Path Distribution Research Articles

Hydrogen-rich gas generation from enhanced catalytic cracking of biomass tar and related model compounds on Ni-Fe/ASA@HZSM-5 catalyst: Pathways and mechanisms

This study focused on the design of complex tar model compounds (CTMC) and synthesized a highly efficient aluminum dross (ASA) combined with HZSM-5 molecular sieve co-loaded bimetallic Ni-Fe catalyst (Ni-Fe/ASA@HZSM-5), comprehensively evaluated the catalytic cracking of real tar and its model compound by adjusting the injection amount and injection state of tar, and catalyst types to obtain hydrogen-rich gas with high H2 and low CO2 content. The results showed under the conditions of the injection amount of 0.6 mL/min, pyrolysis temperature of 600 °C, reforming temperature of 800 °C in the liquid phase, Ni-Fe/ASA@HZSM-5 displays excellent catalytic cracking performance with CTMC conversion efficiency of 87.80 % (79.46 % of gas yield and 34.20 vol% of H2 yield). The possible cracking paths between different components of CTMC in the Ni-Fe catalytic system were summarized by the experimental data and product distribution, the catalytic mechanism of Ni–Fe based catalysts can be attributed to the formation of a Ni-Fe alloy and the synergistic effect of ASA and HZSM-5 co-carrier. Further, the catalytic cracking pathway of CTMC was enhanced in the gas phase, the higher CTMC conversion efficiency of 93.68 %, gas yield (a mass fraction of 82.51 %) and H2 (a volume fraction of 33.59 %) were obtained from catalytic cracking of CTMC, the liquid yield decreased by 12.39 %, significantly improved the cracking degree and gas production capacity of CTMC. The real tar showed the same catalytic pyrolysis path and product distribution. Under the same cracking conditions, the yields of gas, liquid and char were 84.85 %, 12.00 % and 3.15 % respectively, and obtained the hydrogen yield of 55.29 mL/g. The CTMC simplifies the cracking process and reduces polymerization, provides insights into the cracking mechanisms of heavy components in biomass tar. Furthermore, the synergy of Ni and Fe contributed to greater activity for CTMC and real tar cracking, ASA combined with HZSM-5 molecular sieve co-loaded bimetallic Ni-Fe catalysts have a promising application potential as highly efficient catalysts for tar removal and reutilization.

A mathematical model for predicting the electro-mechanical behavior of biomimetic crack sensors: Effect of crack depth and gap

Various biomimetic microstructures, such as porous, cracks, wrinkles, micro-pyramids, and micro-domes, are applied to improve the sensing performance of mechanical sensors. Among them, the crack-based strain sensors are widely investigated due to high sensitivity and fast response time. To clearly describe the relationship between crack morphology and sensor sensitivity, a mathematical model is developed for investigating the performance of a poly(3,4-ethylenedioxythiophene)-silicon oxide/polydimethylsiloxane (SiOx/PDMS) based crack strain sensor. First, the displacement field of a crack tip is calculated based on the theory of fracture mechanics, and the mathematical relationship between the crack depth, crack gap, and strain is obtained. The predicted crack depth of the SiOx thin film's thicknesses in 7.91 μm (SiOx/PDMS-7.91) is 2.82 μm, with the error of 3.75% compared to the experimental result. Correspondingly, the deviation of SiOx/PDMS-7.91 is 5.74% between the predicted crack gap and the experimental data. Second, above the aforementioned crack tip characteristics, the mathematical model based on crack edges contacts probability (CECP) is used to construct the relationship between the crack tip characteristics, applied stress, and device sensitivity. The maximum predicted sensitivity can reach 3562.68 compared with the experimental data of 3800.44, and the deviation is about 6.26%. Moreover, the CECP model has good universality with the other reported crack-based strain sensors. It can be concluded that crack morphology affects the distribution and quantity of conductive paths. When the strain sensor is subjected to external forces, brittle thin films generate a certain number of bumped-like elements for microcracks. The wider and deeper crack will increase the relative resistance change and the decrease of conductive paths, resulting in a rapid increase in the sensitivity of the strain sensor.

Vehicle-drone collaborative distribution path planning based on neural architecture search under the influence of carbon emissions

Intelligent operation and green distribution have become a mainstream task to enable fast development of urban distribution applications. However, how to improve the distribution efficiency with low operating costs, and mitigate environmental pollution with high service quality is still a significant challenge in the practical industry applications. To address the above challenge, in this paper, we take into account both the economic cost and environmental cost, and propose a joint distribution path planning model based on neural architecture search (NAS) for electric vehicles with double-decked drones. More specifically, in our design, the factors such as energy consumption and carbon emissions of vehicles and drones during different distribution stages are considered. Then, a mixed integer linear programming model is established under the constraints of customer time window, vehicle capacity and vehicle battery capacity. Based on this model, a hybrid genetic algorithm is proposed to solve the optimization problem, where the carbon emission cost is estimated by the convolution neural network model, which is optimized by the neural architecture search technique. We conduct extensive experiments to validate the effectiveness of the proposed method. The experimental results show that, compared with CPLEX, the proposed method excelled in both solution quality and speed, which verify the effectives of our hybrid algorithm in dealing with the 2E-VRPD problem, where delivery routes can be well optimized, the efficiency of vehicle-machine collaborative delivery can be improved, and delivery costs are reduced.

Transition Path Dynamics of Non-Markovian Systems across a Rough Potential Barrier.

Transition paths refer to rare events in physics, chemistry, and biology where the molecules cross barriers separating stable molecular conformations. The conventional analysis of the transition path times employs a diffusive and memoryless transition over a smooth potential barrier. However, it is widely acknowledged that the free energy profile between two minima in biomolecular processes is inherently not smooth. In this article, we discuss a theoretical model with a parabolic rough potential barrier and obtain analytical results of the transition path distribution and mean transition path times by incorporating absorbing boundary conditions across the boundaries under the driving of Gaussian white noise. Further, the influence of anomalous dynamics in rough potential driven by a power-law memory kernel is analyzed by deriving a time-dependent scaled diffusion coefficient that coarse-grains the effects of roughness, and the system's dynamics is reduced to a scaled diffusion on a smooth potential. Our theoretical results are tested and validated against numerical simulations. The findings of our study show the influence of the boundary conditions, barrier height, barrier roughness, and memory effect on the transition path time distributions in a rough potential, and the validity of the scaling diffusion coefficient has been discussed.

Experimental investigation of the electromagnetic acceleration mechanisms in an applied-field magnetoplasmadynamic thruster

Abstract The high specific impulse and efficiency of the applied-field magnetoplasmadynamic thruster make it one of the most promising electric thrusters in deep space exploration. However, the crucial electromagnetic acceleration mechanisms are still not clearly investigated, which limits performance improvement. The electromagnetic acceleration mechanism is closely related to the current path and magnetic field distribution in the plume. Experiments are conducted using a water-cooled Hall probe in the steady-state applied-field magnetoplasmadynamic thruster plume. The radial, azimuthal and axial magnetic fields are measured, then the current density and Lorentz force density are calculated. The results show that the outflow current accounts for 63% to 82% of the thruster discharge current depending on the strength of the applied magnetic field. Moreover, the outflow current can extend the range of electromagnetic force action, which in turn increases the effect of electromagnetic acceleration. The radial Lorentz force is numerically dominant, and the combined effect of the radial Lorentz force and axial Lorentz force is to compress the plasma toward the axis. In electromagnetic acceleration, Self-field contributions are less than 5%, while E × B acceleration constitutes -12.2-21.2%, and Diamagnetic acceleration dominates at approximately 76.7-90.5%. Finally, a method for evaluating the rotational velocity was presented based on the MHD equations. The centrifugal force was then calculated by combining this with the plasma density. At the thruster outlet, the centrifugal force is significant and cannot be ignored in comparison to the radial Lorentz force.

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

A Comparative Study on Seismic Behaviour of Plan Irregular Buildings

Abstract: Asymmetrical plan irregularities occur when the horizontal layout of a building is non uniform or unsymmetrical in one or more axes. These irregularities may arise from variations in building shape, size, or configuration, leading to nonuniform distribution of mass, stiffness, or load paths throughout the structure. Considering equivalent static analysis (ESA) and response spectrum analysis (RSA) as per IS 1893-Part 1 (2016) considering seismic zones III, structure with square shaped plan performs better than the other shaped models (viz. T, L, H and Plus shapes) with least values of storey displacement and storey drift ratio, and also with higher value of storey stiffness. Thus, square plan shaped structure is seismically more resistant than T, L, H and Plus shaped structures.

The Wealth Creation in Indian and American Businesses

This paper discusses the comparative analysis of wealth production and distribution by looking at the business contexts and earning functions where there are two sides consisting of earning and spending between India and the USA. This research looks at the business inquiries, wealth-centric cultural attitudes, and economic systems in both countries. The USA has skewed more toward innovation and consumer-driven economies, whereas India is more technology, manufacturing, and services-based - all of which are still driven primarily by the traditional values that are focused on long-term financial security. This study synthesizes the extant body of literature to probe how different earning strategies such as the difference between lowering costs and innovation, the spending patterns including corporate social responsibility initiatives and much more may impact the economic outcomes and societal well-being in each country. This paper will show the comparative analysis between India and the USA which will provide information that will help explain the various paths of wealth distribution and accumulation for the two countries.

Prediction Method for the Distribution of the Effective Dominant Path of Sand Bodies Transporting Oil and Gas and Its Application

To study the oil and gas distribution law in the slope area of a hydrocarbon-bearing basin, one must first define the dominant hydrocarbon transport paths of sand bodies as the superimposed area of the contiguously distributed area of the sand bodies and the paleotectonic ridges at the top interface of the formation. Then, the oil and gas supply area of the oil source faults must be defined, as the overlapped area of the favorable transport paths of oil source faults and the width of their associated fracture zones. Stacking the two areas, we can delineate the distribution of the effective dominant paths of sand bodies transporting oil and gas. The results from our case study of the Qinan area show that the effective dominant paths of sand bodies transporting oil and gas in the lower sub-member within the first member of the Shahejie Formation (Es1L) in the Qinan area are distributed throughout the region, with better development in the west than in the east. And, the effective dominant paths in the eastern part extend from north to south, while those in the western part extend from northeast to southwest. These are favorable for the convergence of oil and gas generated from source rocks in the third member of the Shahejie Formation (Es3) in the main depression of Qikou Sag in the Es1L in the Qinan area. The results are consistent with the fact that oil and gas discovered in the Es1L in the Qinan area are mainly distributed in the central and western regions, with a small amount in the southeast. Therefore, the suggested method is feasible for predicting the effective dominant paths of sand bodies transporting oil and gas.

Analysis of diversion ratio and study of sediment transport in the first canal station of an irrigation area based on Mike21 hydrodynamic model

Abstract Based on MIKE21 hydrodynamic model, the change of diversion ratio and sediment transport at the first canal station in an irrigation area are analyzed. Two-dimensional numerical simulation is carried out by using MIKE21 FM hydrodynamic module, and the split ratio is studied. In the aspect of sediment transport, the transport path and deposition distribution of different sediment particle sizes are simulated by simulating sediment transport. The results show that under the lowest daily average water level, the diversion canal flow can meet the irrigation demand; The particle size of sediment has a great influence on the deposition distribution and thickness of the approach channel at the first station of the canal, and then affects the water diversion capacity of the pumping station.

A cost-efficient content distribution optimization model for fog-based content delivery networks

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

Optimization of distribution paths in cold chain logistics management based on IFW-ABC algorithm

The current cold chain logistics management and distribution routes generally face problems such as environmental pollution and high costs. Therefore, the study proposed an improved fireworks-artificial bee colony for optimizing delivery paths. Firstly, the artificial bee colony was improved by using neighborhood search. Secondly, for the optimal solution, the fireworks explosion operator was used for the final result search. The benchmark function validation showed that the proposed algorithm performed better than other algorithms in solving optimal value, average value, and standard deviation. In the simulation analysis of a fresh enterprise in China, the total delivery cost was reduced by an average of 3.30% and 4.93% compared to other algorithms, considering carbon emission and not considering it, respectively. This proposed algorithm had more advantages in reducing quality loss, pollutant emissions, and CE. Therefore, this improved fireworks-artificial bee colony can effectively reduce the economic costs and environmental pollution of cold chain logistics distribution routes. This ensures economic benefits while considering social benefits, achieving the optimal path planning for logistics distribution.

Optimisation of Urban Logistics Distribution Routing with Time Window

Reasonable optimization of the path can not only reduce unnecessary logistics cost expenditure; efficient circulation speed can also enhance the economic development of the city. In the process of the transformation of the model, it will be directly affected by the change of the importance of logistics enterprises to logistics distribution work. Based on the background of urban logistics, the optimization process of multi-logistics distribution path with time window is comprehensively studied. The relevant theoretical knowledge involved in the distribution link is reviewed, and through the analysis of the existing problems of urban logistics and the vehicle path optimization problem, the heuristic algorithm is used to establish the path optimization model of urban logistics center with time window, which reduces the vehicle transportation cost, thus reducing the total cost of logistics, and at the same time, improves the efficiency of the internal circulation of urban logistics and accelerates the economic development.

The ultrametric backbone is the union of all minimum spanning forests

Minimum spanning trees and forests are powerful sparsification techniques that remove cycles from weighted graphs to minimize total edge weight while preserving node reachability, with applications in computer science, network science, and graph theory. Despite their utility and ubiquity, they have several limitations, including that they are only defined for undirected networks, they significantly alter dynamics on networks, and they do not generally preserve important network features such as shortest distances, shortest path distribution, and community structure. In contrast, distance backbones, which are subgraphs formed by all edges that obey a generalized triangle inequality, are well defined in directed and undirected graphs and preserve those and other important network features. The backbone of a graph is defined with respect to a specified path-length operator that aggregates weights along a path to define its length, thereby associating a cost to indirect connections. The backbone is the union of all shortest paths between each pair of nodes according to the specified operator. One such operator, the max function, computes the length of a path as the largest weight of the edges that compose it (a weakest link criterion). It is the only operator that yields an algebraic structure for computing shortest paths that is consistent with De Morgan's laws. Applying this operator yields the ultrametric backbone of a graph in that (semi-triangular) edges whose weights are larger than the length of an indirect path connecting the same nodes (i.e. those that break the generalized triangle inequality based on max as a path-length operator) are removed. We show that the ultrametric backbone is the union of minimum spanning forests in undirected graphs and provides a new generalization of minimum spanning trees to directed graphs that, unlike minimum equivalent graphs and minimum spanning arborescences, preserves all shortest paths and De Morgan's law consistency.

Research on Joint Distribution Path Planning of Electric Logistics Vehicles with Different Recharge Modes

To address the route planning issues under the community group purchase model for joint delivery, this study thoroughly considers electric logistics vehicles with different recharging methods. The objective is to minimize the sum of operating costs, recharging costs, time window penalty costs, and carbon emission costs. Separate multi-objective optimization models for route planning are constructed for both charging and battery-swapping logistics vehicles. An improved seagull optimization algorithm, guided by the golden sine strategy of the Lévy flight guidance mechanism, is employed to avoid local optima and enhance the solution efficiency. The feasibility of the models and the algorithm is verified through simulation examples. Experimental results show that, at the current stage, battery-swapping logistics vehicles display significant advantages over charging electric logistics vehicles. Although battery-swapping logistics vehicles extend delivery time, they can reduce the total delivery costs to a certain extent. Therefore, the future development prospects of battery-swapping logistics vehicles will be even broader.

Effects of aggregate's type and orientation on stress concentration and crack propagation of modeled concrete applied a shear force

This study focused on planarizing natural modeled aggregate concrete (NMC), recycled modeled aggregate concrete (RMC), and brick modeled aggregate concrete (BMC) for analyzing the influences of aggregates on shearing failure mechanism. A designed apparatus was used to load a shear force for the specimens and analyzed the global and local strain fields near the interface transition zone (ITZ) using digital image correlation (DIC) analysis. Additionally, we employed COMSOL software simulation to understand the crack initiation process and damage evolution mechanism of related concrete. Our findings revealed that the brick aggregate leaded to the lowest modulus of elasticity and compressive strength, resulting in poor shear resistance. However, the recycled aggregate model exhibited the highest shear resistance, primarily due to the old mortar acting as a buffer layer in the ITZ. During loading, the stress in the NMC model concentrated in the load transfer zone and gradually moved to the left and right sides of the built-in aggregate model until fracture, as observed through DIC analysis. The stress distribution of the RMC was similar with those of the NMC, but the BMC showed a distinct stress distribution pattern. Furthermore, the corner of the built-in modeled aggregate significantly influenced the crack propagating path and stress distribution. Therefore, the modeled aggregate corner plays a crucial role in the shear behavior of concrete, affecting both crack propagation and overall mechanical performance. The practical implications of our study can inform the design and application of recycled concrete.

Efficiency and resilience: key drivers of distribution network growth

Networks to distribute goods, from raw materials to food and medicines, are the backbone of a functioning economy. They are shaped by several supply relations connecting manufacturers, distributors, and final buyers worldwide. We present a network-based model to describe the mechanisms underlying the emergence and growth of distribution networks. In our model, firms consider two practices when establishing new supply relations: centralization, the tendency to choose highly connected partners, and multi-sourcing, the preference for multiple suppliers. Centralization enhances network efficiency by leveraging short distribution paths; multi-sourcing fosters resilience by providing multiple distribution paths connecting final buyers to the manufacturer. We validate the proposed model using data on drug shipments in the US. Drawing on these data, we reconstruct 22 nationwide pharmaceutical distribution networks. We demonstrate that the proposed model successfully replicates several structural features of the empirical networks, including their out-degree and path length distributions as well as their resilience and efficiency properties. These findings suggest that the proposed firm-level practices effectively capture the network growth process that leads to the observed structures.

A novel method to identify preferential flow paths by considering the time-varying effect of petrophysical parameters in ultra-high water-cut reservoirs

The continental clastic reservoirs mainly distributed in the eastern region of China are regarded as the “ballast stone” for ensuring national energy security. After long-term waterflooding, reservoir petrophysical parameters have changed drastically. When the ultra-high water-cut period is achieved, the preferential flow paths are widely distributed inside the reservoir, resulting in severe ineffective water circulation and high difficulty in inhibiting water cut rise and stabilizing oil production. Due to the slow calculation speed and low identification accuracy of the existing methods, it is crucial to propose a novel method to identify preferential flow paths by considering the time-varying impact of petrophysical parameters in ultra-high water-cut reservoirs. To tackle this issue, massive dynamic and static data are collected from typical water-drive reservoirs in the Daqing oilfield to analyze the dynamic characteristics of preferential flow wells and the time-variation laws of reservoir parameters. A rapid evaluation index system for preferential flow paths is thereafter established. By describing the time-variation of reservoir permeability and fluid apparent viscosity and introducing a dynamic flow resistance coefficient to infer the inter-well or inter-layer connectivity, a novel method with the time-varying effect of petrophysical parameters considered is developed to quickly identify preferential flow paths in ultra-high water-cut reservoirs. A critical criterion of preferential flow paths is further constructed. Finally, the proposed method is used to determine the spatial distribution of preferential flow paths in a typical block of the Daqing oilfield. Results demonstrate that, once a preferential flow path is formed, some distinct dynamic characteristics of injectors and producers will be exhibited. Based on the estimated dynamic flow resistance coefficient, the preferential flow paths are further classified into four types: non-preferential, primary preferential, intermediate preferential, and strong preferential flow paths. Using the proposed method, the identification accuracy of preferential flow paths exceeds 95%, and the identification speed is more than 10 times faster than the traditional methods.

Optimization of cold chain logistics distribution paths for community buying considering cost and time window: The case of J Center

AbstractIn recent years, the community group buying market has been developing rapidly, and the group buying categories are mainly fresh food, so one of the keys to the success of the community group buying platform lies in the supply chain logistics, in which the cold chain logistics is the key to guarantee the fresh food products for the time and quality. In this paper, on the combing research of the theories and problems of community group purchase, cold chain logistics, and vehicle path planning, in view of the existing problems of cold chain logistics and distribution business in Jinan distribution center of J company's community group purchase, taking into account the actual demand of the grid station and the characteristics of the fresh products to be stored at low temperatures, we constructed the optimization model of the cold chain logistics and distribution path that minimizes the total cost under the demand of the fuzzy time window of the grid station, and designed the distribution area is divided by using K‐means clustering, and then, the distribution path in each sub‐distribution area is optimized and solved by genetic algorithm, and then, the validity of the optimization scheme is verified, the number of distribution trucks used is reduced by two, and the distribution cost is reduced by 12.77%, and optimization measures such as promoting the standardization of warehousing and distribution, and creating a digital and integrated logistics information platform are proposed.

Tracing Opioids Across the US: A High-Resolution Pharmaceutical Distribution Dataset

In the field of pharmaceutical supply chains, there is a lack of comprehensive historical data, representing a significant barrier to advancing research. To address this gap, we introduce a high-resolution dataset comprising drug packages distributed to approximately 300,000 pharmacies, hospitals, and practitioners across the US. We reconstruct 375 million distribution paths from ARCOS, a DEA-maintained database comprising half a billion shipping records between 2006 and 2014. While ARCOS tracks dyadic shipments, it does not provide information on the complete journey of single packages from manufacturers to final destinations. Our algorithm is able to reconstruct complete distribution paths from these dyadic records. The reconstructed dataset, with its high temporal and spatial resolution, offers an unprecedented view of US pharmaceutical distribution and is a valuable resource for investigating supply and distribution networks.