Assessment of genetic diversity and molecular characterization among elite rice varieties of (RM10713, RM279, RM424, RM6266, RM1155, RM289, RM20224 and RM5371) were identified that produced specific alleles only in the aromatic rice varieties and were useful for varietal identification and DNA fingerprinting of these aromatic rice varieties.The findings of this study sould be useful for varietal identification and could help in background selection in backcross breeding programs.

Adequate supply of micronutrients is important for the proper growth and yield of lentil, particularly in poorly fertile soil. This study was carried out to understand the effects of zinc (Zn), boron (B), and molybdenum (Mo) on the growth and yield of lentil, and how these elements can help manage soil fertility issues. In this regard, the morpho-physiological traits of lentils (BARI Masur-7) were collected from two experiments receiving the same treatments carried out during consecutive rabi seasons of 2015-2016 and 2016-2017. The experiments were laid out with a randomized complete block design having eight treatments, and was replicated thrice. The treatments were T 1 (Control), T 2 (Zn 2.0 kg ha −1), T 3 (B 1.

This paper presents the present status of food security and ecological footprint, an indicator of environmental sustainability of the coastal zones of Bangladesh. To estimate the present status of the food security and ecological footprint of the coastal zone of Bangladesh, primary and secondary data were collected, and the present status of food security and environmental degradation (in terms of ecological footprint) were calculated.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

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