Proposed Modeling Framework Research Articles

Model training periods impact estimation of COVID-19 incidence from wastewater viral loads

Wastewater-based epidemiology (WBE) has been deployed broadly as an early warning tool for emerging COVID-19 outbreaks. WBE can inform targeted interventions and identify communities with high transmission, enabling quick and effective responses. As the wastewater (WW) becomes an increasingly important indicator for COVID-19 transmission, more robust methods and metrics are needed to guide public health decision-making. This research aimed to develop and implement a mathematical framework to infer incident cases of COVID-19 from SARS-CoV-2 levels measured in WW. We propose a classification scheme to assess the adequacy of model training periods based on clinical testing rates and assess the sensitivity of model predictions to training periods. A testing period is classified as adequate when the rate of change in testing is greater than the rate of change in cases. We present a Bayesian deconvolution and linear regression model to estimate COVID-19 cases from WW data. The effective reproductive number is estimated from reconstructed cases using WW. The proposed modeling framework was applied to three Northern California communities served by distinct WW treatment plants. The results showed that training periods with adequate testing are essential to provide accurate projections of COVID-19 incidence.

A Riemannian stochastic representation for quantifying model uncertainties in molecular dynamics simulations

A Riemannian stochastic representation of model uncertainties in molecular dynamics is proposed. The approach relies on a reduced-order model, the projection basis of which is randomized on a subset of the Stiefel manifold characterized by a set of linear constraints defining, e.g., Dirichlet boundary conditions in the physical space. We first show that these constraints are, indeed, preserved through Riemannian pushforward and pullback actions to, and from, the tangent space to the manifold at any admissible point. This fundamental property is subsequently exploited to derive a probabilistic model that leverages the multimodel nature of the atomistic setting. The proposed formulation offers several advantages, including a simple and interpretable low-dimensional parameterization, the ability to constraint the Fréchet mean on the manifold, and ease of implementation and propagation. The relevance of the proposed modeling framework is finally demonstrated on various applications including multiscale simulations on graphene-based systems.

Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Customer complaints reflect the needs of citizens and provide valuable information for the efficient management of urban problems. Indoor water leakage management is required to achieve a sustainable water infrastructure and urban development. In this study, a machine learning (ML)-based modeling framework was developed for predicting the spatial distribution of customer complaints about indoor water leakage in the downtown area of Daegu Metropolitan City, South Korea. Two ML algorithms (XGBoost and LightGBM) were used with six resampling methods (e.g., undersampling, oversampling, and hybrid sampling) to compare the prediction performances. The combination of LightGBM and hybrid sampling showed the highest prediction performance. Post hoc analysis using Shapley Additive Explanations indicated that, among the various input features, the land cover type, building and water infrastructure characteristics were of primary importance. High-resolution gridded mapping clearly revealed the spatial pattern of complaint probabilities. These results provide a decision support tool for indoor water leakage management. The proposed modeling framework encompasses data preprocessing and integration, prediction, interpretation, and spatial mapping, and it is applicable to a wide variety of urban problems that require in-depth analysis of their spatial characteristics.

Planning reliable service facility location against disruption risks and last-mile congestion in a continuous space

This paper proposes a methodological framework that incorporates probabilistic facility disruption risks, last-mile customers travel path choices, and the induced traffic congestion near the facilities into the consideration of service facility location planning. The customers can be pedestrians, drones, or any autonomous vehicles that do not have to travel via fixed channels to access a service facility. Analytical models are developed to evaluate the expected performance of a facility location design across an exponential number of facility disruption scenarios. In each of these scenarios, customers travel to a functioning facility through a continuous space, and their destination and path choices under traffic equilibrium are described by a class of partial differential equation (PDE). A closed-form solution to the PDE is derived in an explicit matrix form, and this paper shows how the traffic equilibrium patterns across all facility disruption scenarios can be evaluated in a polynomial time. These new analytical results are then incorporated into continuous and discrete optimization frameworks for facility location design. Numerical experiments are conducted to test the computational performance of the proposed modeling framework.

