402 research outputs found
Partitioning Complexity in Air Traffic Management Task
Cognitive complexity is a term that appears frequently in air traffic control (ATC) research literature, yet there is little principled investigation of the potential sources of cognitive complexity. Three distinctly different sources of
cognitive complexity are proposed which are environmental, organizational, and display. Two experiments were conducted to explore whether or not these proposed components of complexity could be effectively partitioned,
measured, and compared. The findings demonstrate that sources of complexity can be decomposed and measured and furthermore, the use of color in displays, a display design intervention meant to reduce environmental complexity, can actually contribute to it.This research was sponsored by the Civil Aerospace Medical Institute
A statistical analysis of product prices in online markets
We empirically investigate fluctuations in product prices in online markets
by using a tick-by-tick price data collected from a Japanese price comparison
site, and find some similarities and differences between product and asset
prices. The average price of a product across e-retailers behaves almost like a
random walk, although the probability of price increase/decrease is higher
conditional on the multiple events of price increase/decrease. This is quite
similar to the property reported by previous studies about asset prices.
However, we fail to find a long memory property in the volatility of product
price changes. Also, we find that the price change distribution for product
prices is close to an exponential distribution, rather than a power law
distribution. These two findings are in a sharp contrast with the previous
results regarding asset prices. We propose an interpretation that these
differences may stem from the absence of speculative activities in product
markets; namely, e-retailers seldom repeat buy and sell of a product, unlike
traders in asset markets.Comment: 5 pages, 5 figures, 1 table, proceedings of APFA
The a1 isoform of the Na Ăž /K Ăž ATPase is up-regulated in dedifferentiated progenitor cells that mediate lens and retina regeneration in adult newts
Anti-persistence in the global temperature anomaly field
In this study, low-frequency variations in temperature anomaly are investigated by mapping temperature anomaly records onto random walks. We show evidence that global overturns in trends of temperature anomalies occur on decadal time-scales as part of the natural variability of the climate system. Paleoclimatic summer records in Europe and New-Zealand provide further support for these findings as they indicate that anti-persistence of temperature anomalies on decadal time-scale have occurred in the last 226 yrs. Atmospheric processes in the subtropics and mid-latitudes of the SH and interactions with the Southern Oceans seem to play an important role to moderate global variations of temperature on decadal time-scales
Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks
Network theory provides various tools for investigating the structural or
functional topology of many complex systems found in nature, technology and
society. Nevertheless, it has recently been realised that a considerable number
of systems of interest should be treated, more appropriately, as interacting
networks or networks of networks. Here we introduce a novel graph-theoretical
framework for studying the interaction structure between subnetworks embedded
within a complex network of networks. This framework allows us to quantify the
structural role of single vertices or whole subnetworks with respect to the
interaction of a pair of subnetworks on local, mesoscopic and global
topological scales.
Climate networks have recently been shown to be a powerful tool for the
analysis of climatological data. Applying the general framework for studying
interacting networks, we introduce coupled climate subnetworks to represent and
investigate the topology of statistical relationships between the fields of
distinct climatological variables. Using coupled climate subnetworks to
investigate the terrestrial atmosphere's three-dimensional geopotential height
field uncovers known as well as interesting novel features of the atmosphere's
vertical stratification and general circulation. Specifically, the new measure
"cross-betweenness" identifies regions which are particularly important for
mediating vertical wind field interactions. The promising results obtained by
following the coupled climate subnetwork approach present a first step towards
an improved understanding of the Earth system and its complex interacting
components from a network perspective
Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
When network and graph theory are used in the study of complex systems, a
typically finite set of nodes of the network under consideration is frequently
either explicitly or implicitly considered representative of a much larger
finite or infinite region or set of objects of interest. The selection
procedure, e.g., formation of a subset or some kind of discretization or
aggregation, typically results in individual nodes of the studied network
representing quite differently sized parts of the domain of interest. This
heterogeneity may induce substantial bias and artifacts in derived network
statistics. To avoid this bias, we propose an axiomatic scheme based on the
idea of node splitting invariance to derive consistently weighted variants of
various commonly used statistical network measures. The practical relevance and
applicability of our approach is demonstrated for a number of example networks
from different fields of research, and is shown to be of fundamental importance
in particular in the study of spatially embedded functional networks derived
from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure
Statistical Physics in Meteorology
Various aspects of modern statistical physics and meteorology can be tied
together. The historical importance of the University of Wroclaw in the field
of meteorology is first pointed out. Next, some basic difference about time and
space scales between meteorology and climatology is outlined. The nature and
role of clouds both from a geometric and thermal point of view are recalled.
