Framework For Development Of Models Research Articles

Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study

Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based on first-principles are most common, however, data-driven models exploiting patterns in measurements are gaining popularity. This research investigates a hybrid modeling approach, utilizing techniques from both the aforementioned areas of expertise, to model a well production choke. The choke is represented with a simplified set of first-principle equations and a neural network to estimate the valve flow coefficient. Historical production data from the petroleum platform Edvard Grieg is used for model validation. Additionally, a mechanistic and a data-driven model are constructed for comparison of performance. A practical framework for development of models with varying degree of hybridity and stochastic optimization of its parameters is established. Results of the hybrid model performance are promising albeit with considerable room for improvements.

Guidance for Developing Amphibian Population Models for Ecological Risk Assessment.

Despite widespread acceptance of the utility of population modeling and advocacy of this approach for a more ecologically relevant perspective, it is not routinely incorporated in ecological risk assessments (ERA). A systematic framework for situation-specific model development is one of the major challenges to broadly adopting population models in ERA. As risk assessors confront the multitude of species and chemicals requiring evaluation, an adaptable stepwise guide for model parameterization would facilitate this process. Additional guidance on interpretation of model output and evaluating uncertainty would further contribute to establishing consensus on good modeling practices. We build on previous work that created a framework and decision guide for developing population models for ERA by focusing on data types, model structure, and extrinsic stressors relevant to anuran amphibians. Anurans have a unique life cycle with varying habitat requirements and high phenotypic plasticity. These species belong to the amphibian class, which is facing global population decline in large part due to anthropogenic stressors, including chemicals. We synthesize information from databases and literature relevant to amphibian risks to identify traits that influence exposure likelihood, inherent sensitivity, population vulnerability, and environmental constraints. We link these concerns with relevant population modeling methods and structure in order to evaluate pesticide effects with appropriate scale and parameterization. A standardized population modeling approach, with additional guidance for anuran ERA, offers an example method for quantifying population risks and evaluating long-term impacts of chemical stressors to populations. Integr Environ Assess Manag 2020;16:223-233. © 2019 SETAC.

Predicting effects of changed antimicrobial usage on the abundance of antimicrobial resistance genes in finisher’ gut microbiomes

It is accepted that usage of antimicrobials (AMs) in food animals causes the emergence and spread of antimicrobial resistance (AMR) in this sector, while also contributing to the burden of AMR in humans. Curbing the increasing occurrence of AMR in food animals requires in-depth knowledge of the quantitative relationship between antimicrobial usage (AMU) and AMR to achieve desired resistance reductions from interventions targeting AMU. In the observational study, the relationships between lifetime AMU in 83 finisher batches from Danish farms and the AMR gene abundances of seven antimicrobial classes in their gut microbiomes were quantified using multi-variable linear regression models. These relationships and the national lifetime AMU in pigs were included in the predictive modelling that allowed for testing of scenarios with changed lifetime AMU for finishers produced in Denmark in 2014. A total of 50 farms from the observational study were included in validating the observational study and the predictive modelling. The results from the observational study showed that the relationship was linear, and that the parenteral usage of AMs had a high effect on specific AM-classes of resistance, whereas the peroral usage had a lower but broader effect on several classes. Three different scenarios of changed lifetime AMU were simulated in the predictive modelling. When all tetracycline usage ceased, the predicted interval reductions of aminoglycoside, lincosamide and tetracycline resistance were 4–42 %, 0–8 % and 9–18 %, respectively. When the peroral tetracycline usage of the 10 % highest users was replaced with peroral macrolide usage, the tetracycline resistance fell by 1–2 % and the macrolide and MLSb resistance increased by 5–8 %. When all extended-spectrum penicillin usage was replaced with parenteral lincosamide usage, the beta-lactam resistance fell by 2–7 %, but the lincosamide usage and resistance increased by 194 % and 10–45 %, respectively. The external validation provided results within the 95 % CI of the predictive modelling outcome at national level, while the external validation at farm level was less accurate. In conclusion, interventions targeting AMU will reduce AMR abundance, though differently depending on the targeted AM-class and provided the reduction of one AM-class usage is not replaced with usage of another AM-class. Predicting several classes of AMR gene abundance simultaneously will support stakeholders when deciding on interventions targeting AMU in the finisher production to avoid adverse and unforeseen effects on the AMR abundance. This study provides a sound predictive modelling framework for further development, including the dynamics of AMU on AMR in finishers at national level.

