RESUMO

In social robotics, especially with regard to direct interactions between robots and humans, the robotic movements of the body, arms and head must make an adequate displacement to guarantee an adequate interaction, both from a functional and social point of view. To achieve this, the use of closed-loop control techniques that consider the complex nonlinear dynamics and disturbances inherent in these systems is required. In this paper, an implementation of a nonlinear controller for the tracking of trajectories and a profile of speeds that execute the movements of the arms and head of a humanoid robot based on the mathematical model is proposed. First, the design and implementation of the arms and head are initially presented, then the mathematical model via kinematic and dynamic analysis was performed. With the above, the design of nonlinear controllers such as nonlinear proportional derivative control with gravity compensation, Backstepping control, Sliding Mode control and the application of each of them to the robotic system are presented. A comparative analysis based on a frequency analysis, the efficiency in polynomial trajectories and the implementation requirements allowed selecting the non-linear Backstepping control technique to be implemented. Then, for the implementation, a centralized control architecture is considered, which uses a central microcontroller in the external loop and an internal microcontroller (as internal loop) for each of the actuators. With the above, the selected controller was validated through experiments performed in real time on the implemented humanoid robot, demonstrating proper path tracking of established trajectories for performing body language movements.

Assuntos

RESUMO

The purpose of this study is to analyze the contribution of the interactions between electrodes, measured either as correlation or as Jaccard distance, to the classification of two actions in a motor imagery paradigm, namely, left-hand movement and right-hand movement. The analysis is performed in two classifier models, namely, a static (linear discriminant analysis, LDA) model and a dynamic (hidden conditional random field, HCRF) model. The impact of using the sliding window technique (SWT) in the static and dynamic models is also analyzed. The study proved that their combination with temporal features provides significant information to improve the classification in a two-class motor imagery task for LDA (average accuracy: 0.7192 no additional features, 0.7617 by adding correlation, 0.7606 by adding Jaccard distance; p < 0.001) and HCRF (average accuracy: 0.7370 no additional features, 0.7764 by adding correlation, 0.7793 by adding Jaccard distance; p < 0.001). Also, we showed that adding interactions between electrodes improves significantly the performance of each classifier, regarding the nature of the interaction measure or the classifier itself.

RESUMO

Invasive meningococcal disease (IMD) is an uncommon yet unpredictable, severe, and life-threatening disease with the highest burden in young children. In Chile, most IMD is caused by meningococcal serogroup B (MenB) and W (MenW) infection. In response to a MenW outbreak in 2012, a toddler vaccination program was implemented using quadrivalent meningococcal conjugate vaccine against serogroups A, C, W and Y (MenACWY). The vaccine program, however, does not protect infants or other unvaccinated age groups and does not protect against MenB IMD. Since 2017, MenB IMD cases are becoming increasingly prevalent. Using a dynamic transmission model adapted for Chile, this analysis assessed the public health impact (reduction in IMD cases, long-term sequelae, deaths, and quality-adjusted life-years) of six alternative vaccination strategies using MenACWY and/or the four-component MenB (4CMenB) vaccine in infants, toddlers, and/or adolescents compared to the National Immunization Program (NIP) implemented in 2014. Strategies that added infant 4CMenB to MenACWY in toddlers or adolescents would prevent more IMD than the current NIP, observed within the first 5 years of the program. Replacing the NIP by an adolescent MenACWY strategy would prevent more IMD in the longer term, once herd immunity is established to protect unvaccinated infants or older age groups. The strategy that maximized reduction of IMD cases and associated sequelae in all age groups with immediate plus long-term benefits included infant 4CMenB and MenACWY in both toddlers and adolescents. This analysis can help policymakers determine the best strategy to control IMD in Chile and improve public health. A set of audio slides linked to this manuscript can be found at https://doi.org/10.6084/m9.figshare.16837543.

