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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 article deals with the control of a class of robotic systems with constant input time delay through the Active Disturbance Rejection paradigm, by means of Generalized Proportional Integral observers. The system is represented as a linear perturbed system, whose lumped disturbance input is estimated and then compensated by an Extended State Observer-Based Control, which is implemented on a predictor scheme for the time delay compensation. A complete Lyapunov Krasovskii functional was used to perform the stability analysis, leading to an ultimate bound trajectory tracking error. The proposal is tested and validated on a two degrees of freedom robotic manipulator. [ABSTRACT FROM AUTHOR]

Three microbial consortia (C2, C4, C5) grown under photoheterotrophic conditions assimilated acetate and butyrate, as individual and mixed substrates. By controlling the culture conditions, it was possible to manipulate the microbial population composition and thus the yield of polyhydroxyalkanoates (PHA) accumulation. Under limited ammonium conditions, pH control, and a sequential two-step process, C2 and C4 produced PHA. C4 showed the highest production of 44% of the cell dry mass (CDM), close to the theoretical value calculated with a stoichiometric balance. Analysis of the confocal microscopy images confirmed the accumulated biopolymer percentages produced by each consortium, and it was in close correlation with microbial distribution and substrate consumption pattern. 1 H, 13 C, NMR, and MALDI-TOF spectra identified the primary structure of the obtained biopolymers as copolymers of 3-hydroxybutyrate (3HB) and 3-hydroxyvalerate (3HV). This composition allows for better mechanical properties compared to the PHB homopolymer. Microbial characterization showed a similar microbial population with different proportions for C2 and C4. The highest PHA production in C4 was associated with higher abundances of PHA producers, including Clostridium (29%), Pseudomonas (8%) and Rhodopseudomonas (5%). Both microbial consortia showed that a portion of their microbial populations were able to perform syntrophic reactions ( Dysgonomonas and Clostridium ). [ABSTRACT FROM AUTHOR]

In the study presented in this paper , the problem of the design and implementation of a two-axis sun tracking system was addressed by applying a set of two robotic systems, one for the automatic orientation of the photovoltaic modules and the second for providing the reference trajectory (robotic sensor). The design methodology was based on mechatronic concepts, in particular the VDI 2206 standard, according to which the system is divided into interconnected modules to be designed, validated, and integrated. This approach provides an efficient energy collection system in terms of the mechanism, instrumentation system, energy supply, and automatic trajectory tracking control. Experimental results illustrate the behavior of the proposed system, which achieves a better performance than fixed systems, as well as one-axis tracking mechanisms. [ABSTRACT FROM AUTHOR]

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 SiO 2 , specific density of 1701.38 kg/m 3 , a true density of 2492.50 kg/m 3 , 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/m 3 , a true density of 2673.55 kg/m 3 , surface area of 34.92 m 2 /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. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of an output feedback controller based on the super twisting algorithm (STA) that stabilizes the trajectory tracking error of a skid steered mobile robot (SSMR). The control scheme introduces a diffeomorphism based on the mathematical model of the SSMR to transform the original problem into a third order chain of integrators. In this study, the available measurements are the position and orientation of the SSMR. A modified STA working as a step by step differentiator estimates the velocity and acceleration of the mobile robot. Then, a second STA enforces the tracking of a predefined trajectory. Numerical and experimental results comparing the STA with a state feedback controller (SFC) and a first order sliding mode controller (FOSM) justify the control proposal. [ABSTRACT FROM AUTHOR]

Sequential processes appear naturally in all types of industries. Biotechnology is a good example of such schemes. Wastewater treatment using microbiological activity is a particular case having all the characteristics of sequential methods. Sulfate reduction as pre-treatment followed by the decomposition of sulfated compounds using adapted microorganisms is the sequential nonlinear process with state constrains analyzed in this paper. Modeling this procedure is still a difficult task because the number of elements involved in the reaction. This paper presents an adaptive algorithm to obtain a suitable model of this process using continuous neural networks. The adaptive model preserves the sequential nature of the process as well as the bounded nature of all states. The neural network is proposed as a system identifier in terms of the hybrid systems theory. The Lyapunov stability method is used to demonstrate the convergence of the identifier states to the real concentrations of the microbiological system. Experimental results and their corresponding simulation using the adaptive model based on neural networks confirm the theoretical results described in this paper. [ABSTRACT FROM AUTHOR]