A general framework for photoionization calculations applied to nonthermal gas discharges in air

A general framework for photoionization rate calculations in a constant pressure gaseous medium is introduced. The formulation includes the number of photons emitted per unit volume per unit time per unit wavelength due to a radiating source, photobasorption cross sections and density of species comprising the medium, and the photoionization probability (i.e. photoionization yield) of the species being photoionized. We derive a standard integral representation of the photoionization problem that may be readily converted to a set of Helmholtz differential equations for efficient calculation of the photoionization rate. The model is applied to the photoionization problem in air in which , , , , singlet states of N2 are excited due to collisions with electrons generated as a result of nonthermal discharges. Radiative decay from these states gives rise to respective band systems Birge-Hopfield I, Birge-Hopfield II, Worley-Jenkins, Worley, and Carroll-Yoshino, and to photons which are generally energetic enough to ionize O2. The excitation rates and contribution of each band system to photoionization of O2 in air are calculated for the first time. Using recently measured electron impact excitation cross sections of these states, and the recently measured extreme ultraviolet (XUV) spectra of N2, we quantify the emission from each singlet state. Absorption of emission is modeled using measured photoabsorption cross sections of N2 and O2. The photoionization rate of O2 upon absorption of a photon with a certain energy is calculated using experimental values for the photoionization yield of O2. Finally, we introduce a set of coefficients which define the differential representation of the problem of photoionization in air. The developed modeling framework allows accurate solution of photoionization problems in air for the broad range Torr cm, where is the partial pressure of O2 in air in units of Torr ( Torr at atmospheric pressure) and R in cm is a characteristic spatial dimension of the system of interest. The model performance is demonstrated using a set of artificial sources leading to photoionization over a representative range of values and a realistic problem of dynamics of a double-headed streamer in air that was used in previous photoionization literature. The validity of the modeling framework is demonstrated by comparisons with the photo-ion yield function in air, , derived from the classic photoionization model of Zheleznyak et al () and more recent experimental data on photoionization in air.

Packing ovals in optimized regular polygons

We present a model development framework and numerical solution approach to the general problem-class of packing convex objects into optimized convex containers. Specifically, we discuss the problem of packing ovals (egg-shaped objects, defined here as generalized ellipses) into optimized regular polygons in $$ {\mathbb{R}}^{2} $$. Our solution strategy is based on the use of embedded Lagrange multipliers, followed by nonlinear optimization. Credible numerical results are attained using randomized starting solutions, refined by a single call to a local optimization solver. We obtain visibly good quality packings for packing 4 to 10 ovals into regular polygons with 3 to 10 sides in all 224 test problems presented here. Our modeling and solution approach can be extended towards handling other difficult packing problems.

A Systemic Framework for Business Model Design and Development -Part B: Practical Perspective

Profit-making requires innovation in the design of a successful business model that brings about sustainable competitive advantage for the organization. Given the increasing use of mobile phones in financial affairs, the design and development of mobile banking business model is one of the major concerns of the banking sector. Since there are a multitude of stakeholders with different viewpoints on what and how value can be added through mobile, the development of mobile banking business model has turned into a complex problem. In light of this, this study aims at developing a mobile banking business model, using a new framework based on Soft Systems Methodology (SSM). This framework consists of 18 steps, ranging from finding out the problem situation to the implementation of the model. One of the Iranian private banks was selected as the case of the current study. The results revealed that in the context of the problem, the development of mobile banking business model requires the interaction among 7 sub-systems. Which emerge out of the accommodation between three different mindsets involved in the development of mobile banking business model.

A bootstrap-aggregated hybrid semi-parametric modeling framework for bioprocess development.

Hybrid semi-parametric modeling, combining mechanistic and machine-learning methods, has proven to be a powerful method for process development. This paper proposes bootstrap aggregation to increase the predictive power of hybrid semi-parametric models when the process data are obtained by statistical design of experiments. A fed-batch Escherichia coli optimization problem is addressed, in which three factors (biomass growth setpoint, temperature, and biomass concentration at induction) were designed statistically to identify optimal cell growth and recombinant protein expression conditions. Synthetic data sets were generated applying three distinct design methods, namely, Box-Behnken, central composite, and Doehlert design. Bootstrap-aggregated hybrid models were developed for the three designs and compared against the respective non-aggregated versions. It is shown that bootstrap aggregation significantly decreases the prediction mean squared error of new batch experiments for all three designs. The number of (best) models to aggregate is a key calibration parameter that needs to be fine-tuned in each problem. The Doehlert design was slightly better than the other designs in the identification of the process optimum. Finally, the availability of several predictions allowed computing error bounds for the different parts of the model, which provides an additional insight into the variation of predictions within the model components.

