Notes on Approximately Pure Submodules

Let R be a commutative ring with identity 1 and M be a unitary left R-module. A submodule N of an R-module M is said to be approximately pure submodule of an R-module, if for each ideal I of R. The main purpose of this paper is to study the properties of the following concepts: approximately pure essentialsubmodules, approximately pure closedsubmodules and relative approximately pure complement submodules. We prove that: when an R-module M is an approximately purely extending modules and N be Ap-puresubmodulein M, if M has the Ap-pure intersection property then N is Ap purely extending

Generation of Fuzzy Rules by Subtractive ‎ Clustering

هذا العمل يعتمد مرحلتين, المرحلة الاولى  يستخدم خوارزمية  "التجميع"  والتي تستخدم لتحديد العلاقات بين عناصر البيانات لكي يبنى النظام, بحيث كل نقطه في البيانات تجمع مع نقاط اخرى ذات نفس المواصفات لكي تكوﱠن مجاميع. هذه المجاميع (العناقيد) سوف تستخدم في المرحلة الثانية من العمل لبناء مجموعة من القواعد المضببة والتي يصطلح عليها  (IF…THEN rules). والتي ستحدد مسار عمل النظام. عدد القواعد ومتغيرات الادخال والاخراج لديها تعتمد على المجاميع (العناقيد) المتولدة في المرحلة الاولى  بينما يستخدم نظام الاستدلال المسمى(King TSK-Sugeno). طﹹبق العمل لتشخيص مرض القلب.This work depends on two stages. First one, "subtractive method", clustering algorithm, used for identifying the relationships between data points in order to build system, where the data point gathers with other points to make cluster of the same features. These groups will be used in the second part of the work to construct fuzzy IF…THEN rules, which controls how the system works. The number of rules and its parts depend on these clusters. While the Takagi-Sugeno Kang (TSK) fuzzy inference modal was used. The scope of this work is applied to heart disease diagnosis

Design of a microwave based mobile thermo-chemical unit for biomedical waste treatment

Biomedical waste (BMW) contains pathogenic microorganisms that may severely harm the community and environment. Due to the Covid pandemic-2019, isolated wards at health care units and even due to the home treated patients; vast quantities of BMW are generated. Covid-19 converts even ordinary waste such as gloves, testing kits, and personal protective equipment into high-risk BMW. The appropriate disposal of such waste involves safety, affordability, and efficacy; hence can be considered a complex issue. A solution proposed in this article is an OSBMWTU (on-site biomedical waste treatment unit) by using microwave radiation. The possibility of enhancing the thermal effect of microwave radiation by using chemical additives was tested. The proposed machine reduces waste volume, inactivates microorganisms, and disposes BMW on-site. Findings suggest that adding butter spray to microwave radiation enhances thermal effectiveness by 43%, increasing treatment temperature while minimizing time, power, and running costs. The proposed machine will work automatically after filling the BMW, thus, minimizing the human involvement. It prevents bio-hazardous waste accumulation and decreases its volume by up to 80%. The designed machine is characterized by safety, low cost, and small dimensions. A machine that can handle 72 kg BMW/day can be set up on-site in an area of 1.5 m2. The suggestion of the proposed machine as a BMW management and treatment system will reduce environmental pollution due to BMW during COVID-19 and even after the pandemic

Mechanical Evaluation of Sustainable Concrete Used in a Concrete Pavement that Production from Iron Filling Waste

In this research used iron filling as partial replacement fine aggregate at percent 5,10, and 15% by weight of fine aggregate and maintaining the ratio of water to cement at 40% and study observe that produces concrete with high compressive strength, tensile and flexural strength at replacement percent I10% and production of concrete with a lower coefficient of thermal expansion with continued replacement and thus obtaining the maximum distance between expansion joints at the rate of addition of I15%

Green Fabrication and Characterization of Zinc Oxide Nanoparticles using Eucalyptus Leaves for Removing Acid Black 210 Dye from an Aqueous Medium

