Showing papers in "Arabian Journal for Science and Engineering in 2021"

Numerical Computing Paradigm for Investigation of Micropolar Nanofluid Flow Between Parallel Plates System with Impact of Electrical MHD and Hall Current

Abstract: The current study develops a novel application of numerical computing paradigm to inspect the cumulative effects of both electric and magnetic field on a micropolar nanofluid bounded by two parallel plates in a rotating system by using Adams and Explicit Runge–Kutta (RK) solvers. The steady state micropolar nanofluidics flow has been considered between parallel plates under the Hall current effect. The conventional expressions for the governing flow in terms of system of ODEs are engaged. Adams and Explicit RK-based numerical solvers have been exploited for approximate solution of the system dynamics. Accuracy, convergence and stability analyses of both numerical schemes established their correctness. The impact of the Sherwood number on the concentration profile, Nusselt number and coefficient of skin friction on temperature and velocity profiles, respectively, have been analyzed numerically along with thermophoresis investigation, rotation, Hall current and Brownian motion of micropolar nanofluid. The effect of physical quantities including viscosity, magnetic, rotating, coupling, Brownian motion, thermophoretic and micropolar fluid parameters as well as Prandtl and Schmidt numbers has been presented.

A New Improved Model of Marine Predator Algorithm for Optimization Problems

Abstract: The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies.

Toward Design of an Intelligent Cyber Attack Detection System using Hybrid Feature Reduced Approach for IoT Networks

Abstract: With simple connectivity and fast-growing demand of smart devices and networks, IoT has become more prone to cyber attacks. In order to detect and prevent cyber attacks in IoT networks, intrusion detection system (IDS) plays a crucial role. However, most of the existing IDS have dimensionality curse that reduces overall IoT systems efficiency. Hence, it is important to remove repetitive and irrelevant features while designing effective IDS. Motivated from aforementioned challenges, this paper presents an intelligent cyber attack detection system for IoT network using a novel hybrid feature reduced approach. This technique first performs feature ranking using correlation coefficient, random forest mean decrease accuracy and gain ratio to obtain three different feature sets. Then, features are combined using a suitably designed mechanism (AND operation), to obtain single optimized feature set. Finally, the obtained reduced feature set is fed to three well-known machine learning algorithms such as random forest, K-nearest neighbor and XGBoost for detection of cyber attacks. The efficiency of the proposed cyber attack detection framework is evaluated using NSL-KDD and two latest IoT-based datasets namely, BoT-IoT and DS2OS. Performance of the proposed framework is evaluated and compared with some recent state-of-the-art techniques found in literature, in terms of accuracy, detection rate (DR), precision and F1 score. Performance analysis using these three datasets shows that the proposed model has achieved DR up to 90%–100%, for most of the attack vectors that has close similarity to normal behaviors and accuracy above 99%.

Bioconvection in a Convectional Nanofluid Flow Containing Gyrotactic Microorganisms over an Isothermal Vertical Cone Embedded in a Porous Surface with Chemical Reactive Species

Abstract: In this examination, a mathematical model is developed for Darcy free convection with reference to an isothermal vertical cone along with fixed apex half angle, pointing downward in a nanofluid-saturated porous medium. The aim of this methodology is to offer another sort of primary fluid containing nanoparticles and gyrotactic microorganism’s consistence of permeable medium, chemical reaction alongside convective boundary circumstance. The model presents design rules for improvement of imperative fabrication of fertilizer and polymer substance. The present model includes the gyrotactic microorganisms alongside nanoparticles, and cone is dependent on concentration of nanoparticles as well as density of motile microorganisms. Two important impacts Brownian motion as well as thermophoresis are also included in the present model for nanofluids. Reduced system of nonlinear differential equations is derived from governing partial differential of the present flow by using usual transformations along with Oberbeck–Boussinesq approximation. After that, this reduced system is solved numerically with the use of fifth-order Runge–Kutta method in conjunction with the shooting technique. Relevant outcomes are exhibited graphically and talked about quantitatively as for variety in the flow controlling parameters related to the present analysis. Mainly, the observations are bioconvection parameters will in general improve the concentration of the rescaled density of microorganisms and nanoparticles volume fraction and dimensionless motile microorganisms reduce with strong chemical reactions.

