In response to escalating urban traffic congestion, environmental degradation, and mobility inefficiencies, intelligent transportation systems (ITS) and sustainable mobility strategies have been increasingly recognised as vital components of smart city development. In this study, the city of Trabzon, Türkiye, was examined as a representative urban environment facing such challenges. Six major intersections exhibiting persistent traffic congestion were selected for conversion from conventional fixed-time signal control to adaptive, traffic-actuated signalisation systems. Detailed performance evaluations were conducted, incorporating microsimulation modelling and real-time traffic flow analysis. The implementation of adaptive signalisation was found to significantly reduce vehicular delay, queue lengths, and intersection-level emissions, while enhancing operational efficiency and traffic safety. A complementary analysis assessed the economic and environmental impacts of this intervention, revealing considerable annual savings in fuel consumption and marked reductions in carbon dioxide (CO$_2$) emissions, thereby underscoring the long-term sustainability and cost-effectiveness of the proposed system. In parallel, the integration of electric vehicles (EVs) and micromobility solutions—including electric buses, minibuses, passenger cars, bicycles, and scooters—was proposed to further promote sustainable urban mobility. Strategic placement of EV charging infrastructure was suggested, with spatial planning informed by expected demand distribution and intermodal connectivity. Economic modelling demonstrated a reduction in operational fuel expenditure, while environmental projections indicated a substantial decrease in transport-related greenhouse gas emissions. Furthermore, micromobility modes were proposed as critical for addressing first- and last-mile connectivity gaps, mitigating short-distance vehicular traffic, and alleviating urban parking demand. Policy recommendations emphasised the necessity of strong municipal leadership in facilitating infrastructure deployment, public adoption, and behavioural shifts towards low-emission transport alternatives. The findings position Trabzon as a viable model for medium-sized urban centres seeking to implement scalable and replicable smart mobility frameworks. By integrating adaptive traffic control with zero-emission mobility, this study provides actionable insights into the design of efficient, economically viable, and environmentally sustainable urban transportation ecosystems.
To enhance cost-efficiency and streamline logistics operations in industrial manufacturing, centralised warehouse systems have increasingly been adopted as a strategic alternative to decentralised storage structures. In this study, the storage framework of a lubricating oil production facility has been examined to assess the operational implications of decentralised warehousing currently in use. It has been identified that the existing system incurs excessive operational costs, prolongs handling times, and demands a disproportionately high labour force, thereby constraining the overall efficiency of the supply chain. In response to projected increases in production output, the feasibility of constructing a centralised, gravity-fed warehouse equipped with automated and robotic technologies for the handling of palletised goods has been investigated. This proposed facility would be strategically integrated with the product packaging unit to form a unified logistical hub within the manufacturing site. A comprehensive analysis was conducted to determine the optimal location for the central warehouse, with key criteria including material flow, space availability, connectivity to production lines, and scalability. The results indicate that the implementation of a centralised automated storage and retrieval system (AS/RS) would significantly improve warehouse throughput, reduce operational expenditures, and align closely with long-term production expansion plans. Additionally, the integration of advanced storage technologies is expected to enhance inventory visibility, minimise human error, and support real-time production coordination. It is concluded that the establishment of a central warehouse facility, functioning as a core node in the internal logistics network, is essential for achieving sustainable operational efficiency and future-proofing the lubricating oil manufacturing process.
The reliability and precision of stock market forecasting are of paramount importance to investors, regulatory authorities, and financial institutions. Traditional centralized systems for data processing and model deployment have been found to suffer from critical vulnerabilities, including susceptibility to tampering, single points of failure, and a lack of verifiability. To address these limitations, a novel hybrid framework has been developed that integrates advanced deep learning models with decentralized blockchain infrastructure to ensure both predictive accuracy and data integrity in financial time series forecasting. Temporal dependencies in market dynamics are captured through the use of recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures, which have been extensively trained to model non-linear and non-stationary behaviors in high-frequency financial data. In parallel, a private Ethereum-based blockchain has been deployed to record cryptographic hashes of input datasets, model parameters, and forecasting outputs, thereby ensuring transparency, auditability, and immutability across the data lifecycle. To enable computational scalability, deep learning operations have been executed off-chain, while on-chain mechanisms are utilized for secure checkpointing and traceability. Empirical validation has been conducted using real-time data from the Borsa İstanbul (BIST), demonstrating significant improvements in forecasting accuracy when compared with baseline statistical and machine learning (ML) models. Moreover, the integration of blockchain technology has enabled a verifiable audit trail for all predictive operations, enhancing trust in the data pipeline without compromising computational efficiency. The proposed framework represents a significant advancement towards secure, transparent, and trustworthy artificial intelligence (AI) in financial forecasting, with potential implications for the broader decentralized finance (DeFi) ecosystem and regulatory-compliant AI deployments in capital markets.
