Samarra Journal of Pure and Applied Science

The effects of greenhouse gases, like CO2, are becoming more and more visible, from drastic weather changes to global sea level rise. The resultant global warming has become an environmental issue of great concern in recent years. The potential for CO2 capture through metal-organic frameworks has been widely studied and is currently being explored as a way to reduce greenhouse gas emissions. The strong structural and electronic properties of these frameworks make them excellent candidates for capturing CO2 due to their high porosity, tunable composition and good chemical stability. Functionalized metal organic frameworks (MOFs) are important because it allows for the development of MOF materials with properties that are tunable for many different applications. Many of these functions are gas adsorption, catalysis, and separation. Depending on the composition of the linkers and nodes, various functional groups can be introduced into the network through the organic linkers and metal nodes, resulting in MOFs with different functions. Mixed-metal MOFs are composed of several different metals. The variety of metals in a mixed-metal MOF gives the MOF many options for tailoring its properties. The resulting bimetallic MOFs are not only more thermally and chemically stable, but also can absorb more gases. The other objective of this study was to observe the effect of organic functional groups on CO2 adsorption in MOFs. The study found that organic functional groups have a significant effect on CO2 adsorption in MOFs.

Abs
This study was take placed for the period from December 2021 until January 2022, and it included a follow-up of the effect of Outer Membrane Proteins (OMPs) or the so-called (O-antigen) purified from Escherichia coli bacteria on the immune response in (45) male laboratory white rabbits, 15 of them infected with Giardia lamblia and others did not provoke infection, but were treated with O-antigen only. The immune response was studied based on several criteria including changes in phagocytic index, viability of polymorphonuclear cells (PMNs) and Lymphocytes, active and total of T-rosette and B-rosett. The present results showed that the percentage of viability of lymphocytes and PMNs significantly increased for the antigen-treated group compared with the negative control, but in challenge group, it was noted that the viability of lymphocytes and PMNs cells was not affected by the presence of the parasite and remained high. As the current study showed, the phagocytic index was significantly increased in the two groups (treated with O-antigen, and challenge group) compared with the negative control group, as there was a non-significant decrease in challenge group compared with treated with O-antigen group. The results of the current study showed that there were statistical differences in the group of animals treated with O-antigen compared with the negative control, which were not significant for the active T-rosette and significant for the total T-rosette, and as our current results showed, the percentage of active T-rosette significantly increased in challenge group compared with the positive control, and not significant in the case of the total T-rosette. We conclude from the current study the percentage increase of each of the phagocytic index, T-rosette and B-rosette after immunization with antigen (O), as well as the percentage of viability of lymphocytes and PMNs cells after immunization with antigen (O) and their persistence in the presence of G. lamblia. The outer membrane proteins extracted from E. coli and the concentrations used in the current study can be considered as non-specific immunomodulators and non-toxic compounds at the specified concentrations and as stimulators of the immune system against G. lamblia infection in white laboratory rabbits.

A field experiment was carried out during the winter seasons 2020-2021 and 2021-2022 on one of the private farms in the village of Banat Al-Hassan, southeast of the city of Samarra, in gypsum soil. The experiment included 135 experimental units with 45 treatments with three replications resulting from the interaction of organic fertilizers with three concentrations (0, 4, 8) T. D-1 and phosphate fertilizer in three concentrations (0, 80, 160) kg. D-1 and five genotypes (Local, Amigo, Dollap, Azuricum, Hannan). This study aims to estimate the genetic parameters of the characteristics of fruit yield and its components for several sweet fennel genotypes with the effect of organic and phosphate fertilizers. The randomized complete block experiment is designed as a split plot experiment with three replications using Duncan multiple range test at the level of 0.05, and the data were analyzed pooled to find out the effect of seasons. The results show that the highest coefficients of genetic and phenotypic variation were recorded for the biological yield, which amounted to 4359.866, 8364.365, and then the fruit yield, which amounted to 2322.135 and 3107.175, respectively. The highest inheritance rate for the fruit yield was 74.734%, followed by the biological yield with 52.124%. As for the values of genetic improvement, the highest values are recorded for the characteristic of the biological yield, which amounted to 83.901, followed by the characteristic of the fruit yield, which amounted to 73.319. As for the values of the coefficient of genetic variation, the highest values are at the number of umbellets per plant 13.36, followed by the number of fruits per umbellet 10.85. While the coefficient of determination recorded the highest values for the characteristics of weight of 1000 fruits, biological yield and fruit yield, all of which are 0.99. The genetic correlation recorded the highest value for the number of branches trait. A plant with a plant height of 1.631, followed by the number of umbellets with the number of branches. Plant reached 1,597. As for the phenotypic correlation, the highest correlation was given to the fruit yield with the weight of 1000 fruits, which reached 0.989, followed by the correlation of the weight of 1000 fruits with the number of fruits in the umbellet , which reached 0.910. The path analysis indicate that the best direct effect of the characteristic of the weight of 1000 fruits amounted to 0.729, 0.816 for the genetic and phenotypic correlation, respectively, and the highest indirect effect is for the genetic correlation of the number of branches by weight of 1000 fruits amounted to 0.932, and the phenotypic correlation of the characteristic of the number of fruits by weight of 1000 fruits amounted to 0.743.

This paper examines the challenge of accurately computing highway performance measures by estimating traffic-flow between traffic sensors that are geographically dispersed. Consequently, predicting flows vehicle values is one of the most difficult issues in the field of traffic flow prediction. Therefore, there has been a rise in interest in combining machine learning (ML) methods with indicators from technical analysis. In this paper, we suggest a hybrid strategy for generating traffic to help with this issue. Our proposed method utilizes a technical indicator and an ANN technique to predict future flows. That this method can be applied to other technical indicators while still maintaining its simplicity and effectiveness thanks to the hybrid rules is what makes it novel. The performance of the proposed artificial neural network (ANN) was evaluated with a number of other machine learning techniques to help us choose the optimal ML approach. Daily traffic data from the Motorway Incident Detection and Automatic Signalling (MIDAS) system on the M25 highway was used to test the proposed method. The achieved results demonstrate that the predictive power of ML models is augmented when ML techniques are applied to technical analysis indicators.

Combinatorial optimization problems (COPs) are the most important class of optimization problems, with great practical significance. This class is concerned with identifying the best solution from a discrete set of all available options. The transportation (routing) and distribution (scheduling) systems are considered the most challenging optimization examples of the COPs. Given the importance of routing and scheduling problems, many methods have been proposed to address them. These methods can be categorized into traditional (exact and metaheuristics (MHs) methods) and machine learning (ML) methods. ML methods have been proposed to overcome the problems that traditional methods suffer from, especially high computational time and dependence on the knowledge of experts. Recently, ML methods and MHs have been combined to tackle the COPs, and then the learn heuristics term emerged. This combination aims to guide the MHs toward an efficient, effective, and robust search and improve their performance in terms of solution quality. This work reviews the publications in which the collaboration between MHs and ML has been utilized to propose a guideline for the researchers to put forward new algorithms that have a good ability to solve routing and scheduling problems.

In this work we study a notion of strongly Pseudo Nearly 2-Absorbing submodule and some generalization, it is a generalization of 2_Absorbing submodules, and a strong form of (Nearly_2_Absorbing and Pseudo_2_A bsorbing) submodule. Characterization and examples of the suggestion a generalization are given, as well as many different suggestion properties that have been proven.