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Congenital heart disease (CHD) is the most common global congenital defect affecting over 2.4 million individuals in the United States. Ongoing medical and surgical advancements have improved the survival of children with CHD leading to a shift where, as of 2010, adults constitute two-thirds of the CHD patient population. The increasing number and aging of adult congenital heart disease (ACHD) patients present a clinical challenge due to heightened complexity, morbidity, and mortality. Studies indicate that 1 in 13 ACHD patients will develop heart failure (HF) in their lifetime. ACHD-HF patients experience more frequent emergency department visits, higher hospitalization rates, longer hospital stays, and higher mortality compared to non-ACHD patients with heart failure (non-ACHD-HF). Despite HF being the leading cause of death in ACHD patients, there is a notable gap in evidence regarding treatment. While guideline-directed medical therapy (GDMT) has been extensively studied in non-ACHD-HF, research specific to ACHD-HF individuals is limited. This article aims to comprehensively review available literature addressing the pharmacological treatment of ACHD-HF. [ABSTRACT FROM AUTHOR]

The main goals of this research are to develop a method for obtaining the rotor position and speed in a doubly fed induction generator (DFIG) without using sensors in a variable-speed wind turbine installation. The considered method is based on the Model Reference Adaptive System (MRAS). According to this method, electromagnetic torque is used as an error variable for the adaptation process in order to refine the estimate. A good assessment is very important when trying to put into place any strategy that can control the behavior of a DFIG. This method of estimation functions by comparing the actual performance of the DFIG with that of a reference model and adjusting the system parameters to reduce any mismatch between the two. One notable advantage of this developed estimator is its stability across a broad range of speeds. Additionally, it is designed to exhibit resilience in the face of uncertainties in machine parameters. The proportional integral (PI) gains for the MRAS estimator are determined via pole placement. To assess and validate the entire DFIG model and the sensorless estimation method, comprehensive simulations are carried out using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Wind energy systems are based on synchronous machines, which can support high-speed rotation cases due to possible high-speed values of incoming winds. The machines used are affiliated with permanent magnet machines, whereas speed detection can become difficult. This complication appears when high incoming wind speed values increase the motor temperature, which in turn can influence motor parameters, and especially the stator resistance. This can impact the proposed speed software estimator robustness. The proposed high-speed estimator algorithm is based on the Model Reference Adaptive System (MRAS) estimation method, which can be used for motor speed estimation. This concept includes the reactive power model, which ensures the robustness of the estimator when facing any possible stator resistance variation, even at very high speeds. [ABSTRACT FROM AUTHOR]

Genome‐wide association studies (GWAS) have identified coronary artery disease (CAD) susceptibility locus on chromosome 3q22.3. This locus contains a cluster of several genes that includes muscle rat sarcoma virus (MRAS). Common MRAS variants are also associated with CAD causing risk factors such as hypertension, dyslipidemia, obesity, and type II diabetes. The MRAS gene is an oncogene that encodes a membrane‐bound small GTPase. It is involved in a variety of signaling pathways, regulating cell differentiation and cell survival (mitogen‐activated protein kinase [MAPK]/extracellular signal‐regulated kinase and phosphatidylinositol 3‐kinase) as well as acute phase response signaling (tumor necrosis factor [TNF] and interleukin 6 [IL6] signaling). In this review, we will summarize the role of genetic MRAS variants in the etiology of CAD and its comorbidities with the focus on tissue distribution of MRAS isoforms, cell type/tissue specificity, and mode of action of single nucleotide variants in MRAS associated complex traits. Finally, we postulate that CAD risk variants in the MRAS locus are specific to smooth muscle cells and lead to higher levels of MRAS, particularly in arterial and cardiac tissue, resulting in MAPK‐dependent tissue hypertrophy or hyperplasia. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Model Reference Adaptive Systems (MRASs) are one of the most commonly applied techniques to estimate induction motor speed. They ensure simple and algorithmic way to obtain all necessary variables to create a sensorless drive, or to provide the estimated speed signal for redundant system. However, one of the most popular MRAS-type estimators, MRAS-CC (C – Current estimator, C – Current flux simulator) is unstable in a large part of the regenerating mode operation. In this paper it is proposed to apply the multilayer observer theory to ensure the stability, while preserving large insensitivity to motor parameters. The proposed MRAS-ML (ML – MultiLayer) estimator includes two different MRAS-type estimators, unlike previous multilayer solutions with multiple identical observers, but with different parameters. Additionally, no auxiliary parameters need to be selected and it is not necessary to switch off any additional adaptation mechanism. It is shown that the MRAS-ML is stable in the whole regenerating mode operation using both simulation and experimental tests.

