Al-Bayan University

Using the full-potential linearised augmented plane wave approach, we examined the structural, electronic, and optical features of AgXBr3 perovskite materials (X = Ca, Sr, or Ba). The GGA-PBEsol exchange-correlation functional yields equilibrium structure parameters that match the literature. Electronic structure analysis demonstrates that the Tran–Blaha modified Becke–Johnson and screened hybrid HSE06 functionals widen the bandgap compared to GGA-PBEsol. As X’s atomic size rises, its indirect fundamental bandgap lowers. The density of states diagrams, complex dielectric function, electronic energy loss function, absorption coefficient, reflectivity, extinction coefficient, and refractive index were thoroughly explored. Results show that reducing the bandgap increases the dielectric function’s zero frequency limits. Origins of optical spectra peaks and characteristics have been identified.

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO2), Ozone (O3), Sulphur Dioxide (SO2), Fine Particles Matter (PM2.5), Coarse Particles Matter (PM10), and Ammonia (NH3). Hourly datasets were collected for air quality monitoring stations near Delhi, India, from November 25, 2020 to January 24, 2023. In this context, five intelligent models were developed, including Long Short-Term Memory (LSTM), Bidirectional Long-Short Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), Multilayer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). The modelling results revealed that Bi-LSTM model had the best predictability performance for forecasting CO with (R² = 0.979), NO with (R² = 0.961), NO2 with (R² = 0.956), SO2 with (R² = 0.955), PM10 with (R² = 0.9751) and NH3 with (R² = 0.971). Meanwhile, GRU and LSTM models performed better in forecasting O3 and PM2.5 with (R² = 0.9624) and (R² = 0.973), respectively. The current research provides illuminating visuals highlighting the potential of deep learning to comprehend air quality modeling, enabling improved environmental decisions.

The present study was conducted to explore and compare retinopathy and some effective indicators of blood biochemistry in diabetic patients treated with metformin and canagliflozin. The present observational cohort study was conducted in 2021–2022. The research populations were divided into two groups receiving canagliflozin or metformin. Biochemical indices and degree of retinopathy were measured at the beginning of administration, then 9 and 18 months later. Analysis was done in SPSS via the independent-samples T-test and repeated measures analysis of variance. As the present findings showed, the HbA1c index in both groups decreased over time. For HCT in both groups, no significant difference was found. The highest percentage of changes was that of BUN. The Cr level gradually increased in both groups. No significant change was observed in SGOT in the metformin group though it fluctuated in the canagliflozin group. Meanwhile, in SGPT condition, it was the opposite. Also, there is no change in the degree of retinopathy in both groups. Although metformin and canagliflozin both reduce the HbA1c level, they can cause complications especially if these drugs are not used at the right time and the right dose.

A descriptive study design was carried out in the pediatric hospitals in Baghdad City. The study is related to mothers’ knowledge of massage therapy for children with lower respiratory tract infections in pediatric hospitals in Baghdad City from March 1, 2023 to June 30, 2023.The aim of the study was to identify mothers of children with lower respiratory tract infections, who received massage therapy to reduce these infections in pediatric hospitals in Baghdad City. A non-probability (purposive) sample of 100 mothers was selected from medical wards in pediatric hospitals in Baghdad City. The results of the study revealed that in the quarter of mothers’ age in the sample, more than half of the mothers in the sample (52%) were in the age group between 20 and 29 years, 41% of them were government employees, and 84% of them were from urban areas. As for the educational level of the mothers, 27% of them were college and university graduates. As for the degree of kinship between the mother and her spouse, 52% of them were first-degree kinship. Furthermore, the results show that 45% of mothers had three or more children whose age at first birth was between 25 and 29 years. The results reveal a significant association between mothers’ knowledge and their educational level. However, no significant association was found between mothers’ knowledge in terms of their occupation and their place of residence. The study recommended the existence of an education and training program for mothers regarding children with lower chest infections. It focused on the care of children with lower chest infections and on improving the education level of mothers through media and booklets for children with respiratory tract infections, especially lower chest infections.

