Search Results (8,693)

The concept of shared space is proposed to improve the safety and health of vulnerable road users (VRUs) by promoting walking and cycling. However, despite the documented benefits of shared spaces, concerns were raised about the frequency and severity of road user interactions in shared spaces. Thus, the objective of this study is to investigate the microscopic behaviors and psychological characteristics of vulnerable road user interactions (i.e., pedestrian–e-bike interactions and pedestrian–cyclist interactions) in non-motorized shared spaces and their interplay mechanisms. We identify a total of 334 interactions in the same- and opposite-direction using the Dutch Objective Conflict Technique for Operation and Research (DOCTOR) method at four locations in Shenzhen city, China. Trajectories of road users involved in these interactions were extracted to identify key points in trajectories and interaction phases, considering both microscopic behaviors and psychological factors synthetically. The study also compared lateral and longitudinal decision distances, maneuvering distances, maneuvering time, and safety zones across different characteristics, including severity levels, road user types, genders, and whether road users carry large items or not. The results show that the main characteristic of the interaction’s starting and ending points changes in the lateral direction. Road users have a stronger sense of security in swerve-back phases. The average lateral psychological safety distance in shared spaces is about 1.125 m. Moreover, the average safety zone area for road users in opposite and same-direction interactions are 4.83 m² and 9.36 m², respectively. Road users carrying large items perceived a higher risk in shared spaces and required longer lateral psychological safety distances and larger safety zones. The findings of this study can be used to better design shared space facilities, considering the perceived risk of road users and their interactions and psychological behavior. Full article

Wireless Medical Sensor Networks (WMSNs) collect and transmit patients’ physiological data in real time through various sensors, playing an increasingly important role in intelligent healthcare. Authentication protocols in WMSNs ensure that users can securely access real-time data from sensor nodes. Although many researchers have proposed authentication schemes to resist common attacks, insufficient attention has been paid to insider attacks and ephemeral secret leakage (ESL) attacks. Moreover, existing adversary models still have limitations in accurately characterizing an attacker’s capabilities. To address these issues, this paper extends the traditional adversary model to better reflect practical deployment scenarios, assuming a semi-trusted server and allowing adversaries to obtain users’ temporary secrets. Based on this enhanced model, we design an efficient ECC-based authentication and key agreement protocol that ensures the confidentiality of users’ passwords, biometric data, and long-term private keys during the registration phase, thereby mitigating insider threats. The proposed protocol combines anonymous authentication and elliptic curve cryptography (ECC) key exchange to satisfy security requirements. Performance analysis demonstrates that the proposed protocol achieves lower computational and communication costs compared with existing schemes. Furthermore, the protocol’s security is formally proven under the Random Oracle (ROR) model and verified using the ProVerif tool, confirming its security and reliability. Therefore, the proposed protocol can be effectively applied to secure data transmission and user authentication in wireless medical sensor networks and other IoT environments. Full article

Despite the rapid progress in the capabilities of large language models, they still lack a reliable and efficient method of storing and retrieving new information conveyed over the course of their interaction with users upon deployment. In this paper, we use reinforcement learning methods to train a team of smaller language models, which we frame as options, on reward-respecting subtasks, to learn to use SQL commands to store and retrieve relevant information to and from an external SQL database. In particular, we train a storage language model on a subtask for distinguishing between user and assistant in the dialogue history, to learn to store any relevant facts that may be required to answer future user queries. We then train a retrieval language model on a subtask for querying a sufficient number of fields, to learn to retrieve information from the SQL database that could be useful in answering the current user query. We find that training our models on their respective subtasks results in much higher performance than training them to directly optimize the reward signal and that the resulting team of language models is able to achieve performance on memory tasks comparable to existing methods that rely on language models orders of magnitude larger in size. In particular, we were able to able to achieve a 36% gain in accuracy over a prompt engineering baseline and a 13% gain over a strong baseline that uses the much larger GPT-3.5 Turbo on the MSC-Self-Instruct dataset. Full article

