This study applies k-means clustering to group countries based on key economic and health indicators: Gross National Income per capita (GNIP), health expenditure, life expectancy, birth and death rates, and urbanization. The elbow method identified k = 3 as the optimal number of clusters, indicating a significant drop in within-cluster sum of squares (from 5000 to 2000). The results reveal three distinct development groupings. A small cluster of 13 high-performing countries stands out with strong economic (GNIP = 0.658) and health outcomes (Life Expectancy = 0.856), along with low birth (0.116) and death rates (0.113). This group also shows strong internal similarity (silhouette width = 0.58). The remaining countries fall into two broader clusters. The first includes 320 countries with moderate development, higher urbanization (UrbanP = 0.712), and relatively high health spending (healthE = 0.219), but lower GNIP (0.066). The second cluster of 277 countries faces greater challenges, marked by low life expectancy (0.414), high birth rates (0.670), and weak economic indicators (GNIP = 0.067). Both larger clusters show moderate cohesion (silhouette widths = 0.29 and 0.32). These findings highlight the stratified and multidimensional nature of global development, offering a data-driven framework to inform policy decisions and tailor interventions to the unique characteristics of each cluster.

Lipid A in the cell wall of  Salmonella typhi and pseudomonas aeruginosa  were extracted and analyzed by the GC/MS technique. Three concentrations were prepared (50, 100, 200) mg/100g of body weight. The sample was divided into two groups of Albino rats, consisting of three animals for each concentration. Lipid A was calculated and then injected in three doses over three consecutive days. Interleukin-2 and Interleukin-9 levels were measured and were (13.11, 3.150, 3.180),(12.183, 15.766, 36766)pg/ml respectively c compared with the controls (3.205 and 6.433)pg/ml for three concentration respectively for Salmonella typhi whereas Pseudomonas aeruginosa were (3.086, 3.159, 3.168),(12.133, 14.400, 21.100) pg/ml compared with the controls (6.337 and 0.230) pg/ml for the three concentrations, respectively. White blood cell counting and total count were (9.052, 9.2033, 9.118)*10μl in Salmonella  typhi, and were (8.235, 9.239, 8.945)*10μl in Pseudomonas aeruginosa. Neutrophils, lymphocytes and monocytes were (5.408, 5.506, 6.221),(3,716, 2.882, 2.405),(0.087, 0.139, 0.1820)*10μl respectively for  the three concentrations in Salmonella typhi and were(4.353, 5.526, 5.994),(3.944, 3.413, 3.016),(0.123, 0.143, 0.072)*10μl respectively for the three concentrations in Pseudomonas aeruginosa. Eosinophils and basophils were (0.342, 0.266, 0.348),(0.281, 0.004, 0.004)*10μl for the three concentrations for Salmonella typhi while in Pseudomonas aeruginosa were(0.164,0285, 0.136),(0.000, 0.000, 0.000)*10μl respectively for the three concentrations.

        This research studied, the effect of lanthanum oxide as a thermal barrier on the highly effective aluminization process of a nickel-base  superalloy was studied. The aluminization  process was carried out using the solid cementation  method at a temperature of (1000oC) in a vacuum atmosphere and for Periods of (2, 4, 6) hours after stabilizing the thermal barrier material(lanthanum oxide) on the samples. It was observed that the rate of mass gained and the rate of thickness of the resulting coating increase with time until it reaches the saturation point and takes the form of a parabolic curve. Examination with a scanning electron microscope (SEM-EDS) of the surface of a sample painted for six hours showed the presence of a high percentage of aluminum of the element lanthanum  in addition to other elements of the base alloy material, Optical microscope examination showed that the coating layers are composed of three multi-phase regions (the outer layer, the inner layer, and the Interdiffusion  zone), while X-ray analyses  showed presence of some phases with a high melting point, such as (LaNi5, Al2La, and NiAl), which reflected positively on the alloy's resistance to hot corrosion, evident through the stability in the mass of the coated samples after testing them at a temperature of (900oC) in the presence of salt vapor from a mixture of sodium sulphate and sodium chloride.

Alzheimer's disease involves environmental influences and genetic and biochemical factors that lead to the build up of harmful proteins like amyloid and neurofibrillary tangles in brain. This accumulation can disturb the communication between nerve cells. Result in cognitive difficulties. This analysis emphasizes the importance of elements like air pollution and toxic metals in addition to discussing the influence of inflammation oxygen related stress and antioxidants, on disease progression. It also explores how specific hormones and enzymes influence the course of the disease. Alzheimer’s stands out as a condition that leads to cognitive decline and affects nerve tissues in the brain extensively.  It is identified through the development of tangles and the buildup of amyloid proteins outside cells that form plaques hindering communication among neurons.  This inherited degenerative condition affects nerve function and results in a decrease, in abilities and memory loss.   This review delves into biochemical pathways associated with Alzheimers disease; discussing the impact of air pollution and harmful metals along with inflammation and oxidative stress as well as the influence of antioxidants and certain hormones such as insulin and proteins like GO and insulin like growth factor (IGF) along with leptin in the process. It also touches upon the significance of enzymes like choline acetyltransferase and essential proteins in the disease pathway such, as C reactive protein interleukins and E methione. This research is crucial for gaining insights into the processes involved in Alzheimers disease and could help in crafting better prevention and treatment approaches, down the line.

In this paper, we obtained the nuclear energy levels for 48Cr isotope that resided in the pf -shell of shell model calculations. For nucleons that were outside closed and no core, they were calculated using skyrme interactions. The Skyrme (SLY5) was used as an efficient full-space two-body interaction, and the frozen orbitals and restricted occupations technique were adopted in the context of full-space calculation. All wave vectors and analyses are presented using the diagrammatic notation, while all representatives are given in Dirac notations. All the calculations are done with the help of the OXBASH code program and the results are compared with the available experimental data.

