Search

AC microgrids are a contemporary adaptation of traditional power distribution networks, propelled by the rapid integration of renewable energy resources. Yet, their dynamic operational nature poses distinct protection & control challenges. This paper introduces a protection approach for AC microgrids utilizing the Particle Filter Algorithm (PFA). The method involves initial measurement and state estimation of current and voltage signals at the designated bus using PFA. Then, the dual index is computed via PFA encompasses 1). per-phase particle residuals (PPPR), which are derived as an index for fault detection and classification, essentially capturing the difference among estimated & measured current. 2). also, a second index is generated named fifth and seventh root means square (RMS) harmonics distortion (F&SRHD) from the estimated current signals, computed by slight modification in conventional total harmonic distortion. Variances in any of the PPPRs and F&SRHD more than the threshold level indicate the fault within the AC microgrids. Fault localization is accomplished by analyzing the directional patterns of non-fundamental components of 3-phase active energy (3-pRE). Rigorous MATLAB/Simulink 2022b simulations authenticate the efficacy of the presented strategy. Both the high-impedance faults (HIF) and low-impedance solid faults (LISF) are successfully detected across radial and meshed scenarios, with 99% accuracy.

This paper introduces the problem of scheduling jobs on parallel plastic extrusion lines where each line is composed of one or more than one extruder. Although there are some similarities between the introduced problem and the non-identical parallel machines scheduling problems with sequence-dependent setup times, limited additional resources and machine eligibility restrictions, the problem considered in this paper is a generalization of the parallel machine scheduling problem. This is because in parallel machines scheduling each job requires only one machine but in our case some jobs require more than one machine. Thus, our problem reduces to the parallel machine scheduling problem if all jobs require only one machine. This paper describes the problem of scheduling parallel extrusion lines, its industrial context, and develops a mixed-linear formulation to model the problem. This formulation allowed solving instances of up to 15 jobs. In addition, we developed four metaheuristics: a simulated annealing algorithm, a tabu search heuristic, a genetic algorithm, and a greedy randomized adaptive search procedure. These metaheuristics can be used to solve real-life instances of the problem. A numerical experiment shows that the proposed metaheuristics produce excellent solutions. Some of the proposed simulated annealing adaptations and of the tabu search heuristics obtained solutions with less than 2% deviation from the optimum.

This study introduces a novel method for classifying sets of images, called Riemannian geodesic discriminant analysis–minimum Riemannian mean distance (RGDA-MRMD). This method first converts image data into symmetric positive definite (SPD) matrices, which capture important features related to the variability within the data. These SPD matrices are then mapped onto simpler, flat spaces (tangent spaces) using a mathematical tool called the logarithm operator, which helps to reduce their complexity and dimensionality. Subsequently, regularized local Fisher discriminant analysis (RLFDA) is employed to refine these simplified data points on the tangent plane, focusing on local data structures to optimize the distances between the points and prevent overfitting. The optimized points are then transformed back into a complex, curved space (SPD manifold) using the exponential operator to enhance robustness. Finally, classification is performed using the minimum Riemannian mean distance (MRMD) algorithm, which assigns each data point to the class with the closest mean in the Riemannian space. Through experiments on the ETH-80 (Eidgenössische Technische Hochschule Zürich-80 object category), AFEW (acted facial expressions in the wild), and FPHA (first-person hand action) datasets, the proposed method demonstrates superior performance, with accuracy scores of 97.50%, 37.27%, and 88.47%, respectively. It outperforms all the comparison methods, effectively preserving the unique topological structure of the SPD matrices and significantly boosting image set classification accuracy.

This paper proposes a modified primary flux linkage and an improved weighting less model predictive control for linear induction motors (LIMs) to enhance the drive system in terms of linear speed response, wide speed range, efficiency, and computation time. Sliding mode controller is presented in this work to get quick response instead of the use of the PI controller. A weighting less model predictive flux control (MPFC) is employed to eliminate the weighting factor and reduce the computation time. Furthermore, the optimum value of the primary flux linkage is calculated to guarantee higher efficiency under the operation of maximum thrust per ampere, loss minimization control and wider speed range in the field weakening region. The FCS-MPFC uses only the primary flux in the cost function independent on the weighting factor. Moreover, simplified calculation process can be executed greatly in the $\alpha \beta $ -coordinates without transformation matrix, where the end-effect is fully taken into consideration. In comparison with the PI controller under different conditions, the proposed control method can achieve faster dynamics with lower thrust ripple, computation time, and so on. Comprehensive simulation and experimental results based on one prototype with 3 kW linear induction machine have verified the advantages of the proposed strategy in this work.

