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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, the photoheterotrophic consortium C4 was used to produce the copolymer [P(3HB-co-3HV)]. PHA production was enhanced by using response surface methodology (RSM) to determine the effects of different concentrations of acetate and butyrate in mixtures (0.5–3 g L−1), ammonium sulfate and their combinations. This is relevant because PHA accumulation is stimulated by nitrogen limitation. The type and concentration of the substrate determines the monomeric composition and the PHA content (% per cell dry mass (CDM)). The RSM, carbon balance and metabolic behavior analysis results showed that at the lowest ammonium concentration, 0.1 g L−1, and when acetate was in a higher proportion than butyrate, biomass production was favored. In contrast, when the butyrate proportion was high, PHA production increased, reaching a highest production of 58% per CDM. The better conditions were evaluated in a 3-L reactor, and a maximum P(3HB-co-3HV) of 67% was determined. The predominant microbial population consisted of four major species, Macelibacteroides fermentans (37%), Rhodopseudomonas palustris (22%), Acinetobacter sp. (35%), and Clostridium propionicum (2%). Insights into the understanding of copolymer production by photoheterotrophic mixed cultures constitute the basis for developing coupled processes from organic residues. These microorganisms are worth studying since they produce a variety of valuable biotechnological products. [ABSTRACT FROM AUTHOR]

This study introduces the design of an asynchronous event-driven adaptive robust control that regulates a mobile limb orthosis position for the hind legs of a Canis lupus familiaris (CLF). The application of a suitable stability analysis based on a controlled Lyapunov function results in the laws to adjust the adaptive gains of a proportional integral derivative controller (PID). The controller succeeded in compensating external bounded perturbations and non-modeled uncertainties in the active orthosis device. This compensation forces the tracking between the current positions and some reference trajectories obtained by a biomechanical gait cycle analysis of the CLF. The controller starts with the event triggered from the power of the electromyography signal from the frontal legs. If the signal power is higher than a predefined threshold, the movement of the orthosis will initiate. Electromyographic signals were acquired offline and injected into a virtualized orthosis model to test the event-driven control design. A set of numerical simulations confirmed a better performance of tracking reference trajectories and the effect of the event-driven controller on the orthosis operation. The experimental validation of the proposed output feedback controller on the designed orthosis seems to justify a potential automatized rehabilitation therapy based on the proposed electromyography-driven strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aimed to develop a semi non-parametric model of the die casting process of aluminum alloys. This model uses a hierarchical artificial neural network (HANN), with a structure motivated by the relationships of the metals which define the characteristics of the aluminum alloy. These settings depend on the content of seven metals (Sn, Zn, Mn, Cu, Si, Ni, and Mg). The relation between these metals and the alloy characteristics oriented the HANN structure. A distributed back-propagation learning modified with the Levenberg-Marquardt method served to adjust the HANN weights. Two complementary validation methods justified the application of this novel hybrid non-parametric modelling structure. The training set came from standards composition proposed by different international organizations. A set of real aluminum alloys and the experimental results describing their characteristics formed the validation test. An average accuracy value of 3.65% confirmed the ability of the HANN to reproduce the relation between the metal content and the alloy characteristics. These values confirmed how the oriented HANN may predict the aluminum alloy characteristics as function of the metal distribution. This result offers a different alternative to the prediction of aluminum alloy properties using the metal composition as input information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Recently, the study of dynamic systems and signals in the frequency domain motivates the emergence of new tools. In particular, electrophysiological and communications signals in the complex domain can be analyzed but hardly, they can be modeled. This problem promotes an attractive field of researching in system theory. As a consequence, adaptive algorithms like neural networks are interesting tools to deal with the identification problem of this kind of systems. In this study, a new learning process for recurrent neural network applied on complex-valued discrete-time nonlinear systems is proposed. The Lyapunov stability framework is applied to obtain the corresponding learning laws by means of the so-called Lyapunov control functions. The region where the identification error converges is defined by the power of uncertainties and perturbations that affects the nonlinear discrete-time complex system. This zone is obtained as an alternative result of the same Lyapunov analysis. An off-line training algorithm is derived in order to reduce the size of the convergence zone. The training is executed using a set of some off-line measurements coming from the uncertain system. Numerical results are developed to prove the efficiency of the methodology proposed in this study. A first example is oriented to identify the dynamics of a nonlinear discrete time complex-valued system and the second one to model the dynamics of an electrophysiological signal separated in magnitude and phase. [ABSTRACT FROM AUTHOR]

