Modeling-based Approach Research Articles

System Dynamics Modeling-Based Approach for Assessing Seismic Resilience of Hospitals: Methodology and a Case in China

AbstractHospitals play a crucial role in providing badly needed medical care after earthquakes. Meanwhile, hospitals are likely to find themselves subject to earthquake impacts and may fail to func...

CHO cell productivity improvement by genome-scale modeling and pathway analysis: Application to feed supplements

Effective bioprocess development using Chinese hamster ovary (CHO) cells as hosts is hampered by the limited understanding of cellular metabolism under process conditions in bioreactors. Systematic tools such as genome-scale models have been developed, but their value has not been satisfactorily demonstrated and exploited for process development. In this study, we proposed a method using a genome-scale model to analyze existing process studies for bioprocess optimization. First, we used existing industrial CHO cell culture experiments to systematically gain metabolic insights for bioprocess development. Two fed-batch cultures, using the same cell line and process, resulted in different titers by supplementing two different types of feed media. A genome-scale model was applied to calculate fluxomics (i.e., intracellular fluxes) from extracellular metabolomics and then the metabolic differences were further analyzed through pathway analysis between these two cell culture conditions. Transcriptomics data from RNA-Seq were employed at this point for comparison and found to be consistent in pathway analysis with the flux analysis results. At the second stage, we developed a modeling-based approach for media optimization to increase antibody production based on the understanding from the first stage. The new design was tested in silico using a genome-scale model and then verified experimentally, confirming the applicability of this modeling-based approach for bioprocess optimization. The framework proposed in this study can maximize the utilization of existing process studies and minimize the time consumed for empirical work in developing new processes.

Uncertainty analysis of PIV measurements in bubbly flows considering sampling and bubble effects with ray optics modeling

In this paper, a modeling-based approach is developed to investigate the external sources of uncertainty in particle image velocimetry (PIV) measurements for two-phase bubbly flows. Primarily relied on ray optics modeling, this study focuses on the uncertainty in PIV measurements due to bubble-induced light distortion. In addition, the uncertainty due to sampling is also considered through synthetic PIV images generated from Monte Carlo sampling. Based on a PIV-planar laser induced florescence (PLIF) measurement system, three case studies are performed and discussed, considering respectively the sampling effect, bubble effect, and the combination of these two effects. These case studies confirm the validity of the ray optics modeling in obtaining accurate particle images through a benchmark test. Furthermore, it is also found that the bubble effect could significantly influence the particle images. On the other hand, the result of the case study with synthetic PIV images indicates that the bubble effect on particle images can be minimized by removing “outliers” in the obtained velocity vector field through a multi-pass post-processing procedure. However, the uncertainty of the velocity measurements will still increase.

Modeling-Based Response-Guided Glecaprevir-Pibrentasvir Therapy for Chronic Hepatitis C to Identify Patients for Ultrashort Treatment Duration.

We recently showed in a proof-of-concept study that real-time modeling-based response-guided therapy can shorten hepatitis C virus treatment duration with sofosbuvir-velpatasvir, elbasvir-grazoprevir, and sofosbuvir-ledipasvir without compromising efficacy, confirming our retrospective modeling reports in >200 patients. However, retrospective modeling of pibrentasvir-glecaprevir (P/G) treatment has yet to be evaluated. In the current study, modeling hepatitis C virus kinetics in 44 cirrhotic and noncirrhotic patients predicts that P/G treatment might have been reduced to 4, 6, and 7 weeks in 16%, 34%, and 14% of patients, respectively. These results support the further evaluation of a modeling-based response-guided therapy approach using P/G.

