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With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people’s daily life, but at the same time bring traffic congestion. How to relieve traffic congestion and improve its capacity is a hot research area. This paper aims to propose a new simulation framework for crowd transportations to ease traffic congestion.

This paper establishes related simulation models such as vehicles, traffic lights and advisers. Then the paper describes their relationships, gives their interaction mechanism and solidifies the above into a software implementation framework.

This paper proposes a simulation framework for crowd transportations.

In this framework, traffic lights are used as a control method to control the road network and road conditions are used as an Affecter to influence individual behavior. The vehicle passing rate is defined by the correlation between endowment and the start time of the traffic lights. In this framework, members are related, dynamically adjusted according to road conditions and dynamically optimized member decisions. The optimal path is dynamic and real-time adjustments are made for each step forward. It is different from the traditional optimal path in which there is only one fixed one and it is different from the macroscopic optimal path that does not exist.

The purpose of this paper is to study the corrosion rate for X52, X60, X65, X70 and X80 steel immersed in Mexican oilfield produced water. For the electrochemical characterization of the five steels rotating disk electrodes, 20°C, 30°C and 45°C of experimental temperature and 0, 500, 1,000 and 2,000 rpm of rotation speed were taken into account. The temperature dependence was analyzed using Arrhenius law. Thus, Rct values obtained from EIS data in comparison with the corrosion rate obtained from polarization curves data were taken into account. Hydrodynamic effects were analyzed by Rct and corrosion rate data.

Electrochemical impedance spectroscopy and potentiodynamic polarization techniques were used to assess the electrochemical behavior for five pipe steels steel immersed in a natural solution.

The resistance and corrosion rate taken from electrochemical tests decreased as temperature and hydrodynamic condition also decreased. In addition, the Arrhenius parameter revealed that the natural solution increased the corrosion rate as the activation energy decreased. Typical branches related to reduction-oxidation reaction (dissolution-activation process or corrosion products dissolution) on steel surface were discussed. Optical images analysis shows that corrosion products for X65 steel exposed to oilfield produced water can be attributed to more susceptibility to corrosion damage for this steel grade (Quej-Ake et al., 2018), which is increased with the temperature and rotation speed of the working electrode.

Corrosion process of the five steels exposed to oilfield produced water could be perceptive when Arrhenius analysis is taken into account. This is because oilfield produced water is the most aggressive condition (brine reservoir and sour water) for internal pipelines walls and storage tanks (brine tanks). Thus, stagnant condition was considered as a more extreme corrosive condition because produced water is stored in atmospheric stationary tanks as well as it is transported under laminar condition in zones where oilfield produced water is maintaining in the bottom of the pipe during the production, transporting and storing of the crude oil. In addition, a brief operational process for Reynolds number and the flowrate of the stock tank barrel per day (Q in STBD) using field and Reynolds number data is discussed.

The purpose of this paper is to study the corrosion process by examining the deterioration of X80 steel exposed to a real petroleum sample containing condensed hydrocarbon plus oilfield-produced water, which were subjected to stimulated emulsions in flowing media at 50°C.

The impedance and polarization spectra were used to assess the aggressiveness of the petroleum sample and tried to find a washing process using condensed hydrocarbon with deionized water. Mössbauer technique was used to identify the phases in precipitated ions obtained during an oven-drying procedure of the oilfield produced water.

The emulsion, chloride, sulphur compounds, heavy metals and the use of a double hydrodynamic system were the most important factors affecting the corrosion of X80 steel. The corrosion rate of this steel increased when oilfield-produced water was stimulated by a double hydrodynamic system (4.56 mm/year). It was determined to be 7.66 mm/year and 4.01 mm/year when steel was exposed to a stimulated emulsion using the petroleum sample and condensed hydrocarbon with deionized water at 24 h, respectively, suggesting that a significant process of hydrocarbon washing could occur and a more corrosive solution was highlighted. Mössbauer results showed that the ions precipitates included the following phases at Wt.%: magnetite (20.0), greigeite (22.8), siderite (3.2), pyrite (2.9), marcasite (26.7) and mackinawite (24.4).

