ABSTRACT. The global population growth, regulatory compliance, and resource depletion are factors that lead organizations to depart from industrial practices towards more sustainable operations. Environmental impacts of the supply chain are expected to be amplified by 40% - 70% in the absence of green and sustainable practices. Renewable Energy adoption has been emphasized in the existing literature as a key strategy for sustainable improvement in addressing environmental issues. The transaction toward the green supply chain network has grown significantly with the consideration of digitization. This shift impacts firms by initiating integration operations with supportive advanced technologies. Industry 5.0 technologies appear as a driver for achieving green practices in the supply chain network. However, there is a lack of research papers investigating the impact of I5.0 technologies on the RE supply chain network. I5.0 performs in a way to mixes human-centered approaches with advanced technologies, promoting both ecological and community welfare in industrial shifts. This research study aims to address this issue through analysing the enablers of I5.0 technologies and establishing a framework for the impact of I5.0 technologies on the RE supply chain network. The research approach employs a mixed-methods design, utilizing a two-phase decision-making model that incorporates the Entropy Weighting Method (EWM) and the Quality Function Deployment (QFD) approach. The research findings offer novel insights into the realm of I5.0 and RE practices, the Entropy approach analysis revealed that Transparency and Decision-Making are among the most influential criteria; the results of QFD underlie the Internet of Things (IoT), ranked among all estimated I5.0 technologies, the most weighted Technology, followed by a Digital Twin and Artificial Intelligence occupying the second and third ranking. The findings correlate with the advancement of smart mobility in RE practices within supply chain networks, enhancing supply chain activities and achieving resilience.

ABSTRACT. The electrification of public transport fleets is a critical pathway toward achieving national sustainability and emission reduction goals. This study develops a practical optimisation framework to guide the economic and operational transition of university bus fleets from diesel to battery-electric buses (BEBs). The proposed University Bus Transition (UBT) model is formulated as a multi-period integer linear optimisation problem that minimises the Net Present Value (NPV) of total system costs, including vehicle acquisition, battery replacement, charging infrastructure, maintenance, energy, and labour, subject to annual budget and service constraints. The model is applied to the King Fahd University of Petroleum and Minerals (KFUPM) campus to identify the optimal replacement schedule and fleet composition over a 20-year planning horizon. Results show that with a $400,000 annual budget and 50% electrification target, the NPV of UBT model is $12.35 million. Feasibility analysis indicates that the upper electrification limit under the given budget is approximately 60%, beyond which the transition becomes economically infeasible. The findings highlight the financial and operational trade-offs involved in achieving sustainable campus mobility. Although based on KFUPM-specific data, the framework is adaptable to other institutional or regional transport systems with appropriate parameterisation. Future work should incorporate renewable energy integration, real-world charging patterns, and life-cycle emissions analysis to enable a comprehensive evaluation of electrified transport strategies.

ABSTRACT. Energy infrastructure systems are increasingly exposed to uncertainty arising from aging assets, environmental hazards, and operational volatility. This study presents a hybrid reliability-centred statistical framework for quantifying and optimizing the resilience of energy systems, integrating Weibull reliability modelling, Monte Carlo uncertainty analysis, and cost–resilience trade-off optimization. Synthetic failure-time data were simulated using a Weibull distribution (β = 1.8, η = 5000 hr) to represent realistic component degradation patterns. The system resilience index (R) was computed from performance recovery profiles, while uncertainty propagation was analyzed through probabilistic reliability simulations. A Particle Swarm Optimization (PSO) algorithm was then employed to determine the optimal investment level balancing resilience improvement and maintenance cost. Results indicate that moderate investment (~6.8 units) achieves the best trade-off between cost and system resilience (R = 0.82), with Monte Carlo reliability 95% CI of [0.78, 0.92], revealing the influence of stochastic failure behaviour. The proposed framework provides a decision-support tool for policymakers and system engineers to prioritize resource allocation, enhance adaptive capacity, and maintain operational reliability in uncertain environments.

ABSTRACT. Motivated by Saudi Arabia’s Vision 2030, we assess where battery‑electric trucks can economically replace diesel in the Kingdom’s logistics network. We develop a Saudi‑specific total‑cost‑of‑ownership model for representative duty cycles and embed it in an operations optimization that minimizes logistics cost while applying a carbon shadow price. Stochastic simulations vary diesel and electricity prices, tariffs, grid intensity, and policy shocks to test robustness. Results: depot‑based medium‑duty fleets on urban and regional routes are already cost‑competitive under prevailing industrial tariffs, with managed charging enhancing savings. Heavy‑duty long‑haul remains cost‑positive but nears parity when the diesel–electricity price ratio widens or standardized corridor charging is available. Carbon pricing and grid decarbonization cut emissions materially, yet parity hinges mainly on relative energy prices and operational constraints. Roadmap: electrify medium‑duty first; establish corridor pilots for heavy‑duty; hedge capital and tariff risk through contracts and incentives; and coordinate depot/corridor charging with managed charging and on‑site renewables to stabilize power costs and scale reliably.

