Assessing an intuitive condition for stability under a range of traffic conditions via a generalised Lu-Kumar network

We argue the importance both of developing simple sufficientconditions for the stability of general multiclass queueing networks and also of assessing such conditions under a range of assumptions on the weight of the traffic flowing between service stations. To achieve the former, we review a peak-rate stability condition and extend its range of application and for the latter, we introduce a generalisation of the Lu-Kumar network on which the stability condition may be tested for a range of traffic configurations. The peak-rate condition is close to exact when the between-station traffic is light, but degrades as this traffic increases.

Search for supersymmetry in events with opposite-sign dileptons and missing transverse energy using an artificial neural network

In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from a standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4.98 fb-1 of proton-proton collisions at the center-of-mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (EÌ̧T>40 GeV) and total hadronic transverse energy (HT>120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observations, yielding limits in the context of the constrained minimal supersymmetric standard model and on a set of simplified models. © 2013 CERN.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

The diffusion of Co⠶溩 Zrâ â Tiâ â alloy /|nS. Kumar. -- 260 St Catharines [Ont.] : Dept. of Physics, Brock University,

The diffusion of Co60 in the body centered cubic beta phase of a ZrSOTi SO alloy has been studied at 900°, 1200°, and 1440°C. The results confirm earlier unpublished data obtained by Kidson17 • The temperature dependence of the diffusion coefficient is unusual and suggests that at least two and possibly three mechanisms may be operative Annealing of the specimen in the high B.C.C. region prior to the deposition of the tracer results in a large reduction in the diffusion coefficient. The possible significance of this effect is discussed in terms of rapid transport along dislocation network.

RMAC-M: Extending the R-MAC Protocol for an Energy Efficient, Delay Tolerant Underwater Acoustic Sensor Network application with a mobile data mule node

One of the major applications of underwater acoustic sensor networks (UWASN) is ocean environment monitoring. Employing data mules is an energy efficient way of data collection from the underwater sensor nodes in such a network. A data mule node such as an autonomous underwater vehicle (AUV) periodically visits the stationary nodes to download data. By conserving the power required for data transmission over long distances to a remote data sink, this approach extends the network life time. In this paper we propose a new MAC protocol to support a single mobile data mule node to collect the data sensed by the sensor nodes in periodic runs through the network. In this approach, the nodes need to perform only short distance, single hop transmission to the data mule. The protocol design discussed in this paper is motivated to support such an application. The proposed protocol is a hybrid protocol, which employs a combination of schedule based access among the stationary nodes along with handshake based access to support mobile data mules. The new protocol, RMAC-M is developed as an extension to the energy efficient MAC protocol R-MAC by extending the slot time of R-MAC to include a contention part for a hand shake based data transfer. The mobile node makes use of a beacon to signal its presence to all the nearby nodes, which can then hand-shake with the mobile node for data transfer. Simulation results show that the new protocol provides efficient support for a mobile data mule node while preserving the advantages of R-MAC such as energy efficiency and fairness.

Optically transparent magnetic nanocomposites based on encapsulated Fe3O4 nanoparticles in a sol–gel silica network

Composite Fe3O4–SiO2 materials were prepared by the sol–gel method with tetraethoxysilane and aqueous-based Fe3O4 ferrofluids as precursors. The monoliths obtained were crack free and showed both optical and magnetic properties. The structural properties were determined by infrared spectroscopy, x-ray diffractometry and transmission electron microscopy. Fe3O4 particles of 20 nm size lie within the pores of the matrix without any strong Si–O–Fe bonding. The well established silica network provides effective confinement to these nanoparticles. The composites were transparent in the 600–800 nm regime and the field dependent magnetization curves suggest that the composite exhibits superparamagnetic characteristics

Implementation of a Neural Network Classifier for Noise Sources in the Ocean

The paper investigates the feasibility of implementing an intelligent classifier for noise sources in the ocean, with the help of artificial neural networks, using higher order spectral features. Non-linear interactions between the component frequencies of the noise data can give rise to certain phase relations called Quadratic Phase Coupling (QPC), which cannot be characterized by power spectral analysis. However, bispectral analysis, which is a higher order estimation technique, can reveal the presence of such phase couplings and provide a measure to quantify such couplings. A feed forward neural network has been trained and validated with higher order spectral features

