Karbala International Journal of Modern Science

This study presents an in-depth examination of the energy levels and decay properties of Gadolinium (Gd) isotopes
with mass numbers (A ¼ 156e160), utilizing the Interacting Boson Model-1 (IBM-1) within a broken SU(3) symmetry
framework. Through this approach, we systematically calculated and analyzed the energy spectra, B(E2) transition
probabilities, quadrupole moments, and potential energy surface (PES) which provided valuable insights into the shape
and collective behavior of nuclei, as well as the decay properties of the selected Gd isotopes. The broken SU(3) sym
metries provide a good description to the isotopes under study. This comprehensive analysis enhances the under
standing of the nuclear structure of mid-mass Gd isotopes, making valuable contributions to nuclear physics models and
their predictive capabilities.

The study tested the nematicidal effects of essential oils from Bubonium graveolens and Launaea arborescens on the
potato cyst nematode Globodera rostochiens. The chemical composition of the essential oils was analyzed using GC-MS.
To determine the concentration that killed 50% of the nematode population (LC50), five concentrations of the essential
oils were applied to the tested organisms. The effects of essential oils on the hatching of cyst nematode (Globodera
rostochiensis sp.) eggs in vitro demonstrated a wide variety of effects ranging from no impact to mild, moderate, and
strong effects, which increased dramatically with exposure duration and concentration. All the oils examined were
capable of causing Globodera cysts to hatch, although the effects varied depending on the type of oil used, how long it
was exposed to, and its concentration. The percentage of nematode mortalities ranged from 7.14 to 78.56% for B.
graveolens, 7.14e85.71% for L. arborescens, and 64.38e73.31% for the nematode control. This study investigated the
effects of bioactive compounds from B. graveolens and L. arborescens plants on the body and the molecular mechanisms
underlying cyst nematode egg hatching

Thestudy of the luminescence photostability for colloidal nanocrystals is an important task since the understanding of
the corresponding physical processes advances new electronic devices based on semiconductor nanoparticles as well as
other important applications such as biomarkers. In this paper, we provide the first study and comprehensive analysis of
the photostability of the luminescent properties for colloidal CdTe/SiO2 core/shell quantum dots prepared by an
aqueous-based method. The quantum dots were exposed to continuous laser radiation during two time intervals with
prolonged break in between. The photoluminescence intensity of the quantum dots increased over time under
continuous laser irradiation. Also, some processes occurred in the quantum dots in the dark (during the break), leading
to a further increase in the photoluminescence intensity after turning on the laser radiation in the second time interval.
The observed continuous photoluminescence intensity enhancement of the CdTe/SiO2 quantum dots was attributed to
adsorption of water molecules on the surface of the nanocrystals and, as a consequence, to a decrease in the probability
of nonradiative transitions. The positions of the photoluminescence intensity maximum and the colorimetric charac
teristics have been found to be stable against prolonged laser irradiation. This has been explained by the fact that for the
CdTe core capped with a large-bandgap SiO2 shell (with respect to the bandgap of CdTe), photocorrosion, which is often
responsible for the PL blueshift, is a slow process. The results of our work can be used in the development of opto
electronic and nanophotonic devices based on colloidal CdTe/SiO2 nanostructures.

In Indonesia, the treatment with leaves as a traditional medicine is still firmly integrated into the community to
overcome various health problems experienced, one of which is the treatment with Bidara leaves (Ziziphus mauritiana).
Ziziphus mauritiana belongs to family of Rhamnaceae, and it is generally considered a potential source of antioxidant and
cholesterol lowering. This study aimed to examine phytochemical characterization, antioxidant potential, and cholesterol
lowering effects of ethyl acetate fraction derived from Ziziphus mauritiana. Extraction was accomplished using ethyl
acetate, and the resultant extract was fractionated through chromatography with a blend of n-hexane and ethyl acetate as
the eluent. Antioxidant assay was conducted using the scavenging radical 2,2- diphenyl-1-picrylhydrazyl (DPPH)
methods, while cholesterol-lowering activity was assessed with the Lieberman-Burchard method. Additionally, phyto
chemical profiling of potential fractions was determined using LC-MS/MS. The results showed that fraction ethyl acetate
Ziziphus (EAZ) fraction 09 had the highest DPPH radical scavenging with % inhibitions of 30.524 ± 0.095 mg/mL. Based
on LC-MS/MS analysis, fraction EAZ.09 contained five main compounds and EC50 value of cholesterol-lowering activity
was 132.115 mg/mL. In conclusion, fraction EAZ.09 from Z. mauritiana leaves had significant potential based on the
chemical composition, in-vitro antioxidant activity, and cholesterol-lowering effects.

