Differential Neural Networks Prediction Using Slow and Fast Hybrid Learning: Application to Prognosis of Infectionsand Deaths of COVID-19 Dynamics.

This essay discusses a potential method for predicting the behavior of various physical processes and uses the COVID-19 outbreak to demonstrate its applicability. This study assumes that the current data set reflects the output of a dynamic system that is governed by a nonlinear ordinary differential equation. This dynamic system may be described by a Differential Neural Network (DNN) with time-varying weights matrix parameters. A new hybrid learning scheme based on the decomposition of the signal to be predicted. The decomposition considers the slow and fast components of the signal which is more natural to signals such as the ones corresponding to the number of infected and deceased patients who suffered of COVID 2019 sickness. The paper results demonstrate the recommended method offers competitive performance (70 days of COVID prediction) in comparison to similar studies.

Intensification of Hydrogen Production by a Co-culture of Syntrophomonas wolfei and Rhodopseudomonas palustris Employing High Concentrations of Butyrate as a Substrate.

The purpose of this study is to present an effective form of developing a sequential dark (DF) and photo (PF) fermentation using volatile fatty acids (VFAs) and nitrogen compounds as bonding components between both metabolic networks of microbial growing in each fermentation. A simultaneous (co-)culture of Syntrophomonas wolfei (with its ability to consume butyrate and produce acetate) and Rhodopseudomonas palustris (that can use the produced acetate as a carbon source) performed a syntrophic metabolism. The former bacteria consumed the acetate/butyrate mixture reducing the butyrate concentration below 2.0 g/L, permitting Rhodopseudomonas palustris to produce hydrogen. Considering that the inoculum composition (Syntrophomonas wolfei/Rhodopseudomonas palustris) and the nitrogen source (yeast extract) define the microbial biomass specific productivity and the butyrate consumption, a response surface methodology defined the best inoculum design and yeast extract (YE) yielding to the highest biomass concentration of 1.1 g/L after 380.00 h. A second culture process (without a nitrogen source) showed the biomass produced in the previous culture process yields to produce a total cumulated hydrogen concentration of 3.4 mmol. This value was not obtained previously with the pure strain Rhodopseudomonas palustris if the culture medium contained butyrate concentration above 2.0 g/L, representing a contribution to the sequential fermentation scheme based on DF and PF.

Deep Learning Adapted to Differential Neural Networks Used as Pattern Classification of Electrophysiological Signals.

This manuscript presents the design of a deep differential neural network (DDNN) for pattern classification. First, we proposed a DDNN topology with three layers, whose learning laws are derived from a Lyapunov analysis, justifying local asymptotic convergence of the classification error and the weights of the DDNN. Then, an extension to include an arbitrary number of hidden layers in the DDNN is analyzed. The learning laws for this general form of the DDNN offer a contribution to the deep learning framework for signal classification with biological nature and dynamic structures. The DDNN is used to classify electroencephalographic signals from volunteers that perform an identification graphical test. The classification results show exponential growth in the signal classification accuracy from 82 percent with one layer to 100 percent with three hidden layers. Working with DDNN instead of static deep neural networks (SDNN) represents a set of advantages, such as processing time and training period reduction up to almost 100 times, and the increment of the classification accuracy while working with less hidden layers than working with SDNN, which are highly dependent on their topology and the number of neurons in each layer. The DDNN employed fewer neurons due to the induced feedback characteristic.

Multi-link endoscopic manipulator robot actuated by shape memory alloys spring actuators controlled by a sliding mode.

