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(searched for: doi:10.33945/sami/pcbr.2020.2.8)
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Basel Hammoda,
VINE Journal of Information and Knowledge Management Systems; https://doi.org/10.1108/vjikms-07-2021-0114

Abstract:
Purpose: Knowledge is a critical factor for health-care organizations’ sustainability in today’s hyperconnected and technology reliant environment, which presents additional challenges and responsibilities for managing knowledge and its risks in medical practices. This paper aims at developing a taxonomy of knowledge risks (KR) within a health-care context, with relevant descriptions and discussion of their possible impact on health-care organizations. Design/methodology/approach: As KRs have not been discussed yet within a health-care context, the authors reviewed relevant literature on KRs and challenges to knowledge practices in general contexts and in other industries. In addition, the authors reviewed literature on knowledge management (KM) in health care. The authors synthesized their findings and combined it with authors’ insights based on their experience in the health-care and KM fields to develop the taxonomy of KR, with contextual explanations and expounded on their potential effects on health-care organizations. Findings: The authors propose and explain 25 types of KRs in health-care organizations and organized them into three categories: human, operational and technology. Practical implications: Proper identification of clinical and administrative KRs plays a critical role in their effective management and remediation, thus improving the quality of care, promoting efficiency savings and ensuring health-care organizations’ sustainability. This paper will raise the awareness of KR among health-care professionals and offer researchers solid ground for more rigorous research in the field of KR and their management, within the health-care context in specific. Originality/value: To the best of the authors’ knowledge, this paper is the first to comprehensively discuss issues of KRs within a health-care context.
Published: 23 October 2021
by MDPI
Journal: Polymers
Abstract:
Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In this study, MCNP (Monte Carlo N-Particle) code was employed to simulate annular, stratified, and homogeneous regimes. In this approach, two detectors (NaI) were utilized to detect the emitted photons from a cesium-137 source. The registered signals of both detectors were decomposed using a discrete wavelet transform (DWT). Following this, the low-frequency (approximation) and high-frequency (detail) components of the signals were calculated. Finally, various features of the approximation signals were extracted, using the average value, kurtosis, standard deviation (STD), and root mean square (RMS). The extracted features were thoroughly analyzed to find those features which could classify the flow regimes and be utilized as the inputs to a network for improving the efficiency of flow meters. Two different networks were implemented for flow regime classification and void fraction prediction. In the current study, using the wavelet transform and feature extraction approach, the considered flow regimes were classified correctly, and the void fraction percentages were calculated with a mean relative error (MRE) of 0.4%. Although the system presented in this study is proposed for measuring the characteristics of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
Published: 26 September 2021
by MDPI
Journal: Mathematics
Mathematics, Volume 9; https://doi.org/10.3390/math9192391

Abstract:
Deposition of scale layers inside pipelines leads to many problems, e.g., reducing the internal diameter of pipelines, damage to drilling equipment because of corrosion, increasing energy consumption because of decreased efficiency of equipment, and shortened life, etc., in the petroleum industry. Gamma attenuation could be implemented as a non-invasive approach suitable for determining the mineral scale layer. In this paper, an intelligent system for metering the scale layer thickness independently of each phase’s volume fraction in an annular three-phase flow is presented. The approach is based on the use of a combination of an RBF neural network and a dual-energy radiation detection system. Photo peaks of 241Am and 133Ba registered in the two transmitted detectors, and scale-layer thickness of the pipe were considered as the network’s input and output, respectively. The architecture of the presented network was optimized using a trial-and-error method. The regression diagrams for the testing set were plotted, which demonstrate the precision of the system as well as correction. The MAE and RMSE of the presented system were 0.07 and 0.09, respectively. This novel metering system in three-phase flows could be a promising and practical tool in the oil, chemical, and petrochemical industries.
Published: 26 August 2021
by MDPI
Journal: Sensors
Sensors, Volume 21; https://doi.org/10.3390/s21175753

Abstract:
The features that are used in the classification process are acquired from sensor data on the production site (associated with toxic, physicochemical properties) and also a dataset associated with cybersecurity that may affect the above-mentioned risk. These are large datasets, so it is important to reduce them. The author’s motivation was to develop a method of assessing the dimensionality of features based on correlation measures and the discriminant power of features allowing for a more accurate reduction of their dimensions compared to the classical Kaiser criterion and assessment of scree plot. The method proved to be promising. The results obtained in the experiments demonstrate that the quality of classification after extraction is better than using classical criteria for estimating the number of components and features. Experiments were carried out for various extraction methods, demonstrating that the rotation of factors according to centroids of a class in this classification task gives the best risk assessment of chemical threats. The classification quality increased by about 7% compared to a model where feature extraction was not used and resulted in an improvement of 4% compared to the classical PCA method with the Kaiser criterion, with an evaluation of the scree plot. Furthermore, it has been shown that there is a certain subspace of cybersecurity features, which complemented with the features of the concentration of volatile substances, affects the risk assessment of chemical hazards. The identified cybersecurity factors are the number of packets lost, incorrect Logins, incorrect sensor responses, increased email spam, and excessive traffic in the computer network. To visualize the speed of classification in real-time, simulations were carried out for various systems used in Industry 4.0.
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