The Political Economy of Myanmar's Transition

This is an Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the JOURNAL OF CONTEMPORARY ASIA, 07 Feb 2013, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/00472336.2013.764143.Since holding elections in 2010, Myanmar has transitioned from a direct military dictatorship to a formally democratic system and has embarked on a period of rapid economic reform. After two decades of military rule, the pace of change has startled almost everyone and led to a great deal of cautious optimism. To make sense of the transition and assess the case for optimism, this article explores the political economy of Myanmar's dual transition from state socialism to capitalism and from dictatorship to democracy. It analyses changes within Myanmar society from a critical political economy perspective in order to both situate these developments within broader regional trends and to evaluate the country's current trajectory. In particular, the emergence of state-mediated capitalism and politico-business complexes in Myanmar's borderlands are emphasised. These dynamics, which have empowered a narrow oligarchy, are less likely to be undone by the reform process than to fundamentally shape the contours of reform. Consequently, Myanmar's future may not be unlike those of other Southeast Asian states that have experienced similar developmental trajectories

Fluctuations in Serum magnesium and Systemic Arterial Blood Pressures during the Menstrual Cycle in young reproductive women

Introduction: The menstrual cycle involves a sequence of structural, functional, and hormonal changes in the reproductive system. This is linked and controlled by cyclical fluctuations in the levels of FSH, LH, estrogen, and progesterone. Because of these cyclical fluctuations, there might also be associated cyclical changes of magnesium and systemic arterial blood pressures during the menstrual cycle. Purpose: To assess the changes in serum magnesium level and systemic arterial blood pressures during the menstrual cycle in young reproductive women. Methodology: the sample population is 40 apparently healthy young reproductive-aged 18- 25years female students from the University of Medicine, Magway participated in this study. Systemic arterial blood pressures were measured by indirect method. The serum magnesium level was measured by spectrophotometry. These measurements were done in the early follicular phase (EF), the peri-ovulatory phase (PO), and the midluteal phase (ML) of the menstrual cycle. The serum magnesium levels were significantly (p <0.001) lower, and the systolic blood pressures were significantly higher (p <0.05) in the PO than the EF and the ML. In the EF, there was a significant negative correlation between serum magnesium level and diastolic blood pressure (r= - 0.374, p <0.05) and mean arterial pressure (r = -0.354, p < 0.05) but no significant correlation with systolic blood pressure. In the PO, there was no significant correlation between serum magnesium level and systemic arterial blood pressures. In the ML, there was significant negative correlation between serum magnesium level and systolic blood pressure (r = -0.651, p <0.001), diastolic blood pressure (r = -0.607, p <0.001), and mean arterial pressure (r = -0.661, p <0.001). Conclusion: The study concludes that serum magnesium level has a negative effect on blood pressure changes and the blood pressure-lowering effect of magnesium. These changes are related to the fluctuation of estrogen levels during the menstrual cycle. KEYWORDS: Serum magnesium, systemic arterial blood pressures, menstrual cycle reproductive syste

Detection of East/Central/South African Genotype of Chikungunya Virus in Myanmar, 2010

In 2010, chikungunya virus of the East Central South African genotype was isolated from 4 children in Myanmyar who had dengue-like symptoms. Phylogenetic analysis of the E1 gene revealed that the isolates were closely related to isolates from China, Thailand, and Malaysia that harbor the A226V mutation in this gene

Investigation and implementation of image processing algorithms for medical application

Functional Magnetic Resonance Imaging (fMRI) has experienced a rapid growth in the past several years and has found further applications in a wide variety of fields, such as neuroscience, psychology, and political science, in addition to medical applications. Currently, there exist a number of different imaging modalities that allows the users to study the physiological changes that accompany brain activation. Each of these techniques provides a unique perspective on brain function and meeting the individual purposes. Among them, there has been a growing number of neuro-imaging studies performed using fMRI. The fMRI is now solidly established as a noninvasive diagnostic technique in acquisition of physiological and biochemical information and in particular for studying brain activity, as well as tumors and cancerous vicinity. The fMRI takes advantage of the relationship of certain stimuli leads to initiate changes in neuronal activity, which give momentary changes in blood oxygenation/oxygen level (BOLD) to the active region of the brain. In practice, it identifies the brain activity by picking up minute changes in blood flow in response to stimuli that the subject or patient experiencing associated with alternative non-stimulated instance or pause while the scanning is in progress. Changes in the measured signal between individual images are to make inferences regarding task-related activations in the brain. This paper discuses the analysis of fMRI data, from the initial raw data to its use in locating brain activity and to map the brain function. A standard fMRI study gives rise to massive amounts of noisy data as its signal is corrupted by random noise and various components that arise due to both the system hardware reasons and the subjects themselves together with a complicated spatial-temporal correlation structure. This is further denoising those nuisance signals and making corrective measures for the reduction of noises correspond to the subjects. The signal data then have to undergo statistical processing for the development of image representation. The Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used as diagnostic tools for medical practitioners and as evidences to be interpreted by scientists.Bachelor of Engineerin

Puccinia reynoldsii sp. nov. on Clerodendrum (Lamiaceae) from Burma

Puccinia reynoldsii sp. nov. is a new species encountered during mycological exploration on Clerodendrum indicum Kuntze from central Burma in the Pyinmana and the Gyobingauk townships. In this report, P. reynoldsii is described, illustrated, and discussed, based on traditional taxonomic tools

Pile settlement estimation from CAPWAP analysis

This dissertation is prepared for partial fulfillment of the requirements for the degree of Master of Science in Geotechnical Engineering. The main objective of this study is to assess the reliability of CAPWAP program in the estimation of load settlement behavior of pile.Master of Science (Geotechnical Engineering

Efficient Action Recognition based on Salient Object Detection

Action recognition has become an importantresearch topic in the computer vision area. Thispaper presents an efficient action recognitionapproach based on salient object detection. Recently,many features were directly extracted from videoframes; as a result, unsatisfying results wereproduced due to intrinsic textural difference betweenforeground and background. Instead of wholeframes, processing only on salient objects suppressesthe interference of background pixels and also makesthe algorithm to be more efficient. So, the maincontribution of this paper is to focus on salient objectdetection to reflect textural difference. Firstly, salientforeground objects are detected in video frames andonly interest features for such objects are detected.Secondly, we extract features using SURF featuredetector and HOG feature descriptor. Finally, we useKNN classifier for achieving better actionrecognition accuracy. Experiments performed onUCF-Sports action dataset show that our proposedapproach outperforms state-of-the-art actionrecognition methods