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.
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.
The optimization of railway train selection in Pakistan has become increasingly critical due to rapid population growth and rising travel demands. Despite efforts by the Railway Transport (RT) Department to enhance efficiency, productivity, and safety through policy reforms and infrastructure advancements, persistent challenges such as outdated technology, infrastructure bottlenecks, frequent delays, and inadequate maintenance continue to hinder progress. Addressing these issues is imperative to ensuring sustainable, efficient, and resilient railway operations. Given the multifaceted and uncertain nature of railway system modeling and management, decision-making (DM) processes necessitate robust methodologies capable of handling imprecise and ambiguous data. In this study, an innovative DM framework is introduced, leveraging intuitionistic fuzzy sets (IFSs) as an advanced extension of fuzzy sets (FSs) to manage uncertainty and hesitation in complex scenarios. By employing Einstein t-norm and t-conorm-based operators, novel operational laws for intuitionistic fuzzy credibility numbers (IFCNs) are proposed. Three key aggregation techniques—Confidence Intuitionistic Fuzzy Credibility Einstein Weighted Averaging (CIFCEWA), Confidence Intuitionistic Fuzzy Credibility Einstein Ordered Weighted Averaging (CIFCEOWA), and Confidence Intuitionistic Fuzzy Credibility Einstein Hybrid Weighted Averaging (CIFCEHWA) operators—are developed to provide a structured approach for processing and analyzing intuitionistic fuzzy data. To evaluate the practical applicability and reliability of the proposed methodology, a structured DM algorithm is formulated and validated using a real-world railway train selection case study. The incorporation of confidence levels within the IFCN framework enhances DM precision by quantifying the degree of certainty, thereby reducing risk and improving reliability. The findings demonstrate that the proposed approach effectively addresses the inherent uncertainties in railway selection processes, leading to more informed and strategic planning. Furthermore, the applicability of IFCNs extends beyond railway systems, offering valuable insights for domains such as artificial intelligence, financial DM, management science, and engineering, where uncertainty plays a pivotal role.
The retail sector is increasingly confronted with challenges arising from digital disruption and shifts in consumer behaviour. Amidst this transformation, the integration of augmented reality (AR) has been identified as a promising avenue to revitalise the in-store shopping experience, offering a means to engage customers more effectively and enhance competitiveness. This study investigates the extent to which AR applications can improve the shopping experience in physical retail settings, with particular emphasis on their capacity to foster customer flow states. A survey of 239 participants, comprising both general consumers and retail professionals, was conducted to explore the impact of AR on the shopping process. The findings suggest that AR significantly enhances the shopping experience, contributing to heightened customer engagement and immersion. However, while AR is found to influence flow states, the flow experience itself does not mediate the relationship between AR use and the shopping experience. These results offer important insights into the application of AR in brick-and-mortar retail environments, providing a management-oriented perspective on how its strategic implementation can generate sustainable competitive advantages. Moreover, the study contributes to existing AR literature by extending the understanding of its role in traditional retail, highlighting practical considerations for retailers aiming to adopt such technologies. The evidence also underscores the potential of AR in fostering behaviours and experiences that are essential for maintaining the competitiveness of physical stores in the digital age. Therefore, the adoption of AR technologies is not only recommended for enhancing the customer experience but also for driving innovation within the retail industry.
