Sustainability has been one of architecture’s most significant trends over the last twenty years. The environmental consciousness of professionals has put sustainability at the heart of the architectural profession and has contributed to adopting and implementing sustainable designs on the scale of urban landscapes. A green roof or living roof, which is a sustainable solution in architecture, is a roof on the surface of which plants are grown. The roof is covered by plants, covering the waterproof layer beneath the vegetation. However, various types of plants can be used in this scheme. Understanding the influencing factors in choosing the right plant species and the impact that utilizing green roofs has on the overall energy consumption of the building can tremendously help scientists and clarify the possible future research topics in this field. Hence, this article investigates energy optimization in the construction process of a green roof in sustainable architecture and its advantages and challenges. The results of this study show that budget limitations, managerial and organizational policies, legal issues, technical and scientific infrastructure, and cultural and geographical aspects are all affecting the widespread use of green roofs currently and need to be considered in future studies.

Tungsten carbide (WC) and its composites are typically associated with high hardness and high wear resistance, posing challenges in conventional machining processes like turning. To address the machining difficulties of WC-Co, electrical discharge turning (EDT) was proposed. The rotational speed in EDT is a key factor influencing the machining results; however, conflicting reports exist about its impact on the EDT process. Therefore, the effect of rotational speed on three different machining regimes, including roughing, semi-finishing, and finishing, was investigated using energy-dispersive X-ray spectroscopy (EDX), SEM, and roughness tests. Additionally, elemental mapping was applied to illustrate the element distribution on the machined surface. The results indicated that increasing the rotational speed led to a 10% to 17% decrease in the recast layer thickness and a 14% to 54% reduction in the surface roughness (Ra).

Artificial intelligence (AI) models have demonstrated their capabilities across various fields by performing tasks that are currently handled by humans. However, the training of these models faces several limitations, such as the need for sufficient data. This study proposes the use of acoustic signals as training data as this method offers a simpler way to obtain a large dataset compared to traditional approaches. Acoustic signals contain valuable information about the process behavior. We investigated the ability of extracting useful features from acoustic data expecting to predict labels separately by a multilabel classifier rather than as a multiclass classifier. This study focuses on electrical discharge turning (EDT) as a hybrid process of electrical discharge machining (EDM) and turning, an intricate process with multiple influencing parameters. The sounds generated during EDT were recorded and used as training data. The sounds underwent preprocessing to examine the effects of the parameters used for feature extraction prior to feeding the data into the ANN model. The parameters investigated included sample rate, length of the FFT window, hop length, and the number of mel-frequency cepstral coefficients (MFCC). The study aimed to determine the optimal preprocessing parameters considering the highest precision, recall, and F1 scores. The results revealed that instead of using the default set values in the python packages, it is necessary to investigate the preprocessing parameters to find the optimal values for the maximum classification performance. The promising results of the multi-label classification model depicted that it is possible to detect various aspects of a process simultaneously receiving single data, which is very beneficial in monitoring. The results also indicated that the highest prediction scores could be achieved by setting the sample rate, length of the FFT window, hop length, and number of MFCC to 4500 Hz, 1024, 256, and 80, respectively.