PDGFRß Activation Induced the Bovine Embryonic Genome Activation via Enhanced NFYA Nuclear Localization.

Embryonic genome activation (EGA) is a critical step during embryonic development. Several transcription factors have been identified that play major roles in initiating EGA; however, this gradual and complex mechanism still needs to be explored. In this study, we investigated the role of nuclear transcription factor Y subunit A (NFYA) in bovine EGA and bovine embryonic development and its relationship with the platelet-derived growth factor receptor-ß (PDGFRß) by using a potent selective activator (PDGF-BB) and inhibitor (CP-673451) of PDGF receptors. Activation and inhibition of PDGFRß using PDGF-BB and CP-673451 revealed that NFYA expression is significantly (p < 0.05) affected by the PDGFRß. In addition, PDGFRß mRNA expression was significantly increased (p < 0.05) in the activator group and significantly decreased (p < 0.05) in the inhibitor group when compared with PDGFRα. Downregulation of NFYA following PDGFRß inhibition was associated with the expression of critical EGA-related genes, bovine embryo development rate, and implantation potential. Moreover, ROS and mitochondrial apoptosis levels and expression of pluripotency-related markers necessary for inner cell mass development were also significantly (p < 0.05) affected by the downregulation of NFYA while interrupting trophoblast cell (CDX2) differentiation. In conclusion, the PDGFRß-NFYA axis is critical for bovine embryonic genome activation and embryonic development.

Control Design for Uncertain Higher-Order Networked Nonlinear Systems via an Arbitrary Order Finite-Time Sliding Mode Control Law.

The authors proposed an arbitrary order finite-time sliding mode control (SMC) design for a networked of uncertain higher-order nonlinear systems. A network of n+1 nodes, connected via a directed graph (with fixed topology), is considered. The nodes are considered to be uncertain in nature. A consensus error-based canonical form of the error dynamics is developed and a new arbitrary order distributed control protocol design strategy is proposed, which not only ensures the sliding mode enforcement in finite time but also confirms the finite time error dynamics stability. Rigorous stability analysis, in closed-loop, is presented, and a simulation example is given, which demonstrates the results developed in this work.

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches.

An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.

High gain differentiator based neuro-adaptive arbitrary order sliding mode control design for MPE of standalone wind power system.

In this paper, we introduce a novel Maximum Power Point Tracking (MPPT) controller for standalone Wind Energy Conversion Systems (WECS) with Permanent Magnet Synchronous Generators (PMSG). The primary novelty of our controller lies in its implementation of an Arbitrary Order Sliding Mode Control (AOSMC) to effectively overcome the challenges caused by the measurement noise in the system. The considered model is transformed into a control-convenient input-output form. Additionally, we enhance the control methodology by simultaneously incorporating Feedforward Neural Networks (FFNN) and a high-gain differentiator (HGO), further improving the system performance. The FFNN estimates critical nonlinear functions, such as the drift term and input channel, whereas the HGO estimates higher derivatives of the system outputs, which are subsequently fed back to the control inputs. HGO reduces sensor noise sensitivity, rendering the control law more practical. To validate the proposed novel control technique, we conduct comprehensive simulation experiments compared against established literature results in a MATLAB environment, confirming its exceptional effectiveness in maximizing power extraction in standalone wind energy applications.

Enhanced control strategy for photovoltaic emulator operating in continuously changing environmental conditions based on shift methodology.

This article investigates an inventive methodology for precisely and efficiently controlling photovoltaic emulating (PVE) prototypes, which are employed in the assessment of solar systems. A modification to the Shift controller (SC), which is regarded as a leading PVE controller, is proposed. In addition to efficiency and accuracy, the novel controller places a high emphasis on improving transient performance. The novel piecewise linear-logarithmic adaptation utilized by the Modified-Shift controller (M-SC) enables the controller to linearly adapt to the load burden within a specified operating range. At reduced load resistances, the transient sped of the PVE can be increased through the implementation of this scheme. An exceedingly short settling time of the PVE is ensured by a logarithmic modification of the control action beyond the critical point. In order to analyze the M-SC in the context of PVE control, numerical investigations implemented in MATLAB/Simulink (Version: Simulink 10.4, URL: https://in.mathworks.com/products/simulink.html ) were utilized. To assess the effectiveness of the suggested PVE, three benchmarking profiles are presented: eight scenarios involving irradiance/PVE load, continuously varying irradiance/temperature, and rapidly changing loads. These profiles include metrics such as settling time, efficiency, Integral of Absolute Error (IAE), and percentage error (epve). As suggested, the M-SC attains an approximate twofold increase in speed over the conventional SC, according to the findings. This is substantiated by an efficiency increase of 2.2%, an expeditiousness enhancement of 5.65%, and an IAE rise of 5.65%. Based on the results of this research, the new M-SC enables the PVE to experience perpetual dynamic operation enhancement, making it highly suitable for evaluating solar systems in ever-changing environments.

Neuro-adaptive fast integral terminal sliding mode control design with variable gain robust exact differentiator for under-actuated quadcopter UAV.

In this paper, a robust global fast terminal attractor based full flight trajectory tracking control law has been developed for the available regular form which is operated under matched uncertainties. Based on the hierarchical control principle, the aforesaid model is first subdivided into two subsystems, i.e., a fully-actuated subsystem and an under-actuated subsystem. In other words, the under-actuated subsystem is further transformed into a regular form whereby the under-actuated characteristics are decoupled in terms of control inputs. In the proposed design, the nonlinear drift terms, which certainly varies in full flight, are estimated via functional link neural networks to improve the performance of the controller in full flight. Besides, a variable gain robust exact differentiator (VG-RED) is designed to provide us with estimated flight velocities. It has consequently reduced the noise in system's velocities and has mapped this controller as a practical one. The finite-time sliding mode enforcement and the states' convergence are shown, for all flight loops, i.e., forward flight and backward flight, via the Lyapunov approach. All these claims are verified via numerical simulations and experimental implementation of the quadcopter system in a Matlab environment. For a more impressive presentation, the developed simulation results are compared with standard literature.

Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems.

The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to variation in environmental conditions such as temperature and solar radiations. In the presence of these variations, it is necessary to extract the maximum power via the maximum power point tracking (MPPT) controller. This paper presents a nonlinear generalized global sliding mode controller (GGSMC) to harvest maximum power from a PV array using a DC-DC buck-boost converter. A feed-forward neural network (FFNN) is used to provide a reference voltage. A GGSMC is designed to track the FFNN generated reference subject to varying temperature and sunlight. The proposed control strategy, along with a modified sliding mode control, eliminates the reaching phase so that the sliding mode exists throughout the time. The system response observes no chattering and harmonic distortions. Finally, the simulation results using MATLAB/Simulink environment demonstrate the effectiveness, accuracy, and rapid tracking of the proposed control strategy. The results are compared with standard results of the nonlinear backstepping controller under abrupt changes in environmental conditions for further validation.