Generative Artificial Intelligence (AI) refers to advanced systems capable of creating new content by learning from vast datasets, including text, images, and code. These AI tools are increasingly being integrated into various sectors, including education, where they have the potential to enhance learning experiences. While the existing literature has primarily focused on the immediate educational benefits of these tools, such as enhanced learning and efficiency, less attention has been given to how these tools influence broader social sustainability goals, including equitable access and inclusive learning environments. Therefore, this study aims to fill this gap by developing a theoretical research model that combines the information system (IS) success model, technology-environmental, economic, and social sustainability theory (T-EESST), and privacy concerns. To evaluate the developed model, data were collected from 773 university students who were active users of Generative AI and analyzed using the PLS-SEM technique. The findings showed that service quality, system quality, and information quality have a significant positive effect on user satisfaction. Using Generative AI tools is found to be positively affected by user satisfaction. Interestingly, the findings supported the positive role of Generative AI in promoting social sustainability. However, no significant negative correlation was found between privacy concerns and Generative AI use. The findings provide several theoretical contributions and offer insights for various stakeholders in developing, implementing, and managing Generative AI tools in educational settings.

Objective: The aim of the study was to assess knowledge among primary healthcare (PHC) physicians about asthma in Iraq. Methods: A retrospective study was conducted in Iraq. Data were collected from 400 physicians who completed the electronic asthma training course for four weeks starting in March 2023. This study used aconvenience sampling technique via email from participating physicians. A 15-item questionnaire based on the National Asthma Guideline for Primary Care Physicians was used to assess their knowledge about asthma. The Mann–Whitney test was used to compare knowledge scores based on gender, prior training in chronic respiratory disorders, and a personal history of bronchial asthma. The Kruskal–Wallis test was used to compare knowledge scores depending on the specialty and years of practice as a PHC doctor. Results: The mean age of the participating doctors was 32.5 years, 67.9% were female, and 32.1% were male. Overall, the participants’ knowledge score was poor at less than 70%. The Mann–Whitney test revealed no significant difference in the knowledge scores of the participating physicians depending on their gender, previous asthma training, or personal history of bronchial asthma. A significant difference was found when comparing the knowledge scores of participating physicians depending on their specialty and duration of practice as a PHC doctor. Conclusion: This study showed that PHC physicians’ knowledge about asthma based on the recommended national guidelines of Iraq was poor. Further research is required to examine the parameters associated with improving both understanding of asthma and compliance with the guidelines.

Background The first pathogen to be designated a “red-alert” human pathogen is Acinetobacter baumannii , which is on the list of infections that must be treated urgently with new antibiotics. Infections due to this bacterium are on the rise, especially in patients admitted to hospital intensive care units. It can create biofilms on both biotic and abiotic surfaces. Objectives This study aimed to detect biofilm formation by A. baumannii phenotypically and genotypically. Materials and Methods A total of 250 samples were subjected to bacterial identification using the VITEK-2 compact system, which showed 42 A. baumannii isolates. Biofilm formation was phenotypically investigated using the microtiter plate method. Results The results revealed three stages of biofilm formation: 5 (11.6%) nonbiofilm, 13 (30.2%) weak biofilm, 15 (34.9%) moderate, and 10 (23.3%) strong biofilm formation. The isolates from intensive care unit (ICU) patients had strong, moderate, weak, and nonforming biofilm ability in higher rates of biofilm producers compared with the isolates from samples of hospital wards. The polymerase chain reaction (PCR) products showed genotypically positive results as follows: PapII 12 (31.5%), OmpA 11 (28.9%), and LuxR 8 (21%) out of 38 positive samples of A. baumannii for all genes. Conclusion Isolates of A. baumannii appeared in different stages of biofilm formation with a higher percentage rate in the ICU compared with hospitalized patients. The PCR products for isolates of A. baumannii showed that PapII , OmpA , and LuxR showed positive results.

