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This paper aims to study the upgradation of the lubricating performance of the renewable base oil , and to study the tribological behavior of graphene oxide (GO) sheets used as lubricating additives in bio-oil for iron/steel contact.

A multifunctional end-face tribometer was used to characterize the friction coefficient and wear loss of the tribosystem under different lubricants.

The experimental results show that GO sheets with small size benefit lubricating effects and the optimal concentration of GO sheets in bio-oil is 0.4-0.6 per cent, which can form a complete lubricating film on the frictional interfaces and obtain a low friction coefficient and wear loss. Higher concentration of GO sheets can result in a significant aggregation of the sheets, reducing the content of the lubricating components in the bio-oil, which results in the increase in friction and wear; at this stage, the main wear pattern was ascribed to adhesive wear.

These results show a promising prospect of improving the tribological performance of renewable base oil with the introduction of GO sheets as additives.

No literature has covered the tribological behaviour of GO sheets in bio-oil. This study contributes to accelerating the application of bio-oil.

Around the world, prison complexes have always been fundamentally taxing environments. The strained atmosphere within these prisons often contributes to the deterioration of mental health, mostly those who may already be psychologically vulnerable. This paper aims to understand whether there exists a relationship between the built environment of prisons, particularly the central prisons of India and its effects on the mental health of inmates.

By means of literature reviews, the study parameters were found to be connectivity to nature, lighting, acoustics, colour, air quality and thermal comfort. The data collected through interviews and email correspondences with identified experts were analysed thematically using a deductive approach to derive a set of practical recommendations, which could be implemented in Indian prisons.

The built environment of prisons impacts the prison population by further contributing to depressive symptoms. The effects of the built space persist regardless of social factors. A well-designed environment is healthy for its occupants and would yield positive changes. However, it is not the sole contributor to depression; social interactions, prison management, societal acceptance and meaningful activities are equally relevant factors. The sole focus of this paper is the relationship between the built environment and the mental health of inmates.

There is a paucity of research into the intersection between prison architecture and the mental health of inmates in the Indian subcontinent. This paper that addresses the gap may have significant consequences on how criminal reform is perceived, and also encourage further research in this field.

The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm.

To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification.

The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate.

The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm.

The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.