Heterocyclic systems, which are essential in medicinal chemistry due to their promising cytotoxic activity, are one of the most important families of organic molecules found in nature or produced in the laboratory. As a result of coupling N-(4-nitrophenyl)-3-oxo-butanamide (3) using thiourea, indole-3-carboxaldehyde, or piperonal, the pyrimidine derivatives (5a and 5b) were produced. Furthermore, pyrimidine 9 was synthesized by reacting thiophene-2-carboxaldehyde with ethyl cyanoacetate and urea with potassium carbonate as a catalyst. The chalcones 11a and 11b were synthesized by reacting equal molar quantities of 1-naphthaldehy

The contemporary development applications on scientific areas of acyclic and cyclic Schiff bases and their complexes.

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

 In this work, the preparation of some new oxazolidine and thiazolidine derivatives has been conducted. This was done over two steps; the first step included the synthesis of Schiff bases A1-A5 in 72-88% yields by the condensation of isonicotinic acid hydrazide and aldehydes. The second step includes the cyclization of derivatives A1-A5 with glycolic acid and thioglycolic acid to obtain the desired products, oxazolidine derivatives B1-B5 (44-60% yields) and thiazolidine derivatives C1-C5 (41-61% yields), respectively. The structure of the prepared compounds was characterized using FT-IR, 1H NMR, and 13C NMR spectroscopy. Some of the produced compounds were tested for antioxidant properties.  

Natural Bauxite (BXT) mineral clay was modified with a cationic surfactant (hexadecy ltrimethy lammonium bromide (BXT-HDTMA)) and characterized with different techniques: FTIR spectroscopy, X-ray powder diffraction (XRD) and scanning electron microscopy (SEM). The modified and natural bauxite (BXT) were used as adsorbents for the adsorption of 4- Chlorophenol (4-CP) from aqueous solutions. The adsorption study was carried out at different conditions and parameters: contact time, pH value, adsorbent dosage and ionic strength. The adsorption kinetic (described by a pseudo-first order and a pseudo-second order), equilibrium experimental data (analyzed by Langmuir, Freundlich and Temkin isotherm models) and thermodynamic parameters (change in s

The inverse kinematics of redundant manipulators has infinite solutions by using conventional methods, so that, this work presents applicability of intelligent tool (artificial neural network ANN) for finding one desired solution from these solutions. The inverse analysis and trajectory planning of a three link redundant planar robot have been studied in this work using a proposed dual neural networks model (DNNM), which shows a predictable time decreasing in the training session. The effect of the number of the training sets on the DNNM output and the number of NN layers have been studied. Several trajectories have been implemented using point to point trajectory planning algorithm with DNNM and the result shows good accuracy of the end

The optical absorption data of Hydrogenated Amorphous Silicon was analyzed using a Dunstan model of optical absorption in amorphous semiconductors. This model introduces disorder into the band-band absorption through a linear exponential distribution of local energy gaps, and it accounts for both the Urbach and Tauc regions of the optical absorption edge.Compared to other models of similar bases, such as the O’Leary and Guerra models, it is simpler to understand mathematically and has a physical meaning. The optical absorption data of Jackson et al and Maurer et al were successfully interpreted using Dunstan’s model. Useful physical parameters are extracted especially the band to the band energy gap , which is the energy gap in the a

The COVID-19 pandemic has necessitated new methods for controlling the spread of the virus, and machine learning (ML) holds promise in this regard. Our study aims to explore the latest ML algorithms utilized for COVID-19 prediction, with a focus on their potential to optimize decision-making and resource allocation during peak periods of the pandemic. Our review stands out from others as it concentrates primarily on ML methods for disease prediction.To conduct this scoping review, we performed a Google Scholar literature search using "COVID-19," "prediction," and "machine learning" as keywords, with a custom range from 2020 to 2022. Of the 99 articles that were screened for eligibility, we selected 20 for the final review.Our system

In the light of the globalization Which surrounds the business environment and whose impact has been reflected on industrial economic units  the whole world has become a single market that affects its variables on all units and is affected by the economic contribution of each economic unit as much as its share. The problem of this research is that the use of Pareto analysis enables industrial economic units to diagnose the risks surrounding them , so the main objective of the research was to classify risks into both internal and external types and identify any risks that require more attention.

The research was based on the hypothesis that Pareto analysis used, risks can be identified and addressed before they occur.