Search Results (685)

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

Self-medication is a subject of global importance. If practiced responsibly, self-medication represents a part of self-care or positive care of an individual or a community in promoting their own health. However, today’s practices of self-medication are often inappropriate and irresponsible, and as such appear all over the world. Inappropriate self-medication can be connected with possible serious health risks and consequences. Therefore, it represents a global health issue. It can even generate additional health problems, which will eventually become a burden to healthcare systems and can induce significant costs, which also raises socioeconomic concerns. Hence, self-medication attracts the attention of researchers and practitioners globally in efforts to clarify the current status and define feasible measures that should be implemented to address this issue. This narrative review aims to give an overview of the situation in the field of self-medication globally, including current practices and attitudes, as well as implications for actions needed to improve this problem. A PubMed/MEDLINE search was conducted for articles published in the period from 1995 up to March 2025 using keywords “self-medication” or “selfmedication” alone or in combinations with terms related to specific subthemes related to self-medication, such as COVID-19, antimicrobials, healthcare professionals, and storing habits of medicines at home. Studies were included if self-medication was their main focus. Publications that only mentioned self-medication in different contexts, but not as their main focus, were excluded. Considering the outcomes of research on self-medication in various contexts, increasing awareness of responsible self-medication through education and informing, together with surveillance of particular medicines and populations, could lead to more appropriate and beneficial self-medication in the future. Full article

Teleoperation emerged as a promising fallback for situations beyond the capabilities of automated vehicles. Nevertheless, teleoperation still faces challenges, such as reduced situational awareness. Since situational awareness is primarily built through the remote operator’s visual perception, the graphical user interface (GUI) design is critical. In addition to video feed, supplemental informational elements are crucial—not only for the predominantly studied remote driving, but also for emerging desk-based remote assistance concepts. This work develops a GUI for different teleoperation concepts by identifying key informational elements during the teleoperation process through expert interviews (N = 9). Following this, a static and dynamic GUI prototype was developed and evaluated in a click dummy study (N = 36). Thereby, the dynamic GUI adapts the number of displayed elements according to the teleoperation phase. Results show that both GUIs achieve good system usability scale (SUS) ratings, with the dynamic GUI significantly outperforming the static version in both usability and task completion time. However, the results might be attributable to a learning effect due to the lack of randomization. The user experience questionnaire (UEQ) score shows potential for improvement. To enhance the user experience, the GUI should be evaluated in a follow-up study that includes interaction with a real vehicle. Full article

Rapid and comprehensive assessment of building damage caused by earthquakes is essential for effective emergency response and rescue efforts in the immediate aftermath. Advanced technologies, including real-time simulations, remote sensing, and multi-sensor systems, can effectively enhance situational awareness and structural damage evaluations. However, most existing methods rely on isolated time snapshots, and few studies have systematically explored the continuous, time-scaled integration and update of building damage estimates from multiple data sources. This study proposes a stepwise framework that continuously updates time-scaled, single-damage estimation outputs using the best available multi-sensor data for estimating earthquake-induced building damage. We demonstrated the framework using the 2024 Noto Peninsula Earthquake as a case study and incorporated official damage reports from the Ishikawa Prefectural Government, real-time earthquake building damage estimation (REBDE) data, and satellite-based damage estimation data (ALOS-2-building damage estimation (BDE)). By integrating the REBDE and ALOS-2-BDE datasets, we created a composite damage estimation product (integrated-BDE). These datasets were statistically validated against official damage records. Our framework showed significant improvements in accuracy, as demonstrated by the mean absolute percentage error, when the datasets were integrated and updated over time: 177.2% for REBDE, 58.1% for ALOS-2-BDE, and 25.0% for integrated-BDE. Finally, for stepwise damage estimation, we proposed a methodological framework that incorporates social media content to further confirm the accuracy of damage assessments. Potential supplementary datasets, including data from Internet of Things-enabled home appliances, real-time traffic data, very-high-resolution optical imagery, and structural health monitoring systems, can also be integrated to improve accuracy. The proposed framework is expected to improve the timeliness and accuracy of building damage assessments, foster shared understanding of disaster impacts across stakeholders, and support more effective emergency response planning, resource allocation, and decision-making in the early stages of disaster management in the future, particularly when comprehensive official damage reports are unavailable. Full article

