Journal of University of Babylon

Background: Orthodontics is a dental specialty focused on the diagnosis, prevention, and management of malaligned teeth and craniofacial discrepancies, and prompting facial esthetics. The use of fixed orthodontic appliances, creates numerous retentive niches that predispose individuals to increased plaque accumulation and food debris retention. This, in turn complicates effective oral hygiene practices.
Objective: The study’s objective is immunological evaluation of immunoglobulin A (IgA) and interleukin 26(IL-26) in white spot lesions in fixed orthodontic treatment.
Material and method: A total of 100 individuals, including 50(50%) experimental and 50 (50%) controls, 36 males and 64 females, aged 15-35, underwent fixed orthodontic treatment for those measuring salivary biomarkers by ELISA for at least one month after appliance activation. Results: The findings showed that the case group had higher significant differences as opposed to the control group with p value = 0.002 regarding IgA, while IL-26 showed no significant difference between the two groups with p value 0.557. Conclusion: Compared to the control group, which exhibited no sign of white spot lesions, the case group demonstrated elevated levels of salivary IgA, while IL-26 levels remained relatively unchanged.

Background: Solving systems of nonlinear equations (SNLEs) remains a fundamental challenge in computational mathematics, with applications spanning structural mechanics, financial modeling, and engineering design. While the Newton-Raphson method offers rapid local convergence, it is highly sensitive to initial guesses and often fails in high-dimensional or ill-conditioned systems. Metaheuristic algorithms, such as the Grey Wolf Optimizer (GWO), excel in global exploration but lack the precision and speed required for high-accuracy solutions. This study proposes a novel hybrid algorithm that synergistically integrates GWO with a curvature-aware, damped Newton-Raphson method to overcome the limitations of standalone approaches.
Methods: We developed a two-phase hybrid framework: (1) GWO performs global exploration to identify promising solution basins, and (2) a damped Newton-Raphson method, guided by Jacobian conditioning and adaptive damping, refines the solution locally. The transition between phases is criterion-based, triggered only when proximity to a root (∥F(x)∥ < 10⁻⁴) and numerical stability (Jacobian condition number κ < 10⁶) are ensured. The algorithm was rigorously evaluated on benchmark functions (CEC2020, Rosenbrock, Rastrigin) across dimensions ranging from n=2 to n=1000. Performance was statistically validated against classical solvers (fsolve) and pure metaheuristics (GWO, PSO) using Wilcoxon signed-rank tests and ANOVA (p < 0.05).
Results: The hybrid GWO-Newton algorithm achieved a 98% success rate across all benchmarks, significantly outperforming fsolve (72%), standalone GWO (65%), and PSO (70%). It reduced the average number of iterations by 43% compared to fsolve and delivered solutions with a mean residual error of 8.3×10⁻⁹ — three orders of magnitude more precise than metaheuristic alternatives. The adaptive damping and population resizing mechanisms proved critical for maintaining stability in ill-conditioned and high-dimensional problems.
Conclusions: The proposed hybrid framework successfully bridges the gap between global exploration and local precision, offering a robust, scalable, and highly accurate solver for complex nonlinear systems. The integration of adaptive, curvature-informed controls ensures reliable convergence where traditional methods fail. This approach redefines numerical root-finding by harmonizing stochastic and deterministic principles, making it a powerful tool for real-world scientific and engineering applications.

The development of eco-friendly nanomaterials has become a central objective in modern materials science due to growing concerns about environmental sustainability and energy demand. Conventional methods for synthesizing metal–oxide nanocomposites often require toxic precursors and energy-intensive processes, whereas green synthesis provides a safer and more sustainable approach by utilizing plant-derived phytochemicals, aqueous media, and mild reaction conditions. In this study, three nanocomposites—CeO₂–ZnO, ZnO–SnO₂, and NiO–ZnO were prepared using green routes, including plant-mediated extract synthesis, sol–gel, and co-precipitation. The materials were thoroughly characterized by XRD, SEM, TEM, FTIR, UV–Vis, and BET techniques to examine their crystalline structure, morphology, functional groups, optical properties, and surface area. The nanocomposites were subsequently tested for three different applications: photocatalysis, solar cells, and supercapacitors. The results demonstrated that all composites exhibited enhanced performance compared with their single-oxide counterparts. Specifically, they achieved over 90% photocatalytic efficiency in dye degradation, improved power conversion efficiency in dye-sensitized solar cells, and high specific capacitance and cycling stability in supercapacitors. These findings highlight the potential of green-synthesized nanocomposites as multifunctional materials that combine environmental remediation with renewable energy conversion and storage. The outcomes of this work point toward the feasibility of scaling up green synthesis approaches and integrating such materials into hybrid energy and environmental systems.

