Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials.

This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge-Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

Factors Affecting the Satisfaction of Women Employees in Health Sector: A Perception Study in Saudi Arabia.

Purpose: Broad evidence points out that women workers in Saudi Arabia face a range of inequitable work practices despite continued efforts for gender equality. The study aims to assess the satisfaction of Saudi women working in the health sector among certain factors in the work environment, factors that enable them to gain opportunities and benefits and make decisions. Patients and Methods: This is a cross-sectional study, which involved the use of closed-ended surveys on 261 Saudi women working in the healthcare sector. Results: Most of the females were aged between 25 and 34 years (59%) and more than half of them worked in the governmental sector (53%). Fifty-eight percent of the females hold clinical jobs, 25% of the administrative jobs were 37% of them have more than 19 years of working experience. The finding showed a significant association between female workers in health sector satisfaction with factors related to the workplace environment, training, and development, and their involvement in decision-making. Conclusion: Most women felt empowered when they received equitable tasks and were able to reach managerial-level positions in their organizations. Establishing a positive work environment characterized by opportunities has the potential to enhance women workers' satisfaction.

Retraction: Schwannoma: A Rare Etiology of Pancoast Syndrome.

[This retracts the article DOI: 10.7759/cureus.19418.].

Retraction: Sclerosing Encapsulating Peritonitis Following Recovery From COVID-19 Pneumonia.

[This retracts the article DOI: 10.7759/cureus.19306.].

Effects of Repeated in-vitro Exposure to Saudi Honey on Bacterial Resistance to Antibiotics and Biofilm Formation.

Introduction: Although Sumra and Sidr Saudi honey is widely used in traditional medicine due to its potent activity, it is unknown whether its prolonged usage has impact upon bacterial virulence or leading to reduced antibiotic sensitivity. Thus, the study aims to investigate the effect of prolonged (repeated) in-vitro exposure to Saudi honey on the antibiotic susceptibility profiles and biofilm formation of pathogenic bacteria. Methods: Several bacteria, including Staphylococcus aureus, Escherichia coli, and Acinetobacter baumannii, were in-vitro exposed ten times [passaged (P10)]to Sumra and Sider honey individually to introduce adapted bacteria (P10). Antibiotic susceptibility profiles of untreated (P0) and adapted (P10) bacteria were assessed using disc diffusion and microdilution assays. The tendency regarding biofilm formation following in-vitro exposure to honey (P10) was assessed using the Crystal violet staining method. Results: Adapted (P10) bacteria to both Sumra and Sidr honey showed an increased sensitivity to gentamicin, ceftazidime, ampicillin, amoxycillin/clavulanic acid, and ceftriaxone, when compared with the parent strains (P0). In addition, A. baumannii (P10) that was adapted to Sidr honey displayed a 4-fold increase in the minimal inhibitory concentration of the same honey following in-vitro exposure. 3-fold reduction in the tendency toward biofilm formation was observed for the Sumra-adapted (P10) methicillin resistant S. aureus strain, although there was a lower rate of reduction (1.5-fold) in biofilm formation by both the Sumra- and Sidr-adapted A. baumannii (P10) strains. Conclusion: The data highlight the positive impact of prolonged in-vitro exposure to Saudi honey (Sumra and Sider) for wound-associated bacteria since they displayed a significant increase in their sensitivity profiles to the tested antibiotic and a reduction in their ability to form biofilm. The increased bacterial sensitivity to antibiotics and a limited tendency toward biofilm formation would suggest the great potential therapeutic use of this Saudi honey (Sumra and Sidr) to treat wound infections.

Bifurcation analysis on ion sound and Langmuir solitary waves solutions to the stochastic models with multiplicative noises.

This article explores on a stochastic couple models of ion sound as well as Langmuir surges propagation involving multiplicative noises. We concentrate on the analytical stochastic solutions including the travelling and solitary waves by using the planner dynamical systematic approach. To apply the method, First effort is to convert the system of equations into the ordinary differential form and present it in form of a dynamic structure. Next analyze the nature of the critical points of the system and obtain the phase portraits on various conditions of the corresponding parameters. The analytic solutions of the system in an account of distinct energy states for each phase orbit are performed. We also show how the results are highly effective and interesting to realize their exciting physical as well as the geometrical phenomena based on the demonstration of the stochastic system involving ion sound as well as Langmuir surges. Descriptions of effectiveness of the multiplicative noise on the obtained solutions of the model, and its corresponding figures are demonstrated numerically.

Application of a physiologically based pharmacokinetic model in predicting captopril disposition in children with chronic kidney disease.

Over the last several decades, angiotensin-converting enzyme inhibitors (ACEIs) have been a staple in the treatment of hypertension and renovascular disorders in children. One of the ACEIs, captopril, is projected to have all the benefits of traditional vasodilators. However, conducting clinical trials for determining the pharmacokinetics (PK) of a drug is challenging, particularly in pediatrics. As a result, modeling and simulation methods have been developed to identify the safe and effective dosages of drugs. The physiologically based pharmacokinetic (PBPK) modeling is a well-established method that permits extrapolation from adult to juvenile populations. By using SIMCYP simulator, as a modeling platform, a previously developed PBPK drug-disease model of captopril was scaled to renally impaired pediatrics population for predicting captopril PK. The visual predictive checks, predicted/observed ratios (ratiopred/obs), and the average fold error of PK parameters were used for model evaluation. The model predictions were comparable with the reported PK data of captopril in mild and severe chronic kidney disease (CKD) patients, as the mean ratiopred/obs Cmax and AUC0-t were 1.44 (95% CI 1.07 - 1.80) and 1.26 (95% CI 0.93 - 1.59), respectively. The successfully developed captopril-CKD pediatric model can be used in suggesting drug dosing in children diagnosed with different stages of CKD.

Awareness About Total Knee Arthroplasty Among Hail Population.

BACKGROUND: Knee joint osteoarthritis is the most common among older adults. The knee joint needs to be treated surgically. The prevalence of this disorder is believed to be higher than 40% among people over 60. OBJECTIVE: To study public awareness about total knee replacement among the Hail population and to explore the relationship between the level of awareness and different socio-demographic factors. METHODS: A cross-sectional study was carried out in Hail, Saudi Arabia, using an online self-administered questionnaire created via Google Forms. Collected data were coded, entered, and analyzed using the Statistical Package for Social Sciences (SPSS; Version 23). RESULTS: A total of 431 participants enrolled in the study, of which 242 (56.15%) were males and 189 (43.9%) were females. Out of a total of 34 points, the average awareness score was 15 ± 6 (range: 5-33), with a mean percentage of awareness of 44.1%. The most reported causes of total knee replacement were overweight or obesity (82.6%), aging (61%), and rheumatoid arthritis (45.2%). The most known symptoms were pain (85.6%) and clicking sounds during movements (64.7%). Regarding the knowledge about the treatment, both physiotherapy and weight reduction were mentioned by 290 (67.3%), exercise by 244 (56.6%), and surgical intervention by 177 (41.1%) participants. The purpose of knee replacement surgery was to relieve pain according to 357 (82.8%) participants, improve walking quality according to 257 (59.6%) participants, and be able to do prayers (while kneeling) according to 215 (49.9%) participants. The most reported reasons that might prevent the participants from undergoing knee replacement surgery were the pain after surgery (59.4%), unavailability of surgeons (35.5%), and general complications from anesthesia (37.4%). Moreover, 188 (43.6%) participants thought that knee osteoarthritis is diagnosed using magnetic resonance imaging (MRI). Educational level was found to be associated with awareness score (p = .012). Conversely, gender, nationality, residence, and history of chronic disease were not found to be associated with awareness about total knee replacement (p = .548, .357, .734, and .639, respectively). CONCLUSION: An average level of awareness and knowledge regarding total knee replacement was observed. Educational level was found to be significantly associated with this awareness.

Quantitative Study of Non-Linear Convection Diffusion Equations for a Rotating-Disc Electrode.

