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A novel distribution family named the Transmuted Odd Moment Exponential-G (TOME-G) family is introduced, derived from both the Odd Moment Exponential-G and Transmuted families. Within this family, three distinct models are proposed: the transmuted odd moment exponential Lomax (TOMEL) distribution, transmuted odd moment exponential Rayleigh (TOMER) distribution, and transmuted odd moment exponential exponential (TOMEE) distribution. The density function shape for each model, such as symmetric or asymmetric (including right-skewed, left-skewed, increasing, decreasing, and uni-modal), is discussed. Key statistical characteristics of the family, including the expansion of the probability density function (PDF), moments, incomplete moment, Mo generating function, residual life, and reversed residual life functions, are derived. Additionally, three estimation methods—maximum likelihood estimation (MLE), Ordinary Least Squares Estimator (OLSE), and Weighted Least Squares Estimator (WLE)—are examined for estimating model parameters. A simulation study is conducted to compare the performance of these estimation methods. We covered two real-life applications in this study, both involving life tests. One of them is in the engineering domain, aiming to study the time to failure between manufactured units. The other application is in the medical field, focusing on leukemia data for life tests.

AimsThis study aims to evaluate the association between the Oxygenation Index (OI) and the Oxygen Saturation Index (OSI) in extremely preterm infants. In addition, the study seeks to determine the predictive value of these indices for mortality in the first 7 days and Bronchopulmonary Dysplasia (BPD) at 36 weeks postmenstrual age (PMA).MethodsThis is a prospective observational study conducted at King Abdulaziz Medical City, Riyadh between October 2023 and May 2024, involving extremely preterm infants with clinical and ventilator data collected during the first 7 days of life. The predictive capabilities of OI and OSI for mortality within the first 7 days and BPD at 36 wks. PMA were assessed using Area Under the Curve (AUC) analysis, while associations between indices were explored through Spearman's correlation coefficient.ResultsThe study included 85 infants with a mean birth weight of 856 grams (SD = 243) and a mean gestational age of 26 weeks (SD = 1.8). There was a strong positive correlation between OI and OSI overall (r = 0.848, p

Abstract This research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios.

This research proposes the Kavya-Manoharan Unit Exponentiated Half Logistic (KM-UEHL) distribution as a novel tool for epidemiological modeling of COVID-19 data. Specifically designed to analyze data constrained to the unit interval, the KM-UEHL distribution builds upon the unit exponentiated half logistic model, making it suitable for various data from COVID-19. The paper emphasizes the KM-UEHL distribution's adaptability by examining its density and hazard rate functions. Its effectiveness is demonstrated in handling the diverse nature of COVID-19 data through these functions. Key characteristics like moments, quantile functions, stress-strength reliability, and entropy measures are also comprehensively investigated. Furthermore, the KM-UEHL distribution is employed for forecasting future COVID-19 data under a progressive Type-II censoring scheme, which acknowledges the time-dependent nature of data collection during outbreaks. The paper presents various methods for constructing prediction intervals for future-order statistics, including maximum likelihood estimation, Bayesian inference (both point and interval estimates), and upper-order statistics approaches. The Metropolis-Hastings and Gibbs sampling procedures are combined to create the Markov chain Monte Carlo simulations because it is mathematically difficult to acquire closed-form solutions for the posterior density function in the Bayesian framework. The theoretical developments are validated with numerical simulations, and the practical applicability of the KM-UEHL distribution is showcased using real-world COVID-19 datasets.

The arctan uniform distribution (AUD) is a brand-new bounded distribution that may be used for modeling a variety of existing bounded real-world datasets. Ranked set sampling (RSS) is a useful technique for parameter estimation when accurate measurement of the observation is challenging and/or expensive. In the current study, the parameter estimator of the AUD is addressed based on RSS and simple random sampling (SRS) techniques. Some of the popular conventional estimating techniques are considered. The efficiency of the produced estimates is compared using a Monte Carlo simulation. It appears that the maximum product spacing method has an advantage in assessing the quality of proposed estimates based on the outcomes of our simulations for both the SRS and RSS datasets. In comparison to estimates produced from the SRS datasets, it can be seen that those from the RSS datasets are more reliable. This implies that RSS is a more effective sampling technique in terms of generating estimates with a smaller mean squared error. The benefit of the RSS design over the SRS design is further supported by real data results.

