The coefficient of performance of a window type Air-Conditioner system can be improved if a reduction in the work of compressor can be achieved by a suitable technique. The present study investigates the effect of dispersing a low concentration of TiO2 nanoparticles in the mineral oil based lubricant, as well as on the overall performance of a window type Air-Conditioner system using R22 as the working fluid. An enhancement in the COP of the refrigeration system has been observed and the existence of an optimum volume fraction noticed, with low concentrations of nanoparticles suspended in the mineral oil. Results showed that the average compressor work reduced by 13.3%, which ultimately resulted in an increase of 11.99% in the COP due to

In terms of each party's contribution to the common venture, partnership felt in several categories. It would be a complete partnership, that is, one in which all partnership, both capital and service; It could be an arrangement like the commenda (Arabic : Mudãraba , muqãraba , girãd) in which one party supplied the capital and the other service .
In Malikite and Hanaifite Law, It would take the form of a labor partnership, that is, one in which the only investment on the part of all its members was their skill or labor. In this last category of partnership, the captil itself considered solely or primarily of the labor of the partners.

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.

Progesterone is highly used in pregnant women as therapeutic agent to maintain and support pregnancy. To explore the effects of progesterone usage allover gestation till 7days postnatally on mice offspring ovaries development and anogenital distance. Ten pregnant mice were equally divide into control group that was injected with sesame oil which is used as a solvent for progesterone and treated group that is daily intraperitoneally injected with progesterone (dissolved in sesame oil 1: 10) at dose 10.2 mg/kg (the equivalent human dose) all through gestation till7day postnatal then sacrificed and measuring the anogenital distance (the distance between anus and genital papilla). Histological slides were prepared, and Diameters of the ovary, p

Adenosine deaminase (ADA; Ec: 3.5.4.4), 5´- Nucleotidase (5´– NT; Ec: 3.1.3.5), and AMP – amino hydrolase (AMP – deaminase AMPDA; Ec: 3.5.4.6) activities were measured in sera of ovarian cancer patients before surgery, and after chemotherapy. The results indicated that ADA specific activity increased significantly (P<0.05), while 5´-NT and AMPDA specific activity decreased significantly (P<0.05) in ovarian cancer patients before surgery in comparison with those of their corresponding control women and benign tumors groups. When the activities of these enzymes were measured after chemotherapy, a significant decrease (P<0.05) in ADA activity, and a significant increase (P<0.05) in 5´- NT and AMPDA activities w

Distribution of light intensity in the flat photobioreactor for microalgae cultivation as a step design for production of bio-renewable energy was addressed in the current study. Five sizes of bioreactors with specific distances from the main light source were adopted as independent variables in experiential design model. The results showed that the bioreactor’s location according to the light source, determines the nature of light intensity distribution in the reactor body. However, the cross-section area plays an important role in determining the suitable location of reactor to achieve required light homogeneity. This area could change even the expected response of the light passing through the reactor if Beer-Lambert's law is adopted.

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Biomarkers to detect Alzheimer’s disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reductio