Search Results (5,565)

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

Against the backdrop of global energy transition and the widespread adoption of Hydrogen-Blended Natural Gas (HBNG), the safety of urban gas pipeline networks faces severe challenges. This paper systematically reviews the research progress of numerical simulation in the field of natural gas pipeline safety, focusing on its core supporting roles throughout the “Leakage–Dispersion–Evolution–Consequence” disaster chain. First, it analyzes the kinetic modeling of high-pressure leakage holes and property corrections based on real gas equations of state, elaborating on the numerical characterization of HBNG multi-component transport. Second, it compares the dispersion mechanisms and environmental coupling modeling methods in typical scenarios such as buried porous media, confined spaces in utility tunnels, underwater environments, and urban building clusters. Third, it reviews leakage monitoring technologies based on physical field simulation and data-driven approaches (e.g., Convolutional Neural Network, Long Short-Term Memory), emphasizing the value of numerical simulation in constructing digital twin training sets. Furthermore, it explores the dynamic evolution of explosion flame–shock wave interactions and the evaluation models for secondary disaster consequences. Finally, the current research status of grid-based risk pre-warning and emergency response strategies is summarized. In conclusion, numerical simulation is not only a robust method for precisely quantifying and characterizing complex physical mechanisms but also a critical technological foundation for building smart and resilient energy cities. Future research should focus on the deep coupling of multi-physics fields, physics-informed learning, and the development of system-level integrated defense systems. Full article

The location and capacity of electric vehicle charging stations (EVCSs) directly determine the capital invested and construction costs while also affecting the travelling convenience and economy of electric vehicle (EV) users. Furthermore, the siting and sizing of EVCSs has an impact on distribution network flexibility. Therefore, a method for the siting and sizing of EVCSs that takes into account distribution network flexibility is proposed. Firstly, based on the definition of distribution network flexibility, the flexibility deficit is analyzed, and five flexibility assessment indicators are established. Secondly, the travel characteristics of EVs are simulated based on urban road topology and a trip probability matrix, and a model incorporating users’ bounded rationality is adopted to predict the temporal and spatial distribution of EV charging requirements. Furthermore, based on charging requirements and distribution network flexibility deficit, this paper establishes a model for the siting and sizing of EVCSs considering distribution network flexibility. Finally, case studies are conducted with a 29-node transportation network and a 33-node distribution network. The results show that the proposed method can formulate a more reasonable siting and sizing scheme for EVCSs, decrease the flexibility deficit of the distribution network, and reduce the annual comprehensive cost by 11.96%. Full article

Heated crude oil pipelines transporting high-pour-point, high-viscosity, and high-wax-content crude oil are increasingly operated under small-batch and multi-condition scenarios. Under such conditions, fixed-parameter models and experience-based operating strategies may fail to accurately describe the evolving thermo-hydraulic state, resulting in inaccurate temperature-safety assessment and conservative energy use. To address this problem, this study develops a hybrid modelling and simulation framework for the energy-efficient operation of heated crude oil pipelines. The framework integrates operating-state perception, online parameter inversion, transient thermo-hydraulic simulation, data assimilation, and rolling optimization. First, an online parameter inversion method based on inverse problem solving is established to dynamically identify the overall heat-transfer coefficient and friction correction factor from Supervisory Control and Data Acquisition (SCADA) measurements. Second, a transient thermo-hydraulic simulation and data-assimilation model is constructed to predict pressure, temperature, and safety margins under changing boundary conditions. Third, a constraint-aware rolling optimization strategy is introduced to coordinate heating and pumping operations while satisfying temperature and pressure constraints. The proposed framework is validated using a practical crude oil pipeline. Under a representative low-flow-rate condition, online parameter inversion corrects the overestimation of the thermo-hydraulic state by the fixed-parameter model: the total temperature drop along the pipeline is revised from 33.12 °C to 35.65 °C, and the minimum station-inlet oil temperature is revised from 24.77 °C to 21.61 °C. After optimization is introduced, the total operating energy consumption decreases from 11,715.65 kW to 11,287.43 kW, corresponding to a reduction of 3.66%, while all temperature and pressure constraints remain satisfied. Under time-varying boundary conditions, the rolling optimization strategy further adjusts heating-furnace operation according to variations in inlet flow rate, inlet oil temperature, and ambient temperature, thereby reducing cumulative heating energy consumption while maintaining safe operation. The results demonstrate that the proposed framework provides an implementable modelling and simulation approach for online state assessment, transient prediction, and energy-efficient operation of heated crude oil pipelines under variable operating conditions. Full article

Although fine-grained spatial knowledge of the urban population distribution is fundamental for effective urban management, traditional census data lack sufficient resolution. Current disaggregation methods often struggle to probabilistically fuse heterogeneous data, such as noisy mobile signaling and building attributes, while ensuring hierarchical consistency between micro-level predictions and macro-level ground truth. To address these gaps, this study proposes a Bayesian-informed hierarchical learning (BIHL) model framework for building-level population estimation. The methodology integrates three distinct layers: (1) a data-driven prior model using a LightGBM ensemble to generate initial probabilistic estimates and uncertainty weights; (2) an enhanced neural network posterior estimator featuring a multi-branch architecture—incorporating Zone Bias Embedding and Zone Interaction networks—to capture non-linear urban dynamics and spatial heterogeneity; and (3) a constrained optimization layer utilizing a hierarchical loss function that enforces strict consistency between aggregated building estimates and official census data through dynamic curriculum learning. Through empirical validation in Haidian District, Beijing, it is demonstrated that the BIHL framework significantly outperforms baseline models (MLR, Random Forest, and LightGBM), achieving a Mean Absolute Percentage Error (MAPE) of 11.36%. This study confirms that incorporating building-level spatial locations and residential categories is vital for mitigating “spatial smoothing” and systematic under-prediction in high-density areas. This framework provides a robust, high-fidelity solution for generating residential population layers, which are essential for city planning. Full article

With the global energy system undergoing a transition toward green and low-carbon systems, the scale of liquefied natural gas (LNG) maritime transportation has expanded rapidly. Influenced by a combination of factors including the global economy, geopolitics, energy policies, and environmental conditions, the Liquefied Natural Gas Maritime Transportation Network (LMTN) exhibits a high degree of structural complexity and has gradually emerged as a prominent research focus in the field. This study provides a comprehensive review of the current research progress on LMTN. First, the concept of LMTN is introduced and the major stages of its research development are highlighted. Second, LMTN construction methods are systematically summarized with data sources, theoretical foundations, and application scenarios, thereby establishing a technical framework for global LMTN research. Subsequently, bibliometric analysis is also employed to extract representative publications and reveal the knowledge structure, historical evolution, and emerging research frontiers of the field. Finally, from three technical perspectives—methodology, data, and computational power—this study discusses existing limitations and challenges, and identifies future development trends of LMTN research driven by big data and artificial intelligence. Overall, this study aims to provide scientific guidance for future LMTN research and theoretical support for enhancing the security and resilience of global energy transportation systems. Full article

Aqueous zinc-ion batteries offer a safe and economical platform for large-scale energy storage, yet vanadium oxide cathodes remain hindered by sluggish Zn²⁺ migration, poor electronic conductivity, and structural degradation during cycling. Herein, a PEDOT regulated interfacial engineering strategy is proposed to construct surface modified sodium vanadium oxide nanostructures with coordinated ion and electron transport. The 1P-NaVO cathode retains the layered framework while introducing a PEDOT-derived surface component that strengthens interfacial charge transfer and preserves accessible Zn²⁺ diffusion pathways, delivering 655 mAh g⁻¹ at 0.1 A g⁻¹. Kinetic analyses further reveal accelerated charge storage behavior, including an increased pseudocapacitive contribution, a low charge transfer activation energy of 20.6 kJ mol⁻¹, and improved Zn²⁺ diffusion, with D_Zn²⁺ values of approximately 10^−10.8 to 10^−9.8 cm² s⁻¹. Ex situ XRD and SEM disclose a reversible structural response during Zn²⁺ insertion and extraction, involving interlayer perturbation, local framework relaxation, transient electrolyte-derived surface species, and partial morphology recovery after recharge. These findings show that controlled PEDOT-derived surface regulation promotes efficient coupling between interfacial electron transfer and Zn²⁺ diffusion, offering a practical design principle for durable vanadium-based cathodes in aqueous zinc-ion batteries. Full article

The complexity of marine structural experimental devices is usually attributed to the boundary conditions and loads applied to the objects. As a result, the complexity of the device and the volume of the objects make strict requirements on the experimental designs and assembly of these devices. In this study, a virtual assembly system for a structural laboratory (VAL) is developed in the Unity 3D environment, and collision detection algorithms are derived based on bounding box models and mesh models. This research realized the parametric modeling or importing of 3D objects and reconstructed pressure loading experimental scenarios in a 3D environment. The algorithm can automatically select a detection method according to the geometric object, and every assembly step is recorded and visualized. The system can effectively simulate the assembly of a structural experimental device. Moreover, the 3D file importing interface, the rendering of transporting tracks, and interaction detection algorithms can support the construction of a virtual scenario for more experiments. Full article

Against the backdrop of the “Dual Carbon” strategy and global shipping digitalization, data elements have emerged as the key enabling factor and predictive correlate of coastal port smartness. Using panel data for seven coastal provinces/municipalities and eight coastal ports in China from 2017 to 2024, this paper constructs a “base-supply-flow-use” data element development index (DEDI) and a “WSR” coastal port smartness index (CPSI), employing VHSD-EM dynamic model, random forest algorithm, and partial effect model to examine the association patterns, nonlinear responses, and differentiated enhancement pathways between data elements and port smartness. Findings reveal: (1) CPSI and DEDI exhibit a high positive correlation with narrowing regional disparities; (2) CPSI shows stepwise spatial differentiation, with Shanghai and Ningbo-Zhoushan Ports leading, while Guangdong demonstrates “data advancement but smartness lag”; (3) in the random forest model, the predictive contribution of DEDI to CPSI is 13.586%, which ranks behind digital inclusive finance and openness level but is higher than regional economic strength and innovation output. The combined predictive contribution of the DEDI main effect and its interaction terms reaches 32.567%; (4) the univariate partial effect of DEDI on predicted CPSI followed a three-stage nonlinear pattern of initial accumulation, accelerated improvement around a threshold of DEDI ≈ 0.215, and diminishing marginal effects at higher levels; and (5) the joint partial effects of DEDI with digital inclusive finance, economic development, fiscal transportation expenditure, and innovation output showed clear dimensional and regional heterogeneity. Accordingly, four policy pathways are proposed: constructing a full-chain data element system, enabling synergistic empowerment of data and supporting elements, formulating regionally differentiated catch-up strategies, and strengthening the dual-wheel support of digital inclusive finance and opening-up—all aimed at advancing the development of world-class ports. Full article

Transportation emissions raise critical environmental justice concerns, yet most studies overlook the distinct inequity patterns between passenger and freight systems. This study aims to compare the spatial disparities and driving mechanisms of exposure injustice from passenger and freight emissions at the U.S. county level. Using 2020 county-level cross-sectional data, we construct an environmental injustice index (EII) and apply spatial autocorrelation analysis, a two-stage multi-task TabNet model, and SHAP interpretation to identify spatial divergence, key determinants, and heterogeneous effects of urban compactness. Results show that passenger EII features continuous regional clustering, while freight EII concentrates along corridors and nodes with limited spatial overlap. Passenger injustice is driven by population density, auto dependence, and public transit, whereas freight injustice is dominated by truck intensity, freight network location, and logistics employment. Urban compactness has dual impacts on passenger injustice but consistently exacerbates freight injustice. These findings highlight the necessity of differentiated governance and provide empirical support for equitable low-carbon transport policies. Full article

Adverse weather and low-illumination conditions in urban road scenarios substantially degrade license plate image quality, posing a major challenge to robust automatic license plate recognition for urban intelligent transportation systems and smart city construction. To address the limitations of conventional pipelines that optimize detection, enhancement, and recognition in isolation, this study proposes CLEI, a unified framework integrating YOLOv12-based detection, GAN-based image enhancement, and a novel CNN–Mamba network (CMN) for character recognition. Using a curated dataset of 3000 license plate images captured under rain, snow, fog, and nighttime urban roadside conditions, we first benchmarked several mainstream detectors and identified YOLOv12s as the most effective model in terms of accuracy, inference speed, and computational efficiency. To mitigate blur and low-quality degradation in cropped plate regions, DeblurGAN-v2 was employed for adaptive enhancement, achieving PSNR of 16.61 dB, SSIM of 0.8776, and LPIPS of 0.1151. For recognition, the proposed CMN replaces the recurrent module in CRNN with a Mamba-based state-space model, improving sequence modeling efficiency and robustness. CMN achieved 93.3% plate accuracy, outperforming CRNN (91.0%) and LPRNet (88.5%), while the full CLEI framework reached 93.67% accuracy after enhancement. These results demonstrate that collaborative optimization across detection, restoration, and recognition enables accurate and efficient license plate recognition in severely degraded urban traffic environments, providing a reliable technical support for urban traffic monitoring, public security governance and smart city infrastructure construction. Full article