The structural origins of brittle star arm kinematics: An integrated tomographic, additive manufacturing, and parametric modeling-based approach

Brittle stars are known for the high flexibility of their arms, a characteristic required for locomotion, food grasping, and for holding onto a great diversity of substrates. Their high agility is facilitated by the numerous discrete skeletal elements (ossicles) running through the center of each arm and embedded in the skin. While much has been learned regarding the structural diversity of these ossicles, which are important characters for taxonomic purposes, their impact on the arms’ range of motion, by contrast, is poorly understood. In the present study, we set out to investigate how ossicle morphology and skeletal organization affect the flexibility of brittle star arms.Here, we present the results of an in-depth analysis of three brittle star species (Ophioplocus esmarki, Ophiopteris papillosa, and Ophiothrix spiculata), chosen for their different ranges of motion, as well as spine size and orientation. Using an integrated approach that combines behavioral studies with parametric modeling, additive manufacturing, micro-computed tomography, scanning electron microscopy, and finite element simulations, we present a high-throughput workflow that provides a fundamental understanding of 3D structure-kinematic relationships in brittle star skeletal systems.

Social dimension activates the usage and academic impact of Open Access publications in Andean countries: a structural modeling-based approach

Most of the altmetric research in developing countries is descriptive and has not tested models exploring relationships among article-level factors. Therefore, there is a knowledge gap on this issue. The research question for this study was the following: Social dimension is the factor that activates the usage and citation of Open Access publications in Andean countries. DOIs of Andean countries publications in PLoS journals were downloaded using (n = 3,186) using rplos, an Application Programming Interface (API) developed for R: Bolivia (n = 121), Chile (n = 1,180), Colombia (n = 826), Ecuador (n = 266), Peru (n = 622), and Venezuela (n = 171). Data modeling was carried out with Partial Least Squares – Structural Equation Modeling (PLS-SEM). The hypothesized structural model accounted for at least 41% of the explained variance of citations (R2 = 0.406). Among the two associated factors, the usage made the highest contribution (at least 0.639), while the social media showed a negative contribution. The application of PLS-SEM for modeling the information-seeking behavior, at the document level, highlights the positive evidence for the path social → usage → citation, but a negative and nonsignificant association between the social dimension and citation.

Modeling-based mineral system approach to prospectivity mapping of stratabound hydrothermal deposits: A case study of MVT Pb-Zn deposits in the Huayuan area, northwestern Hunan Province, China

An in-depth analysis of the modelled mineral system is prerequisite for producing mineral prospectivity maps. Critical to this is the accurate analysis of key ore-controlling processes and their translation into mappable targeting criteria and their spatial proxies. A case study illustrating and implementing the mineral prospectivity based on the theory of mineral system for stratabound hydrothermal deposits in the Huayuan area, northwestern (NW) Hunan Province, China helps analyze and summary the important mineral system components and promote the subsequent mineral prospectivity mapping. In the light of a local Pb-Zn metallogenic model and regional metallogeny, the ore-bearing strata were deposited in a platform edge shoal-reef facies, which not only facilitated the formation of a fluid reservoir, but also provided organic matter for the thermochemical sulfate reduction and the space for the ore accumulation. The left-lateral strike-slip movement of the Huayuan-Zhangjiajie fault forming the NE-trending secondary tensional fractures provides channels for ore-fluid migration. Based on the aforementioned, the NE-trending syn-ore fractures and thick ore-bearing strata, are crucial mappable targeting criteria for the delineation of prospective area. By detail analysis of the Huayuan-Zhangjiajie fault movement mode and the ore-bearing stratoisohypse mapping, three prospective areas were delineated. Our study from the perspective of mineral system emphasizes the importance of syn-ore fault analyzing, lithofacies paleogeographic and stratoisohypse mapping in the prospecting of MVT deposits in this case study. The method procedure for mineral prospectivity adopted in this contribution is also suitable for the prediction of other stratabound hydrothermal deposits.

