On the Selection of a Composite Material for Two-Wheeler Foot Bracket Failure Prevention through Simulation and Mathematical Modeling

A foot bracket is a metal panel bracket used to mount and support the footrest in two-wheeler systems.It holds the footrest in place while rigidly supporting it.In working conditions,this element has often been observed to fail when specific load-fluctuation conditions are established at its rear end.Appropriate materials therefore need to be identified to overcome such a recurring failure.To address these issues,the present study has been implemented with the specific objective to determine the response of selected Al6061-T6 and Al7075-T6 Hybrid Metal Matrix Composites(HMMC).The results,obtained using the ANSYS Software,show that the selected composites can withstand 636,962 N/m^(2)of maximum stress and 8.88×10^(−6)m of minimum displacement.These results are also compared with relevant mathematical models and it is concluded that the identified material combination provides the required improvement of structural stability that can withstand the load fluctuation on the foot bracket.

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

A simple and distinguished nebulizer approach to prepare CdS thin films

In this paper, we report the substrate temperature induced change in structural, optical, morphological,luminescence and photoelectrochemical properties of CdS films deposited by a simple and facile approach called nebulized spray pyrolysis technique. X-ray diffraction study confirmed the deposited CdS films belong to hexagonal wurtzite structure, with preferential orientation along c-axis,(002) direction perpendicular to the substrate plane. The crack free, uniform, and homogeneously distributed spherical particles are witnessed from AFM image. Various optical parameters like energy band gap, optical conductivity,refractive index, extinction coefficient, dielectric constants, and dispersion energy parameters of the films were evaluated. The strong band edge emission observed in the PL study may be attributed to the recombination of excitations and/or shallowly trapped electron-hole pairs. The first and second overtone of LO modes of CdS at 302 and 600 cm-1are observed in the Raman study. The photoelectrochemical properties of the films were also tested.

An Analytical Study on the Synchronization of Murali-Lakshmanan-Chua Circuits

An explicit analytical solution is presented for unidirectionally coupled two Murali-Lakshmanan-Chua circuits exhibiting chaos synchronization in their dynamics. The transition of the system from an unsynchronized state to a state of complete synchronization under the influence of the coupling parameter is observed through phase portraits obtained from the analytical solutions of the circuit equations characterizing the system.

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.