Rotating-disc electrodes (RDEs) are favored technologies for analyzing electrochemical processes in electrically charged cells and other revolving machines, such as engines, compressors, gearboxes, and generators. The model is based on the concept of the nonlinear entropy convection-diffusion equations, which are constructed using semi-boundaries as an infinite notion. In this model, the surrogate solutions with different parameter values for the mathematical characterization of non-dimensional OH- and H+ ion concentrations at a rotating-disc electrode (RDE) are investigated using an intelligent hybrid technique by utilizing neural networks (NN) and the Levenberg-Marquardt algorithm (LMA). Reference solutions were calculated using the RK-4 numerical method. Through the training, validation, and testing sampling of reference solutions, the NN-BLMA approximations were recorded. Error histograms, absolute error, curve fitting graphs, and regression graphs validated the NN-BLMA's resilience and accuracy for the problem. Additionally, the comparison graphs between the reference solution and the NN-BLMA procedure established that our paradigm is reliable and accurate.

Efficacy of Biomarkers in Predicting Anastomotic Leakage After Gastrointestinal Resection: A Systematic Review and Meta-Analysis.

Our systematic review and meta-analysis were designed to evaluate the published literature from 2016 to 2019 on which the role of biomarkers in predicting the anastomotic leakage (AL) in gastroesophageal cancer surgery was investigated. This extensive literature search was conducted on the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica dataBASE (EMBASE) were used to gather the relevant information. No restrictions were made on the type of biomarkers. Wald or likelihood ratio (LRT) fixed effect tests were used to estimate the pooled prevalence to generate the proportions with 95% confidence intervals (CI) and model-fitted weights. For analyzing heterogeneity, the Cochran Q test and I square test were used. The Egger regression asymmetry test and funnel plot were used for publication. In this meta-analysis, a total of 15 studies were recruited with 1892 patients undergoing the resection. The pooled elevated C-reactive protein (CRP) was observed as 13.9% ranging from 11.6% to 16.1%. The pooled prevalence of other biomarkers with AL was observed as 4.4%. Significant heterogeneity was observed between studies that reported CRP and other biomarkers (92% each with chi-squared values of 78.80 and 122.78, respectively). However, no significant publication was observed between studies (p=0.61 and p=0.11, respectively). We concluded our study on this note that different biomarkers are involved in the diagnosis of AL. However, all these biomarkers are poor predictors with insufficient predictive value and sensitivity.

Dynamical interaction of solitary, periodic, rogue type wave solutions and multi-soliton solutions of the nonlinear models.

This study presents a modification form of modified simple equation method, namely new modified simple equation method. Multiple waves and interaction of soliton solutions of the Phi-4 and Klein-Gordon models are investigated via the scheme. Consequently, we derive various novels and more general interaction, and multiple wave solutions in term of exponential, hyperbolic, and trigonometric, rational function solutions combining with some free parameters. Taking special values of the free parameters, interaction of two dark bells, interaction of two bright bells, two kinks, two periodic waves, kink and soliton, kink-rogue wave solutions are obtained which is the key significance of this method. Properties of the achieved solutions have many useful descriptions of physical behavior, correlated to the solutions are attained in this work through plentiful 3D figures, density plot and 2D contour plots. The results derived may increase the prospect of performing significant experimentations and carry out probable applications.

Analysis of heat transmission in convective, radiative and moving rod with thermal conductivity using meta-heuristic-driven soft computing technique.

Abstract: The present study analyzes the thermal attribute of conductive, convective, and radiative moving fin with thermal conductivity and constant velocity. The basic Darcy's model is utilized to formulate the governing equation for the problem, which is further nondimensionalized using certain variables. Moreover, an effective soft computing paradigm based on the approximating ability of the feedforword artificial neural networks (FANN's) and meta-heuristic approach of global and local search optimization techniques is developed to quantify the effect of variations in significant parameters such as ambient temperature, radiation-conduction number, Peclet number, nonconstant thermal conductivity, and initial temperature parameter on the temperature gradient of the rod. The results by the proposed FANN-AOA-SQP algorithm are compared with radial basis function approximation, Runge-Kutta-Fehlberg method and machine-learning algorithms. An extensive graphical and statistical analysis based on solution curves and errors such as absolute errors, mean square error, standard deviations in Nash-Sutcliffe efficiency, mean absolute deviations, and Theil's inequality coefficient are performed to show the accuracy, ease of implementation, and robustness of the design scheme.

