Basic Structure Of Model Research Articles

Simulation and experimental analysis of melt pool evolution in laser engineered net shaping

In this work, the evolution of melt pool under single-point and single-line printing in the laser engineered net shaping (LENS) process is analyzed. Firstly, the basic structure of the melt pool model of the LENS process is established and the necessary assumptions are made. Then, the establishment process of the multi-physical field model of the melt pool is introduced in detail. It is concluded that the simulation model results are highly consistent with the online measurement experiment results in terms of melt pool profile, space temperature gradient, and time temperature gradient. Meanwhile, some parameters, such as the 3D morphology and surface fluid field of the melt pool, which are not obtained in the online measurement experiment, are analyzed. Finally, the influence of changing the scanning speed on the profile, peak temperature, and temperature gradient of the single-line melt pool is also analyzed, and the following conclusions are obtained: With the increase in scanning speed, the profile of the melt pool gradually becomes slender; The relationship between peak temperature and scanning speed is approximately linear in a certain speed range; The space temperature gradient at the tail of the melt pool under different scanning speeds hardly changes with the scanning speed, and the time temperature gradient at the tail of the melt pool is in direct proportion to the scanning speed.

Implementing of the BOPPS Model on Basic Properties of Fractions Using Hawgent Dynamic Mathematics Software in Elementary Education

The rapid development of information technology, informationization of education has become inevitable. Starting from the necessity of information-based teaching, this paper introduces the basic structure of the BOPPPS model and analyzes the content of each stage of the model. On this basis, the BOPPPS model is applied to the teaching of basic properties of fractions with the help of Hawgent dynamic mathematics software, in order to provide a theoretical basis for teachers to improve the teaching and methods of elementary mathematics, enhance the efficiency of classroom teaching, and help the implementation of "double reduction".

Challenges and Countermeasures of Arab Immigrants and International Trade in the Era of Big Data

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade.

Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots.

The purpose of the study is to improve the performance of intelligent football training. Based on deep learning (DL), the training of football players and detection of football robots are analyzed. First, the research status of the training of football players and football robots is introduced, and the basic structure of the neuron model and convolutional neural network (CNN) and the mainstream framework of DL are mainly expounded. Second, combined with the spatial stream network, a CNN-based action recognition system is constructed in the context of artificial intelligence (AI). Finally, by the football robot, a field line detection model based on a fully convolutional network (FCN) is proposed, and the effective applicability of the system is evaluated. The results demonstrate that the recognition effect of the dual-stream network is the best, reaching 92.8%. The recognition rate of the timestream network is lower than that of the dual-stream network, and the maximum recognition rate is 88%. The spatial stream network has the lowest recognition rate of 86.5%. The processing power of the four different algorithms on the dataset is stronger than that of the ordinary video set. The recognition rate of the time-segmented dual-stream fusion network is the highest, which is second only to the designed network. The recognition rate of the basic dual-stream network is 88.6%, and the recognition rate of the 3D CNN is the lowest, which is 86.2%. Under the intelligent training system, the recognition accuracy rates of jumping, kicking, grabbing, and starting actions range to 97.6, 94.5, 92.5, and 89.8% respectively, which are slightly lower than other actions. The recognition accuracy rate of passing action is 91.3%, and the maximum upgrade rate of intelligent training is 25.7%. The pixel accuracy of the improved field line detection of the model and the mean intersection over union (MIoU) are both improved by 5%. Intelligent training systems and the field line detection of football robots are more feasible. The research provides a reference for the development of AI in the field of sports training.