This research offers detailed insights into managing and preventing meningococcal disease, highlighting Neisseria meningitidis and Neisseria gonorrhoeae as key pathogens. It focuses on the epidemiological diversity and classification of meningococcal serogroups, along with their worldwide distribution. A significant portion of the study is dedicated to advancements in vaccination, especially conjugate and protein-based vaccines, which have markedly enhanced prevention. The study examines the transmission and carriage mechanisms of N. meningitidis, emphasizing its carrier state and potential to cause severe diseases like meningitis and sepsis. It also explores virulence factors such as the polysaccharide capsule, lipooligosaccharide (LPS), and Type IV pili, and their contribution to disease pathogenesis. Additionally, it discusses nasopharyngeal epithelium colonization and the host's immune response role. The research ultimately highlights the crucial need for ongoing surveillance, vaccination, and international public health initiatives in effectively addressing meningococcal disease. The research provides valuable insights into the management and prevention of meningococcal disease. It emphasizes the critical role of vaccination, the need for ongoing surveillance, and the importance of global public health initiatives to mitigate the impact of this disease.

This research offers detailed insights into managing and preventing meningococcal disease, highlighting Neisseria meningitidis and Neisseria gonorrhoeae as key pathogens. It focuses on the epidemiological diversity and classification of meningococcal serogroups, along with their worldwide distribution. A significant portion of the study is dedicated to advancements in vaccination, especially conjugate and protein-based vaccines, which have markedly enhanced prevention. The study examines the transmission and carriage mechanisms of N. meningitidis, emphasizing its carrier state and potential to cause severe diseases like meningitis and sepsis. It also explores virulence factors such as the polysaccharide capsule, lipooligosaccharide (LPS), and Type IV pili, and their contribution to disease pathogenesis. Additionally, it discusses nasopharyngeal epithelium colonization and the host's immune response role. The research ultimately highlights the crucial need for ongoing surveillance, vaccination, and international public health initiatives in effectively addressing meningococcal disease. The research provides valuable insights into the management and prevention of meningococcal disease. It emphasizes the critical role of vaccination, the need for ongoing surveillance, and the importance of global public health initiatives to mitigate the impact of this disease.

There is a rising interest in nanocomposites with unanticipated features that are different from traditional materials to fulfill the demands of these applications. Cobalt oxide (Co3O4) nanoparticles, and cobalt oxide with multi-walled carbon nanotubes (Co3O4/ MWCNTs) as nanocomposites were created. In this study, cobalt oxide nanoparticles (Co3O4 NPs) were synthesized via co-precipitation method chemical using CoCl2 salt as a source of cobalt nanoparticles and sodium hydroxide solution as a factor agent at room temperature. The cobalt hydroxide (Co(OH)2) precipitate was obtained, then it calcinated the cobalt hydroxide precipitate at 600°C to obtain the cobalt oxides nanoparticles (Co3O4 NPs). Also, MWCNTs were prepared by a locally manufactured reactor at 423 k. Binary composites were prepared by solution method using cobalt oxide nanoparticles dispersion over MWCNTs surfaces. The structural, morphological, and spectral properties were confirmed and investigated by using FE-SEM, Raman spectroscopy, and (XRD), respectively. The surface area and pore size obtained by BET and BJH showed that the binary nanocomposites (Co3O4/MWCNTs) have a higher surface area than their pristine cobalt oxide and multi-walled carbon nanotubes nanoparticles. The adsorption processes of nanocomposites have attracted significant care from the scientific public because of their distinctive possessions and aptitude to reduce and adsorb Cu+2 and Ni+2 as heavy metals from water pollution. As a result, Co3O4 and its nanocomposite with MWCNTs were used to remove copper and nickel ions from the dilute aqueous solution.

This research paper includes the design and implementation of a system for mining student and patient data at the College of Dentistry at the University of Mosul using the Microsoft SQL Server database management system to design and implement the database system and WEKA program for database mining, and the Microsoft Visual C# .NET 2012 language was used to program system interfaces. The main steps of the database included analysis, design and implementation, and the mining process included seven steps; data collection, data preprocessing, data exploration, data transformation, data modeling, evaluation, and deployment. The database mining process was divided into two parts; the first part is a smart cluster process for students of the Faculty of Dentistry for the fourth and fifth stages on laboratories (i.e. the number of chairs available for each laboratory) using three famous algorithms (Canopy, K-Means, EM), the second part is the process of classifying patients into four classes according to the type of treatment that each patient needs using three also famous algorithms (SVM, Naïve Bayes, Random Forest). ). After applying the system to the real data of the College of Dentistry at the University of Mosul, it was found that the best cluster algorithm is K-Means and the best classification algorithm is Naïve Bayes.

Alzheimer's disease is a degenerative neurological disorder that primarily strikes older people. Alzheimer's disease is now a major health problem for anyone over the age of 65. The inability to remember what has been said or done before is the first symptom of the condition. Memory loss becomes severe, and daily functioning declines as the disease progresses. The memory-controlling region of the brain shows signs of impairment years before any symptoms occur. There are three possible disease stages: mild, moderate, and severe. The early stage, sometimes known as the middle stage, mild demented (MCI), is an intermediate state between Alzheimer’s patients and healthy individuals When someone is diagnosed with MCI, there is an opportunity to treat or stop the development of the disease into AD, which is the only solution to avoid AD. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for The Early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Based on MRI scans, The four phases of Alzheimer's disease are correctly categorized by the suggested technique with an accuracy of 95.17 % performance, 86.82 % precision, and 93.13 f1 score.