Biologic therapies for rheumatologic illnesses, aimed at targeting molecules implicated in immune system mechanisms, offer an alternative to conventional treatments such as disease-altering anti-rheumatic medicines with additional immunosuppressive medications. Nonetheless, the existing limitations of biologic treatments, including the challenges of intravenous administration, the substantial prices of these pharmaceuticals, and the associated side events hinder their widespread use as first-line treatments. This review is an update on the recent research regarding the novel biologic medicines accessible. The review focuses on eight pharmaceuticals: Tocilizumab, Rituximab, Adalimumab, Ustekinumab, Infliximab Belimumab, Vedolizumab, and Secukinumab, utilized in the treatment of rheumatoid arthritis, spondyloarthritis, systemic lupus erythematosus, systemic sclerosis, or vasculitis.

Background:
Skin cancer remains one of the most prevalent malignancies worldwide, and its prevention and early detection play a crucial role in reducing morbidity and mortality. Recent advances in deep learning have significantly improved the accuracy of automated skin lesion classification. However, several challenges remain in designing a computationally efficient hybrid model that integrates multiple lightweight architectures.
Materials and Methods:
This work proposes a hybrid deep learning model that combines the MobileNetV3+ and ShuffleNetV2 architectures. To enhance model performance, the Elephant Herd Optimization (EHO) algorithm was employed to optimize key hyperparameters, including learning rate, batch size, and the number of epochs. Skin image datasets were collected from SkinDataSe and the ISIC archive, encompassing both benign and malignant cases. Preprocessing involved contrast enhancement and various data augmentation techniques such as rotation, scaling, flipping, and translation to improve model generalization. All images were resized to 224×224×3 pixels and normalized to [0,1] range. The dataset was then divided into 70% for training, 15% for validation, and 15% for testing.
Results:
The hybrid model demonstrated exceptional performance, achieving a training accuracy of 99.72% and an F1-score of 0.93.
Conclusion:
The findings of this study confirm the potential of the proposed hybrid deep learning framework, optimized via EHO, in delivering accurate, swift, and early diagnosis of skin cancer.

Background:
Sentiment analysis algorithms require high-quality annotated data during the training phase. However, this requirement has led to complex, time-consuming and costly manual data annotation process. To address these challenges, this research proposes an automatic data annotation process for sentiment analysis.
Materials and Methods:
Three semantic orientation measures (Pointwise Mutual Information, latent Semantic Analysis, and Word2Vec), five classification algorithms (K-Nearest Neighbors, Logistic Regression, naïve Bayes, Random Forest, Support Vector Machine) and NRC lexicon thesaurus are used to automate the process of tweet annotation for sentiment analysis.
Results:
Tweets were annotated using five classifiers and three semantic measures, forming fifteen combinations. The Inter-Annotator Agreement (IAA) among these combinations was evaluated using Cohen’s Kappa statistic. The obtained results show that (Pointwise Mutual Information + Logistic Regression) and (Pointwise Mutual Information + Naïve Bayes) achieved the highest agreement score of 0.7008.
Conclusion:
These results have shown that the corpus-based semantic orientation measures have provided substantive results. However, it can still be enhanced through the use of a broader vocabulary, the application of contextual information and the implementation of the newest deep learning algorithms.

Many elements on Earth occur in various atomic configurations called isotopes, which have identical atomic numbers but vary in atomic mass. Un-stable elements undergo disintegration through energy emission; isotopes that emit radiation are termed radioisotopes. The application of these isotopes in sectors including manufacturing, farming, medical services, and research institutions is presently of considerable significance. In the medical field, these isotopes are employed in nuclear medicine for diagnostic and therapeutic uses. Radionuclide imaging, or functional imaging, represents a medical specialty that uniquely enables the evaluation of physiological changes resulting from biochemical abnormalities, utilizing the radiotracer technique.
Radioisotopes play a pivotal role in modern medicine, being widely used in diagnosis and therapy, contributing to improved disease detection accuracy and treatment effectiveness. In diagnostics, isotopes like technetium-99m (Tc) have been utilized in organ imaging via single-photon emission computed tomographic (SPECT), whereas fluorine-18 (F) was a crucial component in positron emission tomography (PET), facilitating the high-resolution identification of metabolic alterations within tissues. In therapy, isotopes like iodine-131 (I) were utilized to address thyroid diseases, while lutetium-177 (Lu) is an effective targeted therapy for some cancers. Despite the significant benefits of radioisotopes, challenges related to their availability, high cost, and radiation risks remain, necessitating the development of safer technologies and the production of isotopes using more efficient methods. The future of nuclear medicine depends on continued progress in targeted radiation therapy, improved imaging methods, and enhanced safety measures to ensure maximum therapeutic benefit with the least possible risks.

