Search Results (445)

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

This study proposes an integrated, machine learning-based multi-objective optimization framework to evaluate and optimize the utilization of steel slag in road base layers, simultaneously addressing economic costs and environmental impacts. A comprehensive dataset of 482 scenarios was engineered based on literature-informed parameters, encompassing transport distance, processing energy intensity, initial moisture content, gradation adjustments, and regional electricity emission factors. Four advanced tree-based ensemble regression algorithms—Random Forest Regressor (RFR), Extremely Randomized Trees (ERTs), Gradient Boosted Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR)—were rigorously evaluated. Among these, GBR demonstrated superior predictive performance (R² > 0.95, RMSE < 7.5), effectively capturing complex nonlinear interactions inherent in slag processing and logistics operations. Feature importance analysis via SHapley Additive exPlanations (SHAP) provided interpretative insights, highlighting transport distance and energy intensity as dominant factors affecting unit cost, while moisture content and grid emission factor predominantly influenced CO₂ emissions. Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. The resulting Pareto front revealed a diverse solution space, with significant nonlinear trade-offs between economic efficiency and environmental performance, clearly identifying strategic inflection points. To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO₂ factor of 0.47 kg CO₂/kWh. This scenario offered a substantial reduction (45%) in CO₂ emissions relative to cost-minimized solutions, with a moderate increase (33%) in total cost, presenting a realistic and balanced pathway for sustainable infrastructure practices. Overall, this study introduces a robust, scalable, and interpretable optimization framework, providing valuable methodological advancements for sustainable decision making in infrastructure planning and circular economy initiatives. Full article

Aiming for convenience and the low cost of goods transfer between towns, this paper proposes a trade hub location and allocation method based on a novel artificial eagle-inspired optimization algorithm. Firstly, the trade hub location and allocation model is established, taking the total cost consisting of construction and transportation costs as the objective function. Then, to solve the nonlinear model, a novel artificial eagle optimization algorithm (AEOA) is proposed by simulating the collective migration behaviors of artificial eagles when facing a severe living environment. Three main strategies are designed to help the algorithm effectively explore the decision space: the situational awareness and analysis stage, the free exploration stage, and the flight formation integration stage. In the first stage, artificial eagles are endowed with intelligent thinking, thus generating new positions closer to the optimum by perceiving the current situation and updating their positions. In the free exploration stage, artificial eagles update their positions by drawing on the current optimal position, ensuring more suitable habitats can be found. Meanwhile, inspired by the consciousness of teamwork, a formation flying method based on distance information is introduced in the last stage to improve stability and success rate. Test results from the CEC2022 suite indicate that the AEOA can obtain better solutions for 11 functions out of all 12 functions compared with 8 other popular algorithms. Faster convergence speed and stronger stability of the AEOA are also proved by quantitative analysis. Finally, the trade hub location and allocation method is proposed by combining the optimization model and the AEOA. By solving two typical simulated cases, this method can select suitable hubs with lower construction costs and achieve reasonable allocation between hubs and the rest of the towns to reduce transportation costs. Thus, it is used to solve the trade hub location and allocation problem of Henan province in China to help the government make sound decisions. Full article

The swift expansion of China’s automotive manufacturing industry has spurred a constant rise in the demand for automotive parts production and distribution, making the optimization of distribution routes in complex environments a crucial research topic. Efficiently optimizing these routes not only boosts production efficiency and cuts costs for automotive manufacturers but also enhances supply chain management and advances sustainable development. This study focuses on the optimization of automotive parts distribution routes under a multi-manufacturer collaboration framework. An optimization model is proposed to minimize the total operational costs within a joint distribution system, incorporating an improved Ant Colony Optimization (ACO) algorithm to formulate an effective solution approach. The model considers complex factors such as dynamic demand, time-window constraints, and periodic distribution. A PIVNS algorithm integrating a virtual distribution center with an enhanced variable neighborhood search is designed to efficiently address the problem. The efficacy of the proposed model and algorithm is substantiated through extensive experiments grounded in real-world case studies. The results confirm the high computational efficiency of the proposed approach in solving large-scale problems, which significantly reduces distribution costs while improving overall supply chain performance. Specifically, the PIVNS algorithm achieves an average travel distance of 2020.85 km, an average runtime of 112.25 s, a total transportation cost of CNY 12,497.99, and a loading rate of 86.775%. These findings collectively highlight the advantages of the proposed method in enhancing efficiency, reducing costs, and optimizing resource utilization. Overall, this study provides valuable insights for logistics optimization in automotive manufacturing and offers a significant reference for future research and practical applications in the field. Full article

The micro-scale interplay between the built environment and innovation has attracted increasing scholarly attention. However, discussions on how such microdynamics operate and vary across high-density cities remain insufficient. This study focuses on nine high-density urban centres along the G60 S&T Innovation Valley and employs a fine-grained grid unit, viz. 1 km × 1 km, combined with the gradient boosting decision tree (GBDT) model to address these issues. Results show that urban construction density-related variables, including the building density, floor area ratio, and transportation network density, generally rank higher than the amenity density and proximity-related variables. The former contributes 50.90% of the total relative importance in predicting invention patent application density (IPAD), while the latter two contribute 13.64% and 35.46%, respectively. Threshold effect analysis identifies optimal levels for enhancing IPAD. Specifically, the optimal building density is approximately 20%, floor area ratio is 5, and transportation network density is 8 km/km². Optimal distances to universities, city centres, and transportation hubs are around 1 km, 17 km, and 9 km, respectively. Furthermore, significant city-level heterogeneity was observed: most density-related variables consistently have an overall positive association with IPAD, with metropolitan cities (e.g., Hangzhou and Suzhou) exhibiting notably higher optimal values compared to medium and small cities (e.g., Xuancheng and Huzhou). In contrast, the threshold effects of proximity-related variables on IPAD are more complex and diverse. These findings offer empirical support for enhancing innovation in high-density urban environments. Full article

Objectives: The aim of this systematic review is to analyze the research journals, sample characteristics and research methodology used in the studies about school neighborhood built environment (SNBE) and the physical health of children and adolescents. Methods: Using 124 key terms across four databases (Web of Science, PubMed, Sportdiscus and Transportation Research Board), 8837 studies were identified, and 55 were selected. The research question and evidence search were guided by the “Population, Intervention, Comparison, Outcomes” (PICO) framework. Results: Most studies were published in health-related research journals (67.3%) and conducted in 16 countries, primarily urban contexts (44.4%). Cross-sectional designs dominated (89.1%), with participation ranging from a minimum of 7 schools and 94 students to a maximum of 6362 schools and 979,119 students. Street network distances are often defined by 1000 or 800 m. The SNBE variables (135 total) were often measured via GIS (67.2%). In contrast, 70.6% of the 45 physical health measures relied on self-reports. Conclusions: This systematic review highlights the diverse approaches, gaps, and common patterns in studying the association between the SNBE and the physical health of children and adolescents. Therefore, this manuscript may serve as a valuable resource to examine the current landscape of knowledge and to guide future research on this topic. Full article

Zebrafish (Danio rerio) combine accessible behavioral phenotypes with conserved neurochemical pathways and molecular features of vertebrate brain function, positioning them as a powerful model for investigating early neurodegenerative processes and screening neuroprotective strategies. In this context, integrated behavioral and proteomic analyses provide valuable insights into the initial pathophysiological events shared by conditions such as Parkinson’s disease and related disorders—including mitochondrial dysfunction, oxidative stress, and synaptic impairment—which emerge before overt neuronal loss and offer a crucial window to understand disease progression and evaluate therapeutic candidates prior to irreversible damage. To investigate this early window of dysfunction, zebrafish larvae were exposed to 500 μM 1-methyl-4-phenylpyridinium (MPP⁺) from 1 to 5 days post-fertilization and evaluated through integrated behavioral and label-free proteomic analyses. MPP⁺-treated larvae exhibited hypokinesia, characterized by significantly reduced total distance traveled, fewer movement bursts, prolonged immobility, and a near-complete absence of light-evoked responses—mirroring features of early Parkinsonian-like motor dysfunction. Label-free proteomic profiling revealed 40 differentially expressed proteins related to mitochondrial metabolism, redox regulation, proteasomal activity, and synaptic organization. Enrichment analysis indicated broad molecular alterations, including pathways such as mitochondrial translation and vesicle-mediated transport. A focused subset of Parkinsonism-related proteins—such as DJ-1 (PARK7), succinate dehydrogenase (SDHA), and multiple 26S proteasome subunits—exhibited coordinated dysregulation, as visualized through protein–protein interaction mapping. The upregulation of proteasome components and antioxidant proteins suggests an early-stage stress response, while the downregulation of mitochondrial enzymes and synaptic regulators reflects canonical PD-related neurodegeneration. Together, these findings provide a comprehensive functional and molecular characterization of MPP⁺-induced neurotoxicity in zebrafish larvae, supporting its use as a relevant in vivo system to investigate early-stage Parkinson’s disease mechanisms and shared neurodegenerative pathways, as well as for screening candidate therapeutics in a developmentally responsive context. Full article

