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The WHO Mental Health Gap Action Programme (mhGAP) guideline update reflects 15 years of investment in reducing the treatment gap and scaling up care for people with mental, neurological, and substance use (MNS) conditions. It was produced by a guideline development group and steering group, with support from topic experts, using quantitative and qualitative evidence and a systematic review of use of mhGAP. 90 recommendations from the 2015 guideline update were validated and endorsed for use in their current format. These are joined by 30 revised recommendations and 18 new recommendations, including a new module on anxiety. Psychological interventions are emphasised as treatments and digitally delivered interventions feature across many modules, as well as updated recommendations for psychotropic medicines. Research gaps identified include the need for evidence from low-resource settings and on the views of people with lived experience of MNS conditions. The revised recommendations ensure that mhGAP continues to offer high-quality, timely, transparent, and evidence-based guidance to support non-specialist health workers in low-income and middle-income countries in providing care to individuals with MNS conditions.

The design of incentive-compatible mechanisms for a certain class of finite Bayesian partially observable Markov games is proposed using a dynamic framework. We set forth a formal method that maintains the incomplete knowledge of both the Bayesian model and the Markov system’s states. We suggest a methodology that uses Tikhonov’s regularization technique to compute a Bayesian Nash equilibrium and the accompanying game mechanism. Our framework centers on a penalty function approach, which guarantees strong convexity of the regularized reward function and the existence of a singular solution involving equality and inequality constraints in the game. We demonstrate that the approach leads to a resolution with the smallest weighted norm. The resulting individually rational and ex post periodic incentive compatible system satisfies this requirement. We arrive at the analytical equations needed to compute the game’s mechanism and equilibrium. Finally, using a supply chain network for a profit maximization problem, we demonstrate the viability of the proposed mechanism design.

Purpose: At first, p53 was believed to be present primarily in malignant cells. In fact, p53 turned out to play a major role in vascular remodeling associated with transplantation surgery or intraluminal intervention. The purpose of this review was to describe the multiple processes which include p53 activation and to analyze its impact on cellular proliferation and migration, as well as its involvement in such conditions as pulmonary arterial hypertension, subarachnoid hemorrhage, ischemia and others. Methods: To select the initial sources for our review, we searched the PubMed database with the keywords “p53 mechanisms”, “p53 atherosclerosis”, “p53 cardiovascular”, etc. To maintain the relevance, we selected only papers published in 2018 and later. Results: Overall, p53 is proved to have a strong relationship with the whole cardiovascular system. Available data also indicate a connection of p53 to the atherosclerosis initiation and progression. P53 might become a promising therapeutic target for treatment of acute and chronic cardiovascular diseases due to its ability to influence various vascular mechanisms. Conclusion: Modifying the synthesis of this protein and the expression of the corresponding genes to create models, cellular and animal, that allow us to better understand about complex nature of the pathogenesis of atherosclerosis seems to be promising. Also, p53 can be considered as a possible target for the development of therapeutic strategies. The same is true both for the protein itself and for all participants in the pathway of its metabolism.

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.

Despite the worrying prevalence of mental disorders among adolescents and young people, evidence on predictors of treatment outcome remains scarce. Systematic reviews have focused on specific disorders or treatment modalities and have not targeted this age group in particular. This systematic review presents an overview of the sociodemographic predictors and moderators on the outcome of psychotherapeutic interventions for adolescents and young people with mental disorders across treatment modalities. The search was conducted in PubMed and PsycINFO following the PICOS strategy. The included studies were clinical outcome studies on psychotherapeutic interventions, targeted for young people (aged 12–30 years) with specified mental disorders and published in peer-reviewed journals. During a four-step screening process, 17,359 articles were assessed, among which 114 met the inclusion criteria. Most included studies involved patients with mood, eating, or substance use disorders. Age, gender, and ethnicity were the most frequently reported predictors. For age and gender the findings were mixed, so it is not possible to state which age group or gender would benefit most from treatment across disorder groups. Ethnic minority status and history of traumatic events may predict poorer outcomes. However, the results mainly did not support the relevance of sociodemographic variables for predicting treatment outcome.

The main goal of the current investigation is to present an output feedback robust control that regulates the motion of mobile robotic autonomous devicesbased 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.

