Search Results (76)

5G systems have delivered on their promise of seamless connectivity and efficiency improvements since their global rollout began in 2020. However, maintaining subscriber identity privacy on the network remains a critical challenge. The 3GPP specifications define numerous identifiers associated with the subscriber and their activity, all of which are critical to the operations of cellular networks. While the introduction of the Subscription Concealed Identifier (SUCI) protects users across the air interface, the 5G Core Network (CN) continues to operate largely on the basis of the Subscription Permanent Identifier (SUPI)—the 5G-equivalent to the IMSI from prior generations—for functions such as authentication, billing, session management, emergency services, and lawful interception. Furthermore, the SUPI relies solely on the transport layer’s encryption for protection from malicious observation and tracking of the SUPI across activities. The crucial role of the largely unprotected SUPI and other closely related identifiers creates a high-value target for insider threats, malware campaigns, and data exfiltration, effectively rendering the Mobile Network Operator (MNO) a single point of failure for identity privacy. In this paper, we analyze the architectural vulnerabilities of identity persistence within the CN, challenging the legacy “honest-but-curious” trust model. To quantify the extent of subscriber identities being utilized and exchange within various API calls in the CN, we conducted a study of the occurrence of SUPI as a parameter throughout the collection of 5G SBI (Service-Based Interface) Core VNF (Virtual Network Function) API (Application Programming Interface) schemas. Our extensive analysis of the 3GPP specifications for 3GPP Release 18 revealed a total of 4284 distinct parameter names being used across all API calls, with a total of 171,466 occurrences across the API schema. More importantly, it revealed a highly skewed distribution in which subscriber identity plays a pivotal role. Specifically, the “supi” parameter ranks 57th with 397 occurrences. We found that SUPI occurs both as a direct parameter (“supi”) and within 72 other parameter names that contain subscriber identifiers as defined in 3GPP TS 23.003. For these 73 parameter names, we identified a total of 8757 occurrences. At over 5.11% of all parameter occurrences, this constitutes a disproportionately large share of total references. We also detail scenarios where subscriber privacy can be compromised by internal actors and review future privacy-preserving frameworks that aim to decouple subscriber identity from network operations. By suggesting a shift towards a zero-trust model for CN architecture and providing subscribers with greater control over their identity management, this work also offers a potential roadmap for mitigating insider threats in current deployments and influencing specific standardization and regulatory requirements for future 6G and Beyond-6G networks. Full article

Electric vehicles have strengthened the coupling between transportation systems and power distribution networks, giving rise to new challenges in the coordinated management of traffic flow and charging demand. Monetary incentives, such as tariffs and subsidies, have been widely adopted to influence drivers’ route and charging decisions and to improve system-level performance. This paper proposes a user-centric incentive framework in which a system operator allocates rewards to guide drivers’ behavior, thereby enabling coordinated operation of power–traffic networks. A reward scheme is developed to provide joint subscription-based and path-based incentives that account for drivers’ behavioral responses through a logit choice model for scheme adoption embedded within a traffic assignment model. The resulting interaction is formulated as a bilevel optimization problem, in which a coupled power–traffic system operator determines incentive schemes to achieve system optimality within a given budget constraint, while individual drivers respond by selecting routes and charging strategies to minimize their perceived travel costs. A single-level Karush–Kuhn–Tucker (KKT) reformulation is developed, and linearization techniques are employed to compute the resulting equilibrium, yielding a tractable mixed-integer second-order cone program (MISOCP). Numerical experiments demonstrate the effectiveness of the subscription-based and path-based reward schemes in improving network performance and budget saving. Full article

We compare the publication performance of open-access (OA) and subscription-based (SB) journals in Engineering using journal-level indicators from Scopus (CiteScore 2023 view; data collected on 2 December 2024). We analysed 3013 active Engineering journals with an assigned CiteScore quartile (Q1–Q4, where Q1 denotes the highest CiteScore quartile), of which 770 are labelled OA in Scopus; the remaining journals in each stratum were classified as SB. We stratified journals by CiteScore quartile and by the top 10% CiteScore percentile. We examined four indicators for 2020–2023: CiteScore 2023, total citations, number of published documents, and the percentage of cited articles. Because citation and publication counts are strongly right-skewed, we report medians and use Mann–Whitney tests with effect sizes (Cliff’s delta) and false discovery rate correction; Welch tests on log-transformed counts are used as sensitivity analyses. SB journals exhibit substantially higher citation and document medians across all quartiles and in the top 10% stratum, whereas CiteScore medians are very similar between access models. OA journals represent about one quarter of Engineering journals in Scopus, but remain underrepresented in the top 10% segment (125 of 484). Overall, OA provides a competitive level of impact, while SB titles still dominate accumulated visibility and editorial scale in Engineering. Full article

