Adaptive state restricted barrier Lyapunov-based control of a Stewart platform used as ankle-controlled mobilizer.

In this research project, a closed-chain robotic active ankle orthosis with six degrees of freedom is designed, constructed, numerically valued, instrumented, and experimentally validated. The mechanical arrangement to implement the orthosis corresponds to a six-legged Stewart platform. An adaptive gain control strategy with state constraints based on a state-dependent gains control (that behaves as a diverging function as the states approach the state restrictions) operates the device's motion. The convergence to an invariant positive set centered at the origin of the tracking error space is validated using the stability analysis based on the second method of Lyapunov, with the implementation of a state barrier Lyapunov-like function. The ultimate boundedness of the tracking error is proven with an endorsed gains adjustment method leading to a reachable minimum size of the ultimate bound. Hence, the impact of the state constraints and the formal reason for applying the controller on the suggested orthosis are all established. The orthosis is also controlled using a conventional state feedback strategy to assess the tracking error for an external disturbance and contrast its performance with the proposed control approach. The technology is tested on a few carefully chosen volunteers, successfully limiting the range of motion within a pre-defined region based on the scope of movement reported by patients with ankle illnesses discovered in the literature. Based on a unique mechatronic device, the created system offers a fresh approach to treating this class of impairments.

Sequential Treatment by Ozonation and Biodegradation of Pulp and Paper Industry Wastewater to Eliminate Organic Contaminants.

In this research, the decomposition of toxic organics from pulp and paper mill effluent by the sequential application of ozonation and biodegradation was studied. Ozonation, as a pre-treatment, was executed to transform the initial pollutants into less toxic compounds (such as organic acids of low molecular weights). Biodegradation was executed during three days with acclimated microorganisms that were able to complete the decomposition of the initial organic mixture (raw wastewater) and to achieve a higher degree of mineralization (85-90%). Experiments were performed under three different conditions: (a) only ozonation of the initial contaminants, (b) only biodegradation of residual water without previous treatment by ozone and (c) ozonation followed by biodegradation performed by acclimated microorganisms. In the case of 72 h of biodegradation, the mineralization efficiency reached 85% and 89% after 30 and 60 min of ozonation, respectively. The no significant difference in this parameter coincided with the calculated generalized microorganisms' consortia specific growing rate µmax that was reduced from 2.08 × 10-3 h-1 to 6.05 × 10-4 h-1 when the ozonation time was longer. The identification of the organics composition by gas chromatography with mass detector (GC-MS) before and after treatments confirmed that the proposed combined process served as a more efficient alternative to secondary and tertiary treatments (mineralization degree between 60 and 80% in average) of the paper industry wastewater.

Deep Learning Techniques to Characterize the RPS28P7 Pseudogene and the Metazoa-SRP Gene as Drug Potential Targets in Pancreatic Cancer Patients.

The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were EWSR1, FLT3, GPC3, HIF1A, HLF, and MEN1. The most highly up-regulated genes (>8.5-fold change) in the death group were RPL30, RPL37, RPS28P7, RPS11, Metazoa_SRP, CAPNS1, FN1, H3-3B, LCN2, and OAZ1. None of their corresponding proteins were up or down-regulated in the death group. The mRNA of the RPS28P7 pseudogene could act as ceRNA sponging the miRNA that was originally directed to the parental gene RPS28. We propose RPS28P7 mRNA as the most druggable target that can be modulated with small molecules or the RNA technology approach. These markers could be added as criteria to patient stratification in future PaCa drug trials, but further validation in the target populations is encouraged.

State identification for a class of uncertain switched systems by differential neural networks.

This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.

Differential Neural Network Identifier for Dynamical Systems With Time-Varying State Constraints.

This study presents a state nonparametric identifier based on neural networks with continuous dynamics, also known as differential neural networks (DNNs). The laws for adjusting their parameters are developed using a control barrier Lyapunov functions (BLFs). The motivation for using the BLF comes from the preliminary information of the system states, which remain in a predefined time-depending set characterized by state or purely time-dependent functions. In this study, time-dependent state constraints are supposed to be known in advance continuous-time functions. The obtained learning laws require solving differential continuous-time Riccati equations and nonlinear differential equations for the learning laws that depend on the identification error and the state restrictions. The developed identifier was evaluated concerning the identifier that does not consider the state restrictions. This comparison included the numerical evaluation of the identifier for a robotic arm intended to reproduce a nonstandard flight simulator. This evaluation confirmed that the identification results were improved using the proposed learning laws and considering that the state limits were not transgressed. The quality indicators based on the mean square error were more minor by 4.2 times.

