Search

In the era of automation and artificial intelligence, some problems in the petroleum industry can be efficiently solved by machine-learning models based on big data. Previous researchers have addressed truly precise methods to evaluate reserves from accessible well-test data, although enormous time, effort, and cost were required. However, due to the lack of sufficient conventional material balance (CMB) test data for a large number of wells in the Su Dong (SD) gas field, only 12 wells (0.99%) were generally suitable for previous methods. We substituted the fully built-up reservoir pressures used in CMB with those converted from shut-in casing-pressure data. Suffering from data noise caused by pressure fluctuation, only 22.91% of the plots were linear. The nonlinear plots were enhanced into linear plots by denoising and smoothening according to engineering knowledge. Machine-learning models were developed to perform the enhancement automatically, which constructed an adaptive and feasible workflow. The model improved the percentage of applicable wells to 68.16% (824 out of 1,209) and achieved considerable precision for reserve evaluation. The proposed method is much more suitable than CMB in gas fields featuring unstable production status and provides feasible reserve evaluation. This paper presents an exemplary demonstration of the combination of petroleum engineering and machine learning. [ABSTRACT FROM AUTHOR]

The integration of machine learning (Keplerian paradigm) and more general artificial intelligence technologies with physical modeling based on first principles (Newtonian paradigm) will impact scientific computing in engineering in fundamental ways. Such hybrid models combine first principle-based models with data-based models into a joint architecture. This paper will give some background, explain trends and showcase recent achievements from an applied mathematics and industrial perspective. Examples include characterization of superconducting accelerator magnets by blending data with physics, data-driven magnetostatic field simulation without an explicit model of the constitutive law, and Bayesian free-shape optimization of a trace pair with bend on a printed circuit board. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have emerged as a promising solution for effectively handling non-Euclidean data in construction, including building information models (BIM) and scanned point clouds. However, despite their potential, there is a lack of comprehensive scholarly work providing a holistic understanding of the application of GNNs in the construction domain. This paper addresses this gap by conducting a thorough review of 34 publications on GNNs in construction, presenting a comprehensive overview of the current research landscape. By analyzing the existing literature, this paper aims to identify opportunities and challenges for further advancing the application of GNNs in construction. The findings from this review shed light on diverse approaches for constructing graph data from common construction data types and demonstrate the significant potential of GNNs for the industry. Moreover, this paper contributes to the existing body of knowledge by increasing awareness of the current state of GNNs in the construction industry and offering practical recommendations to overcome challenges in real-world practice. [Display omitted] • A systematic review of GNNs research and applications in the construction industry • Identification of GNNs potential in handling the non-Euclidean data commonly found in construction activities. • Various approaches to developing graph data from common data types in construction • Discussion of current applications and potential of GNNs in construction, as well as its challenges and opportunities. [ABSTRACT FROM AUTHOR]

• A bibliometric analysis on machine learning research in all engineering fields. • VOSviewer is employed to this study based on retrieved 3057 articles. • Research distributions of this topic are identified. • A detailed discussion on the knowledge bases, research hotspots and frontiers. In this work, we conducted a visualized bibliometric analysis to map the research trends of machine learning in engineering (MLE) based on articles indexed in the Web of Science Core Collection published between 2000 and 2019. The research distributions, knowledge bases, research hotspots, and research frontiers for MLE studies are revealed by using VOSviewer software and visualization technology. The growth of the literature related to MLE averaged 24.3% in the past two decades. A total of 3057 peer-reviewed papers from 96 countries published in 1299 different journals were identified. The USA was the most productive country, with 23.73% of the overall articles and 32.25% of the overall citations. The most active research organization was MIT, with 41 publications and 1079 citations, and the Journal of Machine Learning Research had the largest number of citations in the field of MLE. In particular, our findings indicate that the research issues of "random forests", "support vector machine", "extreme learning machine", "deep learning", "statistical learning theory", and "Python machine learning" formed the knowledge bases of MLE from 2000 to 2019, while the research hotspots focused on applications of machine learning benchmark algorithms. Burst detection analysis results showed that more burst keywords emerged and had a higher frequency of change after 2010. This study provides an insight view of the overall research trends of MLE and may help researchers better understand this research field and predict its dynamic directions. [ABSTRACT FROM AUTHOR]

The Smart Home designed to extend older adults independence is emerging as a clinical solution to the growing ageing population. Nurses will and should play a key role in the development and application of Smart Home technology. Accordingly, conceptual frameworks are needed for nurse scientists who are collaborating with multidisciplinary research teams in developing an intelligent Smart Home that assists with managing older adults' health. We present a conceptual framework that is grounded in critical realism and pragmatism, informing a unique mixed methodological approach to generating, analyzing, and contextualizing sensor data for clinician‐based machine learning. This framework can guide nurse scientists in knowledge construction as they participate in multidisciplinary health‐assistive Smart Home and artificial intelligence research. In this paper, we review philosophical underpinnings and explicate how this framework can guide nurse scientists collaborating with engineers to develop intelligent health‐assistive Smart Homes. It is critical that clinical nursing knowledge is integrated into Smart Home and artificial intelligence features. A conceptual framework and practical method will provide needed structure for knowledge construction by nurse scientists. [ABSTRACT FROM AUTHOR]