Differential response of two Pinus spp. to avian nitrogen input as revealed by nitrogen isotope analysis for tree rings

Temporal variations in N concentration and δ¹⁵N value of annual tree rings (1 year of time resolution) of two Japanese Black Pine (Pinus thunbergii) and three Japanese Red Pine (Pinus densiflora) trees under current breeding activity of the Great Cormorant (Pharacrocorax carbo) and the Black-tailed Gull (Larus crassirostris), respectively, in central and northeastern Japan were studied. Both species from control sites where no avian input occurs show negative values (δ¹⁵N = around?4 ‰ to?2 ‰) which are common among higher plants growing under high rainfall regimes. The δ¹⁵N values of P. densiflora show uniformly positive values several years before and after the breeding event, indicating N translocation that moved the absorbed N of a given growth year to tree rings of the previous year while a clear historical value of soil N dynamics was kept intact in the annual rings of P. thunbergii. Long-term N trends inferred from tree rings must take into account tree species with limited translocation rates that can retain actual N annual acquisition.     

Differential response of two Pinus spp. to avian nitrogen input as revealed by nitrogen isotope analysis for tree rings

Temporal variations in N concentration and δ(15)N value of annual tree rings (1 year of time resolution) of two Japanese Black Pine (Pinus thunbergii) and three Japanese Red Pine (Pinus densiflora) trees under current breeding activity of the Great Cormorant (Pharacrocorax carbo) and the Black-tailed Gull (Larus crassirostris), respectively, in central and northeastern Japan were studied. Both species from control sites where no avian input occurs show negative values (δ(15)N = around -4 ‰ to -2 ‰) which are common among higher plants growing under high rainfall regimes. The δ(15)N values of P. densiflora show uniformly positive values several years before and after the breeding event, indicating N translocation that moved the absorbed N of a given growth year to tree rings of the previous year while a clear historical value of soil N dynamics was kept intact in the annual rings of P. thunbergii. Long-term N trends inferred from tree rings must take into account tree species with limited translocation rates that can retain actual N annual acquisition.     

基于时序NDVI与光谱微分变换的森林优势树种识别

基于遥感光谱特征准确识别优势树种类型对于区域林业资源的监测和经营具有重要意义,也是当前亟待解决的重要科学问题。伴随遥感技术的发展,利用时间序列高分影像能够有效获取林分树种不同物候期生长特性及其冠层光谱动态信息,有利于克服区域森林类型精细识别中普遍存在的异物同谱难题。以中国东北地区赤峰市旺业甸国有林场为试验区,采用覆盖完整自然年的共36景高分一号（GF-1）WFV时间序列数据（16 m）,提取包含不同优势树种生长阶段特征的林分冠层光谱归一化植被指数（NDVI）,结合支持向量机（SVM）模型对研究区内5种典型优势树种：油松、落叶松、山杨、白桦和蒙古栎,进行不同时间尺度下（单季相、全季相、逐月和逐旬）的光谱识别研究。同时,分别基于原始时序光谱及其一阶、二阶和三阶微分变换结果,探讨了不同分辨率时序NDVI光谱及其3种微分变换结果对区域森林优势树种的识别效果。结果显示,基于不同尺度的时间序列数据能够获得比不同季节单时相数据更好的树种识别结果（p＜0.05）,其中采用全季相数据的树种总分类精度相比于春、夏和秋不同季节的单季相数据结果,分别提高了7.67%,6.64%和3.6%,表明时间序列影像中所包含的植被物候信息对于区分不同森林树种类型十分重要,同时秋季是采用单时相数据的最佳识别季节（p＜0.05）;在不同时间序列数据中,基于逐旬的NDVI数据显著优于基于逐月和全季相数据的光谱识别结果（p＜0.05）,而基于全季相数据的光谱识别结果最低（p＜0.05）,表明更密集的时序光谱信息有利于区域树种类型识别精度的提升。此外,结合光谱微分变换后的树种识别结果比仅采用原始NDVI时间序列的识别结果精度更高（p＜0.05）,其中基于逐旬和逐月时间分辨率数据的最高识别精度能够达到82.1%和78.74%,分别提升了3.38%和2.95%。研究表明采用基于全年逐旬或逐月尺度的时序光谱数据,并结合相应的微分变换方法,可以有效提高区域尺度优势树种的识别精度,为相关多光谱森林植被精细识别研究提供参考。     

Systemic Losses Due to Counterparty Risk in a Stylized Banking System

We report a study of a stylized banking cascade model investigating systemic risk caused by counterparty failure using liabilities and assets to define banks’ balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to distressed whenever its liabilities are larger than the banks’ assets. The banks are connected through an interbank lending network and, whenever a bank is distressed, its creditor cannot expect the loan from the distressed bank to be repaid, potentially becoming distressed themselves. We solve the problem analytically for a homogeneous system and test the robustness and generality of the results with simulations of more complex systems. We investigate the parameter space and the corresponding distribution of operating banks mapping the conditions under which the whole system is stable or unstable. This allows us to determine how financial stability of a banking system is influenced by regulatory decisions, such as leverage; we discuss the effect of central bank actions, such as quantitative easing and we determine the cost of rescuing a distressed banking system using re-capitalisation. Finally, we estimate the stability of the UK and US banking systems comparing the years 2007 and 2012 by using real data.     

Investigation of major international and Turkish companies via hierarchical methods and bootstrap approach

We present a study, within the scope of econophysics, of the hierarchical structure of 98 among the largest international companies including 18 among the largest Turkish companies, namely Banks, Automobile, Software-hardware, Telecommunication Services, Energy and the Oil-Gas sectors, viewed as a network of interacting companies. We analyze the daily time series data of the Boerse-Frankfurt and Istanbul Stock Exchange. We examine the topological properties among the companies over the period 2006–2010 by using the concept of hierarchical structure methods (the minimal spanning tree (MST) and the hierarchical tree (HT)). The period is divided into three subperiods, namely 2006–2007, 2008 which was the year of global economic crisis, and 2009–2010, in order to test various time-windows and observe temporal evolution. We carry out bootstrap analyses to associate the value of statistical reliability to the links of the MSTs and HTs. We also use average linkage clustering analysis (ALCA) in order to better observe the cluster structure. From these studies, we find that the interactions among the Banks/Energy sectors and the other sectors were reduced after the global economic crisis; hence the effects of the Banks and Energy sectors on the correlations of all companies were decreased. Telecommunication Services were also greatly affected by the crisis. We also observed that the Automobile and Banks sectors, including Turkish companies as well as some companies from the USA, Japan and Germany were strongly correlated with each other in all periods.     

Waiting time distributions in financial markets

We study waiting time distributions for data representing two completely different financial markets that have dramatically different characteristics. The first are data for the Irish market during the 19th century over the period 1850 to 1854. A total of 10 stocks out of a database of 60 are examined. The second database is for Japanese yen currency fluctuations during the latter part of the 20th century (1989-1992). The Irish stock activity was recorded on a daily basis and activity was characterised by waiting times that varied from one day to a few months. The Japanese yen data was recorded every minute over 24 hour periods and the waiting times varied from a minute to a an hour or so. For both data sets, the waiting time distributions exhibit power law tails. The results for Irish daily data can be easily interpreted using the model of a continuous time random walk first proposed by Montroll and applied recently to some financial data by Mainardi, Scalas and colleagues. Yen data show a quite different behaviour. For large waiting times, the Irish data exhibit a cut off; the Yen data exhibit two humps that could arise as result of major trading centres in the World. Received 31 December 2001     

Comparison between volatility return intervals of the S&;P 500 index and two common models

We analyze the S&P 500 index data for the 13-year period, from January 1, 1984 to December 31, 1996, with one data point every 10 min. For this database, we study the distribution and clustering of volatility return intervals, which are defined as the time intervals between successive volatilities above a certain threshold q. We find that the long memory in the volatility leads to a clustering of above-median as well as below-median return intervals. In addition, it turns out that the short return intervals form larger clusters compared to the long return intervals. When comparing the empirical results to the ARMA-FIGARCH and fBm models for volatility, we find that the fBm model predicts scaling better than the ARMA-FIGARCH model, which is consistent with the argument that both ARMA-FIGARCH and fBm capture the long-term dependence in return intervals to a certain extent, but only fBm accounts for the scaling. We perform the Student's t-test to compare the empirical data with the shuffled records, ARMA-FIGARCH and fBm. We analyze separately the clusters of above-median return intervals and the clusters of below-median return intervals for different thresholds q. We find that the empirical data are statistically different from the shuffled data for all thresholds q. Our results also suggest that the ARMA-FIGARCH model is statistically different from the S&P 500 for intermediate q for both above-median and below-median clusters, while fBm is statistically different from S&P 500 for small and large q for above-median clusters and for small q for below-median clusters. Neither model can fully explain the entire regime of q studied.     

Boundary conditions for hemodynamics: The structured tree revisited

The structured tree boundary condition is a physiologically-based outflow boundary condition used in hemodynamics. We propose an alternative derivation that is considerably simpler than the original one and yields similar, but not identical, results. We analyze the sensitivity of this boundary condition to its parameters and discuss its domain of validity. Several implementation issues are discussed and tested in the case of arterial flow in the Circle of Willis. Additionally, we compare results obtained from the structured tree boundary condition to the Windkessel boundary condition and measured data.     

Loan and nonloan flows in the Australian interbank network

High-value transactions between banks in Australia are settled in the Reserve Bank Information and Transfer System (RITS) administered by the Reserve Bank of Australia. RITS operates on a real-time gross settlement (RTGS) basis and settles payments and transfers sourced from the SWIFT payment delivery system, the Austraclear securities settlement system, and the interbank transactions entered directly into RITS. In this paper, we analyse a dataset received from the Reserve Bank of Australia that includes all interbank transactions settled in RITS on an RTGS basis during five consecutive weekdays from 19 February 2007 inclusive, a week of relatively quiescent market conditions. The source, destination, and value of each transaction are known, which allows us to separate overnight loans from other transactions (nonloans) and reconstruct monetary flows between banks for every day in our sample. We conduct a novel analysis of the flow stability and examine the connection between loan and nonloan flows. Our aim is to understand the underlying causal mechanism connecting loan and nonloan flows. We find that the imbalances in the banks’ exchange settlement funds resulting from the daily flows of nonloan transactions are almost exactly counterbalanced by the flows of overnight loans. The correlation coefficient between loan and nonloan imbalances is about −0.9 on most days. Some flows that persist over two consecutive days can be highly variable, but overall the flows are moderately stable in value. The nonloan network is characterised by a large fraction of persistent flows, whereas only half of the flows persist over any two consecutive days in the loan network. Moreover, we observe an unusual degree of coherence between persistent loan flow values on Tuesday and Wednesday. We probe static topological properties of the Australian interbank network and find them consistent with those observed in other countries.     

Tests of the random walk hypothesis for financial data

We propose a method from the viewpoint of deterministic dynamical systems to investigate whether observed data follow a random walk (RW) and apply the method to several financial data. Our method is based on the previously proposed small-shuffle surrogate method. Hence, our method does not depend on the specific data distribution, although previously proposed methods depend on properties of the data distribution. The data we use are stock market (Standard & Poor's 500 in US market and Nikkei225 in Japanese market), exchange rate (British Pound/US dollar and Japanese Yen/US dollar), and commodity market (gold price and crude oil price). We found that these financial data are RW whose first differences are independently distributed random variables or time-varying random variables.     

Rumor Processes in Random Environment on \mathbb{N} and on Galton–Watson Trees

The aim of this paper is to study rumor processes in random environment. In a rumor process a signal starts from the stations of a fixed vertex (the root) and travels on a graph from vertex to vertex. We consider two rumor processes. In the firework process each station, when reached by the signal, transmits it up to a random distance. In the reverse firework process, on the other hand, stations do not send any signal but they “listen” for it up to a random distance. The first random environment that we consider is the deterministic 1-dimensional tree $\mathbb{N}$ with a random number of stations on each vertex; in this case the root is the origin of $\mathbb{N}$ . We give conditions for the survival/extinction on almost every realization of the sequence of stations. Later on, we study the processes on Galton–Watson trees with random number of stations on each vertex. We show that if the probability of survival is positive, then there is survival on almost every realization of the infinite tree such that there is at least one station at the root. We characterize the survival of the process in some cases and we give sufficient conditions for survival/extinction.     

Sector analysis for a FTSE portfolio of stocks

Using a portfolio of stocks from the London Stock Exchange FTSE100 index (FTSE), we study both the time dependence of their correlations and the normalized tree length of the associated minimal spanning tree (MST). The first four moments of the distribution of correlations and lengths of the tree are examined in detail and differences in behavior noted. For different economic groups and industries, clustering is evident. However, comparing the classification used prior to 2006 with that introduced in January 2006 it is clear that the new classification, apart from one or two notable exceptions, is much more compatible with the clustering obtained by the MST analysis. We finally compare the MST for real data with that obtained for a synthetic random market. The latter tree would seem more like the structure found by Coronnello et al. for trees based on high-frequency data.     

Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform

We analyze the correlations in patterns of trading for members of the Italian interbank trading platform e-MID. The trading strategy of a particular member institution is defined as the sequence of (intra-) daily net trading volumes within a certain semester. Based on this definition, we show that there are significant and persistent bilateral correlations between institutions’ trading strategies. In most semesters we find two clusters, with positively (negatively) correlated trading strategies within (between) clusters. We show that the two clusters mostly contain continuous net buyers and net sellers of money, respectively, and that cluster memberships of individual banks are highly persistent. Additionally, we highlight some problems related to our definition of trading strategies. Our findings add further evidence on the fact that preferential lending relationships on the micro-level lead to community structure on the macro-level.     

