Al-Bahir Journal for Engineering and Pure Sciences

Analyze the possibilities of implementing a parallel algorithm to test the singularity of the N-th order matrix. Design
and implement in (C/Cþþ) a solution based on sending messages between nodes using the PVM system library.
Distribute the load among the nodes such that the computation time is as small as possible. Find out how the execution
time and calculation acceleration depend on the number of nodes and the size of the problem (indicate the table and
graphs). Based on the results, estimate the communication latency, for what size the task is (well) scalable on the given
architecture, and what is the maximum size when the calculation is still bearable on the available architecture by divide
the program into master -slave model which is able to send tasks and collect the results to receive it by master computer

In this paper, Rad-⨁-supplemented semimodules are defined as generalization of⨁-supplemented semimodules. Let
R be a semiring. An R-semimodule A is called a Rad-⨁-supplemented semimodule, if each subsemimodule of A has a
Rad-supplement which is a direct summand of A. Here, we investigate some properties of these semimodules and
generalize some results on Rad-⨁-supplemented modules to semimodules. We prove that any finite direct sum of Rad-
⨁-supplemented semimodules is Rad-⨁-supplemented. Also, we prove that if A is a subtractive semimodule with (D3)
then A is Rad-⨁-supplemented if and only if every direct summand to A is Rad-⨁-supplemented

In this work, a procedure based on dispersive liquid₋liquid micro-extraction for lead (Pb) pre-concentration and
quantification in an environmental matrix by flame atomic absorption spectroscopy (FAAS) was applied. A case-study
approach was chosen to obtain further in-depth information on the Pb levels. The green chemistry principles have been
applied for the pretreatment and preparation of real samples by focusing on some features such as the volume of
reagents/sample, employ of energy efficient equipment, and production of waste. A univariate strategy was utilized to
achieve the optimum extraction conditions. 1750 mL of acetonitrile containing 100 mL of carbon tetrachloride, were rapid
injection into: 12.0 mL of a sample solution adjusted the pH at 9.0 and 0.07% (m v¡1) Dithizone. The estimated limits of
detection (LOD) and quantification (LOQ) under optimum conditions were 2.85 and 9.5 mg L¡1, respectively. The
enrichment factors EFs were found to be 60.6 (aqueous standards/DLLME₋FAAS) and 30.8 (matrix matched/DLLME₋-
FAAS). It was applied for the analyzing eleven environmental samples. The detectable Pb levels for water samples were
ranged from 1.87 to 8.11 mg L¡1, while in solid samples were ranged 0.39e4.66 mg g¡1. The relative standard deviation for
three concentrations (low, medium, and high) were found to be 10.64, 6.87, and 2.7, respectively. The presented system
was validated by analyzing eleven environmental samples (water, bivalve molluscs, lettuce, and medicinal plants). The
results indicated that high recoveries were obtained in the range of 92e104 %. The lead concentration was found in these
samples always lower than permissible maximum levels stipulated by Iraqi Standards Specifications 2009 (number 417)
for neutral water. The DLLME/FAAS procedure has proven to be a fast, simple, low-cost, and efficient analytical protocol,
therefore allowing for employing this protocol in routine analysis of trace metals in such matrixes

The motivation of the present study is to study and address the limitations imposed on hydrological models and the
spatial and temporal distribution of streamflow in the Watershed Diyala River (WDR) from January 1996 to April 2023,
which has an area of 25,652 km2, which is considered an important source of water for central and southern Iraq, to
enable decision-makers to make a future management plan for streamflow. To achieve this motivation, the SWAT model
was used and fed by input data such as a digital elevation model with a 30 m resolution, a soil map, the land use and
land cover map with a spatial resolution of 30 m, weather data, and the daily streamflow data. Using the SUFI-2 algorithm
and SWAT-CUP, the model was calibrated automatically. Statistically, using the R2, NSE, and Pbs, the performance
of the model was evaluated. The statistical analysis displays a perfect match between simulated and observed
values when calibrating and verifying the model for streamflow. The results indicate that the monthly evapotranspiration
rate and streamflow constitute 65% and 37% of the rainfall in WDR, respectively, and that the annual average
surface runoff ranged from 320.358 to 8.325 m3/s. Therefore, it is possible to rely on the calibrated model, successfully
verified with high reliability, to simulate the hydrological model in WDR