Allocation Method of Enterprise High-Quality Human Resources Based on Intelligent Big Data

At present, the variation coefficient is used to calculate the difference between personnel and individual posts, which leads to the poor ability of allocating enterprise human resources. Therefore, a high-quality enterprise human resource allocation method based on intelligent big data is proposed. According to the four principles, the process of model building is designed, the hypothesis of model building is made, and the model of optimal allocation of human resources is constructed. From the personnel and post quantity, personnel and post structure, personnel and post quality, system, and other four aspects of optimal allocation, design the model running process, network personnel and posts, and use network indicators to match personnel and posts. The adjustment layer is introduced into the basic CNN model structure, and the input layer of intelligent big data is used to normalize the score of current high-quality human resources of enterprises. After the local characteristic processing is implemented on the human resource scoring matrix, the human resource allocation results are output to realize the allocation of high-quality human resources of enterprises. The experimental results show that the evaluation effect of the method is good, the evaluation time is short, the work efficiency is high, the credibility of human resource structure allocation is good, and the human resource allocation method has a good human resource structure allocation ability.

Influence of Rural Development of River Tourism Resources on Physical and Mental Health and Consumption Willingness in the Context of COVID-19

This study took the Three Gorges Dam as an example and discussed the influence of river regulation decisions on the sustainable development of surrounding villages. The study used mixed research methods, snowball sampling, and convenience sampling to obtain samples. The questionnaire samples were analyzed by basic statistical tests, t-test, and structural equation modeling (SEM). The respondents’ opinions were collected through semi-structured interviews and finally the results were discussed by multivariate analysis. The findings were that even though the villages were not well developed in terms of economy, environment, and natural ecology, as long as the community security could be stable, the living could be safe and convenient, people’s daily life patterns and leisure behaviors could be maintained, and people could stabilize their minds and emotions and maintain physical and mental health in order to meet their living needs and reduce the burden. There would be time and funds to invest in leisure, tourism activities, and consumption behavior. If the above consumption patterns are continued, people will gain positive perceptions, stimulating people’s willingness to invest in property purchases or to make travel plans again.

Economic Forecasting Model Based on Chaos Simulated Annealing Neural Network

In view of the prototype of the traditional chaotic network model, a neural network model with continuously updated chaotic noise is innovatively improved. This model has the advantages of two network models. On this basis, a logic graph is proposed, which can replace the chaotic noise generated by the running process of the model function. Models with these advantages can innovatively solve problems such as constrained optimization with dimensions larger than three, high discreteness, and weak linear relationship between convex and concave. Simulation results can be confirmed that the algorithm of this model is very close to the predicted value. This neural network model is an improved model with strong applicability and can be applied to optimization problems of economic systems or other industrial systems. In order to effectively alleviate the predictive control problem of nonlinear research objects, we propose a control method based on chaotic neural network in this study. Taking the economic model as the nonlinear object, establishing the basic structure of the chaotic neural network model, reconstructing the time and space structure, and obtaining the optimal solution of time continuation and embedding dimension through information entropy and pseudonearest neighbor, then the chaotic properties of nonlinear objects and the topology of chaotic neural networks are determined. In the simulation, test samples and experimental samples are established, and the predicted and true values are compared. The prediction results of the model established by this method in this study prove the effectiveness of the method. The training time of the chaotic neural network will not fall into a local minimum, thereby reducing the training time and ensuring the accuracy of the prediction.

Problems, Common Beliefs and Procedures on the Use of Partial Least Squares Structural Equation Modeling in Business Research

Partial least squares structural equation modeling (commonly referred to as PLS-SEM) was not developed without reason. PLS-SEM was developed as an alternative to covariance-based SEM, allowing researchers to conduct exploratory research. In addition, PLS-SEM is considered capable of providing flexibility related to data characteristics, model complexity, and model specifications. Undoubtedly, PLS-SEM is the most frequently used method in many fields of business research. However, many researchers use PLS-SEM incorrectly and even expect more without understanding the basic structural equation modeling method. For this reason, this article will discuss various types of problems and general beliefs about the use of PLS-SEM in business research. In addition, this article can be used as a reference to make it easier for applied researchers to decide what methods, techniques, and tools will be used to complete their research. In addition, at the end of this article, we will discuss how PLS-SEM can be applied to develop theory in business research through a series of technical introductions taking into account user needs. Subsequently, this article will be equipped with a systematic procedure that discusses the evaluation flow of each PLS-SEM test through illustrations with a notated model using SmartPLS.

