Battling a global health crisis while holding all social responsibilities together left the world in despair. It is just one of the numerous signs that every natural thing interrelates with each other. Novel coronavirus outbreak began in China in December 2019. It continues to alter the society causing economic plight across the globe, including highly developed nations and medical researchers remaining wide awake to seek an effective vaccine. Regardless of race or even status, nobody is spared from the threat of this virus.Termination of this virus considering the sorrowful count of deaths and transfer cases requires the demand to utilize sufficient vital equipment, systematic and prepared facilities-one of these is the application of technology that could support physical distance limitation among individuals referred to as the Internet of Things or IoT.More than ever is a significant period to discuss the gaps concerning IoT. It necessitates professionals and tech specialists to determine these issues with an appropriate methodology for further system refinement. They must also decide involving the certainty of convenience, predominates its market price, and setbacks. Moreover, the congress must construct a bill that would cover IoT users' data and privacy rights.
Road violations that lead to accidents and deaths are increasing significantly. There are about 1.35 million people who die every year because of road accidents, and more than half of these involve a motorcycle. Authorities are strictly implementing traffic laws and making some innovations to capture those motorists violating laws easily. Researchers are also doing their part to help solve the problem; indeed, their studies give a vast contribution and solve road safety issues. However, the papers on road violations were focused more on on-road violations involving four-wheeled vehicles. For this reason, a motorcyclist violation detection and plate recognition with e-mail notification using a DeepLearning algorithm were developed to apprehend motorcyclists violating traffic laws. Tensorflow Object Detection API was used as a framework along with the Faster R-CNN model. The system was developed using Anaconda Environment, Python Scripting, KNN, and MySQL Connector. The conditions and criteria for detecting a violation are based on motorcycle detection, including motorcycle tracking. After violation detection and plate recognition, the violation's image is sent through e-mail together with the details of the offense.
A Leishmaniose Visceral Canina (LVC) é uma doença endêmica de caráter mundial e de grande importância para a Saúde Pública, transmitida através das picadas da fêmea do flebotomíneo Lutzomyia longipalpis, conhecido como mosquito-palha. Possui elevada taxa de óbitos em humanos e animais, sendo de alta ocorrência no Estado do Ceará. Objetivou-se neste trabalho realizar um estudo epidemiológico e sua correlação social acerca da incidência da LVC no município de Fortaleza, com coleta de dados em vários bairros da região, que são realocadas em regionais de I a XII, através do projeto da Prefeitura de Fortaleza chamado de VetMóvel, além de realizar uma correlação entre o número de habitantes e animais abandonados. Foi realizado o teste de triagem para leishmaniose (TR-DPP) em 12.132 animais, referente ao período de Junho de 2018 a Março de 2021, obtendo um resultado positivo de 1,3%. A casuística foi mais observada nos meses com aumento de densidade pluviométrica e nas regionais V e VI, corroborando com estudos anteriores. A quantidade de animais abandonados atingiu uma taxa de 150 mil, obtendo o maior número de casos em regiões com média financeira de meio salário mínimo. É importante projetos governamentais para o controle de doenças em animais para que se tome medidas profiláticas necessárias não só para outros animais sadios como em humanos também. Ressalta-se, ainda, que os animais soro-reagentes não são os principais contribuintes para a disseminação da doença, incluindo condições socioeconômicas e ambientais como fatores predisponentes.
Road violations that lead to accidents and deaths are increasing significantly. There are about 1.35 million people who die every year because of road accidents, and more than half of these involve a motorcycle. Authorities are strictly implementing traffic laws and making some innovations to capture those motorists violating laws easily. Researchers are also doing their part to help solve the problem; indeed, their studies give a vast contribution and solve road safety issues. However, the papers on road violations were focused more on on-road violations involving four-wheeled vehicles. For this reason, a motorcyclist violation detection and plate recognition with e-mail notification using a DeepLearning algorithm were developed to apprehend motorcyclists violating traffic laws. Tensorflow Object Detection API was used as a framework along with the Faster R-CNN model. The system was developed using Anaconda Environment, Python Scripting, KNN, and MySQL Connector. The conditions and criteria for detecting a violation are based on motorcycle detection, including motorcycle tracking. After violation detection and plate recognition, the violation's image is sent through e-mail together with the details of the offense.
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