Knowledge and Decision Systems with Applications
Knowledge and Decision Systems with Applications is a peer-reviewed interdisciplinary journal dedicated to advancing research in data-driven decision-making, artificial intelligence, and knowledge management. The journal serves as a platform for publishing innovative solutions that enhance decision processes, optimize knowledge systems, and address real-world challenges. With a focus on practical applications, the journal appeals to researchers, practitioners, and decision-makers across fields such as healthcare, finance, environmental science, and emerging technologies.
Physics-Informed Machine Learning for Predicting Thermomechanical Stress in Rotating Carbon-Aramid Hybrid Composites
This work presents a robust analytical and computational framework to investigate the thermomechanical behavior of rotating hybrid composites, specifically PAN-based carbon fiber and aramid subjected to high-speed rotation and extreme thermal gradients. By employing plane strain theory, we modeled the structural response of multilayered cylinders to evaluate how angular velocities and temperature fluctuations interact to influence structural integrity. A distinctive feature of this research is the validation of closed-form analytical solutions against machine learning (ML) predictive models at operational speeds of 100 and 200 rad/s, utilizing the Von Mises yield criterion to delineate elastic-plastic boundaries. The results reveal a significant correlation between rotational-thermal synergies and radial displacement, highlighting the unique load-bearing advantages of the carbon-aramid hybrid. While the PAN-carbon phase provides high specific stiffness, the aramid layer enhances toughness and impact resistance. The integration of physics-based modeling with ML validation confirms the framework’s high predictive accuracy and thermal stability. This hybrid approach offers a scalable, intelligent decision-support tool for aerospace and automotive engineering, where lightweight, resilient structural components are paramount for mission-critical performance.
Hybrid Composite Structures; Machine Learning; Predictive Modeling; Thermomechanical Stress Analysis; Von Mises Yield Criterion
Copyright (c) 2026 Firat Kayiran (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Fuzzy Analytic Hierarchy Process and Interval Analytic Hierarchy Process for Multi-Stakeholder Evaluation of Public Transport Supply Quality
Evaluating public transport service quality requires balancing diverse stakeholder perspectives, a complex task essential for sustainable urban mobility. The efficiency of Multi-Criteria Decision-Making (MCDM) techniques, such as the Fuzzy Analytic Hierarchy Process (FAHP), in fostering consensus among diverse groups is still up for debate. This study compares FAHP and Interval AHP (IAHP) to determine which method better achieves stakeholder agreement in public transport quality assessment. Using a three-level criteria hierarchy, data were collected from regular passengers, potential passengers, and government officials in Mersin, Turkey. Both FAHP and IAHP derived priority weights, with consensus measured by Kendall’s coefficient of concordance (W). Results revealed that consensus was strongest for strategic goals and weakened for operational details. Notably, IAHP consistently outperformed FAHP, producing higher concordance across all levels. The findings demonstrate that IAHP is a more effective tool for reconciling divergent preferences, offering transport planners a superior method for fostering legitimate, consensus-driven policies and improvements.
Multi-Criteria Decision Making (MCDM), Fuzzy Analytic Hierarchy Process (FAHP), Interval Analytic Hierarchy Process (IAHP), Stakeholder Analysis, Public Transport Quality, Kendall’s Coefficient of Concordance (W), Urban Mobility
Copyright (c) 2026 Hiba Solieman, Szabolcs Duleba (Author) https://creativecommons.org/licenses/by/4.0
The Evolution of Disclosure Narratives in Audit Firms: Evidence from Dynamic Community Detection of Transparency Reports
Transparency reporting has emerged as a critical regulatory mechanism for enhancing accountability in the audit profession following prominent corporate scandals. Nevertheless, the literature contests the extent to which these mandatory disclosures provide substantive information rather than serving as symbolic compliance tools. The present study aims to address the existing gap in understanding how audit firm disclosure narratives evolve and how firms strategically reposition themselves within the disclosure landscape. The objective of this research is to examine the longitudinal dynamics of disclosure strategies in Turkish audit firm transparency reports from 2013 to 2024, investigating the emergence of narrative communities and firms' strategic repositioning behaviours. A novel methodological framework is employed, integrating computational text analysis with dynamic network science. Specifically, term frequency-inverse document frequency and cosine similarity measures are utilized to assess narrative proximity between transparency reports. The Leiden algorithm for dynamic community detection is then applied to identify and track disclosure communities across 230 firm-year observations from 89 distinct audit firms. The results reveal a fundamental transformation in the disclosure landscape: an initially homogeneous narrative structure dominated by two generalist communities in early years evolved into a fragmented system comprising eight specialized disclosure communities by 2024. Furthermore, audit firms engage in systematic narrative repositioning, with a pronounced gravitation toward compliance-oriented communities during periods of maximum fragmentation. The results show that mandatory transparency rules can have unintended effects. For example, when narratives become more complex, companies tend to use symbolic, procedure-focused disclosure strategies instead of substantive differentiation. The study offers critical implications for regulators seeking to design disclosure frameworks that foster genuine transparency rather than superficial compliance.
