual labelling, this work offers a significant advancement toward generalisable and interpretable road surface analysis technologies.
Artisanal and small-scale mining (ASM) has become increasingly significant in Ecuador, contributing to rural employment and economic stability. However, its environmental consequences, particularly those related to illegal mining and the discharge of untreated waste into water bodies, have raised concerns regarding water quality deterioration. The present study investigates heavy metal contamination in six rivers (Siete, Pagua, Fermín, Villa, Guanache, and 9 de Octubre) within the Ponce Enríquez mining district, where elevated concentrations of heavy metals have been detected. To facilitate the development of effective remediation strategies, an integrated statistical analysis was conducted to elucidate the relationships between pollutants and their potential sources. The methodology encompassed (i) an extensive review of water quality data, (ii) a statistical correlation analysis of predominant heavy metals, and (iii) an evaluation of environmental management approaches. The findings indicate that the Villa, Siete, Fermín, and Guanache rivers exhibit particularly high concentrations of aluminium (Al), iron (Fe), lead (Pb), and zinc (Zn), with contamination levels intensifying during the wet season due to runoff and the influence of the geological composition of the study area. Strong positive correlations (r>0.8) were observed between Fe-Pb, Fe-Al, and Pb-Al in both dry and wet seasons, suggesting that mining activities, mineralogical characteristics of the region, and agricultural runoff contribute to heavy metal accumulation. Based on these findings, sustainable remediation techniques are proposed to mitigate contamination and enhance water quality. The implementation of these measures is expected to facilitate the gradual improvement of riverine ecosystems while promoting economic diversification within the Ponce Enríquez mining district.
The corporate financial performance of Turkish insurance companies was evaluated through the development of a novel hybrid multi-criteria decision-making (MCDM) framework, integrating the Ranking Comparison (RANCOM), Simple Weight Calculation (SIWEC), and Multi-Attribute Ideal-Real Comparative Analysis (MAIRCA) methodologies. Within this framework, financial indicators were selected based on expert input, and indicator weights were determined through the combined application of RANCOM and SIWEC methods. Subsequently, company rankings were established by employing the MAIRCA method. To ensure the robustness and reliability of the proposed framework, extensive sensitivity analyses were conducted. The findings identified the current ratio, defined as the ratio of current assets to current liabilities, as a critical determinant of financial performance. Türkiye Sigorta was consistently ranked as the top-performing company over the analyzed period. The outcomes of the sensitivity analyses confirmed the stability and effectiveness of the proposed decision-making model in assessing corporate financial performance within the insurance industry. This study contributes to the financial performance evaluation literature by demonstrating the applicability and advantages of hybrid MCDM approaches in dynamic and highly regulated sectors such as insurance.
Traditional tensioning monitoring techniques for prestressed concrete structures often exhibit limitations in real-time performance, accuracy, and adaptability to complex stress distributions. To address these challenges, an intelligent monitoring framework is developed based on a Radial Basis Function (RBF) neural network. Using the Dongjiacun aqueduct as a case study, a comprehensive methodology is established, integrating numerical simulation, Machine Learning (ML), and real-time data processing. Initially, Finite Element Analysis (FEA) is conducted to simulate stress distribution during the tensioning process, allowing for the extraction of critical stress data at key structural locations. These data serve as the foundation for training the RBF neural network, which functions as a high-fidelity surrogate model capable of efficiently predicting stress variations with enhanced accuracy. By leveraging the network's strong generalization capabilities, the proposed framework ensures rapid and precise estimation of stress evolution throughout the tensioning process. Furthermore, an intelligent monitoring platform is designed, incorporating real-time data acquisition, automated stress prediction, and visualization functionalities. The platform facilitates prestress control and structural health assessment, contributing to the long-term safety and durability of prestressed concrete structures. Additionally, an interactive user interface is prototyped using Mock Plus to enhance usability and facilitate intuitive interpretation of stress-related insights. The proposed approach not only advances the automation and intelligence of tensioning monitoring but also provides a robust technical foundation for optimizing prestress management in large-scale infrastructure applications.