Three FTO-BiVO 4 photoelectrodes are fabricated modifying the BiVO 4 thickness, and systematically evaluating the influence of FTO substrate on the optical, electrical properties, and photoelectrochemical performance of BiVO 4 semiconductor. The catalysts are characterized using two light sources with back-side or front-side irradiations, to investigate the impacts of different energy sources and configuration illumination on the FTO-BiVO 4 photoactivity. This analysis reveals the existence of an additional charge transfer resistance increasing with thickness film subjected to front-side illumination, while the resistance remarkably diminishes when this interface is directly irradiated under back-side illumination. The highest photocurrent is achieved with the LED lamp under back-side illumination, condition selected to compare the degradation of 20 mg L−1 ciprofloxacin (CIP) in 0.05 M NaCl through electrocatalysis, photocatalysis, and photoelectrocatalysis using front-side or back-side illuminations. In these evaluations, modified FTO contributes to the photogeneration of reactive chlorine species, whence it cannot be considered as a simple substrate. Back-side illumination presents a higher CIP elimination in comparison with front-irradiation. A schematic energy band diagram relying on Tauc and Mott-Schottky plots, and incorporating FTO as a photoactive semiconductor is established to rationalize the formation of oxidant species in the system. A degradation mechanism is established based on HPLC measurements of the different treatment methods. [Display omitted] • Light source affects the formation of a true FTO-BiVO 4 heterojunction. • FTO beyond a current collector influencing the photoelectrochemical performance. • Charge transfer resistance increasing with FTO-BiVO 4 thickness at front radiation. • FTO contributes to photogenerate reactive chlorine species (non-substrate). • Back-side illumination presents higher Ciprofloxacin elimination than front one. [ABSTRACT FROM AUTHOR]

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. • The type of soil affected significantly the DMP elimination using ozone. • DEP ozonation obtained 70% TOC removal on calcinated agricultural soil. • The ozonation generated less toxic by-products than DMP. [ABSTRACT FROM AUTHOR]

This paper uses a multi-head neural transformer to present the text-to-text translation/interpretation of Sign Language (SL) in the context of glosses (written SL). A Spanish to Mexican Sign Language (MSL) gloss dataset was built based on simple and compound sentences and the corresponding interpretation in MSL gloss. The interpretation process was achieved by implementing state-of-the-art tools in the natural language processing (NLP) field called neural transformers. We tried different architectures, varying the number of encoder-decoder layers and hyperparameters. The best of our models achieved 0.68 BLEU in the training phase and 0.33 in the validation phase. MSL glosses are crucial as they rule the grammatical order in which MSL has to be executed. All these quantitative and qualitative results confirm the potential applicability of neural transformers to create effective automatic translators for the Spanish language to MSL, with similar effectiveness shown by other automatic translators for other more likely languages.

This work presents the development of a multilevel electromagnetic actuation system that controls the shape of a flexible rotatory robotic structure. An array of electromagnets is used as the set of actuators that regulate the position of permanent magnets within the flexible device. The primary outcome of this study is the design and experimental validation of the multilevel rotating device. In addition, the theoretical description of the system motion under electromagnetic actuation is formulated using Euler-Lagrange and electromagnetic theories. Given the developed model, a theoretical study leads to designing an adaptive control that considers motion restrictions in the flexible device. The controller aims to modify the current applied to the electromagnets, which changes the interaction forces between the electromagnet and the permanent magnets in the robotic flexible structure. A set of numerical simulations confirms the proposed controller's effectiveness compared to the traditional state feedback approach that does not consider the state restrictions, which is implemented in devices that also operate under an electromagnetic approach. Furthermore, an experimental version of the flexible device allows for testing of the developed controller. The experimental results show the suitability of the proposed control to generate non-oscillatory controlled motion during the regulation of the flexible mechanic device shape., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

Differential neural networks (DiNNs) encounter a trade-off between the approximation quality and structural complexity. One promising approach to address this trade-off is incorporating dynamic complexity adjustment as an integral part of the learning process. Taking inspiration from the Fourier approximation theory, this study introduces a novel method for adapting the architecture of DiNNs, when they serve as nonparametric identifiers for dynamic systems with uncertain mathematical models. The structural adaptation process is executed through a recursive algorithm based on a modification structure strategy, which dynamically adjusts the number of neurons within the network's structure. By applying a projection operator to the set of neurons, this method identifies the most relevant sequence of sigmoidal functions, intending to minimize the mean square error in approximating the trajectories of uncertain systems. This simultaneous reduction in overall complexity enhances the quality of the approximations. Moreover, the proposed method can implement a coarse-to-fine approach, wherein selecting necessary neurons occurs in multiple steps. These steps are determined by an adaptive structure strategy that alters the topology of the DiNN. The resulting framework's effectiveness is demonstrated by evaluating the proposed identifier's performance in approximating the evolution of real-life data associated with the ocular response during controlled motions or virtual reality engagement. In both experimental cases, there was a noticeable improvement in the accuracy of eye motion approximation by the DiNN, thanks to the variable structure approximation basis determined by the adaptive structure strategy. Overall, this study presents a formal method to automatically determine a feasible DiNN topology.