Copyright (c) 2026 Abdullah Kürşat Merter, Murat Özcan, Yavuz Selim Balcıoğlu, Mehmet Günlük (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Study on Fault Detection Techniques for Wheel Bearings in Rotating Machinery: Assessment of the LBF-MABAC model Based on Power Strategies
The wheel bearing is a very important part of the car, because they help or supports rotation and reduces friction among moving parts. A detailed investigation and comparison of the many models used to detect faults or failures in wheel bearings, which are critical and complex techniques in rotating machines such as vehicles, turbines, industrial equi⋕ent, and motors. Four main faults are noticed in the wheel bearing, such as: outer race defects, cage defects, ball/roller defects, and inner race defects, but the most important are preventing catastrophic failures, reducing downtime and repair costs with enables predictive maintenance. The main theme of this study is to choose or develop a technique for engineers’ implementation of condition monitoring systems; therefore, first, we design the model of the linguistic bipolar fuzzy technique, then we evaluate the models of “power averaging technique” and “power geometric technique” for linguistic bipolar fuzzy models. Additionally, we also construct the model of the “multi-attribute border approximation area comparison” technique, which is used for the assessment of the fault detection techniques for wheel bearing in rotating machinery. Finally, we illustrate numerical examples to describe the comparative analysis between our ranking values and the ranking values of old models, to mention the advantages and disadvantages of all approaches.
Bipolar fuzzy logic; Decision-making analysis; Fault detection; Linguistic term sets; Wheel bearing in rotating machinery.
Application of Cost-Effectiveness and Durability of Cement Treated Base in Road Construction
Cement Treated Base (CTB) is a type of stabilized base layer widely used in modern road construction to improve pavement strength and durability. It involves mixing a predetermined amount of cement with aggregate and water, resulting in a bound layer with enhanced load-bearing capacity and resistance to environmental conditions. The expense of producing the base layer of road and material used in pavements, including as drilling, blasting, crushing, and transporting, makes it an expensive choice. However, when subjected to high traffic and/or strong weather, this material does not hold up. The purpose of the research is, to increase the load bearing capacity of the road using CTB at feasible cost. The methods used are the Unconfined compressive strength, Modified proctor test for the cement-treated base. Results showed the optimal strength at the 3.5 % cement ratio, indicating its suitability for high-load-bearing applications. While 4% cement ratio demonstrated the least amount of rutting, making it ideal for high-traffic roads and areas prone to heavy loads. Cracking behaviors was influenced by both the cement content and curing conditions. The CTB methods saved the most money on construction costs of road, focusing on its benefits and inconveniences. In light of the writing survey and contextual investigations, the paper reveals that CTB can be a practical and robust solution for roads, development, especially in regions with frail subgrades or high traffic volumes.
