Ensuring the safety of Autonomous Driving Systems (ADS) at urban intersections remains challenging due to complex interactions between vehicles and traffic management infrastructure. This study validates an ADS equipped with connected perception using Infrastructure-to-Vehicle (I2V) communication within a combined virtual and hybrid testing approach. The validation follows the overall structure and methodology of the SUNRISE Safety Assurance Framework (SAF), which is applied in detail where required by the scope of the study. Five representative urban intersection scenarios, covering both nominal driving conditions and safety-critical edge cases, are evaluated using virtual simulations in MATLAB/Simulink (2014b) and hybrid experiments integrating OMNeT++ (5.7.1)/Veins (5.2)/SUMO (1.12.0) with real-world components. Key Performance Indicators (KPIs) related to safety, decision-making, longitudinal control, passenger comfort, and V2X communication performance are analyzed. The results show strong consistency between virtual and hybrid testing, with ego vehicle speed deviations below 2 km/h and trigger distance differences under 3 m. V2X communication achieves a near-perfect Cooperative Awareness Message (CAM) delivery ratio, with an average latency of approximately 142 ms. While this latency remains within the tolerance of the deployed ADS, the overall end-to-end delay highlights opportunities for further optimization. The study demonstrates how the SUNRISE SAF can effectively structure ADS validation, identifies critical scenarios such as right-of-way violations by non-priority obstacles, and provides insights into improving connectivity handling and low-speed braking behavior for Cooperative, Connected, and Automated Mobility (CCAM) systems in urban environments.

This study examines the impact of cultural tourism on the preservation of local traditions in Penglipuran Village, one of Bali’s recognized tourism villages. While previous research has extensively discussed the economic and environmental consequences of tourism, limited attention has been given to its influence on the sustainability of local cultural traditions at the village level. Moreover, existing studies tend to adopt general perspectives, lacking in-depth analysis of specific socio-cultural dynamics within particular communities. This research employs a qualitative approach using a case study design to explore how cultural tourism interacts with traditional practices in Penglipuran Village. Data were collected through field observation, interviews with community leaders and residents, and document analysis. The findings indicate that although tourism has introduced certain transformations in ritual practices and social organization, the community’s strong adherence to fundamental Hindu philosophical doctrines, such as Widhi Tattwa, Atman Tattwa, Karmaphala Tattwa, Samsara Tattwa, and Moksa Tattwa, serves as a stabilizing framework that sustains cultural continuity. Local perceptions suggest that tourism, rather than eroding tradition, can reinforce cultural preservation due to visitors’ interest in authenticity and ritual life. Strategic measures implemented include participatory tourism governance, cultural education programs, infrastructure development, sustainable mobility systems, community-based partnerships, small enterprise empowerment, experiential and educational tourism packages, regulatory frameworks, systematic documentation, and community capacity building. This study contributes to the discourse on sustainable cultural tourism by demonstrating how religious worldview and community-based management function as key mechanisms in safeguarding local traditions.

This paper investigates the finite-horizon H∞ control problem for mobile robot system under hybrid cyber attacks, where signal transmissions from sensors to the controller are scheduled using an accumulation-based event-triggered mechanism (AETM) to reduce communication load. Compared with traditional event-triggered strategies, the AETM exhibits enhanced robustness against burst signals and owns a lower communication frequency. A more general cyber attack scenario is considered, in which randomly occurring denial-of-service (DoS) attacks and deception attacks coexist, forming a hybrid attack environment. The objective is to develop an AETM-based control strategy that ensures the mobile robot system satisfies the desired finite-horizon H∞ performance under such hybrid attacks. Sufficient conditions are first derived using the stochastic analysis technique to guarantee that the mobile robot system meets the prescribed control performance under the proposed strategy. Then, by recursively solving a sequence of matrix inequalities, a time-varying controller gain is computed in real time. Finally, the effectiveness of the proposed controller is demonstrated through numerical simulations based on the kinematic model of a mobile robot.

Purpose: This study develops a mobile-based financial reporting system for mosques under ISAK 35 to improve the reporting quality and donor trust through real-time updates. Methodology/approach: Using the Rapid Application Development (RAD) method, this study covers system requirement analysis, design, implementation, and testing. Data were collected through observations, Focus Group Discussions (FGDs) with mosque administrators, and literature reviews. Usability was tested using black-box and task-based evaluations. Results: The system generates ISAK 35–compliant reports, including statements of financial position, comprehensive income, cash flow, and changes in net assets. This enables real-time recording and reporting, thereby improving transparency and efficiency. Usability testing showed that administrators found the system to be intuitive and useful, although some struggled with adjustment entries. The system also improves financial literacy and donor confidence. Conclusions: The ISAK 35–based mobile system strengthens mosque financial reporting and offers a practical and replicable model for nonprofit entities. Limitations: The system is limited by database capacity (200 MB, 500 rows), lacks integration with digital payment tools (e.g., QRIS), and does not fully support multi-user or multi-entity functions. Limited accounting knowledge among administrators also affects usage. Contributions: This study presents a digital financial management prototype that integrates ISAK 35 with mobile technology to build donor trust and improve responsible reporting. The model can be adapted for other nonprofits, such as churches, foundations, and NGOs.