This study focuses on developing a continuous differential neural network (DNN) approximating min–max robust control by applying dynamic neural programming. The suggested controller is applied to a class of nonlinear perturbed systems providing satisfactory dynamics for a given cost function depending on both the trajectories of the perturbed system and the designed constrained control actions. The min–max formulation for dynamic programming offers reliable control for restricted modeling uncertainties and perturbations. The suggested design considers control norm restrictions in the optimization problem. DNN’s approximation of the worst (with respect to the admissible class of perturbations and uncertainties) value function of the Hamilton–Jacobi–Bellman (HJB) equation enables the estimation of the closed-loop formulation of the controller. The robust version of the HJB partial differential equation is studied to create the learning law class for the time-varying weights in the DNN. The controller employs a time-varying Lyapunov-like differential equation and the solution of the corresponding learning laws. A recurrent algorithm based on the Kiefer–Wolfowitz technique can be used by modifying the weights’ initial conditions to fulfill the specified cost function’s end requirements. A numerical example tests the robust control proposed in this study, validating the robust optimal solution based on the DNN approximation for Bellman’s value function.

H*-aggregates are a rarely observed type of molecular aggregates, with many of their properties yet to be investigated. The indotricarbocyanine dye under study is the first known compound able to produce both H*- and J-aggregates. Steady-state spectral properties, morphology, photoelectrochemical response, and excited-state dynamics of the H*-aggregates are investigated for the first time. Under 400 nm pumping, the deactivation of electronic excitations in the H*-aggregates follows two paths involving the top and bottom of the exciton band. This is consistent with the presence of a fast 3 ps and a slow 33 ps component in the decay trace of the ground-state bleaching. Under 800 nm pumping, the H*-aggregates are promoted to the bottom of the exciton band. Consequently, only the slow component is present in the decay trace of the ground-state bleaching. For the J-aggregates, the ground-state bleaching follows biexponential decay with the time constants of 1 and 20 ps under both 400 and 800 nm pumping. The relaxation involves only the bottom of the exciton band. The faster deactivation of electronic excitations in the J-aggregates is probably due to greater exciton delocalization: 9 molecules in the J-aggregates versus 4 molecules in the H*-aggregates. The obtained results can be used to create photonic devices containing the H*-aggregates.

The aim of this study is to provide a class of state observers, based on differential neural networks, to approximate a class of advanced oxidation systems, based on the application of ozone high oxidant power and catalyst (the named catalytic ozonation). The study considers the design of a state observer for uncertain systems with the restrictions of the ozonation system, including the positivity of the states, as well as the control action. The observer includes a projection operator which is motivated by the state constraints. The learning laws of the proposed differential neural networks are obtained using a class of controlled state restricted Lyapunov functions. The detailed stability analysis proves the input to state stability with respect to the modeling error, as well as the bounded uncertainties of the ozonation system. The experimental confirmation of the state estimation is also presented. The experimental case considers the ozonation of a toxic organic contaminant (therephtalic acid) which is a regular pollutant of the plastic industry wastewater.

The purpose of this study is to present the design of robust control based on the integral sliding mode control version of the averaged subgradient for a robotic manipulator considering the dynamics of the direct current motor, which is driven by a power converter electrical device. The control action is sequentially developed in the subsystems of the complete model, realizing the so-called backstepping (or cascade) approach, including the perturbed dynamics of the actuator. The proposed control strategy for solving the trajectory tracking problem in each stage implements the averaged subgradient-version of the integral sliding mode technique. The controller design solution is treated as optimizing 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. The main result of this study shows that the minimization of the proposed functional leads to the optimal tracking regime in the presence of significant uncertainties in the mathematical model of the power converter. 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 standard proportional integral derivative (PID) controllers is presented. The proposed controller exhibits a better tracking of the reference trajectory than the PID version, showing a smaller mean square estimation for the tracking error.

