No abstract

ABSTRACT:Every year, during dry season, Chiang Mai and other northern provinces of Thailand face the problem of haze which is mainly generated by the burning of agricultural waste and forest fire, contained high percentage of particulate matter. Particulate matter 10 (PM10), being very small in size, can be inhaled easily to the deepest parts of the human lung and throat respiratory functions. Due to this, it increases the risk of respiratory diseases mainly in the case of continuous exposure to this seasonal smog. MODIS aerosol images (MOD04) have been used for four weeks in March 2007 for generating the hazard map by linking to in-situ values of PM10. Simple linear regression model between PM10 and AOD got fair correlation with R 2 = 0.7 and was applied to transform PM10 pattern. The hazard maps showed the dominance of PM10 in northern part of Chiang Mai, especially in second week of March when PM10 level was three to four times higher than standard. The respiratory disease records and public health station of each village were collected from Provincial Public Health Department in Chiang Mai province. There are about 300 public health stations out of 2070 villages; hence thiessen polygon was created to determine the representative area of each public health station. Within each thiessen polygon, respiratory disease incident rate (RDIR) was calculated based on the number of patients and population. Global Moran's I was computed for RDIR to explore spatial pattern of diseases through four weeks of March. Moran's I index depicted a cluster pattern of respiratory diseases in 2 nd week than other weeks. That made sense for a relationship between PM10 and respiratory diseases infections. In order to examine how PM10 affect the human respiratory system, geographically weighted regression model was used to observe local correlation coefficient between RDIR and PM10 across study area. The result captured a high correlation between respiratory diseases and high level of PM10 in northeast districts of Chiang Mai in second week of March.

<p>The outbreak of fir bark beetles (Polygraphus proximus Blandford) in natural Abies Mariesii forest on Zao Mountain were reported in 2016. With the recent development of deep learning and drones, it is possible to automatically detect trees in both man-made and natural forests including damaged tree detection. However there are still some challenges in using deep learning and drones for sick tree detection in mountainous area that we want to address: (i) mixed forest structure with overlapping canopies, (ii) heterogeneous distribution of species in different sites, (iii) high slope of mountainous area and (iv) variation of mountainous climate condition. The current work can be summarized into three stages: data collection, data preparation and data processing. All the data were collected by DJI Mavic 2 pro at 60-70m flying height from the take off point with ground sampling distance (GSD) are ranging from1.23 cm to 2.54 cm depending on the slope of the sites. To prepare the data to be processed using a Convolutional Neural Network (CNN), all images were stitched together using Agisoft’s metashape software to create five orthomosaics of five study sites. Every site has different percentage of fir according to the change of elevation. We then manually annotated all the mosaics with GIMP to categorize all the forest cover into 6 classes: dead fir, sick fir, healthy fir, deciduous trees, grass and uncovered (pathway, building and soil). The mosaics are automatically divided into small patches with the assigned categories by our algorithm with first trial window size of 200 pixel x 200 pixel, which we temporally see can cover the medium fir trees. We will also try different window sizes and evaluate how this parameter affects results. The resulting patches were finally used as the input for CNN architecture to detect the damaged trees. The work is still on going and we expect to achieve the results with high classification accuracy in terms of deep learning algorithm allowing us to build maps regarding health status of all fir trees.</p><p> </p><p>Keywords: Deep learning, CNN, drones, UAVs, tree detection, sick trees, insect damaged trees, forest</p><p> </p>

