Sana'a University Journal of Applied Sciences and Technology

Punica granatum (Pomegranate) is a valuable source of secondary plant metabolism compounds (SMs) that are widely used in the pharmaceutical, cosmetics, and food industries. The phytochemical screening illustrates that the Sa’dah P. granatum cultivar peel ethanol extract containsvarious beneficial components, including Flavonoids, Alkaloids, Steroids, Phenols, and Tannins. Moreover, the Sa’dah P. granatum cultivar peel ethanol extract (1.3 μg/ml) exhibited significant antioxidant activity compared to low concentrations of ascorbic acid (0.4, 0.6, 0.8, and 1 μg/ml). Furthermore, the Sa’dah P. granatum cultivar peel ethanol extract displays potent antibacterial activities against selected pathogenic bacteria (Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa) compared to two nominated antibiotics (Ampicillin and Gentamicin). This remarkable performance can be attributed to the presence of phenols and flavonoid compounds, which exhibit high antioxidant activity.

The environmentally friendly methods for synthesizing metal oxide nanoparticles effectively save time, money, and effort. These methods involve the phytosynthesis process, which depends on plant extracts. To produce zinc oxide nanoparticles (ZnO − NP_s), we used at this work an extract of Aloe Fleurentinorum Leaves (AFL) as a reducing, capping, and stabilizing agent. As well as the effect of altering some parameters on the synthesis of ZnO − NP_s, such as the Temperature (T), the potential of hydrogen (pH), and the volume ratio (V/V), was studied using an X-ray diffractometer (XRD), crystallite size, and microstrain was determined, and the average crystallite size was between 16.50, and 25.99 nm. Also, ZnO − NP_s were characterized optically by a UV-Vis spectrophotometer and got 3.13 eV band gap energy. Fourier transfer infrared spectroscopy was used for characterization. The antibacterial activities of ZnO − NP_s were investigated against four bacterial species, and the results exhibit a noticeable effect, especially against gram-positive species, with inhibition zones ranging from 17 to 20 mm for the different concentrations. Also, the DPPH free radical method was used to evaluate the antioxidant properties of the synthesized zinc oxide nanoparticles at various concentrations, which showed a moderate scavenging efficiency of 52.74% at 800 µg/mL.

Artificial intelligence (AI) significantly enhances our daily lives, driving many service and financial institutions to seek optimal utilization of it. This paper addresses the problem, which is the inability of some institutions, such as banks, to satisfy customers using outdated methods and solely focusing on acquiring new customers instead of prioritizing customer retention that would be more effective and profitable to them. The main object of this paper is to enhance the quality of service in banks by proposing an intelligent model that leverages customer data to improve services and foster customer loyalty. This paper was motivated by the limited research on customer churn in Yemen and the scarcity of simple intelligent models globally. The methodology employed in this paper includes an in-depth analysis of existing literature on customer churn. Finally, this paper provides a comprehensive and concise conclusion based on the findings.

In the present study, the current status of medical waste management in the city of Hodeidah was presented in terms of its definition, components, sources, and threats that may result from improper handling. This investi-gation aims to study the potential environmental and health impacts of existing medical waste produced by the government hospitals and healthcare entities in Hodeidah City, in addition to comparing the applied methods and procedures with the methods and procedures that are applied in developed countries. The research study was carried out from January 2022 until January 2023, and targeted ten government hospitals and healthcare institutions in Hodeidah City. It relied on descriptive and analytical scientific methods, which aim to describe and analyze medical solid waste generated from the selected institutions. The result of the study showed that the total quantities of waste produced in all studied government health facilities amounted to approximately 599,038 kg/year. The percentage of non-hazardous waste was about 67% against hazardous waste which was 33%. Hospitals that receive more patients and have more beds as well as a multiplicity of their specialties generate larger medical wastes. The present research investigation revealed that government hospitals and healthcare centers in the city of Al-Hodeidah have low levels of health and safety conditions. They do not meet the local and international standards and requirements in the Medical Waste Management Guides and Recommendations. The poor management situation of medical waste could expose Yemeni citizens to danger. The healthcare insti-tutions should be supervised and controlled and ensure management of medical waste following environmental regulations and laws.

With the continued development of cloud computing environments, security measures have become more im-portant than ever. Intrusion detection systems (IDS) are considered one of the most critical security measures in cloud computing. Researchers aim to find effective technologies for detecting intrusions in cloud computing. This paper presents a comprehensive survey of the techniques used for Intrusion Detection in Cloud Computing and their classification. Specifically, it covers a range of techniques such as machine learning, and provides insights for researchers looking to develop more flexible and effective techniques for intrusion detection in cloud computing.

Microarray technology produces data with a large number of dimensions. The abundance of dimensions in the data makes it challenging for machine learning algorithms to extract meaningful information from it. To overcome this limitation, feature selection (FS) techniques can be applied to reduce the dimensionality of the data. FS is a crucial preprocessing step that allows for the handling of high-dimensional data and facilitates more effective and efficient processing. The primary objective of this paper is to develop an efficient FS method that can effectively reduce the dimensionality of microarray data. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized in the first filtering stage to extract informative features. The best feature subset is then determined by using a Particle Swarm Optimization (PSO) algorithm as a wrapper feature selector to identify ideal features. The proposed approach is evaluated using microarray datasets from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and compared with other algorithms. The experimental findings indicate that the proposed approach outperforms the benchmark methods in terms of classification accuracy.

One of the most popular IT subfields today is cloud computing. With a simple Internet connection, it enables users to access cutting-edge information, technology, and infrastructure. Because of the numerous benefits offered, most international organizations have started to move toward cloud computing environments during the last few decades. Companies and organizations find cloud computing appealing because it eliminates the need to plan for provisioning and enables them to start with modest resources and progressively raise them as service demand increases. Cloud computing presents opportunities for research as well as obstacles. The security and privacy of data in cloud systems are important issues. Now that cloud computing technology is developing quickly, it is more convenient for businesses to move their workload there. It asks for a smaller investment and guarantees quick delivery of cutting-edge technologies. Cloud security is of the utmost importance. It is susceptible to cyberattacks that exploit its vulnerabilities in an insecure cloud environment. This puts the cloud environment’s assets, resources, and data in danger. In this paper, we reviewed some cloud computing studies about various attacks on cloud computing services. This paper’s main objective is to give readers a better understanding of security issues and solutions.

4-(N, N-dimethylaminobenzylidene) 1-phenylsemicarbazide Schiff base (SB) was synthesized from 1-phenylsemicarbazide and 4-(N,N-dimethylaminobenzaldehyde). Numerous analytical approaches, such as ele-mental analysis, ¹HNMR, ¹³CNMR, electronic spectra, and FT-IR spectroscopy were utilized to analyze the newly synthesized Schiff base. The Ti (IV) Schiff base complex was produced, and its electrolytic nature, stability, and octahedral structural properties were determined by elemental, IR, UV-Vis spectral, conductivity, and thermal studies (TGA-DTA). From XRD results, the practical size of Schiff base and its complex were in the nano range with crystallinity structure. To evaluate the antibacterial activity, the free ligand and its complex were assessed in vitro against Pseudomonas aeruginosa, Escherichia coli, and Staphylococcus aureus. These results show that the metal complex has stronger antibacterial action than the Schiff base ligand.