ABSTRACT. This study explores how private investment in Artificial Intelligence (AI) influence the improvement of Logistics Performance Index (LPI) for countries in the European Union and the Arabian (MENA) region from 2018 to 2023. The paper use a quantitative approach with secondary data, focusing mainly on AI investment and some baseline economic conditions like GDP per capita, broadband coverage and trade openness. The regression results show that AI investment has a positive and significant impact on LPI when the EU and Arabian countries are combined, but the relationship disappears when only EU countries are tested. The findings suggest that developing or middle-income economies may gain more from AI investment because they have more space for improvement, while richer countries maybe already reached performance limits. This research gives a simple but meaningful understanding of how AI can support logistics and trade competitiveness under different economic environments

ABSTRACT. In recent years, research on Supply Chain Visibility (SCV) and Industry 4.0 technologies has increased significantly. This study applied bibliometric analysis to discover the connection between SCV and Industry 4.0, highlighting key research movements, significant studies, and emerging themes in the field. The authors analyzed 426 peer-reviewed academic papers published from 2011 to 2025 that were indexed in Scopus. The study employed the bibliometrix R-package, with the results visualized using VOSviewer. The analysis reveals a significant acceleration of publications post-2018, coinciding with a mean citation per year surge, indicating the field’s growing maturity and relevance. Geographically, China, India, and the USA lead scientific production. Thematic analysis confirms that Blockchain is the most dominant technology and conceptual driver, exhibiting strong linkages with Supply Chain Management, Traceability, and Sustainability. The high concentration of articles clustered around single technologies (e.g., Blockchain or IoT in isolation) underscores the major research gap: there is a lack of integrated, holistic models.

ABSTRACT. This study examines the extent of Industry 4.0 (I4.0) technology adoption across global supply chain functions and investigates the role of Lean programmes as a foundation for digital transformation. An online survey was distributed to supply chain professionals worldwide, generating 70 valid responses from organisations of varying sizes and sectors. Results show that 74% of organisations are currently deploying I4.0 initiatives, and among these, 69% reported having an established Lean programme prior to digitalisation. These findings highlight the synergistic relationship between Lean and I4.0, where Lean maturity enhances digital readiness and I4.0 technologies strengthen Lean capabilities. Although representation from North and South America was limited, the study provides valuable empirical insights into the current state of Lean–I4.0 integration. Future research should consider larger and more regionally balanced samples or longitudinal case studies to further explore adoption patterns. Overall, the study contributes to a deeper understanding of I4.0 deployment across supply chain functions and its strategic interplay with Lean practices.

ABSTRACT. Logistics play a critical role in enhancing the sustainability and efficiency of waste management systems. This study investigates the potential of integrating electric and autonomous vehicles (EAVs) into waste transport operations to support circular and low-carbon industrial transitions. A comparative case study approach was applied to two large agro-industrial companies in the United Kingdom and Egypt, examining how electrification and automation can enhance the economic and environmental performance of waste-to-energy (WtE) systems. The first case study, Dyson Farming (UK), represents a technologically advanced circular farming model integrating anaerobic digestion, digitalisation, and precision logistics. The second case study, Beyti-Almarai, a large dairy producer in Egypt, utilises four organic waste streams to generate renewable energy through an on-site biogas plant. The proposed study develops a comprehensive techno-economic environmental assessment framework to evaluate the feasibility of replacing internal combustion engine vehicles with EAVs for transporting waste from collection points to the energy generation facility. The analysis integrates lifecycle costing, emissions modelling, and logistics optimisation to provide context-sensitive evidence on low-carbon mobility transitions. The economic assessment considers vehicle acquisition and lifecycle costs, while environmental evaluation measures potential reductions in greenhouse gas emissions. Results indicate that adopting EAVs can reduce emissions by more than 65%, equivalent annual costs by 11%, and cost per ton transported by around 6% compared with conventional diesel trucks. These findings demonstrate the strategic role of logistics decarbonisation in strengthening circular economy performance. The study demonstrates that integrating advanced vehicle technologies with biogas-based waste valorisation can significantly strengthen circular economy performance and industrial decarbonisation.