The aim of this study was to design and evaluate a prototype of a snake-like endoscopic manipulator robot (SLEMR) and its corresponding automatic controller based on the first order sliding mode theory. The SLEMR was controlled with a set of actuators made of shape memory alloys (SMA). The SLEMR device was constructed with a sequential arrangement of links interconnected by a two degree-of-freedom joint. A parallel agonist-antagonist configuration of actuators was implemented to move each joint. The physical relation between temperature and elongation in SMA forced the execution of the movement in the joint. Elongation-temperature model of the SMA actuator served to get a feasible bound of velocity for each joint. Each pair of SMA actuators was controlled by a first order sliding mode controller. This control design solved the tracking trajectory problem for each joint in the SLEMR because of its robustness against uncertainties and external perturbations. The control action was projected into a feasible implementable set of pulse-width modulated signals which was used to regulate the temperature of the corresponding SMA actuator. The controller designed in this study was experimentally validated in a SLEMR made up by a tridimensional printing technique. The control strategy induced the successful trajectory tracking for all the joints in the SLEMR simultaneously. This characteristic of the control design also enforces the tracking of a reference position by the tip of the final link of the SLEMR. An image acquisition system was used to determine the position of the final actuator in the SLEMR. The effectiveness of the controller proposed in this study was confirmed by the evaluation of the tracking error of the final actuator which approached to a bounded region (less than 1.0 mm) near the origin in a finite-time (0.5 s).

Effect of the type of soil on dimethyl phthalate degradation by ozone.

In the present study, ozone was applied for the removal of dimethyl phthalate (DMP) from soil. The effect of several experimental parameters was investigated considering, the initial DMP concentration, ozone flow, the type of soil (sand and agricultural soil) and the presence of α-FeOOH as a potential catalyst in the reaction system with sand. The elimination of DMP using ozone is significantly affected by the type of soil. In the case of sand, conventional ozonation was capable to degrade 74% of the initial DMP concentration (0.5 mg g-1) after 8 h of the reaction, however, the mineralization degree was below 50%. Under the same experimental conditions, the complete elimination of DMP was achieved when calcined agricultural soil was present reaching a 70% of mineralization. The presence of metal oxides in calcined agricultural soil combined with ozone produced oxidants species which were responsible of incrementing the mineralization degree (around 20% in comparison with the sand). The toxicity tests on lettuce seed demonstrated lower toxicity of DMP byproducts after ozonation. The DMP high removal efficiencies and the lower toxicity of generated byproducts in soil prove the applicability of ozone treatment for soil remediation.

Mechatronic design and implementation of a bicycle virtual reality system.

The aim of this study is to design and implement a virtual reality bicycle system based on a functional-based mechatronic design approach. The development of virtual reality technologies with haptic systems demands a proper integration of the involved disciplines to provide immerse experiences for users. The proposed design approach provides a formal manner to gather the subsystems in the mechatronic device. The developed system is divided in a Virtual Reality System (VRS) and a Physical System (PS) for the design process. The former includes an interactive virtual environment in which an Avatar is animated using a simple kinematic bicycle model. The latter includes an adapted mountain bicycle with haptic feedback mechanisms to interact with the user and to produce the corresponding inputs for the bicycle model. Both systems are integrated by a control behavior system that works under two operation modes, where the user carries out virtual tours and gets feedbacks from a stereoscopic display system, audio cues, and haptic mechanisms. A multibody simulation validates the consistency and the integration of the physical system. In addition, a set of experimental results show the performance of instrumentation elements, control strategies, and feedback mechanisms, to provide the user with an immersive experience in the virtual environment. A brief survey was carried out to assess the opinion of users about the virtual bicycle tours, providing feedback for future improvements. The different designed modules and sub-systems allow modifying and enhancing the VRS without major modifications of the PS, or allow enhancing the physical platform without affecting the functionality of the virtual environment.

Hybrid position/force output feedback second-order sliding mode control for a prototype of an active orthosis used in back-assisted mobilization.

