Magnani, Matteo; Rossi, Luca
While most research in Social Network Analysis has focused on single networks, the availability of complex on-line data about individuals and their mutual heterogenous connections has recently determined a renewed interest in multi-layer network analysis. To the best of our knowledge, in this paper...... we introduce the first network formation model for multiple networks. Network formation models are among the most popular tools in traditional network studies, because of both their practical and theoretical impact. However, existing models are not sufficient to describe the generation of multiple...... networks. Our model, motivated by an empirical analysis of real multi-layered network data, is a conservative extension of single-network models and emphasizes the additional level of complexity that we experience when we move from a single- to a more complete and realistic multi-network context....
Gligorijević, Vladimir; Malod-Dognin, Noël; Pržulj, Nataša
Discovering patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. However, the complexity of the multiple network alignment problem grows exponentially with the number of networks being aligned and designing a multiple network aligner that is both scalable and that produces biologically relevant alignments is a challenging task that has not been fully addressed. The objective of multiple network alignment is to create clusters of nodes that are evolutionarily and functionally conserved across all networks. Unfortunately, the alignment methods proposed thus far do not meet this objective as they are guided by pairwise scores that do not utilize the entire functional and evolutionary information across all networks. To overcome this weakness, we propose Fuse, a new multiple network alignment algorithm that works in two steps. First, it computes our novel protein functional similarity scores by fusing information from wiring patterns of all aligned PPI networks and sequence similarities between their proteins. This is in contrast with the previous tools that are all based on protein similarities in pairs of networks being aligned. Our comprehensive new protein similarity scores are computed by Non-negative Matrix Tri-Factorization (NMTF) method that predicts associations between proteins whose homology (from sequences) and functioning similarity (from wiring patterns) are supported by all networks. Using the five largest and most complete PPI networks from BioGRID, we show that NMTF predicts a large number protein pairs that are biologically consistent. Second, to identify clusters of aligned proteins over all networks, Fuse uses our novel maximum weight k-partite matching approximation algorithm. We compare Fuse with the state of the art
Bergenholtz, Carsten; Bjerregaard, Toke
The present study investigates how a high-tech-small-firm (HTSF) can carry out an inter-organizational search of actors located at universities. Responding to calls to study how firms navigate multiple institutional norms, this research examines the different strategies used by a HTSF to balance...
Full Text Available One of the main design challenges in wireless sensor networks (WSNs is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV. In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF and cumulative distribution function (CDF of the received signal-to-noise ratio (SNR are derived. In addition, the average symbol error rate (ASER with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.
Yoo, Wucherl; Sim, Alex
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Full Text Available The demand for wireless services is growing on a daily basis while spectral resources to support this growth are static. Therefore, there is need for the adoption of a new spectrum sharing paradigm. Cognitive Radio (CR is a revolutionary technology aiming to increase spectrum utilization through dynamic spectrum access, as well as mitigating interference among multiple coexisting wireless networks. In many practical scenarios, multiple CR networks may coexist in the same geographical area, and they may interfere with each other and also have to yield to the primary user (PU. In this study, we investigate how much throughput a node in a CR network can achieve in the presence of another CR network and a PU. The results of this study illustrate how the transmission probability and sensing performance affect the achievable throughput of a node in coexisting CR networks. In addition, these results may serve as guidance for the deployment of multiple CR networks.
Guo, Xiaoqiang; Wu, Weiyang; Chen, Zhe
) and synchronization techniques have been presented in the past decades. Most of them make a tradeoff between the accuracy and dynamic response under severe distorted and unbalanced conditions. In this paper, a multiple-complex coefficient-filter-based PLL is presented, and its unique feature lies in the accurate...
Full Text Available The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components. We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by
Yu, Guoxian; Zhu, Hailong; Domeniconi, Carlotta; Guo, Maozu
High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. We address this issue by modeling the optimization of the composite network and the prediction problems within a unified objective function. In particular, we use a kernel target alignment technique and the loss function of a network based classifier to jointly adjust the weights assigned to the individual networks. We show that the proposed method, called MNet, can achieve a performance that is superior (with respect to different evaluation criteria) to related techniques using the multiple networks of four example species (yeast, human, mouse, and fly) annotated with thousands (or hundreds) of GO terms. MNet can effectively integrate multiple networks for protein function prediction and is robust to the input parameters. Supplementary data is available at https://sites.google.com/site/guoxian85/home/mnet. The Matlab code of MNet is available upon request.
Popovska Avramova, Andrijana; Iversen, Villy Bæk
deployments (required for coverage enhancement), increased base station utilization, and reduced overall power consumption. Today, network sharing in the radio access part is passive and limited to cell sites. With the introduction of Cloud Radio Access Network and Software Defined Networking adoption......Mobile operators are moving towards sharing network capacity in order to reduce capital and operational expenditures, while meeting the increasing demand for mobile broadband data services. Radio access network sharing is a promising technique that leads to reduced number of physical base station...... to the radio access network, the possibility for sharing baseband processing and radio spectrum becomes an important aspect of network sharing. This paper investigates strategies for active sharing of radio access among multiple operators, and analyses the individual benefits depending on the sharing degree...
Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed
A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.
Freedman, Mark S
Teriflunomide is an oral, once-daily disease-modifying therapy (DMT) approved in the USA, Australia, and Argentina for the treatment of relapsing forms of multiple sclerosis (RMS). Teriflunomide reversibly limits the expansion of activated T and B cells associated with the inflammatory process purportedly involved in multiple sclerosis pathogenesis, while preserving lymphocytes for routine immune surveillance. In an extensive clinical development program, teriflunomide demonstrated consistent...
Zhang, Qian-Ming; Xu, Xiao-Ke; Zhu, Yu-Xiao; Zhou, Tao
Numerous concise models such as preferential attachment have been put forward to reveal the evolution mechanisms of real-world networks, which show that real-world networks are usually jointly driven by a hybrid mechanism of multiplex features instead of a single pure mechanism. To get an accurate simulation for real networks, some researchers proposed a few hybrid models by mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of multiplex features jointly influence the network evolution is not very clear. In this study, we introduce two methods (link prediction and likelihood analysis) to measure multiple evolution mechanisms of complex networks. Through tremendous experiments on artificial networks, which can be controlled to follow multiple mechanisms with different weights, we find the method based on likelihood analysis performs much better and gives very accurate estimations. At last, we apply this method to some real-world networks which are from different domains (including technology networks and social networks) and different countries (e.g., USA and China), to see how popularity and clustering co-evolve. We find most of them are affected by both popularity and clustering, but with quite different weights.
Freedman, Mark S
Teriflunomide is an oral, once-daily disease-modifying therapy (DMT) approved in the USA, Australia, and Argentina for the treatment of relapsing forms of multiple sclerosis (RMS). Teriflunomide reversibly limits the expansion of activated T and B cells associated with the inflammatory process purportedly involved in multiple sclerosis pathogenesis, while preserving lymphocytes for routine immune surveillance. In an extensive clinical development program, teriflunomide demonstrated consistent benefits on both clinical and magnetic resonance imaging outcomes. In long-term studies, teriflunomide treatment was associated with low rates of relapse and disability progression for up to 8 years. The safety profile of teriflunomide has been well characterized, with adverse events generally mild to moderate in nature and infrequently leading to permanent treatment discontinuation. The evidence reviewed here indicates that teriflunomide is an effective addition to the current DMTs used to treat RMS.
This brief examines current research on improving Vehicular Networks (VANETs), examining spectrum scarcity due to the dramatic growth of mobile data traffic and the limited bandwidth of dedicated vehicular communication bands and the use of opportunistic spectrum bands to mitigate congestion. It reviews existing literature on the use of opportunistic spectrum bands for VANETs, including licensed and unlicensed spectrum bands and a variety of related technologies, such as cognitive radio, WiFi and device-to-device communications. Focused on analyzing spectrum characteristics, designing efficient spectrum exploitation schemes, and evaluating the date delivery performance when utilizing different opportunistic spectrum bands, the results presented in this brief provide valuable insights on improving the design and deployment of future VANETs.
Full Text Available Distributed generation (DG has gained a vital role in distribution utilities. So, it is important to correctly detect islanding of DG units. Frequency relays are one of the most commonly used loss of mains detection method. However, distribution utilities may be faced by concern related to false operation of these frequency relays. The commercially available frequency relays reported considering standard tight setting. This paper investigates some factors related to relays internal algorithm that contribute to their different operating responses. The factors that will be investigated are frequency measuring techniques, measuring windows, time delays and under voltage interlock function. With the increasing penetration of DG into the network, it is becoming common to have multiple DG units connected at the same network location. Two generators connected at the same location and employing frequency relays with the same setting but different characteristics were simulated. When subjected to the same network disturbances the possible interference between the two relays is analyzed.
Semm, S.; Becker, T.; Kolbe, T. H.
Cartographic visualizations of crises are used to create a Common Operational Picture (COP) and enforce Situational Awareness by presenting and representing relevant information. As nearly all crises affect geospatial entities, geo-data representations have to support location-specific decision-making throughout the crises. Since, Operator's attention span and their working memory are limiting factors for the process of getting and interpreting information; the cartographic presentation has to support individuals in coordinating their activities and with handling highly dynamic situations. The Situational Awareness of operators in conjunction with a COP are key aspects of the decision making process and essential for coming to appropriate decisions. Utility networks are one of the most complex and most needed systems within a city. The visualization of utility infrastructure in crisis situations is addressed in this paper. The paper will provide a conceptual approach on how to simplify, aggregate, and visualize multiple utility networks and their components to meet the requirements of the decision-making process and to support Situational Awareness.
Savinainen, A.; Nieminen, P.; Makynen, A.; Viiri, J.
In this paper, we present materials and teaching ideas utilizing multiple representations in the contexts of kinematics and the force concept. These ideas and materials are substantiated by evidence and can be readily used in teaching with no special training. In addition, we briefly discuss two multiple-choice tests based on physics education…
Mitra, Chiranjit; Choudhary, Anshul; Sinha, Sudeshna; Kurths, Jürgen; Donner, Reik V.
Dynamical entities interacting with each other on complex networks often exhibit multistability. The stability of a desired steady regime (e.g., a synchronized state) to large perturbations is critical in the operation of many real-world networked dynamical systems such as ecosystems, power grids, the human brain, etc. This necessitates the development of appropriate quantifiers of stability of multiple stable states of such systems. Motivated by the concept of basin stability (BS) [P. J. Menck et al., Nat. Phys. 9, 89 (2013), 10.1038/nphys2516], we propose here the general framework of multiple-node basin stability for gauging the global stability and robustness of networked dynamical systems in response to nonlocal perturbations simultaneously affecting multiple nodes of a system. The framework of multiple-node BS provides an estimate of the critical number of nodes that, when simultaneously perturbed, significantly reduce the capacity of the system to return to the desired stable state. Further, this methodology can be applied to estimate the minimum number of nodes of the network to be controlled or safeguarded from external perturbations to ensure proper operation of the system. Multiple-node BS can also be utilized for probing the influence of spatially localized perturbations or targeted attacks to specific parts of a network. We demonstrate the potential of multiple-node BS in assessing the stability of the synchronized state in a deterministic scale-free network of Rössler oscillators and a conceptual model of the power grid of the United Kingdom with second-order Kuramoto-type nodal dynamics.
are generated by TCP when performing congestion control . TCP performs congestion control in the network, as defined in RFC 2581, to mitigate the...Available: https://www.wireshark.org/about.html.  Network Working Group. (1999, April). TCP congestion control . [Online]. Available: http...Identification TCP Transport Control Protocol VNC Virtual Network Computing WLAN Wireless Local Area Network xvi THIS PAGE INTENTIONALLY
Li, Dan-Dan; Gao, Fei; Qin, Su-Juan; Wen, Qiao-Yan
In order to realize long-distance and large-scale quantum communication, it is natural to utilize quantum repeater. For a general quantum multiple-unicast network, it is still puzzling how to complete communication tasks perfectly with less resources such as registers. In this paper, we solve this problem. By applying quantum repeaters to multiple-unicast communication problem, we give encoding-decoding schemes for source nodes, internal ones and target ones, respectively. Source-target nodes share EPR pairs by using our encoding-decoding schemes over quantum multiple-unicast network. Furthermore, quantum communication can be accomplished perfectly via teleportation. Compared with existed schemes, our schemes can reduce resource consumption and realize long-distance transmission of quantum information.
Tajima, A.; Kondoh, T.; Ochi, T.; Fujiwara, M.; Yoshino, K.; Iizuka, H.; Sakamoto, T.; Tomita, A.; Shimamura, E.; Asami, S.; Sasaki, M.
The fundamental architecture and functions of secure key management in a quantum key distribution (QKD) network with enhanced universal interfaces for smooth key sharing between arbitrary two nodes and enabling multiple secure communication applications are proposed. The proposed architecture consists of three layers: a quantum layer, key management layer and key supply layer. We explain the functions of each layer, the key formats in each layer and the key lifecycle for enabling a practical QKD network. A quantum key distribution-advanced encryption standard (QKD-AES) hybrid system and an encrypted smartphone system were developed as secure communication applications on our QKD network. The validity and usefulness of these systems were demonstrated on the Tokyo QKD Network testbed.
Mahmood, Nurul Huda; Øien, G.E.; Lundheim, L.
In an underlay Cognitive Radio Network, multiple secondary users coexist geographically and spectrally with multiple primary users under a constraint on the maximum received interference power at the primary receivers. Given such a setting, one may ask "how to achieve maximum utility benefit...
Zhao, Dawei [Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China); National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876 (China); Shandong Provincial Key Laboratory of Computer Network, Shandong Computer Science Center, Jinan 250014 (China); Li, Lixiang [Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China); National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876 (China); Peng, Haipeng, E-mail: firstname.lastname@example.org [Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China); National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876 (China); Luo, Qun; Yang, Yixian [Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China); National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing 100876 (China)
This letter investigates the multiple routes transmitted epidemic process on multiplex networks. We propose detailed theoretical analysis that allows us to accurately calculate the epidemic threshold and outbreak size. It is found that the epidemic can spread across the multiplex network even if all the network layers are well below their respective epidemic thresholds. Strong positive degree–degree correlation of nodes in multiplex network could lead to a much lower epidemic threshold and a relatively smaller outbreak size. However, the average similarity of neighbors from different layers of nodes has no obvious effect on the epidemic threshold and outbreak size. -- Highlights: •We studies multiple routes transmitted epidemic process on multiplex networks. •SIR model and bond percolation theory are used to analyze the epidemic processes. •We derive equations to accurately calculate the epidemic threshold and outbreak size. •ASN has no effect on the epidemic threshold and outbreak size. •Strong positive DDC leads to a lower epidemic threshold and a smaller outbreak size.
Lake, Joe E [ORNL; Allgood, Glenn O [ORNL; Olama, Mohammed M [ORNL; Saffold, JAy [Research Network, Inc
The U.S. military defines antiterrorism as the defensive posture taken against terrorist threats. Antiterrorism includes fostering awareness of potential threats, deterring aggressors, developing security measures, planning for future events, interdicting an event in progress, and ultimately mitigating and managing the consequences of an event. Recent events highlight the need for efficient tools for training our military and homeland security officers for anticipating threats posed by terrorists. These tools need to be easy enough so that they are readily usable without substantial training, but still maintain the complexity to allow for a level of deceptive reasoning on the part of the opponent. To meet this need, we propose to integrate a Bayesian Belief Network (BBN) model for threat anticipation and deceptive reasoning into training simulation environments currently utilized by several organizations within the Department of Defense (DoD). BBNs have the ability to deal with various types of uncertainties; such as identities, capabilities, target attractiveness, and the combinations of the previous. They also allow for disparate types of data to be fused in a coherent, analytically defensible, and understandable manner. A BBN has been developed by ORNL uses a network engineering process that treats the probability distributions of each node with in the broader context of the system development effort as a whole, and not in isolation. The network will be integrated into the Research Network Inc,(RNI) developed Game Distributed Interactive Simulation (GDIS) as a smart artificial intelligence module. GDIS is utilized by several DoD and civilian organizations as a distributed training tool for a multiplicity of reasons. It has garnered several awards for its realism, ease of use, and popularity. One area that it still has room to excel in, as most video training tools do, is in the area of artificial intelligence of opponent combatants. It is believed that by
The presented bachelor's thesis deals with advertisement. It answers the question of what advertisement is, why firms use advertisement and what its benefits are. It concentrates especially on Internet advertisement presented through social networks. These social networks have come to occupy a significant position on the Internet during the last five years and offer new possibilities in terms of creating advertising campaigns (Hypertargeting). The thesis presents the division and comparison o...
Hole, Kjell Jørgen
The author analyzes a technique to prevent multiple simultaneous virus epidemics on any vulnerable computer network with inhomogeneous topology. The technique immunizes a small fraction of the computers and utilizes diverse software platforms to halt the virus outbreaks. The halting technique is of practical interest since a network's detailed topology need not be known.
3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE SOCIAL NETWORKS AND HIGH HEALTHCARE UTILIZATION: BUILDING RESILIENCE...28 3. Evaluating Network Type ...........................................................29 4. Density and Path...studied by showing “ galaxies ” of highly connected individuals and their associated lines of flow, or “emotions,” expressed graphically by links and
Nepal, Sudip; Kumar, Pradeep
Biological systems are capable of producing multiple states out of a single set of inputs. Multistability acts like a biological switch that allows organisms to respond differently to different environmental conditions and hence plays an important role in adaptation to changing environment. One of the widely studied gene regulatory networks underlying the metabolism of bacteria is the lactose utilization network, which exhibits a multistable behavior as a function of lactose concentration. We have studied the effect of temperature on multistability of the lactose utilization network at various concentrations of thio-methylgalactoside (TMG), a synthetic lactose. We find that while the lactose utilization network exhibits a bistable behavior for temperature T >20° C , a graded response arises for temperature T cellular regulation of metabolism.
Underwood, Keith D [Albuquerque, NM; Hemmert, Karl Scott [Albuquerque, NM
A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.
Gómez Fernández, Juan F
In order to satisfy the needs of their customers, network utilities require specially developed maintenance management capabilities. Maintenance Management information systems are essential to ensure control, gain knowledge and improve-decision making in companies dealing with network infrastructure, such as distribution of gas, water, electricity and telecommunications. Maintenance Management in Network Utilities studies specified characteristics of maintenance management in this sector to offer a practical approach to defining and implementing the best management practices and suitable frameworks. Divided into three major sections, Maintenance Management in Network Utilities defines a series of stages which can be followed to manage maintenance frameworks properly. Different case studies provide detailed descriptions which illustrate the experience in real company situations. An introduction to the concepts is followed by main sections including: • A Literature Review: covering the basic concepts an...
Magnani, Matteo; Rossi, Luca
Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...
T. Anji Kumar; Dr MHM Krishna Prasad
Now a days, Internet plays a major role in our day to day activities e.g., for online transactions, online shopping, and other network related applications. Internet suffers from slow convergence of routing protocols after a network failure which becomes a growing problem. Multiple Routing Configurations [MRC] recovers network from single node/link failures, but does not support network from multiple node/link failures. In this paper, we propose Enhanced MRC [EMRC], to support multiple node/l...
This CIGRE green book begins by addressing the specification and provision of communication services in the context of operational applications for electrical power utilities, before subsequently providing guidelines on the deployment or transformation of networks to deliver these specific communication services. Lastly, it demonstrates how these networks and their services can be monitored, operated, and maintained to ensure that the requisite high level of service quality is consistently achieved.
Findrik, Mislav; Grønbæk, Jesper; Olsen, Rasmus Løvenstein
managing this fast flexibility requires two-way data exchange between a controller and sensors/meters via communication networks. In this paper we investigated scheduling of data collection utilizing meta-data from sensors that are describing dynamics of information. We show the applicability...... of this approach for a constraint communication networks of the smart grid and compared three general data access mechanisms, namely, push, pull and event-based....
O'Gorman, Laurel D.; Hogenbirk, John C; Warry, Wayne
Abstract Introduction: Northern Ontario is a region in Canada with approximately 775,000 people in communities scattered across 803,000?km2. The Ontario Telemedicine Network (OTN) facilitates access to medical care in areas that are often underserved. We assessed how OTN utilization differed throughout the province. Materials and Methods: We used OTN medical service utilization data collected through the Ontario Health Insurance Plan and provided by the Ministry of Health and Long Term Care. ...
Egid, Adin [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
We consider the use of a novel weak in- dicator alongside more commonly used weak indicators to help detect anomalous behavior in a large computer network. The data of the network which we are studying in this research paper concerns remote log-in information (Virtual Private Network, or VPN sessions) from the internal network of Los Alamos National Laboratory (LANL). The novel indicator we are utilizing is some- thing which, while novel in its application to data science/cyber security research, is a concept borrowed from the business world. The Her ndahl-Hirschman Index (HHI) is a computationally trivial index which provides a useful heuristic for regulatory agencies to ascertain the relative competitiveness of a particular industry. Using this index as a lagging indicator in the monthly format we have studied could help to detect anomalous behavior by a particular or small set of users on the network.
Dmitry A Rodionov
Full Text Available Hyperthermophilic bacteria from the Thermotogales lineage can produce hydrogen by fermenting a wide range of carbohydrates. Previous experimental studies identified a large fraction of genes committed to carbohydrate degradation and utilization in the model bacterium Thermotoga maritima. Knowledge of these genes enabled comprehensive reconstruction of biochemical pathways comprising the carbohydrate utilization network. However, transcriptional factors (TFs and regulatory mechanisms driving this network remained largely unknown. Here, we used an integrated approach based on comparative analysis of genomic and transcriptomic data for the reconstruction of the carbohydrate utilization regulatory networks in 11 Thermotogales genomes. We identified DNA-binding motifs and regulons for 19 orthologous TFs in the Thermotogales. The inferred regulatory network in T. maritima contains 181 genes encoding TFs, sugar catabolic enzymes and ABC-family transporters. In contrast to many previously described bacteria, a transcriptional regulation strategy of Thermotoga does not employ global regulatory factors. The reconstructed regulatory network in T. maritima was validated by gene expression profiling on a panel of mono- and disaccharides and by in vitro DNA-binding assays. The observed upregulation of genes involved in catabolism of pectin, trehalose, cellobiose, arabinose, rhamnose, xylose, glucose, galactose, and ribose showed a strong correlation with the UxaR, TreR, BglR, CelR, AraR, RhaR, XylR, GluR, GalR, and RbsR regulons. Ultimately, this study elucidated the transcriptional regulatory network and mechanisms controlling expression of carbohydrate utilization genes in T. maritima. In addition to improving the functional annotations of associated transporters and catabolic enzymes, this research provides novel insights into the evolution of regulatory networks in Thermotogales.
T Anji Kumar & Dr MHM Krishna Prasad
Now a days, Internet plays a major role in our day to day activities e.g., for online transactions,online shopping, and other network related applications. Internet suffers from slow convergenceof routing protocols after a network failure which becomes a growing problem. Multiple RoutingConfigurations [MRC] recovers network from single node/link failures, but does not supportnetwork from multiple node/link failures. In this paper, we propose Enhanced MRC [EMRC], tosupport multiple node/link f...
Ma, Xiaoke; Sun, Penggang; Qin, Guimin
Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules. We then transform the condition-specific module discovery problem into a clustering problem in multiple networks. We develop an efficient heuristic algorithm for the Specific Modules in Multiple Networks (SMMN), which discovers the condition-specific modules by considering multiple networks. By using the artificial networks, we demonstrate that SMMN outperforms state-of-the-art methods. In breast cancer networks, stage-specific modules discovered by SMMN are more discriminative in predicting cancer stages than those obtained by other techniques. In pan-cancer networks, cancer-specific modules are more likely to associate with survival time of patients, which is critical for cancer therapy.
Damrongplasit, Kannika; Wangdi, Tshering
This paper uses the Bhutan Living Standards Survey 2012 to assess factors that affect the decision to use outpatient care when ill, outpatient utilization choice, and bypassing decision. Our attention is placed on geographical factors because of the unique geographical landscape in Bhutan, which may act as an important barrier for access to care in the country. We further analyze the pattern of multiple healthcare visits of individuals with the same health symptom. The methods employed for this study consist of binary logit and multinomial logit regressions as well as descriptive statistical approach. The results show that living in rural area, longer travel time, and residing in remote area reduce the chance of receiving formal care when ill, and among those who get formal treatment, these factors lead to higher tendency of visiting primary healthcare facilities and less propensity of getting care from secondary and tertiary providers. We also find that people with lower economic status have less access to care than their richer counterparts. By investigating the pattern of multiple outpatient visits, our analysis reveals incidence of bypassing primary care to higher level of care in Bhutan. There is also evidence of moving up to higher level of care during subsequent visits but in general people are very persistent in their provider choice.
Hlava, Marjorie M. K.
The purposes of this consulting effort are: (1) to evaluate the existing management and production procedures and workflow as they each relate to the successful development, utilization, and implementation of the NASA Technology Utilization Network System (TUNS) database; (2) to identify, as requested by the NASA Project Monitor, the strengths, weaknesses, areas of bottlenecking, and previously unaddressed problem areas affecting TUNS; (3) to recommend changes or modifications of existing procedures as necessary in order to effect corrections for the overall benefit of NASA TUNS database production, implementation, and utilization; and (4) to recommend the addition of alternative procedures, routines, and activities that will consolidate and facilitate the production, implementation, and utilization of the NASA TUNS database.
Full Text Available interaction and communication technologies. Indeed, there has been an emerging movement in the interaction and communication technologies. More specifically, the growth of Web 2.0 technologies has acted as a catalyst for change in the disciplines of education. The social networking websites have gained popularity in recent years; therefore, many research studies have been conducted to explain how the use of social networking websites for instructional purposes. For the best practices, it is essential to understand theories associated with social networking studies because related theories for any subject may provide insights and guideline for professionals and researchers. This theoretical paper was designed to offer a road map through the literature in relation to the utilization of social networking websites by presenting main understandings of theories associated with social networking. The Uses and Gratification Theory, social network theory, connectives, and constructivism were selected to serve as a basis for designing social networking studies regarding instructional purposes. Moreover, common attributes of the theories and specific application areas were also discussed. This paper contributes to this emerging movement by explaining the role of these theories for researchers and practitioners to find ways to beneficially integrate them into their future research endeavors
Manzano, M.; Marzo, J. L.; Calle, E.
on topological characteristics. Recently approaches also consider the services supported by such networks. In this paper we carry out a robustness analysis of five real backbone telecommunication networks under defined multiple failure scenarios, taking into account the consequences of the loss of established......Nowadays the ubiquity of telecommunication networks, which underpin and fulfill key aspects of modern day living, is taken for granted. Significant large-scale failures have occurred in the last years affecting telecommunication networks. Traditionally, network robustness analysis has been focused...... connections. Results show which networks are more robust in response to a specific type of failure....
Delgoda, Dilini; Malano, Hector; Saleem, Syed K.; Halgamuge, Malka N.
This research proposes a novel generic method for irrigation scheduling in a canal network to optimize multiple objectives related to canal scheduling (e.g. maximizing water supply and minimizing imbalance of water distribution) within multiple hierarchical layers (e.g. the layers consisting of the main canal, distributaries) while utilizing traditional canal scheduling methods. It is based on modularizing the optimization process. The method is theoretically capable of optimizing an unlimited number of user-defined objectives within an unlimited number of hierarchical layers and only limited by resource availability (e.g. maximum canal capacity and water limitations) in the network. It allows flexible decision-making through quantification of the mutual effects of optimizing conflicting objectives and is adaptable to available multi-objective evolutionary algorithms. The method's application is demonstrated using a hypothetical canal network example with six objectives and three hierarchical layers, and a real scenario with four objectives and two layers.
O'Gorman, Laurel D; Hogenbirk, John C; Warry, Wayne
Northern Ontario is a region in Canada with approximately 775,000 people in communities scattered across 803,000 km(2). The Ontario Telemedicine Network (OTN) facilitates access to medical care in areas that are often underserved. We assessed how OTN utilization differed throughout the province. We used OTN medical service utilization data collected through the Ontario Health Insurance Plan and provided by the Ministry of Health and Long Term Care. Using census subdivisions grouped by Northern and Southern Ontario as well as urban and rural areas, we calculated utilization rates per fiscal year and total from 2008/2009 to 2013/2014. We also used billing codes to calculate utilization by therapeutic area of care. There were 652,337 OTN patient visits in Ontario from 2008/2009 to 2013/2014. Median annual utilization rates per 1,000 people were higher in northern areas (rural, 52.0; urban, 32.1) than in southern areas (rural, 6.1; urban, 3.1). The majority of usage in Ontario was in mental health and addictions (61.8%). Utilization in other areas of care such as surgery, oncology, and internal medicine was highest in the rural north, whereas primary care use was highest in the urban south. Utilization was higher and therapeutic areas of care were more diverse in rural Northern Ontario than in other parts of the province. Utilization was also higher in urban Northern Ontario than in Southern Ontario. This suggests that telemedicine is being used to improve access to medical care services, especially in sparsely populated regions of the province.
Lim, Bang Hui; Lu, Dongyuan; Chen, Tao; Kan, Min-Yen
We study how users of multiple online social networks (OSNs) employ and share information by studying a common user pool that use six OSNs - Flickr, Google+, Instagram, Tumblr, Twitter, and YouTube. We analyze the temporal and topical signature of users' sharing behaviour, showing how they exhibit distinct behaviorial patterns on different networks. We also examine cross-sharing (i.e., the act of user broadcasting their activity to multiple OSNs near-simultaneously), a previously-unstudied be...
With multisensor data fusion technology, the data from multiple sensors are fused in order to make a more accurate estimation of the environment through measurement, processing and analysis. Artificial neural networks are the computational models that mimic biological neural networks. With high per...
Torenvlied, René; Akkerman, Agnes; Meier, Kenneth J.; O'Toole, Laurence J.
Studies in public management show that agencies draw different types of support from different actors and organizations in their environment. If this is true, we would expect that managers differentiate their networking activity toward different types of external actors and organizations. However,
Torenvlied, R.; Akkerman, A.; Meier, K.J.; O’Toole, L.J.
Studies in public management show that agencies draw different types of support from different actors and organizations in their environment. If this is true, we would expect that managers differentiate their networking activity toward different types of external actors and organizations. However,
...) modulation for utility-packet transmission in Seaweb underwater wireless acoustic communications networks, Seaweb networks require robust channel-tolerant utility packets having a low probability of detection (LPD...
Rao, Nageswara S. [ORNL; Ma, Chris Y. T. [Hang Seng Management College, Hon Kong; Hausken, Kjell [University of Stavanger, Norway; He, Fei [Texas A& M University, Kingsville, TX, USA; Yau, David K. Y. [Singapore University of Technology and Design; Zhuang, Jun [University at Buffalo (SUNY)
We consider an infrastructure of networked systems with discrete components that can be reinforced at certain costs to guard against attacks. The communications network plays a critical, asymmetric role of providing the vital connectivity between the systems. We characterize the correlations within this infrastructure at two levels using (a) aggregate failure correlation function that specifies the infrastructure failure probability giventhe failure of an individual system or network, and (b) first order differential conditions on system survival probabilities that characterize component-level correlations. We formulate an infrastructure survival game between an attacker and a provider, who attacks and reinforces individual components, respectively. They use the composite utility functions composed of a survival probability term and a cost term, and the previously studiedsum-form and product-form utility functions are their special cases. At Nash Equilibrium, we derive expressions for individual system survival probabilities and the expected total number of operational components. We apply and discuss these estimates for a simplified model of distributed cloud computing infrastructure
Tapan Kumar Jain
Full Text Available The wireless sensor network consists of small limited energy sensors which are connected to one or more sinks. The maximum energy consumption takes place in communicating the data from the nodes to the sink. Multiple sink WSN has an edge over the single sink WSN where very less energy is utilized in sending the data to the sink, as the number of hops is reduced. If the energy consumed by a node is balanced between the other nodes, the lifetime of the network is considerably increased. The network lifetime optimization is achieved by restructuring the network by modifying the neighbor nodes of a sink. Only those nodes are connected to a sink which makes the total energy of the sink less than the threshold. This energy balancing through network restructuring optimizes the network lifetime. This paper depicts this fact through simulations done in MATLAB.
Multiple and concurrent sexual partnerships (MCP) are prevalent in southern Africa and have been identified as a primary cause of high HIV prevalence in this region. Sexual liaisons with multiple partners serve to increase the size and diversity of an individual's sexual — and social — network and therefore to increase their ...
Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai
Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...
Zhang, Chongfu; Qiu, Kun; Ma, Chunli
In this paper, we utilize a new study method that is under independent case of multiple optical orthogonal codes to derive the probability function of MOOCS-OPS networks, discuss the performance characteristics for a variety of parameters, and compare some characteristics of the system employed by single optical orthogonal code or multiple optical orthogonal codes sequences-based optical labels. The performance of the system is also calculated, and our results verify that the method is effective. Additionally it is found that performance of MOOCS-OPS networks would, negatively, be worsened, compared with single optical orthogonal code-based optical label for optical packet switching (SOOC-OPS); however, MOOCS-OPS networks can greatly enlarge the scalability of optical packet switching networks.
Tamada, Yoshinori; Bannai, Hideo; Imoto, Seiya; Katayama, Toshiaki; Kanehisa, Minoru; Miyano, Satoru
Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.
Hwangbo, Soonho; Lee, In-Beum [POSTECH, Pohang (Korea, Republic of); Han, Jeehoon [University of Wisconsin-Madison, Madison (United States)
Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network.
Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP) are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making. PMID:28961245
Westerink, E.J.; Westerterp, K.R.
The model of the pseudo-homogeneous, one-dimensional cooled tubular reactor is applied to a multiple-reaction network. It is demonstrated for a network which consists of two parallel and two consecutive reactions. Three criteria are developed to obtain an integral yield which does not deviate more
Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)
Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.
Jensen, Peter Damsgaard; Rubæk, Tonny; Mohr, Johan Jacob
The use of multiple frequencies in a nonlinear microwave algorithm is considered. Using multiple frequencies allows for obtaining the improved resolution available at the higher frequencies while retaining the regularizing effects of the lower frequencies. However, a number of different challenge...
Greene, Derek; Cunningham, Pádraig
In many social networks, several different link relations will exist between the same set of users. Additionally, attribute or textual information will be associated with those users, such as demographic details or user-generated content. For many data analysis tasks, such as community finding and data visualisation, the provision of multiple heterogeneous types of user data makes the analysis process more complex. We propose an unsupervised method for integrating multiple data views to produ...
Full Text Available One point of consensus in the otherwise very controversial discussion about the benefits and dangers of DTC genetics in the health domain is the lack of substantial clinical utility. At the same time, both the empirical and conceptual literature indicate that health-related DTC tests can have value and utility outside of the clinic. We argue that a broader and multi-faceted conceptualization of utility and value would enrich the ethical and social discussion of DTC testing in several ways: First, looking at ways in which DTC testing can have personal and social value for users – in the form of entertainment, learning, or a way to relate to others – can help to explain why people still take DTC tests, and will, further down the line, foster a more nuanced understanding of secondary and tertiary uses of DTC test results (which could very well unearth new ethical and regulatory challenges. Second, considering the economic value and broader utility of DTC testing foregrounds wider social and political aspects than have been dominant in the ethical and regulatory debates surrounding DTC genetics so far. These wider political aspects include the profound power asymmetries that characterize the collection and use of personal genetic data in many contexts.
Turrini, Mauro; Prainsack, Barbara
One point of consensus in the otherwise very controversial discussion about the benefits and dangers of DTC genetics in the health domain is the lack of substantial clinical utility. At the same time, both the empirical and conceptual literature indicate that health-related DTC tests can have value and utility outside of the clinic. We argue that a broader and multi-faceted conceptualization of utility and value would enrich the ethical and social discussion of DTC testing in several ways: First, looking at ways in which DTC testing can have personal and social value for users - in the form of entertainment, learning, or a way to relate to others - can help to explain why people still take DTC tests, and will, further down the line, foster a more nuanced understanding of secondary and tertiary uses of DTC test results (which could very well unearth new ethical and regulatory challenges). Second, considering the economic value and broader utility of DTC testing foregrounds wider social and political aspects than have been dominant in the ethical and regulatory debates surrounding DTC genetics so far. These wider political aspects include the profound power asymmetries that characterize the collection and use of personal genetic data in many contexts.
Frauzem, Rebecca; Gani, Rafiqul
In response to increasing regulations and concern about the impact of greenhouse gases on the environment, carbon dioxide (CO2) emissions are targeted for reduction. One method is the conversion of CO2 to useful compounds via chemical reactions. However, conversion is still in its infancy...... processing block. CO2 conversion processes show promise as an additional method for the sustainable reduction of CO2 emissions....... a superstructure-based approach a network of utilization alternatives is created linking CO2 and other raw materials with various products using processing blocks. This will then be optimized and verified for sustainability. Detailed design has also been performed for a case study on the methanol synthesis...
Full Text Available This paper proposes the use of cognitive radio technology for multiple wireless sensor technologies in mines. The work is motivated by the lack of flexible and scalable sensor networks in mines. The proposed architecture uses cognitive radio...
Berkhout, A.J.; Verschuur, D.J.
