Kumaraswamy autoregressive moving average models for double bounded environmental data

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval $(a,b)$ following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.Comment: 25 pages, 4 tables, 4 figure

Life Cycle Greenhouse Gas Emissions in Maize No-Till Agroecosystems in Southern Brazil Based on a Long-Term Experiment

Brazilian agriculture is constantly questioned concerning its environmental impacts, particularly greenhouse gas (GHG) emissions. This research study used data from a 34-year field experiment to estimate the life cycle GHG emissions intensity of maize production for grain in farming systems under no-tillage (NT) and conventional tillage (CT) combined with Gramineae (oat) and legume (vetch) cover crops in southern Brazil. We applied the Feedstock Carbon Intensity Calculator for modeling the “field-to-farm gate” emissions with measured annual soil N2O and CH4 emissions data. For net CO2 emissions, increases in soil organic C (SOC) were applied as a proxy, where the CT combined with oat was a reference. The life cycle GHG emissions intensity for maize was negative under NT farming systems with Gramineae and legume cover crops, −0.7 and −0.1 kg CO2e kg−1 of maize, respectively. CT with oats as a cover crop had a GHG intensity of 1.0 kg CO2e kg−1 of maize and 2.2 Mg CO2e ha−1. NT with cover crops increased SOC (0.7 C Mg ha−1 yr−1, 0–100 cm) and contributed to the mitigation of life cycle GHG emissions of maize production. This research shows that NT with cover crops is a sustainable solution for farming in southern Brazil

Int-FLBCC: Model for Load Balancing in Cloud Computing using Fuzzy Logic Type-2 and Admissible Orders.

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY and 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLA), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1

Environmental Shaping of Sponge Associated Archaeal Communities

Archaea are ubiquitous symbionts of marine sponges but their ecological roles and the influence of environmental factors on these associations are still poorly understood.We compared the diversity and composition of archaea associated with seawater and with the sponges Hymeniacidon heliophila, Paraleucilla magna and Petromica citrina in two distinct environments: Guanabara Bay, a highly impacted estuary in Rio de Janeiro, Brazil, and the nearby Cagarras Archipelago. For this we used metagenomic analyses of 16S rRNA and ammonia monooxygenase (amoA) gene libraries. Hymeniacidon heliophila was more abundant inside the bay, while P. magna was more abundant outside and P. citrina was only recorded at the Cagarras Archipelago. Principal Component Analysis plots (PCA) generated using pairwise unweighted UniFrac distances showed that the archaeal community structure of inner bay seawater and sponges was different from that of coastal Cagarras Archipelago. Rarefaction analyses showed that inner bay archaeaoplankton were more diverse than those from the Cagarras Archipelago. Only members of Crenarchaeota were found in sponge libraries, while in seawater both Crenarchaeota and Euryarchaeota were observed. Although most amoA archaeal genes detected in this study seem to be novel, some clones were affiliated to known ammonia oxidizers such as Nitrosopumilus maritimus and Cenarchaeum symbiosum.The composition and diversity of archaeal communities associated with pollution-tolerant sponge species can change in a range of few kilometers, probably influenced by eutrophication. The presence of archaeal amoA genes in Porifera suggests that Archaea are involved in the nitrogen cycle within the sponge holobiont, possibly increasing its resistance to anthropogenic impacts. The higher diversity of Crenarchaeota in the polluted area suggests that some marine sponges are able to change the composition of their associated archaeal communities, thereby improving their fitness in impacted environments

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

A contribution to the Int-FLBCC exploring Fuzzy Consensus via penalty functions.

The dynamic consolidation of resources in the infrastructures provided by Computing Clouds is a widely used strategy to improve energy efficiency in Cloud Computing. Determining when it is best to relocate Virtual Machines (VMs) from overloaded hosts, or that are under a very low load, is an aspect that directly influences resource utilization and the quality of service offered by the CN infrastructure. As an important step in the study and research efforts, a Systematic Literature Review was carried out through the formulation of a search string, an inclusion criterion and four exclusion criteria. At the end of this stage, eight works were selected, presenting their main characteristics and the strategies used to manage resources in the NC. In this scenario, this work aims to design an approach for the consolidation of resources in a NC environment, which considers the treatment of information related to Computing Power, Communication Cost and RAM Consumption. Based on these features, the proposed approach extends the Int-FLBCC, which consists of an Interval Valued Fuzzy Logic approach, adding a degree of reliability to the results obtained with an evaluation through consensus measures. Evaluations were carried out exploring two fronts: (i) where consensus measures and penalties in fuzzy values are explored, based on membership functions; and (ii) which considers the imprecision inherent to the Interval Valued Fuzzy Sets related to input and output variables. The resulted evaluations point to promising results in the treatment of imprecision through the functions developed in Int-FLBCC, and also, due to the exploitation of fuzzy consensus, greater reliability is generated in the treatment of the uncertainty present in the information captured from an infrastructure typical of Cloud Computing.Sem bolsaA consolidação dinâmica de recursos nas infraestruturas disponibilizadas pelas Nuvens Computacionais é uma estratégia bastante utilizada para melhorar a eficiência energética na Computação em Nuvem (CN). Determinar quando é melhor realocar Máquinas Virtuais (MVs) de hosts sobrecarregados, ou que estejam com uma carga muito baixa, é um aspecto que influencia diretamente na utilização de recursos e na qualidade de serviço oferecida pela infraestrutura de CN. Enquanto etapa importante dos esforços de estudo e pesquisa, foi realizada uma Revisão Sistemática da Literatura através da formulação de uma string de pesquisa, um critério de inclusão e quatro critérios de exclusão. Ao final desta etapa, foram selecionados oito trabalhos, sendo apresentadas suas principais características e as estratégias usadas para o gerenciamento de recursos na CN. Considerando este contexto, este trabalho tem por objetivo a concepção de uma abordagem para a consolidação de recursos em um ambiente de CN, que considere o tratamento de informações relacionadas ao Poder Computacional, Custo de Comunicação e Consumo de Memória RAM. Para tal, a abordagem proposta estende o Int-FLBCC, que consiste em uma abordagem de Lógica Fuzzy Valorada Intervalarmente, acrescentando um grau de confiabilidade aos resultados obtidos com uma avaliação através de medidas de consenso. Foram feitas avaliações explorando duas frentes: (i) onde são exploradas medidas de consenso e penalidades em valores fuzzy, a partir das funções de pertinência; e (ii) que considera a imprecisão inerente aos conjuntos fuzzy valorados intervalarmente referente às variáveis de entrada e saída. As avaliações realizadas apontam resultados promissores no tratamento da imprecisão através das funções desenvolvidas no Int-FLBCC, e ainda, em virtude da exploração do consenso fuzzy, é gerada uma maior confiabilidade no tratamento da incerteza presente nas informações capturadas de uma infraestrutura típica de CN