Computational Dynamics Model Research Articles

The Global Environmental Impacts of China's Accession to the WTO: A 20-Year Review.

Robust empirical assessments of the long-term cumulative global effects of free trade and economic globalization on the environment are limited. This account fills this gap by constructing a dynamic computable general equilibrium model to estimate the environmental effects of a milestone in the recent history of trade liberalization: China's 20-year World Trade Organization (WTO) accession. The modeling shows that China's accession could have resulted in an increase in the global cumulative greenhouse gases (GHGs), sulfur dioxide (SO2), and nitrogen oxide (NOx) emissions by roughly 14,000 Mt CO2-eq, 64 Mt, and 46 Mt, respectively. The global production scale effect contributed to most of these estimated increases. The regional total output composition effect also caused higher emissions. Meanwhile, the sectoral output composition effect helped reduce total emissions to a limited extent. Fortunately, a package of emission abatement measures led to a decrease in emission factors and a drop in the global cumulative emissions of GHGs, SO2, and NOx. The findings suggest that to enjoy the free trade and economic globalization benefits and minimize the induced emission increases, it is vitally important to systemically reduce emissions across the entire economy and nurture a low-carbon trade regime.

Reputation Without Practice? A Dynamic Computational Model of the Unintended Consequences of Open Scientist Reputations

Practicing open science can have benefits for the career prospects of individual researchers or labs through higher quality work and increased chances of publication. However, being an outspoken advocate of open science might also indirectly benefit individual scientific careers, in the form of status in a scientific community, decisions for tenure, and eligibility for certain kinds of funding. Therefore, it may be profitable for individual labs to appear to engage in open science practices, without actually putting in the associated effort or doing only the bare minimum. In this article, we explore two types of academic behavior through a dynamic computational model (cf. Smaldino & Mcelreath,2016) of an academic community that rewards open science: (1) practicing open science and/or (2) advocating open science. Crossing these two types of behavior leads to four different kinds of labs and we examine which of them thrive in this academic community. We found that labs that practice and advocate open science dominate in a scientific community that values open science. Implications of the model results are discussed.

Understanding the consolidation mechanism of selective laser sintering/powder bed selective laser process of ceramics: Hydroxyapatite case

PurposeThe consolidation process and morphology evolution in ceramics-based additive manufacturing (AM) are still not well-understood. As a way to better understand the ceramic selective laser sintering (SLS), a dynamic three-dimensional computational model was developed to forecast thermal behavior of hydroxyapatite (HA) bioceramic.Design/methodology/approachAM has revolutionized automotive, biomedical and aerospace industries, among many others. AM provides design and geometric freedom, rapid product customization and manufacturing flexibility through its layer-by-layer technique. However, a very limited number of materials are printable because of rapid melting and solidification hysteresis. Melting-solidification dynamics in powder bed fusion are usually correlated with welding, often ignoring the intrinsic properties of the laser irradiation; unsurprisingly, the printable materials are mostly the well-known weldable materials.FindingsThe consolidation mechanism of HA was identified during its processing in a ceramic SLS device, then the effect of the laser energy density was studied to see how it affects the processing window. Premature sintering and sintering regimes were revealed and elaborated in detail. The full consolidation beyond sintering was also revealed along with its interaction to baseplate.Originality/valueThese findings provide important insight into the consolidation mechanism of HA ceramics, which will be the cornerstone for extending the range of materials in laser powder bed fusion of ceramics.

Analysis of energy policy reform in Iran: Energy and emission intensity changes

The Subsidized energy system of Iran, with its high financial burden, failed to achieve its intended economic goals, resulting in increased energy consumption and pollutant emissions. Energy product subsidies in Iran are among the highest in the world and have important implications for developing countries. This study examines the impact of subsidy removal policy on energy and emission intensities using a Dynamic Computable General Equilibrium model. The findings reveal that after subsidy removal, total output experiences an annual growth of around 1.5%, with output composition changes mainly favoring agriculture and services that are less energy dependent. However, prices soar by at least 10% owing to the higher costs of energy products. The decreasing trend in energy intensity after subsidy reform is accompanied by a decrease in emission intensity. Higher prices of energy after subsidy removal and the corresponding improvement in the efficiency of energy consumption lead to lower emission intensity.