Modeling of computer network with fault-tolerance gateway in OMNeT++

Subject of Research. The paper considers the issues of providing fault-tolerance gateways in computer networks with the use of First Hop Redundancy Protocols (FHRP). The description of FHRP family protocols and simulator OMNeT++ is given. The process of simulation model creation in OMNeT++ using ANSA-INET library is described. Detailed description of HSRP algorithm is presented and researching opportunities for simulation study by OMNeT++ simulator are considered. Work Objective. The aim of the work is the study of computer network models creation with fault-tolerance gateway in OMNeT++ simulator on the HSRP protocol example. Method. The proposed approach to the study of FHRP protocols in computer networks is based on carrying out simulation experiments in simulation environment. We show development potential for computer networks based on FHRP protocols in OMNeT++ using ANSA-INET framework which contains a large library of completed network models, components and protocol implementations. The paper describes in brief the process of computer network model development and ANSA-INET framework main abilities concerning the modeling of fault-tolerance gateways. Main Results. Computer network model with routers cluster is developed. Every router works with HSRP protocol. It gives the possibility to perform a router redundancy, that is a gateway in this computer network. Simulation model of computer network with fault-tolerance gateway is developed. We present the method of developing computer network models using FHRP protocols for fault-tolerance gateway implementation. The main stages of model development and completion are described. Practical Relevance. The presented results can be used in the design of high-reliable and fault-tolerance computer networks providing continuity of service at network gateway failure. The described tools and algorithm for development of computer network models give the possibility to carry out research on gateway fault-tolerance in computer networks and create computer-aided design systems for reliable computer networks.

Adsorption-Based Atmospheric Water Harvesting: Impact of Material and Component Properties on System-Level Performance.

Atmospheric water harvesting (AWH) is the capture and collection of water that is present in the air either as vapor or small water droplets. AWH has been recognized as a method for decentralized water production, especially in areas where liquid water is physically scarce, or the infrastructure required to bring water from other locations is unreliable or infeasible. The main methods of AWH are fog harvesting, dewing, and utilizing sorbent materials to collect vapor from the air. In this paper, we first distinguish between the geographic/climatic operating regimes of fog harvesting, dewing, and sorbent-based approaches based on temperature and relative humidity (RH). Because utilizing sorbents has the potential to be more widely applicable to areas which are also facing water scarcity, we focus our discussion on this approach. We discuss sorbent materials which have been developed for AWH and the material properties which affect system-level performance. Much of the recent materials development has focused on a single material metric, equilibrium vapor uptake in the material (kg of water uptake per kg of dry adsorbent), as found from the adsorption isotherm. This equilibrium property alone, however, is not a good indicator of the actual performance of the AWH system. Understanding material properties which affect heat and mass transport are equally important in the development of materials and components for AWH, because resistances associated with heat and mass transport in the bulk material dramatically change the system performance. We focus our discussion on modeling a solar thermal-driven system. Performance of a solar-driven AWH system can be characterized by different metrics, including L of water per m2 device per day or L of water per kg adsorbent per day. The former metric is especially important for systems driven by low-grade heat sources because the low power density of these sources makes this technology land area intensive. In either case, it is important to include rates in the performance metric to capture the effects of heat and mass transport in the system. We discuss our previously developed modeling framework which can predict the performance of a sorbent material packed into a porous matrix. This model connects mass transport across length scales, considering diffusion both inside a single crystal as well as macroscale geometric parameters, such as the thickness of a composite adsorbent layer. For a simple solar thermal-driven adsorption-based AWH system, we show how this model can be used to optimize the system. Finally, we discuss strategies which have been used to improve heat and mass transport in the design of adsorption systems and the potential for adsorption-based AWH systems for decentralized water supplies.

Heterotrophic denitrifiers growing on soluble microbial products contribute to nitrous oxide production in anammox biofilm: Model evaluation

In this work, a model framework was constructed to assess and predict nitrous oxide (N2O) production, substrate and microbe interactions in an anammox biofilm bioreactor. The anammox kinetics were extended by including kinetics of autotrophic soluble microbial products (SMP) formation, which consisted of utilization-associated products (UAP) and biomass-associated products (BAP). Heterotrophic bacteria growing on UAP, BAP and decay released substance (SS) were modelled to perform four-step sequential reductions from nitrate to dinitrogen gas. N2O was modelled as an intermidiate of heterotrophic denitrification via three pathways with UAP, BAP and SS as the electron donors. The developed model framework was evaluated using long-term operational data from an anammox biofilm reactor and satisfactorily reproduced effluent nitrogen and SMP as well as N2O emission factors under different operational conditions. The modeling results revealed that N2O was mainly produced with UAP as the electron donor while BAP and SS play minor roles. Heterotrophic denitrifiers growing on UAP would significantly contribute to N2O emission from anammox biofilm reactor even though heterotrophs only account for a relatively small fraction of active biomass in the anammox biofilm. Comprehensive simulations were conducted to investigate the effects of N loading rate and biofilm thickness, which indicated that maintaining a low N loading rate and a thick biofilm thickness were essential for high total nitrogen removal efficiency and low N2O emission.