ABSTRACT. The increased use of Internet of Things (IoT) technologies in supply chains has led to the increased urgency to know the organisational variables that facilitate successful implementation. Despite the numerous success factors revealed by the current research, the literature is fragmented and systematically uneven, and is not as well-developed analytically. The paper fills this gap by summarising the evidence base and using Interpretive Structural Modelling (ISM) to organise the contextual relationships between IoT success factors in UAE oil and gas Supply chains. An initial search in Scopus retrieved sixty articles, out of which thirty-two qualified to be included. Eighteen papers have clearly mapped out IoT-related success factors, which resulted in sixtytwo that were later aggregated into twenty-nine overarching factors that is related to different sectors. These factors were reduced by a focus group of five experts working in the oil and gas industry in UAE to an acceptable set of twenty-seven, and this step can be done with experts from different indusries. The Structural Self-Interaction Matrix (SSIM) was created using expert judgement and then converted to a Final Reachability Matrix (FRM). A hierarchical ISM model was created based on this. The model also points out a three-level framework that involves key driving factors, a group of strongly interconnected linkage factors, and a group of dependent outcome factors. The results provide a systematic and theoretically based framework for the prioritisation of the implementation of IoT in complicated supply chain settings with application to UAE oil and gas Supply chains.

ABSTRACT. This paper presents the design, development, and evaluation of an IoT-enabled wearable sensor system for real-time health and posture monitoring, aimed at enhancing user safety and performance in both athletic and smart-city mobility contexts. The system integrates two prototypes: a posture-correction vest employing dual MPU6050 inertial measurement units with an on-body buzzer for immediate feedback, and an activity-monitoring glove equipped with an MPU6050 for step detection, a pulse-rate sensor for heart-rate monitoring, and Wi-Fi connectivity for cloud-based data logging via ThingSpeak. Data are processed locally on an Arduino platform and analyzed using a MATLAB-based artificial neural network employing the Levenberg–Marquardt algorithm to generate personalized warm-up or rest recommendations based on heart-rate thresholds. Experimental evaluation demonstrated that the posture-correction vest achieved a sensitivity of 85.5% in detecting poor posture, while the glove reached accuracies of 96.52% and 94.74% for heart-rate monitoring and step counting, respectively. The proposed system offers a low-cost, scalable, and extensible platform that combines real-time feedback with remote analytics, making it suitable for deployment in sports training, occupational health, and smart-city mobility applications.

ABSTRACT. The increasing digitalization of power systems has amplified their exposure to cyber threats, demanding advanced defence mechanisms that integrate strategic reasoning with adaptive control. This research develops a two-player, zero-sum Stackelberg game model that embeds Adaptive Autonomy (AA) into the cybersecurity decision-making process of smart grids. The proposed framework models the interaction between an attacker, aiming to maximize customer interruption costs (CIC) under budget constraints, and a defender, seeking to minimize both CIC and autonomy-implementation costs. The defender’s strategy dynamically regulates the Level of Automation (LOA), optimizing resilience through adaptive trade-offs between human supervision and autonomous control. Both decision spaces are formulated as knapsack problems and solved using a Particle Swarm Optimization (PSO) algorithm, applied to the IEEE Billinton Four-Bus Test System. Simulation results demonstrate that adaptive autonomy enables flexible resource allocation, which significantly reduces interruption costs. Moreover, a cost-sensitivity analysis reveals that when autonomy implementation exceeds approximately 25% of average payout costs, its marginal benefit diminishes sharply. The proposed model provides a rigorous quantitative foundation for balancing economic efficiency and cyber-resilience in smart distribution systems, offering actionable insights for utilities and policymakers. By linking game-theoretic defence modelling with human–automation dynamics, this work introduces a novel paradigm for adaptive, cost-aware cybersecurity management in cyber-physical energy infrastructures.

ABSTRACT. Stress is one of the most common challenges faced by people of nearly all age groups and professions, causing serious health conditions such as hypertension, cardiovascular diseases, and weakened immunity if not properly managed. The early detection of stress is an important concern in maintaining physical and mental health, and the rapidly emerging wearable devices support the continuous collection of physiological signals. Most earlier studies on stress detection are based on controlled laboratory datasets, which still lack real-time personalized systems that may adjust to users' daily changes in physiological states. The present study has proposed, therefore, an intelligent mobile app powered by artificial intelligence for real-time detection and management of stress using data obtained from wearable sensors. The proposed system classifies a subject as either stressed or non-stressed based on electrodermal activity (EDA) and skin temperature (TEMP) signals, using the XGBoost model trained on the open WESAD dataset. As soon as the subject is detected in a stress condition, the mobile application will trigger a personalized relaxation response comprising soothing music, motivational messages, or guided breathing exercises. In the experiments, the XGBoost model achieved an accuracy of 91.18 percent, whereas the SVM model achieved an accuracy of 94.12 percent, demonstrating the strong capability of both models in distinguishing between stress and non-stress states. The integration of AI-based stress detection with wearable technologies shall enable continuous, individualized, real-world stress management, bridging the gap between research in the laboratory and practical application, and paving the way toward widely available evidence-based mental health care.

