Computational Quantum Chemical Study, Drug-Likeness and In Silico Cytotoxicity Evaluation of Some Steroidal Anti-Inflammatory Drugs

This paper contains a theoretical study of ten Anti-inflammatory steroids (AIS) on the understanding of the relationship between the structure and activity of the drug, the pharmacokinetic parameters responsible for bioavailability and bioactivity and finally the toxicity evaluation. DFT calculations with B3LYP/6-31G (d, p) level have been used to analyze the electronic and geometric characteristics deduced for the stable structure of the compounds. Moreover, using the Frontier Molecular orbital (FMO) energies, MEP surface visualizations and the density-based descriptors such as chemical potential (µ), electronegativity (χ), hardness (η) and softness (σ), the chemical stability were determined. Furthermore, in silico, studies showed that Lipinski rules are applied, which means that these (AIS) are expected to have a high probability of good oral bioavailability. On the other side, the bioinformatic Osiris/Molinspiration analyses of the relative cytotoxicity of these derivatives are reported in comparison to Cortisol. In fact, it has been showed that almost of these compounds are non-toxics except for Mometasone that presents a great risk of tumorigenicity during reproduction with a slightly mutagenic structure due to the two chloride atoms. from all results obtained, we can conclude that fluticasone has the best physico-chemical properties which explains its high efficiency. Keywords: Anti-inflammatory steroids, DFT, Lipinski rules, Tumorigenicity

Designing, Cytotoxic Evaluation, Molecular Docking and in Silico Pharmacokinetic Prediction of New Hydrocortisone Derivatives as Anti-Asthmatics Drugs

A series of new 20 corticosteroids were subjected to molecular property prediction. The Molecular, Physicochemical, and Biological properties were determined using Molinspiration Cheminformatics software. These compounds were further subjected to Toxicity Predictions using the Osiris Software. The calculated drug-related properties of the designed molecules were similar to those found in most marketed drugs. Amongst the proposed molecules, fourteen promising candidates can be considered as promising structures for the synthesis of new and more effective anti-asthmatic drugs. Result indicates that the derivatives are orally active molecules.  In-silico ADME and toxicity prediction was accomplished with the help of Swiss-ADMET tool provides the latest and most inclusive for diverse chemicals associated with known Absorption, Distribution, Metabolism, Excretion and Toxicity profiles. furthermore, BBB (Blood brain barrier) penetration, HIA (Human intestinal absorption), Caco-2 cell permeability and Ames test were calculated using ADMET web-based query tools incorporating a molecular build in interface enable the database to be queried by Smiles and structural similarity search. According to molecular docking results, derivatives No 4, 10 and 11 showed better docking Scores values compared to other derivatives and also dexamethasone and hydrocortisone. Keywords: Corticosteroids, Drug-likeness, Lipophilicity, Anti-asthmatic, ADME

System configuration, fault detection, location, isolation and restoration: a review on LVDC Microgrid protections

Low voltage direct current (LVDC) distribution has gained the significant interest of research due to the advancements in power conversion technologies. However, the use of converters has given rise to several technical issues regarding their protections and controls of such devices under faulty conditions. Post-fault behaviour of converter-fed LVDC system involves both active converter control and passive circuit transient of similar time scale, which makes the protection for LVDC distribution significantly different and more challenging than low voltage AC. These protection and operational issues have handicapped the practical applications of DC distribution. This paper presents state-of-the-art protection schemes developed for DC Microgrids. With a close look at practical limitations such as the dependency on modelling accuracy, requirement on communications and so forth, a comprehensive evaluation is carried out on those system approaches in terms of system configurations， fault detection, location, isolation and restoration

A triangular canonical form for a class of 0-flat nonlinear systems

International audienceThis article proposes a triangular canonical form for a class of 0-flat nonlinear systems. Necessary and sufficient geometrical conditions are given in order to guarantee the existence of a local diffeomorphism to transform the studied nonlinear systems into the proposed 0-flat canonical form, which enables us to compute the flat output as well

