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"global influence"
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Which ENSO index best represents its global influences?
Knowledge about the El Niño-Southern Oscillation (ENSO) is the scientific foundation for short-term climate prediction, due to its global influence. In operation and research communities, the ENSO state is often represented by various ENSO indices. However, it is unclear which index is the strongest for capturing ENSO’s global climate influence. By examining the correlations of eleven ENSO indices with monthly mean global precipitation and surface temperature (TS), we illustrate the similarities and differences in the connections, identify the strongest index, and discuss the physics behind the differences. For the global average, the Niño3.4 and relative Niño3.4 indices are the two strongest indices and the warm pool index is the weakest one for capturing the impact of ENSO on global precipitation, while the Niño4 and Niño3.4 indices are the two strongest indices and the Modoki index is the weakest one for capturing the ENSO’s influence on TS variations. In addition to the dependence on the variables and ENSO indices, the representations of climate variability associated with ENSO depend on the region. For example, in Australia, the southern oscillation index has the most significant correlations with precipitation and its correlations with TS are relatively weaker than those of some of the other indices. These differences associated with the various ENSO indices may be due to their representation of the deep convection in the tropical Pacific. These results can serve as a benchmark to understand the global picture of monthly mean precipitation and TS influenced by ENSO and to verify model’s ability in capturing these connections.
Journal Article
The 2008 global financial crisis in retrospect : causes of the crisis and national regulatory responses
This work addresses the causes and consequences of the international financial crisis of 2008. A range of esteemed contributors explore developments in the United States, where the crisis of 2008 originated, as well as the smallest country affected, Iceland, by evaluating developments since 2008. Currently, many countries are facing similar problems as Iceland did in 2008: this book is of interest to economists and policy makers in these countries to study what happened in Iceland, and why the recovery of that economy was strong and swift. The chapters originate from panel discussions and conferences and explore areas including regulation, state projects and inflation.
Book
Ranking influential nodes in complex networks based on local and global structures
Identifying influential nodes in complex networks is an open and challenging issue. Many measures have been proposed to evaluate the influence of nodes and improve the accuracy of measuring influential nodes. In this paper, a new method is proposed to identify and rank the influential nodes in complex networks. The proposed method determines the influence of a node based on its local location and global location. It considers both the local and global structure of the network. Traditional degree centrality is improved and combined with the notion of the local clustering coefficient to measure the local influence of nodes, and the classical k-shell decomposition method is improved to measure the global influence of nodes. To evaluate the performance of the proposed method, the susceptible-infected-recovered (SIR) model is utilized to examine the spreading capability of nodes. A number of experiments are conducted on 11 real-world networks to compare the proposed method with other methods. The experimental results show that the proposed method can identify the influential nodes more accurately than other methods.
Journal Article
Research on the Complex Characteristics of Urban Subway Network and the Identification Method of Key Lines
Based on the complex network theory, we established a topological network of the Beijing subway under Space L, Space P, and Space C. Then, we analyzed the complex characteristics of the subway network under each topological network, proposed the global impact indexes (including aggregation impact coefficient, path length impact coefficient, network efficiency impact coefficient, and connectivity impact coefficient), and interline impact indexes (including degree centrality impact coefficient, near-centrality impact coefficient and intermediate centrality impact coefficient, the higher the value, the more obvious the effect on other lines; degree centrality sensitivity coefficient, near-centrality sensitivity coefficient and intermediate centrality sensitivity coefficient, the higher the value, the more vulnerable to the impact of other lines). At the global and local levels, it is possible to analyze the effect of different lines on the global situation and other lines. The concept of the “line importance index” is proposed to identify the key lines in the Beijing subway network. The network is characterized by scale-free and small-world characteristics under Space P, and scale-free network characteristics but no small-world characteristics under Space L and Space C. Subway Line 10, Line 9, Line 1, Line 2, and Line 5 are the five subway lines with the highest importance. Subway Line S1, Changping Line, Xijiao Line, Capital Airport Line, and Daxing International Airport Line are the five subway lines with the lowest importance.
Journal Article
The Odds Exponential-Pareto IV Distribution: Regression Model and Application
This article introduces the odds exponential-Pareto IV distribution, which belongs to the odds family of distributions. We studied the statistical properties of this new distribution. The odds exponential-Pareto IV distribution provided decreasing, increasing, and upside-down hazard functions. We employed the maximum likelihood method to estimate the distribution parameters. The estimators performance was assessed by conducting simulation studies. A new log location-scale regression model based on the odds exponential-Pareto IV distribution was also introduced. Parameter estimates of the proposed model were obtained using both maximum likelihood and jackknife methods for right-censored data. Real data sets were analyzed under the odds exponential-Pareto IV distribution and log odds exponential-Pareto IV regression model to show their flexibility and potentiality.
Journal Article
Eaarth : making a life on a tough new planet
Argues that a large-scale shift in Earth's climate is unavoidable and explains how humans should live if they are going to sustain themselves on the new planet that their mistakes have created.
Book
A novel multivariate fuzzy time series based forecasting algorithm incorporating the effect of clustering on prediction
Forecasting has often played predominant roles in daily life as necessary measures can be taken to bypass the undesired and detrimental future prompted by this fact, the issue of forecasting becomes one of the most important topics of research for the modern scientists and as a result several innovative forecasting techniques have been developed. Amongst various well-known forecasting techniques, fuzzy time series-based methods are successfully used, though they are suffering from some serious drawbacks, viz., fixed sized intervals, using some fixed membership values (0, 0.5, and 1) and moreover, the defuzzification process only deals with the factor that is to be predicted. Additionally, most of the existing and widely used fuzzy time series-based forecasting algorithms employ their own clustering techniques that may be data-dependent and in turn the predictive accuracy decrease. Prompted by the fact, the present author developed a novel multivariate fuzzy forecasting algorithm that is able to remove all the drawbacks as also can predict the future occurrences with better predictive accuracy. Moreover, the comparisons with the thirteen other existing frequently used forecasting algorithms (viz., conventional, fuzzy time series-based algorithms and ANN) were performed to demonstrate its better efficiency and predictive accuracy. Towards the end, the applicability and predictive accuracy of the developed algorithm has been demonstrated using three different data sets collected from three different domains, such as: oil agglomeration process (coal washing technique), frequently occurred web error prediction and the financial forecasting. The real dataset related to oil agglomeration was collected from CIMFER, Dhanbad, India, that regarding the frequently occurred web error codes of
www.ismdhanbad.ac.in
, the official website of ISM Dhanbad, was collected from the Indian School of Mines (ISM) Dhanbad, India server and the finance data set was collected from the Ministry of Statistical and Program Implementation (Govt. of India).
Journal Article