Financing behavior of R&D investments in the emerging markets : the role of alliance and financial system

This paper examines the financing behaviour of R&D investments in emerging markets. Drawing on institutional theory and using panel data of generalized methods of moment (GMM) estimation for a sample of 302 firms from 20 countries during the period 2003-2015, we find that emerging market firms tend to use internal funds for financing R&D investments. Interesting results emerged when the sample was divided as alliance and non-alliance firms, and bank-based and market-based financial systems. The results show that R&D financing behaves differently for alliance and non-alliance firms. Alliance firms use both internal and external funds for R&D investments, while non-alliance firms do not use external funds. We also document that a country’s financial system influences the choice of available sources of finance. Firms from countries that follow a bank-based financial system tend to rely on external funds while firms from countries that follow a market-based financial system depend more on internal funds for financing R&D investments. This study is important as it provides new evidence on financing R&D investments in emerging countries taking into account the institutional arguments of financing choices, and so should guide stakeholders about appropriate sources of R&D financing

Reynolds number effect on the wake of two staggered cylinders

Author name used in this publication: Y. Zhou2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Application Program Search Data in Vehicle Data Tenants and PT. Yanto Rentcar Motor Using Visualbasic 6.0

Writing about the application view contains information about tenant data and vehicle data in PT. MOTOR YANTO RENTCAR, then it is expected that these applications can provide information quickly, efficiently, and accurately and reliably useful for both employees and the tenants themselves.In this information display applications the author uses visual basic program. Visual Basic is a software that not only limited to building database-based applications, but also can be used for a variety of other needs.With the information display application data in PT. MOTOR RENTCAR YANTO is expected in the data-data searches will be easier and faster in the presentation

Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

Cloud computing CC benefits and opportunities are among the fastest growing technologies in the computer industry Cloud computing s challenges include resource allocation security quality of service availability privacy data management performance compatibility and fault tolerance Fault tolerance FT refers to a system s ability to continue performing its intended task in the presence of defects Fault tolerance challenges include heterogeneity and a lack of standards the need for automation cloud downtime reliability consideration for recovery point objects recovery time objects and cloud workload The proposed research includes machine learning ML algorithms such as na ve Bayes NB library support vector machine LibSVM multinomial logistic regression MLR sequential minimal optimization SMO K nearest neighbor KNN and random forest RF as well as a fault tolerance method known as delta checkpointing to achieve higher accuracy lesser fault prediction error and reliability Furthermore the secondary data were collected from the homonymous experimental high performance computing HPC system at the Swiss Federal Institute of Technology ETH Zurich and the primary data were generated using virtual machines VMs to select the best machine learning classifier In this article the secondary and primary data were divided into two split ratios of 80 20 and 70 30 respectively and cross validation 5 fold was used to identify more accuracy and less prediction of faults in terms of true false repair and failure of virtual machines Secondary data results show that na ve Bayes performed exceptionally well on CPU Mem mono and multi blocks and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction In the case of greater accuracy and less fault prediction primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity Sequential minimal optimizati

Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm

The benefits and opportunities offered by cloud computing are among the fastest growing technologies in the computer industry Additionally it addresses the difficulties and issues that make more users more likely to accept and use the technology The proposed research comprised of machine learning ML algorithms is Na ve Bayes NB Library Support Vector Machine LibSVM Multinomial Logistic Regression MLR Sequential Minimal Optimization SMO K Nearest Neighbor KNN and Random Forest RF to compare the classifier gives better results in accuracy and less fault prediction In this research the secondary data results CPU Mem Mono give the highest percentage of accuracy and less fault prediction on the NB classifier in terms of 80 20 77 01 70 30 76 05 and 5 folds cross validation 74 88 and CPU Mem Multi in terms of 80 20 89 72 70 30 90 28 and 5 folds cross validation 92 83 Furthermore on HDD Mono the SMO classifier gives the highest percentage of accuracy and less fault prediction fault in terms of 80 20 87 72 70 30 89 41 and 5 folds cross validation 88 38 and HDD Multi in terms of 80 20 93 64 70 30 90 91 and 5 folds cross validation 88 20 Whereas primary data results found RF classifier gives the highest percentage of accuracy and less fault prediction in terms of 80 20 97 14 70 30 96 19 and 5 folds cross validation 95 85 in the primary data results but the algorithm complexity 0 17 seconds is not good In terms of 80 20 95 71 70 30 95 71 and 5 folds cross validation 95 71 SMO has the second highest accuracy and less fault prediction but the algorithm complexity is good 0 3 seconds The difference in accuracy and less fault prediction between RF and SMO is only 13 and the difference in time complexity is 14 seconds We have decided that we will modify SMO Finally the Modified Sequential Minimal Optimization MSMO Algorithm method has been proposed to get the highest accuracy less fault prediction errors in terms of 80 20 96 42 70 30 96 42 5 fold cross validation 96 5

