Pure Sciences International Journal of Kerbala

The research involved the synthesis four of new Schiff base derivatives (SC1, SC3, SC4, SC6) through the condensation reaction of Three amines (drugs) with four aldehydes, all conducted under mild conditions. To validate their chemical structures, the produced compounds were analyzed utilizing spectroscopic methods such as FT-IR, 1H-NMR, and 13C-NMR. The antibacterial activity of Schiff base derivatives was tested against (Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Proteus spp., and Staphylococcus aureus) using the agar well diffusion technique.
The findings revealed that SC1 was the most powerful and broad-spectrum antibacterial activity of all of the chemicals tested, with inhibition zone widths ranging from 18 to 30 mm, while SC3 showed moderate activity against most tested strains, with the highest activity against E. coli (21 mm) and S. aureus (25 mm). The structure-activity relationship (SAR) research revealed that electron-donating and electron-withdrawing substituents on the aromatic ring affected biological activity. These findings indicate that Schiff bases may be viable candidates for the creation of novel antibacterial medicines.

This study aims to measure and evaluate the energy density of electromagnetic radiation emitted by cell phone towers in three locations in Karbala, Iraq. The first location is the area between the Holy Shrine of Imam Hussain and Abbas that represents religion area, while the second area is Al-Hussein District Street which represents a commercial location. The third place is Al-Ghadeer District (a residential area). Field measurements were performed by using an HF 59B Analyzer. This device is distinguished by its accuracy in measuring the energy density levels of electromagnetic radiation in the bands used in modern communication systems. The results showed that the highest radiation power density was recorded at 6:00 PM between the two Holy Shrine, with the average radiation intensity in some evening periods exceeding 2,000 µW/m². This is attributed to increased human activity during the evening hours and increased use of mobile phones during that time. In the Al-Ghadeer District, peak radiation was observed at 1:00 PM.

This paper introduces a new version of the exponential distribution, offering a more flexible model for real-life data, namely unit half logistic geometric exponential (UHLGeE). The essential statistical functions and features of the two-parameter proposed distribution are discussed. Different techniques, maximum likelihood, ordinary least squares, weighted least squares, and Cramer-Von-Mises minimum distance, are employed to estimate the two unknown parameters. Consequently, extensive simulation experiments are conducted to evaluate the performance of all estimation methods. Lastly, the efficacy and adaptability of the new distribution are illustrated through an analysis of a real data set. Based on the outcomes of the empirical and real applications, it is recommended to adopt and employ UHLGeE in additional applications due to its features and adaptability.

In this research, manganese dioxide nanoparticles (MnO₂ NPs) were prepared using a simple chemical precipitation method, using sodium thiosulfate (Na₂S₂O₃) as an effective reducing agent to control the size and shape of the nanoparticle crystals. This method represents a promising approach for preparing nanomaterials due to the possibility of tuning their structural properties under mild conditions, opening the way for innovative medical applications. The prepared particles were characterized using advanced analytical techniques, including Ultraviolet-visible (UV-Vis) spectroscopy to study the optical properties, Fourier-transform infrared (FTIR) spectroscopy to determine the chemical bonds, X-ray diffraction (XRD) to analyze the crystal phase and calculate the crystal size. The results showed that the particles belong to the α-MnO₂ phase and crystallized in the tetragonal crystal system, Transmission electron microscopy (TEM) and scanning electron microscopy (FESEM) to study the shape and size, with images showing that the particles exhibit a somewhat spherical structure, with slightly heterogeneous proportions, Energy-dispersive X-ray (EDX) analysis to determine the elemental composition.The results showed that the prepared particles are characterized by their small size, relatively regular shape, and large surface area, characteristics that contribute to improving their effectiveness in biological applications. Manganese dioxide nanoparticles show promising potential in the medical field, especially in nanomaterial-based therapeutic and diagnostic applications.