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition.

The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome's composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome's composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome's influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host-microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome's composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.

Adaptive controller based on barrier Lyapunov function for a composite Cartesian-delta robotic device for precise time-varying position tracking.

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control.

Microbiome distribution modeling using gradient descent strategies for mock, in vitro and clinical community distributions.

The human gut is home to a complex array of microorganisms interacting with the host and each other, forming a community known as the microbiome. This community has been linked to human health and disease, but understanding the underlying interactions is still challenging for researchers. Standard studies typically use high-throughput sequencing to analyze microbiome distribution in patient samples. Recent advancements in meta-omic data analysis have enabled computational modeling strategies to integrate this information into an in silico model. However, there is a need for improved parameter fitting and data integration features in microbial community modeling. This study proposes a novel alternative strategy utilizing state-of-the-art dynamic flux balance analysis (dFBA) to provide a simple protocol enabling accurate replication of abundance data composition through dynamic parameter estimation and integration of metagenomic data. We used a recurrent optimization algorithm to replicate community distributions from three different sources: mock, in vitro, and clinical microbiome. Our results show an accuracy of 98% and 96% when using in vitro and clinical bacterial abundance distributions, respectively. The proposed modeling scheme allowed us to observe the evolution of metabolites. It could provide a deeper understanding of metabolic interactions while taking advantage of the high contextualization features of GEM schemes to fit the study case. The proposed modeling scheme could improve the approach in cases where external factors determine specific bacterial distributions, such as drug intake.

Microalgae-assisted green bioremediation of food-processing wastewater: A sustainable approach toward a circular economy concept.

Wastewater disposal is a major environmental issue that pollutes water, causing eutrophication, habitat destruction, and economic impact. In Mexico, food-processing effluents pose a huge environmental threat due to their excessive nutrient content and their large volume discharged every year. Some of the most harmful residues are tequila vinasses, nejayote, and cheese whey. Each liter of tequila generates 13-15 L of vinasses, each kilogram of cheese produces approximately 9 kg of cheese whey, and each kilogram of nixtamalized maize results in the production of 2.5-3.3 L of nejayote. A promising strategy to reduce the contamination derived from wastewater is through microalgae-based wastewater treatment. Microalgae have a high adaptability to hostile environments and they can feed on the nutrients in the effluents to grow. Moreover, to increase the viability, profitability, and value of wastewater treatments, a microalgae biorefinery could be proposed. This review will focus on the circular bioeconomy scheme focused on the simultaneous food-processing wastewater treatment and its use to grow microalgae biomass to produce added-value compounds. This strategy allows for the revalorization of wastewater, decreases contamination of water sources, and produces valuable compounds that promote human health such as phycobiliproteins, carotenoids, omega-3 fatty acids, exopolysaccharides, mycosporine-like amino acids, and as a source of clean energy: biodiesel, biogas, and bioethanol.

Trajectory tracking controller of a robotized arm with joint constraints, a direct adaptive gain with state limitations approach.

Motion restrictions in robotic devices may introduce complex requirements for any closed-loop control design, mainly when the robot joints must track reference trajectories that force the end-effector to perform planned motions. This study summarizes the comprehensive technical design of an adaptive state feedback controller for multi-link robotic manipulators that consider the effect of position and velocity restrictions on the tracking trajectory control approach. The proposed design is less conservative than other methods because of the explicit inclusion of state restrictions in the control gain dynamics. A logarithm barrier Lyapunov function class supports the design of the adaptive gain for the manipulator. Sufficient conditions based on a Riccati equation simplify the implementation of the adaptive controller with gains depending on the distance between the current state and the restriction sets. Numerical simulations show the advantages of the proposed controller with adaptive gains concerning a similar adaptive controller that does not consider the restrictions and a proportional-integral-derivative form. An implementation for the motion control of a robotic arm is presented to demonstrate the development by implementing the proposed gain, which confirms the suggested improvements enforced by the proposed controller. The performed comparison shows the advantages of the suggested adaptive gain control form, inducing better tracking of reference trajectories and smaller control energy applications.