Cement; Cement Treated Base; Cost estimation, durability, high traffic
Ranking of AI-Driven Strategies for Optimizing the Health Tourism Supply Chain Using Stratified BWM
Health tourism, as an emerging and strategic sector of the tourism industry, plays a significant role in economic growth, the development of international cooperation, and the enhancement of healthcare service quality. By integrating medical needs with recreational services, this sector holds great potential for attracting international patients. In this context, artificial intelligence technologies—including machine learning, data mining, natural language processing, and recommender systems—offer innovative opportunities to improve the efficiency of the health tourism supply chain. From enhancing patient data management to facilitating travel planning and personalizing services, AI can play a pivotal role in optimizing industry processes. This study aims to identify and rank the most effective AI-driven strategies for optimizing the health tourism supply chain. First, a comprehensive literature review and analysis of domain-specific sources were conducted to extract a set of key strategies. Subsequently, these strategies were evaluated and prioritized using the multi-criteria decision-making method known as the Stratified Best-Worst Method (Stratified BWM). The results revealed that the top three strategies were: “Patient Data Management and Analysis” (A1), “Healthcare supply chain optimization” (A2), and “Customer Experience and Digital Services” (A3). These strategies—by leveraging intelligent data usage, optimal resource allocation, and improved digital engagement with patients—significantly contribute to higher efficiency, customer satisfaction, and service quality. The findings of this research provide valuable insights for policymakers, healthcare managers, and tourism stakeholders in designing targeted programs and promoting the sustainable development of the health tourism supply chain.
Health Tourism; Supply Chain Optimization; Artificial Intelligence, Patient Data Analytics, Stratified BWM
The Utilization of Artificial Intelligence in Healthcare: Review, Challenges, and Future Research
Integrating Artificial Intelligence (AI) into the healthcare system has made major progress for diagnosing disease, patient monitoring, and medical imaging, creating a highly interconnected ecosystem for improved medical decision-making. AI-driven disease diagnosis utilizes machine learning models to analyze vast medical datasets, enabling quick and precise identification of diseases. This diagnostic capability is further enhanced by AI-powered medical imaging, where deep learning techniques, including convolutional neural networks (CNNs), refine image analysis, segmentation, and classification, providing critical support for precise diagnosis. Alongside these deep learning techniques, support vector machines (SVMs) offer strong classification powers that work especially well in situations requiring high-dimensional data processing with sparse training data. By combining CNNs for obtaining features and SVMs for categorizing, the advantages of both methods are combined to increase computational efficiency and diagnostic certainty. These AI-based insights are then reinforced through patient monitoring, where wearable sensors and IoT devices continuously track patient health, feeding real-time data into AI models that detect anomalies and predict disease progression. The synergy between these three areas ensures a continuous flow of medical information, enhancing predictive analytics and personalized treatment strategies. This review examines how powered system AI unifies disease diagnosis and patient monitoring using medical imaging into an integrated healthcare system, discussing current challenges such as data security, interpretability, and clinical adoption. The findings highlight AI's role in bridging diagnostic precision, real-time monitoring, and advanced imaging, paving the way for a more proactive and efficient healthcare framework.
Artificial Intelligence; machine learning; disease diagnosis; patient monitoring; medical imaging; IOT Devices; CNN; SVM.
Arabic Text Diacritization Using Deep Neural Networks and Transformer-Based Architectures
This study investigates the application of deep learning architectures for automatic Arabic text diacritization, with a particular focus on character-level neural networks. Four architectures were implemented: a Transformer encoder-decoder, a BiGRU model, a baseline stacked BiLSTM, and a CBHG model. Diacritic Error Rate (DER) and Word Error Rate (WER) were used as evaluation metrics, with training and evaluation conducted on the Tashkeela corpus. The results show that the CBHG model achieved faster inference times while slightly outperforming the Transformer encoder-decoder in diacritic accuracy. However, the findings also suggest that the Transformer model may yield better performance with larger datasets, improved parameter tuning, and increased model capacity.
