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In recent decades many emerging markets (EMFs) have undertaken entrepreneurial transformations to adapt to institutional transition and industrial change. Corporate entrepreneurship (CE) provided EMFs viable ways to revitalize, reconfigure, and transform successfully with the dynamic environment. Although previous research examined government roles on EMFs' CE activities, little is known about the mechanisms of how government exerts influence on CE activities. To fully understand CE of EMFs, we propose a stage model to explore specific roles governments play that affect CE activities over time. In particular, we investigate how governments' grabbing hand, helping hand, and invisible hand roles affected Chinese auto firms' CE activities at different stages from 1980 to 2016. Government involvement is summarized and the advantages and disadvantages of these roles are analyzed.

The purpose of this paper is to examine the current state of corporate entrepreneurship (CE) of emerging market firms (EMFs) and provide direction for future research on the topic.

The authors specifically review the recent literature between the years 2000 and 2019 on CE with the keywords “corporate entrepreneurship,” “emerging economies” and “emerging countries” published in the Australian Business Deans Council list journals. The authors review the existing literature about CE in emerging markets, summarize current achievements and present an agenda for future research.

Based on the review, the authors categorized the macro and micro contexts of CE and summarized the current articles on CE in emerging markets within each macro and micro context. The authors conclude that despite the abundance of research on CE that investigates the three prongs of CE in terms of innovation, strategic renewal and new venturing in developed market contexts, there is a scarcity of literature that focuses on CE in emerging markets from a holistic perspective.

While there is an abundance of literature review on CE in general in terms of the drivers of the construct, the contexts contributing to it and the outcomes, the reviews are lacking about CE specifically within the context of emerging markets. Emerging markets vary from developed markets institutionally, economically, culturally, socially and technologically. However, the questions of how these differences impact the CE activities, as it relates to innovation, venturing and strategic renewal in EMFs, and how these differences provide incentives or hinder the activities that contribute to CE remain mostly unanswered. This paper reviewed the research on CE and emerging market contexts from 2000 to present. It targets to provide a better understanding of the current achievement on this topic and what to be done in the future.

The purpose of this paper is to study the interfacial effect on mechanical properties of the cellulose nano crystal (CNC)–shape memory polymer (SMP) composites by using combination of the theoretical and experimental approaches.

SMP composites were fabricated by introducing CNCs into crystalline shape memory polyurethane. The morphological, thermal and mechanical properties were comprehensively investigated. Theoretical approach based upon the percolation model was used to simulate the storage modulus E’ variation of the composites in crystalline and amorphous states, respectively. The classic two-phase percolation model was used for the amorphous-state composites. Furthermore, a three-phase model consisting of interfacial regions was created for the crystalline-state composites.

The deviation of nano fillers mechanical reinforcements was disclosed as the composites triggered thermal transitions. Modified percolation theory involving the interfacial effects greatly enhanced the simulation accuracy.

The study made the traditional percolating theory suitable for dynamic modulus and polymorphs polymers in terms of mechanics, which may extend the potential application.

The findings may greatly benefit the development of novel interfacial reinforcing theory and intelligent polymeric nanocomposites featuring polymorphs and dynamic properties.

The purpose of this paper is to apply the concept of equivalent initial flaw size (EIFS) to the anisotropic nickel-based single crystal (SX) material, and to predict the fatigue life on this basis. The crack propagation law of SX material at different temperatures and the weak correlation of EIFS values verification under different loading conditions are also investigated.

A three-parameter time to crack initial (TTCI) method with multiple reference crack lengths under different loading conditions is established, which include the TTCI backstepping method and EIFS fitting method. Subsequently, the optimized EIFS distribution is obtained based on the random crack propagation rate and maximum likelihood estimation of median fatigue life. Then, an effective driving force based on anisotropic and mixed crack propagation mode is proposed to describe the crack propagation rate in the small crack stage. Finally, the fatigue life of three different temperature ESE(T) standard specimens is predicted based on the EIFS values under different survival rates.

The optimized EIFS distribution based on EIFS fitting - maximum likelihood estimation (MLE) method has the highest accuracy in predicting the total fatigue life, with the range of EIFS values being about [0.0028, 0.0875] (mm), and the mean value of EIFS being 0.0506 mm. The error between the predicted fatigue life based on the crack propagation rate and EIFS distribution for survival rates ranges from 5% to 95% and the experimental life is within two times dispersion band.

This paper systematically proposes a new anisotropic material EIFS prediction method, establishing a framework for predicting the fatigue life of SX material at different temperatures using fracture mechanics to avoid inaccurate anisotropic constitutive models and fatigue damage accumulation theory.

The tribological properties of muscovite and its thermal-treated products as lubricant additives in lithium grease were investigated. The effect of thermal temperature on the crystal structure and tribological properties of muscovite was studied. This study aims to explore the tribological mechanism of muscovite and optimize a proper thermal activation temperature, thus further improving the tribological properties.

The crystal structure of muscovite samples was characterized by SEM, TG-DSC, XRD and FTIR. The tribological properties of grease samples were investigated using a four-ball tribotester and the worn surface was analyzed by SEM and EDS.

