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Comprehensive mechanized solid backfilling technology exhibits significant advantages in solid waste disposal, “three-under” coal mining, and dynamic disaster control. However, its large-scale application is constrained by low production efficiency, high unit production cost, and high labor intensity. Therefore, industrial upgrading through intelligent technologies is urgently required. In this study, methods including literature review, theoretical analysis, and field measurements are employed to propose three backfilling modes. The configurations of the six core subsystems under each mode are systematically summarized, and the core definition of an intelligent backfilling mine is established. Furthermore, a key technology framework for intelligent backfill mining is developed, based on PLC control and PID algorithms, with a closed-loop architecture centered on “perception–decision–execution.” Engineering applications demonstrate that the surface gangue intelligent pretreatment system achieves functions including automatic vehicle washing, intelligent dust suppression spraying at discharge points, dynamic metering during conveying, and adaptive adjustment of feeding systems. The intelligent surface-to-underground coal gangue vertical feeding system enables full silo alarm and level regulation. The underground jigging intelligent separation system realizes intelligent jigging ratio adjustment, intelligent bed layer measurement and control, and intelligent air volume regulation, with the coal content in gangue discharge maintained below 4%. At the working face, the intelligent solid backfilling system doubles monthly coal output, boosts backfilling efficiency by 50%, and cuts the workforce by 8–10 workers. The intelligent backfilling effectiveness monitoring system operates stably, with a working face weighting factor of 1.12 and precise ground deformation control within Grade I limits. Full article

Child occupants are highly vulnerable to head and neck injuries in vehicle crashes; conventional child restraint systems primarily restrain the torso, with limited ability to directly reduce excessive head excursion and neck loads during frontal collisions. Therefore, effective cushioning and energy absorption are needed to improve head and neck protection in child restraint systems. This study proposed and evaluated a novel child restraint system integrated with an airbag to enhance head and neck protection. A finite element model of a five-point harness child safety seat with an airbag module mounted on the seatbelt buckle was developed. The predictive accuracy of the airbag model and child restraint system was validated through pendulum impact tests and frontal sled tests. Next, the PIPER 3 human model was applied to evaluate the effectiveness of the airbag. Compared with the five-point harness child restraint system without an airbag, the incorporation of the airbag significantly improved head and neck protection. Specifically, the maximum vertical head-T1 displacement decreased from 286 mm to 90 mm; additionally, HIC15, 3 ms resultant head acceleration, peak upper neck tension force, peak upper neck flexion moment, and 3 ms chest acceleration were reduced by 51.8%, 27.8%, 66.9%, 29.6%, and 16.0%, respectively. This study provided a technical basis for the development of passive safety technologies in child restraint systems with airbag applications. Full article

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

Unmanned aerial vehicles (UAVs) are increasingly used for remote dimensional inspection in transportation monitoring and infrastructure control. In such applications, measurement results are often interpreted relative to regulatory thresholds, making the reliability of inspection decisions strongly dependent on measurement uncertainty. This study presents a probabilistic framework for evaluating measurement uncertainty and decision risk in UAV-based dimensional inspection tasks. A measurement model describing uncertainty scaling with observation geometry is formulated, and the probability of exceedance relative to a regulatory limit is derived. The framework integrates probabilistic measurement modeling with a risk-based decision formulation that accounts for false-positive and false-negative inspection outcomes. The resulting integral inspection risk is analyzed for representative sensing modalities commonly used in UAV platforms, including vision-based systems, LiDAR, and radar sensors. The results demonstrate that uncertainty scaling with flight altitude significantly influences exceedance probability and decision reliability. Sensors with lower intrinsic dispersion maintain sharper threshold transitions and therefore provide more stable regulatory decisions. Sensitivity analysis further confirms that moderate variations in measurement uncertainty can substantially affect inspection risk. The proposed framework provides a quantitative tool for evaluating sensing technologies in UAV-based inspection missions and supports the design of reliable drone-assisted dimensional compliance monitoring systems. Full article