Prediction of Heart Disease Using Deep Convolutional Neural Networks

Abstract: Heart diseases are currently a major cause of death in the world. This problem is severe in developing countries in Africa and Asia. A heart disease predicted at earlier stages not only helps the patients prevent it, but I can also help the medical practitioners learn the major causes of a heart attack and avoid it before its actual occurrence in patient. In this paper, we propose a method named CardioHelp which predicts the probability of the presence of cardiovascular disease in a patient by incorporating a deep learning algorithm called convolutional neural networks (CNN). The proposed method is concerned with temporal data modeling by utilizing CNN for HF prediction at its earliest stage. We prepared the heart disease dataset and compared the results with state-of-the-art methods and achieved good results. Experimental results show that the proposed method outperforms the existing methods in terms of performance evaluation metrics. The achieved accuracy of the proposed method is 97%.

Adaptive PID Controller Using Sliding Mode Control Approaches for Quadrotor UAV Attitude and Position Stabilization

Abstract: This paper proposes an auto-tuning adaptive proportional-integral-derivative control (APIDC) system for attitude and position stabilization of quadrotor unmanned aerial vehicle (UAV) under parameter uncertainties and external disturbances. By employing sliding mode control as the adaptive mechanism, this technique can overcome the manual controller’s re-tuning gains in a proportional-integral-derivative controller. Furthermore, a fuzzy compensator is used to eliminate the chattering phenomena caused by the sliding mode control. The auto-tuning process is based on the gradient descent technique and the Lyapunov stability theorem. Using simulations, the proposed APIDC scheme is able to achieve a satisfactory attitude and position tracking performance of the quadrotor UAV. The proposed APIDC system also shows high robustness under parameter uncertainties and external disturbances.

Applications of Blockchain Technology in Clinical Trials: Review and Open Challenges

Abstract: Blockchain technology has disclosed unprecedented opportunities in the healthcare sector by unlocking the true value of interoperability. Specifically, the striking features of blockchain technology, such as data provenance, transparency, decentralized transaction validation, and immutability, can help to compensate for stringent data management issues (e.g., patient recruitment, persistent monitoring, data management, and data analytics and accurate reporting) in clinical trials (CTs). Although several research studies show that blockchain solutions help to improve patient retention, data integrity, privacy and ensure CTs compliance with regulatory policies, a comprehensive survey on this topic is lacking. In this survey, we provide insights into the adoption of blockchain technology in CTs. We categorize and classify the literature by devising a meticulous taxonomy of the decentralized tasks of CT and practices based on indispensable parameters. Furthermore, we provide insights on works in progress toward deploying blockchain solutions in CTs. Finally, we identify and discuss several challenges that hinder the successful implementation of blockchain technologies in CTs.

A New Ensemble-Based Intrusion Detection System for Internet of Things

Abstract: The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few years by drastically transforming human lives to automate their ordinary daily tasks. This is achieved by interconnecting heterogeneous physical devices with different functionalities. Consequently, the rate of cyber threats has also been raised with the expansion of IoT networks which puts data integrity and stability on stake. In order to secure data from misuse and unusual attempts, several intrusion detection systems (IDSs) have been proposed to detect the malicious activities on the basis of predefined attack patterns. The rapid increase in such kind of attacks requires improvements in the existing IDS. Machine learning has become the key solution to improve intrusion detection systems. In this study, an ensemble-based intrusion detection model has been proposed. In the proposed model, logistic regression, naive Bayes, and decision tree have been deployed with voting classifier after analyzing model’s performance with some prominent existing state-of-the-art techniques. Moreover, the effectiveness of the proposed model has been analyzed using CICIDS2017 dataset. The results illustrate significant improvement in terms of accuracy as compared to existing models in terms of both binary and multi-class classification scenarios.

Low-Interacted Multiple Antenna Systems Based on Metasurface-Inspired Isolation Approach for MIMO Applications

Abstract: This paper investigates a simple method based on the metasurface concept to suppress the mutual-coupling between the radiation parts of a 2 × 2 antenna-arrays. The array-antennas have constructed of four circular-patches implemented on the FR-4 substrate, so each patch has separately excited by a waveguide-port. The proposed decoupling-approach inspired the metasurface principle has applied by realizing the rectangular-slots in a linear and series configuration incorporated between the antennas to decrease their interaction and reduce the surface-waves. The proposed slots act like series left-handed capacitors. To achieve more isolation, the metallic via-holes have employed between the rectangular-slots across the substrate-layer, which has caused to suppress the substrate-losses. The via-holes behave like shunt left-handed inductors. By incorporating series slots and via-holes, the metasurafce-inspired decoupling-slab has realized without increasing the physical dimensions. The results show that by the proposed method the substrate-loses, surface-waves, and interaction between the radiation elements have significantly diminished and as resultant the array's performances such as impedance bandwidth, fractional bandwidth, impedance matching, isolation between antennas #1, #1, #1, radiation gain, and efficiency have improved by 2.1 GHz, 21.2%, 4 dB, 12 dB, 16 dB, 13 dB, 3.2 dBi, and 23%, respectively, which exhibit the effectiveness of the proposed metasurface-based isolation-slab. The fabricated proposed 2 × 2 array-antennas with compact dimensions of 40 $$\times $$ 40 $$\times $$ 0.8 mm3 and edge-to-edge distance between the radiation components of 0.16 $${\lambda }_{0}$$ operates over approximately entire X-band spectrum of 8.2–12 GHz, which corresponds to 37.62% practical bandwidth. The array antennas exhibit an average efficiency and gain of 76% and 8.5dBi, which enable it to be applicable for MIMO systems.

