Navegando por Autor "Guidoni, Daniel Ludovico"

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An overview of Brazilian working age adults vulnerability to COVID‐19.
(2022) Souza, Fernanda Sumika Hojo de; Souza, Natália Satchiko Hojo de; Oliveira, Daniela Carine Ramires de; Silva, Cristiano Maciel da; Guidoni, Daniel Ludovico
Brazil is a country of continental dimensions, where many smaller countries would ft. In addition to demographic, socioeconomic, and cultural diferences, hospital infrastructure and healthcare varies across all 27 federative units. Therefore, the evolution of COVID-19 pandemic did not manifest itself in a homogeneous and predictable trend across the nation. In late 2020 and early 2021, new waves of the COVID-19 outbreak have caused an unprecedented sanitary collapse in Brazil. Unlike the frst COVID-19 wave, in subsequent waves, preliminary evidence has pointed to an increase in the daily reported cases among younger people being hospitalized, overloading the healthcare system. In this comprehensive retrospective cohort study, confrmed cases of hospitalization, ICU admission, IMV requirement and in-hospital death from Brazilian COVID-19 patients throughout 2020 until the beginning of 2021 were analyzed through a spatio-temporal study for patients aged 20–59 years. All Brazilian federative units had their data disaggregated in six periods of ten epidemiological weeks each. We found that there is a wide variation in the waves dynamic due to SARS-CoV-2 infection, both in the frst and in subsequent outbreaks in diferent federative units over the analyzed periods. As a result, atypical waves can be seen in the Brazil data as a whole. The analysis showed that Brazil is experiencing a numerical explosion of hospitalizations and deaths for patients aged 20–59 years, especially in the state of São Paulo, with a similar proportion of hospitalizations for this age group but higher proportion of deaths compared to the frst wave.
An urban traffic management system based on vehicle cooperation.
(2023) Gomides, Thiago da Silva; Grande, Robson Eduardo de; Pereira, Rickson Simioni; Meneguette, Rodolfo Ipolito; Souza, Fernanda Sumika Hojo de; Guidoni, Daniel Ludovico
The next generation of smart cities will rely on Intelligent Transport Systems (ITSs) due to the increased complexity and dynamism of traffic caused by continuous urbanization and population growth. The traditional techniques to deal with these challenges are expensive and have a great impact on people’s lives and, in this scenario, the introduction of computational and technological solutions is necessary. In order to minimize the problems caused by congestion in urban centers, we present Let Me Know!, a traffic management system inspired by the communication among vehicles that allows the request and availability of information related to vehicular traffic. The vehicles request information in order to update a distributed database containing momentary analyzes of road traffic. Through extensive performance analysis, we show our system’s ability to reduce traffic congestion with a low impact on the network.
Predictive congestion control based on collaborative information sharing for vehicular ad hoc networks.
(2022) Gomides, Thiago da Silva; Grande, Robson Eduardo de; Meneguette, Rodolfo Ipolito; Souza, Fernanda Sumika Hojo de; Guidoni, Daniel Ludovico
Traffic jams are an essential and continuous challenge in our cities, responsible for socioeconomic and environmental concerns and an ambitious traffic jams management agenda is urgent. The distributed solutions in the literature for Traffic Management Systems (TMS) are heavily based on beacon messages or proactive communication protocols to share vehicular traffic information among vehicles. Thus, these solutions are not scalable when the number of vehicles increases in the network — when there are traffic jams. To overcome these problems, we propose a new VANET-based traffic management system named CoNeCT: Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks. CoNeCT’s primary goal is to support vehicles’ collaboration in analyzing, predicting, and managing congestion. The proposed system was designed to decrease the number of messages by using a novel road segment load assessment that improves traffic flow classification. Vehicles aware of traffic conditions share it with their neighbors, and they can also request traffic views whenever necessary. Additionally, vehicles can detect significant traffic variations and predict future traffic conditions to improve roads’ overall traffic conditions, mitigating the congestion before it arises. Results obtained from an extensive performance analysis show CoNeCT’s ability to reduce traffic congestion with a low impact on the wireless communication medium, outperforming the state-of-art systems.
Profile of Brazilian inpatients with COVID-19 vaccine breakthrough infection and risk factors for unfavorable outcome.