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

In this research project, a closed-chain robotic active ankle orthosis with six degrees of freedom is designed, constructed, numerically valued, instrumented, and experimentally validated. The mechanical arrangement to implement the orthosis corresponds to a six-legged Stewart platform. An adaptive gain control strategy with state constraints based on a state-dependent gains control (that behaves as a diverging function as the states approach the state restrictions) operates the device's motion. The convergence to an invariant positive set centered at the origin of the tracking error space is validated using the stability analysis based on the second method of Lyapunov, with the implementation of a state barrier Lyapunov-like function. The ultimate boundedness of the tracking error is proven with an endorsed gains adjustment method leading to a reachable minimum size of the ultimate bound. Hence, the impact of the state constraints and the formal reason for applying the controller on the suggested orthosis are all established. The orthosis is also controlled using a conventional state feedback strategy to assess the tracking error for an external disturbance and contrast its performance with the proposed control approach. The technology is tested on a few carefully chosen volunteers, successfully limiting the range of motion within a pre-defined region based on the scope of movement reported by patients with ankle illnesses discovered in the literature. Based on a unique mechatronic device, the created system offers a fresh approach to treating this class of impairments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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

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.

This study presents the design of an adaptive event-driven controller for solving the trajectory tracking problem of a composite robotic device made up of a three-dimensional Cartesian and a parallel Delta robot. The proposed composite device has a mathematical model satisfying a standard Lagrangian structure affected by modeling uncertainties and external perturbations. The adaptive gain of the controller is considered to enforce the convergence of the tracking error while the state bounds are satisfied. The barrier Lyapunov function addresses the preconceived state constraints for both robotic devices by designing a time-varying gain that guarantees the ultimate boundedness of the tracking error under the effect of external perturbations. The event-driven approach considers that the Cartesian robot is moving into a predefined invariant zone near to the origin. In contrast, the delta robot can complete the tracking problem once the end-effector is inside the given zone. The suggested controller was evaluated using a virtual representation of the composite robotic device showing better tracking performance (while the restrictions are satisfied) than the performances obtained with the traditional linear state feedback controllers. Analyzing the mean square error and its integral led to confirming the benefits of using the adaptive barrier control., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

Wastewater disposal is a major environmental issue that pollutes water, causing eutrophication, habitat destruction, and economic impact. In Mexico, food-processing effluents pose a huge environmental threat due to their excessive nutrient content and their large volume discharged every year. Some of the most harmful residues are tequila vinasses, nejayote, and cheese whey. Each liter of tequila generates 13-15 L of vinasses, each kilogram of cheese produces approximately 9 kg of cheese whey, and each kilogram of nixtamalized maize results in the production of 2.5-3.3 L of nejayote. A promising strategy to reduce the contamination derived from wastewater is through microalgae-based wastewater treatment. Microalgae have a high adaptability to hostile environments and they can feed on the nutrients in the effluents to grow. Moreover, to increase the viability, profitability, and value of wastewater treatments, a microalgae biorefinery could be proposed. This review will focus on the circular bioeconomy scheme focused on the simultaneous food-processing wastewater treatment and its use to grow microalgae biomass to produce added-value compounds. This strategy allows for the revalorization of wastewater, decreases contamination of water sources, and produces valuable compounds that promote human health such as phycobiliproteins, carotenoids, omega-3 fatty acids, exopolysaccharides, mycosporine-like amino acids, and as a source of clean energy: biodiesel, biogas, and bioethanol., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Cesar E. Najar-Almanzor reports financial support was provided by CONACYT- Mexico. Tlalli Uribe-Velazquez reports financial support was provided by CONACYT- Mexico., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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

Motion restrictions in robotic devices may introduce complex requirements for any closed-loop control design, mainly when the robot joints must track reference trajectories that force the end-effector to perform planned motions. This study summarizes the comprehensive technical design of an adaptive state feedback controller for multi-link robotic manipulators that consider the effect of position and velocity restrictions on the tracking trajectory control approach. The proposed design is less conservative than other methods because of the explicit inclusion of state restrictions in the control gain dynamics. A logarithm barrier Lyapunov function class supports the design of the adaptive gain for the manipulator. Sufficient conditions based on a Riccati equation simplify the implementation of the adaptive controller with gains depending on the distance between the current state and the restriction sets. Numerical simulations show the advantages of the proposed controller with adaptive gains concerning a similar adaptive controller that does not consider the restrictions and a proportional-integral-derivative form. An implementation for the motion control of a robotic arm is presented to demonstrate the development by implementing the proposed gain, which confirms the suggested improvements enforced by the proposed controller. The performed comparison shows the advantages of the suggested adaptive gain control form, inducing better tracking of reference trajectories and smaller control energy applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

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.