BACKGROUND: Since the occurrence of coronavirus disease in 2019 (COVID-19), the global community has witnessed its exponential spread with devastating outcomes within the general population and specifically within hemodialysis patients. OBJECTIVES: Compare the state of immunity to SARS-CoV-2 among hemodialysis patients and staff. DESIGN: Cross-sectional study with a prospective follow-up period. SETTING: Hemodialysis centers in Madinah region. PATIENTS AND METHODS: We prospectively tested for SARS-CoV-2 antibodies in dialysis patients using dialysis centers staff as controls. The participants were tested on four occasions when feasible for the presence of anti-SARS-CoV-2 antibodies. We also analyzed factors that might be associated with seropositivity. MAIN OUTCOME MEASURES: SARS-CoV-2 positivity using immunoglobulin G (IgG) levels SAMPLE SIZE: 830 participants, 677 patients and 153 dialysis centers staff as controls. RESULTS: Of the total participants, 325 (257 patients and 68 staff) were positive for SARS-CoV-2 IgG antibodies, for a prevalence of 38.0% and 44.4% among patients and staff, respectively (P=.1379). Participants with a history of COVID-19 or related symptoms were more likely to have positive IgG (P

Complex lymphatic anomalies (CLAs) are a set of rare diseases with unique osteopathic profiles. Recent efforts have identified how lymphatic-specific somatic activating mutations can induce abnormal lymphatic formations that are capable of invading bone and inducing bone resorption. The abnormal bone resorption in CLA patients has been linked to overactive osteoclasts in areas with lymphatic invasions. Despite these findings, the mechanism associated with progressive bone loss in CLAs remains to be elucidated. In order to determine the role of osteoblasts in CLAs, we sought to assess osteoblast differentiation and bone formation when exposed to the lymphatic endothelial cell secretome. When treated with lymphatic endothelial cell conditioned medium (L-CM), osteoblasts exhibited a significant decrease in proliferation, differentiation, and function. Additionally, L-CM treatment also inhibited bone formation through a neonatal calvaria explant culture. These findings are the first to reveal how osteoblasts may be actively suppressed during bone lymphatic invasion in CLAs.

Having continuous decrease in inertia and being sensitive to load/generation variation are considered crucial challenging problems for modern power grids. The main cause of these problems is the increased penetration capacities of renewables. An unbalanced load with generation power largely affects grids’ frequency and voltage profiles. Load frequency control (LFC) mechanisms are extensively presented to solve these problems. In the literature, LFC methods are still lacking in dealing with system uncertainty, parameter variation, structure changes, and/or disturbance rejection. Therefore, this paper proposes an improved LFC methodology using the hybrid one plus proportional integral double-integral derivative (1+PII2D) cascaded with fractional order proportional-integral-derivative (FOPID), namely, the proposed 1+PII2D/FOPID controller. The contribution of superconducting magnetic energy storage devices (SMES) is considered in the proposed design, also considering hybrid high-voltage DC and AC transmission lines (hybrid HVDC/HVAC). An optimized design of proposed 1+PII2D/FOPID controller is proposed using a new application of the recently presented powerful artificial rabbits optimizers (ARO) algorithm. Various performance comparisons, system changes, parameter uncertainties, and load/generation profiles and changes are considered in the proposed case study. The results proved superior regulation of frequency using proposed 1+PII2D/FOPID control and the ARO optimum parameters.

In this study, an adaptive proportional-integral-derivative (PID) sliding mode control technique combined with the super-twisting algorithm is planned for the stabilization of rotational inverted pendulum in the appearance of exterior perturbation. The state-space model of rotational inverted pendulum in the existence of exterior disturbance is attained. Then, the super-twisting PID sliding mode controller is designed for finite time stability control of the considered underactuated control system. The upper bounds of perturbation are presumed to be unknown; consequently, the adaptive control procedure is taken into account to approximate the uncertain bounds of external disturbances. The stability control of rotational inverted pendulum system is verified by means of the Lyapunov stability theory. In order to validate the accuracy and efficiency of the recommended control technique, some simulation outcomes are prepared and compared with other existing scheme. Finally, the experimental results are implemented to show the success of the designed method.

In this paper, a novel fast terminal sliding mode reaching law (FTSMRL) based sliding mode control (SMC) is proposed to improve the speed dynamic performance of the permanent magnet synchronous motor (PMSM) and drive. The proposed FTSMRL contains two power terms, which can take the leading role in the SMC process, when sliding state is near or far from the selected sliding trajectory. The proposed SMC based FTSMRL can not only suppress the chattering phenomenon effectively, but also improve the reaching velocity of the sliding state variable. In order to further improve the system disturbance rejection ability of the control system, an extended state observer (ESO) is also developed in this work. The proposed observer estimates the disturbances and inputs the signal to a feed-forward compensation controller considering the chattering phenomenon caused by the high switching gain. The proposed method has been fully confirmed by mathematical stability check, numerical analysis and experiments, separately.

The problem of attitude control for rigid spacecraft under the attitude and angular velocity constraints is investigated in this study. Particularly, a simple structure constrained proportional-derivative (PD)-like control is proposed which contains two portions. The first portion is a conventional PD control to provide convergence of the system states; whereas the second portion provides the desired performance specifications such as convergence rate, overshoot and steady-state bound for attitude and rotation velocity to improve the attitude pointing accuracy and pointing stability. The distinctive property of the suggested constrained control method is to ensure the desired performance in transient and steady-state phases for all the system states. It also possesses much simpler structure compared to the existing constrained control techniques since it is based on a new methodology. The simulation results conducted on a rigid spacecraft verify the efficiency of the proposed approach.

