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Aluminium alloys are being employed very widely as galvanic anodes for cathodic protection. Aluminium‐zinc‐mercury and aluminium‐zinc‐indium are the popular alloys that are used as anodes. In general, the alloying ingredients activate aluminium and maintain uniform dissolution. In the present investigation, the role of addition of gallium in the ternary alloy of aluminium‐zinc‐indium has been studied by making use of polarisation studies, capacity measurements and galvanic current measurements.

(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO₂) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO₂) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.

The two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).

(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.

(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.

(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO₂) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO₂) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.

The drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.

All the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

In this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).

The proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.

The steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.

The hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.

The purpose of this study is to propose a model of competition between a formal lender (bank) and an informal lender (moneylender) with informational asymmetry between these two lenders. Further, the authors introduce capacity constraint on the lending capacity of the moneylender and assume that borrowers differ in risk and wealth.

The solution concept of Nash equilibrium has been used to derive the optimal strategies of the lenders.

The equilibrium strategies in most of the results depend on the difference between the expected returns from risky and safe projects where the risky project has higher expected returns. The credit market is segmented in terms of risk and wealth levels. Rationing of poor safe borrowers from the credit market is inevitable when the moneylender's capacity is sufficiently small, suggesting a low-income trap for them. Further, when moneylender has capacity constraint of some form, a zero-profit outcome is never a Nash equilibrium outcome.

There is a possibility of collusion between the lenders. However, the authors do not derive all possible outcomes under capacity constraint

When the informal lender has limited capacity, competition between formal and informal lenders may not alleviate credit rationing, instead aggravate the problem. Thus, the government should devise policies to ensure credit availability to resource poor households

While the literature models strategic interaction between lenders under the assumption of zero-profit (Bertrand Paradox) condition, this study shows that zero profit is not the only outcome under such a set-up. Also, in presence of capacity constraint of the moneylender, a zero-profit outcome is never a Nash equilibrium outcome for the lenders. There is an optimal contract at which the lenders differentiate in terms of repayment and collateral and earn positive profits under certain conditions.

Purpose of this paper are Real power loss reduction, voltage stability enhancement and minimization of Voltage deviation.

In HLG approach as per Henry gas law sum of gas dissolved in the liquid is directly proportional to the partial pressure on above the liquid. Gas dissolving in the liquid which based on Henry gas law is main concept to formulate the proposed algorithm. Populations are divided into groups and all the groups possess the similar Henry constant value. Exploration and exploitation has been balanced effectively. Ranking and position of the worst agents is done in order to avoid the local optima. Then in this work Mobula alfredi optimization (MAO) algorithm is projected to solve optimal reactive power problem. Foraging actions of Mobula alfredi has been imitated to design the algorithm. String foraging, twister foraging and backward roll foraging are mathematically formulated to solve the problem. In the entire exploration space the Mobula alfredi has been forced to discover new regions by assigning capricious position. Through this approach, exploration competence of the algorithm has been improved. In all iterations, the position of the Mobula alfredi has been updated and replaced with the most excellent solution found so far. Exploration and exploitation capabilities have been maintained sequentially. Then in this work balanced condition algorithm (BCA) is projected to solve optimal reactive power problem. Proposed BCA approach based on the conception in physics- on the subject of the mass; incoming, exit and producing in the control volume. Preliminary population has been created based on the dimensions and number of particles and it initialized capriciously in the exploration space with minimum and maximum concentration. Production control parameter and Production probability utilized to control the exploration and exploitation.

Proposed Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA are evaluated in IEEE 30 bus system with L-index (Voltage stability) and also tested in standard IEEE 14, 30, 57, 118, 300 bus test systems without L- index. Real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.

For the first time Henry's Law based -soluble gas optimization (HLG) algorithm, Mobula alfredi optimization (MAO) algorithm and BCA is projected to solve the power loss reduction problem.

The study aims to investigate returns on investment (ROI) from the academic library of India's top ten leading university libraries in terms of research publication. Librarians help academic researchers in a variety of ways. Some of these methods are collection oriented, whilst others are service oriented. The study investigates many factors responsible for the increased or decreased institutional ROI, performance and research out of the institution, such as total library budget, staff, library collection, service and facilities. The ROI % shows the institution's highest and lowest investment return in research publication.

The study is expiation about ROI from the academic library of India's top ten leading university libraries in terms of research publication. The study is based on primary and secondary data gathered from the Ministry of Education, Government of India and universities ranking in June 2020. The data were compiled from the ranking list regarding total library budgets, staff and research publications for 2017–2020. The research is limited to a ROI analysis of university libraries. The study should aid libraries in better understanding the idea of ROI in order to improve library services. The study then looked at various institutions' ROI from 2017 to 2020.

