Amount Of Transport Research Articles

Blown sand environment characteristics and bridge sand hazard mechanisms of expressways in the South Tibet Valley.

South Tibet expressways are built in the Yalu Tsangpo valley along the route to high-altitude and cold-temperature valleys, which are the main environmental characteristics of the region. Given that the blown sand environment and its potential harm are unclear, targeted prevention and control are not conducive, but its absence would result in severe wind-sand hazards on these expressways. In this research, the blown sand environment of the South Tibet Valley and the mechanism of sand damage to expressway bridges were studied via field observations, wind tunnel experiments and numerical simulations. The sand-moving wind is mainly west wind. The sand-moving wind frequency, sand drift potential, and maximum possible sand transport quantity are high in winter and spring (November-April) and low in summer and autumn (May-October). The sand drift direction is in the east direction. Expressway bridges have a considerable impact on the near-surface blown sand environment, forming a zone for increasing the wind speed between the top of the slope shoulder of the windward side of the bridge to the center of the bridge abutment, forming a zone for weakening the wind speed between the - 3H distances of the upwind direction to the bottom of the slope middle of the windward side of the bridge, further forming a zone for weakening the wind speed centered in the slope shoulder of the leeward side of the bridge. The wind speed within a distance of 40H downwind direction of the bridge generally did not recover. The wind-sand flow is partially blocked when it passes through the bridge and accumulates near the bridge, which cause harm. The impact of the bridge on the blown sand environment in the downwind is greater than that in the upwind. The results of this study can provide a reference for preventing and controlling wind-sand hazards on South Tibet expressways.

Environmental transport and sorting of glass retroreflective microbeads and their potential as proxies for road marking paints

Road marking paints are a potentially important contributor to the global microplastic pool but very little reliable information is available on their erosion or environmental distributions. As potential carriers of or proxies for road paints, we determine the concentrations and sorting of retroreflective glass microbeads in marking materials and in fractionated (< 5 mm) local dusts, soils and sediments. As an aid to our investigation, we also determine the concentrations of metals of geochemical significance or components of road paint pigments in markings and geosolids. Concentrations of beads up to 92,800 kg−1 were observed in street dusts, with a median diameter (350 μm) greater than that in road marking samples (270 μm). Few beads were found in adjacent (< 5 m) or more remote soils (six beads in ten 50-g samples) and none were detected in replicates of a sample of roof dust, suggesting that aeolian transport is limited. Concentrations up to 3700 kg−1 were found in estuarine sediments close to bridges or stormwater runoff effluents, and with increasing sediment depth concentrations and median diameter decreased; beads were not, however, detected in sediments 400 m away from any significant roads or runoff effluents. These observations suggest that bead accumulation is constrained locally but that the precise distance travelled and extent of burial in sediments are inversely related to size. Road marking paints sampled from urban streets readily fragmented into pieces smaller than glass microbeads, suggesting that while beads might carry small quantities of paint, transport and dispersion of the two particle types may not be directly coupled. Environmental ratios of V to Bi and Cr to Pb, as markers for BiVO4- and PbCrO4-pigmented yellow paints, respectively, did not correlate with bead distribution, presumably because these metals have a multitude of additional anthropogenic sources. However, an inverse relationship between bead concentrations and K:Ca suggests that this ratio might be a useful proxy of road marking paint in regions that are geologically similar.

