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The development of machines explores the development of a manufacturing system. This perspective offers challenges adjacent to the materials, the product that wants to be manufactured, the technical considerations that must be covered during implementation and the manner of delivering the products to the user. This article addresses the design and development of machines, on the one hand the redesign of a multifunctional platform, and on the other hand a 3D food printer. The second is emphasised, since its main objective is to help the food problems that exist today. The design phases, aspects considered for its implementation such as food safety, the choice of the substrate, the functionality of the food, transportation and the pre- and post-processes required. In this manner, the selection and innovation of materials constitutes a primary source of innovation and research in the integral development of a 3D food printer. The main source of value of these both cases lie in customisation. The experiences of a multidisciplinary research team in the development processes are compiled and the design and creation process are shown, in order to contribute to deal with design and development to tackle fundamental problems of society. [ABSTRACT FROM AUTHOR]

This study presents the development of an event-driven hybrid control for position and force tracking applied on a mobile robotic manipulator for metal recycling tasks. The suggested controller operates in a sequenced strategy starting from a fixed spot, moving the mobile device towards a targeted zone ($\Omega _i^r$ Ω i r ) from where the i-th piece-to-be-recycled is attainable (considering the arm manipulation). Once the event of entering the zone is completed, the mobile robot is fixed at a position, and the end-effector of the robotic arm is enforced towards the piece-to-be-recycled. When the end-effector touches the piece in a given spot ($\Omega _i^e$ Ω i e ), the hybrid control changes to the force tracking intending to carry the piece towards the spot ($\Omega _i^p$ Ω i p ) where it ill be processed. Each piece location is identified based on a vision-based system that applies deep learning tools using convolutional neural networks. A multi-physics numerical simulation illustrated the application of the developed controller in a realistic scenario, showing all the elements of the event-driven operation. To validate the suggested controller, the comparison with a robust control that works on a wide range of carrying mass confirms the operational improvement of the event-driven hybrid position and force design. [ABSTRACT FROM AUTHOR]

Vehicle safety relies on the capacity of vehicle systems to decrease the likelihood of biomechanical injuries to both vehicle occupants and pedestrians. This can take the form of active or passive measures applied to the materials and their geometry in the event of an impact. Nevertheless, due to the nonlinear behaviour of materials and deformation, a distinct collapse process emerges that requires analysis; this analysis can be virtual or through experimental tests. While observed performance can be ascertained through experimental designs, executing such a design for every parameter combination can be time-consuming and costly. This study employs a Long Short-Term Memory (LSTM) to predict the energy absorbed and crushing force of thin-walled aluminium 6063 T5 tubes subject to different collapse triggers and heat treatments. The LSTM model is constructed using experimental data derived from experiments that consider trigger shape, area, trigger position, furnace duration, cooling temperature, and heat treatment soaking method. The LSTM model achieved a Root Mean Square Error (RSME) of 0.56 and 0.0025 for crushing force and energy absorption, respectively. LSTM proves to be a valuable tool for predicting results in nonlinear analysis, particularly in the context of crush behaviour. Also, a comparison of the LSTM and finite element analysis (FEA) predictive performance is presented. [ABSTRACT FROM AUTHOR]

Summary: In non‐cooperative multi‐player games, an average sub‐gradient (ASG) strategy for finding an ϵ$$ \epsilon $$‐Nash equilibrium. For convex (but not necessarily strictly convex) individual cost functions, the function convergence of the multi‐player game is demonstrated. Applying Tanaka's formula that characterizes the Nash equilibrium leads to finding the strategy for each player. A min‐max formulation defines the corresponding solution for each player. The main result is proving the reachability of the desired regime, obtaining an explicit upper bound for Tanaka's function decrement. Detailed analyses for two‐player and multi‐player games are developed. For a class of two‐player games with a convex performance function, a set of numerical studies shows the applicability of the proposed method. So, the tracking of the arm in 3‐links robot is considered as an illustrative example. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of blast furnace slags (BFSs) as catalysts in the ozonation process to degrade complex contaminants such as bezafibrate (BFZ) at different pH levels. The findings reveal that the presence of BFS enhances degradation efficiency, achieving a 10% improvement at pH 10 and a 30% improvement at pH 5.5 compared to simple ozonation. The highest degradation efficiency was observed in the Ozonation–BFS system at pH 10, with 90% decomposition of BFZ. These results were corroborated through ozone consumption analysis, BOD5 measurements, and the identification of oxalic acid as the final decomposition product. Due to the complexity of the reaction system, kinetic characterization was performed using non-parametric modeling based on differential neural networks. The model indicated that the observed reaction rate for BFZ degradation in the presence of ozone and BFS was 4.12 times higher at pH 5.0 and 1.08 times higher at pH 10.0 compared to simple ozonation. These results underscore the potential of using BFS in catalytic ozonation processes for the effective treatment of recalcitrant contaminants in wastewater. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) are used to solve pattern classification problems. As this algorithm is based on establishing a relationship between an image-shaped input and its related output through the CNN structure, the training stage is a significant process in their working basis. This study develops a new-fangled and explainable algorithm to train CNNs. The input filters in the convolution layers are parameterized to keep the filter structure, implementing traditional and explainable image processing filters within the network topology. A back-propagation scheme updates the parameters in the filters and the fully connected section of the CNN. Several traditional image filters (Sobel, averaging, Gaussian, and directional, among others) are used in CNN with a learning strategy that keeps their kernel structures. The method implies that the training of these networks is applied to a single parameter instead of all coefficients in the filters, reducing the uncertainty about how each filter performs the image analysis in CNN. This approach was compared with traditional CNNs considering the analysis of the computational cost (measured in terms of time and floops required for training) and their accuracy results. Three image databases were used to evaluate the proposed algorithm. Using a cross-validation methodology, the new training algorithm based on the filter parameterization achieved higher accuracy (93.7% vs. 91.3% in average). The new algorithm got similar results regarding the computational cost compared to traditional methods. This characteristic makes the proposed training methodology an appropriate option to classify images with more explainable processing at the convolution layers. [ABSTRACT FROM AUTHOR]

