Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience.

This literature review highlights the emergence of the Internet of Things (IoT) and the proliferation of connected devices as the driving force behind the adoption of smart spaces. This review also discusses the various applications of smart spaces, including smart homes, smart cities, and smart healthcare: (1) Background: the aim of this research is to provide a comprehensive overview of the concept of smart spaces, including their key features, technologies, and applications in built environments and urban areas; (2) Methods: The study adopts a qualitative approach, drawing on secondary sources, such as academic journals, reports, and online sources; (3) Results: The findings suggest that smart spaces have the potential to transform the way people interact with their environment and each other. They could improve efficiency, safety, and quality of life.

Engineering Auxetic Cylinders and Intestine to Improve Longitudinal Intestinal Lengthening and Tailoring Procedure.

Auxetic materials can be exploited for coupling different types of tissues. Herein, we designed a material where the microorganism metabolic activity yields the formation of buckled/collapsed bubbles within gelling silicone cylinders thus providing auxetic properties. The finite element model of such hollow auxetic cylinders demonstrated the tubular structure to promote worm-like peristalsis.

Modelling and fabrication procedure for a 3D printed cardiac model - surgical planning of Left Ventricular Aneurysm.

The present paper describes a procedure for the development and production of a physical model for surgical planning of a Left Ventricular Aneurysm. The method is based on the general approach provided in Otton et al. (2017) and was customized to seek a reliable and fast procedure for the production of a specific type of cardiac model - i.

Machine Learning for Renal Pathologies: An Updated Survey.

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available.