General Modeling Framework Research Articles

Development of an integrated prediction-optimization modeling approach for coupled risk management of water and energy nexus systems

In this research, an integrated prediction-optimization approach (IPOA) was developed to manage coupled risks of water and energy nexus (WEN) systems. This approach combined Markov-chain based unbiased grey model (MC-UGM), Copula analysis and inexact optimization into a general modeling framework. The proposed approach was employed for dealing with the joint probability distributions of water and energy resources, and further supporting coordinated decision-making under uncertainties. Then, Beijing Tianjin Hebei Integrated Region (BTHIR), as the leading economic center of north China, was employed as the demonstrative site for the developed approach. Results indicated that IPOA could help predict water and energy demands, excavate the interactions between water and energy, identify and deal with the uncertain and complex features of the WEN system in the region. Optimal allocation schemes of water and energy under multiple coupled risks for multiple users in BTHIR were obtained. It can be found that under certain coupled risks, the lower for water risk, and the greater for energy risks. Also, the net benefits of WEN systems would continue to increase over time. Under most of the risk levels, water resource in BTHIR would be mainly allocated for domestic and industrial sectors. Energy resource would be mainly allocated for secondary and tertiary industrial sectors. The developed IPOA could not only realize the identification and quantification of inherent interactions between energy and water, but also help decision makers formulate reasonable policies balancing system benefits and coupled risks. Besides, IPOA can improve the robustness of utilization decisions regarding water and energy, which could help promote the coordinated development for BTHIR.

Pairs trading with general state space models

This study examines pairs trading using a general state space model framework. It models the spread between the prices of two assets as an unobservable state variable assuming that it follows a mean-reverting process. This new model has two distinctive features: the (1) non-Gaussianity and heteroscedasticity of innovations to the spread, and (2) nonlinearity of the mean reversion of the spread. It shows how to use the filtered spread as the trading indicator in carrying out statistical arbitrage and proposes a new trading strategy which uses a Monte Carlo-based approach to selecting the optimal trading rule. The new model and trading strategy are illustrated by two examples: PEP vs. KO and EWT vs. EWH. The empirical results show that the new approach can achieve 21.86% (31.84%) annualized return for the PEP-KO (EWT-EWH) pair. Then all the possible pairs among the five largest and the five smallest U.S. banks listed on the NYSE are considered. For these pairs, the performance of the proposed approach with that of the existing popular approaches, are compared both in-sample and out-of-sample. In almost all the cases considered, our approach can significantly improve the return and the Sharpe ratio.

Evidence of Neurovascular Un-Coupling in Mild Alzheimer's Disease through Multimodal EEG-fNIRS and Multivariate Analysis of Resting-State Data.

Alzheimer’s disease (AD) is associated with modifications in cerebral blood perfusion and autoregulation. Hence, neurovascular coupling (NC) alteration could become a biomarker of the disease. NC might be assessed in clinical settings through multimodal electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Multimodal EEG-fNIRS was recorded at rest in an ambulatory setting to assess NC and to evaluate the sensitivity and specificity of the methodology to AD. Global NC was evaluated with a general linear model (GLM) framework by regressing whole-head EEG power envelopes in three frequency bands (theta, alpha and beta) with average fNIRS oxy- and deoxy-hemoglobin concentration changes in the frontal and prefrontal cortices. NC was lower in AD compared to healthy controls (HC) with significant differences in the linkage of theta and alpha bands with oxy- and deoxy-hemoglobin, respectively (p = 0.028 and p = 0.020). Importantly, standalone EEG and fNIRS metrics did not highlight differences between AD and HC. Furthermore, a multivariate data-driven analysis of NC between the three frequency bands and the two hemoglobin species delivered a cross-validated classification performance of AD and HC with an Area Under the Curve, AUC = 0.905 (p = 2.17 × 10−5). The findings demonstrate that EEG-fNIRS may indeed represent a powerful ecological tool for clinical evaluation of NC and early identification of AD.

Coupling of multiscale lattice Boltzmann discrete-element method for reactive particle fluid flows.