The next generation migration technology considers multiple scattering as vital information, allowing the industry to generate significantly better images of the subsurface. The proposed full wavefield algorithm (FWM) makes use of two-way wave theory that is formulated in terms of one-way
Xiao, C.; Yilmaz, A.; Lia, S.
Despite having achieved good performance, visual tracking is still an open area of research, especially when target undergoes serious appearance changes which are not included in the model. So, in this paper, we replace the appearance model by a concept model which is learned from large-scale datasets using a deep learning network. The concept model is a combination of high-level semantic information that is learned from myriads of objects with various appearances. In our tracking method, we generate the target's concept by combining the learned object concepts from classification task. We also demonstrate that the last convolutional feature map can be used to generate a heat map to highlight the possible location of the given target in new frames. Finally, in the proposed tracking framework, we utilize the target image, the search image cropped from the new frame and their heat maps as input into a localization network to find the final target position. Compared to the other state-of-the-art trackers, the proposed method shows the comparable and at times better performance in real-time.
Tranmer, Mark; Steel, David; Browne, William J
The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications.
Full Text Available It is a classic topic of social network analysis to evaluate the importance of nodes and identify the node that takes on the role of core or bridge in a network. Because a single indicator is not sufficient to analyze multiple characteristics of a node, it is a natural solution to apply multiple indicators that should be selected carefully. An intuitive idea is to select some indicators with weak correlations to efficiently assess different characteristics of a node. However, this paper shows that it is much better to select the indicators with strong correlations. Because indicator correlation is based on the statistical analysis of a large number of nodes, the particularity of an important node will be outlined if its indicator relationship doesn't comply with the statistical correlation. Therefore, the paper selects the multiple indicators including degree, ego-betweenness centrality and eigenvector centrality to evaluate the importance and the role of a node. The importance of a node is equal to the normalized sum of its three indicators. A candidate for core or bridge is selected from the great degree nodes or the nodes with great ego-betweenness centrality respectively. Then, the role of a candidate is determined according to the difference between its indicators' relationship with the statistical correlation of the overall network. Based on 18 real networks and 3 kinds of model networks, the experimental results show that the proposed methods perform quite well in evaluating the importance of nodes and in identifying the node role.
Full Text Available It is envisaged that in future Cognitive Radio (CR networks deployment, multiple radio access networks may coexist. The networks may have different characteristics in terms of multiple attributes. CRs will have choices of selecting the optimal network out of the available networks. Optimal network selection is a challenging task that can be performed by spectrum handoff with Multiple Attribute Decision Making (MADM. The spectrum handoff decision with MADM provides wider and optimal choice with quality of service. This motivates the devolopment of a spectrum handoff scheme with MADM methods such as simple additive weighting, a technique for order preference by similarity to the ideal solution, a grey relational analysis and a cost function based method, which is the objective of this study. The CR preferences are based on voice, video and data services, called triple play services. The numerical results show that all MADM methods are effective for selecting the optimal network for spectrum handoff with a reduced complexity for the spectrum handoff decision. The paper shows that the proposed spectrum handoff scheme can be effectively implemented to select the optimal network according to triple play services in CR networks.
Yuan, Lina; Chen, Huajun; Gong, Jing
The limited lifetime is one of the important factors restricted wireless sensor networks (WSNs), when possible, wireless nodes often operate with small batteries, while battery replacement is a very difficult and expensive. So the nodes must work long hours in the case of no battery replacement. Therefore, in WSNs, minimizing energy consumption is an important design consideration, at the same time, the transmission strategies of energy efficiency must be used for data forwarding. This paper, using cooperative multiple input multiple output(MIMO) technology combined with multiple hops technology, has put forward a new transmission model, i.e., the MIMO-MISO(multi-input multi-output)/MIMO-MIMO model. Simulation results demonstrate the proposed MIMO-MISO/MIMO-MIMO to minimize energy consumption of each node every node for multi-hop WSNs, to save a great deal of energy for a larger transmission distance, which makes the life of the entire network be extended.
Full Text Available Internet of Things (IoT consists of several tiny devices connected together to form a collaborative computing environment. Recently IoT technologies begin to merge with supervisory control and data acquisition (SCADA sensor networks to more efficiently gather and analyze real-time data from sensors in industrial environments. But SCADA sensor networks are becoming more and more vulnerable to cyber-attacks due to increased connectivity. To safely adopt IoT technologies in the SCADA environments, it is important to improve the security of SCADA sensor networks. In this paper we propose a multiple filtering technique based on whitelists to detect illegitimate packets. Our proposed system detects the traffic of network and application protocol attacks with a set of whitelists collected from normal traffic.
Full Text Available In the mammalian retina, bipolar cells and ganglion cells which stratify in sublamina a of the inner plexiform layer (IPL show OFF responses to light stimuli while those that stratify in sublamina b show ON responses. This functional relationship between anatomy and physiology is a key principle of retinal organization. However, there are at least three types of retinal neurons, including intrinsically photosensitive retinal ganglion cells (ipRGCs and dopaminergic amacrine cells, which violate this principle. These cell types have light-driven ON responses, but their dendrites mainly stratify in sublamina a of the IPL, the OFF sublayer. Recent anatomical studies suggested that certain ON cone bipolar cells make axonal or ectopic synapses as they descend through sublamina a, thus providing ON input to cells which stratify in the OFF sublayer. Using immunoelectron microscopy with 3-dimensional reconstruction, we have identified axonal synapses of ON cone bipolar cells in the rabbit retina. Ten calbindin ON cone bipolar axons made en passant ribbon synapses onto amacrine or ganglion dendrites in sublamina a of the IPL. Compared to the ribbon synapses made by bipolar terminals, these axonal ribbon synapses were characterized by a broad postsynaptic element that appeared as a monad and by the presence of multiple short synaptic ribbons. These findings confirm that certain ON cone bipolar cells can provide ON input to amacrine and ganglion cells whose dendrites stratify in the OFF sublayer via axonal synapses. The monadic synapse with multiple ribbons may be a diagnostic feature of the ON cone bipolar axonal synapse in sublamina a. The presence of multiple ribbons and a broad postsynaptic density suggest these structures may be very efficient synapses. We also identified axonal inputs to ipRGCs with the architecture described above.
Eswaran, Sharanya; Misra, Archan; La Porta, Thomas; Leung, Kin
This paper examines the practical challenges in the application of the distributed network utility maximization (NUM) framework to the problem of resource allocation and sensor device adaptation in a mission-centric wireless sensor network (WSN) environment. By providing rich (multi-modal), real-time information about a variety of (often inaccessible or hostile) operating environments, sensors such as video, acoustic and short-aperture radar enhance the situational awareness of many battlefield missions. Prior work on the applicability of the NUM framework to mission-centric WSNs has focused on tackling the challenges introduced by i) the definition of an individual mission's utility as a collective function of multiple sensor flows and ii) the dissemination of an individual sensor's data via a multicast tree to multiple consuming missions. However, the practical application and performance of this framework is influenced by several parameters internal to the framework and also by implementation-specific decisions. This is made further complex due to mobile nodes. In this paper, we use discrete-event simulations to study the effects of these parameters on the performance of the protocol in terms of speed of convergence, packet loss, and signaling overhead thereby addressing the challenges posed by wireless interference and node mobility in ad-hoc battlefield scenarios. This study provides better understanding of the issues involved in the practical adaptation of the NUM framework. It also helps identify potential avenues of improvement within the framework and protocol.
Ravcheev, Dmitry A; Godzik, Adam; Osterman, Andrei L; Rodionov, Dmitry A
Bacteroides thetaiotaomicron, a predominant member of the human gut microbiota, is characterized by its ability to utilize a wide variety of polysaccharides using the extensive saccharolytic machinery that is controlled by an expanded repertoire of transcription factors (TFs). The availability of genomic sequences for multiple Bacteroides species opens an opportunity for their comparative analysis to enable characterization of their metabolic and regulatory networks. A comparative genomics approach was applied for the reconstruction and functional annotation of the carbohydrate utilization regulatory networks in 11 Bacteroides genomes. Bioinformatics analysis of promoter regions revealed putative DNA-binding motifs and regulons for 31 orthologous TFs in the Bacteroides. Among the analyzed TFs there are 4 SusR-like regulators, 16 AraC-like hybrid two-component systems (HTCSs), and 11 regulators from other families. Novel DNA motifs of HTCSs and SusR-like regulators in the Bacteroides have the common structure of direct repeats with a long spacer between two conserved sites. The inferred regulatory network in B. thetaiotaomicron contains 308 genes encoding polysaccharide and sugar catabolic enzymes, carbohydrate-binding and transport systems, and TFs. The analyzed TFs control pathways for utilization of host and dietary glycans to monosaccharides and their further interconversions to intermediates of the central metabolism. The reconstructed regulatory network allowed us to suggest and refine specific functional assignments for sugar catabolic enzymes and transporters, providing a substantial improvement to the existing metabolic models for B. thetaiotaomicron. The obtained collection of reconstructed TF regulons is available in the RegPrecise database (http://regprecise.lbl.gov).
Rakoff-Nahoum, Seth; Coyne, Michael J.; Comstock, Laurie E.
Summary Background: The human intestine is colonized with trillions of microorganisms important to health and disease. There has been an intensive effort to catalog the species and genetic content of this microbial ecosystem. However, little is known of the ecological interactions between these microbes, a prerequisite to understanding the dynamics and stability of this host-associated microbial community. Here we perform a systematic investigation of public goods-based syntrophic interactions among the abundant human gut bacteria, the Bacteroidales. Results: We find evidence for a rich interaction network based on the breakdown and use of polysaccharides. Species that utilize a particular polysaccharide (producers) liberate polysaccharide breakdown products (PBP) that are consumed by other species unable to grow on the polysaccharide alone (recipients). Cross-species gene addition experiments demonstrate that recipients can grow on a polysaccharide if the producer-derived glycoside hydrolase, responsible for PBP generation, is provided. These producer-derived glycoside hydrolases are public goods transported extracellularly in outer membrane vesicles allowing for the creation of PBP and concomitant recipient growth spatially distant from the producer. Recipients can exploit these ecological interactions and conditionally outgrow producers. Finally, we show that these public good-based interactions occur among Bacteroidales species co-resident within a natural human intestinal community. Conclusions: This study examines public-goods based syntrophic interactions between bacterial members of the critically important gut microbial ecosystem. This polysaccharide-based network likely represents foundational relationships creating organized ecological units within the intestinal microbiota, knowledge of which can be applied to impact human health. PMID:24332541
Berechman, Joseph; deWit, Jaap
. In the meantime, open skies agreements have been concluded between the USA and most of the EU member states to facilitate strategic alliances between airlines of the states involved. As a result of this on-going liberalization the model of the single 'national' carrier using the national home base as its single hub for the designated third, fourth and sixth freedom operations will stepwise disappear. Within the EU the concept of the national carrier has already been replaced by that of the community carrier. State ownership in more and more European carriers is reduced. On the longer run mergers or even bankruptcy will further undermine the "single national carrier - single national hub" model in Europe. In the meantime, strategic alliances between national carriers in Europe will already reduce the airlines' loyalty to a single airport. Profit maximization and accountability to share holders will supersede the loyalty of these newly emerging alliances, probably looking for the opportunities of a multiple hub network to adequately cover the whole European market. As a consequence, some European airports might see a substantial decline in arriving, departing and transfer traffic, thus in revenues and financial solvency, as well as in their connection to other inter-continental and intra-European destinations. At the same time, other airports might realize a significant increase in traffic as they will be sought after by the profit maximizing airlines as their major gateway hubs. Which will be the losing airports and which will be the winning ones? Can airports anticipate the actions of airlines in deregulated markets and utilize policies which will improve their relative position? If so, what should be these anticipatory policies? These questions become the more urgent, since an increasing number of major European airports will be privatized in the near future. Although increasing airport congestion in Europe will also be reflected in a growing demand pressure for
increases do not necessarily come from higher gain but rather from a good front -to- back ratio. The front -to- back ratio was the dominant factor because...registered trademark of Xerox Corporation. Released by D. E . Hurdsman, Head Applied Electromagnetics Branch Under authority of J. McGee, Head...input multiple-output (MIMO) communication systems use multiple anten- nas and transceivers at both ends of a wireless link to utilize spatial
Gerstein, Mark [Yale Univ., New Haven, CT (United States). Gerstein Lab.
In this grant, we have systematically investigated the integrated networks, which are responsible for the coordination of activity between metabolic pathways in prokaryotes. We have developed several computational tools to analyze the topology of the integrated networks consisting of metabolic, regulatory, and physical interaction networks. The tools are all open-source, and they are available to download from Github, and can be incorporated in the Knowledgebase. Here, we summarize our work as follow. Understanding the topology of the integrated networks is the first step toward understanding its dynamics and evolution. For Aim 1 of this grant, we have developed a novel algorithm to determine and measure the hierarchical structure of transcriptional regulatory networks . The hierarchy captures the direction of information flow in the network. The algorithm is generally applicable to regulatory networks in prokaryotes, yeast and higher organisms. Integrated datasets are extremely beneficial in understanding the biology of a system in a compact manner due to the conflation of multiple layers of information. Therefore for Aim 2 of this grant, we have developed several tools and carried out analysis for integrating system-wide genomic information. To make use of the structural data, we have developed DynaSIN for protein-protein interactions networks with various dynamical interfaces . We then examined the association between network topology with phenotypic effects such as gene essentiality. In particular, we have organized E. coli and S. cerevisiae transcriptional regulatory networks into hierarchies. We then correlated gene phenotypic effects by tinkering with different layers to elucidate which layers were more tolerant to perturbations . In the context of evolution, we also developed a workflow to guide the comparison between different types of biological networks across various species using the concept of rewiring , and Furthermore, we have developed
Hedlof, R. M.; Ordonez, C. A.
An analytical model and a Monte Carlo simulation of an antihydrogen gravity experiment that would employ multiple apertures are presented. Such an experiment may be possible at the CERN Antiproton Decelerator facility. The model was developed with the primary goal of reducing the experimental run time necessary to determine the direction of free fall acceleration for antimatter in the gravitational field of the Earth. The experiment would confine cryogenic antihydrogen plasma for producing antihydrogen (e.g., by three-body recombination). A cylindrical drift tube would have a horizontal axis of symmetry, with a series of coaxial apertures positioned on one or both sides of the region for antihydrogen production. The experiment would employ a detector capable of distinguishing between cosmic rays and antihydrogen annihilations. The distribution of annihilations on the drift tube would be azimuthally asymmetric for a short distance beyond each aperture within a shadow region depending on the direction of the gravitational acceleration of antimatter. The analytical model is used to determine the probability that an antiatom would annihilate within one of the shadow regions for specified experimental parameters.
Rami J Oweis
Full Text Available Background: Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. Methods: This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs and adaptive neuro-fuzzy inference systems (ANFIS toolboxes. The methods have been applied to 10 different respiratory sounds for classification. Results: The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. Conclusions: The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.
Oweis, Rami J; Abdulhay, Enas W; Khayal, Amer; Awad, Areen
Computerized lung sound analysis involves recording lung sound via an electronic device, followed by computer analysis and classification based on specific signal characteristics as non-linearity and nonstationarity caused by air turbulence. An automatic analysis is necessary to avoid dependence on expert skills. This work revolves around exploiting autocorrelation in the feature extraction stage. All process stages were implemented in MATLAB. The classification process was performed comparatively using both artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) toolboxes. The methods have been applied to 10 different respiratory sounds for classification. The ANN was superior to the ANFIS system and returned superior performance parameters. Its accuracy, specificity, and sensitivity were 98.6%, 100%, and 97.8%, respectively. The obtained parameters showed superiority to many recent approaches. The promising proposed method is an efficient fast tool for the intended purpose as manifested in the performance parameters, specifically, accuracy, specificity, and sensitivity. Furthermore, it may be added that utilizing the autocorrelation function in the feature extraction in such applications results in enhanced performance and avoids undesired computation complexities compared to other techniques.
Full Text Available In a wireless sensor network (WSN, many applications have limited energy resources for data transmission. In order to accomplish a better green communication for WSN, a hybrid energy scheme can supply a more reliable energy source. In this article, hybrid energy utilization—which consists of constant energy source and solar harvested energy—is considered for WSN. To minimize constant energy usage from the hybrid source, a Markov decision process (MDP is designed to find the optimal transmission policy. With a finite packet buffer and a finite battery size, an MDP model is presented to define the states, actions, state transition probabilities, and the cost function including the cost values for all actions. A weighted sum of constant energy source consumption and a packet dropping probability (PDP are adopted as the cost value, enabling us to find the optimal solution for balancing the minimization of the constant energy source utilization and the PDP using a value iteration algorithm. As shown in the simulation results, the performance of optimal solution using MDP achieves a significant improvement compared to solution without its use.
Eigenvector centrality ......................................................88 xii THIS PAGE INTENTIONALLY LEFT BLANK xiii LIST OF ACRONYMS AND...should be engaged. This determination will be based on simple SNA centrality measures, total degree,9 betweenness,10 closeness,11 and Eigenvector ...11 Closeness centrality measures how close each node is to all the other nodes in a network by their path distance. 12 Eigenvector centrality
Multiple Cognitive Radio Networks The demand for wireless services is growing on a daily basis while spectral resources to support this growth are...static. Therefore, there is need for the adoption of a new spectrum sharing paradigm. Cognitive Radio (CR) is a revolutionary technology aiming to...27709-2211 Cognitive Radio REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR/MONITOR’S ACRONYM(S) ARO 8. PERFORMING
M. E. Migabo
Full Text Available Despite its low computational cost, the Gradient Based Routing (GBR broadcast of interest messages in Wireless Sensor Networks (WSNs causes significant packets duplications and unnecessary packets transmissions. This results in energy wastage, traffic load imbalance, high network traffic, and low throughput. Thanks to the emergence of fast and powerful processors, the development of efficient network coding strategies is expected to enable efficient packets aggregations and reduce packets retransmissions. For multiple sinks WSNs, the challenge consists of efficiently selecting a suitable network coding scheme. This article proposes a Cooperative and Adaptive Network Coding for GBR (CoAdNC-GBR technique which considers the network density as dynamically defined by the average number of neighbouring nodes, to efficiently aggregate interest messages. The aggregation is performed by means of linear combinations of random coefficients of a finite Galois Field of variable size GF(2S at each node and the decoding is performed by means of Gaussian elimination. The obtained results reveal that, by exploiting the cooperation of the multiple sinks, the CoAdNC-GBR not only improves the transmission reliability of links and lowers the number of transmissions and the propagation latency, but also enhances the energy efficiency of the network when compared to the GBR-network coding (GBR-NC techniques.
YAKIN, Ilker; TINMAZ, Hasan
.... The social networking websites have gained popularity in recent years; therefore, many research studies have been conducted to explain how the use of social networking websites for instructional purposes...
Zhan, Jiayi; Zhang, Mingming; Zhou, Mi; Liu, Bin; Chen, Dong; Liu, Yuanyuan; Chen, Qianqian; Qiu, Huayu; Yin, Shouchun
Supramolecular polymer networks have attracted considerable attention not only due to their topological importance but also because they can show some fantastic properties such as stimuli-responsiveness and self-healing. Although various supramolecular networks are constructed by supramolecular chemists based on different non-covalent interactions, supramolecular polymer networks based on multiple orthogonal interactions are still rare. Here, a supramolecular polymer network is presented on the basis of the host-guest interactions between dibenzo-24-crown-8 (DB24C8) and dibenzylammonium salts (DBAS), the metal-ligand coordination interactions between terpyridine and Zn(OTf)2 , and between 1,2,3-triazole and PdCl2 (PhCN)2 . The topology of the networks can be easily tuned from monomer to main-chain supramolecular polymer and then to the supramolecular networks. This process is well studied by various characterization methods such as (1) H NMR, UV-vis, DOSY, viscosity, and rheological measurements. More importantly, a supramolecular gel is obtained at high concentrations of the supramolecular networks, which demonstrates both stimuli-responsiveness and self-healing properties. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Full Text Available This paper presents the experimental performance evaluation results of the IEEE 802.15.4/4g/4e Smart Utility Networks (SUN in applications suited for outdoor environment. SUN is an advanced wireless communications network designed for reliable, low data rate, and low energy consumption networks for command-and-control applications like utility service, sensor network, and so on. IEEE 802.15.4g/4e is the international standard for SUN supported by multiple utility providers and product vendors. In this paper, a comprehensive field test was conducted by employing the implementation we have developed to evaluate the performance of the SUN devices based on IEEE 802.15.4/4g/4e standard. The output power of the implementation is 250 mW for extended range, reducible to 20 mW for short-range scalability and battery preservation. Results showed that in an outdoor line-of-sight environment, the achievable one-hop range of a 50 kbps SUN device was 450 m. Next, in a non-line-of-sight environment involving typical residential concrete building, the communications could be established penetrating obstructions to reach above the 11th storey, reaching the performance degradation limits at the 20th storey. Next, the network of the SUN system was proven to be capable of supporting a typical multihop tree network in a dense populated building, meeting the required performance by the standard.
Sabri, Bushra; Huerta, Julia; Alexander, Kamila A; St Vil, Noelle M; Campbell, Jacquelyn C; Callwood, Gloria B
This study examined knowledge, access, utilization, and barriers to use of resources among Black women exposed to multiple types of intimate partner violence in Baltimore, Maryland and the U.S. Virgin Islands (USVI). We analyzed quantitative survey data collected by 163 women recruited from primary care, prenatal or family planning clinics in Baltimore and the USVI. In addition we analyzed qualitative data from in-depth interviews with 11 women. Quantitative data were analyzed using descriptive statistics and qualitative data were analyzed using thematic analysis. A substantial proportion of Black women with multiple types of violence experiences lacked knowledge of, did not have access to, and did not use resources. Barriers to resource use were identified at the individual, relationship, and community levels. There is need for programs to develop awareness, promote access and utilization of resources, and eliminate barriers to resource use among abused Black women.
Full Text Available The growing energy consumption of communication networks has attracted the attention of the networking researchers in the last decade. In this context, the new architecture of Software-Defined Networks (SDN allows a flexible programmability, suitable for the power-consumption optimization problem. In this paper we address the issue of designing a novel distributed routing algorithm that optimizes the power consumption in large scale SDN with multiple domains. The solution proposed, called DEAR (Distributed Energy-Aware Routing, tackles the problem of minimizing the number of links that can be used to satisfy a given data traffic demand under performance constraints such as control traffic delay and link utilization. To this end, we present a complete formulation of the optimization problem that considers routing requirements for control and data plane communications. Simulation results confirm that the proposed solution enables the achievement of significant energy savings.
Hijazi, I.; Ehlers, M.; Zlatanova, S.; Isikdag, U.
The development of semantic 3D city models has allowed for new approaches to town planning and urban management (Benner et al. 2005) such as emergency and catastrophe planning, checking building developments, and utility networks. Utility networks inside buildings are composed of pipes and cables
Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei
Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.
Kim, Sang Hun; Yoon, Sung-Geun; Chae, Song Hwa; Park, Sunwon
Most chemical companies consume a lot of steam, water and electrical resources in the production process. Given recent record fuel costs, utility networks must be optimized to reduce the overall cost of production. Environmental concerns must also be considered when preparing modifications to satisfy the requirements for industrial utilities, since wastes discharged from the utility networks are restricted by environmental regulations. Construction of Eco-Industrial Parks (EIPs) has drawn attention as a promising approach for retrofitting existing industrial parks to improve energy efficiency. The optimization of the utility network within an industrial complex is one of the most important undertakings to minimize energy consumption and waste loads in the EIP. In this work, a systematic approach to optimize the utility network of an industrial complex is presented. An important issue in the optimization of a utility network is the desire of the companies to achieve high profits while complying with the environmental regulations. Therefore, the proposed optimization was performed with consideration of both economic and environmental factors. The proposed approach consists of unit modeling using thermodynamic principles, mass and energy balances, development of a multi-period Mixed Integer Linear Programming (MILP) model for the integration of utility systems in an industrial complex, and an economic/environmental analysis of the results. This approach is applied to the Yeosu Industrial Complex, considering seasonal utility demands. The results show that both the total utility cost and waste load are reduced by optimizing the utility network of an industrial complex. 2009 Elsevier Ltd. All rights reserved.
Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates
Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng
So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.
Rahadi, Dedi Rianto; Abdillah, Leon Andretti
Nowadays social media (Twitter, Facebook, etc.), not only simply as communication media, but also for promotion. Social networking media offers many business benefits for companies and organizations. Research purposes is to determine the model of social network media utilization as a promotional media for handicraft business in Palembang city. Qualitative and quantitative research design are used to know how handicraft business in Palembang city utilizing social media networking as a promotio...
Voss, Erica A; Makadia, Rupa; Matcho, Amy; Ma, Qianli; Knoll, Chris; Schuemie, Martijn; DeFalco, Frank J; Londhe, Ajit; Zhu, Vivienne; Ryan, Patrick B
To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research. Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results. Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour. The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases. Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.
Becker, T.; König, G.
Cartographic visualizations of crises are used to create a Common Operational Picture (COP) and enforce Situational Awareness by presenting relevant information to the involved actors. As nearly all crises affect geospatial entities, geo-data representations have to support location-specific analysis throughout the decision-making process. Meaningful cartographic presentation is needed for coordinating the activities of crisis manager in a highly dynamic situation, since operators' attention span and their spatial memories are limiting factors during the perception and interpretation process. Situational Awareness of operators in conjunction with a COP are key aspects in decision-making process and essential for making well thought-out and appropriate decisions. Considering utility networks as one of the most complex and particularly frequent required systems in urban environment, meaningful cartographic presentation of multiple utility networks with respect to disaster management do not exist. Therefore, an optimized visualization of utility infrastructure for emergency response procedures is proposed. The article will describe a conceptual approach on how to simplify, aggregate, and visualize multiple utility networks and their components to meet the requirements of the decision-making process and to support Situational Awareness.
Full Text Available This paper proposes a method to take advantage of fog computing and SDN in the connected vehicle environment, where communication channels are unstable and the topology changes frequently. A controller knows the current state of the network by maintaining the most recent network topology. Of all the information collected by the controller in the mobile environment, node mobility information is particularly important. Thus, we divide nodes into three classes according to their mobility types and use their related attributes to efficiently manage the mobile connections. Our approach utilizes mobility information to reduce control message overhead by adjusting the period of beacon messages and to support efficient failure recovery. One is to recover the connection failures using only mobility information, and the other is to suggest a real-time scheduling algorithm to recover the services for the vehicles that lost connection in the case of a fog server failure. A real-time scheduling method is first described and then evaluated. The results show that our scheme is effective in the connected vehicle environment. We then demonstrate the reduction of control overhead and the connection recovery by using a network simulator. The simulation results show that control message overhead and failure recovery time are decreased by approximately 55% and 5%, respectively.
Full Text Available Wireless Sensor Networks (WSNs are subject to node failures because of limited energy and link unreliability which makes the design of routing protocols in such networks a challenging task. The multipath routing scheme is an optimal alternative to address this problem which splits the traffic across multiple paths instead of routing all the traffic along a single path. However, using more paths introduces more contentions which degrade energy efficiency. The problem becomes even more difficult in the scenario of multiple sessions since different source-destination pairs may pass the same link which makes the flow distribution of each link uncertain. Our goal is to minimize the energy cost and provide the robust transmission by choosing the optimal paths. We first study the problem from a theoretical standpoint by mapping it to the multi-commodity network design problem. Since it is hard to build a global addressing scheme due to the great number of sensor nodes, we propose a Distributed Energy Efficient Routing protocol (D2ER. In D2ER, we employ the transportation method which can optimize the flow distribution with minimal energy consumption. Simulation results demonstrate that our optimal algorithm can save energy drastically.
Anwar, Asim; Seet, Boon-Chong; Ding, Zhiguo
Ubiquitous wireless sensor networks (UWSNs) have become a critical technology for enabling smart cities and other ubiquitous monitoring applications. Their deployment, however, can be seriously hampered by the spectrum available to the sheer number of sensors for communication. To support the communication needs of UWSNs without requiring more spectrum resources, the power-domain non-orthogonal multiple access (NOMA) technique originally proposed for 5th Generation (5G) cellular networks is investigated for UWSNs for the first time in this paper. However, unlike 5G networks that operate in the licensed spectrum, UWSNs mostly operate in unlicensed spectrum where sensors also experience cross-technology interferences from other devices sharing the same spectrum. In this paper, we model the interferences from various sources at the sensors using stochastic geometry framework. To evaluate the performance, we derive a theorem and present new closed form expression for the outage probability of the sensors in a downlink scenario under interference limited environment. In addition, diversity analysis for the ordered NOMA users is performed. Based on the derived outage probability, we evaluate the average link throughput and energy consumption efficiency of NOMA against conventional orthogonal multiple access (OMA) technique in UWSNs. Further, the required computational complexity for the NOMA users is presented.
Petrioli, Chiara (Universita di Roma); Carosi, Alessio (Universita di Roma); Basagni, Stefano (Northeastern University); Phillips, Cynthia Ann
via a traveling salesman heuristic, and computing feasible transitions using matching algorithms. This algorithm assumes sinks can get a schedule from a central server or a leader sink. If the network owner prefers the sinks make independent decisions, they can use our distributed heuristic. In this heuristic, sinks maintain estimates of the energy distribution in the network and move greedily (with some coordination) based on local search. This application uses the new SUCASA (Solver Utility for Customization with Automatic Symbol Access) facility within the PICO (Parallel Integer and Combinatorial Optimizer) integer programming solver system. SUCASA allows rapid development of customized math programming (search-based) solvers using a problem's natural multidimensional representation. In this case, SUCASA also significantly improves runtime compared to implementations in the ampl math programming language or in perl.
Buvaneswari, A; Polakos, Paul; Buvaneswari, Arumugam
In order to meet demanding performance objectives in Long Term Evolution (LTE) networks, it is mandatory to implement highly efficient, autonomic self-optimization and configuration processes. Self-optimization processes have already been studied in second generation (2G) and third generation (3G) networks, typically with the objective of improving radio coverage and channel capacity. The 3rd Generation Partnership Project (3GPP) standard for LTE self-organization of networks (SON) provides guidelines on self-configuration of physical cell ID and neighbor relation function and self-optimization for mobility robustness, load balancing, and inter-cell interference reduction. While these are very important from an optimization perspective of local phenomenon (i.e., the eNodeB's interaction with its neighbors), it is also essential to architect control algorithms to optimize the network as a whole. In this paper, we propose a Celnet Xplorer-based SON architecture that allows detailed analysis of network performan...
Wang, Shuliang; Zhang, Jianhua; Duan, Na
To understand the vulnerability of the power network from multiple perspectives, multi-angle and multi-dimensional vulnerability analysis as well as community based vulnerability analysis are proposed in this paper. Taking into account of central China power grid as an example, correlation analysis of different vulnerability models is discussed. Then, vulnerabilities produced by different vulnerability metrics under the given vulnerability models and failure scenarios are analyzed. At last, applying the community detecting approach, critical areas of central China power grid are identified, Vulnerable and robust communities on both topological and functional perspective are acquired and analyzed. The approach introduced in this paper can be used to help decision makers develop optimal protection strategies. It will be also useful to give a multiple vulnerability analysis of the other infrastructure systems.
Full Text Available With the ever-increasing wireless data application recently, considerable efforts have been focused on the designof distributed explicit rate scheme based on Network Utility Maximization (NUM or wireless multi-hop meshnetworks. This paper describes a novel wireless multi-hop multicast flow control scheme for wireless meshnetworks via 802.11, which is based on the distributed self-turning Optimal Proportional plus Second-orderDifferential (OPSD controller. The control scheme, which is located at the sources in the wireless multicastnetworks, can ensure short convergence time by regulating the transmission rate. We further analyze thetheoretical aspects of the proposed algorithm. Simulation results demonstrate the efficiency of the proposedscheme in terms of fast response time, low packet loss and error ration.
Full Text Available This paper will examine the development of sustainable SME methods for tracking tacit (informal knowledge transfer as a series of networks of larger complex system. Understanding sustainable systems begins with valuing tacit knowledge networks and their ability to produce connections on multiple levels. The behaviour of the social or socio aspects of a system in relation to the explicit formal/physical structures need to be understood and actively considered when utilizing methodologies for interacting within complex systems structures. This paper utilizes theory from several previous studies to underpin the key case study discussed. This approach involved examining the behavioural phenomena of an SME knowledge network. The knowledge network elements were highlighted to identify their value within an SME structure. To understand the value of these emergent elements from between tacit and explicit knowledge networks, is to actively, simultaneously and continuous support sustainable development for SME organizations. The simultaneous links within and between groups of organizations is crucial for understanding sustainable networking structures of complex systems.
A. A. Salama
Full Text Available In this paper, we present a review of different recommender system algorithms that are utilized in social networks based e-Learning systems. Future research will include our proposed our e-Learning system that utilizes Recommender System and Social Network. Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache in [21, 22, 23] and Salama et al. in [24-66].The purpose of this paper is to utilize a neutrosophic set to analyze social networks data conducted through learning activities.
Bhuvaneswari, R.; Viswanathan, M
Mobile Ad Hoc Networks (MANET) are the collection of mobile nodes without any centralized infrastructure. The underlying assumption is that the intermediate nodes cooperate in forwarding packets. Mobile nodes collects the route information through overhearing and stores these information in route caches with the use of Dynamic Source Routing (DSR) Protocol. These nodes consume power unnecessarily due to overhearing the transmissions of their neighbors. Due to this, the network performance is ...
Full Text Available The aim of the project was to analyze the behavior of military communication networks based on work with real data collected continuously since 2005. With regard to the nature and amount of the data, data mining methods were selected for the purpose of analyses and experiments. The quality of real data is often insufficient for an immediate analysis. The article presents the data cleaning operations which have been carried out with the aim to improve the input data sample to obtain reliable models. Gradually, by means of properly chosen SW, network models were developed to verify generally valid patterns of network behavior as a bulk service. Furthermore, unlike the commercially available communication networks simulators, the models designed allowed us to capture nonstandard models of network behavior under an increased load, verify the correct sizing of the network to the increased load, and thus test its reliability. Finally, based on previous experience, the models enabled us to predict emergency situations with a reasonable accuracy.
Full Text Available Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN is shown to be able to predict the Air Pollution Index (API with a Mean Squared Error (MSE and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.
García Izquierdo, Mariano; García Izquierdo, Antonio León
The present study examined the utility of decision making in personnel selection comparing multiple and composite criteria by discriminative efficiency, in a bricklaying training program. We have found valid predictors (aptitudes, personality and experience) with different forms of the criterion, the ERPF scale, in logistic and multiple regression analysis. Results seem indicate that there is not a better criterion, so it depends on different conditions and different aims to reach. We proposed criteria as combining measures but not opposed.
utilizes CUDA to accelerate the computation of MD5 hashes for detecting malicious files, a technique commonly used in antivirus products such as clamAV...matching and hash calculation. Kouzinopoulos and Margaritis utilize the GPU to perform string matching using parallel implementations of several...forensics in the area of data carving. The GPU is used to hash byte patterns and search a hash database in GPU memory, realizing a 13x speedup over the CPU
Full Text Available In NDMA (network diversity multiple access, protocol-controlled retransmissions are used to create a virtual MIMO (multiple-input multiple-output system, where collisions can be resolved via source separation. By using this retransmission diversity approach for collision resolution, NDMA is the family of random access protocols with the highest potential throughput. However, several issues remain open today in the modeling and design of this type of protocol, particularly in terms of dynamic stable performance and backlog delay. This paper attempts to partially fill this gap by proposing a Markov model for the study of the dynamic-stable performance of a symmetrical and non-blind NDMA protocol assisted by a multiple-antenna receiver. The model is useful in the study of stability aspects in terms of the backlog-user distribution and average backlog delay. It also allows for the investigation of the different states of the system and the transition probabilities between them. Unlike previous works, the proposed approach considers the imperfect estimation of the collision multiplicity, which is a crucial process to the performance of NDMA. The results suggest that NDMA improves not only the throughput performance over previous solutions, but also the average number of backlogged users, the average backlog delay and, in general, the stability of random access protocols. It is also shown that when multiuser detection conditions degrade, ALOHA-type backlog retransmission becomes relevant to the stable operation of NDMA.
Moussawi, Alaa; Derzsy, Noemi; Lin, Xin; Szymanski, Boleslaw; Korniss, Gyorgy
Flow-driven networks are particularly prone to cascading failures. These failures are non self-averaging and this makes them very difficult to predict or subdue. Previous work has suggested that uniformly increasing edge or node capacities may lead to larger failures. This suggests that some nodes/edges may act as fuses and mitigate cascading failures. We investigate this idea, and analyze how properties of the initiators of the cascade influence its outcome. We also discuss how stochastic node capacity allocation can be utilized to mitigate cascades induced by multiple initiators. We demonstrate the efficacy of these strategies on random geometric graphs (RGG) and the UCTE European electrical power transmission network, with capacities allocated in a fashion similar to the industry standard. Supported in part by DTRA and NSF.
This presentation from a Solar Utilization in Higher Education Networking and Information webinar covers financing and project economics issues related to solar project development in the higher education sector.