Arabic Diacritization; Natural Language Processing (NLP); Sequence-to-Sequence Models; Transformer Architecture
A Hybrid Multi-Criteria Decision-Making Model for Performance Assessment in the Banking Industry
This work presents a novel hybrid multi-criteria decision-making (MCDM) framework designed to assess the financial and non-financial Environmental, Social, and Governance (ESG) performance of the banking sector. To demonstrate its applicability, a longitudinal case study was conducted on a major Turkish bank using annual data from 2012 to 2022. The proposed framework integrates two complementary methodologies: Maximum of Criterion (MAXC) and Range of Value (ROV). The MAXC method was utilized to objectively compute the weights of financial and non-financial performance indicators, while the ROV procedure enabled the ranking of annual performance across both dimensions. The evaluation followed a two-stage structure. In the first phase, MAXC revealed that the most influential financial indicators were return on average assets, return on average equity, and the ratio of non-performing loans to total loans. For the non-financial dimension, community engagement, governance quality, and human rights emerged as the most impactful criteria. In the second phase, the ROV method was employed to calculate composite performance scores and derive annual rankings. The results indicate that 2022, 2012, and 2014 marked the strongest years in terms of financial performance, while 2021, 2022, and 2020 demonstrated the highest ESG-related performance. Finally, a series of sensitivity analyses confirmed the robustness and reliability of the proposed hybrid model, thereby validating its suitability for comprehensive performance evaluation in the banking sector.
Financial Performance; Non-Financial Performance; Banking Sector; Multi-Criteria Decision-Making; Maximum of Criterion; Range of Value
Determining the Optimal Selection Criteria for Unmanned Surface Vehicles in Port Security Using Fuzzy AHP and the Best-Worst Method
The primary objective of this study is to evaluate the selection criteria for unmanned maritime vehicles in the context of port security. The selection criteria were obtained through a comprehensive review of the extant literature and consultation with experts in the field. The objective was to ascertain the relative weights of these criteria using the fuzzy AHP and Best Worst Method. The evaluations of five decision makers were utilised in the implementation phase. Following a thorough evaluation of the available data, it was determined that both methods yielded analogous results. The most significant criteria was determined to be "Endurance," while "Maintenance Cost" and "Investment Cost" were found to be of lesser importance. The study provides port operators with a unique perspective on the use of unmanned maritime vehicles for port security, emphasising the criteria that should be given greater attention.
Unmanned Surface Vehicle; Port Security; Fuzzy AHP; Best-Worst Method; Maritime Security; Multi-criteria Decision Making.
Numerical Technique for Solving Non-Linear Deterministic Hepatitis B Model
Epidemic models classify the process of infectious disease into a series of discrete stages, with transition from susceptible to different states. In this paper, a non-linear epidemic model is proposed to control and understand the dynamics of hepatitis B disease. Epidemic model describes a set of equations where computational tools are used to study the spread of transmissible pathogens in host populations. This model considers the effect of vaccination during the formulation of proposed model. In the work, the numerical solution of the model is further attained through Homotopy Analysis Method, which provides an effective and flexible way for controlling and adjusting the convergence region of the infinite series solution by means of an auxiliary parameter. The outcomes of this paper display that the hepatitis B disease has the inclination over a period of time but it is under control after vaccination.
Hepatitis B; SEIVR model; Initial value problem; Homotopy Analysis method
Evaluating the Electronic Waste Management System by Using Technique for Order Performance by Similarity to Ideal Solution
The waste created after the useful life of electronic or electrical products is labeled as electronic waste. Introduction of better and upgraded options into market increase the rate of disposal of obsolete discarded products from the consumer side. Electronic waste contains many toxic ingredients and major toxicants are heavy metal ions that may find the way to environment matrix after improper disposal of products. In recent years due to rapid technological development and industrialization; electronic waste management has become a major challenge throughout the world and receiving an attention from researchers, governments, and public. Poor management of waste may adversely affect the environment and human health. Thus, it is necessary to segregate all e-waste materials at the point of generation, for appropriate treatment/disposal. The assessment of the most suitable and appropriate framework has been a subject of extreme research interest as it involves many conflicting factors such as cost, type of waste, energy consumption, environmental impact of waste residue, rate of waste generation, treatment efficiency, and practical feasibility, consumer awareness, occupational hazards etc. There are numerous constraints and knowledge gaps to plan a feasible strategy in realistic scenario. The exact amount of disposed of e-waste is very difficult to predict because of many reasons including the unavailability of collection units for e-waste as well lack of awareness to consumer regarding its proper way of disposal. Various approaches that have been proposed needs to be analyzed with respect to every individual factor. To address this problem, we have proposed multi-criteria decision-making(MCDM) based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to develop framework that determine the interdependency among different factors and obtained the priority value of each e-waste treatment method for appropriate selection and successful implementation in real scenario. From the results of the given information gathered from 3 sources, it is observed that re-use method is the preferred over recycle and landfill methods. The findings of this study will support the organizations and stakeholders to take suitable steps for the disposal of electronic waste strategies.
