Model-building Techniques Research Articles

Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

A system dynamics approach to understand Dutch adolescents’ sleep health using a causal loop diagram

BackgroundHealthy sleep is crucial for the physical and mental wellbeing of adolescents. However, many adolescents suffer from poor sleep health. Little is known about how to effectively improve adolescent sleep health as it is shaped by a complex adaptive system of many interacting factors. This study aims to provide insights into the system dynamics underlying adolescent sleep health and to identify impactful leverage points for sleep health promotion interventions.MethodsThree rounds of single-actor workshops, applying Group Model Building techniques, were held with adolescents (n = 23, 12–15 years), parents (n = 14) and relevant professionals (n = 26). The workshops resulted in a multi-actor Causal Loop Diagram (CLD) visualizing the system dynamics underlying adolescent sleep health. This CLD was supplemented with evidence from the literature. Subsystems, feedback loops and underlying causal mechanisms were identified to understand overarching system dynamics. Potential leverage points for action were identified applying the Action Scales Model (ASM).ResultsThe resulting CLD comprised six subsystems around the following themes: (1) School environment; (2) Mental wellbeing; (3) Digital environment; (4) Family & Home environment; (5) Health behaviors & Leisure activities; (6) Personal system. Within and between these subsystems, 16 reinforcing and 7 balancing feedback loops were identified. Approximately 60 potential leverage points on different levels of the system were identified as well.ConclusionsThe multi-actor CLD and identified system dynamics illustrate the complexity of adolescent sleep health and supports the need for developing a coherent package of activities targeting different leverage points at all system levels to induce system change.

A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis.

Machine learning has permeated all fields of research, including chemistry, and is now an integral part of the design of novel compounds with desired properties. In the field of asymmetric catalysis, the preference still lies with models based on a physical understanding of the catalysis phenomenon and the electronic and steric properties of catalysts. However, such models require quantum chemical calculations and are thus limited by their computational cost. Here, we highlight the recent advances in modeling catalyst selectivity by using the 2D structures of catalysts and substrates. While these have a less explicit mechanistic connection to the modeled property, 2D descriptors, such as topological indices, molecular fingerprints, and fragments, offer the tremendous advantages of low cost and high speed of calculations. This makes them optimal for the in-silico screening of large amounts of data. We provide an overview of common quantitative structure-property relationship workflow, model building and validation techniques, applications of these methodologies in asymmetric catalysis design, and an outlook on improving the understanding of 2D-based models.

Understanding the links between human health, ecosystem health, and food systems in Small Island Developing States using stakeholder-informed causal loop diagrams.

Globalized food systems are a major driver of climate change, biodiversity loss, environmental degradation, and the increasing prevalence of overweight and obesity in society. Small Island Developing States (SIDS) are particularly sensitive to the negative effects of rapid environmental change, with many also exhibiting a heavy reliance on food imports and high burdens of nutrition-related disease, resulting in calls to (re)localize their food systems. Such a transition represents a complex challenge, with adaptation interventions in one part of the food system contingent on the success of interventions in other parts. To help address this challenge, we used group model-building techniques from the science of system dynamics to engage food system stakeholders in Caribbean and Pacific SIDS. Our aim was to understand the drivers of unhealthy and unsustainable food systems in SIDS, and the potential role that increased local food production could play in transformative adaptation. We present two causal loop diagrams (CLDs) considered helpful in designing resilience-enhancing interventions in local food systems. These CLDs represent 'dynamic hypotheses' and provide starting points that can be adapted to local contexts for identifying food system factors, understanding the interactions between them, and co-creating and implementing adaptation interventions, particularly in SIDS. The results can help guide understanding of complexity, assist in the co-creation of interventions, and reduce the risk of maladaptive consequences.