The large-scale adoption of electric vehicles has created an urgent need for the orderly management of charging loads in residential communities. While existing research on community-based orderly charging architectures and control strategies primarily focuses on connected charging piles (CPs) equipped with remote power control functions. However, in practical scenarios, most residential communities still rely on unconnected charging piles (UCPs) that lack remote communication capabilities, making it difficult to practically deploy many intelligent orderly architectures and control strategies that rely on communication with charging piles. Therefore, this paper proposes a non-intrusive orderly charging architecture tailored for UCPs. This architecture does not require modifying the hardware of UCPs; instead, it introduces pile-end management units (PMUs) to interact with users for orderly charging, thereby facilitating easier deployment and promotion. Based on this architecture, an optimized control strategy using the GD-SA (greedy-simulated annealing) algorithm for orderly charging is constructed, which considers the dual constraints of transformer capacity and charging demand. Case studies on a typical community in Tianjin, China, demonstrate that with the proposed order charging architecture and strategy, when users fully accept the orderly charging approach, the peak load can be reduced by over 17% compared to uncontrolled charging scenarios. Additionally, the effectiveness of the method has been validated through sensitivity analysis of user acceptance, stress scenario testing, and statistical analysis with a 95% confidence interval. Finally, this paper summarizes the practical value potential of supporting UCPs in achieving orderly charging, while also pointing out the limitations of the current research and identifying directions for further in-depth exploration. Full article

Electroencephalography is a widely used method for emotion recognition. However, it requires specialized equipment, leading to high costs. Additionally, attaching devices to the body during such procedures may cause physical and psychological stress to participants. These issues are addressed in this study by focusing on physiological signals that are noninvasive and contact-free, and a generalized method for estimating emotions is developed. Specifically, the facial skin temperature and eye-opening degree of participants captured via infrared thermography and visible cameras are utilized, and emotional states are estimated while Japanese older adults view digital content. Emotional responses while viewing digital content are often subtle and dynamic. Additionally, various emotions occur during such situations, both positive and negative. Fluctuations in facial skin temperature and eye-opening degree reflect activities in the autonomic nervous system. In particular, expressing emotions through facial expressions is difficult for older adults; as such, emotional estimation using such ecological information is required. Our study results demonstrated that focusing on skin temperature changes and eye movements during emotional arousal and non-arousal using bidirectional long short-term memory yields an F1 score of 92.21%. The findings of this study can enhance emotion recognition in digital content, improving user experience and the evaluation of digital content. Full article

The management of waste has become increasingly complex due to the growing volume and diversity of waste generated by modern societies. Effective collection systems are essential for mitigating environmental impacts and promoting sustainability. However, the increasing complexity of waste management requires a comprehensive approach that considers multiple criteria in order to evaluate the performance of these systems. This study evaluates the environmental performance of waste collection systems by comparing various methods using the Analytic Hierarchy Process (AHP). The research involves identifying key performance indicators (KPIs) that could be relevant for all the stakeholders involved and important for environmental sustainability. These KPIs are then used as criteria for the AHP model, allowing for a detailed comparison of each collection method. Data is collected from a case study in the Friuli-Venezia Giulia region in Italy. The preliminary results indicate significant variations in environmental performance and user fruitfulness across different collection methods. Door-to-door collection was found to be the preferred methodology with an absolute weight of 0.527. The AHP framework proves to be a robust tool for integrating diverse criteria and stakeholder preferences, facilitating informed decision-making in waste management. Moreover, it underscores the importance of adopting a holistic approach to evaluate and improve recycling systems. By leveraging AHP, policymakers and waste management professionals can identify optimal strategies that align with environmental sustainability goals. Full article

