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To facilitate technical managers and field workers to master and understand the provisions of Technical Management Regulations for Railway more accurately, so as to better serve the comprehensive revision of the Regulations, this paper carries out the research on the traceability and evolution of the provisions of the Regulations.

This paper studies and analyzes the evolution of the 11th edition of the Regulations by analyzing the relevance of clauses and summarizes the historical background of the development of calendar editions of the Regulations. The basic research on the traceability and evolution of the Regulations is carried out from four aspects: the continuity of the development of the Regulations, the authority of contents, the relevance of clauses and the richness of historical materials.

From the first edition of the Regulations issued by the former Ministry of Railways in 1950 to the 11th edition, there have been ten comprehensive revisions. There is a strong correlation and continuity between the calendar editions of the Regulations in terms of chapter structure and clauses. Studying the context of the terms of the Regulations is an important way to understand and master the current clauses of the Regulations.

Through the research on the traceability and evolution of the clauses of the Regulations, one is to explore the context of the development of railway technical equipment in China, the other is to clarify the historical background when the provisions were formulated and the third is to trace the development and evolution of the provisions. The revision of the Regulations is based on an accurate grasp of the context of the provisions, which can effectively judge the possible security risks caused by the revision of the provisions and avoid the possible risks in field implementation from the source.

Direct energy deposition (DED) is an additive manufacturing process that allows to produce metal parts with complex shapes. DED process depends on several parameters, including laser power, deposition rate and powder feeding rate. It is important to control the manufacturing process to study the influence of the operating parameters on the final characteristics of these parts and to optimize them. Computational modeling helps engineers to address these challenges. This paper aims to establish a framework for the development, verification and application of meshless methods and surrogate models to the DED process.

Finite pointset method (FPM) is used to solve conservation equations involved in the DED process. A surrogate model is then established for the DED process using design of experiments with powder feeding rate, laser power and scanning speed as input parameters. The surrogate model is constructed using neutral networks (NN) approximations for the prediction of maximum temperature, clad angle and dilution.

The simulations of thin wall built of Ti-6Al-4V titanium alloy clearly demonstrated that FPM simulation is successful in predicting temperature distribution for different process conditions and compare favorably with experimental results from the literature. A methodology has been developed for obtaining a surrogate model for DED process.

This methodology shows how to achieve realistic simulations of DED process and how to construct a surrogate model for further use in optimization loop.

Excellent obstacle surmounting performance is essential for the robotic vehicles in uneven terrain. However, existing robotic vehicles depend on complex mechanisms or control algorithms to surmount an obstacle. Therefore, this paper aims to propose a new simple configuration of an all-terrain robotic vehicle with eight wheels including four-swing arms.

This vehicle is driven by distributed hydraulic motors which provide high mobility. It possesses the ability to change the posture by means of cooperation of the four-swing arms. This ensures that the vehicle can adapt to complex terrain. In this paper, the bionic mechanism, control design and steering method of the vehicle are introduced. Then, the kinematic model of the center of gravity is studied. Afterward, the obstacle surmounting performance based on a static model is analyzed. Finally, the simulation based on ADAMS and the prototype experiment is carried out.

The experiment results demonstrate that the robotic vehicle can surmount an obstacle 2.29 times the height of the wheel radius, which verifies the feasibility of this new configuration. Therefore, this vehicle has excellent uneven terrain adaptability.

This paper proposes a new configuration of an all-terrain robotic vehicle with four-swing arms. With simple mechanism and control algorithms, the vehicle has a high efficiency of surmounting an obstacle. It can surmount a vertical obstacle 2.29 times the height of the wheel radius.

Digital technologies, such as big data and artificial intelligence, significantly impact entrepreneurial activities worldwide. However, research on entrepreneurial activities enabled by digital technologies is fragmented, divergent and delayed. This study aims to provide a structured review of digital entrepreneurship (DE) to identify status, hotspots, knowledge structure, dynamic trends and future developments in this field.

The bibliometric analysis was applied to offer a technological review on DE. In total 704 publications and their 34,083 references from Web of Science were retrieved as the sample set. Basic characteristics of publications, including the most influential documents, authors, journals and countries, were obtained. Then, co-citation and co-occurrence analyses were conducted to sketch the contours of the structure and evolution of DE.

DE has attracted increasing attention in the past three decades, especially after 2013. There are dozens of countries, hundreds of journals and more than 1,000 authors that have contributed to this field. Based on keyword co-occurrence clustering and co-citation clustering, the authors proposed a 3E (empower, evolution and ecosystem) framework of DE to facilitate an interdisciplinary dialogue for evidence-based policymaking and practice. In the future, researchers need to pay more attention to theoretical research and study DE from a holistic and dynamic perspective with consideration to the negative impact of digital technology on entrepreneurial activities.

This study draws an outline of the global advance on DE research. It presents an opportunity to comprehensively understand the contemporary achievements, the march of knowledge and the logical venation underlying academic developments as well as foundations for policymaking.

