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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.

This study aims to provide a comprehensive overview of asset pricing research and identifies the general research trends in the area. The study also aims to provide future direction to the researchers in the area of asset pricing.

The study uses bibliometric analysis techniques to achieve the stated purpose. The study covers 3,007 articles published in the top 50 finance and economics journals, accessed from the Scopus database for a period of 47 years (1973–2020). After initial searching for “asset pricing” as the main keyword in “title, abstract, keywords”, the database yields 6,583 articles. This number further reduces to 3,007 articles when the search is restricted to research and review articles published in the top 50 peer-reviewed journals.

The tabular and pictorial representation obtained from the analysis exhibit that asset pricing is an extensively researched area; however, a sudden rise in the number of publications (242) observed for 2019 demonstrates a growing interest amongst researchers. Further, affiliation statistics indicate that the volume of research is mainly concentrated in the USA and other developed nations; hence it opens vistas for the exploration of risk-return dynamics in the context of emerging markets.

The work presents an exhaustive and comprehensive review along with potential research implications. The present study reconciles various contradictory views of the prior studies under asset pricing such as risk-return trade-off, low-risk anomaly and provides the researchers with potential research gaps.

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

The purpose of this study is to investigate online viewers’ preferences concerning the number and duration of video advertisements to watch during commercial breaks. The goal of the investigations was to assess whether online viewers preferred watching a fewer number of advertisements with longer durations or a greater number of advertisements with shorter durations.

Two studies used experimental research designs to assess viewers’ preferences regarding advertisements. These designs used two independent variables and one dependent variable. The first independent variable manipulated the type of choice options given to online viewers (e.g. one 60 s or two 30 s advertisements). The second independent variable manipulated when the choice was given to online viewers (i.e. at the beginning of the viewing experience or in the middle of the experience). The dependent variable measured viewers’ choices concerning their preferred advertisement option.

The results across both studies found that participants made choices that minimized total advertisement exposure time when possible. When minimizing total exposure time was not possible, participants made choices that minimized the number of exposures instead.

These investigations extend the literature on advertisement choice by examining online viewers’ preferences about the format of their advertising experience rather than the content of the persuasive messages themselves. In addition, these investigations provide value by investigating viewers’ responses to stimuli within realistic online simulations rather than abstract hypotheticals.

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.

The inchworm actuator is widely applied in space industry. One of the major issues in space instrumentation is the reliability, especially under space thermal load. The purpose of this paper is to present a numerical calculation method for the inchworm actuator reliability with considering the effect of space temperature.

First, the structure of designed inchworm actuator is introduced, and the main failure reason is analyzed. Then the wear model is proposed with considering the space temperature, and an experiment device is designed to verify the wear model. Finally, the reliability calculation method is developed based on the working principle of the inchworm actuator.

The numerical calculation method can be applied to calculate the reliability of the inchworm actuator with considering the space temperature. And the results provide a new perspective to discuss the influences of the temperature and driving voltage on the reliability of inchworm actuators.

This work presents a reliability calculation method of inchworm actuators with considering the space temperature.

This paper aims to investigate the influence of the contact status between the seal ring and its support on the seal performance in hydrostatic mechanical face seal.

A thermal fluid-solid interaction (TFSI) model of hydrostatic mechanical face seal is further developed, in which the multi-body contacts between components are particularly given more attention. The numerical models of the flow field and complete seal assemblies are developed. A specific energy equation is obtained to simplify the calculation of film temperature. Based on the mechanism for the continuity condition of the physical quantities at the fluid–solid interface, the TFSI model uses an on-line iterative coupling method.

The contact status between seal ring and its support affects the seal performance significantly. The rotating ring and the stationary ring contribute differently because of the contact status difference.

The contact status between the seal ring and the ring seat is key to gain an insight into the performance of the hydrostatic mechanical face seal thus provides guidance for mechanical seal design.

The purpose of this study is to design a more flexible and larger range of the dimming circuit that achieves the independence of multiple LED strings drive and can time-multiplex the power circuit.

The state-space method is used to model the BUCK circuit working in Pseudo continuous conduction mode, analyze the frequency characteristics of the system transfer function and design the compensation network. Build a simulation platform on the Orcad PSPICE platform and verify the function of the designed circuit through the simulation results. Use Altium Designer 16 to draw the printed circuit board, complete the welding of various components and use the oscilloscope, direct current (DC) power supply and a signal generator to verify the circuit function.

A prototype of the proposed LED driver is fabricated and tested. The measurement results show that the switching frequency can be increased to 1 MHz, Power inductance is 2.2 µH, which is smaller than current research. The dimming ratio can be set from 10% to 100%. The proposed LED driver can output more than 48 W and achieve a peak conversion efficiency of 91%.

The proposed LED driver adopts pulse width modulation (PWM) dimming at a lower dimming ratio and adopts DC dimming at a larger dimming ratio to realize switching PWM dimming to analog dimming. The control strategy can be more precise and have a wide range of dimming.

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.

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.