The seismic fragility of reinforced concrete (RC) bridge piers is critical for urban rail transport systems, as severe pier damage may interrupt post-earthquake operation and threaten network safety. Compared with conventional highway bridge piers, urban rail transport RC solid piers usually have lower axial load ratios, larger cross-sections, and stricter serviceability requirements. However, the combined effects of geometric parameters, reinforcement detailing, and material strength on their cyclic behavior, dynamic response, and seismic fragility remain insufficiently understood. To address this issue, seven 1/4-scale RC solid pier specimens were tested under quasi-static cyclic loading to examine the effects of pier height, transverse reinforcement ratio, and longitudinal reinforcement ratio on damage evolution, hysteretic response, skeleton curves, and energy dissipation. A fiber-based OpenSees model considering bond-slip effects was then established, validated against the tests, and extended to a full-scale prototype pier for parametric analysis. The effects of aspect ratio, axial load ratio, longitudinal reinforcement ratio, stirrup ratio, steel yield strength, and concrete strength were evaluated under cyclic loading and nonlinear dynamic time-history excitations. An incremental dynamic analysis-based probabilistic seismic demand model was further developed using 30 near-fault ground motions, with peak ground acceleration as the intensity measure and displacement ductility as the engineering demand parameter. The results showed that increasing the aspect ratio changed the failure mode from flexure-shear-dominated to flexure-dominated behavior, increasing the ultimate displacement from 122 mm to 155 mm while reducing the peak lateral strength from 263 kN to 248 kN. Increasing the longitudinal reinforcement ratio improved both peak strength and ultimate displacement, from 226 kN to 262 kN and from 120 mm to 160 mm, respectively. The numerical results indicated that aspect ratio, axial load ratio, and longitudinal reinforcement ratio had more pronounced effects on seismic demand and fragility than stirrup ratio. Increasing steel yield strength generally reduced seismic fragility, whereas increasing concrete strength enhanced lateral resistance but did not necessarily improve fragility performance. These findings suggest that the seismic performance of urban rail transport RC solid piers should be evaluated by combining cyclic response, dynamic demand, and fragility-based performance, rather than by maximizing any single design parameter. Full article

Submarine channels are critical conduits for sediment transport by turbidity currents, yet the quantitative influence of channel geometry on flow dynamics and sediment segregation remains poorly understood. Based on computational fluid dynamics, we constructed six three-dimensional numerical models of submarine channels with varying curvatures (R1–R3) and axial slopes (R4–R6) using ANSYS Fluent 17.2, with model settings informed by seafloor morphology from the South China Sea. The Eulerian–Eulerian multiphase model coupled with the standard k-ε turbulence model was used to simulate density fields, velocity structures, and sediment distributions. Results show that low-curvature channels exhibit symmetric density evolution and uniform sediment distribution, whereas high curvature induces pronounced asymmetry with a steep outer-bank density front and triggers secondary flow reversal. Increasing curvature also enhances flow thickness and radial mass flux. Increasing axial slope markedly elevates downstream velocity (0.09 to 0.16 m/s), reduces flow thickness, and shifts sediment distribution toward the inner bank without inducing secondary flow reversal. This study provides a parametric comparison of curvature versus slope effects on turbidity current dynamics and sedimentation patterns under fixed-bed, rectangular-channel assumptions. The findings offer a qualitative reference for interpreting sedimentary architectures in deep-water systems such as those in the South China Sea and analogous rift basins. Results are hypothesis-generating, pending further validation with field data and morphodynamic modeling. Full article

To improve the accuracy and stability of short-term traffic flow prediction in complex road networks and address the limitations of existing models in modeling spatiotemporal dependencies, this paper proposes a traffic flow prediction model based on a Hypergraph Spatio-Temporal Interaction Network (HGSTIN) in the context of intelligent transportation systems. The study constructs a multi-dimensional traffic pattern input tensor by integrating three temporal scales—proximity, intra-day, and intra-week—while taking traffic flow as the prediction target and introducing average speed and lane occupancy as auxiliary features. In terms of temporal modeling, a Transformer architecture integrated with a Dynamic Tanh (DyT) mechanism is adopted to capture multi-period variations. For spatial modeling, a neighborhood hypergraph and a DTW-based semantic hypergraph are combined to enhance the representation of local and global through spatial self-attention and hypergraph neural network branches, and an adaptive feature fusion module is designed to perform adaptively weighted fusion of the outputs from the two branches. In terms of loss function design, a temporal gradient consistency loss function is proposed to enhance the robustness of predictions. Experimental results on the PEMS04 and PEMS08 datasets show that the proposed model achieves average improvements of approximately 5.15%, 1.76%, and 3.88% in MAE, RMSE, and MAPE, respectively, compared to the second-best baseline model. The model exhibits the smallest performance degradation in multi-step prediction scenarios, and the effectiveness of each module is validated through ablation studies. The findings demonstrate that HGSTIN can effectively capture the dynamic spatiotemporal characteristics of complex traffic scenarios, thereby providing high-precision prediction support for intelligent transportation systems. Full article

Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development. Full article