MicroRNA–mRNA interaction network using TSK-type recurrent neural fuzzy network

MicroRNAs are short non-coding RNAs that can regulate gene expression during various crucial cell processes such as differentiation, proliferation and apoptosis. Changes in expression profiles of miRNA play an important role in the development of many cancers, including CRC. Therefore, the identification of cancer related miRNAs and their target genes are important for cancer biology research. In this paper, we applied TSK-type recurrent neural fuzzy network (TRNFN) to infer miRNA–mRNA association network from paired miRNA, mRNA expression profiles of CRC patients. We demonstrated that the method we proposed achieved good performance in recovering known experimentally verified miRNA–mRNA associations. Moreover, our approach proved successful in identifying 17 validated cancer miRNAs which are directly involved in the CRC related pathways. Targeting such miRNAs may help not only to prevent the recurrence of disease but also to control the growth of advanced metastatic tumors. Our regulatory modules provide valuable insights into the pathogenesis of cancer

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.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al‐SiCp metal matrix composites and ANOVA analysis

Metal matrix composites (MMC) having aluminium (Al) in the matrix phase and silicon carbide particles (SiCp) in reinforcement phase, ie Al‐SiCp type MMC, have gained popularity in the re‐cent past. In this competitive age, manufacturing industries strive to produce superior quality products at reasonable price. This is possible by achieving higher productivity while performing machining at optimum combinations of process variables. The low weight and high strength MMC are found suitable for variety of components

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

Re-Engineering Towed Arrays for Quality Enhancement - Network Based Towed Array

The Towed Array electronics is a multi-channel simultaneous real time high speed data acquisition system. Since its assembly is highly manpower intensive, the costs of arrays are prohibitive and therefore any attempt to reduce the manufacturing, assembly, testing and maintenance costs is a welcome proposition. The Network Based Towed Array is an innovative concept and its implementation has remarkably simplified the fabrication, assembly and testing and revolutionised the Towed Array scenario. The focus of this paper is to give a good insight into the Reliability aspects of Network Based Towed Array. A case study of the comparison between the conventional array and the network based towed array is also dealt with

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

Orthogonal polynomials and recurrence equations, operator equations and factorization

This article surveys the classical orthogonal polynomial systems of the Hahn class, which are solutions of second-order differential, difference or q-difference equations. Orthogonal families satisfy three-term recurrence equations. Example applications of an algorithm to determine whether a three-term recurrence equation has solutions in the Hahn class - implemented in the computer algebra system Maple - are given. Modifications of these families, in particular associated orthogonal systems, satisfy fourth-order operator equations. A factorization of these equations leads to a solution basis.

Die Privatisierung der Öffentlichen Personennahverkehrs (ÖPNV) und die damit verbundene Problematik des marktwirtschaftlichen und interventionistischen Dualismus am Beispiel des Schienenpersonennahverkehrs (SPNV) in der Fläche

Der SPNV als Bestandteil des ÖPNV bildet einen integralen Bestandteil der öffentlichen Daseinsvorsorge. Insbesondere Flächenregionen abseits urbaner Ballungszentren erhalten durch den SPNV sowohl ökonomisch als auch soziokulturell wichtige Impulse, so dass die Zukunftsfähigkeit dieser Verkehrsart durch geeignete Gestaltungsmaßnahmen zu sichern ist. ZIELE: Die Arbeit verfolgte das Ziel, derartige Gestaltungsmaßnahmen sowohl grundlagentheoretisch herzuleiten als auch in ihrer konkreten Ausformung für die verkehrswirtschaftliche Praxis zu beschreiben. Abgezielt wurde insofern auf strukturelle Konzepte als auch praktische Einzelmaßnahmen. Der Schwerpunkt der Analyse erstreckte sich dabei auf Deutschland, wobei jedoch auch verkehrsbezogene Privatisierungserfahrungen aus anderen europäischen Staaten und den USA berücksichtigt wurden. METHODEN: Ausgewertet wurden deutschsprachige als auch internationale Literatur primär verkehrswissenschaftlicher Ausrichtung sowie Fallbeispiele verkehrswirtschaftlicher Privatisierung. Darüber hinaus wurden Entscheidungsträger der Deutschen Bahn (DB) und DB-externe Eisenbahnexperten interviewt. Eine Gruppe 5 DB-interner und 5 DB-externer Probanden nahm zusätzlich an einer standardisierten Erhebung zur Einschätzung struktureller und spezifischer Gestaltungsmaßnahmen für den SPNV teil. ERGEBNISSE: In struktureller Hinsicht ist die Eigentums- und Verfügungsregelung für das gesamte deutsche Bahnwesen und den SPNV kritisch zu bewerten, da der dominante Eisenbahninfrastrukturbetreiber (EIU) in Form der DB Netz AG und die das Netz nutzenden Eisenbahnverkehrs-Unternehmen (EVUs, nach wie vor zumeist DB-Bahnen) innerhalb der DB-Holding konfundiert sind. Hieraus ergeben sich Diskriminierungspotenziale vor allem gegenüber DB-externen EVUs. Diese Situation entspricht keiner echten Netz-Betriebs-Trennung, die wettbewerbstheoretisch sinnvoll wäre und nachhaltige Konkurrenz verschiedener EVUs ermöglichen würde. Die seitens der DB zur Festigung bestehender Strukturen vertretene Argumentation, wonach Netz und Betrieb eine untrennbare Einheit (Synergie) bilden sollten, ist weder wettbewerbstheoretisch noch auf der Ebene technischer Aspekte akzeptabel. Vielmehr werden durch die gegenwärtige Verquickung der als Quasimonopol fungierenden Netzebene mit der Ebene der EVU-Leistungen Innovationspotenziale eingeschränkt. Abgesehen von der grundsätzlichen Notwendigkeit einer konsequenten Netz-Betriebs-Trennung und dezentraler Strukturen sind Ausschreibungen (faktisch öffentliches Verfahren) für den Betrieb der SPNV-Strecken als Handlungsansatz zu berücksichtigen. Wettbewerb kann auf diese Weise gleichsam an der Quelle einer EVU-Leistung ansetzen, wobei politische und administrative Widerstände gegen dieses Konzept derzeit noch unverkennbar sind. Hinsichtlich infrastruktureller Maßnahmen für den SPNV ist insbesondere das sog. "Betreibermodell" sinnvoll, bei dem sich das übernehmende EIU im Sinne seiner Kernkompetenzen auf den Betrieb konzentriert. Die Verantwortung für bauliche Maßnahmen sowie die Instandhaltung der Strecken liegt beim Betreiber, welcher derartige Leistungen am Markt einkaufen kann (Kostensenkungspotenzial). Bei Abgabeplanungen der DB Netz AG für eine Strecke ist mithin für die Auswahl eines Betreibers auf den genannten Ausschreibungsmodus zurückzugreifen. Als kostensenkende Einzelmaßnahmen zur Zukunftssicherung des SPNV werden abschließend insbesondere die Optimierung des Fahrzeugumlaufes sowie der Einsatz von Triebwagen anstatt lokbespannter Züge und die weiter forcierte Ausrichtung auf die kundenorientierte Attraktivitätssteigerung des SPNV empfohlen. SCHLUSSFOLGERUNGEN: Handlungsansätze für eine langfristige Sicherung des SPNV können nicht aus der Realisierung von Extrempositionen (staatlicher Interventionismus versus Liberalismus) resultieren, sondern nur aus einem pragmatischen Ausgleich zwischen beiden Polen. Dabei erscheint eine Verschiebung hin zum marktwirtschaftlichen Pol sinnvoll, um durch die Nutzung wettbewerbsbezogener Impulse Kostensenkungen und Effizienzsteigerungen für den SPNV herbeizuführen.

Contribution of the exact Breit operator plus the electron state-dependent screening to the multiplet and fine structure of muon-electron atoms

We investigate for very general cases the multiplet and fine structure splitting of muonelectron atoms arising from the coupling of the electron and muon angular momenta, including the effect of the Breit operator plus the electron state-dependent screening. Although many conditions have to be fulfilled simultaneously to observe these effeets, it should be possible to measure them in the 6h- 5g muonic transition in the Sn region.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of the systems. We consider their underlying data structures – socalled folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag co-occurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

Network properties of folksonomies

Social resource sharing systems like YouTube and del.icio.us have acquired a large number of users within the last few years. They provide rich resources for data analysis, information retrieval, and knowledge discovery applications. A first step towards this end is to gain better insights into content and structure of these systems. In this paper, we will analyse the main network characteristics of two of these systems. We consider their underlying data structures – so-called folksonomies – as tri-partite hypergraphs, and adapt classical network measures like characteristic path length and clustering coefficient to them. Subsequently, we introduce a network of tag cooccurrence and investigate some of its statistical properties, focusing on correlations in node connectivity and pointing out features that reflect emergent semantics within the folksonomy. We show that simple statistical indicators unambiguously spot non-social behavior such as spam.

Semantic network analysis of ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, beside consistency checking, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as (labeled, directed) graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures in general currently receive high attention in the Semantic Web community, there are only very few SNA applications up to now, and virtually none for analyzing the structure of ontologies. We illustrate in this paper the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality based on Hermitian matrices, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size.

Elimination in Operator Algebras

A large class of special functions are solutions of systems of linear difference and differential equations with polynomial coefficients. For a given function, these equations considered as operator polynomials generate a left ideal in a noncommutative algebra called Ore algebra. This ideal with finitely many conditions characterizes the function uniquely so that Gröbner basis techniques can be applied. Many problems related to special functions which can be described by such ideals can be solved by performing elimination of appropriate noncommutative variables in these ideals. In this work, we mainly achieve the following: 1. We give an overview of the theoretical algebraic background as well as the algorithmic aspects of different methods using noncommutative Gröbner elimination techniques in Ore algebras in order to solve problems related to special functions. 2. We describe in detail algorithms which are based on Gröbner elimination techniques and perform the creative telescoping method for sums and integrals of special functions. 3. We investigate and compare these algorithms by illustrative examples which are performed by the computer algebra system Maple. This investigation has the objective to test how far noncommutative Gröbner elimination techniques may be efficiently applied to perform creative telescoping.

Assembly Guidance in Augmented Reality Environments Using a Virtual Interactive Tool

The application of augmented reality (AR) technology for assembly guidance is a novel approach in the traditional manufacturing domain. In this paper, we propose an AR approach for assembly guidance using a virtual interactive tool that is intuitive and easy to use. The virtual interactive tool, termed the Virtual Interaction Panel (VirIP), involves two tasks: the design of the VirIPs and the real-time tracking of an interaction pen using a Restricted Coulomb Energy (RCE) neural network. The VirIP includes virtual buttons, which have meaningful assembly information that can be activated by an interaction pen during the assembly process. A visual assembly tree structure (VATS) is used for information management and assembly instructions retrieval in this AR environment. VATS is a hierarchical tree structure that can be easily maintained via a visual interface. This paper describes a typical scenario for assembly guidance using VirIP and VATS. The main characteristic of the proposed AR system is the intuitive way in which an assembly operator can easily step through a pre-defined assembly plan/sequence without the need of any sensor schemes or markers attached on the assembly components.