Many studies relating the gut microbiota composition and autism spectrum disorder focus on finding the statistical differences in microbiome composition between neurotypical and autistic subjects. Since microbiota composition involves high-dimensional variables, establishing inferential or causal relationships using only statistical information is complex, hindering advances toward early functional treatment. Complementary machine learning strategies related to the study of autism spectrum disorder are focused on early diagnosis, substituting the expensive screening tests without providing a possible guide to future alternatives to reduce autism spectrum disorder symptoms. Such techniques may offer better outcomes as a direct approach complemented with statistical analysis to optimize patient healthcare based on an early and simplified detection process. This work evaluates several classic machine learning models, including random forests, support vector machines, k-nearest neighbors, Naïve Bayes, and artificial neural network models. The developed models were assessed to identify less-known patterns and their underlying structures to prior published research on the relationship between gut microbiome composition and an autism spectrum disorder. The differences and similarities between the discovered patterns and existing research are discussed to detect a minimal set of strains that may define the presence of autism spectrum disorder. The best-evaluated models were an artificial neural network and a k-nearest neighbor model, reaching an accuracy of 94.7% in the testing partition with only two missed classifications from 38 previously unseen testing samples. These outcomes support the potential of machine learning strategies to construct a useful pre-diagnostic tool for autism spectrum disorder based on relative gut microbiome distribution. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to present the design of an artificial signal generation system of electrocardiography continuous signals based on the application of a buck power converter. A first-order sliding mode control application for a buck direct current–direct current power converter induces the generation of continuous variations of a direct current voltage as the response of a power converter. The output voltage reproduces the required electrocardiography signals corresponding to the regular or pathological variants. The equivalent control method permits introducing a novel design of a kind of cascade-like sliding mode controller that can regulate both current and voltage operative loops simultaneously, which force the controlled tracking of the reference bioinspired voltages at the output of the buck power converter. For the developed control design, the current regulation is enforced as the primary outcome of the sliding mode controller. Consequently, the required voltage from the buck converter is produced, regulating the switching operation in the electrical power system. The suggested control operates robustly concerning the internal uncertainties and perturbations for both input and voltage signals. Furthermore, the variation of the needed gain for the designed sliding mode controller is developed by studying the online amplitude of the electrocardiography electrophysiological signals used as references and their time derivatives over time. Two reference signals were developed to validate the controllers with the same quality at the numerical simulation and using some experimental validations. The proposed signals used as references were successfully generated in both evaluated cases. [ABSTRACT FROM AUTHOR]

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 μ m a x 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Functional electrical stimulation (FES) has been proven to be a reliable rehabilitation technique that increases muscle strength, reduces spasms, and enhances neuroplasticity in the long term. However, the available electrical stimulation systems on the market produce stimulation signals with no personalized voltage–current amplitudes, which could lead to muscle fatigue or incomplete enforced therapeutic motion. This work proposes an FES system aided by machine learning strategies that could adjust the stimulating signal based on electromyography (EMG) information. The regulation of the stimulated signal according to the patient's therapeutic requirements is proposed. The EMG signals were classified using Long Short-Term Memory (LSTM) and a least-squares boosting ensemble model with an accuracy of 91.87% and 84.7%, respectively, when a set of 1200 signals from six different patients were used. The classification outcomes were used as input to a second regression machine learning algorithm that produced the adjusted electrostimulation signal required by the user according to their own electrophysiological conditions. The output of the second network served as input to a digitally processed electrostimulator that generated the necessary signal to be injected into the extremity to be treated. The results were evaluated in both simulated and robotized human hand scenarios. These evaluations demonstrated a two percent error when replicating the required movement enforced by the collected EMG information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The primary objective of this research is to determine if the dynamic version of an averaged sub‐gradient (ASG) integral siding mode (ISM) controller can effectively handle the end‐effector acceleration tracking problem for a two‐link robotic arm avoiding singularity conditions. The proposed scheme solves a nonlinear extremum seeking problem that minimizes a non‐strictly convex function that depends on the acceleration tracking error defined by the difference between the desired acceleration trajectory and the cueing end‐effector acceleration of a two‐link robotic arm in a 2D‐plane. A second‐order differential equation drives the proposed dynamic ASG with nonlinear coefficients and a discontinuous right‐hand side term, which allows for the achievement of the necessary ISM regime in the presence of considerable errors in the mathematical model description. A special switch between modes allows the controller to track accelerations even in extreme positions (near to the active boundaries of the joint articulations) effectively. [ABSTRACT FROM AUTHOR]

The mechanical section of the telemetry data from the scientific satellite "Lomonosov", (such as yaw, pitch, and roll angles of the spacecraft's main axes, along with its programmed and measured velocities) is pre-processed (alignment, reflection, and binarization) and used for anomaly behavior propagation. The main goal of this study is to estimate possible abnormal behavior of the system in the future and to help to restore normal behavior during a limited communication session with a spacecraft. The system model uses a recurrent architecture approach, namely tracing methodology, considering time shifts in the target data sequence. A deep learning strategy is used to model the abnormal behavior using the onboard collected mechanical information as inputs. The results are compared with the onboard anomaly detection system (ARO) data. The reproduction of the obtained information shows better performance compared to traditional estimation techniques, using binary cross-entropy and receiver operating characteristic curve (ROCAUC) as comparison criterion. Future model modifications, which can improve its quality, are discussed at end of the study. [ABSTRACT FROM AUTHOR]