Samuel de Barros Moraes; Celi Langhi; Marcos Crivelaro
This case study, based on interviews and technical analysis of a Brazilian water utility with more than 10 million clients, aims to understand what kind of adjusts on a telecommunications network...
Jefferies, Ann; Simmons, Brian; Ng, Eugene; Skidmore, Martin
Competency based medical education involves assessing physicians-in-training in multiple roles. Training programs are challenged by the need to introduce appropriate yet feasible assessment methods. We therefore examined the utility of a structured oral examination (SOE) in the assessment of the 7 CanMEDS roles (Medical Expert, Communicator,…
Full Text Available This paper investigates a wireless cooperative relay network with multiple relays communicating with the destination over orthogonal channels. Proposed is a cooperative transmission scheme that employs two signal-to-noise ratio (SNR thresholds and multiple hard-decision detections (HDD at the destination. One SNR threshold is used to select transmitting relays, and the other threshold is used at the destination for detection. Then the destination simply combines all the hard-decision results and makes the final binary decision based on majority voting. Focusing on the decode-and-forward (DF relaying protocol, the average bit error probability is derived and diversity analysis is carried out. It is shown that the full diversity order can be achieved by setting appropriate thresholds even when the destination does not know the exact or average SNRs of the source-relay links. The performance analysis is further extended to multi-hop cooperation and/or with the presence of a direct link where multiple thresholds are needed. By combining the multiple-SNR threshold method with a selection of the best relaying link, a high spectral-efficiency cooperative transmission scheme is further presented. Simulation results verify the theoretical analysis and demonstrate performance advantage of our proposed schemes over the existing ones.
Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S
Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.
International Trade (IT) plays a fundamental role in today's economy: by connecting world countries production and consumption processes, it radically contributes in shaping their economy and development path. Although its evolving structure and determinants have been widely analyzed in the literature, much less has been done to understand its interplay with other complex phenomena. The aim of this work is, precisely in this direction, to study the relations of IT with International Migration (IM) and Foreign Direct Investments (FDI). In both cases the procedure used is to first approach the problem in a multiple-networks perspective and than deepen the analysis by using ad hoc econometrics techniques. With respect to IM, a general positive correlation with IT is highlighted and product categories for which this effect is stronger are identified and cross-checked with previous classifications. Next, employing spatial econometric techniques and proposing a new way to define country neighbors based on the most ...
Full Text Available This paper deals with the impact of transcoding on the speech quality. We have focused mainly on the transcoding between codecs without the negative influence of the network parameters such as packet loss and delay. It has ensured objective and repeatable results from our measurement. The measurement was performed on the Transcoding Measuring System developed especially for this purpose. The system is based on the open source projects and is useful as a design tool for VoIP system administrators. The paper compares the most used codecs from the transcoding perspective. The multiple transcoding between G711, GSM and G729 codecs were performed and the speech quality of these calls was evaluated. The speech quality was measured by Perceptual Evaluation of Speech Quality method, which provides results in Mean Opinion Score used to describe the speech quality on a scale from 1 to 5. The obtained results indicate periodical speech quality degradation on every transcoding between two codecs.
Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parametrized by many (unknown) parameters....... A goal is to understand how properties of the dynamical systems depend on the parameters. Qualitative properties relating to the behaviour of a dynamical system are locally inferred from the system at steady state. Here, we focus on steady states that are the positive solutions to a parametrized system...... there is one or more solutions is non-trivial. We present a new method, based on so-called injectivity, to preclude or assert that multiple positive solutions exist. The results apply to generalized polynomials and variables can be restricted to the linear, parameter-independent first integrals...
Full Text Available The paper considers the connected target coverage (CTC problem in wireless heterogeneous sensor networks (WHSNs with multiple sensing units, termed MU-CTC problem. MU-CTC problem can be reduced to a connected set cover problem and further formulated as an integer linear programming (ILP problem. However, the ILP problem is an NP-complete problem. Therefore, two distributed heuristic schemes, REFS (remaining energy first scheme and EEFS (energy efficiency first scheme, are proposed. In REFS, each sensor considers its remaining energy and its neighbors’ decisions to enable its sensing units and communication unit such that all targets can be covered for the required attributes and the sensed data can be delivered to the sink. The advantages of REFS are its simplicity and reduced communication overhead. However, to utilize sensors’ energy efficiently, EEFS is proposed. A sensor in EEFS considers its contribution to the coverage and the connectivity to make a better decision. To our best knowledge, this paper is the first to consider target coverage and connectivity jointly for WHSNs with multiple sensing units. Simulation results show that REFS and EEFS can both prolong the network lifetime effectively. EEFS outperforms REFS in network lifetime, but REFS is simpler.
Shih, Kuei-Ping; Deng, Der-Jiunn; Chang, Ruay-Shiung; Chen, Hung-Chang
The paper considers the connected target coverage (CTC) problem in wireless heterogeneous sensor networks (WHSNs) with multiple sensing units, termed MU-CTC problem. MU-CTC problem can be reduced to a connected set cover problem and further formulated as an integer linear programming (ILP) problem. However, the ILP problem is an NP-complete problem. Therefore, two distributed heuristic schemes, REFS (remaining energy first scheme) and EEFS (energy efficiency first scheme), are proposed. In REFS, each sensor considers its remaining energy and its neighbors' decisions to enable its sensing units and communication unit such that all targets can be covered for the required attributes and the sensed data can be delivered to the sink. The advantages of REFS are its simplicity and reduced communication overhead. However, to utilize sensors' energy efficiently, EEFS is proposed. A sensor in EEFS considers its contribution to the coverage and the connectivity to make a better decision. To our best knowledge, this paper is the first to consider target coverage and connectivity jointly for WHSNs with multiple sensing units. Simulation results show that REFS and EEFS can both prolong the network lifetime effectively. EEFS outperforms REFS in network lifetime, but REFS is simpler.
Li, Siqi; Jiang, Huiyan; Pang, Wenbo
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.
Full Text Available This paper proposes a novel paradigm for network congestion control. Instead of perpetual conflict as in TCP, a proof-of-concept first-ever protocol enabling inter-flow communication without infrastructure support thru a side channel constructed on generic FIFO queue behaviour is presented. This enables independent flows passing thru the same bottleneck queue to communicate and achieve fair capacity sharing and a stable equilibrium state in a rapid fashion.
Jawaid, Masood; Khan, Muhammad Hassaan; Bhutto, Shahzadi Nisar
To find out the frequency and contents of online social networking (Facebook) among medical students of Dow University of Health Sciences. The sample of the study comprised of final year students of two medical colleges of Dow University of Health Sciences - Karachi. Systematic search for the face book profiles of the students was carried out with a new Facebook account. In the initial phase of search, it was determined whether each student had a Facebook account and the status of account as ''private'' ''intermediate'' or ''public'' was also sought. In the second phase of the study, objective information including gender, education, personal views, likes, tag pictures etc. were recorded for the publicly available accounts. An in depth qualitative content analysis of the public profiles of ten medical students, selected randomly with the help of random number generator technique was conducted. Social networking with Facebook is common among medical students with 66.9% having an account out of a total 535 students. One fifth of profiles 18.9% were publicly open, 36.6% profiles were private and 56.9% were identified to have an intermediate privacy setting, having customized settings for the profile information. In-depth analysis of some public profiles showed that potentially unprofessional material mostly related to violence and politics was posted by medical students. The usage of social network (Facebook) is very common among students of the university. Some unprofessional posts were also found on students' profiles mostly related to violence and politics.
Martins, Indayara B.; Aldaya, Ivan; Perez-Sanchez, G.; Gallion, Philippe
In this paper, the effects of gridless spectrum allocation in Wavelength Division Multiplexed (WDM) optical networks are examined. The advanced modulation formats and multi-rate transmissions of the signals, which are key parameters in the optical system project, are taken into account. The consumed spectrum, as well as the impact of linear and nonlinear impairments on the signal transmission, are compared to WDM network adopting standard grid and gridless ITU. To analyze the influence of these physical effects, some key network design parameters are monitored and evaluated, such as the guard band size, the signal occupied bandwidth, the laser power and the quality of channels. The applied signal modulation formats were On/Off Keying (OOK), Quadrature Phase Shift keying (QPSK), and Dual Polarization State Phase Modulation (DP-QPSK), whereas the transmission rate per wavelength was varied from 10 Gb/s to 100Ghz. The guard band, signal band, and laser power were swept and the resulted Bit Error Rate (BER) was estimated from the eye-diagram. Analytical calculations and simulations are conducted in order to evaluate the impact of the gridless spectrum allocation on both the spectral consumption and the signal quality of transmission (QoT). Results reveal that a gridless transmission system reduces the spectral consumption while offering an acceptable QoT. This work was carried out with both analytical modeling and numerical calculation using the Optisystem as well as Matlab.
Madsen, Jacob Theilgaard; Kristensen, Thomas le Fevre; Olsen, Rasmus Løvenstein
A smart grid is a complex system consisting of a wide range of electric grid components, entities controlling power distribution, generation and consumption, and a communication network supporting data exchange. This paper focuses on the influence of imperfect network conditions on smart grid con......- trollers, and how this can be counteracted by utilizing Quality of Service (QoS) information from the communication network. Such an interface between grid controller and network QoS is particularly relevant for smart grid scenarios that use third party communication network infrastructure, where...... modification of networking and lower layer protocols are impossible. This paper defines a middleware solution for adaptation of smart grid control, which uses network QoS information and interacts with the smart grid controller to increase dependability. In order to verify the methodology, an example scenario...
AUTHOR|(CDS)2088631; The ATLAS collaboration
Because of their performance characteristics high-performance fabrics like Infiniband or OmniPath are interesting technologies for many local area network applications, including data acquisition systems for high-energy physics experiments like the ATLAS experiment at CERN. This paper analyzes existing APIs for high-performance fabrics and evaluates their suitability for data acquisition systems in terms of performance and domain applicability. The study finds that existing software APIs for high-performance interconnects are focused on applications in high-performance computing with specific workloads and are not compatible with the requirements of data acquisition systems. To evaluate the use of high-performance interconnects in data acquisition systems a custom library, NetIO, is presented and compared against existing technologies. NetIO has a message queue-like interface which matches the ATLAS use case better than traditional HPC APIs like MPI. The architecture of NetIO is based on a interchangeable bac...
Sung, Grace H H; Aoyagi, Takahiro; Hernandez, Marco; Hamaguchi, Kiyoshi; Kohno, Ryuji
For privacy and radio propagation controls, electromagnetic shielding textile could be adopted in WBANs. The effect of including a commercially available electromagnetic shielding apron in WBANs was examined in this paper. By having both the coordinator and the sensor covered by the shielding apron, signal could be confined around the body; however signal strength can be greatly influenced by body movements. Placing the shielding apron underneath both antennas, the transmission coefficient could be on average enhanced by at least 10dB, with less variation comparing to the case when apron does not exist. Shielding textiles could be utilized in designing a smart suit to enhance WBANs performance, and to prevent signals travelling beyond its intended area.
however to expect link proxy routers to provide infinite buffer capacity . Accordingly, the centralized memory manager built into the model requires...utilized within strategically placed backbone routers under military jurisdiction. 5.3.4 Custodial TCP Flow Control Link proxy buffer capacity enables...Moderate and heavy channel losses however would require considerable buffer capacity , 5−8 especially if multiple high capacity TCP flows
Full Text Available The advances in biological technologies make it possible to generate data for multiple conditions simultaneously. Discovering the condition-specific modules in multiple networks has great merit in understanding the underlying molecular mechanisms of cells. The available algorithms transform the multiple networks into a single objective optimization problem, which is criticized for its low accuracy. To address this issue, a multi-objective genetic algorithm for condition-specific modules in multiple networks (MOGA-CSM is developed to discover the condition-specific modules. By using the artificial networks, we demonstrate that the MOGA-CSM outperforms state-of-the-art methods in terms of accuracy. Furthermore, MOGA-CSM discovers stage-specific modules in breast cancer networks based on The Cancer Genome Atlas (TCGA data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm provide an effective way to analyze multiple networks.
Full Text Available The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.
Van Eeckhout, Edward M [Los Alamos National Laboratory; Leishman, Deborah A [Los Alamos National Laboratory; Gibson, William L [Los Alamos National Laboratory
Bayesian network tool (called IKE for Integrated Knowledge Engine) has been developed to assess the probability of undesirable events. The tool allows indications and observables from sensors and/or intelligence to feed directly into hypotheses of interest, thus allowing one to quantify the probability and uncertainty of these events resulting from very disparate evidence. For example, the probability that a facility is processing nuclear fuel or assembling a weapon can be assessed by examining the processes required, establishing the observables that should be present, then assembling information from intelligence, sensors and other information sources related to the observables. IKE also has the capability to determine tasking plans, that is, prioritize which observable should be collected next to most quickly ascertain the 'true' state and drive the probability toward 'zero' or 'one.' This optimization capability is called 'evidence marshaling.' One example to be discussed is a denied facility monitoring situation; there is concern that certain process(es) are being executed at the site (due to some intelligence or other data). We will show how additional pieces of evidence will then ascertain with some degree of certainty the likelihood of this process(es) as each piece of evidence is obtained. This example shows how both intelligence and sensor data can be incorporated into the analysis. A second example involves real-time perimeter security. For this demonstration we used seismic, acoustic, and optical sensors linked back to IKE. We show how these sensors identified and assessed the likelihood of 'intruder' versus friendly vehicles.
Vecchio, Fabrizio; Miraglia, Francesca; Porcaro, Camillo; Cottone, Carlo; Cancelli, Andrea; Rossini, Paolo Maria; Tecchio, Franca
People with multiple sclerosis (MS) frequently complain of excessive fatigue, which is the most disabling symptom for half of them. While the few drugs used to treat MS fatigue are of limited utility, we recently observed the efficacy of a personalized neuromodulation treatment. Here, we aim at strengthening knowledge of the brain network changes that occur when MS fatigue increases, using graph theory. We collected electroencephalographic (EEG; 23 or 64 channels) data in resting state with eyes open in 27 relapsing-remitting (RR) patients with mild MS (EDSS ≤2), suffering a wide range of fatigue as scored by the modified Fatigue Impact Scale (mFIS) (2-69, within a total range 0-84). To estimate graph theory small-world index (SW), we calculated the lagged linear coherence between EEG cortical eLORETA sources, in the standard frequency bands delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-45 Hz). We calculated the SW of these undirected and weighted networks separately in the four left and right frontal (motor) and parieto-occipito-temporal (sensory) brain networks. A correlative analysis demonstrated increased fatigue symptoms along with the SW specifically in the Sensory network of the left dominant hemisphere in the beta1 band (Pearson's r = 0.404, P = .020). Our study indicates a specific involvement of the dominant-hemisphere sensory network in MS fatigue. It suggests that compensatory neuromodulation interventions could enhance efficacy in relieving this debilitating symptom by targeting this area. © The Author(s) 2016.
Oliker, Nurit; Ostfeld, Avi
Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.
Cook, Benjamin Stassen
In this work, the smallest reported inkjet-printed UWB antenna is proposed that utilizes a fractal matching network to increase the performance of a UWB microstrip monopole. The antenna is inkjet-printed on a paper substrate to demonstrate the ability to produce small and low-cost UWB antennas with inkjet-printing technology which can enable compact, low-cost, and environmentally friendly wireless sensor network. © 2012 IEEE.
Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.
The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.
Full Text Available To provide layered multicast with responsiveness, efficiency in network utilization, scalability and fairness (including inter-protocol fairness, intra-protocol fairness, intra-session fairness and TCP-friendliness for layered multicast, we propose in this paper a new multicast congestion control, called Explicit Rate Adjustment (ERA. Our protocol uses an algorithm relying on TCP throughput equation and Packet-bunch Probe techniques to detect optimal bandwidth utilization; then adjusts the reception rate accordingly. We have built ERA into a network simulator (ns2 and demonstrate via simulations that the goals are reached.
Oynhausen, Svenja; Alcauskas, Megan; Hannigan, Christine; Bencosme, Yadira; Müller, Marcus; Lublin, Fred; Krieger, Stephen
There has been no systematic analysis of emergency department (ED) utilization in the multiple sclerosis (MS) population. We investigated the acute-care needs of MS patients using ED as a route for entry into healthcare services. ED visits made by MS patients were identified. Data extracted included demographics, medical/neurological history, and workup/management in the ED. The Mount Sinai ED received 569 visits from 224 MS patients during a 3-year period, of whom 33.5% were covered by Medicaid and 12.9% were uninsured. Patients with an Expanded Disability Status Scale score of ≥6 accounted for 54%, 50.5% of relapsing remitting MS patients were being treated with disease-modifying therapies, and 74.5% of the ED visits were non-neurological. Patients with mild-to-moderate MS were more likely to present to the ED for issues directly related to MS such as acute exacerbations, while those with severe MS presented more often due to medical issues indirectly related to MS, such as urinary tract infections (p<0.0001). Most MS patients seeking ED care suffer from acute non-neurological problems. The MS patients presenting to the ED tended to be underinsured, had high levels of disability, and were undertreated with disease-modifying therapies. The acute-care needs of MS patients evolve over the disease course, as do the resources that must be utilized in providing emergency care across the spectrum of MS severity. Understanding the characteristics, problems, and needs of MS patients utilizing the ED is an important step in improving care in this population from both clinical and public health perspectives.
Duim, van der R.; Ren, C.; Johannesson, G.T.
In this article, we demonstrate how Actor–Network Theory has been translated into tourism research. The article presents and discusses three concepts integral to the Actor–Network Theory approach: ordering, materiality, and multiplicity. We first briefly introduce Actor–Network Theory and draw
Dmitrienko, V D; Zakovorotnyi, A Yu; Leonov, S Yu; Khavina, I P
A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input.
Silverberg, Jonathan I; Simpson, Eric L
Atopic dermatitis (AD) is associated with multiple comorbid conditions, such as asthma and food allergy. We sought to determine the impact of eczema severity on the development of these disorders and other non-atopic comorbidities in AD. We used the 2007 National Survey of Children's Health, a prospective questionnaire-based study of a nationally representative sample of 91,642 children aged 0-17 yr. Prevalence and severity of eczema, asthma, hay fever and food allergy, sleep impairment, healthcare utilization, recurrent ear infections, and visual and dental problems were determined. In general, more severe eczema is correlated with poorer overall health, impaired sleep, and increased healthcare utilization, including seeing a specialist, compared with children with mild or moderate disease (Rao-Scott chi-squared test, p tooth decay (p = 0.13). These data indicate that severe eczema is associated with multiple comorbid chronic health disorders, impaired overall health, and increased healthcare utilization. Further, these data suggest that children with eczema are at risk of decreased oral health. Future studies are warranted to verify this novel association. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Francomano, Jesse A; Harpin, Scott B
Social networking site use has exploded among youth in the last few years and is being adapted as an important tool for healthcare interventions and serving as a platform for adolescents to gain access to health information. The aim of this study was to examine the strengths, weaknesses, and best practices of utilizing Facebook in adolescent health promotion and research via pragmatic literature review. We also examine how sites can facilitate ethically sound healthcare for adolescents, particularly at-risk youth. We conducted a literature review of health and social sciences literature from the past 5 years related to adolescent health and social network site use. Publications were grouped by shared content then categorized by themes. Five themes emerged: access to healthcare information, peer support and networking, risk and benefits of social network site use in care delivery, overcoming technological barriers, and social network site interventions. More research is needed to better understand how such Web sites can be better utilized to provide access to adolescents seeking healthcare. Given the broad reach of social network sites, all health information must be closely monitored for accurate, safe distribution. Finally, consent and privacy issues are omnipresent in social network sites, which calls for standards of ethical use.