ABSTRACT. Reliable traffic data are essential for intelligent transportation systems, yet real-world measurements are often degraded by missing readings and sensor noise resulting from failures, communication loss, or environmental factors. This paper presents a unified Graph Signal Processing (GSP) framework for traffic flow imputation and denoising based on Laplacian regularization. The road network is represented as a directed, weighted graph whose vertices correspond to sensors and whose edges capture spatial proximity and directional connectivity. Three weighting schemes are investigated: a standard Gaussian kernel, a self-tuning Gaussian kernel with locally adaptive bandwidth, and a correlation-aware hybrid kernel that integrates spatial distance with empirical temporal correlation.

Two complementary estimators are developed—a harmonic interpolation model and a Tikhonov regularizer—that jointly perform data recovery and noise suppression through a convex fidelity–smoothness trade-off. Model validation on the PEMS-BAY dataset demonstrates that graph-based methods consistently outperform purely temporal baselines under both random and burst sensor outages. The self-tuning and hybrid kernels achieve up to 25\% lower mean absolute error, while the graph-Tikhonov model attains the lowest RMSE and positive signal-to-noise ratio gains in denoising tasks.

Overall, the proposed GSP framework provides an interpretable and computationally efficient approach for reliable traffic data reconstruction across large-scale spatio–temporal sensor networks.

ABSTRACT. Urban traffic congestion remains a critical challenge for logistics and mobility worldwide, particularly in contexts where the rapid expansion of urban transportation demands more efficient and sustainable solutions. From a technical perspective, traditional forecasting models often overlook the intricate spatio-temporal interdependencies between geographic locations and traffic speed patterns, thereby limiting their ability to accurately predict real-world traffic dynamics. In this work, we address this limitation by implementing a hybrid architecture that combines Graph Convolutional Networks (GCNs) with Long Short-Term Memory (LSTM) networks. Vehicular speed data collected via GPS sensors are transformed into graph-structured representations using geospatial clustering from approximately 22 million GPS-based speed records. We present a comprehensive study that includes data preprocessing, imputation of missing values, and a comparative evaluation of different models. Our experiments show that the proposed GCN-LSTM approach significantly outperforms classical baselines such as linear regression and standalone LSTM models, yielding higher R 2 values, from 0.29 to 0.53 at 1-hour granularity, and reaching 0.64 at 3-hour granularity. The results highlight that incorporating spatial clustering structures through GCNs enables a more accurate modeling of interdependencies across urban zones, leading to substantial improvements in predictive performance, particularly in metrics such as MAE and R 2 . These findings pave the way for the development of graph-based intelligent traffic management systems with direct applications in urban logistics, transportation planning, and decision-support systems.

ABSTRACT. Accurate roadway travel-time prediction is foundational to transportation systems analysis, yet widespread reliance on either data- intensive congestion models or overly na¨ıve heuristics limits scalability and practical adoption in engineering workflows. This paper develops a lightweight estimator for minimally congested car travel times that integrates open road-network data, speed constraints, and sparse control/turn features within a random forest framework to correct bias from shortest-path traversal-time baselines. Using an urban testbed, the pipeline: (i) constructs drivable networks from volunteered geographic data; (ii) solves Di- jkstra routes minimizing edge traversal time; (iii) derives sparse operational features (signals, stops, crossings, yield, roundabouts; left/right/slight/U-turn counts); and (iv) trains a regression ensemble on limited high-quality reference times to generalize predic- tions beyond the training set. Out-of-sample evaluation demonstrates marked improvements over traversal-time baselines across mean absolute error, mean absolute percentage error, mean squared error, relative bias, and explained variance, with no significant mean bias under minimally congested conditions and consistent k-fold stability indicating negligible overfitting. The resulting approach offers a practical middle ground for transportation engineering: it preserves point-to-point fidelity at the metropolitan scale, reduces resource requirements, and supplies defensible performance estimates where congestion feeds are inaccessible or cost-prohibitive, supporting planning, accessibility, and network performance applications under low-traffic operating regimes.

ABSTRACT. Smart mobility is a term that refers to how adopting cleaner, safer, and efficient behaviour towards shifting to clean transportation matters to enhance smart mobility. The term refers to using different modes of transportation, or instead of owning a gas-fuelled vehicle, such as ride sharing, public transportation, car-sharing, biking, or even walking. Finding a reasonable solution to monitor the traffic congestion and the emission of carbon resulted from the grey hydrogen, which is nature gas, is the future of eco-friendly technology towards saving the environment. Following this approach, we will get closer to zero emissions, zero accidents, and zero pollution. There are key principles to achieve smart mobility, flexibility of transportation options, efficiency of the travel with minimum disruption, safety, and clean technology. However, many cities are facing problems in roads and streets, which leads to complex traffic congestion, has a negative impact on the environment. Also, high congestion leads to other problems, such as the disruption of road infrastructure, a high rate of potential accidents, and leads to high CO2 emission resulting from the combustion of grey hydrogen. To mitigate and support this environmental problem, an artificial optimized machine learning approach will be built to predict the levels of CO2 emissions, and predicting traffic conditions, thereby enabling data-driven strategies for cleaner, smarter, and more efficient mobility systems. In this research, regression models will be employed to detect the CO2 emission rate. In addition, classification techniques to predict the traffic conditions Geotab (2024).

