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It is recognized that many emerging market multinationals (EM MNEs) are risk-taking or radical when undertaking outbound FDI. Risk-taking, however, is profoundly endogenous and therefore it is misl...

A growing body of research probes into the unique ownership advantages of emerging market MNEs (EMNEs) to uncover the sources of their counterintuitive success, when judged from the perspective of traditional theories in international business. Although research documents that EMNEs expand into spatially distant countries to prompt a global catch-up in competitiveness, how such geographic dispersion results in improved performance remains unclear. Per the springboard perspective, adaptability and ambidexterity may facilitate post-springboard success. We advance this logic, submitting that EMNEs fulfill adaptability advantages through strategic agility, and ambidexterity advantages through structural mediums that support adaptive integration. We propose that these strategic and structural enablers foster a positive relationship between geographic dispersion and performance. Our study is based on data from 220 MNEs headquartered in China. The results indicate that strategic agility, integration capability, and autonomy delegation promote the positivity of geographic dispersion. These findings enlighten EMNE research in organizing post-springboard activities that involve geographically dispersed operations.

Background: Leber congenital amaurosis (LCA) is a visual disease which is caused by RPE65 mutations and results in retinal degeneration and severe vision loss in early infancy. According to previous researches, mutations of the RPE65 gene account for 16% of all cases of LCA. This study aimed to identify RPE65 gene mutations in Chinese patients with LCA. Methods: We recruited 52 sporadic patients from Peking University Third Hospital in 2016 and applied Sanger sequencing to identify variants among exons responsible for the disease. The genomic DNAs from blood leukocytes of these patients were isolated, and the entire coding region of the RPE65 gene was amplified by polymerase chain reaction. We then determined the sequence of RPE65 using ABI 3100 Genetic Analyzer. Results: Our study identified that only 1 out of the 52 patients with LCA carried the previously unreported homozygosis missense mutation c1174A>C (T392P) of the RPE65 gene. However, the mutation was associated with the disease phenotype and not detected in 100 normal controls. Conclusions: Though we identified a novel missense mutation in the RPE65 gene that causes LCA, our result indicates that RPE65 mutations may not play a major role in the LCA patients in China since only 1 out of the 52 patients carried mutation in the RPE65 gene. Key words: Leber Congenital Amaurosis; Mutation; RPE65

Background Emerging evidence indicates that impaired angiogenesis may contribute to hypertension‐induced cardiac remodeling. The nicotinamide adenine dinucleotide–dependent deacetylase Sirtuin 3 ( SIRT 3) has the potential to modulate angiogenesis, but this has not been confirmed. As such, the aim of this study was to examine the relationship between SIRT 3‐mediated angiogenesis and cardiac remodeling. Methods and Results Our experiments were performed on SIRT 3 knockout and age‐matched wild‐type mice infused with angiotensin II (1400 ng/kg per minute) or saline for 14 days. After angiotensin II infusion, SIRT 3 knockout mice developed more severe microvascular rarefaction and functional hypoxia in cardiac tissues compared with wild‐type mice. These events were concomitant with mitochondrial dysfunction and enhanced collagen I and collagen III expression, leading to cardiac fibrosis. Silencing SIRT 3 facilitated angiotensin II –induced aberrant Pink/Parkin acetylation and impaired mitophagy, while excessive mitochondrial reactive oxygen species generation limited angiogenic capacity in primary mouse cardiac microvascular endothelial cells. Moreover, SIRT 3 overexpression in cardiac microvascular endothelial cells enhanced Pink/Parkin‐mediated mitophagy, attenuated mitochondrial reactive oxygen species generation, and restored vessel sprouting and tube formation. In parallel, endothelial cell–specific SIRT 3 transgenic mice showed decreased fibrosis, as well as improved cardiac function and microvascular network, compared with wild‐type mice with similar stimuli. Conclusions Collectively, these findings suggest that SIRT 3 could promote angiogenesis through attenuating mitochondrial dysfunction caused by defective mitophagy.

This paper examines the impact of cross-border acquisitions on the subsequent innovation of acquiring firms in their home countries using emerging market multinationals (EMNEs) as an example. Drawi...