This paper presents the design, simulation, and experimental validation of a dual-Circularly Polarized (CP) array antenna to be used as single element for a bistatic radar system, aimed at detecting and tracking objects in Low Earth Orbit (LEO). The antenna operates at 412 MHz in reception mode and consists of an array of 19 slotted-patch radiating elements with a cavity-based metallic superstrate, designed to support dual circular polarization. These elements are arranged in a hexagonal configuration, enabling the array structure to achieve a maximum realized gain of 17 dBi and a Side Lobe Level (SLL) below −17 dB while maintaining high polarization purity. Two identical analog feeding networks enable the precise control of phase and amplitude, allowing the independent reception of Right-Hand and Left-Hand Circularly Polarized (RHCP and LHCP) signals. Full-wave simulations and experimental measurements confirm the high performance and robustness of the system, demonstrating its suitability for integration into large-scale Space Situational Awareness (SSA) sensor networks. Full article

As autonomous driving technologies advance, the need for human-in-the-loop systems becomes increasingly critical to ensure safety, adaptability, and public confidence. This paper presents the design and evaluation of a context-aware immersive teleoperation interface that integrates real-time simulation, virtual reality, and multimodal feedback to support remote interventions in emergency scenarios. Built on a modular ROS2 architecture, the system allows seamless transition between simulated and physical platforms, enabling safe and reproducible testing. The experimental results show a high task success rate and user satisfaction, highlighting the importance of intuitive controls, gesture recognition accuracy, and low-latency feedback. Our findings contribute to the understanding of human-robot interaction (HRI) in immersive teleoperation contexts and provide insights into the role of multisensory feedback and control modalities in building trust and situational awareness for remote operators. Ultimately, this approach is intended to support the broader acceptability of autonomous driving technologies by enhancing human supervision, control, and confidence. Full article

The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

Federated Learning (FL) is a privacy-focused technique for training models; however, most existing unlearning techniques in FL fall significantly short of the efficiency and situational awareness required by the GDPR. The paper introduces two new unlearning methods: EG-FedUnlearn, a gradient-based technique that eliminates the effect of specific target clients without retraining, and OFU-Ontology, an ontology-based approach that ranks data importance to facilitate forgetting contextually. EG-FedUnlearn directly eliminates the contributions of specific target data by reversing the gradient, whereas OFU-Ontology utilizes semantic relevance to prioritize forgetting data of the least importance, thereby minimizing the unlearning-induced degradation of models. The results of experiments on seven benchmark datasets demonstrate the good performance of both algorithms. OFU-Ontology yields 98% accuracy of unlearning while maintaining high model utility with very limited accuracy loss under class-based deletion on MNIST (e.g., 95%), surpassing FedEraser and VeriFi on the multiple metrics of residual influence, communication overhead, and computational cost. These results indicate that the cooperation of efficient unlearning algorithms with semantic reasoning, minimized unlearning costs, and operational performance in a distributed environment. This paper becomes the first to incorporate ontological knowledge into federated unlearning, thereby opening new avenues for scalable and intelligent private machine learning systems. Full article

End-users in a decision-oriented Internet of Things (IoT) healthcare system are often left in the dark regarding critical security information necessary for making informed decisions about potential risks. This is partly due to the lack of transparency and system security awareness end-users have in such systems. To empower end-users and enhance their cybersecurity situational awareness, it is imperative to thoroughly document and report the runtime security controls in place, as well as the security-relevant aspects of the devices they rely on, while the need for better transparency is obvious, it remains uncertain whether current systems offer adequate security metadata for end-users and how future designs can be improved to ensure better visibility into the security measures implemented. To address this gap, we conducted table-top exercises with ten security and ICT experts to evaluate a typical IoT-Health scenario. These exercises revealed the critical role of security metadata, identified the available ones to be presented to users, and suggested potential enhancements that could be integrated into system design. We present our observations from the exercises, highlighting experts’ valuable suggestions, concerns, and views, backed by our in-depth analysis. Moreover, as a proof-of-concept of our study, we simulated three relevant use cases to detect cyber risks. This comprehensive analysis underscores critical considerations that can significantly improve future system protocols, ensuring end-users are better equipped to navigate and mitigate security risks effectively. Full article

Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and 30 from autonomous vehicles (AVs), to examine differences in risk triggers, perception paradigms, and interaction failures between humans and artificial intelligence (AI). Results: Our findings reveal that PCS risks are predominantly internal to the system and detectable through deterministic, rule-based mechanisms, whereas AVs’ risks are externally driven and managed via probabilistic, multi-modal sensor fusion. More importantly, despite these architectural differences, both domains exhibit recurring human–AI interaction failures, including over-reliance on automation, mode confusion, and delayed intervention. In the case of PCSs, these failures are historically tied to human–automation interaction; this article extrapolates these patterns to anticipate potential human–AI interaction challenges as AI adaptation increases. Conclusions: This study highlights the need for a hybrid risk perception framework and improved human-centered design to enhance situational awareness and responsiveness. While AI has not yet been implemented in PCS incident studies, this work interprets human–automation failures in these cases as indicative of potential challenges in human–AI interaction that may arise in future AI-integrated process systems. Implications extend to developing safer intelligent systems across industrial and transportation sectors. Full article

The increased occurrence and severity of cyber-attacks on critical infrastructure have underscored the need to embrace systematic and prospective approaches to resilience. The current research takes as its hypothesis that the InfraGuard Cybersecurity Framework—a capability model that measures the maturity of cyber resilience through three functional pillars, Cyber as a Shield, Cyber as a Space, and Cyber as a Sword—is an implementable and understandable means to proceed with. The model treats the significant aspects of situational awareness, active defense, risk management, and recovery from incidents and is measured using globally standardized maturity models like ISO/IEC 15504, NIST CSF, and COBIT. The contributions include multidimensional measurements of resilience, a scored scale of capability (0–5), and domain-based classification enabling organizations to assess and enhance their cybersecurity situation in a formalized manner. The framework’s applicability is illustrated in three exploratory settings of power grids, healthcare systems, and airports, each constituting various levels of maturity in resilience. This study provides down-to-earth recommendations to policymakers through the translation of the attributes of resilience into concrete assessment indicators, promoting policymaking, investment planning, and global cyber defense collaboration. Full article

Background: Systemic sclerosis is a systemic autoimmune disease that also impacts women’s health in very different ways. Methods: This review summarises the most important data on sexuality, fertility, pregnancy, and menopause from the last 10 years. Findings: We identified nine articles with data on sexuality and a prevalence of sexual dysfunction varying between 46 and 90%. Fertility was examined in six studies, with evidence for a negative influence at least on ovarian reserve. With regard to menopause, only three studies are mentioned that show an increased risk for premature menopause in SSc women. Although pregnancies are rare in SSc women after disease onset, there is growing evidence that pregnancies are feasible but go along with a higher maternal and foetal risk compared to healthy controls. Interpretation: SSc is dominated by female gender, but aspects of women’s health influenced by the disease are still often ignored. The treating physician should be aware of the mostly negative impact on sexuality, fertility, and pregnancy and address these topics with the patients to adapt treatment and follow-up examinations to the patients’ complaints and life situation. Full article

Research on augmented reality (AR) and mixed reality (MR) demonstrated their potential to support decision-making in construction. However, most efforts emphasized technological advancements, often overlooking how to present and interact with information to effectively support decision-making in AR/MR environments. This study proposes an interface design method that integrates situation awareness (SA) and immersive analytics (IA), two complementary frameworks that address user information needs and immersive interaction design. The method guides the design of AR/MR interfaces by aligning information content, presentation, and interaction with SA requirements and IA design principles. To evaluate its effectiveness, the method was applied to develop AR and MR interface prototypes for a simulated decision-making task involving field managers during indoor construction activities of high-rise construction projects. Results show high levels of SA achieved by participants, with no statistically significant differences between AR and MR interfaces, demonstrating the method’s effectiveness to support SA in both environments. The proposed method provides a structured approach for designing immersive interfaces that enable better perception, comprehension, and projection in dynamic construction scenarios. Moreover, it provides designers with practical guidance for interface development and allows practitioners to assess existing AR/MR solutions based on their capacity to enhance SA through IA. Full article

To ensure the on-orbit safety of crewed spacecraft and avoid the threat of constellations such as Starlink to manned spacecraft, the industry has started to research equipping phased array radars for situational awareness of collision threat. In order to enhance the resource allocation capability of the space station’s protection radar system, this paper proposes a task scheduling method based on time shifting constraints and pulse interleaving. The time shifting constraint is designed to minimize the deviation between the actual execution and the desired execution time of the task, and it is negatively correlated with the threat degree of the target. Pulse interleaving is intended to utilize the idle time between the transmitted pulse and the received pulse of a task to perform other tasks, thereby improving the utilization of radar resources. Through computer simulation under typical parameters, our proposed method reduces the average time shifting ratio by about 60% compared to traditional task scheduling methods, and the scheduling success ratio is also higher than that of traditional scheduling methods. This demonstrates the effectiveness of the proposed method in enhancing scheduling efficiency and overall system performance. Full article