(2022) Jesus, Matheus Aguiar S. de; Souza, Natália Satchiko Hojo de; Moraes, Thiago Rocha de; Guidoni, Daniel Ludovico; Souza, Fernanda Sumika Hojo de
Objective. To characterize the epidemiological and clinical profile of individuals more likely to become infected with SARS-CoV-2 after the fully vaccination schedule in order to profile priority groups to receive a booster dose in situations of vaccine doses shortage as well as for maintenance of personal protective care. Methods. This cross-sectional study used data from hospitalized COVID-19 patients aged ≥18 years, who had been fully vaccinated and had a SARS-CoV-2 infection positive diagnosis collected from the SIVEP-Gripe database (Influenza Epidemiological Surveillance Information System) from January 18, 2021 to September 15, 2021. Demographic data, clinical symptoms and preexisting medical conditions (comorbidities) were ana- lyzed. The primary outcome was in-hospital death. Results. The majority of hospitalized patients with vaccine breakthrough infection were ≥60 years old, male, with critical or severe COVID-19. The fatality rate was extremely high (50.27%) and more pronounced in elderly groups. The most prevalent symptoms were cough, dyspnea, respiratory distress, and low blood oxygen sat- uration. The most frequent comorbidities were heart disease and diabetes. High fatality rates were observed among patients admitted to the intensive care units (72.88%) and those who required invasive mechanical ventilation (87.82%). The main risk factors for an unfavorable outcome were older age, respiratory compro- mise, inactivated virus vaccine immunization, and preexisting medical conditions. Conclusions. We characterized the profile of hospitalized Brazilian patients with COVID-19 vaccine break- through infection and the risk factors for an unfavorable outcome. These data allow to identify priority groups to receive a booster dose and to continue using personal protection.
Rage : a novel strategy for solving non-polynomial problems through the random generation of solutions and incremental reduction of the number of candidates : a case study applied to the design of the network infrastructure for connected vehicles.
(2023) Silva, Cristiano Maciel da; Sarubbi, João Fernando Machry; Mokhtari, Somayeh; Santos, Leonardo Alvarenga Lopes dos; Silva, Lucas Diniz; Souza, Fernanda Sumika Hojo de; Guidoni, Daniel Ludovico; Nogueira, José Marcos Silva
This work presents RAGE, a novel strategy designed for solving combinatorial optimization problems where we intend to select a subset of elements from a very large set of candidates. For solving the combinatorial problem, RAGE generates a customizable number of random solutions, computes the objective function for each solution, and then scores each candidate element in terms of the value returned by the objective function. After that, RAGE removes a customizable number of candidate elements presenting the smallest score when considering all solutions generated. This cycle is called one iteration. The heuristic loops performing iterations until there are left the exact number of candidates that we are looking for. In order to evaluate the efficiency of RAGE, we perform experiments showing how RAGE behaves when we change the number of random solutions generated per round, and the number of candidate elements removed per round. Finally, we apply RAGE for solving an NP-Hard problem related to the allocation of infrastructure for vehicular communication. The results show that RAGE requires 40,000 evaluations of the objective function to achieve the same result found by the baseline using 175,000 evaluations of the objective function, which, in this case study, represents a drastic reduction of the computational overhead in order to reach the same target.
A robust traffic information management system against data poisoning in vehicular networks.
(2022) Pedroso, Carlos Marcelo; Gomides, Thiago da Silva; Guidoni, Daniel Ludovico; Lima, Michele Nogueira; Santos, Aldri Luiz dos
Attacks against systems supported by vehicular networks, such as Traffic Information Systems, are more frequent and critical because of the real-time demand and high volume of data. Attacks that decrease data reliability, as data poisoning – DaP, are the most damaging because they severely risk data use. However, in general, vehicular network systems do not implement these features. Hence, this work presents MOVE, an efficient, secure, and VANET-based traffic management system against DaP attacks. MOVE relies on watchdog monitoring and relational consensus for attack detection, achieving efficient data authenticity and high availability. The performance evaluation of MOVE on OMNET++ has reached a detection rate of 90%, false- negative and false-positive rates of 4% and 10%, respectively. MOVE decreases vehicle travel time by up to 40%, and average time on traffic jams by 35%. It increases the average speed by 12% compared to ON-DEMAND.
