The study of cycloaddition reactions between cyclopenta-1,3-diene and benzyl-acrylate, as well as benzyl-2-fluoroacrylate with and without the catalyst (TiCl4), was conducted using MEDT. The results of the energy profiles suggest that these reactions are stereoselective, meaning that they favor the formation of certain stereoisomers over others. Furthermore, the addition of TiCl4 as a catalyst appears to enhance the selectivity of these reactions, which is in line with experimental observations. Additionally, a docking study was carried out to predict the effect of stereochemistry and the presence of specific atoms, such as fluorine, on their ability to bind to viral proteins responsible for SARS-Covid-19 and HIV. Moreover, the notable affinity of Ligand 4 for HIV renders it a pivotal contender for extended research, possibly paving the way for enhanced antiretroviral drugs. Similarly, the encouraging affinity demonstrated by Ligand 1 towards the Covid-19 protein highlights its promise for Covid-19 drug development.

Amid the ongoing pandemic, such as the Covid-19 outbreak, there exists a critical need to comprehend and forecast the dynamic trends of daily confirmed cases to effectively prevent and mitigate the impact of its consequences. This study aims to investigate the essential factors acting as predictors for forecasting daily new confirmed cases specifically within the Indonesian setting. Utilizing advanced Deep Learning (DL) methodologies, including Deep Feedforward Neural Networks (DFNN), Long Short-Term Memory (LSTM), one-dimensional convolutional neural networks (CONV1D), and Gated Recurrent Units (GRU), this research endeavors to predict daily confirmed Covid-19 cases in Indonesia. To achieve this, a comprehensive set of 80 variables (predictors), encompassing the effective reproduction number (Rt), was utilized as input parameters. Before model construction, rigorous variable selection procedures and statistical analyses were conducted to enhance data understanding. The effectiveness of the predictive model was assessed using various metrics, such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Scaled Error (MASE), which evaluates MAE relative to a baseline model. Results indicate that DL models incorporating two key predictors—daily confirmed case count and Rt—exhibited superior predictive performance, capable of forecasting daily confirmed cases up to 13 days in advance. The inclusion of additional variables was found to diminish the predictive accuracy of DL algorithms.

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.

The study aims to identify the effect of environmental sustainability accounting (ESA) on competitive advantage (CA), and the effect of ESA on making decisions (funding, investment, and strategic) in Sudanese banking. A questionnaire was used to collect data from 135 accountants and managers of Sudanese banking. A descriptive method was used to confirm that the study's goals had been met. The questionnaire data is analyzed, and hypotheses are tested, using the Smart pls application. The study found a positive relationship between ESA and CA in the Sudanese banking sector. Additionally, ESA has a positive relationship with funding, investment, and strategic decisions in the Sudanese banking sector. Based on these findings, future research can help accountants comprehend the intricacies of ESA according to national and cultural conditions, especially in developing countries. Also, larger sample sizes may be used in future research on this topic, particularly if it is studied internationally.

Metaheuristic algorithms are easy, flexible and nature-inspired algorithms used to optimize functions. To make metaheuristic algorithms better, multiple algorithms are combined and hybridized. In this context, a hybrid algorithm (HSSAOA) was developed by adapting the exploration phase of the arithmetic optimization algorithm (AOA) to the position update part of the salp swarm algorithm (SSA) of the leader salps/salps. And also, there have also been a few new additions to the SSA. The proposed HSSAOA was tested in three different groups using 22 benchmark functions and compared with 7 well-known algorithms. HSSAOA optimized the best results in a total of 16 benchmark functions in each group. In addition, a statistically significant difference was obtained compared to other algorithms.

In the complex and dynamic stock market landscape, investors seek to optimize returns while minimizing risks associated with price volatility. Various innovative approaches have been proposed to achieve high profits by considering historical trends and social factors. Despite advancements, accurately predicting market dynamics remains a persistent challenge. This study introduces a novel deep reinforcement learning (DRL) architecture to forecast stock market returns effectively. Unlike traditional approaches requiring manual feature engineering, the proposed model leverages convolutional neural networks (CNNs) to directly process daily stock prices and financial indicators. The model addresses overfitting and data scarcity issues during training by replacing conventional Q-tables with convolutional layers. The optimization process minimizes the sum of squared errors, enhancing prediction accuracy. Experimental evaluations demonstrate the model's robustness, achieving a 67% improvement in directional accuracy over the buy-and-hold strategy across short-term and long-term horizons. These findings underscore the model’s adaptability and effectiveness in navigating complex market environments, offering a significant advancement in financial forecasting.

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.

This study examines the joint impact of outsourcing, overtime, multi-delivery, rework, and postponement on a multiproduct fabrication problem. A growing/clear trend in today’s customer requirements turned into rapid response and desired quality of multi-merchandises and multiple fixed-amount deliveries in equal-interval time. To satisfy customers’ expectations, current manufacturing firms must effectively design/plan their multiproduct production scheme with minimum fabrication-inventory-shipping expenses and under confined capacity. Motivated by assisting manufacturing firms in making the right production decision, this study develops a decision-support delayed-differentiation model considering multi-shipment, rework, and dual uptime-reducing strategies (namely, overtime and outsourcing). Our delayed-differentiation model comprises stage one, which makes all common/standard parts of multi-end-merchandises, and stage two, which produces multiple end merchandise. For cutting making times, the study proposes subcontracting a portion of the common/standard part’s lot size and adopting overtime-making end merchandise in stage two. The screening and reworking tasks identify and repair faulty items to ensure customers’ desired quality. The finished lots of end merchandise are divided into a few equal-amount shipments and distributed to customers in equal-interval time. We employ mathematical derivation and optimization methodology to derive the annual expected fabrication- inventory-shipping expense and the cost-minimized production-shipping policy. A numerical demonstration is presented to exhibit our research scheme’s applicability and exposes the studied problem’s critical managerial insights, which help the management make beneficial decisions.