Extending MAM5 Meta-Model and JaCalIV E Framework to Integrate Smart Devices from Real Environments

This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.     

On the applicability of the decentralized control mechanism extracted from the true slime mold: a robotic case study with a serpentine robot

A systematic method for an autonomous decentralized control system is still lacking, despite its appealing concept. In order to alleviate this, we focused on the amoeboid locomotion of the true slime mold, and extracted a design scheme for the decentralized control mechanism that leads to adaptive behavior for the entire system, based on the so-called discrepancy function. In this paper, we intensively investigate the universality of this design scheme by applying it to a different type of locomotion based on a 'synthetic approach'. As a first step, we implement this design scheme to the control of a real physical two-dimensional serpentine robot that exhibits slithering locomotion. The experimental results show that the robot exhibits adaptive behavior and responds to the environmental changes; it is also robust against malfunctions of the body segments due to the local sensory feedback control that is based on the discrepancy function. We expect the results to shed new light on the methodology of autonomous decentralized control systems.     

Stability analysis and optimal vaccination of an SIR epidemic model

Almost all mathematical models of diseases start from the same basic premise: the population can be subdivided into a set of distinct classes dependent upon experience with respect to the relevant disease. Most of these models classify individuals as either a susceptible individual S, infected individual I or recovered individual R. This is called the susceptible-infected-recovered (SIR) model. In this paper, we describe an SIR epidemic model with three components; S, I and R. We describe our study of stability analysis theory to find the equilibria for the model. Next in order to achieve control of the disease, we consider a control problem relative to the SIR model. A percentage of the susceptible populations is vaccinated in this model. We show that an optimal control exists for the control problem and describe numerical simulations using the Runge-Kutta fourth order procedure. Finally, we describe a real example showing the efficiency of this optimal control.     

Modelling Size Structured Food Webs Using a Modified Niche Model with Two Predator Traits

The structure of food webs is frequently described using phenomenological stochastic models. A prominent example, the niche model, was found to produce artificial food webs resembling real food webs according to a range of summary statistics. However, the size structure of food webs generated by the niche model and real food webs has not yet been rigorously compared. To fill this void, I use a body mass based version of the niche model and compare prey-predator body mass allometry and predator-prey body mass ratios predicted by the model to empirical data. The results show that the model predicts weaker size structure than observed in many real food webs. I introduce a modified version of the niche model which allows to control the strength of size-dependence of predator-prey links. In this model, optimal prey body mass depends allometrically on predator body mass and on a second trait, such as foraging mode. These empirically motivated extensions of the model allow to represent size structure of real food webs realistically and can be used to generate artificial food webs varying in several aspects of size structure in a controlled way. Hence, by explicitly including the role of species traits, this model provides new opportunities for simulating the consequences of size structure for food web dynamics and stability.     

An adaptive control algorithm for fed-batch culture

The implementation of adaptive control for a fed-batch culture in order to maximize the output of product based on a self-adjusting model is discussed in the present work. Optimization methods were applied to the generalized mathematical model of a fed-batch fermentation process to determine control algorithms that could be used for on-line process control. The efficiency of the proposed adaptive algorithms was investigated by simulating a model system. The model of amylotytic enzyme fermentation that was proposed by the authors was taken from a real process. Dynamic modelling has shown that the main problem of realization is connected with the on-line identification of the adaptive model's parameters. To avoid this problem, we have introduced special limitations on the parameters' time variations that increased the convergence of the identification algorithm. The results of the investigation have shown the efficiency of the proposed adaptive algorithms, and the results of this work should be investigated for real process control.     

Specific and Selective Bacteriophages in the Fight against Multidrug-resistant Acinetobacter baumannii

Acinetobacter baumannii causes serious infections especially in immunocompromised and/or hospitalized patients. Several A. baumannii strains are multidrug resistant and infect wounds, bones, and the respiratory tract. Current studies are focused on finding new effective agents against A. baumannii. Phage therapy is a promising means to fight this bacterium and many studies on procuring and applying new phages against A. baumannii are currently being conducted. As shown in animal models, phages against multidrug-resistant A. baumannii may control bacterial infections caused by this pathogen and may be a real hope to solve this dangerous health problem.     

Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.     

Emerged or imposed: a theory on the role of physical templates and self‐organisation for vegetation patchiness

In this article, we develop a unifying framework for the understanding of spatial vegetation patterns in heterogeneous landscapes. While much recent research has focused on self‐organised vegetation the prevailing view is still that biological patchiness is mostly due to top‐down control by the physical landscape template, disturbances or predators. We suggest that vegetation patchiness in real landscapes is controlled both by the physical template and by self‐organisation simultaneously, and introduce a conceptual model for the relative roles of the two mechanisms. The model considers four factors that control whether vegetation patchiness is emerged or imposed: soil patch size, plant size, resource input and resource availability. The last three factors determine the plant‐patch size, and the plant‐to‐soil patch size ratio determines the impact of self‐organisation, which becomes important when this ratio is sufficiently small. A field study and numerical simulations of a mathematical model support the conceptual model and give further insight by providing examples of self‐organised and template‐controlled vegetation patterns co‐occurring in the same landscape. We conclude that real landscapes are generally mixtures of template‐induced and self‐organised patchiness. Patchiness variability increases due to source–sink resource relations, and decreases for species of larger patch sizes.     

Developing the control criterion for continuous culture of microorganisms

A short survey and critical analysis of previously proposed criteria for growth control of populations of microorganisms in the chemostat are presented. Based on the analysis of a mathematical model of the steady-state of a microbial population in the chemostat, an adequate control criterion is suggested, along with a method to identify the corresponding regulating factors. The new control criterion is expressed as a product of the factor transformation coefficient and the biomass sensitivity coefficient (SC) with respect to the change of the factor at the chemostat inlet (referred to in the sequel as the biomass SC). The control criterion determines the strength of the control exerted by this or that factor. The method of determination of the regulating factors consists in experimental determination of the real SCs for factors and the biomass and in calculating on this basis the corresponding ideal SCs for constant factor transformation coefficients. The ideal SCs are shown to add up to an integer value, a constraint that we call "quantization" relationships. Such relationships are used to test the completeness of the drawn list of control factors. The proposed method was applied to our own and literature data.     

Modeling and Design of H-Infinity Controller for Piezoelectric Actuator LIPCA

<正> We proposed a dynamic model identification and design of an H-Infinity (i.e.H_∞) controller using a LightweightPiezo-Composite Actuator (LIPCA).A second-order dynamic model was obtained by using input and output data, and applyingan identification algorithm.The identified model coincides well with the real LIPCA.To reduce the resonating mode that istypical of piezoelectric actuators, a notch filter was used.A feedback controller using the H_∞ control scheme was designed basedon the identified dynamic model; thus, the LIPCA can be easily used as an actuator for biomemetic applications such as artificialmuscles or macro/micro positioning in bioengineering.The control algorithm was implemented using a microprocessor, analogfilters, and power amplifying drivers.Our simulation and experimental results demonstrate that the proposed control algorithmworks well in real environment, providing robust performance and stability with uncertain disturbances.     

Interest of locally weighted regression to overcome nonlinear effects during in situ NIR monitoring of CHO cell culture parameters and antibody glycosylation

