Framework For Development Of Models Research Articles

Preliminary research of networked-based inquiry model development to improve 21st-century competencies of students on physics learning in senior high school

This research is based on the important of developing a learning model that emphasizes the balance between the scientific approach and use of technology to develop students’ 21st-century competencies. One of learning models that support the goal is inquiry learning which is integrated ICT at infusing level. The model relies on digital technology to provide opportunities for students to investigate scientifically and authentically through information search, problem-solving, and digital collaboration and communication. This study aims to describe the results of needs and context analysis, review of relevant literature, and designing the theoretical framework and concepts for Networked-based Inquiry model development. This research was research and development using the Plomp model. This research has been done at preliminary research that consists of need and context analysis, literature review, developing conceptual theory. The results of needs and context analysis indicates that 21st-century competencies of students is still relatively low, and the integration of ICT in learning physics has enormous potential. The phase review literature revealed that inquiry learning model can improve students’ critical thinking, creative thinking, collaboration, and communication. Then, networked learning can accommodate the use of ICT in infusing level. These findings indicates that the development of Networked-Based Inquiry models can be a solution in accommodating students to develop 21st-century competencies which integrated with digital literacy.

Coupled thermo-hydro-mechanical-chemical modeling on acid fracturing in carbonatite geothermal reservoirs containing a heterogeneous fracture

Acid fracturing and hydraulic fracturing are two useful methods to improve productivity of geothermal wells. Compared with hydraulic fracturing, it remains challenging to select and design an optimal scheme of acid fracturing in geotherm reservoirs due to the complex chemical reactions between acidizing fluids and reservoir rocks. A modeling framework for the coupled thermal-hydro-mechanical-chemical processes during acid fracturing in carbonatite geothermal reservoirs is developed, which is verified using the published experimental results. Effects of natural and human-controlled factors on efficiency of acid fracturing are investigated through parameter sensitivity analysis. The results show that the two human-controlled parameters including acid concentration and injection rate play an important role in determining propagation area, whereas the acid etching pattern is mainly dependent on natural reservoir parameters including aperture field, reservoir temperature and in-situ stress. The developed modeling framework is successfully applied to simulate a field test of acid fracturing in a geothermal well, Beijing, China. The further follow-up research and limitations of the present study are also addressed.

Probing Internal Pressures and Long-Term Stability of Nanobubbles in Water.

Nanobubbles (NBs) in liquid exhibit many intriguing properties such as low buoyancy and high mass transfer efficiency and reactivity as compared to large bulk bubbles. However, it remains elusive why or how bulk NBs are stabilized in water, and particularly, the states of internal pressures of NBs are difficult to measure due to the lack of proper methodologies or instruments. This study employed the injection of high-pressure gases through a hydrophobized ceramic membrane to produce different gaseous NBs (e.g., N2, O2, H2, and CO2) in water, which is different from cavitation bubbles with potential internal low pressure and noncondensed gases. The results indicate that increasing the injection gas pressure (60-80 psi) and solution temperatures (6-40 °C) both reduced bubble sizes from approximately 400 to 200 nm, which are validated by two independent models developed from the Young-Laplace equation and contact mechanics. Particularly, the colloidal force model can explain the effects of surface tension and surface charge repulsion on bubble sizes and internal pressures. The contact mechanics model incorporates the measurement of the tip-bubble interaction forces by atomic force microscopy to determine the internal pressures and the hardness of NBs (e.g., Young's modulus). Both the colloidal force balance model and our contact mechanics model yielded consistent predictions of the internal pressures of various NBs (120-240 psi). The developed methods and model framework will be useful to unravel properties of NBs and support engineering applications of NBs (e.g., aeration or ozonation). Finally, the bulk NBs under sealed storage could be stable for around a week and progressively reduce in concentrations over the next 30-60 days.

Technical note: Low meteorological influence found in 2019 Amazonia fires

Abstract. The sudden increase in Amazon fires early in the 2019 fire season made global headlines. While it has been heavily speculated that the fires were caused by deliberate human ignitions or human-induced landscape changes, there have also been suggestions that meteorological conditions could have played a role. Here, we ask two questions: were the 2019 fires in the Amazon unprecedented in the historical record, and did the meteorological conditions contribute to the increased burning? To answer this, we take advantage of a recently developed modelling framework which optimises a simple fire model against observations of burnt area and whose outputs are described as probability densities. This allowed us to test the probability of the 2019 fire season occurring due to meteorological conditions alone. The observations show that the burnt area was higher than in previous years in regions where there is already substantial deforestation activity in the Amazon. Overall, 11 % of the area recorded the highest early season (June–August) burnt area since the start of our observational record, with areas in Brazil's central arc of deforestation recording the highest ever monthly burnt area in August. However, areas outside of the regions of widespread deforestation show less burnt area than the historical average, and the optimised model shows that this low burnt area would have extended over much of the eastern Amazon region, including in Brazil's central arc of deforestation with high fire occurrence in 2019. We show that there is a 9 % likelihood of the observed August fires being caused by meteorological conditions alone, decreasing to 6 %–7 % along the agricultural–humid forest interface in Brazil's central states and 8 % in Paraguay and Bolivia dry forests. Our results suggest that changes in land use, cover or management are the likely drivers of the substantial increase in the 2019 early fire season burnt area, especially in Brazil. Burnt area for September in the arc of deforestation had a 14 %–26 % probability of being caused by meteorological conditions, potentially coinciding with a shift in fire-related policy from South American governments.

Host contribution to parasite persistence is consistent between parasites and over time, but varies spatially.

Most parasites and pathogens infect multiple hosts, but a great deal of variation exists in the role of those hosts in persistence of infection. Understanding which hosts are most important in maintaining parasites can provide a clearer target for infection control. Recently developed empirical and theoretical approaches provide a way to quantify the relative contribution of hosts within a community and place them in a multi-host framework to better direct control efforts. Amphibians provide a framework for better understanding multi-host-multi-parasite dynamics. Two well-studied amphibian parasites, Batrachochytrium dendrobatidis (Bd) and Ranavirus, infect multiple host species and exhibit a great deal of heterogeneity in how they affect hosts. We used these two parasites and a community of five amphibian species to investigate the relative importance of hosts in parasite persistence, and how any patterns varied spatially and temporally. At two sites (Lake Ercina and Lake Lloroza in the Picos de Europa National Park, Spain) we collected data on the prevalence and shedding rate of parasite infection for both Bd and Ranavirus, and the abundance of each species' life stages. We used these data to parameterize a recently developed modeling framework, which was used to quantify the relative contribution of each host to the community reproductive number, R0 . By comparing each host-category over time and between sites we were able to identify consistencies in which host was responsible for the maintenance of these two parasites. Within a site one species consistently contributed the most to the persistence of both parasites. This consistency did not transfer between sites, the maintenance host species being different for each. At one site (Ercina), life stages of the common midwife toad, Alytes obstetricans, acted as the maintenance host for both Bd and Ranavirus. In contrast, at the second site, Lloroza, the alpine newt, Ichthyosaura alpestris, fulfilled that role. A single host species was responsible for infection persistence of both parasites at each lake. Attempts to control the infection levels and impacts of multiple parasites can benefit from a community epidemiology approach, and provide clarity on which hosts are the foci of mitigation efforts. However, at a small spatial scale, the target host may vary according to the physical qualities of those sites and the demographics of the host community.

Data-driven experimental design and model development using Gaussian process with active learning

Interest in computational modeling of cognition and behavior continues to grow. To be most productive, modelers should be equipped with tools that ensure optimal efficiency in data collection and in the integrity of inference about the phenomenon of interest. Traditionally, models in cognitive science have been parametric, which are particularly susceptible to model misspecification because their strong assumptions (e.g. parameterization, functional form) may introduce unjustified biases in data collection and inference. To address this issue, we propose a data-driven nonparametric framework for model development, one that also includes optimal experimental design as a goal. It combines Gaussian Processes, a stochastic process often used for regression and classification, with active learning, from machine learning, to iteratively fit the model and use it to optimize the design selection throughout the experiment. The approach, dubbed Gaussian process with active learning (GPAL), is an extension of the parametric, adaptive design optimization (ADO) framework (Cavagnaro, Myung, Pitt, & Kujala, 2010). We demonstrate the application and features of GPAL in a delay discounting task and compare its performance to ADO in two experiments. The results show that GPAL is a viable modeling framework that is noteworthy for its high sensitivity to individual differences, identifying novel patterns in the data that were missed by the model-constrained ADO. This investigation represents a first step towards the development of a data-driven cognitive modeling framework that serves as a middle ground between raw data, which can be difficult to interpret, and parametric models, which rely on strong assumptions.

Electric Vehicle Charger Placement Optimization in Michigan Considering Monthly Traffic Demand and Battery Performance Variations

Limited charging infrastructure for electric vehicles (EVs) is one of the main barriers to adoption of these vehicles. In conjunction with limited battery range, the lack of charging infrastructure leads to range-anxiety, which may discourage many potential users. This problem is especially important for long-distance or intercity trips. Monthly traffic patterns and battery performance variations are two main contributing factors in defining the infrastructure needs of EV users, particularly in states with adverse weather conditions. Knowing this, the current study focuses on Michigan and its future needs to support the intercity trips of EVs across the state in two target years of 2020 and 2030, considering monthly traffic demand and battery performance variations. This study incorporates a recently developed modeling framework to suggest the optimal locations of fast EV chargers to be implemented in Michigan. Considering demand and battery performance variations is the major contribution of the current study to the proposed modeling framework by the same authors in the literature. Furthermore, many stakeholders in Michigan are engaged to estimate the input parameters. Therefore, the research study can be used by authorities as an applied model for optimal allocation of resources to place EV fast chargers. The results show that for charger placement, the reduced battery performance in cold weather is a more critical factor than the increased demand in warm seasons. To support foreseeable annual EV trips in Michigan in 2030, this study suggests 36 charging stations with 490 chargers and an investment cost of $23 million.

ЧИСЛЕННОЕ МОДЕЛИРОВАНИЕ ПРОЦЕССА ЭРОДИРОВАНИЯ ТВЕРДЫМИ ЧАСТИЦАМИ В ГАЗОВОМ ПОТОКЕ (ОБЗОР)

The paper studies the contemporary state of the numerical simulation of erosive wear by solid particles in gaseous flow based on representative publications in Russian and foreign journals. It is shown that erosion is primarily numerically studied using two approaches: computational fluid dynamics passed techniques and finite-element modeling based techniques. The review has also revealed that, depending on the application, CFD-based methodologies successfully utilize popular eddy viscosity models such as k-omega shear stress transport, standard k-epsilon models etc., and reynolds-stress models. However, the choice of semi-empirical or analytic erosion models applicable for CFD-based methodologies is still ambiguous and subject to specific application. FEM-based methodologies due to usage of an explicit dynamics solver allow for successful estimation of fundamental wear processes of the surface for various materials and can serve as a framework for further deep research and development of new analytical models. Opposite, CFD-based techniques are suitable for engineering tasks, and receive general system parameters as boundary and initial conditions. Regardless of these propitious conditions, currently no studies are published concerning the development of an entire technique which would allow the use of CFD approach based on the erosion models, pre-developed for a range of surface materials, particle velocities, impingement angles, etc. Such methodology, hence, is proposed for development and validation in future studies.