A novel low-temperature thermo-mechanical coupling model for frost cracking simulation using the finite-discrete element method

Frost cracking is widely prevalent in cold areas and is typically associated with the deterioration of rock masses. A better understanding of the frost cracking mechanism and precise prediction of the associated crack evolution are essential for the safety and long-term service life of engineered rock structures in cold regions. This paper presents a low temperature thermo-mechanical (TM) coupling modeling framework, implemented into the combined finite-discrete element method (FDEM), to simulate the frost crack evolution in rock masses, considering the heat transfer, water–ice phase transition, and the induced frost heaving pressure. Existing benchmark tests were used to calibrate and verify the proposed modeling framework. Moreover, experimental and numerical results from freezing experiments conducted on samples containing pre-existing cracks have been successfully reproduced the complex mechanisms that lead to crack initiation and propagation in jointed rock formations where cracks driven by the water–ice phase transition initiate and propagate from the pre-existing cracks and deviate under the influence of confining stress and multi-crack interactions (i.e., stress shadow effect). This novel numerical framework presents a potentially useful tool to further understand frost cracking mechanisms and frost crack evolution of engineering projects in cold environments.

A fully coupled thermal–microstructural–mechanical finite element process model for directed energy deposition additive manufacturing of Ti–6Al–4V

A fully coupled thermal–microstructural–mechanical finite element modelling framework is developed to investigate the distortion and residual stresses during directed energy deposition (DED) of multi-phase Ti–6Al–4V alloy. The Johnson–Cook constitutive model is used to predict the yield strength of each phase as a function of strain, strain rate and temperature where the flow stress is calculated by a linear mixing rule based on the volumetric phase fractions. A thin-walled rectangular sample is chosen as the reference geometry and the results are compared with experimentally measured in situ thermal history and distortion data, where a reasonable agreement is achieved. The proposed modelling framework with physics-based material constitutive model provides useful information for a better understanding of process–microstructure–property relations in additive manufacturing by DED.

Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study

How to mitigate the spread of infectious diseases like COVID-19 in indoor environments remains an important research question. In this study, we propose an agent-based modeling framework to evaluate facility usage policies that aim to lower the probability of outbreaks. The proposed framework is individual-based, spatially-resolved with time resolution of up to 1 s, and takes into detailed account specific floor layouts, occupant schedules and movement. It enables decision makers to compute realistic contact networks and generate risk profiles of their facilities without relying on wearable devices, smartphone tagging or surveillance cameras. Our demonstrative modeling results indicate that not all facility occupants present the same risk of starting an outbreak, where the driver of outbreaks varies with facility layouts as well as individual occupant schedules. Therefore, generic mitigation strategies applied across all facilities should be considered inferior to tailored policies that take into account individual characteristics of the facilities of interest. The proposed modeling framework, implemented in Python and now available to the public in an open-source platform, enables such strategy evaluation.

Evolving asymmetric and anisotropic hardening of CP-Ti sheets under monotonic and reverse loading: Characterization and modeling

Commercially pure titanium (CP-Ti) sheets are promising to manufacture high-performance and lightweight components in many fields such as aerospace, marine, energy, electrics and healthcare. However, due to its low symmetric hexagonal close-packed (HCP) structure and complex thermal-mechanical processing history, CP-Ti sheets may exhibit significant asymmetry and anisotropy under monotonic and reverse loading. Therefore, the coupling effect of loading-path dependent asymmetric and anisotropic hardening behaviors of CP-Ti sheets need to be systemically characterized and accurately described under monotonic and reverse loading paths. In this study, firstly, the rheological tests were performed to systemically explore the evolving asymmetric and anisotropic hardening behavior of CP-Ti sheet under monotonic and reverse loading modes. With the help of anti-buckling support fixture and DIC system in tension and compression tests, the coupling effect of asymmetric and anisotropic hardening under monotonic and reverse loading of CP-Ti sheet was identified. Secondly, a loading-path dependent constitutive modeling framework was constructed in which the stress invariants-based model (SIM) integrated with distorted hardening (DH) under reverse loading (RL) conditions (SIM+DH+RL) were considered. Within the proposed modeling framework, a judging criterion of stress reversal for explicit solution method and an activation criterion of different reverse loading modes were introduced. Finally, the proposed SIM+DH+RL model was implemented, calibrated and further applied to simulate typical V-/U-bending of CP-Ti sheets. The results show that the proposed SIM+DH+RL model can significantly improve the prediction accuracy of V-/U-bending springback with the relative errors less than 7.8% and 2.1%, compared with 15.2% and 12% relative errors of Mises+IH, 19.5% and 7.3% relative errors of SIM+DH, 15.1% and 8.4% relative errors of Yoon2014+IH, 14.5% and 5.2% relative errors of CPB06+IH, and 17.3% and 11.5% relative errors of Hill48+IH. It is noted that for the thickness distributions after V-/U-bending springback, the above used models have similar prediction accuracies with the relative errors less than 1.8%.