Helicobacter pylori (H. pylori) is a Gram-negative, spiral-shaped bacterium that colonizes the gastric epithelium and is associated with a range of gastrointestinal disorders, exhibiting a global prevalence of approximately 50%. Despite the availability of treatment options, H. pylori frequently reemerges and demonstrates increasing antibiotic resistance, which diminishes the efficacy of conventional therapies. Consequently, it is imperative to explore non-antibiotic treatment alternatives to mitigate the inappropriate use of antibiotics. This review examines H. pylori infection, encompassing transmission pathways, treatment modalities, antibiotic resistance, and eradication strategies. Additionally, it discusses alternative therapeutic approaches such as probiotics, anti-biofilm agents, phytotherapy, phototherapy, phage therapy, lactoferrin therapy, and vaccine development. These strategies aim to reduce antimicrobial resistance and enhance treatment outcomes for H. pylori infections. While alternative therapies can maintain low bacterial levels, they do not achieve complete eradication of H. pylori. These therapies are designed to bolster the immune response, minimize side effects, and provide gastroprotective benefits, rendering them suitable for adjunctive use alongside conventional treatments. Probiotics may serve as adjunctive therapy for H. pylori; however, their effectiveness as a monotherapy is limited. Photodynamic and phage therapies exhibit potential in targeting H. pylori infections, including those caused by drug-resistant strains, without the use of antibiotics. The development of a reliable vaccine is also critical for the eradication of H. pylori. This review identifies candidate antigens such as VacA, CagA, and HspA, along with various vaccine formulations, including vector-based and subunit vaccines. Some vaccines have demonstrated efficacy in clinical trials, while others have shown robust immune protection in preclinical studies. Nevertheless, each of the aforementioned alternative therapies requires thorough preclinical and clinical evaluation to ascertain their efficacy, side effects, cost-effectiveness, and patient compliance.

ObjectiveTo explore the relationship between Oxygenation Index (OI) and Oxygen Saturation Index (OSI) among infants with Congenital Diaphragmatic Hernia (CDH), both within the first 24 h after birth and in extended observations in those who survived until their surgical intervention.MethodsSeven- years retrospective review of CDH cases at a single Level III neonatal intensive care unit. The correlations of various combinations of OI-OSI pairs were assessed using the Spearman's rho Correlation Coefficient. Additionally, during the initial 24 h, the correlations between admission (first), best (lowest), highest, and mean OI and OSI values were determined. The predictive ability of the first 24 h oxygen and oxygen saturation indices for mortality and other adverse outcomes were assessed using the Area Under the Curve (AUC) analysis.ResultsThirty-seven infants with CDH were included in the analysis. A strong correlation was observed between all pairs of OI/OSI (2,289) (Spearman's rho = 0.843), matched pairs of Postductal OI/OSI (1,232 pairs) (Spearman's rho = 0.835) and the unmatched pairs of Postductal OI and Preductal OSI (1,057 pairs) (Spearman's rho = 0.852). Using the regression equations for all pairs, matched and unmatched OI/OSI pairs, we deduced that for clinically pertinent OI thresholds of 10, 15, 20 and 40, the corresponding OSI values were 5, 8, 11, and 23, respectively. Furthermore, in the first 24 h, strong correlations were evident between OI/OSI: at admission (Spearman's rho = 0.783), best OI/OSI (Spearman's rho = 0.848), and highest OI/OSI (Spearman's rho = 0.921). The most robust correlation was observed between the mean OI/OSI with a Spearman's rho of 0.928. First (AUC = 0.849), best (AUC = 0.927), highest (AUC = 0.942) and mean day 1 OI (AUC = 0.946) were all predictive of mortality. Similarly, first (AUC = 1.00), best (AUC = 0.989), highest (AUC = 1.00) and the mean OSI in day 1 (AUC = 0.978) were all predictive of mortality. All of the OIs and OSIs in day 1 except for the admission OSI (AUC = 0.683) were predictive of pulmonary hypertension. Additionally, all of OI and OSI indices in the first 24-hour except for the best day 1 OI (AUC = 0.674) were predictive of the need for rescue HFOV.ConclusionThere were a strong correlation between the OI and OSI in infants with CDH. Oxygenation indices and OSI in the first 24 h were predictive of mortality and other adverse outcomes in infants with CDH.

Objective. To examine the postnatal predictors of survival and adverse outcome among Congenital Diaphragmatic Hernia (CDH) infants. Method. Six-year retrospective review of CDH infants born at tertiary neonatal unit. Results. Forty infants with CDH were included; 27 (67.5%) survived to discharge. Non-survivors had a higher best, mean, and highest oxygenation index (OI) ( P

In this article, a new three-parameter lifetime model called the Gull alpha power exponentiated exponential (GAPEE) distribution is introduced and studied by combining the Gull alpha power family of distributions and the exponentiated exponential distribution. The shapes of the probability density function (PDF) for the GAPEE distribution can be asymmetric shapes, like unimodal, decreasing, and right-skewed. In addition, the shapes of the hazard rate function (hrf) for the GAPEE distribution can be increasing, decreasing, and upside-down shaped. Several statistical features of the GAPEE distribution are computed. Eight estimation methods such as the maximum likelihood, Anderson-Darling, right-tail Anderson-Darling, left-tailed Anderson-Darling, Cramér-von Mises, least-squares, weighted least-squares, and maximum product of spacing are discussed to estimate the parameters of the GAPEE distribution. The flexibility and the importance of the GAPEE distribution were demonstrated utilizing three real-world datasets related to medical sciences. The GAPEE distribution is extremely adaptable and outperforms several well-known statistical models. A bivariate step-stress accelerated life test based on progressive type-I censoring using the model is presented. Minimizing the asymptotic variance of the maximum likelihood estimate of the log of the scale parameter at design stress under progressive type-I censoring yields an expression for the ideal test plan under progressive type-I censoring.