A closed-loop supply chain inventory model with stochastic demand, exchange rate, green investment, and carbon tax

In this paper, a mathematical inventory model for a closed-loop supply chain consisting of a single manufacturer and a single retailer is investigated under a stochastic environment and imperfect production. The manufacturer adopts a remanufacturing policy to recover returned products from the market. The model takes into account exchange rate disparities because the supply chain’s participants are in separate countries. The model also considers carbon emissions, which are expected to be produced from storage, transportation, and production. A carbon policy, namely carbon tax, is implemented to manage the overall emissions resulting from the supply chain. To cope with carbon restrictions, the manufacturer has an opportunity to invest in green technologies. The objective of the study is to find optimal shipment quantity, number of deliveries, safety factor, and green investment such that the joint total cost is minimized. An iterative procedure is suggested to solve the proposed problem, and a numerical example is presented for model validation. The findings imply that variations in the production rate and carbon tax have a significant influence on the model’s performance. Aside from that, the results reveal that uncertainty in the exchange rate and production flaws are important factors that must be considered by managers in making inventory decisions, especially those related to the number of deliveries, frequency of deliveries, and the amount of money invested in green technology. Finally, a sensitivity analysis is provided to explain the behavior of the model.

Legume green manure can intensify the function of chemical nitrogen fertilizer substitution via increasing nitrogen supply and uptake of wheat

Achieving the green development of agriculture requires the reduction of chemical nitrogen (N) fertilizer input. Previous studies have confirmed that returning green manure to the field is an effective measure to improve crop yields while substituting partial chemical N fertilizer. However, it remains unclear how to further intensify the substituting function of green manure and elucidate its underlying agronomic mechanism. Therefore, In a split-plot field experiment in spring wheat, different green manures returned to the field was established in an oasis area since 2018, in order to investigate the effect of green manure and reduced N on grain yield, N uptake, N use efficiency (NUE), N nutrition, soil organic matter, and soil N of wheat in 2020–2022. Our results showed that mixed sown common vetch and hairy vetch can substitute 40 % of chemical N fertilizer without reducing grain yield or N accumulation. Noteworthily, mixed sown common vetch and hairy vetch under reduced N by 20 % showed the highest N agronomy efficiency and recovery efficiency, which were 92.0 % and 46.0 % higher than fallow after wheat harvest and conventional N application rate, respectively. The increase in NUE of wheat was mainly attributed to mixed sown common vetch and hairy vetch, which increased N transportation quantity and rate at pre-anthesis, enhanced N harvest index, optimized N nutrition index, and increased activities of nitrate reductase and glutamine synthetase, respectively. Meanwhile, mixed sown common vetch and hairy vetch under reduced N by 20 % improved soil organic matter and N contents. Therefore, mixed sown common vetch and hairy vetch can substitute 40 % of chemical N fertilizer by enhancing N supply and uptake.

Assessment of Technoeconomic Opportunities in Automation for Nuclear Microreactors

Achieving full decarbonization of all economic sectors remains a challenge, especially in niche markets. For example, remote communities and industrial or mining activities detached from the main electric grid heavily rely on fossil fuels, similar to urban and industrial microgrids with combined heat and power needs. A combination of renewables and energy storage is often not suitable due to cost, reliability, intermittency, and large storage requirements. Small nuclear reactors with a flexible purpose could serve these applications. Microreactors (MR) are a class of reactors that are compact, factory manufactured, transportable, and self-regulating. Typically, they generate much less power than their large reactor counterparts. The main advantages of microreactors include the versatile nature of the energy produced, the reliability of supply, and freedom from having to transport and store large quantities of fuels on-site, coupled with the absence of dependence on an electrical grid. A strong business case is needed to move from the microreactor prototype to the commercialization phase. In fact, fossil fuels are still relatively inexpensive, and in the near term, carbon credits will be available to virtually compensate for emissions. For microreactors, one of the main costs in operation and maintenance (O&M) is their staffing levels. In this study, we investigate how to optimize the number (and thus the cost) of workers, moving from a traditional, fully manned, on-site personnel approach to an unmanned, remote personnel approach. We examine four different staffing models that can be implemented as the technology matures and evolves. We estimate the staffing needs of each model and build a business case to justify the substitution of on-site personnel with adequate technologies. To do so, we propose a cost model to quantify potential cost reductions from automating O&M activities. The model accounts for both the reduction in cost derived from the reduced number of full-time-equivalent (FTE) employees and the increase in cost derived from the need to buy new control hardware as needed. Applying the cost model that we created to different scenarios, an on-site O&M cost reduction exceeding 80% can be expected. Additionally, we found that it is more impactful to focus on automating routine O&M tasks rather than attempting to automate transient management (shutdowns, restarts, monitoring condition deviations). In fact, transients typically account for less than 1% of the total FTE time spent on the reactors.

Dielectric resonance of composites containing randomly distributed ZrB2 particles with continuous dual-peak microwave absorption

Substantial efforts have been devoted to the elaborate component and microstructure design of absorbents (inclusions) in microwave absorbing (MA) composite materials. However, the mesoscopic architecture of composites also plays a significant role in prescribing their electromagnetic properties, which is rarely explored in studies of MA materials. Herein, a composite containing randomly distributed ZrB2 particles is fabricated to offer a mesoscopic cluster configuration, which produces dielectric resonance. The resonance disappears and reoccurs when ZrB2 is coated with insulating and semiconductive ZrO2 layers, respectively, suggesting that it is a plasmon resonance excited by electron transport between ZrB2 particles in clusters rather than any intrinsic resonance of the materials constituting the composite. The resonance strength can be regulated by controlling the quantity of electron transport between particles, which is accomplished by gradually increasing the insulating ZrO2-coated ZrB2 ratio, x, to disturb the electron transport in ternary disordered composites containing ZrB2 and insulating ZrO2-coated ZrB2. When x exceeds 0.7, the electron transport is cut off completely and the resonance thus disappears. The resonance induces double quarter-wavelength (1/4λ) interference cancellations or resonance absorption coupled with 1/4λ interference cancellation, giving rise to continuous dual-peak absorption. This work highlights the significance of mesoscopic architectures of composites in MA material design, which can be exploited to prescribe electromagnetic properties.

Exploring an assessment framework for the supply–demand balance of carbon sequestration services under land use change: Towards carbon strategy

The key challenge created by global climate change is to implement effective carbon–neutral programs. The carbon sequestration service (CSS) establishes spatial and temporal connections between carbon sequestration supply and demand, so accurate scientific quantification of the CSS supply–demand balance is essential. However, a standard assessment framework for quantifying CSS supply–demand is currently lacking. In this study, we developed a spatio-temporal assessment framework for quantifying CSS supply–demand balance under land use change, based on the SPANs model. We followed the flow of CSS from supply to demand areas through spatial visualization based on the main wind direction and defined the transport path and quantity of CSS flow, taking Lancang Mekong River Basin (LMRB) as a case. The results showed that: (1) The total supply of CSS decreased by 1.14 million tons from 2000 to 2020, and total demand for CSS increased by 8.1 million tons. (2) CSS surplus areas were mainly concentrated in forested regions, with a maximum surplus of 4.31 t/ha. Deficit areas were dominated by cultivated land and artificial surfaces, with a maximum deficit of −1101.21 t/ha. There was spatial heterogeneity in CSS distribution and a clear imbalance between supply and demand. (3) Three out of six countries in the basin showed significant changes in CSS flow, with a decrease in demand area observed in Thailand and an increase in demand area observed in Cambodia and Vietnam. (4) CSS flow was mainly from north to south, with higher CSS in Thailand and lower flow in China. Thus the analysis revealed supply–demand imbalances in CSS and provided an objective understanding of the spatial flow of CSS. This scientific and intuitive theoretical basis and data support can be used for regional carbon management in LMRB and global carbon balance control.

Time-dependent density-functional theory study on nonlocal electron stopping for inertial confinement fusion

Understanding laser–target coupling is of the utmost importance for achieving high performance in laser-direct-drive (LDD) inertial confinement fusion (ICF) experiments. Thus, accurate modeling of electron transport and deposition through ICF-relevant materials and conditions is necessary to quantify the total thermal conduction and ablation. The stopping range is a key transport quantity used in thermal conduction models; in this work, we review the overall role that the electron mean free path (MFP) plays in thermal conduction and hydrodynamic simulations. The currently used modified Lee–More model employs various physics approximations. We discuss a recent model that uses time-dependent density functional theory (TD-DFT) to eliminate these approximations in both the calculation of the electron stopping power and corresponding MFP in conduction zone polystyrene (CH) plasma. In general, the TD-DFT calculations showed a larger MFP (lower stopping power) than the standard modified Lee–More model. Using the TD-DFT results, an analytical model for the electron deposition range, λTD−DFT(ρ,T,K), was devised for CH plasmas between ρ=[0.05−1.05] g/cm3, kBT=[100−1000] eV. We implemented this model into LILAC, for simulations of a National Ignition Facility-scale LDD implosion and compared key physics quantities to ones obtained by simulations using the standard model. The implications of the obtained results and the path moving forward to calculate this same quantity in conduction-zone deuterium–tritium plasmas are further discussed, to hopefully close the understanding gap for laser target coupling in LDD-ICF simulations.

A novel method for solving the multi-commodity flow problem on evolving networks

The minimum cost multi-commodity flow (MMCF) problem on evolving networks is the latest challenge to solving the min-cost network flow problem of multi-commodity flowing from multi-source and multi-sink on an evolving network. Previous researches primarily focus on the solution of MMCF problem within static networks. However, researchers ignored the MMCF problem within networks where the structure dynamically changes over time. Evolving networks have a dynamically changing and unpredictable network structure. The existing methods must recalculate when the network structure changes, leading to high computational complexity. This manuscript proposes a dynamic graph computing model for adaptive dynamic path selection and optimization. First, the model selects the path with the least cost for each commodity, assuming the route can transport a specified number of such commodities to find the optimal path faster. Second, we design path selection and flow update strategies for each node in the graph to adapt to changes in the graph structure. At the same time, we can prevent multiple commodity conflicts from exceeding the marginal capacity limit. Finally, the optimal path of each commodity is dynamically pruned based on the original optical path to obtain the tree structure and determine the absolute path to meet the specified transportation quantity of each commodity. Performance analysis results show that our method can efficiently cope with the dynamic changes of the network structure, which provides ideas for solving the multi-commodity network flow problem in dynamic networks.

Calculation of Mixed Oil Volume for Sequential Transportation of Crude Oil Pipelines with Variable Temperature and Low Reynolds Number

In order to meet the differentiated demand of oil refiners for crude oil, improve the operating load ratio of long-distance pipelines, and reduce one-time investment, sequential transportation process has been widely used in long-distance crude oil pipelines in China. There are many studies on the calculation method of the mixed oil quantity of sequential transportation of refined oil, but due to its complex influencing factors, there is no general calculation model accepted by everyone. For the sequential transportation of crude oil pipelines, most of them have the characteristics of variable temperature and low Reynolds number, and it is more difficult to accurately calculate the mixed oil quantity than the refined oil pipeline. Through the analysis of the characteristics of oil mixing and hydraulic characteristics of sequential oil transportation, combined with the operation example of an oil pipeline in China, the calculation results of the diffusion theory formula, the Austin-Palfrey empirical formula and the equivalent pipeline length formula were compared and analyzed. The results show that the “equivalent pipeline length method” considers the influence of flow, pipe diameter and conveying distance on oil mixing, and takes into account the oil mixture caused by the change of initial oil mix and Reynolds number, which is more suitable for calculating the mixed oil quantity of sequential transportation of crude oil pipelines. However, for the sequential transportation of crude oil pipelines with low Reynolds number and variable temperature, the amount of oil mixed will be increased. According to the comparative analysis of actual cases, the correction coefficient is increased by 1.5 times on the basis of theoretical calculation, and the calculated value is in good agreement with the actual value, and the deviation is within 5%. It can be used as a reference for the design and operation management of variable temperature and low Reynolds number crude oil sequential transportation pipeline.

Digital Marketing Strategy for Mangrove Ecotourism towards Sustainable Development Goals (SDG) 8.3: Case Study on Rawa Aopa Watumohai National Park, Southeast Sulawesi

The purpose of this research is to analyze the external and internal environment, including opportunities, challenges, strengths, and weaknesses, regarding the Digital Marketing Strategy for Mangrove Ecotourism towards SDG 8.3 at Rawa Aopa Watumohai National Park. This research method utilizes a qualitative approach with SWOT analysis. Findings reveal that the Park supports various training and capacity-building activities for local entrepreneurs around the Park, reflecting the local community's enthusiasm for entrepreneurship, thus establishing a connection between SDG 8.3 and the Park. Weaknesses include the need for improvement in the quantity of tourism facilities, transportation, public infrastructure, and internet connectivity. Opportunities arise from leveraging social media for optimal promotion of tourism locations to attract tourists. Threats stem from competition with similar tourist destinations, potentially reducing visitor numbers. Additionally, climate change and threats to species diversity in the park necessitate better management planning.

Digital Marketing Strategy for Mangrove Ecotourism Towards Sustainable Development Goals (SDG) 8.3 (Case Study: Rawa Aopa Watumohai National Park, Southeast Sulawesi)

The purpose of this research is to analyze the external and internal environment, including opportunities, challenges, strengths, and weaknesses, regarding the Digital Marketing Strategy for Mangrove Ecotourism towards SDG 8.3 at Rawa Aopa Watumohai National Park. This research method utilizes a qualitative approach with SWOT analysis. Findings reveal that the Park supports various training and capacity-building activities for local entrepreneurs around the Park, reflecting the local community's enthusiasm for entrepreneurship, thus establishing a connection between SDG 8.3 and the Park. Weaknesses include the need for improvement in the quantity of tourism facilities, transportation, public infrastructure, and internet connectivity. Opportunities arise from leveraging social media for optimal promotion of tourism locations to attract tourists. Threats stem from competition with similar tourist destinations, potentially reducing visitor numbers. Additionally, climate change and threats to species diversity in the park necessitate better management planning.

New exact and heuristic algorithms for general production and delivery integration

This study considers a general production and delivery integration problem, commonly faced by a manufacturer that adopts make-to-order and commit-to-delivery business strategies. In the problem, the manufacturer determines acceptance or rejection of customers, produces products for accepted customers, and cooperates with third-party logistics providers who offer multiple shipping modes chosen by the manufacturer to deliver the finished products to customers. To better reflect the practical needs, this problem takes into account nonlinear production cost functions, nonlinear earliness and tardiness penalty functions, and nonlinear shipping cost functions of both shipping time and shipping quantity. The problem is to determine an integrated customer acceptance, production, and delivery plan by minimizing the total cost of production, shipping and inventory holding, and the total penalty of rejection, earliness, and tardiness. We investigate two variants of the problem where splittable delivery for the orders from customers is allowed and where splittable delivery is not allowed. For both the variants, we develop exact algorithms which achieve pseudo-polynomial running time in some practical situations, and design column generation-based heuristic algorithms to find near-optimal solutions efficiently. The computational results demonstrate that the heuristic algorithms are capable of generating near-optimal solutions for the instances generated randomly, with average optimality gap less than 4% in a reasonable running time.

Current Status and Economic Analysis of Green Hydrogen Energy Industry Chain

Under the background of the power system profoundly reforming, hydrogen energy from renewable energy, as an important carrier for constructing a clean, low-carbon, safe and efficient energy system, is a necessary way to realize the objectives of carbon peaking and carbon neutrality. As a strategic energy source, hydrogen plays a significant role in accelerating the clean energy transition and promoting renewable energy. However, the cost and technology are the two main constraints to green hydrogen energy development. Herein, the technological development status and economy of the whole industrial chain for green hydrogen energy “production-storage-transportation-use” are discussed and reviewed. After analysis, the electricity price and equipment cost are key factors to limiting the development of alkaline and proton exchange membrane hydrogen production technology; the quantity, scale and distance of transportation are key to controlling the costs of hydrogen storage and transportation. The application of hydrogen energy is mainly concentrated in the traditional industries. With the gradual upgrading and progress of the top-level design and technology, the application of hydrogen energy mainly including traffic transportation, industrial engineering, energy storage, power to gas and microgrid will show a diversified development trend. And the bottleneck problems and development trends of the hydrogen energy industry chain are also summarized and viewed.

Entropy-ruled nonequilibrium charge transport in thiazolothiazole-based molecular crystals: a quantum chemical study.

The charge and energy fluctuations in molecular solids are crucial factors for a better understanding of charge transport (CT) in organic semiconductors. The energetic disorder-coupled molecular charge transport is still not well-established. Moreover, the conventional Einstein's diffusion (D)-mobility (μ) relation fails to explain the quantum features of organic semiconductors, including nonequilibrium and degenerate transport systems, where kB is the Boltzmann constant, T is the temperature and q is the electric charge. To overcome this issue, a unified version of the entropy-ruled D/μ relation was proposed by Navamani (J. Phys. Chem. Lett., 2024, 15, 2519-2528) for hopping and band transport systems as where d, η and heff are the dimension (d = 1, 2, 3), chemical potential and effective entropy, respectively. Within this context, we investigate the CT properties of 2,5-bis(4-methoxyphenyl)thiazolo[5,4-d]thiazole (MOP-TZTZ) and 2,5-bis(2,4,5 trifluorophenyl)-thiazolo[5,4-d]thiazole (TFP-TZTZ) molecular solids using electronic structure calculations and the entropy-ruled method. The CT key parameters such as charge transfer integral and site energy are computed by matrix elements of the Kohn-Sham Hamiltonian. Using Marcus theory, the charge transfer rate is numerically calculated for MOP-TZTZ and TFP-TZTZ molecular crystals under different site energy disorder (ΔEij(E⃑)) situations. Using our entropy-ruled method, the exact diffusion-mobility (D/μ) and other transport quantities such as thermodynamic density of states, conductivity, and current density are calculated for these derivatives at different applied electric field values via the site energy disorder. The theoretical results show that the molecule TFP-TZTZ has good hole mobility (∼0.012 cm2 V-1 s-1) at a site energy disorder value of 90 meV. The obtained ideality factor from the Navamani-Shockley diode current density equation categorizes the typical transport as either the Langevin-type or Shockley-Read-Hall mechanism in the studied molecular solids. Our analysis clearly shows that both the electron and hole transport in these MOP-TZTZ and TFP-TZTZ molecules follow the trap-free Langevin mechanism, which is indeed ideal for designing charge-transporting molecular devices.

Predicting Future Demand Analysis in the Logistics Sector Using Machine Learning Methods

In this study, the potential of machine learning methods for analyzing future needs in the logistics sector was investigated. The research is conducted using the MATLAB platform. Numeric pallet demand data obtained from a logistics company are employed to train MLP, LSTM, and CNN models. Data security and confidentiality take priority during the data collection process. This dataset, comprising a total of 3,062 daily records, serves as the primary data source for the study. In the data preprocessing phase, missing or erroneous data is rectified, and outliers are detected and corrected. The models are tested to predict pallet quantities over periods of 25 days and 4 weeks. The results are evaluated by comparing the model predictions with actual data. Model performances are assessed using metrics such as MSE, RMSE, NRMSE, MAE, ESD, and RC. The outcomes of the last 25 days demonstrate that the LSTM model exhibits the lowest MSE (6,410.5571) and RMSE (80.0660) values. For the MLP model, the MSE value is calculated as 20,536.5564, and the RMSE value is 143.3058. Performance evaluations for the CNN model yield an MSE of 8,492.4297 and an RMSE of 92.1544. Furthermore, it is observed that the MLP model provides the best results for the 4-week forecasts. The results of this study indicate the success of the models used for predicting pallet transportation quantities in the logistics sector. In addition to this study, a contribution is made toward enabling logistics companies to make more informed and strategic decisions.

Minimization of transportation costs using linear programming

This paper intricately explores the utilization of Linear Programming (LP), a distinguished mathematical optimization technique, within the logistics and transportation sector, a domain persistently pursuing methods to bolster efficiency and curtail expenses. LP, characterized by its adeptness in optimizing a linear objective function subject to linear constraints, emerges as a pragmatic solution. This exposition elucidates how LP can be harnessed to ascertain the most economically viable transportation routes and quantities of goods transported from warehouses to consumers. A case study is introduced to spotlight the pragmatic applicability of LP in authentic scenarios, and the potential fiscal savings corporations can realize through its adoption. While recognizing the merits of LP, such as clarity, versatility, and computational efficacy, the paper also sheds light on its limitations, involving its dependency on certain presumptions, a necessity for precise data, and its concentration on single-objective optimization. The discourse concludes by prospecting the future of LP in transportation, exploring its amalgamation with artificial intelligence, machine learning, multi-objective optimization, and green logistics, intending to underscore the significance of LP in transportation and furnish insights for ensuing research and application.

Validation of the single-event method for low-energy electron transport via stopping power calculations with MCNP

Monte Carlo simulations of low-energy (<50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. In this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

Characteristics of wind-sand transportation along railways in the eastern fringe of the Taklimakan Desert and sand control system

Abstract This paper focuses on the Korla-Ruoqiang desert railway line, utilizing ERA5 (ERA5 is the fifth generation of ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric reanalysis global climate data) wind data to conduct a comprehensive analysis of the wind energy environment along the line in all aspects, reveals the characteristics of wind-sand transport, and puts forward the countermeasures to prevent and control wind-sand disasters. The results of the study indicate that: (1) the wind-sand along the line mainly occurs in the spring and summer seasons, and the dominant sand-moving directions are easterly (ENE, E and NNE) and northeasterly (NE). (2) The average annual sand-moving wind speed ranges from 5.80 m/s to 7.25 m/s, and the annual frequency of sand-moving ranges from 11.99% to 37.26%. (3) The annual sand drift potential (DP) along the line ranges from 69.20 VU to 607.24 VU, with three types of wind energy environments: low, medium and high. The resultant drift potential (RDP) ranges from 45.52 VU to 547.49 VU, and the wind variability index (RDP/DP) is between 0.54 and 0.90. (4) The average sand transport quantity along the line ranges from 2.92 m³/m/a to 9.09 m³/m/a. Based on these results, we optimize the sand blocking, sand fixing and wind erosion prevention measures for different types of wind-sand environments, establish a scientific and efficient wind-sand protection and control system to solve the wind-sand problems and provide a scientific foundation for the prevention and control of wind-sand disasters along the line.

Assessment of the performance of alkali-activated slag/fly ash using liquid and solid activators: early-age properties and efflorescence

The use of solid activators to prepare alkali-activated materials (AAM) holds great potential for on-site applications, as it eliminates the need to transport and store large quantities of concentrated alkaline solutions. This study compared the early-age properties and efflorescence between alkali-activated slag/fly ash (AASF) using solid and liquid sodium silicates as activators. The results revealed that AASF with solid sodium silicates exhibited comparable mechanical strength while possessing reduced initial setting time and flowability. All AASF mixtures were susceptible to efflorescence and carbonation, resulting in varying mineralogical compositions of carbonation products: Vaterite was detected in AASF with solid activators, while aragonite and pirssonite were identified in AASF with liquid activators containing 4 and 6 wt.% Na2O, respectively. The efflorescence of AASF exposed to bottom water was more severe than those exposed to natural conditions, as evidenced by the decalcification of reaction products, migration of alkalis, and formation of crystalline carbonates.