This study aims to present the design of robust control based on the integral sliding mode control version of the averaged sub-gradient for a robotic manipulator considering the dynamics of the direct current motor device driven by a power converter electrical system. The control action is sequentially operated, developing a class of back-stepping approaches, including the perturbed dynamics of the actuator. The proposed control strategy for solving the end-effector trajectory tracking problem in each stage implements the averaged subgradient-version of the integral sliding mode technique aided with an adaptive approximation of the robotic arm dynamics using an artificial neural network with differential evolution. The main result of this study shows that the minimization of the proposed functional leads to the optimal tracking regime. A numerical example proves the effectiveness of the suggested robust dynamic controller. The proposed controller exhibits a better tracking of the reference trajectory than the state feedback version.

In 2013, the American Psychiatric Association (APA) published the 5th edition of its Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). In 2019, the World Health Assembly approved the 11th revision of the International Classification of Diseases (ICD‐11). It has often been suggested that the field would benefit from a single, unified classification of mental disorders, although the priorities and constituencies of the two sponsoring organizations are quite different. During the development of the ICD‐11 and DSM‐5, the World Health Organization (WHO) and the APA made efforts toward harmonizing the two systems, including the appointment of an ICD‐DSM Harmonization Group. This paper evaluates the success of these harmonization efforts and provides a guide for practitioners, researchers and policy makers describing the differences between the two systems at both the organizational and the disorder level. The organization of the two classifications of mental disorders is substantially similar. There are nineteen ICD‐11 disorder categories that do not appear in DSM‐5, and seven DSM‐5 disorder categories that do not appear in the ICD‐11. We compared the Essential Features section of the ICD‐11 Clinical Descriptions and Diagnostic Guidelines (CDDG) with the DSM‐5 criteria sets for 103 diagnostic entities that appear in both systems. We rated 20 disorders (19.4%) as having major differences, 42 disorders (40.8%) as having minor definitional differences, 10 disorders (9.7%) as having minor differences due to greater degree of specification in DSM‐5, and 31 disorders (30.1%) as essentially identical. Detailed descriptions of the major differences and some of the most important minor differences, with their rationale and related evidence, are provided. The ICD and DSM are now closer than at any time since the ICD‐8 and DSM‐II. Differences are largely based on the differing priorities and uses of the two diagnostic systems and on differing interpretations of the evidence. Substantively divergent approaches allow for empirical comparisons of validity and utility and can contribute to advances in the field.

Background: The aim of the elucidation of mechanisms implicated in the chronification of inflammation is to shed light on the pathogenesis of disorders that are responsible for the majority of the incidences of diseases and deaths, and also causes of ageing. Atherosclerosis is an example of the most significant inflammatory pathology. The inflammatory response of innate immunity is implicated in the development of atherosclerosis arising locally or focally. Modified low-density lipoprotein (LDL) was regarded as the trigger for this response. No atherosclerotic changes in the arterial wall occur due to the quick decrease in inflammation rate. Nonetheless, the atherosclerotic lesion formation can be a result of the chronification of local inflammation, which, in turn, is caused by alteration of the response of innate immunity. Objective: In this review, we discussed potential mechanisms of the altered response of the immunity in atherosclerosis with a particular emphasis on mitochondrial dysfunctions. Conclusion: A few mitochondrial dysfunctions can be caused by the mitochondrial DNA (mtDNA) mutations. Moreover, mtDNA mutations were found to affect the development of defective mitophagy. Modern investigations have demonstrated the controlling mitophagy function in response to the immune system. Therefore, we hypothesized that impaired mitophagy, as a consequence of mutations in mtDNA, can raise a disturbed innate immunity response, resulting in the chronification of inflammation in atherosclerosis.

Background: Atherosclerosis is a chronic inflammatory condition that affects different arteries in the human body and often leads to severe neurological complications, such as stroke and its sequelae. Affected blood vessels develop atherosclerotic lesions in the form of focal thickening of the intimal layer, so called atherosclerotic plaques. Objectives: Despite the high priority of atherosclerosis research for global health and the numerous preclinical and clinical studies conducted, currently, there is no effective pharmacological treatment that directly impacts atherosclerotic plaques. Many knowledge gaps exist in our understanding of the mechanisms of plaque formation. In this review, we discuss the role of mitochondria in different cell types involved in atherogenesis and provide information about mtDNA mutations associated with the disease. Results: Mitochondria of blood and arterial wall cells appear to be one of the important factors in disease initiation and development. Significant experimental evidence connects oxidative stress associated with mitochondrial dysfunction and vascular disease. Moreover, mitochondrial DNA (mtDNA) deletions and mutations are being considered as potential disease markers. Further study of mtDNA damage and associated dysfunction may open new perspectives for atherosclerosis treatment. Conclusion: Mitochondria can be considered as important disease-modifying factors in several chronic pathologies. Deletions and mutations of mtDNA may be used as potential disease markers. Mitochondria-targeting antioxidant therapies appear to be promising for the development of treatment of atherosclerosis and other diseases associated with oxidative stress and chronic inflammation.

