Enhancing the mechanical properties' performances coconut fiber and CDW composite in paver block: multiple AI techniques with a Performance analysis.

The present research incorporates five AI methods to enhance and forecast the characteristics of building envelopes. In this study, Response Surface Methodology (RSM), Support Vector Machine (SVM), Gradient Boosting (GB), Artificial Neural Networks (ANN), and Random Forest (RF) machine learning method for optimization and predicting the mechanical properties of natural fiber addition incorporated with construction and demolition waste (CDW) as replacement of Fine Aggregate in Paver blocks. In this study, factors considered were cement content, natural fine aggregate, CDW, and coconut fibre, while the resulting measure was the machinal properties of the paver blocks.

A hybrid approach: remote sensing, analytic hierarchy process and geographic information system for groundwater potential mapping in Dediapada, India.

In recent years, the overexploitation of groundwater accompanied by a significant climatic shift has placed a burden on the world's groundwater supply. This has generated a rising need for potential groundwater zones. In recent year the ever-increasing demand for potable water across the world for various groundwater exploration investigation.

Concepts and criteria defining emerging microbiome applications.

In recent years, microbiomes and their potential applications for human, animal or plant health, food production and environmental management came into the spotlight of major national and international policies and strategies. This has been accompanied by substantial R&D investments in both public and private sectors, with an increasing number of products entering the market. Despite widespread agreement on the potential of microbiomes and their uses across disciplines, stakeholders and countries, there is no consensus on what defines a microbiome application.

Probing of plant transcriptomes reveals the hidden genetic diversity of the family Secoviridae.

Secoviruses are single-stranded RNA viruses that infect plants. In the present study, we identified 61 putative novel secoviral genomes in various plant species by mining publicly available plant transcriptome data. These viral sequences represent the genomes of 13 monopartite and 48 bipartite secovirids.