This study presents the development of an output feedback control for regulating the movement of an autonomous submersible vehicles in the tridimensional space. The controller formulation applies the Averaged Sub-Gradient Integral Sliding Mode which is fed with the estimation of the translational velocity states by a distributed super-twisting algorithm. The structure of the autonomous submersible vehicles dynamics permits the implementation of the ASG algorithm using the estimates of translation velocity. The proposed scheme solves a non-linear extremum seeking problem that aims to minimize a non-strictly convex function that depends on the tracking error defined by the difference of some suitable reference trajectories and the coordinates of the submarine center of mass. The desired reference trajectories were designed to force the autonomous submersible vehicles three-dimensional motion to a continuous circuit in an oscillating shape over the horizontal plane combined with a submersion on the z -axis. The comparison between the proposed controller, the ASG with complete knowledge of the states and the state feedback controller is presented. This comparison confirms the output feedback ASG controller forced the autonomous submersible vehicles to the desired trajectory with a notable difference in the magnitude of the control given the estimation of the states. These outcomes justify the potential contributions of the suggested ASG integrated with the super-twisting algorithm to obtain the local minimization of the evaluated functional depending on the tracking error. The results justify the potential contributions of the suggested ASG with the super-twisting algorithm to regulate the position of the autonomous submersible vehicles to the desired trajectory. [ABSTRACT FROM AUTHOR]

This research focuses on designing a min–max robust control based on a neural dynamic programming approach using a class of continuous differential neural networks (DNNs). The proposed controller solves the robust optimization of a proposed cost function that depends on the trajectories of a system with an uncertain mathematical model satisfying a class of non-linear perturbed systems. The dynamic programming min–max formulation enables robust control concerning bounded modelling uncertainties and disturbances. The Hamilton–Jacobi–Bellman (HJB) equation's value function, approximated by a DNN, permits to estimate the closed-loop formulation of the controller. The controller design is based on an estimated state trajectory with the worst possible uncertainties/perturbations that provide the degree of robustness using the proposed controller. The class of learning laws for the time-varying weights in the DNN is produced by studying the HJB partial differential equation. The controller uses the solution of the obtained learning laws and a time-varying Riccati equation. A recurrent algorithm based on the Kiefer–Wolfowitz method leads to adjusting the initial conditions for the weights to satisfy the final condition of the given cost function. The robust control suggested in this work is evaluated using a numerical example confirming the optimizing solution based on the DNN approximate for Bellman's value function. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The main purpose of this work is to develop an output state-dependent controller that solves the path-tracking deviation error for a skid-steering autonomous vehicle. The controller takes advantage of a nonlinear diffeomorphism that transforms skid-steering autonomous vehicle into a multi-input multi-output chain of integrators. This research assumes that available skid-steering autonomous vehicle variables are its position and its orientation. This in fact motivates the development of a modified super-twisting algorithm operating as a sequential step-by-step differentiator that estimates traslational velocity and acceleration of the studied autonomous vehicle in a finite time, which were used as part of the controller implementation. Based on the estimated states by the step-by-step multi-variable differentiator, an adaptive control design enforces the asymptotic convergence of the tracking trajectories for the skid-steering autonomous vehicle to the origin. The explicit form of the controller gains is derived using a class of control Lyapunov function including the deviation corresponding to the tracking error and a term that defines a matrix norm associated with control gains. Numerical results confirm the workability of the proposed controller considering the reduced norm of tracking error obtained with the proposed controller. Experimental evaluations compared the adaptive control introduced in this study and a state-feedback form justifying the control proposal. The adaptive form enforced smaller tracking errors using the estimated states forced by the step-by-step differentiator and the information obtained from a multi-camera video high-frequency acquisition system. [ABSTRACT FROM AUTHOR]

This paper describes the design and implementation of a novel adaptive control method to track a set of bioinspired reference trajectories. These references define anthropomorphic movements for an exoskeleton robot. The proposed controller implemented the adjustment laws for the variable gains of a state feedback (Proportional-Derivative type) structure. The method to adjust the adaptive gains was determined using a controlled Lyapunov function. The adaptation laws use velocity estimation based on a robust exact differentiator (RED) implemented as a variation of a distributed Super-Twisting algorithm. The adaptive gain controller was evaluated on a simulated exoskeleton structure. The set of simulations considered the presence of external disturbances and modeling uncertainties. The controller proved efficient in rejecting external perturbations/uncertainties affecting the exoskeleton. The proposed controller's performance was superior to the one obtained if the standard fixed-gain proportional derivative controller was evaluated. As an additional benefit of the adaptive PD controller implementation, a controller power reduction of at least 14 % concerning the non-adaptive version of the feedback controller was attained. An experimental evaluation of the proposed controller confirmed the benefits of the proposed controller with adaptive gains. The successful tracking of nine different biomechanically inspired reference trajectories justified the exoskeleton application, which could be used as a potential tool for rehabilitation purposes. [ABSTRACT FROM AUTHOR]

