Specific instability of boreholes drilled through rocks, such as borehole breakout, borehole collapse, and shale swelling, is a significant concern in drilling operations due to the adverse impact it has on drilling safety and efficiency. Despite considerable research on borehole instability, finding a solution to this problem remains elusive. The objective of this review is to examine the primary mechanisms of instability in rocks from a general physical instability principle perspective. A key criterion for the analysis of specific instability problems is the construction of comprehensive phase diagrams. Within this context, three main approaches and their partial implementation are discussed. The first approach involves constructing phase diagrams under the assumption that the borehole has already caved and aims to determine the final position of the cave wall. However, this approach presents several challenges. Caving may lead to severe kick-off or break-off problems, instability during the caving process, and complications in simultaneously modeling caving in a fluid-like porous material alongside flow filtration through the open walls of the borehole. The second approach entails developing phase diagrams that characterize the mechanical and hydraulic instability of the borehole wall being driven. These phase diagrams serve as specific instability criteria, but they face difficulties in generalizing across various failure mechanisms since they are typically non-quasi-static and are disregarded after failure occurs and the propagation ban is implemented. The third approach involves analyzing phase diagrams to investigate the results of the touch modes of compliance. However, these phase diagrams and the collapse of final boundary conditions often overlook the main depletions and in-situ impedances, which are crucial system-specific physical complements that enhance the classical mode of balance equation. Therefore, the integration of these factors is essential for a more comprehensive understanding of borehole instability.

The probability models of two and three mixed gamma distributions, specifically the Lindley and Sujatha distributions, will be enhanced through the application of random variable transformation techniques, resulting in the Power Lindley and Power Sujatha probability models. This study employs four probability models: Lindley, Sujatha, Power Lindley, and Power Sujatha, to analyze the survival time of diabetic patients. All probability models in this study will utilize the maximum likelihood method for parameter estimation. The optimal model will be determined based on a goodness-of-fit test, which will incorporate both graphical methods (density and cumulative distribution graphs) and numerical methods (Akaike's Information Criterion (AIC) and negative log-likelihood). The results of the goodness-of-fit test indicate that the model derived from the random variable transformation yields a superior probability model compared to its original form.

The current study aims to identify adopting Emerging Digital Technologies in developing country Medical. Was model study the, goal this achieve to order in designed starting from the factors influencing the adoption of the emerging digital technologies, up to the intention to adopting Emerging Digital Technologies in developing country Medical, based on the theory that supports technology represented by of adoption the (Push-Pull-Mooring Model) PPM, as according to extracted were factors influencing the that theory represented by the variable of push effects, which included dimensions of (low flexibility, limited scalability, low compatibility, and dissatisfaction). As for the variable of attraction effects, it included dimensions of (low cost, ease of maintenance, attractive alternatives, and expected value). As for the effects of awarding, it included two dimensions (expected risks, inertia), and the variable of behavioral intent to adopt emerging digital technologies., which the on relied study, the reached is that result a represents the and, implementation its for method survey analytical for tool a as studies previous on based designed was questionnaire data collection, as it was disseminated online. There were (203) responders out of the entire study population (575) individuals, and fourteen hypotheses emerged from the study model. The data was analyzed using the (SPSS) (AMOS) program to describe the variables of the study and test its hypotheses statistical the method modeling equations structural the using a significant effect of is there that showed results to the results of the study, the push effects represented by (low flexibility, limited scalability, and low compatibility), have a significant effect on dissatisfaction with the adoption of emerging digital technologies, and that low compatibility has a major impact on the behavioral intend to adopt emerging digital technologies. That the effects of attraction (low cost, ease of maintenance, and attractive alternatives) significantly affect the expected value of adopting emerging digital technologies. Researcher suggested the need to pay attention to adopting emerging digital technologies through the implementation of more studies, to define what emerging digital technologies are and to encourage people and companies to embrace these technologies, highlight their significance and the advantages they offer emerging digital technologies in their organizations.

Indeed, AI on the rise in health has given a way forward to cause revolutionary changes in diagnostic capability. With this, critical issues relating to bias and fairness arise, particularly regarding the under-representative in any given dataset. It attempts to explore the relevance of probably the leading challenge that biases in diagnostic artificial intelligence impose on health and the contribution of representative, various train datasets toward reducing such biases for guaranteeing health equity. This study is a quantitative research in which data through purposive sampling was collected with the use of survey methodology from 160 stakeholders, including clinicians, patients, and AI developers. The structured questionnaires captured perceptions related to AI bias, fairness, and the effectiveness of mitigation strategies. Results findings show great concerns over biases in AI systems, with 48.75% agreeing that bias within the training dataset is a major hindrance in clinical decision-making. A majority of 53.33% said it is the diversity in the datasets that helps in promoting fairness, while 76.88% support frequent algorithmic audits. Other key strategies recommended were collaboration among stakeholders and transparency. The current study, therefore, infers that these biases in diagnostic Artificial Intelligence tools need to be treated along the dimensions of data diversity, auditing algorithms, and collaboration with various stakeholders, to whom the transparency of processing should be allowed. Hence, there is a need for improvement in robust frameworks assurance for the equity of AI so that it could guarantee fairness, reliability, and inclusion among various populations for diagnoses.