Search Results (7,817)

Accurate estimation of residential building energy consumption and associated CO₂ emissions is essential for refined urban carbon management. This study develops a hybrid framework that integrates physics-based simulation and machine learning to estimate residential building energy use and energy-related CO₂ emissions in Shenzhen in 2020. Representative building archetypes were first simulated and then used to train machine-learning models for large-scale applications. Building-level energy estimates were further combined with a bottom-up inventory to generate high-spatiotemporal-resolution maps of residential CO₂ emissions. The results show that: (1) the selected model achieved good accuracy and temporal robustness, with strong agreement between estimated and reference energy use at daily, monthly, and annual scales; (2) residential energy use was primarily driven by meteorological conditions, especially daily mean temperature and the duration of high-temperature conditions, and exhibited clear weekly and seasonal patterns, with higher values on weekends and in summer; (3) residential CO₂ emissions in Shenzhen reflected the combined effects of scale and intensity, with Longgang and Bao’an contributing the largest total emissions, Self-built residential buildings contributing the largest aggregate emissions, and Old residential buildings showing the highest average emissions per building; (4) emissions were highly concentrated in a small number of high-emission buildings, which were more frequently distributed along road-adjacent block perimeters. Overall, the proposed framework improves the fine-scale characterization of residential building CO₂ emissions and provides a useful basis for hotspot identification and targeted mitigation. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

Traditional cognitive practices in the Engineering Management Major (EMM) are often constrained by safety risks, high costs, and geographical limitations. This study proposes a novel Virtual Cognitive Practice (VCP) mode that integrates ubiquitous learning (U-learning) with web-based panoramic maps to overcome these challenges. We developed a VCP system leveraging panoramic data of roads, bridges, and tunnels from commercial web mapping platforms to provide high-fidelity, interactive observation environments. To evaluate its effectiveness, 147 undergraduate students participated in a virtual practice course and subsequently completed a structured questionnaire. The results demonstrate that the accuracy on objective knowledge tests exceeded 80%, alongside a high mean score of 4.27/5 for visualization satisfaction. Statistical analysis using Chi-square tests indicates that students with prior on-site experience are significantly more confident in the VCP mode’s potential as a pedagogical alternative. This research bridges the technical gap in EMM practical education by providing a flexible, ubiquitous learning ecosystem. Full article

The integration of Artificial Intelligence into educational systems has accelerated dramatically with the advent of Large Language Models (LLMs). However, two critical limitations constrain current AI-powered tutoring systems: LLMs hallucinate factually incorrect content in high-stakes pedagogical contexts, and existing systems lack standardized mechanisms to dynamically access and synthesize knowledge from heterogeneous educational sources, including learning management systems, open-access textbook repositories, assessment databases, and real-time educational APIs. This paper presents a systematic survey of the convergence of Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) in educational AI applications. Based on our taxonomy, we identify a critical architectural gap: no current system simultaneously achieves multi-source curriculum retrieval, standardized tool orchestration, learner-adaptive personalization, and citation-aware generation within a unified framework. To address this, we propose EduMSRA (Educational Multi-Source Research Agent)—a novel architecture comprising a Hierarchical Educational RAG Pipeline, an MCP-based Curriculum Tool Orchestration Layer, a Conflict-Aware Fusion Module (CAFM), a Learner Profile Manager (LPM), and a Pedagogical Policy Agent (PPA) aligned with Bloom’s taxonomy. We further provide a comprehensive experimental design road map specifying nine publicly available benchmark datasets and four evaluation experiments. Additionally, we conduct three Bayesian empirical analyses: (1) a random-effects meta-analysis of 12 RAG studies indicating a positive effect direction ($\widehat{\mu }=0.511$, 95% HDI: $[0.250,0.790]$) , $I^2=99.3\%$ heterogeneity flagged as indicative), (2) a BKT simulation illustrating adaptive scaffolding dynamics across five learner profiles, and (3) a Beta-Binomial difficulty characterization of nine benchmark datasets. Our analysis demonstrates that EduMSRA offers a principled, scalable path toward adaptive, grounded, and pedagogically aligned AI tutoring agents. Full article

The decarbonization of multimodal transport systems requires assessment approaches that simultaneously address environmental impacts and economic performance at dynamic operational conditions. Conventional Life Cycle Assessment (LCA) and Life Cycle Costing (LCC), including Total Cost of Ownership (TCO), are widely used for this purpose; however, they often rely on static assumptions and averaged data, limiting their ability to capture real-world variability. This study proposes an AI-enhanced LCA–LCC/TCO framework for the integrated evaluation of decarbonised multimodal Door-to-Port transport systems. Artificial intelligence is embedded directly into the life cycle inventory and cost inventory stages to generate scenario-specific estimates of energy consumption, greenhouse gas emissions, and operational costs. The framework is demonstrated through a case study of a multimodal Door-to-Port transport chain comprising road pre-haulage, rail line-haul, and port terminal operations. Three scenarios are analysed: conventional, partially decarbonised, and fully decarbonised configurations. The results indicate that partial decarbonization reduces greenhouse gas emissions by more than 60% compared to the baseline while achieving the lowest total cost of ownership. Full decarbonization achieves emission reductions exceeding 95% but is associated with slightly higher costs under current assumptions. Sensitivity analysis verifies the robustness of the relative scenario ranking under different energy prices, carbon pricing, and electricity carbon intensity. The proposed framework provides a structured decision-support framework for logistics operators, port authorities, and policymakers seeking cost-effective pathways to low-emission multimodal transport systems. Full article

Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and behave as black-box models with limited explainability. To overcome these limitations, the present work proposes a SoC estimation approach based on the Light Gradient Boosting Machine (LightGBM). The proposed model provides a balanced trade-off between prediction accuracy and computational efficiency. Furthermore, feature engineering is performed to derive additional informative features, improving the model’s ability to learn driving conditions and battery dynamics. In addition, the study incorporates a seasonal analysis by evaluating the model under both summer and winter conditions, allowing the impact of environmental variations on SoC estimation performance to be investigated. Moreover, Explainable Artificial Intelligence (XAI) techniques are employed to interpret the model predictions. Evaluation on real-world on-road data demonstrated that the proposed model achieved substantial improvements in estimation performance compared to recent studies. Full article

Driven by increasingly stringent requirements for energy saving and emission reduction in non-road machinery, hybrid wheel loaders have attracted growing attention as a practical pathway toward cleaner construction equipment. However, conventional energy management strategies often show limited adaptability to highly transient operating cycles and struggle to balance fuel economy, real-time applicability, and battery charge sustainability. To address these issues, this study proposes an improved sparrow-search-algorithm-based equivalent consumption minimization strategy (ISSA-ECMS) for a series hybrid wheel loader. A quasi-static powertrain model was established, while ISSA was used to optimize both the hyperparameters of a Convolutional Neural Network-Long Short-Term Memory (CNN–LSTM) stage-recognition model and the stage-dependent ECMS parameters. A hidden Markov model (HMM)-based post-processing framework was further introduced to improve temporal consistency in operating-stage recognition. The results show that the optimized ISSA-CNN–LSTM achieved 93.22% accuracy, 93.08% Macro-F1, and 93.21% Weighted-F1, while HMM refinement further improved recognition accuracy from 94.02% to 97.92%. In energy management simulations, ISSA-ECMS maintained the terminal state of charge (SOC) at 50.0069%, reduced fuel consumption by 2.1% and 1.4% compared with conventional ECMS and A-ECMS, respectively, and increased the proportion of engine operating points in the economical region to 77.549%. Compared with dynamic programming, its fuel-consumption increase was only 0.28%, while retaining online applicability. These results demonstrate that the proposed method provides an effective and practical solution for real-time energy management of series hybrid wheel loaders. Full article

The use of plastic waste as a filler in concrete, particularly in paving block production, represents an approach aligned with circular economy principles. While previous studies have focused on mechanical properties, the effect of such materials on slip risk remains insufficiently investigated, especially for pedestrian applications. This study evaluates the influence of the volumetric content of recycled plastic waste from electrical cable insulation on slip resistance of concrete paving blocks. A series of specimens was prepared with 0–45% replacement of natural aggregate by granulated cable insulation (GCI). Slip resistance was measured using the British Pendulum Tester and expressed as Skid Resistance Value (SRV) after statistical processing. Two sliders were used, Mounted Shoe 55 and Mounted Shoe 96, corresponding to road and pedestrian conditions. The results show that increasing GCI content reduces mass by approximately 9.6 g per 1% GCI, reaching a reduction of about 20% at 50% GCI. For polished surfaces, SRV increased up to 77 (MS55) and 75 (MS96) at 40–45% GCI. For ground surfaces, optimal performance was observed at 10% GCI, while higher contents reduced SRV and caused mechanical degradation above 30–35% GCI. The results indicate that optimized GCI content can improve slip resistance while reducing material weight. Full article