Reactive particulate systems are of prime importance in varieties of practical applications in process engineering. As an example this study considers extraction of phosphorous from waste water by calcium silicate hydrate particles in the P-RoC process. For such systems modeling has a large potential to help to optimize process conditions, e.g., particle-size distributions or volume flows. The goal of this study is to present a new generic modeling framework to capture relevant aspects of reactive particle fluid flows using combined lattice Boltzmann method and discrete-element method. The model developed is Euler-Lagrange scheme which consist of three-components viz., a fluid phase, a dissolved reactive substance, and suspended particles. The fluid flow and reactive mass transport are described in a continuum framework using volume-averaged Navier-Stokes and volume-averaged advection-diffusion-reaction equations, respectively, and solved using lattice Boltzmann methods. The volume-averaging procedure ensures correctness in coupling between fluid flow, reactive mass transport, and particle motion. The developed model is validated through series of well-defined benchmarks. The benchmarks include the validation of the model with experimental data for the settling of a single particle in a cavity filled with water. The benchmark to validate the multi-scale reactive transport involves comparing the results with a resolved numerical simulation. These benchmarks also prove that the proposed model is grid convergent which has previously not been established for such coupled models. Finally, we demonstrate the applicability of our model by simulating a suspension of multiple particles in fluid with a dissolved reactive substance. Comparison of this coupled model is made with a one-way coupled simulation where the influence of particles on the fluid flow and the reactive solution transport is not considered. This elucidates the need for the two-way coupled model.

Assessing the potential of combined production and energy management in Industrial Energy Hubs – Analysis of a chipboard production plant

To minimize energy consumption in today’s industry, holistic energy efficiency solutions are required. The Energy Hub is a promising concept for optimal energy management of industrial systems with multiple energy carriers like electricity, heat, or gas. However, most of the research conducted in this field focuses on optimal energy management and does not consider the scheduling of product in manufacturing processes. Though, including production scheduling in the energy optimization of manufacturing processes can considerably increase energy efficiency, especially if batch processes are involved. This work identifies the potential of combined energy and production scheduling in Industrial Energy Hubs, using a chipboard production process as a use case. The chipboard production plant includes a combined heat and power unit, units for production, and has two district heating demands as external requirements. A generic modeling and optimization framework, based on mixed-integer linear programming, is used to model and optimize different scenarios. The results show that the scheduling of product has a significant impact on the energy management of the chipboard production plant: The required energy for the production units can be reduced by approximately 30 %. Nevertheless, this potential can only be exploited if process design is adapted and excess heat is utilized appropriately.

A three-step local smoothing approach for estimating the mean and covariance functions of spatio-temporal Data

Spatio-temporal data are common in practice. Existing methods for analyzing such data often employ parametric modelling with different sets of model assumptions. However, spatio-temporal data in practice often have complicated structures, including complex spatial and temporal data variation, latent spatio-temporal data correlation, and unknown data distribution. Because such data structures reflect the complicated impact of confounding variables, such as weather, demographic variables, life styles, and other cultural and environmental factors, they are usually too complicated to describe by parametric models. In this paper, we suggest a general modelling framework for estimating the mean and covariance functions of spatio-temporal data using a three-step local smoothing procedure. The suggested method can well accommodate the complicated structure of real spatio-temporal data. Under some regularity conditions, the consistency of the proposed estimators is established. Both simulation studies and a real-data application show that our proposed method could work well in practice.

Equivalent flow channel model for doublets in heterogeneous porous geothermal reservoirs

This study presents a general numerical modeling framework for application to doublets in heterogeneous porous geothermal reservoirs. Extension of the equivalent flow channel model for fractured media leads to an inverse model that combines an analytical solution of solute transport equation with differential evolution method for global optimization. When constrained by data from tracer tests, the inverse model can calculate the geometric and transport parameters pertaining to equivalent flow channels that link injection and production wells. A distinct forward model is developed based on an analytical solution for heat transport in equivalent flow channels, where the parameters calculated from the inverse model serve as inputs for predicting thermal breakthrough at production wells. A case study uses the modeling framework in combination with data from tracer tests to calculate the parameters of equivalent flow channels that link two distinct doublets at Dezhou geothermal field, Shandong, China, followed by the prediction of production well thermal breakthrough. The results show that flow channel parameter values are similar for the two doublets. A sensitivity analysis is performed to understand the roles of flow channel dimensions and flow rate in equivalent flow channels in thermal breakthrough curves at production wells.

