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Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model's categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification, clustering, or prediction), the models' input and output parameters, the performance metrics, the optimal model, and the model's benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries. [ABSTRACT FROM AUTHOR]

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies. [ABSTRACT FROM AUTHOR]

Forecasting with a precise evaluation of new cases and the rate of occurrence is essential for the effective implementation of governmental initiatives and early prevention of any infectious illness. Despite the extensive research done on the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) virus since the outbreak, not enough knowledge could be gained about the virus in terms of immune function, virus-host interactions, pathogenesis, propagation, and mutations. In this paper, various statistical models, namely Supervised Machine Learning techniques (ML), are being discussed for previous diseases and for the recent COVID-19 pandemic. Namely, the use of the Support Vector Machine (SVM) model and a variety of time series regression models is demonstrated for several infectious diseases, including COVID-19. As infectious diseases evolve throughout time, they provide data on a single variable, that is, "the figure of contaminations that occurred over time"; thus, researchers tend to use time series models to fit the data and make predictions using different evaluation metrics to find the best-fitting model. This review developed ideas about how to enhance the current modeling techniques. Furthermore, findings of current Machine Learning Techniques are being evaluated, which attempts to estimate the COVID-19 spread. Researchers looking for approaches to advance SARS-CoV-2 research as well as individuals curious about the field's current condition will find this review to be helpful. [ABSTRACT FROM AUTHOR]

Glycolipids prepared from palm kernel oil, PKO, in tropical regions represent an excellent opportunity to develop new surfactant formulations for cosmeceutical and pharmaceutical applications while contributing to sustainable economies. Here we report the synthesis of a new mannoside obtained from palm kernel oil, aManPKO, and we characterise its thermotropic properties by using DSC, POM, SAXS, WAXS, FT-IR and broadband dielectric spectroscopy. aManPKO exhibits a monotropic smectic phase with a partially interdigitated bilayer structure, similar to its major unsaturated component, the oleyl mannoside with a C18:1 monounsaturated alkyl chain. The smectic phase of aManPKO vitrifies below room temperature, which is associated to the C18:1 component, but also to the activation of short-range motions, ~4 Å, by cleavage of strong hydrogen bonding between the polar heads of the mannosides. The stability of the liquid crystal phase, on the other hand, is linked to the presence of weak hydrogen bonding, whose breakage leads to the activation of long rate motions in the range of the layer distances, ~39 Å. [ABSTRACT FROM AUTHOR]

In developing building energy sector, employment of smart energy plants with advanced control strategies is essential to reduce energy consumption and to increase inhabitants' comfort. The solar Photovoltaic-Thermal (PVT) panels are considered as the key elements for smart building energy supply. In the present research, a combination of solar PVTs with a reversible heat pump is designed and analyzed to provide electricity, cooling, and heating energies of a case study building. The plant also includes control equipment and energy storage tank to insure smart operation under different climate conditions. The proposed plant can also be employed for a larger scale to provide energy demands of a district area. Thermal characteristics and economic features of the proposed plant are taken into account to assess its transient performance using TRNSYS software for a case study location in China with considering real climate conditions of ambient temperature and sun radiation. The produced electricity as well as cooling and heating duties are evaluated for different specifications of PVT panels and the storage tanks' volumes. The plant performance is appraised in terms of total capital cost, CO 2 saving ratio, and the payback period. Finally a multi-objective optimization is carried out and the Pareto frontier for three objectives is represented. It has been concluded that, under optimal operation, the proposed building energy plant has a 5 year payback period with 1.93 CO 2 saving ratio and 513328 $. Also it is found that, the volume of storage tanks has a significant effect on economic indicators as well as the CO 2 saving ratio. [Display omitted] • A novel building energy plant driven by solar PVT panels is proposed. • The plant is integrated with a reversible heat pump to operate as a CCHP system. • Dynamic modeling and annually transient performance evaluation is represented. • Optimization is conducted based on total cost, CO 2 saving ratio and payback period. [ABSTRACT FROM AUTHOR]