Entities using business models that integrate sustainability principles into business practice are gaining popularity through innovative measures. One such model is the subscription economy, in which customers pay regularly for access to products or services rather than purchasing them once. The study aims to present the subscription economy as a model that can help promote sustainable consumption. The paper uses a diagnostic survey method and a literature analysis and critique. Based on the literature on sustainable business models, the authors have shown that subscription economics, meeting the conditions of a sustainable model, can play an important role in promoting sustainable consumption (in terms of economic, social, and environmental rationality). The authors’ own research showed that Poles are very interested in the subscription model and its greatest importance in terms of economic rationality, which is the most important element of sustainable consumption. This also applies to the younger generation, which, despite being characterized by a high awareness of growing social and environmental problems, identifies sustainability as a secondary motivation to personal benefits such as financial security. Full article

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars. Full article

In educational smart learning contexts, 360° virtual tours deliver authentic, cross-device experiences, but uptake is limited by subscription-based authoring tools and free options that restrict in-tour rich media embedding. To address this, we present a library of eight open-source HTML5 widgets (image gallery, PDF viewer, quiz, 3D model viewer, webpage viewer, audio player, YouTube viewer, and image comparison) that can be embedded directly in the viewer as HTML pop-ups (e.g., CloudPano) or run standalone, with dual packaging (single self-contained HTML or server-hosted assets referenced by URL). Evaluation is limited to technical efficiency (resource size, load performance, and cross-device/browser compatibility), with pedagogical outcomes and learner performance beyond the scope. The architecture minimizes dependencies and enables reuse in virtual classrooms via iframes. We provide a unified web interface and a repository to promote adoption, auditability, and community contributions. The results show that standalone widgets are between 20 and 100 times smaller than H5P equivalents produced with Lumi Education and exhibit shorter measured load times (0.1–0.5 ms). Seamless integration is demonstrated for CloudPano and Moodle. By lowering costs, simplifying deployment, and broadening in-tour media capabilities, the proposed widgets offer a pragmatic pathway to enrich educational 360° tours. Full article

Electrolytic aluminum is one of the most energy-intensive industrial processes and offers strong potential for demand-side flexibility and renewable energy integration. However, existing studies mainly focus on operational scheduling, while comprehensive planning frameworks at the industrial-park scale remain limited. This study proposes an optimal planning framework for electrolytic aluminum that co-optimizes renewable energy investments, waste heat recovery, and green power trading while capturing the temperature safety constraints of electrolytic cells. The electrolytic aluminum process is explicitly modeled with heat exchangers to enable combined cooling–heating–power supply for nearby users. A wind power priority subscription mechanism and green certificate compliance are incorporated to enhance practical applicability and support future decarbonization requirements. Moreover, a two-stage particle swarm-deterministic optimization scheme is developed to provide a tractable solution to the inherently nonconvex mixed-integer nonlinear model. Case studies based on a real plant in Xinjiang, China, demonstrate that the proposed framework can raise the green electricity aluminum share to 60.4%, reduce annual carbon emissions by 52.0%, and significantly increase total system profit compared with the benchmark configuration, highlighting its economic and sustainability benefits for industrial park development. Full article

This article examines the growing relevance of photovoltaic (PV) energy amid rising electricity demand, sustainability goals, and the need for flexible energy management in households and enterprises. It analyzes six PV business models, ownership, leasing, Power Purchase Agreement (PPA), energy communities/peer-to-peer (P2P), crowdfunding, and subscription-based Solar-as-a-Service, using the Business Model Canvas (BMC) framework. A systematic literature review was combined with a unified BMC for each model, enabling structured comparison of value propositions, customer segments, cost structures, revenue streams, and risk allocation. The results show that no single universal model exists; each addresses different financial capacities, risk preferences, and strategic needs of households, SMEs, large enterprises, and energy communities. Significant differences were found in investment requirements, operational involvement, scalability, and potential for energy independence. The study’s novelty lies in providing a coherent, cross-model comparison using a standardized BMC approach, offering insights not systematically explored in previous research. These findings support informed decision-making for organizations considering PV adoption and provide a basis for further research on innovative energy management strategies. The topic is highly relevant in the context of the accelerating global energy transition, technological advances, regulatory changes, and increasingly diverse customer profiles, highlighting the need for comprehensive comparative analyses to guide flexible photovoltaic deployment. Full article