This study examined the correlation between type 2 diabetes mellitus (T2DM), insulin resistance, and fatty liver disease (FLD) using biochemical assessment of 50 diabetic patients diagnosed with FLD in comparison to 40 healthy persons. The findings indicated a substantial elevation in HOMA-IR among the patients (16.25±12.75) relative to the control group (1.74±0.96; p<0.001), signifying an increase of nearly ninefold. The C-peptide level was significantly enhanced in the patients (3.88±2.70) compared to the control group (2.64±1.02; p<0.001), and insulin levels were nearly 3.5 times more (31.61±21.53 vs. 8.70±5.16; p=0.007).
Concerning hepatic functioning, patients exhibited markedly elevated levels of AST (37.00±19.34 vs. 22.53±5.11; p<0.001) and ALT (41.84±22.48 vs. 24.63±7.29; p<0.001), signifying hepatocellular injury, although ALP levels did not demonstrate a significant difference (110.70±45.97 vs. 104.20±22.08; p=0.414). A modest yet statistically significant reduction in albumin levels was noted (4.27±0.58 vs. 4.41±0.30; p=0.001).
The HbA1c level in patients was considerably elevated at 8.24±1.28 compared to the control group at 4.93±0.47 (p<0.001), reflecting inadequate blood sugar regulation and a level about 67% greater in the diabetes-FLD cohort. Clinically, 78% of individuals had non-alcoholic fatty liver disease (NAFLD), whereas 22% presented with alcoholic fatty liver disease, underscoring the prevalence of NAFLD among diabetes patients.

Machine scheduling challenges are growing more intricate and dynamic. The scale and complexity of these problems demand the creation of approaches and solutions that can deliver satisfactory outcomes within an acceptable timeframe. In response, this paper introduces a novel mathematical framework tailored to multi-objective functions, focusing on single-machine scheduling issues by minimizing the discounted total completion time (∑C_j), the total number of tardy jobs (∑▒U_j ), and the range of lateness time (R_L) denoted 1//(∑▒C_j ,∑▒U_j ,R_L ) or CUR-TC. Also, minimizing the sum of total completion time (∑C_j), the total number of tardy jobs (∑▒U_j ), and the range of lateness time (R_L) denoted 1//(∑▒C_j +∑▒U_j +R_L ) or CUR-TO. In this paper, we prove the existence of an efficient sequence for problem 1//(∑▒C_j ,∑▒U_j ,R_L ), where jobs are sorted in ascending order based on their processing times. Certain specific cases are highlighted to provide efficient and, in some instances, optimal solutions to the problem under consideration. The dominance rule can be used to solve problems to improve them and find efficient solutions. Accordingly, we proposed two heuristic methods for solving search problems. These are two fast methods for finding (near-optimal) solutions to optimization problems with the least possible number of tables, in comparison to the complete enumeration method and the branch and bound method. The first method is based on dominance rules, while the second method is based on MST and SPT rules.

Machine learning has achieved wide success in detecting and identifying cancer diseases, especially those based on medical images. To increase the performance of models, machine learning techniques are combined with other techniques in the processing stage. In this research, the ABC algorithm was used in the feature identification process to help the algorithm choose the optimal solution more accurately.The medical images used are CT images of breast cancer collected in the form of a standard dataset of 570 known as WDBC dataset. The proposed model achieved classification accuracy 94.74%.

Cancer remains one of the major threats to human fitness, and although there are many treatments available to address this problem, conventional treatments including radiotherapy and chemotherapy have significant adverse effects and are by no means ideal. Gene therapy is a newly developed treatment modality that provides superior targeting with fewer adverse effects than conventional treatments. To change the expression of gene products, foreign genetic material is injected into the host tissue during gene therapy. Gene therapy provides the opportunity to influence the course of many diseases. Therefore, reliable and safe gene products and vector application coupled with advanced biotechnology will be crucial in the treatment of various diseases in the future. Gene therapy is a treatment strategy of opportunity that has been extensively studied recently. The therapeutic genes are implanted at the specified site. Cell or tissue specific to the majority of tumors these compounds have the ability to either induce cell death or inhibit the rate of cell proliferation. Of most types of cancer. Many viral and non-viral genes and vectors are being used in investigations because this treatment uses unique approaches that involve either silencing inactivated or unwanted genes or activating recovery genes. Activation of tumor suppressor genes is a crucial mechanism that helps control the growth of tumors and suppress them, and controls the function of oncogenes and inhibits their activation. Our explanation in this article covers gene therapy, methods of treating most tumors using genes, the use of approved genetic drugs to treat most malignant tumors, and the future of gene therapy for cancer. . Gene therapy no longer meets the criteria to completely replace conventional treatments. But with a deeper understanding of the process behind cancer treatment, one can choose the optimal course of action and target for future gene therapy, either alone or in combination with other therapies.

