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Will a free trade agreement (FTA) between nations be politically viable? Under political lobbying, which incentives determine whether FTAs will be signed or not? Will FTAs steadily include more countries until we reach a worldwide free trade? The paper addresses these questions using a theoretical analysis model, with “protection for sale” model as the foundation.

Firstly, the economic and political factors are investigated in the theoretical model. Then, the validity of results is tested by econometric analysis with a panel probit model. The data spans 25 key trade nations and covers the period of 2007, 2010 and 2013.

First, the FTA will be endorsed only if the aggregate welfare under FTA, combing lobby contributions with social welfare of both pair nations, is higher than the counterpart without FTA. Otherwise, FTA is rejected. Second, the possibility of concluding a FTA has positive correlation with pair nations’ market sizes and the number of countries with which they have both previously concluded FTAs; the possibility has negative correlation with the distance between pair nations; if pair nations’ aggregate market sizes are large enough, the possibility has positive correlation with government’s sensitivity to social welfare, otherwise the correlation is negative. Third, although FTAs are characterized by the regionalism, they will contribute to multilateral free trade in the long run.

Most researchers do not take the foreign lobbying into account in the manner or the detail that we do here when they study the determinants of FTAs. This paper shows the condition under which FTA is politically viable and incentives behind FTA.

Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.

A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.

The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.

This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.

The purpose of this paper is to explore the relationship between top management team (TMT) internal social capital and strategic decision-making speed, and further explore role of TMT behavioral integration in their relationship. It reveals how TMT internal social capital impacts strategic decision-making speed.

On the basis of the social capital theory and upper echelons theory, at first, a model about TMT internal social capital and strategic decision-making speed is proposed by exploratory case study. Then, the data obtained via questionnaire from 67 TMTs by software SPSS 19.0 and AMOS 17.0 are analyzed, and the theoretical hypotheses as mentioned above are verified.

The empirical study found that different dimensions of TMT internal social capital have significant positive impact on TMT behavioral integrity; TMT behavioral integrity has significant positive impact on strategic decision-making speed; and TMT behavioral integrity as an intermediary variable played a brokering role in the relationship between TMT internal social capital and strategic decision-making speed.

The study enriches the empirical test on the relationship between TMT internal social capital and decision speed, thereby helping the authors further understand how to improve the speed of strategic decision making in TMT.

In recent years, a great number of top conferences and workshops on artificial intelligence (AI) were held in China, showing Chinese AI plays an important role in the world. Meanwhile, Chinese government announced an ambitious scheme, “New Generation Artificial Intelligence Development Plan,” for the country to become a world leader in AI technologies by 2030. The AI research in China has covered various aspects, ranging from chips to algorithms. This chapter attempts to give an overview of the recent advances of AI research and development in China, as well as some perspectives on the future development of AI in China.

This paper aims to investigate the effect of laser energy density on the fretting wear properties of TC4 alloys prepared by selective laser melting to enhance their service life.

The friction test used a ball-to-plane contact structure and simulated the actual working conditions by applying a linear reciprocating motion. In the experiments, the authors prepared TC4 alloys using different laser energy densities and performed fretting wear tests with D = 100 µm on a friction machine, as well as comparative analyses with conventional cast TC4 materials. 3D optical microscope, scanning electron microscopy and energy spectrum x-ray spectrometer were used to analyze the surface morphology and elemental content of the samples.

The results indicate that the friction coefficient and wear volume of selective laser melting (SLM)-TC4 are lower than those of cast TC4, demonstrating superior wear resistance. As the laser energy density increases, the grain size of the SLM-TC4 samples gradually coarsens, leading to an initial improvement followed by a decline in friction coefficient and wear resistance. Among the samples, ET3 (42 J/mm³) exhibits the lowest friction coefficient and wear volume, with the friction coefficient ranging from 0.67 to 0.7 and the wear volume being only 9.92% of that of cast TC4.