Plain Language Summary (PLS)What is the context?Invasive meningococcal disease (IMD) is a severe, sometimes fatal, unpredictable disease with highest rates in infants, young children, and adolescents. It is caused by different serogroups of Neisseria meningitidis bacteria. Most cases in Chile are due to meningococcal serogroups B (MenB) and W (MenW). Following a MenW IMD outbreak in 2012, vaccination was introduced, leading to the current National Immunization Program (NIP) in toddlers with quadrivalent meningococcal conjugate vaccine (MenACWY) (protecting against IMD caused by MenA, C, W, and Y).What is new?A disease model to predict the impact of vaccination strategies in the Chilean population compared six alternative strategies, using the multi-component MenB (4CMenB) vaccine for infants (protecting against MenB, with potential cross-protection against MenW and Y IMD) and/or the MenACWY vaccine for toddlers and/or adolescents.What is the impact?Results, compared to the NIP, show that: Strategy 1 (a program targeting only infants with 4CMenB) would reduce more MenB cases but fewer MenA, C, W and Y cases resulting in a lower reduction of total IMD cases in the long term; Strategy 3 (a program targeting only adolescents with MenACWY) would have a similar effect to the NIP in the short term but a far greater IMD reduction in the long term (as vaccinating this age group eventually reduces transmission to other age groups, reducing their risk of disease); all the other strategies targeted more than one age group, further reducing numbers of IMD cases compared with the NIP. The greatest benefits were seen with infant 4CMenB vaccination combined with toddler and adolescent MenACWY vaccination. Results can help policymakers determine the best IMD strategy to maximize the benefits of available meningococcal vaccines.

Assuntos

RESUMO

The acrosome reaction (AR) is an exocytotic process essential for mammalian fertilization. It involves diverse physiological changes (biochemical, biophysical, and morphological) that culminate in the release of the acrosomal content to the extracellular medium as well as a reorganization of the plasma membrane (PM) that allows sperm to interact and fuse with the egg. In spite of many efforts, there are still important pending questions regarding the molecular mechanism regulating the AR. Particularly, the contribution of acrosomal alkalinization to AR triggering physiological conditions is not well understood. Also, the dependence of the proportion of sperm capable of undergoing AR on the physiological heterogeneity within a sperm population has not been studied. Here, we present a discrete mathematical model for the human sperm AR based on the physiological interactions among some of the main components of this complex exocytotic process. We show that this model can qualitatively reproduce diverse experimental results, and that it can be used to analyze how acrosomal pH (pH a ) and cell heterogeneity regulate AR. Our results confirm that a pH a increase can on its own trigger AR in a subpopulation of sperm, and furthermore, it indicates that this is a necessary step to trigger acrosomal exocytosis through progesterone, a known natural inducer of AR. Most importantly, we show that the proportion of sperm undergoing AR is directly related to the detailed structure of the population physiological heterogeneity.

RESUMO

The self-discharge phenomenon results in a decrease of the open-circuit voltage (OCV), which occurs when an electrochemical device is disconnected from the power source. Although the self-discharge phenomenon has widely been investigated for energy storage devices such as batteries and supercapacitors, no previous works have been reported in the literature about this phenomenon for electrolyzers. For this reason, this work is mainly focused on investigating the self-discharge voltage that occurs in a proton exchange membrane (PEM) electrolyzer. To investigate this voltage drop for modeling purposes, experiments have been performed on a commercial PEM electrolyzer to analyze the decrease in the OCV. One model was developed based on different tests carried out on a commercial-400 W PEM electrolyzer for the self-discharge voltage. The proposed model has been compared with the experimental data to assess its effectiveness in modeling the self-discharge phenomenon. Thus, by taking into account this voltage drop in the modeling, simulations with a higher degree of reliability were obtained when predicting the behavior of PEM electrolyzers.

RESUMO

Abstract Infertility is becoming a growing issue in almost all countries. Assisted Reproductive Technologies (ART) are recent development in treating infertility that give hope to the infertile couples. However, the pregnancy rates achieved with the aid of ART is considerably low, as success in ART is not only based on the treatment but also on many other controllable and uncontrollable biological, social, and environmental features. High expenditures and painful process of ART cycles are the two major barriers for opting for ART. Moreover, ART treatments are not covered by any health insurance schemes. Computational prediction models could be used to improve the success rate by predicting the treatment outcome, before the start of an ART cycle. This may suggest the couples and the doctors to decide on the next course of action i.e. either to opt for ART or opt for correcting determinants or quit the ART. With the intension to improve the success rate of ART by providing decision support system to the physicians as well to the patients before entering into the treatment this research work proposes a dynamic model for ART outcome prediction using Machine Learning (ML) techniques. The proposed dynamic model is partially implemented with the help of an ensemble of heterogeneous incremental classifier and its performance is compared with state-of-art classifiers such as Naïve Bayes (NB), Random Forest (RF), K-star etc.,using ART dataset. Performance of the model is evaluated with various metrics such as accuracy, Precision Recall Curve (PRC), Receiver Operating Characteristic (ROC), F-Measure etc., However, ROC cure area is taken as the chief metric. Evaluation results shows that the model achieves the performance with the ROC area value of 94.1 %.