Sistemas do tipo eixo-raio aplicados a redes de sensores sem fio mdeladas cmo Redes Small World.
(2007) Guidoni, Daniel Ludovico; Aquino, André Luiz Lins de; Cabral, Raquel da Silva; Loureiro, Antônio Alfredo Ferreira; Fernandes, Antônio Otávio
As redes de sensores sem fio possuem restrições de recursos, tais como baixo poder computacional, largura de banda reduzida e especialmente, fonte de energia limitada. O alto consumo de energia pode ser observado quando o fluxo de dados em cada n´o ´e alto e quando o numero de vizinhos e grande. Alem disso, essas redes podem ser modeladas como redes do tipos small world, onde o coeficiente de agrupamento é alto e o caminho médio mínimo entre cada par de nos na rede é pequeno. Através da utilização do conceito de sistemas do tipo eixo-raio, obtivemos uma configuração com um caminho médio mínimo pequeno entre qualquer par de n´os, obtendo assim, uma configuração ótima para a rede, onde alguns n´os são escolhidos como concentradores garantindo o menor consumo de energia
A temporal study of Brazilian pregnant and postpartum women vulnerability for COVID-19 : characteristics, risk factors and outcomes.
(2022) Souza, Natália Satchiko Hojo de; Guidoni, Daniel Ludovico; Silva, Cristiano Maciel da; Souza, Fernanda Sumika Hojo de
Background During the COVID-19 second wave in Brazil, there has been a significant increase in the number of daily cases and deaths, including pregnant and postpartum women. We assess risk factors and outcomes for this priority group compared to the COVID-19 non-pregnant cohort in two epidemic waves. Methods In this retrospective cohort study we evaluated data of hospitalized pregnant, postpartum, and nonpregnant women aged 15-44 years, between epidemiological weeks 2020−8 and 2021−15, who tested positive for SARSCoV-2, retrieved from the Influenza Epidemiological Surveillance Information System maintained by Ministry of Health of Brazil. We analysed in-hospital case fatality rate, crude and adjusted risk ratios on different outcomes aiming to compare data in two waves. Findings The study included pregnant women (n = 7,132), postpartum women (n = 2,405) and nonpregnant women (n = 76,278) hospitalized with COVID-19. Case fatality rates of pregnant women were lower in both waves compared to nonpregnant women, but higher among postpartum women. The risk for admission to the intensive care unit and invasive mechanical ventilation requirement in both waves was significantly higher among postpartum women compared to nonpregnant women. Cardiac disease, diabetes, obesity, and asthma were the most frequent underlying medical conditions in all patient groups. These comorbidities were significantly less frequent among pregnant women. Interpretation Pregnant women with COVID-19 are at lower risk of poor outcome compared to nonpregnant women. On the other hand, postpartum women are at higher risk of adverse outcomes compared to pregnant and nonpregnant women, especially during the second wave. There was a significant increase in the in-hospital case fatality rate for all patient groups during the second wave of COVID-19.
Toward an efficient data dissemination protocol for vehicular ad-hoc networks.
(2022) Guidoni, Daniel Ludovico; Gottsfritz, Euclydes Nasorri; Meneguette, Rodolfo Ipolito; Silva, Cristiano Maciel da; Rocha Filho, Geraldo Pereira; Souza, Fernanda Sumika Hojo de
Data Dissemination protocols are used for several vehicular applications, varying from warning messages to real-time video delivery. The majority of literature solutions consider the distance from the sender to choose the vehicle to forward the message. Basically, the solutions introduce a delay in the forwarding procedure, which is inversely proportional to the distance from the sender vehicle. In order to improve the forwarding procedure, this work introduces the concept of Road Covered Area to improve the overall data dissemination process and we describe how to calculate the road covered area by a node transmission. We present the D&RCA, the combination of Distance and Road Covered Area strategies to enhance the re-transmission during communication. Instead of considering the distance, we propose a function to combine the distance and road covered area to introduce a small delay before re-transmissions. We compare the proposed protocol with literature solutions considering the metrics of number of collisions, network coverage and communication latency for different density of vehicles in the network. When the network has 700 vehicles/km2 , the data dissemination latency and number of collisions of the proposed D&RCA is, respectively, 1.24 and 1.32 times smaller than the literature solutions. When we increase the density of vehicles, all evaluated solutions present a network coverage above 90%.