Algorithm Analysis of Clothing Classification Based on Neural Network

With the rapid development of Internet e-commerce, the online transaction volume of clothing has increased day by day, and the importance of clothing images in transactions has also increased. However, there are many clothing categories and different classification standards. It is difficult for consumers and e-commerce merchants to unify the description of clothing categories, which can easily lead to a poor clothing shopping experience. Neural network has an excellent list in the field of computer vision, which can effectively classify clothing. The purpose of this article is to study the algorithm analysis of clothing classification based on neural network. Starting from the neural network, this paper proposes a clothing image classification algorithm based on a multi-task convolutional neural network (Convolutional Neural Network, CNN). Through hierarchical classification data combined with multi-task technology, the basic structure of the network model is not changed. The accuracy of clothing image classification improves the network’s ability to express refined clothing categories. This paper proposes a clothing classification algorithm based on the feature fusion of Hu invariant matrix and CNN network. The feature fusion of the features extracted by the convolutional neural network is initially explored, the information gain of the feature is calculated, and the shape feature is used to eliminate the feature with less information gain. This paper also designs a clothing classification system based on neural network to realize the recognition, detection and classification of clothing images. The experimental results show that the clothing classification accuracy rates under the four combined tasks are 93.54%, 89.26%, 92.14%, 95.66%, and 93.54%, respectively. It can be seen that the model based on convolutional neural network can further improve the accuracy of clothing classification.

Solving Parametric Volterra Integral Equation from Distributed-Order Rough Heston Model and Option Pricing

The rough Heston model has recently attracted the attention of many financial practitioners and researchers because it maintains the basic structure of the classic Heston model and has an advantage in describing the microstructure foundation of the market. In this paper, we study the distributed-order rough Heston model with an exponential tempered factor, and from the characteristic function of log-price in this model, we obtain a nonlinear parametric Volterra integral equation. Finally, the Fourier-cosine methods are combined with the Adams methods to price the option.

Game Theory-Based Modeling of Multi-Vehicle/Multi-Pedestrian Interaction at Unsignalized Crosswalks

<div class="section abstract"><div class="htmlview paragraph">The improvement of road transport safety requires the development of advanced vehicle safety systems, whose development could be facilitated by using complex interaction models of different road users. To this end, this paper deals with the modeling of multi-vehicle/multi-pedestrian interactions at unsignalized crosswalks. This multi-agent modeling approach extends on the existing basic model covering only single-vehicle/single-pedestrian interactions. The basic model structure and parameters have remained the same, as it was previously experimentally calibrated and thoroughly verified. The proposed modeling procedure employs the basic model within the multi-agent setting based on its application to relevant single-vehicle and single-pedestrian pairs. The resulting, so-called pre-decisions are then used for making final crossing decisions in a current time step for each agent. During the decision making process, each pedestrian takes into account approaching vehicles and their expected future behavior with respect to all pedestrians (i.e., himself/herself and other pedestrians), and vice versa for vehicles. This generalized multi-agent model is conceived to support simulation scenarios concerning multi-lane roads and pedestrians approaching from both sides of a road, as well. The model is verified by means of large-scale simulations and various scenarios.</div></div>

DAESim: A dynamic agro-ecosystem simulation model for natural capital assessment

Threats to sustainable food production are accelerating due to climate change, population growth, depletion of natural capital, and global market instability. This causes significant risks to farmers, consumers, and financial and policy institutions. Understanding agro-ecosystems, and how varying management styles can impact their performance is critical to future wellbeing. To better understand and manage agricultural production, we have developed a dynamic simulation model that accounts for the core natural capital components of agro-ecosystems, including climate, soil, carbon, water, nitrogen, phosphorus, microorganisms, erosion, crops, farm animals and plants. Dynamic Agro-Ecosystem Simulation (DAESim) model can be used to simulate dynamics of soil health and project it into the future to assess vulnerabilities and resilience. This knowledge can inform and guide investment decisions by financial institutions, insurance companies, farmers, and governmental agencies. Here, we describe the basic model structure, sensitivity, and calibration results. We then run a few scenarios to demonstrate the model's ability to analyze alternative agro-ecosystem management options.

