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Background and Aims: The prone position is one of the common surgical positions used in clinical practice. Manoeuvring patients from supine to a prone position can impact respiratory dynamics and result in haemodynamic variations. Methods: This study included 64 patients and was conducted after obtaining approval from the ethics committee and registration of the trial. The primary objective was to evaluate the changes in peak inspiratory pressure (PIP), plateau pressure (P[sub.plat]) and mean airway pressure (MAP) in patients undergoing surgery under general anaesthesia in the prone position with (Group S) and without (Group P) spine frame. The secondary objective was to evaluate and compare the variations in heart rate and blood pressure. Results: On turning the patient prone, there was statistically significant increase in median PIP (Group S 4 cmH[sub.2]O vs. Group P 0.5 cmH[sub.2]O, P < 0.001), P[sub.plat] (Group S 3.5 cmH[sub.2]O vs. Group P 1 cmH[sub.2]O, P = 0.004) and dynamic compliance (Group S -5.513 vs. Group P -2.78, P < 0.004). Conclusions: Our study found that prone positioning with a spine frame led to a significantly greater increase in airway pressures and a decrease in dynamic compliance when compared to patients positioned prone without the spine frame. Keywords: Lung compliance, nephrolithotomy, patient positioning, prone position, respiratory dynamics, spine surgery, spine frame, Author(s): Preeti Kandasamy [1]; Vinayak S. Pujari (corresponding author) [2]; Sandya Rani D. Channaiah [1] INTRODUCTION Prone positioning is one of the common positions used to facilitate surgical access, but [...]

Due to the growing interest in electricity, both conventional and non-conventional energy sources are growing. As the number of powertrains increases, just as the interconnectivity of these components grows terrifyingly, so does the power response along with the variable overcurrent. Which acquires the majority of power shares in an environmentally responsible manner. One thing that sets wind power apart is how little it costs. With a proven capacity of around 21262 MW, India's wind farm ranks fifth in the world with an exceptionally high annual expansion rate [1]. Management New Harmless to Ecosystem Strength (MNRE) and association of Tamil Nadu, Maharashtra and Gujarat movements. Considering the 7.6 Gw of wind cap that has been produced in the last 15 months, the target is to produce an additional 10 Gw in FY 2019[2]. The Twofold Dealt with Acknowledgement Generator (DFIG) structure was found to be affected by the wind in uneven stacking. The electricity produced makes endless sounds available [3]. The proposed study depicts the evaluation of noise produced by wind-related electricity. The combined dynamic channel uses the DFIG system for both direct and indirect loads. This work recreates the sounds produced by direct and indirect loads using mutt dynamic channel reduction for Grid Side Control (GSC) and Rotor Side Control (RSC). The end product of this effort shows that using the unequal weight system, the value of sounds dropped to 4.96% from 29.66%. Additionally, it shows that in cases where the nonlinear weight has no effect on the structure, the consonant value is exactly 0.14%. Various research projects are now being carried out in an effort to develop channels that can suppress consonant levels higher than the dedicated hybrid feed channel. MATLAB R2018a variant is used to generate the model for the proposed work. The model is shown in the Simulink section of MATLAB under Programming.

In this research introduces a method to control strategy of a solar photovoltaic (PV) system connected to the grid. For Maximum Power Point Tracking (MPPT) of the PV array a voltage source converter (VSC) is used in this system. Load at PCC receives the AC power from DC power obtained from PV array. The Positive Sequence Components (PSCs) of unbalanced grid voltages are calculated to evaluate unit templates, and an Artificial Neural Network (ANN) controller is used to reduce Total Harmonic Distortion (THD). The standalone mode is selected when there is failure in the grid reduction in grid current distortions found to be less than 5%. Additionally, a multi-variable filter-based control algorithm is employed to compensate for harmonic load currents and ensure unity power factor operation. A synchronization control ensures smooth connection to the grid when available and disconnection when unavailable. Experiments were conducted under various steady state as well as dynamic conditions. Artificial neural network (ANN) is used as extensive method which give better performance and total harmonic distortion.

This paper proposes a new standalone photovoltaic (PV) inverter topology for optimal solar power usage, which includes a new maximum power point tracking (MPPT) technique based on shading pattern analysis utilizing an adaptive neuro-fuzzy inference system (ANFIS), SIMO converter, and multilayer inverter (MLI). The suggested MPPT scheme is tested in partially shaded conditions. The PV voltage is then sent to the SIMO converter, which generates four distinct voltages (V1, V2, V3, and V4). The MLI reduces harmonic distortion by converting the SIMO converter’s dc output voltage to ac. Artificial Neural Networks (ANN) are employed in traditional systems to reduce harmonic distortion by 3.24 %. Photovoltaic inverter configuration using ANFIS is proposed in this paper. The ANFIS reduces overall harmonic distortion by 2.04 %, which is significantly better than the ANN. This MLI has eight switching devices and delivers 31-level ac output voltage with very low harmonic distortion. The particle swarm optimization (PSO), MPPT scheme harvests maximum power in any weather condition, and MLI feeds the utility, which is a key aspect, linked with the proposed topology. The performance of the MLI and converter is programmed using an FPGA Spartan trainer kit.