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This study aims to solve the realistic dilemma between the importance of entrepreneurship and the high rate of entrepreneurial failure, and to point out the direction of subsequent research.

The paper takes the form of a literature review.

Entrepreneurial activities involve multiple dimensions. Entrepreneurs or entrepreneurial teams will be affected by multiple factors when starting a business, and sufficient attention should be paid to both the factors within the group and the factors outside the group such as institutional quality and market competition. High entrepreneurial failure rate is an essential characteristic of entrepreneurial activities, while solving this problem requires entrepreneurs to maintain passion, clarify their own motivation, improve their learning abilities and adopt appropriate entrepreneurial strategies to improve entrepreneurial performance. Meanwhile, it also urgent to build entrepreneurial teams with common goals, heterogeneous knowledge structure, outstanding learning ability, solid mutual trust, strong social influence and social capital. Successful entrepreneurship should adhere to the perspective of openness and cooperation. It should not only actively strengthen international cooperation but also fully adapt to the country’s system and culture. Sustainable growth of entrepreneurial enterprises requires not only stable commercial revenue but also responsibility to society, which in turn leads to a good reputation and high social recognition.

The authors hope this review can provide some insightful viewpoints for deepening the theoretical system of entrepreneurship, improving the success rate of entrepreneurship and promoting the sustainable growth of enterprises.

Further research can be carried out on the promotion of business growth by entrepreneurship at the micro level in the following aspects: analyze functional mechanism between innovation and entrepreneurship; entrepreneurship research by integrating multiple institutional contexts and cultural traditions; consider the changes in emerging technologies on entrepreneurial activities; diversified mechanism between entrepreneurship education and business growth.

This paper aims to investigate the thermal oxidation behavior of trimethylolpropane trioleate (TMPTO) base oil when exposed to Fe surfaces.

Samples of TMPTO bulk oil were placed in Fe vessels and heated in an oven to accelerate the oxidation at different time intervals, while others were placed in glass vessels and used as experimental controls. Subsequently, the physicochemical properties of the oxidized TMPTOs, including the kinematic viscosity and acid value, were measured and a structural analysis was conducted using the Raman and Fourier transform infrared (FTIR) techniques.

The results demonstrate that the TMPTO bulk oil exhibited an exponential increase in the kinematic viscosity along with the increasing acid value over the oxidation time. The Fe surface significantly increased the kinematic viscosity of TMPTO, while only mildly impacting its acid value compared with the experimental controls. The structural analysis results of the TMPTO suggest that the C = C and = C-H bonds were the vulnerable sites. Furthermore, the results suggest that the Fe surface evidently accelerates the chemical reactions of the C = C and the = C-H bonds, and less alcohols and more carbonyl products were identified in the oil samples that were heated in the Fe vessels.

The results demonstrate that the Fe surfaces affected the oxidation behavior of the TMPTO base oil, and an interaction mechanism between the Fe and the TMPTO is developed.

The purpose of this study is to present and compare the productive efficiency of China Mainland and China Hong Kong contractors, and to identify and investigate the components and sources of their efficiency under different economic and institutional environments.

Data envelopment analysis (DEA) is a non‐parametric approach to examine the relative efficiency among different firms. This study employs DEA based Malmquist Productivity Index (MPI) to compile the efficiency scores of 20 construction companies listed in the Hong Kong Exchange and Clearing Limited (HKEx) from 2004 to 2010.

A decomposition of MPI suggests that catch‐up effect has contributed more to contractor's efficiency than frontier‐shift effect. Compared to their Mainland counterparts, Hong Kong contractors have higher MPI mainly due to higher efficiency scores in catch‐up effect.

Hong Kong contractors have advantage over Mainland contractors in their managerial and strategic capabilities. Hence Hong Kong contractors should lever on their managerial expertise in accounting, financing and legal services when exporting their services. Meanwhile, Mainland contractors should improve their efficiency by making the most use of their technological and human resources, thus improving upon their international entrepreneurship.

This study is the first attempt to apply MPI to compare the productive efficiency of listed contractors in China Mainland and China Hong Kong. The findings contribute to the body of knowledge for productive efficiency measurement.

Accurate and timely cost prediction is critical to the success of construction projects which is still facing challenges especially at the early stage. In the context of rapid development of machine learning technology and the massive cost data from historical projects, this paper aims to propose a novel cost prediction model based on historical data with improved performance when only limited information about the new project is available.

The proposed approach combines regression analysis (RA) and artificial neural network (ANN) to build a novel hybrid cost prediction model with the former as front-end prediction and the latter as back-end correction. Firstly, the main factors influencing the cost of building projects are identified through literature research and subsequently screened by principal component analysis (PCA). Secondly the optimal RA model is determined through multi-model comparison and used for front-end prediction. Finally, ANN is applied to construct the error correction model. The hybrid RA-ANN model was trained and tested with cost data from 128 completed construction projects in China.

The results show that the hybrid cost prediction model has the advantages of both RA and ANN whose prediction accuracy is higher than that of RA and ANN only with the information such as total floor area, height and number of floors.

(1) The most critical influencing factors of the buildings’ cost are found out by means of PCA on the historical data. (2) A novel hybrid RA-ANN model is proposed which proved to have the advantages of both RA and ANN with higher accuracy. (3) The comparison among different models has been carried out which is helpful to future model selection.

The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.

The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.

The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.

The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

The objective of this research is to investigate various neural network (NN) observer techniques for sensors fault identification and diagnosis of nonlinear system in consideration of numerous faults, failures, uncertainties and disturbances. For the importunity of increasing the faults diagnosis and reconstruction preciseness, a new technique is used for modifying the weight parameters of NNs without enhancement of computational complexities.

Various techniques such as adaptive radial basis functions (ARBF), conventional radial basis functions, adaptive multi-layer perceptron, conventional multi-layer perceptron and extended state observer are presented. For increasing the fault detection preciseness, a new technique is used for updating the weight parameters of radial basis functions and multi-layer perceptron (MLP) without enhancement of computational complexities. Lyapunov stability theory and sliding-mode surface concepts are used for the weight-updating parameters. Based on the combination of these two concepts, the weight parameters of NNs are updated adaptively. The key purpose of utilization of adaptive weight is to enhance the detection of faults with high accuracy. Because of the online adaptation, the ARBF can detect various kinds of faults and failures such as simultaneous, incipient, intermittent and abrupt faults effectively. Results depict that the suggested algorithm (ARBF) demonstrates more confrontation to unknown disturbances, faults and system dynamics compared with other investigated techniques and techniques used in the literature. The proposed algorithms are investigated by the utilization of quadrotor unmanned aerial vehicle dynamics, which authenticate the efficiency of the suggested algorithm.

The proposed Lyapunov function theory and sliding-mode surface-based strategy are studied, which shows more efficiency to unknown faults, failures, uncertainties and disturbances compared with conventional approaches as well as techniques used in the literature.

For improvement of the system safety and for avoiding failure and damage, the rapid fault detection and isolation has a great significance; the proposed approaches in this research work guarantee the detection and reconstruction of unknown faults, which has a great significance for practical life.

In this research, two strategies such Lyapunov function theory and sliding-mode surface concept are used in combination for tuning the weight parameters of NNs adaptively. The main purpose of these strategies is the fault diagnosis and reconstruction with high accuracy in terms of shape as well as the magnitude of unknown faults. Results depict that the proposed strategy is more effective compared with techniques used in the literature.

Prosociality may in part determine sustainability behavior. Prior research indicates that pro-environmental behavior correlates with prosocial attitudes, and separately, that prosociality correlates with social support in homes and communities. Therefore, prosociality may constitute a keystone variable linking human well-being with pro-environmental behavior. The purpose of the paper is to test this conjecture.

Data from a multi-year student survey at the University of Maine on environmental behavior, prosociality and experienced social support are used. A two-stage least-squares regression is applied to explore the relationships between these variables, and sub-scale analysis of the pro-environmental responses is performed. Additionally, spatial statistics for the student population across the state are computed.

The data corroborate previous findings and indicates that social support within a community may bolster the prosociality of its members, which in turn may increase pro-environmental behaviors and intentions.

Cross-sectional data do not permit the imputation of causality. Self-reported measures of behavior may also be biased. However, student prosociality surveys may provide an effective and low-cost sustainability metric for large populations.

The results of this study corroborate prior research to suggest that pro-environmental and prosocial behaviors may both be enhanced by bolstering social support efforts at the community level.

It is suggested that prosociality could become a keystone sustainability indicator. The study’s results extend the understanding of the connections between prosociality, social support and pro-environmental behavior. The results of this study suggest that efforts to simultaneously improve the well-being and environmental status might focus on building prosociality and social support systems at the community level.

Static VAR compensators (SVC) have been recognized to be one of the most important flexible AC transmission systems devices used for mitigating the low-frequency electrochemical oscillations occurring in the system and for reactive power compensation, thereby improving the overall dynamic stability and efficiency of the system. The purpose of this paper is to optimize and dynamically tune the control parameters of the classical proportional integral and derivative (PID) controller of the SVC for a two-machine system by designing a new robust optimization technique.

The angular speed deviation between the two machines is used as an auxiliary signal to SVC for generation of the required damping output. To justify the efficacy of the system undertaken, a light load fault at time t = 1 s is projected to the system. The simulation is carried out in MATLAB/Simulink architecture.

The proposed technique helps in the enhancement of system efficiency, reliability and controllability and by effectively responding to the non-linearities taking place in a power grid network. The results obtained are indicative of the fact that the proposed modified brain storming optimization (MBSO) technique reduces system disturbances very quickly, increases the system response in terms of better rise time, settling time and peak overshoot and improves the efficiency of the system.

A detailed comparison of the MBSO technique is compared with the conventional brain storming optimization (BSO) and PID technique. Total harmonic distortion through fast Fourier transform is also compiled to prove that the values of the proposed MBSO method found out to be confined well within the prescribed IEEE-514 boundaries.

The purpose of this paper is to deal with the classification improvement of pollutant using WO3 gases sensors. To evaluate the discrimination capacity, some experiments were achieved using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol via four WO3 sensors.

To improve the classification accuracy and enhance selectivity, some combined features that were configured through the principal component analysis were used. First, evaluate the discrimination capacity; some experiments were performed using three gases: ozone, ethanol, acetone and a mixture of ozone and ethanol, via four WO3 sensors. To this end, three features that are derivate, integral and the time corresponding to the peak derivate have been extracted from each transient sensor response according to four WO3 gas sensors used. Then these extracted parameters were used in a combined array.

The results show that the proposed feature extraction method could extract robust information. The Extreme Learning Machine (ELM) was used to identify the studied gases. In addition, ELM was compared with the Support Vector Machine (SVM). The experimental results prove the superiority of the combined features method in our E-nose application, as this method achieves the highest classification rate of 90% using the ELM and 93.03% using the SVM based on Radial Basis Kernel Function SVM-RBF.

Combined features have been configured from transient response to improve the classification accuracy. The achieved results show that the proposed feature extraction method could extract robust information. The ELM and SVM were used to identify the studied gases.