Recent developments in electrical power grids have witnessed high utilization levels of renewable energy sources (RESs) and increased trends that benefit the batteries of electric vehicles (EVs). However, modern electrical power grids cause increased concerns due to their continuously reduced inertia resulting from RES characteristics. Therefore, this paper proposes an improved fractional-order frequency controller with a design optimization methodology. The proposed controller is represented by two cascaded control loops using the one-plus-proportional derivative (1 + PD) in the outer loop and a fractional-order proportional integral derivative (FOPID) in the inner loop, which form the proposed improved 1 + PD/FOPID. The main superior performance characteristics of the proposed 1 + PD/FOPID fractional-order frequency controller over existing methods include a faster response time with minimized overshoot/undershoot peaks, an ability for mitigating both high- and low-frequency disturbances, and coordination of EV participation in regulating electrical power grid frequency. Moreover, simultaneous determination of the proposed fractional-order frequency controller parameters is proposed using the recent manta ray foraging optimization (MRFO) algorithm. Performance comparisons of the proposed 1 + PD/FOPID fractional-order frequency controller with existing PID, FOPID, and PD/FOPID controllers are presented in the paper. The results show an improved response, and the disturbance mitigation is also obtained using the proposed MRFO-based 1 + PD/FOPID control and design optimization methodology.

The optimal control of reactive powers in electrical systems can improve a system’s performance and security; this can be provided by the optimal reactive power dispatch (ORPD). Under the high penetration of renewable energy resources (RERs) such as wind turbines (WTs), the ORPD problem solution has become a challenging and complex task due to the fluctuations and uncertainties of generated power from WTs. In this regard, this paper solved the conventional ORPD and the stochastic ORPD (SORPD) at uncertainties of the generated power from WTs and the load demand. An Adaptive Manta-Ray Foraging Optimization (AMRFO) was presented based on three modifications, including the fitness distance balance selection (FDB), Quasi Oppositional based learning (QOBL), and an adaptive Levy Flight (ALF). The ORPD and SORPD were solved to reduce the power loss (PLoss) and the total expected PLoss (TEPL), the voltage deviations (VD) and the total expected VD (TEVD). The normal and Weibull probability density functions (PDFs), along with the scenario reduction method and the Monte Carlo simulation (MCS), were utilized for uncertainty representations. The performance and validity of the suggested AMRFO were compared to other optimizers, including SCSO, WOA, DO, AHA, and the conventional MRFO on the IEEE 30-bus system and standard benchmark functions. These simulation results confirm the supremacy of the suggested AMRFO for the ORPD and SORPD solution compared to the other reported techniques.

Renewable energy systems (RESs) have introduced themselves as vital solutions for energy supply in remote regions, wherein main utility supply systems are not available. The construction of microgrid (MG) systems is useful candidate for proper control and management with hybrid RESs. However, RESs-based MGs face reduced power system inertia due to the dependency of RESs on power electronic converter systems. Accordingly, preserving nominal operating frequency and reduced deviations in tie-line power are crucial elements for proper operation of interconnected MGs in remote areas. To overcome this problem, load frequency control (LFC) systems have proven featured solutions. Therefore, this paper proposes a new non-integer LFC method based on the fractional order (FO) control theory for LFC in interconnected MGs in remote areas. The proposed control is based on the three degree of freedom (3DoF) cascaded 1+proportional-integral-derivative-accelerated (PIDA) controller with FOPI controller, namely 3DoF 1+PIDA/FOPI LFC scheme. The proposed 3DoF 1+PIDA/FOPItakes the advantages of the accelerated term of PIDA control to improve power system transients, regarding maximum overshoot/undershoot and settling times. Additionally, it employs outer loop to reduce errors and faster inner loop to mitigate disturbances effects. The contribution of plug-in controlled electric vehicles (EVs) are considered to enhance the frequency regulation functions. An optimized design of the proposed 3DoF 1+PIDA/FOPI LFC scheme is proposed using the newly developed hybrid equilibrium optimizer (EO)-slime mould optimization (SMA) algorithm (namely EOSMA optimizer). The EOSMA combines the features of the EO and SMA powerful optimization algorithms. A two interconnected MGs in remote areas with RESs and EVs inclusions with high penetration levels is selected to verify the proposed 3DoF 1+PIDA/FOPI LFC scheme and the EOSMA optimizer. The results show high ability of the proposed controller and design scheme to minimize MGs’ frequency and tie-line power fluctuations and to preserve frequency stability and security.

One of the tools used to measure the quality of life in hemodialysis (HD) patients is the Kidney Disease Quality of Life (KDQOL) survey. The KDQOL has been through several developmental processes, with the most recent one being the KDQOL-36™. Our study evaluated the validity and reliability of the Arabic-translated KDQOL-36™ survey in Saudi chronic dialysis patients. This cross-sectional study was conducted at four HD centers in Saudi Arabia. The KDQOL-36™ survey was translated into Arabic according to the RAND Corporation’s basic guidelines for translating surveys. The validation process was achieved by assessing reliability and validity. The reliability of the translated survey was established by Cronbach’s alpha to measure internal consistency and the intra-class correlation coefficient (ICC) to measure the test–retest reliability. The validity of the translated survey was established based on content validity and convergent validity. The study included 184 patients (36–65 years; 60.9% of men). Regarding reliability, Cronbach’s alpha for the subscales ranged from 0.63 to 0.89, and ICCs ranged from 0.60 to 0.88. For content validity, an expert panel reviewed the questions in depth. In addition, we found a positive relationship between all sub- and overall health-rated scores (P