Trends in Intelligent and AI-Based Software Engineering Processes: A Deep Learning-Based Software Process Model Recommendation Method.

In recent years, numerous studies have successfully implemented machine learning strategies in a wide range of application areas. Therefore, several different deep learning models exist, each one tailored to a certain software task. Using deep learning models provides numerous advantages for the software development industry. Testing and maintaining software is a critical concern today. Software engineers have many responsibilities while developing a software system, including coding, testing, and delivering the software to users via the cloud. From this list, it is easy to see that each task calls for extensive organization and preparation, as well as access to a variety of resources. A developer may consult other code repositories, websites with related programming content, and even colleagues for information before attempting to build and test a solution to the problem at hand. In this investigation, we aim to identify the factors that led to developing the recommender. This system analyzes the recommender's performance and provides suggestions for improving the software based on users' opinions.

Time-Series Analysis and Healthcare Implications of COVID-19 Pandemic in Saudi Arabia.

The first case of coronavirus disease 2019 (COVID-19) in Saudi Arabia was reported on 2 March 2020. Since then, it has progressed rapidly and the number of cases has grown exponentially, reaching 788,294 cases on 22 June 2022. Accurately analyzing and predicting the spread of new COVID-19 cases is critical to develop a framework for universal pandemic preparedness as well as mitigating the disease's spread. To this end, the main aim of this paper is first to analyze the historical data of the disease gathered from 2 March 2020 to 20 June 2022 and second to use the collected data for forecasting the trajectory of COVID-19 in order to construct robust and accurate models. To the best of our knowledge, this study is the first that analyzes the outbreak of COVID-19 in Saudi Arabia for a long period (more than two years). To achieve this study aim, two techniques from the data analytics field, namely the auto-regressive integrated moving average (ARIMA) statistical technique and Prophet Facebook machine learning technique were investigated for predicting daily new infections, recoveries and deaths. Based on forecasting performance metrics, both models were found to be accurate and robust in forecasting the time series of COVID-19 in Saudi Arabia for the considered period (the coefficient of determination for example was in all cases more than 0.96) with a small superiority of the ARIMA model in terms of the forecasting ability and of Prophet in terms of simplicity and a few hyper-parameters. The findings of this study have yielded a realistic picture of the disease direction and provide useful insights for decision makers so as to be prepared for the future evolution of the pandemic. In addition, the results of this study have shown positive healthcare implications of the Saudi experience in fighting the disease and the relative efficiency of the taken measures.

Manual and Powered Toothbrushing Effectiveness on Autistic Children's Oral Hygiene Status.

Background: Autistic children want a lot of help cleaning their teeth and they have a higher risk of periodontal disorder and less caries than the general public. The study's purpose is to see how helpful manual and powered toothbrushing are for autistic kids aged 6-12 years. Materials and Methods: A total of 40 autistic children aged 6-12 years were chosen at random from Mangalore city schools. The simplified oral hygiene index was used to determine the baseline oral health (OHI-S). Children were split into two classes during an oral review. Children in Group 1 received a manual toothbrush, and those in Group 2 received a powered toothbrush. Many of the children were given Colgate fluoride-free toothpaste. Under the guidance of care professionals, children were taught to brush for 3 min. They were taught to count from 1 to 20 when brushing each buccal and lingual section. The index was rechecked at the conclusion of the 1st month (30th day) and the next month (3rd month) (90th day). On the 60th day, the recently learned techniques were reinforced (end of the 2nd month). Result: In Group 1, a statistically significant reduction in mean OHI-S scores was observed in baseline versus 30 days and baseline versus 90 days, while in Group 2, a statistically highly significant reduction in baseline versus 30 days, baseline versus 90 days, and 30 days versus 90 days was observed. Conclusion: Furthermore, in autism children, powered teeth brushing shows potential. Long-term follow-up and greater sample size are, however, needed.

