Search Results (852)

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Neuronal proteins synthesized locally in axons and dendrites contribute to growth, plasticity, survival, and retrograde signaling underlying these cellular processes. Advances in molecular tools to profile localized mRNAs, along with single-molecule detection approaches for RNAs and proteins, have significantly expanded our understanding of the diverse proteins produced in subcellular compartments. These investigations have also uncovered key molecular mechanisms that regulate mRNA transport, storage, stability, and translation within neurons. The long distances that axons extend render their processes vulnerable, especially when injury necessitates regeneration to restore connectivity. Localized mRNA translation in axons helps initiate and sustain axon regeneration in the peripheral nervous system and promotes axon growth in the central nervous system. Recent and ongoing studies suggest that axonal RNA transport, storage, and stability mechanisms represent promising targets for enhancing regenerative capacity. Here, we summarize critical post-transcriptional regulatory mechanisms, emphasizing translation in the axonal compartment and highlighting potential strategies for the development of new regeneration-promoting therapeutics. Full article

Nitrogen use efficiency remains the primary bottleneck for sustainable maize production. This study elucidates the functional mechanisms of the amino acid transporter ZmAAP1 in nitrogen absorption and stress resilience. Through systematic evolutionary analysis of 55 maize inbred lines, we discovered that the ZmAAP1 gene family exhibits distinct chromosomal localization (Chr7 and Chr9) and functional domain diversification (e.g., group 10-specific motifs 11/12), indicating species-specific adaptive evolution. Integrative analysis of promoter cis-elements and multi-omics data confirmed the root-preferential expression of ZmAAP1 under drought stress, mediated via the ABA-DRE signaling pathway. To validate its biological role, we generated transgenic maize lines expressing Arabidopsis thaliana AtAAP1 via Agrobacterium-mediated transformation. Three generations of genetic stability screening confirmed the stable genomic integration and root-specific accumulation of the AtAAP1 protein (Southern blot/Western blot). Field trials demonstrated that low-N conditions enhanced the following transgenic traits: the chlorophyll content increased by 13.5%, and the aboveground biomass improved by 7.2%. Under high-N regimes, the gene-pyramided hybrid ZD958 (AAP1 + AAP1) achieved a 12.3% yield advantage over conventional varieties. Our findings reveal ZmAAP1’s dual role in root development and long-distance nitrogen transport, establishing it as a pivotal target for molecular breeding. This study provides actionable genetic resources for enhancing NUE in maize production systems. Full article

Generation Z (Gen Z; born 1997–2012) is reshaping global tourism through digital fluency, ethical awareness, and a desire for authentic, sustainable travel experiences. This study surveys 413 Gen Z travelers across France, Portugal, Romania, and the USA to map their booking behaviors, information sources, transport modes, accommodations, dining practices, and leisure activities. The findings reveal a strong preference for independent online bookings and social-media-influenced destination choices (Instagram, TikTok), with air and car travel being used for long-distance journeys and walking/public transit being used for local journeys. Accommodation spans commercial hotels and private rentals, while informal, local dining and nature- or culture-centered leisure prevail. Chi-square tests were performed to identify differences between countries. To reveal distinct traveler segments and their country’s modulations towards sustainability, a hierarchical cluster analysis was performed. The results uncover four segments: “Tech-Active, Nature-Oriented Minimalists” (32.3% in France); “Moderate Digital Planners” (most frequent across all countries, particularly dominant among Romanian respondents); “Disengaged and Indecisive Travelers” (overrepresented in the USA); and “Culturally Inclined, Selective Sustainability Seekers” (>30% in France/Portugal). Although sustainability is widely valued, only some segments of the studied population consistently act on these values. The results suggest that engaging Gen Z requires targeted, value-driven digital strategies that align platform design with the cohort’s diverse sustainability commitments. Full article

Intelligent Transportation Systems (ITSs) play a vital role in improving urban and regional mobility by reducing traffic congestion and enhancing trip planning. A key element of ITS is travel-time prediction, which supports informed decisions for both travelers and traffic management. While non-parametric models offer flexibility, they often require large datasets and significant computation. Parametric models, though easier to fit and interpret, are less adaptable. Fuzzy logic models, by contrast, provide robustness and scalability, adjusting to new data and changing conditions. This paper proposes a cascaded fuzzy logic system for highway travel-time prediction, using the Greenshields model as its reasoning foundation. The system consists of multiple fuzzy subsystems, each representing a highway segment. These subsystems transform traffic flow and density inputs into speed predictions through fuzzification, Greenshields-based rules, and defuzzification. The approach enables localized and segment-specific predictions, enhancing route planning and congestion avoidance. The system’s accuracy is evaluated by comparing its predictions with those of a regression model using real traffic data from the Sun Yat-Sen Highway in Taiwan. Simulation results confirm that the proposed model achieves reliable, adaptable travel-time forecasts, including for long-distance trips. 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

Periodic train timetables provide passengers with easily accessible rail transport services. However, in networked railway operations, some long-distance cross-line trains have high operational priority and pose difficulties for scheduling local services. In this paper, we address the minimal-cycle-length periodic train timetabling problem with high-priority cross-line operations and complex local train types. We propose a special set of constraints to accommodate the prespecified operational times of cross-line operations with regard to system robustness. As the cycle length is regarded as a decision variable, the formulation is nonlinear. To solve the problem, we exploit the connection between cycle length and consumed capacity of periodic timetables and propose high-level cycle-capacity and binary search-guided iterative solution frameworks, which implicitly enumerate the periodic train timetabling problems. Using the real-world operational data of the Guangzhou–Zhuhai Intercity Rail Line, we explore the solution performance of the proposed solution approaches and the straight linearization of the problem, and we also compare the practices of fixing prespecified operational times and our proposed constraints for the cross-line services. The results demonstrate that our proposed method can efficiently achieve flexible while recoverable operational times for the cross-line services and the proposed implicit enumeration algorithms significantly outperform the direct linearization, which increases the search space significantly due to the considerable dimensionality of the periodic decision variables involved. Numerical computations also suggest that our proposed constraints provide a type of approach for balancing the operational convenience and stability margins available in the periodic timetable with the presence of cross-line operations. Full article

Seed development plays a critical role in determining both crop yield and grain quality in rice. As a key nutrient storage organ, the rice endosperm development not only contributes to grain filling but also plays an essential role during the early stages of seed germination. Amino acid metabolism is active during the process of seed development and seed germination. Asparagine is a primary amino acid responsible for long-distance organic nitrogen transport in plants. Asparagine synthetase catalyzes the synthesis of asparagine from aspartate and glutamine. In this study, CRISPR/Cas9-mediated knockout mutants of the OsASN2 gene of rice were generated. Homozygous mutants exhibited complete failure of seed germination, and heterozygotes could not produce homozygous offspring. Endosperm development of homozygous mutant seeds showed severe defects. Additionally, interacting protein screening combined with pull-down and co-immunoprecipitation (Co-IP) assays confirmed that OsASN2 physically interacted with pyruvate phosphate dikinase OsPPDKB, the mutants of which showed impaired endosperm development. These findings collectively indicate that OsASN2 plays a critical role in seed development and germination in rice. 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

Scientifically and accurately assessing the interaction between changes in human activity intensity and the surrounding ecological environment along the Qinghai–Tibet Railway is of great significance for the optimized construction of the railway and the restoration of the regional ecological environment. Based on different spatial distribution scales and construction phases of the Qinghai–Tibet Railway, this study integrates multi-source remote sensing data to construct a long-term spatiotemporal dataset of human activity intensity in the region. Drawing on analytical methods from production theory, a coupling theoretical framework based on remote sensing ecological models is proposed to quantitatively reveal the coupling relationships between the ecological environment and human activities across varying spatiotemporal scales along the Qinghai–Tibet Railway. The study finds that (1) the spatiotemporal distribution of human activity intensity along the Qinghai–Tibet Railway demonstrates clear patterns, with expansion primarily radiating from transportation corridors and their intersections, and marked spatial heterogeneity across different segments. Overall, human activity intensity increased slowly between 1990 and 2002, followed by a significant rise during the construction and opening of the Golmud–Lhasa section (2001–2007). From 2013 to 2020, the growth rate began to slow. Within a 0–30 km buffer zone centered on railway station locations (with a 15 km radius), the growth rate of human activity intensity generally decreased with increasing distance from the railway. In the 30–60 km buffer zone, this trend tended to stabilize. (2) The coupling process between ecological quality and human activity intensity across different spatiotemporal scales along the railway exhibits considerable spatial and temporal heterogeneity and complexity. The decoupling relationship is dominated by strong and weak decoupling patterns, with strong decoupling being the most prevalent. Weak decoupling is mainly distributed along the sides of the railway. Overall, in most areas along the railway, ecological quality has shown a certain degree of improvement alongside increasing human activity intensity; however, the rate of ecological improvement is generally lower than the rate of increase in human activity intensity. In some areas adjacent to the railway, intensified human activities have led to a decline in ecological quality, though the resulting ecological pressure remains relatively low. Full article

This study addresses the critical challenges of hydrogen permeation and embrittlement in metallic pipelines for hydrogen storage and transportation by developing an epoxy resin-based composite coating with enhanced hydrogen barrier properties. Using cold spray technology, the fabricated coatings with controlled 250–320 μm thicknesses incorporating graphene/ceramic composite particles uniformly dispersed in the epoxy matrix. Microstructural characterization revealed dense morphology and excellent interfacial bonding. Electrochemical hydrogen charging tests demonstrated remarkable hydrogen permeation reduction, showing a strong positive correlation between coating thickness and barrier performance. The optimal 320 μm-thick coating achieved a hydrogen content of only 0.28 ± 0.09 ppm, representing an 89% reduction compared to that in uncoated substrates. The superior performance originates from the Al₂O₃/SiO₂ networks providing physical barriers, graphene offering high-surface-area adsorption sites, and MgO chemically trapping hydrogen atoms. Post-charging analysis identified interfacial stress concentration and hydrogen-induced plasticization as primary causes of ceramic particle delamination. This work provides both fundamental insights and practical solutions for designing high-performance protective coatings in long-distance hydrogen pipelines. Full article

Selecting the most suitable machines to use for wood recovery is essential for computing the operating costs of the whole forestry machinery chain (FMC). Nevertheless, a generalized approach for selecting the most suitable FMC and quantifying the corresponding economic performances for wood recovery (i.e., harvesting and long-distance transport) is still missing. The primary aim of this study is to describe a decision support model, called FOREstry MAchinery chain selection (“FOREMA v1”), which is able to (i) select the most feasible FMC and (ii) calculate the costs (such as EUR∙h⁻¹; EUR∙t⁻¹ of dry matter, DM) of each operation (OP) comprising the FMC. The model is made up of three different modules (Ms): machinery chain selection (M1), machinery chain organization (M2), and cost calculation (M3). In M1, feasible FMCs are defined according to seven technical parameters that characterize the forest area. For each FMC, FOREMA v1 defines the sequence of OPs and the types of machines that can potentially be used. Once the characteristics of the area in which wood recovery occurs are processed, the user selects the types of machines to use according to the model’s suggestions. In M2 and M3, the user is supported in organizing the FMC (e.g., calculation of the required time, working productivity, and so on) and computing the operating costs. The secondary aim of this study is to discuss a case study focused on chips production for energy generation, providing empirical evidence on how FOREMA v1 works. The proposed model provides a systematic approach for the selection and optimization of the most suitable FMC to adopt for biomass recovery, thus supporting decision-making processes. The results showed that felling had the lowest cost per unit of time (63.7 EUR·h⁻¹) but the highest cost per unit of mass (35.4 EUR·t DM⁻¹) due to its longer working time and lower productivity. Loading and long-distance transport incurred the highest costs both per unit of time (223.5 EUR·h⁻¹) and per unit of mass (29.4 EUR·t DM⁻¹), attributed to the use of medium–small-sized trailers coupled with tractors operating at low speeds, leading to a high number of cycles. For the entire FMC the costs were equal to 147.3 EUR·h⁻¹ and 101.1 EUR·t DM⁻¹. Full article

In the field of intelligent decision-making, time-series data collected by sensors serves as the core carrier for interaction between the physical and digital worlds. Accurate analysis is the cornerstone of decision-making in critical scenarios, such as industrial monitoring and intelligent transportation. However, the inherent spatio-temporal coupling characteristics and cross-period long-range dependency of sensor data cause traditional time-series prediction methods to face performance bottlenecks in feature decoupling and multi-scale modeling. This study innovatively proposes a Spatio-Temporal Attention-Enhanced Network (TSEBG). Breaking through traditional structural designs, the model employs a Squeeze-and-Excitation Network (SENet) to reconstruct the convolutional layers of the Temporal Convolutional Network (TCN), strengthening the feature expression of key time steps through dynamic channel weight allocation to address the redundancy issue of traditional causal convolutions in local pattern capture. A Bidirectional Gated Recurrent Unit (BiGRU) variant based on a global attention mechanism is designed, leveraging the collaboration between gating units and attention weights to mine cross-period long-distance dependencies and effectively alleviate the gradient disappearance problem of Recurrent Neural Network (RNN-like) models in multi-scale time-series analysis. A hierarchical feature fusion architecture is constructed to achieve multi-dimensional alignment of local spatial and global temporal features. Through residual connections and the dynamic adjustment of attention weights, hierarchical semantic representations are output. Experiments show that TSEBG outperforms current dominant models in time-series single-step prediction tasks in terms of accuracy and performance, with a cross-dataset R² standard deviation of only 3.7%, demonstrating excellent generalization stability. It provides a novel theoretical framework for feature decoupling and multi-scale modeling of complex time-series data. Full article

With the increase in urban–rural disparities in China, rural public transportation systems in low-density areas face unique challenges, especially in the contexts of sparse population, complex topography, and uneven resource allocation; research on public transportation in low-density areas has had less attention compared to high-density urban areas. Therefore, how to solve the dilemma of public transportation service provision in low-density rural areas due to sparse population and long travel distances has become an urgent problem. In this paper, a dynamic optimization model based on a two-layer planning framework was constructed. The upper layer optimized the topology of multimodal transportation nodes through the Floyd shortest path algorithm to generate a composite network of trunk roads and feeder routes; the lower layer adopted an improved Logit discrete choice model, integrating the heterogeneous utility parameters, such as time cost, economic cost, and comfort, to simulate and realize the equilibrium allocation of stochastic users. It was found that the dynamic game mechanism based on the “path optimization–fairness measurement” can optimize the travel time, mode, route, and bus stop selection of rural residents. At the same time, the mechanism can realize the fair distribution of rural transportation network subjects (people–vehicles–roads). This provides a dynamic, multi-scenario macro policy reference basis for the optimization of a rural transportation network layout. Full article

This study provides a bibliometric analysis of life cycle assessments (LCAs) to explore the sustainability potential of mass timber buildings, focusing on glulam. The analysis highlights regional differences in carbon footprint performance within the ISO 14040 and EN 15978 frameworks. LCA results from representative countries across six continents show that wood buildings, compared to traditional materials, have a reduced carbon footprint. The geographical distribution of forest resources significantly influences the carbon footprint of glulam production. Europe and North America demonstrate optimal performance metrics (e.g., carbon sequestration), attributable to advanced technology and investment in long-term sustainable forest management. Our review research shows the lowest glulam carbon footprints (28–70% lower than traditional materials) due to clean energy and sustainable practices. In contrast, Asia and Africa exhibit systemic deficits, driven by resource scarcity, climatic stressors, and land-use pressures. South America and Oceania display transitional dynamics, with heterogeneous outcomes influenced by localized deforestation trends and conservation efficacy. Glulam buildings outperformed concrete and steel across 11–18 environmental categories, with carbon storage offsetting 30–47% of emissions and energy mixes cutting operational impacts by up to 67%. Circular strategies like recycling and prefabrication reduced end-of-life emissions by 12–29% and cut construction time and costs. Social benefits included job creation (e.g., 1 million in the EU) and improved well-being in wooden interiors. To further reduce carbon footprint disparities, this study emphasizes sustainable forest management, longer building lifespans, optimized energy mixes, shorter transport distances, advanced production technologies, and improved recycling systems. Additionally, the circular economy and social benefits of glulam buildings, such as reduced construction costs, value recovery, and job creation, are highlighted. In the future, prioritizing equitable partnerships and enhancing international exchanges of technical expertise will facilitate the adoption of sustainable practices in glulam buildings and advance decarbonization goals in the global building sector. Full article