The present study details the validation of a simple mathematical model of dimethyl phthalate (DMP) ozonation distributed on sand (as a model soil) and the agricultural soil (real soil). The proposed model considers the time-varying parameters that characterize the ozone mass transfer in viscous liquid and different solid matrices and the observed reaction rate of DMP ozonation, described by a system of ordinary differential equations. To simplify the description of the heterogeneous DMP distribution on the studied soils (the calcinated sand and soil and the glass spheres), the model included a global distinguishing parameter, namely, the adsorption constant (kads,min–1), characterizing the ozone adsorbed on the solid phase, and the time-varying function ksat(t), which described the ozone mass transfer into DMP clusters as well as the reaction constant of ozone with DMP. The evaluation of the proposed model was realized by the comparison of the numerical simulation and the experimental data of DMP decomposition in the different solid phases, which demonstrated a good coincidence, considering the correlation factors of 0.93 in average. The conformity between experimental data and the model evaluated with the estimated parameters may provide a justification to exploit the suggested mathematical formulation to study ozonation of different viscous liquids.

Alcohol-related liver disease (ALD), which includes a range of disorders of different severity and is one of the most prevalent types of liver disease worldwide, has recently regained increased attention. Among other reasons, the realisation that any alcohol intake, regardless of type of beverage represents a health risk, and the new therapeutic strategies tested in recently published or undergoing clinical trials spur scientific interest in this area.In April 2019, Gut convened a round table panel of experts during the European Association for the Study of the Liver International Liver Congress in Vienna to discuss critical and up-to-date issues and clinical trial data regarding ALD, its epidemiology, diagnosis, management, pathomechanisms, possible future treatments and prevention. This paper summarises the discussion and its conclusions.

This study is devoted to the design of an adaptive discontinuous control based on differential neural networks (DNNs) for a class of uncertain homogeneous systems. The control is based on the universal approximation properties of artificial neural networks (ANNs) applied on a certain class of homogeneous nonlinear functions. The adaptation laws for the DNNs parameters are obtained with the application of the Lyapunov stability theory and the homogeneity properties of the approximated nonlinear system. The stability analysis of the closed loop system with the proposed controller is presented. The estimation error in the approximation of the uncertain homogeneous functions is considered in the stability analysis. The performance of the controller is illustrated by means of a numerical simulation of a homogeneous model.

The aim of this study was to develop an adaptive state estimator armed with discontinuous learning laws for the catalytic ozonation system. A class of differential neural network served to estimate the uncertain section of the uncertain catalytic process. The learning laws used for adjusting the weights included in the neural network based estimator. A set of numerical simulations demonstrated the application of the DNN based state observer and showed the estimation of the non-measurable information in the catalytic ozonation system. The adaptive state estimator with discontinuous learning laws was also evaluated with experimental information. The comparison of suggested and asymptotically convergent DNN based observer demonstrated the superior estimation performance offered by the estimator introduced in this study.

Uncontrollable dendrite growth and corrosion induced by reactive water molecules and sulfate ions (SO42–) seriously hindered the practical application of aqueous zinc ion batteries (AZIBs). Here we construct artificial solid electrolyte interfaces (SEIs) realized by sodium and calcium bentonite with a layered structure anchored to anodes (NB@Zn and CB@Zn). This artificial SEI layer functioning as a protective coating to isolate activated water molecules, provides high-speed transport channels for Zn2+, and serves as an ionic sieve to repel negatively charged anions while attracting positively charged cations. The theoretical results show that the bentonite electrodes exhibit a higher binding energy for Zn2+. This demonstrates that the bentonite protective layer enhances the Zn-ion deposition kinetics. Consequently, the NB@Zn//MnO2and CB@Zn//MnO2full-battery capacities are 96.7 and 70.4 mAh g–1at 2.0 A g–1after 1000 cycles, respectively. This study aims to stabilize Zn anodes and improve the electrochemical performance of AZIBs by ion-selection sieving.