Coordinative Urban-Rural Solid Waste Management: A Fractional Dual-Objective Programming Model for the Regional Munifcipality of Xiamen

A linear fractional programming based solid waste management planning model was proposed and applied to support the planning of urban-rural solid waste management in Xiamen, China. The model could obtain the best system efficiency while solving the tradeoff between economic and environmental objectives. It aimed to effectively address the urban and rural solid waste management planning through minimizing the system cost and optimizing system efficiency in the developed model framework. Through the model, the optimal waste flow for each facility was obtained, and the problem of overburdened landfill in Xiamen’s urban and rural solid waste management system was solved. The solutions for waste allocation and facility capacity expansion were provided for Xiamen’s urban and rural solid waste management. The planning results showed that about 42.44 × 106 tons of waste would be diverted to other facilities from landfills over the planning period of 2018-2032, and the waste diversion rate would reach 97%, which would greatly reduce the burden on landfills. The economic efficiency of waste diversion would be 5.07 × 103 ton per 106 RMB. All the capacities of Xiamen’s urban and rural solid waste management facilities including incinerators, composting plant, and landfills should be expanded because of increasing waste production rate.

Cross-Interaction Chromatography-Based QSAR Model for Early-Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics.

Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by developability issues such as polyspecificity and lack of solubility, which leads to attrition as well as manufacturing failures. In this study a multitool hybrid quantitative structure-activity relationship (QSAR) model development framework is described. This framework uses four novel datasets derived from the primary sequences of IgG1-κ-humanized mAbs with varying degrees of resolutions. Unsupervised pattern recognition is first performed on the descriptor sets to visualize any intrinsic property-based clustering, followed by regression of descriptors against cross-interaction chromatography (CIC) retention times. Model optimization is performed via unsupervised variable reduction followed by supervised variable selection. Finally, the models and datasets are benchmarked based on the regression model performance metrics such as R2 , Q2 , and RMSE. The results show that datasets containing localized descriptors rather than averaged value over the entire protein have better predictive performance of CIC retention behavior with R2 > 0.8 and RMSE < 0.3. Furthermore, the results indicate the physicochemical, electronic, and topological properties of hypervariable regions of antibodies that contribute most to the CIC retention times. The results of these studies could contribute to early-stage screening and better design of mAbs.

Modeling Exposure-Driven Adverse Event Time Courses in Oncology Exemplified by Afatinib.

Models were developed to characterize the relationship between afatinib exposure and diarrhea and rash/acne adverse event (AE) trajectories, and their predictive ability was assessed. Based on pooled data from seven phase II/III clinical studies including 998 patients, mixed‐effects models for ordered categorical data were applied to describe daily AE severity. Clinical trial simulation aided by trial execution models was used for internal and external model evaluation. The final exposure‐safety model consisted of longitudinal logistic regression models with first‐order Markov elements for both AEs. Drug exposure was included as daily area under the concentration‐time curve (AUC), and drug effects on the AEs were correlated. Clinical trial simulation allowed adequate prediction of maximum AE grades and AE severity time courses but overestimated the proportion of AE‐dependent dose reductions and discontinuations. Both diarrhea and rash/acne were correlated with afatinib exposure. The developed modeling framework allows a prospective comparison of dosing strategies and study designs with respect to safety.

Gsmodutils: a python based framework for test-driven genome scale metabolic model development.

MotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. For example, when genome annotations are updated or new understanding regarding behaviour is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build, test and learn cycle.ResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimizing error between model versions.Availability and implementationThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils.Supplementary information Supplementary data are available at Bioinformatics online.

Design and development of the SLAV-INMIO-CICE coupled model for seasonal prediction and climate research

Abstract SLAV–INMIO–CICE is the coupled atmosphere–ocean–ice model developed at Marchuk Institute of Numerical Mathematics (INM) Russian Academy of Sciences (RAS), Shirshov Institute of Oceanology RAS and Hydrometeorological Centre of Russia (HMCR). The model components are coupled using the new version of the own developed Compact Modeling Framework (CMF). This paper presents design of the coupled model and some computational aspects related to the model components coupling. Preliminary evaluation of the coupled model climate and performance are also given.