The complex puzzle of dietary silver nanoparticles, mucus and microbiota in the gut

ABSTRACTHundreds of consumer and commercial products containing silver nanoparticles (AgNPs) are currently used in food, personal-care products, pharmaceutical, and many other applications. Human exposure to AgNPs includes oral intake, inhalation, and dermal contact. The aim of this review was to focus on oral intake, intentional and incidental of AgNPs where well-known antimicrobial characteristics that might affect the microbiome and mucus in the gastrointestinal tract (GIT). This critical review summarizes what is known regarding the impacts of AgNPs on gut homeostasis. It is fundamental to understand the forms of AgNPs and their physicochemical characterization before and during digestion. For example, lab-synthesized AgNPs differ from “real” ingestable AgNPs used as food additives and dietary supplements. Similarly, the gut environment alters the chemical and physical state of Ag that is ingested as AgNPs. Emerging research on in vitro and in vivo rodent and human indicated complex multi-directional relationships among AgNPs, the intestinal microbiota, and the epithelial mucus. It may be necessary to go beyond today’s descriptive approach to a modeling-based ecosystem approach that might quantitatively integrate spatio-temporal interactions among microbial groups, host factors (e.g., mucus), and environmental factors, including lifestyle-based stressors. It is suggested that future research (1) utilize more representative AgNPs, focus on microbe/mucus interactions, (2) assess the effects of environmental stressors for longer and longitudinal conditions, and (3) be integrated using quantitative modeling.

Assessment of the effect of polymers combination and effervescent component on the drug release of swellable gastro-floating tablet formulation through compartmental modeling-based approach

The aim of this research was to assess the effect of polymer blend and effervescent components on the floating and swelling behaviors of swellable gastro-floating formulation as well as the drug release through a compartmental modeling analysis. Swellable gastro-floating formulation of freely water-soluble drug, metformin HCl as a drug model, was formulated and developed using D-optimal design. Polymer combination between interpolymer complex (IPC) (poly-vinyl acetate-copolymer methacrylate) and hydroxy propyl methyl cellulose (HPMC), and effervescent components were studied and optimized in this work. Several factors affecting the drug release behavior were determined e.g. swelling behavior, erosion behavior, and floating behavior were studied as well as the drug release through compartmental modeling analysis. The results revealed that the hydrophilic polymer was responsible for gas entrapment formed from effervescent reaction, meanwhile IPC contributed on maintaining the swollen matrix integrity through intermolecular polymer interaction. In addition, effervescent components played fundamental role in the formation of porous system as well as inducing burst release effect. Compartmental modeling provided different outlook about the drug release. Presence of IPC at a high proportion (10–15%) of the polymer blend modulated the changes of pattern of the drug release kinetics and mechanism. Finally, compartmental modeling-based approach was more adequate to describe the drug release kinetics and mechanism compared to the monophasic equation model correlating with process understanding of the drug release from swellable gastro-floating formulation.

Requirement Validation for Embedded Systems in Automotive Industry Through Modeling

Requirement validation contributes significantly toward the success of software projects. Validating requirements is also essential to ensure the correctness of embedded systems in the auto industry. The auto industry emphasizes a lot on the verification of car designs and shapes. Invalid or erroneous requirements lead to inappropriate designs and degraded product quality. Considering the required expertise and time for requirement validation, significant attention is not devoted to verification and validation of requirements in the industry. Currently, the failure ratio of software projects is significantly higher and the key reason for that appears to be the inappropriate and invalidated requirements at the early stages in the projects. To that end, we propose a model-based approach that uses the existing V&V model. Through virtual prototyping, the proposed approach eliminates the need to validate the requirements after each stage of the project. Consequently, the model is validated after the design phase and the errors in requirements are detected at the earliest stage. In this research, we performed two different case studies for requirement validation in the auto industry by using a modeling-based approach and formal technique using Petri nets. A benefit of the proposed modeling-based approach is that the projects in the auto industry domain can be completed in less time due to effective requirements validation. Moreover, the modeling-based approach minimizes the development time, cost and increases productivity because the majority of the code is automatically generated using the approach.