The excellent tribological properties of muscovite can be ascribed to the layer structure and lubricant film formed on the worn surface. Thermal temperature at 500-600°C increases the surface activity and oxygen releasing capability, and thus favors the formation of lubricant film and accordingly further improves the tribological properties. However, the layer structure is destroyed and hard phases such as alumina and amorphous appear after thermal temperature activated beyond 1000°C, as it results in the aggravation of friction and wear.

To the authors’ knowledge, it is the first to study the effect of thermal temperature on the crystal structure and tribological properties of muscovite. The tribological mechanism of muscovite particle and its thermal-treated products was disclosed.

The purpose of this paper, is to predict the various phases and crystal structure from multi-component alloys. Nowadays, the concept and strategies of the development of multi-principal element alloys (MPEAs) significantly increase the count of the potential candidate of alloy systems, which demand proper screening of large number of alloy systems based on the nature of their phase and structure. Experimentally obtained data linking elemental properties and their resulting phases for MPEAs is profused; hence, there is a strong scope for categorization/classification of MPEAs based on structural features of the resultant phase along with distinctive connections between elemental properties and phases.

In this paper, several machine-learning algorithms have been used to recognize the underlying data pattern using data sets to design MPEAs and classify them based on structural features of their resultant phase such as single-phase solid solution, amorphous and intermetallic compounds. Further classification of MPEAs having single-phase solid solution is performed based on crystal structure using an ensemble-based machine-learning algorithm known as random-forest algorithm.

The model developed by implementing random-forest algorithm has resulted in an accuracy of 91 per cent for phase prediction and 93 per cent for crystal structure prediction for single-phase solid solution class of MPEAs. Five input parameters are used in the prediction model namely, valence electron concentration, difference in the pauling negativeness, atomic size difference, mixing enthalpy and mixing entropy. It has been found that the valence electron concentration is the most important feature with respect to prediction of phases. To avoid overfitting problem, fivefold cross-validation has been performed. To understand the comparative performance, different algorithms such as K-nearest Neighbor, support vector machine, logistic regression, naïve-based approach, decision tree and neural network have been used in the data set.

In this paper, the authors described the phase selection and crystal structure prediction mechanism in MPEA data set and have achieved better accuracy using machine learning.

This paper describes a new method designed for a quartz tuning‐fork temperature sensor. This sensor is designed with a new cut ZYtw(115°/15°) and it is shown that this worked best in flexural vibration mode. The way for raising signal to noise ratio and reducing equivalent resistor of the sensor were analyzed in theory. A manufacturing method has been determined to form and adjust the precise frequency, which could improve sensitivity and reduce non‐linearity.

A chromate conversion coating was prepared on the surface of bare AA2024 aluminum alloy by direct immersion in the chromating treatment bath, and the corrosion behavior of chromated AA2024 aluminum alloy in 3.5 per cent NaCl solution was studied by electrochemical measurement and microstructural observation.

According to the polarization curve test and the scanning electron microscope observation, the corrosion evolution of chromated AA2024 in 3.5 per cent NaCl solution was divided into the following three stages: coating failure, pitting corrosion and intergranular corrosion (IGC).

In the first stage, the chromate coating degraded gradually due to the combined action of chloride anions and water molecules, resulting in the complete exposure of AA2024 substrate to 3.5 per cent NaCl solution. Subsequently, in the second stage, chloride anions adsorbed at the sites of θ phase (Al2Cu) and S phase (Al2CuMg) on the AA2024 surface preferentially, and some corrosion pits initiated at the above two sites and propagated towards the deep of crystal grains. However, the propagation of a pit terminated when the pit front arrived at the adjacent grain boundary, where the initiation of IGC occurred.

Finally, in the third stage, the corrosion proceeded along the continuous grain boundary net and penetrated the internal of AA2024 substrate, resulting in the propagation of IGC. The related corrosion mechanisms for the bare and the chromated AA2024 were also discussed.

This paper aims to predict and evaluate the wear rate of TiAl-2 Wt.% MoO₃ tabular crystals (TMCs) using the Newton interpolation methods.

The friction and wear behaviors of TMC were examined using pin-on-disc apparatus at different times, namely, 1,200, 2,400, 3,600, 4,800 and 6,000 s. The wear rates of five different times as interpolation nodes were measured and calculated by electron probe microanalysis (EMPA) and field emission electron microscope (FESEM). Then, the prediction formula of wear rate was constructed using the Newton interpolation method. The accuracy of the prediction formula and the relationship with friction layer and worn surface are verified for evaluating the reliability of the prediction formula.

The prediction formula shows a similar variation trend of TMC as the experimental results, indicating that the prediction formula can forecast the wear rate and working condition of TMC. Moreover, the microstructures of friction layer and worn surface also have a strong impact on the prediction formulas.

The prediction formulas of the Newton interpolation polynomial can be adopted to predict working longevity in the mechanical components, which can guide the practical engineering application in industrial fields.