Monitoring changes in global soil organic carbon stocks from space

Soils are under threat globally, with declining soil productivity and soil health in many places. As a key indicator of soil functioning, soil organic carbon (SOC) is crucial for ensuring food, soil, water and energy security, together with biodiversity protection. While there is a global effort to map SOC stock and status, SOC is a dynamic soil property and can change rapidly as a function of land management and land use. Here, we introduce a semi-mechanistic model to monitor SOC stocks at a global scale, underpinned by one of the largest worldwide soil database to date. Our model generates a SOC stock baseline for the year 2001, which is then propagated through time by keeping track of annual landcover changes obtained from remote sensing products with loss and gain dynamics dependent on temperature and precipitation, which finally define the magnitude, rate and direction of the SOC changes. We estimated a global SOC stock in the top 30 cm of around 793 Pg with annual losses due to landcover change of 1.9 Pg SOC yr−1 from 2001 to 2020, 20% larger than the annual production-based emissions of the United States in 2018. The biggest losses were found in the tropic and sub-tropical regions, accounting for almost 50% of the total global loss. This is a considerable contribution to greenhouse gas emissions but it also has a direct impact on agricultural production with more than 16 million hectares yr−1 falling below critical SOC limits. The proposed modelling framework is flexible, allowing it to be updated as more remote sensing and soil data becomes available, offering a first-of-its-kind global spatio-temporal SOC stock assessment and monitoring system.

Modeling between-farm transmission dynamics of porcine epidemic diarrhea virus: Characterizing the dominant transmission routes

The role of transportation vehicles, pig movement between farms, proximity to infected premises, and feed deliveries has not been fully considered in the dissemination dynamics of porcine epidemic diarrhea virus (PEDV). This has limited efforts for disease prevention, control and elimination restricting the development of risk-based resource allocation to the most relevant modes of PEDV dissemination. Here, we modeled nine pathways of between-farm transmission represented by a contact network of pig movements between sites, farm-to-farm proximity (local transmission), four distinct contact networks of transportation vehicles (trucks that transport pigs from farm-to-farm and farm-to-markets, as well as trucks transporting feed and staff), the volume of animal by-products in feed diets (e.g., fat and meat-and-bone-meal) to reproduce PEDV transmission dynamics. The model was calibrated in space and time with weekly PEDV outbreaks. We investigated the model performance to identify outbreak locations and the contribution of each route in the dissemination of PEDV. The model estimated that 42.7% of the infections in sow farms were related to vehicles transporting feed, 34.5% of infected nurseries were associated with vehicles transporting pigs between farms, and for both farm types, local transmission or pig movements were the next most relevant transmission routes. On the other hand, finishers were most often (31.4%) infected via local transmission, followed by the vehicles transporting feed and pigs between farms. Feed ingredients did not significantly improve model calibration metrics, sensitivity, and specificity; therefore, it was considered to have a negligible contribution in the dissemination of PEDV. The proposed modeling framework provides an evaluation of PEDV transmission dynamics, ranking the most important routes of PEDV dissemination and granting the swine industry valuable information to focus efforts and resources on the most important transmission routes.