This study uses an environmentally friendly and low-cost synthesis method to manufacture zinc oxide nanoparticles (ZnO NPs) by using zinc sulfate. Eucalyptus leaf extract is an effective chelating and capping agent for synthesizing ZnO NPs. The structure, morphology, thermal behavior, chemical composition, and optical properties of ZnO nanoparticles were studied utilizing FT-IR, FE-SEM, EDAX, AFM, and Zeta potential analysis. The FE-SEM pictures confirmed that the ZnO NPs with a size range of (22-37) nm were crystalline and spherical. Two methods were used to prepare ZnO NPs. The first method involved calcining the resulting ZnO NPs, while the second method did not. The prepared ZnO NPs were used as adsorbents for removing acid black 210 dye (AB210) from simulated wastewater. The removal efficiency using calcinated and uncalcinated ZnO NPs was 57 % and 59 %, respectively

Optimization of Acetaminophen and Methylparaben Removal within Subsurface Batch Constructed Wetland Systems

The response surface methodology accompanied by Central Composite Design (CCD) was employed in this study to optimize the Alternanthera spp-based phytoremediation process for the individual removal of acetaminophen and methylparaben. Two operational variables, including concentration (A) (20, 60,100 mg/L) and sampling time (B) (7, 14, 21, and 35 days) were involved in the study for removal efficiency (Y) as response. CCD had required a total of 18 experiments for each compound. Analysis of variance (ANOVA) was conducted to verify the adequacy of the proposed mathematical models and revealed good agreement with the experimental data. The observed R2 values (0.9732 and 0.9870), adjusted R2 (0.9620 and 0.9816) and predicted R2 (0.9383 and 0.9721) for AC and MP, respectively, indicated that the developed models were significant at the 95% probability level. Concentration factor was found to be insignificant in the mathematical models; in contrast, sampling time was found to be of a crucial role. The removal of AC and MP were 89.23% and 64.48% under optimum conditions of A = 100 mg/L and B = 35 days respectively. The validation test confirmed the predicted results obtained by Central Composite Design, as the removals achieved under optimum conditions were 91.04% and 59.17% for AC and MP, respectively, which were in good agreement with the results proposed by the theoretical design

Synthesis of CdO NPS for antimicrobial activity

: In this study, (50 - 110) nm cadmium oxide (CdO) nanoparticles were synthesized by chemical method. The optical, structural and topographical properties of the synthesized nanoparticles were investigated by using UV-VIS absorption, Transmission electron microscopy TEM, atomic force microscopy AFM, and x-ray diffraction XRD. The bacterial resistance represents a problem and the outlook for the use of antibiotics in the future is still uncertain. Therefore, it must be taken measures to reduce this problem. Antibacterial activity of the Cadmium oxide nanoparticles were investigated against several pathogenic bacteria, including Klebsiella pneumoniae; Acinetobacter baumannii; Pseudomonas aeruginosa and Staphylococcus aureus by using well diffusion method ,the results showed that Cadmium nanoparticles had inhibitory effect against all pathogenic bacteria with inhibition zone (18 ,18 ,14 and 17 mm) for S. aureus, K. pneumonia, A. baumannii and P.aeruginosa respectively. CdO nanoparticles had inhibitory effect against S. aureus (22 mm) ; K. pneumonia ( 18mm) and A. baumannii (14mm)

Improved artificial neural networks based whale optimization algorithm

Owing to the increasing interest in artificial neural networks (ANNs) across various fields of study, many studies have focused on enhancing their performance through the utilisation of different learning algorithms. This study examines the use of the Whale Optimization Algorithm (WOA) as a training algorithm to improve the classification accuracy of ANNs. To achieve a high level of classification accuracy with ANN models, it is imperative to ensure that the model is appropriately designed in terms of the employed structure, training algorithm and activation function. In this work, WOA was adopted to train ANN models using 10 well-known datasets sourced from the UCI machine learning repository. The classification accuracy of a WOA-trained ANN was compared with that of a backpropagation-trained ANN, and the results showed that the WOA-trained ANN exhibited superior performance