On the genre-fication of music: a percolation approach

We analyze web-downloaded data on people sharing their music library. By attributing to each music group usual music genres (Rock, Pop ...), and analysing correlations between music groups of different genres with percolation-idea based methods, we probe the reality of these subdivisions and construct a music genre cartography, with a tree representation. We also discuss an alternative objective way to classify music, that is based on the complex structure of the groups audience. Finally, a link is drawn with the theory of hidden variables in complex networks.     

Characterization of the branching structure of the lung from "macroscopic" pressure-volume measurements

We analyze the problem of fluid flow in a bifurcating structure containing random blockages that can be removed by fluid pressure. We introduce an asymmetric tree model and find that the predicted pressure-volume relation is connected to the distribution Pi(n) of the generation number n of the tree's terminal segments. We use this relation to explore the branching structure of the lung by analyzing experimental pressure-volume data from dog lungs. The Pi(n) extracted from the data using the model agrees well with experimental data on the branching structure. We can thus obtain information about the asymmetric structure of the lung from macroscopic, noninvasive pressure-volume measurements.     

(3+1)-dimensional localized self-accelerating Airy elegant Ince-Gaussian wave packets and their radiation forces in free space

We construct analytically linear self-accelerating Airy elegant Ince–Gaussian wave packet solutions from(3+1)-dimensional potential-free Schr odinger equation. These wave packets have elliptical geometry and show different characteristics when the parameters(p, m) and ellipticity ε are adjusted. We investigate these characteristics both analytically and numerically and give the 3-dimensional intensity and phase distribution of these wave packets. Lastly, we analyze the radiation forces on a Rayleigh dielectric particle. In addition, we also find an interesting phenomenon that if the energy distribution between every part of wave packets is uneven at the input plane, the energy will be transferred between every part in the process of transmission.     

Coupled Criticality Analysis of Inflation and Unemployment

In this paper, we focus on the critical periods in the economy that are characterized by unusual and large fluctuations in macroeconomic indicators, like those measuring inflation and unemployment. We analyze U.S. data for 70 years from 1948 until 2018. To capture their fluctuation essence, we concentrate on the non-Gaussianity of their distributions. We investigate how the non-Gaussianity of these variables affects the coupling structure of them. We distinguish “regular” from “rare” events, in calculating the correlation coefficient, emphasizing that both cases might lead to a different response of the economy. Through the “multifractal random wall” model, one can see that the non-Gaussianity depends on time scales. The non-Gaussianity of unemployment is noticeable only for periods shorter than one year; for longer periods, the fluctuation distribution tends to a Gaussian behavior. In contrast, the non-Gaussianities of inflation fluctuations persist for all time scales. We observe through the “bivariate multifractal random walk” that despite the inflation features, the non-Gaussianity of the coupled structure is finite for scales less than one year, drops for periods larger than one year, and becomes small for scales greater than two years. This means that the footprint of the monetary policies intentionally influencing the inflation and unemployment couple is observed only for time horizons smaller than two years. Finally, to improve some understanding of the effect of rare events, we calculate high moments of the variables’ increments for various q orders and various time scales. The results show that coupling with high moments sharply increases during crises.     

G-SIMS and SMILES: Simulated fragmentation pathways for identification of complex molecules, amino acids and peptides

G-SIMS is a powerful method for the identification of organics and complex molecules at surfaces. We have previously shown that the molecular structure may be reassembled from fragment ions by studying the evolution of G-SIMS intensities as the surface plasma, with effective temperature T_p, is varied, using a method known as G-SIMS-FPM.Here, we develop a novel approach, based on SMILES (Simplified Molecular Input Line Entry Specification), to assist the reassembly process in an automated way through evaluation of the fragmentation pathways for given molecular structures. A computer program takes a parent structure and goes through every possible fragmentation to provide a tree structure of fragmentation products and simulated fragmentation pathways. For any fragment it is then possible to identify the molecular structure, its mass and a pathway to the parent. We find that there is a good correlation with peak evolution in G-SIMS-FPM data and simulated pathways for two amino acids and a simple peptide. This significantly enhances the application of G-SIMS-FPM to unknown materials.     

Correlation structures in short-term variabilities of stock indices and exchange rates

Financial data usually show irregular fluctuations and some trends. We investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) among financial data from the viewpoint of deterministic dynamical systems. Our method is based on the small-shuffle surrogate method. The data we use are daily closing price of Standard & Poor's 500 and the volume, and daily foreign exchange rates, Euro/US Dollar (USD), British Pound/USD and Japanese Yen/USD. We found that these data are not independent.