Animal cell culture processes have become the standard platform to produce therapeutic proteins such as recombinant monoclonal antibodies (mAb). Since the mAb quality could be subject to significant changes depending on manufacturing process conditions, real time monitoring and control systems are required to ensure mAb specifications mainly glycosylation and patient safety. Up to now, real time monitoring glycosylation of proteins has received scarce attention. In this article, the use of near infrared (NIR) to monitor mAb glycosylation has been reported for the first time. Whereas monitoring models are mainly constructed using linear partial least squares regressions (PLSR), evidences presented in this study indicate nonlinearity relationship between in situ captured spectra and compound concentrations, compromising the PLSR performances. A novel and simple approach was proposed to fit nonlinearity using the locally weighted regression (LWR). The LWR models were found to be more appropriate for handling information contained in spectra so that real time monitoring of cultures were accurately performed. Moreover, for the first time, the LWR calibration models allowed mAb glycosylation to be monitored, in a real time manner, by using in situ NIR spectroscopy. These results represent a further step toward developing active-control feedback of animal cell processes, particularly for ensuring properties of biologics.     

Simple control of fed-batch processes for recombinant protein production with E. coli

A very simple but effective process control technique is proposed that leads to a high batch-to-batch reproducibility with respect to biomass concentration as well as the specific biomass growth rate profiles in E. coli fermentations performed during recombinant protein production. It makes use of the well-established temperature controllers in currently used fermenters, but takes its information from the difference between the controlled culture temperature T (cult) and the temperature T (coolin) of the coolant fed to the fermenter's cooling jacket as adjusted by the fermenter temperature controller. For process control purposes this measured difference is corrected regarding stirrer influences and cumulated before it is used as a new process control variable. As a spin-off of this control, it becomes possible to estimate online the oxygen mass transfer rates and the corresponding k(L)a values during the real cultivation process.     

Dose-response and thresholds in mutagenicity studies: a statistical testing approach

The analysis of dose-response relationships is an important objective in toxicology, and one in which both modelling and testing approaches are used. One particular question is whether a threshold exists at low doses. The concept of a pragmatic threshold is used, i.e. low doses with biologically unimportant effects are assumed to be threshold doses. "Biologically unimportant" means, in statistical terms, a lower effect than the effect of the negative control, or at least a just-tolerable margin delta higher than the effect of the negative control. Therefore, threshold doses can be tested in terms of a one-sided hypothesis of equivalence. A new approach is proposed, assuming, at the least, that the low dose is a threshold dose, and the highest dose is superior to the negative control. By analogy to the k-fold rule commonly used in mutagenicity studies, tests on ratio-to-control are used. The a priori definition of the threshold margin is inherently needed. A further approach proposes the analysis of dose-response relationships by means of order-restricted inference (the so-called trend test). A modification of a multiple-contrast test is used, in which only those contrasts are included that are sensitive for no effects at low doses. A further modification treats the complicated, but real, problem of simultaneous existence of a threshold, a monotonic increase, and a downturn effect at high dose(s). A parametric procedure is considered, together with an extension for proportions. The important problem of a priori sample size definition is discussed. The approaches are demonstrated by means of examples based on real data.     

Modeling effect of homo- or heterozygosity on animal growth intensity

A model has been developed that describes the dependence of a quantitative selective trait on the animal homo- and heterozygosity for the genes that control the biochemical reaction rate (isoenzyme systems). The model includes any cases of gene control of a quantitative trait and is applicable to real, genetically heterogeneous populations. The results of testing the model show that it can be used for identifying the genes involved in additive polygenic determination of quantitative commercially valuable traits.     

Mathematical modeling of calcium homeostasis in yeast cells

In this study, based on currently available experimental observations on protein level, we constructed a mathematical model to describe calcium homeostasis in normally growing yeast cells (Saccharomyces cerevisiae). Simulation results show that tightly controlled low cytosolic calcium ion level can be a natural result under the general mechanism of gene expression feedback control. The calmodulin (a sensor protein) behavior in our model cell agrees well with relevant observations in real cells. Moreover, our model can qualitatively reproduce the experimentally observed response curve of real yeast cell responding to step-like disturbance in extracellular calcium ion concentration. Further investigations show that the feedback control mechanism in our model is as robust as it is in real cells.     

