Meeting sustainable development goals via robotics and autonomous systems.

Robotics and autonomous systems are reshaping the world, changing healthcare, food production and biodiversity management. While they will play a fundamental role in delivering the UN Sustainable Development Goals, associated opportunities and threats are yet to be considered systematically. We report on a horizon scan evaluating robotics and autonomous systems impact on all Sustainable Development Goals, involving 102 experts from around the world. Robotics and autonomous systems are likely to transform how the Sustainable Development Goals are achieved, through replacing and supporting human activities, fostering innovation, enhancing remote access and improving monitoring. Emerging threats relate to reinforcing inequalities, exacerbating environmental change, diverting resources from tried-and-tested solutions and reducing freedom and privacy through inadequate governance. Although predicting future impacts of robotics and autonomous systems on the Sustainable Development Goals is difficult, thoroughly examining technological developments early is essential to prevent unintended detrimental consequences. Additionally, robotics and autonomous systems should be considered explicitly when developing future iterations of the Sustainable Development Goals to avoid reversing progress or exacerbating inequalities.

Logit models, the area under receiver characteristic curves, sensitivity, and specificity for Co-enrollment density in college networks dataset.

This article describes the data related to co-enrollment density (CD), a new network clustering index, that can predict persistence and graduation. The data hold the raw results and charts obtained with the algorithm for CD introduced in ``Co-Enrollment Density Predicts Engineering Students' Persistence and Graduation: College Networks and Logistic Regression Analysis.'' There are data for eight institutions that show CD as a predictor for graduation at four years, graduation at six years, and ever graduated. The files were processed using R to estimate CD at one, two, three, and four years. Logistic regression models, receiver operating characteristic curves, specificity, sensitivity, and cut-off points were estimated for each model. The R code to reproduce the metanalysis for the summary data is included. The displays for the logistic regression models, receiver operating characteristic curves, density curves for classes, models, and parameters are included.

Thermal Energy Storage by the Encapsulation of Phase Change Materials in Building Elements-A Review.

The energy sector is one of the fields of interest for different nations around the world. Due to the current fossil fuel crisis, the scientific community develops new energy-saving experiences to address this concern. Buildings are one of the elements of higher energy consumption, so the generation of knowledge and technological development may offer solutions to this energy demand, which are more than welcome. Phase change materials (PCMs) included in building elements such as wall panels, blocks, panels or coatings, for heating and cooling applications have been shown, when heating, to increase the heat storage capacity by absorbing heat as latent heat. Therefore, the use of latent heat storage systems using phase change materials (PCMs) has been investigated within the last two decades. In the present review, the macro and micro encapsulation methods for construction materials are reviewed, the former being the most viable method of inclusion of PCMs in construction elements. In addition, based on the analysis of the existing papers on the encapsulation process of PCMs, the importance to pay more attention to the bio-based PCMs is shown, since more research is needed to process such PCMs. To determine its thermophysical and mechanical behavior at the micro and macro levels, in order to see the feasibility of substituting petroleum-based PCMs with a more environmentally friendly bio-based one, a section devoted to the excellent PCM with lightweight aggregate (PCM-LWA concrete) is presented due to the lack of description given in other reviews.