A dynamical computational model of theta generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation.

Neurostimulation of the hippocampal formation has shown promising results for modulating memory but the underlying mechanisms remain unclear. In particular, the effects on hippocampal theta-nested gamma oscillations and theta phase reset, which are both crucial for memory processes, are unknown. Moreover, these effects cannot be investigated using current computational models, which consider theta oscillations with a fixed amplitude and phase velocity. Here, we developed a novel computational model that includes the medial septum, represented as a set of abstract Kuramoto oscillators producing a dynamical theta rhythm with phase reset, and the hippocampal formation, composed of biophysically realistic neurons and able to generate theta-nested gamma oscillations under theta drive. We showed that, for theta inputs just below the threshold to induce self-sustained theta-nested gamma oscillations, a single stimulation pulse could switch the network behavior from non-oscillatory to a state producing sustained oscillations. Next, we demonstrated that, for a weaker theta input, pulse train stimulation at the theta frequency could transiently restore seemingly physiological oscillations. Importantly, the presence of phase reset influenced whether these two effects depended on the phase at which stimulation onset was delivered, which has practical implications for designing neurostimulation protocols that are triggered by the phase of ongoing theta oscillations. This novel model opens new avenues for studying the effects of neurostimulation on the hippocampal formation. Furthermore, our hybrid approach that combines different levels of abstraction could be extended in future work to other neural circuits that produce dynamical brain rhythms.

A dynamical computational model of theta generation in hippocampal circuits to study theta-gamma oscillations during neurostimulation

Neurostimulation of the hippocampal formation has shown promising results for modulating memory but the underlying mechanisms remain unclear. In particular, the effects on hippocampal theta-nested gamma oscillations and theta phase reset, which are both crucial for memory processes, are unknown. Moreover, these effects cannot be investigated using current computational models, which consider theta oscillations with a fixed amplitude and phase velocity. Here, we developed a novel computational model that includes the medial septum, represented as a set of abstract Kuramoto oscillators producing a dynamical theta rhythm with phase reset, and the hippocampal formation, composed of biophysically realistic neurons and able to generate theta-nested gamma oscillations under theta drive. We showed that, for theta inputs just below the threshold to induce self-sustained theta-nested gamma oscillations, a single stimulation pulse could switch the network behavior from non-oscillatory to a state producing sustained oscillations. Next, we demonstrated that, for a weaker theta input, pulse train stimulation at the theta frequency could transiently restore seemingly physiological oscillations. Importantly, the presence of phase reset influenced whether these two effects depended on the phase at which stimulation onset was delivered, which has practical implications for designing neurostimulation protocols that are triggered by the phase of ongoing theta oscillations. This novel model opens new avenues for studying the effects of neurostimulation on the hippocampal formation. Furthermore, our hybrid approach that combines different levels of abstraction could be extended in future work to other neural circuits that produce dynamical brain rhythms.

MetaLo: metabolic analysis of Logical models extracted from molecular interaction maps.

Molecular interaction maps (MIMs) are static graphical representations depicting complex biochemical networks that can be formalized using one of the Systems Biology Graphical Notation languages. Regardless of their extensive coverage of various biological processes, they are limited in terms of dynamic insights. However, MIMs can serve as templates for developing dynamic computational models. We present MetaLo, an open-source Python package that enables the coupling of Boolean models inferred from process description MIMs with generic core metabolic networks. MetaLo provides a framework to study the impact of signaling cascades, gene regulation processes, and metabolic flux distribution of central energy production pathways. MetaLo computes the Boolean model's asynchronous asymptotic behavior, through the identification of trap-spaces, and extracts metabolic constraints to contextualize the generic metabolic network. MetaLo is able to handle large-scale Boolean models and genome-scale metabolic models without requiring kinetic information or manual tuning. The framework behind MetaLo enables in depth analysis of the regulatory model, and may allow tackling a lack of omics data in poorly addressed biological fields to contextualize generic metabolic networks along with improper automatic reconstructions of cell- and/or disease-specific metabolic networks. MetaLo is available at https://pypi.org/project/metalo/ under the terms of the GNU General Public License v3.