Modeling machine learning requirements from three perspectives: a case report from the healthcare domain

Implementing machine learning in an enterprise involves tackling a wide range of complexities with respect to requirements elicitation, design, development, and deployment of such solutions. Despite the necessity and relevance of requirements engineering approaches to the process, not much research has been done in this area. This paper employs a case study method to evaluate the expressiveness and usefulness of GR4ML, a conceptual modeling framework for requirements elicitation, design, and development of machine learning solutions. Our results confirm that the framework includes an adequate set of concepts for expressing machine learning requirements and solution design. The case study also demonstrates that the framework can be useful in machine learning projects by revealing new requirements that would have been missed without using the framework, as well as, by facilitating communication among project team members of different roles and backgrounds. Feedback from study participants and areas of improvement to the framework are also discussed.

Decision support for pest management: Using field data for optimizing temperature-dependent population dynamics models

Insect physiology is highly dependent on the environmental temperature, and the relationship can be mathematically defined. Thus, many models that aim to predict insect-pest population dynamics, use meteorological data as input to descriptive functions that predict the development rate, survival and reproduction of pest populations. In most cases, however, these functions/models are laboratory-driven and are based on data from constant-temperature experiments. Therefore, they lack an important optimization and validation steps that test their accuracy under field conditions. Here, we developed a realistic and robust regional framework for modeling the field population dynamics of the global insect pest Bemisia tabaci. First, two non-linear functions, development rate (DR) and female reproduction (EN) were fitted to data collected in constant temperature experiments. Next, nine one-generation field experiments were conducted in order to establish a field-derived database of insect performance, representing a variety of growing conditions (different seasons, regions and host plants). Then, sensitivity analyses were performed for identifying the optimal time-scale for which the running-averaged temperatures should be fed to the model. Setting the time to 6 h (i.e., each of the 24-time steps per day represents the last 6 h average) produced the best fit (RMSD score of 1.59 days, 5.7% of the mean) between the field observations and the model simulations. We hypothesize that the 6 h ‘relevant biological time-scale’ captures the insect's physiological memory of daily cycling temperature events. Lastly, we evaluated the potential of the developed modeling framework to serve as a decision support tool in pest-management programs by correlating the model predictions with field-observations of three pest control inspectors during 2019. The model successfully predicted the first notable appearance of the insect in the field (completion of the third generation in May). Also, the model correctly identified the sharp rise in abundance (outbreak point) in mid-July (completion of the fifth generation), and the persistent rise in abundance through August and September. Comparing the simulations of the 2018 and 2019 seasons indicated that the model can also serve as a tool for retrospective systematic assessment of major decisions. Taken together, these data demonstrate the model robustness and its potential to provide an excellent decision-making support platform in regional control of pest species.

Uncertainty and sensitivity analysis of modeling plant CO2 exchange in the built environment

The dynamics of carbon dioxide (CO2) exchange in the built environment, coupled with local microclimate modeling, is of critical importance to the understanding of emergent patterns of longterm urban climate evolution. In addition to the complex model physics, the difficulty is outstanding to characterize uncertainties inherited in the parameter space and its impact on the model performance and predictive skills. In this study, we conducted a series of numerical simulations based on advanced Markov chain Monte Carlo algorithms to quantify the sensitivity of a recently developed modeling framework by coupling the dynamics of CO2 transport into a single-layer urban canopy model. The results show that urban morphology (canyon aspect ratio), irrigation, and the physiological properties of urban vegetation predominate the processes of plant CO2 exchange in the built environment. In contrast, the CO2 budget is relatively insensitive to material properties of urban facets in the built environment. The findings in this study can help to unravel the interplay of urban carbon dynamics and the built environment, as well as to inform researchers and policy makers for sustainable urban development towards a low carbon city.

Model-driven fatigue crack characterization and growth prediction: A two-step, 3-D fatigue damage modeling framework for structural health monitoring

Prevailing fatigue damage evaluation approaches that make use of the acoustic nonlinearity of guided ultrasonic waves (GUWs) are sustained by simplified models, most of which depict three-dimensional (3-D) fatigue damage in a two-dimensional (2-D) domain [1]. Such approximation risks the evaluation accuracy. With such motivation, this study aspires to a new, two-step modeling framework, aimed at accurately characterizing and continuously monitoring fatigue damage, from its embryonic initiation, through progressive growth to formation of macroscopic crack. In the first step, a 3-D, analytical model based on the theory of elastodynamics sheds light on the generation of contact acoustic nonlinearity in GUWs under the modulation of `breathing' behavior of a non-penetrating fatigue crack, on which basis a crack-area-dependent nonlinear damage index is yielded. In the second step, a 3-D fatigue crack growth model predicts the continuous growth of the identified fatigue crack in the length and depth along crack front. The framework is validated using numerical simulation, followed with experiment, in both of which the initiation and progressive growth of a real corner fatigue crack is monitored, with continuous prediction of the crack growth in length and depth. Results have demonstrated the accuracy and precision of the developed modeling framework for characterizing embryonic fatigue damage.

Inter-Stage Dynamic Performance of an Axial Compressor of a Twin-Shaft Industrial Gas Turbine

In this study, the inter-stage dynamic performance of a multistage axial compressor is simulated through a semi-empirical model constructed in the Matlab Simulink environment. A semi-empirical 1-D compressor model developed in a previous study has been integrated with a 0-D twin-shaft gas turbine model developed in the Simulink environment. Inter-stage performance data generated through a high-fidelity design tool and based on throughflow analysis are considered for the development of the inter-stage modeling framework. Inter-stage performance data comprise pressure ratio at various speeds with nominal variable stator guide vane (VGV) positions and with hypothetical offsets to them with respect to the gas generator speed (GGS). Compressor discharge pressure, fuel flow demand, GGS and power turbine speed measured during the operation of a twin-shaft industrial gas turbine are considered for the dynamic model validation. The dynamic performance of the axial-compressor, simulated by the developed modeling framework, is represented on the overall compressor map and individual stage characteristic maps. The effect of extracting air through the bleed port in the engine center-casing on transient performance represented on overall compressor map and stage performance maps is also presented. In addition, the dynamic performance of the axial-compressor with an offset in VGV position is represented on the overall compressor map and individual stage characteristic maps. The study couples the fundamental principles of axial compressors and a semi-empirical modeling architecture in a complementary manner. The developed modeling framework can provide a deeper understanding of the factors that affect the dynamic performance of axial compressors.

A self-similar model for fretting wear contact with the third body in gross slip

A continuum mechanics approach is applied to model fretting wear in gross-slip regime with accounting for the third-body layer, which is formed by wear debris trapped within contact interface. The developed modeling framework employs the known concept of a third-body conversion factor and utilizes it in a self-similar form. A two-dimensional Hertzian type contact geometry (cylinder on a plate of relatively large thickness) is assumed, though a major part of the presented analysis is curried out for an arbitrary initial gap function. A Winkler foundation model is used to describe the contact deformation of the third-body layer. The effect of elasticity is neglected in describing the self-similar evolution of the contact pressure profile in the advanced stage of fretting wear. A comparison with the FEM-based simulations available in the literature is given. The fact that the elasticity effect plays a minor role has been verified a posteriori in the considered example. Simple formulas are derived to estimate the continuous variation of the third body layer profile, thickness, and volume.

Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

PurposeExtracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study.Design/methodology/approachThis paper presents a literature review from selected previous research that specifically addresses the topics of quotation extraction and quotation attribution. Research works that discuss corpus development related to quotation extraction and quotation attribution are also considered. The findings of the review will be used as a basis for proposing a framework to direct further research.FindingsThere are three findings in this study. Firstly, the extraction process still consists of two main tasks, namely, the extraction of quotations and the attribution of quotations. Secondly, most extraction algorithms rely on a rule-based algorithm or traditional machine learning. And last, the availability of corpus, which is limited in quantity and depth. Based on these findings, a statement extraction framework for Indonesian language corpus and model development is proposed.Originality/valueThe paper serves as a guideline to formulate a framework for statement extraction based on the findings from the literature study. The proposed framework includes a corpus development in the Indonesian language and a model for public figure statement extraction. Furthermore, this study could be used as a reference to produce a similar framework for other languages.