A prediction model using a linear regression model and Artificial Neural Network for the abrasive water jet machining
of Aluminum-alloy 7024 was the main objective of this study. The abrasive water jet experiments were carried out based
on the Taguchi Design. The influence of three independent variables such as pressure (200, 250, 300, and 350 MPa), feed
rate (40, 60, 80, and 100 mm/min), and Gap or standoff distance (1, 2, 3, and 4 mm) as input and use Surface Roughness
(Ra) were examined using analysis of variance (ANOVA) as output. The ANOVA response graphs show that pressure
has the greatest impact on Ra as a function of feed rate and Gap. The regression model was sophisticated between the
studied factors and the response. The confirmation tests show that the regression models are in well approval. The
estimated determination of the coefficient value was (0.96). As a result, the maximum error between the obtained
Artificial Neural Network (ANN) and experimental data is less than the regression model. The prediction model of ANN
was found to be more accurate when compared with the regression model. The ANN results have a good acceptance
between the predicted and experimental data, with a mean square error of training indices equal to (0.001

Economic performance of a nation depends majorly on the stability of foreign exchange rate; the economic viability
hangs on the exchange rate of local currencies against other currencies across the globe. Box e Jenkins Approach was
employed to model the Naira exchange rate to other major currencies using Autoregressive Integrated Moving Average
(ARIMA) and the autoregressive fractional integral moving average (ARFIMA) models. This studies aimed on
measuring forecast ability of Autoregressive Integrated Moving Average (ARIMA) (p,d,q) and autoregressive fractional
integral moving average (ARFIMA) (p, fd, q) models for stationary type series that exhibit features of Long memory
properties. Results indicate autoregressive fractional integral moving average (ARFIMA) is the best model in terms of fit,
serial correlation analysis and accuracy measures. The out-sample forecasts confirmed the competence of the autoregressive
fractional integral moving average (ARFIMA) models as shown by forecast validation tools. Consequently, the
out-sample forecasts result nearly reveal the current economic situation in Nigeria indicating that the autoregressive
fractional integral moving average (ARFIMA) model is appropriate and realistic in modeling and forecasting the
strength of Naira to other currencies.

Bayesian inferences depend solely on specification and accuracy of likelihoods and prior distributions of the observed
data. The research delved into Bayesian estimation method of regression models to reduce the impact of some of the
problems, posed by convectional method of estimating regression models, such as handling complex models, availability
of small sample sizes and inclusion of background information in the estimation procedure. Posterior distributions
are based on prior distributions and the data accuracy, which is the fundamental principles of Bayesian statistics
to produce accurate final model estimates. Sensitivity analysis is an essential part of mathematical model validation in
obtaining a robust inference. Prior sensitivity analysis was examined in regression model, via Bayesian regression and
Bayesian quantile regression analysis; results obtained across the sensitivity analysis were compared using RMSE and
BIAS statistic as model performance evaluation. Empirical studies using Nigeria Economic variables were employed to
analyze the variation in prior sensitivity. Different hyper parameters of the priors were used to check for sensitivity of
the prior distributions, it was ascertained that Bayesian method under the frame work of regression quantiles performs
well with small variance and sample size than Bayesian regression methods

Scheduling is an important technique being used widely in numerous applications especially in industry. Flexible Job
Shop Scheduling Problem (FJSP) is an extension of job scheduling which has several practical applications, (FJSP) is NPhard
combinatorial optimization problem. Owing to its importance and intricacy a lot of attention has been paid to this
topic. Many swarm intelligent algorithms have drawn inspiration from nature; one particularly notable example is Ant
Colony Optimization (ACO), which has shown to be incredibly successful and productive when applied to highcomplexity
(NP-hard) combinatorial optimization tasks. The paper presents a literature survey on ACO variants types
and applications in Scheduling.