Challenges of Climate Change and Bioenergy

Atmospheric concentration of the Green House Gases, Carbon Dioxide, Methane and Nitrous Oxide has increased largely since Industrial Revolution. Continued GHG emissions at or above current rates would cause further warming and induce many changes in global climate system. Climate changes will lead to more intense and longer droughts, water scarcity and many other problems then have been observed. For these reasons concept of development of bioenergy came into existance for climate change mitigation, energy security and agricultural and rural development. But can biofuels really deliver? There is also a negative facet of the story. There are several challenges posed by the increased usage of bioenergy. This paper will cover in detail the challenges posed by Climate Change and Bioenergy to our planet.Climate Change;Bioenergy;Food Crisis;Food Security

Cooperative Coded Data Dissemination for Wireless Sensor Networks

In this poster paper we present a data dissemination transmission abstraction for over the air programming (OAP) protocol which is fundamentally different from the previous hop by hop transmission protocols. Instead of imposing the greedy requirement that at least one node in the ith hop receives all packets before transmitting packets to the next hop and its neighbours, we take advantage of the spatial diversity and broadcast nature of wireless transmission to adopt a cooperative approach in which node broadcast whatever packets it has received with the expectation that it will recover the lost packets with high probability by overhearing the broadcast transmissions of its neighbours. The use of coded transmissions ensures that this does not lead to the broadcast storm problem. We validate the improved performance our of proposed transmission scheme with respect to the previous state of the art OAP protocols on a proof-of-concept two-hops TelosB wireless sensor network testbed.Comment: This paper appears in: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), London, 2016, pp. 1-

Associating factors in postmenopausal women with pelvic organ prolapse

I have reviewed with deep interest the publication "the evaluation between vitamin D level and pelvic organ prolapse in postmenopausal women." As a medical student, I appreciate all authors on their dedicated work and would like to express my gratitude for their findings. This study investigated the association between vitamin D levels and pelvic organ prolapse in postmenopausal women

A robust machine learning method for cell-load approximation in wireless networks

We propose a learning algorithm for cell-load approximation in wireless networks. The proposed algorithm is robust in the sense that it is designed to cope with the uncertainty arising from a small number of training samples. This scenario is highly relevant in wireless networks where training has to be performed on short time scales because of a fast time-varying communication environment. The first part of this work studies the set of feasible rates and shows that this set is compact. We then prove that the mapping relating a feasible rate vector to the unique fixed point of the non-linear cell-load mapping is monotone and uniformly continuous. Utilizing these properties, we apply an approximation framework that achieves the best worst-case performance. Furthermore, the approximation preserves the monotonicity and continuity properties. Simulations show that the proposed method exhibits better robustness and accuracy for small training sets in comparison with standard approximation techniques for multivariate data.Comment: Shorter version accepted at ICASSP 201

Extended compartmental absorption and transit model

Y u and Amidon described an extended compartmental absorption and transit (CAT) model to estimate saturable small intestinal absorption. This model simultaneously considers passive absorption, saturable absorption, degradation, and transit kinetics in the human small intestine Cefatrizine. is an Amino ’’?- lactam antibiotics ’’ that is absorbed by a carrier mediated system (through carriers), it means that follows saturated absorption and might be dose dependent absorption and might be dose dependent absorption to some extent. This type of absorption cannot be explained linear absorption model. This model must be mechanistic and quantitative to estimate dose dependent absorption and degradation in vivo. No saturated absorption occurs in ileum except the first compartment in which transporters continue to decrease from jejunum to ilium. Gastric emptying is considered to be first order, if residence time is 0.25h