Although cross-border acquisitions are prevalent, many of these transactions fail to complete. This study contributes to extant research by examining how to enhance this completion via learning vic...

We analyze the impact of informal entrepreneurship on innovation in emerging economies. We build on agency theory to argue that formal new ventures that were informally created engage more in imita...

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.

Automated segmentation of images has been considered an important intermediate processing task to extract semantic meaning from pixels. In general, the fuzzy c-means approach (FCM) is highly effective for image segmentation. But for the conventional FCM image segmentation algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and the spatial distribution of pixels in an image is not taken into consideration. In this paper, we present a novel FCM image segmentation scheme by utilizing local contextual information and the high inter-pixel correlation inherent. Firstly, a local spatial similarity measure model is established, and the initial clustering center and initial membership are determined adaptively based on local spatial similarity measure model. Secondly, the fuzzy membership function is modified according to the high inter-pixel correlation inherent. Finally, the image is segmented by using the modified FCM algorithm. Experimental results showed the proposed method achieves competitive segmentation results compared to other FCM-based methods, and is in general faster.

VRCASE is a virtual environment based Computer Aided Software Engineering (CASE) tool. It provides a 3D multiuser collaborative software modeling environment with automatic object-class abstraction, class diagram generation, and C++ skeleton generation facilities for assisting Object-Oriented software development. It allows multiple concurrent users to model software system collaboratively. To achieve efficient collaborative software development in VRCASE, we have proposed and implemented a Fine-grained locking and notification mechanism together with visual indicators to maintain system consistency among multiple concurrent users. The system evaluation shows that our approach can effectively support multiuser collaborative software design in VRCASE.

We analyze how multinational subsidiaries overcome deficiencies in infrastructure in developing economies. Drawing insights from the resource dependence theory, we explain how a subsidiary‟s dependence on host country‟s soft infrastructure or institutions and hard or physical infrastructure becomes a critical constraint under which it has to strategically respond. We propose three ideas. First, we propose that the degree of infrastructure deficiencies in a developing economy constrains the subsidiary‟s investment scale in the country. Second, the subsidiary can overcome these constraints using three strategies: adaptation, or gaining experience in operating with deficient infrastructure; influencing or lobbying the government to reduce uncertainty in investment; and escape, or reducing exposure to deficient infrastructure by seeking foreign markets. Third, the first two strategies are more effective in addressing hard infrastructure deficiencies and the last strategy is more effective in redressing soft i...

It is recognized that many emerging market multinationals (EM MNEs) are risk-taking or radical when undertaking outbound FDI. Risk-taking, however, is profoundly endogenous and therefore it is misl...

Building on signaling theory, we analyze how firms from emerging countries can address the negative impact of the liability of emergingness in exports. Because of information asymmetries, firms fro...

Two-roll bending with the elastic medium is an advanced technology for sheet metal forming which combines the principle of traditional roll bending with the advantage of stamping using elastic medium(polyurethane). The process principle of rotary shaping with the elastic medium (RSEM) is introduced, and the experiment investigation of RSEM has been carried out, in which the primary technique factors of RSEM are discussed. A new concept of converted modulus of elasticity of polyurethane E e is put forward, and expressions of E e is derived, by which the elastic medium can be selected reasonably. Moreover, the model of analysis of RSEM is built by finite element analysis software MARC, which has been applied successfully to simulate the whole process of workpiece forming and resilience. The result of the finite element analysis (FEA) are compared with experimental measurements, Simulation shows good agreement with measurements. The results of FEA are helpful to assign the value of the processing parameters of RSEM

We present a composition-based logic toward international expansion by emerging market firms (EMFs) - firms that use compositional investment, compositional competition, and compositional collabora...

We examine the performance implications of co-opetition (simultaneous pursuit of competition and cooperation with key business stakeholders) and how co-opetition and co-competence (simultaneous pos...

We investigate input states, outcome states, and necessary conditions in the process by which diversity improves performance in emerging market multinational enterprises (EM MNEs). We focus on geog...