Mikler, A.R.; Honavar, V.; Wong, J.S.K. [Iowa State Univ., Ames, IA (United States)
Utility theory offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion optimization problem in a dynamic and uncertain environment. In this paper, we incrementally develop a set of heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. We present an analysis of properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics and identify the conditions under which they are guaranteed to route messages along an optimal path. The paper concludes with a discussion of the relevance of the theoretical results presented in the paper to the design of intelligent autonomous adaptive communication networks and an outline of some directions of future research.
Dimov, Ves; Eidelman, Frank
Online social networks are used to connect with friends and family members, and increasingly, to stay up-to-date with the latest news and developments in allergy and immunology. As communication is a central part of healthcare delivery, the utilization of such networking channels in allergy and immunology will continue to grow. There are inherent risks to online social networks related to breaches of patient confidentiality, professionalism and privacy. Malpractice and liability risks should also be considered. There is a paucity of information in the literature on how social network interventions affect patient outcomes. The allergy and immunology community should direct future studies towards investigating how the use of social networks and other technology tools and services can improve patient care.
Ahmad, Hasnat; Taylor, Bruce V; van der Mei, Ingrid; Colman, Sam; O'Leary, Beth A; Breslin, Monique; Palmer, Andrew J
The measurement of health state utility values (HSUVs) for a representative sample of Australian people with multiple sclerosis (MS) has not previously been performed. Our main aim was to quantify the HSUVs for different levels of disease severities in Australian people with MS. HSUVs were calculated by employing a 'judgement-based' method that essentially creates EQ-5D-3L profiles based on WHOQOL-100 responses and then applying utility weights to each level in each dimension. A stepwise linear regression was used to evaluate the relationship between HSUVs and disease severity, classified as mild (Expanded Disability Status Scale (EDSS) levels: 0-3.5), moderate (EDSS levels: 4-6) and severe (EDSS levels: 6.5-9.5). Mean HSUV for all people with MS was 0.53 (95% confidence interval (CI): 0.52-0.54). Utility decreased with increasing disease severity: 0.61 (95% CI: 0.60-0.62), 0.51 (95% CI: 0.50-0.52) and 0.40 (95% CI: 0.38-0.43) for mild, moderate and severe disease, respectively. Adjusted differences in mean HSUV between the three severity groups were statistically significant. For the first time in Australia, we have quantified the impact of increasing severity of MS on health utility of people with MS. The HSUVs we have generated will be useful in further health economic analyses of interventions that slow progression of MS.
Mosha, I.H.; Ruben, R.
Family planning utilization in Tanzania is low. This study was cross sectional. It examined family planning use and socio demographic variables, social networks, knowledge and communication among the couples, whereby a stratified sample of 440 women of reproductive age (18-49), married or cohabiting
Kaadige, Mohan R; Elgort, Marc G; Ayer, Donald E
Glucose and glutamine are the most abundant circulating nutrients and support the growth and proliferation of all cells, in particular rapidly growing and dividing cancer cells. Several recent studies implicate an expanded Myc network in how cells sense and utilize both glucose and glutamine. These studies reveal an unappreciated coordination between glycolysis and glutaminolysis, potentially providing new targets for therapeutic intervention in cancer.
Rosenzweig, Stav; Grinstein, A.; Ofek, Elie
The forces that drive the impact of academic research articles in the marketing discipline are of great interests to authors, editors, and the discipline's policy makers. A key understudied driver is social network utilization by academic researchers. In this paper, we examine how activating one's
van den Berg, D.; van der Heijden, Matthijs C.; Schuur, Peter
We study a multi-item, two-echelon, continuous-review inventory problem at a Dutch utility company. We develop a model for the optimal allocation of service parts in a two-echelon network under an aggregate waiting time constraint. Specific model aspects are emergency shipments in case of stockout,
Full Text Available Introduction: Lupus erythematosus is a chronic, inflammatory autoimmune disease that can affect multiple organs. Lupus can affect many parts of the body, especially in systemic lupus erythematosus (SLE; affected tissues may include the joints, skin, kidneys, heart, lungs, blood vessels, and brain. Case report: A 46-year-old female presented with pruritus, photosensitivity and edema of the cheeks of about 2 years duration, and was evaluated by a dermatologist. On examination, multiple telangiectasias were present on the cheeks, with erythema, edema and a malar rash observed. A review of systems documented breathing difficulty and pleuitic pain, joint pain and joint edema, photosensitivity, cardiac dysrhythmia, and periodic pain in the back close to the kidneys. Methods: Skin biopsies for hematoxylin and eosin testing, as well for direct and indirect immunofluorescence were performed, in addition to multiple diagnostic blood tests, chest radiography and directed immunologic testing. Results: The blood testing showed elevated C-reactive protein. Direct and indirect immunofluorescence testing utilizing monkey esophagus, mouse and pig heart and kidney, normal human eyelid skin and veal brain demonstrated strong reactivity to several components of smooth muscle, nerves, blood vessels, skin basement membrane zone and sweat gland ducts and skin meibomian glands. Anti-endomysium antibodies were detected as well as others, especially using FITC conjugated Complement/C1q, FITC conjugated anti-human immunoglobulin IgG and FITC conjugated anti-human fibrinogen. Conclusions: We conclude that both direct and indirect immunofluorescence using several substrates can unveil previously undocumented autoantibodies in multiple organs in lupus erythematosus, and that these findings could be utilized to complement existing diagnostic testing for this disorder.
Rai, Man Mohan
flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.
Alexander C. D. Royal
Full Text Available The successful operation of buried infrastructure within urban environments is fundamental to the conservation of modern living standards. Open-cut methods are predominantly used, in preference to trenchless technology, to effect a repair, replace or install a new section of the network. This is, in part, due to the inability to determine the position of all utilities below the carriageway, making open-cut methods desirable in terms of dealing with uncertainty since the buried infrastructure is progressively exposed during excavation. However, open-cut methods damage the carriageway and disrupt society's functions. This paper describes the progress of a research project that aims to develop a multi-sensor geophysical platform that can improve the probability of complete detection of the infrastructure buried beneath the carriageway. The multi-sensor platform is being developed in conjunction with a knowledge-based system that aims to provide information on how the properties of the ground might affect the sensing technologies being deployed. The fusion of data sources (sensor data and utilities record data is also being researched to maximize the probability of location. This paper describes the outcome of the initial phase of testing along with the development of the knowledge-based system and the fusing of data to produce utility maps.
A. V. Smirnov
Full Text Available In the article the problem of ﬁnding the maximal multiple ﬂow in the network of any natural multiplicity k is studied. There are arcs of three types: ordinary arcs, multiple arcs and multi-arcs. Each multiple and multi-arc is a union of k linked arcs, which are adjusted with each other. The network constructing rules are described. The deﬁnitions of a divisible network and some associated subjects are stated. The important property of the divisible network is that every divisible network can be partitioned into k parts, which are adjusted on the linked arcs of each multiple and multi-arc. Each part is the ordinary transportation network. The main results of the article are the following subclasses of the problem of ﬁnding the maximal multiple ﬂow in the divisible network. 1. The divisible networks with the multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in k −1 network parts. In this case the problem can be solved in a polynomial time. 2. The divisible networks with the weak multi-arc constraints. Assume that only one vertex is the ending vertex for a multi-arc in s network parts (1 ≤ s < k − 1 and other parts have at least two such vertices. In that case the multiplicity of the multiple ﬂow problem can be decreased to k − s. 3. The divisible network of the parallel structure. Assume that the divisible network component, which consists of all multiple arcs, can be partitioned into subcomponents, each of them containing exactly one vertex-beginning of a multi-arc. Suppose that intersection of each pair of subcomponents is the only vertex-network source x0. If k = 2, the maximal ﬂow problem can be solved in a polynomial time. If k ≥ 3, the problem is NP-complete. The algorithms for each polynomial subclass are suggested. Also, the multiplicity decreasing algorithm for the divisible network with weak multi-arc constraints is formulated.
Full Text Available Due to the battery resource constraints, saving energy is a critical issue in wireless sensor networks, particularly in large sensor networks. One possible solution is to deploy multiple sink nodes simultaneously. Another possible solution is to employ an adaptive clustering hierarchy routing scheme. In this paper, we propose a multiple sink cluster wireless sensor networks scheme which combines the two solutions, and propose an efficient transmission power control scheme for a sink-centric cluster routing protocol in multiple sink wireless sensor networks, denoted as MSCWSNs-PC. It is a distributed, scalable, self-organizing, adaptive system, and the sensor nodes do not require knowledge of the global network and their location. All sinks effectively work out a representative view of a monitored region, after which power control is employed to optimize network topology. The simulations demonstrate the advantages of our new protocol.
Gilmore, Adrian W; Nelson, Steven M; McDermott, Kathleen B
The manner by which the human brain learns and recognizes stimuli is a matter of ongoing investigation. Through examination of meta-analyses of task-based functional MRI and resting state functional connectivity MRI, we identified a novel network strongly related to learning and memory. Activity within this network at encoding predicts subsequent item memory, and at retrieval differs for recognized and unrecognized items. The direction of activity flips as a function of recent history: from deactivation for novel stimuli to activation for stimuli that are familiar due to recent exposure. We term this network the 'parietal memory network' (PMN) to reflect its broad involvement in human memory processing. We provide a preliminary framework for understanding the key functional properties of the network. Copyright © 2015 Elsevier Ltd. All rights reserved.
Honarmand, Kimia; Akbar, Nadine; Kou, Nancy; Feinstein, Anthony
As many as two-thirds of multiple sclerosis (MS) patients are unable to retain employment. Neurological and cognitive status are known to be significant predictors of unemployment, but the relationship between the two is unclear. Furthermore, the association between employment status and depression, anxiety, and personality has not been adequately explored in MS patients. This study examined the demographic, neurological, neuropsychological, and personality factors associated with unemployment in MS. We also sought to determine the utility of the Multiple Sclerosis Functional Composite (MSFC), a measure of MS-related disability incorporating physical and cognitive measures, in predicting employment status. A consecutive sample of 106 MS patients (61.3% unemployed) completed the Brief Repeatable Battery of Neuropsychological Tests (BRBN), Hospital Anxiety and Depression Scale (HADS), and NEO Five-Factor Personality Inventory. The MSFC emerged as the most robust predictor of employment status in MS patients, exceeding the predictive value of the EDSS. Together with NEO "Agreeableness" and HADS Depression subscore, the MSFC accounted for 49.8% of the variance in employment status. Unemployment was also associated with a progressive disease course, longer disease duration, and being female. While Global Cognitive Impairment did not differentiate between groups, unemployed patients scored significantly lower on three of five BRBN indices: Symbol Digit Modality Test, Paced Auditory Serial Addition Test, and Word List Generation. The findings highlight the utility of the MSFC as a predictor of unemployment in MS. Furthermore, a strong association was found between unemployment and the personality construct "Agreeableness", and severity of depression.
LaPlante, R.A.; Douw, L.; Tang, W.; Stufflebeam, S.M.
In analysis of the human connectome, the connectivity of the human brain is collected from multiple imaging modalities and analyzed using graph theoretical techniques. The dimensionality of human connectivity data is high, and making sense of the complex networks in connectomics requires
Derzsy, N.; Molnar, F., Jr.; Szymanski, B. K.; Korniss, G.
Dominating sets provide key solution to various critical problems in networked systems, such as detecting, monitoring, or controlling the behavior of nodes. Motivated by graph theory literature [Erdos, Israel J. Math. 4, 233 (1966)], we studied maximal independent sets (MIS) as dominating sets in scale-free networks. We investigated the scaling behavior of the size of MIS in artificial scale-free networks with respect to multiple topological properties (size, average degree, power-law exponent, assortativity), evaluated its resilience to network damage resulting from random failure or targeted attack [Molnar et al., Sci. Rep. 5, 8321 (2015)], and compared its efficiency to previously proposed dominating set selection strategies. We showed that, despite its small set size, MIS provides very high resilience against network damage. Using extensive numerical analysis on both synthetic and real-world (social, biological, technological) network samples, we demonstrate that our method effectively satisfies four essential requirements of dominating sets for their practical applicability on large-scale real-world systems: 1.) small set size, 2.) minimal network information required for their construction scheme, 3.) fast and easy computational implementation, and 4.) resiliency to network damage. Supported by DARPA, DTRA, and NSF.
Takeuchi, Susumu; Teranishi, Yuuichi; Harumoto, Kaname; Shimojo, Shinji
Almost all companies are now utilizing computer networks to support speedier and more effective in-house information-sharing and communication. However, existing systems are designed to support communications only within the same department. Therefore, in our research, we propose an in-house communication support system which is based on the “Information Propagation Model (IPM).” The IPM is proposed to realize word-of-mouth communication in a social network, and to support information-sharing on the network. By applying the system in a real company, we found that information could be exchanged between different and unrelated departments, and such exchanges of information could help to build new relationships between the users who are apart on the social network.
Full Text Available Recent neuroimaging studies have revealed normal aging-related alterations in functional and structural brain networks such as the default mode network (DMN. However, less is understood about specific brain structural dependencies or interactions between brain regions within the DMN in the normal aging process. In this study, using Bayesian network (BN modeling, we analyzed grey matter volume data from 109 young and 82 old subjects to characterize the influence of aging on associations between core brain regions within the DMN. Furthermore, we investigated the discriminability of the aging-associated BN models for the young and old groups. Compared to their young counterparts, the old subjects showed significant reductions in connections from right inferior temporal cortex (ITC to medial prefrontal cortex (mPFC, right hippocampus (HP to right ITC, and mPFC to posterior cingulate cortex (PCC and increases in connections from left HP to mPFC and right inferior parietal cortex (IPC to right ITC. Moreover, the classification results showed that the aging-related BN models could predict group membership with 88.48% accuracy, 88.07% sensitivity and 89.02% specificity. Our findings suggest that structural associations within the DMN may be affected by normal aging and provide crucial information about aging effects on brain structural networks.
Bergman, Elizabeth J; Haley, William E
Bereavement services are an important part of comprehensive end-of-life care with potential to ameliorate physical, psychological, and spiritual distress. We studied bereaved spouses of hospice patients to examine bereavement service utilization, barriers, and preferences regarding content, structure, and delivery of potential bereavement services. We also examined the impact of depressive symptoms and social network. Retrospective cohort study of bereaved spousal caregivers of patients of three hospices in Tampa Bay, Florida. Descriptive and univariate analyses assessed demographics, depressive symptoms, social network, service utilization, barriers, and preferences. Nearly half utilized at least one type of specialized professional bereavement intervention to aid in coping with their loss. The most frequently used services were provided by clergy members and physicians. Primarily attitudinal in nature, barriers included the finding that more than one third felt available services did not fit their needs or interests. Individual and spiritually-based services were highly endorsed, as were services designed to provide tools to reframe the loss and cope with accompanying changes and emotions. Lower social network was associated with higher content preferences for services consistent primarily with restoration-oriented coping. Clinicians and service providers may facilitate coping by routinely screening for depressive symptoms and social network and tailoring interventions to those identified as experiencing elevated distress or lacking social resources. Attitudinal barriers and preferences suggest that even in the service-rich environment of hospice some modification of bereavement services might reach more bereaved spouses. Future studies might address whether preferences lead individuals to services of the greatest benefit.
Reches, A.; Kutcher, J.; Elbin, R. J.; Or-Ly, H.; Sadeh, B.; Greer, J.; McAllister, D. J.; Geva, A.; Kontos, A. P.
ABSTRACT Background: The clinical diagnosis and management of patients with sport-related concussion is largely dependent on subjectively reported symptoms, clinical examinations, cognitive, balance, vestibular and oculomotor testing. Consequently, there is an unmet need for objective assessment tools that can identify the injury from a physiological perspective and add an important layer of information to the clinician’s decision-making process. Objective: The goal of the study was to evaluate the clinical utility of the EEG-based tool named Brain Network Activation (BNA) as a longitudinal assessment method of brain function in the management of young athletes with concussion. Methods: Athletes with concussion (n = 86) and age-matched controls (n = 81) were evaluated at four time points with symptom questionnaires and BNA. BNA scores were calculated by comparing functional networks to a previously defined normative reference brain network model to the same cognitive task. Results: Subjects above 16 years of age exhibited a significant decrease in BNA scores immediately following injury, as well as notable changes in functional network activity, relative to the controls. Three representative case studies of the tested population are discussed in detail, to demonstrate the clinical utility of BNA. Conclusion: The data support the utility of BNA to augment clinical examinations, symptoms and additional tests by providing an effective method for evaluating objective electrophysiological changes associated with sport-related concussions. PMID:28055228
Stubbs, Derek F.
We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results
Full Text Available Abstract Background Clearly visualized biopathways provide a great help in understanding biological systems. However, manual drawing of large-scale biopathways is time consuming. We proposed a grid layout algorithm that can handle gene-regulatory networks and signal transduction pathways by considering edge-edge crossing, node-edge crossing, distance measure between nodes, and subcellular localization information from Gene Ontology. Consequently, the layout algorithm succeeded in drastically reducing these crossings in the apoptosis model. However, for larger-scale networks, we encountered three problems: (i the initial layout is often very far from any local optimum because nodes are initially placed at random, (ii from a biological viewpoint, human layouts still exceed automatic layouts in understanding because except subcellular localization, it does not fully utilize biological information of pathways, and (iii it employs a local search strategy in which the neighborhood is obtained by moving one node at each step, and automatic layouts suggest that simultaneous movements of multiple nodes are necessary for better layouts, while such extension may face worsening the time complexity. Results We propose a new grid layout algorithm. To address problem (i, we devised a new force-directed algorithm whose output is suitable as the initial layout. For (ii, we considered that an appropriate alignment of nodes having the same biological attribute is one of the most important factors of the comprehension, and we defined a new score function that gives an advantage to such configurations. For solving problem (iii, we developed a search strategy that considers swapping nodes as well as moving a node, while keeping the order of the time complexity. Though a naïve implementation increases by one order, the time complexity, we solved this difficulty by devising a method that caches differences between scores of a layout and its possible updates
Full Text Available With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF, for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC, which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER with much less resource consumption.
