The Current Situation and Improvement of Informatization Construction in Universities in the Era of Digital Education—Taking the Nanchong Campus of Southwest Petroleum University as an Example

Digitization of education is an important component of Digital China Strategy. China is deeply implementing the digitalization of education strategy, promoting educational reform and innovation, and accelerating the construction of a learning society and a learning country where everyone can learn, learn everywhere, and learn at all times [1]. As the cradle and gathering place of talent cultivation, universities should be at the forefront of educational informatization construction. As a comprehensive energy university in the northeastern region of Sichuan, the Nanchong Campus of Southwest Petroleum University plays an irreplaceable role in the local economic development. This article summarizes the current situation of educational informatization construction on this campus and proposes suggestions for its improvement path, providing constructive reference opinions for the development of similar universities.

Speed limit effect during lane change in a two-lane lattice model under V2X environment

Speed limit measures are ubiquitous due to the complexity of the road environment,which can be supplied with the help of vehicle to everything(V2X)communication technology.Therefore,the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during the lane change process under V2X environment.Accordingly,the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis.Moreover,the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability in the cases of strong reaction intensity and high limited speed.

A new method of weight choice in InSAR least squares unwrapping

The de-coherence phenomena such as Low-SNR radar signal, shadows and layover caused by topography, etc. , causing phase data discontinuity, makes the result of unwrapping phase inaccuracy or even completely wrong. Based on the analysis of influencing factors to weight choice, this thesis develops a new method to choose the weights based on the measure of the confidence in the frequency domain. Experiments show that it could overcome the defect of sub-estimate to the slope of least squares method very well, which has a better rationale, stability and performance.

A new method of weighted choice in InSAR Least Squares unwrapping

The decorelation phenomena such as Low-SNR radar signal, shadows and layover caused by topography etc, causes phase data discontinuous and makes the result of unwrapping phase inaccurate or completely wrong. Based on the analysis of influencing factors to the weight selection, this paper develops a new method to choose the weights based on the measurement of confidence in frequency domain. Results show that it is more precise and robust than other methods, and can make up for the defect of sub-estimate to the slope of least squares method.

The concept, structure and characteristics of the distributed virtual coal-mine geographical environment

Distributed virtual coal-mine geography environment（CM-DVGE） researches the virtual reality simulation of coal-mine environment in cyberspace and the performance of coal production process. The goal is to deal with the practical safety problems of observation, analysis, design and training during the underground coal production process. This paper first probed into the evolution of CM-DVGE from a historical point of view, then respectively analyzed the characteristics of its physical progressive levels according to the cognitivd law and its logical structure of data on the basis of data flow, and concluded by putting forward the prospect of the application of CM-DVGE, thus enriched and developed the method of synthetic study of coal mine.

A first-principles study on zigzag phosphorene nanoribbons terminated by transition metal atoms

We have investigated the electronic and magnetic properties of zigzag phosphorene nanoribbons(ZPNRs)with transition metal(TM)passivated atoms,it can be found that the ZPNRs with TM passivated atoms exhibit different magnetisms except for the Ni-passivated system.Meanwhile,the results show that the magnetic moments of ZPNRs with TM passivated atoms are larger than that of ZPNRs with other passivated non-metals/groups.Interestingly,it can be found that Fe-passivated ZPNR exhibits magnetic semiconducting character,which provides the possbility for the application of phosphorene in information storage.For Mn-passivated ZPNRs,it exhibits the half-metallicity.These results may be useful for potential applications of phosphorene in electronic and high-performance spintronic devices.

VAGUE MAPPINGS

In this paper, the concepts of （α,β） vague mappings, surjective （α,β） vague mappings, injective （α,β） vague mappings, bijective （α,β） vague mappings, （α,β） adjoin vague mappings, （α,β） vague monomorphism, （α,β） vague epimorphism, （α,β） vague isomorphism, （α,β） Vague par-tition are introduced through the so-called （α,β） hierarchical divide vague relations We extend the results on fuzzy mappings, and obtain some of their properties.

