Long distance calling? Spatial preference patterns in enterprise microblogging in the retail industry

We examine enterprise social network usage data obtained from a community of store managers in a leading Australian retail organization, over a period of fifteen months. Our interest in examining this data is in spatial preferences by the network users, that is, to ascertain who is communicating with whom and where. We offer several contrasting theoretical perspectives for spatial preference patterns and examine these against data collected from over 12,000 messages exchanged between 530 managers in 897 stores. Our findings show that interactions can generally be characterized by individual preferences for local communication but also that two different user communities exist – locals and globals. We develop empirical profiles for these social network user communities and outline implications for theories on spatial influences on communication behaviours on enterprise social networks.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Protection of a Static Frequency Converter fed traction network, a case study

Aurizon, Australia's largest rail freight operator, is introducing the Static Frequency Converter (SFC) technology into its electric railway network as part of the Bauhinia Electrification Project. The introduction of SFCs has significant implications on the protection systems of the 50kV traction network. The traditional distance protection calculation method does not work in this configuration because of the effect that the SFC in combination with the remote grid has on the apparent impedance, and was substantially reviewed. The standard overcurrent (OC) protection scheme is not suitable due to the minimum fault level being below the maximum load level and was revised to incorporate directionality and under-voltage inhibit. Delta protection was reviewed to improve sensitivity. A new protection function was introduced to prevent back-feeding faults in the transmission network through the grid connection. Protection inter-tripping was included to ensure selectivity between the SFC protection and the system downstream.

Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis

Tuberculosis continues to be a major health challenge, warranting the need for newer strategies for therapeutic intervention and newer approaches to discover them. Here, we report the identification of efficient metabolism disruption strategies by analysis of a reactome network. Protein-protein dependencies at a genome scale are derived from the curated metabolic network, from which insights into the nature and extent of inter-protein and inter-pathway dependencies have been obtained. A functional distance matrix and a subsequent nearness index derived from this information, helps in understanding how the influence of a given protein can pervade to the metabolic network. Thus, the nearness index can be viewed as a metabolic disruptability index, which suggests possible strategies for achieving maximal metabolic disruption by inhibition of the least number of proteins. A greedy approach has been used to identify the most influential singleton, and its combination with the other most pervasive proteins to obtain highly influential pairs, triplets and quadruplets. The effect of deletion of these combinations on cellular metabolism has been studied by flux balance analysis. An obvious outcome of this study is a rational identification of drug targets, to efficiently bring down mycobacterial metabolism.

Jointly optimal power control and routing for a single cell, dense, ad hoc wireless network

We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Theta(opt) bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form d(opt)((P) over bar (t)) x Theta(opt) with d(opt) scaling as (P) over bar (1/eta)(t), where (P) over bar (t) is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then provide a simple characterisation of the optimal operating point.

Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination

This paper aims at evaluating the methods of multiclass support vector machines (SVMs) for effective use in distance relay coordination. Also, it describes a strategy of supportive systems to aid the conventional protection philosophy in combating situations where protection systems have maloperated and/or information is missing and provide selective and secure coordinations. SVMs have considerable potential as zone classifiers of distance relay coordination. This typically requires a multiclass SVM classifier to effectively analyze/build the underlying concept between reach of different zones and the apparent impedance trajectory during fault. Several methods have been proposed for multiclass classification where typically several binary SVM classifiers are combined together. Some authors have extended binary SVM classification to one-step single optimization operation considering all classes at once. In this paper, one-step multiclass classification, one-against-all, and one-against-one multiclass methods are compared for their performance with respect to accuracy, number of iterations, number of support vectors, training, and testing time. The performance analysis of these three methods is presented on three data sets belonging to training and testing patterns of three supportive systems for a region and part of a network, which is an equivalent 526-bus system of the practical Indian Western grid.