Virtual reality has been widely used in various industries, such as entertainment, communication, and healthcare, to mention a few. In the health industry, in combination with the brain–computer interfaces (BCIs), virtual reality could produce rehabilitation measures that may contribute novel strategies such as remote rehabilitation or telerehabilitation. The design and development of BCIs integrate different processes, including biosignals acquisition and processing, feature extraction and selection, classification of signals, and application of the technology to patients under rehabilitation treatments. This manuscript presents a literature review of the papers focused on the implementation of BCI and assistive technologies for remote rehabilitation based on virtual reality implementation. The purpose of this comprehensive review is to identify those studies that take advantage of virtual reality in combination with a biomedical technology to improve the performances of diverse rehabilitation processes. Various revisited studies provided a complete system for remote rehabilitation. These findings could lead to applying these models in diverse rehabilitation tasks. [ABSTRACT FROM AUTHOR]

Mixed cultures represent better alternatives to ferment organic waste and dark fermentation products in anerobic conditions because the microbial associations contribute to electron transfer mechanisms and combine metabolic possibilities. The understanding of the microbial interactions in natural and synthetic consortia and the strategies to improve the performance of the processes by bioaugmentation provide insight into the physiology and ecology of the mixed cultures used for biotechnological purposes. Here, synthetic microbial communities were built from three hydrogen (bioH2) and poly-hydroxy-alkanoates (PHA) producers, Clostridium pasteurianum, Rhodopseudomonas palustris and Syntrophomonas wolfei, and a photoheterotrophic mixed consortium C4, and their performance was evaluated during photofermentation. Higher hydrogen volumetric production rates (H2VPR) were determined with the consortia (28–40 mL/Lh) as compared with individual strains (20–27 mL/Lh). The designed consortia reached the highest bioH2 and PHA productions of 44.3 mmol and 50.46% and produced both metabolites simultaneously using dark fermentation effluents composed of a mixture of lactic, butyric, acetic, and propionic acids. When the mixed culture C4 was bioaugmented with S. wolfei, the bioH2 and PHA production reached 32 mmol and 50%, respectively. Overall, the consumption of organic acids was above 50%, which accounted up to 55% of total chemical oxygen demand (COD) removed. Increased bioH2 was observed in the condition when S. wolfei was added as the bioaugmentation agent, reaching up to 562 mL of H2 produced per gram of COD. The enhanced production of bioH2 and PHA can be explained by the metabolic interaction between the three selected strains, which likely include thermodynamic equilibrium, the assimilation of organic acids via beta-oxidation, and the production of bioH2 using a proton driving force derived from reduced menaquinone or via electron bifurcation. [ABSTRACT FROM AUTHOR]

This study presents the design of a robust control based on the sliding-mode theory to solve both; the stabilization and the trajectory tracking problems of nonlinear systems subjected to a class of full-state restrictions. The selected nonlinear system satisfies a standard Lagrangian structure affected by nonparametric uncertainties. A barrier Lyapunov function is used to ensure the state constraints by designing a time-varying gain, which guarantees the fulfillment of the predefined state constraints even under external perturbations. The proposed design methodology for the barrier sliding-mode control (BSMC) ensures the convergence of the sliding surface in finite time to the origin. Consequently, the asymptotic convergence of the states to the corresponding equilibrium point is achieved. The finite-time stability of the origin in the closed-loop system with the proposed controller has been demonstrated using the second Lyapunov stability method. The suggested controller was evaluated on a two-link robotic manipulator. Then, the obtained results showed better stabilization and tracking performances (while the restrictions are satisfied) than the traditional first-order sliding-mode or linear state feedback controllers. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop an adaptive automatic control method for solving the trajectory tracking problem for a biped robotic device (BRD) and taking into account that each articulation is mobilized by a linear actuator. Each extremity of the BRD has three articulations with a linear actuator enforcing the controlled motion for each articulation. The control problem considers the task of tracking reference trajectories that define a regular gait cycle. The suggested adaptive control form has state-dependent gains that drive the tracking error into an invariant and attractive ellipsoidal with a center at the origin; meanwhile, the articulation restrictions are satisfied permanently. The stability analysis based on a controlled Lyapunov function depending on the tracking error leads to the explicit design of the state-dependent adaptive gains. Taking into account the forward complete setting of the proposed BRD, an output feedback formulation of the given adaptive controller is also developed using a finite-time and robust convergent differentiator based on the super-twisting algorithm. A virtual dynamic representation of the BRD is used to test the proposed controller using a distributed implementation of the adaptive controller. Numerical simulations corroborate the convergence of the tracking error, while all the articulation restrictions are satisfied using the adaptive gains. With the purpose of characterizing the proposed controller, a sub-optimal tuned regular state feedback controller is used as a comparative approach for validating the suggested design. Among the compared controllers, the analysis of the convergence of the mean square error of the tracking error motivates the application of the designed adaptive variant. [ABSTRACT FROM AUTHOR]

This study intends to present an automatic control design for a Cartesian electrospinning system with a dual moving platforms for the collector and the syringe subsystems which are mobilized using linear actuators. The proposed device is including a set of dual robotized structures, that is also carrying a structure to create the Taylor cone that regulates the nano-fiber deposition over the supporting platform. The suggested control form considers the synchronization between the syringe tip and the collector using a cascade tracking structure. A class of distributed state-dependent terminal sliding mode control form solves the tracking problem that yields the syringe-collector pair moves in a synchronized form tracking a bidimensional design that should be reproduced with the fibers deposition made of polyvinyl acid–based polymer. This controller also considers the working space restrictions in the Cartesian electrospinning system device using state-dependent gains which are obtained using a control barrier Lyapunov function. The application of the second stability method of Lyapunov serves to both design the state-dependent gains and prove that the tracking error trajectory is ultimate bound. The proposed controller is evaluated using a cyber-physical representation of the Cartesian electrospinning system device where the proposed controller is evaluated. This evaluation considers a comparison of the control effectiveness (using the root mean square value of the tracking error) with a traditional non-adaptive state feedback and a first-order sliding mode control. This comparison shows that the proposed adaptive controller induces the smallest root mean square among the tested control forms while reducing the magnitude of the overshoot for each actuator. [ABSTRACT FROM AUTHOR]