During a field survey conducted in 2022 in citrus groves in Alhusaynia territory, Karbala Province, Iraq, leaf spot symptoms were observed on orange foliage. A fungal pathogen was regularly isolated from diseased leaves and identified as Alternaria alternata based on colony and conidial morphology and internal transcribed spacer (ITS-rDNA; PX763615.1) sequence analysis. Pathogenicity was evaluated using a detached-leaf assay, and typical symptoms were reproduced on inoculated leaves, followed by re-isolation of the same fungus. This investigation is the first documented occurrence of A. alternata associated with leaf spot on orange in Karbala Province, Iraq

A field experiment was conducted on the west side of AL-Jadwal Al-Garbi /Hindia/ Karbala, Iraq, during the spring agricultural season of 2023. The objective was to evaluate the effect of G power Ca + Nanocomposite spraying and Bio-Fertilization using Pseudomonas bacteria on sunflower yield and seed quality. Two factors were studied. First foliar spraying of G Power Ca+ Nanocomposite at four concentrations (0,2.5, 4 and 5 ml.L-1). Second, Soil application of Pseudomonas bacteria at four levels (0, 10, 20 and 60 ml .L-1) injected into Rhizosphere. The examined proceeded in accordance with the experiment design using the Gene stat program ,the arithmetic averages comparison employed the least significant difference (LSD0.05) test.
Measured parameters included Plant height, biological yield , seed content of oil, number of seeds per disc, and, protein in seed , and nitrogen in seed . The highest values were recorded at a concentration of 5 ml/L of G Power Ca+ nanoparticles and 60 ml/L of Pseudomonas bacteria for all studied traits, with observed (191.56 cm , 12.76 ton .ha-1, 5.76 % , 1245 seed.disk-1, 11.47 % , 2.99 % ) showing statistically significant differences compared to the control treatments(122.87 cm , 6.56 ton .ha-1, 1.87 % , 754 seed.disk-1 , 6.23%,1.04%)respectively . These results highlight the potential for improving sunflower productivity and nutritional quality in arid conditions by combining nanoparticle treatment with biofertilization.

Polymeric hydrogels have been recognized as a new generation of material for multifunctional use owing to their high water retention capacity, three-dimensional crosslinked network structure, and responsiveness to external stimuli like pH, temperature, and ionic strength. These unique physicochemical characteristics of the hydrogels have made these materials critical in a multitude of biomedical and environmental uses. This review describes the dual-functional applications of polymeric hydrogels in two important domains: water purification and wound healing. Hydrogels are considered sustainable, ecologically friendly, and inexpensive adsorbent materials in the field of water treatment. Their excellent porous structure coupled with tunable surface chemistry aid in the efficient removal of a diverse spectrum of contaminants—heavy metals [Pb²⁺, Cd²⁺, Hg²⁺], synthetic dyes, and organic contaminants from industrial and domestic wastewater. The addition of functional groups including carboxyl, hydroxyl, amine and sulfonic groups improves their adsorption power and selectivity to particular pollutants. Moreover, by incorporating the nanomaterials metal oxide nanoparticles or carbon-based nanostructures, the mechanical stability, reuse rate and overall adsorption performance of hydrogels would be improved substantially. Conversely, polymeric hydrogels have demonstrated excellent activity in wound healing since they can mimic the natural extracellular matrix (ECM). Their soft, fluid-rich and biocompatible structure offers a perfect microenvironment for the adhesion, proliferation and regeneration of the cells. Hydrogels provide a moist wound microenvironment conducive to accelerating healing, preventing dehydration and minimizing infection risks, enhancing angiogenesis and promoting healing. Furthermore, they possess the possibility to be controlled drug delivery agents, allowing long-term release of various therapeutic agents as antibiotics such as antimicrobial compounds, anti-inflammatories and growth factors, as localized system. Their adjustable degradation rate and mechanical characteristics improve the application of a variety of materials to wounds, including chronic and burn wounds. Hydrogel design innovations in recent years have been more focused on a smart and stimuli-responsive design, which can be dynamically designed to adapt and change their properties for different environmental conditions. Advancements in physical and chemical crosslinking systems are increasing structural durability and performance. Yet, they still encounter several limitations, such as poor mechanical strength in some formulations, scalability and long-term durability in practical applications. Polymeric hydrogels serve as a promising platform for transforming hydrogels and with a strong potential to address global challenges in an age of continuous environmental preservation, sustainability, and health systems

Modeling lifetime data plays an indispensable role in reliability engineering and survival analysis, where the primary goal is to accurately predict time-to-event phenomena. While classical probability distributions, such as the widely applied Weibull-Exponentiated Exponential (W-EE) hybrid model, offer significant flexibility in capturing versatile hazard rates, they possess a fundamental limitation. Specifically, traditional models inherently assume that the observed data and underlying parameters are precise and well-defined. However, this assumption is rarely satisfied in practical scenarios. In real-world applications, data collection is frequently plagued by measurement errors, interval-censored information, environmental noise, and inherent vagueness. Relying strictly on classical statistical methods under such uncertain conditions often yields biased parameter estimates and misleading reliability inferences. To address these critical challenges, this study introduces the Neutrosophic Weibull–Exponentiated Exponential (NW–EE) distribution, a novel extension designed to model lifetime data characterized by indeterminacy. By integrating neutrosophic components into the classical W-EE model, we effectively represent observations and parameters as neutrosophic intervals rather than exact point values. In this paper, we rigorously derive the key statistical functions of the new distribution, including the neutrosophic cumulative distribution function (NCDF), the neutrosophic probability density function (NPDF), and vital reliability measures such as the survival and hazard rate functions. Furthermore, we investigate essential mathematical properties such as neutrosophic moments and variance. Additionally, the model parameters are estimated utilizing the neutrosophic maximum likelihood estimation (NMLE) technique, and the model is validated using real datasets. The results demonstrate that the NW-EE model offers superior flexibility in handling imprecise observations in reliability engineering and survival analysis, providing a highly accurate and realistic alternative to classical distributions. The introduced Neutrosophic Weibull-Exponentiated Exponential (NW-EE) distribution is a sophisticated analytical tool designed to model lifetime data in environments where imprecision and indeterminacy are unavoidable.

In this article a new weighted Rayleigh distribution has been proposed. The main difference between the proposed distribution and the other existing Rayleigh families is that, usually, when families add such a parameter, it creates new behavior, such as ( tail). In the proposed distribution, the new parameter η interacts with β to adjust the spreading. It is simpler and more computationally.
The distribution's properties have been analyzed, and the maximum-likelihood method was suggested for the estimation of the parameters of the new distribution. The novel weighted distribution was exemplified through its application to an actual lifetime dataset.

In this paper, the k-ideals in Q-algebras are studied in the framework of single-valued neutrosophic sets. After recalling some basic notions related to Q-algebras and single-valued neutrosophic sets, the concept of single-valued neutrosophic k-ideals are introduced. Several properties and characterizations of single-valued neutrosophic k-ideals are investigated. Moreover, the relationships between single-valued neutrosophic k-ideals and single-valued neutrosophic ideals are discussed. Illustrative examples are provided to support the theoretical results.