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As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

The digital revolution is transforming the financial services sector. Risk is not static; emerging risks continue to pose threats to the financial services sector which influences financial stability and consumer protection regulation mandates. This novel study presents a comparative bibliometric analysis of China and India in examining the effect of trends on the scholarly research outputs discussing the emerging risks in the fintech-driven digital banking environment. Furthermore, the mapping presents the geographical dynamics of Asia, followed by country-level perspectives. The period of study was from 2015 to 2024. Leveraging the Scopus database, data was extracted based on a specified query using the SPAR 4 SLR protocol. Analysis was performed on 162 articles from an initial list of 1257 articles using Scival and Vos viewer tools. Performance indicator metrics and science mapping enabled the answering of research questions. The findings revealed that research output is inclined towards India rather than China; this is despite China domiciling some big tech firms. Comparatively, India dominates when it comes to performance analysis metrics compared to China. The scientific mapping depicted in both countries shows the multifaceted effects of fintech on banking, including trends in user acceptance, competition, emerging risks, technological innovation, and financial stability. The strong connections in both countries across clusters highlight how fintech research is multi-disciplinary, spanning consumer behavior, finance, economics, and financial technology. This study provides a foundation on which a robust risk management framework, which is customized to digital banking existence, can be developed in the face of emerging risks. Full article

In the process of digital transformation, the performance diagnosis of server systems is crucial for ensuring service continuity and enhancing user experience. Addressing the issues of invasiveness, poor universality, and difficulty in precisely locating abnormal bottlenecks in service requests with traditional performance analysis methods, this paper proposes a nonintrusive diagnosis method named Cross-Environment Server Diagnosis with Fusion (CSDF), which is based on the fusion of network traffic and Application Performance Management (APM) metrics. This CSDF method uses a traffic replay tool to reproduce real service requests captured via network cards in a production environment at a 1:1 ratio in a replay environment, comparing performance differences between the two environments to identify abnormal bottlenecks. By integrating Key Performance Indicator (KPI) metrics collected from APM systems, a correlation model between metrics and bottlenecks is established using the Random Forest algorithm within CSDF to pinpoint the root cause at the host resource layer. Simultaneously, it supplements network layer bottleneck analysis by parsing network transmission characteristics of data packets as an important part of CSDF. Experimental results demonstrate that this CSDF method can effectively identify abnormal bottlenecks in specific service requests, verifying its effectiveness in China Tower’s production system—the correlation coefficient between 1 min average load and latency reached 0.87, and the optimization effect was significant. This study provides a general framework for the precise diagnosis and optimization of server systems via CSDF, possessing strong practical value and promising application prospects. Full article

With the rapid development of information and communication technology, the intelligent transformation process of traditional power grid continues to accelerate. As an important innovation in the field of power service, smart grid completely revolutionizes the traditional power supply process, and relies on an agile and efficient communication network to realize the two-way interaction between users and the power grid, which significantly improves the power supply flexibility and service quality. However, the two-way communication process is vulnerable to all kinds of network attacks, but most of the current intrusion detection schemes are difficult to effectively identify the emerging attack types, even if incremental learning methods are adopted, they are often trapped in catastrophic forgetting problems. In order to meet the above challenges, this paper proposes smart grid intrusion detection system (Grid-IDS). By establishing an incremental learning method based on tree structure, it can not only accurately detect existing attacks, but also incrementally learn new attack types, and at the same time relief the catastrophic forgetting problem caused by incremental learning. Experiments show 99.65% accuracy on CICIDS2017 with performance superior to baselines, and competitive accuracy and precision on WUSTL-IIoT-2018, indicating good generalization under heterogeneous traffic. Full article