Configurable virtual MIMO via UAV swarm: Channel modeling and spatial correlation analysis

Air-to-ground wireless channel modeling for unmanned aerial vehicle (UAV) communications has been widely studied. However, channel modeling for UAV swarm-enabled cooperative communication still needs investigation, where the impact of UAV positions on the spatial channel characteristics is of particular importance. In this paper, we consider a UAV swarm-enabled virtual multiple input multiple output (MIMO) system, where multiple single-antenna UAVs cooperatively transmit to multiple ground users (GUs). We establish a common coordinate system, as well as a UAV swarm-oriented coordinate system, to describe the relative positions of the GUs and the UAV elements, respectively. Based on the established coordinate systems, geometric ray superposition method is applied to describe the spatial channel matrix. The proposed modeling framework can be directly used to describe the line-of-sight and two-ray propagations, and can be extended for including more practical spatial features such as multipath scattering, inter-UAV blockage, and random UAV jittering, etc. Based on the proposed model, we further analyze the spatial correlation among the virtual MIMO links of GUs located at different positions. Via extensive simulations, we show that thanks to the flexible deployment of UAVs, the virtual MIMO array structure can be conveniently configured to get desired channel properties, such as the channel capacity, eigenvalue and condition number distribution, and spatial correlation distribution. This shows the possibility and importance of exploiting a new design dimension, i.e., the UAV swarm pattern, in such cooperative virtual MIMO systems.

Optimal contract selection for contract manufacturing organizations in the secondary pharmaceutical industry

During the last two decades, an increasing number of large pharmaceutical companies have decided to outsource part of their operations to reduce costs and mitigate their risk exposure. Contract Manufacturing Organizations (CMOs) utilize their facilities to manufacture products for multinational pharmaceutical companies on a contract basis. These organizations must define which contracts to accept to maximize their profit while considering their risk tolerance. This work presents an integrated tactical planning and medium-term scheduling framework for the optimal contract appraisal problem of Contract Manufacturing Organizations in the secondary pharmaceutical industry under demand uncertainty. An aggregated MILP-based planning model is proposed, along with a general precedence MILP model for the scheduling of multi-stage, multiproduct, batch industries. Production targets are defined in the planning decision level using a rolling horizon framework, while the scheduling model takes batch-sizing, sequencing and timing decisions in detail. A three-phase, scenario-based solution algorithm is introduced to model demand uncertainty considering Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) measures, while both systematic and unsystematic risk are considered. Results illustrate that the proposed modelling framework provides a systematic approach for the contract appraisal problem of Contract Manufacturing Organizations as it can select the optimal contract mixture depending on the corresponding risk tolerance.

An integrated deep learning-based model of spatial cells that combines self-motion with sensory information.

A special class of neurons in the hippocampal formation broadly known as the spatial cells, whose subcategories include place cells, grid cells, and head direction cells, are considered to be the building blocks of the brain's map of the spatial world. We present a general, deep learning-based modeling framework that describes the emergence of the spatial-cell responses and can also explain responses that involve a combination of path integration and vision. The first layer of the model consists of head direction (HD) cells that code for the preferred direction of the agent. The second layer is the path integration (PI) layer with oscillatory neurons: displacement of the agent in a given direction modulates the frequency of these oscillators. Principal component analysis (PCA) of the PI-cell responses showed the emergence of cells with grid-like spatial periodicity. We show that the Bessel functions could describe the response of these cells. The output of the PI layer is used to train a stack of autoencoders. Neurons of both the layers exhibit responses resembling grid cells and place cells. The paper concludes by suggesting the wider applicability of the proposed modeling framework beyond the two simulated studies.

The integrated modeling of microgrid cyber physical system based on hybrid automaton

To fully reveal the interplay of the cyber system and physical system in the microgrid, this paper proposes a generic hierarchical modeling framework for cyber-physical integration modeling of microgrid, including two layers: physical device layer and controller layer. Each layer includes two parts: the continuous part (characterizing the physical system in the microgrid) and the discrete part (characterizing the cyber system in the microgrid). In this paper, firstly, detailed mathematical expressions describing the dynamic characteristics of each layer (including continuous characteristics and discrete characteristics) and describing the interplay of the continuous part and discrete part in each layer are given, which contributes to a better understanding of the interplay of the information flow and energy flow in the microgrid. Secondly, the cyber-physical integration modeling of each unit in the proposed framework is established using hybrid automaton, which can clearly present the operation state of each unit of the microgrid and its state transfer process, which is beneficial to design the optimal operation state trajectory of the system. The proposed modeling framework is generic and can be extended to any dynamic system with cyber-physical integration. Finally, a typical microgrid system is taken as an example to verify the feasibility of the proposed modeling approach.

