modeling-based instructional practices have been successfully used in science classrooms for several decades. These have mostly focused on diagrammatic models, such as those that students can construct on whiteboards....
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modeling-based instructional practices have been successfully used in science classrooms for several decades. These have mostly focused on diagrammatic models, such as those that students can construct on whiteboards. While these diagrammatic models are time efficient and effective for illustrating student understanding of most content, they break down when trying to determine cause and effect or when interpreting dynamically changing systems. One way to overcome some of the limitations of diagrammatic models is to utilizeSageModeler, a free browser-based computational modeling program that allows students to run simulations that they can compare to empirical data. Supplemented into the classroom at appropriate times, computational modeling can help students build and improve on systems thinking skills, gaining a more complete understanding of the phenomena they are investigating.
A novel strategy delivering both metronidazole and L. crispatus via 3D-printed scaffolds was recently shown to target pathogens in bacterial vaginosis (BV) while promoting beneficial microflora with sustained probioti...
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A novel strategy delivering both metronidazole and L. crispatus via 3D-printed scaffolds was recently shown to target pathogens in bacterial vaginosis (BV) while promoting beneficial microflora with sustained probiotic release, with the objective to facilitate user treatment adherence. This study developed an integrated experimental/computational platform to evaluate dual therapeutic strategy efficacy over a wide range of system dynamics, towards the goal of personalized therapy design. Experiments evaluated Gardnerella and L. crispatus interactions under controlled glucose concentrations in vitro , including bacterial growth, glucose consumption, lactic acid production, and pH. These data informed parameters of a novel computational model simulating the vagina, incorporating nutrient dynamics, bacterial interactions, and dual release of antibiotics and probiotics from 3D-printed scaffolds. Efficacy of varying concentrations of antibiotics and probiotics was assessed via sensitivity analyses. Experimental results demonstrate that L. crispatus outcompetes Gardnerella at lower glucose concentrations, while Gardnerella dominates at higher glucose levels. The computational model replicated these dynamics and projected that dual therapy could significantly suppress Gardnerella while promoting L. crispatus , even at lower drug dosages and probiotic CFU counts. Results were validated against data from 3D-printed dual release scaffolds. Simulated dual treatment enhanced lactic acid production and decreased vaginal pH, creating an unfavorable environment for pathogenic bacteria and shifting the microbiome composition towards the beneficial microflora. We conclude that an integrated experimental/computational modeling approach enables detailed evaluation of pathogenic and host bacteria interactions in the vaginal microbiome. This approach could advance personalized treatment for BV that eradicates pathogens while simultaneously restoring beneficial microflora.
A long-standing theoretical debate exists in linguistics concerning argument structure processing, with separationism focusing on syntactic structure and projectionism on semantic properties. To investigate whether ar...
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A long-standing theoretical debate exists in linguistics concerning argument structure processing, with separationism focusing on syntactic structure and projectionism on semantic properties. To investigate whether argument structure processing is primarily influenced by syntactic structure or semantic properties, this study employed integrative neurocomputational modeling to link brain functions with explicitly defined computational models. We analyzed naturalistic functional magnetic resonance imaging (fMRI) data from participants listening to a story, with a focus on subject noun phrase + verb chunks. The methodological framework integrated a general linear model (GLM) analysis of the fMRI data with computational modeling using natural language processing algorithms. These components were integrated using representational similarity analysis (RSA), allowing us to assess the relatedness of two symbolic computational models—one relying on syntactic information from parse trees and the other based on semantic selectional preference information of verbs—to brain activities. The GLM analysis identified significant neural correlates of argument structure processing largely consistent with previous findings, including the precuneus, the right superior temporal gyrus, and the right middle temporal gyrus. Some deviations from previous studies likely reflect the naturalistic nature of the stimuli and our contrast design. The RSA results favored the model utilizing semantic information—a finding further supported by effects observed in brain regions associated with argument structure processing in the literature and by an additional RSA comparing constructions with varying levels of transitivity. These findings suggest that during naturalistic story listening, humans rely heavily on semantic information to interpret argument structure. This study demonstrates an alternative method to engage with the debate on argument structure, highlighting a collaborative effort between t
The Boundary Element Fast Multipole Method for computational electromagnetic modeling of transcranial neurostimulation methods is applied to the high-resolution MIDA human head model to investigate the effects of canc...
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ISBN:
(纸本)9781728143378;9781728143385
The Boundary Element Fast Multipole Method for computational electromagnetic modeling of transcranial neurostimulation methods is applied to the high-resolution MIDA human head model to investigate the effects of cancellous bone and dura mater on transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (TES). After validating the model and method against commercial electromagnetic software, we show that the cancellous bone of the skull and the dura mater have a negligible effect on TMS but a substantial effect on TES. Further investigation shows that the electric field induced by TES at the inner cortical surface for a simplified model might be proportional to the electric field at the same location for the full model, but this simple scaling operation likely does not hold for the outer cortical surface.
computational modeling provides an excellent vehicle for raising scientific awareness of emergent and topical phenomena such as COVID-19. Now more than ever, it is crucial to provide students with factual information ...