Green Synthesis of Carbon Dots and Their Application as Photocatalyst in Dye Degradation Studies

Abstract: This paper presents work on green synthesis of the carbon dots from leaves of Elettaria cardamomum (E.C) using simple ultra-sonication technique. In X-ray diffraction analysis, peak at 2θ value of 22.9° confirmed the amorphous nature of synthesized carbon dots. Furthermore, Raman investigations of the synthesized carbon dots illustrated D and G band at 1365 cm−1 and 1575 cm−1, respectively, showing graphitic structure of carbon dots. Similarly, Fourier transform infrared spectrum of carbon dots confirmed the presence of different functional groups such as C=O, C=C, OH, C–OH at absorption peaks values of 1715 cm−1, 1634 cm−1, 3257 cm−1 and 1027 cm−1, respectively. Photoluminescence spectral analysis of carbon dots confirms photoluminescent nature by exhibiting two emission peaks at 520 nm and 850 nm, respectively. UV–visible spectrophotometric investigations confirmed the presence of carbon dots showing two absorption peaks at 220 nm and 272 nm. After confirmation and characterization, the synthesized carbon dots were utilized for studying visible light-induced degradation of congo red and methylene blue dyes. This further investigation showed that the maximum degradation of congo red dye was observed in acidic media at pH 4 with a dye concentration of 5 ppm in a time interval of 50 min. For methylene blue dye, optimum degradation was observed in alkaline medium at a pH value of 8 with 5 ppm dye concentration in a time interval of 55 min.

EMHD Flow of Radiative Second-Grade Nanofluid over a Riga Plate due to Convective Heating: Revised Buongiorno’s Nanofluid Model

Abstract: Exploiting the impact of Lorentz force and convective heating boundary on second-grade nanofluid flow alongside a Riga pattern is the main objective of the present work. Modelling of the present work is done through Grinberg term and a Lorentz force applied parallel to the wall of a Riga plate. The nanoparticles fraction on the solid surface of Riga pattern maintained a strong retardation because of zero mass flux. Theories of Cattaneo–Christov heat flux and generalized Fick’s relations are employed by following the modern aspects of heat and mass transportations. In the current study, additional features of thermal radiation are also included in the energy equation in terms of linear expressions. In order to make the analysis more worthy, effect of chemical reaction is also included. By applying the suitable variables, constituted problem is converted into dimensionless form. Solution of the problem with desired accuracy is obtained by utilizing popular method called Runge–Kutta–Fehlberg. The graphical representations are used to illustrate the flow controlling parameters involved by their attractive physical consequences. Velocity distribution is observed for the increase with the second-grade parameter. Further, an improved nanoparticles temperature distribution is observed with the increase in radiation parameter and Biot number. Additionally, the distribution of the concentration of nanoparticles increases with increase in values of the thermophoretic parameter. Based on the scientific calculations obtained, it is established that the reported results may play a useful role in production processes and in the improvement of energy and thermal resources.

Nodal/Saddle Stagnation Point Slip Flow of an Aqueous Convectional Magnesium Oxide–Gold Hybrid Nanofluid with Viscous Dissipation

Abstract: In this analysis, convective heat transfer characteristics of a hybrid nanofluid mixture containing magnesium oxide (MgO) and gold (Au) nanoparticles are numerically studied. The impact of slip effects on nodal/saddle stagnation point boundary layer flow with viscous dissipation effect is mathematically modeled. The behavior of nanofluids is studied by employing Tiwari–Das nanofluid model. Pure water is the base fluid in this analysis. The governing partial differential equations with many independent variables are reduced to ordinary differential equations with one independent variable and then numerically solved by the Runge–Kutta–Fehlberg method with the desired accuracy. The outputs showed that MgO–Au/water hybrid nanofluid sharply raises the base fluid's thermal behavior. Results reveal that in the nodal and saddle point areas, the impact of higher slip effects significantly increases the local heat transfer rate.

A New Fusion of ASO with SA Algorithm and Its Applications to MLP Training and DC Motor Speed Control

Abstract: An improved version of atom search optimization (ASO) algorithm is proposed in this paper The search capability of ASO was improved by using simulated annealing (SA) algorithm as an embedded part of it The proposed hybrid algorithm was named as hASO-SA and used for optimizing nonlinear and linearized problems such as training multilayer perceptron (MLP) and proportional-integral-derivative controller design for DC motor speed regulation as well as testing benchmark functions of unimodal, multimodal, hybrid and composition types The obtained results on classical and CEC2014 benchmark functions were compared with other metaheuristic algorithms, including two other SA-based hybrid versions, which showed the greater capability of the proposed approach In addition, nonparametric statistical test was performed for further verification of the superior performance of hASO-SA In terms of MLP training, several datasets were used and the obtained results were compared with respective competitive algorithms The results clearly indicated the performance of the proposed algorithm to be better For the case of controller design, the performance evaluation was performed by comparing it with the recent studies adopting the same controller parameters and limits as well as objective function The transient, frequency and robustness analysis demonstrated the superior ability of the proposed approach In brief, the comparative analyses indicated the proposed algorithm to be successful for optimization problems with different nature

Study on Strength Behavior of Basalt Fiber-Reinforced Loess by Digital Image Technology (DIT) and Scanning Electron Microscope (SEM)

Abstract: Basalt fiber has unique advantages in the reinforcement of slopes and foundation. In this study, triaxial shear tests under undrained unconsolidated conditions, digital image technology and scanning electron microscope (SEM) tests were carried out to investigate the mechanism of fiber reinforcement to improve the shear strength of loess. Results show that the shear strength of reinforced sample is significantly higher than that of the unreinforced case, but not monotonically with increasing fiber length or fiber content, but with a maximum value at 0.6% fiber content and 12 mm fiber length. The failure morphology and strain field indicate that samples at higher fiber content exhibit bulging failure while those without fiber or at low fiber content show shear band failure. SEM images implies that the fibers in the soil tend to be orderly distributed as the fiber content increases while prone to bending and entanglement as the fiber length grows, which limits the mechanical response of the fibers. The statistical analysis proves that the hyperbolic strength model fits better than the parabola in the tension-shear and the shear domain. A unified joint strength model for basalt fiber-reinforced loess was obtained by fitting test data under different fiber contents and its rationality was verified by the error analysis.

Predicting Bond Strength of FRP Bars in Concrete Using Soft Computing Techniques

Abstract: Fiber-reinforced plastic (FRP) rebars can be the futuristic potential reinforcing material in place of mild steel (MS) rebars which are highly prone to corrosion. However, the bond properties of the FRP rebars are not consistent with those of mild steel rebars. Therefore, determination of bond strength properties of FRP rebars becomes essential. In this study, an investigation was conducted on 222 samples for bond strength data set for FRP rebars using various soft computing techniques such as multilinear regression, random forests, random tree, M5P, bagged-M5P tree, stochastic-M5P, and Gaussian process. Outcomes of accuracy assessment parameters, i.e., CC, MAE, and RMSE, suggest that bagged-M5P tree-based model is outperforming than other developed models CC, MAE, and RMSE whose values are 0.9530, 0.8970, and 1.2531, respectively, for testing stages. On assessing the data and the results, it was found that GP_PUK model is more appropriate than GP_RBF-based model for predicting the bond strength of FRP (MPa). On comparison of the RF and RT models, it was concluded that RF-based model performs better than RT models with CC, MAE, and RMSE values of 0.9427, 0.8674, and 1.3424, respectively, for testing stages. The results of the study also suggest that bagged-M5P model attains higher correlation with lesser RMSE values. Taylor diagram also verifies that bagged-M5P model performs better than other developed models. Sensitivity analysis suggests that bar embedment length to bar diameter (l/d) is the most influencing parameter for the prediction of bond strength of FRP.

A Comprehensive Review on Machining of Titanium Alloys

Abstract: Titanium (Ti) alloys are produced with the combination of titanium and alloying elements. Titanium and titanium alloys are lightweight materials with high toughness, corrosion resistance and tensile strength at high temperatures. Titanium and its alloys have been widely used in several industrial applications, such as marine, biomedicine, chemical, energy and other industries. However, machining of Ti alloys is extremely difficult due to the high cutting temperature, high tool wear, and built-up edge formation. Therefore, this research aimed to extensively review the impact of machining inputs on the machining force, chip formation, cutting temperature, tool wear, mechanical properties (residual stress, fatigue and hardness) and surface integrity (surface roughness, surface defect and microstructure) during turning and milling of Ti alloys. Moreover, laser-assisted machining, ultrasonically assisted machining and different cooling systems were reviewed and discussed. It was found that these techniques can significantly improve Ti alloys’ machinability. They can improve the surface integrity, tool life and mechanical properties as well as reducing the machining force and cutting temperature. The findings of this research can help manufacturers and researchers who work on machining processes, specially machining of Ti alloys.

Removal of Heavy Metals from Industrial Wastewater by Chemical Precipitation: Mechanisms and Sludge Characterization

Abstract: Chemical precipitation using lime (Ca(OH)2), caustic soda (NaOH) and soda ash (Na2CO3) for the removal of simultaneous heavy metals (Cu(II) and Zn(II)) from industrial wastewater of the cable industry was carried out in laboratory by jar tests For each reagent used, an improvement in copper and zinc removal efficiency was obtained by increasing the precipitating reagent dose (10–400 mg/L) Efficiencies of over 90% can be achieved Chemical precipitation efficiency is related to the pH of the treatment At a high final pH level (8 < pH < 10), the removal efficiency of copper for each precipitating agent is slightly higher than that of zinc and the residual metal contents were in conformity with industrial discharge standards In sludge product, zinc and copper were precipitated as amorphous hydroxides including Zn(OH)2 and Cu(OH)2 Based on XRD analysis, the presence of an amount of other additional phases was noticed for copper SEM images show that sludges produced are not large in size and are compact in structure Corresponding EDX (energy-dispersive X-ray spectroscopy) shows that the amount of copper is higher than the amount of zinc in all recovered sludge Wastewater treatment with soda ash resulted in a lower volume and a large product size of sludge As a result, drying steps can be less expensive This is a significant advantage comparably with the other precipitating agents Soda ash may be considered as cost-effective precipitating agent for Cu(II) and Zn(II) in the industrial wastewater of the cable industry

Thermal Enhancement of Radiating Magneto-Nanoliquid with Nanoparticles Aggregation and Joule Heating: A Three-Dimensional Flow

Abstract: This article studies the effect of nanoparticle aggregation on the 3D flow of titanium nanoliquid based on ethylene glycol $$ ( {\text{C}}_{ 2} {\text{H}}_{ 6} {\text{O}}_{2} - {\text{TiO}}_{2} ) $$ due to an exponentially elongated surface. Thermal analysis is carried out considering linear thermal radiation, Joule heating, and mechanisms of the heat source/sink, while the aspect of the homogeneous single-order chemical reaction is included in the analysis of the solute. The variable magnetic field is also accounted. The modified Maxwell model (Maxwell–Bruggeman) is implemented to estimate the effective conductivity of the nanoliquid. The displayed equations are moderated in quantities without dimensions. The 2-point nonlinear boundary value problem (BVP) is solved by the shooting procedure. The importance of effective parameters is described through graphs. Numerical data are presented to study the friction factor, the heat transfer rate, and the mass transfer rate. It has been established that the aggregation of nanoparticles significantly improves the thermal field. Furthermore, the effect of magnetism is more in ordinary fluid than in nanofluid.

Internet of Things with Artificial Intelligence for Health Care Security

Abstract: In recent years, health care facilities are moving towards technological advancements for precise patient monitoring and record management. Though it is technically advanced, the health care information and communication technology network's security is a significant challenge for health care. With the aid of standard algorithms, unstructured data existing outside organized databases (i.e., electronic documents and reports) is difficult to arrange and secure. The existing clustering method has a disadvantage of efficiency issues for recovering data transfer. This paper proposes the Internet of Things with Artificial Intelligence System (IoT-AIS) for health care security. Wireless sensor networks are developed by IoT technology. IoT network is used to bridge the physical and digital world. IoT-AIS is used to monitor the patient’s data and encrypt them. The encrypted data are stored in the cloud to maintain the patient data to access remotely. The IoT-AIS dashboard provides an individualized user interface for individual patients to maintain their records individually with single-user access. The proposed paper's simulation analysis proved that the Patient Record of health care could be encrypted and provide individualized access. The experimental results of IoT-AIS achieve the highest data transmission rate to 98.14% and the highest delivery rate of (98.90%), high period of standard responses (93.79%), less delay estimation (10.76%), improved throughput (98.23%), effective bandwidth monitoring (83.14%) energy usage (8.56%) and highest performance rate (98.4%) when compared to other methods.