An integrated approach of artificial neural networks and polynomial chaos expansion for prediction and analysis of yield and environmental impact of oil shale retorting process under uncertainty

Shale oil reserves exploration is getting huge investments due to the depletion of conventional reserves. Computational methods have been used to realize optimum design and operation of shale oil and gas reserves exploration and processing. The uncertainty associated with the composition of shale reserves and operating conditions during processing put a challenge to the realization of high yield and mitigation of environmental impact. In the current work, machine learning (ML) based models are proposed for the estimation of the yield and environmental impact of the oil shale retorting process under uncertainty. An artificial uncertainty of 1% was inserted in feed composition and process conditions of an Aspen model of the process to generate data for the development of the ML models. Artificial Neural Network (ANN), Least Square Boosting (LSB), and Bagging techniques were compared to find the best ML model. ANN models, with the highest correlation coefficient of 0.995 and 0.999 for the oil yield and CO2 content respectively, are used as surrogates in a Polynomial Chaos Expansion (PCE) framework for the uncertainty analysis of the process. For 1% uncertainty in feed composition and process conditions, a mean absolute deviation of 0.319 and 0.580 was obtained for the oil yield and carbon dioxide emissions respectively. To find the hierarchy in the process inputs in terms of their effect on the oil yield and carbon dioxide emissions, the ANN model is used as a surrogate in sensitivity analysis through Sobol and Fourier Amplitude Sensitivity Test (FAST) indices. The most sensitive input variables were feed temperature and air molar flow rate. The proposed modeling framework will provide a base for future real-time monitoring and analysis of the oil shale retorting processes.

Assessing farmers' adaptation responses to water conservation policies through modular recursive hydro-micro-macro-economic modeling

Farmers' adaptation responses to water conservation policies involve a complex decision-making process that depends on a range of criteria, including water availability, profits, and risks, which are in turn dependent on (and might have consequences at) broader scale processes including water systems and the macroeconomy. The non-consideration of the complex interactions between and within natural and human systems often leads to unforeseen consequences and sub-optimal water policy design. There exists a fundamental need to improve our understanding of complex human-water (e.g. hydro-economic) and human-human (e.g. micro-macroeconomic) systems' interactions so to better inform policy-makers. This paper develops an innovative modeling framework for capturing the richness of interactions in complex human-water systems by: i) considering the rationale behind farmers' behavior and responses through microeconomic models; ii) assessing the complex interactions among economic sectors and regions within an economy through macroeconomic models; iii) simulating responses on water cycle dynamics within a river basin by means of hydrologic modeling; and iv) representing the interconnected dynamics and two-way feedback responses between human-water and human-human (through micro-macro-economic) systems. The proposed modeling framework operates through a recursive modular approach built from independent modules which are, in turn, connected through a set of protocols that control the exchange of information. Methods are illustrated considering an incremental agricultural water charging policy in the Spanish part of the Douro River Basin (DRB). Results show that local land use reallocations have an impact on the supply of irrigated (rainfed) agricultural commodities at the macroeconomic level, which further leads to higher (or lower) commodity prices that partially offset changes in crop profitability due to changing water charges and readjustments in the crop portfolio. These, in turn, result in non-linear responses in land and water use with non-trivial impacts on water system's dynamics, where evapotranspiration, surface runoff, and groundwater evapotranspiration are the main hydrologic components affected. We conclude that the integration of hydro-micro-macro-economic modules through a set of protocols can provide crucial information for promoting the efficient design of agricultural water policies.