Threshold-Based Subgroup Testing in Logistic Regression Models in Two-Phase Sampling Designs

The effect of treatment on binary disease outcome can differ across subgroups characterized by other covariates. Testing for the existence of subgroups that are associated with heterogeneous treatment effects can provide valuable insight regarding the optimal treatment recommendation in practice. Our research in this paper is motivated by the question of whether host genetics could modify a vaccine's effect on HIV acquisition risk. To answer this question, we used data from an HIV vaccine trial with a two-phase sampling design and developed a general threshold-based model framework to test for the existence of subgroups associated with the heterogeneity in disease risks, allowing for subgroups based on multivariate covariates. We developed a testing procedure based on maximum of likelihood-ratio statistics over change planes and demonstrated its advantage over alternative methods. We further developed the testing procedure to account for bias sampling of expensive (i.e. resource-intensive to measure) covariates through the incorporation of inverse probability weighting techniques. We used the proposed method to analyze the motivating HIV vaccine trial data. Our proposed testing procedure also has broad applications in epidemiological studies for assessing heterogeneity in disease risk with respect to univariate or multivariate predictors.

A construction method of digital twin model for contact characteristics of assembly interface

The paper aims to study the accurate modeling of the assembly interface and the update of the digital twin model to keep the consistency of physical and digital information. It purposes a construction method of the digital twin model of assembly interface contact characteristics, based on the interface virtual thin-layer element with nonuniform and replaceable attribute parameters. Firstly, the general framework of the digital twin model of assembly interface contact characteristics is given. Secondly, the typical assembly interface characteristics are analyzed. Aiming at the precise modeling of the assembly interface, the interface gradient virtual material thin-layer element is developed by integrating the measured data. Based on the surrogate model and genetic algorithm, the real-time assimilation and fusion of test data and simulation data are carried out. The thin-layer element parameters are optimized, and the dynamic updating of the assembly digital twin model is realized. Finally, the twin data model is applied to the dynamic performance analysis of the machining center and compared with the Takashi Yoshimura method. The results show that the analysis results of the twin data model are in good agreement with the test data, and the analysis accuracy is higher than that of the Takashi Yoshimura method, which verifies the feasibility of the digital twin model modeling method for assembly contact characteristics. The twin data model of contact characteristics can be extended and used to predict the mechanical performance of multiple joint structures in real time.

Thermoelectromagnetic Lumped-Parameter Model of High Temperature Superconductor Generators for Transient Stability Analysis

A refined dynamic lumped-parameter model of a REBCO field coil synchronous generator including details of the thermoelectromagnetic phenomenology of the superconductor has been developed in the classic dq0 frame of reference. The idea is to build a generic modelling framework that can be used in power systems engineering studies to infer trends on the behavior of HTS field coil machines in power grids. In the present work, the transient stability of a hybrid High Temperature Superconducting (HTS) generator is analyzed under the most severe operating condition applying a three-phase short circuit at its terminals. For the study, we chose the parameters of a wind generator at 1 MW. Two cases are considered: 1) no full transition of the HTS to the normal-resistive state under stable condition, 2) full transition of the HTS under unstable condition. The response of the HTS machine is compared against that of a conventional machine.