Sulfur LIMitation1 (SLIM1) transcription factor coordinates gene expression in plants in response to sulfur deficiency (−S). SLIM1 belongs to the family of plant-specific EIL transcription factors with EIN3 and EIL1, which regulate the ethylene-responsive gene expression. The EIL domains consist of DNA binding and dimerization domains highly conserved among EIL family members, while the N- and C-terminal regions are structurally variable and postulated to have regulatory roles in this protein family, such that the EIN3 C-terminal region is essential for its ethylene-responsive activation. In this study, we focused on the roles of the SLIM1 C-terminal region. We examined the transactivation activity of the full-length and the truncated SLIM1 in yeast and Arabidopsis. The full-length SLIM1 and the truncated form of SLIM1 with a deletion of C-terminal 106 amino acids (ΔC105) transactivated the reporter gene expression in yeast when they were fused to the GAL4 DNA binding domain, whereas the deletion of additional 15 amino acids to remove the C-terminal 120 amino acids (ΔC120) eliminated such an activity, identifying the necessity of that 15-amino-acid segment for transactivation. In the Arabidopsis slim1-2 mutant, the transcript levels of SULTR1;2 sulfate transporter and the GFP expression derived from the SULTR1;2 promoter-GFP (PSULTR1;2-GFP) transgene construct were restored under −S by introducing the full-length SLIM1, but not with the C-terminal truncated forms ΔC105 and ΔC57. Furthermore, the transcript levels of −S-responsive genes were restored concomitantly with an increase in glutathione accumulation in the complementing lines with the full-length SLIM1 but not with ΔC57. The C-terminal 57 amino acids of SLIM1 were also shown to be necessary for transactivation of a −S-inducible gene, SHM7/MSA1, in a transient expression system using the SHM7/MSA1 promoter-GUS as a reporter. These findings suggest that the C-terminal region is essential for the SLIM1 activity.

Recently, photocatalysis process has shown great potential as a low-cost, environmentally friendly, and sustainable method for the water/wastewater treatment. Among that, g-C3N4 is one of the most promising photocatalyst and widely used for a variety of applications. In spite of some unique features such as strong reduction ability, active under visible light, nontoxic, and high stability, g-C3N4 photocatalytic capability under visible light is limited due to fast recombination rate of reactive charges. To deal with this issue, in this study, g-C3N4 is combined with GaN-ZnO for reducing the recombination rate of charge carriers and increasing the active sites. The g-C3N4/GaN-ZnO composite was characterized by several methods such as SEM, EDX, XRD, FT-IR, UV-Vis, and BET. It is also observed that the composite with outstanding features can work effectively under visible light; thus, it is likely to be widely applied in environment treatment, especially in antibiotic residue with more than 90% of tetracycline was decomposed after 3 hours.

The South China Sea is one of the most important trade pathways in the world. Its strategic economic importance and its geographic location at the confluence of several spheres of influence have rendered it one of the “world’s hotspots”. The South China Sea issue began as a territorial dispute over the sovereignty of the islands and sea territory involving China, five ASEAN countries including Vietnam, the Philippines, Malaysia, Brunei, Indonesia, and Taiwan. While the South China Sea has been the subject of disputes of sovereignty for some time, the conflict began to intensify when China established its nine-dash line in 2012 outlining its territorial claims in the body of water. China’s aggressive stance has prompted reactions from ASEAN countries as well as the US. The South China Sea is an area with relevance to U.S.’s national economic, strategic, security interests, so that increased tension within this area may threaten U.S.'s national interests. Vietnam is also aware that the United States is a superpower that shares concerns about China, as well as its influence in the region can play an important role in balancing power in the South China Sea Conflict. U.S presence help to contain China's aggressive actions, and multilateralization or internationalization of the South China Sea issues is also a contributing factor to control conflict. Therefore, the dispute in the South China Sea is a factor making a closer relationship between the U.S. and Vietnam. Vietnam and the United States established a Comprehensive Partnership in 2013, under which the two countries will strengthen and expand cooperation. In the future, U.S. - Vietnam cooperation will promote strong development, including sensitive fields, because of based on common strategic interests, including "sensitive" fields such as security and defense.

Nitrogen dioxide (NO2) in the atmosphere can be measured using the tropospheric NO2 columns, indicating the number of molecules of NO2 in an atmospheric column from the ground surface to the top of the atmosphere above a square centimeter of the surface. In this study, the temporal variations of tropospheric NO2 columns in Vietnam during 2015–2020 were investigated. To do this, data on the columnar NO2 obtained from the Ozone monitoring instrument (OMI) onboard the NASA’s Earth orbiting satellite Aura were used. Consequently, northeastern Vietnam showed the highest values of the tropospheric NO2 columns over the whole study period (2015–2020), suggesting that this area would be a hot spot of NO2 pollution in Vietnam. In addition, the lowest and highest mean levels of columnar NO2 were found in 2020 and 2016, respectively. However, there is no statistical significance among the columnar NO2 in 2015–2020. Regarding the monthly variation, March and April exhibited the highest levels of tropospheric NO2 columns, which would be affected by frequent combustion activities (e.g., post-harvesting combustion) and meteorological conditions, such as lower air temperature. Results of this study can contribute to an understanding of NO2 pollution in Vietnam over long period.