Domestic hot water consumption prediction models suited for dwellings in central-southern parts of Chile

Domestic hot water (DHW) consumption in dwellings can play a key role in the development of policies that are focused on energy poverty, and in improving energy efficiency, among other aspects. There is an important variability observed with DHW among different countries due to technical, sociological, climatic, and economic factors. Most studies that deal with DHW predictions are based on stochastic models, and only a few apply time series or statistical methods. In the case of Chile, the country is undergoing a policy development process, and there is little information about DHW consumption. As a result, it is fundamental to have DHW consumption prediction models that are focused on dwelling. For this reason, the study analysed the possibility of using time series models to make future estimations about monthly domestic hot water (DHW) consumption. To this end, consumption data obtained from 98 apartments between 2015 and 2021 were used, and 3 approaches were applied namely, exponential smoothing, basic structural model (BSM), and state-space model (SSM). The results showed that exponential smoothing and state-space methods allowed to obtain satisfactory results with regard to percentage error and confidence levels. Therefore, these models could be used to make future estimations of domestic hot water (DHW) consumption.

СИНТЕЗ ПРИСТРОЮ КЕРУВАННЯ НА БАЗІ R-АВТОМАТА ДЛЯ АСОЦІАТИВНОГО ПРОЦЕСОРА

This article discusses one of the well-known options for the synthesis of control devices with rigid logic. Such control devices provide a significant response speed compared to control devices with programmable logic, but they have a laborious process at the stages of abstract and structural synthesis of a microprogram automaton (MPA) as the basic structural model of this type of control device. Synthesis of an MPA based on a shift register as an R-automaton provides a compromise solution among the implementations of an MPA based on a parallel register as a T-automaton or a counter as a CT-automaton. This is due to the load on the memory of the MPA by reducing the complexity of the logical part of the MPA, namely its combinational scheme. The use of a specific single positional coding of the MPA states makes it possible to significantly simplify the complex process of the formation of Boolean functions corresponding to the excitation functions in the abstract synthesis of the MPA. And this, in turn, leads to a simplification of the combinational part of the MPA. At the same time, the complication of the memory part of the MPA based on the shift register leads to an increase in its bit rate without reducing the regularity of its structure. The article proposes a non-standard approach to the synthesis of a control device and develops its functional scheme. As a result, synthesis on the basis of the R - automaton of the control device structure for the associative processor confirmed the perspective of this version of MPA synthesis by simplifying the process of the corresponding abstract and structural synthesis. This is due to the simplicity of forming direct and simplified structural tables using single positional coding of MPA states. It is planned to place the developed structure of the control device in a programmable logic IC (FPGA), which will allow the placement of the entire associative processor in the FPGA. This approach will ensure the compactness of the organization and the speed of the associative processor.

Effects of Leisure Obstacles, Job Satisfaction, and Physical and Mental Health on Job Intentions of Medical Workers Exposed to COVID-19 Infection Risk and Workplace Stress.

The purpose of this study was to investigate the effects of leisure obstacles, job satisfaction, physical and mental health, and work intentions of medical workers in Taiwan. SPSS 26.0 and AMOS 24.0 statistical software were used to analyze 208 questionnaires by basic statistical tests, t-tests, and structural model analysis. Results: Under the epidemic, medical workers were unable to develop job identity due to low promotion opportunities and low job achievement. The lack of recreational exercise skills, time, and information created leisure obstacles. In addition, they were unable to express their true selves freely at work, which led to health problems such as reduced enthusiasm, mental weakness, and emotional irritability. In particular, female medical workers felt more strongly about the issues of leisure obstacles and the intention to stay in their jobs. The study found that the higher their job satisfaction, the higher their intention to stay in the job, while the more pronounced the leisure obstacles and physical and mental health problems, the more pronounced their intention to leave.

Box-Jenkins and State Space Model in Forecasting Malaysia Road Accident Cases

Road accident is one of the main causes of death and injury worldwide in this fast-paced modern world. Many developing countries, including Malaysia, are facing serious road accident problems. Forecasting road accident cases has become an important step towards setting the road safety target. Hence, this study aims to develop forecasting models and forecast future trends of monthly road accident cases in Malaysia. The data set on monthly number of accident cases from January 2001 to December 2019 was provided by Polis Diraja Malaysia (PDRM). Box-Jenkins and State space models were developed using the data under study. The models were then evaluated based on in-sample and out-sample evaluation using lowest root mean square error, mean absolute percentage error and mean absolute error. The results show that the basic structural state space model with trend and seasonal component was the most appropriate model in forecasting road accident cases in Malaysia. The 10-year ahead forecast from January 2020 to December 2030 shows that monthly road accident cases in Malaysia have a constant inclining pattern for each year. It is hoped that the finding from this study could become a reference for the authorities of Malaysia in making recommendations in order to improve road safety and reduce road traffic accidents in Malaysia.

On the thermodynamics and its connection to structure in the Pt-Pd-Cu-Ni-P bulk metallic glass forming system

Contrary to basic hard sphere structure models, recent studies revealed, significant structural differences between Pt-Cu-Ni-P and Pd-Cu-Ni-P metallic glass-forming liquids with the same stoichiometry. To cover the compositional space between both systems, platinum is subsequently replaced by palladium in the composition (Pt/Pd)42.5Cu27Ni9.5P21. For this systematic set of alloys, the thermodynamic properties, such as isobaric heat capacity, enthalpy and Gibbs free energy are assessed. A systematic drop of the Gibbs free energy difference between crystal and liquid, providing a lower estimate of the driving force for crystallization was observed, underlining the high glass-forming ability of the Pd-rich systems. Contrary to kinetic fragility data, a change of the thermodynamic fragility can be observed, drawing the picture of an increasing thermodynamically strong behavior with rising Pd-content. Further, the temperature induced changes of the total structure factors S(Q) were monitored using high-energy synchrotron X-ray diffraction. Focus was laid on the changes on the medium-range length scale, by analyzing changes of the first sharp diffraction peak. Here a good correlation of the changes in peak-width and the thermodynamic fragility was found. From the determination of the excess enthalpy, large amounts of residual enthalpy in the glassy state were observed for the Pt-rich alloys, supporting the increased ductility of these alloys. The current findings further carve out the different roles of the topologically similar Pt and Pd in the Pt/Pd-Cu-Ni-P alloy system and how the change of the structural motifs on the medium range order is influencing thermal properties such as enthalpy and heat capacity.

Research on Roof Fracture Characteristics of Gob-Side Entry Retaining with Roof Cutting and Non-pillar Mining in Thick Coal Seam, China

Based on the S1201-2 large height mining in the 2–2 coal seam of Ningtiaota colliery with on-site microseismic measurement, physical simulation and theoretical analysis methods, this paper explores the rule of roof movement in thick coal seams with roof cutting and non-pillar (hereinafter referred to as RCN-P) mining, so as to obtain scientific and effective theoretical basis for entry support and to summarize the regional structural characteristics and dynamic periodic fracture characteristics. As can be seen from microseismic events, the entry roof is featured by "two zones and one line" along the horizontal direction, namely, the crack generation area, the roof movement area. Additionally, and the obvious lateral breaking of the entry roof on the coal wall is a typical feature of the thick coal seam with RCN-P mining. The roof is vertically divided into "three zones", the crack generation area, the roof movement area and the crack development area. The roof cutting activity mainly affects the overburden activity within the basic roof height range, which is also the roof movement area. In addition, the distribution frequency and the intensity of microseismic events indicate the roof periodic breaking characteristics. The "breaking pressure relief,” “advanced crack development,” and “the limit breaking state” of roof breaking corresponds to the initial, middle, and final stage of breaking in the periodic weighting process, respectively. Compared with the normal mining, the RCN-P mining reduces the periodic weighting length and increases the pressure strength. As is shown in the physical simulation experiment, the basic roof and the cutting control layer in the "regional structural characteristics" constitute the “large” and “small” structures with RCN-P mining. The basic roof key layer is the core to control the stability of the strata, and the breaking process from the cantilever beam to the short masonry beam of the roof-cutting control layer is the main cause of the entry stress. Correspondingly, the basic structure model of “short masonry-hinged” roof was proposed and the calculation method of support was established for the entry with RCN-P mining in thick coal seam, providing a research foundation for scientific and effective rock formation control.

