AUTHORS I - J
Have a look at the UNIGOU Remote Publications developed by students
participating in the Scientific Training part of the UNIGOU Remote Program:
The Opioid Crisis: Approaches to Treatment and Prevention
Itabaiana Nicolau Antunes, Fernanda
Abstract:
The opioid crisis has become a global health challenge, marked by escalating rates of opioid misuse, addiction, and overdose deaths. This article examines the multifaceted dimensions of the crisis and explores innovative approaches to both treatment and prevention. The opioid crisis has its roots in the widespread use of opioids for pain management, which has led to an epidemic of addiction. This article also delves into the factors contributing to the crisis, including the role of prescription opioids, the rise of synthetic opioids, and the societal impact. Treatment strategies are a crucial aspect of addressing the crisis, with a focus on medication-assisted therapy (MAT) and harm reduction programs. MAT combines medications with counseling and therapy to support recovery, while harm reduction initiatives aim to reduce the negative consequences of opioid misuse. Preventative measures also play a pivotal role, ranging from prescription drug monitoring programs to public health campaigns. Effective prevention involves a multi-pronged approach that includes education, early intervention, and policy reforms. In a world where the opioid crisis continues to affect communities and healthcare systems, this article serves as a comprehensive guide to understanding the crisis and highlights the need for a holistic approach that combines treatment and prevention strategies.
Keywords:
Opioid crisis, Opioid addiction, Treatment strategies, Prevention measures, Medication-assisted therapy.
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J
The Amazon Fund, Retrospect and Possibilities (2008 -?)
Jukemura, Felipe
Abstract:
During a growing national and international climate agenda, one of the most important international climate funds emerged, the Amazon Fund (AF), along with it, increasing drops in the level of deforestation were observed. However, this Fund was destabilized with the arrival of the Bolsonaro government to the presidency of Brazil, leading, among others, to the freezing of resources by the main funders of the AF. Today, with the new Intergovernmental Panel on Climate Change (IPCC) report and after the 26th Conference of the Parties (COP26), it is necessary to analyse what the AF has been doing so far and what are the possible paths it may take in the coming years. To answer these questions, an analysis of primary sources was carried out – such as the AF activities report, INPE data, and others -, in addition to a broad bibliographic review, consultation of articles, and press news. It was observed that throughout its years of operation, the AF has positioned itself as a mechanism of great innovation, but despite the possible criticisms of it, hardly anyone could say that the Fund had a negative balance in the period, because in addition to the remarkable numbers presented by the AF in its years of operation, there was a constant learning curve for both the BNDES and the other actors. In the Bolsonaro government, despite efforts to maintain continuity in its actions, the AF suffered serious blows, which reflected in its performance, but with the prospect of new governments and policies, a window opens for the Fund to reinvent itself and reappear. as the avant-garde instrument, it was created to be.
Keywords:
Amazon Fund, Sustainable Development, Bolsonaro; Deforestation; Green Gas Emission.
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Deep Learning Methods Apply in the Power Transformer Differential Protection
Junio Muller Coelho, Elias
Abstract:
Transformers play a key role in the transmission and distribution of power systems. Diagnostic faults of the power transformer is very important to ensure safe and stable operation of the power system. The objective of this article is to show methods for deep learning already applied in transformer differential protection and to share summary results of these methods. The methods addressed are Accelerated Convolutional Neural Network, Signal Localised Convolutional Neural Network, Fast GRNN and Dynamic Differential Current in real time with CNN. In the analysis of the articles, many analyses were carried out in different cases, with accuracy well above 95%, where in some cases it reached more than 99.5%. Therefore, the deep learning methods presented are effective and accurate, enabling for possible more advanced studies.
Keywords:
Deep learning, transformer differential protection, electrical power system.