The main contribution of this work is to provide a new reference for improving the wear resistance of TC4 prepared by selective laser melting. By studying the effect of different laser energy densities on the wear resistance of TC4, it provides a theoretical basis for the preparation of SLM-TC4 alloys for industrial applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0414/

Laser powder bed fusion (LPBF) can be used to fabricate complex extrusion die without the limitation of structures. Layer-by-layer processing leads to differences in microstructures and wear properties. This study aims to investigate the microstructure evolution and effects of tungsten carbide (WC) on the wear properties of LPBF-printed 18Ni300.

Economical spherical granulation-sintering-deoxygenation (GSD) WC-reinforced 18Ni300 steel matrix composites were produced by LPBF from powder mixtures of WC and 18Ni300. The effects of WC contents on anisotropic microstructures and wear properties of the composites were investigated.

The relative density is more than 99% for all the composites except 25% WC/18Ni300 composite. The grain sizes distributed on the top cross-section are smaller than those on the side cross-section. After adding WC particles, more high-angle grain boundaries and larger Schmid factor generate, and deformed grains decrease. With increasing WC contents, the hardness first decreases and then increases but the wear volume loss decreases. The side cross-section of the composite has higher hardness and better wear resistance. The 18Ni300 exhibits adhesive wear accompanying with abrasive wear, while plowing and fatigue wear are the predominant wear mechanisms of the composites.

Economical spherical GSD WC particles can be used to improve the wear resistance. The novel WC/18Ni300 composites are suitable for the application under the abrasive wear condition with low stress.

In the online environment, consumers increasingly feel vulnerable due to firms’ expanding capabilities of collecting and using their data in an unsanctioned manner. Drawing from gossip theory, this research focuses on two key suppressors of consumer vulnerability: transparency and control. Previous studies conceptualize transparency and control from rationalistic approaches that overlook individual experiences and present a unidimensional conceptualization. This research aims to understand how individuals interpret transparency and control concerning privacy vulnerability in the online environment. Additionally, it explores strategic approaches to communicating the value of transparency and control.

An interpretivism paradigm and phenomenology were adopted in the research design. Data were collected through semi-structured interviews with 41 participants, including consumers and experts, and analyzed through thematic analysis.

The findings identify key conceptual dimensions of transparency and control by adapting justice theory. They also reveal that firms can communicate assurance, functional, technical and social values of transparency and control to address consumer vulnerability.

This research makes the following contributions to the data privacy literature. The findings exhibit multidimensional and comprehensive conceptualizations of transparency and control, including user, firm and information perspectives. Additionally, the conceptual framework combines empirical insights from both experiencers and observers to offer an understanding of how transparency and control serve as justice mechanisms to effectively tackle the issue of unsanctioned transmission of personal information and subsequently address vulnerability. Lastly, the findings provide strategic approaches to communicating the value of transparency and control.

Template matching is one of the most suitable choices for full six degrees of freedom pose estimation in many practical industrial applications. However, the increasing number of templates while dealing with a wide range of viewpoint changes results in a long runtime, which may not meet the real-time requirements. This paper aims to improve matching efficiency while maintaining sample resolution and matching accuracy.

A multi-pyramid-based hierarchical template matching strategy is proposed. Three pyramids are established at the sphere subdivision, radius and in-plane rotation levels during the offline template render stage. Then, a hierarchical template matching is performed from the highest to the lowest level in each pyramid, narrowing the global search space and expanding the local search space. The initial search parameters at the top level can be determined by the preprocessing of the YOLOv3 object detection network to further improve real-time performance.

Experimental results show that this matching strategy takes only 100 ms under 100k templates without loss of accuracy, promising for real industrial applications. The authors further validated the approach by applying it to a real robot grasping task.

The matching framework in this paper improves the template matching efficiency by two orders of magnitude and is validated using a common template definition and viewpoint sampling methods. In addition, it can be easily adapted to other template definitions and viewpoint sampling methods.