The efficient classification of transport vehicles is critical to the optimization of modern transportation systems, yet significant challenges persist, particularly in distinguishing Heavy Transport Vehicles (HTVs) from Light Transport Vehicles (LTVs). These challenges arise due to considerable variations in vehicle size, shape, orientation, and external factors such as camera perspective, lighting conditions, and occlusions. In this study, a novel classification framework is proposed, integrating geometric feature extraction with a soft computing approach based on fuzzy logic. Key geometric attributes, including bounding box length, width, area, and aspect ratio, are extracted through image processing techniques. Initial classification is performed via threshold-based rules to eliminate non-HTV instances using predefined feature thresholds. To address uncertainties inherent in real-world surveillance conditions, fuzzy logic inference is subsequently applied, enabling flexible and robust decision-making in the presence of imprecise or noisy data. This hybrid methodology, combining deterministic thresholding and soft computing principles, enhances classification reliability and adaptability under diverse environmental and operational conditions. Extensive real-world experiments have been conducted to validate the proposed framework, demonstrating superior performance in terms of accuracy, robustness, and computational efficiency when compared with conventional classification methods. The results underscore the potential of the framework for deployment in intelligent traffic monitoring systems where precise vehicle categorization is essential for traffic management, infrastructure planning, and safety enforcement.
Accurate crowd density estimation has become critical in applications ranging from intelligent urban planning and public safety monitoring to marketing analytics and emergency response. In recent developments, various methods have been used to enhance the precision of crowd analysis systems. In this study, a Convolutional Neural Network (CNN)-based approach was presented for crowd density detection, wherein the Congested Scene Recognition Network (CSRNet) architecture was employed with a Visual Geometry Group (VGG)-16 backbone. This method was applied to two benchmark datasets—Mall and Crowd-UIT—to assess its effectiveness in real-world crowd scenarios. Density maps were generated to visualize spatial distributions, and performance was quantitatively evaluated using Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics. For the Mall dataset, the model achieved an MSE of 0.08 and an MAE of 0.10, while for the Crowd-UIT dataset, an MSE of 0.05 and an MAE of 0.15 were obtained. These results suggest that the proposed VGG-16-based CSRNet model yields high accuracy in crowd estimation tasks across varied environments and crowd densities. Additionally, the model demonstrates robustness in generalizing across different dataset characteristics, indicating its potential applicability in both surveillance systems and public space management. The outcomes of this investigation offer a promising direction for future research in data-driven crowd analysis, particularly in enhancing predictive reliability and real-time deployment capabilities of deep learning models for population monitoring tasks.
The increasing demand for efficient and sustainable last-mile delivery solutions has presented a significant challenge in the evolving landscape of e-commerce logistics. To address this issue, a systematic evaluation and prioritization of six alternative delivery methods—namely, home delivery, workplace delivery, delivery to a neighbor or acquaintance, staffed pickup points, unstaffed (automated) pickup points, and third-party drop-off locations—has been conducted. These alternatives have been assessed against a comprehensive set of criteria, including delivery time flexibility, accessibility, cost-efficiency, security, speed of service, and ease of product return. To capture the nuanced preferences and subjective judgements of stakeholders, the Fuzzy Factor Relationship (FARE) method has been employed to determine the relative importance of each criterion through a structured fuzzy logic framework. Subsequently, the Aggregated Decision-Making (ADAM) method has been applied to rank the delivery alternatives, integrating evaluations from key stakeholder groups—consumers, retailers, and logistics service providers. The findings reveal that unstaffed pickup points, particularly those leveraging automated systems, represent the most balanced and sustainable solution, offering superior performance in terms of cost-effectiveness, user accessibility, and operational flexibility. In contrast, while home delivery continues to be favored for its convenience, it remains constrained by elevated operational costs and limited scheduling flexibility. The methodological integration of Fuzzy FARE and ADAM ensures a robust and transparent decision-support mechanism that accounts for both qualitative and quantitative factors. These insights are expected to guide strategic decision-making in last-mile logistics (LML), contributing to service quality enhancement, operational cost reduction, and the advancement of environmentally responsible delivery systems. This evaluation framework offers practical relevance to e-commerce platforms, third-party logistics providers, and urban mobility planners seeking to implement scalable and customer-centric delivery models in complex urban environments.