A fact based analysis of decision trees for improving reliability in cloud computing

The popularity of cloud computing (CC) has increased significantly in recent years due to its cost-effectiveness and simplified resource allocation. Owing to the exponential rise of cloud computing in the past decade, many corporations and businesses have moved to the cloud to ensure accessibility, scalability, and transparency. The proposed research involves comparing the accuracy and fault prediction of five machine learning algorithms AdaBoostM1, Bagging, Decision Tree (J48), Deep Learning (Dl4jMLP), and Naive Bayes Tree (NB Tree). The results from secondary data analysis indicate that the Central Processing Unit CPU-Mem Multi classifier has the highest accuracy percentage and the least amount of fault prediction. This holds for the Decision Tree (J48) classifier with an accuracy rate of 89.71 for 80/20, 90.28 for 70/30, and 92.82 for 10-fold cross-validation. Additionally, the Hard Disk Drive HDD-Mono classifier has an accuracy rate of 90.35 for 80/20, 92.35 for 70/30, and 90.49 for 10-fold cross-validation. The AdaBoostM1 classifier was found to have the highest accuracy percentage and the least amount of fault prediction for the HDD Multi classifier with an accuracy rate of 93.63 for 80/20, 90.09 for 70/30, and 88.92 for 10-fold cross-validation. Finally, the CPU-Mem Mono classifier has an accuracy rate of 77.87 for 80/20, 77.01 for 70/30, and 77.06 for 10-fold cross-validation. Based on the primary data results, the Naive Bayes Tree (NB Tree) classifier is found to have the highest accuracy rate with less fault prediction of 97.05 for 80/20, 96.09 for 70/30, and 96.78 for 10 folds cross-validation. However, the algorithm complexity is not good, taking 1.01 seconds. On the other hand, the Decision Tree (J48) has the second-highest accuracy rate of 96.78 , 95.95 , and 96.78 for 80/20, 70/30, and 10-fold cross-validation, respectively. J48 also has less fault prediction but with a good algorithm complexity of 0.11 seconds. The difference in accuracy and less fa

Apocynin prevented inflammation and oxidative stress in carbon tetrachloride induced hepatic dysfunction in rats

Background: Liver fibrosis is a leading pathway to cirrhosis and a global clinical issue. Oxidative stress mediated tissue damage is one of the prime causes of hepatic dysfunction and fibrosis. Apocynin is one of many strong antioxidants. Objective: To evaluate the effect of apocynin in the CCl4 administered hepatic dysfunction in rats. Methods: Female Long Evans rats were administered with CCl4 orally (1 mL/kg) twice a week for 2 weeks and were treated with apocynin (100 mg/kg). Both plasma and liver tissues were analyzed for alanine aminotransferase (ALT), aspartate aminotransferase (AST) and alkaline phosphatase activities. Oxidative stress parameters were also measured by determining malondialdehyde (MDA), nitric oxide (NO), myeloperoxidase (MPO), advanced protein oxidation product (APOP). In addition, antioxidant enzyme activities such as superoxide dismutase (SOD) and catalase activities in plasma and liver tissues were analyzed. Moreover, inflammation and tissue fibrosis were confirmed by histological staining of liver tissue sections. Results: Apocynin significantly reduced serum AST, ALT, and ALP activities in carbon tetrachloride treated rats. It also exhibited a considerable reduction of the oxidative stress markers (MDA, MPO, NO, and APOP level) which was elevated due to CCl4 administration in rats. Apocynin treatment also restored the catalase and superoxide dismutase activity in CCl4 treated rats. Histological analysis of liver sections revealed that apocynin prevented inflammatory cells infiltration and fibrosis in CCl4 administered rats. Conclusion: These results suggest that apocynin protects liver damage induced by CCl4 by inhibiting lipid peroxidation and stimulating the cellular antioxidant system

The transcriptional repressor protein NsrR senses nitric oxide directly via a [2Fe-2S] cluster

The regulatory protein NsrR, a member of the Rrf2 family of transcription repressors, is specifically dedicated to sensing nitric oxide (NO) in a variety of pathogenic and non-pathogenic bacteria. It has been proposed that NO directly modulates NsrR activity by interacting with a predicted [Fe-S] cluster in the NsrR protein, but no experimental evidence has been published to support this hypothesis. Here we report the purification of NsrR from the obligate aerobe Streptomyces coelicolor. We demonstrate using UV-visible, near UV CD and EPR spectroscopy that the protein contains an NO-sensitive [2Fe-2S] cluster when purified from E. coli. Upon exposure of NsrR to NO, the cluster is nitrosylated, which results in the loss of DNA binding activity as detected by bandshift assays. Removal of the [2Fe-2S] cluster to generate apo-NsrR also resulted in loss of DNA binding activity. This is the first demonstration that NsrR contains an NO-sensitive [2Fe-2S] cluster that is required for DNA binding activity

Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide