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This Paper proposed an enhanced Montgomery and efficient implementation of Modular Multiplication. Cryptographyoprocess is usedufor providingphigh informationmsecurity when a data is transferredmfrom transmitter to receiver. Various using methods like RSA, ECC, the Digital Signature Algorithm. The propose Montgomery algorithm usin RSA algorithim of cryptography is implemented in two different input both the inputs are 8 bit input. Coding have been done in Verilog language and the results are simulated on Vivado Software. For physical testing, we have used an FPGA NESYS 4 DDR hardware board that have Artix-7 FPGA chip on it produced by digilent company. The propose method shows good results in term of the number of slice flip flop, LUTs, and number of IOBs and power consumption. The proposed method shows better results as compare to other previous methods in term of different result parameters.

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL. [ABSTRACT FROM AUTHOR]

During gastrulation, physical forces reshape the simple embryonic tissue to form the complex body plans of multicellular organisms.sup.1. These forces often cause large-scale asymmetric movements of the embryonic tissue.sup.2,3. In many embryos, the gastrulating tissue is surrounded by a rigid protective shell.sup.4. Although it is well-recognized that gastrulation movements depend on forces that are generated by tissue-intrinsic contractility.sup.5,6, it is not known whether interactions between the tissue and the protective shell provide additional forces that affect gastrulation. Here we show that a particular part of the blastoderm tissue of the red flour beetle (Tribolium castaneum) tightly adheres in a temporally coordinated manner to the vitelline envelope that surrounds the embryo. This attachment generates an additional force that counteracts tissue-intrinsic contractile forces to create asymmetric tissue movements. This localized attachment depends on an [alpha]PS2 integrin (inflated), and the knockdown of this integrin leads to a gastrulation phenotype that is consistent with complete loss of attachment. Furthermore, analysis of another integrin (the [alpha]PS3 integrin, scab) in the fruit fly (Drosophila melanogaster) suggests that gastrulation in this organism also relies on adhesion between the blastoderm and the vitelline envelope. Our findings reveal a conserved mechanism through which the spatiotemporal pattern of tissue adhesion to the vitelline envelope provides controllable, counteracting forces that shape gastrulation movements in insects. In the red flour beetle (Tribolium castaneum) and fruit fly (Drosophila melanogaster), spatiotemporally coordinated integrin-dependent attachments between the blastoderm and vitelline envelope counteract tissue-intrinsic contractile forces to create asymmetric movements of embryonic tissue., Author(s): Stefan Münster [sup.1] [sup.2] [sup.3] [sup.4] , Akanksha Jain [sup.1] , Alexander Mietke [sup.1] [sup.2] [sup.3] [sup.5] , Anastasios Pavlopoulos [sup.6] , Stephan W. Grill [sup.1] [sup.3] [sup.4] [sup.7] [...]