This study introduces a novel SI2HR model,where“I2”denotes two infectious classes representing asymptomatic and symptomatic infections,aiming to investigate and analyze the cost-effective optimal control measures fo...
详细信息
This study introduces a novel SI2HR model,where“I2”denotes two infectious classes representing asymptomatic and symptomatic infections,aiming to investigate and analyze the cost-effective optimal control measures for managing *** model incorporates a novel concept of infectious density-induced additional screening(IDIAS)and accounts for treatment ***,the model considers the possibility of reinfection and the loss of immunity in individuals who have previously *** validate and calibrate the proposed model,real data from November–December 2022 in Hong Kong are *** estimated parameters obtained from this calibration process are valuable for prediction purposes and facilitate further numerical *** analysis of the model reveals that delays in screening,treatment,and quarantine contribute to an increase in the basic reproduction number R0,indicating a tendency towards *** particular,from the elasticity of R0,we deduce that normalized sensitivity indices of baseline screening rate(θ),quarantine rates(γ,αs),and treatment rate(α)are negative,which shows that delaying any of these may cause huge surge in R0,ultimately increases the disease ***,by the contour plots,we note the two-parameter behavior of the infectives(both symptomatic and asymptomatic).Expanding upon the model analysis,an optimal control problem(OCP)is formulated,incorporating three control measures:precautionary interventions,boosted IDIAS,and boosted *** Pontryagin's maximum principle and the forward-backward sweep method are employed to solve the *** numerical simulations highlight that enhanced screening and treatment,coupled with preventive interventions,can effectively contribute to sustainable disease ***,the cost-effectiveness analysis(CEA)conducted in this study suggests that boosting IDIAS alone is the most economically efficient and cost-effective approach compared to other *** CEA resul
Managing the current high penetration of renewable energy sources globally poses a significant challenge due to the distributed and diverse nature of generation components. To maximize real-time power generation, thes...
详细信息
Managing the current high penetration of renewable energy sources globally poses a significant challenge due to the distributed and diverse nature of generation components. To maximize real-time power generation, these resources need to perform optimally. Digital twin technology offers a comprehensive framework for managerial support by replicating grid features in a digital environment. This research creates a digital twin of the microgrid to optimize power generation, focusing on computational efficiency and self-healing control. The framework is tested in a laboratory microgrid, with modeling performed using a polynomial regression algorithm. Optimization is achieved through a gradient descent algorithm, and the self-healing model is implemented using a logistic regression algorithm. Real-time data extracted from the microgrid drives this process. The results can be utilized for predictive analysis before deploying a microgrid or to optimize generation in existing systems using the digital twin model. Even though the research focuses on a single microgrid unit, it introduces a framework proposal for extensively distributed microgrids integrating multiple renewable energy sources.
processes in healthcare are complex and data-intensive. process mining uses data recorded during process execution to obtain an understanding of the actual execution of a process. Due to the complexity of healthcare p...
详细信息
ISBN:
(纸本)9780998133164
processes in healthcare are complex and data-intensive. process mining uses data recorded during process execution to obtain an understanding of the actual execution of a process. Due to the complexity of healthcare processes, it is useful to consider and analyse the process execution of certain cohorts, such as old and young patients, separately. While such analysis is facilitated by process variant analysis techniques, existing approaches for process variant analysis only consider a comparison based on the control flow and performance perspectives. Given the large amount of event data attributes available in healthcare settings, we propose the first data-based process variant analysis approach. Our approach allows comparing process variants based on differences in event data attributes by building on statistical tests. We applied our approach on the MIMIC-IV real-world data set on hospitalizations in the US, where we demonstrate that the approach is feasible and can actually provide relevant medical insights.
Solid oxide fuel cell (SOFC) is a common fuel cell type that has high efficiency. SOFC is a complex non-linear system, subject to aging and manufacturing variation, which makes performance prediction difficult. To est...
