Abstract
This study identified and characterized the factors influencing managers' intentions to adopt collaborative robots (cobots) in manufacturing companies. Based on a conceptual framework that integrates three technology adoption theories (Diffusion of Innovation, Technology-organization-environment and Institutional theory) and following an exploratory qualitative research design, this paper identifies 39 factors influencing the intention to adopt cobots in three contexts (internal, external and technology). Twelve of these factors are new as contrasted with previous literature. The findings of this study can assist organizations in their process of adoption of cobots and in the development of managerial practices that consider the role of these factors.

Abstract
The goal of the Semantic Web is to allow the software agents around us and AIs to extract information from the Internet as easily as humans do. This semantic web is a network of connected graphs, where relations between concepts and entities make up a layout that is very easy for machines to navigate. At the moment, there are only a few tools that enable humans to navigate this new layer of the Internet, and those that exist are for the most part very specialized tools that require from the user a lot of pre-existing knowledge about the technologies behind this structure. In this article we report on the development of DAOLOT, a search engine that allows users with no previous knowledge of the semantic web to take full advantage of its information network. This paper presents its design, the algorithm behind it and the results of the validation testing conducted with users. The results of our validation testing show that DAOLOT is useful and intuitive to users, even those without any previous knowledge of the field, and provides curated information from multiple sources instantly about any topic.

Abstract

Abstract
Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to "trust" the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning research. This paper proposes a series of visualization tools that explain the relationship between the expected predictive performance of black box regression models and the values of the input variables of any given test case. This type of information thus allows end-users to correctly assess the risks associated with the use of a model, by showing how concrete values of the predictors may affect the performance of the model. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user. Furthermore, a thorough evaluation of the proposed tools is performed to showcase the reliability of this approach.

Abstract
In this study, municipal solid waste composts obtained from mechanical biological treatment has been considered as a source of adsorbents for CO2 capture. Three samples derived from the maturated compost in the municipal solid wastes were modified to produce activated carbon. The first sample was treated with sulfuric acid, the second one was thermally treated at 800? C and the last one was modified chemically and thermally with sulfuric acid and at 800? C. Then, the CO2 uptake capacity of prepared samples was measured through breakthrough adsorption experiments at the post combustion operational conditions to collect isotherm data. Also a fixed bed adsorption mathematical model was developed by applying mass and energy balances. Results showed the municipal solid wastes have an excellent capacity to be considered as source of adsorbent for CO2 capture also the mathematical model is able to predict breakthrough data. © 2020 Taylor & Francis Group, London, UK.

Abstract
An important scheduling function of manufacturing systems is controlled order release. While there exists a broad literature on order release, reported release procedures typically use simple sequencing rules and greedy heuristics to determine which jobs to select for release. While this is appealing due to its simplicity, its adequateness has recently been questioned. In response, this study uses an integer linear programming model to select orders for release to the shop floor. Using simulation, we show that optimisation has the potential to improve performance compared to 'classical' release based on pool sequencing rules. However, in order to also outperform more powerful pool sequencing rules, load balancing and timing must be considered at release. Existing optimisation-based release methods emphasise load balancing in periods when jobs are on time. In line with recent advances in Workload Control theory, we show that a better percentage tardy performance can be achieved by only emphasising load balancing when many jobs are urgent. However, counterintuitively, emphasising urgency in underload periods leads to higher mean tardiness. Compared to previous literature we further highlight that continuous optimisation-based release outperforms periodic optimisation-based release. This has important implications on how optimised-based release should be designed.