The main goals of this research are to develop a method for obtaining the rotor position and speed in a doubly fed induction generator (DFIG) without using sensors in a variable-speed wind turbine installation. The considered method is based on the Model Reference Adaptive System (MRAS). According to this method, electromagnetic torque is used as an error variable for the adaptation process in order to refine the estimate. A good assessment is very important when trying to put into place any strategy that can control the behavior of a DFIG. This method of estimation functions by comparing the actual performance of the DFIG with that of a reference model and adjusting the system parameters to reduce any mismatch between the two. One notable advantage of this developed estimator is its stability across a broad range of speeds. Additionally, it is designed to exhibit resilience in the face of uncertainties in machine parameters. The proportional integral (PI) gains for the MRAS estimator are determined via pole placement. To assess and validate the entire DFIG model and the sensorless estimation method, comprehensive simulations are carried out using MATLAB/Simulink.

This work applies an adaptive PD controller based on MRAS (Model Reference Adaptive System) using Lyapunov theory with sliding mode theory to a Fixed-wing MAV (Mini Aerial Vehicle). The objective is to design different adjustment mechanisms to obtain a robust adaptive control law in the presence of unknown perturbation due to wind gusts. Four adjustment mechanisms applied to an adaptive PD controller are compared. The adjustment mechanisms are Lyapunov theory, Lyapunov theory with first-order sliding mode, Lyapunov theory with second-order sliding mode, and Lyapunov theory with high-order sliding mode. Finally, after several simulations, a significant reduction and almost elimination of the unknown perturbations are presented with the addition of the sliding mode theory in the design of the adjustment mechanism for the adaptive PD controller. [ABSTRACT FROM AUTHOR]

Due to their advantages in ensuring low harmonic distortion and high power factors, single-phase Pulse-Width Modulated (PWM) rectifiers are widely employed in several industrial applications. Generally, the conventional control loop of a single-phase PWM rectifier uses both voltage and current sensors. Hence, in case of sensor fault, the performance and the availability of the converter can be seriously compromised. Therefore, diagnosis approaches and fault-tolerant control (FTC) strategies are mandatory to monitor these systems. Accordingly, this paper introduces a novel multiple-sensor FTC scheme for a single-phase PWM rectifier. The proposed fault diagnosis approach relies on joining several Generalized Proportional Integral (GPI) and Model Reference Adaptive System (MRAS) observers with a residual generation technique to detect and isolate sensor faults in a simple and reliable manner. While conventional sensor FTC methods dedicated to PWM rectifiers can only deal with single faults, the suggested approach guarantees a very good effectiveness level of sensor fault detection, isolation (FDI) and FTC of multiple-sensor fault occurrence scenarios. Consequently, the single-phase PWM rectifier can work with only the survivable single sensor with the guarantee of very good performance as in healthy operation mode. The effectiveness of the proposed sensor FDI approach and its control reconfiguration performance are demonstrated through both extensive simulation and experimental results. [ABSTRACT FROM AUTHOR]

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics.

RAF inhibitors unexpectedly induce ERK signaling in normal and tumor cells with elevated RAS activity. Paradoxical activation is believed to be RAS dependent. In this study, we showed that LY3009120, a pan-RAF inhibitor, can unexpectedly cause paradoxical ERK activation in KRASG12C-dependent lung cancer cell lines, when KRAS is inhibited by ARS1620, a KRASG12C inhibitor. Using H/N/KRAS-less mouse embryonic fibroblasts, we discovered that classical RAS proteins are not essential for RAF inhibitor-induced paradoxical ERK signaling. In their absence, RAF inhibitors can induce ERK phosphorylation, ERK target gene transcription, and cell proliferation. We further showed that the MRAS/SHOC2 complex is required for this process. This study highlights the complexity of the allosteric RAF regulation by RAF inhibitors, and the importance of other RAS-related proteins in this process.

This paper gives a clear visual appearance of the speed predictive control by a Model Reference Adaptive System (MRAS) observer for a Permanent-Magnet Synchronous Motor which is the heart of the electric vehicle’s powertrain, The proposed approach used is Field Oriented Control (FOC) of the PMSM drive in case of speed sensor failure and the importance of substituted it by MRAS observer system to ensure the continuity of precaution situation drive and even get rid of its problems at all, thus improve the reliability of electric vehicle powertrain system and securing drivers and pedestrians from dangers. The performances and robustness tests of the traction chain are achieved by real scenarios (driving cycles) such as NEDC, SFTP US06, and JP-15 combined with the vehicle dynamic model. This effectiveness study of (PMSM) drives enhanced by an observer in an electric car application confirms the high estimation accuracy and robustness of torque control of the proposed sensorless approach in several real road situations.

Mineralocorticoid receptor antagonists (MRAs) are a cornerstone drug class for heart failure therapy. Several clinical studies have demonstrated its role in heart failure therapy. However, due to the recommendation of sodium-glucose cotransporter-2 (SGLT-2) inhibitors for the treatment of heart failure, there is a lack of sufficient evidence regarding whether MRAs can continue to play a cornerstone role in heart failure treatment. A meta-analysis was performed on subgroups of the DAPA-HF and EMPEROR-Reduced trials. Using trial-level data, we performed a meta-analysis to assess the effects of SGLT-2 inhibitors and MRAs on various clinical endpoints of heart failure. The incidence of cardiovascular-related death or heart failure hospitalization was the primary outcome. In addition, we assessed cardiovascular death, all-cause death, heart failure hospitalization, renal outcomes, and hyperkalemia. This study has already been registered with PROSPERO, CRD42022385023. Compared with SGLT-2 inhibitor monotherapy, combined treatment did not demonstrate more significant advantages in terms of heart failure or cardiovascular death (RR = 1.00; 95% CI: 0.78–1.28), cardiovascular death (RR = 0.96; 95% CI: 0.61–1.52), heart failure hospitalization (RR = 0.92; 95% CI: 0.79–1.07), all-cause death (RR = 1.00; 95% CI: 0.63–1.59) and composite kidney endpoint (RR = 0.85; 95% CI: 0.49–1.46). Moreover, in comparison to SGLT-2 inhibitors, combined therapy increased the risk of moderate-severe hyperkalemia (blood potassium > 6.0 mmol/l) (RR = 4.13; 95% CI: 2.23–7.65). In patients with HFrEF who have started MRAs treatment, the addition of an SGLT-2 inhibitor provides significant clinical benefit. However, the addition of MRAs to SGLT-2 inhibitors to treat heart failure is not essential. [ABSTRACT FROM AUTHOR]

Climate change has highlighted a need to transition to more sustainable forms of transportation. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) offer a promising alternative to conventional gasoline powered vehicles. However, advancements in power electronics and advanced control systems have made the implementation of high performance traction drives for EVs and HEVs easy. In this paper, a novel sliding mode control model reference adaptive system (SMC-MRAS) speed estimator in traction drive control application is presented. However, due to the unpredictable operational uncertainties of the machine parameters and unmodelled non-linear dynamics, the proportional-integral (PI)-MRAS may not produce a satisfactory performance. The Proposed estimator eliminates the PI controller employed in the conventional MRAS. This method utilizes two loops and generates two different error signals from the rotor flux and motor torques. The stability and dynamics of the SMC law are obtained through the Lyapunov theory. The potential of the proposed SMC-MRAS methodology is simulated and experimentally validated for an electric vehicle application. Matlab-Simulink environment is developed and proposed scheme is employed on indirect vector control method. However, for the experimental validation, the dSPACE 4011 R & D controller board was utilized. Furthermore, the SMC-MRAS performance is differentiated with PI-MRAS for speed regulation performance, tracking and estimation error, as well as the fast minimization of the error signal. The results of the proposed scheme illustrate the enhanced speed estimation, load disturbance rejection ability and fast error dynamics. [ABSTRACT FROM AUTHOR]

When the PMSM motor rotates at high speed, the motor winding current contains a lot of harmonics, and the harmonic current will increase the heat of the motor, thereby reducing the operating efficiency and service life of the motor. In view of the above problems, in the EV motor control, a rotor speed observer based on model reference adaptation was designed. This method was taken the stator current equation containing rotor position parameters as adjustable model, and the stator current equation of the motor itself as adjustable model. The reference model was designed the adaptive rate based on the Popov superstability theory; at the same time, the feed-forward compensation based on the sliding mode observer was designed to suppress harmonics. The simulation and experimental results have shown that the method can effectively reduce the current harmonics, speed error and torque ripple content of the EV motor compared with the MRAS speed sensorless control.

In this article, the performance of multilevel inverter-fed position sensorless permanent magnet synchronous motor (PMSM) drive is analyzed using modified model predictive torque control (MPTC) method using two-level and three-level inverters, respectively. The MPTC method helps to control electromagnetic torque directly, which is not possible in model predictive current control, whereas the conventional model predictive torque and flux control method requires weighing factors that are similar to PI controllers used in field-oriented control or direct torque and flux control method. To simplify the control structure, most of the researchers are looking forward to weighing factorless predictive control strategies. The multilevel inverters required complex control algorithms such as space vector modulation. However, the model predictive control methods made it simple by using an embedded control structure. Furthermore, position sensorless control strategies are much needed because speed sensors are much prone to failures. Therefore, in this article, a model reference adaptive system (MRAS)–based speed sensorless approach is used to eliminate the requirement of a position sensor. The proposed multilevel inverter-fed position sensorless PMSM drive using modified MPTC is simulated in MATLAB/SIMULINK environment under different operating conditions such as under no-load, load, and change in speed. The simulation results are validated with the experimental results. [ABSTRACT FROM AUTHOR]

In this study, the stator currents-based model reference adaptive system (MRAS) estimators, which uses adaptive filtering algorithms, called the least mean squares (LMS) and least mean Kurtosis (LMK) algorithms, in the adaptation mechanism, are proposed for speed estimation of the PMSM. The proposed MRAS estimators have been directly estimated the rotor speed of the PMSM by taking into account the error between the measured stator currents called the reference model and the stator currents at the output of the adaptive model. The performance of proposed estimators and the finite control set model predictive current control (FCS-MPCC) based speed/position sensorless PMSM driver are tested and verified in simulations. In addition, the proposed estimators are compared to a fixed-gain PI controller-based MRAS. Simulation results and mean square errors (MSEs) obtained from both LMS and LMK-based MRAS algorithms demonstrated that the proposed estimators have higher performance compared to MRAS using a traditional PI controller. Moreover, the proposed estimators have eliminated the need for a fixed-gain PI controller, which is often used in MRAS structures. [ABSTRACT FROM AUTHOR]

This paper gives a clear visual appearance of the speed predictive control by a Model Reference Adaptive System (MRAS) observer for a Permanent-Magnet Synchronous Motor which is the heart of the electric vehicle’s powertrain, The proposed approach used is Field Oriented Control (FOC) of the PMSM drive in case of speed sensor failure and the importance of substituted it by MRAS observer system to ensure the continuity of precaution situation drive and even get rid of its problems at all, thus improve the reliability of electric vehicle powertrain system and securing drivers and pedestrians from dangers. The performances and robustness tests of the traction chain are achieved by real scenarios (driving cycles) such as NEDC, SFTP US06, and JP-15 combined with the vehicle dynamic model. This effectiveness study of (PMSM) drives enhanced by an observer in an electric car application confirms the high estimation accuracy and robustness of torque control of the proposed sensorless approach in several real road situations. [ABSTRACT FROM AUTHOR]

Single current sensor-based speed sensorless vector controlled PMSM (“Permanent Magnet Synchronous Motor”) drive is presented in this paper. Speed, position, and currents are estimated using single current sensor information of 3- $\Phi $ PMSM drive. 2- $\Phi $ currents in the $dq-axes$ are calculated and closed in the loop using $i_{qs}^{\ast }$ and a phase current information obtained from the single current sensor. The proposed method applies to all types of 3- $\Phi $ PMSM; current estimation is independent of machine parameters and inverter switching states. Drive is made speed and position sensorless by estimating using Y-MRAS (Model Reference Adaptive System). Y-MRAS is developed using reference voltages and estimated currents. The speed estimator depends on stator resistance; any variation in it will affect the drive performance. So, stator resistance needs to be estimated online and compensated in the speed estimation technique. Modified P-MRAS technique is used for stator resistance estimation. Here, the drive performance is also validated under stator resistance variation and its compensation. The proposed drive is independent of switching states, integrator terms, and differentiator terms. The single sensor drive reduces the overall cost of the drive and can be implemented into the existing system for sensor condition monitoring and to make the drive fault tolerant against the sensor failure without any extra hardware. The proposed single sensor-based drive is theoretically-modeled and simulated in the MATLAB/SIMULINK platform. The stability of the proposed drive is verified through a stability analysis. It is experimentally validated using a laboratory-developed PMSM drive prototype with a dSPACE-1104 controller board.