The National Recovery and Resilience Plan (NRRP) represents a transformative opportunity to reduce territorial, gender and generational disparities in Italy. It plays an even more important role for inner areas, which make up about three-fifths of the entire national territory and require structural investment to improve infrastructure, social services and access to healthcare services. This study aims to analyse the distribution of funds by project type, and to develop a geostatistical analysis-based methodology to critically evaluate two key aspects: the ability of small municipalities to access resources, and the effectiveness of the funding programme in meeting the specific needs of inner areas. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. This methodology is then applied to a case study of the Abruzzo region (Italy), which is considered particularly interesting due to its physical, historical and socio-economic characteristics that make it particularly vulnerable to natural disasters. The developed methodology consists of several steps aimed at collecting, standardising, geo-spatialising and analysing data relating to NRRP funds. The results of the spatial autocorrelation and cluster analyses were then overlapped and compared with the internal areas defined by the National Strategy for Inner Areas (NSIA). The outcomes reveal how investments interact with existing spatial planning instruments and development strategies, underscoring the critical role of accessibility, infrastructure, and public services in fostering equitable and sustainable regional development. The analysis offers insights into addressing structural disparities and enhancing territorial cohesion, with implications for policy alignment across multiple levels of governance. Full article

In this paper, an energy efficiency (EE)–spectral efficiency (SE) trade-off scheme is investigated for the distributed reconfigurable intelligent surface (RIS)-assisted backscatter vehicle-to-infrastructure (V2I) communication system. Firstly, a multi-objective optimization framework balancing EE and SE is established using the linear weighting method, and the quadratic transformation is utilized to recast the optimization problem as a strictly convex problem. Secondly, an alternating optimization (AO) approach is applied to partition the original problem into two independent subproblems of the BS and RIS beamforming, which are, respectively, designed by the weighted minimization mean-square error (WMMSE) and the Riemannian conjugate gradient (RCG) algorithms. Finally, according to the trade-off factor, the power reflection coefficients of backscatter devices (BDs) are dynamically optimized with the BS beamforming vectors and RIS phase shift matrices, considering their activation requirements and the vehicle minimum quality of service (QoS). The simulation results verify the effectiveness of the proposed algorithm in simultaneously improving SE and the EE in practical V2I applications through rational optimization of the BD power reflection coefficient. Full article

In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to enhance efficiency and resilience. Experimental validation on three E-government datasets (95,000–230,000 records) demonstrates that the proposed model improves processing speed by up to 40% and enhances adversarial robustness by maintaining accuracy reductions below 2.5% under attack scenarios. Compared with baseline neural networks, the architecture achieves higher accuracy (up to 95.1%), security (up to 98% attack prevention), and efficiency (processing up to 1600 records/sec). These results confirm the model’s applicability for large-scale, real-time E-government systems, providing a practical path for sustainable and secure digital public administration. Full article

The fast and accurate calculation of the internal temperature rise in the oil-immersed transformer is the premise to realize the thermal health management and load energy evaluation of the in-service transformer. In view of the influence of nanofluids on the heat transfer process of transformer, a numerical simulation algorithm based on lattice Boltzmann method (LBM) and finite difference method (FDM) is proposed to study the heat and mass transfer process inside nano-modified oil-immersed transformer. Firstly, the D2Q9 lattice model is used to solve the fluid and thermal lattice Boltzmann equations inside the oil-immersed transformer at the mesoscopic scale, and the temperature field and velocity field are obtained by macroscopic transformation. Secondly, the electric field distribution inside the oil-immersed transformer is calculated by FDM. The viscous resistance in LBM analysis and the electric field force in FDM analysis, as well as the gravity and buoyancy of particles, are used to explore the motion characteristics of nanoparticles and metal particles. Finally, compared with the thermal ring method and the finite volume method (FVM), the relative error is less than 5%, which verifies the effectiveness of the numerical model and provides a method for studying the internal electrothermal convection of nano-modified oil-immersed transformers. Full article

This study investigates how Corporate Social Responsibility (CSR) influences financial performance in the Middle Eastern banking sector through the mediating roles of corporate reputation, employee engagement, and innovation orientation. Drawing on stakeholder theory and the resource-based view, a survey of 297 senior banking executives was analyzed using structural equation modeling. The results show that CSR has both a direct positive impact on financial performance and an indirect effect by strengthening intangible resources. Among the mediators, innovation orientation emerged as the strongest pathway, followed by employee engagement and reputation. Collectively, the model accounted for more than 60% of the variance in financial performance, confirming that socially responsible strategies are not symbolic but yield tangible economic value. In the Middle Eastern banking sector—characterized by regulatory turbulence, cultural expectations, and digital transformation—CSR initiatives such as financial inclusion programs, green financing, and Sharia-compliant services provide both legitimacy and resilience. These findings highlight the strategic importance of embedding CSR into banking practices, showing that socially responsible institutions not only secure reputational gains but also cultivate motivated employees, foster innovation, and achieve sustainable profitability. By situating CSR within the unique context of Middle Eastern banking, this study extends the literature on CSR—performance linkages in emerging markets and demonstrates how intangible capabilities can be mobilized to secure long-term financial sustainability. Full article