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Velasco-Álvarez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Electromagnetic actuation results suitable for wireless driven motion, where the estimation of the force between magnetic elements is usually required. This force can lead to states where the magnetic-mechanical system remains fixed, requiring constraints to avoid the transgression of these states, and Barrier Lyapunov Functions (BLF) are useful for this purpose. This work presents an adaptive controller with BLF in a magnetic pendulum with state restrictions. It employs fixed electromagnets to induce motion on a pendulum with a permanent magnet as its bob. The force between the magnetic elements is obtained through approximation functions. A new implementation strategy for the control gains introduces the effect of state restrictions on the control action based on a control BLF. Results are analyzed in both simulations and experimental stages, which prove the advantages of employing BLF controllers in mechanical systems that require the avoidance of specific boundaries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

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

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., Competing Interests: Conflict of interestAll the authors declare no conflict of interest., (© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Acceleration tracking is a significant problem in aeronautics, automotive, and biomedical technical areas because its solution may yield effective simulation of motion cues. In the case of aeronautics, the proper solution for the tracking problem improves the in-flight simulations for the training of plane pilots. These simulators can be set up using robotic devices that develop controlled motions with the end-effector following the required three-dimensional reference accelerations robustly. Hence, the primary goal of this study is the effective application of the integral sliding mode controller to solve the acceleration tracking problem for the end-effector of a two-link robotic arm. The control design problem is formulated as an optimization of a convex (non-strict) performance functional depending on the difference between the acceleration of the robotic arm and the desired acceleration using the averaged sub-gradient (ASG) descendant method. A novel sliding surface considers the sensitiveness threshold for acceleration dynamics, inspired by the limit of detection in the pilot vestibular apparatus. The proposed controller was analyzed in terms of the finite-time convergence of the sliding surface and the practical stability analysis for the tracking error dynamics. Our main contribution is the design of the online averaged sub-gradient optimization controller based on integral SMCs. The controller solves the end-effector acceleration tracking for a two-link robotic arm, which implements a simplified version of a flight simulator that is considered to be operated under uncertain scenarios and assumes the presence of perturbations and modeling errors. The controller considers the case of incomplete knowledge of the robotic arm model, which adds an extra degree of robustness to the control design. The numerical evaluations demonstrate the attributes of the ASG formulation compared to traditional state feedback control, using the performance functional, the norm of the acceleration tracking error, and the control input variation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

This study describes the development (design, construction, instrumentation, and control) of a nursing mobile robotic device to monitor vital signals in home-cared patients. The proposed device measures electrocardiography potentials, oxygen saturation, skin temperature, and non-invasive arterial pressure of the patient. Additionally, the nursing robot can supply assistance in the gait cycle for people who require it. The robotic device's structural and mechanical components were built using 3D-printed techniques. The instrumentation includes electronic embedded devices and sensors to know the robot's relative position with respect to the patient. With this information together with the available physiological measurements, the robot can work in three different scenarios: (a) in the first one, a robust control strategy regulates the mobile robot operation, including the tracking of the patient under uncertain working scenarios leading to the selection of an appropriate sequence of movements; (b) the second one helps the patients, if they need it, to perform a controlled gait-cycle during outdoors and indoors excursions; and (c) the third one verifies the state of health of the users measuring their vital signs. A graphical user interface (GUI) collects, processes, and displays the information acquired by the bioelectrical amplifiers and signal processing systems. Moreover, it allows easy interaction between the nursing robot, the patients, and the physician. The proposed design has been tested with five volunteers showing efficient assistance for primary health care. Graphical Abstract Main stages of the home-care nursing controlled mobile robot., (© 2022. International Federation for Medical and Biological Engineering.)

