Abstract
A few years back organizations were rushing into social media environments following the worldwide trend to create a social presence in multiple channels and / or to explore their potential. Currently, after having gone through a period of experimentation and consolidation of that presence, it is important to understand and to report on how the performance and communication efficiency of organizations has evolved. On previous studies, where we focused on the public higher education sector, we have identified a set of organizations that presented behaviour which was typical from yearly social media adopters, with very low relative performance and communication efficiency. Using data and text mining tools, and techniques, we showed that these organizations revealed very low frequency of publication of messages and very low engagement among their audiences. At the time, the analysis of this sector posed challenges to the confirmation of whether these content strategies were representative enough and if they were a result of an effective and permanent organizational behaviour on social media, or just a result of a stage of social media adoption. In this paper, we present a longitudinal study that portrays the evolution of the organizational behaviour of these organizations on social media, concerning their relative performance and their communication efficiency after a four-year period. Our analysis is based on how and if they have evolved from that stage by fine-tuning their social media communications. We also present findings concerning the content strategy structure evolution along the past four years, concerning the type of content used in higher education institutions' social media strategies, to obtain the best possible return on engagement from the publics (fans), demonstrating how these organizations have either dropped Facebook or optimized their type of content to foster higher return. Thus, on this longitudinal study we present and benchmark the current state of performance of public higher education institutions, concerning the path they undertook in the past four years.

Abstract
Computer-based modelling tools allow students to express their theories in models that can be simulated. In this way, students can use their theories operationally, confronting themselves with the consequences of their ideas. The ability of students to form and express a mental model will be expanded if they are given an opportunity to become aware of their own mental model by expressing this same model and comparing it to other models, like a consensus model. The building of numerical models of biophysical phenomena, such as the mechanics of breathing, or blood circulation, has the potential for student motivation as well as long-term learning. Our theory is that by re-building well known numerical models of physiological phenomena students will have the opportunity to change their perceptions about the relevance of the contents addressed, simultaneously improving their learning in the topics covered and increasing their motivation in the basic science disciplines in their curricula. For the implementation of computer numerical models historically it was necessary to use some programming language, such as MATLAB, BASIC, C++, JAVA. With the development of computer science, it is now possible to these students "construct" models of physical phenomena expressed through dedicated computer tools without necessarily having to do so in a programming language. As for example we have STELLA, MODELLUS, or STARLOGO. There is also the possibility of using a spreadsheet such as Microsoft EXCEL, Open Office CALC, Google SHEETS, or others, as tools that allow students to express physical models. The current spreadsheets, even those available for free, are very powerful, having many integrated tools, in terms of calculation, and we can count on several other features, such as graphs of various types, buttons and other tools that allow interaction with the model, and databases that can be integrated into the spreadsheet. There are several advantages of using spreadsheets in science education due to its general access, through smartphones, tablet's and computers, ease of implementation for the basic operations, and ease of the "debug" process, relative to other types of software. Also, it does not require prior knowledge of programming languages, or about complex mathematical software, which would an obstacle to the learning in itself. There is also the positive side effect of learning how to use a spreadsheet that is a plus in itself for the future professional's. This paper will have a review of the state of the art of using spreadsheets in Modelling Based Learning. Also, it will be presented a study with first year undergraduate students of a health care course, using Biophysical models historically very important in the physiology and medicine development.

Abstract
We present MixMash, an interactive tool to assist users in the creation of music mashups based on cross-modal associations between musical content analysis and information visualisation. Our point of departure is a harmonic mixing method for musical mashups by Bernardes et al. [1]. To surpass design limitations identified in the previous method, we propose a new interactive visualisation of multidimensional musical attributes-hierarchical harmonic compatibility, onset density, spectral region, and timbral similarity-extracted from a large collection of audio tracks. All tracks are represented as nodes whose distances and edge connections indicate their harmonic compatibility as a result of a force-directed graph. In addition, we provide a visual language that aims to enhance the tool usability and foster creative endeavour in the search for meaningful music mixes.

Abstract

Abstract
Many of nowadays organizations can be said to be knowledge-based. That is, they have relevant decision-making processes that are supported by data and data mining processes. These data may be created/collected by the organization or acquired from external sources (e.g. open data portals). In any case, the quality of the data will, ultimately, be one of the main drivers of decision quality. In this context, it is important that data-producing organizations also produce relevant meta-information characterizing the provenance of the data, its context or the representation standards used. This paper presents a framework to facilitate this process, promoting the inclusion of information concerning representation standards, provenance, trust and permissions at the data level. The main goal is to promote data usability and, consequently, its value for the organizations. © Springer Nature Switzerland AG 2019.

Abstract
This paper presents a new state estimation (SE) method including equality constraints to model voltage dependent loads and zero injections. Formulation of conventional SE, assuming simple constant power model for the system's loads, is modified to incorporate voltage dependent load models. Assuming reliable load models and zero injections, it is analytically proved that modeling the equality constraints leads to better SE accuracy. To numerically validate the analytical findings, the proposed SE method is implemented on the IEEE 118-bus test system and the large-scale real-world Iran's power system, and its obtained results are compared with the results of conventional SE. Also, it is shown that considering voltage dependent load model in SE formulation leads to better performance of bad data detection. Moreover, it is illustrated that the accuracy of the proposed SE has low sensitivity to load model identification error.