Socialized services for rural characteristic industries are becoming a key support for promoting rural industries’ transformation and upgrading. They are permeating the development process of modern agricultural service technologies, achieving significant progress in specialized fields such as mechanized operations and plant-protection services. However, challenges remain, including low efficiency in matching service resources and limited spatiotemporal coordination capabilities. With the deep integration of spatiotemporal information technology and knowledge graph technology, the enormous potential of semantic-level feature spatial representation in intelligent scheduling of service resources has been fully demonstrated, providing a new technical pathway to solve the above problem. This paper systematically analyzes the technological evolution trends of socialized services for rural characteristic industries and proposes a collaborative scheduling framework based on semantic feature space and spatiotemporal maps for characteristic industry service resources. At the technical architecture level, the paper aims to construct a spatiotemporal graph model integrating geographic knowledge graphs and temporal tree technology to achieve semantic-level feature matching between service demand and supply. Regarding implementation pathways, the model significantly improves the spatiotemporal allocation efficiency of service resources through cloud service platforms that integrate spatial semantic matching algorithms and dynamic optimization technologies. This paper conducts in-depth discussions and analyses on technical details such as agricultural semantic feature extraction, dynamic updates of rural service resources, and the collaboration of semantic matching and spatio-temporal matching of supply and demand relationships. It also presents relevant implementation methods to enhance technical integrity and logic, which is conducive to the engineering implementation of the proposed methods. The effectiveness of the proposed collaborative scheduling framework for service resources is proved by the synthesis of principal analysis, logical deduction and case comparison. We have proposed a practical “three-step” implementation path conducive to realizing the proposed method. Regarding application paradigms, this technical system will promote the transformation of rural industry services from traditional mechanical operations to an intelligent service model of “demand perception–intelligent matching–precise scheduling”. In the field of socialized services for rural characteristic industries, it is suggested that relevant institutions promote this technical framework and pay attention to the development trends of new technologies such as knowledge services, spatio-temporal services, the Internet of Things, and unmanned farms so as to promote the sustainable development of rural characteristic industries. Full article

Providing age-friendly metro service substantially enhances the elderly’s mobility and well-being. Despite recent progress in user profiling and mobility prediction, the prediction of the elderly’s metro travel patterns remains limited. To fill this gap, this study proposes a framework integrating user profiling and knowledge graph embedding to predict the elderly’s activity types at metro trip destinations, utilizing 180,143 smart card records and 885,072 points of interest (POI) records from Chongqing, China in 2019. First, an elderly metro travel profile (EMTP) tag system is developed to capture the elderly’s spatiotemporal metro travel behaviors and preferences. Subsequently, an elderly metro travel knowledge graph (EMTKG) is constructed to support semantic reasoning, transforming the activity types prediction problem into a knowledge graph completion problem. To solve the completion problem, the Temporal and Non-Temporal ComplEx (TNTComplEx) model is introduced to embed entities and relations into a complex vector space and distinguish between time-sensitive and time-insensitive behavioral patterns. Fact plausibility within the graph is evaluated by a scoring function. Numerical experiments validate that the proposed model outperforms the best-performing baselines by 13.37% higher Accuracy@1 and 52.40% faster training time per epoch, and ablation studies further confirm component effectiveness. This study provides an enlightening and scalable approach for enhancing age-friendly metro system service. Full article