Abstract Alzheimer's disease, often known as dementia, is a severe neurodegenerative disorder that causes irreversible memory loss by destroying brain cells. People die because there is no specific treatment for this disease. Alzheimer's is most common among seniors 65 years and older. However, the progress of this disease can be reduced if it can be diagnosed earlier. Recently, artificial intelligence has instilled hope in the diagnosis of Alzheimer's disease by performing sophisticated analyses on extensive patient datasets, enabling the identification of subtle patterns that may elude human experts. Researchers have investigated various deep learning and machine learning models to diagnose this disease at an early stage using image datasets. In this paper, a new Deep learning (DL) methodology is proposed, where MRI images are fed into the model after applying various pre‐processing techniques. The proposed Alzheimer's disease detection approach adopts transfer learning for multi‐class classification using brain MRIs. The MRI Images are classified into four categories: mild dementia (MD), moderate dementia (MOD), very mild dementia (VMD), and non‐dementia (ND). The model is implemented and extensive performance analysis is performed. The finding shows that the model obtains 97.31% accuracy. The model outperforms the state‐of‐the‐art models in terms of accuracy, precision, recall, and F‐score.

Hepatocellular carcinoma (HCC) is one of the most prevalent malignant tumors globally, significantly affecting liver functions, thus necessitating the identification of biomarkers and effective therapeutics to improve HCC-based disabilities. This study aimed to identify prognostic biomarkers, signaling cascades, and candidate drugs for the treatment of HCC through integrated bioinformatics approaches such as functional enrichment analysis, survival analysis, molecular docking, and simulation. Differential expression and functional enrichment analyses revealed 176 common differentially expressed genes from two microarray datasets, GSE29721 and GSE49515, significantly involved in HCC development and progression. Topological analyses revealed 12 hub genes exhibiting elevated expression in patients with higher tumor stages and grades. Survival analyses indicated that 11 hub genes (CCNB1, AURKA, RACGAP1, CEP55, SMC4, RRM2, PRC1, CKAP2, SMC2, UHRF1, and FANCI) and three transcription factors (E2F1, CREB1, and NFYA) are strongly linked to poor patient survival. Finally, molecular docking and simulation identified seven candidate drugs with stable complexes to their target proteins: tozasertib (−9.8 kcal/mol), tamatinib (−9.6 kcal/mol), ilorasertib (−9.5 kcal/mol), hesperidin (−9.5 kcal/mol), PF−562271 (−9.3 kcal/mol), coumestrol (−8.4 kcal/mol), and clofarabine (−7.7 kcal/mol). These findings suggest that the identified hub genes and TFs could serve as valuable prognostic biomarkers and therapeutic targets for HCC-based disabilities.

Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model’s performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality.

Abstract Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

A neutrosophic set (NS) is a novel computing technique that accesses uncertain information by using three memberships. The main goal of this study is to come up with a novel approach called the ”possibility neutrosophic soft expert set” (PNSE-set), which is based on the idea that each element of the universe of discourse has a certain level of possibility. Based on this new approach, the set-theoretical operations on PNSE set (i.e complement, subset, equality, union, intersection, DeMorgans laws, AND-product, and OR-product operations) are introduced, along with illustrative examples and relevant laws. A generalized algorithm is proposed and applied to decision-making problems. Meanwhile, a similarity measure of two PNSE-sets is offered, and it’s tested in real-life applications involving medical diagnosis applications. Finally, this work is supported by a comparative analysis of three recent methods.

Background: The interpretation and reading of Electrocardiography (ECG) is one of the core medical skills, as there could be a negative impact on the management and safety of the patients if misinterpreted. Therefore, Our study aims to assess electrocardiographic interpretation competency among medical students at Umm Al-Qura University, Makkah, Saudi Arabia. Methods: A cross-sectional study was carried out in Umm Al-Qura University. Participants were under-graduated medical students. An online questionnaire was distributed in January during the 2021-2022 academic year. Results: 427 students from clinical years participated in the study, the distribution of clinical academic years was comparable, fourth year (126; 29.5%), fifth year (150; 35.1%), and sixth year (151; 35.4%). The mean score for all the students out of 10 is: (4.58, SD: 2.32). The number of students who scored 7.5 or more out of 10 (passed): 74 (17.3%). Conclusion: The under-graduated medical student’s overall performance was limited regarding their competency in ECG interpretation. Thus, it is recommended to provide more organized educational courses and supervised self-learning on the respective topic to enhance overall ECG interpretation skills.