A stimulated hydrocarbon/water emulsions with a more homogeneous solution containing high concentrations of saline compounds and heavy metals were used to simulate the susceptibility to corrosion on the internal pipeline steels exposed to any type of immiscible liquids such as condensed hydrocarbon, or crude oil, containing water. A practical application of the presented research could provide a novel framework for understanding the internal corrosion in pipelines from the simulation of washed hydrocarbons after the stimulated emulsions that can be found in the field. Because more susceptibility to corrosion for pipeline steels would be expected at the end of the transportation of the fluid. It is possible to investigate the possible corrosion mechanisms by using a dried oilfield-produced water sample interacting with the pipeline steels.

Crowd network systems have been deemed as a promising mode of modern service industry and future economic society, and taking crowd network as the research object and exploring its operation mechanism and laws is of great significance for realizing the effective governance of the government and the rapid development of economy, avoiding social chaos and mutation. Because crowd network is a large-scale, dynamic and diversified online deep interconnection, its most results cannot be observed in real world, and it cannot be carried out in accordance with traditional way, simulation is of great importance to put forward related research. To solve above problems, this paper aims to propose a simulation architecture based on the characteristics of crowd network and to verify the feasibility of this architecture through a simulation example.

This paper adopts a data-driven architecture by deeply analyzing existing large-scale simulation architectures and proposes a novel reflective memory-based architecture for crowd network simulations. In this paper, the architecture is analyzed from three aspects: implementation framework, functional architecture and implementation architecture. The proposed architecture adopts a general structure to decouple related work in a harmonious way and gets support for reflection storage by connecting to different devices via reflection memory card. Several toolkits for system implementation are designed and connected by data-driven files (DDF), and these XML files constitute a persistent storage layer. To improve the credibility of simulations, VV&A (verification, validation and accreditation) is introduced into the architecture to verify the accuracy of simulation system executions.

Implementation framework introduces the scenes, methods and toolkits involved in the whole simulation architecture construction process. Functional architecture adopts a general structure to decouple related work in a harmonious way. In the implementation architecture, several toolkits for system implementation are designed, which are connected by DDF, and these XML files constitute a persistent storage layer. Crowd network simulations obtain the support of reflective memory by connecting the reflective memory cards on different devices and connect the interfaces of relevant simulation software to complete the corresponding function call. Meanwhile, to improve the credibility of simulations, VV&A is introduced into the architecture to verify the accuracy of simulation system executions.

This paper proposes a novel reflective memory-based architecture for crowd network simulations. Reflective memory is adopted as share memory within given simulation execution in this architecture; communication efficiency and capability have greatly improved by this share memory-based architecture. This paper adopts a data-driven architecture; the architecture mainly relies on XML files to drive the entire simulation process, and XML files have strong readability and do not need special software to read.

The purpose of this study is to develop laboratory techniques to evaluate the inhibition efficiency of salt neutralizer (SN) solutions in the corrosion protection of metal alloys associated with winter maintenance equipment.

The corrosion resistance of alloys A36, B36 and B152 treated with SNs was evaluated by accelerated corrosion testing (ASTM B117) and electrochemical polarization curves. Characterization of inhibition solutions was performed by contact angle measurements, scanning electron microscopy, ultraviolet-visible spectroscopy and Fourier transform infrared spectroscopy.

Salt neutralizer systems act as mixed inhibitors in acidic media by changing the corrosion resistance ability of metal alloys because of the adsorption of surfactant molecules through their hydrophilic heads. The correlation of the corrosion rate of metal alloys and the inhibitor efficiency showed the influence of the SN type, its concentration, its effective adsorption constant and its contact angle on the alloy surface. Salt neutralizers with higher manufacturer’s recommended wash concentrations (MRWC) to critical wash concentration ratio, lower contact angle on the alloy surface and higher K_eff were more successful at preventing corrosion on the alloys tested.

The results of this work provide, for the first time, both quantitative and qualitative information of the properties of washing techniques in the use of effective cleaning strategies for protecting winter maintenance equipment from corrosion. Other state departments of transportation facing similar weather conditions will be benefited by identifying measures and techniques to increase the corrosion resistance of their equipment assets.

The purpose of this paper is to develop a kind of low cost measuring system based on binocular vision sensor to detect both the weld pool geometry and root gap simultaneously for robot welding process.

Two normal charge coupled device cameras are used for capturing clear images from two directions; one of them is used to measure the root gap and another one is used to measure the geometric parameters of the weld pool. Efforts are made from both hardware and software aspects to decrease the strong interferences in pulsed gas tungsten arc welding process, so that clear and steady images can be obtained. The grey level distribution characteristics of root gap edge and weld pool edge in images are analyzed and utilized for developing the image processing algorithms.

