Karbala International Journal of Modern Science

Nitrophenols are notorious aquatic organic contaminants found as degradation products of various parent compounds,
including pesticides and industrial chemicals that persist in the environment and must be removed. Catalyticdegradation is
one ofthe feasible routes to clean the contaminated water systems, however, environmental contamination with catalysts is
also widespread. Herein, we report an environmentally friendly catalyst based on composited Fe-Schiff's base with exfoli
ated layered double hydroxides (LDH)ofaluminumandnickel(hydrotalcite).Thecompositeshowedagglomeratedpleated
LDH structures sheathed with Fe(III)SB. Nitrogen adsorption isotherm data exhibited improved surface area and narrow
pores patterns for composite as compared to LDH indicating Fe(III) SB-induced change in the morphology of LDH. Also,
significant improvement in catalytic efficiency was observed for Fe(III)SB-LDH over pristine LDH. 4-nitrophenol (10 mg/L)
degradation of 99% in 5 min was achieved at pH 6 using a catalyst-to-volume ratio of 1:20 and 20 mM H2O2 as oxidant. It is
concluded that phenomenal improvement in catalyst efficiency can be attributed to Fe-Schiff's base modification.

In this paper, we introduce a novel approach to numerical solving for nuclear reactivity using the inverse equation of
point kinetics as a model. Our method leverages the Gauss-Lobatto quadrature, incorporating multiple time steps and
nodes derived from Legendre polynomials of varying orders. To enhance the accuracy of the differential component of
the inverse equation, we employed a differential scheme based on the Gauss-Lobatto quadrature nodes. To address
f
luctuations in neutron population density, we implemented a second-order low-pass Butterworth filter with a minimal
window length of M ¼ 3 samples. Through extensive numerical simulations, we assessed the precision of our proposed
method and filtering process by varying time steps and standard deviations associated with noise or uncertainty.
Additionally, we benchmarked our results against the SavitzkyeGolay filter, which uses a significantly larger sampling
window of M ¼ 225. Our findings reveal that the integration of the Gauss-Lobatto quadrature method with the But
terworth filter not only significantly reduces fluctuations but also demonstrates potential for effective implementation in
digital reactivity meters.

Tungsten (W) is being developed as a plasma-facing material for fusion reactors, where it is subjected to MeV neutron
irradiation, low-energy helium isotope particles, and high temperatures. These conditions lead to the formation of point
defects, dislocation loops, voids, and transmutation into rhenium (Re) and osmium (Os), which form precipitates that
significantly impact dislocation motion and increase hardness. This study uses molecular dynamics modeling to examine
the interaction between an edge dislocation and Re-rich particles of various stoichiometries, specifically coherent bcc
phase particles and noncoherent s-phase precipitates. Results show that shear stress increases by approximately
20e40 %withlarger particle size (3e5 nm diameter) and higher Re content (50e75 at.%), while temperature (600e1400 K)
has a weak effect on critical shear stress. Coherent bcc-phase particles are weak barriers to dislocations, whereas
noncoherent s-phase precipitates are strong obstacles. The study reveals that dislocations typically cut through these
particles, except for noncoherent spherical ones, which are bypassed via the Orowan mechanism.

Internet of Hybrid Vehicle Networks (IoHV) is a network generated by merging the Internet with a Hybrid Vehicular
Ad-Hoc Network (H-VANET). In IoHV, various types of electric and fuel vehicles create tasks. However, executing
several tasks by electric vehicles affects their lifetime because they suffer from energy limitation issues which is one of
the IoHV challenges. On the other hand, fuel vehicles and fog nodes have unlimited energy and can be used to execute
most tasks of electric vehicles quickly. In this paper, we produce a new Energy Resource management Technique for
IoHV called ERTH that aims to offload the tasks of electric vehicles to the fuel vehicles and fog nodes. The main goal of
ERTH is saving energy and the lifetime of electric vehicles. As a result, the probability of switching off electric vehicles
and the number, time, and cost of recharging their batteries will be reduced. Moreover, we propose a new energy-aware
clustering method for IoHV to connect electric vehicles, fuel vehicles, and fog nodes. It can help save the energy of electric
vehicles and balance the load. The results showed that ERTH is better than PLIFS and RMOIE according to EC. Moreover,
NDEV with ERTH is approximately 47.2 % and 42.4 % with different numbers of fuel vehicles and 26.19 % and 14.1 %
with various mobility speeds less than PLIFS and RMOIE, respectively. Finally, PET with ERTH is 2.1 % and 3.09 % with
various numbers of fuel vehicles and 1.9 % and 3.18 % with various speeds more than PLIFS and RMOIE, respectively.

