GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh.

The frequency of landslides and related economic and environmental damage has increased in recent decades across the hilly areas of the world, no exception is Bangladesh. Considering the first step in landslide disaster management, different methods have been applied but no methods found as best one. As a result, landslide assessment using different methods in different geographical regions has significant importance. The research aims to prepare and evaluate landslide susceptibility maps (LSMs) of the Chattogram district using three machine learning algorithms of Logistic Regression (LR), Random forest (RF) and Decision and Regression Tree (DRT). Sixteen landslide conditioning factors were determined considering topographic, hydro-climatic, geologic and anthropogenic influence. The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. The LSMs showed that almost 9-12 % of areas of the Chattogram district are highly susceptible to landslides. The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. The ROC values for training data were 0.943, 0.917 and 0.947 and testing data were 0.963, 0.934 and 0.905 for LR, RF and DRT models, respectively. The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management.

Role of transport network accessibility in the spread of COVID-19 - a case study in Savar Upazila, Bangladesh.

The aim of this research was to test the hypothesis that people in a typical high-transport zone are particularly vulnerable with respect to transmission of coronavirus disease 2019 (COVID-19), a new contagious disease that has rapidly developed into a highrisk global problem. A case study was carried out in Savar Upazila, a sub-district of the capital city Dhaka in Bangladesh, which is traversed by a prominent national highway (Dhaka- Aricha-N5) that also passes the concentric industrial export processing zone surrounding Dhaka. This municipality is thus part of a high-density transport network with extensive economic activities. COVID-19 data were collected from local records at the Upazila Health Complex, while spatial data of the Savar Upazila, including the pertinent road network, were identified and digitized using geographical information systems software. The presence of COVID-19 in in Savar Upazila was found to be strongly associated with the reach and mechanism of transport networks (Pearson correlation = 0.76 with 99% confidence interval).

Spectral lights trigger biomass accumulation and production of antioxidant secondary metabolites in adventitious root cultures of Stevia rebaudiana (Bert.).

Stevia rebaudiana (S. rebaudiana) is the most important therapeutic plant species and has been accepted as such worldwide. It has a tendency to accumulate steviol glycosides, which are 300 times sweeter than marketable sugar. Recently, diabetic patients commonly use this plant as a sugar substitute for sweet taste. In the present study, the effects of different spectral lights were investigated on biomass accumulation and production of secondary metabolites in adventitious root cultures of S. rebaudiana. For callus development, leaf explants were excised from seed-derived plantlets and inoculated on a Murashige and Skoog (MS) medium containing the combination of 2,4-dichlorophenoxy acetic acid (2, 4-D, 2.0mg/l) and 6-benzyladenine (BA, 2.0mg/l), while 0.5mg/l naphthalene acetic acid (NAA) was used for adventitious root culture. Adventitious root cultures were exposed to different spectral lights (blue, green, violet, red and yellow) for a 30-day period. White light was used as control. The growth kinetics was studied for 30days with 3-day intervals. In this study, the violet light showed the maximum accumulation of fresh biomass (2.495g/flask) as compared to control (1.63g/flask), while red light showed growth inhibition (1.025g/flask) as compared to control. The blue light enhanced the highest accumulation of phenolic content (TPC; 6.56mg GAE/g DW), total phenolic production (TPP; 101mg/flask) as compared to control (5.44mg GAE/g DW; 82.2mg GAE/g DW), and exhibited a strong correlation with dry biomass. Blue light also improved the accumulation of total flavonoid content (TFC; 4.33mg RE/g DW) and total flavonoid production (TFP; 65mg/flask) as compared to control. The violet light showed the highest DPPH inhibition (79.72%), while the lowest antioxidant activity was observed for control roots (73.81%). Hence, we concluded that the application of spectral lights is an auspicious strategy for the enhancement of the required antioxidant secondary metabolites in adventitious root cultures of S. rebaudiana and of other medicinal plants.