Magnetohydrodynamics Natural Convection of a Triangular Cavity Involving Ag-MgO/Water Hybrid Nanofluid and Provided with Rotating Circular Barrier and a Quarter Circular Porous Medium at its Right-Angled Corner

Abstract: The current paper studied the behavior of a triangular cavity occupied with Ag-MgO/water nanofluid under MHD natural convection and provided with a rotating circular barrier, while the right-angled corner is equipped with quarter-circle porous medium and maintained at a fixed hot temperature Th. Several parameters are tested such as Rayleigh number (103 ≤ Ra ≤ 106), Hartmann number (0 ≤ Ha ≤ 80) and Darcy number (10−5 ≤ Da ≤ 0.15). The obtained results depict the enhancing effect of Ra and the controlling role of the magnetic parameter on heat transport. Increasing the characteristics of the porous media such as the porosity and the permeability showed a substantial impact on the heat transport efficiency within the enclosure. Moreover, the novelty findings in this paper are principally illustrated in the boosting impact of raising the porous medium thickness when it is associated with the growing up of the heated parts of the geometry by increasing the dimension of the radius (rp). Also, the rotational velocity (ω) and the radius (rob) of the circular obstacle are tested and showed an important influence on the energy transport within the cavity. Moreover, the obtained results by modifying the length (a) prove its pertinent influence on the heat transfer performance.

Carboxymethyl Cellulose-Based Hydrogel: Dielectric Study, Antimicrobial Activity and Biocompatibility

Abstract: In this work, we intended to investigate the antimicrobial activity, biocompatibility, and study the electrical conductivity of the polypyrrole on the surface of the conducting hydrogel such as carboxymethyl cellulose-g-poly (acrylamide-co-acrylamido-2-methyl-1-propane sulfonic acid). Broadband dielectric spectroscopy was employed to follow up the electrochemical double layer that developed at the electrode surfaces. Biocompatible conducting hydrogel showed the establishment of the electrical double layer in a wide range of frequencies, and the DC-conductivity values were in top of the semiconductors range. The addition of polypyrrole not only diminishes the effect of water transformations on conductivity, but also manifests the permittivity’s value (from 1.7 × 106 to 2.4 × 108). In addition, it lowers the charging–discharging loss of energy. Comparing the prepared conductive hydrogels to the ionic liquids, it showed that hydrogels have more ability to be applicable in the energy storage systems. Also, the prepared hydrogels biocompatibility was tested against normal cell line (Vero cells) which recorded the excellent compatibility with cells. The antimicrobial activity was examined against some pathogens; (i) Gram-negative bacteria: Escherichia coli (NCTC-10416) and Pseudomonas aeruginosa (NCID-9016); (ii) Gram-positive bacteria: Bacillus subtilis (NCID-3610); (iii) unicellular fungi: Candida albicans (NCCLS-11) and (iv) filamentous fungi Aspergillus niger (ATCC-22342).

Numerical Study and Chaotic Analysis of Meminductor and Memcapacitor Through Fractal–Fractional Differential Operator

Abstract: This paper investigates the dynamical characteristics for meminductor and memcapacitor via fractal–fractional-order domain of Caputo–Fabrizio. A chaos circuit is modeled for the highly nonlinear and non-fractional governing differential equations of meminductor and meminductor for knowing the hyperchaos, abrupt chaos and coexisting attractors. The time-scale transformation on dynamical equations is invoked within non-classical approach through newly presented fractal–fractional differential operator of Caputo–Fabrizio. The nonlinear fractionalized governing differential equations of meminductor and meminductor have been simulated by means of Adams–Bashforth–Moulton method. In order to disclose the functionalities of capacitive and inductive elements so-called meminductor and memcapacitor, we specified the fractal–fractional differential operator of Caputo–Fabrizio in three categories as (i) variation in both fractional and fractal parameters, (ii) variation in fractional parameter keeping fractal parameters equal to one, and (iii) variation in fractal parameter keeping fractional parameters equal to one. At the end, our numerically simulated results elaborate that chaotic behavior and unpinched hysteresis loops obtained via fractal–fractional approach are more efficient than ordinary approach.

COBERT: COVID-19 Question Answering System Using BERT.