Background/Objectives: Accurate diagnosis, prognosis, and prediction of treatment response are essential in managing gynecologic cancers and maintaining patient quality of life. Computational pathology, powered by artificial intelligence (AI), offers a transformative opportunity for objective histopathological assessment. This review provides a comprehensive, user-oriented overview of existing AI tools for the characterization of gynecological cancers, critically evaluating their clinical applicability and identifying key challenges for future development. Methods: A systematic literature search was conducted in PubMed and Web of Science for studies published up to 2025. The search focused on AI tools developed for the diagnosis, prognosis, or treatment prediction of gynecologic cancers based on histopathological images. After applying selection criteria, 36 studies were included for in-depth analysis, covering ovarian, uterine, cervical, and other gynecological cancers. Studies on cytopathology and pure tumor detection were excluded. Results: Our analysis identified AI tools addressing critical clinical tasks, including histopathologic subtyping, grading, staging, molecular subtyping, and prediction of therapy response (e.g., to platinum-based chemotherapy or PARP inhibitors). The performance of these tools varied significantly. While some demonstrated high accuracy and promising results in internal validation, many were limited by a lack of external validation, potential biases from training data, and performance that is not yet sufficient for routine clinical use. Direct comparison between studies was often hindered by the use of non-standardized evaluation metrics and evolving disease classifications over the past decade. Conclusions: AI tools for gynecologic cancers represent a promising field with the potential to significantly support pathological practice. However, their current development is heterogeneous, and many tools lack the robustness and validation required for clinical integration. There is a pressing need to invest in the creation of clinically driven, interpretable, and accurate AI tools that are rigorously validated on large, multicenter cohorts. Future efforts should focus on standardizing evaluation metrics and addressing unmet diagnostic needs, such as the molecular subtyping of rare tumors, to ensure these technologies can reliably benefit patient care. Full article

This feasibility study explored the integration of physiological monitoring into a virtual reality (VR) intervention for pediatric pain management. The goal of this study is to identify a feasible strategy for collecting physiologic data in the context of a VR intervention currently being developed for youth with chronic pain. We assess the potential of Cognitive Load (CL)—derived from heart rate and pupillometry/eye-tracking data—as a marker of arousal and user engagement in a VR simulation to promote school functioning in youth with chronic pain. The HP Reverb G2 Omnicept headset and Polar H10 heart-rate sensor were utilized. The Child Presence Questionnaire (CPQ) assessed participants’ self-reported immersion and engagement. Data collection focused on feasibility and utility of physiologic data in assessing arousal and correlations with self-reported experience. Nine participants engaged in the simulation, with eight yielding complete data. The simulation and headset were well tolerated. CPQ Transportation subscale showed trend-level correlation with mean CL. Due to small sample and feasibility focus, individual-level results were examined. Combining multiple physiologic markers into a construct like CL is intriguing, but data interpretability was limited. Pupillometry and related metrics show promise as feasible markers of engagement and arousal for VR-based intervention but require appropriate expertise to fully interpret. The study found that integration of physiologic monitoring is feasible, but further work is needed to standardize metrics and identify the most useful and user-friendly markers. Full article

Driver emotion recognition is vital for intelligent driver assistance systems, where the accurate detection of emotional states enhances both safety and user experience. Current approaches, however, require extensive labeled datasets, perform poorly under real-world conditions, and degrade with class imbalance. To overcome these challenges, we propose the Active Learning and Deep Attention Mechanism (ALDAM) framework. ALDAM introduces three key innovations: (1) an active learning cycle that reduces labeling effort by ~40%; (2) a weighted-cluster loss that mitigates class imbalance; and (3) a deep attention mechanism that strengthens feature selection under occlusion, pose variation, and illumination changes. Evaluated on four benchmark datasets (FER-2013, AffectNet, CK+, and EMOTIC), ALDAM achieves an average accuracy of 97.58%, F1-score of 98.64%, and AUC of 98.76% surpassing CNN-based models and advanced baselines such as SE-ResNet-50. These results establish ALDAM as a robust and efficient solution for real-time driver emotion recognition. Full article

Background: The achievement of the goals of the Immunization Agenda 2030 (IA2030) requires vaccines to be developed and implemented that meet the needs and requirements of the final users: vaccinators and vaccinees. A detailed and shared understanding of these needs should inform policy and programme guidance directing stakeholders’ efforts and investments. Currently, relevant guidance documents only partially capture vaccine users’ perspectives. Method: To help overcome this gap, we propose an operational research method grounded in the principles of the design approach that systematically maps and integrates user perspectives in vaccine development, policy, and implementation decisions. Results: The method, named the seven Ws, guides researchers through a three-step process. First, it clarifies the contribution of a vaccine to solving a public health problem—the solution–problem fit. Second, it maps potential implementation strategies for the vaccine in different settings. Lastly, it describes the relevant vaccine’s use cases across the implementation strategies, elucidating the user requirements for the vaccine to be successfully implemented—the solution–provider and solution–user fits. Conclusions: By explicitly pursuing these three fits, policymakers, vaccine developers, and programme managers will be able to better contribute towards the achievement of the IA2030 goals. This framework is intended as a conceptual contribution rather than an empirical validation study. Full article