A new T-S fuzzy model predictive control for nonlinear processes

Abstract: In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems

A geometrical characterization of a class of 00-flat affine dynamical systems

International audienceThis paper gives a description of a class of $0$-flat dynamical systems. This class is characterized by the involutivity of a distribution associated naturally to multi-output affine dynamical systems and the Lie bracket of some control vector fields fulfilling some conditions. We will also show that these conditions are a generalization of the well-known result on $0$-flatness of codimension $1$ affine systems

Quelques formes normales non linéaires plates

International audienceCet article propose quelques formes "normales" non linéaires plates. L'idée est de trouver les conditions nécessaires et su±santes pour qu'un système soit équivalent par difféomorphisme à un système que l'on sait plat. Ainsi nous obtenons des conditions su±santes qui permettent de conclure si un système non linéaire est plat

Observer-Based Robust Fault Predictive Control for Wind Turbine Time-Delay Systems with Sensor and Actuator Faults

This paper presents a novel observer-based robust fault predictive control (OBRFPC) approach for a wind turbine time-delay system subject to constraints, actuator/sensor faults, and external disturbances. The proposed approach is based on an augmented state-space representation that contains state-space variables and estimation errors. The proposed augmented representation is then used to synthesize a robust predictive controller. In addition, an observer is developed and used to estimate both state variables and actuator/sensor faults. To ensure that the proposed approach has disturbance rejection capabilities, the disturbance estimates were merged with the prediction model. In addition, the disturbance rejection capabilities and fault tolerance were insured by formulating the control process as an optimization problem subject to constraints in terms of linear matrix inequalities (LMIs). As a result, the controller gains are acquired by solving an LMI problem to guarantee input-to-state stability in the presence of sensor and actuator faults. A simulation example is conducted on a nonlinear wind turbine (1 MW) model with 3 blades, a horizontal axis, and upwind variable speed subject to actuator/sensor faults in the pitch system. The results demonstrate the ability of the proposed method in dealing with nonlinear systems subject to external disturbances and keeping the control performance acceptable in the presence of actuator/sensor faults

Predictive control for delay systems: theory and applications

International audienceIn industrial processes, time delay often occurs in many dynamical systems, such as chemical processes, communication systems, and vehicle systems. Presence of time delay in a process increases the difficulty of controlling such systems. These delays can affect the state, input, and output; they can be constant or time varying, known or unknown, deterministic or stochastic depending on the systems under consideration. In this chapter, we propose a robust model predictive control (MPC) algorithm for a class of uncertain discrete-time systems with both states and input delays. We consider the constant and time-varying delay cases as well as the state feedback case. The uncertainty is assumed polytopic with a known upper bound. By the augmented system description we reduce a robust model predictive control law to a convex optimization involving linear matrix inequalities (LMIs). After defining an optimization problem that minimizes a cost function at each time instant, we compute a state feedback by minimizing the upper bound of the cost function subject to constraints on inputs. We give closed-loop stability conditions on the systematic construction of a Lyapunov–Krasovskii functional and compare its robustness properties with the standard MPC in the presence of parameter uncertainties and delay systems. Finally, we study the constrained control problem for a quarter-vehicle model and nonlinear system using the proposed robust MPC

Recent advances in electrical engineering and control applications

This book of proceedings includes papers presenting the state of art in electrical engineering and control theory as well as their applications. The topics focus on classical as well as modern methods for modeling, control, identification and simulation of complex systems with applications in science and engineering. The papers were selected from the hottest topic areas, such as control and systems engineering, renewable energy, faults diagnosis—faults tolerant control, large-scale systems, fractional order systems, unconventional algorithms in control engineering, signals and communications. The control and design of complex systems dynamics, analysis and modeling of its behavior and structure is vitally important in engineering, economics and in science generally science today. Examples of such systems can be seen in the world around us and are a part of our everyday life. Application of modern methods for control, electronics, signal processing and more can be found in our mobile phones, car engines, home devices like washing machines is as well as in such advanced devices as space probes and systems for communicating with them. All these technologies are part of technological backbone of our civilization, making further research and hi-tech applications essential. The rich variety of contributions appeals to a wide audience, including researchers, students and academics.