Abstract: In the current situation of worldwide pandemic COVID-19, which has infected 62.5 Million people and caused nearly 1.46 Million deaths worldwide as of Nov 2020. The profoundly powerful and quickly advancing circumstance with COVID-19 has made it hard to get precise, on-request latest data with respect to the virus. Especially, the frontline workers of the battle medical services experts, policymakers, clinical scientists, and so on will require expert specific methods to stay aware of this literature for getting scientific knowledge of the latest research findings. The risks are most certainly not trivial, as decisions made on fallacious, answers may endanger trust or general well being and security of the public. But, with thousands of research papers being dispensed on the topic, making it more difficult to keep track of the latest research. Taking these challenges into account we have proposed COBERT: a retriever-reader dual algorithmic system that answers the complex queries by searching a document of 59K corona virus-related literature made accessible through the Coronavirus Open Research Dataset Challenge (CORD-19). The retriever is composed of a TF-IDF vectorizer capturing the top 500 documents with optimal scores. The reader which is pre-trained Bidirectional Encoder Representations from Transformers (BERT) on SQuAD 1.1 dev dataset built on top of the HuggingFace BERT transformers, refines the sentences from the filtered documents, which are then passed into ranker which compares the logits scores to produce a short answer, title of the paper and source article of extraction. The proposed DistilBERT version has outperformed previous pre-trained models obtaining an Exact Match(EM)/F1 score of 80.6/87.3 respectively.

Detecting the Stages of Alzheimer’s Disease with Pre-trained Deep Learning Architectures

Abstract: Deep learning algorithms have begun to be used in medical image processing studies, especially in the last decade. MRI is used in the diagnosis of Alzheimer’s disease, a type of dementia disease, which is the 7th among the diseases that cause death in the world. Alzheimer’s disease has no known cure in the literature, so it is important to attempt treatment before starting the irreversible path by diagnosing the pre-illness stages. In this study, the previous stages of Alzheimer’s disease were classified as normal, mild cognitive impairment, and Alzheimer’s disease through brain MRIs. Different models using CNN architecture were used to classify 2182 image objects obtained from the ADNI database. The study was presented in a very comprehensive comparison framework, and the performances of 29 different pre-trained models on images were evaluated. The accuracy values of each model and the precision, specificity, and sensitivity rates of each class were determined. In the study, the EfficientNetB0 model provided the highest accuracy at the test stage with an accuracy rate of 92.98%. In the comparative evaluation stage with the confusion matrix, the highest rates of precision, sensitivity, and specificity values of the Alzheimer’s disease class were achieved by EfficientNetB3 (89.78%), EfficientNetB2 (94.42%), and EfficientNetB3 (97.28%) models, respectively. The results of the study showed that among the pre-trained models, EfficientNet models achieved a high rate of classification performance as the models with the highest performance. This study will contribute to clinical studies in early prevention by detecting Alzheimer’s disease before it occurs.

An improved faster-RCNN model for handwritten character recognition

Abstract: Existing techniques for hand-written digit recognition (HDR) rely heavily on the hand-coded key points and requires prior knowledge Training an efficient HDR network with these preconditions is a complicated task Recently, work on HDR is mainly focused on deep learning (DL) approaches and has exhibited remarkable results However, effective detection and classification of numerals is still a challenging task due to people’s varying writing styles and the presence of blurring, distortion, light and size variations in the input sample To cope with these limitations, we present an effective and efficient HDR system, introducing a customized faster regional convolutional neural network (Faster-RCNN) This approach comprises three main steps Initially, we develop annotations to obtain the region of interest Then, an improved Faster-RCNN is employed in which DenseNet-41 is introduced to compute the deep features Finally, the regressor and classification layer is used to localize and classify the digits into ten classes The performance of the proposed method is analyzed on the standard MNIST database, which is diverse in terms of changes in lighting conditions, chrominance, shape and size of digits, and the occurrence of blurring and noise effects, etc Additionally, we have also evaluated our technique over a cross-dataset scenario to prove its efficacy Experimental evaluations demonstrate that the approach is more competent and able to accurately detect and classify numerals than other recent methods

Impact of Inclined Lorentz Force and Schmidt Number on Chemically Reactive Newtonian Fluid Flow on a Stretchable Surface When Stefan Blowing and Thermal Radiation are Significant

Abstract: The influence of inclined magnetic field and heat and mass transfer of a hydromagnetic fluid on stretching/shrinking sheet with Stefan blowing effects and radiation has been investigated. The elementary viscous equations for momentum, heat and mass transfer, which are highly nonlinear partial differential equations, are mapped into highly nonlinear ordinary differential equations with the help of similarity transformation. The subsequent highly nonlinear differential equation is solved analytically. The exact solution of heat and mass transfer appearances is found in terms of the incomplete gamma function. The species and temperature boundary conditions are assumed to be a linear function of the distance from the origin. Further, the impact of various parameters, such as Chandrasekhar number, thermal radiation, inclined Lorentz force and mass transpiration on velocity and temperature summaries, are conferred in detail.