详细信息
Solid oxide fuel cell (SOFC) is a common fuel cell type that has high efficiency. SOFC is a complex non-linear system, subject to aging and manufacturing variation, which makes performance prediction difficult. To estimate the SOFC performance data-driven methods allow a trade-off between computation cost and accuracy. Eight different SOFC tubular cells with different properties are fabricated and experimentally tested in 18 different operating conditions. A deep neural network (DNN) is used to predict the output voltage of the cells. The input features of this network are the cell physical properties determined by scanning electron microscope (SEM) analysis and the operating parameters. As a first step, all measurable features are provided to principal component analysis (PCA) for feature selection resulting in a 50% reduction in features, resulting in a corresponding 50% reduction in the training time of the DNN. This trained DNN is able to capture the non-linear voltage drop of concentration polarization in the current density-voltage (J-V) curves. The prediction performance of the network is evaluated using three performance metrics of coefficient of determination (R-2), root mean square error (RMSE), and mean absolute percentage error (MAPE) with satisfactory accuracy for both the training and test datasets. For all predictions, R-2 is 0.99, MAPE is less than 1%, and RMSE is 0.0001 on the test dataset. During the training process, both the validation loss and training loss approach zero indicating that the trained model is not overfitted. The DNN model can be useful for design and operation optimization purposes. Copyright (c) 2024 The Authors.
The aim of this study is to identify the barriers that affect the implementation of Industry 4.0, establish the relationship among the barriers using interpretive structural modeling (ISM), and identify the driving po...
详细信息
The aim of this study is to identify the barriers that affect the implementation of Industry 4.0, establish the relationship among the barriers using interpretive structural modeling (ISM), and identify the driving power and dependence of the identified barriers using matriced' impacts croised-multiplication applique' and classment (cross-matrix multiplication applied to classification) (MICMAC) analysis. Industry 4.0, a different acronym for the fourth industrial revolution, is considered an important concept for the digitalization of the manufacturing sector as it results in the efficient use of resources, reduced lead time, and improved product quality. A contextual relationship matrix is constructed based on questionnaire responses from industry and academia. Then, a hierarchical relationship among the identified barriers is established using the ISM method. Subsequently, driving power and dependence of the identified barriers are identified using MICMAC analysis. The analyzed results help determine the significance of the identified barriers and their relative importance, which will in turn help researchers and policymakers in the implementation of the Industry 4.0 concept. In this article, we also suggest that the government should frame the policy to provide financial and technical support and subsidies for transforming conventional factories into smart factories, and proper training should be given to the workforce so that they can cope with the real-time needs of the industries.
In the presented article, problems associated with the modeling features in automation and control of processes occurring in a primary oil refining technological installation are studied and investigated. In the artic...
详细信息
Based on the data of Beijing-Tianjin-Hebei region from 2011 to 2019,this paper measures the medical efficiency of Beijing-Tianjin-Hebei region in recent ten years by using the super efficiency SBM model, and uses the ...
详细信息
Capability analysis of manufacturing processes needs to appropriately model quality characteristics (QCs). The sample data for modeling can be divided into three categories: (a) original measurement data of QCs, (b) t...
详细信息
Flowsheet modeling is a powerful digital tool for analyzing industrial processes. However, some models require a significant number of experiments to fit their parameters, so, it is important to understand what model ...
详细信息
The mathematical model of ship maneuvering motion is a crucial foundation for both prediction of ship navigation state and automatic control of ship motion. This paper proposes a practical and robust nonparametric ide...
详细信息
ISBN:
(纸本)9780791887837
The mathematical model of ship maneuvering motion is a crucial foundation for both prediction of ship navigation state and automatic control of ship motion. This paper proposes a practical and robust nonparametric identification method based on echo state Gaussian process (ESGP) to establish the nonparametric model of ship maneuvering motion. The traditional echo state networks (ESNs) and basic Gaussian processes (GPs) have found successfully applications in nonparametric modeling and time- series forecasting. The proposed method combines the strengths of both ESN and GP approaches. On the one hand, it offers a more robust alternative to conventional reservoir computing networks;on the other hand, it can directly generate confidence intervals for prediction results. The KVLCC2 ship model is taken as the object of this research. The datasets collected from several standard and nonstandard zigzag maneuvers of free-running model, which are provided by SIMMAN 2008, are used as training and testing data. To assess the robustness and generalization ability of the established nonparametric model, the prediction results of the ESGP method are compared with the experimental data. It is shown that the ESGP method proposed in this paper can achieve high prediction accuracy and provide a measure of credibility for the output results, which makes it more practical and applicable for modeling and prediction of ship maneuvering motion.
暂无评论