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The rapid electrification of transportation has accelerated the demand for high-power electric vehicle (EV)-charging systems with high efficiency, compact size, galvanic isolation, and flexible scalability. Conventional isolated EV chargers typically adopt cascaded AC–DC and DC–DC conversion stages, which require additional semiconductor devices, passive components, and bulky dc-link capacitors, thereby increasing system complexity and limiting power density. This paper proposes a scalable three-phase modular parallel quasi-single-stage isolated single-ended primary-inductor converter (SEPIC) for high-power EV fast-charging applications. The proposed converter integrates power factor correction, voltage regulation, and high-frequency isolation within a unified SEPIC-based conversion cell, eliminating the intermediate dc-link capacitor while reducing the number of magnetic components and power conversion stages. By employing a Δ-connected three-phase input and input/output-parallel modular configuration, the proposed architecture provides a flexible power expansion approach based on a 9 kW basic module, with the potential to extend to higher power levels, such as 54 kW, through paralleling multiple identical modules. The operating principle, steady-state characteristics, continuous conduction mode (CCM)/discontinuous conduction mode (DCM) transition mechanism, current-sharing behavior, and control strategy are systematically investigated. An 18 kW prototype consisting of two parallel modules is experimentally validated under 380 V three-phase AC input and 400 V DC output conditions. The experimental results demonstrate a peak efficiency of 97.5%, a rated efficiency of 97.3%, a power factor (PF) of 0.999, and an input current total harmonic distortion (THD) of 2.55%, confirming the effectiveness and scalability of the proposed converter for high-power EV fast-charging applications. Full article

The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations. Full article

Repeated non-equidistant unmanned aerial vehicle (UAV) inspections of slope-road cracks require measurements from different distances, poses, and image scales to remain comparable and sufficiently precise for engineering-state decisions. Existing studies rarely integrate cross-view physical conversion, measurement uncertainty, and a project-defined minimum detectable change. We develop an engineering-detectability framework that defines cross-period criteria for crack width and displacement and derives equivalent widths for ideal, representative non-standard, and arbitrary viewpoints. First-order error propagation and reliability allocation convert the minimum detectable change into accuracy requirements for range, field of view, and normalized image coordinates. Crack-boundary coordinates and localization uncertainties provide a common interface for interchangeable detection and photogrammetric modules. Validation combines a controlled fixed-camera sequence with a close-range field-camera multiview test of seven physical openings under local coplanarity. All six determinate stages in the controlled sequence agreed with the digital image correlation (DIC) comparison, while one borderline stage required review. Across the seven openings, the four-view means gave a mean absolute error (MAE) of 0.196 mm and a root mean square error (RMSE) of 0.270 mm, with cross-view coefficients of variation (CVs) of 0.33–4.93%. An illustrative error budget demonstrates reverse screening of system configurations from project thresholds. The framework therefore connects viewpoint-equivalent measurements, uncertainty constraints, and engineering-state decisions in an auditable chain. Full article

Microtransit services fill gaps within public transportation systems across the United States (U.S.), but there are questions about whether they complement and compete with fixed-route services. The successful integration of microtransit is important for sustainability because it has the potential to improve the ridership and viability of public transit, which has implications for reducing emissions and increasing vehicle occupancy. Moreover, microtransit may serve as a more cost-effective way to provide transit service in low-density regions, relative to fixed-route services. We analyzed survey and trip activity data from three microtransit operations in California, including the Silicon Valley Hopper (N = 457), Richmond Moves (N = 131), and the Via West Sac (N = 224). For the Bay Area systems, surveys were deployed in November 2024, while activity data spanned June 2022 to November 2024. Via West Sac is one of the oldest microtransit systems in the U.S., and data from a May 2019 survey was integrated into the analysis. The survey showed that 35% of Richmond Moves, 31% of Silicon Valley Hopper, and 17% of Via West Sac respondents connected to and/or from public transit during their most recent microtransit trip. We estimated travel and wait times were lower on microtransit than public transit for 53% of Richmond Moves, 83% of Silicon Valley Hopper, and 85% of Via West Sac trips. We also found that 27% of Richmond Moves, 37% of Silicon Valley Hopper, and 52% of Via West Sac trips had no viable fixed-route transit alternative. Insights from these findings and expert interviews were used to define planning and design recommendations to improve complementarity. Key recommendations include providing comparative travel time information between microtransit and public transit options, highlighting faster fixed-route alternatives to requested trips, and offering transfer credits for microtransit trips that connect to transit. Full article