The usage of socially assistive robots for autism therapies has increased in recent years. This novel therapeutic tool allows the specialist to keep track of the improvement in socially assistive tasks for autistic children, who hypothetically prefer object-based over human interactions. These kinds of tools also allow the collection of new information to early diagnose neurodevelopment disabilities. This work presents the integration of an output feedback adaptive controller for trajectory tracking and energetic autonomy of a mobile socially assistive robot for autism spectrum disorder under an event-driven control scheme. The proposed implementation integrates facial expression and emotion recognition algorithms to detect the emotions and identities of users (providing robustness to the algorithm since it automatically generates the missing input parameters, which allows it to complete the recognition) to detonate a set of adequate trajectories. The algorithmic implementation for the proposed socially assistive robot is presented and implemented in the Linux-based Robot Operating System. It is considered that the optimization of energetic consumption of the proposal is the main contribution of this work, as it will allow therapists to extend and adapt sessions with autistic children. The experiment that validates the energetic optimization of the proposed integration of an event-driven control scheme is presented., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor MA declared a shared affiliation with the authors at the time of review., (Copyright © 2023 Fuentes-Alvarez, Morfin-Santana, Ibañez, Chairez and Salazar.)

This study aims to propose an adaptive state-dependent gain finite-time convergent controller (using the fundamentals of the sliding mode theory) that solves the trajectory tracking for a class of state constraint master-slave robotic system (M-SRS) formed by two manipulators with the same number of articulations. The control design considers the effect of state constraints by implementing a state dependent adaptive gain. A Lyapunov-stability analysis leads to design the gain variation laws yielding proving the finite-time convergence of the sliding surface as well as the asymptotic convergence of the tracking error. The state constraints of the slave system motivate the characterization of the convergence-time as a function of the bounded uncertainties affecting the M-SRS dynamics. The forward-complete setting of the M-SRS justified the application of a robust and exact differentiator which estimated the articulation velocities for the slave robot. The estimated velocities are used as part of the realization of the output feedback controller. Numerical simulations demonstrate that the proposed control scheme provides a smaller quadratic norm of the tracking error compared with the obtained with other controllers (proportional-derivative and conventional sliding modes). The proposed control approach satisfies the state constraints while the sliding manifold converges to the origin in finite-time as justified by the theoretical stability analysis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

A new design of a non-parametric adaptive approximate model based on Differential Neural Networks (DNNs) applied for a class of non-negative environmental systems with an uncertain mathematical model is the primary outcome of this study. The approximate model uses an extended state formulation that gathers the dynamics of the DNN and a state projector (pDNN). Implementing a non-differentiable projection operator ensures the positiveness of the identifier states. The extended form allows producing continuous dynamics for the projected model. The design of the learning laws for the weight adjustment of the continuous projected DNN considered the application of a controlled Lyapunov-like function. The stability analysis based on the proposed Lyapunov-like function leads to the characterization of the ultimate boundedness property for the identification error. Applying the Attractive Ellipsoid Method (AEM) yields to analyze the convergence quality of the designed approximate model. The solution to the specific optimization problem using the AEM with matrix inequalities constraints allows us to find the parameters of the considered DNN that minimizes the ultimate bound. The evaluation of two numerical examples confirmed the ability of the proposed pDNN to approximate the positive model in the presence of bounded noises and perturbations in the measured data. The first example corresponds to a catalytic ozonation system that can be used to decompose toxic and recalcitrant contaminants. The second one describes the bacteria growth in aerobic batch regime biodegrading simple organic matter mixture., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This study introduces a design of robust finite-time controllers that aims to solve the trajectory tracking of robot manipulators with full-state constraints. The control design is based on the construction of a distributed state constraint non-singular terminal sliding mode (CNTSM). The CNTSM design includes the gain self-adapting tuning method, which can ensure finite-time convergence to the sliding surface aside from the states to its corresponding reference trajectories. The implementation of the time-varying gain ensures the fulfillment of the accurate tracking for the references while the position and velocity constraints are satisfied permanently. A barrier Lyapunov function is proposed to develop the finite-time stability analysis of the designed controllers. The CNTSM realization uses the tracking error as well as its estimated derivative, which is calculated using a variant of adaptive super-twisting algorithm operating as robust differentiator. The proposed CNTSM is numerically evaluated on a two-link RM with uncertain inertia and Coriolis matrices. Simulation and experimental results evidence the efficiency of the CNTSM controller demonstrating a better tracking performance while the full-state constraints are satisfied in counterpart with the classical non-singular terminal sliding mode which is not able to keep such restrictions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 ISA. Published by Elsevier Ltd. All rights reserved.)