Aging is characterized by the age-dependent decline of physiologic function that in turn raises susceptibility to death. This process of degeneration is characteristic for all forms of life and constitutes a major driving force behind many diseases including obesity, type 2 diabetes mellitus, Alzheimers disease and heart disease. Failures in insulin, an essential hormone, have been shown to be associated with most of the age-related diseases. Insulin is a critical hormone controlling metabolism, development, cell division and differentiation. It is regulated by two major mechanisms: release and clearance of insulin from the blood as well as sensitivity to a tissue's tissues. Aging disrupts these processes, which contributes to insulin dysfunction, and increases the incidence of disease and death. Insulin action in aging is the purpose of this project, and its contribution to age-related diseases. Also indicate that improving insulin function may be an effective way to promote healthier and longer life.

Nanotechnology has emerged as a new, as a new approach to tackling antimicrobial resistance. AgNPs have been demonstrated. AgNPs have received extensive attention requiring their exceptional properties, such as chemical stability and of high quality conductivity. Unlike other metal nanoparticles, silver is non-toxic and harmless to the human body at low concentrations. These pathogenic bacteria exhibit several virulence factors, including the cytotoxic necrosis factor (colicin) and surface structures such as flagella and a capsule. They also possess cilia, which help them adhere to host tissues, enabling them to form biofilms. Given the importance of biofilms in the development of pathological infections and increased drug resistance, AgNPs have proven effective in inhibiting biofilm formation and secondary reactions. Scientists have been searching for a suitable method to control and prevent the growth of biofilms, given their good compatibility with a mixture of AgNPs. Studies have shown that the diameter of the nanoparticles affects the bacteria.

Conclusion
Antibiotic resistance patterns raise concerns about the emergence and resurgence of multidrug-resistant pathogens. Developing or modifying antimicrobial compounds to improve their ability to kill bacteria is a modern priority. Nanotechnology provides effective principles for improving and developing nanostructures with promising applications in various fields. These silver nanoparticles have proven effective as weapons against potent biofilm producers, such as E. coli, which causes urinary tract infections. These particles can also be used synergistically with certain antibiotics, increasing the effectiveness of the antibiotic against bacterial resistance.

Background: The assessment of lung function in children with asthma is not routinely performed. The use of spirometry in children is difficult to be apply in this age group because it requires active cooperation with forceful expiration; therefore, the use of the forced oscillation technique resolved this problem.
Patients and method: This cross-sectional study included 80 children with asthma who were regarded as the patient group and were compared with 80 healthy children as the control group. It was performed in the period from in the period from April 2023 to May 2024. Pulmonary function in both groups was tested using forced oscillation technique. Respiratory resistance (Rrs) and reactance (Xrs) were measured at 5 Hz, and 19 Hz.
Results: The mean age of asthmatic children was 6.66 years, and the means of weight, height, and body mass index were 26.36±9.8, 120.75±14.005, 17.58±3.6, respectively. There were highly significant differences between them according to Rrs5, Rrs19, Xrs5,Xrs19, and expiratory flow limitation. The study also showed a significant correlation between weight, height, and body mass index with some forced oscillation technique parameters (Rrs5hzex, Rrs19hzex, Xrs5hz, and Xrs19hz). Simulated ROC analysis confirmed the clinical utility of the forced oscillation technique. Expiratory flow limitation and Rrs5 showed excellent diagnostic performance (AUC > 0.85); with high sensitivity and specificity at the optimal cut-offs values.
Conclusions: From the results of this study, we conclude that the forced oscillation technique seems to be an important instrument in the assessment of asthma in children, and we can depend on the values of expiratory flow limitation in determining disease severity. Therefore, further studies are warranted.