Microhabitats are smaller pockets of ecosystems with unique conditions and they serve as major contributors of species
richness. Mangrove plants offer a diverse array of such microhabitats (rhizosphere, phyllosphere, endosphere etc), and the
inhabitant bacteria benefit the host by providing better salinity tolerance, faster and better nutrient mobilization, pro
tection from phytopathogens and assistance in seed germination. Identification of such beneficial bacteria, their growth
requirements and metabolism, will have direct application in the field of agriculture and ecosystem restoration. Hence in
this study, effort has been made to elucidate the seasonal numerical and compositional profile of pneumatophore bacterial
consortia of Avicennia officinalis, from the mangrove ecosystem of Poovar, South Kerala, India. The physico-chemical
parameters of mangrove water, micronutrient availability within the pneumatophore and the count, morphology and
biochemical characteristics of the endosymbiont bacterial consortia were analyzed simultaneously. Significant variation is
observed across the rainy and the non-rainy seasons, in the water parameters and in the endobacterial CFU count.
Antagonistically, the micronutrient content and the endobacterial composition of the pneumatophore remained stable
across the seasons. The biochemical profile of the endobacterial consortia illustrated the metabolic co-dependence and
complementarity among the host and the symbiotic consortia, which substantiate the need for such a solid core bacterial
consortium within the plant host. Further studies are warranted in this lane to trace the beneficial metabolic pathways of
symbiotic bacterial strains, which could find wide application in the field of agriculture and human health.

The disposal of heavy metals from various activities has become a pervasive issue. The investigation of sustain-able,
low-cost adsorbents for the remediation of these hazardous pollutants has been widely disregarded. This research
emphasizes the recovery of selenium ions from contaminated water using lemon peels as a cost-effective adsorbent. The
adsorption was explored in a batch unit under various operational parameters, including initial selenium concentration
(1e90 ppm), pH (1e11), agitation speed (100e500 rpm), adsorbent dose (0.4e5.5 g), contact time (5e180 min), and tem
perature (25e55 C). The BET surface area of the lemon peels was found to be 27.86 m2/g before adsorption and 2.18 m2/g
after adsorption. FTIR analysis exposed the disappearance or shifting of several peaks, while the FESEM analysis
showed significant changes in the surface morphology of the lemon peels due to its interaction with selenium. The
results indicated that lemon peels are highly effective at removing selenium, attaining an efficiency of over 87% under
optimal conditions: pH 8, agitation speed of 450 rpm, initial selenium concentration of 86 ppm, adsorbent dose of 5 g,
contact time of 150 min, and temperature of 25 C. The experimental results were analyzed using isothermal, kinetic, and
thermodynamic methodologies. The mathematical model confirmed that the Langmuir theory is the most effective at
representing the adsorption isotherm, while in terms of kinetic, the adsorption followed the inter-particle diffusion
model, as indicated by the correlation coefficient (R2) values. Moreover, the adsorption process was found to be spon
taneous, exothermic, and associated with low entropy over the studied temperature range.

This study aimed to produce a-cellulose from Bacterial Cellulose SCOBY (BCS) using alternative substrates from
fermented fruit and vegetable byproducts: katuk leaves (KT), kale leaves (KL), guava (JB), dragon fruit (NG), and banana
(PS). BCS production involved juice extraction, SCOBY inoculation, and sucrose addition, followed by 21 days of
fermentation. Initially, the NG medium had the highest concentration of Total Reducing Sugars (TRS), but all media
showed a decline as sugars were consumed. Fermentation reduced pH and increased total polyphenols, with KL and JB
showing the highest rise (0.13e0.15 mg GAE/mL). Flavonoid levels varied, decreasing in KL and PS but increasing in KL,
JB, and NG. Antioxidant activity decreased in NG (13.21%), while KL increased by 43.49%. BCS characteristics varied,
with KL producing the thickest BCS (3.41 ± 0.40 mm wet, 0.73 ± 0.06 mm dry). The JB medium yielded the highest dry
BCS (17.20 ± 1.86%), with lower water content (81.13 ± 1.71%). XRD analysis after alkali treatment revealed increased
crystallinity in a-BCS, with crystallite sizes of 4.78e8.40 nm, larger than standard a-cellulose. a-BCS from kale showed
higher DMSO compatibility than water. These results demonstrate the diverse properties of BCS from alternative
substrates, highlighting potential industrial and biomedical applications.