A solid foundation for seam tracking and penetration control of robot welding process can be established based on the binocular vision sensor.

The results show that the algorithms can extract the root gap edges and the contour of weld pool effectively, and then some geometric parameters can be calculated from the results.

The binocular vision system provides a new method for sensing of robot welding process.

This research investigates the influence of bank loans on Chinese listed companies’ performance by collecting data on bank loan amounts and indicators used to measure performance, such as return on assets (ROA) and Tobin’s Q, semiannually from 2015 to 2020. Pooling panel regression models are employed to determine the relationship between firms’ performance and their amount of bank loans. This study contributes to the literature by controlling for additional bank loan characteristics and comparing the relevance between bank loans and bond issuance. The authors also find that the relationship between firm performance and bank loans shows a nonlinear concave relationship, suggesting the negative impact is more severe in the high loan-to-asset region. The subsample after 2018 shows a significantly positive relationship, indicating that the impact of COVID-19 might alter the prevalent relationship. In addition, short-term debt has a more noticeable negative impact on firm performance than long-term debt. Both results become weaker after COVID-19. This chapter can help listed companies to trade off using long-term or short-term bank loans as their debt financing methods and approach a better capital structure.

The study examined the impact of feedback types through a learning management system (LMS) on employees’ training performance. The purpose of this study is to establish effective feedback on advanced technologies for promoting corporate training.

A total of 148 trainees were recruited from a multinational medical company. Employees were randomly assigned to receive feedback from shallow to more constructive details on their learning performance with LMS. Data sources included are employees’ goal setting (GS) performance evaluated by the experts and their posttest scores obtained from the LMS. A series of statistical analyses were performed to investigate the impact of feedback intervention on employees’ GS and their impacts on corporate training results.

GS has a significant impact on learning outcomes. Employees who set greater specific goals attained higher scores. Furthermore, feedback with more formative evaluation and constructive developmental advice resulted in the most significant positive influence on the relationship between participants’ GS and learning outcomes.

Organizations can benefit from delivering appropriate feedback using LMS to enhance employees’ GS and learning efficacy in corporate training.

This study is one of the first to examine the moderating effect of feedback provided by LMS on GS and online learning performance in corporate training. This study contributes to GS theory for practical application and proposes a viable method for remote learning. The current study’s findings can be used to provide educational psychological insights for training and learning in industrial contexts.

Recognition and guidance of initial welding position (IWP) is one of the most important steps of automatic welding process, also a key technology of autonomous welding process. The purpose of this paper is to advance an improved Harris Algorithm and grey scale scanning method (GSCM) to raise the precision of image processing.

Through the configuration of “single camera and double positions,” a new set of image processing algorithms is adopted to extract feature points by using the pattern of rough location and subtle extraction, so as to restructure three‐dimensional information to guide robot move to IWP in the practical welding environment.

Experiments showed that mean square errors (MSEs) in X, Y, Z‐directions for both flat butt joint and flat flange are 0.4491, 0.8178, 1.4797, and 0.5398, 0.4861, 1.1071 mm, respectively.

It has a limitation in providing guidance for only one step, and would be more accurate if fractional steps are adopted.

Guidance experiments of IWPs on oxidant tank's simulating parts are carried out, whose success rate is up to 95 percent and MSEs are 0.7407, 0.7971, and 1.3429 mm. It meets the demands of continuous and automatic welding process.

Improved Harris Algorithm and GSCM are advanced to raise the precision of image processing which influenced guidance precision most.

This paper aims to evaluate the early effects of the pandemic of coronavirus disease 2019 (COVID-19) and accompanying stay-at-home orders on restaurant demand in US counties.

The following two sets of daily restaurant demand data were collected for each US county: foot traffic data and card transaction data. A two-way fixed-effects panel data model was used to estimate daily restaurant demand from February 1 to April 30, 2020.

Results show that a 1% increase in daily new COVID-19 cases led to a 0.0556% decrease in daily restaurant demand, while stay-at-home orders were collectively associated with a 3.25% drop in demand. The extent of these declines varied across counties; ethnicity, political ideology, eat-in habits and restaurant diversity were found to moderate the effects of the COVID-19 pandemic and stay-at-home orders.

These results characterize the regional restaurant industry’s resilience to COVID-19 and identify particularly vulnerable areas that may require pubic policies and managerial strategies for intervention.

This study represents a pioneering attempt to investigate the economic impact of COVID-19 on restaurant businesses.