Driven by the rapid expansion of e-commerce in China, there is a growing demand for high-efficiency, sustainability-oriented logistics solutions in rural regions, particularly for the time-sensitive distribution of perishable agricultural commodities. Traditional logistics systems face considerable challenges in these geographically complex regions due to limited infrastructure and extended travel distances. To address these issues, this study proposes an intelligent cooperative delivery system that integrates automated drones with conventional trucks, aiming to enhance both operational efficiency and environmental sustainability. A mixed-integer linear programming (MILP) model is developed to account for the diverse terrain characteristics of rural China, including forest, lake, and mountain regions. To optimize distribution strategies, the model incorporates an improved Fuzzy C-Means (FCM) algorithm combined with a hybrid genetic simulated annealing algorithm. The performance of three transportation modes, namely truck-only, drone-only, and truck–drone integrated delivery, was evaluated and compared. Sustainability-related externalities, such as carbon emission costs and delivery delay penalties, are quantitatively integrated into the total transportation cost objective function. Simulation results indicate that the cooperative delivery model is especially effective in lake regions, significantly reducing overall costs while improving environmental performance and service quality. This research offers practical insights into the development of sustainable intelligent transportation systems tailored to the unique challenges of rural logistics. Full article

The maritime industry remains a significant contributor to global greenhouse gas (GHG) emissions. In this article, a systematic study has been performed on the alternative fuel emissions of large cargo ships under different route scenarios and propulsion systems. For this purpose, a set of key performance indicators (KPIs) are evaluated, including total equivalent CO₂ emissions (CO_2eq), CO_2eq emissions per unit of transport mass and CO_2eq emissions per unit of transport mass per distance. The emissions analysis demonstrates that Liquified Natural Gas (LNG) paired with Marine Gas Oil (MGO) emerges as the most viable short-term solution in comparison with the conventional fuel oil propulsion. Synthetic methanol (eMeOH) paired with synthetic diesel (eDiesel) is identified as the most promising long-term fuel combination. When comparing the European Union (EU) emission calculation system (FuelEU) with the International Maritime Organization (IMO) emission metrics, a discrepancy in emissions reduction outcomes has been observed. The IMO approach appears to favor methanol (MeOH) and liquefied natural gas (LNG) over conventional fuel oil. This is attributed to the fact that the IMO metrics do not consider unburned methane emissions (methane slip) and emissions in the production of fuels (Well-to-Tank). Full article

The production of AISI 304 stainless steel (a corrosion-resistant alloy prone to solidification defects from high alloy content) particularly benefits from twin-roll strip casting—a short-process green technology enabling sub-rapid solidification (the maximum cooling rate exceeds 1000 °C/s) control for high-performance steels. However, the internal phenomena within its molten pool remain exceptionally challenging to monitor. This study developed a multiscale numerical model to simulate coupled fluid flow, heat transfer, and solidification in AISI 304 stainless steel twin-roll strip casting. A quarter-symmetry 3D model captured macroscopic transport phenomena, while a slice model resolved mesoscopic solidification structure. Laboratory experiments had verified that the deviation between the predicted temperature field and the measured average value (1384.3 °C) was less than 5%, and the error between the solidification structure simulation and the electron backscatter diffraction (EBSD) data was within 5%. The flow field and flow trajectory showed obvious recirculation zones: the center area was mainly composed of large recirculation zones, and many small recirculation zones appeared at the edges. Parameter studies showed that, compared with the high superheat (110 °C), the low superheat (30 °C) increased the total solid fraction by 63% (from 8.3% to 13.6%) and increased the distance between the kiss point and the bottom of the molten pool by 154% (from 6.2 to 15.8 mm). The location of the kiss point is a key industrial indicator for assessing solidification integrity and the risk of strip fracture. In terms of mesoscopic solidification structure, low superheat promoted the formation of coarse columnar crystals (equiaxed crystals accounted for 8.9%), while high superheat promoted the formation of equiaxed nucleation (26.5%). The model can be used to assist in the setting of process parameters and process optimization for twin-roll strip casting. Full article

Inverse Laplace transforms (ILTs) are fundamental to a wide range of scientific and engineering applications—from diffusion NMR spectroscopy to medical imaging—yet their numerical inversion remains severely ill-posed, particularly in the presence of noise or sparse data. The primary objective of this study is to develop robust and efficient numerical methods that improve the stability and accuracy of ILT reconstructions under challenging conditions. In this work, we introduce a novel family of Kaczmarz-based ILT solvers that embed advanced regularization directly into the iterative projection framework. We propose three algorithmic variants—Tikhonov–Kaczmarz, total variation (TV)–Kaczmarz, and Wasserstein–Kaczmarz—each incorporating a distinct penalty to stabilize solutions and mitigate noise amplification. The Wasserstein–Kaczmarz method, in particular, leverages optimal transport theory to impose geometric priors, yielding enhanced robustness for multi-modal or highly overlapping distributions. We benchmark these methods against established ILT solvers—including CONTIN, maximum entropy (MaxEnt), TRAIn, ITAMeD, and PALMA—using synthetic single- and multi-modal diffusion distributions contaminated with 1% controlled noise. Quantitative evaluation via mean squared error (MSE), Wasserstein distance, total variation, peak signal-to-noise ratio (PSNR), and runtime demonstrates that Wasserstein–Kaczmarz attains an optimal balance of speed (0.53 s per inversion) and accuracy (MSE = $4.7\times {10}^{-8}$), while TRAIn achieves the highest fidelity (MSE = $1.5\times {10}^{-8}$) at a modest computational cost. These results elucidate the inherent trade-offs between computational efficiency and reconstruction precision and establish regularized Kaczmarz solvers as versatile, high-performance tools for ill-posed inverse problems. Full article

With the ongoing acceleration in urban development, the volume of construction and demolition waste continues to rise, while the availability of natural aggregates is steadily declining. Utilizing recycled aggregates in concrete has become a vital approach to fostering sustainability within the construction sector. This research develops a life cycle-based environmental impact evaluation model for recycled aggregate concrete, applying the Life Cycle Assessment (LCA) framework. Through the eFootprint platform, a quantitative evaluation is carried out for C30-grade concrete containing varying levels of recycled aggregate replacement. Four replacement ratios of recycled coarse aggregate (30%, 50%, 70%, and 100%) were evaluated. The assessment includes six key environmental indicators: Global Warming Potential (GWP), Primary Energy Demand (PED), Abiotic Depletion Potential (ADP), Acidification Potential (AP), Eutrophication Potential (EP), and Respiratory Inorganics (RI). The findings reveal that higher substitution rates of recycled aggregate lead to noticeable reductions in RI, EP, and AP, indicating improved environmental performance. Conversely, slight increases are observed in GWP and PED, especially under long transport distances. Analysis of contributing factors and sensitivity indicates that cement manufacturing is the principal driver of these increases, contributing over 80% of the total GWP, PED, and ADP impacts, with aggregate transport as the next major contributor. This study offers methodological insights into the environmental evaluation of recycled aggregate concrete and supports the green design and development of low-carbon strategies in construction. Full article

The vast majority of ocean plastics originate from land and are transported over long distances to their final sink. Yet, our current understanding of transfer mechanisms through rivers and estuaries remains poor due to a lack of consistent methods for assessing and monitoring plastic waste. In this study, we quantify and characterize the abundance of plastics in the Tijuana River estuary, located along the U.S.–Mexico border. We found a total of 2804 plastic debris items, of which 79.3% were sampled during heavy rainfalls and 20.7% during the dry period. Overall, most plastics were attributed to five economic sectors: packaging, food, construction, fishing, and tourism, highlighting losses during the use and waste management phases of the plastic’s value chain. Based on the results of the analysis, consistent monitoring of plastic pollution is recommended for managing variable plastic loads. Full article

The growing need for healthy and sustainable food alternatives has led to a rapid increase in vegan burgers on the market. Specifically, plant-based burgers using legumes as a protein substitute are amongst the most widespread choices for consumers. While these products can offer environmental benefits over traditional meat-based options, further optimization in both ecological and economic aspects can be achieved. This study conducted a life cycle assessment (LCA) and life cycle costing (LCC) analysis to evaluate and optimize the environmental and economic life cycle of a legume-based vegan burger. LCA was performed in accordance with the recommendations of the ISO 14040 and 14044 series, and ReCiPe 2016 Hierarchist served as the impact assessment methodology. For this purpose, a base case scenario, relying on imported raw materials and conventional packaging for a legume-based vegan burger, was established to serve as the comparison benchmark, and various alternative scenarios were examined, focusing on minimizing the distance between cultivation and processing areas for key legume ingredients and improving packaging materials. The results indicate that reducing transportation distances for raw ingredients and using bio-polyethylene packaging significantly enhance sustainability. Specifically, the legume-based vegan burger of the base case scenario had a carbon footprint of 1.30 kg CO₂ eq. and a total life cycle cost of EUR 2.43 per two pieces. In contrast, the optimized scenario, which incorporated shorter transportation distances and bio-polyethylene packaging, achieved a carbon footprint of 0.51 kg CO₂ eq. and a reduced cost of EUR 2.37. The findings of the present work highlight the potential for further environmental and economic improvements in vegan burger production through logistics optimization and selection of climate-friendly packaging solutions, thus contributing to sustainable development. Full article

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems. Full article