Discrete element modelling of large soil deformations under heavy vehicles

This paper addresses the challenges of creating realistic models of soil for simulations of heavy vehicles on weak terrain. We modelled dense soils using the discrete element method with variable parameters for surface friction, normal cohesion, and rolling resistance. To find out what type of soils can be represented, we measured the internal friction and bulk cohesion of over 100 different virtual samples. To test the model, we simulated rut formation from a heavy vehicle with different loads and soil strengths. We conclude that the relevant space of dense frictional and frictional-cohesive soils can be represented and that the model is applicable for simulation of large deformations induced by heavy vehicles on weak terrain.     

Multifunctional material Bi4Ge3O12: thermodynamic properties

Thermodynamic characteristics of a single crystal of bismuth orthogermanate (Bi₄Ge₃O₁₂), which are necessary to improve device portfolio, have been studied. It has been shown that bismuth orthogermanate is thermodynamically stable against decomposition into binary oxides at 50 °C, which gives us grounds to consider this compound promising for various applications.     

Influence of oxygen on soot formation during acetylene pyrolysis

Kinetic modeling of pyrolysis of acetylene diluted with argon showed a strong influence of small additives of oxygen on the routes of formation of soot nuclei. The influence of oxygen on various channels of formation and consumption of propargyl radicals C₃H₃, which are important precursors of soot formation, as well as the fundamental possibility of controlling the process of soot formation and its properties are considered.     

Group mobility assisted network selection framework in 5G vehicular cognitive radio networks

The heterogeneity nature of networks is the most eminent characteristic in 5G vehicular cognitive radio networks across complex radio environments. Since multiple communicating radios may be in motion at the same time in a vehicle. So, group mobility is the most prominent characteristic that requires to be a deep investigation. Therefore, different communication radios that are moving on a train/bus needed to select the networks simultaneously. Without considering the group mobility feature, there is a possibility that the same network may be selected by each moving node and cause congestion in a particular network. To overcome this problem, a novel network selection technique considering the group mobility feature is proposed to improve the throughput of the network. In this work, a 5G vehicular cognitive radio network scenario is also realized using USRP-2954 and LabVIEW communications system design suite testbed. The performance metrics like transmission delay, packet loss rate, reject rate and, channel utilization for vehicular nodes, are gained to analyze the proposed technique in vehicular cognitive radio networks environment. The proposed technique demonstrates a remarkable improvement in channel utilization for vehicular nodes and outperformed conventional schemes.     

Classify the Higgs decays with the PFN and ParticleNet at electron–positron colliders

Various Higgs factories are proposed to study the Higgs boson precisely and systematically in a model- independent way. In this study, the Particle Flow Network and ParticleNet techniques are used to classify the Higgs decays into multicategories, and the ultimate goal is to realize an "end-to-end" analysis. A Monte Carlo simulation study is performed to demonstrate the feasibility, and the performance looks rather promising. This result could be the basis of a "one-stop" analysis to measure all the branching fractions of the Higgs decays simultaneously.     

Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

正电子冲击下氦电离三重微分截面的理论计算

我们发展了一种正电子碰撞原子电离的畸变波Born近似方法， 在这个方法中，正负电子偶素通道通过一个ab initio的光学势附加到入射粒子和靶的相互作用势上，且通道对电离作用被第一次被考虑在正电子碰撞原子电离的过程中. 应用这个方法计算了在50 eV入射能量范围氦的电离的三重微分截面，计算结果和实验数据很好的符合.     

Mechanism of [3+2] Cycloaddition of Alkynes to the [Mo3S4(acac)3(py)3][PF6] Cluster

A study, involving kinetic measurements on the stopped‐flow and conventional UV/Vis timescales, ESI‐MS, NMR spectroscopy and DFT calculations, has been carried out to understand the mechanism of the reaction of [Mo₃S₄(acac)₃(py)₃][PF₆] ([ 1 ]PF₆; acac=acetylacetonate, py=pyridine) with two RC?CR alkynes (R=CH₂OH (btd), COOH (adc)) in CH₃CN. Both reactions show polyphasic kinetics, but experimental and computational data indicate that alkyne activation occurs in a single kinetic step through a concerted mechanism similar to that of organic [3+2] cycloaddition reactions, in this case through the interaction with one Mo(μ‐S)₂ moiety of [ 1 ]⁺. The rate of this step is three orders of magnitude faster for adc than that for btd, and the products initially formed evolve in subsequent steps into compounds that result from substitution of py ligands or from reorganization to give species with different structures. Activation strain analysis of the [3+2] cycloaddition step reveals that the deformation of the two reactants has a small contribution to the difference in the computed activation barriers, which is mainly associated with the change in the extent of their interaction at the transition‐state structures. Subsequent frontier molecular orbital analysis shows that the carboxylic acid substituents on adc stabilize its HOMO and LUMO orbitals with respect to those on btd due to better electron‐withdrawing properties. As a result, the frontier molecular orbitals of the cluster and alkyne become closer in energy; this allows a stronger interaction.     

Compressibility of random walker trajectories on growing networks

We find that the simple coupling of network growth to the position of a random walker on the network generates a traveling wave in the probability distribution of nodes visited by the walker. We argue that the entropy of this probability distribution is bounded as the network size tends to infinity. This means that the growth of a space coupled to a random walker situated in it constrains its dynamics to a set of typical random walker trajectories, and walker trajectories inside the growing space are compressible.