Curved multi-layer beams, such as leaf springs, are widely used in vehicle suspension systems for both road and rail vehicles in automotive industry due to their capacity for high loads and their vibrational damping properties. To design suspension systems that experience a large number of load types and complexities of friction, we must first understand the nonlinear dynamic behavior of curved beams. In this paper, the governing equations for the nonlinear vibrations of curved two-layer beams in the presence of interlayer slip are first derived. Then, the characteristic equation, the longitudinal and transverse mode shapes of the beam, are determined independently using eigenvalue problem solutions. Subsequently, using the calculated mode shapes, different phases of the dynamics of these structures are investigated, taking into account interlayer friction. The results of numerical simulations are compared and validated with finite element analysis using ANSYS software. The results show that the dynamic behavior of curved two-layer beams experiences chaotic regimes after initial slip. Different regimes of periodic, quasi-periodic and chaotic motions are found in the dynamics of the system.
As market saturation and competitive pressure intensify within the banking sector, the mitigation of customer churn has emerged as a critical concern. Given that the cost of acquiring new clients substantially exceeds that of retaining existing ones, the development of highly accurate churn prediction models has become imperative. In this study, a hybrid customer churn prediction model was developed by integrating Sentence Transformers with a stacking ensemble learning architecture. Customer behavioral data containing textual content was transformed into dense vector representations through the use of Sentence Transformers, thereby capturing contextual and semantic nuances. These embeddings were combined with normalized structured features. To enhance predictive performance, a stacking ensemble method was employed to integrate the outputs of multiple base models, including random forest, Gradient Boosting Tree (GBT), and Support Vector Machine (SVM). Experimental evaluation was conducted on real-world banking data, and the proposed model demonstrated superior performance relative to conventional baseline approaches, achieving notable improvements in both accuracy and the area under the curve (AUC). Furthermore, the analysis of model outputs revealed several salient predictors of customer attrition, such as anomalous transaction behavior, prolonged inactivity, and indicators of dissatisfaction with customer service. These insights are expected to inform the development of targeted intervention strategies aimed at strengthening customer retention, improving satisfaction, and fostering long-term institutional growth and stability.
Significant advancements in artificial intelligence (AI) have transformed clinical decision-making, particularly in disease detection and management. Endometriosis, a chronic and often debilitating gynecological disorder, affects a substantial proportion of reproductive-age women and is associated with pelvic pain, infertility, and a reduced quality of life. Despite its high prevalence, non-invasive and accurate diagnostic methods remain limited, frequently resulting in delayed or missed diagnoses. In this study, a novel diagnostic framework was developed by integrating deep learning (DL) with explainable artificial intelligence (XAI) to address existing limitations in the early and non-invasive detection of endometriosis. Abdominopelvic magnetic resonance imaging (MRI) data were obtained from the Crestview Radiology Center in Victoria Island, Lagos State. Preprocessing procedures, including Digital Imaging and Communications in Medicine (DICOM)-to-PNG conversion, image resizing, and intensity normalization, were applied to standardize the imaging data. A U-Net architecture enhanced with a dual attention mechanism was employed for lesion segmentation, while Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated to visualize and interpret the model’s decision-making process. Ethical considerations, including informed patient consent, fairness in algorithmic decision-making, and mitigation of data bias, were rigorously addressed throughout the model development pipeline. The proposed system demonstrated the potential to improve diagnostic accuracy, reduce diagnostic latency, and enhance clinician trust by offering transparent and interpretable predictions. Furthermore, the integration of XAI is anticipated to promote greater clinical adoption and reliability of AI-assisted diagnostic systems in gynecology. This work contributes to the advancement of non-invasive diagnostic tools and reinforces the role of interpretable DL in the broader context of precision medicine and women's health.