Collective Network Capability in International Project Business Networks - A Case Study of the Business Network for the Ashanti Electrification Project in Ghana

Despite thirty years of research in interorganizational networks and project business within the industrial networks approach and relationship marketing, collective capability of networks of business and other interorganizational actors has not been explicitly conceptualized and studied within the above-named approaches. This is despite the fact that the two approaches maintain that networking is one of the core strategies for the long-term survival of market actors. Recently, many scholars within the above-named approaches have emphasized that the survival of market actors is based on the strength of their networks and that inter-firm competition is being replaced by inter-network competition. Furthermore, project business is characterized by the building of goal-oriented, temporary networks whose aims, structures, and procedures are clarified and that are governed by processes of interaction as well as recurrent contracts. This study develops frameworks for studying and analysing collective network capability, i.e. collective capability created for the network of firms. The concept is first justified and positioned within the industrial networks, project business, and relationship marketing schools. An eclectic source of conceptual input is based on four major approaches to interorganizational business relationships. The study uses qualitative research and analysis, and the case report analyses the empirical phenomenon using a large number of qualitative techniques: tables, diagrams, network models, matrices etc. The study shows the high level of uniqueness and complexity of international project business. While perceived psychic distance between the parties may be small due to previous project experiences and the benefit of existing relationships, a varied number of critical events develop due to the economic and local context of the recipient country as well as the coordination demands of the large number of involved actors. The study shows that the successful creation of collective network capability led to the success of the network for the studied project. The processes and structures for creating collective network capability are encapsulated in a model of governance factors for interorganizational networks. The theoretical and management implications are summarized in seven propositions. The core implication is that project business success in unique and complex environments is achieved by accessing the capabilities of a network of actors, and project management in such environments should be built on both contractual and cooperative procedures with local recipient country parties.

Lessons from the Sparse Sensor Network Deployment in Rural India

We discuss the key issues in the deployment of sparse sensor networks. The network monitors several environment parameters and is deployed in a semi-arid region for the benefit of small and marginal farmers. We begin by discussing the problems of an existing unreliable 1 sq km sparse network deployed in a village. The proposed solutions are implemented in a new cluster. The new cluster is a reliable 5 sq km network. Our contributions are two fold. Firstly, we describe a. novel methodology to deploy a sparse reliable data gathering sensor network and evaluate the ``safe distance'' or ``reliable'' distance between nodes using propagation models. Secondly, we address the problem of transporting data from rural aggregation servers to urban data centres. This paper tracks our steps in deploying a sensor network in a village,in India, trying to provide better diagnosis for better crop management. Keywords - Rural, Agriculture, CTRS, Sparse.

Communication pathways from the anti-codon region to the aminoacylation site in Methionyl tRNA Synthetase: Molecular dynamics simulations and the structure network analysis

The enzymes of the family of tRNA synthetases perform their functions with high precision by synchronously recognizing the anticodon region and the aminoacylation region, which are separated by ?70 in space. This precision in function is brought about by establishing good communication paths between the two regions. We have modeled the structure of the complex consisting of Escherichia coli methionyl-tRNA synthetase (MetRS), tRNA, and the activated methionine. Molecular dynamics simulations have been performed on the modeled structure to obtain the equilibrated structure of the complex and the cross-correlations between the residues in MetRS have been evaluated. Furthermore, the network analysis on these simulated structures has been carried out to elucidate the paths of communication between the activation site and the anticodon recognition site. This study has provided the detailed paths of communication, which are consistent with experimental results. Similar studies also have been carried out on the complexes (MetRS + activated methonine) and (MetRS + tRNA) along with ligand-free native enzyme. A comparison of the paths derived from the four simulations clearly has shown that the communication path is strongly correlated and unique to the enzyme complex, which is bound to both the tRNA and the activated methionine. The details of the method of our investigation and the biological implications of the results are presented in this article. The method developed here also could be used to investigate any protein system where the function takes place through long-distance communication.

On Network-Error Correcting Convolutional Codes under the BSC Edge Error Model

Convolutional network-error correcting codes (CNECCs) are known to provide error correcting capability in acyclic instantaneous networks within the network coding paradigm under small field size conditions. In this work, we investigate the performance of CNECCs under the error model of the network where the edges are assumed to be statistically independent binary symmetric channels, each with the same probability of error pe(0 <= p(e) < 0.5). We obtain bounds on the performance of such CNECCs based on a modified generating function (the transfer function) of the CNECCs. For a given network, we derive a mathematical condition on how small p(e) should be so that only single edge network-errors need to be accounted for, thus reducing the complexity of evaluating the probability of error of any CNECC. Simulations indicate that convolutional codes are required to possess different properties to achieve good performance in low p(e) and high p(e) regimes. For the low p(e) regime, convolutional codes with good distance properties show good performance. For the high p(e) regime, convolutional codes that have a good slope ( the minimum normalized cycle weight) are seen to be good. We derive a lower bound on the slope of any rate b/c convolutional code with a certain degree.