Featured Application: The proposed solution belongs to the group of fault-tolerant control algorithms that are used in industrial drives. Systems of this type are particularly applicable in industries where an increased level of safety is required, such as electric vehicle drive systems, including electric and hybrid aircraft drive systems. This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers has been extended with neural networks. This enables improved detection efficiency and increases immunity to false alarms. In addition, the use of neural networks allowed for the classification of the failure type. Three types of failures are considered in the paper: signal loss, scaling error, and signal interference. The detection efficiency of the proposed solution is about 97%. On the other hand, the classification of the worst type of failure—signal loss—was achieved at the level of 100%. Other considered failure types are classified at the level of 80–90%. In addition, tests were carried out for two types of observers—model reference adaptive system and sliding mode observer—to compare the results. The work presents experimental results carried out for various operating conditions of the drive system. The failure classification times in the experimental tests were achieved at a level of less than 30 ms. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work applies an adaptive PD controller based on MRAS (Model Reference Adaptive System) using Lyapunov theory with sliding mode theory to a Fixed-wing MAV (Mini Aerial Vehicle). The objective is to design different adjustment mechanisms to obtain a robust adaptive control law in the presence of unknown perturbation due to wind gusts. Four adjustment mechanisms applied to an adaptive PD controller are compared. The adjustment mechanisms are Lyapunov theory, Lyapunov theory with first-order sliding mode, Lyapunov theory with second-order sliding mode, and Lyapunov theory with high-order sliding mode. Finally, after several simulations, a significant reduction and almost elimination of the unknown perturbations are presented with the addition of the sliding mode theory in the design of the adjustment mechanism for the adaptive PD controller.

Despite the crucial role of RAF kinases in cell signaling and disease, we still lack a complete understanding of their regulation. Heterodimerization of RAF kinases as well as dephosphorylation of a conserved "S259" inhibitory site are important steps for RAF activation but the precise mechanisms and dynamics remain unclear. A ternary complex comprised of SHOC2, MRAS, and PP1 (SHOC2 complex) functions as a RAF S259 holophosphatase and gain-of-function mutations in SHOC2, MRAS, and PP1 that promote complex formation are found in Noonan syndrome. Here we show that SHOC2 complex-mediated S259 RAF dephosphorylation is critically required for growth factor-induced RAF heterodimerization as well as for MEK dissociation from BRAF. We also uncover SHOC2-independent mechanisms of RAF and ERK pathway activation that rely on N-region phosphorylation of CRAF. In DLD-1 cells stimulated with EGF, SHOC2 function is essential for a rapid transient phase of ERK activation, but is not required for a slow, sustained phase that is instead driven by palmitoylated H/N-RAS proteins and CRAF. Whereas redundant SHOC2-dependent and -independent mechanisms of RAF and ERK activation make SHOC2 dispensable for proliferation in 2D, KRAS mutant cells preferentially rely on SHOC2 for ERK signaling under anchorage-independent conditions. Our study highlights a context-dependent contribution of SHOC2 to ERK pathway dynamics that is preferentially engaged by KRAS oncogenic signaling and provides a biochemical framework for selective ERK pathway inhibition by targeting the SHOC2 holophosphatase.

Speed feedback is indispensable not only for closed-loop controlled induction motors (IM) but also for the state monitoring systems of open-loop controlled IMs. Replacing speed sensors with speed estimations has many advantages including, e.g., hardware complexity reduction, sensor cabling elimination, and reduction of IM drive costs. From the various methods for IM speed estimation proposed in the literature, the model reference adaptive system (MRAS)-based speed estimator stands out because of its straightforward synthesis technique and lower computational complexity. On the other hand, this estimator is inherently sensitive to mismatching of IM equivalent circuit parameters. During transients, when the slip frequency cannot be temporarily controlled over the assumed operating point, rotor impedance varies with the slip frequency changes. This phenomenon is even more significant for open-loop controlled high-slip IMs (NEMA design D IMs), which operate under the cycling load like in, e.g., oil well pumps. The rate and the range of rotor impedance variability are related to the rate and the range of the slip frequency changes. Therefore, a speed estimator capable of modelling the slip frequency dependent variability of the rotor impedance may be required, particularly for the state monitoring systems of open-loop controlled IMs. This paper presents an MRAS-based speed estimator developed on the IM space vector model with the rotor impedance variability modelled by the parallel connected branches of the series rotor equivalent resistance and leakage inductance. The experimental test results confirm the precise speed estimation of the tested IMs achieved by the devised MRAS-based speed estimator in the considered slip frequency range and indicate its potential industrial application. [ABSTRACT FROM AUTHOR]

