Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets.

The construction industry is the major contributor to waste generation in the world. Construction and demolition waste comprised more than 60% of the total UK waste generation in 2020. This presents challenges to material resource efficiency, sustainable development and fundamentally a transition from a linear to circular economy. The analysis of circular economy implementation in the construction industry can help identify strategies and design action plans to improve performance towards net zero carbon and zero avoidable waste targets. The aim of this article was to identify current circular practices in the construction activity in the UK, as well as challenges and drivers for its application, and make recommendations for accelerating circularity performance. As a particular case study, this paper analysed the results of a survey about this implementation by the companies linked to Morgan Sindall supply chain in the Northwest branch. A total of 60 responses were collected from companies ranging from micro to large enterprises, as well as different stakeholders participating in the construction process, corresponding to manufacturers/suppliers, designers, and contractors/subcontractors. The results obtained showed the perception of the participants’ companies and helped identify main recommendations to support a common strategic plan for collaboration and implementation of practices towards circular economy. The findings of this research can contribute to the transition of construction companies to a circular economy at a wider scale.

Skin cancer is one of the widespread diseases that typically develop on the skin due to continuous exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer reports account for over half of all cancer occurrences worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of the morphological variety and indistinguishable characteristics across skin malignancies. Recently, Deep Learning models have been used in the field of image-based lesion diagnosis, and it has demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep Convolutional Neural Network (CNN) termed SkinLesNet has been built in this study. The ResNetV50 and VGG16 models have been carefully compared to review the performance of the proposed model. The dataset used in this study, PAD-UFES-20, contains 1314 samples in total and includes three common forms of skin lesions. The proposed approach, SkinLesNet, significantly outperforms the well-known compared models in the given conditions