Nanocrystal-ZnS-loaded graphene was synthesized by a facile co-precipitation route. The Graphene was affected on the characterization of ZnS which has been investigated. XRD results reveal that ZnS has a cubic system while the hexagonal structure has been observed by loading graphene during preparation ZnS. D.c-conductivity proves that ZnS and ZnS/Gr have semiconductor behavior. The sensing properties of ZnS/Gr against NO₂ gas were investigated as a function of operating temperature and time under optimal condition. The sensitivity, response time and recovery time were calculated with different operating temperatures (100, 150, 200)^oC.

To show the impact of 790-805 nm diode laser irradiations on wound healing as a supplementary treatment in women underwent episiotomies, and to assess the laser parameters that were used .Material and methods: Eighteen female patients were included in this study; all of them underwent mediolateral episiotomy. Ten patients received laser therapy- diode laser (K Laser) (790-805) nm in CW mode of operation (and eight patients were the control group. Spot size of 8mm, time for exposure for each spot was 30 seconds. The power used was 0.6 W .The power density for each spot of treatment was 1.19 W/cm2 per session (non contact mode of application of laser therapy).The group studied received 2 sessions of laser radiation, day 4, and day 8 after

Crude-colicin  was extracted from g.coli isolated from urinary tract

infection patients. Toxicity of various concentrations of crudt: colicin

on the viability of immune cells [phagocytic ceUs(M0,PMN) and lymphocytes]  was studied.  Results  indicated  that  toxicity  ciTect of crude colicin is dependent on concentration. Low concentrations (I0,

25, 50  mg/ml)  didn't  have  toxic  effects,  but  when  there  was an increase  in  the  consternation  of  this  extract, the    viability  of  the immune cell s began to decrease.

Photoacoustic is a unique imaging method that combines the absorption contrast of light or radio frequency waves with ultrasound resolution. When the deposition of this energy is sufficiently short, a thermo-elastic expansion takes place whereby acoustic waves are generated. These waves can be recorded and stored to construct an image. This work presents experimental procedure of laser photoacoustic two dimensional imaging to detect tumor embedded within normal tissue. The experimental work is accomplished using phantoms that are sandwiched from fish heart or blood sac (simulating a tumor) 1-14mm mean diameter embedded within chicken breast to simulate a real tissue. Nd: YAG laser of 1.064μm and 532nm wavelengths, 10ns pulse duration, 4

    The main purpose of the work is to apply a new method, so-called LTAM, which couples the Tamimi and Ansari iterative method (TAM) with the Laplace transform (LT). This method involves solving a problem of non-fatal disease spread in a society that is assumed to have a fixed size during the epidemic period. We apply the method to give an approximate analytic solution to the nonlinear system of the intended model. Moreover, the absolute error resulting from the numerical solutions and the ten iterations of LTAM approximations of the epidemic model, along with the maximum error remainder, were calculated by using MATHEMATICA® 11.3 program to illustrate the effectiveness of the method.

Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating opti

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog