Accurate detection of road surface anomalies remains a fundamental challenge in ensuring vehicular safety, particularly within the domain of intelligent transportation systems and autonomous driving technologies. Among such anomalies, crash stones—defined as irregular, protruding, and often unstructured fragments on the road—pose considerable risks due to their heterogeneous morphologies and unpredictable spatial distributions. In this study, a novel mathematical model is proposed, formulated through a functional energy minimization framework tailored specifically for the detection and segmentation of crash stones. The model incorporates three principal components: geometric edge energy to emphasize structural discontinuities, local variance descriptors to capture micro-textural heterogeneity, and fuzzy texture irregularity measures designed to quantify non-uniform surface patterns. These components are integrated into a unified total energy functional, which, when minimized, facilitates the precise localization of obstacle regions under diverse illumination, weather, and pavement conditions. Final detection is achieved through adaptive thresholding informed by fuzzy logic-based classification, enabling robust performance in scenarios with high noise or low contrast. Unlike deep learning-based methods, the proposed approach is fully interpretable, non-reliant on extensive annotated datasets, and computationally efficient, making it well-suited for real-time applications in resource-constrained environments. Experimental validations demonstrate high detection accuracy across varied real-world datasets, substantiating the model's generalizability and resilience. The framework contributes a mathematically rigorous, scalable, and explainable solution to the enduring problem of small obstacle detection, with direct implications for the enhancement of road safety in next-generation transportation systems.
As the most widely played and commercially influential sport worldwide, football (soccer) demands increasingly data-driven and methodologically sound decision-making across tactical, operational, and financial domains. In recent years, Multi-Criteria Decision-Making (MCDM) methods have been increasingly adopted to address the complex, multi-dimensional challenges faced by stakeholders in the sport. To comprehensively examine the current state of research, a systematic literature review (SLR) was conducted focusing on the application of MCDM techniques in football-related decision contexts. The analysis was performed using articles indexed in the Scopus and Web of Science databases, with the Novelty, Impact, Relevance, and Prestige (NIRP) method employed to filter and prioritize the most impactful publications. A final portfolio of 27 articles published between 2000 and 2024 was identified and examined. The selected works were analyzed to identify prevailing MCDM techniques, thematic concentrations, and methodological trends within the domain, providing a comprehensive overview of developments in this field. This review is expected to serve as a foundational reference for academics and practitioners seeking to leverage decision-making frameworks in the evolving landscape of football analytics.
Detailed Understanding of Roman concrete requires context from Roman military and civil engineering. The Romans prioritized durable infrastructure due to the impracticality of maintaining temporary wooden structures across their vast empire. This led to the development of long-lasting roads, bridges, and fortifications, many of which still exist today. Roman construction techniques, including concrete use, evolved significantly over time. Although Vitruvius documented early methods in the 1st century BC, later advancements—such as “hot mixing”—were not included in his texts. Roman concrete’s durability, especially in late Empire formulations, contributed to its longevity and continued use through the medieval period. In modern times, concrete construction shifted towards heavily reinforced structures, often without adequate protection. This has led to durability issues, highlighted by events like the collapse of the Morandi Bridge. In contrast, Roman concrete demonstrates superior longevity and self-healing properties despite being unreinforced. The study of Roman concrete offers valuable insights for modern construction, suggesting that minimally reinforced or unreinforced methods inspired by Roman practices could enhance durability and sustainability.
Energy remains a cornerstone of national economic development and societal advancement. However, the current trajectory of global energy production—dominated by fossil fuels and driven by escalating demand—is environmentally unsustainable. Electricity, as a versatile and high-grade form of energy, offers the advantage of being generable from both conventional and renewable sources. Nevertheless, fossil fuel–based electricity generation continues to contribute significantly to local and global environmental degradation. In response to the dual imperatives of meeting rising energy demand and reducing greenhouse gas emissions, the identification and prioritisation of sustainable electricity generation technologies have become imperative. Renewable energy sources (RES)—such as solar, wind, hydro, and biogas—offer viable alternatives, yet their relative merits must be evaluated through a rigorous and systematic approach. In this study, a multi-criteria decision-making (MCDM) framework has been employed to assess and rank RES in the Republic of Serbia. Key evaluation criteria have included construction cost, payback period, ecological impact, annual generation capacity, and potential for integration with alternative energy modes. The assessment has been conducted using the FANMA method (a novel hybrid technique named after its developers) and the Weighted Aggregated Sum Product Assessment (WASPAS) method, both of which are established tools for handling complex decision-making scenarios. The findings have provided a data-driven basis for prioritising renewable energy technologies in national energy strategies. The insights derived are expected to inform policy decisions in Serbia and offer a transferable framework for energy planning in other developing economies aiming to transition towards more sustainable power generation systems.
To address the evolving preferences of residents in smart community development and the uncertainty inherent in expert-driven technology adoption decisions, an integrated Quality Function Deployment (QFD) framework has been proposed. This framework combines Interval Type-2 Fuzzy Sets (IT2 FSs), a modified Kano Model, Regret Theory, and the Grey-Entropy Technique for Order Preference by Similarity to Ideal Solution (GETOPSIS). IT2 FSs were employed to accommodate the semantic ambiguity of user demands, enabling more precise interpretation of linguistic input. A refined Kano classification was used to categorise 15 demand indicators, from which 5 Customer Requirements (CRs) and 10 Design Requirements (DRs) were derived. Regret Theory was incorporated to model behavioural biases commonly observed in expert evaluations, particularly the tendency to avoid perceived short-term losses. Additionally, a dynamic weight adjustment mechanism was introduced based on corporate life cycle theory, revealing strategic divergences between early-stage enterprises, which prioritise basic security infrastructure, and mature firms, which emphasise sustainable, energy-efficient technologies. The GETOPSIS method was further enhanced to improve the robustness of technology prioritisation under uncertainty. The principal contributions of this study are threefold: (1) the provision of a QFD framework capable of modelling high-order uncertainty through linguistic variables, (2) the integration of behavioural decision theory to better reflect real-world expert judgement, and (3) the development of an improved GETOPSIS approach for more reliable multi-criteria decision-making. The proposed framework provides theoretical and methodological foundations for advancing adaptive technology adoption strategies in smart communities and may serve as a decision-support tool for policymakers and developers in rapidly evolving urban environments.
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.
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