Natural pesticides for pest control in agricultural crops: an alternative and eco-friendly method

Biological pesticides are pesticides derived from natural materials such as bacteria, plants, and minerals that are applied to crops to kill pests. Biopesticides are targeted, inexpensive, eco-friendly, sustainable, leave no trace, and are not associated with the production of greenhouse gases. It contributes significantly to the agricultural bio-economy's sustainability. The advantages to the ecosystem provided by many significant biological resources justify the incorporation of biopesticides in Integrated Pest Management (IPM) programs. Through advancements in research and development, the use of biopesticides has significantly reduced environmental contamination. The development of biopesticides promotes agricultural modernization and will surely result in a gradual phase-out of chemical pesticides. Although synthetic pesticides have positive effects on crop yield and productivity, they also have some negative impacts on soil biodiversity, animals, aquatic life, and humans. In general, synthetic pesticides make the soil brittle, decrease soil respiration, and reduce the activity of some soil microorganisms, such as earthworms. Pesticide buildup in bodies of water can spread from aquatic life to animals including people, as their biomagnification can cause fatal diseases like cancer, kidney disease, rashes on the skin, diabetes, etc. Biopesticides, on the other hand, have surfaced and have proven to be quite beneficial in the management of pests and are safe for the environment and hence have emerged as very useful in the control of pests with a lot of merits.  The present review provides a broad perspective on the different kinds of pesticides. We analyzed suitable and environmentally friendly ways to improve the acceptance and industrial application of microbial herbicides, phytopesticides, and nano biopesticides for plant nutrition, crop protection/yield, animal/human health promotion, as well as their potential integration into the integrated pest management system

A global two-layer meta-model for response statistics in robust design optimization

Robust design optimization (RDO) of large-scale engineering systems is computationally intensive and requires significant CPU time. Considerable computational effort is still required within conventional meta-model assisted RDO frameworks. The primary objective of this article is to minimize further the computational requirements of meta-model assisted RDO by developing a global two-layered approximation based RDO technique. The meta-model in the inner layer approximates the response quantity and the meta-model in the outer layer approximates the response statistics computed from the response meta-model. This approach eliminates both model building and Monte Carlo simulation from the optimization cycle, and requires considerably fewer actual response evaluations than a single-layered approximation. To demonstrate the approach, two recently developed compressive sensing enabled globally refined Kriging models have been utilized. The proposed framework is applied to one test example and two real-life applications to illustrate clearly its potential to yield robust optimal solutions with minimal computational cost

Long Noncoding RNAs are Frontier Breakthrough of RNA World and RNAi-based Gene Regulation

General complexities in versatile animals are not always proportional to their genome size. A notable example is that the salamander genome size is 15-fold larger than that of human, which mostly contains unfolded “junk DNA.” A vast portion of this non-protein-coding unfolded DNA undergoes transcriptional regulation and produces a large number of long noncoding RNAs (lncRNAs). LncRNAs play key roles in gene expression and therapies of different human diseases. Recently, novel lncRNAs and their function on the silencing or activation of a particular gene(s) are regularly being discovered. Another important component of gene regulation is high packing of chromatin, which is composed of mainly repetitive sequences with negligible coding potential. In particular, an epigenetic marker determines the state of the gene associated with it, whether the gene will be expressed or silenced. Here, we elaborately discuss the biogenesis pathway of lncRNAs as well as their mechanism of action and role in gene silencing and regulation, including RNA interference. Moreover, several lncRNAs are the common precursors of small regulatory RNAs. It is thus becoming increasingly clear that lncRNAs can function via numerous paradigms as key regulatory molecules in different organisms

Testing of K(R,T)-gravity through gravastar configurations

In this article, we are reporting for the first time the existence of gravastar configurations in the framework of K(R,T)-gravity, which can be treated as an alternative to a black hole (Mazur and Mottola). This strengthens how much this new gravity theory may be physically demanding to the gravity community in the near future. We first develop the gravastar field equations for a generic K(R,T) functional and then we study four different models within this theory. We find that the solutions for the interior region are regular everywhere regardless of the exact form of the K(R,T) functional. The solutions for the shell region indicate that two of the four models subjected to the study are physically feasible. In addition, the junction conditions are considered at each interface by using the Lanczos equations that yield the surface density and pressure at the thin shell. We investigate various characteristics of the gravastar structure such as the proper length, energy, and entropy of the spherical distribution

DeepGAR: Deep Graph Learning for Analogical Reasoning

Analogical reasoning is the process of discovering and mapping correspondences from a target subject to a base subject. As the most well-known computational method of analogical reasoning, Structure-Mapping Theory (SMT) abstracts both target and base subjects into relational graphs and forms the cognitive process of analogical reasoning by finding a corresponding subgraph (i.e., correspondence) in the target graph that is aligned with the base graph. However, incorporating deep learning for SMT is still under-explored due to several obstacles: 1) the combinatorial complexity of searching for the correspondence in the target graph; 2) the correspondence mining is restricted by various cognitive theory-driven constraints. To address both challenges, we propose a novel framework for Analogical Reasoning (DeepGAR) that identifies the correspondence between source and target domains by assuring cognitive theory-driven constraints. Specifically, we design a geometric constraint embedding space to induce subgraph relation from node embeddings for efficient subgraph search. Furthermore, we develop novel learning and optimization strategies that could end-to-end identify correspondences that are strictly consistent with constraints driven by the cognitive theory. Extensive experiments are conducted on synthetic and real-world datasets to demonstrate the effectiveness of the proposed DeepGAR over existing methods.Comment: 22nd IEEE International Conference on Data Mining (ICDM 2022

Multisketches: Practical Secure Sketches Using Off-the-Shelf Biometric Matching Algorithms

Biometric authentication is increasingly being used for large scale human authentication and identification, creating the risk of leaking the biometric secrets of millions of users in the case of database compromise. Powerful ``fuzzy\u27\u27 cryptographic techniques for biometric template protection, such as secure sketches, could help in principle, but go unused in practice. This is because they would require new biometric matching algorithms with potentially much-diminished accuracy. We introduce a new primitive called a multisketch that generalizes secure sketches. Multisketches can work with existing biometric matching algorithms to generate strong cryptographic keys from biometric data reliably. A multisketch works on a biometric database containing multiple biometrics --- e.g., multiple fingerprints --- of a moderately large population of users (say, thousands). It conceals the correspondence between users and their biometric templates, preventing an attacker from learning the biometric data of a user in the advent of a breach, but enabling derivation of user-specific secret keys upon successful user authentication. We design a multisketch over tenprints --- fingerprints of ten fingers --- called TenSketch. We report on a prototype implementation of TenSketch, showing its feasibility in practice. We explore several possible attacks against TenSketch database and show, via simulations with real tenprint datasets, that an attacker must perform a large amount of computation to learn any meaningful information from a stolen TenSketch database