Commercial automotive technologies are advancing rapidly in areas such as electrification, connectivity, digital transformation, and autonomous systems. As military organizations increasingly incorporate commercial technologies, it is important to understand how commercial trends align with future battlefield requirements. This paper presents a framework for forecasting military ground vehicle requirements through 2040 by integrating commercial automotive technology trends with observations from contemporary warfare. This analysis identified automotive technology trajectories through a synthesis of published bibliometric studies covering major automotive research domains from 2016 to 2026. Military operational requirements were derived from battlefield narratives published by the Institute for the Study of War (ISW) and open-source vehicle loss data reported by Oryxspioenkop during the Russia–Ukraine War. The automotive and military analyses were then aligned to identify areas of convergence between commercial technology development and future battlefield requirements. The results indicate that future battlefields will be characterized by persistent surveillance, precision fires, contested logistics, contested electromagnetic environments, high attrition, and rapid battlefield adaptation. Future military vehicles will increasingly leverage commercial technologies to improve autonomous operations, predictive sustainment, fuel efficiency, resilient communications, and signature management while incorporating military-specific capabilities where required. The resulting framework provides a structured methodology for linking commercial automotive innovation with military vehicle modernization, acquisition planning, and future capability development. Full article

Road traffic surveys require sensing systems that can be deployed rapidly without modifying the road surface or requiring a permanent power connection. This paper presents the design, embedded implementation, and evaluation of a low-power roadside magnetic sensor that performs vehicle-event detection and classification directly at the edge. The sensing node integrates two RM3100 three-axis magnetometers (PNI Sensor, Santa Rosa, CA, USA) with an NXP MK22FN512VLH12 microcontroller (NXP Semiconductors N.V., Eindhoven, The Netherlands) based on a 120 MHz Arm Cortex-M4F core with 512 kB Flash and 128 kB SRAM. Magnetic-field data are acquired at 250 Hz and processed locally using baseline removal, low-pass filtering, signal-energy calculation, and peak-based event detection. Detected magnetic signatures are classified using an integer-quantised one-dimensional convolutional neural network implemented directly on the microcontroller. The model processes four synchronised 512-sample channels representing the three magnetic-field axes and their combined signal energy. Model development was supported by approximately 50,000 annotated events obtained from 36 h of real-world traffic measurements at eight locations. The selected model achieved an overall classification accuracy of 91.1% for the considered operational categories. The implemented network requires 288,128 multiply–accumulate operations per inference, while its quantised weights and biases occupy approximately 23 kB of Flash memory. Complete three-axis event signatures are stored locally for subsequent verification, whereas only the timestamp and predicted vehicle category are transmitted through the wireless interface. Based on the capacity of the applied LiFePO₄ battery and the estimated consumption of the implemented hardware, the expected autonomous operating period is approximately 41 days. The results demonstrate the feasibility of integrating magnetic sensing, embedded signal processing, and Edge AI on a conventional resource-constrained Cortex-M4 platform for non-invasive road traffic monitoring. Full article

Ordinary architectural conditions interact through arrangements that static representations often detach from time and use. This study examines six selected Turkish popular films as a mediated comparative corpus of apartments, neighborhood interfaces, mobile (route-vehicle) interiors, and domestic spaces in use. Comparative visual–spatial analysis of 24 scene-clusters traces how spatial domains and boundary/connection devices organize visibility, audibility, access, movement, bodily proximity, and collective attention. We define architectures of exposure as an event-level mismatch between scene-supported expected control over sight, sound, access, or bodily distance and the interactional field enabled by a configuration. A provisional, construction-corpus-derived layered model separates four primary spatial domains from boundary/connection devices, six interactional mechanisms, four control relations, three intensifiers, and contingent outcomes. Ten clusters directly demonstrate the mismatch; three show strategic or transformative widening, six provide partial or qualified evidence, and five delimit the concept through intended permeability or retained control. Cross-case comparison shows that the same device can preserve refusal, transmit distress, enable rescue, or support solidarity, while comparable spatial mechanisms acquire different narrative and ethical significance under comic and non-comic framing. The study contributes a scene-cluster method for treating fiction film as mediated architectural evidence and a diagnostic framework for future observation, acoustic assessment, visibility analysis, and post-occupancy research. Full article

The aluminum/steel hybrid body structure represents one of the key breakthrough directions for automotive lightweighting. However, aluminum and steel differ significantly in their thermophysical properties, making it difficult to achieve high-quality joining between them using conventional fusion welding methods. To address this challenge, the present study employs ultrasonic welding technology to achieve spot welding in a steel/aluminum/steel three-layer plate configuration. The experimental welding of the three-layer sheets and interfacial phase identification were first carried out, followed by the development of an ultrasonic vibration–thermal–mechanical coupled numerical simulation model, the accuracy of which was verified through experiments. On this basis, the dynamic evolution of the temperature and stress fields during the ultrasonic welding process was systematically revealed. Furthermore, this novel engineering simulation case was introduced into the teaching of the course Engineering Finite Element and Numerical Computation yielding favorable educational outcomes. Full article

Maritime routes ensure connectivity between nations, carrying a vast flow of goods across borders, while ports serve as the critical junctions within this network, managing a wide spectrum of commodities from raw materials to finished goods. Ports also generate employment across numerous sectors and underpin a broad range of allied industries. A seaport is a maritime facility equipped with docks, cranes, and storage infrastructure for international trade, where ships load and unload cargo, containers, and passengers. Key functions of seaports include customs processing, warehousing, and vessel services, with major global hubs such as Shanghai, PSA Singapore, DP World, and Rotterdam handling immense volumes of cargo each year. In contrast, Bangladesh’s ports, Chittagong, Mongla, and Payra, play a vital role in sustaining regional commerce. Today, ports are widely recognized as essential capital infrastructure and prime movers of economic activity. Smart ports are automated facilities that leverage advanced digital technologies, including sensors, big data analytics, artificial intelligence (AI), machine learning (ML), deep learning (DL), augmented reality (AR), digital twins, the Internet of Things (IoT), and various automation systems, to optimize overall operational efficiency. These tools streamline cargo movement while embedding sustainable practices to protect the environment. Beyond operational gains, smart ports deliver faster, more advanced services to all stakeholders involved in port operations, including shipping companies, customs agencies, local communities, and other relevant parties. Renewable energy sources, electric vehicle charging stations, onshore power supply, and smart logistics infrastructure are among the defining sustainability features of smart ports in the present-day context. This study examines the current status and future development trajectory of Bangladesh’s sea ports in relation to the broader global imperative toward smart port transformation. Full article

Driven by the vision of sixth-generation (6G) communication networks, Space–Air–Ground Integrated Vehicular Networks (SAGVNs) address the connectivity blind spots inherent in traditional networks by integrating unmanned aerial vehicles (UAVs) as highly mobile relay nodes. However, the high bit error rates (BERs) and prolonged propagation delays characteristic of satellite links, coupled with the highly dynamic topologies and multi-hop transmission nature of UAVs and terrestrial vehicles, present significant challenges to reliable end-to-end data streaming. To mitigate the performance degradation caused by link asymmetries in heterogeneous networks, this paper proposes a reliable transmission optimization scheme for UAV-relayed SAGVNs. By comprehensively modeling the transmission dynamics of long-delay, high-BER satellite links and mobile multi-hop UAV networks, the proposed scheme introduces an enhanced slow-start mechanism to accelerate throughput growth, thereby mitigating the startup lag induced by extensive propagation delays. Furthermore, an accurate packet loss differentiation model is established during the congestion avoidance phase. This model effectively decouples non-congestion packet losses—triggered by random channel errors or topology handovers due to high-speed node mobility—from genuine congestion-induced losses caused by buffer overflows at bottleneck nodes. Simulation results demonstrate that the proposed adaptive scheme demonstrates notable improvements over classical loss-based and delay-based baselines in reducing queuing delays at UAV relay nodes, enhances the transmission efficiency of multi-hop terminals, and effectively maintains end-to-end goodput stability in high-latency environments. Full article

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support. Full article

LiDAR–camera fusion is widely used for 3D perception in intelligent connected vehicles, but a clean camera branch does not necessarily compensate for structured LiDAR corruption. We propose CMS-Attack, a cross-modal search framework in which only the LiDAR point cloud is perturbed while the camera input remains unchanged; here, “cross-modal” denotes that a single-modality LiDAR perturbation propagates through the LiDAR–camera fusion process and disrupts the multimodal detector, rather than simultaneous perturbation of both modalities. The framework has the following two access-dependent routes: the gray-box route contains FB-CMS, which uses camera-BEV, LiDAR-BEV, fused-BEV, and detection-head responses to construct a target-aware prior and prune an over-complete candidate pool, and Adaptive CMS, which substitutes architecture-specific intermediate responses; the decision-only black-box route contains FC-CMS, which refines candidates solely from display-level target states. On the nuScenes validation split, FB-CMS reduced matched target confidence from 0.80 to 0.03 under 140 injected points, corresponding to a 96.1% relative drop and 100% ASR@0.3. The ten-query FC-CMS achieved 58.97% ASR@0.3, compared with 6.17% for random frustum spoofing. On the query-based FUTR3D detector, Adaptive CMS reduced the mean matched-target score from 0.642 to 0.058, corresponding to a 90.97% relative reduction and 93.42% ASR@0.3. Intermediate camera-, LiDAR-, and fused-BEV region energies changed by less than 0.8% despite target suppression, indicating disruption at the fusion-decision stage (defined here as the decoder/detection-head and post-processing path from fused representations to final object predictions) rather than a collapse of BEV feature magnitude. These results show that structured LiDAR fabrication is substantially more disruptive than information removal and that generic outlier filtering incurs a robustness–accuracy tradeoff. Full article