This paper aims to focus on the liquefaction of soybean protein to obtain a homogeneous protein solution with a high solid/protein content but low viscosity, which may improve the bond properties and technological applicability of soybean protein adhesive.

The liquefactions of soybean protein in the presence of various amounts of sodium sulphite, urea and sodium dodecyl sulphate (SDS) are investigated, and their effects on the main properties of liquefied soybean protein and soybean protein adhesives are characterized by Fourier transform infrared spectroscopy (FT-IR), gel permeation chromatography (GPC), viscosity tracing and plywood evaluation. Meanwhile, the applicability of soybean protein adhesive composed of liquefied protein for particleboard is also investigated.

Soybean protein can be effectively liquefied to form a homogeneous protein solution with a soybean protein content of 25 per cent and viscosity as low as 772 mPa.s; the addition of sodium sulphite, urea and SDS are beneficial for the liquefaction of soybean protein and have important effects on the technological applicability and water resistance of the obtained adhesive. The optimal liquefying technology of soybean protein is obtained in the presence of 1.5 Wt.% of sodium sulphite, 5 Wt.% of urea, 1.5 Wt.% of SDS and 3 Wt.% of sodium hydroxide. The optimal soybean protein adhesive has the desired water resistance in terms of the boiling-dry-boiling aged wet bond strength, which is up to 1.08 MPa higher than the required value (0.98 MPa) for structural use according to the commercial standard JIS K6806-2003. The optimal liquefied protein has the great potential to prepare particleboard.

The protein content of liquefied soybean protein is expected to further increase from 25 to 40 Wt.% or even higher to further reduce the hot-pressing cycle or energy consumption of wood composites bonded by soybean protein adhesives.

The soybean protein adhesive composed of optimal liquefied protein has potential use in the manufacturing of structural-use plywood and has comparable applicability as a commercial urea-formaldehyde resin for the manufacturing of common particleboard.

Soybean protein adhesive is an environmentally safe bio-adhesive that does not lead to the release of toxic formaldehyde, and the renewable and abundant soybean protein can be used with higher value added by the application as wood adhesive.

A novel liquefaction approach of soybean protein is proposed, and the soybean protein adhesive based on the liquefied protein is obtained with good technological applicability and desired bond properties that extend the applications of the soybean protein adhesive from interior plywood to particleboard and exterior or structural plywood.

The traditional vision system cannot automatically adjust the feature point extraction method according to the type of welding seam. In addition, the robot cannot self-correct the laying position error or machining error. To solve this problem, this paper aims to propose a hierarchical visual model to achieve automatic arc welding guidance.

The hierarchical visual model proposed in this paper is divided into two layers: welding seam classification layer and feature point extraction layer. In the welding seam classification layer, the SegNet network model is trained to identify the welding seam type, and the prediction mask is obtained to segment the corresponding point clouds. In the feature point extraction layer, the scanning path is determined by the point cloud obtained from the upper layer to correct laying position error. The feature points extraction method is automatically determined to correct machining error based on the type of welding seam. Furthermore, the corresponding specific method to extract the feature points for each type of welding seam is proposed. The proposed visual model is experimentally validated, and the feature points extraction results as well as seam tracking error are finally analyzed.

The experimental results show that the algorithm can well accomplish welding seam classification, feature points extraction and seam tracking with high precision. The prediction mask accuracy is above 90% for three types of welding seam. The proposed feature points extraction method for each type of welding seam can achieve sub-pixel feature extraction. For the three types of welding seam, the maximum seam tracking error is 0.33–0.41 mm, and the average seam tracking error is 0.11–0.22 mm.

The main innovation of this paper is that a hierarchical visual model for robotic arc welding is proposed, which is suitable for various types of welding seam. The proposed visual model well achieves welding seam classification, feature point extraction and error correction, which improves the automation level of robot welding.