Late-onset Pompe disease (LOPD) presents with progressive muscle weakness, often leading to functional impairment that is challenging to monitor with conventional assessments. This study aims to develop and validate novel gait-based outcome measures for monitoring disease progression in individuals with LOPD. Longitudinal study with genetically confirmed LOPD patients and age-and gait velocity-matched healthy controls that were assessed over a two-year period using the Ephion Mobility system, which integrates inertial sensors, plantar pressure insoles, and surface electromyography. All participants completed a free walking test (10-15m at self-selected pace) and the 6-minute walk test (6MWT). Differences in gait features were identified using a three-stage feature selection framework that includes linear mixed-effects model, ElasticNet-regularized and bootstrap analysis. To explore intra-group variability within the LOPD cohort, we performed a clustering analysis. Based on the selected features and their weighted temporal changes, we developed a Pompe Mobility-Derived Progression Index (Pompe-MDPI) by training a Linear Discriminant Analysis (LDA) to discriminate between control and LOPD data. We calculated the Minimum Clinically Important Difference and compared its performance against the 6MWT distance. 24 LOPD and 39 healthy controls were included in the study. 46 gait features were found to significantly differentiate individuals with LOPD from controls (Holm-corrected p < 0.05), comprising 16 from trunk and pelvis joints, 18 from lower limb joints, 4 from force profiles, and 8 from EMG.Hierarchical clustering analysis revealed two distinct subgroups within the LOPD cohort, based on nine gait features. The computed Pompe-MDPI successfully discriminated between LOPD and healthy controls (AUC = 0.95), outperforming the 6MWT distance (AUC = 0.84). The Pompe-MDPI was also strongly associated with the 6MWT (p < 0.0001) and demonstrated significant change over time in the LOPD group (p = 0.02). The Pompe Mobility-Derived Progression Index (Pompe-MDPI) was developed and validated as a sensitive biomarker of disease progression. Longitudinal analysis demonstrated that Pompe-MDPI captured gait deterioration over one year, outperforming traditional measures like the six-minute walk test in sensitivity. These findings support the use of wearable gait analysis as a clinically meaningful, scalable tool for monitoring motor function in LOPD, with implications for both patient care and therapeutic trials.

Autonomous driving technology is a core enabler for new energy vehicle industrial upgrading and a critical pillar for achieving sustainable development goals (SDGs), especially sustainable urban mobility, low-carbon transportation, and efficient intelligent transportation systems (ITS). However, unstable nighttime low-light perception severely restricts autonomous driving deployment, hindering sustainable transportation development—rooted in visual feature degradation and cross-modal imbalance that impair 3D object detection (autonomous driving’s core perception technology). To address this and advance sustainable autonomous driving, this paper proposes a Bird’s-Eye View (BEV)-based multi-modal 3D object detection approach tailored for nighttime scenarios, integrating low-light adaptive components while preserving the original BEV pipeline. Without modifying core inference, it enhances low-light robustness and cross-modal fusion stability, ensuring reliable perception for sustainable autonomous driving operation. Extensive experiments on the nuScenes nighttime subset quantify performance via rigorous metrics (NDS, mAP, mATE). Results show the method outperforms BEVFusion with negligible parameter/inference overhead, achieving 1.13% NDS improvement. This validates its effectiveness and provides a sustainable technical tool for autonomous driving perception, promoting new energy vehicle popularization, optimizing urban ITS efficiency, reducing perception-related accidents and carbon emissions, and directly contributing to transportation and socio-economic sustainability.

Artificial Intelligence is increasingly embedded in urban mobility and emergency response systems, where real-time decision-making, infrastructure coordination, and public safety converge. This article examines Urban Artificial Intelligence (Urban AI) through the domain of traffic management and emergency mobility, using these systems as a strategic entry point for analyzing broader questions of governance, equity, and resilience in AI-enabled cities. The paper develops a theoretical framework that distinguishes among cognitive, data-driven, and hybrid Urban AI models, highlighting how each approach shapes urban knowledge production, operational performance, and accountability. This framework is grounded through three U.S.-based case studies: AI-enabled emergency vehicle preemption in Fremont, California; AI-assisted subway infrastructure monitoring in New York City; and AI-driven signal coordination for emergency routing in Seattle. Together, these cases illustrate how Urban AI systems are deployed in real-world contexts to enhance efficiency, safety, and resilience. The analysis demonstrates that while Urban AI can significantly improve urban operations, its long-term legitimacy depends on integrating principles of equity, transparency, environmental responsibility, and participatory governance. The article concludes by arguing for integrated Urban AI models that balance technical effectiveness with democratic oversight, positioning Urban AI as a critical component of just, resilient, and inclusive urban futures.