To address the challenges of missing fine-grained objects, blurred boundaries, and the suppression of shallow details by deep semantic features during cross-scale fusion in traffic scene semantic segmentation, this paper proposes a discrepancy-guided semantic segmentation method with boundary detail enhancement. First, to improve the semantic completeness of fine-grained regions, a Gated Collaborative Context Module (GCCM) is introduced between the encoder and decoder. By leveraging gating-guided channel selection and multi-scale contextual modeling, GCCM adaptively captures semantic dependencies across different scales. Second, to alleviate boundary ambiguity and detail loss, a Frequency–Edge Guided Enhancement Module (FEGE) is designed in the decoder. This module explicitly models low-frequency structural information and high-frequency edge components via frequency decomposition, and further enhances high-frequency details using the Scharr operator and lightweight convolution, thereby improving the structural representation of object contours and boundary regions. Furthermore, to mitigate the suppression of shallow details during cross-scale feature fusion, a Discrepancy-aware Pixel-Adaptive Gating Fusion module (D-PagFM) is proposed. By jointly modeling feature similarity and local discrepancy, the module adaptively regulates pixel-wise fusion, enhancing detail integration in structurally consistent regions while suppressing misleading fusion in inconsistent regions, thereby improving the robustness of feature fusion and boundary consistency. Experimental results on the Cityscapes and CamVid datasets demonstrate that the proposed method achieves mIoU scores of 80.08% and 82.97%, respectively. Moreover, it shows more significant improvements in boundary-sensitive fine-grained categories such as road boundaries, poles, and traffic signs, indicating its effectiveness and application potential for high-precision semantic segmentation in traffic scenes. Full article

Island reef road construction faces a complex marine service environment characterized by high salinity and high humidity. Meanwhile, rapid construction and prompt subgrade repair are urgently required, creating a strong demand for novel calcareous-sand-based stabilization materials that combine excellent mechanical performance with resistance to seawater erosion. To this end, this study developed an early-strength cemented calcareous-sand reinforcement material for road base construction. Sulfoaluminate cement (SAC) and ferrite-aluminate cement (FAC), both featuring rapid setting/early strength development and superior corrosion resistance, were used to cement calcareous sand (CS) and to investigate its mechanical and microstructural characteristics under different water environments. Unconfined compressive strength tests (UCS) showed that SC-CS and FC-CS could meet subgrade requirements at 1 d and 7 d, with SC-CS and FC-CS reaching 3.12 MPa and 3.44 MPa at 1 d, and 3.26 MPa and 3.67 MPa at 7 d, respectively, under seawater SS conditions. Seawater mixing and immersion were found to promote the early strength and stiffness development of both SC-CS and FC-CS, with a more pronounced effect observed for FC-CS. Based on experimental results, a damage model for the stabilized specimens was established with a fitting accuracy of R² > 0.97. This constitutive model accurately describes the stress–strain relationship of the material and quantitatively characterizes its damage evolution. Microscopic XRD and SEM analyses indicated that the main hydration product in freshwater-cured specimens was ettringite, and the interparticle connection of CS was dominated by bridging through rod-like ettringite. In contrast, under seawater conditions, the ettringite content decreased, while hydrotalcite and calcium aluminate hydrate increased, forming massive and lamellar bridging products. Compared with SC-CS, the bridging structure in FC-CS was denser. Moreover, the compactness of the bridging structure not only affected its mechanical properties but also governed the movement mode of CS particles, thereby influencing the damage evolution and failure mode of the specimens. The findings provide theoretical support for the construction needs of island road. Full article

The paper introduces a non-linear method for modeling the luminance coefficient (Qd) of asphalt (bituminous) mixtures using a Generalized Additive Model (GAM). Developed from observations after three and six months of service, the model accounts for the effects of aggregate luminance, binder content, and air voids, as well as temporal and non-linear dependencies. It showed a high goodness-of-fit (R² = 0.91) and strong predictive accuracy (RMSE = 4.8 mcd/m²/lx). The analysis revealed that the service period significantly influences luminance, with values after six months being, on average, 12.6 mcd/m²/lx higher than at three months. The impact of aggregate luminance was non-linear, displaying a saturation effect, while asphalt content and air voids varied in their influence over time. Results indicate that the factors affecting bituminous mixture luminance are complex and vary over time; moreover, high aggregate luminance alone does not guarantee a high Qd. Applying the additive model confirms the importance of accounting for non-linear effects and temporal interactions when assessing road surface optical properties. Full article