The current study presents an adaptive control approach to solve the tracking trajectory problem for a robotic manipulator that uses a gripper based on bimorph piezoelectric actuators. The development of an adaptive gain state feedback form that considers the state restrictions is proposed using a novel class of barrier Lyapunov function that drives the effective control of joints and piezoelectric actuators. The proposed method allows for the inclusion of complex combinations of state restrictions in the Lyapunov function, yielding the construction of differential forms for the gains in the controller that can handle the evolution of trajectories of the robotic arm inside the restricted region. The proposed control design successfully tracks reference trajectories for both joints of the robotic arm as well as the motion of the piezoelectric device during several operative scenarios. A comprehensive experimental study evaluates the effect of introducing state-dependent gain considering state restrictions of the ellipsoidal type. The comparison of the mean square error confirms the contributions of the developed control action, showing better tracking quality for less control power with the same evaluation, which is a desirable characteristic in the controlled motion of micromanipulators. The proposed controller solves the tracking trajectory problem for the micromanipulation system, satisfies the motion restrictions, and allows better tracking performance to be enforced. Furthermore, comparison of the obtained trajectories seems to validate the proposed controller's contribution concerning a feedback form with fixed gains. [ABSTRACT FROM AUTHOR]

This study has the aim of introducing a new type of trajectory tracking robust controllers for a class of rehabilitation robotic system considering the articulations restrictions. The robotic device consists of a suspended biped configuration. The suggested robust control considers the application of state depending gains which provide finite-time convergence for the tracking deviation. The state restrictions are fulfilled by the implementation of controller gains estimated by a class of the controlled tangent barrier Lyapunov function. Stability analysis for the tracking error yields the explicit design of the state dependent gains. The rate of convergence for the controller design is enhanced using a matrix inequality convex optimization method. Based on the forward complete characteristic of the suggested rehabilitation device, it is allowed using a finite-time convergent super-twisting-based differentiator to concrete an output feedback realization of the proposed controller. A computerized model of the tendered rehabilitation robot provides a reliable testing platform to the suggested roust controller. Numerical evaluations appear to serve as an indirect confirmation for the tracking error convergence, satisfying the articulation restrictions, and the effect of the gain optimization design. For comparison purposes, the regular state feedback control design is considered as benchmark. The faster convergence of the mean square estimation of the tracking error justifies the design of the proposed control design as well as the state feedback structure justifies the origin is a fixed-time stable equilibrium point for the space of tracking error at the same time that state space restrictions remain satisfied. The experimental evaluations of the proposed controller justifies the barrier controller which, in spite of the modeling uncertainties and the implementation issues, tracked the reference trajectories. [ABSTRACT FROM AUTHOR]

The main goal of the current investigation is to present an output feedback robust control that regulates the motion of mobile robotic autonomous devices based on the application of the dual averaged sub-gradient descendant method. Sub-gradient realization aims to solve the design of a proposed extremum seeking control to stabilize the trajectory tracking error between the three-dimensional position of mobile robotic autonomous devices and some attainable reference trajectories in the same spatial domain. The controller belongs to a class of sliding mode controller with variable gain based on a sliding surface depending on the tracking error and its time derivative. This derivative is estimated with the application of a distributed time-dependent super-twisting robust differentiator. The proposed mobile robotic autonomous device dynamics considers the dynamics of actuators (direct current motors) which drives the mobilization of mobile robotic autonomous devices. The dynamic nature of the mobile robotic autonomous devices regulated by motor actuators induces a backstepping-like controller design based on a two-stage sequential application of the sub-gradient strategy, one for the mobile robotic autonomous devices and the second for the motor dynamics. The mixed strategy appears as a novel formulation to solve the path track for reference trajectories of mobile robotic systems. The proposed control considers the optimization of the tracking error dependent functional without the full knowledge of the mobile robotic autonomous device dynamics. With the aim of proving the effectiveness of the suggested design, two numerical examples are developed. In these examples, the tracking performance, minimization of the proposed functional as well as control evolution are evaluated. The controlled tracking outcomes of the proposed integral sliding mode control are contrasted with the outcomes produced by a set of proportional–derivative and selected sliding mode (first-order type) controllers. The suggested controller shows a superior tracking of the reference trajectory than the other two comparative controllers, using quality measures such as least mean square error and its integral. [ABSTRACT FROM AUTHOR]

A drum boiler is the most critical part of power generation plants. The control of a highly nonlinear, multiple input and multiple output drum boiler system (DBS) is a challenging task. In this work super-twisting sliding mode controllers (STSMCs) are designed for the control-oriented model of the DBS, which maintain a desired water level and pressure of the drum. The unknown state of the DBS required for the control design is estimated using a gain-scheduled Utkin observer (GSUO). A rigorous mathematical analysis is also included to show that the closed-loop system is locally stable. The simulation results show that the closed-loop system yields the required performance even in the presence of parametric uncertainty, process and measurement noises, and external disturbance. To make a fair comparison, a quantitative analysis is also carried out for STSMC and PI controllers. Both controllers consume similar control energy, but STSMC shows better tracking performance. [ABSTRACT FROM AUTHOR]