Background and Objective: Dyslipidemia, characterized by abnormal levels of total cholesterol (TC), low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), and triglycerides (TG), is a major risk factor for cardiovascular diseases. Although the ketogenic diet (KD) is widely used as a very low-carbohydrate, high-fat therapeutic approach, its effects on lipid metabolism and cardiac health remain controversial. This study aimed to compare the impact of different types of ketogenic diets on serum lipid profile, atherogenic ratios, and cardiac histopathological changes in male albino rats.
Methods: Thirty-five male albino rats were randomly divided into five groups of seven: a control group, a high-fat ketogenic diet (70% coconut oil), a high-fat ketogenic diet (70% animal fat), a high-protein ketogenic diet (35% casein, 60% coconut oil), and a cyclical ketogenic diet (70% coconut oil). The dietary intervention lasted for 50 days. At the end of the intervention, serum lipid parameters (TC, TG, LDL-C, HDL-C) and lipid ratios (TC/HDL-C, TG/HDL-C, LDL-C/HDL-C) were measured. Also, histopathological examination of heart tissue was performed using hematoxylin-eosin (H&E) staining. SPSS25 software was used for statistical analysis (p<0.05).
Results: High-fat and high-protein diets significantly increased TC, TG, LDL-C, and VLDL levels in rats, with the coconut oil ketogenic diet showing the most adverse changes. HDL-C levels showed no significant changes across groups. Lipid ratios indicating atherogenic risk were increased dramatically in the high-fat and high-protein groups. Histological examinations of the heart in the coconut oil group showed fat accumulation, inflammatory cell infiltration, and cardiomyocyte destruction, while these changes were less severe in the animal fat group and were rarely observed in the control group.
Conclusion: According to the results obtained, the effects of ketogenic diets are significantly influenced by the macronutrient composition, especially the fat source. High-fat diets, especially those enriched with coconut oil, have significant adverse effects on metabolic od lipids and cardiac tissue, while high-protein and periodic diets are considered comparatively safer, though further studies are needed to confirm their long-term effects. These findings highlight the importance of dietary fat composition when considering ketogenic diets for potential clinical use.

Social sustainability plays a significant role in the entire sustainable framework. It can be described in a variety of ways, such as: The capacity of a city to continue to serve as a long-term viable environment for social interaction, communication, and cultural development. At the same time, it is an aspect of sustainability that is often overlooked. Where the focus is only on the economic and environmental aspects
Materials and Methods:
This study deals with social sustainability estimation using remote sensing and surveying techniques. The study focuses on Sheikh Fars area, which is in eastern Missan, because of the economic significant of the area. The research hypothesis assumes that Sheikh Fars city is a good social sustainability according to its significance.
Results:
The results of the questionnaire survey (direct process) were analyzed used collected data (face to face interview) for residents using Likert scale approach. The results indicated that the study area is classified a poorly social sustainable area. as well as its neighboring areas, as they have the same characteristics as the region.
Conclusion:
In conclusion, this study weakens the first hypothesis, indicating that the Sheikh Fars area is socially unsustainable regardless of its economic significance. The findings show a significant gap between economic and social performance. This emphasizes the importance of incorporating direct social assessments into sustainable development plans.

Background:
The world is more and more interested in sustainable waste management, for which intelligent systems performing automatic sorting of different kinds of waste are needed.
Materials and Methods:
In this study, the hybrid deep learning model is composed of transfer learning with the DenseNet121 model, convolutional block attention module (CBAM) and a simple recurrent neural network (Simple RNN) to enhance feature representation and sequential dependencies of waste images. In order to handle the class imbalance and enrich the data set, preprocessing techniques like normalization, murder and brightness adjusted and strategic data bird mooding (rotating, flipping) were performed.
Results:
The experimental results on a multi-class waste dataset with twelve categories, namely batteries, biowaste, glass types, cardboard, clothing, metals, paper, plastics, shoes, regular waste, and white glass, demonstrated that the model can yield good performance, showing well-balanced classification across all classes. This model reduces the training and parameter size remarkably by using pre-trained weights, and the proposed CBAM mechanism concentrates on learning significant features. The proposed hybrid model achieved an accuracy of 99%, outperforming conventional baselines.

Conclusion:
The experimental results demonstrate the potential to apply the proposed approach in real-world smart recycling systBackground:
The world is more and more interested in sustainable waste management, for which intelligent systems performing automatic sorting of different kinds of waste are needed.
Materials and Methods:
In this study, the hybrid deep learning model is composed of transfer learning with the DenseNet121 model, convolutional block attention module (CBAM) and a simple recurrent neural network (Simple RNN) to enhance feature representation and sequential dependencies of waste images. In order to handle the class imbalance and enrich the data set, preprocessing techniques like normalization, murder and brightness adjusted and strategic data bird mooding (rotating, flipping) were performed.
Results:
The experimental results on a multi-class waste dataset with twelve categories, namely batteries, biowaste, glass types, cardboard, clothing, metals, paper, plastics, shoes, regular waste, and white glass, demonstrated that the model can yield good performance, showing well-balanced classification across all classes. This model reduces the training and parameter size remarkably by using pre-trained weights, and the proposed CBAM mechanism concentrates on learning significant features. The proposed hybrid model achieved an accuracy of 99%, outperforming conventional baselines.