The rapid evolution of Information Technologies is deeply transforming manufacturing, demanding innovative and enhanced production paradigms. Industry 5.0 further advances that transformation by fostering a more resilient, sustainable, and human-centric industrial ecosystem, built on the seamless integration of all value chains. This shift requires the timely collection and intelligent analysis of vast, heterogeneous data sources, including IoT devices, digital services, crowdsourcing platforms, and last but not least important human input, which is essential to drive innovation. With sustainability as a key priority, pervasive monitoring not only enables optimization to reduce greenhouse gas emissions but also plays a strategic role across the manufacturing economy. This work introduces Observability platform for Industry 5.0 (ObsI5), a novel observability framework specifically designed to support Industry 5.0 environments. ObsI5 extends cloud-native observability tools, originally developed for IT service monitoring, into manufacturing infrastructures, enabling the collection, analysis, and control of data across both IT and OT domains. Our solution integrates human contributions as active data sources and leverages standard observability practices to extract actionable insights from all available resources. We validate ObsI5 through a dedicated test bed, demonstrating its ability to meet the strict requirements of Industry 5.0 in terms of timeliness, security, and modularity. Full article

The aim of this article is to assess the digital maturity of Polish enterprises and to identify the most and least developed dimensions of maturity within these organizations in the context of their potential to achieve sustainable development goals. The authors pose research questions regarding the overall level of digital maturity in Polish enterprises, its variation depending on the type of business activity, and the specific dimensions of digital maturity that were rated the highest and lowest. The main thesis of the article assumes that the level of digital maturity determines a company’s sustainable orientation. The article presents the results of empirical research conducted among 697 Polish enterprises operating in the manufacturing, trade, and service sectors. The study employed the seven-dimensional Digitalcheck Mittelstand model for assessing digital maturity. The average scores of digital maturity, both by industry and by specific dimensions, were mapped to six levels of digital maturity adapted for Polish enterprises. The findings confirm that Polish enterprises demonstrate a moderate level of digital maturity. Among the analyzed sectors, manufacturing enterprises exhibit the highest level of maturity. The study also confirmed that the highest maturity levels are observed in the areas of organization and processes. Conversely, the lowest level of digital advancement is found in the environmental dimension, indicating a gap in aligning corporate strategies with green funding programs and eco-initiatives. Future research should take into account causal mechanisms and disruptive factors affecting digital transformation in organizations. Full article

The integrity of urban water supply pipelines, an essential element of municipal infrastructure, is frequently undermined by internal defects such as corrosion, tuberculation, and foreign matter. Traditional inspection methods relying on CCTV are time-consuming, labor-intensive, and prone to subjective interpretation, which hinders the timely and accurate assessment of pipeline conditions. This study proposes YOLOv8-VSW, a systematically optimized and lightweight model based on YOLOv8 for automated defect detection in in-service pipelines. The framework is twofold: First, to overcome data limitations, a specialized defect dataset was constructed and augmented using photometric transformation, affine transformation, and noise injection. Second, the model architecture was improved on three levels: a VanillaNet backbone was adopted for lightweighting, a C2f-Star module was introduced to enhance multi-scale feature fusion, and the WIoUv3 dynamic loss function was employed to improve robustness under complex imaging conditions. Experimental results demonstrate the superior performance of the proposed YOLOv8-VSW model. This study validates the framework on a curated, real-world image dataset, where YOLOv8-VSW achieved mAP@50 of 83.5%, a 4.0% improvement over the baseline. Concurrently, GFLOPs were reduced by approximately 38.9%, while the inference speed was increased to 603.8 FPS. The findings validate the effectiveness of the proposed method, delivering a solution that effectively balances detection accuracy, computational efficiency, and model size. The results establish a strong technical basis for the intelligent and automated control of safety in urban water supply systems. Full article

The development of the computing power network has brought about a revolutionary effect on network routing architecture. As a result, the computing-aware network routing problem has been raised to explore routing various computational tasks to appropriate computing resources in the dynamic network. In this study, we propose a heuristic-based computing-aware routing algorithm to achieve the optimal routing path by considering the dynamic network performance and computing resource status simultaneously. Our proposed approach models the dynamic network using time-varying node and edge weights, which are obtained by mapping basic performance indicators to weights according to quality-of-service requirements. This allows us to improve the user’s experience more effectively during the routing process. Moreover, a novel heuristic-based algorithm, which creatively transforms the computing-aware routing problem into a single-source shortest path problem, has been designed to achieve the comprehensive optimal routing path. The experimental results, based on both simulated networks and a real dedicated network in Zhejiang, demonstrate that our proposed method can obtain the comprehensive optimal routing path with a lower computing time cost than enumerating search. Furthermore, our proposed computing-aware routing method has been proven to be robust to the dynamics of the network, computing resources, and service load changes. Full article