Standard Dynamic Model Research Articles

Non-Inertial Dynamic Analysis of 3-SPS/U Parallel Platform by Screw Theory and Kane’s Method

This paper presents an improved method for the non-inertial dynamic analysis of the 3-SPS/U parallel platform (3-SPS/U PM), employing the screw theory and Kane’s method, where S, P, and U denote spherical, prismatic, and universal joints, respectively. The proposed method extends the traditional inertial dynamic analysis to non-inertial systems. First, the generalized screw method is introduced, followed by the derivation of a transformation formula that adapts the screw method to various co-ordinate systems. Subsequently, the velocities and accelerations of each rigid body within the platform under non-inertial conditions are examined by combining the extended screw method with the system’s inverse kinematics model. The extended screw method is not only conceptually simple, but also adaptable to other non-inertial systems. Finally, the standard non-inertial dynamic model of the 3-SPS/U PM is derived through the Kane’s method and validated by the co-simulations with RecurDyn (V9R5) and MATLAB/Simulation (2019b).

Modeling dynamics of acute HIV infection incorporating density-dependent cell death and multiplicity of infection.

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models-1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)-to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.

Assessing the impact of autologous neutralizing antibodies on rebound dynamics in postnatally SHIV-infected ART-treated infant rhesus macaques.

The presence of antibodies against HIV in infected children is associated with a greater capacity to control viremia in the absence of therapy. While the benefits of early antiretroviral treatment (ART) in infants are well documented, early ART may interfere with the development of antibody responses. In contrast to adults, early treated children lack detectable HIV-specific antibodies, suggesting a fundamental difference in HIV pathogenesis. Despite this potential adverse effect, early ART may decrease the size of the latent reservoir established early in infection in infants, which can be beneficial in viral control. Understanding the virologic and immunologic aspects of pediatric HIV is crucial to inform innovative targeted strategies for treating children living with HIV. In this study, we investigate how ART initiation time sets the stage for trade-offs in the latent reservoir establishment and the development of humoral immunity and how these, in turn, affect posttreatment dynamics. We also elucidate the biological function of antibodies in pediatric HIV. We employ mathematical modeling coupled with experimental data from an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Infant Rhesus macaques (RMs) were orally challenged with SHIV.C.CH505 375H dCT four weeks after birth and started treatment at different times after infection. In addition to viral load measurements, antibody responses and latent reservoir sizes were measured. We estimate model parameters by fitting viral load measurements to the standard HIV viral dynamics model within a nonlinear fixed effects framework. This approach allows us to capture differences between rhesus macaques (RMs) that develop antibody responses or exhibit high latent reservoir sizes compared to those that do not. We find that neutralizing antibody responses are associated with increased viral clearance and decreased viral infectivity but decreased death rate of infected cells. In addition, the presence of detectable latent reservoir is associated with less robust immune responses. These results demonstrate that both immune response and latent reservoir dynamics are needed to understand post-rebound dynamics and point to the necessity of a comprehensive approach in tailoring personalized medical interventions.

Comparative analysis of within-host dynamics of acute infection and viral rebound dynamics in postnatally SHIV-infected ART-treated infant rhesus macaques.

