The COVID-19 surge has mostly affected people and wreaked havoc on multiple sectors of the global economy. This study uses artificial neural networks (ANN) to develop COVID-19 prediction models to minimize the perilous situation. With positive infection data, these hybrid artificial neural network models looked at COVID-19 cases in Andhra Pradesh, India. Then, COVID-19 data were divided into training and testing for simulation. The developed model that takes the previous 14 days into account outperforms the others, depending on the results. According to the developed ANN models for Andhra Pradesh districts, the prediction model that works well and yields positive results is the one that receives lower values of error metrics like MSE, RMSE, MAE, and MAPE and higher values of R2. The hybrid neural network model that considers the previous 14 days for prophecy has suggested anticipating daily positive suffering, notably in areas of Andhra Pradesh, as a result of the collected data. Linear regression, ARIMA, and LSTM have been used for model assessment. The proposed 14-day model statistically surpasses all metrics in RMSE, MAE, MAPE, and R2. This study showed that an ANN-based model can predict the COVID-19 outbreak as well as other epidemics.

Due to highly functionalized molecular structure, furfural has made a huge impact in renewable platforms. As there is a big need of producing renewable sources, this review reveals several attempts made by researchers distributed in every nook and corner of the world to produce furfural-based derivatives. As furfurals are converted to various forms, they reflect many applications both with respect to economy and environment friendly approach. In this review, a summarized detail of various reactions of furfural substrates are provided which includes Diels-Alder reactions, Oxidations, Hydrogenations, Hydrogenolysis, Conversions to C6 Carboxylic acids, Furfuryl amines, Cyclopentenones and other useful compounds. These compounds and their applications in various fields are mentioned systematically. The chemical as well as biocatalytic conversions are explained in detail which will help to know the future challenges and hurdles when design and optimization of furfural derivatives are concerned.

We have synthesized all drugs using previously identified active pharmacophores through molecular hybridization. This paper explains a simple method for making N-((2-(piperazine-1-yl)-2,3-dihydro-1H-benzo[d][1,2,3]triazol-1-yl)methyl)aniline analogs 5(A–L) through steps. We used mass spectrometry, ¹H NMR, and ¹³C NMR to do a spectrum analysis to confirm the structure of the synthesized end products. We evaluated all synthesized compounds for their in vitro antimicrobial, antimalarial, and antitubercular activities. We have also examined the research on structure-activity relationships (SAR). Target chemicals demonstrate significant effectiveness against bacteria, fungal diseases, and malaria.

This paper recommends a new Many-Objective Teaching-Learning-Based Optimizer (MaOTLBO) to handle the Many-Objective Optimal Power Flow (MaO-OPF) problem of modern complex power systems while meeting different operating constraints. A reference point-based mechanism is utilized in the basic version of Teacher Learning-Based Optimizer (TLBO) to formulate the MaOTLBO algorithm and directly applied to DTLZ test benchmark functions with 5, 7, 10-objectives and IEEE-30 bus power system with six different objective functions, namely the minimization of the voltage magnitude deviation, total fuel cost, voltage stability indicator, total emission, active power loss, and reactive power loss. The results obtained from the MaOTLBO optimizer are compared with the well-known standard many-objective algorithms, such as the Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) and Non-Dominated Sorting Genetic Algorithm-version-III (NSGA-III) presented in the literature. The results show the ability of the proposed MaOTLBO to solve the MaO-OPF problem in terms of convergence, coverage, and well-Spread Pareto optimal solutions. The experimental outcomes indicate that the suggested MaOTLBO gives improved individual output and compromised solutions than MOEA/D-DRA and NSGA-III algorithms.