Research Progress and Trends of Domestic Smart Learning Environment

In order to explore the main progress and current status of domestic research on smart learning environment, this paper takes 260 core and CSSCI journal papers included in the CNKI database as the research objects, and uses CiteSpace visual analysis software and uses bibliometrics and knowledge graph analysis as the main research methods, summarizes and analyzes the time distribution of the literature, the distribution of institutions and authors, co-occurrence and clustering of keywords, and research hotspots,etc.

Development Elements of VR Classrooms in China

VR classroom is an integrated teaching environment of content, hardware and platform. Its use is affected by the following three aspects, including price and performance of hardware equipment, cost and difficulty of courseware development, and quality of teaching by VR courseware. Therefore, the future development trend of in VR classroom should be the trinity of terminal+courseware+SaaS.

Image Edge Detection Method Based on Ant Colony Algorithm

Ant colony algorithm has good results in finding the optimal solution in a certain field;and image edge detection is an essential foundation for all kinds of image processing. How to improve image edge detection becomes a hot topic in image processing. In this paper, the ant colony algorithm is applied to image edge detection, and the ant colony algorithm’s discreteness, parallelism and positive feedback are fully utilized. Through repeated iteration, pheromone acquisition and pheromone matrix were continuously updated to search for images step by step. The experimental results show that the ant colony algorithm can effectively detect the edge of the image, and the detection effect of the algorithm is significantly improved compared with the Roberts algorithm.

Identifying Transportation Modes from Raw GPS Data

Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transportation mode is required. In this paper, we present a robust framework without geographic information, which can effectively and automatically identify transportation modes including car, bus, bike and walk. Firstly, a trajectory segmentation algorithm is designed to divide raw GPS trajectory into single mode segments. Secondly, several modern features are proposed which are more discriminating than traditional features. At last, an additional postprocessing procedure is adopted with considering the wholeness of trajectory. Based on Random Forest classifier, our framework can achieve a promising accuracy by distance of 82.85% for identifying transportation modes and especially 91.44% for car mode.

Deep Broad Learning for Emotion Classification in Textual Conversations

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

Multi-Parameter and Multi-Objective Optimization of Occupant Restraint System in Frontal Collision

To solve the constraints of multi-objective optimization of the driver system and high nonlinear problems, according to the relevant dimensions of a car, we build a simulation model with Hybrid Ⅲ 50th dummy driver constraint system. The comparison of the driver mechanics index of the experimental data with the simulation data in the frontal crash shows that the accuracy of simulation model meets the requirements. The optimal Latin test design is adopted, and the global sensitivity analysis of the design parameters is carried out based on the Kriging model. The four most sensitive parameters are selected, and the parameters are solved by a multi-island genetic algorithm.And then the nonlinear programming quadratic line(NLPQL) algorithm is used to search for accurate optimization. The optimal parameters of the occupant restraint system are determined: the limiting force value of force limiter 2 985.603 N, belt extension 12.684%, airbag point explosion time 27.585 ms, and airbag vent diameter 27.338 mm, with the weighted injury criterion(WIC) decreased by 12.97%, the head injury decreased by 22.60%, and the chest compression decreased by 7.29%. The results show that the system integration of passive safety devices such as seat belts and airbags can effectively protect the driver.

A Mixed-Integer Programming Formulation for a Simplified Model of the Double Row Layout Problem

The double row layout problem(DRLP)is to assign facilities on two rows in parallel so that the total cost of material handling among facilities is minimized.Since it is vital to save cost and enhance productivity,the DRLP plays an important role in many application fields.Nevertheless,it is very hard to handle the DRLP because of its complex model.In this paper,we consider a new simplified model for the DRLP(SM-DRLP)and provide a mixed integer programming(MIP)formulation for it.The continuous decision variables of the DRLP are divided into two parts:start points of double rows and adjustable clearances between adjacent facilities.The former one is considered in the new simplified model for the DRLP with the purpose of maintaining solution quality,while the latter one is not taken into account with the purpose of reducing computational time.To evaluate its performance,our SM-DRLP is compared with the model of a general DRLP and the model of another simplified DRLP.The experimental results show the efficiency of our proposed model.