Refining the causal loop diagram: A tutorial for maximizing the contribution of domain expertise in computational system dynamics modeling.

Complexity science and systems thinking are increasingly recognized as relevant paradigms for studying systems where biology, psychology, and socioenvironmental factors interact. The application of systems thinking, however, often stops at developing a conceptual model that visualizes the mapping of causal links within a system, e.g., a causal loop diagram (CLD). While this is an important contribution in itself, it is imperative to subsequently formulate a computable version of a CLD in order to interpret the dynamics of the modeled system and simulate "what if" scenarios. We propose to realize this by deriving knowledge from experts' mental models in biopsychosocial domains. This article first describes the steps required for capturing expert knowledge in a CLD such that it may result in a computational system dynamics model (SDM). For this purpose, we introduce several annotations to the CLD that facilitate this intended conversion. This annotated CLD (aCLD) includes sources of evidence, intermediary variables, functional forms of causal links, and the distinction between uncertain and known-to-be-absent causal links. We propose an algorithm for developing an aCLD that includes these annotations. We then describe how to formulate an SDM based on the aCLD. The described steps for this conversion help identify, quantify, and potentially reduce sources of uncertainty and obtain confidence in the results of the SDM's simulations. We utilize a running example that illustrates each step of this conversion process. The systematic approach described in this article facilitates and advances the application of computational science methods to biopsychosocial systems. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Distribution of economic damages due to climate-driven sea-level rise across European regions and sectors

Economic costs of climate change are conventionally assessed at the aggregated global and national levels, while adaptation is local. When present, regionalised assessments are confined to direct damages, hindered by both data and models’ limitations. This article goes beyond the aggregated analysis to explore direct and indirect economic consequences of sea level rise (SLR) at regional and sectoral levels in Europe. Using a dynamic computable general equilibrium model and novel datasets, we estimate the distribution of losses and gains across regions and sectors. A comparison of a high-end scenario against a no-climate-impact baseline suggests a GDP loss of 1.26% (€871.8 billion) for the whole EU&UK. Conversely our refined assessments show that some coastal regions lose 9.56–20.84% of GDP, revealing striking regional disparities. Inland regions grow due to the displaced demand from coastal areas, but the GDP gains are small (0–1.13%). While recovery benefits the construction sector, public services and industry face significant downturns. We show that prioritising recovery of critical sectors locally reduces massive regional GDP losses, at negligible costs to the overall European economy. Our analysis traces regional economic restructuring triggered by SLR, underscoring the necessity of region-specific adaptation policies that embrace uneven geographic impacts and unique sectoral profiles to inform resilient strategy design.

Virtual neural network-guided optimization of non-invasive brain stimulation in Alzheimer's disease.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimer's disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simulated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density (alpha bands, 8-10 Hz and 10-13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort.

Disaggregated effect of construction investments on the Saudi economy: a dynamic computable general equilibrium model of Saudi Arabia

The role of the construction industry in economic growth has been widely discussed in the extant literature, but existing studies have not investigated the disaggregated impact of construction investments on the production and social sectors. This study examines the disaggregated effect of construction investments on the Saudi economy. The study uses a social accounting matrix of Saudi Arabia and constructs a dynamic computable general equilibrium model. The findings reveal that construction investments significantly boosted GDP and aggregate investments in the first two periods; however, the growth declined in the following three periods. This finding underlines the importance of long-term investments in the construction sector and calls for continuous monitoring and updating of the investment policy for sustainable development. This study also presents the disaggregated impact of investments on the value-added by each sector of the economy. The ranking of sectors exhibits that mining and quarry activities underwent a high increase in value-added, second to construction activities. Other economic activities also experienced growth in value-added and some of them changed their ranks within the five years.