Seismic time-frequency masking for suppression of seismic speckle noise

Seismic speckle noise is the primary factor causing severe reflection distortions caused by small-scale near-surface scattering. As in the case of speckle noise in optics and acoustics, deterministic velocity model-building techniques cannot recover these heterogeneities which are much smaller than a wavelength. Conventional processing techniques struggle to perform when multiplicative noise remains unsuppressed. Although local and global stacking mitigates the effects of speckle noise, it leads to a severe loss of higher frequencies reducing the vertical resolution of the seismic data. The foundation for attacking speckle noise is a recently proposed mathematical model that includes two concurrent random multiplicative noise types: type 1 defining residual statics and type 2 describing random frequency-dependent phase perturbations that mimic small-scale near-surface scattering. Using this model, we have developed seismic time-frequency masking to suppress speckle noise on prestack data. The time-dependent and non-surface-consistent nature of scattering noise dictates window-based approaches borrowed from local stacking techniques. Separate manipulation of phase and amplitude spectra is achieved through modified time-frequency masking inspired by speech processing. A beamformed data set from local stacking is used as a guide for designing phase and amplitude masks that are directly applied to raw data in the time-frequency domain. The phase mask allows the restoration of coherency by repairing phase distortions caused by near-surface scattering. The amplitude mask corrects power spectrum (PS) distortions caused by multiplicative and additive noises. An amplitude mask is implemented using a data-driven minimum statistic approach that estimates the noise PS in each local window. The minimum statistics approach is adapted from speech processing using beamformed data as an initial signal estimate and as a guide to designing an improved amplitude mask. Synthetic and field data examples suggest significant improvements in coherency and signal-to-noise ratio after the suppression of multiplicative noise, which makes the data processable by conventional techniques subsequently.

Model Building Techniques for a Multi-Layered Social System to Explore Measures to Control COVID-19 Infection

Model Building Techniques for a Multi-Layered Social System to Explore Measures to Control COVID-19 Infection

Building capacity for the use of systems science to support local government public health planning: a case study of the VicHealth Local Government Partnership in Victoria, Australia

ObjectiveTo present an approach to build capacity for the use of systems science to support local communities in municipal public health and well-being planning.DesignCase study.SettingLocal government authorities participating in the...

Data-driven strategies for extractive distillation unit optimization

We provide insights for the development of a fast, dynamic, and adaptive way of modeling and optimizing chemical processes. We investigate two data-driven methodologies to optimize the energy cost of an extractive distillation process using an Aspen simulator. The first method uses surrogate-based optimization to explore two model-building techniques: Automated Learning of Algebraic Models’s (ALAMO) generalized linear models and neuron network models with rectifier (ReLU) activation function. We compare the accuracy and performance of these models when embedded into mathematical programming models. The second approach uses black-box optimization (BBO) to optimize problems directly using simulation results. We compare four BBO solvers and three different penalty functions to address constraints. We find that ALAMO performs well for a less complex system, whereas the ReLU network performs better for a more complex one. BBO with a smooth penalty function for constraints is the more effective approach for problems considered in this study.

A system dynamics-based approach towards sustainability of a rural milk dairy venture

India, being one of the largest producers of milk in the world, is witnessing unprecedented shutdowns amongst small-scale dairies due to business sustainability challenges. This could be attributed to adoption of event-based focus on systems and ignoring the time delays between the cause and the effects by the entrepreneurs. These shutdowns can be critical as the small-scale dairies heavily contribute to the country's overall milk production capacity. Through this article, we attempt to present a real-life case of a rural dairy highlighting an entrepreneurial dilemma and demonstrate the usage of group model building (GMB) techniques for conducting system dynamics (SD) simulation using Vensim®. The study is relevant as it enables the entrepreneur to foresee the impact of the different options regarding the business sustainability of his dairy. Also, it demonstrates the usage of GMB techniques and unlocks the potential to apply SD modelling using group techniques for policy analysis in diverse domains for the academicians.