This paper analyzes the mathematical modeling procedure to describe the decomposition of adsorbed phenanthrene in prototypical and real soil samples (sand and agricultural soil, respectively) by ozone. The modeling scheme considered a set of ordinary differential equations with time varying coefficients. This model used the adsorbed ozone in the soil, the ozone reacting with the contaminant and the phenanthrene concentration in the soil sample. The main parameters involved in the mathematical model included a time varying ozone saturation function ( k (t)) and reaction constants ( k ). These parameters were calculated using the ozone concentration variation at the reactor output, named as ozonogram, and the measurements of phenanthrene decomposition through ozonation. The model was validated using two series of experiments: (1) soil saturated with ozone in the absence of the contaminant and (2) soil artificially contaminated with phenanthrene. In both cases, the proposed parametric identification method yields to validate the mathematical model. This fact was confirmed by the correspondence between numerical simulations and experimental data. In particular, total decomposition of phenanthrene adsorbed in two different systems (ozone-sand and ozone-agricultural soil) was obtained after 15 and 30 min of reaction, respectively. This difference was obtained as a consequence of soil physicochemical characteristics: specific surface area and pore volume. The ozonation reaction rate constants of phenanthrene in the sand and agricultural soil were calculated using the same parameter identification scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Dark fermentation for bio-hydrogen (bio-H) production is an easily operated and environmentally friendly technology. However, low bio-H 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-H release from the reactor. This operating condition alters the metabolic pathways and increased the bio-H 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 H, CO, and volatile fatty acids production. The effect of reducing the bio-H partial pressure (bio-H pp) to enhance bio-H production was evaluated in a 30 L continuous stirred tank reactor. Three bio-H release strategies were followed: uncontrolled, intermittent, and constant. In the so called uncontrolled fermentation, without bio-H pp control, a bio-H molar yield of 1.2 mol/mol glucose was obtained. A sustained low bio-H pp of 0.06 atm increased the bio-H production rate from 16.1 to 108 mL/L/h with a stable bio-H percentage of 55 % ( v/ v) and a molar yield of 1.9 mol/mol glucose. Biogas release enhanced bio-H production because lower bio-H pp, CO concentration, and reduced volatile fatty acids accumulation prevented the associated inhibitions and bio-H consumption. [ABSTRACT FROM AUTHOR]

Hydrogen (H) 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 H production obtained were successfully adjusted by a logistic model. The kinetic parameters ( μ = 0.101 h, K = 2.56 g/L) of an H-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 H production of 700 mL was attained. The feasibility of producing H 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 H production and waste organic material degradation was determined. The highest H production rate (1.7 mmol/day), the highest cumulative H 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 H production rates were obtained with relatively low initial substrate concentrations of 5 and 11 g COD/L. The H production rates with FVW were also characterized by the logistic model. Similar cumulative H production was obtained when glucose and FVW were used as substrates. [ABSTRACT FROM AUTHOR]

A dynamic neural network is applied to estimate the state of the “phenol-water-ozone” chemical system. A new method based on dynamic neural observers with sliding mode (signum) term is used to estimate the dynamics of decomposition of phenols by ozone and to identify their kinetic parameters without the use of any process model. Decomposition of phenols and their mixtures by ozone in a semi-batch reactor is regarded as a dynamic process with an uncertain model (“black box”). Only the content of gaseous ozone is measured at the reactor output during ozonization. Variations of this variable are used to construct a total characteristic curve of the ozonization process. A dynamic state observer is used to estimate the phenol ozonization constant at different pH values from 2 to 12. Experimental data on decomposition dynamics are in good agreement with their estimates. Our method is helpful in on-line monitoring of water purification process without the use of special chemical sensors. [ABSTRACT FROM AUTHOR]