This study uses an effective, recently extended Farlie–Gumbel–Morgenstern (EFGM) family to derive the distribution of concomitants of K-record upper values (CKRV). For this CKRV, the negative cumulative residual extropy (NCREX), weighted NCREX (WNCREX), negative cumulative extropy (NCEX), and weighted NCEX (WNCEX) are theoretically and numerically examined. This study presents several beautiful symmetrical and asymmetric relationships that these inaccuracy measurements satisfy. Additionally, empirical estimations are provided for these measures, and their visualizations enable users to verify their accuracy.

The mathematical characteristics of the mixture of Lindley model with 2-component (2-CMLM) are discussed. In this paper, we investigate both the practical and theoretical aspects of the 2-CMLM. We investigate several statistical features of the mixed model like probability generating function, cumulants, characteristic function, factorial moment generating function, mean time to failure, Mills Ratio, mean residual life. The density, hazard rate functions, mean, coefficient of variation, skewness, and kurtosis are all shown graphically. Furthermore, we use appropriate approaches such as maximum likelihood, least square and weighted least square methods to estimate the pertinent parameters of the mixture model. We use a simulation study to assess the performance of suggested methods. Eventually, modelling COVID-19 patient data demonstrates the effectiveness and utility of the 2-CMLM. The proposed model outperformed the two component mixture of exponential model as well as two component mixture of Weibull model in practical applications, indicating that it is a good candidate distribution for modelling COVID-19 and other related data sets.

BackgroundPost-acute coronavirus disease 2019 (COVID-19) syndrome, also known as long COVID, is a prolonged illness after the acute phase of COVID-19. Hospitalized patients were known to have persisting symptoms of fatigue, headache, dyspnea, and anosmia. There is a need to describe the characteristics of individuals with post-COVID-19 symptoms in comparison to the baseline characteristics.PurposeTo investigate the clinical and biochemical characteristics of people who recovered from COVID-19 after 6 months of discharge from the hospital.MethodsThis was a prospective follow-up investigation of hospitalized and discharged COVID-19 patients. Adult patients admitted to King Saud University Medical City, Riyadh, Saudi Arabia, with laboratory-confirmed COVID-19 and discharged were recruited. The baseline demographic information, comorbidities, vital signs and symptoms, laboratory parameters, COVID-19 therapy, and outcomes were collected from the medical records. Blood samples were collected for cytokines estimation. A detailed interview about signs and symptoms was undertaken during the follow-up.ResultsHalf of the followed-up people reported experiencing at least one of the COVID-19-related symptoms. The mean blood pressure was found higher in follow-up. People with the symptoms were characterized by low lymphocyte count, lower serum calcium levels, and hyperglycemia compared to people without any post-COVID-19 symptoms. Cytokines IL-8, VEGF, and MCP-1 were higher in people with the most frequent symptoms.ConclusionPeople with post-COVID-19 symptoms were characterized by lower lymphocyte count, lower serum calcium levels, and hyperglycemia compared to people without symptoms. Individuals with the most frequent post-COVID-19 symptoms had higher baseline pro-inflammatory, chemotactic, and angiogenic cytokines.

Background: COVID-19 is diagnosed using RT-PCR assays of samples from nasal and oropharyngeal swabs. People with negative RT-PCR often presented with clinical manifestations of COVID-19. The data on such patients are lacking. The present study aims to characterize the patients who were suspected COVID-19 cases and tested negative in RT-PCR compared to patients who had been tested RT-PCR positive. Methods: This is a retrospective, observational study of adult suspected and confirmed patients of COVID-19 admitted to King Saud University Medical City, Riyadh, Saudi Arabia, from 1st March 2020 until 30th November 2020. Laboratory confirmation is done through nasal/pharyngeal swab specimens, tested positive in RT-PCR assay. Patients with initial negative RT-PCR test results were assessed again within 48−72 h to avoid false-negative results. Patient data were extracted from the electronic medical files of each included patient using a predesigned case report form. Results: The study included 488 (80.93%) patients with RT-PCR swab results positive, and 115 (19.07%) patients who were negative. Respiratory rate and diastolic blood pressure were higher among the swab-positive cases. More number of swab-negative patients had comorbidities such as coronary heart disease, chronic kidney disease, and carcinoma. Fever, cough, and shortness of breath were reported higher among the swab-positive cases. ALT and AST, and LDH levels were found higher among RT-PCR-positive patients. Serum creatinine, blood urea nitrogen and troponin were more elevated in RT-PCR-negative patients. Antibiotics, anticoagulants, and corticosteroids were used more by swab-positive patients. Significantly higher number of RT-PCR-positive patients required proning, high-flow nasal cannula, non-invasive mechanical ventilation, and invasive mechanical ventilation. Acute cardiac ischemia and death were found to be similar among the patients. However, deaths occurred significantly earlier among the swab-positive cases when compared to the swab-negative group. Conclusion: Distinctive symptoms and markers of COVID-19 are more frequent among patients who had RT-PCR-positive results.

Recently, a new lifetime distribution known as a generalized Quasi Lindley distribution (GQLD) is suggested. In this paper, we modified the GQLD and suggested a two parameters lifetime distribution called as a weighted generalized Quasi Lindley distribution (WGQLD). The main mathematical properties of the WGQLD including the moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis, stochastic ordering, median deviation, harmonic mean, and reliability functions are derived. The model parameters are estimated by using the ordinary least squares, weighted least squares, maximum likelihood, maximum product of spacing's, Anderson-Darling and Cramer-von-Mises methods. The performances of the proposed estimators are compared based on numerical calculations for various values of the distribution parameters and sample sizes in terms of the mean squared error (MSE) and estimated values (Es). To demonstrate the applicability of the new model, four applications of various real data sets consist of the infected cases in Covid-19 in Algeria and Saudi Arabia, carbon fibers and rain fall are analyzed for illustration. It turns out that the WGQLD is empirically better than the other competing distributions considered in this study.

Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.

In this article, we intend to introduce and study a new two-parameter distribution as a new extension of the power Topp–Leone (PTL) distribution called the Kavya–Manoharan PTL (KMPTL) distribution. Several mathematical and statistical features of the KMPTL distribution, such as the quantile function, moments, generating function, and incomplete moments, are calculated. Some measures of entropy are investigated. The cumulative residual Rényi entropy (CRRE) is calculated. To estimate the parameters of the KMPTL distribution, both maximum likelihood and Bayesian estimation methods are used under simple random sample (SRS) and ranked set sampling (RSS). The simulation study was performed to be able to verify the model parameters of the KMPTL distribution using SRS and RSS to demonstrate that RSS is more efficient than SRS. We demonstrated that the KMPTL distribution has more flexibility than the PTL distribution and the other nine competitive statistical distributions: PTL, unit-Gompertz, unit-Lindley, Topp–Leone, unit generalized log Burr XII, unit exponential Pareto, Kumaraswamy, beta, Marshall-Olkin Kumaraswamy distributions employing two real-world datasets.

During the current COVID-19 pandemic, timing and preparedness for healthcare facilities are fundamental. This descriptive article narrates the Department of Internal Medicine at the College of Medicine and King Saud University Medical City (KSUMC) response before and during the COVID-19 outbreak by focusing on the preparedness and mitigation plans the department implemented for its clinical services. A COVID-19 taskforce was created to implement the pathways for healthcare workers' workflow organization, plan the effective use of isolation facilities and personal protective equipment, and enhance liaisons between departments in the medical city. Telehealth was utilized for outpatient services, and medications were delivered to patients via courier. Aerosol-generating procedures were conducted after thoroughly reviewing the clinical indications and time-related risks. Medical day unit services followed a strict multistep screening protocol and rescheduled nonurgent investigations and therapeutics when possible. The Department of Internal Medicine prepandemic preparedness helped significantly mitigate the pandemic impact on KSUMC. Prioritizing in-hospital clinical care according to the patients' conditions, full adherence to infection control measures, clear and efficient communication between departments, and utilizing telehealth were the key elements in maintaining state-of-the-art clinical care during the pandemic.

Statistical models perform an essential role in data analysis, and statisticians are constantly looking for novel or pretty recent statistical models to fit data sets across a broad variety of fields. In this study, we used modified Kies generalized transformation and the new power function to suggest an unique statistical model. We present and discuss a linear illustration of the density function. Theoretically, quantile function, characteristic function, stochastic ordering, mean, and moments are just a few of the structure properties we discuss. By defining an ideal statistical distribution for assessing the COVID-19 mortality rate, an attempt is performed to determine the model of COVID-19 spread in different nations like the United Kingdom and Italy. In some countries, the novel distribution have been shown to be more appropriate than existing competing models when fitted to COVID-19.

In this paper, we present the half logistic inverted Nadarajah–Haghigh (HL-INH) distribution, a novel extension of the inverted Nadarajah–Haghigh (INH) distribution. The probability density function (PDF) for the HL-INH distribution might have a unimodal, right skewness, or heavy-tailed shape for numerous parameter values; however, the shape forms of the hazard rate function (HRF) for the HL-INH distribution may be decreasing. Four specific entropy measurements were investigated. Some useful expansions for the HL-INH distribution were investigated. Several statistical and computational features of the HL-INH distribution were calculated. Using simple (SRS) and ranked set sampling (RSS), the parameters for the HL-INH distribution were estimated using the maximum likelihood (ML) technique. A simulation analysis was executed in order to determine the model parameters of the HL-INH distribution using the SRS and RSS methods, and RSS was shown to be more efficient than SRS. We demonstrate that the HL-INH distribution is more adaptable than the INH distribution and other statistical distributions when utilizing three real-world datasets.

Autism Spectrum Disorder (ASD) is a neurological impairment condition that severely impairs cognitive, linguistic, object recognition, interpersonal, and communication skills. Its main cause is genetic, and early treatment and identification can reduce the patient’s expensive medical costs and lengthy examinations. We developed a machine learning (ML) architecture that is capable of effectively analysing autistic children’s datasets and accurately classifying and identifying ASD traits. We considered the ASD screening dataset of toddlers in this study. We utilised the SMOTE method to balance the dataset, followed by feature transformation and selection methods. Then, we utilised several classification techniques in conjunction with a hyperparameter optimisation approach. The AdaBoost method yielded the best results among the classifiers. We employed ML and statistical approaches to identify the most crucial characteristics for the rapid recognition of ASD patients. We believe our proposed framework could be useful for early diagnosis and helpful for clinicians.

Three-dimensional video services delivered through wireless communication channels have to deal with numerous challenges due to the limitations of both the transmission channel’s bandwidth and receiving devices. Adverse channel conditions, delays, or jitters can result in bit errors and packet losses, which can alter the appearance of stereoscopic 3D (S3D) video. Due to the perception of dissimilar patterns by the two human eyes, they can not be fused into a stable composite pattern in the brain and hence try to dominate by suppressing each other. Thus, a psychovisual sensation that is called binocular rivalry occurs. As a result, undetectable changes causing irritating flickering effects are seen, leading to visual discomforts such as eye strain, headache, nausea, and weariness. This study addresses the observer’s quality of experience (QoE) by analyzing the binocular rivalry impact on the macroblock (MB) losses in a frame and its error propagation due to predictive frame encoding in stereoscopic video transmission systems. To simulate the processing of experimental videos, the Joint Test Model (JM) reference software has been used as it is recommended by the International Telecommunication Union (ITU). Existing error concealing techniques were then applied to the contiguous lost MBs for a variety of transmission impairments. In order to validate the authenticity of the simulated packet loss environment, several objective evaluations were carried out. Standard numbers of subjects were then engaged in the subjective testing of common 3D video sequences. The results were then statistically examined using a standard Student’s t-test, allowing the impact of binocular rivalry to be compared to that of a non-rivalry error condition. The major goal is to assure error-free video communication by minimizing the negative impacts of binocular rivalry and boosting the ability to efficiently integrate 3D video material to improve viewers’ overall QoE.

As all countries around the world, Saudi Arabia has been affected by the spread of covid-19 that started in a Chinese city by the end of December 2019. The impact of spread can be evaluated in different levels, loss of life as the country has already recorded more than 7500 deaths, the impacts on Saudi economy, schooling systems and many others sectors that have been impacted. The main aim of this work is to provide a statistical and deterministic analysis on the spread of covid-19 within the Kingdom of Saudi Arabia. Statistical investigation on collected data were performed, including fitting of data using some statistical distributions, seven days moving average for deaths, recovered and infected. Additionally, a simple SIR mathematical model based on the concept of piecewise modeling, where the model is defined within interval with the aim to capture the crossover displaying by the spread within the kingdom of Saudi Arabia.

In this article, a four parameter lifetime model called the Topp–Leone modified Weibull distribution is proposed. The suggested distribution can be considered as an alternative to Kumaraswamy Weibull, generalized modified Weibull, extend odd Weibull Lomax, Weibull-Lomax, Marshall-Olkin alpha power extended Weibull and exponentiated generalized alpha power exponential distributions, etc. The suggested model includes the Topp-Leone Weibull, Topp-Leone Linear failure rate, Topp-Leone exponential and Topp-Leone Rayleigh distributions as a special case. Several characteristics of the new suggested model including quantile function, moments, moment generating function, central moments, mean, variance, coefficient of skewness, coefficient of kurtosis, incomplete moments, the mean residual life and the mean inactive time are derived. The probability density function of the Topp–Leone modified Weibull distribution can be right skewed and uni-modal shaped but, the hazard rate function may be decreasing, increasing, J-shaped, U-shaped and bathtub on its parameters. Three different methods of estimation as; maximum likelihood, maximum product spacing and Bayesian methods are used to estimate the model parameters. For illustrative reasons, applications of the Topp–Leone modified Weibull model to four real data sets related to medical and engineering sciences are provided and contrasted with the fit reached by several other well-known distributions.

The origin of precursory signals that precede the P-waves is controversial, as it may be caused by ground effect (seismic nucleation phase) or instrumental effect due to a digitizer. Such signals cause errors in the automatic detection of P-wave arrival times and consequently decrease the accuracy of earthquake parameter estimations. In this paper, we proposed spectral analysis techniques to distinguish between different types of precursory segments, and to find if there is a relation between existence of these precursors and their corresponding earthquakes. These algorithms are applied to 1015 local events recorded by eleven permanent seismic stations belonging to the Egyptian National Seismic Network. Manual inspection of these events reveals the existence of these precursors. The investigations provided evidence that one type of these precursors originated because of an instrumental effect. This study shows the underlying causes of the precursory segment’s appearance prior to P-wave impulses. Many events had similar parameters; however, some of them had such precursors while others did not, in this inspection, multi resolution analysis details of the Discrete Wavelet Transform have been used. When large amplitudes for high frequency P-wave detail exceed 104 counts (1 count = 1 nm/s), the appearance percentage for the precursory segment is 100%.

A new class of distribution called the Fréchet binomial (FB) distribution is proposed. The new suggested model is very flexible because its probability density function can be unimodal, decreasing and skewed to the right. Furthermore, the hazard rate function can be increasing, decreasing, up-side-down and reversed-J form. Important mixture representations of the probability density function (pdf) and cumulative distribution function (cdf) are computed. Numerous sub-models of the FB distribution are explored. Numerous statistical and mathematical features of the FB distribution such as the quantile function (QUNF); moments (MO); incomplete MO (IMO); conditional MO (CMO); MO generating function (MOGF); probability weighted MO (PWMO); order statistics; and entropy are computed. When the life test is shortened at a certain time, acceptance sampling (ACS) plans for the new proposed distribution, FB distribution, are produced. The truncation time is supposed to be the median lifetime of the FB distribution multiplied by a set of parameters. The smallest sample size required ensures that the specified life test is obtained at a particular consumer’s risk. The numerical results for a particular consumer’s risk, FB distribution parameters and truncation time are generated. We discuss the method of maximum likelihood to estimate the model parameters. A simulation study was performed to assess the behavior of the estimates. Three real datasets are used to illustrate the importance and flexibility of the proposed model.

With the aim of identifying a probability model that not only correctly describes the stochastic behavior of extreme environmental factors such as excess rain, acid rain pH level, and concentrations of ozone, but also measures concentrations of NO2 and leads deliberations, etc., for a specific site or multiple site forms as well as for life testing experiments, we introduced a novel class of distributions known as the Sine Burr X−G family. Some exceptional prototypes of this class are proposed. Statistical assets of the presented class, such as density function, complete and incomplete moments, average deviation, and Lorenz and Bonferroni graphs, are proposed. Parameter estimation is made via the likelihood method. Moreover, the application is explained by using four real data sets. We have also illustrated the significance and elasticity of the proposed class in the above-mentioned stochastic phenomenon.

In this article, a new one parameter survival model is proposed using the Kavya–Manoharan (KM) transformation family and the inverse length biased exponential (ILBE) distribution. Statistical properties are obtained: quantiles, moments, incomplete moments and moment generating function. Different types of entropies such as Rényi entropy, Tsallis entropy, Havrda and Charvat entropy and Arimoto entropy are computed. Different measures of extropy such as extropy, cumulative residual extropy and the negative cumulative residual extropy are computed. When the lifetime of the item under use is assumed to follow the Kavya–Manoharan inverse length biased exponential (KMILBE) distribution, the progressive-stress accelerated life tests are considered. Some estimating approaches, such as the maximum likelihood, maximum product of spacing, least squares, and weighted least square estimations, are taken into account while using progressive type-II censoring. Furthermore, interval estimation is accomplished by determining the parameters’ approximate confidence intervals. The performance of the estimation approaches is investigated using Monte Carlo simulation. The relevance and flexibility of the model are demonstrated using two real datasets. The distribution is very flexible, and it outperforms many known distributions such as the inverse length biased, the inverse Lindley model, the Lindley, the inverse exponential, the sine inverse exponential and the sine inverse Rayleigh model.

A new continuous version of the inverse flexible Weibull model is proposed and studied. Some of its properties such as quantile function, moments and generating functions, incomplete moments, mean deviation, Lorenz and Bonferroni curves, the mean residual life function, the mean inactivity time, and the strong mean inactivity time are derived. The failure rate of the new model can be “increasing-constant,” “bathtub-constant,” “bathtub,” “constant,” “J-HRF,” “upside down bathtub,” “increasing,” “upside down-increasing-constant,” and “upside down.” Different copulas are used for deriving many bivariate and multivariate type extensions. Different non-Bayesian well-known estimation methods under uncensored scheme are considered and discussed such as the maximum likelihood estimation, Anderson Darling estimation, ordinary least square estimation, Cramér-von-Mises estimation, weighted least square estimation, and right tail Anderson Darling estimation methods. Simulation studies are performed for comparing these estimation methods. Finally, two real datasets are analyzed to illustrate the importance of the new model.

In survival analysis, the two-parameter inverse Lomax distribution is an important lifetime distribution. In this study, the estimation of R=P Y

In this paper, a new flexible generator of continuous lifespan models referred to as the Topp-Leone Weibull G (TLWG) family is developed and studied. Several mathematical characteristics have been investigated. The new hazard rate of the new model can be “monotonically increasing,” “monotonically decreasing,” “bathtub,” and “J shape.” The Farlie Gumbel Morgenstern (FGM) and the modified FGM (MFGM) families and Clayton Copula (CCO) are used to describe and display simple type Copula. We discuss the estimation of the model parameters by the maximum likelihood (MLL) estimations. Simulations are carried out to show the consistency and efficiency of parameter estimates, and finally, real data sets are used to demonstrate the flexibility and potential usefulness of the proposed family of algorithms by using the TLW exponential model as example of the new suggested family.