Effect of interphase on elastic and shear moduli of metal matrix nanocomposites

In the present work, the effect of interphase between nanofiller materials and base metal materials has been analyzed. The analysis has been performed to analyze the possible enhancement of mechanical properties in the presence of interphase between nanofiller material and base metal material of resultant nanocomposites. The carbon nanotube (CNT) and single-layer graphene sheet (SLGS) have been used as nanofiller materials. The 3-dimensional continuum solid modeling-based simulation approach has been considered for the analysis, which can easily model the bonding between base materials and interphase as well as bonding between interphase and nanofiller material. Also, the CNT/SLGS-based metal matrix composites are analyzed to analyze the effect of interphase due to variation in size of the nanofiller materials (variation in outer radius of CNTs and variation in width of the SLGSs) on the elastic and shear moduli of the metal matrix nanocomposites. The presented 3-dimensional continuum solid modeling-based simulation approach has been validated by comparing obtained results with the analytical results based on the continuum solid mechanics-based formulations (rule of mixture). The obtained results depict that the consideration of interphase between nanofiller material and base metal material enhances the elastic and shear moduli of resultant metal matrix nanocomposite. And, the variation in size of nanofiller material affects the overall mechanical properties of resultant metal matrix nanocomposites. Also, the obtained results depict that the effect of variation in outer radii of CNTs is found more significant compared to the variation in width of SLGSs. As the presented 3-dimensional continuum solid modeling-based simulation approach reduces the complexity of the analytical formulations, it is found to be an efficient tool for mechanical characterization of metal matrix nanocomposites in the presence of interphase.

Benefit evaluation of investment in CCS retrofitting of coal-fired power plants and PV power plants in China based on real options

Coal-fired power plays an important role in China's power supply, even though the photovoltaic (PV) power has developed rapidly in recent years. Carbon capture and storage (CCS) technology is essential for achieving anticipated long-term climate change mitigation targets. However, China's government subsidy policies tend to support PV, while incentives for CCS are few. Given this situation, a trinomial tree modeling-based real options approach was developed to evaluate CCS retrofitting investment in coal-fired power plants versus PV power investment. This model includes setting different policy scenarios by considering electricity tariff subsidies and Chinese emission trading scheme (CN-ETS). The results showed that coal-fired power with CCS has a cost advantage over PV power in most provinces in China, if the electricity tariff and other subsidy policies remain the same; however, PV power will be cheaper than coal-fired power with CCS in Yunnan, Tianjin, Hebei, Shanghai and Shanxi provinces in the above cases. Incorporating CCS into CN-ETS could cut the former's application cost, but the incentive effect of CN-ETS is not obvious, because the carbon price in China is currently too cheap, and the incentive effect of a power tariff subsidy (subsidy at the PV power tariff level) is much better for PV than for CN-ETS. Increasing the load factor is an important way to reduce the CCS application cost for power plants. The results of this study provide reference and guidance for the collaborative development of CCS and PV.

A Modeling-Based Approach to Calculus

In our modern world, we are inundated and grapple with data daily. As mathematicians, we are often more comfortable discussing the behavior of functions presented analytically, in contrast with the data-driven or tabular presentations of functions ubiquitous in our culture. This paper introduces an entry-level course, Mathematical Modeling and Applied Calculus, for students who will most likely take only one calculus-level course in college and who often have a weak algebraic background that suggests they would most likely not be successful in a traditional calculus course. During the first half of the course, students review precalculus ideas while learning techniques for identifying elementary functional models whose graphs correspond to the underlying shapes of data sets, refining their models in response to new data, and using the method of least squares to find “best possible” models of a given type. During the second half of the course, these functional models are analyzed using the tools of calculus. Mathematical Modeling and Applied Calculus focuses on real-life contexts and data sets, uses RStudio to study large data sets, and explores big picture conceptual understandings in order to develop the insights and skills needed to describe, model, and analyze many aspects of our world.