This article presents a novel strategy for the design of planar phased arrays using Fibonacci-based partitioning combined with a random multi-objective search. This approach intends to minimize the number of phase shifters used by the system while maintaining the radiation characteristics required for Ku-band user terminals in Low Earth Orbit (LEO) satellite communications. This methodology efficiently tessellates a $16\times 16$ antenna array, reducing the solution search space size and improving algorithmic computational time. From a total of 409,600 possible configurations, an optimal candidate solution was obtained in 2 h. This configuration achieves a balanced trade-off between radiation performance metrics, including side lobe level (SLL), first null beamwidth (FNBW), and the number of phase shifters. This optimal design maintains a value of SLL below $-15$ dB across all the azimuth scanning angles, with a beam steering capability of $\theta =-{40}^{\circ }$ and $0^{\circ }\le \varphi \le {360}^{\circ }$. These results demonstrate the suitability of this novel approach regarding Ku-band satellite communications, providing efficient and practical solutions for high-demand internet services via LEO satellite systems. Full article

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new ($\xi$, $ϵ$)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the ($\xi$, $ϵ$)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article

The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow. The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%. Full article

Public service provision is still limited to document-centric procedures that require citizens to submit data and information needed for the execution of a service via documents. This, amongst others, is time-consuming, error-prone and hinders progress towards data-centricity. This study proposes a data-centric framework for a public service recommender system that combines knowledge graphs (KGs) and large language models (LLMs) in a neuro-symbolic AI architecture. The framework expresses public service preconditions as machine-readable rules based on data standards and provides dynamic recommendations for public services based on citizens’ profiles through automated reasoning. LLMs are utilized to extract preconditions from unstructured textual regulations and create RDF-based evidence models, while KGs provide validation of preconditions through SHACL rules and explainable reasoning towards semantic interoperability. A prototype use case on students applying for housing allowance showcases the feasibility of the proposed framework. The analysis indicates that combining KGs with LLMs for identifying relevant public services for different citizens’ profiles can improve the quality of public services and reduce administrative burdens. This work contributes and promotes the proactive “No-Stop Government” model, where services are recommended to users without explicit requests. The findings highlight the promising potential of employing neuro-symbolic AI to transform e-government processes, while also addressing challenges related to legal complexity, privacy and data fragmentation for large-scale adoption. Full article

Green hydrogen offers a promising yet underexplored pathway for agricultural decarbonisation, requiring technological readiness and coordinated action from policymakers, industry, and farmers. This paper integrates techno-economic modelling with stakeholder engagement (semi-structured interviews and an expert workshop) to assess its potential. Analyses were conducted for farms of 123 hectares and clusters of 10 farms, complemented by seven interviews and a workshop with nine sector experts. Findings show both opportunities and barriers. While on-farm hydrogen production is technically feasible, it remains economically uncompetitive due to high levelised costs, shaped by seasonal demand variability and low utilisation of electrolysers and storage. Pooling demand across multiple users is essential to improve cost-effectiveness. Stakeholders identified three potential business models: fertiliser production via ammonia synthesis, cooperative-based models, and local refuelling stations. Of these, cooperative hydrogen hubs emerged as the most promising, enabling clusters of farms to jointly invest in renewable-powered electrolysers, storage, and refuelling facilities, thereby reducing costs, extending participation to smaller farms, and mitigating risks through collective investment. By linking techno-economic feasibility with stakeholder perspectives and business model considerations, the results contribute to socio-technical transition theory by showing how technological, institutional, and social factors interact in shaping hydrogen adoption in agriculture. With appropriate policy support, cooperative hubs could lower costs, ease concerns over affordability and complexity, and position hydrogen as a practical driver of agricultural decarbonisation and rural resilience. Full article