Developing language strategies for international companies: the contribution of translation studies.

This article introduces translation studies in order to theorize about the ways in which multiple languages in international companies can be combined. Its purpose is to develop different language strategies based on different theoretical perspectives within translation studies. Considering the historical developments in this discipline, we identify three perspectives each with a different conception of translation and language use. These conceptions are the theoretical basis on which we develop three language strategies: a mechanical, cultural and political language strategy. For each strategy, we discuss the selection of language(s), the role of translators and the validation method, and formulate proposition about the types of texts being produced. These propositions indicate that, through their international communication process, international companies become scripted as a particular type of multilingual organization, be it a uniform, a culturally sensitive or a hybrid text.Strategy; International; Companies; Studies; Selection; Validation; Text; Communication; Processes;

Down-regulation of transforming growth factor-β type II receptor (TGF-βRII) protein and mRNA expression in cervical cancer

<p>Abstract</p> <p>Background</p> <p>Cervical carcinogenesis is a multistep process initiated by "high risk" human papillomaviruses (HR-HPV), most commonly HPV16. The infection <it>per se </it>is, however, not sufficient to induce malignant conversion. Transforming Growth Factor β (TGF-β) inhibits epithelial proliferation and altered expression of TGF-β or its receptors may be important in carcinogenesis. One cofactor candidate to initiate neoplasia in cervical cancer is the prolonged exposure to sex hormones. Interestingly, previous studies demonstrated that estrogens suppress TGF-β induced gene expression. To examine the expression of TGF-β2, TGF-βRII, p15 and c-myc we used <it>in situ </it>RT-PCR, real-time PCR and immunohistochemistry in transgenic mice expressing the oncogene E7 of HPV16 under control of the human Keratin-14 promoter (K14-E7 transgenic mice) and nontransgenic control mice treated for 6 months with slow release pellets of 17β-estradiol.</p> <p>Results</p> <p>Estrogen-induced carcinogenesis was accompanied by an increase in the incidence and distribution of proliferating cells solely within the cervical and vaginal squamous epithelium of K14-E7 mice. TGF-β2 mRNA and protein levels increased in K14-E7 transgenic mice as compared with nontransgenic mice and further increased after hormone-treatment in both nontransgenic and transgenic mice. In contrast, TGF-βRII mRNA and protein levels were decreased in K14-E7 transgenic mice compared to nontransgenic mice and these levels were further decreased after hormone treatment in transgenic mice. We also observed that c-myc mRNA levels were high in K14-E7 mice irrespective of estrogen treatment and were increased in estrogen-treated nontransgenic mice. Finally we found that p15 mRNA levels were not increased in K14-E7 mice.</p> <p>Conclusion</p> <p>These results suggest that the synergy between estrogen and E7 in inducing cervical cancer may in part reflect the ability of both factors to modulate TGF-β signal transduction.</p

Controlled oxygen vacancy induced p-type conductivity in HfO{2-x} thin films

We have synthesized highly oxygen deficient HfO$_{2-x}$ thin films by controlled oxygen engineering using reactive molecular beam epitaxy. Above a threshold value of oxygen vacancies, p-type conductivity sets in with up to 6 times 10^{21} charge carriers per cm3. At the same time, the band-gap is reduced continuously by more than 1 eV. We suggest an oxygen vacancy induced p-type defect band as origin of the observed behavior.Comment: 4 pages, 3 figure

Selecting appropriate machine learning classifiers for DGA diagnosis

Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machine learning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Determining appropriate data analytics for transformer health monitoring

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring

Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%

Efficient Market Hypothesis in South Africa: Evidence from a threshold autoregressive (TAR) model

This study deviates from the conventional use of a linear approach in testing for the efficiency market hypothesis (EMH) for the Johannesburg Stock Exchange (JSE) between the periods 2001:01 to 2013:07. By making use of a threshold autoregressive (TAR) model and corresponding asymmetric unit root tests, our study demonstrates how the stock market indexes evolve as highly persistent, nonlinear process and yet for a majority of the time series under observation, the formal unit root tests reject the hypothesis of stationarity among the variables. These results bridge two opposing contentions obtained from previous studies by concluding that while a number of stock prices under the JSE stock market may not evolve as pure unit root processes, the time series are, however, highly persistent to an extent of being able to be deemed as weak-form efficient

Efficient Market Hypothesis in South Africa: Evidence from a threshold autoregressive (TAR) model

This study deviates from the conventional use of a linear approach in testing for the efficiency market hypothesis (EMH) for the Johannesburg Stock Exchange (JSE) between the periods 2001:01 to 2013:07. By making use of a threshold autoregressive (TAR) model and corresponding asymmetric unit root tests, our study demonstrates how the stock market indexes evolve as highly persistent, nonlinear process and yet for a majority of the time series under observation, the formal unit root tests reject the hypothesis of stationarity among the variables. These results bridge two opposing contentions obtained from previous studies by concluding that while a number of stock prices under the JSE stock market may not evolve as pure unit root processes, the time series are, however, highly persistent to an extent of being able to be deemed as weak-form efficient