ABSTRACT. Efficient healthcare logistics play a vital role in improving health outcomes by ensuring the rapid delivery of medical supplies, equipment, and patient care services. This study investigates the key logistics factors influencing transportation efficiency in healthcare operations and their subsequent effects on service performance and patient outcomes. The research employs a multi-criteria decision-making approach combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify, analyse, and prioritize logistics factors and technological enablers that enhance transportation efficiency. Literature review established six critical criteria: timeliness, safety, accessibility, cost-effectiveness, coordination, and reliability. Findings from the AHP indicate that timeliness exerts the highest influence on healthcare transportation performance (priority weight = 0.625), followed by safety, accessibility, and reliability. Reliable and punctual transport was found to significantly improve patient outcomes and operational efficiency, highlighting the need to balance patient-centric service with cost-effective logistics management. Using TOPSIS, eight transport technologies were evaluated to determine their contribution to efficiency and reliability. Telematics ranked first owing to its capability for real-time tracking, optimization, and monitoring. Internet of Things (IoT) and automated scheduling systems followed, enhancing responsiveness and reliability, while Artificial Intelligence (AI) and Blockchain ranked lower, reflecting limited maturity in healthcare logistics applications. The study recommends prioritizing investments in advanced navigation systems, real-time monitoring technologies, and route optimization tools to strengthen punctuality and safety. Moreover, healthcare organizations should integrate digital logistics management platforms to improve coordination, decision-making, and resource allocation. Customized transport services for remote or underserved communities are essential to enhance accessibility. Compliance with regulatory frameworks such as Good Distribution Practices (GDP) ensures quality, safety, and continuity of care. Overall, the research concludes that digital innovation, operational reliability, and timely service are fundamental to achieving efficient and patient-oriented healthcare logistics systems.

ABSTRACT. A growing penetration of renewable generation and electrified loads is pushing distribution systems toward DC microgrids, where tight DC-bus voltage regulation is critical, especially in islanded operation and in transport-adjacent power systems (e.g., depot or station DC buses). This paper presents a comparative study of classical PID and fractional-order PID (FOPID) controllers for a two-distributed generation (DG) microgrid modeled from a standard reference. Both controllers are implemented in MIMO form and tuned via four metaheuristic algorithms: particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO), and differential evolution (DE), using ITSE as the objective. All optimizations use an 80-iteration budget with bounded decision variables. Simulations in MATLAB/Simulink show that, relative to PID, FOPID consistently yields lower overshoot, faster settling, and smaller ITSE, while PSO and GWO converge faster than DE and GA. The results highlight the utility of fractional-order control with metaheuristic tuning for robust DC-bus voltage regulation in microgrids.

ABSTRACT. Electrified logistics and future transport increasingly depend on massive, stochastic, bidirectional energy flows between electric‑vehicle (EV) fleets and smart grids. Prior physics‑informed machine learning (PIML) integrated model predictive control (MPC) formulations demonstrated strong frequency regulation but provided limited guarantees on voltage behavior under logistics‑driven reactive‑power swings. This paper introduces a voltage‑augmented PIML–MPC that jointly predicts active/reactive EV disturbances and coordinates distributed energy resources (DERs) for co‑regulation of generator frequency and bus voltages. The PIML surrogate embeds swing and nodal‑voltage dynamics and is trained with sparse data via a hybrid data‑and‑physics loss with curriculum on the physics weight. The ensuing nonlinear MPC (NMPC) minimizes horizon‑wise state and input deviations under soft bounds using slack variables and employs a Lyapunov‑consistent terminal cost for recursive feasibility and asymptotic stability. On the IEEE 39‑bus system with EV clusters at depot‑like buses, the proposed controller reduces RMS frequency/voltage deviations versus conventional MPC and a frequency‑only PIML baseline, while preserving real‑time feasibility. Sensitivity to scaled fleet intensity confirms robustness and scalability. The results establish voltage‑aware PIML–MPC as a deployable cornerstone for resilient, EV‑integrated logistics grids.

