Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory.

The simultaneous monitoring of both process mean and dispersion, particularly in normal processes, has garnered significant attention within the field. In this article, we present a new Bayesian Max-EWMA control chart that is intended to track a non-normal process mean and dispersion simultaneously. This is accomplished through the utilization of the inverse response function, especially in cases where the procedure follows a Weibull distribution. We used the average run length (ARL) and the standard deviation of run length (SDRL) to assess the efficacy of our suggested control chart. Next, we contrast our suggested control chart's performance with an already-existing Max-EWMA control chart. Our results show that compared to the control chart under consideration, the proposed control chart exhibits a higher degree of sensitivity. Finally, we present a useful case study centered around the hard-bake process in the semiconductor manufacturing sector to demonstrate the performance of our Bayesian Max-EWMA control chart under different Loss Functions (LFs) for a Weibull process. The case study highlights how flexible the chart is to various situations. Our results offer strong proof of the outstanding ability of the Bayesian Max-EWMA control chart to quickly identify out-of-control signals during the hard-bake procedure. This in turn significantly contributes to the enhancement of process monitoring and quality control.

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques.

The independent detection and classification of brain malignancies using magnetic resonance imaging (MRI) can present challenges and the potential for error due to the intricate nature and time-consuming process involved. The complexity of the brain tumor identification process primarily stems from the need for a comprehensive evaluation spanning multiple modules. The advancement of deep learning (DL) has facilitated the emergence of automated medical image processing and diagnostics solutions, thereby offering a potential resolution to this issue. Convolutional neural networks (CNNs) represent a prominent methodology in visual learning and image categorization. The present study introduces a novel methodology integrating image enhancement techniques, specifically, Gaussian-blur-based sharpening and Adaptive Histogram Equalization using CLAHE, with the proposed model. This approach aims to effectively classify different categories of brain tumors, including glioma, meningioma, and pituitary tumor, as well as cases without tumors. The algorithm underwent comprehensive testing using benchmarked data from the published literature, and the results were compared with pre-trained models, including VGG16, ResNet50, VGG19, InceptionV3, and MobileNetV2. The experimental findings of the proposed method demonstrated a noteworthy classification accuracy of 97.84%, a precision success rate of 97.85%, a recall rate of 97.85%, and an F1-score of 97.90%. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring brain tumor types. The technique exhibited commendable generalization properties, rendering it a valuable asset in medicine for aiding physicians in making precise and proficient brain diagnoses.

The optimal control chart selection for monitoring COVID-19 phases: a case study of daily deaths in the USA.

Epidemiologists frequently adopt statistical process control tools, like control charts, to detect changes in the incidence or prevalence of a specific disease in real time, thereby protecting against outbreaks and emergent health concerns. Control charts have proven essential in instantly identifying fluctuations in infection rates, spotting emerging patterns, and enabling timely reaction measures in the context of COVID-19 monitoring. This study aims to review and select an optimal control chart in epidemiology to monitor variations in COVID-19 deaths and understand pandemic mortality patterns. An essential aspect of the present study is selecting an appropriate monitoring technique for distinct deaths in the USA in seven phases, including pre-growth, growth, and post-growth phases. Stage-1 evaluated control chart applications in epidemiology departments of 12 countries between 2000 and 2022. The study assessed various control charts and identified the optimal one based on maximum shift detection using sample data. This study considered at Shewhart ($\bar X$, $R$, $C$) control charts and exponentially weighted moving average (EWMA) control chart with smoothing parameters λ = 0.25, 0.5, 0.75, and 1 were all investigated in this study. In Stage-2, we applied the EWMA control chart for monitoring because of its outstanding shift detection capabilities and compatibility with the present data. Daily deaths have been monitored from March 2020 to February 2023. Control charts in epidemiology show growing use, with the USA leading at 42% applications among top countries. During the application on COVID-19 deaths, the EWMA chart accurately depicted mortality dynamics from March 2020 to February 2022, indicating six distinct stages of death. The third and fifth waves were extremely catastrophic, resulting in a considerable loss of life. Significantly, a persistent sixth wave appeared from March 2022 to February 2023. The EWMA map effectively determined the peaks associated with each wave by thoroughly examining the time and amount of deaths, providing vital insights into the pandemic's progression. The severity of each wave was measured by the average number of deaths $W5(1899)\,\gt\,W3(1881)\,\gt\,W4(1393)\,\gt\,W1(1036)\,\gt\,W2(853)\,\gt\,(W6(473)$. The USA entered a seventh phase (6th wave) from March 2022 to February 2023, marked by fewer deaths. While reassuring, it remains crucial to maintain vaccinations and pandemic control measures. Control charts enable early detection of daily COVID-19 deaths, providing a systematic strategy for government and medical staff. Incorporating the EWMA chart for monitoring immunizations, cases, and deaths is recommended.

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning.

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.

Improved adaptive EWMA control chart for process location with applications in groundwater physicochemical parameters and glass manufacturing industry.

The adaptive exponentially weighted moving average (AEWMA) control charts are the advanced form of classical memory control charts used for efficiently monitoring small-to-large shifts in the process parameters (location and/or dispersion). These AEWMA control charts estimate the unknown shifts using exponentially weighted moving average (EWMA) or cumulative sum (CUSUM) control charts statistics. The hybrid EWMA (HEWMA) control chart is preferred over classical memory control charts to detect early shifts in process parameters. So, this study presents a new auxiliary information-based (AIB) AEWMA (IAEWMAAIB) control chart for process location that estimates the unknown location shift using HEWMA statistic. The objective is to develop an unbiased location shift estimator using HEWMA statistic and then adaptively update the smoothing constant. The shift estimation using HEWMA statistic instead of EWMA or CUSUM statistics boosts the performance of the proposed IAEWMAAIB control chart. The Monte Carlo simulation technique is used to get the numerical results. Famous performance evaluation measures like average run length, extra quadratic loss, relative average run length, and performance comparison index are used to evaluate the performance of the proposed chart with existing counterparts. The comparison reveals the superiority of the proposed control chart. Finally, two real-life applications from the glass manufacturing industry and physicochemical parameters of groundwater are considered to show the proposed control chart's implementation procedure and dominance.

Ethnomedicinal and cultural practices of mammals and birds in the vicinity of river Chenab, Punjab-Pakistan.

BACKGROUND: Although, use of animal species in disease treatment and culture practices is as ancient as that of plant species; however ethnomedicinal uses and cultural values of animal species have rarely been reported. Present study is the first report on the medicinal uses of mammals and bird species in Pakistan. METHODS: Questionnaires and semi-structured interviews were applied to collect qualitative and quantitative data from local informants (N = 109). Relative frequency of mention (RFM), fidelity level (FL), relative popularity level (RPL), similarity index (SI) and rank order priority (ROP) indices were used to analyzed the data. RESULTS: One hundred and eight species of animals, which include: 83% birds and 17% mammals were documented. In total 30 mammalian and 28 birds' species were used to treat various diseases such as rheumatic disorders, skin infections and sexual weakness among several others. Fats, flesh, blood, milk and eggs were the most commonly utilized body parts. Bos taurus, Bubalus bubalis, Capra aegagrus hircus, Felis domesticus, Lepus nigricollis dayanus and Ovis aries (mammals) and Anas platyrhynchos domesticus, Columba livia, Coturnix coturnix, Gallus gallus and Passer domesticus (birds) were the highly utilized species. Medicinal and cultural uses of 30% mammals and 46% birds were reported for the first time, whereas 33% mammals and 79% birds depicted zero similarity with previous reports. CONCLUSION: Present study exhibits significant ethnozoological knowledge of local inhabitants and their strong association with animal species, which could be helpful in sustainable use of biodiversity of the region. Additionally, in vitro and in vivo evaluation of biological activities in the mammalian and birds' species with maximum fidelity level and frequency of mention could be important to discover animal based novel drugs. Some commonly used mammals and birds species of the study area.

Door to needle time of streptokinase and ST segment resolution assessing the efficacy of reperfusion therapy at Karachi Institute of Heart Diseases.

BACKGROUND: Early start of treatment including coronary revascularisation has been recognised as crucial variable in the outcome of acute ST-segment Elevation Myocardial Infarction (STEMI). Objectives of the study were to determine the magnitude of ST-segment resolution after thrombolytic therapy predicts short- and long-term outcomes in patients with an Acute Myocardial Infarction (AMI). METHODS: The duration of quasi experimental study was 3 years, from July 2006 to June 2009, conducted at Karachi Institute of Heart Diseases. Total 1,023 patients of STEMI treated with streptokinase (SK) were enrolled in the study. RESULT: Of the total 1023, 689 (67.3%) patients were males and 334 (32.6%) were females. Six hundred and twenty-nine (61.5%) were successfully resolved after thrombolytic therapy while in 395 (38.5%) patients ST-segment could not resolve into 3 conventional ST-segment resolution categories at 60 minute and 90 minute after thrombolysis. Three hundred and twelve (30%) and 444 (43.4%) with complete resolution, 344 (33.62%) and 325 (31.76%) with partial resolution, 367 (35.8%) and 491 (19.29%) were with no resolution at 60 and 90 minutes respectively. CONCLUSION: Shock, congestive heart failure, and recurrent angina and ischemia occurred more often in patients with partial or no ST resolution as compare to complete resolution.