Trajectory tracking control with state restricted gains for a magnetic pendulum using electromagnetic actuators.

Electromagnetic actuation results suitable for wireless driven motion, where the estimation of the force between magnetic elements is usually required. This force can lead to states where the magnetic-mechanical system remains fixed, requiring constraints to avoid the transgression of these states, and Barrier Lyapunov Functions (BLF) are useful for this purpose. This work presents an adaptive controller with BLF in a magnetic pendulum with state restrictions. It employs fixed electromagnets to induce motion on a pendulum with a permanent magnet as its bob. The force between the magnetic elements is obtained through approximation functions. A new implementation strategy for the control gains introduces the effect of state restrictions on the control action based on a control BLF. Results are analyzed in both simulations and experimental stages, which prove the advantages of employing BLF controllers in mechanical systems that require the avoidance of specific boundaries.

Home-care nursing controlled mobile robot with vital signal monitoring.

This study describes the development (design, construction, instrumentation, and control) of a nursing mobile robotic device to monitor vital signals in home-cared patients. The proposed device measures electrocardiography potentials, oxygen saturation, skin temperature, and non-invasive arterial pressure of the patient. Additionally, the nursing robot can supply assistance in the gait cycle for people who require it. The robotic device's structural and mechanical components were built using 3D-printed techniques. The instrumentation includes electronic embedded devices and sensors to know the robot's relative position with respect to the patient. With this information together with the available physiological measurements, the robot can work in three different scenarios: (a) in the first one, a robust control strategy regulates the mobile robot operation, including the tracking of the patient under uncertain working scenarios leading to the selection of an appropriate sequence of movements; (b) the second one helps the patients, if they need it, to perform a controlled gait-cycle during outdoors and indoors excursions; and (c) the third one verifies the state of health of the users measuring their vital signs. A graphical user interface (GUI) collects, processes, and displays the information acquired by the bioelectrical amplifiers and signal processing systems. Moreover, it allows easy interaction between the nursing robot, the patients, and the physician. The proposed design has been tested with five volunteers showing efficient assistance for primary health care. Graphical Abstract Main stages of the home-care nursing controlled mobile robot.

Rational Continuous Neural Network Identifier for Singular Perturbed Systems With Uncertain Dynamical Models.

This study aims at designing a robust nonparametric identifier for a class of singular perturbed systems (SPSs) with uncertain mathematical models. The identifier structure uses a novel identifier based on a differential neural network (DNN) with rational form, which can take into account the multirate nature of SPS. The identifier uses a mixed learning law including a rational formulation of neural networks which is useful to solve the identification of the fast dynamics in the SPS dynamics. The rational form of the design is proposed in such a way that no-singularities (denominator part of the rational form never touches the origin) are allowed in the identifier dynamics. A proposed control Lyapunov function and a nonlinear parameter identification methodology yield to design the learning laws for the class of novel rational DNN which appears as the main contribution of this study. A complementary matrix inequality-based optimization method allows to get the smallest attainable convergence invariant region. A detailed implementation methodology is also given in the study with the aim of clarifying how the proposed identifier can be used in diverse SPSs. A numerical example considering the dynamics of the enzymatic-substrate-inhibitor system with uncertain dynamics is showing how to apply the DNN identifier using the multirate nature of the proposed DNN identifier for SPSs. The proposed identifier is compared to a classical identifier which is not taking into account the multirate nature of SPS. The benefits of using the rational form for the identifier are highlighted in the numerical performance comparison based on the mean square error (MSE). This example justifies the ability of the suggested identifier to reconstruct both the fast and slow dynamics of the SPS.

Averaged sub-gradient integral sliding mode control design for cueing end-effector acceleration of a two-link robotic arm.