Assuntos

RESUMO

Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot's mobility. Additionally, the wheel friction and the force of gravity force are also relevant issues that can compromise the climbing ability if they are not well modeled. This work presents a model-based torque controller for velocity tracking in a four-wheeled climbing robot specially designed to inspect storage tanks. The model-based controller (MPC) compensates for the effects of nonlinearities due to the forces of gravity, friction, and adhesion through the dynamic and kinematic modeling of the climbing robot. Dynamic modeling is based on the Lagrange-Euler approach, which allows a better understanding of how forces and torques affect the robot's movement. Besides, an analysis of the interaction force between the robot and the contact surface is proposed, since this force affects the motion of the climbing robot according to spatial orientation. Finally, simulations are carried out to examine the robot's dynamics during the climbing movement, and the MPC is validated through the redrobot simulator V-REP and practical experiments. The presented results highlight the compensation of the nonlinear effects due to the robot's climbing motion by the proposed MPC controller.

RESUMO

BACKGROUND: Balance control deteriorates with age and nearly 30% of the elderly population in the United States reports stability problems. Postural stability is an integral task to daily living reliant upon the control of the ankle and hip. To this end, the estimation of joint parameters can be a useful tool when analyzing compensatory actions aimed at maintaining postural stability. METHODS: Using an analytical approach, this study expands on previous work and analyzes a two degrees of freedom human model. The first two modes of vibration of the system are represented by the neuro-mechanical parameters of a second-order, time-varying Kelvin-Voigt model actuated at the ankle and hip. The model is tested using a custom double inverted pendulum and healthy volunteers who were subjected to a positional step-like perturbation during quiet standing. An in silico sensitivity analysis of the influence of inertial parameters was also performed. RESULTS: The proposed method is able to correctly identify the time-varying visco-elastic parameters of of a double inverted pendulum. We show that that the parameter estimation method can be applied to standing humans. These results appear to identify a subject-independent strategy to control quiet standing that combines both the modulation of stiffness, and the use of an intermittent control. CONCLUSIONS: This paper presents the analysis of the non-linear system of differential equations representing the control of lumped muscle-tendon units. It utilizes motion capture measurements to obtain the estimates of the system's control parameters by constructing a simple time-dependent regressor for estimating the time-varying parameters of the control with a single perturbation. This work is a step forward into the understanding of the neuro-mechanical control parameters of human recovering from a fall. In previous literature, the analysis is either restricted to the first vibrational mode of an inverted-pendulum model or assumed to be time-invariant. The proposed method allows for the analysis of hip related movement for stability control and highlights the importance of core training.

Assuntos

RESUMO

BACKGROUND: The determination of kinetic parameters and the development of mathematical models are of great interest to predict the growth of microalgae, the consumption of substrate and the design of photobioreactors focused on CO2 capture. However, most of the models in the literature have been developed for CO2 concentrations below 10%. RESULTS: A nonaxenic microalgal consortium was isolated from landfill leachate in order to study its kinetic behavior using a dynamic model. The model considered the CO2 mass transfer from the gas phase to the liquid phase and the effect of light intensity, assimilated nitrogen concentration, ammonium concentration and nitrate concentration. The proposed mathematical model was adjusted with 13 kinetic parameters and validated with a good fit obtained between experimental and simulated data. CONCLUSIONS: Good results were obtained, demonstrating the robustness of the proposed model. The assumption in the model of DIC inhibition in the ammonium and nitrate uptakes was correct, so this aspect should be considered when evaluating the kinetics with microalgae with high inlet CO2 concentrations.