Since tires from end-of-life vehicles are not entirely biodegradable and pose a serious environmental problem, their disposal has become a significant global environmental concern. One technique to decrease these environmental issues is incorporating waste rubber to make sustainable green concrete. This study examined the usage of waste supplementary cementitious materials (SCMs) such as fly ash (FA), metakaolin (MK), marble powder (MP), slag (SL), and silica fume (SF) for surface precoating of crumb rubber (CR) to improve the mechanical properties of the produced crumb rubber concrete (CRC) by strengthening the bond between CR and cement paste in the interfacial transition zone (ITZ). The CR replaced (0, 15%, and 25%) of sand by weight in the preparation of CRC mixtures. A total of eleven CRC mixes were cast to investigate the fresh properties, compressive strength, and splitting tensile strength. In addition, the compressive stress-strain curve was investigated, and peak stress, peak strain, energy absorption, toughness, and modulus of elasticity have been evaluated. The outcomes showed that precoating CR using FA, followed by MK, has the strongest effect on increasing CRC compressive performance. The 25% substitution of sand with FA-treated CR increased compressive strength after 28 days, splitting tensile strength, peak stress, toughness, and modulus of elasticity by 34.7%, 23.7%, 34.8%, 26.1%, and 25.2%, respectively, in comparison to the same percentage of untreated CR. The proposed approach demonstrates a viable pathway for integrating waste materials and SCM-based technologies to develop high-performance, sustainable cementitious composites. Full article

Unmanned Aerial Vehicle (UAV) clusters have broad applications in agricultural detection, traffic control, and disaster rescue, where navigation and positioning serve as the core technology. However, satellite navigation fails to meet the requirements of region-wide navigation due to the urban canyon effect. Although the China Satellite Network (CSN) boasts advantages such as high landing power and low latency, it can only achieve single-link communication. Consequently, exploring how to realize cooperative positioning via UAV clusters has become an urgent research need. In this paper, an Unmanned Aerial Vehicle Cluster Communication–Navigation Integrated Cooperative Positioning (UCNCP) algorithm is proposed. This algorithm combines the communication and navigation characteristics of the CSN, establishes a single pseudorange measurement model and cluster geometric topology, and constructs an architecture for cooperative positioning based on UAV cluster pseudorange measurements and inter-UAV ranging data, thereby achieving reliable navigation and positioning of UAV clusters. Comparative experiments between the proposed method and other low-orbit satellite positioning methods demonstrate that the UCNCP algorithm exhibits higher positioning stability. When abrupt changes occur in navigation information, it can effectively mitigate the impact of abrupt change errors on positioning accuracy, improving the positioning stability of UAV clusters by more than 30%. Full article

With the deepening integration of 5G-Advanced (5G-A) technology into smart manufacturing, the large-scale deployment of dynamic terminals—such as mobile robots and automated guided vehicles (AGVs)—within industrial private networks introduces complex, time-varying penetration and path losses. This significantly degrades the accuracy of conventional signal quality and capacity estimation methods, which were primarily designed for static terminal scenarios, thereby posing substantial challenges to coverage and deployment planning of industrial 5G access points, with downstream implications for power capacity dimensioning. To address this problem, this paper proposes a coverage-driven base station deployment optimization method formulated as a combinatorial optimization problem. The study constructs a signal strength assessment and network throughput calculation model tailored for dynamic industrial environments. This model captures the joint impact of terminal mobility and environmental obstacles on signal propagation, thereby enabling more reliable estimation of coverage performance and power consumption. Furthermore, by formulating the base station placement optimization as a combinatorial optimization problem, and by introducing mechanisms for equivalent transformation of the objective function and data preprocessing, the proposed method substantially reduces redundant computations during heuristic iterations. Simulation results verify that, compared with conventional static planning approaches, the proposed scheme enhances both the accuracy and computational efficiency of deployment planning while maintaining coverage quality. This work provides a theoretical foundation and a practical methodology for deploying reliable and energy-efficient industrial 5G-A private networks. Full article

Digital Twin (DT) technologies are increasingly adopted in the automotive domain to support real-time monitoring, predictive analytics, and connected decision-making across vehicles, drivers, and road infrastructure. However, research on Vehicle, Driver, and Road Digital Twins (VDTs, DrDTs, and RDTs) remains fragmented, with heterogeneous definitions, architectural assumptions, and integration strategies. This paper presents a critical review of seventy-six studies published between 2008 and 2025, examining how these three DT domains are modeled, evaluated, and connected within intelligent mobility scenarios. The review synthesizes recurring architectural patterns, communication and computing choices, and the role of interoperability and standardization in multi-twin systems. It also highlights open challenges involving distributed coordination, semantic alignment, real-time operation, and driver-aware adaptation. Based on this analysis, the paper presents a unified conceptual framework for connected automotive digital twins and discusses key directions for building scalable and safety-aware mobility services. Full article

To address transportation-related emissions, Taiwan’s 2022 net-zero strategy sets targets to increase the adoption of battery electric vehicles (BEVs). However, current policy frameworks insufficiently consider the technological diversity of low-emission alternatives, particularly hydrogen fuel cell electric vehicles (FCEVs). This study integrates a well-to-wheel life cycle assessment (LCA) with system dynamics modeling to evaluate and compare the environmental and health impacts of transitioning from internal combustion engine vehicles (ICEVs) to BEVs and hydrogen FCEVs. The framework incorporates LCA-based carbon emissions and disability-adjusted life years (DALYs) into a dynamic population simulation. Results show that, while DALY effects on life expectancy and population growth are limited, low-carbon vehicle adoption substantially reduces environmental burdens and helps moderate population decline. Projections to 2050 highlight significant emission-reduction potential, with hydrogen FCEV carbon emissions decreasing as renewable energy in hydrogen production increases. Adoption of green hydrogen could achieve a net-negative carbon balance for hydrogen FCEVs by 2049, positioning them as a sustainable long-term alternative to BEVs. Full article

Driven by global energy transformation and the progress of artificial intelligence technology, traditional automotive engineering is undergoing profound changes. Transportation is rapidly advancing toward electrification and intelligence. Against this background, this paper identifies three emerging paradigms for the development of electric vehicles: Heart Revolution, Brain Evolution, and Network Integration. This paper points out that automobiles are evolving from traditional one-way energy consumers to dynamic energy nodes in smart grids. With the support of artificial intelligence technology, the role of automobiles is also shifting from a simple means of transportation to an intelligent mobile terminal. At the same time, this paper focuses on analyzing the application of the integration theory of “Four Networks and Four Flows” in automobile upgrading. The theory does not focus on the optimization of a single node unit but emphasizes a systematic perspective to improve overall performance and support sustainable development. This paper suggests that the development of the automobile industry must be deeply integrated with the humanity world, information world and physical world. By building a five-in-one architecture of “Human–Vehicle–Road–Cloud–Satellite”, the automobile industry could follow a practical pathway toward coordinated development. At the same time, breakthroughs in core technologies such as solid-state batteries and wide-bandgap semiconductors are also imminent. This paper aims to provide a sustainable and high-performance automobile development path and integrate the concept of human-oriented design into it. Meanwhile, China’s new energy vehicle industry is used as a representative context to illustrate its engineering and industrial implementation. Full article

This study presents a multivocal literature review (MLR) on the implementation of Shared Autonomous Vehicles (SAVs), a relatively new concept in urban mobility that merges autonomous driving with shared transportation. The purpose of this review is to analyse the feasibility, challenges and potential impacts of SAV deployment by aggregating and synthesising insights from the academic literature and grey sources. The review addresses factors influencing deployment, including social acceptance, environmental impact, business models, policy frameworks, needs and barriers, and lessons from existing pilot programmes. The findings reveal that successful SAV implementation depends on combining technology, regulation and infrastructure. Public trust and perception of safety, cost and convenience can also significantly influence the adoption of this technology, as well as potential sustainability benefits (like reduced emissions and fewer private vehicles). Case studies from cities like Phoenix, San Francisco and Singapore show promising results but also context-specific challenges. This study concludes that future research should apply these insights to specific cities, where urban layouts and public transport reliance demand customised approaches to successfully deploy SAVs. Full article

Road freight contributes over half of China’s transport carbon emissions, making its decarbonization critical for carbon neutrality. This study combines total cost of ownership (TCO) and life cycle assessment (LCA) to analyze the economic efficiency and carbon emission effects of diesel, electric, and hydrogen fuel cell trucks. Combined with the LSTM neural network and vehicle ownership model, this study predicts the fleet emission reduction potential from 2020 to 2050. The results show that all new energy trucks can achieve TCO parity with diesel trucks before 2050, and electrification shows better economic competitiveness than hydrogen fuel cell technology across all vehicle types in the Chinese context. Fuel cell trucks powered via solar-powered water electrolysis exhibit the lowest carbon intensity, and grid decarbonization can significantly improve the emission reduction effects of electric and fuel cell trucks. Freight fleet carbon emissions are expected to peak around 2030. In an ideal scenario, emission reductions of 19.5%, 41.9%, and 82.9% can be achieved by 2030, 2040, and 2050, respectively. Heavy-duty trucks are the main emission contributors (47–58%) and the main target of emission reduction strategies. Short-term reduction depends on fuel economy, while long-term reduction prioritizes new energy substitution. Policy recommendations include promoting alternative fuel trucks, upgrading emission standards, and adopting differential taxation. Full article

This systematic literature review (SLR) synthesizes current research on integrating machine learning (ML) into robust optimization (RO) frameworks for solving Green Vehicle Routing Problems (Green-VRP) under uncertainty. The key contributions include utilizing the EmbedSLR 2.0 framework for objective screening, establishing a functional ML role taxonomy, and mapping uncertainty sets to computational tractability. Following PRISMA guidelines, searches across Scopus, Sage, and Dimensions identified 82 eligible studies validated through a three-point quality assessment scale. Bibliometric analysis indicates that the VRP has evolved into an interdisciplinary field that combines the power of rigorous RO with the integration capabilities of ML to achieve sustainability and resilience goals. Based on the results of the literature review, it was found that ML plays four crucial functional roles: as an end-to-end problem solver, a tool for predicting input parameters, a guide for search subroutines, and a mechanism for constructing more precise uncertainty sets. Various frameworks such as Adjustable Robust Optimization (ARO), Distributionally Robust Optimization (DRO), and Data-Driven Robust Optimization (DDRO) have been reported in various studies to offer improved cost efficiency and robustness compared to conventional static RO models by utilizing data more dynamically to reduce the level of conservatism. The integration of these environmental factors is carried out through emission and energy consumption parameters, which systematically give rise to operational trade-offs. This SLR has several limitations, including database and language limitations, the absence of cross-reference validation in EmbedSLR 2.0, and limitations in quality assessment. This publication is funded by the Universitas Padjadjaran through the LPDP on behalf of the Indonesian Ministry of Higher Education, Science and Technology and managed under the EQUITY Program (Contract No. 4303/B3/DT.03.08/2025 and 3927/UN6.RKT/HK.07.00/2025), as well as the Universitas Padjadjaran Research Grant under Research Grant for Graduate Students (Hibah Riset Melibatkan Mahasiswa Pascasarjana - RMMP) with contract number 5598/UN6.3.1/PT.00/2025. This systematic review was registered on the Open Science Framework (OSF) on 8 May 2026. Full article

Radiotherapy remains one of the main pillars of cancer treatment and is used in more than half of all oncological patients. Despite continuous technological improvements, ionizing radiation inevitably causes damage to surrounding healthy tissues, leading to acute and chronic complications affecting multiple organs, including the skin, mucosa, heart, lungs, bones and gastrointestinal tract. Radiation-induced injuries significantly impair patients’ quality of life, limit therapeutic doses, and represent a major unmet clinical challenge. Hydrogels have emerged as promising biomaterials for managing radiation-induced damage due to their high content of water, tunable mechanics, and ability to mimic the extracellular matrix. Recent innovations have introduced functional systems, including stimuli-responsive, injectable, and bioactive hydrogels, capable of delivering antioxidants, growth factors, or living cells. Unlike traditional material-based reviews, this work proposes a novel classification framework based on the hydrogel’s mechanism of action within the pathophysiology of radiation injury. We evaluate how specific designs, such as ROS-scavenging matrices, barrier-forming injectable shields, and bioactive delivery vehicles, address distinct phases of inflammation and fibrosis. By providing a comprehensive overview of radiation-induced injuries across different organs, this review summarizes current hydrogel-based strategies for both prevention and therapy. We highlight the potential of these mechanistically aligned systems to protect healthy tissues, suppress chronic inflammation, and promote effective tissue regeneration. Full article

To address the insufficient strength of friction-stir-welded (FSW) ultra-high-strength Al–Cu–Li alloy joints, the effects of post-weld heat treatment (PWHT) on microstructural evolution and mechanical properties were systematically investigated. The as-welded joint showed a “W”-shaped microhardness profile, with the minimum value located in the thermo-mechanically affected zone (TMAZ), mainly caused by the dissolution of T₁ phases and precipitation of coarse AlCu, AlCuMg, and AlCuMn phases during welding. Direct artificial aging at 155 °C for 24 h failed to improve joint strength due to solute depletion induced by pre-existing coarse secondary phases. Solution treatment re-dissolved coarse precipitates into the matrix, and subsequent aging led to uniform precipitation dominated by T₁ and θ′ phases, with a consistent microhardness of ~155 HV across all zones. By introducing pre-stretching deformation after solution treatment, T₁ became the dominant strengthening phase in all regions, accompanied by a remarkable increase in both microhardness and tensile strength. With 3% pre-stretching, the microhardness reached 185 HV, and the ultimate tensile strength of the joint reached 600 MPa, corresponding to a joint efficiency as high as 95%, which is superior to most reported values for Al–Li alloy FSW joints. This study clarifies the precipitation evolution mechanism under tailored PWHT and provides an effective strategy for property regulation of high-performance Al–Cu–Li alloy FSW structures in aerospace applications. Full article