This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.

Purpose- The number of excess deaths in England and Wales during the second heatwave period in 2022 was the greatest it has been since 2018. Greater London is particularly at risk for indoor overheating due to the predicted increase in hot summers from 50–60% by 2050, which can have serious negative effects on the socio-economic state of the city. The purpose of this study is to assess and predict the risk of overheating in a residential apartment building in a high-risk London heatwave location.Methodology-This study uses computer simulation and on-site measurements to create a virtual model for IES VE dynamic thermal modelling and perform overheating assessment simulations in a London apartment complex using the CIBSE TM59 methodology.Findings- The percentage hours with exceeding temperature surpassed 50% above the standard threshold in the 2020s scenario, which increased two folds for the 2050s and 2080s. Moreover, the bedrooms’ operational temperatures are found to be terribly above the safety standard.Implications- The findings implicate examining the economic viability and effectiveness of various solutions in the future to prevent residential building overheating in London. Further investigation can be done by conducting in-depth experiments on the susceptibility of various floors to overheating.Originality/value- The paper offers novel insights into the overheating risk assessment in London, particularly focusing on households in high-risk areas. The study contributes to the conversation on sustainability practices and their socio-economic implications by highlighting the urgent need for retrofitting frameworks to promote sustainable building design and practices.