Digital platforms face a fundamental strategic decision between subscription-only, advertising-only, and freemium (hybrid) monetization models. We develop a game-theoretic framework that unifies these strategies, explicitly modeling consumer heterogeneity in both willingness-to-pay and advertising disutility, while incorporating network effects through the platform’s valuation of user-base size. Our analysis yields closed-form solutions identifying optimal strategy thresholds based on advertising market conditions. We show that subscription-only dominates when advertising prices are low, advertising-only prevails when prices are high, and freemium emerges as strictly optimal in the intermediate region. Under freemium, we demonstrate strategic complementarity: both subscription fees and advertising intensity exceed their levels in pure strategies because each instrument’s effectiveness is amplified by the other through user reallocation across tiers. Network effects universally reduce monetization intensity but alter instruments’ relative sensitivities differently across regimes—when advertising prices are moderate, freemium adjusts ad length more aggressively, while the opposite holds at high prices. Critically, freemium’s profitability requires sufficient consumer heterogeneity in ad tolerance. As consumer preferences converge, the screening mechanism fails and freemium collapses to the superior pure strategy. These results provide operational guidance for platform monetization decisions and clarify when hybrid models create value beyond traditional approaches. Full article

Sentiment analysis is essential for understanding consumer opinions, yet selecting the optimal models and embedding methods remains challenging, especially when handling ambiguous expressions, slang, or mismatched sentiment–rating pairs. This study provides a comprehensive comparative evaluation of sentiment classification models across three paradigms: traditional machine learning, pre-transformer deep learning, and transformer-based models. Using the Amazon Magazine Subscriptions 2023 dataset, we evaluate a range of embedding techniques, including static embeddings (GloVe, FastText) and contextual transformer embeddings (BERT, DistilBERT, etc.). To capture predictive confidence and model uncertainty, we include categorical cross-entropy as a key evaluation metric alongside accuracy, precision, recall, and F1-score. In addition to detailed quantitative comparisons, we conduct a systematic qualitative analysis of misclassified samples to reveal model-specific patterns of uncertainty. Our findings show that FastText consistently outperforms GloVe in both traditional and LSTM-based models, particularly in recall, due to its subword-level semantic richness. Transformer-based models demonstrate superior contextual understanding and achieve the highest accuracy (92%) and lowest cross-entropy loss (0.25) with DistilBERT, indicating well-calibrated predictions. To validate the generalisability of our results, we replicated our experiments on the Amazon Gift Card Reviews dataset, where similar trends were observed. We also adopt a resource-aware approach by reducing the dataset size from 25 K to 20 K to reflect real-world hardware constraints. This study contributes to both sentiment analysis and sustainable AI by offering a scalable, entropy-aware evaluation framework that supports informed, context-sensitive model selection for practical applications. Full article

This article presents the development of a modular software suite for automated analysis of scientific publications in PDF format. The system integrates vectorization, clustering, topic modelling, dimensionality reduction, and fuzzy logic to combine both formal (vector-based) and semantic (topic-based) approaches. Interactive 3D visualization supports intuitive exploration of thematic clusters, allowing users to highlight relevant documents and adjust analytical parameters. Validation on a maritime safety case study confirmed the system’s ability to process large publication collections, identify relevant sources, and reveal underlying knowledge structures. Compared to established frameworks such as PRISMA or Scopus/WoS Analytics, the proposed tool operates directly on full-text content, provides deeper thematic classification, and does not require subscription-based databases. The study also addresses the limitations arising from data bias and reproducibility issues in the semantic interpretability of safety-critical decision-making systems. The approach offers practical value for organizations in safety-critical domains—including transportation, energy, cybersecurity, and human–machine interaction—where rapid access to thematically related research is essential. Full article

Background: This study deploys the Lean Six Sigma DMAIC framework to achieve systemic optimization of the school subscription process in Tunisia’s public transport service, a critical administrative operation affecting efficiency and customer satisfaction across the urban mobility network. Methods: Beyond conventional applications, the research integrates advanced analytical and process engineering tools, including capability indices, measurement system analysis (MSA), variance decomposition, and root-cause prioritization through Pareto–ANOVA integration, supported by a structured control plan aligned with ISO 9001:2015 and ISO 31000:2018 risk-management standards. Results: Quantitative diagnosis revealed severe process instability and nonconformities in information flow, workload balancing, and suboptimal resource allocation that constrained effective capacity utilization. Corrective interventions were modeled and validated through statistical control and real-time performance dashboards to institutionalize improvements and sustain process stability. The implemented actions led to a 37.5% reduction in cycle time, an 80% decrease in process errors, a 38.5% increase in customer satisfaction, and a 38.9% improvement in throughput. Conclusions: This study contributes theoretically by positioning Lean Six Sigma as a data-centric governance framework for stochastic capacity optimization and process redesign in public service systems, and practically by providing a replicable, evidence-based roadmap for operational excellence in governmental organizations within developing economies. Full article