This study evaluated different carbon and nitrogen sources on the growth and production of carbohydrates, protein, lipids, and chlorophyll of Spirulina platensis LEB-52 through an easy successive methodology under aqueous conditions. Spirulina platensis was cultivated at 120 rpm and light intensity of 156 µmol m -2 s -1 in a 500 mL Erlenmeyer flask with a working volume of 250 mL, using Zarrouk's medium. The biomass, carbohydrate, and protein production together with the specific growth rate did not show a significant difference between NaHCO 3 and Na 2 CO 3 . The salts of urea and ammonium are not an alternative nitrogen sources of low cost for Spirulina platensis cultivation. From the experimental results obtained in this study, a successful estimate of carbohydrate, protein, lipids, and chlorophyll content inside Spirulina platensis was achieved without use advanced analytical techniques, allowing saves resources and time. This method can be extrapolated to other microorganisms and cultivation regimens., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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, µ max ) 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 µ max values was observed when culture media (agar, lactose, MEA) is supplemented with coconut water. The largest values of µ max 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., (© 2021. The Author(s).)

In this paper, a non-parametric model of the neuromusculoskeletal system for the biceps brachii is presented. The model serves to generate angular paths for the control of a virtual active orthosis. The path generator uses a differential neural network (DNN) identifier that obtains the reference angular position and velocities using the raw electromyographic (EMG) signals as input. The model is validated using experimental data. The training and closed-loop implementation of the proposed model are described. The control strategy ensures that the user reaches a set-point with a predefined position constraint and that the device follows the natural reference path that corresponds to the raw EMG signal.

This study describes the design, instrumentation and control of an exoskeleton for lower limb children rehabilitation with nine degrees of freedom. Three degrees of freedom in each leg exert the movements of hip, knee and ankle in the sagittal plane, and three control the drive track system composed by a caterpillar-like robot. The control scheme presents a model free decentralized output feedback adaptive high-order sliding mode control to solve the trajectory tracking problem in each degree of freedom of the exoskeleton. A high order sliding mode differentiator estimates the unmeasured states and, by means of a dynamical state extension, it approximates the unknown dynamical model of the exoskeleton. A second-order adaptive sliding mode controller based on the super-twisting algorithm drives the exoskeleton articulations to track the proposed reference trajectories, inducing an ultimate boundedness for the tracking error. Numerical and experimental simulation results demonstrate the effect of the adaptive gain on the super-twisting control design. Such evaluations confirmed the superior tracking performance forced by the adaptive law for the controller with a smaller chattering amplitude and smaller mean tracking error., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

The microbial co-cultures or consortia are a natural set of microorganisms formed from different species or the same species but different strains, in which members can interact with each other. The co-culture systems have wide variety of technological applications such as the production of foods, treatment of wastewater, removal of toxic substances, environmental recovery, and all these without the need to work in sterile conditions. Therefore, the need of understanding communication mechanisms between cell-to-cell within co-culture will allow to construct and to program their biological behavior from the use of complex substrates to produce biocompounds. The technology of co-culture systems enables the development of biorefinery platforms to obtain biofuels, and high value compounds through biomass transformation by sustainable process. This review focuses on understanding the roles of consortia microbial to design and built co-culture systems to produce high value compounds in terms a sustainable biorefinery., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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-Fe 2 O 3 ), and gibbsite (γ-Al(OH) 3 ) in combination with O 3 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-Fe 2 O 3  < α-FeOOH. Among these three oxides, goethite has the highest OH groups density. The reaction of OH groups and O 3 favors the formation of oxidant species, such as O 2 • - 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 O 3 -soil system, while only 40% was obtained with O 3 -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 O 3 decomposition. The toxicity tests of the three reaction systems (O 3 -sand, O 3 -sand (α-FeOOH), and O 3 -soil) were evaluated on lettuce seed germination before and after DEP ozonation.

The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifier based on a class of continuous artificial neural networks (ANNs). The design of adaptive controllers used the estimated weights on the identifier structure yielding a compensating structure and a linear correction element on the tracking error. The stability of both the identification and tracking errors, considering the DNN, uses a barrier Lyapunov function (BLF) that grow to infinity whenever its arguments approach some finite limits for the state satisfying some predefined ellipsoid bounds. The analysis guarantees the semi-globally uniformly ultimately bounded (SGUUB) solution for the tracking error, which implies the achievement of an invariant set. The suggested controller produces closed-loop bounded signals. This article also presents the comparison between the tracking states forced by the adaptive controller estimated with the DNN based on BLF and quadratic Lyapunov functions as well. The effectiveness of the proposal is demonstrated with a numerical example and an implementation in a real plant (mass-spring system). This comparison confirmed the superiority of the suggested controller based on the BLF using the estimates of the upper bounds for the system states.

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)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

Improving the competitiveness of biodiesel production by microalgae cultures requires the application of several strategies to obtain a high content of lipids, rapid biomass growth and a capacity to adapt to different kinds of environment, with the aim of using non-renewable nutrient sources. Therefore, the use of an individual indigenous microalgae strain or a consortium from natural or anthropogenic sites is now considered an alternative for biofuel production. This study examined the temporal behaviour of secondary metabolites produced by a native microalgae and yeast consortium isolated from wastewater, which was characterized by a genetic identification method based on the MiSeq system. The predominant species in the consortium was Scenedesmus obliquus, representing 68% of the organisms. In addition, the consortium contained a number of yeast species, including Candida pimensis (43%), Arthroderma vanbreuseghemii (23%), Diaporthe aspalathi/Diaporthe meridionalis (25%) and Hericium americanum (3%). This indigenous co-culture of microalgae and yeast showed biomass productivity of 0.06 g l -1  day -1 , with a content of 30% (w/w) carbohydrates, 4% (w/w) proteins and 55% (w/w) lipids. Transesterification of the extracted lipids produced fatty acid methyl esters (FAMEs), which were analysed by gas chromatography (GC). The FAMEs included methyl pentadecanoate (1.90%), cis-10-pentanedecanoic acid methyl ester (1.36%), methyl palmitate (2.64%), methyl palmitoleate (21.36%), methyl oleate (64.95%), methyl linolenate (3.83%) and methyl linolelaidate (3.95%). This composition was relevant for biodiesel production based on the co-culture of indigenous microalgae and yeast consortia.

The main goal of this study is developing an adaptive controller which can solve the trajectory tracking for a class of quadcopter unmanned aerial system (UAS), namely a quadrotor. The control design introduces a new paradigm for adaptive controllers based on the implementation of a set of differential neural networks (DNNs) in the consequence section of a Takagi-Sugeno (T-S) fuzzy inference system. This dynamic fuzzy inference structure was used to approximate the UAS description. The particular form of interaction between neural networks and fuzzy inference systems proposed in the present work received the name of dynamic neural fuzzy system (DNFS). An adaptive controller based on this DNFS form was the main solution attained in this study. This DNFS controller was focused on the estimation and compensation of the uncertain section of the Quadrotor dynamics and then, forced the UAS to perform a hover flight while the tracking of desired angular positions succeeded, which results in tracking a desired trajectory in the X-Y plane. The control design methodology supported on the Lyapunov stability theory guaranteed ultimate boundedness of the estimation and tracking errors simultaneously. Several experimental tests in an outdoor environment by using a real Quadrotor platform was performed by using an RTK-GPS (Real Time Kinematic) system to determine the position of the vehicle in the X-Y plane. The experimental results confirmed the superior performance of the proposed algorithm based on the combination of DNNs and T-S techniques with respect to classical robust controllers., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

The carbamazepine (CBZ) abatement is herein evaluated using catalytic ozonation at different NiO concentrations as catalyst: 100, 300, and 500 mg L -1 , revealing its total destruction after 5 min of reaction either by conventional or catalytic ozonation. The NiO incorporation in the reactor does not increase the destruction rate, but the catalyst presence enhances the partial mineralization of the contaminant by conversion into oxalic and formic acids and the removal of total organic carbon (TOC) associated with the formation of oxidant species such as hydroxyl radical. Evidence for this behavior is the accumulation rate of the above acids which rise proportionally to the NiO concentration. The highest NiO concentration (500 mg L -1 ) reached a maximum TOC removal of 79.2%, which exceeds by 50% the outcome of the conventional treatment. The accumulation-decomposition profiles of oxalic and formic acids suggest the occurrence of simultaneous reaction mechanisms (hydroxyl radicals and complex formations) on the catalyst during CBZ ozonation. According to XPS analysis, the presence of nitrogen species in the NiO-ozonated was attributable to byproducts of CBZ decomposition. The toxicity bioassay based on Lactuca sativa seeds demonstrate that ozonated samples attained similar plant germination than the reference substance (water) after 120 min of treatment. This result is comparable with or without the catalyst presence, indicating the formation of non-toxic accumulated byproducts at the end of the ozonation reaction.