This paper suggests a novel continuous-time robust extremum seeking algorithm for an unknown convex function constrained by a dynamical plant with uncertainties. The main idea of the proposed method is to develop a robust closed-loop controller based on sliding modes where the sliding surface takes the trajectory around a zone of the optimal point. We assume that the output of the plant is given by the states and a measure of the function. We show the stability and zone-convergence of the proposed algorithm. In order to validate the proposed method, we present a numerical example.

The Eleventh Revision of the International Classification of Diseases (ICD‐11) was formally published in May 2019. Alcohol use disorders form a key part of the section of Disorders due to Substance Use and Addictive Behaviours. This review describes and discusses the alcohol diagnoses within this section of ICD‐11, including Alcohol Dependence, Harmful Pattern of Use of Alcohol, and entities such as Alcohol Intoxication, Alcohol Withdrawal, and several alcohol‐induced mental disorders, and briefly covers Hazardous Alcohol Use, which is listed separately as a health risk factor. We summarize the historical background to the development of these diagnoses, including work within the World Health Organization since the 1970s, and the corresponding diagnoses in the current ICD‐10. The process by which ICD‐11 diagnoses have been made is described and may be summarized as a conceptual–pragmatic–confirmatory one. The available empirical data supporting the ICD‐11 diagnoses are presented, particularly in relation to the diagnostic guidelines for Alcohol Dependence. Comparison is made with the corresponding diagnoses in ICD‐10 and their nearest counterparts in the fourth and fifth editions of the Diagnostic and Statistical Manual of Mental Disorders. Field testing of the ICD‐11 diagnoses is currently in progress. A plea is made for matching of diagnoses, diagnostic guidelines/criteria, and the assessment tools intended to capture these diagnoses. In ICD‐11 the diagnosis of Alcohol Dependence has been retained, in simplified format, as the central alcohol use disorder. Concordance with its ICD‐10 and DSM‐IVequivalents is excellent, but it does not tally well with the broadly formulated DSM‐5 Alcohol Use Disorder. Work is needed to develop the most effective crosswalk between the two. ICD‐11 Harmful Alcohol Use is a non‐dependence disorder; it signifies repeated alcohol consumption which has caused harm or through the person's behaviour, harm to others.

Following approval of the ICD‐11 by the World Health Assembly in May 2019, World Health Organization (WHO) member states will transition from the ICD‐10 to the ICD‐11, with reporting of health statistics based on the new system to begin on January 1, 2022. The WHO Department of Mental Health and Substance Abuse will publish Clinical Descriptions and Diagnostic Guidelines (CDDG) for ICD‐11 Mental, Behavioural and Neurodevelopmental Disorders following ICD‐11’s approval. The development of the ICD‐11 CDDG over the past decade, based on the principles of clinical utility and global applicability, has been the most broadly international, multilingual, multidisciplinary and participative revision process ever implemented for a classification of mental disorders. Innovations in the ICD‐11 include the provision of consistent and systematically characterized information, the adoption of a lifespan approach, and culture‐related guidance for each disorder. Dimensional approaches have been incorporated into the classification, particularly for personality disorders and primary psychotic disorders, in ways that are consistent with current evidence, are more compatible with recovery‐based approaches, eliminate artificial comorbidity, and more effectively capture changes over time. Here we describe major changes to the structure of the ICD‐11 classification of mental disorders as compared to the ICD‐10, and the development of two new ICD‐11 chapters relevant to mental health practice. We illustrate a set of new categories that have been added to the ICD‐11 and present the rationale for their inclusion. Finally, we provide a description of the important changes that have been made in each ICD‐11 disorder grouping. This information is intended to be useful for both clinicians and researchers in orienting themselves to the ICD‐11 and in preparing for implementation in their own professional contexts.

In this paper, a non parametric identifier for homogeneous nonlinear systems affine in the input is proposed. The identification algorithm is based on the neural networks using sigmoidal activation functions. The learning algorithm is derived by means of Lyapunov function method and homogeneity theory. A numerical example demonstrates the performance of the proposed identifier.

The successful preparation of the titania nanotubular layers (TNT) in different deep eutectic solvent based electrolytes was performed in the current work. Choline chloride based deep eutectic solvents with various hydrogen bond donors, such as ethylene glycol, urea and malonic acid were used. All results were compared with standard ethylene glycol based electrolyte. The influence of the solution temperature, fluoride ion concentration, applied voltage and anodization time on morphology of the titania nanotubular layers was explored. It was demonstrated for the first time that the use of very viscous electrolytes request use of higher temperature. General trends for deep eutectic solvent based electrolytes and optimum anodization parameters for each system were developed. It was found that the morphology of the films prepared in choline chloride with urea and malonic acid systems is closer to TNT prepared in water solution than in TNT obtained from organic based electrolytes. Influence of the viscous electrolytes on pores formation rate was demonstrated. The texture of the annealed films was investigated by XRD and shows strong differences in properties depending on hydrogen bond donors. Moreover, physicochemical properties were measured, including photocurrent and apparent donor concentration.

This study presents experimental criteria for developing a sequential treatment that couples continuous ozonation with aerobic biodegradation to degrade 4-chlorophenol (4-CP). The ozonation of 4-CP (60, 120, and 240 mg L−1) was carried out in semi-continuous and continuous modes to select suitable experimental conditions in terms of the decomposition efficiency (DE) of 4-CP and its toxic ozonation products (for continuous mode). Complete degradation of 4-CP (98–100% DE) was achieved within 10–30 min of ozonation in the semi-continuous mode and with hydraulic retention times (HRT) of 30, 45, and 60 min in the continuous system. Continuous ozonation resulted in 46.7% and 36.8% reduction of total organic carbon (TOC) for 4-CP initial concentrations of 120 and 240 mg L−1at 60 min of HRT, respectively. Based on these results, the combination of ozonation and biological treatment was evaluated. Biodegradation experiments were conducted in batch, fed-batch, and continuous operation modes using a microbial consortium adapted to the pre-ozonized 4-CP. In this step, the TOC of pre-ozonized solutions was reduced by 93%. The global efficiency of ozonation followed by biodegradation system reached up to 96% of mineralization. Suitable experimental conditions were obtained through mathematical modeling of continuous ozonation and biodegradation data. The mineralization achieved was the target result of the proposed treatment scheme, with the maximum mineralization of ozonation products in the bioprocess serving as a critical criterion for its efficacy. Based on a proposed mathematical model, the experimental results were used to conduct kinetic studies of ozonation and biodegradation in continuous mode.

The use of psychoactive, potentially dependence-producing substances is highly prevalent around the world, and contributes substantially to global disease burden. There is a major gap between the need for treatment for substance use disorders. Changes proposed for the classification of substance use disorders in the Eleventh Revision of the International Classification of Diseases and Related Health Problems, based on a public health approach, have important implications for the conceptualisation, structure and availability of services. These include: (1) an updated and expanded range of substance classes; (2) greater specification of different harmful patterns of substance use, which may be continuous or episodic and recurrent; (3) a new category to denote single episodes of harmful use; (4) a category describing hazardous use of substances; and (5) simplification of diagnostic guidelines for substance dependence. This paper describes these changes and the opportunities they present for improved prevention, treatment, monitoring and health policy.

