Search Results (6,352)

With the escalation of global warming and environmental pollution, electric products characterized by zero emissions, low vibration, and minimal pollution are increasingly favored by consumers. As a pivotal loading and transportation tool, the electrification of forklifts progressed earlier and is relatively mature. However, the prevalent low-voltage systems (72 V or 80 V) in current electric forklifts exhibit issues such as elevated heat loss, restricted motor instantaneous power due to voltage constraints, susceptibility to electrical erosion, and challenges in achieving rapid charging. To address these challenges, a powertrain solution employing high-voltage lithium batteries (320 V) as energy storage units for electric forklifts is proposed. The key parameters of the high-voltage lithium battery were meticulously calculated and selected. The powertrain architecture of the high-voltage lithium battery electric forklift was analyzed, and operational conditions were thoroughly examined. To verify the superior energy efficiency performance of the proposed high-voltage electric forklift in comparison to its low-voltage counterparts, a test prototype was constructed, and comprehensive tests, including average energy consumption and thermal equilibrium assessments, were conducted. The test results demonstrated that under average energy consumption conditions, the operational duration ranged from 8.89 to 13.34 h, surpassing the 7.5 h achieved by low-voltage electric forklifts. The thermal equilibrium temperatures of all electrical control units remained below 43 °C, significantly lower than the 80 °C shutdown protection threshold allowed for low-voltage forklifts. These findings indicate that the proposed high-voltage lithium battery electric forklift exhibits relatively low energy consumption, significantly enhances overall operational efficiency, and ensures stable operation, providing a viable solution and reference for the electrification of forklifts and other construction machinery. Full article

The growing need for sustainable and resource-efficient materials increasingly promotes the use of block-glued laminated timber (glulam) in buildings and civil structures such as bridges. While timber is renewable and sustainable, the formaldehyde-based adhesives commonly used in glulam raise environmental and health concerns. This study addresses this gap by presenting one of the first combined life cycle assessment (LCA) and life cycle cost (LCC) analyses of bio-based versus synthetic adhesives for block-glued glulam. A pedestrian bridge in Zwolle, the Netherlands, serves as a case study. Three synthetic adhesives—melamine-urea formaldehyde (MUF), phenol resorcinol formaldehyde (PRF), and phenol formaldehyde (PF)—and two bio-based alternatives—lignin phenol glyoxal (LPG) and tannin-furfuryl alcohol formaldehyde (TFF)—are analyzed. The LCA covers raw material sourcing, transport, and end-of-life scenarios, with impacts assessed in accordance with EN 15804+A2 using Earthster and the Ecoinvent v3.11 database. The proposed method integrates environmental and economic assessments, with results presented both per kilogram of adhesive and per cubic meter of glulam to ensure comparability. Results show that synthetic adhesives have higher environmental impacts than bio-based adhesives: the carbon footprint of 1 kg of adhesive averages 0.60 kg CO₂-eq for bio-based adhesives and 2.01 kg CO₂-eq for synthetic adhesives. LCC are similar across adhesives, averaging EUR 400 per m³ of glulam. These findings suggest that bio-based adhesives can compete environmentally and economically, but their limited availability and uncertain long-term performance remain barriers. Overall, the study highlights trade-offs between sustainability and structural reliability and provides guidance for sustainable adhesive selection in timber engineering. Full article

The increasing demand for sustainable alternatives to synthetic agrochemicals underscores the need for novel, naturally derived plant growth regulators (PGRs) with high specificity and minimal environmental impact. In this study, we propose agavenin (AG), a steroidal saponin from Agave species, as a promising candidate and evaluate its potential role in plant growth regulation through a comprehensive in silico approach. Using molecular docking, molecular dynamics simulations, ADME profiling, and FTIR spectroscopy, we analyzed the interaction of AG with three key protein receptors (KPRs) that regulate major hormonal pathways: GA3Ox2 (gibberellin), IAA7 (auxin), and BRI1 (brassinosteroid). AG showed strong and stable binding to GA3Ox2 and IAA7, with affinities comparable to their endogenous ligands, while exhibiting low interaction with BRI1—suggesting receptor selectivity. Molecular dynamics confirmed the stability of AG–GA3Ox2 and AG–IAA7 complexes over 100 ns, and ADME profiling highlighted favorable properties for bioavailability and transport. Collectively, these findings indicate that AG could function as a selective, receptor-targeted modulator of gibberellin and auxin signaling pathways. Beyond demonstrating the molecular basis of AG’s bioactivity, this work establishes a computational foundation for its future experimental validation and potential development as a sustainable, naturally derived growth regulator for plant biotechnology and agriculture. Full article

Accurate forecasting of the electric vehicle (EV) charging load is a prerequisite for developing coordinated charging and discharging strategies. This study proposes a method for predicting the EV charging load by incorporating user travel characteristics and charging behavior. First, a transportation network–distribution network coupling framework is established based on a road network model with multi-source information fusion. Second, considering the multiple-intersection features of urban road networks, a time-flow model is developed. A time-optimal path selection method is designed based on the topological structure of the road network. Then, an EV driving energy consumption model is developed, accounting for both the mileage energy consumption and air conditioning energy consumption. Next, the user travel characteristics are finely modeled under two scenarios: working days and rest days. A user charging decision model is established using a fuzzy logic inference system, taking into account the state of charge (SOC), average electricity price, and parking duration. Finally, the Monte Carlo method is applied to simulate user travel and charging behavior. A simulation of the spatiotemporal distribution of the EV charging load was conducted in a specific area of Jiangning District, Nanjing. The simulation results show that there is a significant difference in the time distribution of EV charging loads between working days and rest days, with peak-to-valley differences of 3100.8 kW and 3233.5 kW, respectively. Full article

Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts. Full article

The development of alternative transportation methods has become paramount in the context of sustainable urban population connectivity. The promise of hyperloop as a high-speed, low-emission travel means motivates both academic and industrial interests. The present work centers on the design of hyperloop levitation systems. A component-level optimization is outlined for the appropriate selection of levitation module geometric parameters, followed by an integration into a capsule and bogie system. Two heteropolar levitation module types are numerically studied in realistic operating conditions: a hybrid electromagnet configuration with permanent magnets and a fully active one. To give means for comparison, both configurations are designed with the aid of a general multi-objective optimization approach. For the hybrid case, a position controller is synthesized with a zero-power policy and a specific frequency response function. The active configuration features comparable behavior. Two main power consumption streams are considered: gap control and magnetic drag. While the former depends on the position control effort, the latter depends on the losses of ferromagnetic elements. The two systems are compared in smooth and irregular track conditions over the studied speed range of 400–700 km/h. This study demonstrates that the hybrid heteropolar case achieves a minimum of 97.6% in specific power consumption reduction at the maximum speed of 700 km/h under smooth track conditions. Under irregular track conditions, a benefit in average specific consumption reduction is noted up to 662 km/h for the hybrid case. The maximum reduction in specific consumption is 57.2% at the minimum speed of 400 km/h. Full article

Background/Objectives: Increasing the bioavailability of poorly absorbed drugs is a continuous challenge in modern pharmaceutical technology. This is due to the problematic nature of BCS class IV active pharmaceutical ingredients: these drugs possess poor solubility and membrane permeability. Moreover, many undergo immediate efflux and/or rapid systemic metabolism after absorption. This project aimed to improve the bioavailability of BCS class IV drugs by formulating gastroretentive self-emulsifying systems using curcumin as a model drug. Methods: The base of the systems was created by melting emulsifying agents, dissolution retardants, and PEGs together. Curcumin was added after the mixture was cooled slightly. Aqueous dispersions of several compositions were characterized by dynamic light scattering. After screening these results, the viscosities of the selected formulations were evaluated. Dissolution retardants were selected and added to the most superior samples, and their dissolution profiles were compared. Gastroretention of the final formulation was achieved by dispersing air in the molten system through melt foaming; internal structure was assessed by microCT, and physicochemical properties by PXRD and DSC. Cytotoxicity was measured in Caco-2 cells using MTT and Neutral Red assays, and transcellular transport was also studied. Results: Based on these results, a homogeneous gastric floating system was developed. We observed an advantageous cytotoxic profile and increased bioavailability. Conclusions: Overall, we were able to create a self-emulsifying gastroretentive formulation displaying extended release and gastric retention with a low amount of cost-efficient excipients. Full article

Hydrogen compressors are key components of emerging hydrogen infrastructure. They are needed to meet the growing demand for hydrogen as an energy carrier. One of the challenges in their design is selecting a material and geometry for the impeller that ensures safe operation at high rotational speeds. This paper presents a numerical and structural analysis of a high-speed impeller designed for the first stage of a hydrogen compressor intended for pipeline transmission. The impeller geometry was developed using a 0D design algorithm and verified with CFD simulations. Stress and deformation were assessed using finite element method tools. The operating conditions considered were 28,356 rpm and a compression ratio of 1.25 at an isentropic efficiency of 75%. Four materials were analysed: aluminium 7075-T6, aluminium 2024 T851, stainless steel AISI 420, and titanium alloy Ti-6Al-2Sn-2Zr-2Mo. Equivalent stresses obtained from simulations were compared to the yield strengths of the materials. This study showed that aluminium 7075-T6 is the most suitable material due to its strength, machinability, and availability. It showed an equivalent stress of 398 MPa at a yield strength of 460–530 MPa. The results support the development of safe and efficient impellers for hydrogen compressors that can operate in future energy systems. Full article

Resonant acoustic technology utilizes low-frequency vertical harmonic vibrations to induce full-field mixing effects in processed materials, and it is regarded as a “disruptive technology in the field of energetic materials”. Although numerous scholars have investigated the mechanisms of resonant acoustic mixing, there remains a lack of parameter selection methods for improving product quality and production efficiency in engineering practice. To address this issue, this study employs phase-field modeling and fluid–structure coupling methods to numerically simulate the transport process of glycerol during resonant acoustic mixing. The research reveals the mass transfer mechanism within the flow field, establishes a liquid-phase distribution index for quantitatively characterizing mixing effectiveness, and clarifies the enhancement effect of fluid transport on solid particle mixing through particle tracking methods. Furthermore, parameter studies on vibration frequency and amplitude were conducted, yielding a critical curve for guiding parameter selection in engineering applications. The results demonstrate that Faraday instability first occurs at the fluid surface, generating Faraday waves that drive large-scale vortices for global mass transfer, followed by localized mixing through small-scale vortices. The transport process of glycerol during resonant acoustic mixing comprises three distinct stages: stable Faraday wave oscillation, rapid mass transfer during flow field destabilization, and localized mixing upon stabilization. Additionally, increasing either vibration frequency or amplitude effectively enhances both the rate and effectiveness of mass transfer. These findings offer theoretical guidance for optimizing process parameters in resonant acoustic mixing applications. Full article

This study systematically evaluates the spatial effects and driving mechanisms of Passenger Throughput (PT) within the Multi-airport System (MAS) of the Yangtze River Delta (YRD) region in China, using data from 22 cities between 2011 and 2019. Initially, the Air Passenger Location Entropy (APLE) index is introduced to quantify the spatial agglomeration within the MAS. Subsequently, both global and local Moran’s I indices are employed to assess the spatial autocorrelation of PT. Finally, Lagrange Multiplier (LM) tests, Wald test and Likelihood Ratio (LR) tests are utilized to select the appropriate spatial econometric model under different spatial weight matrices. Key findings include: (1) Air transport activity within the MAS exhibits a dynamic trend toward intensified spatial agglomeration and enhanced regional equilibrium; (2) APLE with higher value primarily concentrated in the southeastern coastal cities; (3) APLE has a significant positive impact on PT, with a 1% increase in APLE, leading to an average increase of 0.429% in PT; and (4) in cities with a well-developed air transport system, PT is predominantly influenced by APLE (0.915), whereas in cities with less robust air transport infrastructure, PT is more strongly influenced by tertiary industry value added (0.839) and GDP (0.442). These findings underscore the pivotal role of spatial dynamics in shaping PT and emphasize the necessity of spatially informed policy interventions to foster balanced regional development, strengthen system resilience, and advance the sustainable evolution of the MAS. Full article

This study aimed to quantitatively evaluate the potential health effects of exposure to major air pollutants inside newly manufactured automobiles and to develop a grading system for automobile indoor air quality based on this assessment. To achieve this, the concentrations of 28 air pollutants were measured in five different automobile models. Among these, 18 substances were selected for health risk assessment based on the availability of acute and chronic toxicity data and the requirement that each substance had been detected at least once under one or more of the automobile test modes (AM, PM, and DM). Acute hazard quotients (HQ_acute), chronic non-carcinogenic hazard quotients (HQ), and excess lifetime cancer risks (ECR) were subsequently calculated. The results of acute and chronic health risk assessments showed significant variation depending on the automobile test mode, and some automobiles exceeded health-based reference values for certain pollutants. Based on these findings, this study developed a 10-level grading system for automobile indoor air quality by comprehensively integrating pollutant-specific health risk levels and exceedances of the recommended limits outlined in Ministry of Land, Infrastructure, and Transport’s “Indoor Air Quality Guidelines for Newly Manufactured Automobiles.” The grading scale ranges from Grade 1 (Excellent) to Grade 10 (Hazardous), reflecting both acute and chronic health risks as well as legal standards, thereby improving upon conventional concentration-based management approaches. The proposed grading system enables a quantitative interpretation of automobile indoor air quality from a health-based perspective and is expected to be applicable in various fields, including automobile manufacturers’ air quality control, consumer information disclosure, and policy development. Full article

Increasing the frequency and duration of extreme heat events significantly compromises asphalt pavement performance, particularly in critical urban infrastructure such as heavily trafficked pavements, BRT lanes, and intersections subjected to slow-moving heavy traffic under extreme temperatures. This study systematically investigates rutting formation mechanisms through integrated theoretical and numerical approaches, addressing significant knowledge gaps regarding rutting evolution under coupled extreme-temperature (70 °C), heavy-load (100 kN–225 kN), and braking conditions (1 m/s²–7 m/s²). A three-dimensional thermo-mechanical finite element model integrating solar radiation heat transfer with the Bailey–Norton creep law was developed to quantify synergistic effects of axle loads, travel speeds, and braking accelerations. Results demonstrate that when the pavement surface temperature rises from 34 °C to 70 °C, the rutting depth is increased by 4.83 times. When the axle load is increased from 100 kN to 225 kN, the rutting of conventional asphalt pavements under 70 °C is increased by 56.4%. Rutting is exacerbated by braking acceleration; due to prolonged loading duration under low acceleration, the rutting depth is increased by 30–40% compared with that under emergency braking. These findings establish theoretical foundations for optimizing pavement design and material selection in slow-moving heavy-load environments, delivering significant engineering value for transportation infrastructure. Full article

This study explores how the integration of generative artificial intelligence, multi-objective evolutionary optimization, and reinforcement learning can enable sustainable and cost-effective decision-making in supply chain strategy. Using real-world retail demand data enriched with synthetic sustainability attributes, we trained a Variational Autoencoder (VAE) to generate plausible future demand scenarios. These were used to seed a Non-Dominated Sorting Genetic Algorithm (NSGA-II) aimed at identifying Pareto-optimal sourcing strategies that balance delivery cost and CO₂ emissions. The resulting Pareto frontier revealed favorable trade-offs, enabling up to 50% emission reductions for only a 10–15% cost increase. We further deployed a deep Q-learning (DQN) agent to dynamically manage weekly shipments under a selected balanced strategy. The reinforcement learning policy achieved an additional 10% emission reduction by adaptively switching between green and conventional transport modes in response to demand and carbon pricing. Importantly, the agent also demonstrated resilience during simulated supply disruptions by rerouting decisions in real time. This research contributes a novel AI-based decision architecture that combines generative modeling, evolutionary search, and adaptive control to support sustainability in complex and uncertain supply chains. Full article

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of the dynamical system governing the subject vehicle’s motion. Unlike conventional machine learning models that typically seek a single generalized representation across all drivers, our framework develops individualized models that explicitly preserve driver heterogeneity. Using both synthetic data from multiple traffic models and real-world naturalistic driving datasets, we demonstrate that DMDc accurately captures nonlinear vehicle interactions and achieves high-fidelity short-term predictions. Analysis of the estimated system matrices reveals that DMDc naturally approximates kinematic relationships, further reinforcing its interpretability. Importantly, this is the first study to apply DMDc to model and predict car-following behavior using real-world driving data. The proposed framework offers a computationally efficient and interpretable tool for traffic behavior analysis, with potential applications in adaptive traffic control, autonomous vehicle planning, and human-driver modeling. Full article

Element doping and functional group modification engineering serve as efficient approaches that contribute to the improvement of the functional efficiency in graphitic carbon nitride (CN) materials. A CN photocatalyst co-modified with sulfur (S) and cyano moieties was prepared through thermal condensation polymerization. The introduced S species modulated the band structure, increased charge carrier mobility, and significantly promoted charge separation and transport. Additionally, the introduction of cyano groups extended light absorption range and improved the material’s selective adsorption of reactant molecules. The as-prepared sulfur-modified CN photocatalyst obtained after a 6 h thermal treatment, which was capable of degrading organic pollutants and producing hydrogen (H₂) efficiently and stably, exhibited excellent catalytic performance. The photocatalyst’s photocatalyst exhibited a significantly enhanced photocatalytic activity, with a Rhodamine B (RhB) removal efficiency reaching 97.3%. Meanwhile, the H₂ production level reached 1221.47 μmol h⁻¹g⁻¹. Based on four-cycle experiments, the photocatalyst exhibited excellent recyclability and stability in both H₂ production processes and photocatalytic organic pollutant degradation. In addition, mechanistic studies confirmed the dominant role of ·OH and ·O₂⁻ as active species responsible for the reaction system’s performance. This study highlights that the co-decoration of heteroatoms and functional groups can markedly enhance the photocatalytic performance of CN-based materials, offering considerable potential for future applications in energy conversion and environmental remediation. Full article