The strategic positioning of distribution, sales, and service facilities plays a critical role in ensuring the efficiency, reliability, and cost-effectiveness of supply chains. In particular, the location of such facilities within the transshipment network significantly influences both operational costs and consumer satisfaction by affecting delivery times and service quality. This study introduces a mixed-integer linear programming (MILP) model designed to optimize the layout of a postal supply chain network. The model aims to minimize the key cost components, including transportation, facility location, and holding costs, within a four-echelon supply chain consisting of suppliers, warehouses, retailers, and recipients. Parcels are initially collected by suppliers and delivered to regional warehouses, which then allocate them to selected retail locations. The selection of optimal retail locations is based on a cost minimization criterion, after which parcels are transported to the final delivery points—post offices situated in various cities. A distinctive feature of the proposed model is the assumption that demand at the recipient level is determined at the supplier level, thereby facilitating more centralized demand management and reducing uncertainties in the planning process. The model incorporates several constraints, such as flow balance, capacity limitations, and retailer selection. The optimization problem is solved using LINGO 16 software, and a comprehensive analysis is conducted to identify the optimal configuration of retailer locations and parcel flow distribution. A numerical example is provided to demonstrate the practical application of the model, and sensitivity analysis is performed to assess the impact of key parameters—such as retailer capacity and initial inventory levels—on the overall cost. The results indicate that increasing retailer capacity leads to a reduction in total supply chain costs, highlighting the benefits of economies of scale and parcel consolidation. However, an increase in the initial quantity of parcels results in higher costs due to elevated transportation and holding expenses. These findings offer valuable insights for decision-makers seeking to optimize postal supply chains, balancing the need for cost efficiency with the provision of high-quality service.
This systematic review seeks to synthesize the existing literature on the integration of blockchain technology into sustainable finance, with a particular focus on its role in enhancing transparency and accountability. A bibliometric analysis was conducted using the PRISMA methodology, incorporating a meta-analysis of scholarly articles published between 2018 and 2023. The analysis was based on data extracted from databases such as Springer Link, Dimensions, and Google Scholar, using the search terms "blockchain," "sustainable," "finance," "transparency," and "accountability." Open-access articles from reputable, peer-reviewed journals were selected to ensure the reliability of the data. Research questions were framed following the PICo method, addressing the specific impacts of blockchain technology on sustainable finance systems. The review highlights that blockchain has the potential to significantly enhance transparency and accountability in sustainable finance by providing robust mechanisms for transaction traceability and verification. Notably, blockchain technology has been applied to improve carbon market management, facilitate green bond issuance, and support the disclosure of Environmental, Social, and Governance (ESG) data. Despite these promising applications, several challenges remain, including regulatory uncertainties, technological limitations, and integration complexities, which could hinder its widespread adoption. To facilitate the global integration of blockchain in sustainable finance, it is recommended that financial institutions invest in technological infrastructure and training. Furthermore, policymakers should work towards harmonizing regulatory frameworks, while researchers are urged to pursue interdisciplinary, empirical studies to address the potential and limitations of blockchain technology. A shift in academic curricula to include blockchain’s implications in finance and sustainability is also recommended to better prepare future professionals. In conclusion, while blockchain holds significant promise for improving transparency and accountability, its broader adoption will require addressing technological, regulatory, and socio-economic barriers.
The transformation of public services into electronic formats (e-services) has gained significant momentum with the advancement of information and communication technologies, particularly due to the widespread use of the Internet and increasing citizen expectations. This transition has not only enhanced the efficiency of traditional public services but also facilitated new forms of e-governance that promote greater interaction, transparency, accessibility, and accountability between citizens and the state. Within this context, this study seeks to address the question: What are the key factors influencing citizens' satisfaction with e-services? The case of student satisfaction with the e-services provided by Anadolu University in Eskişehir, Turkey, serves as the focal point for the investigation. A survey conducted among 1,000 students from eight faculties and one graduate school at Anadolu University assessed their satisfaction with a variety of e-services, including Anasis, Mergen, Anadolu Mobil, E-Mail, library services, cafeteria services, and others. The collected data were analyzed using a combined methodology that integrated the E-GovQual model and the Importance-Performance Analysis (IPA) method. The E-GovQual model provided a comprehensive framework for evaluating the quality of e-services, allowing for an in-depth understanding of students' perceptions. The IPA method, on the other hand, facilitated the identification of performance gaps in e-service delivery and highlighted areas in need of improvement, based on students' expectations. The findings of the analysis were used to formulate strategic recommendations for decision-makers, students, and researchers. This research contributes to the growing body of knowledge on e-governance and user satisfaction in educational institutions, offering practical insights for optimizing online platforms to better meet student needs and expectations.
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