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The temperature drop, especially in the edge of rolled steel in the hot rolling cooling has a catastrophic effect on the steel quality. The purpose of this paper is to study the coupled eddy current-temperature field of a C-type edge induction heater to provide references for engineering applications and designs.

Three-dimensional finite element analysis (FEA) model of a C-type edge induction heater is developed. Especially, a numerical methodology to couple the eddy current and temperature fields is proposed for coupled eddy current and temperature problems involving movement components. FEA software ANSYS is used to solve the coupled eddy current and temperature fields. The heat loss from the eddy current fields is abstracted and processed, and taken as internal heat source in the analysis of the temperature field. The temperature distribution of the rolling steel is obtained.

The numerical results can predict exactly the temperature rise of the rolled steel by means of the edge induction heating system.

The proposed numerical methodology for coupling eddy current and temperature fields can be applied to engineering coupled eddy current and temperature problems involving movement components. Also, the developed model and method can be used in the analysis and design of the edge induction heating system.

A numerical methodology to couple eddy current and temperature field for solving multi-physics field problems involving movement components is proposed and implemented in available commercial software. A three-dimensional model of the C-type edge induction heat heater is developed. Finite element method is employed to study the coupled eddy current-thermal problem. A method to deal with the movement of the strip steel is proposed. The proposed methodology can be applied to other coupled eddy current-temperature field problem with moving components.

Existing hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.

This study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.

The capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.

The initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

The purpose of this paper is to generate quantitative managerial insights for the improvement of the energy-saving potential and the coordinated development between economic growth and environmental protection.

A novel data envelopment analysis (DEA) model, based on the classical DEA theory, is developed from the perspective of emission reduction.

The empirical results indicate that China’s overall environmental efficiency is low and that there is huge improvement space for energy saving. Under the concerns of emission reduction, the energy-saving potential of the central region exceeds that of both the eastern and western regions. With regard to water, electricity and gas consumption, the electricity-saving potential exceeds the potential for both water saving and gas saving.

Previous studies rarely focused on the energy-saving potential, while considering environmental pollution. This paper applies a novel DEA method to evaluate the energy-saving potential of 30 Chinese provinces for 2015 with a focus on emission reduction concerns. Furthermore, both regional differences and energy type differences of the saving potential were analyzed.

The aim of this paper is to solve the problem of low accuracy of traditional fatigue crack growth (FCG) prediction methods.

The GMSVR model was proposed by combining the grey modeling (GM) and the support vector regression (SVR). Meanwhile, the GMSVR model parameter optimal selection method based on the artificial bee colony (ABC) algorithm was presented. The FCG prediction of 7075 aluminum alloy under different conditions were taken as the study objects, and the performance of the genetic algorithm, the particle swarm optimization algorithm, the n-fold cross validation and the ABC algorithm were compared and analyzed.

The results show that the speed of the ABC algorithm is the fastest and the accuracy of the ABC algorithm is the highest too. The prediction performances of the GM (1, 1) model, the SVR model and the GMSVR model were compared, the results show that the GMSVR model has the best prediction ability, it can improve the FCG prediction accuracy of 7075 aluminum alloy greatly.

A new prediction model is proposed for FCG combined the non-equidistant grey model and the SVR model. Aiming at the problem of the model parameters are difficult to select, the GMSVR model parameter optimization method based on the ABC algorithm was presented. the results show that the GMSVR model has better prediction ability, which increase the FCG prediction accuracy of 7075 aluminum alloy greatly.

The application of industrial robots in modern production is becoming increasingly widespread. In the context of flexible production lines, quickly and accurately identifying and grasping specified workpieces is particularly important. This study aims to propose a grasping scheme that combines traditional methods with deep learning to improve grasping accuracy and efficiency.

First, a dataset generation method is proposed, which constructs a point cloud dataset close to the real scene without the need for extensive data collection. Then, the 3D object detection algorithm PointPillars is improved based on the features of the scene point cloud, allowing for the analysis of part poses to achieve grasping. Finally, a grasp detection strategy is proposed to match the optimal grasp pose.

Experimental results show that the proposed method can quickly and easily construct high-quality datasets, significantly reducing the time required for preliminary preparation. Additionally, it can effectively grasp specified workpieces, significantly improving grasping accuracy and reducing computation time.

The main contribution of this paper is the integration of a novel dataset generation method, improvements to the PointPillars algorithm for 3D object detection and the development of an optimal grasp detection strategy. These advancements enable the grasping system to handle real-world scenarios efficiently and accurately, demonstrating significant improvements over traditional methods.

With the rapid development of the 3C industry, the problem of automated operation of 3C wire is becoming increasingly prominent. However, the 3C wire has high flexibility, and its deformation is difficult to model and control. How to realize the automation operation of flexible wire in 3C products is still an important issue that restricts the development of the 3C industry. Therefore, this paper designs a system that aims to improve the automation level of the 3C industry.

