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Innovativeness is crucial for industrial cluster firms to gain sustained competitive advantage. This study aims to investigate the effects of inter-firm coopetition on firm innovativeness within a cluster and examines the moderating role of institutional support.

This research adopts an empirical survey method using multi-source data from 181 industrial cluster firms. Regression is used to test the hypotheses of this study.

The results show that cooperation and constructive conflict promote firm innovativeness, while destructive conflict is detrimental to firm innovativeness. Moreover, the study also finds that cooperation interacts with both types of conflict to affect firm innovativeness, where cooperation and constructive conflict interact negatively on firm innovativeness, while cooperation and destructive conflict interact positively on firm innovativeness. In addition, institutional support weakens the effects of cooperation and destructive conflict on innovativeness, respectively, but has no significant moderating effect on the relationship between constructive conflict and innovativeness.

These findings enrich the current research on coopetition. The interaction effects of cooperation and both types of conflict on innovativeness deepen the concept of coopetition and responds to the call to further explore the interaction effects within coopetition. The moderating role of institutional support fills a gap in the empirical research on the role of institutional factors affecting coopetition on innovation and also provides valuable suggestions for firm managers and governments in industrial clusters.

Because of indeterminateness of the environment and delay of the communication, deep space spacecraft is required to be autonomous. Planning technology is studied in order to realize the spacecraft autonomy. First, a multi‐agent planning system (MAPS) based on temporal constraint satisfaction is proposed for concurrency and distribution of spacecraft system. Second, timeline concept is used to describe simultaneous activity, continue time, resource and temporal constraints. Third, for every planning agent in the MAPS, its layered architecture is designed and planning algorithm based on the temporal constraint satisfaction is given in detail. Finally, taking some key subsystems of deep space explorer as an example, the prototype system of MAPS is implemented. The results show that with the communication and cooperation of the planning agents, the MAPS is able to produce complete plan for explorer mission quickly under the complex constraints of time and resource.

The purpose of this paper is to identify the energy losses factors during the hydro-mechanical conversion process at high pressure via a novel reduced order dynamic model.

A novel reduced order dynamic model of the axial piston motor was proposed, which provides an explicit insight to the compression flow losses and the Coulomb friction losses. A fully coupled dynamic model of the piston motor was obtained based on the array bond graph method. And then, a reduced order model was obtained by the composition analysis of flow and torque of the axial piston motor. After that, the energy losses estimation model was presented to predict the energy loss of the piston motor under a wide range of working conditions. The model was verified by comparing the experimental and simulation results.

The simulation result indicates that the flow loss caused by oil compression accounts for 59 per cent of the total flow loss, and the Coulomb friction torque accounts for 40 per cent of the total torque loss under a specific working condition. The compression flow loss and Coulomb friction torque are the major factors that lead to the aggravation of energy loss under extreme working conditions of the piston motor.

At high-pressure condition, the compression flow losses due to fluid compressibility cannot be neglected, and the hydro-mechanical losses in varies friction pairs should involve Coulomb friction losses. Flow and torque loss analytical expression in the model involve the design and control parameters of the piston equipment, which can realize the parameter optimization of the piston equipment for the purpose of energy-saving.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

Studies in teacher research have seldom discussed the effects of writing publications and teachers’ views on it. This paper aims to examine how teachers in China regard the purpose of writing up research papers and its impact on their professional practice. It also investigates facilitating factors in preparing and writing publications.

This is a qualitative study which mainly analyses interview data gathered from 14 teachers, who have experience in writing publications, from three schools in Shanghai, China. Documentary analysis includes selected published journal papers and book chapters of the interviewees.

Findings show that teachers perceive producing publications as serving both an instrumental purpose of career advancement and a developmental purpose of enhancing their individual professional competency which in turn improves the quality of the teaching profession through knowledge transfer and knowledge transportation.

Contributing factors to help teachers to write include the provision of systematic training courses for teachers and the development of network and research partners.

Findings of this study help school leaders and teacher educators better their understanding of improving the quality of practice of teachers through equipping them with a “stance of inquiry”. Findings suggest ways to provide relevant support to enhance the writing capacity of teachers. More importantly, a revision of the existing teacher promotion system may make the practice of writing publications of more direct value to teachers’ daily practice.

Shang, (from which Qun evolved), came into being before Ku, and was used to cover the lower body. Combined with the upper garment Yi, Ku and Shang constituted a complete outfit in the traditional Chinese clothing system. This article briefly describes the development and evolution of Chinese Ku. It postulates that the opening and closing rise of Ku, was a metaphor for the spirit and the cultivation of Chinese literati. Qun, as a kind of lower cover, was actually unable to cover the body completely. Ku, the Chinese trousers for protection of legs, covered the lower limbs, but was not adopted by the Chinese literati in ancient times. Therefore, half-openness of the skirt and half-closeness of the rise of Ku, metaphorically representing half fantasy and half reality, were combined to complete the covering. This system for covering the body with a clothing combination that was partly open (thus exposing parts of the body) reveals an interesting aspect of Chinese traditional culture. Contemporary designers may find unique inspiration through the study of Chinese traditional clothing systems, for current fashion designs that have rich historical significance.

Lattice Boltzmann method (LBM) is employed to explore friction factor of single-phase fluid flow through porous media and the effects of local porous structure including geometry of grains in porous media and specific surface of porous media on two-phase flow dynamic behavior, phase distribution and relative permeability. The paper aims to discuss this issue.

The 3D single-phase LBM model and the 2D multi-component multi-phase Shan-Chen LBM model (S-C model) are developed for fluid flow through porous media. For the solid site, the bounce back scheme is used with non-slip boundary condition.

The predicted friction factor for single-phase fluid flow agrees well with experimental data and the well-known correlation. Compared with porous media with square grains, the two-phase fluids in porous media with circle grains are more connected and continuous, and consequently the relative permeability is higher. As for the factor of specific porous media surface, the relative permeability of wetting fluids varies a little in two systems with different specific surface areas. In addition, the relative permeability of non-wetting fluid decreases with the increasing of specific surface of porous media due to the large flow resistance.

Fluid-fluid interaction and fluid-solid interaction in the SC LBM model are presented, and schemes to obtain immiscible two-phase flow and different contact angles are discussed. Two-off mechanisms acting on the wetting fluids is proposed to illustrate the relative permeability of wetting fluids varies a little in two systems with different specific surface.

The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years.

The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence.

The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on.

The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.

This study is concerned with markets operating in Turkey in the Istanbul Stock Exchange (BIST), which have been observed and studied in relation to herd behavior. During the research part of the study, the existence of herd behavior was investigated with the help of the daily closing price data of the firms in BIST between January 2011 and December 2017. In the research section of the study, the authors used regression analysis. In the analysis, the authors used the index value of the BIST whole Index. The average value of the index value of BIST whole Index was taken. Then, according to this average, 1% percentile and 5% percentile were taken. In the periods in the 1% percentile (at the dates) the result was that herd behavior was present. The herd behavior was observed for the periods (for dates) included in the percentile of 1%. On the other hand, the results of the analysis for the 5% percentile show that the herd behavior is only seen in the upper extremes.