RouteSpray: A multiple-copy routing algorithm based on transit routes

Vehicular networks represent a special type of wireless network that has gained the attention of researchers over the past few years. Routing protocols for this type of network must face several challenges, such as high mobility, high speeds and frequent network disconnections. This paper proposes a vehicular routing algorithm called RouteSpray that in addition to using vehicular routes to help make routing decisions, uses controlled spraying to forward multiple copies of messages, thus ensuring better delivery rates without overloading the network. The results of experiments performed in this study indicate that the RouteSpray algorithm delivered 13.46% more messages than other algorithms reported in the literature. In addition, the RouteSpray algorithm kept the buffer occupation 73.38% lower.Keywords: wireless networks, vehicular networks, routing protocols

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Cyber-physical production systems retrofitting in context of industry 4.0.

Industry 4.0 is the new industrial revolution involving the introduction of new technologies in the industrial field. However, changing the technological level of an outdated industry is not a simple task. By retrofitting all old equipment into new equipment in industries, the retrofitting concept emerges as a rapid and low-cost solution, aimed at reusing existing equipment, with the addition of new technologies. However, retrofitting changes according to the type and model of industrial equipment, making it challenging to upgrade industrial equipment to Cyber-Physical Production Systems (CPPS). In this paper, the standardization of the retrofitting process to transform old equipment into a CPPS is presented. The standardization is done with the support of a platform that has features to work independently of the model or type of equipment. To implement the platform, we define the requirements, components, and technologies necessary to retrofit industrial equipment. The entire process is based on Reference Architectural Model for Industry 4.0 (RAMI 4.0) a widespread architecture of Industry 4.0. With the retrofitting platform based on RAMI 4.0, makes consistent the process of upgrading industrial equipment, providing Industry 4.0 functionality. A prototype with an industrial robotic arm was implemented to validate the process and the retrofitting platform. The results show the benefits of the CPPS Retrofitting process in industrial equipment

Counting time in drops : views on the role and importance of smartwatches in dew computing.

A large amount of data, called the big data, generated by the devices that are part of the Internet of Things, is expected in the coming years. This scenario creates challenges for sending, processing, and storing all data centrally in the cloud. Recent works propose a decentralization of the processing and storage of this data in local devices close to the user to solve such challenges. This paradigm, called dew computing, has been gaining attention from academia. Several works apply this proposal through devices such as desktops, laptops, and smartphones. However, after a systematic review, no studies were found that applied this proposal to smart wearable devices. Thus, this work shows the research, evaluation, analysis, and discussion of smartwatches for the dew computing environment. The results of this work showed that smartwatches could extend local device functionalities through performing services, cooperating with decentralizing cloud computing, and helping to reduce the negative impacts of the big data

TIPOLOGIA DOS CRIMES AMBIENTAIS REFERENTES À FAUNA SILVESTRE NO ESTADO DA BAHIA: UMA ANÁLISE DOS AUTOS DE INFRAÇÃO DO INEMA E DO IBAMA ENTRE 2001 E 2015

O presente trabalho analisou a tipologia dos crimes ambientais cometidos contra a fauna silvestre no estado da Bahia entre os anos de 2001 a 2015, partindo dos autos de infração emitidos pelo Instituto do Meio Ambiente e Recursos Hídricos do estado da Bahia (INEMA) e pelo Instituto Brasileiro e dos Recursos Naturais Renováveis (IBAMA). Os dados foram quantificados, qualificados e agrupados por municípios e territórios de identidade, o que possibilitou identificar as regiões no Estado da Bahia com maior incidência de crimes e os municípios com as maiores arrecadações por conta das multas aplicadas