How should governments respond to energy price crises? A horse-race between fiscal policies

This paper compares the welfare and distributional implications of fiscal policies aimed at reducing a sudden and significant increase in the price of energy. A dynamic computable general equilibrium model with households disaggregated by income groups is used to compare the effectiveness of five energy price-reducing fiscal policies. The policies are assessed under two financing options, pure government debt and a mix of debt and windfall taxation on energy companies. Results from simulations demonstrate that targeted demand-side policies are more effective at reducing overall energy-driven inflation and increasing welfare. Supply-side policies and mixed demand and supply policies achieve a smaller reduction in the consumer price index but are more expansionary. Financing the policies partly through windfall taxation does not impact the ranking of policies but it delivers better distributional outcomes and higher welfare. The results motivate the use of windfall taxation if governments face high interest rates on debt financing and/ or if households care sufficiently about the provision of public goods. The optimal policy is likely a mix of supply-side measures such as production tax reductions or general price subsidies and either targeted energy price subsidies or targeted income subsidies financed where possible through windfall taxation.

BioModME for building and simulating dynamic computational models of complex biological systems.

Molecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed "BioModME," a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. All relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME.

Forward dynamics computational modelling of a cyclist fall with the inclusion of protective response using deep learning-based human pose estimation

Single bicycle crashes, i.e., falls and impacts not involving a collision with another road user, are a significantly underestimated road safety problem. The motions and behaviours of falling people, or fall kinematics, are often investigated in the injury biomechanics research field. Understanding the mechanics of a fall can help researchers develop better protective gear and safety measures to reduce the risk of injury. However, little is known about cyclist fall kinematics or dynamics.Therefore, in this study, a video analysis of cyclist falls is performed to investigate common kinematic forms and impact patterns. Furthermore, a pipeline involving deep learning-based human pose estimation and inverse kinematics optimisation is created for extracting human motion from real-world footage of falls to initialise forward dynamics computational human body models. A bracing active response is then optimised for using a genetic algorithm. This is then applied to a case study of a cyclist fall.The kinematic forms characterised in this study can be used to inform initial conditions for computational modelling and injury estimation in cyclist falls. Findings indicate that protective response is an important consideration in fall kinematics and dynamics, and should be included in computational modelling. Furthermore, the novel reconstruction pipeline proposed here can be applied more broadly for traumatic injury biomechanics tasks. The tool developed in this study is available at https://kevgildea.github.io/KinePose/.

КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕ ДИНАМИКИ ЦЕНЫ РЕАЛИЗАЦИИ ПРОДУКЦИИ НЕФТЕПЕРЕРАБАТЫВАЮЩИХ ЗАВОДОВ

PJSC Tatneft produces highly competitive, environmentally friendly products, including Euro-5 motor gasoline grades AI-92, AI-95, Euro-5 diesel fuel and others. Petroleum products are a commodity, the prices for which in Russia represent an important indicator of socio-economic well-being. Therefore, the study of trends in the dynamics of petroleum product selling prices and the search for approaches to their forecasting is an urgent task. The purpose of this work is to study the dynamics of petroleum product selling prices using computer modelling methods to solve forecasting problems in the context of both current planning and management issues and strategic level tasks. The research was carried out for the following nomenclature of oil products: motor gasoline (AI-92, 95), diesel fuel (DT), heating oil (HO) at the selling price from the refineries of Tatarstan for the period 2018-2023. The article shows the results obtained using Statistica and MS Excel software packages. The choice of models for predicting trends in the dynamics of changes in salling prices is justified, and methods of analytical leveling and exponential smoothing are applied. It is shown that to identify the dynamics of forecast values, it is necessary to use statistics from both a long period and information from the last year. All proposed models provide results with an acceptable error. The results obtained from computer modeling of the pricing dynamics for selling prices of petroleum products are recommended for forecasting trends in the dynamics of price changes as an effective management tool in planning and making management decisions in order to gain competitive advantages in the development of a sustainable business process.

