Augmenting Matching Algorithm on the Bipartite Graphs

Discrete or Decision Mathematics is auseful subject for making the undesirable orimpossible decisions especially for the assignmentproblems. These problems can be expressed in thegraphs because graphs are used to solve problemsin many fields and applications. Some applicationsare scheduling, finding the shortest paths andmatching the elements of two different sets whichare called the assignment problems. Although thereare many kinds of graphs, the bipartite graphsprovide the most useful way to represent thematching problems. This paper represents how thematching problems are implemented in the bipartitegraphs and (0, 1) matrices. It also shows how theaugmenting matching algorithm is applied in theseproblems to get the complete matching. It alsoshows that the original or the user chosen matchingis already maximal for some cases. In this paper, thepersonnel assignment problem is used as the typicalmatching problem

Assessment of Mining Extent and Expansion in Myanmar Based on Freely-Available Satellite Imagery

Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer of potential mining areas. We used this layer to guide a systematic scan of freely-available fine-resolution imagery, such as Google Earth, in order to digitize likely mining areas. During this process, each mining area was assigned a ranking indicating our certainty in correct identification of the mining land use. Finally, we identified areas of recent mining expansion based on the change in albedo, or brightness, between Landsat images from 2002 and 2015. We identified 90,041 ha of potential mining areas in Myanmar, of which 58% (52,312 ha) was assigned high certainty, 29% (26,251 ha) medium certainty, and 13% (11,478 ha) low certainty. Of the high-certainty mining areas, 62% of bare ground was disturbed (had a large increase in albedo) since 2002. This four-month project provides the first publicly-available database of mining areas in Myanmar, and it demonstrates an approach for large-scale assessment of mining extent and expansion based on freely-available data

Genomic Tracking of SARS-CoV-2 Variants in Myanmar

In December 2019, the COVID-19 disease started in Wuhan, China. The WHO declared a pandemic on 12 March 2020, and the disease started in Myanmar on 23 March 2020. In December 2020, different variants were brought worldwide, threatening global health. To counter those threats, Myanmar started the COVID-19 variant surveillance program in late 2020. Whole genome sequencing was done six times between January 2021 and March 2022. Among them, 83 samples with a PCR threshold cycle of less than 25 were chosen. Then, we used MiSeq FGx for sequencing and Illumina DRAGEN COVIDSeq pipeline, command line interface, GISAID, and MEGA version 7 for data analysis. In January 2021, no variant was detected. The second run, during the rise of cases in June 2021, showed Alpha, Delta, and Kappa variants. The third and the fourth runs in August and December showed only a Delta variant. Omicron and Delta variants were detected during the fifth run in January 2022. The sixth run in March 2022 showed only Omicron BA.2. Amino acid mutation at the receptor binding domain of Spike glycoprotein started since the second run coupling with high transmission, recurrence, and vaccine escape. We also found the mutation at the primer targets used in current RT-PCR platforms, but there was no mutation at the existing antiviral drug targets. The occurrence of multiple variants and mutations claimed vigilance at ports of entry and preparedness for effective control measures. Genomic surveillance with the observation of evolutionary data is required to predict imminent threats of the current disease and diagnose emerging infectious diseases

Losing a jewel—Rapid declines in Myanmar’s intact forests from 2002-2014

<div><p>New and rapid political and economic changes in Myanmar are increasing the pressures on the country’s forests. Yet, little is known about the past and current condition of these forests and how fast they are declining. We mapped forest cover in Myanmar through a consortium of international organizations and environmental non-governmental groups, using freely-available public domain data and open source software tools. We used Landsat satellite imagery to assess the condition and spatial distribution of Myanmar’s intact and degraded forests with special focus on changes in intact forest between 2002 and 2014. We found that forests cover 42,365,729 ha or 63% of Myanmar, making it one of the most forested countries in the region. However, severe logging, expanding plantations, and degradation pose increasing threats. Only 38% of the country’s forests can be considered intact with canopy cover >80%. Between 2002 and 2014, intact forests declined at a rate of 0.94% annually, totaling more than 2 million ha forest loss. Losses can be extremely high locally and we identified 9 townships as forest conversion hotspots. We also delineated 13 large (>100,000 ha) and contiguous intact forest landscapes, which are dispersed across Myanmar. The Northern Forest Complex supports four of these landscapes, totaling over 6.1 million ha of intact forest, followed by the Southern Forest Complex with three landscapes, comprising 1.5 million ha. These remaining contiguous forest landscape should have high priority for protection. Our project demonstrates how open source data and software can be used to develop and share critical information on forests when such data are not readily available elsewhere. We provide all data, code, and outputs freely via the internet at (for scripts: <a href="https://bitbucket.org/rsbiodiv/" target="_blank">https://bitbucket.org/rsbiodiv/</a>; for the data: <a href="http://geonode.themimu.info/layers/geonode:myan_lvl2_smoothed_dec2015_resamp" target="_blank">http://geonode.themimu.info/layers/geonode:myan_lvl2_smoothed_dec2015_resamp</a>)</p></div

Boxplot comparing mean canopy cover as measured by Hansen et al. (2013) for intact and degraded forests in different forest regions of Myanmar between 2002 and 2014.

<p>Northern Sagaing = mostly mixed deciduous and evergreen forest; Southern Sagaing = mixed deciduous and dry dipterocarp forest; Tanintharyi = tropical evergreen forest. (A) Canopy cover values for intact forest; (B) Canopy Cover values for degraded forest; (C) Location of Landsat tiles used for comparison.</p

Remaining and lost intact forest (ha) for Myanmar townships between 2002 and 2014.

<p>Remaining and lost intact forest (ha) for Myanmar townships between 2002 and 2014.</p

Remaining large and intact forest landscapes in Myanmar 2014.

<p>Remaining large and intact forest landscapes in Myanmar 2014.</p