Search Results (1,431)

The rapid growth of urban populations intensifies congestion, air pollution, and energy demand. Green mobility is central to sustainable smart cities, and the Internet of Things (IoT) offers a means to monitor, coordinate, and optimize transport systems in real time. This paper presents an Internet of Things (IoT)-based architecture integrating heterogeneous sensing with edge–cloud orchestration and AI-driven control for green routing and coordinated Electric Vehicle (EV) charging. The framework supports adaptive traffic management, energy-aware charging, and multimodal integration through standards-aware interfaces and auditable Key Performance Indicators (KPIs). We hypothesize that, relative to a static shortest-path baseline, the integrated green routing and EV-charging coordination reduce (H1) mean travel time per trip by ≥7%, (H2) CO₂ intensity (g/km) by ≥6%, and (H3) station peak load by ≥20% under moderate-to-high demand conditions. These hypotheses are tested in Simulation of Urban MObility (SUMO) with Handbook Emission Factors for Road Transport (HBEFA) emission classes, using 10 independent random seeds and reporting means with 95% confidence intervals and formal significance testing. The results confirm the hypotheses: average travel time decreases by approximately 9.8%, CO₂ intensity by approximately 8%, and peak load by approximately 25% under demand multipliers ≥1.2 and EV shares ≥20%. Gains are attenuated under light demand, where congestion effects are weaker. We further discuss scalability, interoperability, privacy/security, and the simulation-to-deployment gap, and outline priorities for reproducible field pilots. In summary, a pragmatic edge–cloud IoT stack has the potential to lower congestion, reduce per-kilometer emissions, and smooth charging demand, provided it is supported by reliable data integration, resilient edge services, and standards-compliant interoperability, thereby contributing to sustainable urban mobility in line with the objectives of SDG 11 (Sustainable Cities and Communities). Full article

Graph neural networks (GNNs) are deep learning models that process structured graph data. By leveraging their graphs/node classification and link prediction capabilities, they have been effectively applied in multiple domains such as community detection, location sharing services, and drug discovery. These powerful applications and the vast availability of graphs in diverse fields have facilitated the adoption of GNNs in privacy-sensitive contexts (e.g., banking systems and healthcare). Unfortunately, GNNs are vulnerable to the leakage of sensitive information through well-defined attacks. Our main focus is on membership inference attacks (MIAs) that allow the attacker to infer whether a given sample belongs to the training dataset. To prevent this, we introduce three LLM-guided defense mechanisms applied at the posterior level: posterior encoding with noise, knowledge distillation, and secure aggregation. Our proposed approaches not only successfully reduce MIA accuracy but also maintain the model’s performance on the node classification task. Our findings, validated through extensive experiments on widely used GNN architectures, offer insights into balancing privacy preservation with predictive performance. Full article

This paper presents TwinP2G, a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling, including electricity, green hydrogen, natural gas, and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles, including data scientists and energy experts, using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally, TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid. Full article

The expansion of Internet of Things (IoT) networks has enabled real-time data collection and automation across smart cities, healthcare, and agriculture, delivering greater convenience and efficiency; however, exposure to diverse threats has also increased. Machine learning-based Intrusion Detection Systems (IDSs) provide an effective means of defense, yet they require large volumes of data, and the use of raw IoT network data containing sensitive information introduces new privacy risks. This study proposes a novel privacy-preserving synthetic data generation model based on a tabular diffusion framework that incorporates Differential Privacy (DP). Among the three diffusion models (TabDDPM, TabSyn, and TabDiff), TabDiff with Utility-Preserving DP (UP-DP) achieved the best Synthetic Data Vault (SDV) Fidelity (0.98) and higher values on multiple statistical metrics, indicating improved utility. Furthermore, by employing the DisclosureProtection and attribute inference to infer and compare sensitive attributes on both real and synthetic datasets, we show that the proposed approach reduces privacy risk of the synthetic data. Additionally, a Membership Inference Attack (MIA) was also used for demonstration on models trained with both real and synthetic data. This approach decreases the risk of leaking patterns related to sensitive information, thereby enabling secure dataset sharing and analysis. Full article

As a key technology in multimodal information processing, infrared and visible image fusion holds significant application value in fields such as military reconnaissance, intelligent security, and autonomous driving. To address the limitations of existing methods, this paper proposes the Hierarchical Triple-Feature Interaction Fusion Network (TFI-Fusion). Based on a hierarchical triple-stream feature interaction mechanism, the network achieves high-quality fusion through a two-stage, separate-model processing approach: In the first stage, a single model extracts low-rank components (representing global structural features) and sparse components (representing local detail features) from source images via the Low-Rank Sparse Decomposition (LSRSD) module, while capturing cross-modal shared features using the Shared Feature Extractor (SFE). In the second stage, another model performs fusion and reconstruction: it first enhances the complementarity between low-rank and sparse features through the innovatively introduced Bi-Feature Interaction (BFI) module, realizes multi-level feature fusion via the Triple-Feature Interaction (TFI) module, and finally generates fused images with rich scene representation through feature reconstruction. This separate-model design reduces memory usage and improves operational speed. Additionally, a multi-objective optimization function is designed based on the network’s characteristics. Experiments demonstrate that TFI-Fusion exhibits excellent fusion performance, effectively preserving image details and enhancing feature complementarity, thus providing reliable visual data support for downstream tasks. Full article

Speaker verification (SV) is a core technology for security and personalized services, and its importance has been growing with the spread of wearables such as smartwatches, earbuds, and AR/VR headsets, where privacy-preserving on-device operation under limited compute and power budgets is required. Recently, self-supervised learning (SSL) models such as WavLM and wav2vec 2.0 have been widely adopted as front ends that provide multi-layer speech representations without labeled data. Lower layers contain fine-grained acoustic information, whereas higher layers capture phonetic and contextual features. However, conventional SV systems typically use only the final layer or a single-step temporal attention over a simple weighted sum of layers, implicitly assuming that frame importance is shared across layers and thus failing to fully exploit the hierarchical diversity of SSL embeddings. We argue that frame relevance is layer dependent, as the frames most critical for speaker identity differ across layers. To address this, we propose Masked Multi-layer Feature Aggregation (MMFA), which first applies independent frame-wise attention within each layer, then performs learnable layer-wise weighting to suppress irrelevant frames such as silence and noise while effectively combining complementary information across layers. On VoxCeleb1, MMFA achieves consistent improvements over strong baselines in both EER and minDCF, and attention-map analysis confirms distinct selection patterns across layers, validating MMFA as a robust SV approach even in short-utterance and noisy conditions. Full article

Due to their vast number and heterogeneity, local public administrations can act as entry points (or attack surfaces) for adversaries targeting national infrastructure. The individual vulnerabilities of these entities function as entry points that can be exploited to compromise higher-level government assets. This study presents a nationwide risk analysis of the exposed perimeter of 7000 municipalities, achieved through the massive collection of 93 technological and contextual variables over three consecutive years and the application of supervised machine learning algorithms. The findings of this study demonstrate that geographical factors are a key predictor of external perimeter cyber risk, suggesting that supra-local entities providing unified, shared security services are better positioned in terms of risk exposure and therefore more resilient. Furthermore, the analysis confirms, contrary to conventional wisdom, that IT budget allocation lacks a significant statistical correlation with external perimeter risk mitigation. It is concluded that large-scale data collection frameworks, enhanced by Artificial Intelligence, provide policymakers with an objective and transparent tool to optimize cybersecurity investments and protection strategies. 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

As the European Union’s energy systems are transforming towards achieving climate goals, this article examines the energy balances of EU member states. This analysis covers, among other things, the dynamics of energy dependence and strategies for decoupling economic growth from the level of emissions in the European Union (EU), with particular emphasis on Poland, which is strongly influenced by its historical reliance on coal in the energy balance. Using panel data from 1990 to 2022, the article investigates differences in energy dependence between individual countries, shaped by economic structures and national energy policies. The study results confirm significant heterogeneity between member states and emphasize that the stability and direction of decoupling economic growth from greenhouse gas (GHG) emissions are strongly dependent on the composition of the energy mix and vulnerability to external conditions. Based on scenario analysis, potential paths for Poland’s energy transition are assessed. We demonstrate that a high share of renewable energy sources (RES) significantly reduces CO₂ emissions, provided it is accompanied by infrastructure modernization and the development of energy storage. Furthermore, integrating nuclear energy as a stabilizing element of the energy mix offers an additional path to deep decarbonization while ensuring supply reliability. Finally, we demonstrate that improving energy efficiency and demand management can effectively increase energy security and reduce emissions, even in a scenario with a stable coal share. The study addresses a research gap by integrating decoupling analysis with scenario-based stochastic modeling for Poland, a country for which few comprehensive transition assessments exist. The results provide practical guidance for developing resilient, low-emission energy policies in Poland and the EU. Results are reported for 2025–2050 (with 2040 as an interim milestone). Full article

With the advancement of technologies such as 5G, digital twins, and edge computing, the Internet of Things (IoT) as a critical component of intelligent systems is profoundly driving the transformation of various industries toward digitalization and intelligence. However, the exponential growth of network connection nodes has expanded the attack exposure surface of IoT devices. The IoT devices with limited storage and computing resources struggle to cope with new types of attacks, and IoT devices lack mature authorization and authentication mechanisms. It is difficult for traditional data-sharing solutions to meet the security requirements of cloud-based shared data. Therefore, this paper proposes a blockchain-based multi-authority IoT data-sharing scheme with attribute-based searchable encryption for intelligent system (BM-ABSE), aiming to address the security, efficiency, and verifiability issues of data sharing in an IoT environment. Our scheme decentralizes management responsibilities through a multi-authority mechanism to avoid the risk of single-point failure. By utilizing the immutability and smart contract function of blockchain, this scheme can ensure data integrity and the reliability of search results. Meanwhile, some decryption computing tasks are outsourced to the cloud to reduce the computing burden on IoT devices. Our scheme meets the static security and IND-CKA security requirements of the standard model, as demonstrated by theoretical analysis, which effectively defends against the stealing or tampering of ciphertexts and keywords by attackers. Experimental simulation results indicate that the scheme has excellent computational efficiency on resource-constrained IoT devices, with core algorithm execution time maintained in milliseconds, and as the number of attributes increases, it has a controllable performance overhead. Full article

Key drivetrain components in wind turbines are prone to misalignment faults due to long-term operation under fluctuating loads and harsh environments. Because misalignment develops gradually rather than occurring instantly, reliable evaluation of structural designs and surface treatments requires long-duration, multi-sensor, and multi-condition experiments that generate massive heterogeneous datasets. Traditional data management relying on manual folders and USB drives is inefficient, redundant, and lacks traceability. To address these challenges, this study presents a dedicated misalignment experimental data management platform specifically designed for wind power applications. The innovation lies in its ability to synchronize vibration, electrostatic, and laser alignment data streams in long-term tests, establish a traceable and reusable data structure linking experimental conditions with sensor outputs, and integrate laboratory results with field SCADA data. Built on Laboratory Information Management System (LIMS) principles and implemented with an MVC + Spring Boot + B/S architecture, the platform supports end-to-end functions including multi-sensor data acquisition, structured storage, automated processing, visualization, secure sharing, and cross-role collaboration. Validation on drivetrain shaft assemblies confirmed its ability to handle multi-terabyte datasets, reduce manual processing time by more than 80%, and directly integrate processed results into fault identification models. Overall, the platform establishes a scalable digital backbone for wind turbine misalignment research, supporting structural reliability evaluation, predictive maintenance, and intelligent operation and maintenance. Full article

Background/Objectives: As global aging accelerates, there is a pressing and empirically substantiated demand for integrated and sustainable strategies, as evidenced by the rising prevalence rates of chronic conditions, social isolation, and digital exclusion among older adults worldwide. These factors underscore the urgent need for multidimensional interventions that simultaneously target physical, psychological, and social well-being. The HOPE-FIT (Hybrid Outreach Program for Exercise and Follow-up Integrated Training) model and the SAGE (Senior Active Guided Exercise) program were designed to address this need through a hybrid framework. These programs foster inclusive aging by explicitly bridging digitally underserved groups and mobility-restricted populations into mainstream health promotion systems through tailored exercise, psychosocial support, and smart-home technologies, thereby functioning as a scalable meta-model across healthcare, community, and policy domains. Methods: HOPE-FIT was developed through a formative, multi-phase process grounded in the RE-AIM framework and a Hybrid Type II effectiveness–implementation design. The program combines professional health coaching, home-based and digital exercise routines, Acceptance and Commitment Performance Training (ACPT)-based psychological strategies, and smart-home monitoring technologies. Empirical data from pilot studies, large-scale surveys (N = 1000), and in-depth user evaluations were incorporated to strengthen validity and contextual adaptation. Culturally tailored content and participatory feedback from older adults further informed ecological validity and program refinement. Implementation Strategy/Framework: The theoretical foundation integrates implementation science with behavioral and digital health. The RE-AIM framework guided reach, fidelity, and maintenance planning, while the Hybrid E–I design enabled the concurrent evaluation of effectiveness outcomes and contextual implementation strategies. Institutional partnerships with community centers, public health organizations, and welfare agencies further facilitated the translation of the model into real-world aging contexts. Dissemination Plan: The multi-pronged dissemination strategy includes international symposia, interdisciplinary academic networks, policy briefs, localized community deployment, and secure, authenticated data sharing for reproducibility. This design facilitates evidence-informed policy, empowers practitioners, and advances digital health equity. Ultimately, HOPE-FIT constitutes a scalable and inclusive model that concretely addresses health disparities and promotes active, dignified aging across systems and disciplines. Full article

The rapid expansion of mobile financial services (MFSs) has brought about benefits in terms of financial inclusion in developing countries; however, threats have also emerged on the sides of cybersecurity and privacy. Traditional fraud-detection strategies are usually not responsive in time or adaptive to changing threat scenarios. This study investigates how artificial intelligence (AI) can be employed to strengthen fraud detection and methods to address user privacy concerns within MFS platforms in emerging markets. A mixed-method approach was adopted, i.e., a quantitative survey (n = 151) and a qualitative analysis of open-ended response. A reliability analysis showed internal consistency (Cronbach’s alpha > 0.70 across constructs). The descriptive results demonstrate that 95.4% of those questioned raised privacy concerns, whereas 78.2% recognized the benefits of AI-driven fraud detection. Regression analysis showed that AI significantly improved perceived security (β = 0.63, p < 0.01), although transparency and explainability were critical determinants of trust. The findings indicate that users consider AI a capable real-time fraud detection tool; however, doubts remain regarding data transparency, sharing with third parties, and lack of user-opted control, resulting in the erosion of user trust. The study also indicates that the socio-cultural factors and weak regulatory contexts weigh heavily on users’ acceptance of these AI-powered systems. This study proposes the promotion of Explainable AI (XAI) systems along with privacy-by-design user controls and localized communication approaches to foster trust and further adoption. The study contained within are thus a critical guide for policymakers, fintech developers, and providers, who seek to innovate with user protection within digital fintech. Full article

Technological advancements are reshaping New Mobility Services (NMS) by enhancing trip planning, booking, and payment processes, while also improving fleet management, infrastructure utilization, and data-driven decision-making. Despite these developments, challenges persist in integrating technologies into cohesive and interoperable mobility systems. This study draws insights from 163 stakeholders across the NMS ecosystem to examine both the opportunities and barriers associated with the effective integration of technology into NMS, particularly within urban and metropolitan contexts. Using statistical methods, these responses were analyzed across eight stakeholder groups to determine whether their views converge or diverge. Findings reveal a broad consensus on the technologies expected to have the greatest impact, as well as on the main challenges of integrating these technologies into NMS. Divergences arise in the perceived influence on specific mobility attributes, such as environmental sustainability, security, safety, equity, and social inclusion, and in the services considered most likely to benefit. Notably, investors express a more optimistic view across nearly all technologies, prioritizing shared vehicle services and anticipating the strongest impacts in environmental sustainability. The rest of the stakeholder groups emphasize the potential of technology to enhance modal integration and identify Mobility-as-a-Service (MaaS) as the NMS with the greatest expected benefits. These insights help identify strategic priorities and redirect efforts toward promoting investment in technologies with the highest potential to deliver transformative benefits across the NMS ecosystem. Full article

Semantic communication (SemCom), as a revolutionary paradigm for next-generation networks, shifts the focus from traditional bit-level transmission to the delivery of meaning and purpose. Grounded in the Data, Information, Knowledge, Wisdom, Purpose (DIKWP) model and its mapping framework, together with the relativity of understanding theory, the discussion systematically reviews advances in semantic-aware communication and personalized semantic security. By innovatively introducing the “Purpose” dimension atop the classical DIKW hierarchy and establishing interlayer feedback mechanisms, the DIKWP model enables purpose-driven, dynamic semantic processing, providing a theoretical foundation for both SemCom and personalized semantic security based on cognitive differences. A comparative analysis of existing SemCom architectures, personalized artificial intelligence (AI) systems, and secure communication mechanisms highlights the unique value of the DIKWP model. An integrated cognitive–conceptual–semantic network, combined with the principle of semantic relativity, supports the development of explainable, cognitively adaptive, and trustworthy communication systems. Practical implementation paths are explored, including DIKWP-based semantic chip design, white-box AI evaluation standards, and dynamic semantic protection frameworks, establishing theoretical links with emerging trends such as task-oriented communication and personalized foundation models. Embedding knowledge representation and cognitive context into communication protocols is shown to enhance efficiency, reliability, and security significantly. In addition, key research challenges in semantic alignment, cross-domain knowledge sharing, and formal semantic metrics are identified, while future research directions are outlined to guide the evolution of intelligent communication networks and provide a systematic reference for the advancement of the field. Full article