SliceChip: a benchtop fluidic platform for organotypic culture and serial assessment of human and rodent pancreatic slices.

Enzymatically isolated pancreatic islets are the most commonly used ex vivo testbeds for diabetes research. Recently, precision-cut living slices of human pancreas are emerging as an exciting alternative because they maintain the complex architecture of the endocrine and exocrine tissues, and do not suffer from the mechanical and chemical stress of enzymatic isolation. We report a fluidic pancreatic SliceChip platform with dynamic environmental controls that generates a warm, oxygenated, and bubble-free fluidic pathway across singular immobilized slices with continuous deliver of fresh media and the ability to perform repeat serial perfusion assessments. A degasser ensures the system remains bubble-free while systemic pressurization with compressed oxygen ensures slice medium remains adequately oxygenated. Computational modeling of perfusion and oxygen dynamics within SliceChip guide the system's physiomimetic culture conditions. Maintenance of the physiological glucose dependent insulin secretion profile across repeat perfusion assessments of individual pancreatic slices kept under physiological oxygen levels demonstrated the culture capacity of our platform. Fluorescent images acquired every 4 hours of transgenic murine pancreatic slices were reliably stable and recoverable over a 5 day period due to the inclusion of a 3D-printed bioinert metallic anchor that maintained slice position within the SliceChip. Our slice on a chip platform has the potential to expand the useability of human pancreatic slices for diabetes pathogenesis and the development of new therapeutic approaches, while also enabling organotypic culture and assessment of other tissue slices such as brain and patient tumors.

A Computable Dynamic Oligopoly Model of Capacity Investment

A Computable Dynamic Oligopoly Model of Capacity Investment

Ideonamic: An integrative computational dynamic model of ideomotor learning and effect-based action control.

According to ideomotor theory, actions are represented, controlled, and retrieved in terms of the perceptual effects that these actions experientially engender. When agents perform a motor action, they observe its subsequent perceptual effects and establish action-effect associations. When they want to achieve this effect at a later time, they use the action-effect associations to preactivate the action by internally activating the effect representation. Ideomotor theory has received extensive support in recent years. To capture this particular effect-based view on action control and goal-directed behavior, we developed IDEONAMIC, an integrative computational model based on dynamic field theory that represents the specific components of the action control process as dynamic neural fields. We show that IDEONAMIC applies conveniently to different types of experimental ideomotor settings, simulates key findings, generates novel predictions from the dynamics of data, and allows reapproaching the underlying cognitive mechanisms from a computational point of view. We encourage the application of IDEONAMIC to more types of ideomotor settings to gain insights into effect-based action control. The model is available at https://osf.io/hbc6n. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Impact of introducing Chinese certified emission reduction scheme to the carbon market: Promoting renewable energy

The China Certified Emission Reduction scheme (CCER) and the Cap-and-Trade (CaT) mechanisms jointly constitute the Emission Trading Scheme (ETS), which is expected to have a profound impact on China's social and economic decarbonization. However, previous studies have primarily focused on the impact of the CaT mechanism, neglecting the fact that CCERs serve as a carbon offset trading mechanism. This paper focuses on the impact of the CCER mechanism on China's energy economy and environment by constructing a recursive dynamic computable general equilibrium model that couples CaT and CCER mechanisms. Introducing the CCER mechanism based on the CAT mechanism leads to increased output and reduced prices in renewable energy sectors, cascading positive effects across various energy-utilizing sectors to counter the decline in Gross Domestic Product (GDP) and residential welfare. The finding suggests that China's unique CCER mechanism is poised to play a more important role in promoting renewable energy to mitigate global warming, given its potential to transform the structure of energy production and consumption. Finally, targeted policy recommendations are proposed for implementing the CCER mechanism.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response.

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.