Conclusion:
The experimental results demonstrate the potential to apply the proposed approach in real-world smart recycling systems, which is an effective way to realize waste sorting and environmental protection. Future work will investigate generalization of this technique to other environmental areas, and validate its performance on a larger and more diverse dataset.
ems, which is an effective way to realize waste sorting and environmental protection. Future work will investigate generalization of this technique to other environmental areas, and validate its performance on a larger and more diverse dataset.

In this study, three different types of commercial solid detectors (CR-39, CN-85 and LR-115) were used for alpha particle detection, which are the most common and widely used in scientific laboratories for alpha particle detection. The detectors were cut and irradiated with a 570 kBq Americium (Am-241) radioactive source for 3 hours. After that, the detectors were immersed in NaOH solution for chemical scraping at different temperatures. An optical microscope was used to calculate the tracks of alpha particles, which are the traces and diameters of the alpha particles resulting from the irradiation, as well as the sensitivity and scraping efficiency of the detectors. Through calculations and practical results, it became clear that the LR-115 detector had the highest alpha path density (602 tr./mm²), while the CR-39 detector showed the largest path diameter (2µm), highest sensitivity (1.667), and highest efficiency (0.398) compared to the other detectors.

In recent decades, the life sciences have undergone a paradigm shift in the strategies used to characterize genes, proteins, transcriptomes, and metabolites. This transformation has been propelled by the advent of high-throughput analytical platforms, which enable the simultaneous interrogation of vast numbers of biomolecules with unprecedented efficiency and resolution. Among these technologies, mass spectrometry has emerged as a cornerstone methodology, owing to its unrivaled sensitivity, selectivity, and quantitative power in ion detection. By facilitating the accurate reconstruction of the molecular and chemical architecture of complex biological systems, mass spectrometry has become indispensable to contemporary biological and biomedical research, driving advances in systems biology, drug discovery, and personalized medicine.
MS enables high-throughput measurement of complex protein or peptide mixtures, sequencing and quantifying proteins or peptides, detecting post-translational modifications (PTMs) of proteins, and predicting their 3D folding and topology. Peptides and proteins are typically studied using mass spectrometry because traditional ionization techniques easily generate protonated species. Peptide drug conjugates (PDC) are a prodrug type in which drug molecules are conjugated to peptides by covalent bonding via specific linkers. The fragmentation of this PDC type depends on several parameters, such as instrument settings, solvent composition, and fragment ion intensities and types, which also depend on the structure. A novel conjugate of Anthracycline and peptide provides safe and effective cancer therapy. For its structure elucidation via MS, several detection techniques would be complementary.

This study is investigating the relation between the soil sample alpha-decay and lung cancer risk from selected districts in the Garmian zone, Kurdistan region, Iraq. The presence of the oil and gas blocks is due to the geological properties of the zone. Also, the formation of the zone consists of the lower Fars that contain naturally occurring radioactive materials in the environment. The samples were collected from 41 locations across 27 districts, the samples were prepared and analyzed by an alpha spectrometer to determine the alpha decay activity concentration. The results define that the activity ranged from 4.19 Bq/m3 to 179 Bq/m3 with an average of 32.4 Bq/m3. The lung cancer cases per year per million people range from 2- 37 cases. This article determines the importance of analyzing soil radioactivity as a factor in the environment to the health risk. This research results underscore the need for more studies to improve the effectiveness of the alpha- decay from natural radionuclides to inform the risk that is associated with public health.

