The theme of accounting in blockchain environment has been referenced and theoretically discussed over the last few years in international publications, both of academic and professional nature. However, until today, there are no other known works that pursued a path in order to perform accounting records in blockchain. In the present work the authors go beyond this, as they will demonstrate, also proposing a practical solution for its implementation and use in a generalized way. The authors present two new proofs of concept, which are part of an ongoing work process to obtain the development of the Portuguese accounting system using blockchain. The most recent of the sequential steps was designing and developing the tokenization process and a Dapp that will be used to perform and consult accounting operations through any mobile device, by developing smart contracts. In a near future, this system will allow accounting operations to be carried out, in a simple way, on the blockchain, assuming implications for the way in which accountants, users of financial information, and software houses work.

The paper presents a monitoring system developed for an energy conversion system based on the sun and known as thermophotovoltaic panel (PVT). The project was implemented using two embedded microcontrollers platforms (arduino Leonardo and arduino yún), wireless transmission systems (WI-FI and XBEE) and net computing ,commonly known as cloud (Google cloud). The main objective of the project is to provide remote access and real-time data monitoring (like: electrical current, electrical voltage, input fluid temperature, output fluid temperature, backward fluid temperature, up PV glass temperature, down PV glass temperature, ambient temperature, solar radiation, wind speed, wind direction and fluid mass flow). This project demonstrates the feasibility of using inexpensive microcontroller’s platforms and free internet service in theWeb, to support the remote study of renewable energy systems, eliminating the acquisition of dedicated systems typically more expensive and limited in the kind of processing proposed.

This paper proposes a data-driven approach to develop a taxonomy in a data structure on list for triple bottom line (TBL) metrics. The approach is built from the authors reflection on the subject and review of the literature about TBL. The envisaged taxonomy framework grid to be developed through this approach will enable existing metrics to be classified, grouped, and standardized, as well as detect the need for further metrics development in uncovered domains and applications. The approach reported aims at developing a taxonomy structure that can be seen as a bi-dimensional table focusing on feature interrogations and characterizing answers, which will be the basis on which the taxonomy can then be developed. The interrogations column is designed as the stack of the TBL metrics features: What type of metric is it (qualitative, quantitative, or hybrid)? What is the level of complexity of the problems where it is used? What standards does it follow? How is the measurement made, and what are the techniques that it uses? In what kinds of problems, subjects, and domains is the metric used? How is the metric validated? What is the method used in its calculation? The column of characterizing answers results from a categorization of the range of types of answers to the feature interrogations. The approach reported in this paper is based on a screening tool that searches and analyzes information both within abstracts and full-text journal papers. The vision for this future taxonomy is that it will enable locating for any specific context, discern what TBL metrics are used in that context or similar contexts, or whether there is a lack of developed metrics. This meta knowledge will enable a conscious decision to be made between creating a new metric or using one of those that already exists. In this latter case, it would also make it possible to choose, among several metrics, the one that is most appropriate to the context at hand. In addition, this future framework will ease new future literature revisions, when these are viewed as updates of this envisaged taxonomy. This would allow creating a dynamic taxonomy for TBL metrics. This paper presents a computational approach to develop such taxonomy, and reports on the initial steps taken in that direction, by creating a taxonomy framework grid with a computational approach.