Factors Influencing the Use and Abuse of Drugs by Commercial Drivers: A Case of Commercial Drivers in Ghana

Introduction: Significant number of road accidents can be attributed to drug use and drunk driving globally. The increase in the number of road traffic accidents in a report by the Ghana National Road Safety Commission, calls for a review of drunk driving in the country. The study was conducted to determine the types and use of drugs by drivers in Ghana. This will determine the social and cultural factors that influence drug driving. Methodology: We developed and introduced a self-administered questionnaire using a sample of 300 questionnaires. These were administered and carefully edited to ensure some level of consistency, clarity and reliability in the information gathered. Purposive sampling approach was used in the selection of commercial bus stations and cargo stations of selected regions depending on the locations of these stations and the population of vehicles. Backward elimination regression model-building technique was used in the selection of significant variable(s) into a fitted logistic regression model. Five percent statistical level of significance was required for a variable to stay in the model. Results: Commercial bus drivers who responded or participated in this study were male adults within the active age and forty one percent were illiterate. About, thirty four percent of these commercial drivers admitted to using drug when driving and seventy percent of these drivers learned how to drive from unapproved driving institutions. Educational levels of these drivers, hours used to drive, how the commercial drivers were trained and distance they traveled were the most significant variables associated with the use of drug by commercial drivers. Conclusion: In conclusion, there exists significant association between Levels of educational, distance traveled, time used in driving and drug Use by these commercial drivers. Drunk driving is a major threat to the development of Ghana. This threatens our transportation industry and measures ought to be taking to address this problem.

Multitasking in the gut: the X-ray structure of the multidomain BbgIII from Bifidobacterium bifidum offers possible explanations for its alternative functions.

β-Galactosidases catalyse the hydrolysis of lactose into galactose and glucose; as an alternative reaction, some β-galactosidases also catalyse the formation of galactooligosaccharides by transglycosylation. Both reactions have industrial importance: lactose hydrolysis is used to produce lactose-free milk, while galactooligosaccharides have been shown to act as prebiotics. For some multi-domain β-galactosidases, the hydrolysis/transglycosylation ratio can be modified by the truncation of carbohydrate-binding modules. Here, an analysis of BbgIII, a multidomain β-galactosidase from Bifidobacterium bifidum, is presented. The X-ray structure has been determined of an intact protein corresponding to a gene construct of eight domains. The use of evolutionary covariance-based predictions made sequence docking in low-resolution areas of the model spectacularly easy, confirming the relevance of this rapidly developing deep-learning-based technique for model building. The structure revealed two alternative orientations of the CBM32 carbohydrate-binding module relative to the GH2 catalytic domain in the six crystallographically independent chains. In one orientation the CBM32 domain covers the entrance to the active site of the enzyme, while in the other orientation the active site is open, suggesting a possible mechanism for switching between the two activities of the enzyme, namely lactose hydrolysis and transgalactosylation. The location of the carbohydrate-binding site of the CBM32 domain on the opposite site of the module to where it comes into contact with the catalytic GH2 domain is consistent with its involvement in adherence to host cells. The role of the CBM32 domain in switching between hydrolysis and transglycosylation modes offers protein-engineering opportunities for selective β-galactosidase modification for industrial purposes in the future.

Effect of Fluids on the Elastic Properties of 3D-Printed Anisotropic Rock Models

Laboratory physical models play an important role in understanding rock properties and wave propagation, both theoretically and at the field scale. In some cases, 3D-printing technology can be adopted to construct complex rock models faster, more inexpensively, and with more specific features than previous model-building techniques. In this study, we use 3D-printed rock models to assist in understanding the effects of various fluids (air, water, engine oil, crude oil, and glycerol) on the models’ elastic properties. We first used a 3D-printed, 1-in. cube-shaped layered model. This model was created with a 6% primary porosity and a bulk density of 0.98 g/cc with VTI anisotropy. We next employed a similar cube but with horizontal inclusions embedded in the layered background, which contributed to its total 24% porosity (including primary porosity). For air to liquid saturation, P-velocities increased for all liquids in both models, with the highest increase being with glycerol (57%) and an approximately 45% increase for other fluids in the inclusion model. For the inclusion model (dry and saturated), we observed a greater difference between two orthogonally polarized S-wave velocities (Vs1 and Vs2) than between two P-wave velocities (VP0 and VP90). We attribute this to the S2-wave (polarized normal to both the layering and the plane of horizontal inclusions), which appears more sensitive to horizontal inclusions than the P-wave. For the inclusion model, Thomsen’s P-wave anisotropic parameter (ɛ) decreased from 26% for the air case to 4% for the water-saturated cube and to 1% for glycerol saturation. The small difference between the bulk modulus of the frame and the pore fluid significantly reduces the velocity anisotropy of the medium, making it almost isotropic. We compared our experimental results with theory and found that predictions using Schoenberg’s linear slip theory combined with Gassmann’s anisotropic equation were closer to actual measurements than Hudson’s isotropic calculations. This work provides insights into the usefulness of 3D-printed models to understand elastic rock properties and wave propagation under various fluid saturations.

