Good Living: rethinking the creation of a new lifestyle in the post-Covid-19 age

Unexpectedly the new Covid-19's Pandemic has joined to the multiple interdependent syndromes which configure the accelerated worsening of the global socio-ecological crisis. This suggests the necessity to drastically review the lifestyle in practically all latitudes of the world. The uncertainties generated new civilizing designs almost half a century after the Stockholm Conference. The predictable impacts on the dominant regulation logic of economic and political-institutional systems represent a decisive vector that points in this direction. From this new and disturbing profile of the global crisis, the objective is to analyze the potential contained in the "post-development ecocentric" Good Living's approach to take a renewed position in the most recent ecopolitical debate - now from the perspective of new lines of reflection that emerged with the outbreak of the Covid-19's Pandemic. It uses an essay based on a descriptive stage with a literature review and critical theoretical analysis established on the interface and the post-development proposal of Good Living itself overall using the human-scale development thesis of Manfred Max-Neef and the eco-socioeconomics thesis by Karl William Kapp and Ignacy Sachs. The intention is to create a new line of academic reflection on the cognitive and ethical-political limits of the hegemonic neoliberal ideological thought in face of the increasingly disturbing signs of almost irreversible impairment of the System-Earth.

EMERGENCY'S ADMINISTRATIVE LAW AND COVID-19: THE SUBMISSION OF LEGAL RELATIONS AND THE REFLECTIONS OF PROVISIONAL MEASURE N. 966/2020 REGARDING THE RESPONSIBILITY OF PUBLIC AGENTS

This article aims to analyze the consequences of Provisional Measure (MP) No. 966/2020, which provides for the accountability of public agents for action and omission in acts related to the Covid-19 pandemic, thus characterizing the so-called "administrative law of emergency" which, brings to light transfigurations of the entire system affecting pre-existing legal relationships. First, in order to contextualize the issue, the civil liability of the public agent will be identified, as well as that of the State before the national legal system. Then, the need and relevance of that measure is investigated, in contrast to the already existing normative commandments, in particular, the Law of Introduction to the Rules of Brazilian Law (LINDB) and the Regulatory Decree No. 9,830/2019. Finally, the impacts of the acts of public agents under MP no. 966/2020 will be analyzed in the light of Brazilian legislation, as well as the interpretation of the limits imposed by the Supreme Court, given the tacit rejection, due to the flow of the legal term.

The review of the mandatory clauses in covenant pandemic times 19 in connection with the restrictive measures imposed by the state

The object of this study is to investigate the revision of the mandatory clauses in times of Pandemic Covid 19 due to the restrictive measures imposed by the federal, state and municipal government. In this context, the problem is: does State intervention imposing restrictive measures in times of pandemic COVID 19 make it possible to revise mandatory clauses? The proposed hypothesis is that: the mandatory clauses should not be modified based on the principle of minimum intervention and exceptionality of the contractual review;except in unpredictable and / or extraordinary situations, as in the case of business formalized before the start of the COVID 19 pandemic and which has its execution periodically continued in the course of the pandemic. To analyze this hypothesis, this article is divided into three parts: the VUCA world, its characteristics and Law 4.0;the COVID 19 pandemic and its economic effects in the face of restrictive measures imposed by the State;and the review of contractual clauses. © 2021, University of Passo Fundo. All rights reserved.

A Chatbot Solution for Self-Reading Energy Consumption via Chatting Applications

To mitigate financial loss and follow the recommended sanitary measures due to the COVID-19 pandemic, self-reading, a method in which a consumer reads and reports his own energy consumption, has been presented as an efficient alternative for power companies. In such context, this work presents a solution for self-reading via chatbot in chatting applications. This solution is under development as part of a research and development (R&D) project. It is integrated with a method based on image processing that automatically reads the energy consumption and recognizes the identification code of a meter for validation purposes. Furthermore, all processes utilize cognitive services from the IBM Watson platform to recognize intentions in the dialog with the consumers. The dataset used to validate the proposed method for self-reading contains examples of analogical and digital meters used by Equatorial Energy group. Preliminary results presented accuracies of 77.20% and 84.30%, respectively, for the recognition of complete reading sequences and identification codes in digital meters and accuracies of 89% and 95.20% in the context of analogical meters. Considering both meter types, the method obtains an accuracy per digit of 97%. The proposed method was also evaluated with UFPR-AMR public dataset and achieves a result comparable to the state of the art. © 2021, Brazilian Society for Automatics--SBA.

Deep Learning in Image Analysis for COVID-19 Diagnosis: a Survey

COVID-19 achieved the highest concentration of confirmed cases in the Americas with a significant impact in Latin America and the Caribbean region, where access to water and sanitation is restricted. In this scenario, we surveyed deep learning techniques applied to extract information from images to detect pneumonia caused by SARS-COV-2, directly assisting health professionals through an automatic case screening. We identify the main public and private image datasets and deep network architectures. Thereby, we identified challenges and research directions. Thus, our goal is to provide a theoretical basis to contribute to the development of computational systems to aid the diagnosis of COVID-19.

Detection of COVID-19 in chest X-ray images using transfer learning with deep convolutional neural network

Over the years, Computer-Aided Diagnosis (CAD) systems have been proving their effectiveness in classifying many pathologies. With the advent of the COVID-19 pandemic, new systems were developed quickly. The chest radiography is one of the least expensive among the imaging exams that assist in the detection of COVID-19. Despite not having high sensitivity for pattern detection compared to other tests - such as ground-glass opacities in computed tomography - this test helps screen infected patients. Therefore, in this work, we propose a methodology for detecting COVID-19 in chest radiography considering three possible scenarios: the healthy, presence of COVID-19, and presence of other pathologies. We developed the methodology by evaluating transfer learning techniques in five well know pre-trained Convolutional Neural Networks (CNNs) architectures. For training CNNs, we used 1,932 healthy images, 3,651 of other pathologies, and 1,436 images related to the presence of COVID-19. We obtained an accuracy of 94.36% in the scenario COVID-19 vs. healthy, 99.80% for COVID-19 vs. others pathologies, and 95.01% differentiating in three classes. The results are considered promising when compared to state of the art since the database used in this work has the largest number of examples for the class COVID-19. © 2021 ACM.