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network. Full article

This paper presents a novel system-oriented counterfactual deep learning framework, termed Hybrid Prediction–Intervention Neural Architecture (HPINA) for intelligent traffic accident risk prediction and proactive safety intervention in smart transportation systems. Unlike conventional data-driven models that rely solely on observational correlations, the proposed system integrates multi-domain data fusion, temporal deep representation learning, a continuous spatio-temporal risk field, and a latent-space counterfactual reasoning module within a unified decision-support architecture. The framework enables accurate prediction of traffic accident risk and simulation of “what-if” intervention scenarios to support real-time safety optimization in intelligent transportation environments. By leveraging heterogeneous inputs, including traffic dynamics, environmental conditions, road attributes, and temporal patterns, the system constructs a high-dimensional representation that captures complex nonlinear dependencies and evolving risk propagation across the network. A key innovation lies in the integration of a causal intervention mechanism and policy-guided decision layer, which jointly quantify intervention impact and identify optimal strategies for minimizing risk. The experimental results demonstrate that HPINA achieves a Test F1-score of 0.958 and an AUC of 0.989, outperforming strong baselines by up to 5.0% and 3.4%, while achieving a relative risk reduction of 0.091 and improved convergence stability with a validation loss of 0.042. These findings highlight the effectiveness of the proposed framework as an intelligent, scalable, and deployable system for real-world traffic safety management and smart city applications. Full article

Decarbonizing heavy-duty road freight transportation requires efficient energy management in zero-emission powertrains. This study investigates energy management strategies (EMSs) for a heavy-duty Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV). Rather than the typical charge-sustaining operation, these strategies are designed for charge-depleting operation, in which each route begins with a charged battery and ends at a lower state of charge (SOC), leveraging the vehicle’s plug-in capability. The EMSs are evaluated primarily in terms of energy consumption, while battery C-rate and fuel cell ramp rate are used as simple stress indicators for comparative analysis. A backward-facing vehicle model is developed to test several EMSs, including both optimization- and rule-based strategies. The Equivalent Consumption Minimization Strategy (ECMS) emerged as a promising option, motivating further testing with a forward-facing model and additional drive cycles. The simulation results show that ECMS consumed only 1.1% more energy than the global optimal solution found by Pontryagin’s Minimum Principle (PMP) and 7.5% less energy than a simple rule-based strategy, on average across five drive cycles. These results show that ECMS can be effective for a heavy-duty FC-PHEV operating in charge-depleting mode, extending its demonstrated applicability beyond charge-sustaining and light-duty vehicles. Full article

Objectives: Management of unstable pelvic fractures during pregnancy presents a major therapeutic challenge, requiring careful multidisciplinary evaluation to balance maternal benefits and fetal radiation risks. Methods: We report the case of a 32-year-old patient who presented with a pelvic fracture due to a road traffic accident at three months of pregnancy. A left sacroiliac osteosynthesis was performed to treat a left sacroiliac diastasis with pelvic osteosynthesis using a trans-iliosacral approach under cone-beam CT (CBCT) guidance using a very-low-dose protocol. Radiation parameters and fetal dose estimates were calculated in advance in collaboration with a medical physicist. Tight beam collimation, a reduced field of view, and minimization of fluoroscopic checks were applied to keep fetal exposure as low as reasonably achievable. This article aims to demonstrate the feasibility of managing a complex pelvic fracture using interventional radiology and to review the literature on management options and gestational age-dependent fetal risks. Results: The estimated cumulative fetal dose from initial imaging, open surgery, and CBCT-guided osteosynthesis remained below 70 mGy using a pregnant phantom (Duke Organ Dose–Dosewatch–General Electric system), which is below thresholds associated with deterministic effects. The procedure achieved optimal screw positioning with less than 40 s of fluoroscopy. Maternal postoperative recovery was favorable, and follow-up revealed normal fetal development. Conclusions: This case demonstrates that CBCT-guided percutaneous iliosacral screw fixation can be safely performed during pregnancy with meticulous planning, dose-reduction strategies, and multidisciplinary collaboration, maintaining fetal radiation exposure below accepted safety thresholds. Full article