This paper designed a visual servo control system. Based on the perception of the flexible wire, a Jacobi matrix is used to relate the deformation of the wire to the action of the robot end; by building and optimizing the Jacobi matrix, the robot can control the flexible wire.

By using the visual servo control system, the shape and deformation of the flexible wire are perceived, and based on this, the robot can control the deformation of the flexible wire well. The experimental environment was built to evaluate the accuracy and stability of the system for controlling the deformation of the flexible wire.

An image-based visual servo system is proposed to operate the flexible wire, including the vision system, visual controller and joint velocity controller. It is a scheme suitable for flexible wire operation, which has helped to automate flexible wire-related industries. Its core is to correlate the motion of the robot end with the deformation of the flexible wire through the Jacobian matrix.

The purpose of this paper is to investigate the wetting behaviors of Sn-5Sb-CuNiAg solders on copper substrates in different soldering processes and the effects of alloying elements on the wettability.

Sn-5Sb-CuNiAg solder balls (750 µm in diameter) were spread and wetted on 40 × 40 × 1 mm copper plates, in different fluxes, soldering temperatures and time. The contact angles were obtained by a home-made measuring instrument. The samples were polished and deep etched before analyzed by scanning electron microscopy. Energy dispersive X-ray spectroscopy was used to identify the composition of the joints.

The effects of different soldering processes and alloying elements on the wetting behaviors of Sn-5Sb-CuNiAg solders on copper substrates were calculated and expounded. The rosin-based flux could effectively remove oxidation layers and improve the wettability of Sn-5Sb-CuNiAg solders. Then with the increase of soldering temperature and time, the contact angles decreased gradually. The soldering processes suited for Sn-5Sb-CuNiAg solders were RMA218, 280°C and 30 s. Considered the effects of alloying elements, the wettability of Sn-5Sb-0.5Cu-0.1Ni-0.5Ag was relatively favorable on copper substrates. Besides, Ni could accumulate at the solder/Cu interface and form a jagged (Cu,Ni)6Sn5 IMC.

This work was carried out with our handmade experiment equipment and the production of the quinary lead-free solder alloy used in wetting tests belongs to us. The investigated Sn-5Sb-CuNiAg alloys exhibited higher melting point and preferable wettability, that was one of the candidates for high-temperature lead-free solders to replace high-Pb solders, and applied extremely to high temperature and frequency working environments of the third-generation semiconductors components, with a greater potential research and development value.

The purpose of this paper is to emphasize that it is necessary to consider possible “switches” when constructing dynamic models. We consider a class of differential games with switching utility functions.

We assume that the players do not have exact information about the switching moments. The problem is to compute the optimal estimate of the unknown such moment that minimizes the players’ losses in the worst case. Two scenarios are considered: (1) the players have the same estimate and (2) the players have different estimates of the switching moment. An example of an investment problem is given, and optimal controls and trajectories are calculated analytically using Pontryagin’s maximum principle.

The analysis provides a simple rule for choosing the optimal estimate. It is shown that the players’ optimal estimates are identical in both scenarios.

To date, no research has addressed the optimal estimation of uncertainty switching moments in a two-player differential game involving utility function switching. Our study offers detailed estimates of switching moments in the cooperative case. This allows players to thoroughly evaluate the performance of various estimated switching times and make well-informed conclusions.

This study aims to examine the effect of the chief executive officer (CEO) characteristics on corporate performance in private listed firms in China.

Fixed effects regressions are used to explore the connection of CEO age, tenure, political connection, duality and gender with firm performance. The final panel data sample consists of 16,010 firm-year observations from 2010 to 2020, including A-share private firms listed in the Shenzhen and Shanghai Stock Exchanges.

Five hypotheses are proposed, and results show that certain CEO characteristics, such as age, tenure and political connection, are positively related to corporate performance. Contrary to expectations, CEO duality and gender do not affect firm performance.

Findings present implications for future research on corporate governance and political connections of private listed firms.

The purpose of this study is to use the grounded theory to summarize the types of celebrity persona and to construct a theoretical model for celebrity persona on consumer purchase intention. Based on the study results, it provides better suggestions for merchants and live streamers and is an expansion of previous research on live-streaming e-commerce.

The grounded theory is recognized as the most scientific qualitative research method and is the ideal explorative method for generating theory. First, the participants were interviewed, and interview data were collected. Then the interview data were organized and analyzed. Finally, this paper summarizes the types of celebrity persona and constructes a theoretical model framework of celebrity persona on consumers' purchase intention.

The results show that the celebrity live streamer persona can be divided into two types: personalized persona and professional persona. Through emotional attachment, the celebrity's persona affects the consumer's purchase intentions. As well as, product type plays a moderating role between celebrity persona and consumer purchase intentions.

The contribution of this research is to start from the celebrity persona, link the celebrity persona with the consumer purchase intentions and expand the research scope of the celebrity persona. It opens the “black box” of the heterogeneity of celebrity live streamers' characteristics on consumer purchase intentions.