Breast cancer accounts for 25 % of all cancer diagnoses and 16 % of cancer-related deaths among women globally, with
high mortality rates due to late diagnosis. Early detection relies on imaging techniques such as mammography, histo
pathology, and breast ultrasound, with mammography being the gold standard due to its proven to detect breast cancer,
thus it is effective for breast cancer treatment. However, mammogram images often produce noise and artefacts,
complicating early-stage cancer detection and emphasizing the need for advanced image processing. Clustering algo
rithms such as K-means and Expectation Maximization- Gaussian Mixture Model (EM-GMM) have shown potential in
image segmentation. This study investigates the application of EM-GMM for mammogram image segmentation, a
relatively underexplored area compared to K-means, focusing on identifying potentially cancerous regions. EM-GMM,
which models data based on Gaussian distribution through iterative EM algorithm application, was used on grayscale
images of malignant tissue. The optimal cluster number for detecting cancerous areas was determined to be nine using
the Bayesian Information Criterion (BIC). This was tested and compared with segmentations using seven and eight
clusters. Results demonstrate that the segmentation with nine-cluster achieves the highest accuracy for segmenting and
identifying cancerous regions. These findings support early detection and thorough breast health assessment, potentially
enhancing diagnostic precision and lowering breast cancer mortality and morbidity.

Dyes used in industry, especially textile dyes, are one of the water pollutants that receive much attention because they
are potentially toxic, carcinogenic, mutagenic, and generally challenging to decompose naturally. Textile dyes from
wastewater can be removed through coagulation-flocculation. However, conventional coagulation-flocculation based on
Fe and Al salts and synthetic polymers often leaves residual pollution. In this research, the performance of the new
biopolymer-based flocculant, namely starch-ethylene glycol dimetacrylate-chitosan (SEC), which can act as coagulants
and flocculants in solid-liquid separation of textile dyes, has been optimized using response surface methodology (RSM)
approach. The influences of several independent variables, such as initial pH, SEC dosage, and rapid stirring time, were
studied. The design experiment was the box-Behnken design (BBD), which involved three factors, three levels, and 15
treatment combinations. Based on the fitting results, the quadratic model is significant and accurate in predicting the
solid-liquid separation of Dypro 19 (p-value 0.0002, F-value 51.94, and R2 0.9894). Numerical optimization based on the
desirability function shows that using initial pH variables of 8, SEC dosage of 2.2 mL L¡1, and rapid stirring time of
3 mincan produce a predicted removal of Dypro 19 of 97.01 %. Model validation through experiments on these variables
shows that 95.71 % of Dypro 19 can be removed. These results indicate that the proposed model's validity, accuracy, and
acceptability are good.

Intestinal barrier function depends on epithelial adhesion, which restricts permeability and microbial invasion from the
internal environment. Impairment of barrier integrity and gut function is closely linked to pro-inflammatory changes.
Inflammationisoftentheprimaryfactorthatprovokesgutfunctiondisorders.Theanti-inflammatorypotentialofpostbiotics
has been reported in recent years. Muramyl peptides (MPs) are small signaling molecules that stimulate intracellular
pathogen receptors and can regulate cell responses. However, the molecular mechanisms of MPs' effects on intestinal cells
remain unknown. The study of MPs treatment on lipopolysaccharide (LPS)-challenged Caco-2 intestinal cells aimed to
investigate the postbiotic effects on intestinal barrier integrity, autophagy, and inflammation. An in vitro inflammation
modelwasconstructedusingCaco-2cellsexposedto10e100mg/mLofLPS.Theresultsshowedadose-dependentdecreasein
cell viability and E-cadherin content. Conversely, TNF-a content was increased, confirming proinflammatory disturbances.
Nosignificant differences were detected in cells exposed to 5e50 mg/mL doses of MPs. However, treatment with 50 mg/mL
MPsamelioratedTNF-aproductioninLPS-exposedcells.Theapplicationof20and50mg/mLMPsexhibitedacytoprotective
effect on cell viability in a dose-dependent manner. Furthermore, the 50 mg/mL dose ofMPs ameliorated the reductionof E
cadherin content induced by LPS exposure. MPs treatment did not modulate the conversion of the autophagosome marker
LC3-I into LC3-II but upregulated its content in LPS-challenged cells. These results demonstrated that MPs may be a
promising tool to restore inflammatory balance and maintain intestinal barrier function.