Acceleration tracking is a significant problem in aeronautics, automotive, and biomedical technical areas because its solution may yield effective simulation of motion cues. In the case of aeronautics, the proper solution for the tracking problem improves the in-flight simulations for the training of plane pilots. These simulators can be set up using robotic devices that develop controlled motions with the end-effector following the required three-dimensional reference accelerations robustly. Hence, the primary goal of this study is the effective application of the integral sliding mode controller to solve the acceleration tracking problem for the end-effector of a two-link robotic arm. The control design problem is formulated as an optimization of a convex (non-strict) performance functional depending on the difference between the acceleration of the robotic arm and the desired acceleration using the averaged sub-gradient (ASG) descendant method. A novel sliding surface considers the sensitiveness threshold for acceleration dynamics, inspired by the limit of detection in the pilot vestibular apparatus. The proposed controller was analyzed in terms of the finite-time convergence of the sliding surface and the practical stability analysis for the tracking error dynamics. Our main contribution is the design of the online averaged sub-gradient optimization controller based on integral SMCs. The controller solves the end-effector acceleration tracking for a two-link robotic arm, which implements a simplified version of a flight simulator that is considered to be operated under uncertain scenarios and assumes the presence of perturbations and modeling errors. The controller considers the case of incomplete knowledge of the robotic arm model, which adds an extra degree of robustness to the control design. The numerical evaluations demonstrate the attributes of the ASG formulation compared to traditional state feedback control, using the performance functional, the norm of the acceleration tracking error, and the control input variation.

Adaptive modeling of nonnegative environmental systems based on projectional Differential Neural Networks observer.

A new design of a non-parametric adaptive approximate model based on Differential Neural Networks (DNNs) applied for a class of non-negative environmental systems with an uncertain mathematical model is the primary outcome of this study. The approximate model uses an extended state formulation that gathers the dynamics of the DNN and a state projector (pDNN). Implementing a non-differentiable projection operator ensures the positiveness of the identifier states. The extended form allows producing continuous dynamics for the projected model. The design of the learning laws for the weight adjustment of the continuous projected DNN considered the application of a controlled Lyapunov-like function. The stability analysis based on the proposed Lyapunov-like function leads to the characterization of the ultimate boundedness property for the identification error. Applying the Attractive Ellipsoid Method (AEM) yields to analyze the convergence quality of the designed approximate model. The solution to the specific optimization problem using the AEM with matrix inequalities constraints allows us to find the parameters of the considered DNN that minimizes the ultimate bound. The evaluation of two numerical examples confirmed the ability of the proposed pDNN to approximate the positive model in the presence of bounded noises and perturbations in the measured data. The first example corresponds to a catalytic ozonation system that can be used to decompose toxic and recalcitrant contaminants. The second one describes the bacteria growth in aerobic batch regime biodegrading simple organic matter mixture.

Mannitol Production by Heterofermentative Lactic Acid Bacteria: a Review.

Obesity, diabetes, and other cardiovascular diseases are directly related to the high consumption of processed sugars with high caloric content. The current food industry has novel trends related to replacing highly caloric sugars with non-caloric or low-calorie sweeteners. Mannitol, a polyol, represents a suitable substitute because it has a low caloric content and does not induce a glycemic response, which is crucial for diabetic people. Consequently, this polyol has multiple applications in the food, pharmaceutical, and medicine industries. Mannitol can be produced by plant extraction, chemical or enzymatic synthesis, or microbial fermentation. Different in vitro processes have been developed regarding enzymatic synthesis to obtain mannitol from fructose, glucose, or starch-derived substrates. Various microorganisms such as yeast, fungi, and bacteria are applied for microbial fermentation. Among them, heterofermentative lactic acid bacteria (LAB) represent a reliable and feasible alternative due to their metabolic characteristics. In this regard, the yield and productivity of mannitol depend on the culture system, the growing conditions, and the culture medium composition. In situ mannitol production represents a novel approach to decrease the sugar content in food and beverages. Also, genetic engineering offers an interesting option to obtain mannitol-producing strains. This review presents and discusses the most significant advances that have been made in the mannitol production through fermentation by heterofermentative LAB, including the pertinent and critical analysis of culture conditions considering broth composition, reaction systems, and their effects on productivities and yields.

Non-singular terminal sliding-mode control for a manipulator robot using a barrier Lyapunov function.

