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The aim of this paper is to study the hot corrosion behaviour of super 304H stainless steel for marine applications.

The investigation was carried out with three different combinations of salt mixture (Na₂SO₄, NaCl and V₂O₅) at two different temperatures (800 and 900°C).

The spalling and growth of oxide layer was observed more with the presence of V₂O₅ in the salt mixture at 900°C during experimentation than what was observed in 800°C. The mass change per unit area is calculated to study the corrosion kinetics and also the influence of salt mixture. Further, the samples are analysed through materials characterisation techniques using optical image, scanning electron microscope (SEM), energy dispersive X-ray (EDAX) and X-ray diffraction (XRD) analysis. The presence of V₂O₅ in the salt mixture was the most important influencing species for accelerating hot corrosion.

SEM, EDAX and XRD analysis confirmed the formation of Fe₂O₃ and Cr₂O₃ at 900°C showing contribution in corrosion protection.

The purpose of this paper is to fabricate and investigate sensing properties of a novel, flexible resistive tensile load cells based on multi-walled carbon nano-tubes (MWCNTs)/rubber composites. The use of carbon nanotubes makes it very attractive for being used as sensors.

On thin rubber substrate, MWCNTs powder was deposited and pressed at elevated temperature. Two types of samples were prepared: first sample was made by depositing MWCNTs suspension in water on the substrate, then the sample was dried at room temperature; the second sample was prepared by applying dry MWCNTs powder directly on the substrate.

The resistances of the cells made from wet MWCNT powder are much lower than those made with dry powder. It was found that the fabricated load cells were highly sensitive to the force and showed good repeatability. The resistance of the flexible resistive tensile MWCNTs/rubber composite load cells increased 1.37 times, on average, with the increasing force (up to 0.045 N). The sensitivity of the cells was equal to 142 N-1.

The device fabrication method used here provides a simple, less expensive and effective approach for preparing resistive tensile load cells.

A novel, flexible resistive tensile load cells using MWCNTs/rubber composites have been successfully fabricated and investigated. MWCNTs, in dry and wet form, have been deposited on thin rubber substrates by adopting a very simple and inexpensive technique.

This paper aims to study the feasibility of using machine learning in hot corrosion prediction of Inconel 617 alloy.

By examination of the experimental studies on hot corrosion of Inconel 617, a data set was built for machine learning models. Apart from the alloy composition, this paper included the condition of hot corrosion like time and temperature, and the composition of the saline medium as independent features, while the specific mass change is set as the target feature. In this paper, linear regression, random forest and XGBoost are used to predict the specific mass gain of Inconel 617.

XGBoost yields the coefficient of determination (R²) of 0.98, which was highest among models. Also, this model recorded the lowest value of mean absolute error (0.20). XGBoost had the best performance in predicting specific mass gain of the alloy in different times at temperature of 900°C. In sum, XGBoost shows highest accuracy in predicting specific mass gain for Inconel 617.

Using machine learning to predict hot corrosion in Inconel 617 marks a substantial progress in this domain and holds promise for simplifying the development and evaluation of novel materials featuring enhanced hot corrosion resilience.

This chapter discusses about the advancement in the field of telemedicine and how often the general public are using the services that are provide by the telehealth and telemedicine market. This chapter starts with the introduction of the medicine in the world, which were the earliest medical practice and how all that thing leads to the today’s telehealth market. This chapter also describes the models that are being used in today’s world, and how these models as implemented and how telemedicine services are implemented more efficiently. Telemedicine and telehealth market is growing day by day and a lot people are getting to know about it, but there is still some section of the society, specially the lower middle class and the people in the rural areas that do not have any access or knowledge about the concept of telehealth services.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

This paper aims to focus on compliance of workplace sexual harassment-related provisions under Indian companies and securities law, based on an empirical analysis of companies’ sexual harassment-related disclosures contained within their directors’ annual reports (ARs). Specifically, sections devoted to sexual harassment-related disclosures, inbuilt within directors’ ARs for the financial year 2019–2020 for a selected sample of companies listed under the National Stock Exchange, have been analysed.

To examine the nature of companies’ disclosures to demonstrate their compliance with statutory requirements under the POSH law, aligned with the Companies (Accounts) Rules, 2014 and Securities and Exchange Board of India’s regulations, an empirical-based, descriptive content analysis of ARs of 200 listed companies were used.

This study primarily finds that the majority of companies from the sample have disclosed to have prepared a corporate-level policy, as required under the POSH law. As also required under the POSH law, companies, reportedly, have constituted an Internal Complaints Committee to adjudicate and dispose of incidents related to sexual misconduct reported at their workplaces. However, companies lack in disclosing qualitative information, with sufficient detail, on many important aspects related to prevention and resolution of reported cases of workplace sexual harassment.

This paper adds to the broader narrative of the lacunae within the disclosure and reporting requirements on enhancing the liabilities of the companies to prevent and address sexual harassment under India’s corporate and securities regulations.

The purpose of this paper was to solve the shortage of carrying energy in probing robot and make full use of wind resources in the Antarctic expedition by designing a four-wheel land-yacht. Land-yacht is a new kind of mobile robot powered by the wind using a sail. The mathematical model and trajectory of the land-yacht are presented in this paper.

The mechanism analysis method and experimental modeling method are used to establish a dual-input and dual-output mathematical model for the motion of land-yacht. First, the land-yacht’s model structure is obtained by using mechanism analysis. Then, the models of steering gear, servomotors and force of wing sail are analyzed and validated. Finally, the motion of land-yacht is simulated according to the mathematical model.

The mathematical model is used to analyze linear motion and steering motion. Compared with the simulation results and the actual experimental tests, the feasibility and reliability of the proposed land-yacht modeling are verified. It can travel according to the given signal.

This land-yacht can be used in the Antarctic, outer planet or for harsh environment exploration.

A land-yacht is designed, and the contribution of this research is the development of a mathematical model for land-yacht robot. It provides a theoretical basis for analysis of the land-yacht’s motion.

The purpose of the study is to machine the composites at lower machining time with higher accuracy without causing delamination.

Abrasive jet machining is the technology appropriate for machining composite materials to obtain good dimensional accuracy without causing de-lamination. The central composite design was followed in deciding the number of experiments to be carried out.

The influence of abrasive jet machining process parameters on machining time, material removal rate (MRR) and kerf characteristics were investigated. The experimental results proved the newly designed internal threaded nozzle increased MRR, thereby reducing the machining time.

Machining of glass fibre reinforced polymer (GFRP) is one of the challenging tasks given its non-linear and in-homogeneous properties. In this investigation, newly developed threaded and unthreaded nozzles in machining were used for making holes on the GFRP composites.