Electronic waste; Waste management; Multi criteria decision making; Environment; Health effects; Solid Waste
Optimizing Cluster Head Selection for Reliable and Efficient WSNs under Uncertainty
WSNs are often deployed in harsh and challenging environments where nodes may have limited energy and be prone to failure, reliability is an essential component of these networks. Cluster reliability is one of the approaches that can lead WSNs to reliable communication and energy efficiency at the highest degree of order. Cluster reliability is defined as the ability of a cluster to retain its structure and functionality over time, especially in the event of node failures or other unfavourable circumstances. The majority of current clustering protocol-based research focuses on (i) cluster Head Role (ii) cluster formation, (iii) data accuracy, and (iv TDMA schedule creation, all of which entirely ignore the importance of appropriate cluster head selections, which have a significant impact on WSN energy efficiency. The network has been divided into optimum clusters in this study employing a hierarchical clustering agglomerative approach after the optimal number of clusters has been established and confirmed by beta CV cluster validity Indexing. Each node's value is determined by the fitness function proposed in this study using parameters such as (i) residual energy, (ii) distance from sink, (iii) SNR (signal to noise ratio), and (iv) average distance. The weight parameter is determined using the entropy-weighted technique. It has been noted that our suggested method, which is shown in Figure 5, has 20% and 10% longer network lifetimes in comparison to the LEACH and LEACH-FC protocols. Finally, the obtained results have been validated by the testing of statistical hypotheses.
A Multi-Criteria Decision-Making Model for Prioritizing the Causes of Inflation in Libya Using the Best-Worst Method and Analytic Hierarchy Process
Inflation remains one of the most persistent and destabilizing economic challenges facing Libya, affecting purchasing power, investment, and overall economic stability. Understanding its root causes is essential for designing effective and targeted policy interventions. This study investigates the main drivers of inflation in Libya by applying a hybrid Multi-Criteria Decision-Making (MCDM) approach that combines the Best-Worst Method (BWM) and the Analytic Hierarchy Process (AHP). Eight critical inflation-related factors were identified through expert consultations and economic literature, including structural, monetary, and institutional elements. The BWM was first used to determine initial weight coefficients by comparing the most and least influential factors, followed by the AHP to conduct comprehensive pairwise comparisons. The final weights were derived by aggregating the results from both methods to ensure robustness and consistency. The findings indicate that weak domestic production and political instability are the most significant contributors to inflation in Libya, followed by currency devaluation and monetary expansion. These results emphasize the need for integrated policy responses that not only address monetary dynamics but also confront deeper structural constraints. The study offers a transparent and replicable framework for inflation analysis and contributes to more informed and context-specific economic policymaking in fragile and transitional economies.
Multi-Criteria Decision-Making; s the Best- Worst Method; Analytic Hierarchy Process; Inflation; policymaking
A Decade of Picture Fuzzy Sets in Multi-Criteria Decision-Making: A Comprehensive Review of Trends, Gaps, and Future Directions
Picture fuzzy sets (PFSs) are extensively utilized in medical diagnostics and multi-criteria decision-making (MCDM) due to their enhanced flexibility, distinguishing capability, and applicability in handling uncertainty and hesitation, particularly in complex domains such as healthcare, transportation, environmental decision-making, artificial intelligence (AI), and machine learning. Guided by four research questions, this review employed descriptive statistics to analyse the extent of research focused on PFSs. An intensive literature search was performed across leading publishers, including IEEE Xplore, SpringerLink, ScienceDirect, the Association for Computing Machinery (ACM), and the Multidisciplinary Digital Publishing Institute (MDPI). Findings revealed significant scholarly efforts to adopt PFSs in healthcare (22.6%), transportation (24.5%), environmental decision-making (7.5%), AI and machine learning (5.7%), and other domains (39.6%). Research from 2013 to 2024 demonstrated notable advancements in mathematical operations and extensions (25%), aggregation operators and similarity measures (30%), hybrid approaches and MCDM applications (20%), domain-specific implementations (15%), and theoretical developments (10%). MCDM emerged as a prominent tool for enhancing decision-making across diverse fields. The study highlights the need to explore additional areas of application for PFSs, particularly in refining distance measures to further enhance their utility.