This paper presents an inclusive review of the cyber-physical (CP) attacks, vulnerabilities, mitigation approaches on the power electronics and the security challenges for the smart grid applications. With the rapid evolution of the physical systems in the power electronics applications for interfacing renewable energy sources that incorporate with cyber frameworks, the cyber threats have a critical impact on the smart grid performance. Due to the existence of electronic devices in the smart grid applications, which are interconnected through communication networks, these networks may be subjected to severe cyber-attacks by hackers. If this occurs, the digital controllers can be physically isolated from the control loop. Therefore, the cyber-physical systems (CPSs) in the power electronic systems employed in the smart grid need special treatment and security. In this paper, an overview of the power electronics systems security on the networked smart grid from the CP perception, as well as then emphases on prominent CP attack patterns with substantial influence on the power electronics components operation along with analogous defense solutions. Furthermore, appraisal of the CPS threats attacks mitigation approaches, and encounters along the smart grid applications are discussed. Finally, the paper concludes with upcoming trends and challenges in CP security in the smart grid applications.

Model predictive control (MPC), manly based on a direct use of an explicit and identifiable model, has been widely used in controller design in different applications both by academia and industry. The reason for such popularity is due to its strong ability for providing high performance electric drive systems, as highly recognized as the most reliable control approach compared with field-oriented control (FOC) and direct torque control (DTC). In general, the MPC has numerous features and advantages, such as direct switching states to the converter without any modulation, online optimization with multivariable control, low current total harmonic distortion, low switching loss, etc. The aim of this paper is to provide a comprehensive review for major development and achievements of the recent progress on the MPC for electrical machines and drives. This review begins with the innovative topologies and operating principles of fundamental MPC, and ends to summary on different advanced MPC algorithms. Typical MPC techniques have been fully adopted to enhance the drive performance of the electrical drives, mainly including finite-set model predictive control (FS-MPC) based on tuning weighting factors, without weighting factors, maximum torque per ampere, low number of switching vectors, and multi voltage vectors in one sample period. Finally, great attention has been paid to the discussion of the new trends and future research topics.

In this paper, a novel optimal adaptive-gains super-twisting sliding-mode control (OAGSTSMC) using actor-critic approach is proposed for a high-speed permanent-magnet synchronous motor (PMSM) drive system. First, the super-twisting sliding-mode controller (STSMC) is adopted for reducing the chattering phenomenon and stabilizing the PMSM drive system. However, the control performance may be destroyed due external disturbances and parameter variations of the drive system. In addition, the conservative selection of the STSMC gains may affect the control performance. Therefore, for enhancing the standard super-twisting approach performance via avoiding the constraints on knowing the disturbances as well as uncertainties upper bounds, and to achieve the drive system robustness, the direct heuristic dynamic programming (HDP) is utilized for optimal tuning of STSMC gains. Consequently, an online actor-critic algorithm with HDP is designed for facilitating the online solution of the Hamilton-Jacobi-Bellman (HJB) equation via a critic neural network while pursuing an optimal control via an actor neural network at the same time. Furthermore, based on Lyapunov approach, the stability of the closed-loop control system is assured. A real-time implementation is performed for verifying the proposed OAGSTSMC efficacy. The experimental results endorse that the proposed OAGSTSMC control approach achieves the PMSM superior dynamic performance regardless of unknown uncertainties as well as exterior disturbances.

Traffic congestion is an important socio-economic problem that swelled in the last few decades. It affects the social mobility of people, length of trips, quality of life, and the economy of countries. As a major problem in most countries, it has been tackled by governments, universities, and advanced research using intelligent transportation systems (ITS) to solve the problem or at least ease its adverse effects. Hidden Markov Models (HMM) represent one of the methods that are suitable for congestion prediction. In this paper, a new model, based on Hidden Markov Model and Contrast, is proposed to define the traffic states during peak hours in two dimensional space (2D). The proposed model uses mean speed and contrast to capture the variability in traffic patterns. Empirical evaluation shows that the proposed approach has improved prediction error in comparison to HMM related work and neuro-fuzzy approaches.

In this paper, a high-speed microturbine (MT) permanent magnet assisted synchronous reluctance generator (PMa-SynRG) real-time emulation based on linear impedance regulator (LIR) using power hardware-in-the-loop (PHIL) for wind energy generation tests is presented. The LIR is designed without any feedback control loop for reshaping the $s$ -domain performances of the current filter along with the converter inside the PMa-SynRG emulated system. The PHIL platform not only provides a method for eliminating the high cost of using real renewable energy hardware but also it enables the developers to create new, rapid, and reliable controllers for renewable energy testing. This platform can be used in investigating the performance of energy system under various conditions even if the generator prototype is not yet developed or unavailable. PMa-SynRG mathematical model is emulated in the real-time using PHIL platform while the output voltage of the proposed emulator imitates the generated voltage through the simulated model. In addition, a voltage source converter is employed as a voltage amplifier for imitating the PMa-SynRG performance when supplying nonlinear/linear loads. In this paper, the proportional-integral resonant (PIR) controller is utilized at the voltage control loop for tracking the distorted output reference signal voltage. In order to investigate the performance of the proposed PMa-SynRG emulator, it has been simulated and compared with MATLAB/SIMULINK environment.

In this study, the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance is investigated. For this reason, an adaptive neural network using a backstepping control strategy is designed. The difference between the actual values of the upper-limb exoskeleton robot system and the desired values is considered as the tracking error. Afterward, the auxiliary variable based on the tracking error is defined and the virtual control input is obtained. Then, by using the backstepping control procedure and Lyapunov stability concept, the convergence of the position tracking error is proved. Moreover, for the compensation of the model uncertainty and the external disturbance that exist in the upper-limb exoskeleton robot system, an adaptive neural-network procedure is adopted. Furthermore, for the estimation of the unknown coefficient related to the parameters of the neural network, the adaptive law is designed. Finally, the simulation results are prepared for demonstration of the effectiveness of the suggested method on the upper-limb exoskeleton robot system.

In recent decades, there has been a growing interest in the design and development of power loss reduction in electric drive systems. To realize the full potential of the Brushless DC motor (BLDCM), the design methodology of an effective intelligent controller is vital. On the other hand, the Landsman converter is used in a bridgeless configuration to enhance the power factor and minimize the power loss of the BLDCM. In addition, BLDCM utilizes the different intelligent controllers that are used to decrease power loss. The performance of the Landsman converter and CSC converter is analyzed concerning various configurations/stages, and the numerical MATLAB 2016a simulation results are discussed to arrive at the suitable intelligent controller for the BLDC motor, and comparison results are presented. The proposed converter is designed and validated using hardware implementation by minimizing power loss. Effectiveness can be tested and valued for the proposed controller for the various intelligent controllers of BLDCM.

In this paper, an adaptive neural network approach is developed based on the integral nonsingular terminal sliding mode control method, with the aim of fixed-time position tracking control of a wheelchair upper-limb exoskeleton robot system under external disturbance. The dynamical equation of the upper-limb exoskeleton robot system is obtained using a free and typical model of the robotic manipulator. Afterward, the position tracking error between the actual and desired values of the upper-limb exoskeleton robot system is defined. Then, the integral nonsingular terminal sliding surface based on tracking error is proposed for fixed-time convergence of the tracking error. Furthermore, the adaptive neural network procedure is proposed to compensate for the external disturbance which exists in the upper-limb exoskeleton robotic system. Finally, to demonstrate the effectiveness of the proposed method, simulation results using MATLAB/Simulink are provided.

Cable robots are a type of parallel robot in which cables have replaced the usual rigid arms. In cable robots, due to the tensile strength of the cable, the workspace analysis is much more complex than in conventional robots. In this paper, we design an adaptive fuzzy controller for a cable-driven parallel robot (CDPR). In the proposed controller, the results show that the accuracy of the system performance in tracking the reference value as well as the controller performance speed is better than that of the robust method. In one of the simulation modes, the performance speed of the control system for convergence is reduced and its error is very small, which indicates the proper performance of the proposed adaptive fuzzy method. It should be noted that all simulations are performed in a MATLAB software environment.

The permanent magnet synchronous motors (PMSMs) as the completely symmetrical three-phase machines, which are usually driven by symmetrical voltage signals. Unfortunately, a PMSM system usually suffers from the different lumped disturbances, such as internal parametric perturbations and external load torques, the speed regulation problem should be addressed within the different operation situations. Characterizing by the current variation speed of the motor winding is much faster than that of the mechanical dynamic velocity, a dual-time-scale sliding mode control (SMC) method for the surface-mounted PMSMs is proposed in this paper. Firstly, the mathematical model of PMSMs is established in the two-phase synchronous rotating orthogonal reference coordinate system, and the slow and fast variation subsystems are obtained based on the quasi-steady-state theory. Secondly, a tracking differentiator (TD)-based and exponential reaching law-based sliding mode controllers are individually designed within dual-time-scale, respectively. As a result, the eventual SMC strategy is presented, and the stability of control system is analyzed by applying the Lyapunov stability theory. The main contribution of this study is to present an alternative control framework for the PMSMs servo system, where the dual-time-scale characteristic is involved, and thus a non-cascade control structure that different from the traditional drive strategy is proposed in the motor community. Finally, the model of whole system is built and carried out on the simulation platform. Research results demonstrate that the presented servo control system can accurately track the reference angle velocity signal, while the strong robustness and fast response performance are guaranteed in the presence of external disturbances. In addition, the three-phase current transient response values are completely symmetrical with the rapid adjustment characteristic.

In this article, the position tracking control of the wheelchair upper-limb exoskeleton robotic system is investigated with the aim of rehabilitation of disabled people. Hence, the fuzzy nonsingular terminal sliding mode control method by using the state observer with a fixed-time convergence rate is designed in three main parts. In the first part, the fixed-time state observer is proposed for estimation of the states of the system. Secondly, the fixed-time convergence of position tracking error of the upper-limb exoskeleton robot system is examined by using the nonsingular terminal sliding mode control approach. In the third part, with the target of the improvement of the controller performance for removal of the chattering phenomenon which diminishes the controller performance, the fuzzy control method is used. Finally, the efficiency and proficiency of the proposed control method on the upper limb exoskeleton robotic system are demonstrated via the simulation results which are provided by MATLAB/Simulink software. In this part, simulation results are obtained based on different initial conditions in two examples using various desired values. Thus, it can be demonstrated that the proposed method applied to the upper-limb exoskeleton robot system is robust under various initial conditions and desired values.