Anisotropy of Mass Transfer During Sintering of Powder Materials with Pore–Particle Structure Orientation

A micromechanical model for the shrinkage anisotropy during sintering of metallic powders is proposed and experimentally assessed. The framework developed for modeling sintering based on the mechanism of grain boundary diffusion is extended to take into account the dislocation pipe-enhanced volume diffusion. The studied iron powder samples are pre-shaped into their green forms by uniaxial cold pressing before sintering step. The resultant green bodies are anisotropic porous structures, with inhomogeneous plastic deformation at the inter-particle contacts. These non-uniformities are considered to be the cause of the anisotropic dislocation pipe diffusion mechanisms, and thus of the undesired shape distortion during shrinkage. The proposed model describes the shrinkage rates in the compaction loading and transverse directions, as functions of both structural and geometric activities of the samples. Dislocation densities can be estimated from such equations using dilatometry and image analysis data. The reliability and applicability of the developed modeling framework are verified by comparing the calculated dislocation densities with outcomes of nanoindentation and electron backscatter diffraction-derived lattice rotations.

Framework for Green School Model Development in Malaysia

In parallel to global modernization especially in developing country like Malaysia, the urging for energy consumption conservation unwaveringly increases. The initiative of implementing the green building concept and practices is being developed to promote the sustainable development of global society at many levels. Green school model is among the initiatives being explored in this paper. In Malaysia, currently there is no significant studies or guidelines have been developed for green school model development. Thus, the aim of this paper is to review and analyze the requirement for green school model establishment which ultimately will act as a guideline for the implementation of green school model in Malaysia. To achieve this, study on general rules adopted to assess the green school model development by various countries is ventured. In particular, the focus is based on the energy efficiency, water efficiency, thermal comfort, indoor air quality, lighting efficiency, visual health, acoustics, and space utilization. The challenges for the development of green school model in Malaysia is also highlighted. Detailed review on the recent Malaysia’s policies and energy development plan is presented. The outcome of this study is a framework for green school model development in Malaysia addressing one of the prime key subject which is the lighting efficiency and its impact towards students’ performance.

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. In the other model, we consider interdependency among the cells, where cellular communication determines their collective behavior and evolution under a temporally evolving growth condition. We then demonstrate the framework’s capability by simulating a full-scale model of bacterial quorum sensing, where the dynamics of a population of bacterial cells is dictated by the intercellular communications in a time-evolving growth environment.

Stochastic finite element analysis framework for modelling thermal conductivity of particulate modified polymer composites

Abstract Polymers possess desirable properties such as low specific gravity and good corrosion resistance, which make them attractive for a variety of applications. However, polymers typically lack in mechanical, thermal and electrical properties. Therefore, considerable research efforts are spent to improve polymer properties using particulate fillers. A multitude of filler materials and shapes are available, such as fiber/rod, plate-like or spherical geometries. Composite morphologies may also differ based on particle distribution, dispersion and alignment, all of which may be affected by filler loading and manufacturing routes. Given the significant efforts associated with experimental analyses, modeling approaches are expedient for providing a fundamental understanding for particulate modified polymers. In the present study a numerical modeling framework was developed for predicting the effective properties, specifically, thermal conductivity, of particulate modified polymers considering filler-matrix and particle-to-particle interfacial effects. Employing a stochastic finite element analysis, composites with randomly distributed spherical particles were modeled and validated against experimental data. The developed modeling framework, which can accommodate a broad range of filler shapes and composite morphologies, provides an effective means for elucidating experimental studies and guiding the design of particulate modified polymer composites.

Atomistically informed multiscale modeling of radially grown nanocomposite using a continuum damage mechanics approach

An atomistically-informed multiscale modeling framework to investigate damage evolution and failure in radially-grown carbon nanotube (CNT) architecture is detailed in this paper. Molecular dynamics (MD) simulations are performed to investigate the effects of nano-reinforcements on the elastic-plastic characteristics of the constituent interphase in the radially-grown nanocomposite. An interphase damage model is developed using the continuum damage mechanics approach with damage evolution equations derived using atomistic simulations. The developed damage model is integrated with a high-fidelity micromechanical analysis and captures the underlying physical behavior that could be attributed to the enhancement of the out-of-plane properties at higher length scales. The mechanical properties of the nanocomposite obtained from micromechanical simulations are compared to experimental values reported in the literature, to validate the developed modeling framework. Conclusions are presented by comparing the material response of radially-grown CNT architectures with the traditional fiber reinforced polymer (FRP) with dispersed CNT architecture.