Malignant arrhythmia risk assessment based on lead-I mobile ECG measurements using machine learning.

Usability assessment for cave automatic virtual environment-based immersive education system on novice nurses and nursing students: A mixed methods study.

Anomaly detection in gamma-ray spectra using autoencoders with small form factor CZT detectors.

Stochastic Modeling for Integrating Bike-Sharing Services with Public Transport in Urban Mobility Systems

The rapid expansion of urban populations has increased pressure on conventional waste-collection practices, which largely depend on manual operations and lack real-time monitoring capabilities. To address these limitations, this paper presents the Smart Street-Level Autonomous Dustbin (SSAD), a mobile robotic system designed for door-to-door waste collection and ondevice segregation. The SSAD integrates an Internet of Things (IoT) framework with embedded sensing modules, GPS-assisted navigation, and lightweight machine-learning-based waste classification. Ultrasonic and infrared sensors support obstacle detection and bin identification, while a convolutional neural network deployed on an onboard processor categorizes waste into biodegradable and non-biodegradable types. The system transmits operational data—including bin status, location, and segregation logs—to a cloud-based dashboard for real-time monitoring. Experimental evaluation demonstrates an average classification accuracy of 85–90% and a 25% improvement in route efficiency compared to manual collection. The proposed system illustrates the potential of autonomous mobile platforms for enhancing waste-management efficiency and enabling scalable smart-city solutions.

On-device intelligence has emerged as a critical requirement for modern mobile systems due to increasing concerns around latency, privacy, reliability, and regulatory compliance. While prior work has demonstrated the feasibility of executing isolated machine learning models on mobile hardware, there remains a lack of system-level frameworks that integrate behavioral modeling, temporal analysis, and adaptive decision-making in a manner suitable for large-scale iOS deployment. This paper presents an original, end-to-end on-device intelligence framework for iOS applications that models user interaction behavior using structured feature engineering and lightweight machine learning pipelines optimized through Apple Core ML and the Neural Engine. The system captures fine-grained interaction signals, performs deterministic preprocessing and behavioral feature extraction, and applies on-device inference to generate adaptive application responses in real time. Unlike cloud-centric approaches, the proposed framework eliminates network dependency and preserves user privacy by design. Experimental evaluation demonstrates that the framework achieves consistently low latency, reduced energy consumption, and stable performance across devices and usage contexts. The technical contributions of this work lie in its system architecture, modeling methodology, and deployment strategy, offering a practical and scalable blueprint for privacy-preserving mobile intelligence. This work constitutes an independent and original contribution to the field of mobile computing and on-device machine learning.

This work investigates the process of real-time resource management of an information system on a mobile platform under intermittent connectivity, destructive influences, and nonstationary resource availability. The scientific task relates to the fact that forecast errors and the inertia of platform reconfiguration can induce oscillatory resource redistribution, causing critical processes to intermittently lose the minimum required resource at each control time-step. A time-step-based predictive adaptive redistribution method with dynamic reservation has been devised in this study. The method introduces an operational survivability constraint. It is formulated as a requirement to maintain a minimum guaranteed resource profile for critical processes. The method adapts the reservation volume according to the assessed reliability of the current forecast. It also constrains the frequency of reconfigurations within a sliding window. These constraints are combined with dispatching of critical processes by urgency and allowable delay. By combining forecast-adaptive reservation with inertia-consistent reconfiguration constraints, the control loop reduces the amplitude and the cumulative intensity of reconfigurations. In particular, under bursty critical workload, the maximum reconfiguration step decreases by about 52%, while the cumulative magnitude of profile changes decreases by about 14%. These effects are explained by the fact that reserved resources compensate for forecast degradation, whereas reconfiguration constraints prevent abrupt control actions and stabilize time-step allocation. The results could be implemented to build resource management information systems for robotic platforms, sensor networks, as well as mobile systems under intermittent connectivity and real-time resource degradation.