Waveform detection has been an area of continuous investigation for many years. One important waveform in sleep
stage 2 is the k-complex. Numerous researchers have created various strategies for auto-k-complex detection; some of
these strategies state that the automated detection techniques are adequate. Because of its analytically relevant resolu
tion, the Electroencephalogram (EEG) is a commonly utilized technique to analyze the k-complexes in order to under
stand the nervous system activity of the brain. Several researchers have classified waveforms using EEGs in a variety of
ways. It appears that the majority of the waveform detection had limitations. The necessary analyzes took a lot of time to
complete, no execution time was indicated in any of the earlier research, and they were too complex for real-world use.
Furthermore, it was revealed that numerous experiments were done without window size and were utilized to detect
waveform characteristics. Additionally, the research used one or two evaluation instruments to analyze the performance
outcomes. For the dataset, a maximum accuracy of 94%e75% was reported. Because of its significance, several analysts
have developed an automated technique to use EEG data to study k-complexes. This work proposes a novel approach to
feature detection for k-complexes utilizing a least square support vector machine (LS-SVM) classifier. The sliding
window method divides EEG signals into a number of segments. Subsequently, distinct feature sets are obtained from
every time interval. Every EEG segment was visible in the obtained twenty-seven features as vectors. That means,
twenty-seven features were extracted using the Katz algorithm and the Tunable Q-factor wavelet transform for each
segment. These features were analyzed, to select the most important features, using an Analysis of Variance (ANOVA)
and the F-test. Finally, the vector of features was used as input to the LS-SVM classifier. When it came to identifying
events of (non) k-complexes, the suggested novel technique demonstrated noteworthy performance results with sensi
tivity, accuracy, and specificity of 98.3%, 96.5%, and 91.6%, respectively. This high accuracy rate has not been discovered
in any method yet. When compared to other classifiers and methods in this field of research, the LS-SVM classifier
approach yielded the best results.

Identifying microorganism species, such as Lactobacillus, is essential in ensuring the food products' quality and
safety. Traditional laboratory practice requires expert knowledge and experience, but the method is expensive and time
consuming due to complex sample preparation. Faster, more accurate, and cheaper computational methods, such as
transfer learning technology, are needed for the Lactobacillus species classification. The technique has been effective in
a variety of image recognition contexts. Deep learning architecture can also be applied as an innovative strategy for
digital image-based identification. Therefore, this research aims to compare several deep-learning architectures in
classifying bacterial strains of Lactobacillus. The four architectures (Inception V3, MNASNet, RegNet, and Xception)
used have excellent performance with accuracy above 95 %. Among those architectures, the mobile neural architecture
search network (MNASNet) exhibits the most potential, with 99.15 % accuracy, 99.09 % precision, 99.14 % sensitivity, and
99.11 % F1 score. This performance is particularly notable given that MNASNet operates with 3.1 million parameters.
Although RegNet has a slightly lower parameter count at 2.68 million, it achieves the accuracy, precision, sensitivity, and
F1 score of 98.73 %, 98.73 %, 98.77 %, and 98.75 %, respectively. Xception, with over 20 million parameters, attains 98.99 %
accuracy, 98.71 % precision, 98.78 % sensitivity, and 98.74 % F1 score. In addition, the MNASNet architecture is highly
efficient, making it very suitable to be implemented or embedded on mobile devices. The model is promising regarding
the deep learning architecture prospects for classifying microscopic images of Lactobacillus species

Since drones cannot fly in any kind of weather, they are not safe for time-sensitive activities. The study examines how
the passage of weather systems in Iraq leads to the ban on drone flights, and how these weather conditions impact the
aerodynamic forces of the drone. Hourly climate data for the study area were obtained from ECMWF ERA5 and CAMS
in NetCDF format for four climate stations (Erbil, Baghdad, Rutbah, and Basrah). A ScanEagle drone was chosen for this
study. The Python programming language was used to perform mathematical operations to calculate the ban on drone
f
lights. ArcGIS 10.8 was used to create maps of the spatial distribution of the Python results. 80 ScanEagle flight sce
narios were investigated in a wind tunnel simulation using computational fluid dynamics. Ansys 2019 was used to
perform the simulation. The results showed that Red Sea and mid-latitude depressions had the most effects on drone
f
light in Iraq because they increased aerodynamic forces (lift and drag), which in turn increased the power needed for
constant speed flight. This resulted in an increase in fuel consumption and flight duration. Red Sea depressions mostly
affect the center and southern parts of Iraq; on the other hand, mid-latitude depressions mostly affect the northern and
western regions, and when flying at 700 hPa, they affect the entire country.

One of the most critical challenges for self-driving vehicles is accurately identifying traffic signs, which are essential
for self-navigation and decision-making. Systems for the detection and recognition of road signs play a crucial role in
this process by providing vital information for the vehicle's decision-making. This study proposes an approach for road
sign identification and recognition utilising the TensorFlow Object Detection API and the SSD MobileNet V2 FPN Lite
model.
In this proposal, we combine the efficiency and accuracy of SSD with the lightweight architecture of MobileNet to
achieve excellent performance in object detection benchmarks while maintaining a small model size and low processing
power requirements. The model was trained using the German Traffic Sign Recognition Benchmark (GTSRB) dataset.
The proposed methodology achieved a mean detection accuracy of 100% while requiring 0.317 s to detect and recognise
each sign.