Opinion formation dynamics — Swift collective disillusionment triggered by unmet expectations

We propose a microscopic model to describe how individual opinions shared between interacting agents initiate excessive collective expectations about a new idea or an innovation, followed by a swift collapse towards a dramatic collective disillusionment. The basic assumption which underlies the dynamics is that the information gathering process is not instantaneous but requires maturation. Agents steadily refine and update their personal opinion via a recurrent consultation of a public pool which stores information tokens (ITs). The expectation for the innovative idea is monitored in real-time by counting the number of stored ITs. The flow dynamics of ITs is assimilated to a single node queuing system (QS) with feedback loop. It incorporates the information pool (the waiting room), an IT inflow, and a service outflow that stylizes the information gathering process. Contrary to basic queuing theory, here the ITs roaming the QS are endowed with time-dependent internal variables. This additional dynamic information is used to construct the information maturation process. Such a maturation of the information introduces response delays into the dynamics, which ultimately generates the collective disillusionment trough. We illustrate the introduced generic modeling framework by considering in details the hype cycle dynamics, a key managerial topic when dealing with diffusion of innovation. In a second part of the paper, we introduce a stylized framework to detect, as soon as possible, the onset of the collective disillusionment phase, while minimizing the frequency of false alarms.

Understanding fundamental trade-offs in nanomechanical resonant sensors

Nanomechanical resonators are used as high performance detectors in a variety of applications such as mass spectrometry and atomic force microscopy. Initial emphasis in nanomechanical resonant sensors based on tracking resonance frequency deviations was on increasing the sensitivity to the level of a single molecule, atom, and beyond. On the other hand, there are applications where the speed of detection is crucial, prompting recent works that emphasize sensing schemes with improved time resolution. Here, we first develop a general modeling framework and a comprehensive theory encompassing all resonance frequency tracking schemes currently in use. We then explore the fundamental trade-offs between accuracy and speed in three resonant sensor architectures, namely, the feedback-free open-loop approach, positive-feedback based self-sustaining oscillator, and negative-feedback based frequency-locked loop scheme. We comparatively analyze them in a unified manner, clarify some misconceptions and confusion that seem to exist in the literature, and unravel their speed vs accuracy characteristics.

A historical review of the traditional methods and the internal state variable theory for modeling composite materials

A review of the development and the usages of traditional non-internal state variable (ISV) models and ISV theory for modeling behaviors of composite materials are presented in this paper. The history of earliest composites models for predictions of material stiffness, failure, viscoelasticity and plasticity is discussed first. Then following the ISV theory, a fundamental thermodynamic framework that governs the dissipative effects caused by the inelastic phenomena is introduced. Applying the thermodynamic restrictions obtained from that framework, different researchers have employed the ISV theory to develop constitutive models for composites to capture their viscoelasticity, rate-independent plasticity, viscoplasticity, and continuum damage. ISVs introduced into those models for describing each inelastic behavior of composites as well as their evolution rules are also discussed. The knowledge obtained from this review in terms of the usages of the ISV theory for composite material modeling forms a theoretical background for the development of a general modeling framework to predict all inelastic phenomena of a wide variety of composite materials.

Flexible modeling and control of capacitive-deionization processes through a linear-state-space dynamic Langmuir model

While black-box models such as neural networks have been powerful in many applications, direct physical modeling (white box) remains crucial in many fields where experimental data are difficult or time-consuming to obtain. Here, we demonstrate with an example from desalination by capacitive deionization (CDI), how an existing physical model could be strengthened by combining a general modeling framework with physical insights (gray box). Thus, a dynamic Langmuir (DL) model is extended to a linear-state-space DL model (LDL). Results obtained show the new LDL model could incorporate general structural and operational modes, including membrane CDI and constant-current operation. The formulation removes the need for direct measurements of detailed device properties without adding model complexity, and MATLAB code for automatically implementing the model is provided in the Supplementary Information. We conclude the new LDL model is widely applicable, offering great flexibility in calibration data, and enabling prediction over general operating modes.

A General 3D Non-Stationary Wireless Channel Model for 5G and Beyond

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

A novel elastic net-based NGBMC(1,n) model with multi-objective optimization for nonlinear time series forecasting

Nonlinear grey Bernoulli multivariate model NGBMC (1, n) is known as a novel forecasting model for nonlinear time series with small samples. However, ill-posed problem would make it less efficient and even cause large errors. In order to improve its generality, a hybrid method combining Elastic Net and multi-objective optimization is introduced in this work. This method effectively solves the essential defect of the ill-posed problem of the NGBMC (1, n) model, making the NGBMC (1, n) model more stable, more reliable, and more interpretable. The parameter identification of the new model uses the alternating direction method of multipliers, and the nonlinear parameter of the model and the regularization parameter of Elastic Net regression are optimized by the multi-objective grey wolf optimizer (MOGWO). Eight numerical cases all show that the use of the Elastic Net regularization method and multi-objective optimization technology can significantly improve the prediction accuracy of the NGBMC (1, n) model for future data. In addition, the hybrid method combing the regularization and optimization strategies proposed in this paper is a general framework for the grey prediction models, which has a high potential in improving the other grey models.