In this paper, a robust adaptive dynamic surface control (RADSC) is proposed with a recurrent cerebellar model articulation controller (RCMAC) based on the function link neural network (FLNN) for two-axis X-Y table derived by two permanent-magnet synchronous motors (PMSMs) servo drives. Initially, an optimal computed torque controller (OCTC) is used to ensure the stability of the two-axis X-Y table system. But there may be destroyed in the control performance due to the presence of parameter uncertainties. This might be due to the fact that linear optimal control possesses inherent robustness within a specified spectrum of model uncertainties. Due to the above reasons, the RADSC is developed according to the system requirements in order to increase the robustness of the control system. In the proposed control scheme, a dynamic surface controller (DSC) and an RCMAC uncertainty observer with a robust controller are combined to constitute the RADSC scheme. In the proposed RADSC, the DSC is utilized to get rid of the complexity of explosion present in the backstepping design. Furthermore, the RCMAC uncertainty observer is developed such that it can approximate the nonlinear parameter uncertainty terms online, whereas the robust controller is designed to recover the residual of the approximation error of the RCMAC. Based on the Lyapunov stability analysis, the online adaptive control laws are derived. The experimental results confirm that the x-axis and y-axis motions are controlled separately, whereas it can be concluded that the proposed RADSC's dynamic behaviors accomplish robust tracking performance though there were parameter uncertainties.

This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. The proposed RABC achieves high performance operation by incorporating an ideal backstepping controller (IBC), a recurrent radial basis function neural network (RRBFNN) uncertainty observer, and a robust controller. The Lyapunov stability theorem is utilized to design the IBC as a position controller of the HSPMSM servo drive system. To enhance the disturbance rejection capability during parameter changes, certain information is needed within the backstepping control law so that the system performance would not sorely be affected. To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties. Moreover, the robust controller is intended to retrieve the remaining of the RRBFNN approximation errors. To assure the stability of the proposed RABC, the Lyapunov stability analysis is used to derive the online adaptive control laws. The performance of the proposed RABC is verified by simulation and experimental analysis under different operating conditions and parameter uncertainties. The test results validate the effectiveness of the proposed RABC scheme to achieve preferable tracking performance regardless of external disturbances and parameter uncertainties.

Complex Lymphatic Anomalies (CLA) are lymphatic malformations with idiopathic bone and soft tissue involvement. The extent of the abnormal lymphatic presentation and boney invasion varies between subtypes of CLA. The etiology of these diseases has proven to be extremely elusive due to their rarity and irregular progression. In this review, we compiled literature on each of the four primary CLA subtypes and discuss their clinical presentation, lymphatic invasion, osseous profile, and regulatory pathways associated with abnormal bone loss caused by the lymphatic invasion. We highlight key proliferation and differentiation pathways shared between lymphatics and bone and how these systems may interact with each other to stimulate lymphangiogenesis and cause bone loss.

This study proposes an adaptive barrier functions-based non-singular terminal sliding mode control approach for the trajectory tracking of a quadrotor unmanned aerial vehicle subject to bounded uncertainties and input constraints. First, the state-space equations of the six degrees-of-freedom quadrotor system is introduced in the presence of bounded uncertainty and constrained input. Then, a compensation system is designed with the aim of removing the constrained input and leading to high performance. Afterwards, a linear switching surface is defined using the tracking error and virtual control input to guarantee the convergence of the tracking error in the presence of parametric uncertainties and input saturation. Later, a non-singular terminal sliding surface is proposed for fast convergence of the linear switching surface. To eliminate the need for approximating the upper bounds of uncertainties and ensure the fast convergence of the non-singular terminal sliding surface to a pre-specified neighborhood of the origin, we considered an adaptive barrier function scheme. The fast convergence rate of the proposed approach is verified via the Lyapunov stability theory. The accuracy and performance of the proposed approach is assessed using MATLAB/Simulink simulations and robustness analysis using the random number noise.

In this paper, a disturbance observer based on the non-singular terminal sliding mode control method was presented for the quadrotor in the presence of wind perturbation. First, the position and attitude dynamical equation of the quadrotor was introduced in the existence of windy perturbation. It was difficult to exactly determine the upper bound of the perturbations in the practical systems such as robot manipulators and quadrotor UAVs. Then, a disturbance observer was designed for the estimation of wind perturbation which was entered to the quadrotor system at any moment. Afterward, a non-singular terminal sliding surface was proposed based on the disturbance observer variable. Furthermore, finite time convergence of the closed-loop position and attitude models of the quadrotor was proved using Lyapunov theory concept. Unlike the existing methods, the new adaptive non-singular terminal sliding mode tracker for quadrotor unmanned aerial vehicles enabled accurate tracking control, robust performance, and parameter tuning. Through the combination of the finite time tracker and disturbance observer, the position and attitude tracking of quadrotor UAVs could be accurately performed not only in the nominal environment but also in the existence of different types of perturbations. Finally, simulation results based on the recommended method were provided to validate the proficiency of the suggested method. Moreover, comparison results with another existing study were presented to prove the success of the proposed method.

The aim of this review is to provide an updated review of the epigenetic factors involved in the onset and development of osteoarthritis (OA). OA is a prevalent degenerative joint disease characterized by chronic inflammation, ectopic bone formation within the joint, and physical and proteolytic cartilage degradation which result in chronic pain and loss of mobility. At present, no disease-modifying therapeutics exist for the prevention or treatment of the disease. Research has identified several OA risk factors including mechanical stressors, physical activity, obesity, traumatic joint injury, genetic predisposition, and age. Recently, there has been increased interest in identifying epigenetic factors involved in the pathogenesis of OA. In this review, we detail several of these epigenetic modifications with known functions in the onset and progression of the disease. We also review current therapeutics targeting aberrant epigenetic regulation as potential options for preventive or therapeutic treatment.