Secure and reliable operations with information are needed for the sustainability of a context-aware mobile open learning environment. Students' attendance prediction can be maximized to optimize resource usage, enhance operational effectiveness, and increase confidence in the institution. The challenge is that conventional statistical techniques don't take into account temporal, contextual, and resource security issues in a decentralized mobile learning setting. This paper presents a model that provides the background for LSTM networks (Long Short-Term Memory) in the context of realtime student attendance prediction. The model can capture an intricate time series while preserving confidential student data, and possessing a touch of privacy-protected lightweight mobile streams keeps the model's ability to interface intact. In the evaluation of the model within the framework, it is evident that the model exhibits satisfactory performance (training: accuracy = 99%, precision = 98%, recall = 99%, F1 = 98%). Furthermore, the model's performance has been sustained even in the presence of adversarial attacks, data silos, and data exfiltration. The MAX LOAD behavior is considered good support for the viability of the model and as supportive evidence that AI-based prediction in the mobile environment can be undertaken with reasonable confidence. This effort addresses the task of reasonably integrating predictive modeling into the problem of obtaining appropriate measures of security for use in modeling, towards intelligent mobile learning systems that are adaptive, sustainable, private yet trusted, and hence maximize institutional trust in digital learning spaces.

Student performance evaluation is a crucial aspect of improving the quality of higher education. This study aims to develop and test an intelligent mobile system based on expert systems for evaluating students’ academic performance. The model is designed to identify key factors influencing student performance and provide more objective, data-driven assessments. Structural equation modeling (SEM) is used to analyze the relationships between variables involved in this evaluation system. Data were collected from students in Universitas Negeri Padang, with students from several departments, and analyzed using SEM to test the validity and reliability of the developed model. The findings indicate that this intelligent mobile system enhances the accuracy of student performance evaluation and provides deeper insights for academic decision-makers. With the implementation of this expert system, educational institutions can optimize learning strategies and academic management more effectively.

Aviation emissions have been the focus of research and regulation since the 1970s. Switzerland's Federal Office of Civil Aviation (FOCA) and research groups in Switzerland have contributed significantly to an improved understanding of these emissions and their impact on the environment and health. This paper presents a brief history of aviation emissions research in Switzerland. We also present the direction of future research on aviation emissions using the Swiss mobile aircraft emissions measurement system (SMARTEMIS).

Design guidelines play a crucial, but often under-appreciated, role in developing mobility infrastructures and cultures, including cycling. Design guidelines have long histories of codifying engineering practice, largely out of the public eye. Such engineering standards often describe actual practice better than policy documents, which are more subject to the vagaries of politics and express visions. Created outside the political arena, manuals wield outsized social power through standard setting but are rarely analysed in mobility studies. Specifically, this paper compares the development of bicycle lane design guidelines in two mature cycling nations, China and the Netherlands. Our qualitative analysis of such bikeway design manuals and their social and historical contexts, coupled with a modal split analysis, takes a long-term perspective (100 years+). A ‘5-Factor Cycling Cities’ analysis – considering urban form, mobility alternatives, transport policy, social movements, and cycling’s cultural status – focuses on the unexamined role of manuals in explaining the development of (urban) cycling over time. This paper initiates a theorization of the power of design manuals in the long-term shaping of mobilities and transitions. Manuals’ roles are often hidden, while they play a fundamental role in policy, with long-term impact on the design, planning, and use of mobility systems.

In​‍​‌‍​‍‌ emergency situations, rapid medical assistance is vital for saving lives but traditional ambulance services, which rely heavily on the road infrastructure, often face challenges when the terrain is difficult, roads are damaged, or the accessibility is limited. To counter these issues, TRAYANA is introduced as a combination of multi-terrain emergency response vehicle that can perform emergency operations through air, water, and land. The innovative vehicle is a VTOL (Vertical Take-Off and Landing) enabled quad rotor configuration powered by a turboshaft engine and features an amphibious operation element with a watertight and buoyant structure as well as a rugged ground traction system for all-terrain mobility. Such a versatile vehicle can operate under tough and unreceptive conditions including mountainous areas, flood zones, river deltas, and off-road areas. The design features a centrally located turboshaft engine that is mechanically connected to four independent rotors thus providing high power, prolonged operation time, and fast refueling. Brushless DC motors together with a buffer battery are used to power the auxiliary propulsion and other onboard systems. A sophisticated navigation and guidance system utilizes GPS, inertial measurement units (IMU), altimeters, and digital compasses to allow accurate flight and smooth handling. Supplementary situational awareness is provided by laser imaging detection and ranging (LiDAR), wind gauges, and barometers thus enabling the system to adapt swiftly to the prevailing environmental conditions. A twin communication system that combines telemetry for long distances and IoT-based 4G/5G connectivity guarantees live monitoring, unmanned control, and uninterrupted healthcare data streaming.

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling.