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Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. Backpropagation neural networks, one of the supervised learning algorithms, is commonly used to train data networks by optimizing the error between actual and predicted values. To optimize this process of training data, Levenberg-Marquardt algorithm is applied; particularly beneficial for solving nonlinear fluid flow problems. Little knowledge is known about the ternary-hybrid nanofluid flow caused by a stretching surface with heat generation, viscous dissipation, magnetic effect and porosity etc. This article presents a novel machine-learning approach using backpropagation neural networks augmented with the Levenberg-Marquardt procedure to figure out the ternary-hybrid nanofluid flow generated by a stretching sheet. It uniquely examines the mixture of copper, Iron oxide and silicon dioxide nanoparticles inside a single base fluid within a magnetic field, tackling the research gaps in the effects of heat generation, viscous dissipation, porosity, and magnetic effects on fluid flow system and heat transfer. Shooting numerical technique (RK-5th) is used for solving the governing ordinary equations. Graphical illustration, error analysis, mean squared error, histograms and regression analysis justify the proposed method, showing better performance for ternary nanofluids.

Adult patients who have suffered acute cardiac or pulmonary failure are increasingly being treated using extracorporeal membrane oxygenation (ECMO), a cardiopulmonary bypass technique. While ECMO has improved the long-term outcomes of these patients, neurological injuries can occur from underlying illness or ECMO itself. Cerebral autoregulation (CA) allows the brain to maintain steady perfusion during changes in systemic blood pressure. Dysfunctional CA is a marker of acute brain injury and can worsen neurologic damage. Monitoring CA using invasive modalities can be risky in ECMO patients due to the necessity of anticoagulation therapy. Diffuse correlation spectroscopy (DCS) measures cerebral blood flow continuously, noninvasively, at the bedside, and can monitor CA. In this study, we compare DCS-based markers of CA in veno-arterial ECMO patients with and without acute brain injury. Adults undergoing ECMO were prospectively enrolled at a single tertiary hospital and underwent DCS and arterial blood pressure monitoring during ECMO. Neurologic injuries were identified using brain computerized tomography (CT) scans obtained in all patients. CA was calculated over a twenty-minute window via wavelet coherence analysis (WCA) over 0.05 Hz to 0.1 Hz and a Pearson correlation (DCSx) between cerebral blood flow measured by DCS and mean arterial pressure. Eleven ECMO patients who received CT neuroimaging were recruited. 5 (45%) patients were found to have neurologic injury. CA indices WCOH, the area under the curve of the WCA, were significantly higher for patients with neurological injuries compared to those without neurological injuries (right hemisphere p = 0.041, left hemisphere p = 0.041). %DCSx, percentage of time DCSx was above a threshold 0.4, were not significantly higher (right hemisphere p = 0.268, left hemisphere p = 0.073). DCS can be used to detect differences in CA for ECMO patients with neurological injuries compared to uninjured patients using WCA.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T–X family of distributions. The proposed family is called a “Novel Generalized Exponent Power X Family” of distributions. A three-parameters special sub-model of the proposed method is derived and named a “Novel Generalized Exponent Power Weibull” distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan–Quinn information criterion (HQINC), the Cramer–von-Misses (CRMI), and the ANDA (Anderson–Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

The objective of this study is to investigate the behavior of the Bayesian exponentially weighted moving average (EWMA) control chart in the presence of measurement error (ME). It explores the impact of different ranked set sampling designs and loss functions on the performance of the control chart when ME is present. The analysis incorporates a covariate model, multiple measurement methods, and a conjugate prior to account for ME. The performance evaluation of the proposed Bayesian EWMA control chart with ME includes metrics such as average run length and standard deviation of run lengths. The findings, obtained through Monte Carlo simulation and real data application, indicated that ME significantly affects the performance of the Bayesian EWMA control chart when RSS schemes are employed. Particularly noteworthy is the superior performance of the median RSS scheme compared to the other two schemes in the presence of ME.

The memory-type control charts, such as cumulative sum (CUSUM) and exponentially weighted moving average control chart, are more desirable for detecting a small or moderate shift in the production process of a location parameter. In this article, a novel Bayesian adaptive EWMA (AEWMA) control chat utilizing ranked set sampling (RSS) designs is proposed under two different loss functions, i.e., square error loss function (SELF) and linex loss function (LLF), and with informative prior distribution to monitor the mean shift of the normally distributed process. The extensive Monte Carlo simulation method is used to check the performance of the suggested Bayesian-AEWMA control chart using RSS schemes. The effectiveness of the proposed AEWMA control chart is evaluated through the average run length (ARL) and standard deviation of run length (SDRL). The results indicate that the proposed Bayesian control chart applying RSS schemes is more sensitive in detecting mean shifts than the existing Bayesian AEWAM control chart based on simple random sampling (SRS). Finally, to demonstrate the effectiveness of the proposed Bayesian-AEWMA control chart under different RSS schemes, we present a numerical example involving the hard-bake process in semiconductor fabrication. Our results show that the Bayesian-AEWMA control chart using RSS schemes outperforms the EWMA and AEWMA control charts utilizing the Bayesian approach under simple random sampling in detecting out-of-control signals.

In this paper, we propose a new family of probability distributions called the new flexible odd type-G family of distributions. We have used the Weibull distribution as the base reference to introduce a new heavy-tailed distribution, which we have fittingly named the new flexible odd-type Weibull distribution. The hazard function of the newly proposed distribution exhibits various shapes, such as increasing, decreasing, unimodal, S-shaped, J-shaped, reverse J-shaped, and bathtub shapes. For the proposed distribution, we have derived various distributional properties, including moments, moment generating function, characteristic function, mean deviation from median, and order statistics. Furthermore, actuarial measures, such as Value at Risk and Tail Value at Risk, are calculated, and it is empirically demonstrated that the proposed distribution exhibits heavy-tailed behavior. To estimate the model parameter, we applied the maximum likelihood estimation method and conducted a simulation study to assess its performance for the proposed distribution. From the simulation results, it is confirmed that as the sample size increases, the mean square error and biases decrease and approach zero. Finally, we evaluated three real data sets from the biomedical field to demonstrate the flexibility and superiority of the proposed distribution in comparison to the other nine probability distributions. The analyzed results (numerically as well as graphically) show that the newly proposed model provides a superior fit compared to the competing distributions.

In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient’s health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

Hepatitis C virus (HCV) transmission remains a significant public health concern. It is well documented globally, however, Nowshera district, Pakistan, is lacking such profile. This study aims to explore the relationship between HCV infection and several risk factors, including socio-demographic, clinical and personal life-style factors. This study using a cross-sectional design, examined 606 randomly selected individuals visiting the Pathology department at Qazi Hussain Medical Complex and District Headquarter (DHQ) Hospital Nowshera between May 1, 2022 and Jun 30, 2023. This research investigation employed a methodical approach involving formal interviews in conjunction with structured questionnaires to gather comprehensive information related to socio-demographic characteristics, clinical history, and personal hygiene practices. We collected 10 ml of blood samples and tested the separated serum to identify markers using the Immuno-Chromatographic Test (ICT) and the Enzyme-Linked Immunosorbent Assay (ELISA). IBM SPSS Statistics version 27 was used for data analysis. To measure the association between dependent variable and independent variables, a chi-square and risk analysis was carried out; multiple logistic regression was employed for modeling the risk factors associated with independent variable. A statistical significance level was established at a P-value of less than 0.05. In present study, which included 606 participants, 12% were found to be infected with HCV. Importantly it revealed that facial shave at barber (adjusted odds ratio, aOR = 40.65, p  = 0.000) represents the most prevalent mode of HCV transmission. Additionally, a strong association was observed between HCV infection and history of past surgery (RR = 2.98, p  = 0.001), HCV infected family member (aOR = 4.28, p  = 0.001), workplace injuries (aOR = 6.68, p  = 0.000), history of hospital admission (RR = 2.09), practicing ear and nose piercing (aOR = 5.01, p  = 0.001), dental treatment (RR = 2.31) and the frequency of injections (aOR = 8.607, p  = 0.000). These findings underscore the pressing need for targeted interventions. This study highlights the paramount importance of understanding the modes of HCV transmission and their associated risk factors. The results emphasize the need for educational initiatives, both within the healthcare sector and among the general public, to combat HCV transmission effectively. By implementing sterilization procedures and increasing awareness, we can make significant strides in reducing the burden of HCV infection. Moreover, proactive measures within families can help contain the spread of the virus, ultimately contributing to improved public health.

In this article, we introduce a novel Bayesian Max-EWMA control chart under various loss functions to concurrently monitor the mean and variance of a normally distributed process. The Bayesian Max-EWMA control chart exhibit strong overall performance in detecting shifts in both mean and dispersion across various magnitudes. To evaluate the performance of the proposed control chart, we employ Monte Carlo simulation methods to compute their run length characteristics. We conduct an extensive comparative analysis, contrasting the run length performance of our proposed charts with that of existing ones. Our findings highlight the heightened sensitivity of Bayesian Max-EWMA control chart to shifts of diverse magnitudes. Finally, to illustrate the efficacy of our Bayesian Max-EWMA control chart using various loss functions, we present a practical case study involving the hard-bake process in semiconductor manufacturing. Our results underscore the superior performance of the Bayesian Max-EWMA control chart in detecting out-of-control signals.

Sudden cardiac arrest (SCA) remains a critical public health challenge with mortality rates close to 90%. Current prognostication methods commonly analyze data of individual modalities separately and delay assessment until 72 hours post-arrest, creating a critical gap in early decision-making. Here, we introduce contrastive language and image reasoning with masked autoencoders (CLAIR), a novel multimodal framework integrating head computed tomography (CT) imaging with non-imaging clinical patient information through a cross-attention mechanism and contrastive learning approach to predict cerebral performance category (CPC) score in patients after cardiac arrest. In a retrospective study of 208 patients, we evaluated CLAIR against CT-based imaging-only assessment, as well as clinical evaluation by two experienced ICU neurologists. Our method achieved an AUC-ROC of 0.94 (CI: 0.90-0.97) when trained on a combination of multiplanar CT reconstructions and non-imaging clinical data, significantly outperforming CT scan-based imaging-only methods (AUC-ROC: 0.80, CI: 0.74-0.86) with statistical significance (p = 0.03). In a structured evaluation, the clinicians suggested that CLAIR assisted assessments resulted in fewer prognostic errors than non-assisted evaluations. Further, we demonstrate the applicability of our approach for early neurologic outcome prediction using CT scans obtained within the first 24 hours post-arrest (median acquisition time: 3.1 hours). Our results suggest that CLAIR can contribute value as a clinical assistive tool aiming at reliable early prognostication for post-cardiac arrest patients, potentially enabling more timely clinical decision-making, family counseling, and resource allocation.