A Markov reward process-based framework for resilience analysis of multistate energy systems under the threat of extreme events

Energy systems are increasingly exposed to the threats of extreme events like floods, earthquakes and hurricanes. In practice, the behaviors of the systems affected by these extreme events are often modeled by multistate models to facilitate the analysis. In this paper, we develop a generic framework for resilience modeling and analysis of multistate energy systems. A multistate resilience model is developed based on a Markov reward process model, where the degradation and recovery of system performance are characterized by a continuous time discrete state Markov chain and the losses caused by the extreme event is modeled by the reward rates associated with the sojourns in the degradation states and the transitions among the states. Four numerical metrics are defined to describe different aspects of system resilience, i.e. the resistant, absorption, recovery and overall resilience. A simulation-based algorithm is proposed for resilience analysis of multistate energy systems. The developed methods are applied for resilience modeling and analysis of a Nuclear Power Plant (NPP) under the threat of earthquakes. The Markov reward process model is developed following a probabilistic seismic hazard analysis, a fragility analysis and an event tree modeling of accident evolutions. Both a time-static and time-dependent resilience analysis are conducted and the results show that the developed model is able to comprehensively describe the resilience of multistate energy systems under the threats of extreme events.

Stability-Based Controller Design of Cloud Control System With Uncertainties

A cloud control system (CCS) is inherently uncertain due to the dynamic services and resources in cloud computing. In this paper, an approach of modeling and controller design for CCS is proposed that considers the uncertainties existing in the controlled plant, network, and controller simultaneously; then, a general framework for modeling and controlling uncertain control systems is constructed. First, a typical CCS structure is presented, and the uncertainties in the CCS are analyzed and decomposed. On this basis, a generalized uncertain state-space model is established, which includes the interval controlled plant and the stochastic network, considering the time-delay and packet loss. Meanwhile, the cloud controller model with interval parameters and stochastic time-delay is designed, which includes the state observer and control law. Then, based on the Lyapunov stability theorem and the linear matrix inequality (LMI) method, a stability criterion for obtaining the parameters of the cloud controller is proposed, in which all the results are expressed in the form of the LMI. Finally, simulation results show the effectiveness and generalization performance of the designed cloud controller.

A General Framework for Fractional Order Compartment Models

Compartment models are a widely used class of models that are useful when considering the flow of objects, people, or energy between different labeled states, referred to as compartments. Classic e...

Graph-based Embedding Smoothing for Sequential Recommendation

In real-world scenarios, a user's interactions with items could be formalized as a behavior sequence, indicating his/her dynamic and evolutionary preferences. To this end, a series of recent efforts in recommender systems aim at improving recommendation performance by considering the sequential information. However, impacts of sequential behavior on future interactions may vary greatly in different scenarios. Additionally, semantic item relations underlying item attributes have not been well exploited in sequential recommendation models, which could be crucial for measuring item similarities in recommendation. To deal with the above problems, this paper provides a general embedding smoothing framework for sequential recommendation models. Specifically, we first construct a hybrid item graph by fusing sequential item relations derived from user-item interactions with semantic item relations built upon item attributes. Second, we perform graph convolutions on the hybrid item graph to generate smoothed item embedding. Finally, we equip sequential recommendation models with the smoothed item representations to enhance their performances. Experimental results demonstrate that with our embedding smoothing framework, the state-of-the-art sequential recommendation model, SASRec, achieves superior performance to most baseline methods on three real-world datasets. Moreover, the results show that most mainstream sequential recommendation models could benefit from our framework.