Automated surface feature detection using fringe projection: An autoregressive modeling-based approach

An automatic surface defect detection algorithm is proposed in a fringe projection profilometry setup. An exponential phase field associated with the fringe projected surface image is analyzed using two-dimensional auto-regressive (AR) model. The AR model coefficients capture the local fringe frequency information. The variation in the fringe frequency from the defect-free to defective surface region is utilized as a signature for defect identification and localization. The fringe frequency threshold required for the image segmentation into defective and defect free regions is derived from the mean frequency computed over the same image which obviates the need for the reference image. In order to enhance the computation efficiency of the algorithm the image is divided into small patches and analysis is performed patch-wise. The simulation and experimental results demonstrate the advantages of the proposed phase based surface evaluation method over the commonly used intensity based methods.

Performance in three shell functions predicts the phenotypic distribution of hard-shelled turtles.

Adaptive landscapes have served as fruitful guides to evolutionary research for nearly a century. Current methods guided by landscape frameworks mostly utilize evolutionary modeling (e.g., fitting data to Ornstein-Uhlenbeck models) to make inferences about adaptive peaks. Recent alternative methods utilize known relationships between phenotypes and functional performance to derive information about adaptive landscapes; this information can then help explain the distribution of species in phenotypic space and help infer the relative importance of various functions for guiding diversification. Here, data on performance for three turtle shell functions-strength, hydrodynamic efficiency, and self-righting ability-are used to develop a set of predicted performance optima in shell shape space. The distribution of performance optima shows significant similarity to the distribution of existing turtle species and helps explain the absence of shells in otherwise anomalously empty regions of morphospace. The method outperforms a modeling-based approach in inferring the location of reasonable adaptive peaks and in explaining the shape of the phenotypic distributions of turtle shells. Performance surface-based methods allow researchers to more directly connect functional performance with macroevolutionary diversification, and to explain the distribution of species (including presences and absences) across phenotypic space.

A Modeling-Based Approach for Soil Frost Detection in the Northern Boreal Forest Region With C-Band SAR

This paper presents a new approach for monitoring soil frost in the northern boreal forest region using co-polarized C-band synthetic aperture radar (SAR) data. Due to the high sensitivity of the C-band signal to vegetation, estimating the soil freeze/thaw (F/T) state directly from the measured backscatter is not feasible over dense vegetation, such as boreal forests. The presented method is based on applying a simple zeroth-order model to estimate the contribution of the ground and the forest canopy on the observed total backscatter. The retrieved ground and canopy backscatter values were compared with in situ information on soil F/T state. By using a linear least sum of square errors classification algorithm, the retrieved ground and canopy backscatter values representing frozen and thawed ground were successfully separated. The method was tested for various soil types and incidence angles. For soil types with higher water holding capacities and lower infiltration rates such as fine Haplic Podzol and Umbric Gleysol, the estimation accuracy of the F/T state was over 97%, whereas for drier, well-drained soil types such as Haplic Arenosol and Coarse Haplic Podzol it was over 94%. Estimation accuracy slightly increased with higher incidence angle. The method is not feasible in rocky terrain due to very low water content, or in wet snow conditions due to lack of penetration of the C-band SAR signal through wet snow. With low ancillary data and computational requirements, the proposed method is applicable for continuous near real-time monitoring of soil F/T state.

Thermodynamics-Based Modeling of Iron- and Steelmaking Processes Using Flow Sheet-Based Approach Employing METSIM

Iron- and steelmaking processes consume an enormous amount of materials in a large-scale production unit. The overall process consists of the flow of material as well as heat across large number of interconnected unit process reactors. Almost all reactors have deviations from thermodynamic equilibrium in overall sense, but they can be classified as large number of interconnected localized equilibrium reactors with restricted interactions among them. Therefore, the role of thermodynamic package like FactSage [1] and sequential modular-based package like METSIM [2] becomes very important for process design and control as well as for studying the parameters to achieve minimum cost and CO2 emissions. In the paper flow sheet, modeling-based approach with coupled application of METSIM is demonstrated for modeling of blast furnace ironmaking, BOF steelmaking and ladle furnace processes. These processes are demonstrated using the capabilities of METSIM for effective process control.