Picture Fuzzy Sets; Multi-Criteria Decision-Making; Uncertainty Management; Aggregation Operators and Artificial Intelligence
An Innovative Decision Support Model for the Financial Performance Assessment: A Study of BIST Cement Firms
The regularly assessment of the financial performance of firms in the real sector is highly essential, both in terms of increasing the level of operational efficiency and in terms of effectively managing potential risk factors and obtaining sustainable competitive advantage. This paper introduces a new decision algorithm for the assessment of firm performance. This decision algorithm consists of the integration of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Modified Standard Deviation (MSD), Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS), Multi-Objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA), Multi Atributive Ideal-Real Comparative Analysis (MAIRCA) and Borda Count methodologies. In order to test the presented decision-making procedure, a real-time case study was applied as part of the study. This case study is focused on analyzing the financial performance of 13 cement industry firms whose shares are listed on the Borsa Istanbul (BIST) for the year 2023. In the process of assessing the performance of these companies, 10 performance indicators were selected with the help of previous literature. LOPCOW and MSD algorithms were applied to determine the final importance weights of these indicators, while CRADIS, MOOSRA, MAIRCA and Borda Count algorithms were employed to rank the firms. The findings of the final weighting algorithm indicate that the three most important performance indicators are total debt to total equity ratio, total debt to total assets ratio and cash ratio. Moreover, according to the final ranking results obtained based on the CRADIS, MOOSRA, MAIRCA and Borda Count algorithms, BUCIM is the most financially successful firm in 2023. Additionally, the findings of the robustness analyses also support the conclusion that the results obtained from the presented model are reliable and applicable.
Financial Performance; BIST Cement Sector Firms; MCDM; LOPCOW; MSD; CRADIS, MOOSRA; MAIRCA; Borda Count.
A Cooperative Heterogeneous Multi-Agent System Leveraging Deep Reinforcement Learning
The agent plays a pivotal role in the multi-agent environment. Previous studies have primarily focused on homogeneous agent collaboration, but the approaches used in these studies cannot be directly applied to heterogeneous agent collaboration. There is a need for a model that supports collaboration among heterogeneous agents, as real-world systems consist of agents with diverse shapes, functions, and tasks. This study proposes a framework that uses visual and vector observations to facilitate collaboration among heterogeneous agents. The seaport environment is simulated for this study. Visual observations and vector observations are employed to interpret signals from other agents and calculate distance between the agents in the environment. Two types of agents are used: the crane agent, which lifts and lowers heavy materials between the ship and the designated location, and the lifter agent, which transports bulky materials from the seaport to the storeroom. These agents are heterogeneous due to differences in size, capabilities, and tasks. Separate neural networks are trained for each agent. The results show that the proposed model outperforms the base model in terms of mean reward, extrinsic reward, episode length, and value estimation. In terms of value loss and policy loss, the proposed model performs similarly to the base model.
Multi-Agents; Deep Reinforcement Learning; Heterogeneous-Agents
Evaluating Financial Performance with SPC-LOPCOW-MARCOS Hybrid Methodology: A Case Study for Firms Listed in BIST Sustainability Index
Periodic analysis of financial performance is instrumental in enabling manufacturing firms to enhance their operational efficiency, manage risks effectively, make strategic decisions, maintain a competitive advantage, ensure sustainability, and promote good corporate governance. This research introduces an innovative decision-making methodology for evaluating and ranking the financial performance of firms by utilizing financial ratio metrics. To this end, the present study puts forward a novel decision-making methodology for evaluating the performance of a corporation, integrating the Symmetry Point Criterion (SPC), the Logarithmic Percentage Change Based Objective Weighting (LOPCOW), and the Measurement of Alternatives and Ranking by Consensus Solution (MARCOS). The feasibility of the proposed decision approach in the existing work is evaluated through a real-time case study. The case analysis concentrates on the financial performance assessment of 16 real sector firms whose shares are traded in the Borsa Istanbul (BIST) Sustainability 25 Index (XSD25). In order to examine the financial performance of firms in the real sector, ten financial performance indicators are selected based on earlier literature. The SPC and LOPCOW procedures were applied to ascertain the weight values of the assessment indicators, and the MARCOS procedure was employed to rank the firms' financial performance. The findings of the weighting process indicate that the three most influential criteria are the ratio of total debt to total equity, the return on equity, and the average price-earnings ratio. According to the MARCOS ranking procedure, ENKAI was the company with the highest financial performance compared to its peers during the analysis period.
