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The purpose of this paper is to analyse nanosecond pulsed laser ablation on both metallic materials and non-metallic materials; a comparison between metallic materials and non-metallic materials has also been included.

In this paper, FEM method has been used to calculate the result by means of the finite element method. Furthermore, all the analyses are based on thermal theories.

The paper presents a comparison of metallic and non-metallic materials. Besides, the effect of how laser parameter changes would influence the ablation depth has also been assessed.

All studies in this paper are based on classical thermal theories. Thermal theories are not applicable some times.

With the results of this paper, suggestions are made so that experiments and manufactures could be optimised and improved.

This study aims to explore how and when learning from others promotes creative performance over the contributor’s tenure in the context of open innovation communities.

The authors analyze a publicly available data set that includes 25,923 innovative items developed by 2,194 contributors from an open innovation community of an online game spanning eight years. Logistic regression model is used for analyzing the data.

The results show that multicultural experiences are negatively related to contributor’s creative performance, and this negative relationship weakens as contributor’s tenure increases. While diverse skills are positively related to contributor’s creative performance, and this positive relationship strengthens as contributor’s tenure increases.

This research highlights the importance of online team collaboration in knowledge transfer through learning from others in open innovation communities. By identifying two outcomes of learning from others through online team collaboration, the authors demonstrate the double-edged role of learning from others and advance the understanding on how the effect of learning from others varies over the contributor’s tenure. These results expand the understanding of online team collaboration and provide a new perspective for research on learning from others.

The development of information technologies has fueled the emergence of online self-organizing teams that involve members with diverse backgrounds to work on a shared goal voluntarily. However, the differences in members' attributes give rise to diversity. Therefore, the authors’ research is to figure out how diversity affects team performance in the context of online self-organizing teams and how this effect changes over team tenure.

The authors use a dynamic approach to the diversity-team performance relationship and collect a publicly longitudinal dataset on 3,970 collaborative items from 2,550 online self-organizing teams spanning nine years in an open innovation community of an online game.

The empirical results show that culture separation is negatively related to team performance, and this negative relationship weakens as team tenure increases. While skill variety and contribution disparity are positively related to team performance, and these positive relationships strengthen as team tenure increases.

The study provides a research framework to examine the relationship between diversity and team performance and explore how this relationship varies over team tenure in the context of online self-organizing teams. The results not only demonstrate the double-edged role of diversity in affecting the success of online self-organizing teams but also advance the understanding on the temporal effect of diversity on team performance.

In the face of solving the urban traffic congestion problem radically, emphasis has been laid on the research on slow traffic planning of urban built environment. Hence, research on slow traffic demand forecasting can provide a basis for the planning of urban slow traffic systems. Based on land use, the overall planning of the new Guangming (GM) district, and the population prediction results, the slow traffic demand within the scope of the new district was forecasted by combining the per capita trip frequency, and the spatial distribution of the slow traffic flow of the new GM district was forecasted per the forecasted demand quantity for slow traffic. The following research conclusions were obtained. Within the new GM district, the correlation of the total demand for slow traffic with the land use functions and population distribution was high, and the cross-zone traffic was mainly decided by the land usage of this district. The cross-unit slow traffic flow was concentrated in the Gongming central, Guangming central, high-tech zone, and Yutian zones. This research provides a guideline for the layout of slow traffic facilities in the future.

The purpose of this study is to empirically investigate the extent to which two types of commercial partnerships (business partner and non-business partner) affect the collaborative innovation of firms in emerging economies. Specifically, the roles of two commercial partnerships are investigated. Additionally, the study explores the moderating effect of external technological uncertainty and internal dynamic capabilities on the relationship between two commercial partnerships and on collaborative innovation.

Using a sample of 370 high-tech firms in China, the authors applied the partial least squares structural equation modeling approach to model these relationships.

The findings reveal opportunities and challenges for companies according to two intensities of commercial partnership for collaborative innovation. The partnership contribution to innovation and competiveness is different within the two routes and ranges. The findings indicate that (1) intense commercial relationships with business partners have a stronger positive significant impact on collaborative innovation than those with non-business partners and (2) non-business partners have a weaker positive impact on collaborative innovation at high external technological uncertainty. It was also found that (3) the positive impact of business partners on collaborative innovation is weakened when a firm has high dynamic capabilities, whereas the positive impact of non-business partners is strengthened.

Insight into the roles of two commercial partnerships in achieving collaborative innovation facilitates the advancement of the theoretical understanding of the circumstances under which cooperative innovation can be more effective under different partnerships.

A key strategic question is whether comprehensiveness enables firms to make better strategic decisions in various environments. In the process of innovation, companies must choose different types and quantities of partners, and they must regulate their partners’ innovative behavior by establishing a corresponding network structure and relationship rules. The current study focuses on analysis of how different intensities of commercial partnerships affect collaborative innovation. This research provides a theoretical framework that creates a new classification of commercial relations with regard to collaborative innovation, and it highlights the difference between the two types of partnerships. This study finds that there are many problems in the selection of innovative partners in China’s high-tech companies. Therefore, companies should strengthen their understanding of cooperative innovation, and they should build and manage highly efficient innovation networks. This study helps companies, high-tech industry associations, academia and government to take enhanced, informed actions.

With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket.

Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation framework of spatial multi-target recognition is given. Second, by comparing and analyzing convolutional neural networks, a convolutional neural network model based on YOLOv3 is designed. Finally, seven satellite scale models are constructed based on systems tool kit (STK) and Solidworks. Multi targets, such as nozzle, star sensor, solar,etc., are selected as the recognition objects.

By labeling, training and testing the image data set, the accuracy of the proposed method for spatial multi-target recognition is 90.17%, which is improved compared with the recognition accuracy and rate based on the YOLOv1 model, thereby effectively verifying the correctness of the proposed method.

This paper only recognizes space multi-targets under ideal simulation conditions, but has not fully considered the space multi-target recognition under the more complex space lighting environment, nutation, precession, roll and other motion laws. In the later period, training and detection can be performed by simulating more realistic space lighting environment images or multi-target images taken by upper-level rocket to further verify the feasibility of multi-target recognition algorithms in complex space environments.

The research in this paper validates that the deep learning-based algorithm to recognize multiple targets in the space environment is feasible in terms of accuracy and rate.

The paper helps to set up an image data set containing six satellite models in STK and one digital satellite model that simulates spatial illumination changes and spins in Solidworks, and use the characteristics of spatial targets (such as rectangles, circles and lines) to provide prior values to the network convolutional layer.

With the aim of shedding new light on the characteristics of human capital in its relationship with organizational innovation, this paper develops a novel theoretical and empirical exploration of the characteristics of human capital, both executives' experience and employees' average education level, as well as the moderating effect of female ownership, on two different aspects of organizational innovation.

Data were obtained from the World Bank's China private manufacturing enterprise questionnaire survey. The study employs regression analysis of a logistic model using 1,598 samples, because the dependent variable of an organization's innovation index is a binary variable.

Using World Bank survey data of Chinese private manufacturing enterprises, the authors find that executives' experience has a significantly positive effect on process innovation. Female ownership strengthens the relationship between executives' experience and process innovation. Moreover, the results indicate that employees' average educational level has a significantly positive effect on product innovation. Female ownership strengthens the relationships between employees' average educational level and organizational innovation including product innovation and process innovation. This study highlights the importance of simultaneously testing the effects of human capital and gender heterogeneity on organizational innovation activities.

This study explores the impact of human capital on organizational innovation activities in the context of the Chinese manufacturing industry. Moreover, organizational innovation activities are divided into two aspects: product innovation and process innovation. This study separately discusses the effect of human capital on these two kinds of innovation in detail. Finally, female ownership is selected as a moderating variable, and it is demonstrated that interactions of female owners with executives' experience and employees' average educational level have a positive impact on increasing different kinds of organizational innovation. The authors identify new boundary conditions for the domain of female research that are sorely lacking in the present literature.