Assuntos

RESUMO

This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

RESUMO

Connectivity studies of the brain are usually based on functional Magnetic Resonance Imaging (fMRI) experiments involving many subjects. These studies need to take into account not only the interaction between areas of a single brain but also the differences amongst those subjects. In this paper we develop a methodology called the group-structure (GS) approach that models possible heterogeneity between subjects and searches for distinct homogeneous sub-groups according to some measure that reflects the connectivity maps. We suggest a GS method that uses a novel distance based on a model selection measure, the Bayes factor. We then develop a new class of Multiregression Dynamic Models to estimate individual networks whilst acknowledging a GS type dependence structure across subjects. We compare the efficacy of this methodology to three other methods, virtual-typical-subject (VTS), individual-structure (IS) and common-structure (CS), used to infer a group network using both synthetic and real fMRI data. We find that the GS approach provides results that are both more consistent with the data and more flexible in their interpretative power than its competitors. In addition, we present two methods, the Individual Estimation of Multiple Networks (IEMN) and the Marginal Estimation of Multiple Networks (MEMN), generated from the GS approach and used to estimate all types of networks informed by an experiment -individual, homogeneous subgroups and group networks. These methods are then compared both from a theoretical perspective and in practice using real fMRI data.

RESUMO

The increase in atmospheric CO2 due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO2 with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO2 fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO2 levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO2. Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions (i.e., low CO2-heterotrophic and high CO2-mixotrophic). Our results suggest that cells growing at high CO2 (i.e., photoautotrophic and mixotrophic conditions) have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO2-heterotrophic and high CO2-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO2 fixation, biomass accumulation, and bioenergy production.

RESUMO

Myxococcus xanthus is a myxobacterium that exhibits aggregation and cellular differentiation during the formation of fruiting bodies. Therefore, it has become a valuable model system to study the transition to multicellularity via cell aggregation. Although there is a vast set of experimental information for the development on M. xanthus, the dynamics behind cell-fate determination in this organism's development remain unclear. We integrate the currently available evidence in a mathematical network model that allows to test the set of molecular elements and regulatory interactions that are sufficient to account for the specification of the cell types that are observed in fruiting body formation. Besides providing a dynamic mechanism for cell-fate determination in the transition to multicellular aggregates of M. xanthus, this model enables the postulation of specific mechanisms behind some experimental observations for which no explanations have been provided, as well as new regulatory interactions that can be experimentally tested. Finally, this model constitutes a formal basis on which the continuously emerging data for this system can be integrated and interpreted.

Assuntos

RESUMO

Los aneurismas cerebrales son lesiones arteriales caracterizadas por el debilitamiento y la dilatación de un segmento del vaso sanguíneo. Representan una gran amenaza para la vida del paciente debido al riesgo de ruptura, trombo-embolias o compresión del tejido adyacente. Los aneurismas cerebrales rotos son la causa más común de la hemorragia subaracnoidea y puede causar una significativa morbilidad y mortalidad. Con el fin de entender el comportamiento hemodinámico de los aneurismas cerebrales se han desarrollado estudios computacionales que simulan las condiciones y propiedades de dichas lesiones en modelos virtuales similares a la realidad; la mayoría de ellos se realizan en un sistema experimental conocido como dinámica de fluido computacional. Este artículo presenta una revisión del estado de la técnica aplicada a hemodinámica de flujo en aneurismas y pretende recopilar los avances más importantes del método que servirán en un futuro, para el desarrollo de una herramienta de apoyo al diagnóstico y tratamiento de estas dolencias.

Intracranial aneurysms are lesions of the arterial wall characterized by weakening and dilation of an arterial segment. These lesions are a major threat to the patient’s life because of the risk of rupture, thrombo-emboli, or compression of adjacent tissue. The rupture of an intracranial aneurysm causes subarachnoid hemorrhage associated with high mortality and morbidity rates. In order to understand the intracranial aneurysm hemodynamics, it has been developed computational studies, which simulate the boundary conditions and properties of these lesions in virtual models (models similar to reality), most of them are made in a computational fluid dynamic model (CFD). This study reviews the state of arts of the CFD technique applied to the aneurysm flow hemodynamics that claims to collect the most important progress of the method that will be useful in the tool’s developments that will become a rely on a diagnosis and treatment tool.

