Who you are or whom you know? Consumption interdependences in social networks

This paper presents an analysis of whether a consumer's decision to switch from one mobile phone provider to another is driven by individual consumer characteristics or by actions of other consumers in her social network. Such consumption interdependences are estimated using a unique dataset, which contains transaction data based on anonymized call records from a large European mobile phone carrier to approximate a consumer's social network. Results show that network effects have an important impact on consumers' switching decisions: switching decisions are interdependent between consumers who interact with each other and this interdependence increases in the closeness between two consumers as measured by the calling data. In other words, if a subscriber switches carriers, she is also affecting the switching probabilities of other individuals in her social circle. The paper argues that such an approach is of high relevance to both switching of providers and to the adoption of new products. © 2013 Copyright Taylor and Francis Group, LLC.

Combining algorithms to predict bacterial protein sub-cellular location:parallel versus concurrent implementations

We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.

Making motes intelligent:an agent-based approach to wireless sensor networks

In-Motes is a mobile agent middleware that generates an intelligent framework for deploying applications in Wireless Sensor Networks (WSNs). In-Motes is based on the injection of mobile agents into the network that can migrate or clone following specific rules and performing application specific tasks. By doing so, each mote is given a certain degree of perception, cognition and control, forming the basis for its intelligence. Our middleware incorporates technologies such as Linda-like tuplespaces and federated system architecture in order to obtain a high degree of collaboration and coordination for the agent society. A set of behavioral rules inspired by a community of bacterial strains is also generated as the means for robustness of the WSN. In this paper, we present In-Motes and provide a detailed evaluation of its implementation for MICA2 motes.

In-Motes Bins:a real time application for environmental monitoring in wireless sensor networks

In-Motes Bins is an agent based real time In-Motes application developed for sensing light and temperature variations in an environment. In-Motes is a mobile agent middleware that facilitates the rapid deployment of adaptive applications in Wireless Sensor Networks (WSN's). In-Motes Bins is based on the injection of mobile agents into the WSN that can migrate or clone following specific rules and performing application specific tasks. Using In-Motes we were able to create and rapidly deploy our application on a WSN consisting of 10 MICA2 motes. Our application was tested in a wine store for a period of four months. In this paper we present the In-Motes Bins application and provide a detailed evaluation of its implementation. © 2007 IEEE.

Performance investigation of IEEE 802.11 MAC in multihop wireless networks

Medium access control (MAC) protocols have a large impact on the achievable system performance for wireless ad hoc networks. Because of the limitations of existing analytical models for ad hoc networks, many researchers have opted to study the impact of MAC protocols via discreteevent simulations. However, as the network scenarios, traffic patterns and physical layer techniques may change significantly, simulation alone is not efficient to get insights into the impacts of MAC protocols on system performance. In this paper, we analyze the performance of IEEE 802.11 distributed coordination function (DCF) in multihop network scenario. We are particularly interested in understanding how physical layer techniques may affect the MAC protocol performance. For this purpose, the features of interference range is studied and taken into account of the analytical model. Simulations with OPNET show the effectiveness of the proposed analytical approach. Copyright 2005 ACM.

ACARS:adaptive context - aware rate selection for DSRC in vehicular networks

Throughput plays a vital role for data transfer in Vehicular Networks which is useful for both safety and non-safety applications. An algorithm that adapts to mobile environment by using Context information has been proposed in this paper. Since one of the problems of existing rate adaptation algorithm is underutilization of link capacity in Vehicular environments, we have demonstrated that in wireless and mobile environments, vehicles can adapt to high mobility link condition and still perform better due to regular vehicles that will be out of communication range due to range checking and then de-congest the network thereby making the system perform better since fewer vehicles will contend for network resources. In this paper, we have design, implement and analyze ACARS, a more robust algorithm with significant increase in throughput performance and energy efficiency in the mist of high mobility of vehicles.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanismbehind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Novel cost-effective PON-to-mm-wave RoF photonic bridge for multigigabit access networks

A novel low-cost photonic PON-to-RoF bridge is presented, allowing simultaneous wireline and wireless multi-Gbps data transmission with minor impact on deployed PON networks. Simulation results show that the proposed scheme does not require narrow-linewidth lasers when envelope detector mobile terminals are used, since the transmission performance is not limited by the phase noise but by the RIN and the frequency difference between the two beating lasers. © 2013 IEEE.