Method for Quick Prediction of Hydraulic Conductivity and Soil-Water Retention of Unsaturated Soils

Hydraulic conductivity and soil-water retention are two critical soil properties describing the fluid flow in unsaturated soils. Existing experimental procedures tend to be time consuming and labor intensive. This paper describes a heuristic approach that combines a limited number of experimental measurements with a computational model with random finite element to significantly accelerate the process. A microstructure-based model is established to describe unsaturated soils with distribution of phases based on their respective volumetric contents. The model is converted into a finite element model, in which the intrinsic hydraulic properties of each phase (soil particle, water, and air) are applied based on the microscopic structures. The bulk hydraulic properties are then determined based on discharge rate using Darcy’s law. The intrinsic permeability of each phase of soil is first calibrated from soil measured under dry and saturated conditions, which is then used to predict the hydraulic conductivities at different extents of saturation. The results match the experimental data closely. Mualem’s equation is applied to fit the pore size parameter based on the hydraulic conductivity. From these, the soil-water characteristic curve is predicted from van Genuchten’s equation. The simulation results are compared with the experimental results from documented studies, and excellent agreements were observed. Overall, this study provides a new modeling-based approach to predict the hydraulic conductivity function and soil-water characteristic curve of unsaturated soils based on measurement at complete dry or completely saturated conditions. An efficient way to measure these critical unsaturated soil properties will be of benefit in introducing unsaturated soil mechanics into engineering practice.

Multiscale statistical approach to assess habitat suitability and connectivity of common leopard (Panthera pardus) in Kailash Sacred Landscape, India

In this study, we assess potential habitats and connectivity for the common leopard (Panthera pardus) in the Indian part of the Kailash Sacred Landscape to predict suitable areas for future dispersal within the landscape. We used a modeling-based approach, which incorporates sixteen landscape variables to identify priority areas for leopard conservation in the Kailash Sacred Landscape. We opportunistically collected 205 presence locations across the 7120 km2 landscape during 2009–2015. The importance of each variable was evaluated using univariate regression analysis across five different spatial scales. The best spatial scale of each response variable was selected based on lowest Akaike’s Information Criterion (AIC). Due to multicollinearity in the landscape variables we computed Principal Components and used eight of the sixteen components as predictors in a multivariate generalized linear model of habitat suitability. A resistance surface model was developed to compute the leopard movement or connectivity across the landscape. We identified corridors between the largest forest patches in the landscape with UNICOR. Here we predict that a large area of the landscape comprising suitable leopard habitat is mostly found at the middle elevational range. This covers about 19.13% of the study area, of which 0.06% area was found to be highly suitable, 4.19% suitable and 14.87% moderately suitable. Further, we have also identified four priority connective corridors. We propose that the conservation of the leopard in this region requires immediate attention by applying integrated landscape-level management to arrest their recent high levels of mortality risk caused due to Human–Leopard conflict.

Exploring Technological Trends in Logistics: Topic Modeling-Based Patent Analysis

With the strategic importance of discerning opportunities and threats from technological development to achieve sustainable competitiveness, exploring technological trends becomes critical for a successful technology strategy in logistics. Given the rapid pace of development and varying technological options, logistics also increasingly requires methodological support and appropriate data to reduce the complexity and burden of exploring technology trends. While previous research has largely relied on experts’ insights, the value of patent-based approaches for exploring technological trends has been underestimated in logistics. To address this gap, this study proposes a topic modeling-based approach using logistics-related patents registered at the United States Patents and Trademark Office (USPTO). The core of the suggested approach is latent Dirichlet allocation (LDA), allowing the identification of logistics-related technological topics behind patents. The topics identified by LDA are further investigated regarding both filed-level and firm-level trends. The suggested approach is expected to offer implications of the use of patents for the purpose of exploring the trends of technology development outside the organization in logistics. In addition, we believe that the information on the technological topics and their trends generated by the suggested approach can offer an enhanced understanding of the technological landscape in logistics.