Electromyography-Based Validation of a Musculoskeletal Hand Model.

Regarding the prevention of injuries and rehabilitation of the human hand, musculoskeletal simulations using an inverse dynamics approach allow for insights of the muscle recruitment and thus acting forces on the hand. Currently, several hand models from various research groups are in use, which are mainly validated by the comparison of numerical and anatomical moment arms. In contrast to this validation and model-building technique by cadaver studies, the aim of this study is to further validate a recently published hand model [1] by analyzing numerically calculated muscle activities in comparison to experimentally measured electromyographical signals of the muscles. Therefore, the electromyographical signals of 10 hand muscles of five test subjects performing seven different hand movements were measured. The kinematics of these tasks were used as input for the hand model, and the numerical muscle activities were computed. To analyze the relationship between simulated and measured activities, the time difference of the muscle on- and off-set points was calculated, which resulted in a mean on- and off-set time difference of 0.58 s between the experimental data and the model. The largest differences were detected for movements that mainly addressed the wrist. One major issue comparing simulated and measured muscle activities of the hand is cross-talk. Nevertheless, the results show that the hand model fits the experiment quite accurately despite some limitations and is a further step toward patient-specific modeling of the upper extremity.

Combing machine learning and elemental profiling for geographical authentication of Chinese Geographical Indication (GI) rice

Identification of geographical origin is of great importance for protecting the authenticity of valuable agri-food products with designated origins. In this study, a robust and accurate analytical method that could authenticate the geographical origin of Geographical Indication (GI) products was developed. The method was based on elemental profiling using inductively coupled plasma mass spectrometry (ICP-MS) in combination with machine learning techniques for model building and feature selection. The method successfully predicted and classified six varieties of Chinese GI rice. The elemental profiles of 131 rice samples were determined, and two machine learning algorithms were implemented, support vector machines (SVM) and random forest (RF), together with the feature selection algorithm Relief. Prediction accuracy of 100% was achieved by both Relief-SVM and Relief-RF models, using only four elements (Al, B, Rb, and Na). The methodology and knowledge from this study could be used to develop reliable methods for tracing geographical origins and controlling fraudulent labeling of diverse high-value agri-food products.

The impact of acquisition geometry on full-waveform inversion updates

Full-waveform inversion (FWI) is a high-resolution model-building technique that uses the entire recorded seismic data content to build the earth model. Conventional FWI usually utilizes diving and refracted waves to update the low-wavenumber components of the velocity model. However, updates are often depth limited due to the limited offset range of the acquisition design. To extend conventional FWI beyond the limits imposed by using only transmitted energy, we must utilize the full acquired wavefield. Analyzing FWI kernels for a given geology and acquisition geometry can provide information on how to optimize the acquisition so that FWI is able to update the velocity model for targets as deep as basement level. Recent long-offset ocean-bottom node acquisition helped FWI succeed, but we would also like to be able to utilize the shorter-offset data from wide-azimuth data acquisitions to improve imaging of these data sets by developing the velocity field with FWI. FWI models are heading toward higher and higher wavenumbers, which allows us to extract pseudoreflectivity directly from the developed velocity model built with the acoustic full wavefield. This is an extremely early start to obtaining a depth image that one would usually produce in much later processing stages.

Volumetric procedural models for shape representation

This article describes a volumetric approach for procedural shape modeling and a new Procedural Shape Modeling Language (PSML) that facilitates the specification of these models. PSML provides programmers the ability to describe shapes in terms of their 3D elements where each element may be a semantic group of 3D objects, e.g., a brick wall, or an indivisible object, e.g., an individual brick. Modeling shapes in this manner facilitates the creation of models that more closely approximate the organization and structure of their real-world counterparts. As such, users may query these models for volumetric information such as the number, position, orientation and volume of 3D elements which cannot be provided using surface based model-building techniques. PSML also provides a number of new language-specific capabilities that allow for a rich variety of context-sensitive behaviors and post-processing functions. These capabilities include an object-oriented approach for model design, methods for querying the model for component-based information and the ability to access model elements and components to perform Boolean operations on the model parts. PSML is open-source and includes freely available tutorial videos, demonstration code and an integrated development environment to support writing PSML programs.

