Preparation of Economic Belite Cement from Saudi Raw Materials
Hopf Bifurcation on Fractional Ordered Glucose-Insulin System with Time-Delay
Albaha University Journal of Basic and Applied Sciences (BUJBAS) publishes English language, peer-reviewed papers focused on the integration of all areas of sciences and their application. Supporting the concept of interdisciplinary research, BUJBAS welcomes submissions in various academic areas such as medicine, dentistry, pharmacy, engineering, computer sciences, veterinary medicine, biology, chemistry, physics, agriculture, mathematics and geology.
Hopf Bifurcation on Fractional Ordered Glucose-Insulin System with Time-Delay
- Sayed Saber, Salem Mubarak Alzahrani
- Reseived: 8 April 2019 Received in Revised Form: 7 September 2019 Accepted: 15 September 2019
This paper considers a class of fractional-order glucose-insulin interaction with time delay for analyzing the dynamic behaviors such as Hopf bifurcation, local asymptotic stability and global asymptotic stability. The stability of the equilibrium state is investigated by analyzing the eigenvalue of the corresponding characteristic matrix for the fractional-order time delay models using a Laplace transformation for the Caputo-fractional derivatives. Some sufficient conditions are established to guarantee the uniqueness of the equilibrium point. Numerical simulations have been used to verify the theoretical analysis.
Stability Analysis of a Fractional Order Delayed Glucose-Insulin Model
- Sayed Saber, Salem Mubarak Alzahrani
- Reseived: 29 October 2018 Received in Revised Form: 18 March 2019 Accepted: 11 April 2019
In this paper, we investigate the stability analysis of a fractional order delayed glucose-insulin model. The equilibrium points are computed and stability of the equilibrium points are analyzed. Local and global stability of existence steady states and Hopf bifurcation with respect to the delay is investigated, with fractional order α∈(0,1]. The phase portraits are obtained for different sets of parameter values. Numerical simulations are performed and it is shown that the system exhibits rich dynamical behaviors.
Preparation of Economic Belite Cement from Saudi Raw Materials
- Abdulaziz Ali Alomari
- Reseived: 8 October 2018 Received in Revised Form: 5 May 2019 Accepted: 15 May 2019
This research investigates the preparation of economic belite cement from the marble of Gabal Al-Qaren Al-Abyad (Gabal Almarmr) and white sand from Riyadh. Lime was produced from the marble after calcination at 950˚C for 2 hours. A mixture of lime and white sand (CaO/SiO2=2) in 2 M NaOH solution with the solution/solid ratio 5 was hydrothermally treated in a stainless steel capsule at 135˚C for 3 hours and calcined at 1000˚C for 3 hours. FTIR, XRD, and SEM-EDX confirmed the formation belite in addition to calcium and sodium silicate phases. A semi-quantitative phase analysis derived from XRD results estimated that the obtained economic belite cement contains 73.9% β-C2S.
Hematological Indices of Pregnant Sudanese Woman Attended Wad Medani Health Care Centers in Gezira State, Sudan
- Algurashi A. Abuelgasim, Hajir Mohammed Hussien Omer, Khalid Eltahir Khalid, Badawi A. Alghadir Taha, Abd Elrahim Haggaz
- Reseived: 1 May 2018 Received in Revised Form: 2 April 2019 Accepted: 12 April 2019
Objectives: The study aimed to determine the overall mean value of selected hematological parameters at different trimesters of pregnancy. Methods: Observational cross-sectional case control study involved 321 healthy pregnant women attending for ANC at secondary health care centers in Wad Medani, Gezira State, Sudan in the period between Feb. to Jul. 2016. They were divided into their first (n= 89), second (n= 173), and third (n= 59) trimester of pregnancy, and 29 healthy non-pregnant women as control. A full blood count was performed on each sample using automated hematology analyzer. Results: The pregnant and non-pregnant women mean age was 26.34±6.51 years (range 14-53 years), their weight range between 40-93 kg. A significant decrease in RBC count (p = 0.002), Hb concentration (p = 0.01), PCV (p = 0.000), platelet counts (p = 0.000) and TIBC (p = 0.04), and a remarkable increase in WBC count (p = 0.03) and MCHC (p = 0.02) in pregnant compared with non-pregnant women. RBC (p = 0.02), PLT (p = 0.01), lymphocytes (p = 0.00) and Iron (p = 0.01) were significantly decreased along the different trimesters of pregnancy, respectively. On the other hand, WBC (p = 0.00), MCV (p = 0.01), MCH (p = 0.01), MCHC (p = 0.03) and Neutrophils (p = 0.00) were significantly increased. The different trimesters of pregnancy correlated positively with WBCs and Neutrophils (r = 0.26 and 0.32, respectively), and negatively with lymphocytes (r = -0.31). Conclusions: The alteration of the hematological parameters at different trimesters of pregnancy necessitate the monitoring of these parameters during pregnancy. The significant decrease of Hb concentration and serum iron level suggest iron therapy for all women.
Analysis of Mobile Malwares Attacks Using Deep Learning Classification
- Mohammad Eid Alzahrani
- Reseived: 15 October 2018 Received in Revised Form: 10 March 2019 Accepted: 10 April 2019
This paper discusses the dynamic analysis of Android malwares by investigating their behaviors. Although many works on the analysis have been conducted with some levels of success, additional processes are needed to improve the accuracy of malware detection system, due to fact that current technologies indicate that malware attackers find different ways of escaping detection. This paper proposes an Android malware detection system that applies deep learning model. To verify the model and avoid the overfitting, this work performs 5-folds cross validation on a filtered Androzoo dataset. Training accuracy of the deep learning model used in the system was 99.40%, and the testing accuracy was 99.70%. Experiments on the rest of 2000-labeled data (1008 benign and 992 malware) of the dataset were conducted for the 5 folds data to measure the system accuracy via True Positive (TP) and False Positive (FP), True Negative (TN), and False Negative (FN) metrics measurements. Overall accuracy was 99.7%. Outstanding results indicate that this work has shown that the permission features are useful to predict unknown malware.
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