Al-Bahir Journal for Engineering and Pure Sciences

Conventional poultry management techniques are failing to meet increased demand for poultry products as the
population continues to grow. As a result, this issue has become a major concern for small-scale farmers, particularly
those in low-income areas, in terms of food security. One of the main reasons for this is that the farmers rely on intensive
farming methods which are inefficient for automating daily poultry operations. However, intensive farming methods
pose major environmental concerns to ecosystems and poultry health. Also, the environmental conditions, welfare, and
productivity of poultry operations may be harmed by the global climate crisis and poultry waste products can be
disastrous to poultry health and need to be managed effectively. This demands for enhanced poultry management
systems to alleviate the environmental conditions and boost optimal poultry well-being. This paper presents a compact,
low-cost internet of things-based poultry management system for a small escale farmer. The system employs a synergistic
combination of cloud and Internet of Things technologies to monitor and regulate temperature, humidity, water
level, ammonia gas concentration, and lighting systems in real time. The hardware prototype was successfully implemented
and tested at the Obasanjo poultry farm home in Nigeria. The experimental results demonstrate that the daily
temperature is maintained between 27 C and 32 C. The relative humidity ranges from 71 % to 72 %, and the ammonia
gas (NH3) level increased intermittently for the first three days before remaining steady, reaching a maximum of 30 ppm.
The illumination is optimally adjusted using a pre-configured algorithm to promote maximum egg production and
poultry health.

The powder metallurgy method was employed to fabricate aluminum-based composite samples reinforced with nano
silica at varying weight fractions of 2, 4, 6, 8, and 10 wt. %. The powders were mixed, dried, and compacted using a
hydraulic press at 7 tons of pressure for 1 min, followed by thermal sintering in an oven at 600 C for 2 h. Structural
characterization was conducted using scanning electron microscopy (SEM) and X-ray diffraction (XRD), while thermal
analysis included thermal conductivity, heat capacity, thermal diffusivity, thermal flux, and thermal resistance. The
results revealed that the optimal nano silica reinforcement ratio was 10 %, yielding notably improved properties. SEM
images confirmed surface homogeneity and strong granular cohesion, whereas XRD and energy-dispersive X-ray
spectroscopy (EDX) identified aluminum in the cubic crystal system, while nano silica exhibited partial crystallization
and a broad maximum peak at (2q ¼ 22e25) with a Miller index of (111). Thermal analysis indicated a gradual decline in
thermal conductivity as the nano silica content increased, with the lowest thermal conductivity recorded at 112 W/m.K
and the lowest thermal effusively at 265.990 W s1/2/m2.K. Similarly, the thermal diffusivity reached a minimum of
0.1098 mm2/s at 10 wt. % silica reinforcement. Furthermore, the thermal capacity decreased, whereas thermal resistance
increased with higher reinforcement ratios, reaching a minimum heat capacity of 302.251 J/kg.K and a maximum thermal
resistance of 3.366 £ 10¡6 W/m2.K at 10 wt. % silica. These findings highlight the potential of nano silica reinforcement
in enhancing the thermal performance of aluminum-based composites.

Nanoparticles were prepared by the laboratory thermal method from copper, magnesium, and zinc oxides. Different
nanoparticles were added to epoxy at weight fractions of (0, 2, 4, 6, 8, 10) wt. %. The structural results from X-ray
diffraction showed the appearance of epoxy in a random system, and when reinforced with nanopowders at higher
addition ratios, we find the appearance of nanoparticles in a clear crystalline form, while the SEM results showed an
increase in consistency and homogeneity with each increase in the proportions of nanoparticles, which appeared
clearly during the EDX examination. The hardness of Shore type D was the best at 10 % for all particles, as it was
(90.2HD) for (EP-10 %CuO), (88.56HD) for (EP-10 %MgO), and (87.5HD) for (EP-10 %ZnO). As for the impact resistance,
magnesium oxide nanoparticles showed the best value at 10 % at (12.3 kJ/m2). It was found that the thermal
conductivity value reached its highest at the EP-10 %CuO ratio of (0.5528 W/m.K), while for the EP-10 %ZnO composite
at the same concentration its value was (0.4683 W/m.K), while it was the lowest in the EP-10 %MgO composite at
the same addition ratio and reached (0.0453 W/m.K). It was found that the thermal conductivity value reached its
highest at the EP-10 %CuO ratio of (0.5528 W/m.K), while for the EP-10 %ZnO composite at the same concentration its
value was (0.4683 W/m.K), while it was the lowest in the EP-10 %MgO composite at the same addition ratio and
reached (0.0453 W/m.K).

