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This study aims to obtain the mechanistic insights for the fabrication of pure copper thin wall components by selective infrared (IR) laser melting (SLM) and correlated with microstructure development, microhardness, surface morphology and phase analysis. Experimental processes for single track and selection of substrate materials have been studied using a combination of different laser powers and scanning speeds.

SLM of pure copper was performed on a YONGNIAN Laser YLMS-120 SLM machine using an Nd: YAG fiber laser operating at 1,060 nm in the NIR region. Single-track experiments and processing parameters are investigated through different combinations of laser power and scanning speed. The microstructure of the fabricated pure copper samples by SLM technique was analyzed by means of X-ray diffraction, scanning electron microscope equipped with energy disperse spectrometer, optical microscope (OM) and micro-hardness tester.

Steel-based substrates were found suitable for pure copper manufacturing due to sufficient heat accumulation. The width of a single track was determined by liner energy density, showing discontinuities and irregular morphologies at low laser powers and high scanning speeds. As a result of instability of the molten pool induced by Marangoni convection, cracks and cavities were observed to appear along grain boundaries in the microstructure. The top surface morphology of SLM-processed component showed a streamflow structure and irregular shapes. However, the powder particles attached to side surface, which manifest copper powders, are even more sensitive to melt pool of contour track. The crystal phase characteristics of copper components indicated increasing crystallite size of a-Cu, and the decreasing intensity of diffraction peak was attributed to the presence of defects during SLM. The maximum relative density and microhardness were 82 per cent and 61.48 HV0.2, respectively. The minimum thickness of a pure copper thin wall component was 0.2 mm.

This paper demonstrated the forming mechanism and explored feasibility of pure copper thin wall parts by SLM technology in the NIR region. The surface morphology, microstructure and crystal structure were preliminary studied with laser processing parameters.

The purpose of this paper is to examine the relation between Buddhism/Taoism and dividend payout decisions among Chinese listed firms during 2003-2013.

The authors include all Chinese A-share listed stocks in their sample during 2003-2013 and use a multiple regression method to conduct their analyses.

Their findings suggest that firms in regions with high influence of Buddhism and Taoism lean toward having high dividend payouts. The results are robust to a battery of alternative specifications in dividend payout, religiosity measures, research methods and dividend regulation regimes.

They show that the religions of Buddhism/Taoism play a role in determining dividend payout, complementing other informal institution studies of dividend policy. They complement the literature by providing insights into the impact of Buddhism and Taoism on corporate behaviors beyond immoral or unethical practices. They are able to relate specific doctrinal tenets of Buddhism and Taoism to corporate behavior rather than using only the general moral and ethical guidelines of religiosity.

In multihop wireless networks, the number of neighbors has an important role in the network performance since links are dynamically formed between a node and its neighbors. This paper aims to investigate this issue.

The paper quantitatively studies the effects of the average number of neighbors in multihop wireless networks on the network connectivity, the number of hops needed to traverse a certain distance, which can be used to determine the hop diameter of a network, and the total energy consumed by packet transmission, which can be used to choose an optimum average number of neighbors that minimizes the energy consumption. This paper also presents an analysis of the energy consumption that can be applied to a wide range of access protocols and show the effect of a variety of factors.

Results show that the minimum average number of neighbors to guarantee the overall network connectivity depends on the size of a network coverage. There is a sharp knee in the network connectivity with decrease of the average number of neighbors, N. If the distance between a source and destination, d, is known, the number of hops needed to reach the destination is usually between d/R∼2d/R, where R is the transmission range. A larger average number of neighbors N leads to a smaller number of hops to traverse a certain distance, which in turn results in a smaller traffic load caused by relaying packets. However, a bigger N also causes more collisions when a contention medium access scheme is used, which leads to more energy consumed by packet transmission. The results show that the optimum N which minimizes the energy is obtained by balancing several factors affecting the energy.

The paper provides a useful study on the effects of the number of neighbors in multihop wireless networks.

The purpose of this paper is to investigate the effect of chief executive officer (CEO) characteristics and involvement in networks on strategic management accounting (SMA) and, in turn, the indirect effect of SMA on company performance.

A model is advanced and tested using partial least-squares path modelling and data were collected from a sample of 121 service small and medium-sized enterprises (SMEs) in Malaysia.

The results indicate significant and positive relationships between the CEO education and the application of SMA as well as between involvement in networks and SMA. Moreover, it is found that SMA has an indirect effect in relations of CEO education, involvement in networks and company performance.

SMEs’ leaders may realize their important role in affecting outcomes by their choices, which are in turn affected by their characteristics and activities.

This study provides an empirical evidence on the impact of two new factors on the SMA by considering contingency theory and upper echelons theory simultaneously for explaining relationships and developing a new model.

The purpose of this paper is to investigate the pose control of rigid spacecraft subject to dead-zone input, unknown external disturbance and parametric uncertainty in space maneuvering mission.

First, a 6-Degree of Freedom (DOF) dynamic model of rigid spacecraft with dead-zone input, unknown external disturbances and parametric uncertainty is derived. Second, a super-twisting-like fixed-time disturbance observer (FTDO) with strong robustness is developed to estimate the lumped disturbances in fixed time. Based on the proposed observer, a non-singular fixed-time terminal sliding-mode (NFTSM) controller with superior performance is proposed.

Different from the existing sliding-mode controllers, the proposed control scheme can directly avoid the singularity in the controller design and speed up the convergence rate with improved control accuracy. Moreover, no prior knowledge of lumped disturbances’ upper bound and its first derivatives is required. The fixed-time stability of the entire closed-loop system is rigorously proved in the Lyapunov framework. Finally, the effectiveness and superiority of the proposed control scheme are proved by comparison with existing approaches.

The proposed NFTSM controller can merely be applied to a specific type of spacecrafts, as the relevant system states should be measurable.

A NFTSM controller based on a super-twisting-like FTDO can efficiently deal with dead-zone input, unknown external disturbance and parametric uncertainty for spacecraft pose control.

This investigation uses NFTSM control and super-twisting-like FTDO to achieve spacecraft pose control subject to dead-zone input, unknown external disturbance and parametric uncertainty.

This study examines the effect of political networking capability (PNC) and strategic capability on exploratory innovation/exploitative innovation through the mediation of absorptive capability (AC).

Using empirical survey data collected from 153 traditional manufacturing firms (TMFs) in China, the authors apply partial least squares structural equation modeling (PLS-SEM) combined with mediation analyses to test hypotheses.

PNC has a higher impact on exploratory innovation than exploitative innovation through AC. The authors thus provide novel empirical insights into independent variables of firms' ambidextrous innovation and their implementation mechanisms.

The authors highlight a unique situation of China and contribute to the literature on PNC and AC. The findings demonstrate that AC plays an important role in configuring government-obtained external resources into new products, thus influencing ambidextrous innovation strategic decisions.

TMFs' executives should enhance PNC to obtain more resources to conduct exploratory and exploitative innovation. Government officials and policymakers should strengthen the supervision of TMFs' innovation activities and adopt effective measures to ensure that TMFs could conduct more exploratory innovation as governments expected.

This study provides new insights by bridging research gaps in the literature and advances the insights of how TMFs' PNC/strategic capability directly and indirectly fosters exploratory and exploitative innovation via the mediating role of AC in China.

This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy operator (GAE) of synthetic aperture radar (SAR) satellites’ task planning algorithm.

The GAE abbreviated as GAE introduces the entropy value of each orbit task into the fitness calculation of the genetic algorithm, which makes the orbit with higher entropy value more likely to be selected and participate in the remaining process of the genetic algorithm.

The simulation result shows that in a condition of the same calculate ability, 85% of the orbital revisit time is unchanged or decreased and 30% is significantly reduced by using the GAE compared with traditional task planning genetic algorithm, which indicates that the GAE can improve the efficiency of satellites’ task planning.

The GAE is an optimization of the traditional genetic algorithm. It combines entropy in thermodynamics with task planning problems. The algorithm considers the whole lifecycle of task planning and gets the desired results. It can greatly improve the efficiency of task planning in observation satellites and shorten the entire task execution time. Then, using the GAE to complete SAR satellites’ task planning is of great significance in reducing satellite operating costs and emergency rescue, which brings certain economic and social benefits.

The purpose of this paper is to propose a new fuel-balanced formation keeping reference trajectories planning method based on selecting the virtual reference center(VRC) in a fuel-balanced sense in terms of relative eccentricity and inclination vectors (E/I vectors).

By using the geometrical intuitive relative E/I vectors theory, the fuel-balanced VRC selection problem is reformulated as the geometrical problem to find the optimal point to equalize the distances between the VRC and the points determined by the relative E/I vectors of satellites in relative E/I vectors plane, which is solved by nonlinear programming method.

Numerical simulations demonstrate that the new proposed fuel-balanced formation keeping strategy is valid, and the new method achieves better fuel-balanced performance than the traditional method, which keeps formation with respect to geometrical formation center.

The new fuel-balanced formation keeping reference trajectories planning method is valid for formation flying mission whose member satellite is in circular or near circular orbit in J2 perturbed orbit environment.

The new fuel-balanced formation keeping reference trajectories planning method can be used to solve formation flying keeping problem, which involves multiple satellites in the formation.

The fuel-balanced reference trajectories planning problem is reformulated as a geometrical problem, which can provide insightful way to understand the dynamic nature of the fuel-balanced reference trajectories planning issue.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The need for infrastructure is growing as urbanization picks up speed, and the infrastructure REITs financing model has been crucial in reviving the vast infrastructure stock, alleviating the pressure on government funds and diversifying investment entities. This study aims to propose a framework to better assess the risks of infrastructure REITs, which can serve for the researchers and the policy makers to propose risk mitigation strategies and policy recommendations more purposively to facilitate successful implementation and long-term development of infrastructure REITs.

The infrastructure REITs risk evaluation index system is established through literature review and factor analysis, and the optimal comprehensive weight of the index is calculated using the combination weight. Then, a risk evaluation cloud model of infrastructure REITs is constructed, and experts quantify the qualitative language of infrastructure REITs risks. This paper verifies the feasibility and effectiveness of the model by taking a basic REITs project in China as an example. This paper takes infrastructure REITs project in China as an example, to verify the feasibility and effectiveness of the cloud evaluation method.

The research outcome shows that infrastructure REITs risks manifest in the risk of policy and legal, underlying asset, market, operational and credit. The main influencing factors in terms of their weights are tax policy risk, operation and management risk, liquidity risk, termination risk and default risk. The financing project is at a higher risk, and the probability of risk is 64.2%.

This research contributes to the existing body of knowledge by supplementing a set of scientific and practical risk evaluation methods to assess the potential risks of infrastructure REITs project, which contributes the infrastructure financing risk management system. Identify key risk factors for infrastructure REITs with underlying assets, which contributes to infrastructure REITs project management. This research can help relevant stakeholders to control risks throughout the infrastructure investment and financing life cycle, provide them with reference for investment and financing decision-making and promote more sustainable and healthy development of infrastructure REITs in developing countries.