An NIR-based PAT approach for real-time control of loading in Protein A chromatography in continuous manufacturing of monoclonal antibodies

Control of column loading in Protein A chromatography is a crucial part of development of robust and flexible process platforms for continuous production of monoclonal antibody (mAb) products. In this paper, we propose a control system that uses near infrared spectroscopy (NIRS) flow cells to accomplish the above. Two applications have been demonstrated using a periodic counter-current continuous chromatography setup. The first application involves use of single NIR flow cell before the inlet of the loading column to measure the concentration of mAb in the harvested broth. Measurement was in real-time (every 3 s) and within ±0.05 mg/ml, significantly better than making UV-based concentration estimations. The second application involved use of an additional NIR flow cell at the outlet of the loading column to measure column breakthrough in real time. The concentration data was transferred to a Python-based monitoring and control algorithm layered over a Cadence BioSMB system. The program could successfully run a three-column periodic counter current method on the BioSMB whereas controlling loading to ensure optimal resin utilization in each loading cycle phase based on precharacterized dynamic binding capacity models, whereas maintaining periodic elutions. The system was tested with multiple perturbations in harvest concentration, modeled after deviations that could arise downstream of a perfusion cell culture system. The results show that the proposed control is a spectroscopy-based process analytical technology tool that facilitates real time monitoring and control of loading in process chromatography. It is adaptable to any continuous chromatography equipment and is very well suited for implementation in a continuous mAb production train.     

Effect of training datasets on support vector machine prediction of protein-protein interactions

Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived from the use of shuffled sequences. The real protein sequences of hypothetical noninteracting proteins are generated from an exclusion analysis in combination with subcellular localization information of interacting proteins found in the Database of Interacting Proteins. Prediction accuracy using real protein sequences is 76.9% compared to 94.1% using artificial shuffled sequences. The discrepancy likely arises from the expected higher level of difficulty for separating two sets of real protein sequences than that for separating a set of real protein sequences from a set of artificial sequences. The use of real protein sequences for training a SVM classification system is expected to give better prediction results in practical cases. This is tested by using both SVM systems for predicting putative protein partners of a set of thioredoxin related proteins. The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein-protein interaction prediction.     

More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses

Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.     

Thermal soaring flight of birds and unmanned aerial vehicles

Thermal soaring saves much energy, but flying large distances in this form represents a great challenge for birds, people and unmanned aerial vehicles (UAVs). The solution is to make use of the so-called thermals, which are localized, warmer regions in the atmosphere moving upward with a speed exceeding the descent rate of birds and planes. Saving energy by exploiting the environment more efficiently is an important possibility for autonomous UAVs as well. Successful control strategies have been developed recently for UAVs in simulations and in real applications. This paper first presents an overview of our knowledge of the soaring flight and strategy of birds, followed by a discussion of control strategies that have been developed for soaring UAVs both in simulations and applications on real platforms. To improve the accuracy of the simulation of thermal exploitation strategies we propose a method to take into account the effect of turbulence. Finally, we propose a new GPS-independent control strategy for exploiting thermal updrafts.     

Monitoring bioreactors using principal component analysis: production of penicillin G acylase as a case study

The complexity of biological processes often makes impractical the development of detailed, structured phenomenological models of the cultivation of microorganisms in bioreactors. In this context, data pre-treatment techniques are useful for bioprocess control and fault detection. Among them, principal component analysis (PCA) plays an important role. This work presents a case study of the application of this technique during real experiments, where the enzyme penicillin G acylase (PGA) was produced by Bacillus megaterium ATCC 14945. PGA hydrolyzes penicillin G to yield 6-aminopenicilanic acid (6-APA) and phenyl acetic acid. 6-APA is used to produce semi-synthetic β-lactam antibiotics. A static PCA algorithm was implemented for on-line detection of deviations from the desired process behavior. The experiments were carried out in a 2-L bioreactor. Hotteling’s T ² was the discrimination criterion employed in this multivariable problem and the method showed a high sensibility for fault detection in all real cases that were studied.