Political transition and emergent forest-conservation issues in Myanmar.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long-running civil war. With these transitions in mind, we used a horizon-scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land-tenure insecurity, large-scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure- and energy-project planning, and reforming land-tenure and environmental-protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions

Political transition and emergent forest-conservation issues in Myanmar.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long-running civil war. With these transitions in mind, we used a horizon-scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land-tenure insecurity, large-scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure- and energy-project planning, and reforming land-tenure and environmental-protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions

Comparison of Performance of Machine Learning Algorithms for Wine Type Classification

Supervised Machine Learning is the search for algorithms that reason from externally supplied instances to produce general hypothesis, which then make predictions about future instances. This paper describes various Supervised Machine Learning classification techniques, compares several machine learning algorithms for classifying wine types. Wine dataset is taken from UCI datasets. Six different machine learning algorithms that involve Logistic Regression(LR), Linear Discriminant Analysis(LDA), k-Nearest Neighbors(KNN), Classification and Regression Trees(CART), Gaussian Naïve Bayes (NB) and support vector machine(SVM) are proposed and assessed for this classification. The result shows that LDA was found to be the algorithm with most precision and accuracy, Gaussian Naïve Bayes and LR algorithms are found to be the next accurate after LDA accordingly. Machine Learning algorithms requires precision, accuracy and minimum error to have supervised predictive machine learning

Sale Forecasting for Hot-Drink Productivity Using Naïve Bayesian Classification

Classification is one of the most popular data mining tasks with a wide ranges of application and lots of algorithms have been proposed to build scalable classifiers. Several data mining techniques and classification methods have been widely applied to extract knowledge from databases. Naïve Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it the conditional independence assumption on which it is based, rarely true in real-world applications. This system will present sale forecasting productivity using Naïve Bayesian Classification

Effectiveness and safety of 3 and 5 day courses of artemether–lumefantrine for the treatment of uncomplicated falciparum malaria in an area of emerging artemisinin resistance in Myanmar

Abstract Background Artemisinin resistance in Plasmodium falciparum has emerged and spread in Southeast Asia. In areas where resistance is established longer courses of artemisinin-based combination therapy have improved cure rates. Methods The standard 3-day course of artemether–lumefantrine (AL) was compared with an extended 5-day regimen for the treatment of uncomplicated falciparum malaria in Kayin state in South-East Myanmar, an area of emerging artemisinin resistance. Late parasite clearance dynamics were described by microscopy and quantitative ultra-sensitive PCR. Patients were followed up for 42 days. Results Of 154 patients recruited (105 adults and 49 children < 14 years) 78 were randomized to 3 days and 76 to 5 days AL. Mutations in the P. falciparum kelch13 propeller gene (k13) were found in 46% (70/152) of infections, with F446I the most prevalent propeller mutation (29%; 20/70). Both regimens were well-tolerated. Parasite clearance profiles were biphasic with a slower submicroscopic phase which was similar in k13 wild-type and mutant infections. The cure rates were 100% (70/70) and 97% (68/70) in the 3- and 5-day arms respectively. Genotyping of the two recurrences was unsuccessful. Conclusion Despite a high prevalence of k13 mutations, the current first-line treatment, AL, was still highly effective in this area of South-East Myanmar. The extended 5 day regimen was very well tolerated, and would be an option to prolong the useful therapeutic life of AL. Trial registration NCT02020330. Registered 24 December 2013, https://clinicaltrials.gov/NCT0202033