Fire and restoration of the largest urban forest of the world in Rio de Janeiro City,Brazil

National Park of Tijuca in Rio de Janeiro (Brazil) is about 3,300 ha and considered the largest urban forest in the world. Its floristic composition is typical of Atlantic Rain Forest. The reserve is being altered because of fire occurrences and urban expansion. This study identified locations and causes of forest fires, and makes management recommendations to restore damaged areas. From 1991 to 2000, forest firefighters recorded an average of 75-fire occurrences/year. Identified causes included hot air balloons (24%), intentional (24%), rubbish burning (21%) and religious practices (17%). Primary fuels included invasive grasses and ferns. Although hot air balloons destroyed larger areas of forest in each occurrence, a greater number of fires started in the invasive vegetation along roads that bisect the forest. In response to recurrent forests, invasive vegetation has spread gradually into the forest increasing forest degradation. To decrease fire damage, sites with high fire frequencies and density of invasive vegetation were planted with less flammable species. Results indicate that fire frequency decreased and density of invasive vegetation declined. This approach appears to prevent fire incidence, reduce the need for fire fighting, and preserve existing biodiversity.     

Choosing between Cox proportional hazards and logistic models for interval- censored data via bootstrap

This work develops a new methodology in order to discriminate models for interval- censored data based on bootstrap residual simulation by observing the deviance difference from one model in relation to another, according to Hinde (1992). Generally, this sort of data can generate a large number of tied observations and, in this case, survival time can be regarded as discrete. Therefore, the Cox proportional hazards model for grouped data (Prentice & Gloeckler, 1978) and the logistic model (Lawless, 1982) can be fitted by means of generalized linear models. Whitehead (1989) considered censoring to be an indicative variable with a binomial distribution and fitted the Cox proportional hazards model using complementary log-log as a link function. In addition, a logistic model can be fitted using logit as a link function. The proposed methodology arises as an alternative to the score tests developed by Colosimo et al. (2000), where such models can be obtained for discrete binary data as particular cases from the Aranda-Ordaz distribution asymmetric family. These tests are thus developed with a basis on link functions to generate such a fit. The example that motivates this study was the dataset from an experiment carried out on a flax cultivar planted on four substrata susceptible to the pathogen Fusarium oxysoprum . The response variable, which is the time until blighting, was observed in intervals during 52 days. The results were compared with the model fit and the AIC values.     

Risk Modeling, Assessment, and Management of Lahar Flow Threat

The 1991 eruption of Mount Pinatubo in the Philippines is considered one of the most violent and destructive volcanic activities in the 20th century. Lahar is the Indonesian term for volcanic ash, and lahar flows resulting from the massive amount of volcanic materials deposited on the mountain's slope posed continued post-eruption threats to the surrounding areas, destroying lives, homes, agricultural products, and infrastructures. Risks of lahar flows were identified immediately after the eruption, with scientific data provided by the Philippine Institute of Volcanology, the U.S. Geological Survey, and other research institutions. However, competing political, economic, and social agendas subordinated the importance of scientific information to policy making. Using systemic risk analysis and management, this article addresses the issues of multiple objectives and the effective integration of scientific techniques into the decision-making process. It provides a modeling framework for identifying, prioritizing, and evaluating policies for managing risk. The major considerations are: (1) applying a holistic approach to risk analysis through hierarchical holographic modeling, (2) applying statistical methods to gain insight into the problem of uncertainty in risk assessment, (3) using multiobjective trade-off analysis to address the issue of multiple decisionmakers and stakeholders in the decision-making process, (4) using the conditional expected value of extreme events to complement and supplement the expected value in quantifying risk, and (5) assessing the impacts of multistage decisions. Numerical examples based on ex post data are formulated to illustrate applications to various problems. The resulting framework from this study can serve as a general baseline model for assessing and managing risks of natural disasters, which the Philippines' lead agency-the National Disaster Coordinating Council (NDCC)-and other related organizations can use for their decision-making processes.     

Global Innovation Networks – Organizations and People

Innovation is increasingly the outcome of global networks that connect geographically dispersed knowledge centers. The international business (IB) literature has developed a sophisticated understanding of the multinational enterprise (MNE) as an organization generates value by integrating knowledge across national borders. We advance this literature by making three key arguments. First we highlight the three megatrends that shape the strategy of the modern MNE: the disaggregation of the value chain into ever narrower activities, the migration of value to knowledge-intensive intangibles and the rise of huge emerging markets like China and India, whose domestic firms can grow to enormous size before venturing abroad. Second, we trace these three megatrends to falling spatial transaction costs, enabled in the main, by digitalization. Third, we draw on earlier research on the generic forms of global linkages, arguing that the IB literature has limited itself to organizational pipelines, while paying relatively little attention to personal relationships. The latter are particularly important in the genesis of both entrepreneurship and radical innovation, but are particularly sensitive to the anti-globalization policies. An environment where technology continues to reduce spatial transaction costs, while policy raises them, strengthens large MNEs at the expense of innovative international new ventures.     

