Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers

The Grünwald–Letnikov method for fractional differential equations

AbstractThis paper is devoted to the numerical treatment of fractional differential equations. Based on the Grünwald–Letnikov definition of fractional derivatives, finite difference schemes for the approximation of the solution are discussed. The main properties of these explicit and implicit methods concerning the stability, the convergence and the error behavior are studied related to linear test equations. The asymptotic stability and the absolute stability of these methods are proved. Error representations and estimates for the truncation, propagation and global error are derived. Numerical experiments are given

The dominance of big teams in china’s scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams of both large and small sizes are essential in research, prompting us to analyze the extent to which a country’s scientific work is carried out by big or small teams. Here, using over 26 million publications from Web of Science, we find that China’s research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are written by big teams, China’s drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative work does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small teams than the National Science Foundation (NSF) of the United States does, implying that big teams are preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which indicates a need to balance small and big teams. © 2020 Linlin Liu, Jianfei Yu, Junming Huang, Feng Xia, and Tao Jia. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license

The dominance of big teams in China's scientific output

Modern science is dominated by scientific productions from teams. A recent finding shows that teams with both large and small sizes are essential in research, prompting us to analyze the extent to which a country's scientific work is carried out by big/small teams. Here, using over 26 million publications from Web of Science, we find that China's research output is more dominated by big teams than the rest of the world, which is particularly the case in fields of natural science. Despite the global trend that more papers are done by big teams, China's drop in small team output is much steeper. As teams in China shift from small to large size, the team diversity that is essential for innovative works does not increase as much as that in other countries. Using the national average as the baseline, we find that the National Natural Science Foundation of China (NSFC) supports fewer small team works than the National Science Foundation of U.S. (NSF) does, implying that big teams are more preferred by grant agencies in China. Our finding provides new insights into the concern of originality and innovation in China, which urges a need to balance small and big teams

The effect of cooling rate on the wear performance of a ZrCuAlAg bulk metallic glass

In the present work, the local atomic ordering and the wear performance of ZrCuAlAg bulk metallic glass (BMG) samples with different diameters have been studied using transmission electron microscopy (TEM) plus autocorrelation function analysis, and pin-on-disc dry sliding wear experiments. Differential scanning calorimetry and TEM studies show that smaller diameter BMG sample has higher free volume and less local atomic ordering. The wear experiments demonstrate that with the same chemical composition, the smaller BMG sample exhibits higher coefficient of friction, higher wear rate, and rougher worn surface than those of the larger ones. Compared with larger BMG sample, the faster cooling rate of the smaller sample results in looser atomic configuration with more free volume, which facilitates the formation of the shear bands, and thus leads to larger plasticity and lower wear resistance. The results provide more quantitative understanding on the relationship among the cooling rate, the local atomic ordering, and the wear performance of BMGs

Productivity Evaluation Method of Horizontal Well Volume Fracturing in Tight Oil Reservoir

Tight oil resources in north Songliao basin is rich and abundant, which is the most important energy sources foundation of stable and raising oil production in Daqing oil field. However, it is difficult to develop such oil resources by the regular ways for the poor reservoir property and thin reservoir thickness. Using the way of horizontal well by volume fracturing can increase contract area of well and the reservoir, improve reservoir flow performance and reach the high oil production, which has showed good results up till now. The accurate productivity evaluation of volume fracturing horizontal well is an important content of reservoir and production engineering field, which is also to develop solutions and decision-making basis. The current formula of horizontal well in low permeability reservoirs production did not consider the effect of seepage volume form fracturing, so it is poorly adapt to calculate the productivity of volume fracturing horizontal well. Based on the tight oil reservoir geological characteristics and seepage characteristics, equation are solved coupling with flow through fractures in the substrate, productivity prediction model is established and the innovation is based on considering horizontal well reservoir heterogeneity, fracturing scale and any artificial fracture distribution form, the results of which can provides a reliable theoretical basis for tight oil reservoir developed effectively

MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing

4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals for device-free human sensing. In this paper, we propose MM-Fi, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks. MM-Fi consists of over 320k synchronized frames of five modalities from 40 human subjects. Various annotations are provided to support potential sensing tasks, e.g., human pose estimation and action recognition. Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks. We envision that MM-Fi can contribute to wireless sensing research with respect to action recognition, human pose estimation, multi-modal learning, cross-modal supervision, and interdisciplinary healthcare research.Comment: The paper has been accepted by NeurIPS 2023 Datasets and Benchmarks Track. Project page: https://ntu-aiot-lab.github.io/mm-f