Merlin—A unified modeling framework for data analysis and methods development in Stata

The challenges in statistics and data science are rapidly growing because access to a multitude of data types continues to increase, as well as the sheer quantity of data. Analysts are now presented with multivariate data, sometimes measured repeatedly, and often requiring the ability to model nonlinear relationships and hierarchical structures. In this article, I present the merlin command, which attempts to provide an extremely general framework for data analysis. From simple settings such as fitting a linear regression model or a Weibull survival model to more complex settings such as fitting a three-level logistic mixed-effects model or a multivariate joint model of multiple longitudinal outcomes (of different types) and a recurrent event and survival with nonlinear effects, merlin can fit them all. I will take a single dataset and attempt to show you the full range of capabilities of merlin and discuss some future directions for the implementation in Stata.

Development and testing of volume models for Pinus nigra Arn., Fagus sylvatica L., and Quercus pubescens Willd.

Volume models were developed and tested for Pinus nigra Arn., Fagus sylvatica L., and Quercus pubescens Willd. to accurately predict the woody stem volume at tree level, since despite the economic importance of these species in the Mediterranean type of forests standard models do not apply. Using a total non-destructive sample of 471 trees in mountain forests of northern Greece, two fundamental volume models, the Hohenadl–Krenn and the standard equation were fitted using a generalised least squares (GLS) estimator along with a power function, to regulate the unequal variance and the normality assumption of the residuals. The results showed that both models presented good fitting performance explaining the largest part of stem volume variance and therefore they can be used in operational forest management planning. In addition, the normality assumption of the distribution of the residuals can be satisfied through weighting techniques for reducing the potential heteroscedastic trends during the fitting procedure. The GLS may provide the necessary framework for stem volume model development on an accurate and unbiased basis.

Microstructural Rearrangements and Their Rheological Signature in Coarsening of Cocontinuous Polymer Blends

Domains of cocontinuous polymer blends coarsen during annealing. In general, coarsening undergoes two regimes: linear growth followed by slower nonlinear growth. While the linear regime is well understood by several theories, the number of studies on the nonlinear regime is scarce and often inconclusive. Herein, we examine the entire spectrum of the coarsening of cocontinuous polymer blends using an in situ high-temperature confocal rheology technique. By linking 4-dimensional microscopic details (time evolution of three-dimensional microscopic details) with the rheological properties, we demonstrate that the transition to the nonlinear regime is associated with the onset of droplet formation during the coarsening of these blends. Such transitions and morphological changes occurred only in the blends with high interfacial tension to zero-shear viscosity ratio (Γ/η₀). This phenomenon provides a framework for comprehensive model development. By analyzing the data from the literature, we propose that there exists a critical Γ/η₀ ratio beyond which the system undergoes such transitions.

A modelling framework for developing early warning systems of COPD emergency admissions

Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.

Unified constitutive model for granular–fluid mixture in quasi-static and dense flow regimes

Most granular materials encountered in nature and industry lie either in the quasi-static regime or the intermediate dense flow regime. Debris materials are a typical granular material with viscous interstitial fluid and show solid-like behaviors before failure and fluid-like behaviors after failure. Based on Bagnold’s pioneering work on granular–fluid flows, we propose a framework for constitutive model development, which has an additive form. Based on this framework, a unified constitutive model for granular–fluid material in the quasi-static and dense flow regimes is developed. The main intergranular interactions and granular–fluid interactions controlling the mechanical behaviors are taken into account using the Mohr–Coulomb model and a Bagnold-type relation. Dry granular flows in three simple configurations, i.e., plain shear, vertical chute flow and flow on an inclined plane, are studied. Analytical solutions based on the presented unified model are obtained. Comparisons between results from the presented model and the $$\mu (I)$$ model indicate that the explicit partition of frictional and collisional stress components provides insights into dense granular flows. In addition, the new model is used to predict the stress–strain relations in two annular shear tests. The applicability and advantages of the unified model are discussed.