In this study, an induction motor (IM) drive based on speedsensorless predictive torque control (PTC) is designed to perform the high-performance control of the IMs by utilizing the least mean square (LMS) algorithm for the adaptation mechanism of the model reference adaptive system (MRAS). Here, the MRAS with LMS adaptation is based on the stator currents and of the IM. Moreover, the rotor fluxes are obtained by the current model, which requires the rotor mechanical speed along with and In contrast to the other MRAS based studies using proportional-integral (PI) in the adaptation mechanisms to estimate state or parameter, it is possible to determine the states and/or parameters as weight coefficients in the MRAS with LMS adaptation which are calculated and updated in each iteration. Here, value is estimated and updated in each iteration as weight coefficient. Furthermore, the MRAS with LMS adaptation is compared to the MRAS using conventional PI in simulations. The simulation results clearly visualize both the estimation performance of stator current based MRAS using LMS adaptation and the effectiveness of the proposed PTC based IM drive. [ABSTRACT FROM AUTHOR]

Voltage-source-inverter (VSI) nonlinearity compensation is an important scheme for improve motor performance. The traditional off-line method is cumbersome and the compensation effect is poor because it does not simulate the real operation of the motor. To solve this problem, an on-line identification method for compensating multiphase machine based on non-sinusoidal power supply is proposed in this paper. First, a full order air-gap flux observer on harmonic plane was established. Then the voltage error is estimated based on a model reference adaptive system (MRAS) and compensated to the given voltage. In the process of identification, the adaptive law of dead-time voltage is designed, and a feedback gain matrix satisfying Lyapunov stability is also designed. Finally, the feasibility and the effectiveness for harmonic suppression of the proposed method are verified on an experimental platform of a seven-phase induction motor platform.

Dephosphorylation of the inhibitory "S259" site on RAF kinases (S259 on CRAF, S365 on BRAF) plays a key role in RAF activation. The MRAS GTPase, a close relative of RAS oncoproteins, interacts with SHOC2 and protein phosphatase 1 (PP1) to form a heterotrimeric holoenzyme that dephosphorylates this S259 RAF site. MRAS and SHOC2 function as PP1 regulatory subunits providing the complex with striking specificity against RAF. MRAS also functions as a targeting subunit as membrane localization is required for efficient RAF dephosphorylation and ERK pathway regulation in cells. SHOC2's predicted structure shows remarkable similarities to the A subunit of PP2A, suggesting a case of convergent structural evolution with the PP2A heterotrimer. We have identified multiple regions in SHOC2 involved in complex formation as well as residues in MRAS switch I and the interswitch region that help account for MRAS's unique effector specificity for SHOC2-PP1. MRAS, SHOC2, and PPP1CB are mutated in Noonan syndrome, and we show that syndromic mutations invariably promote complex formation with each other, but not necessarily with other interactors. Thus, Noonan syndrome in individuals with SHOC2, MRAS, or PPPC1B mutations is likely driven at the biochemical level by enhanced ternary complex formation and highlights the crucial role of this phosphatase holoenzyme in RAF S259 dephosphorylation, ERK pathway dynamics, and normal human development.

Copyright of Adab Al-Kufa is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: In this paper, a stator current‐based model reference adaptive system (SCMRAS) for indirect vector control of induction motor fed to hybrid electric vehicle (HEV) for improving the transient response during limp home period has been proposed. In the proposed SCMRAS, the measured stator currents are employed in voltage model to eliminate the integrator in reference model. The stator currents are estimated and are compared with actual current components to estimate the rotor speed. Further, to improve the performance of SCMRAS during limp home period, the PI controller in the adaptation mechanism is replaced with type 2 fuzzy logic controller (T2FLC). The prototype model of the proposed SCMRAS using dSPACE DS 1104 R&D controller board has been developed for implementing speed sensorless indirect vector control of induction motor drive. The performance of SCMRAS and proposed SCMRAS using T2FLC estimators during limp home period is compared. [ABSTRACT FROM AUTHOR]