Viral dynamics of acute HIV infection and HIV rebound following suspension of antiretroviral therapy may be qualitatively similar but must differ given, for one, development of adaptive immune responses. Understanding the differences of acute HIV infection and viral rebound dynamics in pediatric populations may provide insights into the mechanisms of viral control with potential implications for vaccine design and the development of effective targeted therapeutics for infants and children. Mathematical models have been a crucial tool to elucidate the complex processes driving viral infections within the host. Traditionally, acute HIV infection has been modeled with a standard model of viral dynamics initially developed to explore viral decay during treatment, while viral rebound has necessitated extensions of that standard model to incorporate explicit immune responses. Previous efforts to fit these models to viral load data have underscored differences between the two infection stages, such as increased viral clearance rate and increased death rate of infected cells during rebound. However, these findings have been predicated on viral load measurements from disparate adult individuals. In this study, we aim to bridge this gap, in infants, by comparing the dynamics of acute infection and viral rebound within the same individuals by leveraging an infant nonhuman primate Simian/Human Immunodeficiency Virus (SHIV) infection model. Ten infant Rhesus macaques (RMs) orally challenged with SHIV.C.CH505 375H dCT and given ART at 8 weeks post-infection. These infants were then monitored for up to 60 months post-infection with serial viral load and immune measurements. We use the HIV standard viral dynamics model fitted to viral load measurements in a nonlinear mixed effects framework. We find that the primary difference between acute infection and rebound is the increased death rate of infected cells during rebound. We use these findings to generate hypotheses on the effects of adaptive immune responses. We leverage these findings to formulate hypotheses to elucidate the observed results and provide arguments to support the notion that delayed viral rebound is characterized by a stronger CD8+ T cell response.

Assessment of Lightning Current Injection Tests in Replicating the In-flight Current Distribution on Aircraft

Lightning is one of the natural threats to an aircraft. Laboratory tests are usually limited to aircraft component levels and scaled prototypes. For small aircraft, sometimes the whole aircraft could be subjected to the lightning current specified in the relevant standards. Lightning current injection tests are carried out with standard components A to D. This is in anticipation of producing expected levels of fields and current values during a lightning strike. However, generating a current waveform with a rise time and magnitude the same as component A is very difficult in practice. Further, standards specify a return conductor (RC) arrangement for testing. Fields generated due to RC current can affect the current and field distribution. Hence, the emulated current and field in such tests could differ from that in an actual strike. The present work evaluates the current distribution for different lightning current waveforms suggested in standards and that realised in the laboratory. Also, the effect of the return cage on the current/field distribution is studied. The current and voltage distribution on aircraft skin is obtained by employing the impedance network method. A discretised model of Standard Dynamics Model (SDM) aircraft is employed for the present study. It is found that the surface current distribution on aircraft during a laboratory test can differ from that during the actual strike. Owing to the inductance of the aircraft, the rise time of the lightning current realised in the laboratory could be slow compared to that specified in standards; hence, it could also affect the current distribution on aircraft. This work is intended to provide insight into the inherent limitations of laboratory-level testing, which could be helpful for designers.

Assessment of marine fish stocks within India’s ExclusiveEconomic Zone: Status report 2022

India is one of the major fishing nations in the Indian Ocean, with commercial marine fishlandings in the country comprising of nearly 1000 species. A comprehensive and periodicassessment of the stocks is crucial for achieving the goals of sustainable fisheries harvestand management. The ICAR-Central Marine Fisheries Research Institute, Ministry ofAgriculture and Farmers’ Welfare, Govt. of India, is mandated to monitor and assess themarine fisheries resources in the Indian EEZ. In the present study, 135 stocks across 70species were assessed using monthly length-frequency data. The health status of the stockis projected based on a combination of indicators such as relative biomass or spawningstock biomass (B/Bmsy or SSB/SSB0) and relative fishing pressure (F/Fmsy) estimated usinga standard length-based dynamic pool model (i.e., Thompson and Bell Model). Based onthe comparison of estimated values of indicators against the thresholds, the stocks wereclassified as sustainable, overfishing, overfished and rebuilding. The study revealed that91.1% of the assessed fish stocks have a healthy biomass level (sustainable stocks andthose subjected to overfishing), 8.2% of stocks are overfished, whereas 0.7% are in theprocess of rebuilding. Biology-based, Healthy stocks, Length-frequency,Stock assessment, Sustainable, Tropical

Semiparametric Bayesian estimation of dynamic discrete choice models

We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions in the multinomial choice settings and the flexibility of the location-scale mixtures. We implement the Bayesian approach to inference using Hamiltonian Monte Carlo and an approximately optimal reversible jump algorithm. In our simulation experiments, we show that the standard dynamic logit model can deliver misleading results, especially about counterfactuals, when the shocks are not extreme value distributed. Our semiparametric approach delivers reliable inference in these settings. We develop theoretical results on approximations by location-scale mixtures in an appropriate distance and posterior concentration of the set identified utility parameters and the distribution of shocks in the model.

Beyond expectations: residual dynamic mode decomposition and variance for stochastic dynamical systems

Koopman operators linearize nonlinear dynamical systems, making their spectral information of crucial interest. Numerous algorithms have been developed to approximate these spectral properties, and dynamic mode decomposition (DMD) stands out as the poster child of projection-based methods. Although the Koopman operator itself is linear, the fact that it acts in an infinite-dimensional space of observables poses challenges. These include spurious modes, essential spectra, and the verification of Koopman mode decompositions. While recent work has addressed these challenges for deterministic systems, there remains a notable gap in verified DMD methods for stochastic systems, where the Koopman operator measures the expectation of observables. We show that it is necessary to go beyond expectations to address these issues. By incorporating variance into the Koopman framework, we address these challenges. Through an additional DMD-type matrix, we approximate the sum of a squared residual and a variance term, each of which can be approximated individually using batched snapshot data. This allows verified computation of the spectral properties of stochastic Koopman operators, controlling the projection error. We also introduce the concept of variance-pseudospectra to gauge statistical coherency. Finally, we present a suite of convergence results for the spectral information of stochastic Koopman operators. Our study concludes with practical applications using both simulated and experimental data. In neural recordings from awake mice, we demonstrate how variance-pseudospectra can reveal physiologically significant information unavailable to standard expectation-based dynamical models.

Prediction of chronic kidney disease progression using recurrent neural network and electronic health records

Chronic kidney disease (CKD) is a progressive loss in kidney function. Early detection of patients who will progress to late-stage CKD is of paramount importance for patient care. To address this, we develop a pipeline to process longitudinal electronic heath records (EHRs) and construct recurrent neural network (RNN) models to predict CKD progression from stages II/III to stages IV/V. The RNN model generates predictions based on time-series records of patients, including repeated lab tests and other clinical variables. Our investigation reveals that using a single variable, the recorded estimated glomerular filtration rate (eGFR) over time, the RNN model achieves an average area under the receiver operating characteristic curve (AUROC) of 0.957 for predicting future CKD progression. When additional clinical variables, such as demographics, vital information, lab test results, and health behaviors, are incorporated, the average AUROC increases to 0.967. In both scenarios, the standard deviation of the AUROC across cross-validation trials is less than 0.01, indicating a stable and high prediction accuracy. Our analysis results demonstrate the proposed RNN model outperforms existing standard approaches, including static and dynamic Cox proportional hazards models, random forest, and LightGBM. The utilization of the RNN model and the time-series data of previous eGFR measurements underscores its potential as a straightforward and effective tool for assessing the clinical risk of CKD patients concerning their disease progression.

Dynamical Model of Influenza Disease with Vaccination

Influenza disease is generally found in Thailand. It is a viral infection of the respiratory system, including the nose, throat, and lungs. It can be contacted between people. The influenza transmission model with vaccination is considered in this study. We separated the human population into susceptible, exposed, infectious and recovered human populations. We analyze the dynamical model by using standard dynamical modeling method. The equilibrium states are found. The basic reproduction number is found. The local stability for each equilibrium states are determined. The numerical simulations for our dynamical model are shown.

Development of a Dynamic Gray‐Box Model of a Fermentation Process for Spore Production

AbstractFermentation processes are difficult to describe using purely mechanistic relations as the underlying biochemical phenomena are complex and often not fully understood. In order to cope with this challenge, we developed an approach to augment standard dynamic model equations by data‐based components that are fitted to data using machine learning techniques, which results in dynamic gray‐box models. This methodology is applied here to the batch fermentation process of the sporulating bacterium Bacillus subtilis, using experimental data from a lab‐scale fermenter. The key step in developing the model is the estimation of a training set for the machine learning submodels. The quality of the resulting model is analyzed, and the predictions are compared with real data.