In this study, an intelligent control scheme is developed for induction motors (IMs). The dynamics of IMs are unknown and are perturbed by the variation of rotor resistance and load changes. The control system has two stages. In the identification stage, the group method of data-handling (GMDH) neural network (NN) was designed for online modeling of the IM. In the control stage, the GMDH-NN was applied to compensate for the impacts of disturbances and uncertainties. The stability is shown by the Lyapunov approach. Simulations demonstrated the good accuracy of the suggested new control approach under disturbances and unknown dynamics.

In this study, an adaptive nonsingular terminal sliding mode control technique according to the barrier function is designed for the performance improvement and robust stability of nonlinear systems with outdoor disturbances. For this reason, a novel nonlinear sliding surface is presented based on the states of the system. The nonlinear sliding surface forces the states of the system to converge from initial conditions to zero. Subsequently, a non-singular terminal sliding control scheme is advised for the purpose of finite-time stability of the nonlinear switching surface. Finite-time stabilization of the non-singular terminal sliding surface is verified by the Lyapunov theory. For improvement of the system performance against exterior perturbation, the barrier function adaptive technique is employed to estimate the unknown upper bounds of the exterior disturbance. Finally, the advantage and productivity of the recommended control method is investigated based on the simulation results. In the simulation part, the plasma torch jerk chaotic system is considered as a case study, such that the obtained results are given in different scenarios.

Abstract Background Neuroinflammation is one of the hallmarks of neurodegenerative diseases, such as Parkinson’s disease (PD). Activation of glial cells, including microglia and astrocytes, is a characteristic of the inflammatory response. Glycoprotein non-metastatic melanoma protein B (GPNMB) is a transmembrane glycoprotein that releases a soluble signaling peptide when cleaved by ADAM10 or other extracellular proteases. GPNMB has demonstrated a neuroprotective role in animal models of ALS and ischemia. However, the mechanism of this protection has not been well established. CD44 is a receptor expressed on astrocytes that can bind GPNMB, and CD44 activation has been demonstrated to reduce NFκB activation and subsequent inflammatory responses in macrophages. GPNMB signaling has not been investigated in models of PD or specifically in astrocytes. More recently, genetic studies have linked polymorphisms in GPNMB with risk for PD. Therefore, it is important to understand the role this signaling protein plays in PD. Methods We used data mining techniques to evaluate mRNA expression of GPNMB and its receptor CD44 in the substantia nigra of PD and control brains. Immunofluorescence and qPCR techniques were used to assess GPNMB and CD44 levels in mice treated with MPTP. In vitro experiments utilized the immortalized mouse astrocyte cell line IMA2.1 and purified primary mouse astrocytes. The effects of recombinant GPNMB on cytokine-induced astrocyte activation was determined by qPCR, immunofluorescence, and measurement of nitric oxide and reactive oxygen production. Results Increased GPNMB and CD44 expression was observed in the substantia nigra of human PD brains and in GFAP-positive astrocytes in an animal model of PD. GPNMB treatment attenuated cytokine-induced levels of inducible nitric oxide synthase, nitric oxide, reactive oxygen species, and the inflammatory cytokine IL-6 in an astrocyte cell line and primary mouse astrocytes. Using primary mouse astrocytes from CD44 knockout mice, we found that the anti-inflammatory effects of GPNMB are CD44-mediated. Conclusions These results demonstrate that GPNMB may exert its neuroprotective effect through reducing astrocyte-mediated neuroinflammation in a CD44-dependent manner, providing novel mechanistic insight into the neuroprotective properties of GPNMB.

We report what we believe is the first case of posterior reversible encephalopathy syndrome (PRES) secondary to dialysis disequilibrium syndrome (DDS) in patients in whom all other possible causes of PRES were excluded and in whom a transient episode of tactile hallucination also occurred. We believe that this case of DDS was particularly severe, leading to PRES because of the late institution of dialysis therapy and the concomitant severe degree of metabolic acidosis on presentation.

Summary: Dialysis patients are more likely than the general population to develop active tuberculosis (TB). In these patients, the availability of a highly sensitive and specific test to diagnose latent TB will ensure earlier treatment and decreased progression to active disease. In the current study, the Quanti-FERON-TB Gold In-Tube (QFT-G) test was compared with the tuberculin skin test (TST) for the diagnosis of latent tuberculosis infection (LTBI) among 200 hemodialysis patients and 15 confirmed TB disease cases in a tertiary care center in Saudi Arabia. Among the LTBI cases, 26 (13%) were TST positive, and 65 (32.5%) were positive by the QTF-G test, with an overall agreement between the 2 tests of 75.5% (k = 0.34) being observed. Among the confirmed tuberculosis disease cases, none were positive by TST, and 10 (66.7%) were positive by the QTF-G test, resulting in an overall agreement of 33.3% (k = 0). A comparison between the TST and the QTF-G test was performed based on the sensitivity, specificity, and area under the curve (AUC) obtained for the tests. The QTF-G test was more sensitive and less specific than the TST in predicting the confirmed TB disease cases. When we tested the correspondence of the AUC values between the 2 diagnostic modalities, the obtained p-value was 0.0003. In conclusion, the AUCs of the examined diagnostic modalities are significantly different in predicting LTBI and tuberculosis. Keywords: Tuberculosis (TB), Tuberculin skin test (TST), QTF-G test, Dialysis, Hemodialysis, Chronic renal failure, Latent tuberculosis infection