Better together: Extending JMP® with open‐source software

JMP is commercial software designed for interactive data analysis and exploration. JMP's high‐level, visual interface makes it an outstanding tool for teaching best practices, methods and model building techniques. JMP is also designed for extensibility, with features that allow the embedding of and deployment to open‐source packages and environments. In this paper, we will explore use cases that illustrate how JMP users can take advantage of the broad support and rapid pace of change of open‐source software while still benefiting from JMP's streamlined interface and robust capabilities.

ПРИМЕР ПОСТРОЕНИЯ И УТОЧНЕНИЯ ГЛУБИННО-СКОРОСТНОЙ МОДЕЛИ С ПРИМЕНЕНИЕМ МИГРАЦИОННЫХ И ТОМОГРАФИЧЕСКИХ ПРЕОБРАЗОВАНИЙ

The article is devoted to the overview of the main methods for constructing velocity-depth models (VDM), applied today and the effectiveness of their use in complicated seismic and geological conditions. In the present work, the method of integrated approach is considered along with well-known and generally accepted algorithms. The method of integrated approach is based on a combination of two model-building techniques for the VDM. The concerned approach allows researchers to combine positive aspects of combined methods while leveling their disadvantages. Using the example of real modern data obtained on the territory of East Siberia near fold-thrust dislocations of the Cis-Patom regional trough, the possibility of improving the quality of seismic image is demonstrated due to the use of the integrated approach and tomographic transformations for complexly constructed environments in the process of deep migration.

Rule-based knowledge discovery of satellite imagery using evolutionary classification tree

The classification tree (CT) may be used to establish explicit classification rules for Satellite Imagery (SI). However, the accuracy of explicit classification rules attained by this method is poor. Back-propagation networks (BPN) and the support vector machine (SVM) may both be used to establish highly accurate models for predicting the classification of SI. However, neither is able to generate explicit rules. This study proposes the evolutionary classification tree (ECT) as a novel mining rule method. Composed of the particle bee algorithm (PBA) and classification tree (CT), the ECT produces self-organized rules automatically to predict the classification of SI. In ECT, CT serves as the architecture to represent explicit rules and PBA acts as the optimization mechanism to optimize CT in order to fit the experimental data. A total of 600 experimental datasets were used to compare the accuracy and complexity of four model-building techniques: CT, BPN, SVM, and ECT. The results demonstrate the ability of ECT to produce rules that are more accurate than CT and SVM but less accurate than BPN. However, because BPN is black box model, the ability of ECT to generate explicit rules makes ECT the best model for users wanting to mine the explicit rules and knowledge in practical applications.

How Does Economic Knowledge Have a Politics? On the Frustrated Attempts of John K. Galbraith and Robert M. Solow to Fix the Political Meaning of Economic Models in The Public Interest

In the late 1960s, two big shots of postwar economics debated model-building techniques in the Public Interest. Robert M. Solow argued that the new methods enabled precise state intervention. To John K. Galbraith, they marginalized critical economic thinking and abetted the prevailing growthist ideology to the detriment of the public good. Both claimed influence over policy making but through different channels. Both argued vigorously to fix the political meaning of modeling before a general audience. This article focuses on the actors’ frustrated attempts to establish clear-cut relationships between research practices, modes of intervention, and political ideals. Despite their strenuous exertions, they did not succeed in equipping their competing methodological stances with equally distinct politics. Where these efforts failed, they resorted to constructions of scientific self and other. Personae glued together practices (including assumptions, tools, and standards of evidence) and politics where perhaps no inherent bond existed. Our reading (1) elucidates the confusion over mathematical models just as they became the objects of political debate, and (2) aims to confound the idea that there is a politics of knowledge that is clearly delineable and transparent to historical actors and retrospective observers alike.