Phenolic compounds from olive mill wastewater (PCO) of Algerian origin were used to produce sodium alginate
based active films using the casting method. The effects of adding various concentrations of PCO (0%, 0.1%, and 0.2% w/
v) were evaluated regarding the molecular, morphological, thermal, physicochemical, optical, barrier, biodegradability,
antimicrobial, and antioxidant properties of the alginate films. The FTIR and SEM results elucidated the development of
a coherent cross-linked structure attributed to hydrogen bonding interactions between PCO and alginate chains.
Consequently, the films exhibited enhanced crystallinity and thermal stability, as revealed by DSC analysis. Moreover,
PCO addition positively influenced several film properties, including color attributes. As the PCO concentration
increased from 0% to 0.2%, significant improvements were observed in tensile strength, thickness, phenolic content, and
opacity, with maximum values reaching 19.666 MPa, 0.107 mm, 80.264 mg/g, and 0.869, respectively. Additionally, the
water barrier capacity and oxidative stability of sunflower oil improved significantly, with the lowest values of
0.009 kg cm m¡2 kPa¡1d¡1 and 8.94 meq/Kg observed at a 0.2% PCO concentration. On the other hand, the film
exhibited biodegradability, as evidenced by water resistance tests. The results showed that sodium alginate film coating
containing PCO exhibited good anti-foodborne pathogen capacities against Escherichia coli, Listeria monocytogenes, and
Salmonella enterica with an inhibition zone between 14.6 and 19.6 mm, as well as a significant enhancement of anti
oxidant properties (79.506e98.682%) was observed. In conclusion, sodium alginate-based active films incorporated with
PCO have demonstrated intriguing properties, making them suitable for food packaging.

Obesity is caused by an energy imbalance that increases chronic low-grade inflammation, including macrophage cell
infiltration. Adipose tissue macrophages are polarized into pro-inflammatory macrophage type 1 (M1), secrete large
amounts of pro-inflammatory cytokines, and activate transcription factors. Yoghurt with probiotics is popular at all ages
for its health benefits and must be protected by microencapsulation. Green tea (Camellia sinensis L.) fortification pro
vides yoghurt nutrients while improving its functional qualities and bioactivity. This study aimed to evaluate the effect
of microencapsulation of Lacticaseibacillus paracasei E1 in green tea yoghurt (GTY) on the profile of M1 macrophages in
mice fed a high-fat fructose diet (HFFD). Thirty-five male mice Mus musculus Balb/c (±20 g, 4 to 5-week-old) were
divided into seven groups: normal; simvastatin dosage 1.3 mg/kg BW; plain yoghurt dosage 5 g/kg BW; GTY doses at 2.5,
5, and 10 g/kg BW. Mice were fed a diet for 12 weeks and treated with yoghurt daily for four weeks. Macrophages were
obtained from isolated adipose cells using enzyme digestion and labeled with antibodies. The levels of CD146þNF-kBþ,
CD146þSTAT1þ, CD146þTNF-aþ, CD146þCD11cþIL-6þ, and CD146þLp-PLA2
þ were measured by flow cytometer. The
data were analyzed using one-way ANOVA (p < 0.05). Yoghurt at all doses significantly reduced NF-kb, STAT1, TNF-a,
IL-6, and Lp-PLA2 levels compared to the HFFD group. These findings suggest that combining green tea with probiotics
in yoghurt may serve as a dietary intervention to manage obesity-induced inflammation by alleviating oxidative stress
and regulating metabolism.