Synthesis and Characterization for New Nanometer Cu(II) Complexes, Conformational Study and Molecular Docking Approach Compatible with Promising in Vitro Screening

Abstract: Synthesis for new Cu(II) complexes from acetamidopyridine derivitives and their characterization. Applying analytical, spectral and conformational techniques, the characterization for all new synthesizes, was achieved. Equimolar ratio (1:1) was proposed for all complexes in which the ligands behaved as bidentate toward copper center. The molar conductivity measurements defined state of acetate presence according to coordination sphere. Two structural forms were proposed for five complexes, square-planer and octahedral based on UV–Vis and ESR data. Conformational study was executed for all compounds by DFT method in Gaussian 09 under 6-31G basis set. The optimal structural forms were obtained besides other significant files (log, chk & fchk). Also, functional parameters were calculated, to put a well view about physical features. QSAR parameters were obtained from HyperChem program, also to assert on some characteristics. MOE-docking module, which considered the most powerful program simulates interaction of tested compound with biological systems, was also used to strengthen the study. The data extracted orient to great expectation for biological efficiency of complexes against breast cancer cells. The most shining point was the anticancer screening results, which reflect vigorous toxic behavior for most tested complexes against breast cancer cell line that actually exceeds than doxorubicin itself.

An Improved Blockchain Consensus Algorithm Based on Raft

Abstract: To address the problem of performance degradation caused by the blockchain backup mechanism as well as the high throughput and low latency requirements characteristic of blockchain applications, this paper optimizes the Raft consensus algorithm for the Hyperledger Fabric platform in terms of both log replication and leader election. In the log replication phase, the improvement of log replication based on apportionment idea aims to reduce the communication complexity of leader node by involving peer node in distributing log information. In the leader election phase, the improvement of leader election based on vote change mechanism changes the vote affiliation of peer node based on a comparison of votes from candidate nodes, aims to reach consensus in a round of election and reduces the election time. Through the performance test of the blockchain, it is demonstrated that the improved AdRaft algorithm improves 5.8% in throughput and reduces 1.3% in latency over the original Raft algorithm.

Free Vibrational Behaviour of Multi-Directional Porous Functionally Graded Structures

Abstract: The free vibrational frequencies of a multi-directional functionally graded (FG) structure are investigated for the first time in this paper considering the influences of variable grading (power-law, sigmoid, exponential) and porosity distribution (even and uneven type). Also, the structural properties (Young’s modulus, density and Poisson's ratio) varied along with two different directions simultaneously, i.e., the longitudinal and transversional ones, respectively. The present multi-directional grading model of the FG structure is reconstructed mathematically for the numerical analysis by considering adequate state-space deformation kinematics with the help of higher-order displacement functions and shear stress continuity. The general motion equation of a multi-graded structure is expressed using Hamilton’s principle and the finite element method including the necessary porosity effect. Initially, model consistency is verified and the eigenvalues obtained in the analysis are compared with the ones found in the literature. The comparison also includes the directional grading effect on their frequencies. Further, the influence of different parameters, i.e., power exponents (nz and nx), aspect ratio, thickness ratio, geometry, end support conditions, curvature ratio, porosity index, porosity distribution and material grading patterns, on the vibration characteristics of the multi-directional FG structure is computed. The analysis of the numerical results confirms that material grading; porosity distribution pattern and other design parameters have a significant influence on the frequency response characteristics of a single/multi-directional porous FG structure.

A Theoretical Investigation on the Heat Transfer Ability of Water-Based Hybrid (Ag–Au) Nanofluids and Ag Nanofluids Flow Driven by Electroosmotic Pumping Through a Microchannel

Abstract: This article explores the peristaltically regulated electroosmotic pumping of water-based hybrid (Ag–Au) nanofluids through an inclined asymmetric microfluidic channel in a porous environment. A newly developed model termed as modified Buongiorno model which studies the impact of thermophoretic and Brownian diffusion phenomenon along with the inclusion of thermophysical attributes of nanoparticles is employed to predict the heat transfer attributes. Governing equations of the present model are linearized through Debye–Huckel and lubrication linearization principle. Mathematical software Maple 17 is applied to simulate the numerical results. Salient attributes of the electroosmotic peristaltic pumping subject to various physical parameters are assessed through graphical results. Visualization of fluid flow is presented by preparing contour plots for stream function. Moreover, a comparative study for water-based hybrid (Ag–Au) nanofluid and the silver nanofluid is made. It is found that the hybridity of nanofluid facilitates to achieve a much higher heat transfer rate as compared to silver-water nanofluid and thermophysical properties are remarkably improved in the case of hybrid nanofluids. The heat transfer rate is inversely related to the size of suspended nanoparticles. Furthermore, the mechanism of heat transfer is boosted through electroosmosis by reducing the thickness of the electric double layer and applying the electric field. This model will be applicable to developing biomicrofluidics devices for drug delivery systems.