The synthesis, characterization and mesomorphic properties of two new series of triazine-core based liquid crystals have been investigated. The amino triazine derivatives were characterized by elemental analysis, Fourier transforms infrared (FTIR), 1HNMR and mass spectroscopy. The liquid crystalline properties of these compounds were examined by differential scanning calorimetry (DSC) and polarizing optical microscopy (POM). DSC and POM confirmed nematic (N) and columnar mesophase textures of the materials. The formation of mesomorphic properties was found to be dependent on the number of methylene unit in alkoxy side chains.

The detection of diseases affecting wheat is very important as it relates to the issue of food security, which poses a serious threat to human life. Recently, farmers have heavily relied on modern systems and techniques for the control of the vast agricultural areas. Computer vision and data processing play a key role in detecting diseases that affect plants, depending on the images of their leaves. In this article, Fuzzy- logic based Histogram Equalization (FHE) is proposed to enhance the contrast of images. The fuzzy histogram is applied to divide the histograms into two subparts of histograms, based on the average value of the original image, then equalize them freely and independently to conserve the brightness of the image. The prop

The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed a

Modeling the microclimate of a greenhouse located in Baghdad under its weather conditions to calculate the heating and cooling loads by computer simulation. Solar collectors with a V-corrugated absorber plate and an auxiliary heat source were used as a heating system. A rotary silica gel desiccant dehumidifier, a sensible heat exchanger, and an evaporative cooler were added to the collectors to form an open-cycle solar assisted desiccant cooling system. A dynamic model was adopted to predict the inside air and the soil surface temperatures of the greenhouse. These temperatures are used to predict the greenhouse heating and cooling loads through an energy balance method which takes into account the soil heat gain. This is not included in

The goal of this paper is to study dynamic behavior of a sporadic model (prey-predator). All fixed points of the model are found. We set the conditions that required to investigate the local stability of all fixed points. The model is extended to an optimal control model. The Pontryagin's maximum principle is used to achieve the optimal solutions. Finally, numerical simulations have been applied to confirm the theoretical results.

The world is currently facing a medical crisis. The epidemic has affected millions of people around the world since its appearance. This situation needs an urgent solution. Most countries have used different solutions to stop the spread of the epidemic. The World Health Organization has imposed some rules that people should adhere. The rules are such, wearing masks, quarantining infected people and social distancing. Social distancing is one of the most important solutions that have given good results to confront the emerging virus. Several systems have been developed that use artificial intelligence and deep learning to track social distancing. In this study, a system based on deep learning has been proposed. The system includes monitor

This work is aimed to design a system which is able to diagnose two types of tumors in a human brain (benign and malignant), using curvelet transform and probabilistic neural network. Our proposed method follows an approach in which the stages are preprocessing using Gaussian filter, segmentation using fuzzy c-means and feature extraction using curvelet transform. These features are trained and tested the probabilistic neural network. Curvelet transform is to extract the feature of MRI images. The proposed screening technique has successfully detected the brain cancer from MRI images of an almost 100% recognition rate accuracy.