Recent studies of scaling laws for atmospheric variables are mentioned, like
studies on cirrus ice content, brightness temperature, liquid water path
fluctuations, cloud base height fluctuations, .... Technical time series
analysis approaches based on modern statistical physics considerations are
outlined.Comment: Short version of an invited paper at the XXIth Max Born
symposium,Ladek Zdroj, Poland; Sept. 200
Climate Dynamics: A Network-Based Approach for the Analysis of Global Precipitation
Precipitation is one of the most important meteorological variables for defining the climate dynamics, but the spatial patterns of precipitation have not been fully investigated yet. The complex network theory, which provides a robust tool to investigate the statistical interdependence of many interacting elements, is used here to analyze the spatial dynamics of annual precipitation over seventy years (1941-2010). The precipitation network is built associating a node to a geographical region, which has a temporal distribution of precipitation, and identifying possible links among nodes through the correlation function. The precipitation network reveals significant spatial variability with barely connected regions, as Eastern China and Japan, and highly connected regions, such as the African Sahel, Eastern Australia and, to a lesser extent, Northern Europe. Sahel and Eastern Australia are remarkably dry regions, where low amounts of rainfall are uniformly distributed on continental scales and small-scale extreme events are rare. As a consequence, the precipitation gradient is low, making these regions well connected on a large spatial scale. On the contrary, the Asiatic South-East is often reached by extreme events such as monsoons, tropical cyclones and heat waves, which can all contribute to reduce the correlation to the short-range scale only. Some patterns emerging between mid-latitude and tropical regions suggest a possible impact of the propagation of planetary waves on precipitation at a global scale. Other links can be qualitatively associated to the atmospheric and oceanic circulation. To analyze the sensitivity of the network to the physical closeness of the nodes, short-term connections are broken. The African Sahel, Eastern Australia and Northern Europe regions again appear as the supernodes of the network, confirming furthermore their long-range connection structure. Almost all North-American and Asian nodes vanish, revealing that extreme events can enhance high precipitation gradients, leading to a systematic absence of long-range patterns
Estimating the Fractal Dimension, K_2-entropy, and the Predictability of the Atmosphere
The series of mean daily temperature of air recorded over a period of 215
years is used for analysing the dimensionality and the predictability of the
atmospheric system. The total number of data points of the series is 78527.
Other 37 versions of the original series are generated, including ``seasonally
adjusted'' data, a smoothed series, series without annual course, etc. Modified
methods of Grassberger and Procaccia are applied. A procedure for selection of
the ``meaningful'' scaling region is proposed. Several scaling regions are
revealed in the ln C(r) versus ln r diagram. The first one in the range of
larger ln r has a gradual slope and the second one in the range of intermediate
ln r has a fast slope. Other two regions are settled in the range of small ln
r. The results lead us to claim that the series arises from the activity of at
least two subsystems. The first subsystem is low-dimensional (d_f=1.6) and it
possesses the potential predictability of several weeks. We suggest that this
subsystem is connected with seasonal variability of weather. The second
subsystem is high-dimensional (d_f>17) and its error-doubling time is about 4-7
days. It is found that the predictability differs in dependence on season. The
predictability time for summer, winter and the entire year (T_2 approx. 4.7
days) is longer than for transition-seasons (T_2 approx. 4.0 days for spring,
T_2 approx. 3.6 days for autumn). The role of random noise and the number of
data points are discussed. It is shown that a 15-year-long daily temperature
series is not sufficient for reliable estimations based on Grassberger and
Procaccia algorithms.Comment: 27 pages (LaTex version 2.09) and 15 figures as .ps files, e-mail:
[email protected]
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