White, Joshua S.; Hall, Robert T.; Fields, Jeremy; White, Holly M.
Utilization of traditional sentiment analysis for predicting the outcome of an event on a social network depends on: precise understanding of what topics relate to the event, selective elimination of trends that don't fit, and in most cases, expert knowledge of major players of the event. Sentiment analysis has traditionally taken one of two approaches to derive a quantitative value from qualitative text. These approaches include the bag of words model", and the usage of "NLP" to attempt a real understanding of the text. Each of these methods yield very similar accuracy results with the exception of some special use cases. To do so, however, they both impose a large computational burden on the analytic system. Newer approaches have this same problem. No matter what approach is used, SA typically caps out around 80% in accuracy. However, accuracy is the result of both polarity and degree of polarity, nothing else. In this paper we present a method for hybridizing traditional SA methods to better determine shifts in opinion over time within social networks. This hybridization process involves augmenting traditional SA measurements with contextual understanding, and knowledge about writers' demographics. Our goal is to not only to improve accuracy, but to do so with minimal impact to computation requirements.
Fecko, Mariusz A.; Wong, Larry; Kang, Jaewong; Cichocki, Andrzej; Kaul, Vikram; Samtani, Sunil
To efficiently use alternate paths during periods of congestion, we have devised prioritized Dynamic Routing Control Agent (pDRCA) that (1) selects best links to meet the bandwidth and delay requirements of traffic, (2) provides load-balancing and traffic prioritization when multiple topologies are available, and (3) handles changes in link quality and traffic demand, and link outages. pDRCA provides multiplatform load balancing to maximize SATCOM (both P2P and multi-point) and airborne links utilization. It influences link selection by configuring the cost metrics on a router's interface, which does not require any changes to the routing protocol itself. It supports service differentiation of multiple traffic priorities by providing more network resources to the highest priority flows. pDRCA does so by solving an optimization problem to find optimal links weights that increase throughput and decrease E2E delay; avoid congested, low quality, and long delay links; and exploit path diversity in the network. These optimal link weights are sent to the local agents to be configured on individual routers per traffic priority. The pDRCA optimization algorithm has been proven effective in improving application performance. We created a variety of different test scenarios by varying traffic profile and link behavior (stable links, varying capacity, and link outages). In the scenarios where high priority traffic experienced significant loss without pDRCA, the average loss was reduced from 49.5% to 13% and in some cases dropped to 0%. Currently, pDRCA is integrated with an open-source software router and priority queues on Linux as a component of Open Tactical Router (OTR), which is being developed by ONR DTCN program.
Many individuals wait until alcohol use becomes severe before treatment is sought. However, social networks, or the number of social groups an individual belongs to, may play a moderating role in this relationship. Logistic regression examined the interaction of alcohol consumption and social networks as a predictor of treatment utilization while adjusting for sociodemographic and clinical variables among 1,433 lifetime alcohol-dependent respondents from wave 2 of the National Epidemiologic Survey on Alcohol Related Conditions (NESARC). Results showed that social networks moderate the relationship between alcohol consumption and treatment utilization such that for individuals with few network ties, the relationship between alcohol consumption and treatment utilization was diminished, compared to the relationship between alcohol consumption and treatment utilization for individuals with many network ties. Findings offer insight into how social networks, at times, can influence individuals to pursue treatment, while at other times, influence individuals to stay out of treatment, or seek treatment substitutes. PMID:24462223
Zhang, Jian; Yang, Xiao-hua; Chen, Xiao-juan
Due to nonlinear and multiscale characteristics of temperature time series, a new model called wavelet network model based on multiple criteria decision making (WNMCDM) has been proposed, which combines the advantage of wavelet analysis, multiple criteria decision making, and artificial neural network. One case for forecasting extreme monthly maximum temperature of Miyun Reservoir has been conducted to examine the performance of WNMCDM model. Compared with nearest neighbor bootstrapping regr...
Chae, Song Hwa; Kim, Sang Hun; Yoon, Sung-Geun; Park, Sunwon [Department of Chemical and Bio-molecular Engineering, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701 (Korea)
Development of an eco-industrial park (EIP) has drawn attention as a promising approach seeking for the mutual benefit to the economy and environment. In recent years, the reduction of energy consumption has become a global necessity due to the high oil price and environmental regulations. In order to find energy strategies in an EIP, a framework to investigate waste heat of an industrial complex was proposed. A mathematical model was developed to synthesize a waste heat utilization network, including nearby companies and communities. A case study of an existing petro-chemical complex in Yeosu, South Korea showed that the total energy cost and the amount of waste heat of the region can be reduced by more than 88% and 82% from the present values, respectively, applying the suggested waste heat utilization networks. (author)
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can neither be used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This
Price, True; Wee, Chong-Yaw; Gao, Wei; Shen, Dinggang
Characterization of disease using stationary resting-state functional connectivity (FC) has provided important hallmarks of abnormal brain activation in many domains. Recent studies of resting-state functional magnetic resonance imaging (fMRI), however, suggest there is a considerable amount of additional knowledge to be gained by investigating the variability in FC over the course of a scan. While a few studies have begun to explore the properties of dynamic FC for characterizing disease, the analysis of dynamic FC over multiple networks at multiple time scales has yet to be fully examined. In this study, we combine dynamic connectivity features in a multi-network, multi-scale approach to evaluate the method's potential in better classifying childhood autism. Specifically, from a set of group-level intrinsic connectivity networks (ICNs), we use sliding window correlations to compute intra-network connectivity on the subject level. We derive dynamic FC features for all ICNs over a large range of window sizes and then use a multiple kernel support vector machine (MK-SVM) model to combine a subset of these features for classification. We compare the performance our multi-network, dynamic approach to the best results obtained from single-network dynamic FC features and those obtained from both single- and multi-network static FC features. Our experiments show that integrating multiple networks on different dynamic scales has a clear superiority over these existing methods.
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
Currently available (classical) testing procedures for the network autocorrelation can only be used for falsifying a precise null hypothesis of no network effect. Classical methods can be neither used for quantifying evidence for the null nor for testing multiple hypotheses simultaneously. This
Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.
Amr A. Adly; Abd-El-Hafiz, Salwa K.
Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in ma...
Berg, Anthony P; Mekel-Bobrov, Nitzan; Goldberg, Edward; Huynh, Dat; Jain, Roshini
Advances in spinal cord stimulation (SCS) have improved patient outcomes, leading to its increased utilization for chronic pain. Chronic pain is dynamic showing exacerbations, variable severity, and evolving pain patterns. Given this complexity, SCS systems that provide a broad range of stimulation waveforms may be valuable. The aim of this research was to characterize the usage pattern of stimulation waveforms and field shapes in chronic pain patients implanted with the Spectra System. A review of daily device usage in a cohort of 250 patients implanted for a minimum duration of one month was conducted. With follow-ups ranging between 1 month and 1 year post-implant, 72.8% of patients used Standard Rate, 34.8% Anode Intensification, 23.2% Higher Rate, and 8.4% Burst stimulation waveforms. Collectively, 60% used 1 or more advanced waveforms, either exclusively or along with Standard Rate. A trend showed patients continuing to use up to 3 programs one year post-implant. When given a choice, SCS patients often utilize a variety of waveforms, suggesting that patients may benefit from a single system that provides multiple waveforms and field shapes to customize therapy and improve efficacy.
This paper uses a novel dataset and research design to examine the effects of information networks on immigrants' access to health care. The dataset consists of an unusually large sample of undocumented immigrants and contains a direct indicator of information networks-whether an immigrant was referred to health care opportunities by a strong social tie (relative or friend). This measure allows to overcome some of the major identification issues that afflict most of the existing literature on network effects and to concentrate on one of the channels through which social contacts might operate. The analysis focuses on the time spent in Italy before an immigrant first receives medical assistance. Estimates indicate that networks significantly foster health care utilization: after controlling for all available individual characteristics and for ethnic heterogeneity, I find that relying on a strong social tie reduces the time to visit by 30%. The effect of information networks is stable across specifications and it is relatively large. Further investigation seems to confirm the quantitative importance of networks as an information device.
Nov 27, 2015 ... Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters deﬁned at one level appear as ...
tion of the key players can be used to develop drugs targeted specifically towards these molecules . On a much larger ... cial markets  and brain functional networks [16,17]. Figures 1a–b shows empirical ... nections between cortical regions in the cat  and macaque  brains obtained from anatomical studies.
Duim, van der V.R.; Ren, C.; Jóhannesson, G.T.
The recent surfacing of actor-network theory (ANT) in tourism studies correlates to a rising interest in understanding tourism as emergent thorough relational practice connecting cultures, natures and technologies in multifarious ways. Despite the widespread application of ANT across the social
Wijnands, José Ma; Kingwell, Elaine; Zhu, Feng; Zhao, Yinshan; Fisk, John D; Evans, Charity; Marrie, Ruth Ann; Tremlett, Helen
Little is known about infection risk in multiple sclerosis (MS). We examined infection-related health care utilization in people with and without MS. Using population-based health administrative data from British Columbia, Canada, people with MS were followed from their first demyelinating claim (1996-2013) until death, emigration, or study end (2013). Infection-related hospital, physician, and prescription data of MS cases were compared with sex-, age-, and geographically matched controls using adjusted regression models. Sex and age differences (18-39, 40-49, 50-59, 60+ years) were explored. Relative to 35,837 controls, 7179 MS cases were over twice as likely to be hospitalized for infection (adjusted odds ratio: 2.39; 95% confidence interval (CI): 2.16-2.65), had 41% more physician visits (adjusted rate ratio (aRR): 1.41; 95% CI: 1.36-1.47), and filled 57% more infection-related prescriptions (aRR: 1.57; 95% CI: 1.49-1.65). Utilization was disproportionately higher in MS men than women and was elevated across all ages. MS cases had nearly twice as many physician visits and two to three times more hospitalizations for pneumonia, urinary system infections, and skin infections (aRRs ranged from 1.6 to 3.3) and over twice as many hospitalizations for intestinal infections (aRR = 2.6) and sepsis (aRR = 2.2). Infection-related health care utilization was increased in people with MS across all age groups, with a higher burden for men.
Memon, Pyar Ali
The Clutha is the largest river in New Zealand. The last two decades have witnessed major conflicts centered on the utilization of the water resources of the upper Clutha river. These conflicts have by no means been finally resolved. The focus of this article is on institutional arrangements for water resource management on the Clutha, with particular reference to the decision-making processes that have culminated in the building of the high dam. It critically evaluates recent experiences and comments on future prospects for resolving resource use conflicts rationally through planning for multiple utilization in a climate of market led policies of the present government. The study demonstrates the inevitable conflicts that can arise within a public bureaucracy that combines dual responsibilities for policy making and operational functions. Hitherto, central government has been able to manipulate the water resource allocation process to its advantage because of a lack of clear separation between its two roles as a policy maker and developer. The conflicts that have manifested themselves during the last two decades over the Clutha should be seen as part of a wider public debate during the last two decades concerning resource utilization in New Zealand. The Clutha controversy was preceded by comparable concerns over the rising of the level of Lake Manapouri during the 1960s and has been followed by the debate over the “think big” resource development projects during the 1980s. The election of the fourth Labour government in 1983 has heralded a political and economic policy shift in New Zealand towards minimizing the role of public intervention in resource allocation and major structural reforms in the relative roles of central and regional government in resource management. The significance of these changes pose important implications for the future management of the Clutha.
Xin Wang; Jari Nurmi
Two network-on-chip (NoC) designs are examined and compared in this paper. One design applies a bidirectional ring connection scheme, while the other design applies a code-division multiple-access (CDMA) connection scheme. Both of the designs apply globally asynchronous locally synchronous (GALS) scheme in order to deal with the issue of transferring data in a multiple-clock-domain environment of an on-chip system. The two NoC designs are compared with each other by their network structures, ...
Admon, Lindsay K; Winkelman, Tyler N A; Heisler, Michele; Dalton, Vanessa K
Our objective was to measure obstetric outcomes and delivery-related health care utilization and costs among pregnant women with multiple chronic conditions. We used 2013-2014 data from the National Inpatient Sample to measure obstetric outcomes and delivery-related health care utilization and costs among women with no chronic conditions, 1 chronic condition, and multiple chronic conditions. Women with multiple chronic conditions were at significantly higher risk than women with 1 chronic condition or no chronic conditions across all outcomes measured. High-value strategies are needed to improve birth outcomes among vulnerable mothers and their infants.
Dilmaghani, R S; Bobarshad, H; Ghavami, M; Choobkar, S; Wolfe, C
This paper presents the design of a novel wireless sensor network structure to monitor patients with chronic diseases in their own homes through a remote monitoring system of physiological signals. Currently, most of the monitoring systems send patients' data to a hospital with the aid of personal computers (PC) located in the patients' home. Here, we present a new design which eliminates the need for a PC. The proposed remote monitoring system is a wireless sensor network with the nodes of the network installed in the patients' homes. These nodes are then connected to a central node located at a hospital through an Internet connection. The nodes of the proposed wireless sensor network are created by using a combination of ECG sensors, MSP430 microcontrollers, a CC2500 low-power wireless radio, and a network protocol called the SimpliciTI protocol. ECG signals are first sampled by a small portable device which each patient carries. The captured signals are then wirelessly transmitted to an access point located within the patients' home. This connectivity is based on wireless data transmission at 2.4-GHz frequency. The access point is also a small box attached to the Internet through a home asynchronous digital subscriber line router. Afterwards, the data are sent to the hospital via the Internet in real time for analysis and/or storage. The benefits of this remote monitoring are wide ranging: the patients can continue their normal lives, they do not need a PC all of the time, their risk of infection is reduced, costs significantly decrease for the hospital, and clinicians can check data in a short time.
Full Text Available Argentina is among the four largest producers of soybeans, sunflower, corn, and wheat, among other agricultural products. Institutional and policy changes during the 1990s fostered the development of Argentine agriculture and the introduction of innovative process and product technologies (no-till, agrochemicals, GMO, GPS and new investments in modern, large-scale sunflower and soybean processing plants. In addition to technological changes, a "quiet revolution" occurred in the way agricultural production was carried out and organized: from self-production or ownership agriculture to a contract-based agriculture. The objective of this paper is to explore and describe the emergence of networks in the Argentine crop production sector. The paper presents and describes four cases that currently represent about 50% of total grain and oilseed production in Argentina: "informal hybrid form", "agricultural trust fund", "investor-oriented corporate structure", and "network of networks". In all cases, hybrid forms involve a group of actors linked by common objectives, mainly to gain scale, share resources, and improve the profitability of the business. Informal contracts seem to be the most common way of organizing the agriculture process, but using short-term contracts and sequential interfirm collaboration. Networks of networks involve long-term relationships and social development, and reciprocal interfirm collaboration. Agricultural trust fund and investor-oriented corporate structures have combined interfirm collaboration and medium-term relationships. These organizational forms are highly flexible and show a great capacity to adapt to challenges; they are competitive because they enjoy aligned incentives, flexibility, and adaptability.
Armon, A; Gutner, S; Rosenberg, A; Scolnicov, H
We report on the design, deployment, and use of TaKaDu, a real-time algorithmic Water Infrastructure Monitoring solution, with a strong focus on water loss reduction and control. TaKaDu is provided as a commercial service to several customers worldwide. It has been in use at HaGihon, the Jerusalem utility, since mid 2009. Water utilities collect considerable real-time data from their networks, e.g. by means of a SCADA system and sensors measuring flow, pressure, and other data. We discuss how an algorithmic statistical solution analyses this wealth of raw data, flexibly using many types of input and picking out and reporting significant events and failures in the network. Of particular interest to most water utilities is the early detection capability for invisible leaks, also a means for preventing large visible bursts. The system also detects sensor and SCADA failures, various water quality issues, DMA boundary breaches, unrecorded or unintended network changes (like a valve or pump state change), and other events, including types unforeseen during system design. We discuss results from use at HaGihon, showing clear operational value.
Full Text Available Several drugs have been approved for treatment of multiple sclerosis. Dimethyl fumarate (DMF is utilized as an oral drug to treat this disease and is proven to be potent with less side effects than several other drugs. On the other hand, monomethyl fumarate (MMF, a related compound has not been examined in greater details although it has the potential as a therapeutic drug for multiple sclerosis and other diseases. The mechanism of action of DMF or MMF is related to their ability to enhance the antioxidant pathways and to inhibit reactive oxygen species. However, other mechanisms have also been described which include effects on monocytes, dendritic cells, T cells, and natural killer cells. It is also reported that DMF might be useful for treating psoriasis, asthma, aggressive breast cancers, hematopoeitic tumors, inflammatory bowel disease, intracerebral hemorrhage, osteoarthritis, chronic pancreatitis, and retinal ischemia. In this article we will touch on some of these diseases with an emphasis on the effects of DMF and MMF on various immune cells.
Wei, Peng; Pan, Wei
We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.
Al-Gumaei, Yousef Ali; Noordin, Kamarul Ariffin; Reza, Ahmed Wasif; Dimyati, Kaharudin
Spectrum scarcity is a major challenge in wireless communications systems requiring efficient usage and utilization. Cognitive radio network (CRN) is found as a promising technique to solve this problem of spectrum scarcity. It allows licensed and unlicensed users to share the same licensed spectrum band. Interference resulting from cognitive radios (CRs) has undesirable effects on quality of service (QoS) of both licensed and unlicensed systems where it causes degradation in received signal-to-noise ratio (SIR) of users. Power control is one of the most important techniques that can be used to mitigate interference and guarantee QoS in both systems. In this paper, we develop a new approach of a distributed power control for CRN based on utility and pricing. QoS of CR user is presented as a utility function via pricing and a distributed power control as a non-cooperative game in which users maximize their net utility (utility-price). We define the price as a real function of transmit power to increase pricing charge of the farthest CR users. We prove that the power control game proposed in this study has Nash Equilibrium as well as it is unique. The obtained results show that the proposed power control algorithm based on a new utility function has a significant reduction in transmit power consumption and high improvement in speed of convergence.
Full Text Available The dynamic deployment technology of the virtual machine is one of the current cloud computing research focuses. The traditional methods mainly work after the degradation of the service performance that usually lag. To solve the problem a new prediction model based on the CPU utilization is constructed in this paper. A reference offered by the new prediction model of the CPU utilization is provided to the VM dynamic deployment process which will speed to finish the deployment process before the degradation of the service performance. By this method it not only ensure the quality of services but also improve the server performance and resource utilization. The new prediction method of the CPU utilization based on the ARIMA-BP neural network mainly include four parts: preprocess the collected data, build the predictive model of ARIMA-BP neural network, modify the nonlinear residuals of the time series by the BP prediction algorithm and obtain the prediction results by analyzing the above data comprehensively.
