Functional Outlier Detection by Means of h-Mode Depth and Dynamic Time Warping

Finding outliers in functional infinite-dimensional vector spaces is widely present in the industry for data that may originate from physical measurements or numerical simulations. An automatic and unsupervised process of outlier identification can help ensure the quality of a dataset (trimming), validate the results of industrial simulation codes, or detect specific phenomena or anomalies. This paper focuses on data originating from expensive simulation codes to take into account the realistic case where only a limited quantity of information about the studied process is available. A detection methodology based on different features, such as h-mode depth or the dynamic time warping, is proposed to evaluate the outlyingness both in the magnitude and shape senses. Theoretical examples are used to identify pertinent feature combinations and showcase the quality of the detection method with respect to state-of-the-art methodologies of detection. Finally, we show the practical interest of the method in an industrial context thanks to a nuclear thermal-hydraulic use case and how it can serve as a tool to perform sensitivity analysis on functional data.

Assessment of gridded precipitation products in the hydrological modeling of a flood-prone mesoscale basin

Precipitation plays a critical role in hydrometeorological studies. A predictive analysis of gridded rainfall datasets may provide a cost-effective alternative to conventional rain gauge observations. Here, our objective is to evaluate the performance of satellite and reanalysis precipitation products in the hydrological modeling of a mesoscale watershed. The research also examines the accuracy of hydrological simulations in a sizeable flood-prone watershed in the absence of observed data associated with the myriad water retaining structures present in the catchment. We use three precipitation products, namely Tropical Rainfall Measurement Missions (TRMM) 3B42 Version 7, Climate Forecast System Reanalysis (CFSR), and daily precipitation data recorded at multiple rain gauges in the upper Huai River Basin to simulate streamflow. The Soil & Water Assessment Tool (SWAT) is utilized for runoff modeling, while SWAT-CUP is used to perform sensitivity analysis and to calibrate and validate the simulation results. Nash–Sutcliffe efficiency, percent bias, and Kling-Gupta efficiency (KGE) are employed to evaluate modeling efficiency for three precipitation datasets on different temporal scales. The results indicate that TRMM and CFSR datasets provide satisfactory results on both daily and monthly scales. Specifically, the SWAT model performs better at monthly simulations than daily simulations for all precipitation datasets used.

Co-Optimization of CO2 Storage and Enhanced Gas Recovery Using Carbonated Water and Supercritical CO2

CO2-based enhanced gas recovery (EGR) is an appealing method with the dual benefit of improving recovery from mature gas reservoirs and storing CO2 in the subsurface, thereby reducing net emissions. However, CO2 injection for EGR has the drawback of excessive mixing with the methane gas, therefore, reducing the quality of gas produced and leading to an early breakthrough of CO2. Although this issue has been identified as a major obstacle in CO2-based EGR, few strategies have been suggested to mitigate this problem. We propose a novel hybrid EGR method that involves the injection of a slug of carbonated water before beginning CO2 injection. While still ensuring CO2 storage, carbonated water hinders CO2-methane mixing and reduces CO2 mobility, therefore delaying breakthrough. We use reservoir simulation to assess the feasibility and benefit of the proposed method. Through a structured design of experiments (DoE) framework, we perform sensitivity analysis, uncertainty assessment, and optimization to identify the ideal operation and transition conditions. Results show that the proposed method only requires a small amount of carbonated water injected up to 3% pore volumes. This EGR scheme is mainly influenced by the heterogeneity of the reservoir, slug volume injected, and production rates. Through Monte Carlo simulations, we demonstrate that high recovery factors and storage ratios can be achieved while keeping recycled CO2 ratios low.

Nursing rescheduling problem with multiple rescheduling methods under uncertainty

The nursing rescheduling problem is a challenging decision-making task in hospitals. However, this decision-making needs to be made in a stochastic setting to meet uncertain demand with insufficient historical data or inaccurate forecasting methods. In this study, a stochastic programming model and a distributionally robust model are developed for the nurse rescheduling problem with multiple rescheduling methods under uncertain demands. We show that these models can be reformulated into an integer program. To illustrate the applicability and validity of the proposed model, a study case is conducted on three joint hospitals in Chengdu, Chongzhou, and Guanghan, Sichuan Province. The results show that the stochastic programming model and the distributionally robust model can reduce the cost by 78.71% and 38.92%, respectively. We also evaluate the benefit of the distributionally robust model against the stochastic model and perform sensitivity analysis on important model parameters to derive some meaningful managerial insights.

Mathematical modelling of heat and mass transfer in a roof type solar distillation system

This research examined the performance of a roof type solar distillation system. A model was developed that captured the influence of the cover angle on still performance in terms of evaluating the heat transfer coefficient, yield and efficiency. The previous models of evaluating these matric has been shown to be unsatisfactory due to over prediction. The objectives are to clarify the misconception on the efficiency, to validate the derived expression for the Nusselt’s number of condensation under an inclined surface and to perform sensitivity analysis on the dimensionless parameters with derived models. The derived model has a practical significance because it provides much information on the dependence of the heat transfer coefficient on the cover angle. The model was solved with MATLAB, and results show a well correlated trends with the established work of literature and the proposed model having the lest efficiency as the model is without the over prediction inherent in other models due to non-inclusion of evaporation in the analysis of free convection of air. The peak yield of all the models occurs at about 11:00 AM, with the proposed model having a peak yield of about 0.045kg.