State of the Art in Risk Analysis of Workforce Criticality Influencing Disaster Preparedness for Interdependent Systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.     

Bayesian Classifiers Applied to the Tennessee Eastman Process

Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.     

Pandemic Recovery Analysis Using the Dynamic Inoperability Input-Output Model

Economists have long conceptualized and modeled the inherent interdependent relationships among different sectors of the economy. This concept paved the way for input-output modeling, a methodology that accounts for sector interdependencies governing the magnitude and extent of ripple effects due to changes in the economic structure of a region or nation. Recent extensions to input-output modeling have enhanced the model's capabilities to account for the impact of an economic perturbation; two such examples are the inoperability input-output model^{( 1 , 2 )} and the dynamic inoperability input-output model (DIIM).^{( 3 )} These models introduced sector inoperability, or the inability to satisfy as-planned production levels, into input-output modeling. While these models provide insights for understanding the impacts of inoperability, there are several aspects of the current formulation that do not account for complexities associated with certain disasters, such as a pandemic. This article proposes further enhancements to the DIIM to account for economic productivity losses resulting primarily from workforce disruptions. A pandemic is a unique disaster because the majority of its direct impacts are workforce related. The article develops a modeling framework to account for workforce inoperability and recovery factors. The proposed workforce-explicit enhancements to the DIIM are demonstrated in a case study to simulate a pandemic scenario in the Commonwealth of Virginia.     

Confidence intervals based on the deviance statistic for the hyperparameters in state space models

The main objective of this work is to evaluate the performance of confidence intervals, built using the deviance statistic, for the hyperparameters of state space models. The first procedure is a marginal approximation to confidence regions, based on the likelihood test, and the second one is based on the signed root deviance profile. Those methods are computationally efficient and are not affected by problems such as intervals with limits outside the parameter space, which can be the case when the focus is on the variances of the errors. The procedures are compared to the usual approaches existing in the literature, which includes the method based on the asymptotic distribution of the maximum likelihood estimator, as well as bootstrap confidence intervals. The comparison is performed via a Monte Carlo study, in order to establish empirically the advantages and disadvantages of each method. The results show that the methods based on the deviance statistic possess a better coverage rate than the asymptotic and bootstrap procedures.     

Landscape urbanization threatens plant phylogenetic diversity in the Brazilian Atlantic Forest

Urbanization causes species loss around the world, but its effects on phylogenetic diversity are poorly known in tropical forests. Using a patch-landscape approach in an urbanizing region of the Brazilian Atlantic Forest, we tested whether the increase in landscape urbanization reduces plant species density, phylogenetic richness and divergence, and increases the relatedness among co-occurring individuals and species. We assessed plant responses to urbanization in adult (diameter at breast height?>?10 cm) and sapling communities (2.5–10 cm diameter) separately, as saplings are proxies of the future flora. We sampled 2860 woody plants belonging to 155 species in nine circular landscapes with urbanization level varying from 0% to 45%, and estimated the relatedness among the species that have increased and decreased in relative abundance in more urbanized landscapes (winner and losers, respectively). As expected, species density and phylogenetic richness decreased with the increase in urbanization. These responses were consistent for adult and sapling communities, suggesting a persistent loss of species and lineages in more urbanized landscapes. Contrary to our expectations, phylogenetic divergence and structure did not respond to urbanization, indicating that the more urbanized landscapes still retain much evolutionary history. However, because the relatedness among winners was greater than among losers, it is likely that the phylogenetic divergence gradually reduces and the relatedness increases, resulting in impoverished forests with uncertain ability to provide ecosystem services such as carbon storage and pest control. This environmental cost should be taken into account to align urban sprawl with biodiversity conservation.     

Rainfall interception capacity of tree species used in urban afforestation

Afforestation is inserted in the urban environment and interferes directly in the surface runoff. In order to quantify the rainfall interception by tree species, an experiment was carried out in the city of Uruaçu, Goiás, Brazil, with the four most common tree species in the urban afforestation of the region. The species chosen were the Mangifera indica (mango tree), the Caesalpinia peltophoroides (sibipiruna tree), the Pachira aquatica (munguba tree) and the Licania tomentosa (oiti). In order to register precipitation, ten pluviographs were used. They are capable of measuring precipitation over time in terms of duration, volume, and intensity. For events with a mean cumulative precipitation of 16.7 mm, the mean values of interception found in each species were: Mangifera indica - 8.0 mm; Pachira aquatica - 7.4 mm; Licania tomentosa - 7.2 mm; and Caesalpinia peltophoroides - 4.8 mm. The research confirms the importance of studies related to the identification of the species’ individual characteristics that interfere in the rainfall interception capacity and that can consequently contribute to urban drainage actions. In addition, it proves the existence of variations of rainfall interception in relation to rainfall events and during their occurrences and emphasizes the need for a careful definition of the species that will compose the urban afforestation.