Previously, we reported the preparation and preclinical studies of 99m Tc-labeled gold nanoparticles-mannose ( 99m Tc-AuNP-mannose) with potential for sentinel lymph node (SLN) detection by using nuclear medicine procedures. This study aimed to evaluate the biokinetics and hybrid (2D/3D) dosimetry of 99m Tc-AuNP-mannose in five patients with breast cancer under a sentinel lymph node detection protocol. Anterior and posterior whole-body planar images (2D, at 0.5, 2, 6, and 24 h) and single-photon emission computed tomography (3D at 6.5 h)/computed tomography (SPECT/CT) images were acquired after 99m Tc-AuNP-mannose administration (37 MBq). Through a hybrid quantification method, activity in tissues of interest at the different acquisition times was determined and integrated over time to obtain the total nuclear transformations ( N ), as well as the mean residence time, in each tissue. N values and the OLINDA code were used for estimating the internal radiation absorbed doses. Results demonstrated that 99m Tc-AuNP-mannose successfully accumulates and remains up to 24 h in the sentinel lymph node without detectable migration to other lymph nodes and no side effects on patients. Negligible absorption of the radiolabeled nanoparticles into the circulatory system was observed, from which the radio-nanosystem is rapidly eliminated by kidneys. Hybrid (2D/3D) dosimetry evaluations showed equivalent doses to SLN, breast, and kidneys of 172.34, 5.32, and 0.08 mSv/37 MBq, respectively, with an effective dose of 2.05 E  - 03 mSv/MBq. The mean effective residence time in SLN was 0.92 h. This preliminary study indicates that the use of 99m Tc-AuNP-mannose for successful SLN detection in patients is safe, producing an effective dose at the level recommended for diagnostic studies (<10 mSv)., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2020 Gerardo Ramírez-Nava et al.)

The design of an artificial neural network (ANN) based sub-optimal controller to solve the finite-horizon optimization problem for a class of systems with uncertainties is the main outcome of this study. The optimization problem considers a convex performance index in the Bolza form. The dynamic uncertain restriction is considered as a linear system affected by modeling uncertainties, as well as by external bounded perturbations. The proposed controller implements a min-max approach based on the dynamic neural programming approximate solution. An ANN approximates the Value function to get the estimate of the Hamilton-Jacobi-Bellman (HJB) equation solution. The explicit adaptive law for the weights in the ANN is obtained from the approximation of the HJB solution. The stability analysis based on the Lyapunov theory yields to confirm that the approximate Value function serves as a Lyapunov function candidate and to conclude the practical stability of the equilibrium point. A simulation example illustrates the characteristics of the sub-optimal controller. The comparison of the performance indexes obtained with the application of different controllers evaluates the effect of perturbations and the sub-optimal solution., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)

This article summarizes the comprehensive design of an adaptive control that solves the trajectory tracking of a four-rotor unmanned aerial vehicle supplied by an own-designed grasping device. The controller design uses the regular proportional-derivative (PD) structure which is common to solve regulation, as well as tracking problems for robotic systems. The PD structure needs the estimation of the time-derivative to be exerted on-line. The super-twisting algorithm served to operate as a decentralized estimator of the derivatives for the tracking errors. The controller gains were adjusted by the differential laws aimed to track attainable reference trajectories. The adaptive strategy adjusts the controller gains enforcing the convergence of both the estimation and the tracking errors. A numerical example showed the observer/controller performance. A comparison with a classic non-adaptive PD controller confirms the effectiveness on the tracking task by the proposed design. In addition, a set of virtual numerical evaluations using the parameters of a real quadcopter system confirmed the performance benefits of the adaptive controller based on the PD structure aided with the estimation of the velocity obtained by the observer., (Copyright © 2019. Published by Elsevier Ltd.)

This work evaluated the inhibition effect of low molecular weight alcohol (ethanol) on naproxen (NAP) degradation by conventional and catalytic ozonation. The reaction system considered the ethanol as complementary organic matter in water. The conventional ozonation and in the presence of nickel oxide (O 3 -NiO) achieved 98% NAP degradation during the first 15 min of reaction despite the presence of ethanol. However, NAP degradation presented a delaying effect during the first minutes of treatment with this alcohol. The latter phenomenon indicates that ethanol concentration played a meaningful role in ozonation effectiveness in comparison with the presence of NiO catalyst. The presence of NiO did not generate differences in the byproducts in comparison with conventional ozonation. The intermediates were detected using the Electrospray Ionization Mass Spectrometry technique and have only one aromatic ring in their chemical structure. In samples without ethanol, these byproducts appeared only in the first 5 min of reaction. The TOC study demonstrated the increment of 25% in the mineralization degree with the presence of NiO due to the formation of ·OH species., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

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., (Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.)