Real-time CO2 emissions estimation in Spain and application to the COVID-19 pandemic.

CO2 emissions are one of the major contributors to global warming. The variety of emission sources and the nature of CO2 hinders estimating its concentration in real time and therefore to adopt flexible policies that contribute to its control and, ultimately, to reduce its effects. Spain is not exempted from this challenge and CO2 emissions are published only at the end of the year and as an aggregated value for the whole country, without recognising the existing differences between the regions (the so-called, Autonomous Communities). The recent COVID-19 pandemic is a clear example of the need of accurate and fast estimation methods so that policies can be tailored to the current status and not to a past one. This paper provides a method to estimate monthly emissions of CO2 for each AACC in Spain based on data that are published monthly by the relevant administrations. The paper discusses the approximations needed in the development of the method, predicts the drop in emissions due to the reduced industrial activity during the pandemic in Spain and provides the estimation of future emissions under three recovery scenarios after the pandemic.

Submodelling approach to screw-to-bone interaction in additively manufactured subperiosteal implant structures.

Thanks to new digital technologies, complex cases of severe maxillary atrophy may now be treated with additively manufactured subperiosteal implant structures (AMSISs). However, there are few studies addressing this topic and most of them focus on the mechanical behaviour of the AMSIS itself without considering its interaction with the maxilla bone. The aim of this study is to provide a methodology based on finite element analysis (FEA) to evaluate the effect of interaction between the maxilla bone and the screws fixing the AMSIS. The mechanical performance of an AMSIS was examined via a FEA based on submodelling. Significant differences were encountered in displacements and reaction forces when bone-screw interaction was considered. Stress in the cortical layer was found to be close to the maximum strength while the trabecular layer seems to have no effect on the results; stresses in the AMSIS are lower than the fatigue stress limit. Finally, the comparison of stresses between models with and without osseointegration shows how stresses drop once osseointegration is complete. The proposed submodelling approach considerably reduces the computational effort and enables both a detailed model of the interaction between the thread of the screws and the bone and an accurate evaluation of displacement and stress fields on the interface. The results have shown that stresses in the cortical bone are highly affected by the initial geometry of the thread inside the bone, which demonstrates the importance of modelling the effect of the thread.

Systematic measuring cortical thickness in tibiae for bio-mechanical analysis.

BACKGROUND AND OBJECTIVE: Measuring the thickness of cortical bone tissue helps diagnose bone diseases or monitor the progress of different treatments. This type of measurement can be performed visually from CAT images by a radiologist or by semi-automatic algorithms from Hounsfield values. This article proposes a mechanism capable of measuring thickness over the entire bone surface, aligning and orienting all the images in the same direction to have comparable references and reduce human intervention to a minimum. The objective is to batch process large numbers of patients' CAT images obtaining thicknesses profiles of their cortical tissue to be used in many applications. METHODS: Classical morphological and Deep Learning segmentation is used to extract the area of interest, filtering and interpolation to clean the bones and contour detection and Signed Distance Functions to measure the cortical Thickness. The alignment of the set of bones is achieved by detecting their longitudinal direction, and the orientation is performed by computing their principal component of the center of mass slice. RESULTS: The method processed in an unattended manner 67% of the patients in the first run and 100% in the second run. The difference in the thickness values between the values provided by the algorithm and the measures done by a radiologist was, on average, 0.25 millimetres with a standard deviation of 0.2. CONCLUSION: Measuring the cortical thickness of a bone would allow us to prepare accurate traumatological surgeries or study their structural properties. Obtaining thickness profiles of an extensive set of patients opens the way for numerous studies to be carried out to find patterns between bone thickness and the patients' medical, social or demographic variables.

A Geometry-Based Welding Distortion Prediction Tool.

The prediction of welding distortion requires expertise in computer simulation programs, a clear definition of the nonlinear material properties, and mesh settings together with the nonlinear solution settings of a coupled thermal-structural analysis. The purpose of this paper is to present the validation of an automatic simulation tool implemented in Ansys using Python scripting. This tool allows users to automate the preparation of the simulation model with a reduced number of inputs. The goal was, based on some assumptions, to provide an automated simulation setup that enables users to predict accurate distortion during the welding manufacturing process. Any geometry prepared in a CAD software can be used as the input, which gave us much geometrical flexibility in the shapes and sizes to be modeled. A thermomechanical loosely coupled analysis approach together with element birth and death technology was used to predict the distortions. The automation of the setup enables both simulation and manufacturing engineers to perform welding-induced distortion prediction. The results showed that the method proposed predicts distortion with 80-98% accuracy.

Improvement of an additively manufactured subperiosteal implant structure design by finite elements based topological optimization.

To design a new subperiosteal implant structure for patients suffering from severe Maxillary Atrophy that lowers manufacturing cost, shortens surgical time and reduces patient trauma with regard to current implant structures. A 2-phase finite-element-based topology optimization process was employed with implants made from biocompatible materials via additive manufacturing. Five bite loading cases related to standard chewing, critical chewing force, and worst conditions of fastening were considered along with each specific result to establish the areas that needed to be subjected to fatigue strength optimization. The 2-phase topological optimization tested in this study performed better than the reference implant geometry in terms of both the structural integrity of the implant under tensile-compressive and fatigue strength conditions and the material constraints related to implant manufacturing conditions. It returns a nearly 28% lower volumetric geometry and avoids the need to use two upper fastening screws that are required with complex surgical procedures. The combination of topological optimization methods with the flexibility afforded by additively manufactured biocompatible materials, provides promising results in terms of cost reduction, minimizing the surgical trauma and implant installation impact on edentulous patients.