International Opportunity Discovery of Born Global Firms: The Role of Institutions

The study sets out to investigate the international opportunity discovery process of born global firms embedded in two different institutional contexts: an emerging economy, China, and a developed country, Italy. Drawing on the opportunity-based view and institutional theory, the study explores and draws comparative insights into how home country institutions of born global firms can influence the international opportunity discovery process. Using a case study approach, we examine the international opportunity discovery process of six born global firms from China and Italy. The findings reveal that home institutions played an influential, yet, differentiating role on the international opportunity discovery processes of the Chinese and Italian firms. The institutional context of the Italian firms shaped their opportunity discoveries through product innovation, whereas their Chinese counterparts discovered opportunities mainly through networks embedded their home institutional context

Stabilization of DC–DC buck converter with unknown constant power load via passivity-based control plus proportion-integration

Abstract It is known that constant power load (CPL) may cause a negative impedance, which seriously affects the stability of power system. In this paper, a new control algorithm for DC–DC buck converter feeding unknown CPL is proposed. First, under the assumption of known extracted power load, the standard passivity–based control (PBC) is presented to reshape the system energy and compensate for the negative impedance and a proportion‐integration (PI) action around passive output is added to improve disturbance rejection performance, which forms the PBC plus PI (PBC+PI). Then, a parameter estimation algorithm is developed, based on immersion and invariance (I&I) technique, in order to online estimate the extracted power load. In the next step, the online estimation scheme is adopted to construct an adaptive strategy. Finally, the stability analysis of the cascaded system containing a closed‐loop control system and observer error dynamics is conducted. Simulation and experimental results are demonstrated to validate the performance of the proposed controller

An Artificial Intelligence Approach for Tunnel Construction Performance

As massive tunneling projects become more and more popular, predicting the performance of Tunnel Boring Machine (TBM) has been a problem that arose recently. A TBM is a modern piece of machinery that is specially assembled to excavate a tunnel more efficiently and safely. However, the performance of TBM is very difficult to estimate due to the different geological formations and geotechnical factors. This research aims to predict the penetration rate (PR) of TBM utilizing statistical and artificial intelligence methods that are based on the rock mass and rock material properties: rock mass rating, rock quality designation, and rock strength. To achieve this goal, we used two neural network-based models: artificial neural network (ANN) and group method of data handling (GMDH), to forecast the TBM PR values. Then, we compared the performance of these two models using the well-known indices and a ranking system and selected the model with the highest degree of performance. As a result, an ANN model with one hidden layer and seven neurons showed the highest level of capability in predicting TBM PR. Correlation coefficient values of 0.947 and 0.921 for the training and testing phases, respectively, were obtained for the best model in this study. Our research can serve as a fundamental study for future geotechnical engineers or researchers who would like to predict TBM performance with similar rock mass and material properties to this study

International opportunity development of born global firms: The role of institutions

Purpose: The purpose of the study is to investigate the international opportunity development process of born global firms embedded in two different institutional contexts: China, an emerging economy, and Italy, a developed country. Drawing on the entrepreneurial opportunity literature and institutional theory, this study explores and draws insights into how home country institutions of born globals can influence the international opportunity development process of the firms. Design/Methodology/Approach: We adopt a qualitative case study approach with in-depth, semi-structured interviews of six born global companies from China and Italy. In doing so, we employ a flexible pattern matching design, which is consistent with the qualitative research design of the paper. Findings: The findings of the study indicate that home institutions play an influential, yet differential role on the international opportunity development processes of Chinese and Italian born global firms. While the Italian firms shape their opportunities mainly through product innovation, their Chinese counterparts develop opportunities primarily through networks embedded in their home institutional context. Originality: The key contributions of the paper relate to an integrated analysis of the international opportunity development process of born globals in China and Italy based on institutional theory, which has received limited attention in the International Entrepreneurship (IE) literature. In addition, our study advances the similarities and differences in the international opportunity development process in two different countries, thus providing valuable insights for policymakers and practitioners to enter international markets successfully

C9 ORF 72 expansion in a family with bipolar disorder

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98405/1/bdi12063.pd

Review: optical fiber sensors for civil engineering applications

Optical fiber sensor (OFS) technologies have developed rapidly over the last few decades, and various types of OFS have found practical applications in the field of civil engineering. In this paper, which is resulting from the work of the RILEM technical committee “Optical fiber sensors for civil engineering applications”, different kinds of sensing techniques, including change of light intensity, interferometry, fiber Bragg grating, adsorption measurement and distributed sensing, are briefly reviewed to introduce the basic sensing principles. Then, the applications of OFS in highway structures, building structures, geotechnical structures, pipelines as well as cables monitoring are described, with focus on sensor design, installation technique and sensor performance. It is believed that the State-of-the-Art review is helpful to engineers considering the use of OFS in their projects, and can facilitate the wider application of OFS technologies in construction industry

