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In a distributed generation system, the nonlinear dynamics of a grid-interfaced inverter (GII) cause dynamic instability and power quality issues. Because traditional linear approaches rely on parametric fluctuations in controller parameters, adequate tuning is required. Some of the foregoing issues can be addressed using feedback and feedforward damping methods, but they require extra sensors and are ineffective in maintaining resilience against grid impedance variations and system parametric changes. Due to the digital control delay, they also add negative damping in a given frequency band, resulting in non-minimum phase behavior. The suggested control solution adds an observer-based adaptive control architecture to the existing inverter control. The concept of an energy function is used to depict grid dynamics at the GII terminals. On this dynamic energy signal, the nonlinear function will act to derive an ideal control parameter for the enhanced control algorithm. The proposed algorithm, which considers the system's nonlinearities, provides more robustness amid high- and medium-frequency grid dynamics, as demonstrated by simulation and experimental studies. [ABSTRACT FROM AUTHOR]

In the recent era of evolution in deep learning, several ANN techniques have been developed to forecast weather conditions. Temperature forecasting is a subset of weather forecasting which employ the use of these ANN techniques to predict the future trend in temperature variation. The modern ANN technologies have facilitated the prediction process due to its virtue of handling lengthy sequences which have been encorporated by many reserachers to train their models. These ANN models require an optimisation technique to mitigate the error between the forecasted and the true value, thus a lot of exploration has been done in the improvement of these optimisation strategies. In this research work, a unique derivative free optimiser known as Unscented Kalman Filter (UKF) is implemented for optimisation along with an upgraded form of Recurrent Neural Network called Long Short Term Memory (LSTM) to forecast the Global Land-Ocean index temperature. In the proposed model, LSTM is used as a base neural network due to its probity to handle very long sequences of data and resolving the issue of vanishing and exploding gradients suffered by RNN. The prediction results obtained from the propsed LSTM-UKF model was compared with Gradient Descent (GD) optimizers such as, Adam optimizer, RMSprop optimizer, and an evolutionary optimisation algorithm called Genetic Algorithm (GA) to demostrate its optimising strength. Well established predictive models such as Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) were also trained using UKF and the results were compared with the base model along with the stated optimisation techniques. [ABSTRACT FROM AUTHOR]

The purpose of this study is to demonstrate results on fixed point theory in quasi-partial b-metric space recognizing a new type of mapping, which is a blend of F-contraction and Kannan contraction, and to establish the fixed point results in F-expanding type mappings. Additionally, the obtained results are the application of the contractive mappings to functional equations. Furthermore, Mathematica software is used to demonstrate the 3D shapes of the examples discussed here. [ABSTRACT FROM AUTHOR]

dvances in systematic computational biology and rapid elucidation of synergistic interplay between cis and trans factors governing transcriptional control have facilitated functional annotation of gene networks. The generation of data through deconstructive, reconstructive and database assisted promoter studies, and its integration to principles of synthetic engineering has started an era of designer promoters. Exploration of natural promoter architecture and the concept of cis engineering have not only enabled fine tuning of single or multiple transgene expression in response to perturbations in the chemical, physiological and environmental stimuli but also provided researchers with a unique answer to various problems in crop improvement in the form of bidirectional promoters. [ABSTRACT FROM AUTHOR]

It is reported that coal consumption in the Asia-Pacific region is going to increase to about 87.2 percent by 2035. Management of coal combustion residues (CCRs) generated by industries is a major bottleneck towards handling the repercussions of coal usage. The present study investigates a management technique for these potentially hazardous wastes by means of vermicomposting. In the present investigation, studies were made on the effects of various concentrations of vermicomposted fly ash (VCF) added to agricultural soil, on the growth and yield of tomato (Lycopersicon esculentum Mill.) and brinjal (Solanum melongena L.) plants. The toxicity of trace elements in VCF were estimated using coefficient of pollution and potential ecological risk index, which revealed no apparent risks to the environment. A gradual increase in VCF concentrations in the agricultural soil improved the physico-chemical properties, enzymatic activities, microbial biomass, carbon and microbial population upto 90 days after sowing of seeds. The VCF amendments significantly (p < 0.05) improved the soil quality (2.86% nitrogen and 1.05% Phosphorous) and germination percentage (82.22%) of seeds in L. esculentum and also in S. melongena. The results of this study reveal that, CCRs can be effectively managed in agriculture specially in developing economies. [ABSTRACT FROM AUTHOR]