RESUMO

The contribution of covalently closed circular DNA (cccDNA) and dendritic cells (DCs) to the progression of chronic hepatitis B virus (HBV) infection remains largely unknown. A dynamic model with seven cell types was proposed based on the biological mechanisms of viral replication and the host immune response. The cccDNA self-amplification rate was found to be closely related to both the basic reproduction number of the virus and the immune response. The combination of the cccDNA self-amplification rate and the initial activated DC count induces rich dynamics. Applying our model to the clinical data of untreated patients, we found that chronic patients have a high cccDNA self-amplification rate. For antiviral treatment, an overall drug effectiveness was introduced and the critical drug effectiveness was obtained. The model predicts that timely long-term therapy is needed to reduce the symptoms of HBV and to maintain the benefits of treatment.

Assuntos

RESUMO

UNLABELLED: The knowledge and understanding of Bacillus coagulans inactivation during a thermal treatment in tomato pulp, as well as the influence of temperature variation during thermal processes are essential for design, calculation, and optimization of the process. The aims of this work were to predict B. coagulans spores inactivation in tomato pulp under varying time-temperature profiles with Gompertz-inspired inactivation model and to validate the model's predictions by comparing the predicted values with experimental data. B. coagulans spores in pH 4.3 tomato pulp at 4 °Brix were sealed in capillary glass tubes and heated in thermostatically controlled circulating oil baths. Seven different nonisothermal profiles in the range from 95 to 105 °C were studied. Predicted inactivation kinetics showed similar behavior to experimentally observed inactivation curves when the samples were exposed to temperatures in the upper range of this study (99 to 105 °C). Profiles that resulted in less accurate predictions were those where the range of temperatures analyzed were comparatively lower (inactivation profiles starting at 95 °C). The link between fail prediction and both lower starting temperature and magnitude of the temperature shift suggests some chemical or biological mechanism at work. Statistical analysis showed that overall model predictions were acceptable, with bias factors from 0.781 to 1.012, and accuracy factors from 1.049 to 1.351, and confirm that the models used were adequate to estimate B. coagulans spores inactivation under fluctuating temperature conditions in the range from 95 to 105 °C. PRACTICAL APPLICATION: How can we estimate Bacillus coagulans inactivation during sudden temperature shifts in heat processing? This article provides a validated model that can be used to predict B. coagulans under changing temperature conditions. B. coagulans is a spore-forming bacillus that spoils acidified food products. The mathematical model developed here can be used to predict the spoilage risk following thermal process deviations for tomato products.

Assuntos

RESUMO

Los estudios referentes al trauma conocidos se caracterizan por ser multicausales, pero al observar el impacto de las intervenciones sobre sus causas, se observa que no clarifican cuál es el camino más idóneo para su prevención y control. Objetivo: abordar el problema del trauma desde un enfoque integrador que facilite entender el fenómeno desde sus interrelaciones complejas. Metodología: aplicando la dinámica de sistemas planteada por Forrester para proponer un modelo dinámico que permita prever situaciones relacionadas con su revención y la atención, para plantear políticas públicas hacia la disminución de la incidencia y la mortalidad. El proceso incluyó los seis pasos de la dinámica de sistemas que permite entregar un modelo para el análisis de los escenarios actuales y posibles en su atención, basados en simulaciones del comportamiento del trauma, incluidas las variables de incidencia y prevención en interrelación con la atención prehospitalaria y hospitalaria. Resultados: fue posible la propuesta de escenario ideal en la atención del trauma planteada en la hipótesis dinámica formulada: “la atención oportuna del paciente indicado, en la institución adecuada, es garantía para la disminución de la letalidad por trauma.

Studies relating to trauma are mainly multicausal, but when we observe the impact of interventions on their causes, there is no clarity about the best way for prevention and control. Objetive: To approach the problem of trauma from its complex interrelationships. Methodology: using the system dynamics raised by Forrester to propose a dynamic model capable of predicting situations related to prevention and care, to raise public policies towards reducing the incidence and mortality. The process included six steps of the dynamics of systems to deliver model for the analysis of existing and potential scenarios in their care, based on simulations of the behavior of the trauma, including the incidence and prevention of variables in interaction with prehospital care and hospital. Results: the proposal was ideal in the care of trauma described in the dynamic scenario put "appropriate care of patient described in the appropate institution, is guaranteed to reduce the mortality for trauma".

Assuntos