The decline in stomach cancer mortality: exploration of future trends in seven European countries

Mortality from stomach cancer has fallen steadily during the past decades. The aim of this paper is to assess the implication of a possible continuation of the decline in stomach cancer mortality until the year 2030. Annual rates of decline in stomach cancer mortality from 1980 to 2005 were determined for the Netherlands, United Kingdom, France, and four Nordic countries on the basis of regression analysis. Mortality rates were extrapolated until 2030, assuming the same rate of decline as in the past, using three possible scenarios. The absolute numbers of deaths were projected taking into account data on the ageing of national populations. Stomach cancer mortality rates declined between 1980 and 2005 at about the same rate (3.6–4.9% per year) for both men and women in all countries. The rate of decline did not level off in recent years, and it was not smaller in countries with lower overall mortality rates in 1980. If this decline were to continue into the future, stomach cancer mortality rates would decline with about 66% between 2005 and 2030 in most populations, while the absolute number of stomach cancer deaths would diminish by about 50%. Thus, in view of the strong, stable and consistent mortality declines in recent decades, and despite population ageing, stomach cancer is likely to become far less important as a cause of death in Europe in the future

The role of mechanotransduction versus hypoxia during simulated orthodontic compressive strain—an in vitro study of human periodontal ligament fibroblasts

During orthodontic tooth movement (OTM) mechanical forces trigger pseudo-inflammatory, osteoclastogenic and remodelling processes in the periodontal ligament (PDL) that are mediated by PDL fibroblasts via the expression of various signalling molecules. Thus far, it is unknown whether these processes are mainly induced by mechanical cellular deformation (mechanotransduction) or by concomitant hypoxic conditions via the compression of periodontal blood vessels. Human primary PDL fibroblasts were randomly seeded in conventional six-well cell culture plates with O-2-impermeable polystyrene membranes and in special plates with gas-permeable membranes (Lumox (R), Sarstedt), enabling the experimental separation of mechanotransducive and hypoxic effects that occur concomitantly during OTM. To simulate physiological orthodontic compressive forces, PDL fibroblasts were stimulated mechanically at 2 g.cm(-2) for 48 h after 24 h of pre-incubation. We quantified the cell viability by MTT assay, gene expression by quantitative real-time polymerase chain reaction (RT-qPCR) and protein expression by western blot/enzyme-linked immunosorbent assays (ELISA). In addition, PDL-fibroblast-mediated osteoclastogenesis (TRAP(+) cells) was measured in a 72-h coculture with RAW264.7 cells. The expression of HIF-1 alpha, COX-2, PGE2, VEGF, COL1A2, collagen and ALPL, and the RANKL/OPG ratios at the mRNA/protein levels during PDL-fibroblast-mediated osteoclastogenesis were significantly elevated by mechanical loading irrespective of the oxygen supply, whereas hypoxic conditions had no significant additional effects. The cellular-molecular mediation of OTM by PDL fibroblasts via the expression of various signalling molecules is expected to be predominantly controlled by the application of force (mechanotransduction), whereas hypoxic effects seem to play only a minor role. In the context of OTM, the hypoxic marker HIF-1 alpha does not appear to be primarily stabilized by a reduced O-2 supply but is rather stabilised mechanically

How Can Selection of Biologically Inspired Features Improve the Performance of a Robust Object Recognition Model?

Humans can effectively and swiftly recognize objects in complex natural scenes. This outstanding ability has motivated many computational object recognition models. Most of these models try to emulate the behavior of this remarkable system. The human visual system hierarchically recognizes objects in several processing stages. Along these stages a set of features with increasing complexity is extracted by different parts of visual system. Elementary features like bars and edges are processed in earlier levels of visual pathway and as far as one goes upper in this pathway more complex features will be spotted. It is an important interrogation in the field of visual processing to see which features of an object are selected and represented by the visual cortex. To address this issue, we extended a hierarchical model, which is motivated by biology, for different object recognition tasks. In this model, a set of object parts, named patches, extracted in the intermediate stages. These object parts are used for training procedure in the model and have an important role in object recognition. These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. Our reported results indicate that these patches are more informative than usual random patches. We demonstrate the strength of the proposed model on a range of object recognition tasks. The proposed model outperforms the original model in diverse object recognition tasks. It can be seen from the experiments that selected features are generally particular parts of target images. Our results suggest that selected features which are parts of target objects provide an efficient set for robust object recognition