ABSTRACT. Traditional forecasting approaches face challenges to accurate model the high temporal volatility of characteristics of electric vehicle (EV) charging demand, which is influenced by factors such as socioeconomic conditions, seasonal cycles, and regulatory incentives. In this paper, we present a Temporal Fusion Transformer (TFT) based predictive modeling framework for short-term, multi-horizon forecasting of station-level occupancy rates. The model combines static station metadata, known future calendar/tariff signals, and observed-past local history with spatial neighbor aggregates to provide attention-based interpretability through variable selection and masked, interpretable multi-head attention. Evaluated on the ST-EVCDP dataset, TFT achieved very high accuracy when compared to other methods across nine stations at 5-minute resolution with an average mean absolute error of 0.0064 and high coefficient of determination (R2) of 0.959, indicating outstanding predictive performance and resilience.

ABSTRACT. This paper presents a robust converter-level control strategy for a fuel-cell hybrid electric vehicle (FCEV) integrating a fuel cell, battery, and ultracapacitor through a common DC-link. An Adaptive Disturbance–Observer–Based Sliding Mode Controller (ADOB–SMC) is proposed to achieve precise DC-bus voltage regulation and fuel-cell current tracking under multiple source-side uncertainties. The controller combines real-time disturbance estimation with a continuous super-twisting sliding-mode law, effectively suppressing chattering while ensuring fast and stable convergence. A comprehensive nonlinear model of the hybrid powertrain was developed, and the control law was designed based on Lyapunov stability theory to guarantee global boundedness and robustness. Simulation studies were performed in MATLAB/Simulink under varying voltage disturbances of the battery, ultracapacitor, and fuel cell. The results confirm that the proposed controller maintains the DC-link voltage at 400~V with negligible steady-state error and rapid error convergence, even under combined source-voltage variations. The fuel-cell current adapts smoothly to load dynamics, ensuring coordinated energy sharing among all sources. Overall, the ADOB–SMC provides a highly effective and computationally feasible solution for stable and efficient operation of next-generation hybrid fuel-cell powertrains.

ABSTRACT. The rapid expansion of electric and hydrogen powered vehicles is reshaping urban mobility, but it introduces new challenges for charging and refueling infrastructure planning, grid stability, and low carbon energy utilization. This paper proposes a transformer enhanced multi agent reinforcement learning framework to coordinate heterogeneous actors, including electric vehicles, hydrogen trucks, charging stations, and grid operators, under a centralized training and decentralized execution paradigm. Agents learn policies through a joint objective that balances grid stress mitigation, equitable access, and emission reduction while respecting station and feeder constraints. A proof of concept simulation is evaluated on a smart city setting with 10,000 EVs, 2,000 hydrogen trucks, and 150 stations under peak demand surges, renewable intermittency, and station outage conditions. Compared to a MILP scheduler and a greedy decentralized scheduling baseline, the proposed approach reduces peak to average grid load ratio by 23% and 37%, respectively. It also lowers average waiting time by 18% versus MILP and 41% versus greedy scheduling, and improves fairness by 26%. When aligned with renewable availability windows, the framework achieves a 15% reduction in CO₂ emissions. Under outages affecting 10% of stations, it restores stable operation in 15 steps, compared to 34 for MILP and more than 50 for greedy scheduling. These results indicate a scalable and adaptive solution for sustainable smart city charging ecosystems.

ABSTRACT. The behaviour of people and usefulness/function have always been major considerations for design, and walkways are no exception. Professionals in the built industry have studied human behaviour to adequately cater to their design needs. Pedestrian walkway design involves creating safe and efficient paths for people to walk, jog, and use mobility devices in outdoor spaces. Effective pedestrian walkway systems on university campuses are important for students and teachers to connect, reach into different destinations which enhance their satisfaction of living in campus and academic experience. To improve usefulness of walkway system in university campuses, it is essential to study on how students use, perceive walkways, their safety, comfort, and satisfaction meet their needs. This study aims to analyze and understand the criteria of useful university campus walkways to satisfy the users through a case study of KFUPM. To achieve the aim a multimethod approach is adopted whereby observational walk-in audits of the King Fahd University of Petroleum and Minerals (KFUPM) campus walkways, in-depth interviews and questionnaire survey is employed to obtain qualitative and quantitative data from 10 experts and 85 students living inside KFUPM campus. The outcome of this study shows that lack of universal accessibility, way finding and amenities along the walkways are the major drawbacks in KFUPM walkway system. On the other hand, the students and teachers are overall satisfied with the comfort level while using walkways in KFUPM campus.

ABSTRACT. Urban transportation is central to achieving Sustainable Development Goal 11 (SDG 11), which calls for inclusive, safe, resilient, and sustainable cities. However, the rapid emergence of smart mobility technologies has outpaced our understanding of how these innovations actually contribute to urban sustainability. Existing research often isolates single technologies or single sustainability dimensions, leaving policymakers uncertain about their comparative impacts. Here, we systematically review 55 peer-reviewed studies published between 2010 and 2024 to evaluate the sustainability performance of five smart transport technologies, Autonomous Vehicles (AVs), Mobility-as-a-Service (MaaS), Intelligent Transport Systems (ITS), Green Infrastructure (GI), and Data-Driven Decision-Making (DDDM), across environmental, social, and economic pillars. Using the PRISMA 2020 framework and normalized comparative scoring, we find that MaaS and ITS deliver the most immediate and scalable sustainability gains, primarily through emission reduction, efficiency, and modal integration. AVs and GI provide conditional, long-term benefits that depend on clean-energy use, governance, and spatial investment, while DDDM acts as a systemic enabler that strengthens coordination and resource efficiency across all technologies. These results demonstrate that no single innovation guarantees sustainability in isolation. Achieving SDG 11 requires governance models that integrate digital and ecological strategies, ensure data interoperability, and promote social equity. The review provides policymakers with comparative evidence to prioritize smart transport interventions that balance technological innovation with inclusive urban development.

ABSTRACT. Urban Air Mobility (UAM) is evolving to be a main component of smart and cognitive cities. However, the key to achieving sustainable scalability lies in societal acceptance, as opposed to merely possessing the technology ready. This paper aims to conduct a critical review of global surveys on public perception and ethical frameworks regarding the adoption of UAM. It identifies the main socio-technical barriers associated with UAM adoption. The reviews indicated that the public varies greatly across regions, with high utility. Severely congested markets (e.g., Mexico City, Los Angeles) demonstrated the strongest support yet also displaying the maximum overall public apprehension, indicating a systemic conflict between perceived necessity and inherent risk. The main obstacles identified are persistently identified as operational safety, closely ranked next is the environmental challenge of noise, especially the cognitive disruption resulted from the novel acoustic profiles of eVTOLs (noise and visual blight). Ethical scrutiny emphasizes significant risks related to aerial surveillance, data privacy, and the prospect of UAM worsening social inequality when governance is ineffective. We concluded that an effective implementation depends on obtaining a Social Licence to Operate (SLO) using proactive governance grounded in rights. Cities should make strategies obligatory such as ‘Privacy-by-Design’, integrate transparency through Explainable AI (XAI) for autonomous systems, and create local coordination frameworks (e.g., U-space coordinators) to secure balanced access and establish durable public trust before full implementation.

ABSTRACT. Urban transportation systems form the backbone of modern city development, linking economic growth, environmental sustainability, and quality of life. With rising urbanization and rapid motorization, Indian cities increasingly face congestion, air and noise pollution, and escalating greenhouse gas emissions. Srinagar, the largest urban center in Jammu and Kashmir, exemplifies these challenges while offering distinct geographical and climatic attributes that necessitate context-specific, resilient, and adaptive transport planning approaches. This study evaluates Srinagar’s transportation system and explores potential pathways toward a sustainable, low-emission mobility network by simulating three progressive development scenarios: Business-as-Usual (BAU), Moderate Intervention (MI), and Sustainable Green Transition (SGT). Employing an integrated analytical framework that combines transport-demand modeling, emission estimation, and sustainability indicator assessment, the study provides a comprehensive understanding of the city’s mobility dynamics. The analysis indicates that private vehicles currently account for approximately 62% of total daily trips, underscoring Srinagar’s heavy reliance on personalized motorized transport and the inadequate performance of its existing public transit system. Average travel speeds range from 16-22 km/h, with key corridors such as Lal Chowk and Jahangir Chowk exhibiting volume-to-capacity ratios above 0.9, signifying oversaturation. Emission modeling indicates that transitioning from BAU to SGT could reduce CO₂ emissions by 27%, NOx by 19%, and PM2.5 by 29%, while improving average travel speed to 22 km/h and the Public Transport Accessibility Index from 2.5 to 4.2 stops/km². The SGT scenario further demonstrates an 18% reduction in transport energy intensity and potential prevention of 15-20 premature deaths annually linked to lower PM2.5 exposure. Beyond engineering measures, research emphasizes institutional coordination, citizen participation, and digital innovations as critical enablers of sustainable transport governance. The findings affirm that sustainable mobility in Srinagar is not only an environmental necessity but a strategic imperative to enhance public health, economic resilience, and urban livability in the Kashmir region.

ABSTRACT. This paper presents a novel non-invasive Brain-Computer Interface (BCI) system aimed at improving the independence and quality of life of individuals suffering from chronic motor disabilities such as quadriplegia and Amyotrophic Lateral Sclerosis (ALS). The proposed system enables users to control both mobility and smart home devices using a combination of mental commands and facial expressions, eliminating the need for physical movement or external assistance. The system employs the EMOTIV EPOC X EEG headset to acquire brain activity and facial gesture data. Through training sessions conducted in the EMOTIV BCI software, users can associate specific cognitive tasks (e.g., “Push,” “Pull,” “Left,” “Right”) and facial gestures (e.g., winking, raising eyebrows) with desired actions. These signals are transmitted using the Open Sound Control (OSC) protocol and received by an Arduino Uno microcontroller, which processes the commands and controls actuators accordingly. The system supports robotic wheelchair movement and the control of room conditions such as turning lights or fans ON or OFF.Experimental results demonstrate the system’s reliability, achieving a peak accuracy of 96.6% for the “raising eyebrows” facial gesture and 93.3% for the “Push” mental command, with a minimum response time of just 0.8 seconds. To enhance user safety during mobility, the system integrates a GPS module for real-time location tracking and an ultrasonic sensor for obstacle detection and collision avoidance. Unlike many existing systems that focus solely on motion control or require invasive procedures, this design offers a dual-mode, low-cost, and extensible solution suitable for deployment in low-resource settings. By combining cognitive and facial control channels with environmental and navigational awareness, the system provides a practical pathway toward greater autonomy for users with severe physical impairments.

ABSTRACT. This research presents a Markov Decision Process (MDP) framework for task (drive) management in Socially Assistive Robots (SARs). The proposed model enables the robot to motivate its owner in order to complete some essential tasks (like taking medication or exercising) by considering their interest levels and optimizing the robot’s decision-making strategy. The framework comprises state variables for drives and human interest, transition probabilities which are derived from a Bayesian network, and a reward function that balances task urgency with user engagement. A case study is employed to demonstrate how optimal policies can be obtained using value iteration, showing that the model effectively prioritizes drives and adapts strategies to maximize task compliance. The results highlight the potential of the proposed MDP-based task management to enhance human-robot interaction and promote long-term user adherence through adaptive and personalized motivation strategies.

ABSTRACT. The growing deployment of autonomous mobile robots in factories and warehouses introduces the need for navigation systems that ensure both safety and social compliance in shared spaces with human workers. Traditional rule-based or trajectory-predictive approaches handle proxemic constraints but lack adaptability to dynamic industrial environments where humans and robots coexist. This paper presents a learning-based social navigation framework that enables robots to navigate collaboratively and safely among workers and robots in industrial settings. The proposed method employs Deep Reinforcement Learning (DRL) integrated with Imitation Learning (IL) for efficient and stable training. An attention-based interaction encoder captures the varying importance of surrounding agents, while an asymmetric social awareness mechanism distinguishes between human comfort constraints and robot collision avoidance. The framework is evaluated across three representative industrial scenarios: open floor, aisle, and fixed-layout environments, demonstrating smooth, anticipatory motion, effective collision avoidance, and socially acceptable distancing. Quantitative results show a success rate of 89\% and a near-zero collision rate (0.15\%), outperforming comparable state-of-the-art models in safety and comfort while maintaining practical efficiency. These findings highlight the framework’s potential for real-world deployment in smart mobility and logistics systems requiring adaptive, human-centered navigation.

ABSTRACT. Extended Reality (XR) technologies, encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), arereshaping how humans interact with intelligent systems across many fields, including emerging mobility and logistics ecosystems,medicine, and industry. The effectiveness of these systems depends largely on the design of their interfaces and interaction tech-niques, which mediate perception, control, and collaboration between humans and digital agents. This paper presents a systematicreview of the principal XR interaction modalities, vision-based overlays, gesture and gaze tracking, speech input, haptic feedback,and multimodal integration with emphasis on their role in enabling cognitive human–machine teaming. By comparing XR applica-tions across different domains, the paper identifies recurring design strategies, domain-specific adaptations, and transferable lessonsrelevant to emerging smart mobility and logistics ecosystems. Key challenges such as usability, scalability, and standardization arealso discussed, along with opportunities for building unified, context-aware XR interfaces that enhance decision-making, trust, andcollaboration between humans and intelligent systems.

ABSTRACT. Artificial intelligence has become a central driver of next-generation prosthetic mobility, particularly through EMG-based movement and intent recognition. This review examines 16 recent studies covering a wide spectrum of AI techniques applied to neuromuscular signal interpretation for prosthetic and rehabilitation applications. A structured taxonomy is introduced to classify models into five main algorithm families, revealing that traditional machine learning methods constitute 56.25% of all reported approaches, while deep learning and hybrid architectures each represent 12.5%, highlighting a slow but emerging shift toward more advanced temporal–spatial modeling. The analysis identifies a substantial imbalance in the literature, with most systems developed for upper-limb control and very limited translation to lower-limb prosthetics, despite the unique demands of gait stability, terrain adaptation, and real-time user safety. The review further discusses methodological trends, dataset limitations, and computational considerations, ultimately outlining a focused research agenda for developing robust, lower-limb-specific AI models capable of delivering intuitive and adaptive human–machine teaming in mobility contexts.