Structural model for formation of packages of clinical and diagnostic tests to organize personalized medical care for patients with malignant tumors of the prostate gland

BACKGROUND: Prostate carcinoma is a serious social and economic problem; it ranks second among the most frequently diagnosed malignant tumors worldwide and it ranks sixth in the structure of causes of death from cancer in men. The correct organization of prevention and screening as well as the use of the latest methods of clinical instrumental and molecular analysis at all stages of treatment and diagnostic process can fundamentally improve the outcomes of the disease.
 AIM: This study aimed to analyze of the best international experience in the development of basic clinical and instrumental test packages that determine the personalized choice of medical prescriptions for the treatment and diagnostic process of prostate cancer.
 METHODS: The study included papers published after January 1, 2008, with an emphasis on the analysis of results posted in the last 2 years in the PubMed/Medline electronic database as reliable sources of information.
 RESULTS AND DISCUSSION: This study explored the basic structural model of the formation of clinical and diagnostic packages at the implementation of a personalized treatment and diagnostic process in prostate cancer. The main biomarkers of prostate cancer used in clinical practice have been identified, and the informative value of the newest biomarker tests has been established. Diagnostic tools that are ready for wider implementation in oncological practice are predictive models, such as the 4K algorithm, Score, SelectMDx, Stockholm-3 model, justifying the need to perform a prostate biopsy in a particular patient. Some promising biomarker characteristics of prostate cancer, assessed at the nonclinical, experimental stage, have been demonstrated.
 CONCLUSIONS: This study found that (1) Revision of the algorithms for personalized treatment is becoming an important element in the provision of patient-oriented cancer care. (2) Periodic reassessment of the individual portrait of the tumor process is necessary for the correct organization of treatment and diagnostic measures in a particular patient. (3) Finally, for the implementation of the whole range of possibilities of individual treatment, close interaction with the representatives of various medical specialties in the framework of the implementation of translational medicine programs in oncologyis important.

Modelling short-and long-term marketing effects in the consumer purchase journey

The role of advertising in consumer demand has received much attention in the marketing literature, where the marketing response model has emerged as a dominant analytical framework. In recent years, the basic model structure has evolved in three directions. Firstly, the incorporation of online paid, owned and earned media to accommodate the off-online path-to-purchase. Secondly, the introduction of consumer mindset metrics to capture the emotional foundations of brand-building. Finally, combinations of both to offer a complete view of the consumer purchase journey. In this paper, we argue that no one development successfully provides a fully holistic representation of the role of advertising. We thus propose a more comprehensive structural approach: one that combines search, paid, owned and earned media with a cointegrating mechanism for brand-building, linking mindset metrics directly to long-term base sales. The result is a practical modelling system for marketing mix practitioners with a rigorous economic and statistical foundation for short and long-term advertising effects. The proposed approach is applied to a global electronics manufacturer in the US market, where results fall into three broad areas. Firstly, we demonstrate how off and online marketing interact in the journey structure, the part played by search, owned and earned media and how specific media types play different roles. Secondly, we illustrate how consumer touchpoint experience is a key element of the process, improving measurement of in-store marketing. Finally, we show how consumer mindset metrics and social media play an important word-of-mouth role, with paid and earned media working together to drive long-term demand evolution.