This study presents the development of a hybrid active disturbance rejection control (H-ADRC) implementing a homogeneous extended state observer (ESO) to solve the trajectory tracking problem of an inchworm bioinspired flexible robotic device (IBFRD) with partially unknown dynamics. The proposed controller is implemented on a self-designed IBFRD instrumented with shape memory alloys (SMAs) operating as actuators in each joint of the robotic mobile device. The set of SMAs constitutes an agonist–antagonist mechanism together with regular springs that counteract the SMA displacement. The characteristic two-anchor crawling of an inchworm is modeled as a hybrid system and exerted by a bistable pneumatic circuit that switches suction cups at the terminal links of the IBFRD to exert the inchworm walking. The corresponding reference trajectories emulate an inchworm in a regular gait cycle. Experimental results compare the H-ADRC with a proportional derivative and a proportional–integral–derivative (PID) controller. The obtained results show that the proposed H-ADRC enforces a reduced tracking error than the other controllers used for validation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article deals with the practical implementation of implicit homogeneous controllers (IHCs) for linearized mechanical systems. The control design includes the methodology to get gains of the IHC based on the linearized approximation of the system. If the approximation error enforced by the linearization is vanishing with the state, locally measurable and bounded, the IHC can lead the state to the origin in finite-time. This IHC control allows accelerating the convergence rate of the states. A semiexplicit algorithm is provided to exert the digital implementation of the controller. The application of the bisection method estimates the controller gain ensuring the finite-time convergence of the state to the origin. A complementary analysis provides a simplified algorithm with a reduced number of computation stages but equally efficient gain estimation. The proposed IHC is applied to a rotary inverted pendulum QUBE $^{\text{{{}}}}$ Servo 2 platform of Quanser $^\text{{{{}}}}$. The obtained results for the state convergence are compared with other classical feedback controllers to validate the effectiveness of the proposed scheme. The comparative analysis of the state convergence provides evidence of the faster convergence for the state trajectory to a zone centered on the origin with a smaller hypervolume than the one gotten with the classical controllers. [ABSTRACT FROM AUTHOR]

Dynamic motion simulators cannot provide the same stimulation of sensory systems as real motion. Hence, only a subset of human senses should be targeted. For simulators providing vestibular stimulus, an automatic bodily function of vestibular–ocular reflex (VOR) can objectively measure how accurate motion simulation is. This requires a model of ocular response to enforced accelerations, an attempt to create which is shown in this paper. The proposed model corresponds to a single-layer spiking differential neural network with its activation functions are based on the dynamic Izhikevich model of neuron dynamics. An experiment is proposed to collect training data corresponding to controlled accelerated motions that produce VOR, measured using an eye-tracking system. The effectiveness of the proposed identification is demonstrated by comparing its performance with a traditional sigmoidal identifier. The proposed model based on dynamic representations of activation functions produces a more accurate approximation of foveal motion as the estimation of mean square error confirms. [ABSTRACT FROM AUTHOR]

This manuscript proposes the design of a speech synthesis algorithm based on measured electroencephalographic (EEG) signals previously classified by a class of neural network with continuous dynamics. A novel multilayer differential neural network (MDNN) classifies a database with the EEG studies of 20 volunteers. The database contains information described by input-output pairs corresponding to EEG signals and a corresponding word imagined by the volunteer. The suggested MDNN estimates the unknown relationship between the information instances and suggests the most-likely word that the user wants mentioning. The proposed MDNN satisfactory classifies over 95% a set of words obtained from the suggested EEG study in which, the users have to watch four different geometric figures on a screen. [ABSTRACT FROM AUTHOR]

Morels are gourmet wild edible mushrooms that can grow on several substrates with significant growth rate variations. Such variations have hindered the development of a standardized culture media to promote morel's sustainable production. The aim of this study is developing a novel culture media that takes advantage of coconut water as a complementary component of culture media. Coconut water has been extensively used as a growth-promoting component for plant tissue cultures; however, its application as component of fungi cultivation medium has not been fully developed. This study confirms that coconut water can be efficiently used as culture media component for morels using a kinetic characterization. Morchella sp. kinetic growth is evaluated in different cultures: agar, malt extract agar (MEA), lactose, coconut water (15%) and combinations of them. Kinetic growth parameters (lag phase, λ and maximum specific growth rate, µmax) are estimated using primary modeling methods. Among the selected models, the best fit is achieved using Baranyi's model. A significant increase from 15.8% to 43.4% of the µmax values was observed when culture media (agar, lactose, MEA) is supplemented with coconut water. The largest values of µmax are obtained in MEA-coconut cultures (21.13 ± 0.43–22.57 ± 0.35). Micro-sclerotia and late sclerotia are observed in all cultures containing coconut water justifying the development of a feasible and cost-effective way of culturing morels. The results demonstrate that coconut water can be used for formulation of standard media for morel cultivation leading to a cheap alternative to produce dense mycelium and promote sclerotia formation. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop robust guidance laws for the control motion of an underwater autonomous vehicle (UAV) in a three-dimensional (3D) space. The control design is based on the use of Averaged Sub-Gradient (ASG) version of a class of dynamic integral sliding mode (ISM) method being sequentially applied to the subsystems of the complete model realizing, the so-called backstepping (or cascade) approach. The mathematical form of the UAV model induces a backstepping formulation for solving the tracking trajectory problem sequentially for the position, translation velocity, angular velocity and actuators (thrusters) dynamics. The solution of the trajectory tracking problem at each stage implements the ASG-version of the ISM method. This problem is treated as the optimization of a suitable convex (not obligatory strongly convex) cost functional, depending on the tracking error and reaching its minimal value at the origin of the error tracking space. This study shows that the minimization of the proposed functional leads to the optimal tracking regime under the presence of uncertainties in the mathematical model description. A numerical example proves the effectiveness of the suggested robust dynamic controller. The comparison between the obtained trajectory tracking results and the outcomes produced by a set of standard proportional integral derivative (PID) controllers, is presented. The proposed controller exhibits a better tracking of the reference trajectory compared with the PID version, showing a smaller mean square estimation for the tracking error. [ABSTRACT FROM AUTHOR]

The removal of phenol compounds (such as phenol (Ph), 4-chlorophenol (4-CPh), and 4-phenolsulfonic acid (4-APS)) was studied using catalytic ozonation and cerium oxide (CeO2) as the catalyst. Conventional ozonation removes 40% and 30% of total organic carbon (TOC) for 4-CPh and 4-APS, respectively. The presence of the catalyst doubled the TOC removal in comparison with ozone alone. Chloride and sulfate ions, detached from the target compounds, did not affect the mineralization degree. The effect of ions was studied by adding NaCl and Na2SO4 during the ozonation of Ph with CeO2 as a catalyst. The O3-CeO2 process was responsible for 89% TOC removal, while the presence of NaCl and Na2SO4 decreased TOC removal by nearly 50%. Ozonation alone only removed 20% of TOC at 120 min with or without inorganic salts. The presence of salts in the Ph ozonation provoked in detrimental effect in comparison when inorganic ions were produced along with the decomposition of 4-CPh and 4-APS. [ABSTRACT FROM AUTHOR]

The design of an output-based robust disturbance rejection controller, aimed to solve the state tracking for the articulations of an experimental biped robot, was the main outcome of this study. The robust disturbance rejection controller included an auxiliary hybrid observer entailed to recover the angular velocity for each articulation. The estimated states served to perform the approximation of disturbances and non-modeled parts in the biped robot dynamics by implementing an extended state observer structure. The observer used the tracking position errors as input information, as well as considering the limb articular constraints, which are natural for biologically inspired biped robots. The effect of state constraints motivated the implementation of a hybrid observer with saturated output error injection. The controller design used the estimation of constraint velocity for solving the design of a tracking trajectory control to resolve the reproduction of the gait cycle by the bipedal robotic system. The Lyapunov stability theory served to obtain the laws which adjust the observer gains as well as to prove the ultimate boundedness of the tracking error as well. The evaluation of the suggested controller was realized on a numerical representation of the biped robot. These simulations illustrated the tracking performance of the hybrid robust disturbance rejection controller for all biped robot articulations in a decentralized structure. Experimental evaluations were also considered to validate the robust disturbance rejection controller design. A fully actuated biped robot was constructed and controlled by the robust disturbance rejection controller. The tracking results obtained by the robust disturbance rejection controller (in both the numerical and experimental evaluations) overcame the classical approach performances of diverse controllers as state feedback (proportional-derivative form) and regular robust disturbance rejection controller which did not consider the articulation constraints. [ABSTRACT FROM AUTHOR]

The aim of this study was to design a first-order sliding mode controller for dual AC/DC/AC power converters to regulate the production of a corona discharge ozone generator. The design is presented in the two following parts: (a) The first part regulates a three-branch (phase) AC/DC converter to control the DC intermedium voltage that is required to produce the desired ozone concentration, while (b) the second part regulates a one-phase DC/AC power conversion circuit that regulates amplitude of the AC voltage that must be injected into the reaction chamber of the ozone generator. For the first section, the controller regulates the switching sequence yielding the control of the input currents, which defines the amplitude of the DC voltage. In the second, the switching control regulates the amplitude of the expected output AC signal, which depends on the obtained DC voltage from the AC/DC section. This structure represents a novel form to adjust the functioning of the AC/DC/AC power device, which has not been used before in the context of ozone production. Lyapunov's second method was the main tool for confirming the existence of an asymptotical equilibrium state point for the trajectory tracking deviation for both sections of the power converter. The voltage amplitude was tested on a previously validated mathematical model of a corona-effect ozone generator performance. Several numerical evaluations illustrated the efficient performance of the suggested discontinuous controller and considering the proposed switching sequence that realizes the control action. The proposed controller was contrasted with a usual proportional/integral/derivative (extended state) design, which is a common technique to regulate the ozone generation system. [ABSTRACT FROM AUTHOR]

The design of a state feedback and robust controller for regulating the tridimensional (3D) movement of autonomous underwater mobile crafts (AUMCs). The controller design is based on the application of Averaged Sub-Gradient Integral Sliding Mode Realisation (ASGISMR). The application of the ASGISMR yields the solution of the closed-loop extreme seeking control for a non-strictly convex functional depending on the tracking error between reference and 3D position of the AUMC. The design of reference trajectories was proposed to enforce the AUMC to follow a continued submersion and ellipsoidal detouring. The mechanical dynamical form of AUMC is well posed for applying the extended version of ASGISMR, considering that integral term represents an ASG associated to the euclidean norm of the tracking error. The time evolution of the functional over the controlled trajectories of the AUMC is compared with the corresponding functional enforced by a traditional state feedback controller with gravity effect compensation. The proposed controller exhibits better tracking and similar control magnitude than the considered reference state feedback realisation. These outcomes justify the potential contributions of the suggested ASGISMR to obtain the local minimisation of the evaluated functional, considering such controller as an extreme seeking realisation for the proposed AUMC. [ABSTRACT FROM AUTHOR]

