Eco-Arquitectura ?

Setting aside the technical component linked to the specificity of Eco-architecture and Eco-urbanism, which has been amply divulged and explored, this article is centred on references, concepts and ideas that consubstantiate the thesis of the current emergence of a new behavioural paradigm, of an eco-centric nature, to which architecture and urbanism, as cultural acts, cannot ignore

Unemployment and entrepreneurship: a cyclical relationship?

This paper presents a cyclical model for unemployment and entrepreneurship. The estimated periodicity of the cycles for the US, the UK, Spain and Ireland is between 5 and 10 years, and the orders of integration are smaller (greater) than 1 if the underlying disturbances are autocorrelated (white noise), corresponding to dampen cycles (limit cycle).New firms, Employment creation, cycles.

Persistence on airline accidents

This paper analyses airline accidents data from 1927-2006. The fractional integration methodology is adopted. It is shown that airline accidents are persistent and (fractionally) cointegrated with airline traffic. Thus, there exists an equilibrium relation between air accidents and airline traffic, with the effect of the shocks to that relationship disappearing in the long run. Policy implications are derived for countering accidents events.

Persistence in Airline Accidents

This paper analyses airline accident data from 1927-2006, through fractional integration. It is shown that airline accidents are persistent and (fractionally) cointegrated with airline traffic. There exists a negative relation between air accidents and airline traffic, with the effect of the shocks to that relationship disappearing in the long run. Policy implications are derived for countering accident events.Accidents; airline; Time series; Persistence; Long memory; Cointegration.

Adapted control methods for cerebral palsy users of an intelligent wheelchair

The development of an intelligent wheelchair (IW) platform that may be easily adapted to any commercial electric powered wheelchair and aid any person with special mobility needs is the main objective of this project. To be able to achieve this main objective, three distinct control methods were implemented in the IW: manual, shared and automatic. Several algorithms were developed for each of these control methods. This paper presents three of the most significant of those algorithms with emphasis on the shared control method. Experiments were performed by users suffering from cerebral palsy, using a realistic simulator, in order to validate the approach. The experiments revealed the importance of using shared (aided) controls for users with severe disabilities. The patients still felt having complete control over the wheelchair movement when using a shared control at a 50% level and thus this control type was very well accepted. Thus it may be used in intelligent wheelchairs since it is able to correct the direction in case of involuntary movements of the user but still gives him a sense of complete control over the IW movement

Energy Resources Management Enabled by Internet of Things Devices

The participation of small end-users in the smart grid brings benefits for the end-users and for the smart grid. This paper will treat end-users using communities involving energy sharing between private buildings (residential and commercial) and public buildings. The energy can be shared among end-users and the community can be managed centralized. The paper uses IoT devices to enable the active participation of end-users. The use of this type of devices is growing and more and more market available product are appearing. The remote control and monitor capabilities, provided by the normality of IoT devices, can and should be used in energy management systems as enablers. This paper uses IoT devices, located in end-users, to enable the participation of these player in the community. The paper will propose a smart energy community platform and show its results.The present work was done and funded in the scope of the following projects: European Union's Horizon 2020 project DOMINOES (grant agreement No 771066), and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

SocNav1: A Dataset to Benchmark and Learn Social Navigation Conventions

Datasets are essential to the development and evaluation of machine learning and artificial intelligence algorithms. As new tasks are addressed, new datasets are required. Training algorithms for human-aware navigation is an example of this need. Different factors make designing and gathering data for human-aware navigation datasets challenging. Firstly, the problem itself is subjective, different dataset contributors will very frequently disagree to some extent on their labels. Secondly, the number of variables to consider is undetermined culture-dependent. This paper presents SocNav1, a dataset for social navigation conventions. SocNav1 aims at evaluating the robots’ ability to assess the level of discomfort that their presence might generate among humans. The 9280 samples in SocNav1 seem to be enough for machine learning purposes given the relatively small size of the data structures describing the scenarios. Furthermore, SocNav1 is particularly well-suited to be used to benchmark non-Euclidean machine learning algorithms such as graph neural networks. This paper describes the proposed dataset and the method employed to gather the data. To provide a further understanding of the nature of the dataset, an analysis and validation of the collected data are also presented

BRICKS: Building’s reasoning for intelligent control knowledge-based system

Building energy management systems have been largely implemented, focusing on specific domains. When installed together, they lack interoperability to make them work correctly and to achieve a centralized user interface. The Building's Reasoning for Intelligent Control Knowledge-based System (BRICKS) overcomes these issues by developing an interoperable building management system able to aggregate different interest domains. It is a context-aware semantic rule-based system for intelligent management of buildings' energy and security. Its output can be a set of alarms, notifications, or control actions to take. BRICKS itself, and its features are the innovative contribution of the present paper. It is very important for buildings' energy management, namely in the scope of demand response programs. In this paper, it is shown how semantics is used to enable the knowledge exchange between different devices, algorithms, and models, without the need for reprogramming the system. A scenario is deployed in a real building for demonstration.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the projects UID/EEA/00760/2019, PTDC/EEI-EEE/28954/2017 (MAS-Society), and SFRH/BD/118487/2016.info:eu-repo/semantics/publishedVersio

A Contextual Reinforcement Learning Approach for Electricity Consumption Forecasting in Buildings

The energy management of buildings plays a vital role in the energy sector. With that in mind, and targeting an accurate forecast of electricity consumption, in the present paper is aimed to provide decision on the best prediction algorithm for each context. It may also increase energy usage related with renewables. In this way, the identification of different contexts is an advantage that may improve prediction accuracy. This paper proposes an innovative approach where a decision tree is used to identify different contexts in energy patterns. One week of five-minutes data sampling is used to test the proposed methodology. Each context is evaluated with a decision criterion based on reinforcement learning to find the best suitable forecasting algorithm. Two forecasting models are approached in this paper, based on K-Nearest Neighbor and Artificial Neural Networks, to illustrate the application of the proposed methodology. The reinforcement learning criterion consists of using the Multiarmed Bandit algorithm. The obtained results validate the adequacy of the proposed methodology in two case-studies: building; and industry.This article is a result of the project REal-Time support Infrastructure and Energy management for Intelligent carbon-Neutral smArt cities (RETINA) (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and grant CEECIND/02887/2017. The authors acknowledge the work facilities and equipment provided by the Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD) research center (UIDB/00760/2020) to the project team.info:eu-repo/semantics/publishedVersio