Cardiac Surgery-Associated Acute Kidney Injury (CSA-AKI) in Adults and Pediatrics; Prevention is the Optimal Management.

BACKGROUND: Cardiac surgery-associated acute kidney injury (CSA-AKI) is a significant and severe complication that affects morbidity and mortality. We studied both pediatric and adult patients using the Acute Kidney Injury Network (AKIN) definition. METHODS: This was an observational retrospective cohort study done at King Abdulaziz University Hospital in Jeddah, Saudi Arabia, and approved by the ethical committee. The exclusion criteria were baseline serum creatinine (SCr) ≥ 4 mg/dL or preexisting renal failure requiring dialysis, reoperation, death within 24 hours postoperatively, and operative mortality or missing data. We included 941 patients in the analysis using statistical software SPSS, version 15.0. RESULTS: Of the total number of patients, 28.68% in the adult group and 20.07% in the pediatric group developed CSA-AKI. Adult risk factors included the age group 60-69 years, cardiopulmonary bypass (CPB), number of grafts, and hypertension. In the pediatric group, CPB, aortic cross-clamping (ACX), and the lower preoperative SCr were the main risk factors Conclusion: Conventional conservative management and preoperative identification of predictor risk factors are essential for preventing CSA-AKI, constituting the primary strategy for optimal management.

Heat Transfer Analysis of Nanofluid Flow in a Rotating System with Magnetic Field Using an Intelligent Strength Stochastic-Driven Approach.

This paper investigates the heat transfer of two-phase nanofluid flow between horizontal plates in a rotating system with a magnetic field and external forces. The basic continuity and momentum equations are considered to formulate the governing mathematical model of the problem. Furthermore, certain similarity transformations are used to reduce a governing system of non-linear partial differential equations (PDEs) into a non-linear system of ordinary differential equations. Moreover, an efficient stochastic technique based on feed-forward neural networks (FFNNs) with a back-propagated Levenberg-Marquardt (BLM) algorithm is developed to examine the effect of variations in various parameters on velocity, gravitational acceleration, temperature, and concentration profiles of the nanofluid. To validate the accuracy, efficiency, and computational complexity of the FFNN-BLM algorithm, different performance functions are defined based on mean absolute deviations (MAD), error in Nash-Sutcliffe efficiency (ENSE), and Theil's inequality coefficient (TIC). The approximate solutions achieved by the proposed technique are validated by comparing with the least square method (LSM), machine learning algorithms such as NARX-LM, and numerical solutions by the Runge-Kutta-Fehlberg method (RKFM). The results demonstrate that the mean percentage error in our solutions and values of ENSE, TIC, and MAD is almost zero, showing the design algorithm's robustness and correctness.

Investigation of Three-Dimensional Condensation Film Problem over an Inclined Rotating Disk Using a Nonlinear Autoregressive Exogenous Model.

This paper analyzed the three-dimensional (3D) condensation film problem over an inclined rotating disk. The mathematical model of the problem is governed by nonlinear partial differential equations (NPDE's), which are reduced to the system of nonlinear ordinary differential equations (NODE's) using a similarity transformation. Furthermore, the system of NODEs is solved by the supervised machine learning strategy of the nonlinear autoregressive exogenous (NARX) neural network model with the Levenberg-Marquardt algorithm. The dimensionless profiles of velocity, acceleration, and temperature are investigated under the effect of variations in the Prandtl number and normalized thickness of the film. The results demonstrate that increasing the Prandtl number causes an increase in the fluid's temperature profile. The solutions obtained by the proposed algorithm are compared with the state-of-the-art techniques that show the accuracy of the approximate solutions by NARX-BLM. The mean percentage errors in the results by the proposed algorithm for Θ(η), Ψ(η), k(η), -s(η), and (θ(η)) are 0.0000180%, 0.000084%, 0.0000135%, 0.000075%, and 0.00026%, respectively. The values of performance indicators, such as mean square error and absolute errors, are approaching zero. Thus, it validates the worth and efficiency of the design scheme.