Al-Bahir Journal for Engineering and Pure Sciences

In the laboratories of the Zarazma Mineral Studies Company in Tehran, the Islamic Republic of Iran, measurements of
Uranium content in soil samples were made utilizing ICP-MS (inductively coupled plasma mass spectrometry) in 36
samples of surface soil from selected locations (Some of which, according to the authors, were measured for the first
time) in the Northern Basrah governorate. Uranium concentrations were found to range from (0.9 ppm) in Al-Awjan to
(1.5 ppm) in Al-Ez River soils. The findings are given and contrasted with those of other research. The soil samples
analyzed were uranium less than 100 ppm, which implies an amount of overburden and reserves instead of mineable
reserves. The concentration of uranium in the Northern Basrah Governorates is presented and evaluated in this article.
The study also indicates that uranium levels in 36 samples of surface soil are below the detection limit. The current
findings demonstrate that the uranium contents in the surface soil samples examined were lower than the UNSCEARrecommended
limit of 11.7 ppm.

Bernstein polynomials are one of the first and main tools for function approximation. On the other hand, neural
networks have many useful applications in approximation and other fields as well. In this paper, we study how we
benefit from properties of Bernstein polynomials to define a new version of neural networks, that can be fit approximating
functions in terms of modulus of continuity. Numerically, we use neural networks to approximate some types of
continuous functions. For that purpose, we use GRNN algorithm to approximate functions uniformly by using Matlab,
giving some examples that confirm good rate approximation.

Background and objectives: Malaria, a parasitic protozoan disease caused primarily by Plasmodium falciparum, has killed
millions of people in Africa, particularly those with meager or no access to orthodox medical facilities and therapies.
Extracts from the Azadirachta indica (neem) plant is believed to possess antimalaria properties among the locals that rely
on herbs. Numerous in - vivo studies have suggested the antimalarial properties of neem extract and phytochemicals.
This study employs an in - silico method through molecular docking techniques to provide insight while adding
credence to the antimalarial potential of phytochemicals of neem plants as claimed in folkloric medicine.
Methods: e The crystal structure of P. falciparum a causative parasite of malaria was retrieved from the Protein Data
Bank, and Azadirachta indica phytochemicals were obtained from the PubChem database. Molecular docking through
virtual screening was carried out on the characterized phytochemicals. The bioactive compounds from the Azadirachta
indica plant were investigated by docking with the crystal structure of P. falciparum receptor and compared with
standard antimalarial drugs (lumenfrantrine and artemisinin).
Results: Three Azadirachta indica phytochemicals (gedunnin, nimbinene and salanin) shows a competing binding
energy and affinity when compared to the approved antimalarial drugs (lumefrantrine and artemisinin). While the
binding affinities for azadirachtin, nimbandiol and quercetin is lower than the affinity in artemisinin but comparable
with lumefrantrine.
Conclusion: This virtual screening verified and identified a potential phytochemical component of antimalarial properties
against a protein target: 1m7o, P. falciparum triosephosphate isomerase (PfTIM)

The fast increase in volume and speed of information created by mobile devices, along with the availability of webbased
applications, has considerably contributed to the massive collection of data. Approximate Nearest Neighbor
(ANN) is essential in big size databases for comparison search to offer the nearest neighbor of a given query in the field
of computer vision and pattern recognition. Many hashing algorithms have been developed to improve data management
and retrieval accuracy in huge databases. However, none of these algorithms took bandwidth into consideration,
which is a significant aspect in information retrieval and pattern recognition. As a result, our work created a Geo-SPEBH
algorithm to solve this basic gap. The paper then assesses the performance of the Geo-SPEBH algorithm in terms of
bandwidth in a distributed computing environment. Geo-performance SPEBH\'s was compared to existing state-of-theart
approaches using a network analyzer called Wire shark. The simulation results reveal that during retrieval, the same
kb/sec of data is carried from source to destination and from destination to user. When the coding length is 8bit, the
findings show that 0.091 kb/s of data is required to transport data from source to destination. Each algorithm with the
same bit code requires the same amount of bandwidth to convey data.

and number of vehicles in Karbala are considered among the independent (explanatory) variables taken into
consideration. These explanatory variables ' historical data have been gathered and examined. The historical data of each
explanatory variable has been linked to the historical demand for asphalt concrete in the governorate using regression
techniques in the Statistical Package for the Social Sciences (SPSS) program version 26.
A model to forecast the governorate's demand for asphalt concrete has been developed based on these factors. The
forecasts have been determined for the years 2023e2032. According to the analysis, the demand for asphalt concrete in
the Karbala governorate in 2032 will be 1,718,227 Tonne