Al-Bahir Journal for Engineering and Pure Sciences

Sarcocephalus latifolius (sm.) of the family, Rubiaceae, is an important plant used in folkloric medicine for the
management of various disease conditions. An innovative green route was adopted for the direct characterization of
the underutilized tropical plant which was profiled for the fatty acid composition and examined for the in vitro anticholinesterase
potential. The fatty acid methyl esters obtained via direct methylation was characterized using Gas
Chromatography-Mass Spectrometry, GC-FID/GC-MS. With donepezil hydrochloride as a reference, the cholinesterase
enzyme inhibition capacity of the FAMEs was further assessed through standard assay. Four fatty acids were identified
from the total ion chromatogram of the two samples, with 10-methylheptadecanoic acid common to both leaf and fruit.
Palmitic acid (41.55 %, 26.89 %) and 10-methylheptadecanoic acid (40.32 %, 2.27 %) were predominant in the leave and
fruit with linoleic acid (18.13 %, 20.83 %) also appearing significantly in the leaf and fruit respectively. 8,11,14-
heptadecatrienoic acid (29.55 %, RT 16.89) was only detected in the fruit. The presence of linoleic acid, an important
polyunsaturated omega-6 fatty acid is presumed to contribute significantly to the anticholinesterase activity of the
plant. Although 10-methylheptadecanoic acid was also present in the fruit, it was the least available in it as 8, 11, 14-
heptadecatrienoic acid (29.55 %), hexadecanoic acid (26.89 %) and octadeca-9,12-dienoic acid (20.83 %) were more
abundant. The leave which had higher extraction yield indicated a reduced activity with IC50 of 231.3 ± 1.66 mg/mL
compared to the fruit (29.14 ± 12.89) and the standard, donepezil hydrochloride (127.70 ± 5.77 mg/mL). The rapid
determination of S. latifolius fatty acid profile of the leave and fruit was made possible by the direct trans-esterification
method. This green approach is feasible, time saving, consumes less solvents, and lowers the risk of contamination that
comes with traditional multistage procedures. Following the exploration of the chemical makeup and the observed
antioxidant potential of S. latifolius obtained in this study, the folkloric applications is being established. The data
obtained in this study revealed that S. latifolius contains fatty acids with significant antioxidant potential that warrant
further exploration.

Land Surface Temperature maps are handy tools in a wide variety of scientific and practical fields. Monitoring climate
change, researching the consequences of global warming, and evaluating the health of ecosystems are some of the
applications that are used extensively. In addition, they have uses in agriculture, such as determining the amount of
water that vegetation needs and monitoring agricultural regions that are experiencing drought. This study utilized a
remote sensing approach to estimate land surface temperature with Landsat imagery from two periods, respectively, 2000
and 2024. The Landsat user's guide provided the algorithm to convert the images from DN to TOA radiance. Almost all
stations show a significant increase in LST from 2000 to 2024. Some stations show significant increases of more than
10 C. RMSE slightly increased from 1.89 to 1.98, indicating minor forecast inaccuracies. The average temperature rose
from 34.76 C to 40.25 C, with maximum temperatures increasing from 41.14 C to 48.13 C and minimum temperatures
from 26.29 C to 33.66 C. The standard deviation increased from 3.57 to 3.92, indicating greater temperature variability.
These findings suggest notable climate changes and support the global warming hypothesis. The general increase in
Earth's surface temperatures indicates the effects of global warming and climate change at the local scale. Noticeable
increases may be due to urbanization, deforestation, and changes in land use

Improving biofuel production in countries like Nigeria is important to the development and sustainability of the energy
sector in the country. With the growing population in the country, the energy demand will increase, and conversely,
the threat to climate and ecosystems will increase. To cushion this effect, attention needs to be shifted to farm wastes as a
sustainable option for energy production. Briquette production has been at the focal point of researchers for many years.
This study explores 12 different wood species commonly found around Nigeria with starch and polyvinyl alcohol (PVA)
as binding agents. Briquettes from these wood species were made with PVA and starch separately using the same amount
of mass for analysis. The results obtained from this study showed that briquettes from Ayo and Wawa sawdust produced
the best calorific values of 31,760 J/g and 31,763 J/g, respectively, when polyvinyl alcohol (PVA) is used as a binder. While
Afzelia sawdust briquette produced the lowest percentage moisture content (PMC) of 1.67% but a higher ash content of
3.73% with polyvinyl alcohol, which could be due to affinity for water molecules, hence the high percentage ash contents.
In general, the thermo-physical properties of all the wood samples examined in this study showed that polyvinyl alcohol
improved these properties when compared with common binders like starch

Soil stabilization is one of the processes that have used widely to enhance pavement structure. Using natural and manmade
resources as efficiently as possible while inflicting little environmental harm is known as sustainable roadway
building. Reducing energy consumption during construction and making optimal use of materials to reduce waste are
two approaches to make roads more sustainable. This project work is concern in chemical soil stabilization with a waste
or by-product material. The experiential work included improve sandy soil by using Paper Waste Ash (PWA) and ordinary
Portland cement (OPC). Laboratory tests included sieve analysis, standard proctor compaction test, California
Bearing Ratio Test, Atterberg Limits and Compressive Strength test to determine soil properties. The results showed that
mixing of PWA and cement can improve compressive strength of subgrade, reduce pavement layers’ thicknesses and
then reduce the total cost of construction. It concluded that using PWA reduce the impact on natural materials, landfill
and preserve the environment by rotate rubbish and used it instead of cement or other additives in stabilization of
unbound pavement layers.

This study concentrates on the connection between the commercial streets and the sustainability of neighborhoods
by using Al-Adhamiya in Baghdad as a case study. The gap of knowledge lays in the limited realization of how the
spatial features of commercial streets contribute to the sustainability of the neighborhood. The purpose of the study
is to assess the current condition of the commercial streets for the sake of determining how their spatial characteristics
contribute to the sustainable urban development in a quantitative way. The research used the mixed-method
approach (analysis of previous studies and development of a theoretical framework combining the selected research
vocabulary, quantitative analysis using computer programs of the research case study). The research uses DepthmapX
0.8.0 metrics, just like connectivity, choice, control, integration, intelligibility, synergy and visual graph
analysis. These metrics are used to evaluate the permeability and social dynamics of the streets in the area of the
study, which consists a historic and commercial centre presenting a mix of the traditional and the modern architectural
styles. The study highlights the fundamental role of the commercial streets, such as Corniche Street and
Imam Al-Azam Street, in enhancing accessibility and promoting social interaction, which are crucial elements in
achieving the sustainable urban development. However, some challenges such as the hybrid nature of the urban
fabric affects the intelligibility and synergy of the area. The study emphasizes the importance of the integrated
urban planning in the improvement of connectivity and balance historical preservation with the urban expansion of
long-term sustainability.

This study investigates the airflow properties around the airfoil using numerical simulation, addressing the longstanding
challenge of accurately predicting transition phenomena in turbulent flows. The importance of this research lies
in its potential to improve the design and performance of airfoils in various engineering applications, such as wind
turbines, and aircraft. A custom 2-dimensional airfoil model was created using Airfoil instruments, featuring a 6 %
maximum curvature, 40 % curvature position, and 20 % thickness. COMSOL Multiphysics software performed computations
at a velocity of over 19 h, with data analyzed at 60-min intervals. The Transition turbulent model was
employed to simulate the airflow properties, including pressure distribution, velocity profiles, and turbulence intensity.
The numerical results were validated against experimental data, demonstrating excellent agreement. The key findings
reveal that the Transition model accurately captures the laminar-turbulent transition and predicts the airflow properties
with high accuracy. The results also show that the airfoil's performance is significantly affected by the Reynolds number
and angle of attack. This study contributes to the existing literature by providing a comprehensive understanding of the
airflow properties around the airfoil using advanced numerical simulation techniques. The novelty of this work lies in
its application of the Transition model to airfoil simulations, which has not been extensively explored in previous
studies. The findings of this research have important implications for the design and optimization of airfoils in various
engineering applications.

The literature contains innumerable probability distributions for modeling over-dispersed and under-dispersed
count datasets from various fields of study. However, some of these proposed distributions are inadequate due to
empirical or theoretical characteristics. Therefore, minimizing information loss during modeling has prompted the
demand to modify the classical discrete distributions. A new two-parameter count distribution is proposed by
combining Poisson and Lindley-quasi XGamma distributions via a continuous mixture technique. Some statistical
properties have been derived and studied, including factorial moments, raw moments, probability generating function,
moment generating function, characteristic function, mean, variance, dispersion index, skewness, and kurtosis. The
shape of the PMF and dispersion index suggest that the proposed distribution is right-skewed with a heavy tail, overdispersed,
and approximately equi-dispersed. The unknown parameters of the proposed model are estimated using
both maximum likelihood and Bayesian techniques. The usefulness and flexibility of the proposed distribution are
measured using two distinctive datasets. The application results reveal that the developed distribution provides
maximum fit to the given datasets compared to the other eight standard discrete distributions. The Poisson Lindleyquasi
XGamma distribution should therefore be considered by researchers when modeling over-dispersed count data
from all fields of study.

This paper deals with the review of various existing technique for the microcracks detection in silicon solar cell and
wafer. In addition to this, we proposed a novel approach for the machine learning technique for the inspection of the
cracks those are existed in the solar cell and wafer and not able to detect by the naked eyes. There are many techniques
have been developed by the various researchers around the world to inspect solar cells for defect. All the techniques
discussed in this article having some features and some weakness too. This paper present here gives the two-fold solution
for the microcrack detection that will benefit the scientist and engineers for the development of the machine
vision system to find out the cracks in the solar cells and solar wafers

Forecasting volatility in financial time series remains challenging due to their asymmetric nature and excess kurtosis.
This study evaluates and compares the performance of four variant of GARCH models incorporating skewed non-
Gaussian error innovation distribution. The performances of these GARCH family of models under the skewed error
innovation distributions were evaluated for three different unique data sets to have a more robust assessment of the
performance of these skewed error innovation distributions. This study leverage on daily closing prices of Bitcoin, Naira
to Dollar Exchange rates and daily Nigeria All Share Index between January 1, 2015 and January 26, 2024. Model fit was
assessed using Log-likelihood and Akaike Information Criterion (AIC). Forecasting accuracy was evaluated with Mean
Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results confirmed the
stationarity of returns and presence of ARCH effects at p < .05 validating the use of these volatility models. The skewed
parameters in most models were significant, justifying the use of skewed innovation densities. Out-of-sample forecast
showed that the skewed student-t distribution consistently outperformed other skewed innovations. Model performance
varied by asset since GJR-GARCH(1,1) with Skewed Student-t was optimal for all share index based on MSE ¼ 6.7355,
RMSE ¼ 2.5953, MAE ¼ 1.7536. EGARCH(1,1)-sstd having MSE ¼ 1.5576, RMSE ¼ 1.2481, MAE ¼ 0.8506 for USD-Naira
and GARCH(1,1)-sged with MSE ¼ 0.00009, RMSE ¼ 0.00928, MAE ¼ 0.00804 for Bitcoin. This study therefore signified
the superiority of the skewed Student-t distribution in most of the cases considered. These findings offer valuable insights
for investors on when and how to invest their assets