In this paper, we present a multi-objective solution of the Pareto front for a certain class of discrete-time ergodic controllable Markov chains. For solving the problem, we transform the original multi-objective problem into an equivalent nonlinear programming problem implementing the Lagrange principle. We then propose to adopt the Tikhonov’s regularization method to ensure the convergence of the cost functions to a unique point into the Pareto front. We show that Pareto policies are characterized as optimal policies. One of the fundamental problems related to the construction of the Pareto front is the existence and characterization of both, Pareto and strong Pareto policies. By introducing the Tikhonov’s regularizator, we ensure the existence of strong Pareto policies. This paper proposes a real solution to this problem. We formulate the original problem considering several constraints: (a) we employ the c-variable method for the introduction of linear constraints over the nonlinear problem and (b) restrict the cost functions, allowing points in the Pareto front to have a small distance from one another. The constraints imposed by the c-variable method make the problem computationally tractable and the restriction imposed by the small distance change ensures the continuation of the Pareto front. The resulting equation in this nonlinear system is an optimization problem for which the necessary and efficient condition of a minimum is solved using the projected gradient method. Moreover, we also prove the convergence conditions and compute the estimate rate of convergence of variables corresponding to the Lagrange principle and the Tikhonov’s regularization, respectively. We provide all the details needed to implement the proposed method in an efficient way. The usefulness of the method is successfully demonstrated by a numerical example.

The aim of this study was to design a discontinuous controller with adaptive gains, based on the sliding-mode theory. The adaptive controller solved the trajectory tracking problem for a class of systems with state constraints, specifically for a class of multi-articulated robot manipulator with a constraint work-space. The controller implemented a state dependent adaptive gain that grows in value when the trajectories approach the boundary of the set that characterized the states constraints. The design of the adaptive gain guaranteed the existence of a finite-time stable equilibrium point for a special sliding surface depending on the tracking trajectory error. The specific law used to adjust the gain was obtained by a Lyapunov-based stability analysis. Some numerical simulations proved that the sliding-mode controller showed a superior performance, measured by the mean square value of the tracking result, compared to the non-adaptive gain first order sliding-mode controller.

This study solves a finite horizon optimal problem for linear systems with parametric uncertainties and bounded perturbations. The control solution considers the uncertain part of the system in the sub-optimal control solution by proposing a min-max problem solved by a dynamic neural programming approximate solution. The structure of the neural network was proposed to satisfy the charcateristics of the value function including possitiveness and continuity. The impact of the presence of bounded perturbation over the Hamiltonian maximization was analyzed in detail. The explicit learning law used to adjust the weights was obtained directly from the Hamilton-Jacobi-Bellman (HJB) approximate solution. The weights adjustment to the proposed algorithm is based on an on-line state dependent Riccati-like equation. A numerical simulation is presented to illustrate the results of the sub-optimal algorithm including its comparison against the classical linear regulator solved considering the non-perturbed system.

This paper presents the results of the survey on the stabilization of the Triple-Link Inverted Pendulum as underactuated system. The mathematical model of the coupled electromechanical system is presented in the standard robotics format, where the geometry of its links is taken into account, further, the DC motor is considered. For the pursued aim, Attractive Ellipsoid Method is used in order to design robust stabilizing controller for electromechanical systems in a general form. In order to compare the performance for the suggested methodology, different robust Sliding Mode Control strategies are designed. The obtained algorithms are tested in numerical simulation.

In this paper we present the problem of parameter estimation in continuous-time stochastic systems under coloured or correlated noises. The Least Squares Method (LSM) is a classical approach, but the estimation usually presents bias in stochastic systems. Here we combine this technique with a Kalman filter, that will minimize the noise effect, and will improve the estimation algorithm performance. The Instrumental Variable method will be implemented too, in order to analyse which method is more suitable for systems with coloured noises. The effectiveness of the proposed methods will be illustrated with a numerical example.

The authors suggest an algorithm for extremum seeking based on a random process optimization approach employing a gradient descent method with the synchronous detection technique. The problem consists on finding the minimum of a strongly convex function which is unknown but may be measured in any testing point subject to a noise perturbation. The suggested extremum seeking procedure is based on the estimated gradient obtained by the modified version of the Synchronous Detection Method. We have added a first order low-pass filter to the gradient estimator to attenuate the noise in the estimations. We prove the mean-squared convergence in probability of the suggested algorithm. To validate the contributions of the paper we present a numerical example.

This paper introduces and evaluates a robust control algorithm based on sliding mode control theory, in which together with the use of the LMI methods the stabilization of an underactuated nonlinear system can be guaranteed. The obtained results are applied to a mechanical system which consists of two single gimbal control moment gyroscopes for triaxial attitude control of a gravity-gradient stabilized tethered satellite system. These objectives are investigated first using analytical methods and then verified by numerical simulations.