This research aims to explore the impact of digital virtual anchors, such as virtual presenters and singers, on the entertainment industry, with a focus on user perceptions and market changes. The study analysed data on the behaviour of Chinese youth, including their perceptions of virtual presenters on platforms such as Bilibili, and their influence on preferences and consumer decisions. The methodology included surveys and statistical analysis to assess the degree of engagement, users’ willingness to interact with virtual anchors and their influence on the overall growth of interest in virtual platforms. The results showed that 78% of respondents had a positive perception of virtual anchors, and 62% said that such technologies increased their interest in platforms. The analysis also revealed a significant impact of virtual anchors on market structure, including revenue growth in streaming, virtual concerts and e-commerce. Study participants also noted increased interest in augmented reality (AR) technologies and their integration with virtual anchors. The study’s findings emphasize the importance of the industry adapting to new technologies to attract audiences and remain competitive. The long-term potential of virtual anchors includes opportunities to expand business models, introduce personalized solutions and develop new products, creating significant prospects for their continued use in the entertainment industry.
For reducing uncertainty in data gathered from real-world scenarios, the picture fuzzy rough set (PFRS) framework is a reliable resource. This article presents new aggregation operators (AOs) based on the Schweizer-Sklar t-conorm (SS-TC) and Schweizer-Sklar t-norm (SS-TN). They present the PFRS framework with SS, which aims to handle the intricacies in contexts where decision-making is marked by ambiguity and uncertainty. In the context of Green Supply Chain Management (GSCM), where supply chain procedures incorporate sustainability considerations, this framework is especially pertinent. GSCM places a strong emphasis on minimizing environmental impacts by employing techniques such as effective resource management and sustainable sourcing. The adaptability and versatility required to assess and optimize these inexperienced practices are significantly improved with the aid of our expert PFRS framework. Businesses can keep operational efficiency and align their supply chain operations with environmental desires with the aid of using this framework. By considering both the blessings and disadvantages of environmental sustainability, using PFRS in GSCM enhances decision-making and promotes environmental sustainability. To handle picture fuzzy rough values (PFRVs), these operators include picture fuzzy rough weighted averaging (PFRSSWA) and picture fuzzy rough weighted geometric (PFRSSWG) operators. We investigate these recently created AOs' basic characteristics and use them to solve multi-attribute group decision-making (MAGDM) issues under the framework of picture fuzzy (PF) data. Our results demonstrate how the outcomes in SS-TN and SS-TC vary with varying parameter values. We also contrast these outcomes with the ones obtained from pre-existing AOs. In addition, we provide a graphic representation of all observations and findings to show how flexible and successful the suggested operators are at handling MAGDM problems.
Image segmentation remains a foundational task in computer vision, remote sensing, medical imaging, and object detection, serving as a critical step in delineating object boundaries and extracting meaningful regions from complex visual data. However, conventional segmentation methods often exhibit limited robustness in the presence of noise, intensity inhomogeneity, and intricate region geometries. To address these challenges, a novel segmentation framework was developed, integrating fuzzy logic with geometric principles. Uncertainty and overlapping intensity distributions within regions were modeled through fuzzy membership functions, allowing for more flexible and resilient region characterization. Simultaneously, geometric principles—specifically image gradients and curvature—were incorporated to guide boundary evolution, thereby improving delineation precision. A fuzzy energy functional was constructed to jointly optimize region homogeneity, edge preservation, and boundary smoothness. This functional was minimized through an iterative level-set evolution process, allowing dynamic adaptation to varying image characteristics while maintaining computational efficiency. The proposed model demonstrated robust performance across diverse image modalities, including those with high noise levels and complex regional structures, outperforming traditional methods in terms of segmentation accuracy and stability. Its applicability to tasks demanding high-precision region-based analysis highlights its potential for widespread deployment in advanced imaging applications.
Selection of the optimal warehouse location represents a key strategic decision in modern logistics, particularly in the context of the rapid development of e-commerce and the increasing complexity of supply chains. The aim of this research is to identify the most favorable warehouse location within the urban area of Belgrade by applying multi-criteria decision-making (MCDM) methods. Specifically, a hybrid methodology that integrates the Step-wise Weight Assessment Ratio Analysis (SWARA) and Additive Ratio Assessment (ARAS) methods was employed to evaluate five real-world alternative locations based on eight relevant criteria. The considered criteria include: land cost, delivery time, infrastructure accessibility, labor availability, access to multiple modes of transport, site capacity, environmental conditions and regulatory compliance, as well as the competitiveness of the location itself. Criterion weights were determined through expert evaluation using the SWARA method, while the ARAS method was applied to rank the alternatives based on their normalized performance scores. The analysis indicated that the location in Batajnica (A1) is the most favorable, closely followed by the location on Pančevački Road (A3), owing to their balanced performance across economic, infrastructural, and operational dimensions. In contrast, the location in Kaluđerica/Leštane (A4) proved to be the least suitable, primarily due to poor infrastructure access and limited labor availability. The results confirm the applicability and effectiveness of combining SWARA and ARAS methods for solving complex decision-making problems involving multiple, often conflicting, criteria.
An advanced machine learning (ML)-driven web platform was developed and deployed to automate the identification of rice insect pests, addressing limitations associated with traditional pest detection methods and conventional ML algorithms. Historically, pest identification in rice cultivation has relied on expert evaluation of pest species and their associated crop damage, a process that is labor-intensive, time-consuming, and prone to inaccuracies, particularly in the misclassification of pest species. In this study, a subset of the publicly available IP102 benchmark dataset, consisting of 7,736 images across 12 rice pest categories, was curated for model training and evaluation. Two classification models—a Support Vector Machine (SVM) and a deep Convolutional Neural Network (CNN) based on the Inception_ResNetV2 architecture—were implemented and assessed using standard performance metrics. Experimental results demonstrated that the Inception_ResNetV2 model significantly outperformed SVM, achieving an accuracy of 99.97%, a precision of 99.46%, a recall of 99.81%, and an F1-score of 99.53%. Owing to its superior performance, the Inception_ResNetV2 model was integrated into a web-based application designed for real-time pest identification. The deployed system exhibited an average response time of 5.70 seconds, representing a notable improvement in operational efficiency and usability over previous implementations. The results underscore the potential of artificial intelligence in transforming agricultural practices by enabling accurate, scalable, and timely pest diagnostics, thereby enhancing pest management strategies, mitigating crop losses, and supporting global food security initiatives.
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.
Open Access