This article shows the design of a robust second-order sliding mode controller to solve the trajectory tracking problem of an active orthosis for assisting back physiotherapies. The orthosis was designed in agreement with morphological dimensions and its articulations distribution followed the same designing rules. The orthosis has six articulated arms attached to an articulated column. The orthosis was fully instrumented with actuators and position sensors at each articulation. The controller implemented a class of hybrid/position controller depending on the relative force exerted by the patient and the orthosis movement. The position information provided by each articulation was supplied to a distributed super-twisting differentiator to recover the corresponding angular velocity. A set of twisting controllers was implemented to regulate the position of the robot in agreement to predefined reference trajectories. Reference trajectories were obtained from a biomechanical-based analysis. The hybrid tracking control problem solved the automation of the assisted therapy to the patient, including the force feedback. The performance of the orthosis was tested with different dummy bodies with different resistance. The robust output feedback controller successfully tracked the reference trajectories despite the material of the dummy used during the testing. The orthosis was evaluated with two volunteers using a simple reference trajectory. Graphical Abstract General structure of the active back assisted orthosis.

Robust control of uncertain feedback linearizable systems based on adaptive disturbance estimation.

In this paper, an adaptive disturbance estimation-based control of a class of uncertain feedback linearizable systems with the presence of, both, external perturbations as well as non-modeled dynamics is considered. The aim of the control design was to solve the tracking trajectory problem for a class of output-based linearizable uncertain systems. An adaptive scheme is proposed for developing a state estimator of the uncertain dynamics. The estimation of both, the states and the uncertain dynamics is attained despite the limited knowledge of the plant and the information contained in the output signal. The uncertain section in the linearized system was approximated by a class of time-dependent combination of the system states. The observer implemented a parametric identifier to obtain the time varying parameters associated to the estimation of the uncertain section. This method ensured the adequate estimation process of the uncertainties/perturbations, measured in terms of the mean square error. Simultaneously, an adaptive gain associated to the observer adjusts its trajectories to provide the ultimate boundedness of the estimation error. Once the states of the uncertain system are obtained, a feedback controller rejects actively the perturbations that affect the system by a compensation scheme. Two numerical examples were developed to show the observer-based control performance.

Performance intensification of a stirred bioreactor for fermentative biohydrogen production.

In this study, the biohydrogen (bioH2) production of a microbial consortium was optimized by adjusting the type and configuration of two impellers, the mixing regimen and the mass transfer process (KLa coefficients). A continuous stirred-tank reactor (CSTR) system, with a nonstandard geometry, was characterized. Two different mixing configurations with either predominant axial (PB4 impeller) or radial pumping (Rushton impeller) were assessed and four different impeller configurations to produce bioH2. The best configuration for an adequate mixing time was determined by an ANOVA analysis. A response surface methodology was also used to fully elucidate the optimal configuration. When the PB4 impellers were placed in best configuration, c/Dt = 0.5, s/Di = 1, the maximum bioH2 productivity obtained was 440 mL L-1 hr-1, with a bioH2 molar yield of 1.8. The second best configuration obtained with the PB4 impellers presented a bioH2 productivity of 407.94 mL L-1 hr-1. The configurations based on Rushton impellers showed a lower bioH2 productivity and bioH2 molar yield of 177.065 mL L-1 hr-1 and 0.71, respectively. The experiments with axial impellers (PB4) showed the lowest KLa coefficient and the highest bioH2 production, suggesting that mixing is more important than KLa for the enhanced production of bioH2.

Phenanthrene degradation in soil by ozonation: Effect of morphological and physicochemical properties.

The aim of this study was to characterize the ozone reaction with phenanthrene adsorbed in two types of soils (sand and agricultural). The effect of soil physicochemical properties (texture, bulk density, particle density, porosity, elemental composition, permeability, surface area and pore volume) on the phenanthrene decomposition was evaluated. Commercial sand has a uniform morphology (spherical) with a particle size range between 0.178 and 0.150 mm in diameter, regular elemental composition SiO2, specific density of 1701.38 kg/m3, a true density of 2492.50 kg/m3, with an effective porosity of 31%. On the other hand, the agricultural soil had heterogeneous morphology, particle size between 0.1779 and 0.05 mm in diameter, elemental composition was montmorrillonite silicon oxide, apparent density of 999.52 kg/m3, a true density of 2673.55 kg/m3, surface area of 34.92 m2/g and porosity of 57%. The percentage of phenanthrene decomposition in the sand was 79% after 2 h of treatment. On the other hand, the phenanthrene degradation in the agricultural soil was 95% during the same reaction time. The pore volume of soil limited the crystal size of phenanthrene and increased the contact surface with ozone confirming the direct impact of physicochemical properties of soils on the decomposition kinetics of phenanthrene. In the case of agricultural soil, the effect of organic matter on phenanthrene decomposition efficiency was also investigated. A faster decomposition of initial contaminant and byproducts formed in ozonation was obtained in natural agricultural soil compared to the sand. The partial identification of intermediates and final accumulated products produced by phenanthrene decomposition in ozonation was developed. Among others, phenanthroquinone, hydroquinone, phenanthrol, catechol as well as phthalic, diphenic, maleic and oxalic acids were identified.