Background: Polycystic ovary syndrome (PCOS) is a metabolic condition that impacts females during their reproductive years. PCOS is marked by the presence of multiple cyst-like follicles on the ovaries, irregular or absent ovulation, and resistance to insulin. This research seeks to investigate the consequences of flaxseed extract on the ovary in the PCOS rat’s model by histological techniques and serum hormone level.
Materials and Methods: Sixteen female albino rats (6–8 weeks old, estrous cycle in female rats repeats every 4–5 days) were assigned to four groups: the first group was the control, second group received 0.2 mg/kg body weight of letrozole by gavage, the third group was given 100 mg/kg body weight flaxseed extract intraperitoneally, and the mixed group received both letrozole and flaxseed daily for 21 days. The total period of the study was 2 months, including 15-20 days for preparation, 21 days for animal experimentation, and 20 days for histological and statistical analysis.
Results: Histological examination of the ovary revealed letrozole-induced ovarian cysts and disruption of follicles. Flaxseed treatment significantly restored ovarian morphology, with a recovery of follicular development. Hormonal assay indicated that testosterone and AMH were elevated, while estrogen and progesterone were decreased in the letrozole group to other groups. Extraction of flaxseed supplementation restored hormonal equilibrium, lowering testosterone and increasing estrogen and progesterone.
Conclusion: These results demonstrate that flaxseed extract may offer a potentially effective treatment option for PCOS through modulating ovarian and endocrine dysregulation.

Understanding and automatically recognizing sign language is crucial for equal opportunities for deaf and hard-of-hearing individuals in society. In the world of Arabic sign language communication, people share their ideas, thoughts, and feelings and engage in meaningful daily interactions. While artificial intelligence and computer vision technologies have improved, studies focused on Arabic sign language processing have come first and continue to be a research frontier compared to other world languages. This research proposed a methodology to leverage the HSV color space model for initial segmentation to eliminate background noise to refine the input data. This process segments the hand or semantic sign area. Then, the data is passed onto a Convolutional Neural Network (CNN) for precise sign classification. This method’s novelty is the coordinated interplay of differential data simplification through color space manipulation and the profound representational power of the CNN for improved overall system performance for hood sign recognition.
The research also seeks to fill an existing gap in the field of Arabic signal processing by presenting an integrated model, opening up broad horizons for applications in the fields of education, healthcare, and smart government services. The proposed method showed promising results, as the results of the used criteria ranged between Accuracy 95.3%, Precision 93.5%, Recall 92.8%, and F1-score 93.1%.
Background:
Recognizing sign languages has always been a vital topic in societies, especially with the recent massive expansion of the deaf and mute community. It serves as the main form of communication for over 70 million deaf and hard-of-hearing individuals globally. Thus, the development of simple, accurate, and effective methods that utilize readily available resources deserves attention. Among the various forms of sign language, sign language and Arabic are especially significant, as Arabic is predominantly spoken in the Middle East and North Africa. Yet, the challenges that arise from the communication gap between this population and the hearing members of society continue to affect various areas of the deaf and hard-of-hearing individuals educational, social, and professional for educational, social, and, professional integration. Hence, creating automated systems that can comprehend Arabic Sign Language is an essential development in helping socially integrate and bridge communication gaps [1].
Materials and Methods:
The proposed hand gesture recognition system relies on a multi-stage methodology aimed at high-resolution image processing and efficient feature extraction. The methodology begins with the data collection phase, where a set of images and videos representing letters and words in Arabic Sign Language is collected on the Kaggle Dataset.
Results:
The proposed Arabic Sign Language recognition system was developed through two principal stages: (1) segmentation using the HSV color space with experimentally defined thresholds, and (2) classification via a Convolutional Neural Network (CNN). The segmentation stage, relying on the HSV model with the defined lower range (H=0,S=30,V=60) and upper range (H=20,S=150,V=255), demonstrated effectiveness in isolating the hand region from complex backgrounds, contributed significantly to noise reduction and the removal of irrelevant small objects, and enhanced the clarity of the extracted hand region prior to classification.
Conclusion:
Recognizing sign languages has always been a vital topic in societies, especially with the recent massive expansion of the deaf and mute community. Therefore, it is important to focus on easy and highly accurate techniques that can be relied upon with relatively available resources. The proposed methodology is highly flexible and scalable, as the HSV color space boundaries can be modified or the CNN architecture optimized to suit research requirements and the characteristics of different datasets.

