JOURNAL OF EDUCATION AND SCIENCE

Differential equations are equations that relate a function to one or more of its derivatives. Nonlinear means that the equation is not directly proportional to the function and its derivatives. In other words, its variables cannot be separated simply. The dependent variable and its derivatives are exponential, i.e. not of first order. Fractional order indicates that the derivatives in the equation are not necessarily of integer order (such as the first derivative or the second) and can even be a fraction, meaning that the order is a fractional number. To solve nonlinear ordinary differential equations with fractional derivation, the homotopy analytical method was used because solving these equations is not easy using the usual and well-known methods. However, the results of the homotopy method were not as accurate as required, so algorithms were used to improve the results of the homotopy method. Among these algorithms is the bird flock algorithm (HAM-PSO). In this research, the Runge-Kutta algorithm (HAM-OBE) was used to improve the results of the homotopy method. Through the examples in the diagram, it is noted that the bird flock algorithm (HAM-PSO) improved the results of the homotopy method by an error of 4.17e-02 As for the Runge-Kutta algorithm (HAM-OBE), it improved the results by an error of 4.6835e-07 By comparing the amount of errors with the Runge-Kutta algorithm (HAM-OBE), the results were improved by 99.99% compared to the exact solution, where the root mean square error (RMSE) was used as a fitness function to know the amount of improvement compared to the exact solution. This improvement is useful because nonlinear differential equations with spherical debt are useful in physics, chemistry, medical industries, body motion, and artificial intelligence. It has life applications such as studying sound waves and the movement of objects through fractional derivatives.

The research dealt with a molecular taxonomic study of 6 cultivars of species of the genus Bougainvillea Comm. ex Juss., which included the cultivars ʽMary palmerʼ, ʽRasberry iceʼ, for the species B. spectabilis Willd, ʽRoyal purpleʼ, ʽFormosaʼ for the species B. glabra Choisy, ʽThimmaʼ for the species B. peruviana Hum. And Bon., and ʽCalifornia goldʼ for the species B. x buttiana using eight different random primers for PCR, which was used to multiply and amplify the DNA fragments using random amplified polymorphic DNA (RAPD) markers. Through sample migration on an agarose gel, DNA was extracted from the leaves of cultivars and genetic diversity between amplified pieces for each cultivar was found. The findings indicated that differences in the locations on the genomes of the studied cultivars and the production of different bands revealed on the agarose gel, which reflects the magnitude of genetic variation depending on the primer used. As the primers produced (309) total bands resulting from (95) binding sites, including (12) monomorphic binding sites and (83) polymorphic binding sites distinct for the studied cultivars. The results of the cluster analysis (dendrogram) for the cultivars species of the genus Bougainvillea which depend on the values ​​of the similarity showed the presence of clear variations among the studied cultivars.
The study concluded that the RAPD-PCR technique used in molecular classification based on molecular markers is a powerful tool for identifying and classifying cultivars of species of the genus Bougainvillea Comm. ex Juss. and detecting genetic relationships among them.

Networks and especially network security, have recently become an area of essential concern in data communication due to the ever-present need for data security. Especially that data is to be exposed to the public and traveled from one network to another. Recently, network security has had to be carefully deployed in data communication, especially real-time communication, such as voice over the Internet protocol. SIP is a real-time protocol that functions as a call initiator and controller, ensuring its communication is secure through various means. A cryptographic algorithm is a promising approach to secure SIP end-to-end communication. However, that does not come without cost and penalty, especially with performance. In this paper, we looked at how the SIP server performs using different RSA algorithms, including RSA-Standard, RSA Chinese Remainder Theorem, and Multi-prime RSA, by measuring CPU usage and how quickly calls are answered. The experiment reveals that when applying 1000 calls, the RSA standard consumes the highest CPU percentage. The decryption computations during the inbound calls scenario result in extra CPU usage for the three algorithms. In addition, call response time in all cases is still within an acceptable call response time level to spend 50 ms in higher call load 1000 calls. In sum, from these results, SIP service providers will gain a clear comprehension when it comes to deploying RSA algorithms in their services, which in turn facilitates smooth implementation in the voice communication industry and provides benchmarked material for research communities

Indirect electrochemical properties characteristics of free prostate-specific antigen (FPSA) were investigated using the differential pulse voltammetric (DPV) technique, employing a three-electrode system. This setup included a 2 mm diameter glassy carbon electrode as the working electrode, a 2 mm diameter platinum wire serving as the auxiliary electrode, and a silver/silver chloride saturated potassium chloride electrode as the reference electrode. (Ag/AgCl sat KCl). The method is based on observing the decrease in the manganese acetate reduction peak current which is due to its interaction with FPSA. Acetate buffer solution (pH4) was used, the potential scanned between -1.0 V to 0.5V and the optimum conditions were tested. A calibration curve for free prostate-specific antigen (FPSA) was established based on a series of sequential additions of the analyte. The response to FPSA additions was linear within the studied concentration range (5.4980 - 13.6633) x10-7 M, with a correlation coefficient of 0.9581 and calibration equation y = 0.0073x + 0.6451. The binding constant (k), as well as the values of ΔG, ΔH, and ΔS were calculated to assess the thermodynamic characteristics of the Mn+2- FPSA binding.

The widespread growth of Internet of Things (IoT) devices, in industries has led to a rise in energy requirements underscoring the importance of sustainable power management solutions. This study talks about the methods for optimizing power usage and harnessing energy to improve efficiency within networks shown in 2 and 2.1. By combining dynamic power management strategies with energy harvesting from sources like thermal and vibrational energies this research explores the potential for creating durable and eco-friendly IoT systems. Through a combination of simulations and real world experiments, We assess the efficacy of these approaches. The results demonstrate that customized power management algorithms paired with context energy harvesting can significantly lower the energy consumption of devices without compromising their performance levels. This investigation enhances our knowledge of networking by suggesting a framework for implementing energy efficient practices in IoT by thermal and vibrational energies infrastructures paving the way, for more eco friendly technological environments. This study does not confirm the viability of self sustaining networks but also suggests future research paths to further enhance energy efficiency and resilience in IoT setups or systems.

The study aims to utilise modern statistical techniques to analyze data often sensitive to small changes. Quality control charts are valuable tools for monitoring and analyzing data and identifying and extracting points that fall outside the control limits. On the other hand, wavelet shrinkage is an effective technique for removing noise from data and identifying deviations while preserving significant signals, thus enhancing the accuracy of the analysis. Alongside using actual data that represents the qualitative feature of children's weights- a crucial indicator for evaluating overall health and early detection of nutritional or health issues- this chart was simulated using MATLAB. However, weight measurements are frequently susceptible to noise and interference due to factors such as measurement errors or natural variations. The study concluded that the proposed chart (weighted moving average using wavelet shrinkage) is superior to the classical chart because it performs better in identifying the differences between the upper and lower limits of the two charts. We also observed that the difference between the two limits in the proposed chart is smaller

Lipid A in the cell wall of Salmonella typhi and pseudomonas aeruginosa were extracted and analyzed by the GC/MS technique. Three concentrations were prepared (50, 100, 200) mg/100g of body weight. The sample was divided into two groups of Albino rats, consisting of three animals for each concentration. Lipid A was calculated and then injected in three doses over three consecutive days. Interleukin-2 and Interleukin-9 levels were measured and were (13.11, 3.150, 3.180),(12.183, 15.766, 36766)pg/ml respectively c compared with the controls (3.205 and 6.433)pg/ml for three concentration respectively for Salmonella typhi whereas Pseudomonas aeruginosa were (3.086, 3.159, 3.168),(12.133, 14.400, 21.100) pg/ml compared with the controls (6.337 and 0.230) pg/ml for the three concentrations, respectively. White blood cell counting and total count were (9.052, 9.2033, 9.118)*10μl in Salmonella typhi, and were (8.235, 9.239, 8.945)*10μl in Pseudomonas aeruginosa. Neutrophils, lymphocytes and monocytes were (5.408, 5.506, 6.221),(3,716, 2.882, 2.405),(0.087, 0.139, 0.1820)*10μl respectively for the three concentrations in Salmonella typhi and were(4.353, 5.526, 5.994),(3.944, 3.413, 3.016),(0.123, 0.143, 0.072)*10μl respectively for the three concentrations in Pseudomonas aeruginosa. Eosinophils and basophils were (0.342, 0.266, 0.348),(0.281, 0.004, 0.004)*10μl for the three concentrations for Salmonella typhi while in Pseudomonas aeruginosa were(0.164,0285, 0.136),(0.000, 0.000, 0.000)*10μl respectively for the three concentrations.