A Small Beautiful Example of Sustainability in the Heart of Zimbabwe

Zimbabwe has diligently started pursuing the Sustainable Development Goals (SDGs) defined by the United Nations in 2015. While making progress and being aware of it, will be a shot in the arm, with success breeding more success, the journey may seem daunting at times. However, the journey – sustainable development towards the targets set – is what matters more than the final destination, as philosophers and savants often remind us. This article, while dwelling on sustainable development in this southern-African landlocked country in general, presents a beautiful example of a collaborative venture, undertaken by dedicated and determined international partners, and predicated on SDG#4 (Quality Education) and SDG#5 (Gender Equality), and harnessing the complementarities and synergies with the other SDGs, which set root during the COVID-pandemic, in Mwenezi in southern Zimbabwe. The pandemic while being a scourge, tended to have some silver linings to it, as it gave birth to many collaborations and made human beings realise that one's own happiness is dependent to a very great extent on that of others. This venture emphasizes girls' education and skills-development, which open the doors, synergistically, to sustained growth, development and progress. Education, indisputably, is a key component of the freedom necessary to live a life of value. To quote Nelson Mandela, "It is the most powerful weapon which you can use to change the world.”. © 2023, Scientific Publishing House of the Cardinal Stefan Wyszynski University. All rights reserved.

Transition Modelling of Land Use Land Cover Dynamics in Bardez Taluka of Goa-India (1991-2021)

In the age of global climate change, land use and land cover mapping help us to understand the vital modifications taking place in our environment. LULC mapping assumes great significance in planning, management of resources and keeping track of various programmes at different levels. The data acquired from the land use and land cover investigations are vital for policy formulation and sustainable development of our towns, cities and villages and also to track the disorganized growth of urban areas. Tourism is a tool for economic development in many developing countries of the world. The unplanned tourism growth has led to many ecological problems. This study makes an earnest effort to examine the LULC change using the transition model in the Bardez taluka, which is a well-known global tourist destination in Goa, India. The study has been investigated by using satellite imageries and GIS technologies have been used to analyse the changes occurring in LULC patterns for the years 1991, 2001 and 2021. The result indicates that the area under the built-up class has increased substantially by 11.12 sq. km. as a result of the rise in commercialization, tourism growth and tourism-related activities. Bardez taluka is known for some of the most breath-taking beaches in the world. During 2019-20, just before Covid-19, about 25, 33,234 domestic and 2, 74,840 foreign tourists visited the enchanting beaches of Bardez taluka. Land use classes such as residential, commercial and services, industrial, transportation and utilities also witnessed the growth in their land use and land cover classes whereas classes like agricultural land, coconut plantation, cashew plantation, barren land, DM and FDM forest land, open scrub and fairly dense scrub witnessed a negative change in their class values. © 2023, World Research Association. All rights reserved.

An investigation on the molecular structure, interaction with metal clusters, anti-Covid-19 ability of 2-deoxy-D-glucose: DFT calculations, MD and docking simulations

Studies of the geometrical, vibration, absorption and physicochemical properties of 2-deoxy-D-glucose with and without metal clusters are reported using the DFT method. 2-Deoxy-D-glucose forms stable clusters with transition metal clusters of copper, silver and gold. Frontier molecular orbitals and molecular electrostatic potential of 2-deoxy-D-glucose and associated metal clusters (Cu6, Au6, Ag6, 2-DGCu6, 2-DGCu5Au, 2-DGCu5Ag, 2-DGAu6, 2-DGAu5Ag, 2-DGAu5Cu, 2 -DGAg6, 2-DGAg5Au, 2-DGAg5Cu) are examined with the B3LYP / LANL2DZ basis set. It is observed that the stability and chemical properties of 2-deoxy-D-glucose strongly depends on the cluster size. The molecular electrostatic potential maps were developed to provide information about the chemical reactivity of the molecules to explain intermolecular interactions. Then, to explore the ligand-protein recognition properties molecular docking and molecular dynamic (MD) simulation analyses have shown that the compound under consideration possesses potential activity as anti-cancer, anti-SARS-CoV-2, anti-SARS-CoV. Each of these analyzes contributes significantly to our understanding of the biological effects of the molecules outlined.

RNA Sequence Based Prediction of Coronavirus Using Machine Learning

The detection of the virus is a basic concern for the doctors and the virology for over a decades due to the dynamic behavior and mutations of the virus makes it difficult to detect the virus and study its behaviors. Latest computational techniques enables scientists to crate models that are proficient of learning patterns from the data as well as used to make predictions for unseen data.. As machine learning techniques predicts the corona viruses by allowing for their differing genetic purposeful characteristics, we propose machine learning supported coronavirus prediction method Novel-COV-2 Predictor wherever RNA sequences of SARSCoV-1, MERS, and SARS-CoV-2 are used to instruct a classifier so that it can expect any indefinite sequence of these viruses. The RNA sequence is given in the form of the large text files. Consequently, it becomes a text classification complexity. We convert these data in the text files into numerical data using the count vectorization and utilize machine learning to create a model to know the patterns. In this regard, we have considered Support Vector Machine (SVM) algorithm to evaluate and so that SARSCoV-2 can be predicted as untimely as potential to save human life. © 2021 IEEE.