Pre-osteoblast adhesion and interaction with extracellular matrix (ECM) proteins through integrin receptors result in activation of signaling pathways regulating osteoblast differentiation. Connective tissue growth factor (CTGF/CCN2) is a matricellular protein secreted into the ECM. Prior studies in various cell types have shown that cell adhesion to CTGF via integrin receptors results in activation of specific signaling pathways that regulate cell functions, such as differentiation and cytoskeletal reorganization. To date, there are no studies that have examined whether CTGF can serve as an adhesive substrate for osteoblasts. In this study, we used the MC3T3-E1 cell line to demonstrate that CTGF serves as an adhesive matrix for osteoblasts. Anti-integrin blocking experiments and co-immunoprecipitation assays demonstrated that the integrin αvβ1 plays a key role in osteoblast adhesion to a CTGF matrix. Immunofluorescence staining of osteoblasts cultured on a CTGF matrix confirmed actin cytoskeletal reorganization, enhanced spreading, formation of focal adhesions, and activation of Rac1. Alkaline phosphatase (ALP) staining and activity assays, as well as Alizarin red staining demonstrated that osteoblast attachment to CTGF matrix enhanced maturation, bone nodule formation and matrix mineralization. To investigate whether the effect of CTGF on osteoblast differentiation involves integrin-mediated activation of specific signaling pathways, we performed Western blot, chromatin immunoprecipitation (ChIP) and qPCR assays. Osteoblasts cultured on a CTGF matrix showed increased total and phosphorylated (activated) forms of focal adhesion kinase (FAK) and extracellular signal-regulated kinase (ERK). Inhibition of ERK blocked osteogenic differentiation in cells cultured on a CTGF matrix. There was an increase in runt-related transcription factor 2 (Runx2) binding to the osteocalcin gene promoter, and in the expression of osteogenic markers regulated by Runx2. Collectively, the results of this study are the first to demonstrate CTGF serves as a suitable matrix protein, enhancing osteoblast adhesion (via αvβ1 integrin) and promoting cell spreading via cytoskeletal reorganization and Rac1 activation. Furthermore, integrin-mediated activation of ERK signaling resulted in increased osteoblast differentiation accompanied by an increase in Runx2 binding to the osteocalcin promoter and in the expression of osteogenic markers.

Lung cancer is the second most common cancer and the leading cause of cancer related mortality in both men and women. Each year, more people die of lung cancer than of colon, breast, and prostate cancers combined. It is widely accepted that tumor metastasis is a formidable barrier to effective treatment of lung cancer. The bone is one of the frequent metastatic sites for lung cancer occurring in a large number of patients. Bone metastases can cause a wide range of symptoms that could impair quality of life of lung cancer patients and shorten their survival. We strongly believe that molecular targets (tumor-related and bone microenvironment based) that have been implicated in lung cancer bone metastases hold great promise in lung cancer therapeutics. Thus, this paper discusses some of the emerging molecular targets that have provided insights into the cascade of metastases in lung cancer with the focus on bone invasion. It is anticipated that the information gathered might be useful in future efforts of optimizing lung cancer treatment strategies.

This study elucidates exposure-response relationships between performance of repetitive tasks, grip strength declines, and fibrogenic-related protein changes in muscles, and their link to inflammation. Specifically, we examined forearm flexor digitorum muscles for changes in connective tissue growth factor (CTGF; a matrix protein associated with fibrosis), collagen type I (Col1; a matrix component), and transforming growth factor beta 1 (TGFB1; an upstream modulator of CTGF and collagen), in rats performing one of two repetitive tasks, with or without anti-inflammatory drugs.To examine the roles of force versus repetition, rats performed either a high repetition negligible force food retrieval task (HRNF), or a high repetition high force handle-pulling task (HRHF), for up to 9 weeks, with results compared to trained only (TR-NF or TR-HF) and normal control rats. Grip strength declined with both tasks, with the greatest declines in 9-week HRHF rats. Quantitative PCR (qPCR) analyses of HRNF muscles showed increased expression of Col1 in weeks 3-9, and CTGF in weeks 6 and 9. Immunohistochemistry confirmed PCR results, and also showed greater increases of CTGF and collagen matrix in 9-week HRHF rats than 9-week HRNF rats. ELISA, and immunohistochemistry revealed greater increases of TGFB1 in TR-HF and 6-week HRHF, compared to 6-week HRNF rats. To examine the role of inflammation, results from 6-week HRHF rats were compared to rats receiving ibuprofen or anti-TNF-α treatment in HRHF weeks 4-6. Both treatments attenuated HRHF-induced increases in CTGF and fibrosis by 6 weeks of task performance. Ibuprofen attenuated TGFB1 increases and grip strength declines, matching our prior results with anti-TNFα.Performance of highly repetitive tasks was associated with force-dependent declines in grip strength and increased fibrogenic-related proteins in flexor digitorum muscles. These changes were attenuated, at least short-term, by anti-inflammatory treatments.