Search Results (633)

Personal Rapid Transit (PRT) and Group Rapid Transit (GRT) are classes of fully automated public transport systems, where passengers can travel in small vehicles on an interconnected, grade-separated network of guideways, non-stop, from origin to destination. PRT and GRT are considered sustainable as they are low-emission and able to attract car drivers. The parameterized cost modeling framework developed in this paper has the advantage that profitability of different PRT/GRT systems can be rapidly verified in a transparent way and in function of a variety of relevant system parameters. This framework may contribute to a more transparent, rapid, and low-cost evaluation of PRT/GRT schemes for planning and decision-making purposes. The main innovation is the introduction of the “peak hour network saturation” S: the number of vehicles in circulation during peak hour divided by the maximum number of vehicles running at line speed with minimum time headways. It is an index that aggregates the main uncertainties in the planning process, namely the demand level relative to the supply level. Furthermore, a maximum S can be estimated for a PRT/GRT project, even without a detailed demand estimation. The profit per trip is analytically derived based on S and a series of more certain parameters, such as fares, capital and maintenance costs, daily demand curve, empty vehicle share, and physical properties of the system. To demonstrate the ability of the framework to analyze profitability in function of various parameters, we apply the methods to a single vehicle PRT, a platooned PRT, and a mixed PRT/GRT. The results show that PRT services with trip length proportional fares could be profitable already for $S>0.25$. The PRT capacity, profitability, and robustness to tripled infrastructure costs can be increased by vehicle platooning or GRT service during peak hours. Full article

The spatiotemporal evolution of the regional water use structure holds significant theoretical value for optimizing regional water resource allocation, adjusting industrial structures, and achieving sustainable water resource development. Shandong Province, located at the lowest reach of the Yellow River Basin in China, is a major economic, agricultural, and populous province, as well as a region with one of the most prominent water supply–demand imbalances in the country. As a result, exploring how water use patterns change over time and space in this region has become crucial. Using analytical methods like the Lorenz curve, Gini coefficient, cluster analysis, and spatial statistics, we examine shifts in Shandong’s water use structure from 2001 to 2023. We find that while agriculture remained the largest water consumer over this period, industrial, household, and ecological water use steadily increased, signaling a move toward more balanced resource distribution. Across Shandong’s 16 regions (cities), the water use patterns varied considerably, particularly in terms of agriculture, industry, and ecological needs. Among these, agricultural, industrial, and domestic water use were distributed relatively evenly, whereas ecological water use showed greater regional disparities. These results may have the potential to guide policymakers in refining water allocation strategies, improving industrial planning, and boosting the water use efficiency in Shandong and the country ore broadly. Full article

In this work, research on liquid-based resistive switching devices is carried out, using bottom printable electrodes fabricated from Silver (Ag) paste and silver nitrate (AgNO₃) solution. The self-crossing I-V curves are observed and repeatedly shown by applying 100 sweep cycles, demonstrating repeatability and stability. This liquid device can be refreshed by adding extra droplets of AgNO₃ so that self-crossing I-V hysteresis with up to 493 dual sweeps can be obtained. The ability to be refreshed by supplying a new liquid solution demonstrates an advantage of liquid-based memristive devices, in comparison to their solid counterparts, where the switching layer is fixed after fabrication. The switching mechanism is attributed to Ag migration in the liquid, which narrows the gap between electrodes, giving rise to the observed phenomenon. The devices further show some synaptic properties including excitatory post-synaptic current (EPSC) and potentiation-depression, presenting opportunities to utilize the devices in mimicking some functions of biological neurons. The simplicity and cost-effectiveness of these devices may advance research into fluidic memristors, in which devices with versatile forms and shapes could be fabricated. Full article

The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration. Full article

With the current acceleration of climate change, there is a global demand for sustainable development and carbon emission reduction. As a major link in the global supply chain, the logistics industry’s green and low-carbon transformation has become a critical breakthrough in achieving the objective of reducing carbon emissions. This study develops a multidimensional assessment index method for the green logistics level. The study selects 51 major economies worldwide from 2000 to 2022 as research subjects. The cloud model–entropy value–TOPSIS method is applied to measure the green logistics level. The results of the green logistics level are analyzed from the perspectives of developed and developing countries, and their spatiotemporal evolution characteristics are explored. The study shows that (1) the green logistics level in developed countries is relatively high, mainly due to policy-driven, core technology advantages. However, they continue to encounter issues, such as regional imbalance and excessive green costs. (2) The green logistics level in developing countries is in the middle to lower level, limited by technological dependence, outdated infrastructure, and so on. They are generally caught in a “high-carbon lock-in” situation. (3) From the perspective of time, the global level of green logistics shows a rising trend year by year. The peak of the kernel density curve of the green logistics level is characterized by an “I” shape. There is a significant disparity in each country’s green logistics level, although it is narrowing every year. (4) From the spatial perspective, the green logistics level in each country shows a rising trend year by year vertically, while the horizontal disparity between countries is enormous. The development of the green logistics level between continents is unbalanced. The study presents several recommendations, including boosting technology transfer, giving financial support, strengthening international cooperation, and developing green infrastructure, to promote the global logistics industry’s green and low-carbon transformation to accomplish sustainable development goals. Full article

The thermal error caused by the temperature rise in the service condition of the hydrostatic turntable system has a significant impact on the accuracy of the machine tool. The temperature rise is mainly caused by the friction heat of the bearing and the heat of the oil pump. The amount of heat mainly depends on the working parameters, such as the oil supply pressure and the oil film gap. The unreasonable parameter setting will cause the reduction in the internal flow of the hydrostatic bearing and the increase in the oil pump power, which makes the heat of the lubricating oil increase and the heat dissipation capacity decrease during the movement. Based on the established hydrostatic turntable system, in order to explore the main influencing factors of its thermal error, the temperature field model of the component is established by calculating the thermal balance of the key components of the system. The thermal coupling analysis of the component is carried out by using the model, and the temperature rise, deformation and strain curves of the hydrostatic turntable system under different service conditions are obtained. The results show that with the increase in the temperature, the deformation and strain of the bearing increase monotonously. For every 1 °C increase, the total deformation of the bearing increases by about 0.285 $\mathsf{\mu }\mathrm{m}$. The higher the oil supply pressure, the higher the temperature rise in the system. The larger the oil film gap, the lower the temperature rise in the system. The oil supply pressure has a greater influence on the temperature rise and thermal deformation than the oil film gap. This study provides a valuable reference for reducing the thermal error generated by the hydraulic turntable of the ultra-precision lathe. Full article

As an important unconventional oil and gas resource, tight oil exploration and development is of great significance to ensure energy supply under the background of continuous growth of global energy demand. Low-porosity and low-permeability reservoirs are characterized by tight rock properties, poor physical properties, and complex pore structure, and as a result the fine calculation of logging reservoir parameters faces great challenges. In addition, the crude oil in this area has high viscosity, the formation water salinity is low, and the oil reservoir resistivity shows significant spatial variability in the horizontal direction, which further increases the difficulty of oil and water reservoir identification and affects the accuracy of oil saturation calculation. Targeting the above problems, the Nutcracker Optimization Algorithm (NOA) was used to optimize the hyperparameters of the random forest classification model, and then the optimal hyperparameters were input into the random forest model, and the conventional logging curve and oil test data were combined to identify and classify the reservoir fluids, with the final accuracy reaching 94.92%. Compared with the traditional Hingle map intersection method, the accuracy of this method is improved by 14.92%, which verifies the reliability of the model for fluid identification of low-porosity and low-permeability reservoirs in the research block and provides reference significance for the next oil test and production test layer in this block. Full article

China’s National Famous, Special, Excellent, and New Agricultural Products are key rural economic assets, yet their spatial patterns and sustainability drivers remain underexplored. Based on the geospatial data of 1932 National Famous, Special, Excellent and New Agricultural Products in China, this study systematically analyzes their spatial distribution pattern by using GIS spatial analysis techniques, including the standard deviation ellipse, kernel density estimation, geographic concentration index and Lorenz curve, and quantitatively explores the driving factors of sustainable development by using geographic detectors. The research results of this paper are as follows. (1) The spatial distribution shows a significant non-equilibrium characteristic of “high-density concentration in the central and eastern part of the country and low-density sparseness in the western part of the country” and the geographic concentration index (G = 22.95) and the standard deviation ellipse indicate that the center of gravity of the distribution is located in the North China Plain (115° E–35° N), and the main direction extends along the longitude of 110° E–120° E. (2) Driving factor analysis showed that railroad mileage (X10) (q = 0.5028, p = 0.0025 < 0.01), highway mileage (X11) (q = 0.4633, p = 0.0158 < 0.05), and population size (X3) (q = 0.4469, p = 0.0202 < 0.05) are the core drivers. (3) Three-dimensional kernel density mapping reveals that the eastern coast and central plains (kernel density > 0.08) form high-density clusters due to the advantages of the transportation network and market, while the western part shows a gradient decline due to the limitation of topography and transportation conditions. The study suggests that the sustainable development of National Famous, Special, Excellent, and New Agricultural Products should be promoted by strengthening transportation and digital logistics systems, enhancing cold-chain distribution for perishable goods, tailoring regional branding strategies, and improving synergy among local governments, thereby providing actionable guidance for policymakers and producers to increase market competitiveness and income stability. The study provides a quantitative, policy-oriented assessment of China’s branded agricultural resource allocation and its sustainability drivers, offering specific recommendations to guide infrastructure investment, e-commerce logistics enhancement, and targeted subsidy design for balanced regional development. The study highlights three key contributions: (1) an innovative integration of geospatial analytics and geographical detectors to reveal spatial patterns; (2) clear empirical evidence for policymakers to prioritize transport and digital logistics investments; and (3) practical guidance for producers and brand managers to enhance product market reach, optimize supply chains, and strengthen regional competitiveness in line with sustainable development goals. Full article

Biochar enhances soil available potassium and plant uptake, yet its effects on soil potassium supply capacity and crop potassium accumulation require clarification. This study used a pot experiment with three soil types (albic, brown, and sandy soils) and four biochar application rates (0, 10, 20, and 30 g·kg⁻¹) to investigate potassium supply capacity and soybean potassium accumulation using the potassium site coordination theory and Q/I curve analysis. The results showed that biochar significantly increased the available potassium content in soil. At the highest biochar application rate (30 g·kg⁻¹), the available potassium in the albic, sandy, and brown soils increased by 24.84%, 60.90%, and 24.84%, respectively, compared to the control. The biochar boosted the instantaneous potassium supply (elevated AR₀ and ΔK values) through direct water-soluble potassium input. However, the potential potassium supply capacity (PBC) varied by soil type: the PBC increased in the brown soil at low application rates but decreased in the albic and sandy soils with higher rates. The biochar enhanced soybean potassium accumulation through two pathways: the direct enrichment of soil potassium pools and the indirect improvement in soil properties to promote biomass accumulation. These findings provide theoretical insights for optimizing biochar use in agriculture to maximize potassium availability and crop efficiency. Full article

This study examines the symmetric and asymmetric impacts of international trade on consumption-based carbon emissions (CBEs) in the People’s Republic of China (PRC) and the United States of America (USA) from 1990 to 2018. The analysis uses autoregressive distributed lag (ARDL) and non-linear ARDL (NARDL) methodologies to capture short- and long-run trade emissions dynamics, with economic growth, oil prices, financial development and industry value addition as control variables. The findings reveal that exports reduce CBEs, while imports increase them, across both economies in the long and short run. The asymmetric analysis highlights that a fall in exports increases CBEs in the USA but reduces them in the PRC due to differences in supply chain flexibility. The PRC demonstrates larger coefficients for trade variables, reflecting its reliance on energy-intensive imports and rapid trade growth. The error correction term shows that the PRC takes 2.64 times longer than the USA to return to equilibrium after short-run shocks, reflecting systemic rigidity. These findings challenge the Environmental Kuznets Curve (EKC) hypothesis, showing that economic growth intensifies CBEs. Robustness checks confirm the results, highlighting the need for tailored policies, including carbon border adjustments, renewable energy integration and CBE-based accounting frameworks. Full article

The trend towards shorter supply chains and home delivery has rapidly increased delivery van traffic. Consequently, in the 20 years prior to 2018, delivery traffic has increased by 71%, while passenger vehicles have increased only by 13%. This drastic change in traffic patterns presented new challenges to decision makers and fortunately coincided with changes in the automotive industry, i.e., the advent of automation. However, the design of a controller is not straightforward due to the complex and nonlinear vehicle dynamics and the nonlinear relationship between the controller, tracking error and trajectory. This paper proposes a novel hybrid artificial-intelligence-based lateral control system for an autonomous delivery van to address these challenges to achieve the lowest value of tracking error. The strategy consists of multiple simultaneously operating fuzzy controllers. Their output signals are optimally weighted by a genetic algorithm to determine the proper allocation of control signals for calculating the final steering angle. Six different scenarios are implemented to evaluate the algorithm. A comparative analysis is then performed with two alternative state-of-the-art methods: (i) manually weighted and (ii) geometrically weighted controllers. During the tests, the vehicle’s speed varied, and the roads considered ranged from simple roads to a series of curves. The results show that the proposed strategy leads to a reduction of up to 91.2% and 61.1% in tracking error compared to the manually and geometrically weighted alternatives, respectively. Full article

This study investigates the effects of anode and cathode inlet flow rates (ṁ) on the power performance of bipolar plates in a polymer electrolyte membrane fuel cell (PEMFC). The primary objective is to derive optimal flow rate conditions by comparatively analyzing concentration loss in the I−V curve and crossover phenomena at the anode, thereby establishing flow rates that prevent reactant depletion and water flooding. A single-cell computational model was constructed by assembling a commercial bipolar plate with a gas diffusion layer (GDL), catalyst layer (CL), and proton exchange membrane (PEM). The model simulates current density generated by electrochemical oxidation-reduction reactions. Hydrogen and oxygen were supplied at a 1:3 ratio under five proportional flow rate conditions: hydrogen (${\dot{m}}_{H_2}$ = 0.76–3.77 LPM) and oxygen (${\dot{m}}_{O_2}$ = 2.39–11.94 LPM). The Butler–Volmer equation was employed to model voltage drop due to overpotential, while numerical simulations incorporated contact resistivity, surface permeability, and porous media properties. Simulation results demonstrated a 24.40% increase in current density when raising ${\dot{m}}_{H_2}$ from 2.26 to 3.02 LPM and ${\dot{m}}_{O_2}$ from 7.17 to 9.56 LPM. Further increases to ${\dot{m}}_{H_2}$ = 3.77 LPM and ${\dot{m}}_{O_2}$ = 11.94 LPM yielded a 10.20% improvement, indicating that performance enhancements diminish beyond a critical threshold. Conversely, lower flow rates (${\dot{m}}_{H_2}$ = 0.76 and 1.5 LPM, ${\dot{m}}_{O_2}$ = 2.39 and 4.67 LPM) induced hydrogen-depleted regions, triggering crossover phenomena that exacerbated anode contamination and localized flooding. Full article

The exploration and development of shale oil offers significant potential as an alternative to address oil and gas supply. The complex geological conditions characterized by inter-lithology and strong heterogeneity pose substantial challenges in fracturing reconstruction. This paper established an integrated geoengineering model of Gulong shale in the Songliao Basin using multi-source data to accurately characterize the vertical and lateral heterogeneity of the reservoir. Based on the reconstruction economy, the compressibility evaluation of different reservoirs showed that the advantage of reconstruction was highest in the Q9 reservoir and lowest in the Q3 reservoir. Analysis of the fluctuation in the reservoir compressibility and lithology characteristics revealed limited fracture reconstruction volume in highly plastic reservoirs with elevated mudstone concentrations, which affected reconstruction efficacy. The number of clusters could be increased while the spacing between the clusters could be reduced in the lithologically brittle region. In areas characterized by strong lithology and plasticity, the number of clusters was appropriately decreased while the inter-cluster spacing increased. The research results allow local adjustments according to the logging curve, inform differentiated decisions on fracturing reconstruction, and support the efficient fracturing reconstruction of the Gulong shale oil reservoir. Full article

In this study, we conceptualize the demands imposed on emergency supply chains during extraordinary emergency events as “stress” and develop a scenario-based stress evolution (SE) analytical approach in emergency mobilization decision-making. First, we characterize emergency supply chain stress by uncertainty, abruptness, urgency, massiveness of scale, and latency. Leveraging lifecycle theory and aligning it with the event’s natural lifecycle progression, we construct a dual-cycle model—the emergency event-stress dual-cycle curve model—to intuitively conceptualize the SE process. Second, taking China’s emergency medical supply chain as an illustrative example, we employ set theory to achieve a structured representation of emergency supply chain stress evolution (ESCSE). Third, we propose a novel ESCSE modeling methodology based on stochastic Petri nets and establish both an ESCSE model and a corresponding isomorphic Markov chain model. To address parameter uncertainties inherent in the modeling process, the fuzzy theory is integrated for parameter optimization, enabling realistic simulation of emergency supply chain stress evolution dynamics. Finally, the SE of the ibuprofen supply chain in Beijing during the COVID-19 pandemic is presented as a case study to demonstrate the working principle of the model. The results indicate that the ESCSE model effectively simulates the SE process, identifies critical states, and triggers actions. It also reveals the evolution trends of key scenario elements, thereby assisting decision-makers in deploying more targeted mobilization strategies in dynamic and changing environments. Full article

Plants are commonly exposed to fluctuating illumination under natural light conditions, causing dynamic photosynthesis and further affecting plant growth and productivity. In this context, although the vital role of potassium (K) in steady-state photosynthesis has been well-established, knowledge of the dynamic changes in photosynthesis mediated by K remains scarce. Here, the gas-exchange and chlorophyll fluorescence parameters under steady-state and dynamic photosynthetic responses were quantified in Phaseolus vulgaris L. seedlings grown under K-deficient (−K, 0.02 mM K) and normal K (+K, 2 mM K) conditions. After a transition from low to high light, the time course–induction curves of the net photosynthetic rate (A), stomatal conductance (g_s), mesophyll conductance (g_m), and maximum carboxylation rate (V_cmax) showed an obvious decline in the −K treatment. In comparison with the +K treatment, however, there were no statistical differences in the initial A and V_cmax values in P. vulgaris supplied with deficient K, suggesting that the K-deficiency-induced decreases in A and V_cmax were light-dependent. Interestingly, the time to reach 90% of the maximum A, g_s, and g_m significantly decreased in the −K treatment in comparison with the +K treatment by 27.2%, 45.6%, and 52.9%, respectively, whereas the time to reach 90% of the maximum V_cmax was correspondingly delayed by almost two-fold. The photosynthetic limitation during the induction revealed that the biochemical limitation was the dominating factor that constrained A under the −K conditions, while, under the +K conditions, the main limiting factor changed from biochemical limitation to stomatal limitation over time. Moreover, g_m imposed the smallest limitation on A during induction in both K treatments. These results indicate that a decreased K supply decreases the photosynthetic performance under fluctuating light in P. vulgaris and that improving the induction responses of biochemical components (i.e., V_cmax) has the potential to enhance the growth and productivity of crops grown in K-poor soil. Full article