Background
Q-switching is a laser technique that temporarily stores energy in the laser medium before releasing it all at once to produce extremely brief, intense light pulses. "Q" stands for the optical cavity's quality factor, which is a gauge of how effectively it stores energy. When using Q-switching, the cavity's Q is initially kept low to avoid lasing as the population inversion increases. The stored energy can then be released in a strong pulse that is frequently nanoseconds long but has peak powers thousands of times higher than continuous wave output when the Q is abruptly switched to high.
Objective
The Co-ZnFe₂O₄ nanostructur is synthesis by pulsed laser ablation in liquid (PLAL) method, It is then mixed with the previously prepared Rhodamine B dye to boost optical absorption efficiency.
Materials
Uniform, surfactant-free nanoparticles are produced using a Q-switched Nd:YAG laser (1064 nm, 300 mJ). UV-Vis spectroscopy verified that the resultant nanocomposite showed increased absorption in the visible range when combined with Rhodamine B, exhibiting a discernible redshift in the absorption peak. demonstrating effective energy transfer and robust dye nanoparticle interactions.
Results
highlight the potential of Rhodamine B–Co-ZnFeO₄ nanostructures for high-performance uses in photocatalysis, optical sensing, and solar energy harvesting.
Conclusion
Our research shows that PLAL is a quick, easy, and efficient way to customize nanomaterials with exceptional optical qualities.

Background: In this study, we decompose a non-homogeneous second-order partial differential equation into a homogeneous part and a non-homogeneous part. Previous research on the existence and uniqueness of solutions for both sections provide a clear mathematical foundation. The main goal of this research is to divide the partial differential equation into two separate parts: one homogeneous and one non-homogeneous. These parts are then addressed using initial conditions along with various boundary and overdetermination conditions.
Materials and Methods: We use the finite difference method to numerically solve both the homogeneous and non-homogeneous components. Various boundary conditions are considered, including Dirichlet, Neumann, and mixed conditions, each requiring specific supplementary information. To assess the stability of the numerical solutions, we introduce different noise levels and apply Tikhonov regularization to stabilize the results.
Results: Several numerical examples are presented to evaluate the effectiveness of the proposed approach. The findings indicate that the finite difference method yields accurate solutions under a range of boundary conditions, and that Tikhonov regularization effectively enhances stability in noisy environments.
Conclusion: The numerical results validate the robustness and reliability of the proposed method. By combining Tikhonov regularization with finite difference discretization, this study establishes an efficient framework for solving non-homogeneous second-order partial differential equations under various boundary conditions. The research results indicate that, under various boundary, extra, and initial conditions, we can solve the problem and find convergent solutions for both parts.

Background:
This study presents a useful new framework that uses Bernstein polynomials to improve a spectral collocation technique for numerically solving Fredholm integro-differential equations of fractional orders with variable coefficients and multi-time constant delay (FIFDEs-Delays) under boundary conditions.
Materials and Methods:
The approximate solutions are assumed to be in the form of the truncated Bernstein polynomial series. This novel approach is based on the use of a matrix technique to convert the display equation with conditions into an algebraic linear system of equations with unknown Bernstein coefficients.
Results:
This approach improves the accuracy of the solutions found while simultaneously simplifying the problem. The solution of this system determines the coefficients of the assumed solution. In addition, the integral operators employed in this technique were quantitatively evaluated using the Clenshaw-Curtis formula.
Conclusion:
we provide specific examples to showcase the accuracy of the method, and we employ the least-squares error methodology to minimize error terms within the given domain. Ultimately, the most common application suggested for the numerical approaches is implemented in a Python program.

Background:
The daily consumption of green tea is one of the most important beverages that contains many bioactive compounds, such as polyphenols and flavonoids, in large proportions. It has important health and disease prevention benefits and has antioxidant and anti-inflammatory properties and plays an important role in preventing and preserving the structure and function of body cells. Our study was designed to investigate the effect of daily green tea intake on inflammatory markers, including IL-2, IL-6, and IL-12, as well as TNF-α, C-reactive protein, lipid profile, glucose status, and markers of liver, kidney, and heart function in men in Erbil.
Materials and Methods:
The study involved 21 healthy male volunteers, aged between 21 and 40 years, who consumed two cups of green tea daily and assessed inflammatory markers and physiological and biochemical tests by comparing the results before and after four weeks of daily intake of green tea.
Results:
Serological results of blood samples after 4 weeks of daily intake of green tea revealed a significant reduction (P < 0.001, P < 0.005) in IL-2, IL-6, IL-12, TNF-α, and C-reactive protein, and showed a significant reduction in blood glucose, HbA1c, TG, TC, and LDL cholesterol. It also showed significant reductions in liver, kidney and cardiac function.
Conclusion: Our findings suggest that daily intake of green tea reduces the levels of inflammatory markers, reduces the blood lipid and sugar profiles, improves the physiological functions of the liver, kidneys and heart, and increases the effectiveness and activity of the body's immune system.