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 3: Network Theory and Terminology

In this class, we will discuss network theory fundamentals, including concepts such as diameter, distance, clustering coefficient and others. We will also discuss different types of networks, such as scale-free networks, random networks etc. Readings: Graph structure in the Web, A. Broder and R. Kumar and F. Maghoul and P. Raghavan and S. Rajagopalan and R. Stata and A. Tomkins and J. Wiener Computer Networks 33 309--320 (2000) [Web link, Alternative Link] Optional: The Structure and Function of Complex Networks, M.E.J. Newman, SIAM Review 45 167--256 (2003) [Web link] Original course at: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

Convergencia de vías de señalización en un modelo de apoptosis

La sepsis es un evento inflamatorio generalizado del organismo inducido por un daño causado generalmente por un agente infeccioso. El patógeno más frecuentemente asociado con esta entidad es el Staphylococcus aureus, responsable de la inducción de apoptosis en células endoteliales debida a la producción de ceramida. Se ha descrito el efecto protector de la proteína C activada (PCA) en sepsis y su relación con la disminución de la apoptosis de las células endoteliales. En este trabajo se analizó la activación de las quinasas AKT, ASK1, SAPK/JNK y p38 en un modelo de apoptosis endotelial usando las técnicas de Western Blotting y ELISA. Las células endoteliales (EA.hy926), se trataron con C2-ceramida (130μM) en presencia de inhibidores químicos de cada una de estas quinasas y PCA. La supervivencia de las células en presencia de inhibidores químicos y PCA fue evaluada por medio de ensayos de activación de las caspasas 3, 7 y 9, que verificaban la muerte celular por apoptosis. Los resultados evidencian que la ceramida reduce la activación de AKT y aumenta la activación de las quinasas ASK, SAPK/JNK y p38, en tanto que PCA ejerce el efecto contrario. Adicionalmente se encontró que la tiorredoxina incrementa la activación/fosforilación de AKT, mientras que la quinasa p38 induce la defosforilación de AKT.

The automatic layout model in the sequencing of construction activities of multi-objective diaphragm wall unit by using self-learning neural network

Foundation construction process has been an important key point in a successful construction engineering. The frequency of using diaphragm wall construction method among many deep excavation construction methods in Taiwan is the highest in the world. The traditional view of managing diaphragm wall unit in the sequencing of construction activities is to establish each phase of the sequencing of construction activities by heuristics. However, it conflicts final phase of engineering construction with unit construction and effects planning construction time. In order to avoid this kind of situation, we use management of science in the study of diaphragm wall unit construction to formulate multi-objective combinational optimization problem. Because the characteristic (belong to NP-Complete problem) of problem mathematic model is multi-objective and combining explosive, it is advised that using the 2-type Self-Learning Neural Network (SLNN) to solve the N=12, 24, 36 of diaphragm wall unit in the sequencing of construction activities program problem. In order to compare the liability of the results, this study will use random researching method in comparison with the SLNN. It is found that the testing result of SLNN is superior to random researching method in whether solution-quality or Solving-efficiency.

A socio-technical network approach to the adoption of low and zero carbon technologies in new housing

Housing in the UK accounts for 30.5% of all energy consumed and is responsible for 25% of all carbon emissions. The UK Government’s Code for Sustainable Homes requires all new homes to be zero carbon by 2016. The development and widespread diffusion of low and zero carbon (LZC) technologies is recognised as being a key solution for housing developers to deliver against this zero-carbon agenda. The innovation challenge to design and incorporate these technologies into housing developers’ standard design and production templates will usher in significant technical and commercial risks. In this paper we report early results from an ongoing Engineering and Physical Sciences Research Council project looking at the innovation logic and trajectory of LZC technologies in new housing. The principal theoretical lens for the research is the socio-technical network approach which considers actors’ interests and interpretative flexibilities of technologies and how they negotiate and reproduce ‘acting spaces’ to shape, in this case, the selection and adoption of LZC technologies. The initial findings are revealing the form and operation of the technology networks around new housing developments as being very complex, involving a range of actors and viewpoints that vary for each housing development.