Wire-driven parallel robot suspension system for SDM in a low-speed wind tunnel

A wire-driven parallel robot with eight wires (WDPR-8) acting as the suspension system for an aircraft model in a low-speed wind tunnel is introduced in this paper. A kinematics model for WDPR-8 is established here. A theoretical basis is also provided, through which movement control of the model is achieved by changing the wire length. A model motion control subsystem, a monocular vision measurement subsystem for the model position, and a data acquisition subsystem were also developed for WDPR-8. A prototype of WDPR-8 was built and mounted in a low-speed wind tunnel with an open test section. A six-component strain-gauge balance was installed inside the test model to acquire aerodynamic parameters. The aerodynamic coefficients of the static test show reasonable agreement with reference results. The difference between the test result of WDPR-8 and the results from reference is smaller in the positive angle of attack than the negative angle of attack, and the total difference between WDPR-8 and other facilities is under 10%. Pitch dynamic tests with different amplitudes and frequencies were used to verify the aerodynamic hysteresis phenomena. The research indicates it is successful that the solution of measuring aerodynamic parameters with strain-gauge balance set into the standard dynamic model suspended by WDPR. It demonstrates that WDPR-8 is effective and feasible as a model suspension system in a low-speed wind tunnel. Furthermore, test results without interference correction in accordance with the reference data proves that the interference of Wire-Driven support system to flow field during wind tunnel tests is negligible. Finally, WDPR suspension system introduced in this paper will remarkably improve the efficiency and accuracy in wind tunnel test.

Urbanization-Growth-Environment: How Are They Related? An Evidence from the Global Asia-Pacific Region

Asia and the Pacific have made rapid progress in economic growth but still have work to do in environmental degradation. Logical consequence because pursuing economic progress has raised environmental problems and has become one of the central issues for development. The Asia-Pacific region became the most significant contributor and growth in carbon dioxide emissions compared to other regions. The research objective is to analyze the impact of urbanization on environmental quality and its interaction through economic growth using the standard Dynamic Model of the Autoregressive Distributed Lag approach. The result show demonstrates consistency in developed countries and the High-Income countries categories. Urbanization has a significant negative effect on emissions, and the Growth variable is significantly positive; nevertheless, the EKC curve does not occur. Urbanization deteriorates the relationship between growth and emissions. It is not proven in developing countries and the lower and upper middle income. Equitable development needs to be carried out by every country by considering the quality and sustainable economic institutions and pro-environmental innovations, especially in developing or lower and upper-middle countries.

The Forward Guidance Puzzle

This paper argues that standard dynamic stochastic general equilibrium models grossly overestimate the impact of forward guidance on the macroeconomy, a phenomenon we call the “forward guidance puzzle.” Using data from a panel of Blue Chip survey expectations, we show that accommodative forward guidance had effects on output and inflation expectations that were positive and nontrivial, but still 28 and 8 times smaller, respectively, than implied by standard models. We show that incorporating a perpetual youth structure into the benchmark provides one possible resolution to the puzzle.

Continuous ordinal patterns: Creating a bridge between ordinal analysis and deep learning.

We introduce a generalization of the celebrated ordinal pattern approach for the analysis of time series, in which these are evaluated in terms of their distance to ordinal patterns defined in a continuous way. This allows us to naturally incorporate information about the local amplitude of the data and to optimize the ordinal pattern(s) to the problem under study. This last element represents a novel bridge between standard ordinal analysis and deep learning, allowing the achievement of results comparable to the latter in real-world classification problems while also retaining the conceptual simplicity, computational efficiency, and easy interpretability of the former. We test this through the use of synthetic time series, generated by standard chaotic maps and dynamical models, data sets representing brain activity in health and schizophrenia, and the dynamics of delays in the European air transport system. We further show how the continuous ordinal patterns can be used to assess other aspects of the dynamics, like time irreversibility.