The accurate estimation of the longitudinal dispersion coefficient is crucial for predicting solute transport in natural water bodies. In this study, an analytical (integral) method based on first principles is compared with Fischer’s widely used empirical approach, which is implemented in hydraulic modeling software such as the Hydrologic Engineering Center-River Analysis System (HEC-RAS). The primary objective is to evaluate the accuracy, applicability, and limitations of both methods under varying hydraulic conditions. A key advantage of the analytical approach is its ability to estimate the dispersion coefficient using velocity data alone, eliminating the need for high-cost tracer experiments that rely on solute concentration measurements. The determination index suggests an acceptable level of agreement between the two methods; however, the empirical approach systematically overestimates dispersion coefficients. Furthermore, a clear inverse relationship is observed between the slope of the channel and the magnitude of the dispersion coefficient, which is attributed to the increasing influence of shear velocity on the diffusion process. As slope values increase, solute separation time decreases, and concentration gradients become steeper. Conversely, at lower slopes, solute dispersion occurs over a broader time frame, resulting in lower concentration peaks. These findings indicate that while Fischer’s method provides a robust empirical framework, it should be supplemented with field measurements to improve reliability. In contrast, the analytical method offers a more theoretically grounded alternative that may enhance predictive accuracy in solute transport modeling. The implications of these results extend to water quality management, contaminant transport studies, and hydraulic engineering applications, where the selection of an appropriate dispersion estimation method significantly influences predictive outcomes.
Effective business system management necessitates strategic planning, efficient resource monitoring, and consistent team coordination. In practice, decision-making (DM) processes are frequently challenged by uncertainty, imprecision, and the need to aggregate diverse information sources. To address these complexities, a confidence-based algebraic aggregation framework incorporating the $p, q, r$-Fraction Fuzzy model has been proposed to enhance decision accuracy under uncertain environments. Within this framework, four novel aggregation operators are introduced: the Confidence $p, q, r$-Fraction Fuzzy Weighted Averaging Aggregation ($Cpqr$-FFWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Averaging Aggregation ($Cpqr$-FFOWAA) operator, the Confidence $p, q, r$-Fraction Fuzzy Weighted Geometric Aggregation ($Cpqr$-FFWGA) operator, and the Confidence $p, q, r$-Fraction Fuzzy Ordered Weighted Geometric Aggregation ($Cpqr$-FFOWGA) operator. These operators are designed to capture the inherent vagueness and subjectivity in business-related decision inputs, thereby facilitating robust assessments. The theoretical properties of the proposed operators—such as idempotency, boundedness, and monotonicity—are rigorously analyzed to ensure mathematical soundness and operational reliability. To illustrate the practical applicability of the model, a detailed case study is provided, demonstrating its effectiveness in maintaining resource sufficiency, preventing financial disruptions, and ensuring organizational coherence. The use of these aggregation mechanisms allows for systematic integration of expert confidence levels with varying degrees of fuzzy information, resulting in optimized decisions that are both data-informed and uncertainty-resilient. The methodological contributions are positioned to support real-world business contexts where dynamic inputs, incomplete data, and human judgment intersect. Consequently, the proposed approach offers a substantial advancement in intelligent decision-support systems, providing a scalable and interpretable tool for business performance enhancement.
This study investigates the relationship between municipal management and sustainable tourism in an urban protected area, specifically the Los Pantanos de Villa Wildlife Refuge in Lima, Peru. The research adopts a quantitative, correlational, non-experimental, cross-sectional design, focusing on a sample of 67 employees from the Municipal Authority. A probabilistic sampling technique was employed to select the sample from a population of 80 workers. Data were collected through two separate questionnaires, each tailored to measure one of the key variables, with responses recorded on a Likert scale ranging from 1 to 5. The study area, Los Pantanos de Villa, is an urban protected area situated in a densely populated region where challenges such as pollution, waste management, and urban sprawl exert significant pressure on environmental sustainability. Findings revealed that 88.06% of respondents assessed municipal management in the protected area as "good," while 76.12% rated sustainable tourism positively. Statistical analysis revealed a Pearson correlation coefficient of 0.590, with a p-value of 0.000, indicating a significant positive correlation between effective municipal management and the promotion of sustainable tourism. These results emphasize the crucial role of municipal governance in enhancing both environmental stewardship and sustainable tourism development within urban protected areas. Effective management practices can contribute to balancing the dual objectives of ecological conservation and urban development, thereby fostering a sustainable tourism model in highly urbanised contexts. This study underscores the importance of governance frameworks in mitigating urban pressures and advancing sustainability in Natural Protected Area (NPA).