Diethyl phthalate (DEP) is a pollutant which can be found on soils as a result of its widespread application in plastic industry. Soil contaminated with DEP requires the application of different chemical methods to attain its remediation. Among these methods, ozonation has proven to be effective against toxic soil pollutants. The presence of metal oxides in soil is a possible source of catalytic effect. In this study, it was analyzed the catalytic effect of goethite (α-FeOOH), hematite (α-Fe2O3), and gibbsite (γ-Al(OH)3) in combination with O3 to achieve DEP decomposition. The DEP elimination efficiency by ozonation on the sand increased according to the following order: without catalyst < γ-Al(OH)3 < α-Fe2O3 < α-FeOOH. Among these three oxides, goethite has the highest OH groups density. The reaction of OH groups and O3 favors the formation of oxidant species, such as O2•− and OH•. The effect of the moisture content, the catalyst concentration, and the type of soil (sand and calcined soil) were also studied. The latter had a significant influence on the total organic carbon (TOC) removal. The mineralization degree was 84% in the O3–soil system, while only 40% was obtained with O3–sand (α-FeOOH) in dry sand after 8 h of treatment. Calcined soil promoted the increase of TOC removal due to the presence of different metal oxides, which were active centers for O3 decomposition. The toxicity tests of the three reaction systems (O3–sand, O3–sand (α-FeOOH), and O3–soil) were evaluated on lettuce seed germination before and after DEP ozonation. [ABSTRACT FROM AUTHOR]

The aim of this study was to design a constrained robust output feedback control strategy for stabilizing linear systems affected by uncertainties and output perturbations. The input is constrained by a saturation function. A classical Luenberger observer reconstructed the states from output observations. The state feedback control employed the estimated states given by the observer. This work proposed a Barrier Lyapunov Function (BLF) to manage the saturation problem in the control input. Moreover, an extended version of the attractive ellipsoid method (AEM) characterized the zone of convergence due the presence of perturbations in the output. A convex optimization procedure, formulated as a set of matrix inequalities, yielded the control parameters and the region of attraction for the close‐loop system as well as a minimal ultimately bounded set for the system trajectories. Numerical simulations supported the theoretical results formulated in this study. [ABSTRACT FROM AUTHOR]

The aim of this brief was to develop a class of state estimator modified with a projection function to reconstruct the non-measurable variables of a state-constraint system. The main characteristic of this observer was that the estimated states remain inside the compact set defined by the given variables constraints, independently of the output noise and internal modeling uncertainties. The application of the projection operator introduced a non-smooth form for the observer design. Hence, integral Lyapunov functions can be used to realize the stability study. The design of a Lyapunov-Krasovskii functional overcome the smoothness problem and justifies the ultimate boundedness of the estimation error. The upper bound for the estimation error is rationally related to the observer’s lag and it is nonlinear associated with the power of output noises and modeling errors. Two numerical examples (the Chua’s circuit and the ozonation of toxic compounds adsorbed in soil) illustrated the application of the suggested observer. [ABSTRACT FROM AUTHOR]

Summary: This study aims to design a robust state feedback controller for uncertain and perturbed linear systems with state constraints described by a polytope. This novel design incorporates the use of a composite barrier Lyapunov function (CBLF) and the convex hull of a set of ellipsoids inscribed in the given polytopic constraint set. The CBLF is used to ensure that this convex hull is an invariant set for the perturbed system states. Then, an optimization scheme is implemented to maximize the size of this invariant set to use it as a safe set. This is a set of initial conditions ensuring that the system solutions conform to the constraints for any subsequent time instant. Additionally, a minimal ultimate bound for the states is calculated to ensure asymptotic convergence to a region as close to the origin as possible. This region is characterized by a second convex hull of ellipsoids using the well‐known attractive ellipsoid method and the CBLF. Numerical simulations illustrate and compare the obtained results against a similar approach, considering the classical quadratic Lyapunov function, instead of the CBLF. [ABSTRACT FROM AUTHOR]

Enhancing the productivity of biomass, carbohydrates, proteins, and lipids in microalgae cultures is an essential task to justify the feasibility of using biofuels as energetic alternatives to fossil fuels. Diverse strategies can be used to get a biologically plausible optimization of the microalgae culture such as using indigenous microalgae added with other microorganisms such as fungi or bacteria, analyzing the effect of culture broth composition and reaction experimental conditions. A wastewater isolated co-culture microalgae-yeast (CCMY) was evaluated as potential biomass and lipids-efficient producer. The Box-Behnken experimental design and the response surface methodology yield to identify the best attainable composition of inorganic nutrient variables in culture media: sodium nitrate (NaNO3), potassium phosphate (K2HPO4), and ferrous sulfate (FeSO4). The results of the experimental design revealed that the NaNO3, K2HPO4, and FeSO4 were three factors significantly influencing the biochemical content. The detected optimal conditions correspond to a highest biomass production of 1.68 g L−1; the carbohydrate content was 16.75%, for proteins 37.11%, and the maximum lipid content was 27.77%. These values were about two times higher than the results obtained with the original version of the culture broth (BBM). The complexity of inorganic salts effect on the biomass and lipid accumulation motivates the proposal of a quartic order mathematical model which characterizes the productivity of microalgae-yeast co-culture as function of the inorganic micronutrient contents. [ABSTRACT FROM AUTHOR]

The current study presents the treatment of water contaminated with the dye Direct Red 28 in the presence of Na2SO4 at three different concentrations (10, 40 and 80 g/L) by simple ozonation. This wastewater treatment was considered in a wastewater recycling process. At the end of each ozonation cycle, ozonation dynamics, ozone consumption, pH, electrical conductivity and UV/Vis spectra variation were analyzed and correlated with the stoichiometric analysis balance. The efficiency of the wastewater treatment in each reuse cycle was evaluated by a novel image processing method. The image results show that the presence of sulfate improves more than 45% the dye fixation on a cotton sample. However, at high additive concentrations, the color quality decreases 25%, compared with the system with low additive concentration, due to the presence of peroxysulfate ions and the compounds accumulated through the ozonation process. [ABSTRACT FROM AUTHOR]