Control of discrete time systems based on recurrent Super-Twisting-like algorithm.

Most of the research in sliding mode theory has been carried out to in continuous time to solve the estimation and control problems. However, in discrete time, the results in high order sliding modes have been less developed. In this paper, a discrete time super-twisting-like algorithm (DSTA) was proposed to solve the problems of control and state estimation. The stability proof was developed in terms of the discrete time Lyapunov approach and the linear matrix inequalities theory. The system trajectories were ultimately bounded inside a small region dependent on the sampling period. Simulation results tested the DSTA. The DSTA was applied as a controller for a Furuta pendulum and for a DC motor supplied by a DSTA signal differentiator.

Pattern recognition for electroencephalographic signals based on continuous neural networks.

This study reports the design and implementation of a pattern recognition algorithm to classify electroencephalographic (EEG) signals based on artificial neural networks (NN) described by ordinary differential equations (ODEs). The training method for this kind of continuous NN (CNN) was developed according to the Lyapunov theory stability analysis. A parallel structure with fixed weights was proposed to perform the classification stage. The pattern recognition efficiency was validated by two methods, a generalization-regularization and a k-fold cross validation (k=5). The classifier was applied on two different databases. The first one was made up by signals collected from patients suffering of epilepsy and it is divided in five different classes. The second database was made up by 90 single EEG trials, divided in three classes. Each class corresponds to a different visual evoked potential. The pattern recognition algorithm achieved a maximum correct classification percentage of 97.2% using the information of the entire database. This value was similar to some results previously reported when this database was used for testing pattern classification. However, these results were obtained when only two classes were considered for the testing. The result reported in this study used the whole set of signals (five different classes). In comparison with similar pattern recognition methods that even considered less number of classes, the proposed CNN proved to achieve the same or even better correct classification results.

Robust Parameter Identification to Perform the Modeling of pta and poxB Genes Deletion Effect on Escherichia Coli.

The aim of this study was to design a robust parameter identification algorithm to characterize the effect of gene deletion on Escherichia coli (E. coli) MG1655. Two genes (pta and poxB) in the competitive pathways were deleted from this microorganism to inhibit pyruvate consumption. This condition deviated the E. coli metabolism toward the Krebs cycle. As a consequence, the biomass, substrate (glucose), lactic, and acetate acids as well as ethanol concentrations were modified. A hybrid model was proposed to consider the effect of gene deletion on the metabolism of E. coli. The model parameters were estimated by the application of a least mean square method based on the instrument variable technique. To evaluate the parametric identifier method, a set of robust exact differentiators, based on the super-twisting algorithm, was implemented. The hybrid model was successfully characterized by the parameters obtained from experimental information of E. coli MG1655. The significant difference between parameters obtained with wild-type strain and the modified (with deleted genes) justifies the application of the parametric identification algorithm. This characterization can be used to optimize the production of different byproducts of commercial interest.

Characterization of nitrogen substrate limitation on Escherichia coli's growth by parameter identification tools.