A Retrieval Algorithm of Sheet Metal Parts Based on Relationships of Features

With the rapid increase in the number of three-dimensional （3D） models each year, to quickly and easily find the part desired has become a big challenge of enterprises. Meanwhile, many methods and algorithms have been proposed for part retrieval. However, most of the existing methods are designed lbr mechanical parts, and can not be well worked for sheet metal part re- trieval. An approach to feature-based retrieval of sheet metal parts is presented. Firstly, the features frequently used in sheet metal part design are chosen as the ＂＇key words＂ in retrieval. Based on those features, a relative position model is built to express the different relationships of the features in 3D space. Secondly, a description method of the model is studied. With the descrip- tion method the relative position of features in sheet metal parts can be expressed by four location description matrices. Thirdly, based on the relative position model and location description matrices, the equivalent definition of relationships of two feature groups is given which is the basis to calculate the similarity of two sheet metal parts. Next, the tbrmula of retrieval algorithm for sheet metal parts is given. Finally, a prototype system is developed to test and verify the effectiveness of the retrieval method suggested. Experiments verify that the new method is able to meet the requirements of searching sheet metal parts and possesses potentials in practical application.

Establishment and verification of prediction models for evaluating the physical and chemical properties of soilless substrates

In soilless culture,a suitable mixed substrate that provides a balanced and stable rhizosphere environment is vital for promoting plant growth.The present study was undertaken to establish seven prediction models of physical and chemical properties,including bulk density(DB),total porosity(TP),water-holding porosity(WHP),air porosity(AP),WHP/AP,electrical conductivity(EC)and cation exchange capacity(CEC)of mixed substrate based on regression equations of measured values from 76 substrate combinations.These seven models were verified using the measured values of 12 mixed substrates,and the average relative prediction errors(REs)were all less than 10%.A comprehensive property prediction model was established by weighted summation of the seven models of physical and chemical properties.According to the set values of DB,TP,WHP,AP,WHP/AP,EC and CEC,the comprehensive property model predicted the six mixture proportions of mixed-substrate,as verified using the measured values.This study is the first to establish prediction models for the physical and chemical properties of mixed substrates.The comprehensive property model could be used to evaluate the physical and chemical properties of commercial mixed substrates,and to provide the optimal mixture substrate formulations according to the setting property value of production requirement.

Scoring the collective effects of SNPs: association of minor alleles with complex traits in model organisms

It has long been assumed that most parts of a genome and most genetic variations or SNPs are non-functional with regard to reproductive fitness.However,the collective effects of SNPs have yet to be examined by experimental science.We here developed a novel approach to examine the relationship between traits and the total amount of SNPs in panels of genetic reference populations.We identified the minor alleles(MAs)in each panel and the MA content(MAC)that each inbred strain carried for a set of SNPs with genotypes determined in these panels.MAC was nearly linearly linked to quantitative variations in numerous traits in model organisms,including life span,tumor susceptibility,learning and memory,sensitivity to alcohol and anti-psychotic drugs,and two correlated traits poor reproductive fitness and strong immunity.These results suggest that the collective effects of SNPs are functional and do affect reproductive fitness.

A Binary Particle Swarm Optimization for the Minimum Weight Dominating Set Problem

The minimum weight dominating set problem （MWDSP） is an NP-hard problem with a lot of real-world applications. Several heuristic algorithms have been presented to produce good quality solutions. However, the solution time of them grows very quickly as the size of the instance increases. In this paper, we propose a binary particle swarm optimization （FBPSO） for solving the MWDSP approximately. Based on the characteristic of MWDSP, this approach designs a new position updating rule to guide the search to a promising area. An iterated greedy tabu search is used to enhance the solution quality quickly. In addition, several stochastic strategies are employed to diversify the search and prevent premature convergence. These methods maintain a good balance between the exploration and the exploitation. Experimental studies on 106 groups of 1 060 instances show that FBPSO is able to identify near optimal solutions in a short running time. The average deviation between the solutions obtained by FBPSO and the best known solutions is 0.441%. Moreover, the average solution time of FBPSO is much less than that of other existing algorithms. In particular, with the increasing of instance size, the solution time of FBPSO grows much more slowly than that of other existing algorithms.