The implementation of design methodologies for supporting shared decision making in healthcare services: A systematic review.

Objectives: This systematic review examines how design methodologies support Shared Decision Making (SDM), identifies the most suitable for future use, explores types of methodologies used, challenges faced, and the impact on patients, clinicians, and care pathways.

Methods: Studies were searched on Medline, Web of Science, Scopus and grey literature (Google Scholar, CORDIS) up to July 2024, following PRISMA guidelines.

Results: were analysed to identify patient involvement, design strategies, SDM solutions, and their impact on care paths, professionals, and patients.

Integrated sustainability assessment of wood building products: The case of larch and chestnut cascading systems in Northern Italy.

Wood is increasingly being appreciated in construction due to its valuable environmental attributes. This paper explores the environmental and market performance of two wood supply chains in Northern Italy. Larch and chestnut wood are extracted and processed to obtain beams, planks, MDF panels and energy.

Energy Self-Sufficiency Urban Module (ESSUM): GIS-LCA-based multi-criteria methodology to analyze the urban potential of solar energy generation and its environmental implications.

The concentration of the population in cities has turned them into sources of environmental pollution, however, cities have a great potential for generating clean energy through renewable sources such as a responsible use of solar energy that reaches its rooftops. This work proposes a methodology to estimate the level of energy self-sufficiency in urban areas, particularly in a district of the city of Zaragoza (Spain). First, the Energy Self-Sufficiency Urban Module concept (ESSUM) is defined, then the self-sufficiency capacity of the city or district is determined using Geographical Information Systems (GIS), Light Detection and Ranging (LiDAR) point clouds and cadastral data.

Performance Analysis and Architecture of a Clustering Hybrid Algorithm Called FA+GA-DBSCAN Using Artificial Datasets.

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used algorithm for exploratory clustering applications. Despite the DBSCAN algorithm being considered an unsupervised pattern recognition method, it has two parameters that must be tuned prior to the clustering process in order to reduce uncertainties, the minimum number of points in a clustering segmentation MinPts, and the radii around selected points from a specific dataset Eps. This article presents the performance of a clustering hybrid algorithm for automatically grouping datasets into a two-dimensional space using the well-known algorithm DBSCAN.