Treatment of chronic heart failure (CHF) is very controversial. The issue of optimal doses of beta-blockers, ACE inhibitors, aldosterone receptor antagonists, statins in patients with CHF has not been conclusively addressed. Achieving the maximum tolerated doses of drugs, though related to reduced mortality, but is accompanied by an increase in adverse drug reactions. The aim. To present and discuss our own clinical and scientific data concerning the role of beta-blockers and inhibitors of the renin-angiotensin aldosterone system, diuretics, statins in the treatment of CHF patients and optimization of dosage schemes. Material and methods. The study included 88 patients with CHF of ischemic origin, with sinus rhythm, stage II AB, NYHA FC II–IV, 58 – with reduced LV EF (HFrEF) and 30 – with preserved LV EF (HFpEF). The mean age of patients was 69.18 ± 9.97 years, men 52 % (n = 46). The median follow-up of the CHF patients was 396 days, the maximum number of follow-up days was 1302. During the observation period, 14 endpoints were registered, which accounted for 15.91 % of events: 7 deaths (8.0 %), 2 strokes (2.3 %), 2 cases of acute coronary syndrome (2.3 %), 3 progressive heart failure cases (3.4 %). Kaplan–Mayer curves were drawn to assess survival rate, and the significance of difference between groups was calculated by the criteria of Gehan–Wilcoxon, Cox–Mantel and log-rank test. Risk factors were determined, and prognostic uni- and multi-variant Cox proportional hazards regression models were used. The cut-off values of quantitative risk factors were obtained by ROC analysis. Results. The increase in the relative risk of adverse cardiovascular events in the CHF patients regardless of LV EF was associated with a daily carvedilol dose of more than 25 mg (HR = 1.05; 95 % CI 1.009–1.093; P = 0.0171); eplerenone – more than 12.5 mg (HR = 1.073; 95 % CI 1.005–1.144; P = 0.034), torasemide – more than 5 mg (HR = 1.13; 95 % CI 1.021–1.255; P = 0.019); rosuvastatin – more than 10 mg (HR = 1.107; 95 % CI 1.007–1.203; P = 0.035), and the trend in using atorvastatin at a dose of less than 10 mg (HR = 1.05; 95 % CI 0.951–1.165; P = 0.327). The use of ramipril in a daily dose of less than 2.5 mg was accompanied by a trend towards the 22 % reduced relative risk of adverse cardiovascular events (HR = 0.78; 95 % CI 0.384–1.580; P = 0.491). Conclusions. Positive treatment outcomes in the CHF patients, regardless of the phenotype, were associated with low daily doses of ramipril (10.0 mg).

This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers has been extended with neural networks. This enables improved detection efficiency and increases immunity to false alarms. In addition, the use of neural networks allowed for the classification of the failure type. Three types of failures are considered in the paper: signal loss, scaling error, and signal interference. The detection efficiency of the proposed solution is about 97%. On the other hand, the classification of the worst type of failure—signal loss—was achieved at the level of 100%. Other considered failure types are classified at the level of 80–90%. In addition, tests were carried out for two types of observers—model reference adaptive system and sliding mode observer—to compare the results. The work presents experimental results carried out for various operating conditions of the drive system. The failure classification times in the experimental tests were achieved at a level of less than 30 ms.

To handle the suspended rotor speed self-identification problem of a bearingless induction motor (BL-IM), a sensorless control strategy based on fuzzy PI fractional model reference adaptive system (MRAS) is proposed. First, based on the flux linkage error, fractional MRAS adaptive law is designed to accelerate the response speed of the controlled object, and Lyapunov theorem is used to prove the stability of the adaptive law. Then, the fuzzy control rules in the fuzzy PI controller are used to adjust PI parameters of adaptive law online in real time. Finally, the speed under no-load operation, abrupt speed change and abrupt change of load is estimated and sensorless control is realized. The simulation and experimental results show that the proposed fuzzy PI fractional MRAS control system of the BL-IM can not only effectively realize self-identification of speed under no-load, speed change and abrupt change of load, but also have good suspension characteristics of the motor suspended rotor, realizing high reliability and low costs of BL-IM. [ABSTRACT FROM AUTHOR]

RAF inhibitors unexpectedly induce ERK signaling in normal and tumor cells with elevated RAS activity. Paradoxical activation is believed to be RAS dependent. In this study, we showed that LY3009120, a pan-RAF inhibitor, can unexpectedly cause paradoxical ERK activation in KRASG12C-dependent lung cancer cell lines, when KRAS is inhibited by ARS1620, a KRASG12C inhibitor. Using H/N/KRAS-less mouse embryonic fibroblasts, we discovered that classical RAS proteins are not essential for RAF inhibitor-induced paradoxical ERK signaling. In their absence, RAF inhibitors can induce ERK phosphorylation, ERK target gene transcription, and cell proliferation. We further showed that the MRAS/SHOC2 complex is required for this process. This study highlights the complexity of the allosteric RAF regulation by RAF inhibitors, and the importance of other RAS-related proteins in this process. [ABSTRACT FROM AUTHOR]