Carbon-to-nitrogen ratio (CNR) has shown to be a relevant factor in microorganisms growth and metabolites production. It is usual that this factor compromises the productivity yield of different microorganisms. However, CNR has been rarely modeled and therefore the nature of its specific influence on metabolites production has not been understood clearly. This paper describes a parametric characterization of the CNR effect on the Escherichia coli metabolism. A set of parameters was proposed to introduce a mathematical model that considers the biomass, substrate and several byproducts dynamical behavior under batch regimen and CNR influence. Identification algorithm used to calculate the parameters considers a novel least mean square strategy that formalizes the CNR influence in E. coli metabolism. This scheme produced a step-by-step method that was suitable for obtaining the set of parameters that describes the model. This method was evaluated under two scenarios: (a) using the data from a set of numerical simulations where the model was tested under the presence of artificial noises and (b) the information obtained from a set of experiments under different CNR. In both cases, a leave-one-experiment-out cross-validation study was considered to evaluate the model prediction capabilities. Feasibility of the parametric identification method was proven in both considered scenarios.

Switching robust control for ozone generators using the attractive ellipsoid method.

This paper deals with a switching robust tracking feedback design for a corona-effect ozone generator. The generator is considered as a switched systems in the presence of bounded model uncertainties as well as external perturbations. Three nonlinear dynamic models under arbitrary switching mechanisms are considered assuming that a sample-switching times are known. The stabilization issue is achieved in the sense of a practical stability. We apply the newly elaborated (extended) version of the conventional attractive ellipsoid method (AEM) for this purpose. The same analysis was efficient to obtain the minimal size of region where the tracking error between the trajectories of the ozone generator and reference states converges. The numerically implementable sufficient conditions for the practical stability of systems are derived based on bilinear matrix inequalities (BMIs).

Continuous neural identifier for uncertain nonlinear systems with time delays in the input signal.

Time-delay systems have been successfully used to represent the complexity of some dynamic systems. Time-delay is often used for modeling many real systems. Among others, biological and chemical plants have been described using time-delay terms with better results than those models that have not consider them. However, getting those models represented a challenge and sometimes the results were not so satisfactory. Non-parametric modeling offered an alternative to obtain suitable and usable models. Continuous neural networks (CNN) have been considered as a real alternative to provide models over uncertain non-parametric systems. This article introduces the design of a specific class of non-parametric model for uncertain time-delay system based on CNN considering the so-called delayed learning laws analysis. The convergence analysis as well as the learning laws were produced by means of a Lyapunov-Krasovskii functional. Three examples were developed to demonstrate the effectiveness of the modeling process forced by the identifier proposed in this study. The first example was a simple nonlinear model used as benchmark example. The second example regarded the human immunodeficiency virus dynamic behavior is used to show the performance of the suggested non-parametric identifier based on CNN for no fictitious neither academic models. Finally, a third example describing the evolution of hepatitis B virus served to test the identifier presented in this study and was also useful to provide evidence of its superior performance against a non-delayed identifier based on CNN.

Controlled continuous bio-hydrogen production using different biogas release strategies.

Dark fermentation for bio-hydrogen (bio-H2) production is an easily operated and environmentally friendly technology. However, low bio-H2 production yield has been reported as its main drawback. Two strategies have been followed in the past to improve this fact: genetic modifications and adjusting the reaction conditions. In this paper, the second one is followed to regulate the bio-H2 release from the reactor. This operating condition alters the metabolic pathways and increased the bio-H2 production twice. Gas release was forced in the continuous culture to study the equilibrium in the mass transfer between the gaseous and liquid phases. This equilibrium depends on the H2, CO2, and volatile fatty acids production. The effect of reducing the bio-H2 partial pressure (bio-H2 pp) to enhance bio-H2 production was evaluated in a 30 L continuous stirred tank reactor. Three bio-H2 release strategies were followed: uncontrolled, intermittent, and constant. In the so called uncontrolled fermentation, without bio-H2 pp control, a bio-H2 molar yield of 1.2 mol/mol glucose was obtained. A sustained low bio-H2 pp of 0.06 atm increased the bio-H2 production rate from 16.1 to 108 mL/L/h with a stable bio-H2 percentage of 55% (v/v) and a molar yield of 1.9 mol/mol glucose. Biogas release enhanced bio-H2 production because lower bio-H2 pp, CO2 concentration, and reduced volatile fatty acids accumulation prevented the associated inhibitions and bio-H2 consumption.

Uniform step-by-step observer for aerobic bioreactor based on super-twisting algorithm.

This paper describes a fixed-time convergent step-by-step high order sliding mode observer for a certain type of aerobic bioreactor system. The observer was developed using a hierarchical structure based on a modified super-twisting algorithm. The modification included nonlinear gains of the output error that were used to prove uniform convergence of the estimation error. An energetic function similar to a Lyapunov one was used for proving the convergence between the observer and the bioreactor variables. A nonsmooth analysis was proposed to prove the fixed-time convergence of the observer states to the bioreactor variables. The observer was tested to solve the state estimation problem of an aerobic bioreactor described by the time evolution of biomass, substrate and dissolved oxygen. This last variable was used as the output information because it is feasible to measure it online by regular sensors. Numerical simulations showed the superior behavior of this observer compared to the one having linear output error injection terms (high-gain type) and one having an output injection obtaining first-order sliding mode structure. A set of numerical simulations was developed to demonstrate how the proposed observer served to estimate real information obtained from a real aerobic process with substrate inhibition.

Biohydrogen production based on the evaluation of kinetic parameters of a mixed microbial culture using glucose and fruit-vegetable waste as feedstocks.

Hydrogen (H2) production from the organic fraction of solid waste such as fruit and vegetable waste (FVW) is a novel and feasible energy technology. Continuous application of this process would allow for the simultaneous treatment of organic residues and energy production. In this study, batch experiments were conducted using glucose as substrate, and data of H2 production obtained were successfully adjusted by a logistic model. The kinetic parameters (µ max = 0.101 h(-1), K s = 2.56 g/L) of an H2-producing microbial culture determined by the Monod and Haldane-Andrews growth models were used to establish the continuous culture conditions. This strategy led to a productive steady state in continuous culture. Once the steady state was reached in the continuous reactor, a maximum H2 production of 700 mL was attained. The feasibility of producing H2 from the FVW obtained from a local market in Mexico City was also evaluated using batch conditions. The effect of the initial FVW concentration on the H2 production and waste organic material degradation was determined. The highest H2 production rate (1.7 mmol/day), the highest cumulative H2 volume (310 mL), and 25 % chemical oxygen demand (COD) removal were obtained with an initial substrate (FVW) concentration of 37 g COD/L. The lowest H2 production rates were obtained with relatively low initial substrate concentrations of 5 and 11 g COD/L. The H2 production rates with FVW were also characterized by the logistic model. Similar cumulative H2 production was obtained when glucose and FVW were used as substrates.

Effect of additives on ozone-based decomposition of Reactive Black 5 and Direct Red 28 dyes.

In this research, ozonation of Reactive Black 5 (RB5) and Direct Red 28 (DR28) under the presence of Na2SO4 and Na2CO3 used as textile additives was investigated. The effect of these salts on discoloration, degradation dynamics, and the composition of the final compounds were studied. Different systems were evaluated; such as RB5-Na2SO4 (100 g/L), RB5-Na2CO3 (30 g/L), RB5-Na2SO4/Na2CO3 (100 g/ L/30 g/L), and DR28-Na2SO4 (10 g/L, 40 g/L, and 80 g/L) with dye concentrations of 50, 150, and 250 mg/L without pH adjustment. Discoloration of RB5 and DR28 with and without additives was determined by visible and UV (UV-Vis) spectroscopy. Decomposition of the dyes and the dynamics of intermediates and final byproducts were followed by high performance liquid chromatography. The presence of additives accelerated discoloration and decomposition for both dyes (more than 50%). The accumulation of oxalic and formic acids was observed. Possible mechanism schemes of ozonation for both dyes are proposed.