Poisson Multi-Bernoulli Mixture Filter With General Target-Generated Measurements and Arbitrary Clutter

This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, for this multi-target measurement model and the standard multi-target dynamic model with Poisson birth model, the predicted and filtering densities are PMBMs. We derive the corresponding PMBM filtering recursion. Based on this result, we implement a PMBM filter for point-target measurement models and negative binomial clutter density in which data association hypotheses with high weights are chosen via Gibbs sampling. We also implement an extended target PMBM filter with clutter that is the union of Poisson-distributed clutter and a finite number of independent clutter sources. Simulation results show the benefits of the proposed filters to deal with non-standard clutter.

The Role of International Reserves in Sovereign Debt Restructuring under Fiscal Adjustment

Highly indebted developing economies commonly also hold large external reserves. This behavior seems puzzling given that governments in these countries borrow with an interest rate penalty to compensate lenders for default risk. Reducing debt to the same extent as reserves would maintain net liabilities constant while decreasing interest payments. However, holding reserves can have insurance benefits in a financial crisis. To rationalize the levels of international reserves and external debt observed in the data, a standard dynamic model of equilibrium default is extended to include distortionary taxation and debt restructuring. This paper shows that fiscal adjustments induced by sovereign default can generate large demand for reserves if taxation is distortionary. At the same time, a non-negligible position in reserves modifies the debt restructuring negotiations upon default. A calibrated version of the model produces recovery rate schedules that are increasing with reserves, as seen in the data, being also able to replicate large positions of reserves and debt to GDP. Finally, I study how both mechanisms play a key quantitative role to generate such result, in fact, not including them, produces a counterfactual demand for reserves that is close to zero.

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.

Macroeconomic Effects of Credit Deepening in Latin America

AbstractWe augment a standard dynamic general equilibrium model with financial frictions, in order to quantify the macroeconomic effects of the credit deepening process observed in Latin America in the 2000s—most notably in Brazil. In the model, a stylized banking sector intermediates credit from patient households to impatient households and entrepreneurs. Motivated by the Brazilian experience, we allow the credit constraint faced by households to depend on labor income. Our model is designed to isolate the effects of credit deepening through demand‐side channels, and abstracts from potential effects of credit supply on total factor productivity. In the calibrated model, credit deepening generates only modest above‐trend growth in consumption, investment, and GDP. Since Brazil has experienced one of the most intense credit deepening processes in Latin America, we argue the quantitative effects that hinge on the channels captured by the model are unlikely to be sizable elsewhere in Latin America.

Vaccination’s Role in Combating the Omicron Variant Outbreak in Thailand: An Optimal Control Approach

COVID-19 is the name of the new infectious disease which has reached the pandemic stage and is named after the coronavirus (COVs) which causes it. COV is a single-stranded RNA virus which in humans leads to respiratory tract symptoms which can lead to death in those with low immunities, particularly older people. In this study, a standard dynamic model for COVID-19 was proposed by comparing a simple model and the optimal control model to reduce the number of infected people and become a guideline to control the outbreak. Control strategies are the vaccination rate and vaccine-induced immunity. An analysis was performed to find an equilibrium point, the basic reproduction number (R0), and conditions that generate stability by using Lyapunov functions to prove the stability of the solution at the equilibrium point. Pontryagin’s maximum principle was used to find the optimal control condition. Moreover, sensitivity analysis of the parameters was performed to learn about the parameters that might affect the outbreak in order to be able to control the outbreak. According to the analysis, it is seen that the efficacy of vaccines (b) and the infection rate (βan,βsn,βav,βsv) will affect the increased (decreased) incidence of the outbreak. Numerical analyses were performed on the Omicron variant outbreak data collected from the Thailand Ministry of Health, whose analyses then indicated that the optimal control strategy could lead to planning management and policy setting to control the COVID-19 outbreak.