The development of functional astrocyte-neuron networks derived from stem cells

There is currently great scientific and medical interest in the potential of tissue grown from stem cells. These cells present opportunities for generating model systems for drug screening and toxicological testing which would be expected to be more relevant to human outcomes than animal based tissue preparations. Newly realised astrocytic roles in the brain have fundamental implications within the context of stem cell derived neuronal networks. If the aim of stem cell neuroscience is to generate functional neuronal networks that behave as networks do in the brain, then it becomes clear that we must include and understand all the cellular components that comprise that network, and which are important to support synaptic integrity and cell to cell signalling. We have shown that stem cell derived neurons exhibit spontaneous and coordinated calcium elevations in clusters and in extended processes, indicating local and long distance signalling (1). Tetrodotoxin sensitive network activity could also be evoked by electrical stimulation. Similarly, astrocytes exhibit morphology and functional properties consistent with this glial cell type. Astrocytes also respond to neuronal activity and to exogenously applied neurotransmitters with calcium elevations, and in contrast to neurons, also exhibited spontaneous rhythmic calcium oscillations. Astroctyes also generate propagating calcium waves that are gap junction and purinergic signalling dependent. Our results show that stem cell derived astrocytes exhibit appropriate functionality and that stem cell neuronal networks interact with astrocytic networks in co-culture. Using mixed cultures of stem cell derived neurons and astrocytes, we have also shown both cell types also modulate their glucose uptake, glycogen turnover and lactate production in response to glutamate as well as increased neuronal activity (2). This finding is consistent with their neuron-astrocyte metabolic coupling thus demonstrating a tractable human model, which will facilitate the study of the metabolic coupling between neurons and astrocytes and its relationship with CNS functional issues ranging from plasticity to neurodegeneration. Indeed, cultures treated with oligomers of amyloid beta 1-42 (Aβ1-42) also display a clear hypometabolism, particularly with regard to utilization of substrates such as glucose (3). Both co-cultures of neurons and astrocytes and purified cultures of astrocytes showed a significant decrease in glucose uptake after treatment with 2 and 0.2 μmol/L Aβ at all time points investigated (p <0.01). In addition, a significant increase in the glycogen content of cells was also measured. Mixed neuron and astrocyte co-cultures as well as pure astrocyte cultures showed an initial decrease in glycogen levels at 6 hours compared with control at 0.2 μmol/L and 2 μmol/L P <0.01. These changes were accompanied by changes in NAD+/NADH (P<0.05), ATP (P<0.05), and glutathione levels (P<0.05), suggesting a disruption in the energy-redox axis within these cultures. The high energy demands associated with neuronal functions such as memory formation and protection from oxidative stress put these cells at particular risk from Aβ-induced hypometabolism. As numerous cell types interact in the brain it is important that any in vitro model developed reflects this arrangement. Our findings indicate that stem cell derived neuron and astrocyte networks can communicate, and so have the potential to interact in a tripartite manner as is seen in vivo. This study therefore lays the foundation for further development of stem cell derived neurons and astrocytes into therapeutic cell replacement and human toxicology/disease models. More recently our data provides evidence for a detrimental effect of Aβ on carbohydrate metabolism in both neurons and astrocytes. As a purely in vitro system, human stem cell models can be readily manipulated and maintained in culture for a period of months without the use of animals. In our laboratory cultures can be maintained in culture for up to 12 months months thus providing the opportunity to study the consequences of these changes over extended periods of time relevant to aspects of the disease progression time frame in vivo. In addition, their human origin provides a more realistic in vitro model as well as informing other human in vitro models such as patient-derived iPSC.

Performance analysis of IEEE 802.11 cognitive radio ad hoc networks

IEEE 802.11 standard is the dominant technology for wireless local area networks (WLANs). In the last two decades, the Distributed coordination function (DCF) of IEEE 802.11 standard has become the one of the most important media access control (MAC) protocols for mobile ad hoc networks (MANETs). The DCF protocol can also be combined with cognitive radio, thus the IEEE 802.11 cognitive radio ad hoc networks (CRAHNs) come into being. There were several literatures which focus on the modeling of IEEE 802.11 CRAHNs, however, there is still no thorough and scalable analytical models for IEEE 802.11 CRAHNs whose cognitive node (i.e., secondary user, SU) has spectrum sensing and possible channel silence process before the MAC contention process. This paper develops a unified analytical model for IEEE 802.11 CRAHNs for comprehensive MAC layer queuing analysis. In the proposed model, the SUs are modeled by a hyper generalized 2D Markov chain model with an M/G/1/K model while the primary users (PUs) are modeled by a generalized 2D Markov chain and an M/G/1/K model. The performance evaluation results show that the quality-of-service (QoS) of both the PUs and SUs can be statistically guaranteed with the suitable settings of duration of channel sensing and silence phase in the case of under loading.