The study aims to investigate ROI from the academic library of India's top ten leading university libraries in terms of research publication. This study investigates many factors responsible for the increased or decreased institutional ROI, performance and research out of the institution, such as total library budget, staff, library collection, service and facilities. The study found that the overall highest research output is by the Indian Institute of Science and the lowest by Jamia Millia Islamia among the ten leading university libraries in India.

The ROI study displays the importance of libraries in terms of research publication of the institutions. This research can also support decision-making, library collection development and institutional library system analysis. The library can assist with publications, sponsorships, grants, finances and teaching enhancement research output. The last point justifies the library's increased importance in establishing an organisation's status and obtaining accreditation.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

This paper aims to explore the double diffusive magneto-hydrodynamic (MHD) squeezed flow of (Cu–water) nanofluid between two analogous plates filled with Darcy porous material in existence of chemical reaction and external magnetic field.

The governing nonlinear equations are transformed into ordinary differential equations by means of similarity transforms, and the coupled mass and heat transference equations are resolved analytically with the application of differential transform method (DTM). The effects of different relevant parameters on velocity, temperature and concentration, including the squeeze number, magnetic parameter, Biot number, Darcy number and chemical reaction parameter, are illustrated with figures. In addition, for various parameters, the local skin friction coefficient, local Nusselt number and local Sherwood number are computed and are graphically displayed.

It is observed that the squeeze number has a direct relationship with Sherwood number and an inverse relationship with skin friction as Biot number increases. With enhanced Biot numbers, the temperature value increases during both squeeze and non-squeeze moments, but the temperature values are higher for squeeze moments compared to the other case.

This research has potential applications in various large-scale enterprises that might benefit from increased productivity.

The results are useful to thermal science community.

Unique and valuable insights are provided by studying the impact of chemical reaction on double diffusive MHD squeezing copper–water nanofluid flow between parallel plates filled with porous medium. In addition, this research has potential applications in various large-scale enterprises that might benefit from increased productivity.

The stability and input voltage saturation is a common problem associated with an active magnetic bearing (AMB) system. The purpose of this paper is to design a control scheme that stabilizes the single degree of freedom AMB system and also tackle the problem of input voltage saturation in the AMB system.

The proposed control technique is a combination of two separate control schemes. First, the Backstepping control scheme is designed to stabilize and control the AMB system and then Chebyshev neural network (CNN)-based compensator is designed to tackle the input voltage saturation when the system control action is saturated.

The mathematical and simulation results are presented to validate the effectiveness of proposed methodology for single-degree freedom AMB system.

This paper introduces a CNN-based compensator with Backstepping control strategy to stabilize and tackle the problem of input voltage saturation in the 1-DOF AMB systems.

Non-linear power–voltage characteristics of solar cell and frequently changing output due to variation in solar irradiance caused by movement of clouds are the major issues need to be considered in photovoltaic (PV) penetration to maintain the power quality of the grid. It is important for a PV module to always function at its maximum available power point to increase the efficiency and to maintain the grid stability. A possible solution to mitigate these generation fluctuations is the use of an electric double-layer capacitor or supercapacitor energy storage device, which is an efficient storage device for power smoothing applications. This study aims to propose a power smoothing control approach to smoothen out the output power variations of a solar PV system using a supercapacitor energy storage device.

To extract the maximum possible power from a PV panel, there are several maximum power points tracking (MPPT) algorithms developed in literature. Fuzzy logic controller-MPPT method is used in this work as it is a very efficient and popular technique which responds quickly under varying ecological conditions, reduced computational complexity and does not depend on any system constraints. Fuzzy logic-based MPPT controller by Boost DC–DC converter is developed for operating the PV panels at available maximum power point. Fuzzy logic-proportional integral (PI) charge controller is implemented by Buck–Boost converter to provide the constant current and suitable voltage for supercapacitor and to achieve better power smoothing. PI charge controller is preferred in this work as it offers better outcomes and is very easy to implement.

Simulation results conclude that the proposed power smoothing control approach can efficiently smooth out the power variations under variable irradiance and temperature situations. To confirm the accurateness of the proposed system, it is validated for poly-crystalline PV module and comparison of results is done by using different case study with and without the use of an energy storage system under change in irradiance condition. The proposed system is developed and examined on MATLAB/Simulink environment.

The performance comparison between PV power output with and without the use of a supercapacitor energy storage device under different Case Studies shows that the improved performance in smoothing of power output was achieved with the use of a supercapacitor energy storage device.