A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing

In mobile crowd computing (MCC), smart mobile devices (SMDs) are utilized as computing resources. To achieve satisfactory performance and quality of service, selecting the most suitable resources (SMDs) is crucial. The selection is generally made based on the computing capability of an SMD, which is defined by its various fixed and variable resource parameters. As the selection is made on different criteria of varying significance, the resource selection problem can be duly represented as an MCDM problem. However, for the real-time implementation of MCC and considering its dynamicity, the resource selection algorithm should be time-efficient. In this paper, we aim to find out a suitable MCDM method for resource selection in such a dynamic and time-constraint environment. For this, we present a comparative analysis of various MCDM methods under asymmetric conditions with varying selection criteria and alternative sets. Various datasets of different sizes are used for evaluation. We execute each program on a Windows-based laptop and also on an Android-based smartphone to assess average runtimes. Besides time complexity analysis, we perform sensitivity analysis and ranking order comparison to check the correctness, stability, and reliability of the rankings generated by each method.

Ceramic supplier selection using analytical hierarchy process method

This study tried to implement the Analytical Hierarchy Process (AHP) and the weights of the criteria and sub-criteria to find the best supplier. According to QCDFR (quality, cost, delivery, flexibility, and responsiveness). This study took place in one of the biggest tile producers, ranks fifth in the world and the first in Indonesia. However, the company currently only uses quality, cost, and delivery methods to choose the best supplier of raw material, namely feldspar. This research tries to use the systematic method to find the best supplier based on the importance of the criteria. The method used the quantitative approach to enumerate the data to analyze the information. The company analyzed six suppliers. The primary tool used in this research is a Super Decision Software version 3.2 to create and manage the AHP model, enter the judgments, get results, and perform sensitivity analysis on the results. The result found that Semarang is the best supplier. The company will choose Semarang to become the company's business partner compared to the other suppliers because Semarang has met the criteria that the company prioritizes the most. By having the best supplier selection, the company can provide the right material consistency and suitable material suitability.

Waning Immunity and the Second Wave: Some Projections for SARS-CoV-2

This paper offers projections of future transmission dynamics for SARS-CoV-2 in an SEIRS model with demographics and waning immunity. In a stylized optimal control setting calibrated to the United States, we show that the disease is endemic in steady state and that its dynamics are characterized by damped oscillations. The magnitude of the oscillations depends on how fast immunity wanes. The optimal social distancing policy both curbs peak prevalence and postpones the infection waves relative to the uncontrolled dynamics. Last, we perform sensitivity analysis with respect to the duration of immunity, the infection fatality rate, and the planning horizon. (JEL I12, I18, J11)

Calibration, inversion and sensitivity analysis for hydro-morphodynamic models

The development of reliable, sophisticated hydro-morphodynamic models is essential for protecting the coastal environment against hazards such as flooding and erosion. There exists a high degree of uncertainty associated with the application of these models, in part due to incomplete knowledge of various physical, empirical and numerical closure related parameters in both the hydrodynamic and morphodynamic solvers. This uncertainty can be addressed through the application of adjoint methods. These have the notable advantage that the number and/or dimension of the uncertain parameters has almost no effect on the computational cost associated with calculating the model sensitivities. Here, we develop the first freely available and fully flexible adjoint hydro-morphodynamic model framework. This flexibility is achieved through using the pyadjoint library, which allows us to assess the uncertainty of any parameter with respect to any output functional, without further code implementation. The model is developed within the coastal ocean model Thetis constructed using the finite element code-generation library Firedrake. We present examples of how this framework can perform sensitivity analysis, inversion and calibration for a range of uncertain parameters based on the final bedlevel. These results are verified using so-called dual-twin experiments, where the `correct' parameter value is used in the generation of synthetic model test data, but is unknown to the model in subsequent testing. Moreover, we show that inversion and calibration with experimental data using our framework produces physically sensible optimum parameters and that these parameters always lead to more accurate results. In particular, we demonstrate how our adjoint framework can be applied to a tsunami-like event to invert for the tsunami wave from sediment deposits.

Multi-Criteria Decision-Making Model using Intuitionistic Fuzzy Entropy and Variable Weight Theory

The aim of this research is to develop a new multi-criteria decision-making method that integrates an intuitionistic fuzzy entropy measure and variable weight theory to be implemented in different fields to provide a solution for MCDM problems when the available information is incomplete. A limited number of studies have considered determining decision maker’s weights by performing objective techniques, and almost all of these researches detected a constant weights for the decision makers. In addition, most of the MCDM studies were not formulated to perform sensitivity analysis. The new method is based on the TOPSIS model with an intuitionistic fuzzy entropy measure in the exponential-related function form and the engagement of the variable weight theory to determine weights for the decision-makers that vary as per attibutes. Lastly, a mathematical model was developed in this research to be as an input for developing the mobile-aplication based method in future for virtual use of the new MCDM method.