Advances in correlation clustering

The task of clustering is to partition a given dataset in such a way that objects within a cluster are similar to each other while being dissimilar to objects from other clusters. One challenge to this task arises when dealing with datasets where the objects are characterized by an increased number of features. Objects within a cluster may exhibit correlations among a subset of features. In order to detect such clusters, within the past two decades significant contributions have been made which yielded a wealth of literature presenting algorithms for detecting clusters in arbitrarily oriented subspaces. Each of them approaches the correlation clustering task differently, by relying on different underlying models and techniques. Building on the current progress made, this work addresses the following aspects: First, it is dedicated to the research question of how to actually measure and therefore evaluate the quality of a correlation clustering. As an initial endeavor, it is investigated how far objectives for internal evaluation criteria can be derived from existing correlation clustering algorithms. The results from this approach, however, exhibited limitations rendering the derived internal evaluation measures not suitable. As a consequence endeavors have been made to identify commonalities among correlation clustering algorithms leading to a cost function that is introduced as an internal evaluation measure. Experiments illustrate its capability to assess clusterings based on aspects that are inherent to all correlation clustering algorithms studied so far. Second, among the existing correlation clustering algorithms, one takes a unique approach. Clusters are detected in a space spanned by the parameters of a given function, known as Hough space. The detection itself is achieved by finding so-called regions of interest (ROI) in Hough space. While the de- tection of ROIs in the existing algorithm performs well in most cases, there are conditions under which the runtime deteriorates, especially in data sets with high amounts of noise. In this work, two different novel strategies are proposed for ROI detection in Hough space, where it is elaborated on their individual strengths and weaknesses. Besides the aspect of ROI detection, endeavors are made to go beyond linearity by proposing approaches for detecting quadratic and periodic correlated clusters using Hough transform. Third, while there exist different views, like local and global correlated clusters, explorations are made in this work with the question in mind, in how far both views can be unified under a single concept. Finally, approaches are proposed and investigated that enhance the resilience of correlation clustering methods against outliers.Die Aufgabe von Clustering besteht darin einen gegebenen Datensatz so zu partitionieren dass Objekte innerhalb eines Clusters ähnlich zueinander sind, während diese unähnlich zu Objekten aus anderen Clustern sind. Eine Herausforderung bei dieser Aufgabe kommt auf, wenn man mit Daten umgeht, die sich durch eine erhöhte Anzahl an Merkmalen auszeichnen. Objekte innerhalb eines Clusters können Korrelationen zwischen Teilmengen von Merkmalen aufweisen. Um solche Cluster erkennen zu können, wurden innerhalb der vergangenen zwei Dekaden signifikante Beiträge geleistet. Darin werden Algorithmen vorgestellt, mit denen Cluster in beliebig ausgerichteten Unterräumen erkannt werden können. Jedes der Verfahren verfolgt zur Lösung der Correlation Clustering Aufgabenstellung unterschiedliche Ansätze indem sie sich auf unterschiedliche zugrunde liegende Modelle und Techniken stützen. Aufbauend auf die bislang gemachten Fortschritte, adressiert diese Arbeit die folgenden Aspekte: Zunächst wurde sich der Forschungsfrage gewidmet wie die Güte eines Correlation Clustering Ergebnisses bestimmt werden kann. In einer ersten Bestrebung wurde ermittelt in wie fern Ziele für interne Evaluationskriterien von bereits bestehenden Correlation Clustering Algorithmen abgeleitet werden können. Die Ergebnisse von dieser Vorgehensweise offenbarten Limitationen die einen Einsatz als interne Evaluations- maße ungeeignet erschienen ließen. Als Konsequenz wurden Bestrebungen unternommen Gemeinsamkeiten zwischen Correlation Clustering Algorithmen zu identifizieren, welche zu einer Kostenfunktion führten die als ein internes Evaluationsmaß eingeführt wurde. Die Experimente illustrieren die Fähigkeit, Clusterings auf Basis von Aspekten die inherent in allen bislang studierten Correlation Clustering Algorithmen vorliegen zu bewerten. Als einen zweiten Punkt nimmt ein Correlation Clustering Verfahren unter den bislang existierenden Methoden eine Sonderstellung ein. Die Cluster werden in einem Raum erkannt welches von den parmetern einer gegebenen Funktion aufgespannt werden welches als Hough Raum bekannt ist. Die Erkennung selbst wird durch das Finden von sogenannten "Regions of Interest" (ROI) im Hough Raum erreicht. Während die Erkennung von ROIs in dem bestehenden Verfahren in den meisten Fällen gut verläuft, gibt es Bedingungen, unter welchen die Laufzeit sich verschlechtert, insbesondere bei Datensätzen mit großen Mengen von Rauschen. In dieser Arbeit werden zwei verschiedene neue Strategien für die ROI Erkennung im Hough Raum vorgeschlagen, wobei auf die individuellen Stärken und Schwächen eingegangen wird. Neben dem Aspekt der ROI Erkennung sind Forschungen unternommen worden um über die Linearität der Correlation Cluster hinaus zu gehen, indem Verfahren entwickelt wurden, mit denen quadratisch- und periodisch korrelierte Cluster mittels Hough Transform erkannt werden können. Der dritte Aspekt dieser Arbeit widmet sich den sogenannten "views". Während es verschiedene views gibt wie z.B. bei lokal oder global korrelierten Clustern, wurden Forschungen unternommen mit der Fragestellung, in wie fern beide Ansichten unter einem einzigen gemeinsamen Konzept vereinigt werden können. Zuletzt sind Ansätze vorgeschlagen und untersucht worden welche die Resilienz von Correlation Clustering Methoden hinsichtlich Ausreißer erhöhen

A usability study of elliptic curves

In the recent years, the need of information security has rapidly increased due to an enormous growth of data transmission. In this thesis, we study the uses of elliptic curves in the cryptography. We discuss the elliptic curves over finite fields, attempts to attack; discrete logarithm, Pollard’s rho algorithm, baby-step giant-step algorithm, Pohlig-Hellman algorithm, function field sieve, and number field sieve. The main cryptographic reason to use elliptic curves over finite fields is to provide arbitrarily large finite cyclic groups having a computationally difficult discrete logarithm problem