Usage-Based Insurance (UBI), also referred to as telematics-based insurance, has been experiencing a growing global diffusion. In addition to being well established in countries such as Italy, the United States, and the United Kingdom, UBI adoption is also accelerating in emerging markets such as Japan, South Africa, and Brazil. In Japan, telematics insurance has shown significant growth in recent years, with a steadily increasing subscription rate. In South Africa, UBI adoption ranks among the highest worldwide, with market penetration placing the country among the top three globally, just after the United States and Italy. In Brazil, UBI adoption is expanding, supported by government initiatives promoting road safety and innovation in the insurance sector. According to a MarketsandMarkets report of February 2025, the global UBI market is expected to grow from USD 43.38 billion in 2023 to USD 70.46 billion by 2030, with a compound annual growth rate (CAGR) of 7.2% over the forecast period. This growth is driven by the increasing adoption of both electric and internal combustion vehicles equipped with integrated telematics systems, which enable insurers to collect data on driving behavior and to tailor insurance premiums accordingly. In this paper, we analyze a large dataset consisting of trips recorded over five years from 100,000 policyholders across the Italian territory through the installation of black-box devices. Using univariate and multivariate statistical analyses, as well as Generalized Linear Models (GLMs) with Zero-Inflated Poisson distribution, we examine claims frequency and assess the relevance of various synthetic indicators of driving behavior, with the aim of identifying those that are most significant for insurance pricing. Full article

Smart logistics systems generate massive amounts of data, such as images and videos, requiring real-time processing in edge clusters. However, the edge cluster systems face performance bottlenecks in reception and forwarding high-concurrency data streams from numerous smart terminals, resulting in degraded processing efficiency. To address this issue, a novel high-performance data stream model called CBPS-DPDK is proposed. CBPS-DPDK integrates the DPDK framework from Intel corporations with a content-based publish/subscribe model enhanced by semantic filtering. This model adopts a three-tier optimization architecture. First, the user-space data plane is restructured using DPDK to avoid kernel context switch overhead via zero-copy and polling. Second, semantic enhancement is introduced into the publish/subscribe model to reduce the coupling between data producers and consumers through subscription matching and priority queuing. Finally, a hierarchical load balancing strategy ensures reliable data transmission under high concurrency. Experimental results show that CBPS-DPDK significantly outperforms two baselines—OSKT (kernel-based data forwarding) and DPDK-only (DPDK). Relative to the OSKT baseline, DPDK-only achieves improvements of 37.5% in latency, 11.1% in throughput, and 9.1% in VMAF; CBPS-DPDK further increases these to 51.8%, 18.3%, and 11.2%, respectively. In addition, compared with the traditional publish–subscribe system NATS, CBPS-DPDK maintains lower delay, higher throughput, and more balanced CPU and memory utilization under saturated workloads, demonstrating its effectiveness for real-time, high-concurrency edge scenarios. Full article

With the rapid growth of user demand for large language models (LLMs) in their work, the application market is driving intense competition among large language model providers (LLMPs). Users have different preferences and psychological perceptions towards the charging models of different LLMPs. LLMPs with different intelligence levels must design pricing strategies based on diverse user characteristics. To investigate the impact of user heterogeneity on the strategic pricing of competing LLMPs, this paper establishes a competitive model with two providers, comprising a highly intelligent initial LLM provider and a follower provider. Both providers can independently decide to adopt either a subscription model or a pay-per-use model, resulting in four pricing mode combinations (dual subscription SS, subscription-pay-per-use SD, pay-per-use-subscription DS, dual pay-per-use DD). The study shows that when the pay-per-use model is adopted, the user’s psychological perception of the “tick-tock effect” reduces the provider’s service price and profit, as the perceived psychological cost lowers the user’s valuation of the product, thereby decreasing demand. Furthermore, we analyze the equilibrium strategies for pricing mode selection by the two providers. The results indicate that the subscription model is not always advantageous for providers. Both providers will only choose to adopt the subscription model when both user usage frequency and perceived psychological cost are high. Conversely, when both user usage frequency and perceived psychological cost are low, the two providers will not simultaneously adopt the subscription model. Interestingly, as the product intelligence levels of the two providers converge, their choices of pricing modes are also more inclined to diverge. These insights guide LLMPs to strategically adjust their pricing models based on user behavioral patterns to maximize profitability in the competitive AI market. Full article