Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs

Wireless Personal Communications - This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes,...     

An extended hesitant fuzzy set using SWARA-MULTIMOORA approach to adapt online education for the control of the pandemic spread of COVID-19 in higher education institutions

The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.

     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.     

Development,optimization, and characterization of a novel tea tree oil nanogel using response surface methodology

Purpose: To develop and optimize nanoemulsion (NE)-based emulgel (EG) formulation as a potential vehicle for topical delivery of tea tree oil (TTO).

Methodology: Central composite design was adopted for optimizing the processing conditions for NE preparation by high energy emulsification method viz. surfactant concentration, co-surfactant concentration, and stirring speed. The optimized NE was developed into emulgel (EG) using pH sensitive polymer Carbopol 940 and triethanolamine as alkalizer. The prepared EG was evaluated for its pH, viscosity, and texture parameters, ex vivo permeation at 37?°C and stability. Antimicrobial evaluation of EG in comparison to conventional gel and pure TTO was also carried out against selected microbial strains.

Results and discussion: Optimized NE had particle size and zeta potential of 16.23?±?0.411?nm and 36.11?±?1.234?mV, respectively. TEM analysis revealed the spherical shape of droplets. The pH of EG (5.57?±?0.05?) was found to be in accordance with the range of human skin pH. EG also illustrated efficient permeation (79.58?μL/cm²) and flux value (J_SS) of 7.96?μL cm²/h through skin in 10?h. Viscosity and texture parameters, firmness (9.3?±?0.08?g), spreadability (2.26?±?0.06?mJ), extrudability (61.6?±?0.05?mJ), and adhesiveness (8.66?±?0.08?g) depict its suitability for topical application. Antimicrobial evaluation of EG with same amount of TTO as conventional gel revealed broader zones of growth inhibitions against all the selected microbial strains. Moreover, EG was also found to be nonirritant (PII 0.0833). These parameters were consistent over 90 d.

Conclusion: TTO EG turned out to be a promising vehicle for the topical delivery of TTO with enhanced therapeutic efficacy.     

Experimental investigations into electric discharge grinding and ultrasonic vibration-assisted electric discharge grinding of Inconel 601

The paper presents experimental investigations into electric discharge grinding (EDG) and ultrasonic vibration-assisted electric discharge grinding (UVAEDG) of Inconel 601. The process parameters selected for both processes were duty cycle, discharge current, pulse on time, grinding wheel speed, work speed, and speed ratio to study their influence on responses like surface roughness (R_a) and material removal rate (MRR). It was found that duty cycle, wheel speed, work speed, discharge current, speed ratio, and pulse duration significantly influenced MRR and R_a. It was inferred that MRR increased with increase in duty cycle, wheel speed, current, work speed, and pulse duration in both EDG and UVAEDG processes. It was also inferred that R_a increased with rise in duty factor, pulse on time, and discharge current in EDG and UVAEDG processes.     

Modulated Triple‐Material Nano‐Heterostructures: Where Gold Influenced the Chemical Activity of Silver in Nanocrystals

For efficient charge separations, multimaterial hetero‐nanostructures are being extensively studied as photocatalysts. While materials with one heterojunction are widely established, the chemistry of formation of multijunction heterostructures is not explored. This needs a more sophisticated approach and modulations. To achieve these, a generic multistep seed mediated growth following controlled ion diffusion and ion exchange is reported which successfully leads to triple‐material hetero‐nanostructures with bimetallic‐binary alloy‐binary/ternary semiconductors arrangements. Ag₂S nanocrystals are used as primary seeds for obtaining AuAg‐AuAgS bimetallic‐binary alloyed metal–semiconductor heterostructures via partial reduction of Ag(I) using Au(III) ions. These are again explored as secondary seeds for obtaining a series of triple‐materials heterostructures, AuAg‐AuAgS‐CdS (or ZnS or AgInS₂), with introduction of different divalent and trivalent ions. Chemistry of each step of the gold ion–induced changes in the rate of diffusion and/or ion exchanges are investigated and the formation mechanism for these nearly monodisperse triple material heterostructures are proposed. Reactions without gold are also performed, and the change in the reaction chemistry and growth mechanism in presence of Au is also discussed.     

Nanocrystalline Nd–Fe–B Anisotropic Magnets by Flash Spark Plasma Sintering

Flash spark plasma sintering (flash SPS) is an attractive method to obtain Nd–Fe–B magnets with anisotropic magnetic properties when starting from melt-spun powders. Compared to the benchmark processing route via hot pressing with subsequent die upsetting, flash SPS promises electroplasticity as an additional deformation mechanism and reduced tool wear, while maximizing magnetic properties by tailoring the microstructure—fully dense and high texture. A detailed parameter study is conducted to understand the influence of Flash SPS parameters on the densification and magnetic properties of commercial MQU-F powder. It is revealed that the presintering conditions and preheating temperature before applying the power pulse play a major role for tailoring grain size and texture in the case of hot deformation via Flash SPS. Detailed microstructure and magnetic domain evaluation disclose the texture enhancement with increasing flash SPS temperature at the expense of coercivity. The best compromise between remanence and coercivity (1.37 T and 1195 kA m⁻¹, respectively) is achieved through a combination of presintering at 500 °C for 120 s and preheating temperature of 600 °C, resulting in a magnet with energy product (BH)_max of 350 kJm⁻³. These findings show the potential of flash SPS to obtain fully dense anisotropic nanocrystalline magnets with high magnetic performance.     

Charge transport and structural morphology of HCl-doped polyaniline

The d.c. and a.c. (100 Hz–1 MHz) conductivities of HCl-doped polyaniline have been measured in the temperature range 77–300 K. At 77 K, the a.c. conductivity data, (), can be described by the relation ()=As, where the parameter s lies close to unity and decreases with increase in the doping level. The ratio of measured a.c. to d.c. conductivity shows dispersion at 77 K, which decreases with increase in the doping level. This decrease is found to be sharp around pH3.0. In the temperature range 77–150 K, the observed d.c. conductivity data can be described by Mott's three dimensional variable range hopping (VRH) model. Scanning electron microscopy studies reveal a sharp change in structural morphology of HCl-doped polyaniline at a pH3.0. A strikingly remarkable structural morphology has been observed in the formc of a channel at this pH value. This change is accompanied by a rapid increase in d.c. conductivity, dielectric constant, along with sharp changes in structural morphology, which indicates the existence of a doping-induced structural conductivity correlation in this system. © 1998 Chapman & Hall     

Studies on microbridges of superconducting YBCO thin film

Critical current densities of the superconducting Y-Ba-Cu-O (YBCO) films have been observed to decrease with the increase of power of microwave radiation. Presence of Josephson type of junctions in the microbridges has been established from the microwave irradiation and magnetic field studies. BCS energy gap parameter (2/kT _c ) has been calculated from thedI/dV characteristics and found to be 3.7 at 13 K.