This review article examines the integration of Artificial Intelligence (AI) in Sequential Batch Reactors (SBRs) to enhance operational efficiency in wastewater treatment. SBRs facilitate the treatment process through distinct phases—fill, react, settle, and decant—within a single tank. The transitions between these phases are pivotal for optimizing treatment performance. Traditional control strategies often rely on manual interventions and fixed schedules, which may lead to inefficiencies and suboptimal outcomes. AI technologies, including machine learning and predictive analytics, present a transformative opportunity to improve operational management by enabling real-time monitoring, adaptive control, and predictive maintenance. This article discusses various AI applications in SBR systems, highlighting their potential to enhance treatment efficiency, reduce energy use, and improve effluent quality. Furthermore, it addresses the challenges and future directions of implementing AI within wastewater treatment frameworks, emphasizing the need for continued innovation to meet evolving environmental standards. Through a comprehensive analysis, this review underscores the role of AI in revolutionizing wastewater treatment processes, paving the way for more sustainable and efficient practices in the management of water resources.

Abstract: Abattoir waste can be detrimental to the environment if improperly managed. In Nigeria, such wastes are dump in the open or nearby stream/river course, causing high oxygen demand, becoming heavy breeders of pathogens and micro-organism as well as increasing the risks of zoonotic disease outbreak threatening the quality of life and public health. This study explored the use of vermifiltration; a low-cost, odorless and sustainable wastewater treatment method that uses earthworms and microorganisms to break down abattoir waste effluent. A bio-reactor (presence of earthworms) and a control reactor system (absence of earthworms) each consisting of a collection tank, vermifiltration bed and a fabricated treated water collection tank used to treat the abattoir wastewater by varying the flow rate and concentration of the effluent that entered the vermifiltration system. This was across four levels and three replicates with Hydraulic Retention Time of 42,011.49 sec (116.69hrs) and Hydraulic Loading Rate of 8.7x10-6 m/s. Physico-chemical properties of discharge from bio-reactor recorded 11.010±1.755NTU for turbidity, 24.228±6.319mg/L for dissolved oxygen, 7.562±3.402mg/L for total suspended solid, 51.27±14.00mg/L for total dissolved solid, 6.3562±0.2584 for pH, 250.0±101.1CFU/mL for total coli count and 1296±200CFU/mL for total bacteria count respectively as against results from the control reactor which had mean values of 36.351±3.993NTU for turbidity, 14.277±2.241mg/L for dissolved oxygen, 19.569±1.996mg/L for total suspended solid, 137.21±37.40mg/L for total dissolved solid, 5.0958±0.5596 for pH, 2500.0±522.7CFU/mL for total coli count and 6413±1725CFU/mL for total bacteria count. The vermifiltration process was thus able to greatly reduce and filter out the pathogens found in the influent through the combined action of the earthworms and the filter layer of fine sand and gravel to a high degree. Thus, confirming that vermifiltration system could achieve good performance in the treatment of abattoir waste water.

Abstract: The purpose of this study is to correctly assess the current status of railway development and forecast the development trend, assess the advantages, limitations and make recommendations to perfect the railway development strategy to 2045. To have arguments to assess the current status and make accurate forecasts correctly, this study studies the State and Ho Chi Minh City documents on the current status of railway construction and development in the past and the railway development plan to 2045. The results show that, in the past, urban railways have received great attention and investment, but the results have not been high, only 01 railway line has been put into operation. The implementation of other railway lines still faces many difficulties. Based on the assessment of the current situation and the railway development strategy of the government and Ho Chi Minh City, this study identifies the limitations and causes of these limitations, thereby proposing solutions to improve the railway development strategy in the future.

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.

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.

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.

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.