The purpose of this paper is to investigate characterizations of weakly pseudo semi-2-absorbing submodules in terms
of some types of modules. We provide characterizations for the class of multiplication modules with the help of some
types of modules such as faithful, non-singular, Z-regular, and projective modules. Furthermore, we add some conditions
to proof the residual of a weakly pseudo semi-2-absorbing submodule is a weakly pseudo semi-2-absorbing ideal

Water is essential for life, so monitoring water quality is important. This research focuses on measuring heavy metals
(Lead, Pb; Cadmium, Cd; Chromium, Cr (in 14 healthy water samples of two types; bottled water and filtered water used
by newborn children in Al-Najaf governorate, Iraq. Atomic absorption spectroscopy (AA-7000, Japan) was used to
conduct the experimental analysis. Also, health risks parameters (non-carcinogenic and carcinogenic) due to Pd, Cd, and
Cr concentrations were calculated. The results of Pb, Cd, and Cr concentrations in mg/L for bottled water samples were
ranged 0.03e0.83, 0.01e0.69, and 0.01e0.76, respectively, while the range values for filter water samples were 0.06e4.54,
0.08e1.2, and 0.62e1.62, respectively. Statically using ANOVA-test confirmed, that the result is no significant at p > 0.05,
where the average values of Pb, Cd, and Cr in bottled water samples were lower than in filter water samples. Accordingly,
the results of Pb, Cd, and Cr concentrations in healthiest water samples for two types of Al-Najaf governorate,
were larger than safe limit that were recommended by several organizations and commissions, including the WHO,
USEPA, ECE, and Iraq. Meanwhile, the results of non-cancer and cancer health risks were within safe limits according to
EPA and WHO.

Cybersecurity is seriously threatened by web attacks, which makes it necessary to create strong classification models as
soon as possible for early detection and prevention. Networked system security is now a crucial global concern affecting
people, businesses, and governments. Attacks on networked systems are occurring far more often, and the attackers'
strategies are always changing. One defense against these attacks is intrusion detection. Machine learning is a popular
and useful method for creating intrusion detection systems (IDS). Having more representative and discriminative traits
greatly enhances an IDS's performance. We do a thorough review of feature selection and classification methods for the
purpose of classifying web attacks in this work.
This work aims to use Decision Tree, Random Forest, and Logistic Regression as classifiers and evaluate their accuracy
with the phishing website dataset. To improve the performance of the selected classifiers, subsequently, various feature
selection methods, including Recursive Feature Elimination (RFE) and the Chi-square test, were used to identify the
most relevant features for classification. Feature selection is a key topic for dimension reduction and classification in
high-dimensional datasets. During the feature selection procedure, only the most relevant attributes from the datasets
will be chosen.
Logistic Regression, Decision Tree, and Random Forest. We assess the models' performance based on metrics such as
accuracy and classification reports to determine their effectiveness in classifying web attacks. Our findings provide
insights into the effectiveness of different feature selection and classification techniques for web attack classification,
contributing to the advancement of cybersecurity research.