Cooperative adaptive cruise control (CACC) platoons rely on Vehicle-to-Vehicle communication and GPS to maintain sub-second headways, creating cyberattack surfaces underrepresented in standard surrogate-safety metrics. We built a fully equation-based, Routh–Hurwitz- and Lp-string-stability-verified simulation of a ten-follower (eleven-vehicle, including the leader) CACC platoon (point-mass dynamics, actuator lag, PD spacing control) and subjected it to a two-channel GPS-spoofing attack corrupting both the attacked vehicle’s control loop and its broadcast position; velocity and acceleration broadcasts, and the CACC feed-forward term they drive, are left uncorrupted, so the reported boundaries are conditional on this restricted, single-channel threat model and should be read as a lower bound on attack severity rather than a worst case. Across a 64-cell severity–duration grid (2–20 m, 1–10 s; h = 0.6 s), minimum time-to-collision fell from 31.7 s to a simulated collision in 6/64 cells (9.4%), driven more by magnitude than duration; the disturbance decays sharply after the first hop rather than cascading down the platoon, so the resulting risk is local, not cascading. A 48-cell headway grid showed h ≥ 0.7 s eliminated all collisions at the originally tested attack duration (3/8 → 0/8 at fixed severity), a result that held under two alternative controller-gain sets tested for sensitivity and was largely, though not universally, robust to a substantially stiffer third set. A position sweep found risk invariant across nine of ten platoon positions. Batch-computed first-hop propagation and tail-to-origin amplification ratios showed the disturbance transiently amplifies (ratio > 1) at its first hop in a third of tested attacks despite decaying three orders of magnitude by the platoon’s tail, a behavior distinct from the front-injected Lp string stability verified separately. Peak root-mean-squared jerk stayed within the comfortable range (≤1 m/s³) in every tested cell, including collisions, showing collision and comfort risk are governed by different parameters. Embedding a representative detection and elastic-control layer alongside headway optimization eliminated collisions within the tested range and remained robust at three times that severity, where headway alone failed; because the detector’s residual is computed directly from the true offset magnitude and detector failure is not modeled, this joint-defense result is illustrative rather than a validated-detector-calibrated estimate. These results give a reproducible, quantified basis for headway- and detection-based mitigation policy in connected-vehicle platoons. Full article

Ride quality—encompassing vehicle comfort, vibration isolation, and noise, vibration, and harshness (NVH)—has become a key competitive differentiator in modern automobiles. This paper presents a comprehensive update to the foundational literature on passenger car ride quality, capturing the rapidly growing literature and new technological paradigms that have emerged over the past decade. Established approaches to human vibration response, vehicle dynamics modelling, and road surface characterisation are examined within the ISO 2631 framework. This review critically surveys advances driven by battery electric vehicle (BEV) powertrains—where the absence of internal combustion engine noise unmasks motor whine, inverter switching noise, and tyre–road excitation, lowering the perceptual ride–NVH boundary from ~25 Hz toward 15–18 Hz—as well as intelligent semi-active and active suspension technologies, deep reinforcement learning for suspension control, machine learning for ride quality prediction, and connected vehicle infrastructure enabling predictive preview control. Key research gaps are identified: the absence of validated ISO 2631 frequency weightings for autonomous vehicle postures, the lack of standardised open benchmark datasets for cross-study comparison, and the unresolved sim-to-real validation gap for data-driven suspension controllers. Ten priority research directions are proposed for the coming decade. Full article

With the increasing integration of distributed photovoltaics, energy storage systems, electric vehicle chargers, and intelligent terminals, accurate identification of heterogeneous edge-end devices in distribution networks has become challenging due to their diverse operating characteristics and similar current signatures. This paper proposes an identification method based on current time–frequency features and Random Forest. Equivalent grid-connected current models are developed for five types of edge-end devices, considering different capacity levels, operating states, ripple characteristics, and transient behaviors. A 13-dimensional feature set is extracted from time-domain, frequency-domain, and time–frequency characteristics, covering 11 device subclasses. Feature analysis is conducted to evaluate the separability of the extracted features, and Random Forest is employed for multi-class device identification. The results show that the proposed method achieves an overall accuracy above 98% on independent test samples and 96.91% in the IEEE 33-bus validation case, demonstrating its effectiveness for distribution-network edge-end device identification. Full article