A Port Hamiltonian approach to dynamical chemical process systems network modeling and analysis

This work focuses on the investigation of the applicability of port Hamiltonian systems (PHS) approach to model chemical process plant systems and the generalization of the PHS originally proposed for reversible thermodynamic systems to irreversible thermodynamic system characterized by coupled physicochemical phenomenon and significant irreversible entropy generation. To this end, the first part of this work presents a definition and analysis of the proposed generalized class of conservative pseudo port Hamiltonian system for general open multi-input multi-output (MIMO) irreversible thermodynamic system. In the second part of the work, the generalized PHS approach is applied to multiphase process systems with coupled irreversible entropy generation. The proposed modeling framework is extended to model chemical process systems network with recycle, which is prevalent in chemical process plants.

Chemical signatures to identify the origin of solid ashes for efficient recycling using machine learning

Solid ashes constitute the world's leading environmental problem and their efficient recycling requires specific origin information. Chemical signatures encode the physicochemical and structural characteristics may reveal their origins. In this study, we collected 310 solid ash samples from various continents, countries, and origins and combined particle swarm optimization, random forest (RF), and novel interpretation methods to build an accurate ash origin detection system. The RF model took major oxides as inputs, without further feature-engineering, and automatically classified the solid ash into four origins. Our model predicted solid ash origins with 98.5% accuracy on a training set and with 91.5% accuracy on an independent testing set, even without expert knowledge of the operating conditions. Our approach also demonstrated feature importance and interpreted the decision-making mechanisms underlying optimum RF models. These results demonstrated the feasibility of the proposed modeling framework as a blueprint for automated machine-learning origin detection of solid ashes.

Estimating effectiveness of speed reduction measures for pedestrian crossing treatments using an empirically supported speed choice modeling framework

Pedestrian crossing treatments, such as Pedestrian Hybrid Beacons (PHBs) and Rectangular Rapid-Flashing Beacons (RRFBs), are traffic control devices implemented to help pedestrians safely cross busy or higher-speed roadways at midblock crossings and uncontrolled intersections. This study aims to assess the effectiveness of the PHB and RRFB by analyzing drivers’ speeding behavior under different roadway types with real-life traffic conditions. In order to understand the effect of pedestrian crossing treatments (i.e., PHB and RRFB) have on drivers’ speeding behavior, this paper analyzes four zones (i.e., one upstream zone and three consecutive downstream zones). For the four analysis zones, different indexes were computed which corresponds to the differences in drivers’ speed when encountered with the pedestrian crossing treatments. A grouped random effect hurdle beta regression model is estimated for the indexes, with a fixed effect hurdle beta model used for comparison and validating the importance of considering the grouped random heterogeneity across participants. The proposed analysis framework was validated by the means of an empirical driving simulator study, based on two urban roads in the Central Florida region, North Alafaya Trail (SR-434) and South Orange Blossom Trail (US-441). The results revealed that the proposed modeling framework reflects drivers’ difference in speed for the different pedestrian crossing treatments. The results suggest that with proper understanding of the PHB, the PHB can reduce drivers speed even beyond the location of the PHB. Meanwhile, the RRFB does have some effect in speed reduction beyond the location of the RRFB, however many drivers failed to acknowledge the RRFB. It is suggested that when drivers’ have proper education on the use of the PHB to reduce speed safely and for the installation of RRFB be on roads with two or less lanes and a speed limit less than 40 mph. While the main purpose of the pedestrian crossing treatments is to help pedestrians cross safely, speed reduction can be considered a byproduct as revealed in this study.

Image Difference Captioning with Pre-training and Contrastive Learning

The Image Difference Captioning (IDC) task aims to describe the visual differences between two similar images with natural language. The major challenges of this task lie in two aspects: 1) fine-grained visual differences that require learning stronger vision and language association and 2) high-cost of manual annotations that leads to limited supervised data. To address these challenges, we propose a new modeling framework following the pre-training-finetuning paradigm. Specifically, we design three self-supervised tasks and contrastive learning strategies to align visual differences and text descriptions at a fine-grained level. Moreover, we propose a data expansion strategy to utilize extra cross-task supervision information, such as data for fine-grained image classification, to alleviate the limitation of available supervised IDC data. Extensive experiments on two IDC benchmark datasets, CLEVR-Change and Birds-to-Words, demonstrate the effectiveness of the proposed modeling framework. The codes and models will be released at https://github.com/yaolinli/IDC.