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This study aims to investigate profoundly the protection of oil painting from deterioration using molybdenum trisulphide quantum dots (MoS3 QDs) against microbe, dirt accumulation and ultraviolet (UV) degradation.

The protection of painting against different deterioration factors necessitates the sustainable methods and advanced techniques. Scanning electron microscopy and transmission electron microscopy have been used to investigate the morphological structure of the painting and MoS3 QDs, respectively, and optical microscopy was used to examine antibacterial activity of MoS3 QDs towards different types of bacteria. To investigate the protection of painting against deterioration, the Fourier transform IR spectroscopy (FTIR) was used to investigate the paintings left in open air for a year. Chemical composition and crystal structure of MoS3 QDs have been studied using X-ray diffraction and X-ray photoelectron spectroscopy analysis, respectively.

The addition of MoS3 nanoparticles into painted coatings enhances the durability of linseed oil-based paintings toward UV ageing regarding the change in colour which confirmed by FTIR analysis. The protection of oil painting opposed to various deterioration factors was developed by involving of MoS3 QDs in the coating of the painting. Antibacterial effect of MoS3 QDs was tested against different types of bacteria such as Pseudomonas aeruginosa confirming that the MoS3 QDs involved in the coatings of oil paintings produces a high protection layer for the paintings against several microbial attacks. In addition, coatings containing MoS3 QDs reduce the accumulation of dirt on oil paintings when subjected to open air for a year.

The novel MoS3 QDs was used to form a protective and transparent coating layer for the oil painting to overcome the deterioration, displays the promising protection and can be applied for different oil paintings.

Cracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.

This paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.

It was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.

Literature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.