This document presents a systematic review of Multimodal Human–Computer Interaction. It shows how different types of interaction technologies (virtual reality (VR) and augmented reality, force and vibration feedback devices (haptics), and tracking) are used in different domains (concepts, medicine, physics, human factors/user experience design, transportation, cultural heritage, and industry). A systematic literature search was conducted identifying 406 articles initially. From these articles, we selected 112 research works that we consider most relevant for the content of this article. The articles were analyzed in-depth from the viewpoint of temporal patterns, frequency of usage in types of technology in different domains, and cluster analysis. The analysis allowed us to answer relevant questions in searching for the next steps in work related to multimodal HCI. We looked at the typical technology type, how the technology type and frequency have changed in time over each domain, and how papers are grouped across metrics given their similarities. This analysis determined that VR and haptics are the most widely used in all domains. While VR is the most used, haptic interaction is presented in an increasing number of applications, suggesting future work on applications that configure VR and haptic together.

Decision-Making Centers (DMCs) Environment facilitates stakeholders' decision-making processes using predictive models and diverse what-if scenarios. An essential element of this environment is the management of Decision Support Components (e.g., models or systems) that need to be created with mature methodologies and good delivery time. However, there has been a gap in the understanding of project management best practices in DMC environments and in the application of methodologies to ease project execution. In the following paper, we address that gap by analyzing six predictive analytics projects executed in a Mexican DMC using Process Mining techniques. We perform process discovery using a detailed activity event log, which has not been possible in previous studies. Additionally, we perform a compliance evaluation versus the de facto methodology to identify the current process alignment gaps, and finally, we analyze the social networks present in the process execution. The research reveals that (1) process mining models are helpful to address management issues of PA/DM projects (2) PA/DM projects require alignment to mature methodologies to improve process performance and avoid execution problems (3) PA/DM project execution should be revised at the activity level to identify issues and to propose specific strategies. This study's findings can help project managers to perform process analyses and to make informed decisions in PA/DM projects. The following paper is an extension of the article "Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision-Making Center¨ presented in the 2019 International Conference on Systems and Informatics (ICSAI 2019).

Bottom-up energy models are considered essential tools to support policy design of electricity end-use efficiency. However, in the literature, no study analyzes their contribution to support policy design of electricity end-use efficiency, the modeling techniques used to build them, and the policy instruments supported by them. This systematic review fills that gap by identifying the current capability of bottom-up energy models to support specific policy instruments. In the research, we review 192 publications from January 2015 to June 2020 to finally select 20 for further examination. The articles are analyzed quantitatively in terms of techniques, model characteristics, and applied policies. The findings of the study reveal that: (1) bottom-up energy models contribute to the support of policy design of electricity end-use efficiency with the application of specific best practices (2) bottom-up energy models do not provide a portfolio of analytical methods which constraint their capability to support policy design (3) bottom-up energy models for residential buildings have limited policy support and (4) bottom-up energy models’ design reveals a lack of inclusion of key energy efficiency metrics to support decision-making. This study’s findings can help researchers and energy modelers address these limitations and create new models following best practices.