The proliferation of sophisticated, digitally disseminated misinformation poses a critical threat to public discourse and democratic processes. Existing fake news detection systems, primarily reliant on content veracity or superficial stylistic features, struggle to adapt to the evolving, multi-faceted nature of deceptive communication. Problem: Current models fail to explicitly account for the author’s underlying, often complex, manipulative intent, leading to limited generalizability and interpretability. Solution: This paper presents the Intent-Aware Fake News Detector (IAFND), a novel predictive system that employs a multi-label classification framework to identify five distinct authorial intents (Deceive, Sensationalize, Propagandize, Manipulate, and Incite) using fine-grained linguistic features. Key Findings: Through rigorous experimental validation on a large, publicly identified dataset (25,000 articles from LIAR, FakeNewsNet, and CoAID), the IAFND demonstrates statistically significant performance improvements over state-of-the-art baselines (p<0.001). Furthermore, the system’s intent-based interpretability module is quantitatively shown to be more robust and actionable than established XAI methods (LIME/SHAP), providing a transparent and scalable solution for combating real-world disinformation.

This paper examines the structural complexity of the Zagros Simple Folded Zone (ZSFZ), which covers northeast Iraq, with a significant major emphasis on geological units and fold morphology, as well as tectonic deformation processes occurring in that region. The Zagros Mountain Belt, which covers a large portion of northeast Iraq, formed as a result of plate convergence between Arabia and Eurasia.
The study area, which shows different fold styles such as symmetrical, asymmetrical, overturned, and fault propagation folds, depending upon lithology contrast or tectonic stress, is an arc-shaped region in the Kurdistan Region. Seismic reflection data were analyzed for subsurface information, which shows that the Foothill Zone, where most of the studied anticlines are found, shows structural control by various thrust faults, decollement planes, and episodes of back thrusting.
Important anticlines like Chamchamal, Dari, Qara Dagh, Pulkhana, and Chia Surkh were individually studied for their geometric characteristics and evolutionary development of folds, as well as the effects of major thrusting episodes. It appears from tectonic evolution that existing faults, as well as sediment layers, are of great importance for the development of folds.
This information offers key insight for hydrocarbon exploration, pinpointing structural elements of a trap as well as underground features that increase reservoir capacities. Knowledge about deformation processes in the Zagros Fold-Thrust Belt improves models for geophysical exploration. This work stresses that it is crucial to combine information about seismic studies, fieldwork, and structural geology to acquire a profound understanding of its tectonic settings.

Solid waste management has become a critical environmental challenge due to its direct implications for public health and resource sustainability. Rapid population growth, urban expansion, and shifts in consumption patterns have led to a steady increase in waste generation, while existing infrastructure often struggles to keep pace with these rising quantities. The complexity of the issue stems from the diverse composition of solid waste—ranging from household and industrial refuse to medical and plastic materials—each of which requires specialized treatment methods to reduce environmental harm.
Key obstacles include inefficient collection and sorting systems, reliance on open dumping or uncontrolled burning, and the resulting contamination of air, soil, and groundwater. The problem is further exacerbated by limited public awareness and insufficient recycling policies. Addressing these challenges calls for an integrated approach that prioritizes waste reduction at the source, promotes reuse and recycling, and supports the development of safe and efficient treatment technologies.
Improving solid waste management ultimately depends on coordinated efforts among governmental institutions, the private sector, and local communities. Establishing clear regulatory frameworks and supporting sustainable practices can help mitigate the environmental impacts of waste and transform it into a valuable resource that contributes to long-term environmental and economic resilience.

Dry mouth caused by decreased or absent salivary flow is known as xerostomia. Although xerostomia is not a disease, it can be a sign of several illnesses, a side effect of drugs, or a result of radiation exposure. When xerostomia is untreated, it can lower the pH levels of the mouth and create a favorable environment for the growth of certain microflora, such as Streptococci, Actinomycetes, and Lactobacilli, that can lead to dental cavities, fungal infections, and other problems.
Numerous causes, problems, preventative strategies, and treatment modalities are covered in detail in this analysis. Dehydration, damage to the salivary glands, and disruption of neuronal transmission are the main causes of xerostomia. Acting on the cause, preserving salivary function, and avoiding complications are all examples of preventive approaches. There are four primary approaches to treating xerostomia: symptomatic or palliative, systemic and local stimulation, and preventing complications.
The aim of this investigation is to clarify xerostomia, its causes, its complications, its prevention, treatment, and its effect on oral microbial growth. From this review we conclude that the patient\'s oral and overall health are impacted by xerostomia, or dry mouth, which lowers their quality of life.