The major challenges in applying a conventional speed controller in DC motor are the effects of motor non-linearity. The non-linear characteristics of a DC motor like, saturation and friction could degrade the performance of conventional controllers. The parameters of such a dynamic system changes with time and drive the system beyond the stability margins. The conventional feedback control system thereby fails to maintain the control especially when the plant parameters are unknown. To overcome these problems, an adaptive control system is proposed which can cope up with the changes in motor dynamics. The control scheme used here is the model reference adaptive system (MRAS) where the output of the unknown plant is tuned to track the output of the ideal reference model. The perfect adaptation is achieved by an adaptive estimator implemented based on MIT rule. The plant output is stabilized by an auto-PID controller (PID controller that tunes its parameters by its own) along with the adaptive estimator. The adaptation mechanism modulates the controller and update the controller parameters to minimize error and track the ideal output. The entire proposed system is modelled and simulated in MATLAB, SIMULINK. The results are analyzed and compared over conventional PI control scheme as a part of the study. The proposed system showed better resistance to the forced perturbations induced, with good decay ratio and fine settling. The system showed satisfactory results when operated in low, medium and high speeds. The motive of the thesis is to implement a self-adaptive and autonomous DC motor speed control for variable orbit tracking applications in robotics, launch vehicles, space probes, satellites, unmanned rovers etc. [ABSTRACT FROM AUTHOR]

Objectives: Methicillin-resistant staphylococci are important causes of nosocomial infections. Due to the presence of these bacteria in hospitals as a significant challenge in hospital infection control, the identification of effective disinfectants against methicillin-resistant staphylococci is necessary. This study was designed to evaluate the effectiveness of common hospital disinfectants against methicillin-resistant staphylococci. Materials and Methods: In this cross-sectional study, the effectiveness of 4 surface disinfectant cleaners (Deconex 50 AF, Microzed GP-H, Peranacid M1 and Surfocept quick) against methicillin-resistant staphylococci (10 Staphylococcus aureus, 10 Staphylococcus saprophyticus, and 55 Staphylococcus epidermidis) was evaluated using broth dilution method, disc diffusion assay, and cell viability assay. Results: The minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of Peranacid and Surfocept against the isolates were higher than those of Deconex and Microzed. The results of disk diffusion assay showed that staphylococcus strains exhibited greater inhibition zone diameter than other disinfectants at different concentrations of Deconex and Microzed. According to the results of cell viability assay, S. aureus, S. epidermidis, and S. saprophyticus isolates did not grow at three concentrations (0.5%, 1% and 2%) of Deconex, Microzed, and Peranacid, respectively. Conclusions: Deconex and Microzed had more antimicrobial properties than the 2 other agents and methicillin-resistant staphylococcus isolates had a higher resistance to both Peranacid and Surfosept.

Speed feedback is indispensable not only for closed-loop controlled induction motors (IM) but also for the state monitoring systems of open-loop controlled IMs. Replacing speed sensors with speed estimations has many advantages including, e.g., hardware complexity reduction, sensor cabling elimination, and reduction of IM drive costs. From the various methods for IM speed estimation proposed in the literature, the model reference adaptive system (MRAS)-based speed estimator stands out because of its straightforward synthesis technique and lower computational complexity. On the other hand, this estimator is inherently sensitive to mismatching of IM equivalent circuit parameters. During transients, when the slip frequency cannot be temporarily controlled over the assumed operating point, rotor impedance varies with the slip frequency changes. This phenomenon is even more significant for open-loop controlled high-slip IMs (NEMA design D IMs), which operate under the cycling load like in, e.g., oil well pumps. The rate and the range of rotor impedance variability are related to the rate and the range of the slip frequency changes. Therefore, a speed estimator capable of modelling the slip frequency dependent variability of the rotor impedance may be required, particularly for the state monitoring systems of open-loop controlled IMs. This paper presents an MRAS-based speed estimator developed on the IM space vector model with the rotor impedance variability modelled by the parallel connected branches of the series rotor equivalent resistance and leakage inductance. The experimental test results confirm the precise speed estimation of the tested IMs achieved by the devised MRAS-based speed estimator in the considered slip frequency range and indicate its potential industrial application.

Summary: This paper presents a simple parameter independent speed estimation algorithm for vector‐controlled permanent magnet synchronous motor (PMSM) drive. The speed estimation algorithm is formed using modified X‐MRAS (modified fictitious quantity model reference adaptive system), independent of machine parameter, integrator, and differentiator terms. Any variation in the machine parameters will not affect the presented speed/position estimation algorithm. The speed sensorless method makes the drive more compact, reliable, reduces the cost, and mitigates environmental issues. The functional quantity of modified X‐MRAS is developed using rotor reference frame voltages and currents. The proposed speed estimator is found to be stable in all the four quadrants of operation. This algorithm performs satisfactorily for variable speed and load operations. The proposed algorithm is verified through computer‐based simulation using the MATLAB/SIMULINK platform and supported with experimental validation through the PMSM laboratory‐developed prototype using the dSPACE‐1104. [ABSTRACT FROM AUTHOR]