This paper aims to propose a framework for optimizing the pose in the assembly process of the non-ideal parts considering the manufacturing deviations and contact deformations. Furthermore, the accuracy of the method would be verified by comparing it with the other conventional methods for calculating the optimal assembly pose.

First, the surface morphology of the parts with manufacturing deviations would be modeled to obtain the skin model shapes that can characterize the specific geometric features of the part. The model can provide the basis for the subsequent contact deformation analysis. Second, the simulated non-nominal components are discretized into point cloud data, and the spatial position of the feature points is corrected. Furthermore, the evaluation index to measure the assembly quality has been established, which integrates the contact deformations and the spatial relationship of the non-nominal parts’ key feature points. Third, the improved particle swarm optimization (PSO) algorithm combined with the finite element method is applied to the process of solving the optimal pose of the assembly, and further deformation calculations are conducted based on interference detection. Finally, the feasibility of the optimal pose prediction method is verified by a case.

The proposed method has been well suited to solve the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the effectiveness of the method with an example of the shaft-hole assembly.

The method proposed in this paper has been well suited to the problem of the assembly process for the non-ideal parts with complex geometric deviations. It can obtain the reasonable assembly optimal pose considering the constraints of the surface morphological features and contact deformations. This paper has verified the method with an example of the shaft-hole assembly.

The different surface morphology influenced by manufacturing deviations will lead to the various contact behaviors of the mating surfaces. The assembly problem for the components with complex geometry is usually accompanied by deformation due to the loading during the contact process, which may further affect the accuracy of the assembly. Traditional approaches often use worst-case methods such as tolerance offsets to analyze and optimize the assembly pose. In this paper, it is able to characterize the specific parts in detail by introducing the skin model shapes represented with the point cloud data. The dynamic changes in the parts' contact during the fitting process are also considered. Using the PSO method that takes into account the contact deformations improve the accuracy by 60.7% over the original method that uses geometric alignment alone. Moreover, it can optimize the range control of the contact to the maximum extent to prevent excessive deformations.

Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

This study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.

This research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).

The study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.

For hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.

The study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.

Existing studies have found that most merger and acquisition (M&A) activities do not create the intended synergy. These studies have mainly investigated how firms' internal factors contribute to M&A successes or failures. The current study differs from the earlier ones by exploring how governments' activities can contribute to the creation of acquisition synergy.

A novel technique based on multi-objective optimization by ratio analysis and complex proportional assessment method under an interval-valued intuitionistic fuzzy (IVIF) environment is proposed to prioritize these government roles needed during the M&A process focusing on the Chinese M&A market.

Enactments of regulations and loan guarantees are the most important strategies to help Chinese acquirers overcome acquisition failures. While tax relief ranks third, government training support ranks fourth. Finally, the result shows that government institutional support is the least to help acquirers overcome acquisition failures.

The government has a role to play in the acquisition success. Although this study has prioritized governments' role in relative importance order, the authors recommend that governments capable of providing all these strategies should do so without any specific order. However, if otherwise, governments should not neglect the strategies with less weight completely but rather consider reducing capital allocations to such strategies. Moreover, this study shows how firms with stronger business ties with government officials may enjoy success during acquisition activities. The authors recommend that firms intending to make acquisitions develop stronger ties with governments in order to benefits from governments.

This is the first study to develop a theoretical framework showing how government can contribute to M&A success. The study achieves this by extending Keynesian's arguments and identifies five (5) ways in which governments can ensure acquisition success. Second, within fuzzy multi-criteria decision-making (F-MCDM) research, this study is the first to show the applicability of integrated multi-objective optimization by ratio analysis (MULTIMOORA) and complex proportional assessment (COPRAS) techniques in an IVIF environment. The novel methodology proposed in this study offers an insightful research method to future studies focusing on group decision problems.

With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

The purpose of this study is to investigate structure parameters that influence the mixing process of droplets-gas in underwater depth-adjustable launcher cooling chamber and help engineers who design the launcher to distinguish the most important factor that impacts mixing performance in the cooling chamber.

Euler–Lagrangian droplet tracking method was used to simulate droplets-gas mixing process in the cooling chamber. The SST k-w model was adopted to simulate turbulence. Droplet breakup was described by KHRT hybrid model using modified contains which are more fit to the supersonic main flow condition.

The results show the counter-rotating vortex pairs which caused by injected liquid accelerate the mixing process. High-pressure supersonic freestream makes the liquid jet break into more small droplets due to the high momentum of the main stream. Axial injection angle has the greatest influence on Sauter mean diameter (SMD). Penetration height, SMD and total pressure loss slightly change in different tangential injection conditions. However, mixedness decreases with reduction of tangential injection angle due to a more limited space for spray developing. Enlarging orifice diameter raises penetration and mixedness greatly, while SMD and total pressure loss increase slightly.

The findings of this study confirm the key structure parameter to improve mixing performance in the cooling chamber. Engineers who design the underwater depth-adjustable launcher can refer the findings in this study to make control of launching power more accurate.