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ISBN:
(纸本)9781450380621
computational modeling provides an excellent vehicle for raising scientific awareness of emergent and topical phenomena such as COVID-19. Now more than ever, it is crucial to provide students with factual information about how diseases spread and how their own actions can impact that spread. In order to both encourage computational thinking skills and build scientific knowledge of the COVID-19 pandemic, we have created a series of programming activities through which students construct their own computational models based on the emerging scientific consensus around COVID-19. Students are able to model everyday situations such as being in a crowded area or going to stores while unknowingly infected, and immediately see the consequences of those actions. By including accurate scientific variables such as the reproductive number of the virus, incubation period, and period of communicability, students are able to create their own epi-curves that demonstrate the severity of the disease and provide students with visual representation of how quickly COVID-19 spreads. We also use the scientific model and associated modeling activities to reinforce best practices at home and in the community. Finally, this curriculum development effort demonstrates how block-based computational modeling activities lend themselves to agile curricular re-design around emerging and topics of local interest
Tissue-engineered vascular grafts hold great promise in many clinical applications, especially in pediatrics wherein growth potential is critical. A continuing challenge, however, is identification of optimal scaffold...
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Tissue-engineered vascular grafts hold great promise in many clinical applications, especially in pediatrics wherein growth potential is critical. A continuing challenge, however, is identification of optimal scaffold parameters for promoting favorable neovessel development. In particular, given the countless design parameters available, including those related to polymeric microstructure, material behavior, and degradation kinetics, the number of possible scaffold designs is almost limitless. Advances in computationally modeling the growth and remodeling of native blood vessels suggest that similar simulations could help reduce the search space for candidate scaffold designs in tissue engineering. In this study, we meld a computational model of in vivo neovessel formation with a surrogate management framework to identify optimal scaffold designs for use in the extracardiac Fontan circulation while comparing the utility of different objective functions. We show that evolving luminal radius and graft compliance can be matched to that of the native vein by the end of the simulation period with judicious combinations of scaffold parameters, although the inability to match these metrics at all times reveals constraints engendered by current materials. We emphasize further that there is yet a need to examine additional metrics, and combinations thereof, when seeking to optimize functionality and reduce the potential for adverse outcomes. Impact Statement Tissue-engineered vascular grafts have considerable promise for treating myriad conditions, and multiple designs are now in FDA-approved trials. Nevertheless, the search continues for the optimal design of the underlying polymeric scaffold. We present a novel melding of a computational model of vascular adaptation and a formal method of optimization that can aid in identifying optimal design parameters, with potential to save development time and costs while improving clinical outcomes.
An overarching computational framework unifying several optical theories to describe the temporal evolution of gold nanoparticles (GNPs) during a seeded growth process is presented. To achieve this, we used the inexpe...
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An overarching computational framework unifying several optical theories to describe the temporal evolution of gold nanoparticles (GNPs) during a seeded growth process is presented. To achieve this, we used the inexpensive and widely available optical extinction spectroscopy, to obtain quantitative kinetic data. In situ spectra collected over a wide set of experimental conditions were regressed using the physical model, calculating light extinction by ensembles of GNPs during the growth process. This model provides temporal information on the size, shape, and concentration of the particles and any electromagnetic interactions between them. Consequently, we were able to describe the mechanism of GNP growth and divide the process into distinct genesis periods. We provide explanations for several longstanding mysteries, for example, the phenomena responsible for the purple-greyish hue during the early stages of GNP growth, the complex interactions between nucleation, growth, and aggregation events, and a clear distinction between agglomeration and electromagnetic interactions. The presented theoretical formalism has been developed in a generic fashion so that it can readily be adapted to other nanoparticulate formation scenarios such as the genesis of various metal nanoparticles.
An AISI 1045 steel modified with vanadium (V) and niobium (Nb) was studied to evaluate microstructural conditioning prior to and throughout a rapid heat treat process. In order to accomplish this, both computational a...
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An AISI 1045 steel modified with vanadium (V) and niobium (Nb) was studied to evaluate microstructural conditioning prior to and throughout a rapid heat treat process. In order to accomplish this, both computational and physical simulation techniques have been employed with the goal of assessing the microstructural evolution in a medium-carbon bar steel during the rapid austenitization and quenching procedures involved in an induction hardening process. The appropriate thermal profiles for induction hardening were obtained through finite element modeling using Flux 2D software. Physical simulations of the induction hardening process were carried out using a Gleeble (R) 3500. Analysis of prior austenite grain size is complemented by observation of nanoscale carbonitride precipitation via transmission electron microscopy, scanning transmission electron microscopy, and high-energy synchrotron small-angle x-ray scattering. Through a combination of characterization techniques, this study presents a deeper understanding of nano- and microstructural changes occurring in a microalloyed steel during an induction hardening process.
Selective laser melting (SLM) is a powder-based additive manufacturing technique which creates parts by fusing together successive layers of powder with a laser. The quality of produced parts is highly dependent on th...
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Selective laser melting (SLM) is a powder-based additive manufacturing technique which creates parts by fusing together successive layers of powder with a laser. The quality of produced parts is highly dependent on the proper selection of processing parameters, requiring significant testing and experimentation to determine parameters for a given machine and material. computational modeling could potentially be used to shorten this process by identifying parameters through simulation. However, simulating complete SLM builds is challenging due to the difference in scale between the size of the particles and laser used in the build and the size of the part produced. Often, continuum models are employed which approximate the powder as a continuous medium to avoid the need to model powder particles individually. While computationally expedient, continuum models require as inputs effective material properties for the powder which are often difficult to obtain experimentally. Building on previous works which have developed methods for estimating these effective properties along with their uncertainties through the use of detailed models, this work presents a part scale continuum model capable of predicting residual thermal stresses in an SLM build with uncertainty estimates. Model predictions are compared to experimental measurements from the literature.
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