Network Error Correction for Unit-Delay, Memory-Free Networks Using Convolutional Codes

A single source network is said to be memory-free if all of the internal nodes (those except the source and the sinks) do not employ memory but merely send linear combinations of the symbols received at their incoming edges on their outgoing edges. In this work, we introduce network-error correction for single source, acyclic, unit-delay, memory-free networks with coherent network coding for multicast. A convolutional code is designed at the source based on the network code in order to correct network- errors that correspond to any of a given set of error patterns, as long as consecutive errors are separated by a certain interval which depends on the convolutional code selected. Bounds on this interval and the field size required for constructing the convolutional code with the required free distance are also obtained. We illustrate the performance of convolutional network error correcting codes (CNECCs) designed for the unit-delay networks using simulations of CNECCs on an example network under a probabilistic error model.

Network Rhythms Influence the Relationship between Spike-Triggered Local Field Potential and Functional Connectivity

Characterizing the functional connectivity between neurons is key for understanding brain function. We recorded spikes and local field potentials (LFPs) from multielectrode arrays implanted in monkey visual cortex to test the hypotheses that spikes generated outward-traveling LFP waves and the strength of functional connectivity depended on stimulus contrast, as described recently. These hypotheses were proposed based on the observation that the latency of the peak negativity of the spike-triggered LFP average (STA) increased with distance between the spike and LFP electrodes, and the magnitude of the STA negativity and the distance over which it was observed decreased with increasing stimulus contrast. Detailed analysis of the shape of the STA, however, revealed contributions from two distinct sources-a transient negativity in the LFP locked to the spike (similar to 0 ms) that attenuated rapidly with distance, and a low-frequency rhythm with peak negativity similar to 25 ms after the spike that attenuated slowly with distance. The overall negative peak of the LFP, which combined both these components, shifted from similar to 0 to similar to 25 ms going from electrodes near the spike to electrodes far from the spike, giving an impression of a traveling wave, although the shift was fully explained by changing contributions from the two fixed components. The low-frequency rhythm was attenuated during stimulus presentations, decreasing the overall magnitude of the STA. These results highlight the importance of accounting for the network activity while using STAs to determine functional connectivity.

Jointly Optimal Power Control and Hop Length for a Single Cell, Dense, Ad hoc Wireless Network

We consider a dense, ad hoc wireless network confined to a small region, such that direct communication is possible between any pair of nodes. The physical communication model is that a receiver decodes the signal from a single transmitter, while treating all other signals as interference. Data packets are sent between source-destination pairs by multihop relaying. We assume that nodes self-organise into a multihop network such that all hops are of length d meters, where d is a design parameter. There is a contention based multiaccess scheme, and it is assumed that every node always has data to send, either originated from it or a transit packet (saturation assumption). In this scenario, we seek to maximize a measure of the transport capacity of the network (measured in bit-meters per second) over power controls (in a fading environment) and over the hop distance d, subject to an average power constraint. We first argue that for a dense collection of nodes confined to a small region, single cell operation is efficient for single user decoding transceivers. Then, operating the dense ad hoc network (described above) as a single cell, we study the optimal hop length and power control that maximizes the transport capacity for a given network power constraint. More specifically, for a fading channel and for a fixed transmission time strategy (akin to the IEEE 802.11 TXOP), we find that there exists an intrinsic aggregate bit rate (Thetaopt bits per second, depending on the contention mechanism and the channel fading characteristics) carried by the network, when operating at the optimal hop length and power control. The optimal transport capacity is of the form dopt(Pmacrt) x Thetaopt with dopt scaling as Pmacrt 1 /eta, where Pmacrt is the available time average transmit power and eta is the path loss exponent. Under certain conditions on the fading distribution, we then pro- - vide a simple characterisation of the optimal operating point.

Optimal placement of Distributed Generation for a projected load increase using Relative Electrical Distance approach

There is a lot of pressure on all the developed and second world countries to produce low emission power and distributed generation (DG) is found to be one of the most viable ways to achieve this. DG generally makes use of renewable energy sources like wind, micro turbines, photovoltaic, etc., which produce power with minimum green house gas emissions. While installing a DG it is important to define its size and optimal location enabling minimum network expansion and line losses. In this paper, a methodology to locate the optimal site for a DG installation, with the objective to minimize the net transmission losses, is presented. The methodology is based on the concept of relative electrical distance (RED) between the DG and the load points. This approach will help to identify the new DG location(s), without the necessity to conduct repeated power flows. To validate this methodology case studies are carried out on a 20 node, 66kV system, a part of Karnataka Transco and results are presented.