Relation between the eigenfrequencies of Bogoliubov excitations of Bose-Einstein condensates and the eigenvalues of the Jacobian in a time-dependent variational approach

We study the relation between the eigenfrequencies of the Bogoliubov excitations of Bose-Einstein condensates, and the eigenvalues of the Jacobian stability matrix in a variational approach which maps the Gross-Pitaevskii equation to a system of equations of motion for the variational parameters. We do this for Bose-Einstein condensates with attractive contact interaction in an external trap, and for a simple model of a self-trapped Bose-Einstein condensate with attractive 1/r interaction. The stationary solutions of the Gross-Pitaevskii equation and Bogoliubov excitations are calculated using a finite-difference scheme. The Bogoliubov spectra of the ground and excited state of the self-trapped monopolar condensate exhibits a Rydberg-like structure, which can be explained by means of a quantum defect theory. On the variational side, we treat the problem using an ansatz of time-dependent coupled Gaussians combined with spherical harmonics. We first apply this ansatz to a condensate in an external trap without long-range interaction, and calculate the excitation spectrum with the help of the time-dependent variational principle. Comparing with the full-numerical results, we find a good agreement for the eigenfrequencies of the lowest excitation modes with arbitrary angular momenta. The variational method is then applied to calculate the excitations of the self-trapped monopolar condensates, and the eigenfrequencies of the excitation modes are compared.Comment: 15 pages, 12 figure

Multi-variate analyses of flood loss in Can Tho city, Mekong delta

Floods in the Mekong delta are recurring events and cause substantial losses to the economy. Sea level rise and increasing precipitation during the wet season result in more frequent floods. For effective flood risk management, reliable losses and risk analyses are necessary. However, knowledge about damaging processes and robust assessments of flood losses in the Mekong delta are scarce. In order to fill this gap, we identify and quantify the effects of the most important variables determining flood losses in Can Tho city through multi-variate statistical analyses. Our analysis is limited to the losses of residential buildings and contents. Results reveal that under the specific flooding characteristics in the Mekong delta with relatively well-adapted households, long inundation durations and shallow water depths, inundation duration is more important than water depth for the resulting loss. However, also building and content values, floor space of buildings and building quality are important loss-determining variables. Human activities like undertaking precautionary measures also influence flood losses. The results are important for improving flood loss modelling and, consequently, flood risk assessments in the Mekong delta

Flood loss models and risk analysis for private households in can Tho City, Vietnam

Vietnam has a long history and experience with floods. Flood risk is expected to increase further due to climatic, land use and other global changes. Can Tho City, the cultural and economic center of the Mekong delta in Vietnam, is at high risk of flooding. To improve flood risk analyses for Vietnam, this study presents novel multi-variable flood loss models for residential buildings and contents and demonstrates their application in a flood risk assessment for the inner city of Can Tho. Cross-validation reveals that decision tree based loss models using the three input variables water depth, flood duration and floor space of building are more appropriate for estimating building and contents loss in comparison with depth-damage functions. The flood risk assessment reveals a median expected annual flood damage to private households of US$3340 thousand for the inner city of Can Tho. This is approximately 2.5%of the total annual income of households in the study area. For damage reduction improved flood risk management is required for the Mekong Delta, based on reliable damage and risk analyses

Umsatz und Speicherung von Bodenkohlenstoff entlang eines Breitengradgradienten in Wäldern der sibirischen Taiga und der Tundra

Boreale Wälder nehmen etwa 17% der Landfläche der Erde ein und sind ein wichtiger Speicher für organischen Kohlenstoff (OC). Es wird erwartet, dass sich diese Ökosysteme durch den Klimawandel stark verändern, wobei mit zunehmenden Störungen durch Feuer, und einer Expansion der Wälder in die angrenzende Tundra im Norden gerechnet wird. Die Folgen für die C-Speicherung im Boden sind ungewiss, da wenig über die OC-Vorräte, deren Stabilität und Umsatzzeiten in den wenig zugänglichen Wäldern Sibiriens und der angrenzenden Tundra bekannt ist. Deshalb haben wir insgesamt 20 Bodenprofile bis 30 cm Tiefe entlang des 96ten Längengrades von Breitengrad 55 bis 73 untersucht, von denen 13 im borealen Wald und 7 in der Tundra lagen. Neben der Bestimmung der OC- und N-Vorräte wurden eine Dichtefraktionierung der Böden durchgeführt und die 14C-Gehalte bestimmt. Die Ergebnisse zeigen, dass im Waldbereich der Anteil des im Boden gespeicherten OC mit zunehmendem Breitengrad von 40% bei 55° auf über 90% bei 67° zunimmt. Trotz einer Abnahme der mittleren Temperatur von über 10°C und der Baum-Biomasse, findet man weder in der Auflage noch im Mineralboden eine Abhängigkeit der OC-Vorräte oder des 14C-Gehalts vom Breitengrad. Dies kann für das 14C damit zusammenhängen, dass insgesamt langsamere Umsatzzeiten bei niedrigen Temperaturen zu einem Anstieg des 14C-Gehalts von schnellen Pools (Bombenpeak), aber einer Abnahme des 14C-Gelhalts von langsamen Pools (radioaktiver Zerfall) führen würden, so dass sich im Gesamtboden beides ausgleichen kann. Zudem sind die Umsatzzeiten der normalerweise rasch abbaubaren leichten Fraktion durch pyrogenen Kohlenstoff überprägt, so dass in den Oberböden aller Waldstandorte bis zum 64sten Breitengrad die leichte Fraktion älter ist als die mineralassoziierte. Dies zeigt, dass Störungen durch Feuer die Bildung von stabilem OC in diesen Systemen beeinflussen. Trends zu Änderungen beobachten wir aber erst beim Übergang von der Taiga zur Tundra um den 67sten Breitengrad, wenn die 14C-Gehalte der organischen Auflage von 112±2 auf 77±27‰ und im Mineralboden (0-30 cm) von -132±29 auf -240±173‰ abnehmen. Dabei ist die räumliche Variabilität der 14C-Gehalte in der Tundra deutlich höher ist als im Wald. Die niedrigeren 14C-Gehalte der Böden der Wälder Sibiriens im Vergleich zu Europäischen Wäldern der gemäßigten Breiten deuten auf eine höhere mittlere Stabilität des OC in Sibirien hin. Ergebnisse der Dichtefraktionierung der Tundra-Standorte stehen noch aus

Bayesian Data-Driven approach enhances synthetic flood loss models

Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk

Improving effectiveness of honeypots: predicting targeted destination port numbers during attacks using J48 algorithm

During recent years, there has been an increase in cyber-crime and cybercriminal activities around the world and as countermeasures, effective attack prevention and detection mechanisms are needed. A popular tool to augment existing attack detection mechanisms is the Honeypot. It serves as a decoy for luring attackers, with the purpose to accumulate essential details about the intruder and techniques used to compromise systems. In this endeavor, such tools need to effectively listen and keep track of ports on hosts such as servers and computers within networks. This paper investigates, analyzes and predicts destination port numbers targeted by attackers in order to improve the effectiveness of honeypots. To achieve the purpose of this paper, the J48 decision tree classifier was applied on a database containing information on cyber-attacks. Results revealed insightful information on key destination port numbers targeted by attackers, in addition to how these targeted ports vary within different regions around the world