A novel electronic voting system (EVS) was developed by integrating blockchain technology and advanced facial recognition to enhance electoral security, transparency, and accessibility. The system integrates a public, permissionless blockchain—specifically the Ethereum platform—to ensure end-to-end transparency and immutability throughout the voting lifecycle. To reinforce identity verification while preserving voter privacy, a facial recognition technology based on the ArcFace algorithm was employed. This biometric approach enables secure, contactless voter authentication, mitigating risks associated with identity fraud and multiple voting attempts. The confluence of blockchain technology and facial recognition in a unified architecture was shown to improve system robustness against tampering, data breaches, and unauthorized access. The proposed system was designed within a rigorous research framework, and its technical implementation was critically assessed in terms of security performance, scalability, user accessibility, and system latency. Furthermore, potential ethical implications and privacy considerations were addressed through the use of decentralized identity management and encrypted biometric data storage. The integration strategy not only enhances the verifiability and auditability of election outcomes but also promotes greater inclusivity by enabling remote participation without compromising system integrity. This study contributes to the evolving field of electronic voting by demonstrating how advanced biometric verification and distributed ledger technologies can be synchronously leveraged to support democratic processes. The findings are expected to inform future deployments of secure, accessible, and transparent electoral platforms, offering practical insights for governments, policymakers, and technology developers aiming to modernize electoral systems in a post-digital era.
This study investigates the features of regional development within the Blue Economy system, focusing on sustainable growth and resilience in coastal regions. The Blue Economy emphasizes the sustainable and equitable use of marine resources, requiring a development model that integrates economic, ecological, and social dimensions. This research explores how regional development under the Blue Economy can be understood, assessed, and supported through analytical tools. Using a multi-step tool that combines interquartile range (IQR) analysis, clustering methods, and z-score normalization, representative coastal economies are identified to provide insights into the stability, specialization, and economic efficiency of the Blue Economy. Additionally, a radar chart tool is introduced to assess and visualize the region’s profiles, offering an accessible means for planning by highlighting economic strengths, vulnerabilities, and sectoral dependencies. The findings emphasize the need for a balanced development approach tailored to each region’s socio-economic and ecological context to foster resilience and sustainability. Further enhancements to these tools are proposed, including incorporating additional socio-economic and ecological indicators, to broaden their applicability for comprehensive assessments of the development of the regions in the Blue Economy system. This research thus provides valuable tools for stakeholders to monitor and strengthen the economic health of coastal regions, supporting sustainable regional development within the Blue Economy.
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.
Deposit-taking Savings and Credit Co-operative Societies (DT-SACCOs) have been recognized globally as pivotal financial institutions that facilitate economic development and financial inclusion. Despite this significance, 35.55% of DT-SACCOs in Kenya have been reported as financially unsustainable, a condition attributed primarily to deficient cash management practices. On average, four Savings and Credit Co-operative Societies (SACCOs) are delicensed annually due to financial distress, raising substantive concerns regarding the sector's sustainability. This study was undertaken to investigate the extent to which firm size moderates the relationship between cash management practices and financial sustainability within Kenyan DT-SACCOs. Grounded in cash management theory, the research adopted a positivist paradigm and employed a cross-sectional survey design. A total of 176 finance managers representing 176 licensed DT-SACCOs constituted the study population. Using Yamane’s formula, a sample of 122 respondents was determined, with data collected through structured questionnaires yielding a 98% response rate. Descriptive and inferential statistical techniques were applied in the data analysis. A statistically significant positive relationship between cash management practices and financial sustainability was identified (p = 0.001). Moreover, an increase in the Nagelkerke R2 statistic indicated that firm size exerted a moderating effect on this relationship. It is recommended that DT-SACCOs prioritize the adoption of integrated digital treasury management systems to centralize and automate cash operations, including collections, disbursements, reconciliation, and liquidity monitoring, thereby enhancing financial resilience and long-term sustainability.
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.
The ongoing depletion of conventional fossil fuel reserves, coupled with escalating environmental concerns and the volatility of global oil markets, has intensified the search for cleaner and more sustainable energy alternatives for transportation. Among various low-emission fuels—such as biodiesel, ethanol, methanol, ammonia, hydrogen, and Compressed Natural Gas (CNG)—Liquefied Natural Gas (LNG) has emerged as a particularly viable option for Medium- and Heavy- Duty Vehicles (M&HDVs). LNG offers several advantages, including higher volumetric energy density, reduced tailpipe emissions, and compatibility with high-efficiency engine technologies. Its adoption is of strategic relevance to countries such as India, where transportation remains one of the largest contributors to Greenhouse Gas (GHG) emissions and is predominantly dependent on imported crude oil. The utilisation of LNG in M&HDVs has been identified as a means to simultaneously reduce GHG emissions and enhance national energy security. In this context, a comprehensive assessment is presented, encompassing LNG production pathways, distribution logistics, cryogenic storage technologies, and economic feasibility, as well as supportive government policies and international best practices. Key challenges, such as Boil-off gas (BOG) management, refuelling infrastructure gaps, cost parity with diesel, and engine retrofitting, have also been critically evaluated. Particular attention has been given to recent technological advancements and their potential to improve lifecycle emissions performance and cost-effectiveness. It is suggested that the integration of LNG into national energy and transportation strategies may yield substantial environmental and economic benefits, especially when supported by policy instruments, public–private investment models, and standardised regulatory frameworks. The findings indicate that LNG is poised to play a pivotal role in the decarbonisation of the freight and commercial transport sector, both in India and globally, thereby contributing to long-term sustainability objectives.
To address the issue of estimating energy consumption in computer systems, this study investigates the contribution of various hardware parameters to energy fluctuations, as well as the correlation between these parameters. Based on this analysis, the CM model was proposed, selecting the most representative and monitorable parameters that reflect changes in system energy consumption. The CMP (Chip Multiprocessors) model adapts to different task states of the computer system by identifying primary components driving energy consumption under varying conditions. Energy consumption estimation was then conducted by monitoring these dominant parameters. Experiments across various task states demonstrate that the CMP model outperforms traditional FAN (Fuzzy Attack Net) and Cubic models, particularly when the computer system engages in data-intensive tasks.
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.
Open Access