The Truncated Cauchy Power Weibull-G class is presented as a new family of distributions. Unique models for this family are presented in this paper. The statistical aspects of the family are explored, including the expansion of the density function, moments, incomplete moments (IMOs), residual life and reversed residual life functions, and entropy. The maximum likelihood (ML) and Bayesian estimations are developed based on the Type-II censored sample. The properties of Bayes estimators of the parameters are studied under different loss functions (squared error loss function and LINEX loss function). To create Markov-chain Monte Carlo samples from the posterior density, the Metropolis–Hasting technique was used with posterior density. Using non-informative and informative priors, a full simulation technique was carried out. The maximum likelihood estimator was compared to the Bayesian estimators using Monte Carlo simulation. To compare the performances of the suggested estimators, a simulation study was carried out. Real-world data sets, such as strength measured in GPA for single carbon fibers and impregnated 1000-carbon fiber tows, maximum stress per cycle at 31,000 psi, and COVID-19 data were used to demonstrate the relevance and flexibility of the suggested method. The suggested models are then compared to comparable models such as the Marshall–Olkin alpha power exponential, the extended odd Weibull exponential, the Weibull–Rayleigh, the Weibull–Lomax, and the exponential Lomax distributions.

Introduction: Segmental intestinal volvulus without underlying causes is called Primary Segmental Volvulus. Herein, we report a rare case of segmental ileal volvulus associated with acute appendicitis. Case Presentation: A 3-year-old boy presented with complaints of diarrhoea associated with vomiting, central abdominal pain, and decreased activity for 2 days. He was referred to paediatric surgery as a case of acute appendicitis. Ultrasound of the abdomen showed acute appendicitis with ileoileal target sign. Laparotomy was done, which showed segmental ileal volvulus without malrotation associated with inflamed appendix and fecolith at the tip. Appendectomy plus intestinal resection and anastomosis were also done. Discussion: Primary Segmental Intestinal Volvulus is a very rare condition, and only a few cases have been reported in the literature. It has a prevalence of 22.9%–26.3% in children.In our case, ultrasound findings are suggestive of acute appendicitis and suspicion of intussusception. Intraoperatively, segmental intestinal volvulus was observed along with inflamed appendix and a fecolith at its tip. Conclusion: Primary Segmental Intestinal Volvulus should be considered as a differential diagnosis of any child patient who came with small bowel obstruction symptom. Keywords: Segmental intestinal volvulus, Malrotation, Fecolith, Acute appendicitis, Case report

This work will address the problem of estimating the parameters for the Nadaraj ah–Haghighi (NH) distribution using progressive Type-1 censoring (PT1C) utilizing Bayesian and non-Bayesian approaches. To apply PT1C, censoring times for each stage of censoring needed to be known before the experiment started. To solve this issue of censoring time selection, qauntiles from the NH lifetime distribution will be used as PT1C censoring time points. Maximum likelihood (ML) estimators (MLEs) and asymptotic confidence intervals (ACoIs) are produced with a focus on the censoring technique. Bayes estimates (BEs) and accompanying maximum posterior density (PD) credible interval estimations are also created via the squared error (SEr) loss function. The BEs are evaluated using the Markov Chain Monte Carlo (MCMC) technique and the Metropolis–Hasting (MH) algorithm. An analysis of an actual data set demonstrates the theoretical implications of MLEs and BEs for defined schemes of PT1C samples. Finally, simulation results will be used to compare the performance of the various recommended estimators.

Exponentiated power Lindley distribution is proposed as a generalization of some widely well-known distributions such as Lindley, power Lindley, and generalized Lindley distributions. In this paper, the exact explicit expressions for moments of order statistics from the exponentiated power Lindley distribution are derived. By using these relations, the best linear unbiased estimates of the location and scale parameters, based on type-II right-censored sample, are obtained. Next, the mean, variance, and coefficients of skewness and kurtosis of some certain linear functions of order statistics are calculated and then used to derive the approximate confidence interval for the location and scale parameters using the Edgeworth approximation. Finally, some numerical illustrations and two real data applications are presented.

In this paper, we obtained several recurrence relations for the single and product moments under progressively Type-II right censored order statistics and then use these results to compute the means and variances of two parameter reduced Kies distribution. Besides, these moments are then utilized to derived best linear unbiased estimators of the scale and location parameters of two parameter reduced Kies distribution. The parameters of the two parameter reduced Kies distribution are estimated under progressive type-II censoring scheme. The model parameters are estimated using the maximum likelihood estimation method. Further, we explore the asymptotic confidence intervals for the model parameters. Monte Carlo simulations are performed to compare between the proposed estimation methods under progressive type-II censoring scheme. Based on our study, we can conclude that maximum likelihood estimators is decreasing with respect to an increase of the schemes and comparing the three censoring schemes, it is clear that the mean sum of squares, confidence interval lengths are smaller for scheme 1 than schemes 2 and 3.