This study introduces a design of robust finite-time controllers that aims to solve the trajectory tracking of robot manipulators with full-state constraints. The control design is based on the construction of a distributed state constraint non-singular terminal sliding mode (CNTSM). The CNTSM design includes the gain self-adapting tuning method, which can ensure finite-time convergence to the sliding surface aside from the states to its corresponding reference trajectories. The implementation of the time-varying gain ensures the fulfillment of the accurate tracking for the references while the position and velocity constraints are satisfied permanently. A barrier Lyapunov function is proposed to develop the finite-time stability analysis of the designed controllers. The CNTSM realization uses the tracking error as well as its estimated derivative, which is calculated using a variant of adaptive super-twisting algorithm operating as robust differentiator. The proposed CNTSM is numerically evaluated on a two-link RM with uncertain inertia and Coriolis matrices. Simulation and experimental results evidence the efficiency of the CNTSM controller demonstrating a better tracking performance while the full-state constraints are satisfied in counterpart with the classical non-singular terminal sliding mode which is not able to keep such restrictions.

Growth kinetics and quantification of carbohydrate, protein, lipids, and chlorophyll of Spirulina platensis under aqueous conditions using different carbon and nitrogen sources.

This study evaluated different carbon and nitrogen sources on the growth and production of carbohydrates, protein, lipids, and chlorophyll of Spirulina platensis LEB-52 through an easy successive methodology under aqueous conditions. Spirulina platensis was cultivated at 120 rpm and light intensity of 156 µmol m-2 s-1 in a 500 mL Erlenmeyer flask with a working volume of 250 mL, using Zarrouk's medium. The biomass, carbohydrate, and protein production together with the specific growth rate did not show a significant difference between NaHCO3 and Na2CO3. The salts of urea and ammonium are not an alternative nitrogen sources of low cost for Spirulina platensis cultivation. From the experimental results obtained in this study, a successful estimate of carbohydrate, protein, lipids, and chlorophyll content inside Spirulina platensis was achieved without use advanced analytical techniques, allowing saves resources and time. This method can be extrapolated to other microorganisms and cultivation regimens.

Adaptive sliding-mode trajectory tracking control for state constraint master-slave manipulator systems.

This study aims to propose an adaptive state-dependent gain finite-time convergent controller (using the fundamentals of the sliding mode theory) that solves the trajectory tracking for a class of state constraint master-slave robotic system (M-SRS) formed by two manipulators with the same number of articulations. The control design considers the effect of state constraints by implementing a state dependent adaptive gain. A Lyapunov-stability analysis leads to design the gain variation laws yielding proving the finite-time convergence of the sliding surface as well as the asymptotic convergence of the tracking error. The state constraints of the slave system motivate the characterization of the convergence-time as a function of the bounded uncertainties affecting the M-SRS dynamics. The forward-complete setting of the M-SRS justified the application of a robust and exact differentiator which estimated the articulation velocities for the slave robot. The estimated velocities are used as part of the realization of the output feedback controller. Numerical simulations demonstrate that the proposed control scheme provides a smaller quadratic norm of the tracking error compared with the obtained with other controllers (proportional-derivative and conventional sliding modes). The proposed control approach satisfies the state constraints while the sliding manifold converges to the origin in finite-time as justified by the theoretical stability analysis.

Energetic optimization of an autonomous mobile socially assistive robot for autism spectrum disorder.

The usage of socially assistive robots for autism therapies has increased in recent years. This novel therapeutic tool allows the specialist to keep track of the improvement in socially assistive tasks for autistic children, who hypothetically prefer object-based over human interactions. These kinds of tools also allow the collection of new information to early diagnose neurodevelopment disabilities. This work presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for autism spectrum disorder under an event-driven control scheme. The proposed implementation integrates facial expression and emotion recognition algorithms to detect the emotions and identities of users (providing robustness to the algorithm since it automatically generates the missing input parameters, which allows it to complete the recognition) to detonate a set of adequate trajectories. The algorithmic implementation for the proposed socially assistive robot is presented and implemented in the Linux-based Robot Operating System. It is considered that the optimization of energetic consumption of the proposal is the main contribution of this work, as it will allow therapists to extend and adapt sessions with autistic children. The experiment that validates the energetic optimization of the proposed integration of an event-driven control scheme is presented.