Benzotriazole, commonly referred to as BTAH, has garnered significant interest across several scientific disciplines due to its outstanding characteristics and wide-ranging applications. This document provides a concise overview of benzotriazole and its use in challenging corrosion scenarios. Furthermore, we emphasize the versatility of this molecule and the extensive research commitment associated with it. This thorough analysis examines the synthesis of benzotriazoles and their derivatives in-depth and encapsulates the numerous methods involved. Additionally, we conducted a thorough analysis of toxicity studies and the pertinent implications of benzotriazoles on human health, marine life, and the ecosystem. Our purpose is to provide readers with a thorough understanding of the potential dangers of this chemical by undertaking an extensive examination of associated risks and bad effects. During this review, we emphasize significant research discoveries and trends, offering useful insights into the current status of benzotriazole research and its future potential. The development of such research advances from 2010 to 2024.

Heterocyclic molecules are found in many parts of living things, so they are being used more and more in medicinal chemistry. Benzimidazoles, which are naturally occurring compounds, exhibit a diverse array of pharmaceutical properties that have been extensively documented. Benzimidazole derivatives serve as valuable intermediates or subunits in the synthesis of pharmaceutical or biologically significant compounds. Substituted benzimidazole derivatives have been utilized in various therapeutic domains, encompassing but not limited to antiulcer, anticancer agents, and anthelmintic species. This study provides a systematic and comprehensive overview of the latest advancements in benzimidazole-based compounds within the field of synthetic organic chemistry.

Methodology is reported for the synthesis of a six-membered heterocyclic ring involving cyclocondensation reaction through sulfur-sulfur bond formation. This method of synthesis allows for developing a new class of dithiadiazines. A series of bis-[6-tetra-O-acetyl-β-D-glucopyranosylimino-1,2,4-triazolo[3,4-b]-1,2,4,5-dithiadiazin-4-yl] alkanes have been synthesized by using a reagent, tetra-O-acetyl-β-D-glucopyranosylimino chloromethane sulfenyl chloride and bis-(4-amino-5-mercapto-1,2,4-triazol-3-yl) alkanes. These newly formulated N-glycosylated molecules could serve as a potential biological entity.

The past decade has witnessed significant progress in synthesizing structurally diverse and biologically relevant pyrano[2,3-c]pyrazole derivatives through the integration of green methodologies. A straightforward and environmentally friendly one-step method has been developed to synthesize divergent pyranopyrazoles in good yields with the aid of zinc acetate as a Lewis acid catalyst in toluene as solvent under reflux conditions.

Through the implementation of a simple, environment friendly one-pot multicomponent reaction with 1 mmol of methyl acetoacetate, 2 mmol of different substituted aromatic aldehydes, and 2 mmol of different substituted aromatic anilines in the presence of triethylammonium hydrogen sulfate [Et3NH][HSO4] bronsted acid as an ionic liquid catalyst and Ethanol: water as a green solvent at 60°C temperature. We have developed an affordable and dynamic procedure for the synthesis of novel, medicinally essential tetrahydropyridine derivatives. One-pot multicomponent synthesis is an energetic topic in heterocyclic chemistry due to its vital advantages of simple reaction process, high atom economy, low cost, less amount of waste generation, and simple workup procedures, which make this process economically productive for industrial applications. The final compounds were confirmed via FTIR, ¹H-NMR, and ¹³C-NMR spectroscopy and were in contrast with their reported methods.

This study seeks to examine the various attributes that impact health and safety in construction (HSIC) across different companies, stakeholders, and nations. The objective is to identify these attributes and organize them within a framework to facilitate a clearer understanding. The research identified common characteristics that promote the adoption of HSIC, yielding advantages for governmental, private, and public entities. The United Kingdom, the United States, Canada, Australia, and Hong Kong are considered the leading countries in terms of conducting research on HSIC attributes. There exists significant potential for enhancing the contributions of developing countries. The proposed framework acknowledges a comprehensive set of 61 attributes, which are categorized into four distinct groups: Corporate regulatory, Employee's self-supportive, Workplace regulatory, and Federal regulatory attributes. These attributes function as a framework for clients and policymakers to enhance the quality of HSIC. In forthcoming periods, it is recommended to prioritize the utilization of empirical surveys conducted across diverse locations in order to ascertain the attributes that are deemed of utmost importance and necessitate significant attention.