Bacterial samples were collected from 100 patients suffering from chest (respiratory tract) infections, particularly pneumonia, admitted in Baghdad hospitals (Al-Yarmouk Teaching Hospital and Al-Karama Teaching Hospital) between January 2023 and March 2023. The patients were of both genders: males 51/100 (51%) and females 49/100 (49%). Sputum from infected patients was collected in sterile containers and then inoculated onto MacConkey through a swab containing crystal violet medium. The samples were inoculated onto the nutrient agar medium and incubated at 378C for 24-48 hours. The basic biochemical tests diagnosed bacterial isolates. The results showed Pseudomonas aeruginosa (39/100, 39%), Klebsiella pneumoniae (34/100, 34%), and Acinetobacter baumanii were (27/100, 27%) were isolated from respiratory tract infected patients who presented as outpatients and inpatients. These isolates were cultured on Mueller-Hinton agar medium to study their antibiotic resistance. The results of the antibiotic susceptibility test showed that K. pneumoniae were resistant to Piperacillin (PRL) 100 mg (31/34, 91.18%), Augmentin (AUG) 30 mg (28/34, 82.35%), Aztreonam (ATM) 30 mg (24/34, 70.59%), Imipenem (IMI) 10 mg (26/34, 76.47%), Meropenem (MRP) 10 mg (28/34, 82.35%), Vancomycin (VA) 30 mg (24/34, 70.59%), Ceftriaxone (CRO) 30 mg (24/34, 70.59%), Cefotaxime (CTX) 30 mg (26/34, 76.48%), Ceftazidime (CAZ) 30 mg (20/34, 58.82%), Cefepime (FEP) 30 mg (19/34, 55.89%), Azithromycin (AZM) 15 mg (22/34, 64.70%), Gentamycin (CN) 10 mg (25/34, 73.53%), Tetracycline (TE) 30 mg (23/34, 67.68%), Amikacin (AK) 30 mg (21, 61.77%), and Sulfamethoxazole (SMX) 100 mg (23/34, 67.68%). The aim of this study was to determine the effectiveness of antibiotics used in killing or inhibiting bacterial growth, and to study bacterial resistance to these antibiotics to reduce unnecessary antibiotics that lead to increased bacterial resistance through antibiotics for a cure.

The concept of love of life, which refers to a positive attitude towards one's own life, care for it and attachment to it, has recently captured the attention of researchers in the field of positive psychology. Despite its growing importance, there is a lack of research investigating the underlying mechanisms through which love of life impacts the flourishing and well-being of individuals. For the first time, the present study examined the mediating roles of optimism and hope in the association between love of life and flourishing in Turkish youth. The study comprised 374 young adults, aged between 18 and 24 years (55.3% female; Mean age = 20.94; SD = 1.78 years), who participated in an online survey assessing their levels of love of life, optimism, hope, and flourishing. Results from the mediation analysis revealed that love of life significantly predicted optimism, hope, and flourishing. Furthermore, optimism and hope had significant predictive effects on flourishing. Importantly, optimism and hope played a partial mediating role in explaining the positive influence of the love of life on individuals' flourishing. The findings suggest a positive association between love of life and heightened levels of optimism and hope. These psychological attributes, in turn, emerge as crucial factors contributing to increased flourishing. These results hold significant implications for the development of interventions focused on understanding how to foster the love of life and flourish through the cultivation of psychological strengths.

Currently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality.

This work explains application of Levy type solution method for bending analysis of a novel auxetic metamaterials reinforced doubly curved shell. The two opposite edges of the shell are assumed simply supported and two others one are assumed clamped. A shear deformable-based kinematic model is extended and the governing equations are derived using virtual work principle. After applying the Levy type solution on the governing equations, the obtained ordinary differential equation is solved using the eigenvalue–eigenvector method for clamped–clamped boundary conditions. The numerical results including displacement components are listed along the length and thickness directions for various material characteristics of graphene origami. The main novelty of this work is effect of clamped boundary conditions on the static responses of graphene origami reinforced cylindrical shell. This aim is performed using an analytical method.