Malaria remains a global health challenge, leading to severe hematological complications. Natural alternatives such as
Zingiber officinale and Hibiscus sabdariffa, known for their antioxidant and bioactive properties, are being investigated
for their therapeutic potential. This study examines the hematological benefits of Z. officinale and H. sabdariffa in mice
infected with the common malaria research model, NK65 chloroquine-sensitive Plasmodium berghei. Forty-two albino
mice, averaging 24.5 ± 1.23 g, were randomly divided into six groups. Mice in group A were uninfected which served as
the normal control group. Mice in groups B, C, D, E, and F were inoculated intraperitoneally with P. berghei. Group B
were not treated while Groups C, D, E, and F received oral treatments for four days with 5 mg/kg body weight of
chloroquine, 100 mg/kg body weight of Z. officinale, 100 mg/kg body weight of H. sabdariffa, and extract mixture at
150 mg/kg body weight (100 mg/kg bw of H. sabdariffa: 50 mg/kg bw of Z. officinale), respectively. Hematological
markers that were measured and compared with the control group were hemoglobin, packed cell volume (PCV), platelet
counts white blood cells (WBC), and red blood cells (RBC). Treatments with Z. officinale and H. sabdariffa extracts
significantly (p < 0.05) improved the platelet counts, RBC, WBC, Hb, and PCV, in comparison with untreated P. bergheiinfected
mice. The extracts combinations significantly improved hematological health in malaria-infected mice, suggesting
potential interactions between the biologically active substances in both plants and highlighting their therapeutic
potential. The mechanisms behind these synergistic effects and the efficiency of this herbal combination in
clinical settings require more investigation

Constructed wetlands (CWs) serve as a sustainable and eco-friendly solution for wastewater treatment, particularly in
the removal of eutrophication-causing pollutants. This review focuses on the comparative performance of Vertical Flow
(VF) and Horizontal Flow (HF) Subsurface Flow (SSF) systems, assessing their efficiency in removing Biochemical
Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). VF systems
demonstrate superior pollutant removal, particularly for nitrogen, due to enhanced aeration and efficient
oxygenation processes. In addition, their compact design reduces land area requirements, making them advantageous in
space-limited applications. Conversely, HF systems are more effective at supporting nutrient gradient development,
particularly for phosphorus removal, but they require more land and exhibit slower treatment rates due to limited
oxygenation. The review synthesizes current literature on the mechanisms of pollutant removal in these systems,
emphasizing the role of phytoremediation plants and microbial interactions. Our analysis underscores that, while both
VF and HF systems offer substantial environmental benefits, VF systems consistently outperform HF systems in terms of
pollutant removal efficiency and spatial economy. This makes VF systems particularly valuable in settings where land
availability is constrained and nitrogen reduction is prioritized. These findings highlight the critical role of VF-SSF
systems in advancing wastewater treatment, positioning them as essential components in strategies to mitigate eutrophication
and enhance environmental sustainability. Scientifically and academically, future research should focus on
optimizing VF system designs, enhancing phosphorus removal, and ensuring resilience across diverse climatic conditions
for long-term, global water management solutions.

The CRISPR/Cas9 system is a revolutionary genome-editing tool that enables precise and inheritable modifications,
transforming plant genetic engineering. Previous literature has indicated that, in agriculture, CRISPR has improved crop
yield, biofortification, and resistance to environmental stressors, fostering resilient and high-yielding crops essential for
sustaining a growing global population. In nutraceuticals, CRISPR has enhanced the biosynthesis of bioactive compounds
such as anthocyanins, flavonoids, and omega-3 fatty acids, improving the nutritional and therapeutic value of crops.
Despite its immense potential, CRISPR technology faces technical, ethical, and regulatory challenges, including offtarget
effects, accessibility concerns, and public acceptance. Addressing these issues is crucial for its sustainable adoption.
Future innovations, including the integration of CRISPR with complementary technologies, will further improve its
precision and accessibility, which will facilitate advancements in food security and in the nutraceutical industry. This
review aims to explore the applications of CRISPR in enhancing food security and nutraceutical development, assess its
impact on crop improvement, and discuss the challenges associated with its adoption