Financial Performance; Real Sector Firms; SPC; LOPCOW;MARCOS
Fuzzy Multi-Criteria Decision-Making Based Security Management: Risk Assessment and Countermeasure Selection in Smart Cities
The aim of this study is to develop a fuzzy MCDM based framework for security management in smart cities. The study focuses on the assessment of security risks and the selection of appropriate security measures. To address the uncertainties and complexities in the assessment of security risks and measures, this research employs two different fuzzy methods: weighting the risk factors using the Fuzzy weight by envelope and slope (F-WENSLO) method and selecting the most appropriate security measures using the Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) method. The proposed approach is applied to a case study on identifying and assessing potential security risks in a smart city infrastructure. The risk factors used in the study are identified as flash floods, traffic accidents and transportation security, robbery and public safety threats, cybersecurity threats, health crises and pandemics, and energy infrastructure and outages. Security systems are defined as physical security systems, emergency response and crisis management systems, software and cybersecurity systems, monitoring and surveillance systems, and sensor-based early warning systems. The results show that physical security measures play a critical role in preventing and responding to incidents, while emergency response and crisis management systems perform less well. It is also stated that software and cybersecurity systems provide an effective solution against digital threats, but their integration with other systems is important. The results show that the fuzzy-based model significantly increases the accuracy in prioritizing risks and provides a systematic method for selecting the most effective security measures. This study contributes to the literature by presenting an innovative decision-making tool for security management in smart cities.
Fuzzy MCDM; Smart Cities; Security Management; Risk Assessment.
Analysis of the Corporate Financial Performance Based on Grey PSI and Grey MARCOS Model in Turkish Insurance Sector
The use of MCDM approaches in the insurance sector provides important information to the top management or decision maker’s authorities of insurance companies on issues such as accurate performance measurement, identification of strengths and weaknesses, optimisation of operations and improvement of strategic decisions. This, in turn, helps senior management to balance profitability and risk and contributes to the establishment of a sustainable business model. The present study aims to measure and rank corporate financial performance in the non-life insurance sector by using the Grey PSI and Grey MARCOS integrated model. For the purpose of this study, the proposed decision-making approach is applied through a case study. The case study focuses on the corporate financial performance assessment process of nine companies operating in the Turkish non-life insurance sector and consistently ranked in the top ten in terms of premium production in the period 2021-2023.In the current study, twelve financial performance indicators were identified for corporate financial performance analysis, taking into account previous studies in the literature. The importance weights of these performance criteria are calculated using the Grey PSI procedure. The Grey MARCOS method was then employed to determine the ranking of the decision alternatives. The results obtained from the Grey PSI method indicate that the net profit-to-total equity, net premiums received-to-gross premiums received and total operating expenses-to-gross premiums received are the three most significant criteria affecting the corporate financial performance of the relevant non-life insurance companies. The application of the grey MARCOS ranking method shows that Türkiye Sigorta has superior corporate financial performance for the period 2021-2023 compared to other insurance companies analysed in the study. The assessment and evaluation of corporate financial performance in the insurance sector is of critical importance for sector managers, policyholders, regulatory authorities, policymakers and other stakeholders, due to the significant benefits insurance companies provide at both micro and macroeconomic levels.
Corporate Financial Performance; Insurance Sector; MCDM; Grey System Theory; Grey PSI; Grey MARCOS
A Multi-Criteria Utility Approach to Bridge Maintenance Prioritization
Maintaining bridges optimally presents a challenge for the decision-makers because of the complexity of the objectives. Bridges should have a maintenance planning system for a better organization and controlling mechanism. These systems allow the decision-making process to be performed simpler and the limited public fund to be spent better. In this study, the multi-attribute utility theory (MAUT) is applied as a practical multi-objective decision-making method to prioritize 20 bridge networks in Western Turkey. The objective of this study is to use MAUT to a bridge maintenance problem in Turkey and providing improvements to the existing Turkish bridge management system (BMS) decision-making method in the literature. The criteria list given in the Turkish BMS is reexamined, and one more criterion is added. The current prioritization method is replaced with the MAUT approach to explain the uncertainty situation by taking the risk preferences of the decision-makers. Additionally, the additive utility independence assumption of MAUT is inquired to avoid a suboptimal solution for bridge maintenance planning. The results show that how calculating risk preferences, interrogating more decision-makers with different expertise, and adding a new objective criterion might change the priority order for the same dataset.
Multi-attribute utility theory; multi-criteria decision making; bridge maintenance planning; bridge prioritization
Thermal comfort and adaptive behaviour of the elderly: a systematic review
Thermal comfort is linked to our health, well-being, and productivity. The thermal environment is one of the main factors that influence thermal comfort and, consequently, the productivity of occupants inside buildings. Meanwhile, behavioural adaptation is well known to be the most critical contributor to the adaptive thermal comfort model. This systematic review aims to provide evidence regarding indoor thermal comfort temperature and related behavioural adaptation. Studies published between 2010 and 2025 examining adaptive behaviour of the elderly people. According to the above literature review on the adaptive behaviour of the elderly, opening windows or closing windows, turning on or off air-conditioner and clothing insulation adjustment is the most common strategy for the elderly to adopt indoor thermal environment. In addition, older people tend to adopt indoor thermal environments by some behaviours without financial expenditure, such as opening or closing windows and clothing insulation adjustment. However, the elderly regard turning on the air conditioner as a priority strategy in the public space.
Thermal comfort; Adaptive behaviour; Elderly people
Weapon System Selection for Capability-Based Defense Planning using Lanchester Models integrated with Fuzzy MCDM in Computer Assisted Military Experiment
Capability-based planning (CBP) is an approach that is increasingly gaining importance in modern defense strategies. Unlike traditional threat-focused planning, capability-based planning aims to enable countries to develop flexible and sustainable military capabilities against changing security environments. In this context, weapon system selection should be evaluated not only against current threats but also in terms of suitability for possible future operational requirements. The selection of weapon systems requires consideration of a large number of technical, operational and financial criteria. In this process, Fuzzy Multi-Criteria Decision Making (FMCDM) methods are frequently used. In addition, simulation techniques are a critical tool for testing the effectiveness of weapon systems on the battlefield by creating scenarios close to reality. Lanchester equations, in particular, are an effective tool for analyzing the combat dynamics of forces. In this study, an Anti Guided Tank Missiles selection methodology in which the Fuzzy Analytic Hierarchy Process (FAHP) is integrated with Lanchester equations is applied to an example. JCATS was used for computer assisted military experiments scenario analysis. The results show that Lanchester equations are valid.
Capability-based planning; Weapon Selection; Lanchester models; MCDM; Fuzzy AHP;
Machine Learning-Based Investigation of Stress in Carbon Fiber Rotating Cylinders
This study analyzed elastic stresses in a rotating cylinder consisting of carbon fiber material using analytical methods and machine learning techniques. Elastic stress ranges were determined according to the Von Mises yield criterion, which provided a comprehensive understanding of the material's behavior under rotational loads. The analytical results were validated and improved through machine learning-based predictions, demonstrating the potential of these approaches in stress analysis. The results are presented graphically for clarity and comparison. The study emphasizes that there is an inverse relationship between the rotational speed parameter and the elastic stress that occurs inside the cylinder. These findings contribute to understanding stress behavior in high-performance composite materials but also demonstrate the effectiveness of machine learning in predicting stress distributions for complex engineering.
Machine learning, stress analysis, cylinders