Novel design of weighted differential evolution for parameter estimation of Hammerstein-Wiener systems.

INTRODUCTION: Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability. OBJECTIVES: The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters. METHODS: The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials. RESULTS: Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme. CONCLUSION: Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios.

Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator.

Piezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hysteresis effects of piezoelectric actuators are mathematically represented as a second-order system using the Dahl hysteresis model. In this paper, artificial intelligence-based neurocomputing feedforward and backpropagation networks of the Levenberg-Marquardt method (LMM-NNs) and Bayesian Regularization method (BRM-NNs) are exploited to examine the numerical behavior of the Dahl hysteresis model representing a piezoelectric actuator, and the Adams numerical scheme is used to create datasets for various cases. The generated datasets were used as input target values to the neural network to obtain approximated solutions and optimize the values by using backpropagation neural networks of LMM-NNs and BRM-NNs. The performance analysis of LMM-NNs and BRM-NNs of the Dahl hysteresis model of the piezoelectric actuator is validated through convergence curves and accuracy measures via mean squared error and regression analysis.

Pre-therapeutic efficacy of the CDK inhibitor dinaciclib in medulloblastoma cells.

Medulloblastoma (MB) is the most common aggressive paediatric brain tumour and, despite the recent progress in the treatments of MB patients, there is still an urgent need of complementary or alternative therapeutic options for MB infants. Cyclin Dependent Kinase inhibitors (CDKi) are at the front-line of novel targeted treatments for multiple cancers and the CDK4/6 specific inhibitor palbociclib has been pre-clinically identified as an effective option for MB cells. Herein, we identified the pan-CDKi dinaciclib as a promising alternative to palbociclib for the suppression of MB cells proliferation. We present evidence supporting dinaciclib's ability to inhibit MB cells in vitro proliferation at considerably lower doses than palbociclib. Sequencing data and pathway analysis suggested that dinaciclib is a potent cell death inducer in MB cells. We found that dinaciclib-triggered apoptosis is triggered by CDK9 inhibition and the resultant reduction in RNA pol II phosphorylation, which leads to the downregulation of the oncogenic marker MYC, and the anti-apoptotic protein MCL-1. Specifically, we demonstrated that MCL-1 is a key apoptotic mediator for MB cells and co-treatment of dinaciclib with BH3 mimetics boosts the therapeutic efficacy of dinaciclib. Together, these findings highlight the potential of multi-CDK inhibition by dinaciclib as an alternative option to CDK4/6 specific inhibition, frequently associated with drug resistance in patients.

Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems.

In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global search competency of backtracking search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel's inequality coefficient as well as complexity measures.