Ma, Ning; Brown, Guy J.; May, Tobias
This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for binaural localisation of multiple speakers in reverberant conditions. DNNs are used to map binaural features, consisting of the complete crosscorrelation function (CCF) and interaural...... acoustic scenarios in which multiple speakers and room reverberation are present....
John, V.; Englebienne, G.; Kröse, B.J.A.
In this article we present an automatic camera calibration algorithm using multiple trajectories in a multiple camera network with non-overlapping field-of-views (FOV). Visible trajectories within a camera FOV are assumed to be measured with respect to the camera local co-ordinate system.
Full Text Available An artificial neural network (ANN was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction.
Penfold, Christopher A; Shifaz, Ahmed; Brown, Paul E; Nicholson, Ann; Wild, David L
Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.
Sørensen, Jesper Hemming; Østergaard, Jan; Popovski, Petar
its description and forward it. Such a compression can also be done already at the source node; however, the feedback arrives more timely and reliably to intermediate nodes that are closer to the final receiver. In this paper we investigate the performance of such adaptation at the source node......This paper concerns multi path video streaming using adaptive multiple description coding. The adaptation leverages on the fact that multiple descriptions are correlated. Thus if an intermediate node gets feedback telling that another path is likely to deliver a description, this node can compress...
Frauzem, Rebecca; Fjellerup, Kasper; Gani, Rafiqul
are being investigated and implemented . Carbon Capture and Storage (CCS) is the dominant method that is discussed. However, CO2 utilization is receiving increased attention for its ability to help in long-term CO2 reduction and the formation of various chemical products. One of the primary elements...... is the sustainable linkage of carbon capture to produce the CO2 feed and the subsequent conversion processes. A manipulation of an MEA absorption process, the current industrial standard for carbon capture , is investigated. The resulting CO2 stream can be directly fed into a variety of conversion processes...... hydrogenation highlights the application. This case study illustrates the utility of the utilization network and elements of the methodology being developed. In addition, the conversion process is linked with carbon capture to evaluate the overall sustainability. Finally, the production of the other raw...
Frauzem, Rebecca; Fjellerup, Kasper; Gani, Rafiqul
Climate change is a global issue that has come to the forefront of environmental concern. With the increasing emissions of greenhouse gases, efforts have increased to reduce carbon dioxide (CO2) emissions. Regulatory guidelines are becoming more stringent and efforts for long-term reduction...... are being investigated and implemented . Carbon Capture and Storage (CCS) is the dominant method that is discussed. However, CO2 utilization is receiving increased attention for its ability to help in long-term CO2 reduction and the formation of various chemical products. One of the primary elements...... of utilization is the conversion of CO2 to valuable products via chemical reactions with other raw materials. In order for this to be implemented at a large and industrial level, further work is necessary. As part of this, the work focuses on the formulation and design of a CO2 utilization network via...
Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee
is used for safety and security monitoring purposes. In this paper, we evaluate different access strategies to remote dynamic information and compare between achieving information reliability (mismatch probability) and the associated power consumption. Lastly, based on the models, we propose an adaptive......Accessing information remotely to dynamically changing information elements cannot be avoided and has become a required functionality for various network services. Most applications require up-to-date information which is reliable and accurate. The information reliability in terms of using correct...... information is challenged by dynamic nature of information elements. These challenges are more prominent in case of wireless sensor network (WSN) applications, as the information that the sensor node collects are mostly dynamic in nature (say, temperature). Therefore, it is likely that there can be a mismatch...
Warmflash, Aryeh; Francois, Paul; Siggia, Eric D
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.
Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou
The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.
The tradition of keeping written records of gift received during household ceremonies in many countries offers researchers an underutilized means of data collection for social network analysis. This paper first summarizes unique features of the gift record data that circumvent five prevailing sampling and measurement issues in the literature, and we discuss their advantages over existing studies at both the individual level and the dyadic link level using previous data sources. We then document our research project in rural China that implements a multiple wave census-type household survey and a long-term gift record collection. The pattern of gift-giving in major household social events and its recent escalation is analyzed. There are significantly positive correlations between gift network centrality and various forms of informal insurance. Finally, economic inequality and competitive marriage market are among the main demographic and socioeconomic determinants of the observed gift network structure.
Full Text Available The tradition of keeping written records of gift received during household ceremonies in many countries offers researchers an underutilized means of data collection for social network analysis. This paper first summarizes unique features of the gift record data that circumvent five prevailing sampling and measurement issues in the literature, and we discuss their advantages over existing studies at both the individual level and the dyadic link level using previous data sources. We then document our research project in rural China that implements a multiple wave census-type household survey and a long-term gift record collection. The pattern of gift-giving in major household social events and its recent escalation is analyzed. There are significantly positive correlations between gift network centrality and various forms of informal insurance. Finally, economic inequality and competitive marriage market are among the main demographic and socioeconomic determinants of the observed gift network structure.
Coll-Mayor, D.; Picos, R.; Garcia-Moreno, E. [University of Balearic Islands (Spain). Physics Department
The world of energy has lately experienced a revolution, and new rules are being defined. The climate change produced by the greenhouse gases, the inefficiency of the energy system or the lack of power supply infrastructure in most of the poor countries, the liberalization of the energy market and the development of new technologies in the field of distributed generation (DG) are the key factors of this revolution. It seems clear that the solution at the moment is the DG. The advantage of DG is the energy generation close to the demand point. It means that DG can lower costs, reduce emissions, or expand the energy options of the consumers. DG may add redundancy that increases grid security even while powering emergency lighting or other critical systems and reduces power losses in the electricity distribution. After the development of the different DG and high efficiency technologies such as co-generation and tri-generation, the next step in the DG world is the interconnection of different small distributed generation facilities which act together in a DG network as a large power plant controlled by a centralized energy management system (EMS). The main aim of the EMS is to reach the targets of low emissions and high efficiency. The EMS gives priority to renewable energy sources instead of the use of fossil fuels. This new concept of energy infrastructure is referred to as virtual utility (VU). The VU can be defined as a new model of energy infrastructure which consists of integrating different kind of distributed generation utilities in an energy (electricity and heat) generation network controlled by a central energy management system (EMS). The electricity production in the network is subordinated to the heat necessity of every user. The thermal energy is consumed on site; the electricity is generated and distributed in the entire network. The network is composed of one centralized control with the EMS and different clusters of distributed generation utilities
Wan Alwi, S R; Manan, Z A; Samingin, M H; Misran, N
Water pinch analysis (WPA) is a well-established tool for the design of a maximum water recovery (MWR) network. MWR, which is primarily concerned with water recovery and regeneration, only partly addresses water minimization problem. Strictly speaking, WPA can only lead to maximum water recovery targets as opposed to the minimum water targets as widely claimed by researchers over the years. The minimum water targets can be achieved when all water minimization options including elimination, reduction, reuse/recycling, outsourcing and regeneration have been holistically applied. Even though WPA has been well established for synthesis of MWR network, research towards holistic water minimization has lagged behind. This paper describes a new holistic framework for designing a cost-effective minimum water network (CEMWN) for industry and urban systems. The framework consists of five key steps, i.e. (1) Specify the limiting water data, (2) Determine MWR targets, (3) Screen process changes using water management hierarchy (WMH), (4) Apply Systematic Hierarchical Approach for Resilient Process Screening (SHARPS) strategy, and (5) Design water network. Three key contributions have emerged from this work. First is a hierarchical approach for systematic screening of process changes guided by the WMH. Second is a set of four new heuristics for implementing process changes that considers the interactions among process changes options as well as among equipment and the implications of applying each process change on utility targets. Third is the SHARPS cost-screening technique to customize process changes and ultimately generate a minimum water utilization network that is cost-effective and affordable. The CEMWN holistic framework has been successfully implemented on semiconductor and mosque case studies and yielded results within the designer payback period criterion.
Full Text Available Crystal Watson,1 Christine Prosser,2 Sebastian Braun,2 Pamela B Landsman-Blumberg,3 Erika Gleissner,4 Sarah Naoshy1 1Health Economics and Outcomes Research, Global Market Access, Biogen, Cambridge, MA, USA; 2Real World Evidence, Xcenda GmbH, Hanover, Germany; 3Applied Data Analytics, Xcenda LLC, Palm Harbor, FL, USA; 4Market Access, Biogen, Ismaning, Germany Background: Multiple sclerosis (MS, a progressive neurodegenerative disease, greatly impacts the quality of life and economic status of people affected by this disease. In Germany, the total annual cost of MS is estimated at €40,000 per person with MS. Natalizumab has shown to slow MS disease progression, reduce relapses, and improve the quality of life of people with MS.Objective: To evaluate MS-related and all-cause health care resource utilization and costs among German MS patients during the 12 months before and after initiation of natalizumab in a real-world setting.Methods: The current analysis was conducted using the Health Risk Institute research database. Identified patients were aged ≥18 years with ≥1 diagnosis of MS and had initiated natalizumab therapy (index, with 12-month pre– and post–index-period data. Patients were stratified by prior disease-modifying therapy (DMT usage or no DMT usage in the pre-index period. Outcome measures included corticosteroid use and number of sick/disability days, inpatient stays, and outpatient visits. Health care costs were calculated separately for pre- and post-index periods on a per-patient basis and adjusted for inflation.Results: In a final sample of 193 natalizumab-treated patients, per-patient MS-related corticosteroid use was reduced by 62.3%, MS-related sick days by 27.6%, and inpatient costs by 78.3% from the pre- to post-index period. Furthermore, the proportion of patients with MS-related hospitalizations decreased from 49.7% to 14.0% (P<0.001; this reduction was seen for patients with and without prior DMT use
David J. Muth Jr.
This paper examines the use of graph based evolutionary algorithms (GBEAs) to find multiple acceptable solutions for heat transfer in engineering systems during the optimization process. GBEAs are a type of evolutionary algorithm (EA) in which a topology, or geography, is imposed on an evolving population of solutions. The rates at which solutions can spread within the population are controlled by the choice of topology. As in nature geography can be used to develop and sustain diversity within the solution population. Altering the choice of graph can create a more or less diverse population of potential solutions. The choice of graph can also affect the convergence rate for the EA and the number of mating events required for convergence. The engineering system examined in this paper is a biomass fueled cookstove used in developing nations for household cooking. In this cookstove wood is combusted in a small combustion chamber and the resulting hot gases are utilized to heat the stove’s cooking surface. The spatial temperature profile of the cooking surface is determined by a series of baffles that direct the flow of hot gases. The optimization goal is to find baffle configurations that provide an even temperature distribution on the cooking surface. Often in engineering, the goal of optimization is not to find the single optimum solution but rather to identify a number of good solutions that can be used as a starting point for detailed engineering design. Because of this a key aspect of evolutionary optimization is the diversity of the solutions found. The key conclusion in this paper is that GBEA’s can be used to create multiple good solutions needed to support engineering design.
Full Text Available Jing Wang, Hongfeng Guo, Xin Zhou Department of Hematology, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China Abstract: Multiple myeloma (MM is an incurable hematologic malignancy caused by the autonomous growth of malignant plasma cells. In the last decade, the introduction of novel targeted agents such as thalidomide, bortezomib, and lenalidomide has dramatically improved the clinical outcome of MM patients in both the frontline and recurrent settings. Lenalidomide is a synthetic derivative of thalidomide, which has been shown to significantly improve overall survival, time to progression, and overall response rates in patients with MM. The China Food and Drug Administration approved the use of lenalidomide in patients with MM in 2013. In a Phase II trial, lenalidomide plus low-dose dexamethasone was associated with a high response rate and acceptable safety profile in heavily pretreated Chinese patients with relapsed/refractory MM, including those with renal impairment and IgD subtype. However, lenalidomide will remain as a second-line antimyeloma drug in the near future because of its high price and the policy of health insurance reimbursement in People’s Republic of China. In this review, we summarize the clinical utility and patient considerations in the use of lenalidomide for MM in Chinese patients. Further studies with larger sample sizes are required to investigate the better quality, longer duration, and more clinically meaningful outcomes of lenalidomide in the treatment of MM in Chinese patients. Keywords: lenalidomide, multiple myeloma, clinical efficacy, Chinese patients
This report for the International Energy Agency (IEA) made by Task 5 of the Photovoltaic Power Systems (PVPS) programme takes a look at the probability of islanding in utility networks due to grid-connected photovoltaic power systems. The mission of the Photovoltaic Power Systems Programme is to enhance the international collaboration efforts which accelerate the development and deployment of photovoltaic solar energy. Task 5 deals with issues concerning grid-interconnection and distributed PV power systems. This report summarises the results on a study on the probability of islanding in power networks with a high penetration level of grid connected PV-systems. The results are based on measurements performed during one year in a Dutch utility network. The measurements of active and reactive power were taken every second for two years and stored in a computer for off-line analysis. The area examined and its characteristics are described, as are the test set-up and the equipment used. The ratios between load and PV-power are discussed. The general conclusion is that the probability of islanding is virtually zero for low, medium and high penetration levels of PV-systems.
Varkey, Divya A; Pitcher, Tony J; McAllister, Murdoch K; Sumaila, Rashid S
Proposals for marine conservation measures have proliferated in the last 2 decades due to increased reports of fishery declines and interest in conservation. Fishers and fisheries managers have often disagreed strongly when discussing controls on fisheries. In such situations, ecosystem-based models and fisheries-stock assessment models can help resolve disagreements by highlighting the trade-offs that would be made under alternative management scenarios. We extended the analytical framework for modeling such trade-offs by including additional stakeholders whose livelihoods and the value they place on conservation depend on the condition of the marine ecosystem. To do so, we used Bayesian decision-network models (BDNs) in a case study of an Indonesian coral reef fishery. Our model included interests of the fishers and fishery managers; individuals in the tourism industry; conservation interests of the state, nongovernmental organizations, and the local public; and uncertainties in ecosystem status, projections of fisheries revenues, tourism growth, and levels of interest in conservation. We calculated the total utility (i.e., value) of a range of restoration scenarios. Restricting net fisheries and live-fish fisheries appeared to be the best compromise solutions under several combinations of settings of modeled variables. Results of our case study highlight the implications of alternate formulations for coral reef stakeholder utility functions and discount rates for the calculation of the net benefits of alternative fisheries management options. This case study may also serve as a useful example for other decision analyses with multiple stakeholders. Conservation Biology © 2013 Society for Conservation Biology No claim to original US government works.
Tobgay, Sonam; Olsen, Rasmus Løvenstein; Prasad, Ramjee
set of requirements. Lastly, the paper suggests a mechanism by which the information access or acquisition can be adapted as per the requirements of the application. The main parameters focused in this paper are mismatch probability  and power dissipation with respect to sampling rate....... specific WSN considering its resource constraints, neglecting the return-of-investment and usefulness of the system. In this paper, we bring out the WSN scenario which supports multiple applications and study the challenges that would pose in implementation as each specific application has its own specific...
Al-Jaderi, Zaidoon; Maghazachi, Azzam A.
Several drugs have been approved for treatment of multiple sclerosis (MS). Dimethyl fumarate (DMF) is utilized as an oral drug to treat this disease and is proven to be potent with less side effects than several other drugs. On the other hand, monomethyl fumarate (MMF), a related compound, has not been examined in greater details although it has the potential as a therapeutic drug for MS and other diseases. The mechanism of action of DMF or MMF is related to their ability to enhance the antioxidant pathways and to inhibit reactive oxygen species. However, other mechanisms have also been described, which include effects on monocytes, dendritic cells, T cells, and natural killer cells. It is also reported that DMF might be useful for treating psoriasis, asthma, aggressive breast cancers, hematopoeitic tumors, inflammatory bowel disease, intracerebral hemorrhage, osteoarthritis, chronic pancreatitis, and retinal ischemia. In this article, we will touch on some of these diseases with an emphasis on the effects of DMF and MMF on various immune cells. PMID:27499754
Holbrook, Mark; Pitts, Robert Lee; Gifford, Kevin K.; Jenkins, Andrew; Kuzminsky, Sebastian
The International Space Station (ISS) is in an operational configuration and nearing final assembly. With its maturity and diverse payloads onboard, the opportunity exists to extend the orbital lab into a facility to exercise and demonstrate Delay/Disruption Tolerant Networking (DTN). DTN is an end-to-end network service providing communications through environments characterized by intermittent connectivity, variable delays, high bit error rates, asymmetric links and simplex links. The DTN protocols, also known as bundle protocols, provide a store-and-forward capability to accommodate end-to-end network services. Key capabilities of the bundling protocols include: the Ability to cope with intermittent connectivity, the Ability to take advantage of scheduled and opportunistic connectivity (in addition to always up connectivity), Custody Transfer, and end-to-end security. Colorado University at Boulder and the Huntsville Operational Support Center (HOSC) have been developing a DTN capability utilizing the Commercial Generic Bioprocessing Apparatus (CGBA) payload resources onboard the ISS, at the Boulder Payload Operations Center (POC) and at the HOSC. The DTN capability is in parallel with and is designed to augment current capabilities. The architecture consists of DTN endpoint nodes on the ISS and at the Boulder POC, and a DTN node at the HOSC. The DTN network is composed of two implementations; the Interplanetary Overlay Network (ION) and the open source DTN2 implementation. This paper presents the architecture, implementation, and lessons learned. By being able to handle the types of environments described above, the DTN technology will be instrumental in extending networks into deep space to support future missions to other planets and other solar system points of interest. Thus, this paper also discusses how this technology will be applicable to these types of deep space exploration missions.
This presentation from a Solar Utilization in Higher Education Networking and Information webinar covers contracts, Request for Proposals (RFPs), and administrative issues related to solar project development in the higher education sector.
Bellotti, Amadeo; Steffes, Paul G.
The Juno Microwave Radiometer (MWR) has six channels ranging from 1.36-50 cm and has the ability to peer deep into the Jovian atmosphere. A minimization algorithm utilizing surrogate models has been developed and implemented to perform retrievals for Jovian constituent profiles using Juno MWR data. An artifical neural network algorithm is used as the surrogate for the Juno Atmospheric Microwave Radiative Transfer (JAMRT) model in this minimization. The neural network is trained by simulating emissions at the six wavelengths computed using JAMRT. By exploiting the speed of this surrogate model, retrievals for Jovian constituents profiles, such as ammonia and water vapor, can be rapidly and accurately performed. Retrieved abundance profiles for the first six perijoves during which the Juno MWR was operational will be presented.This work was supported by NASA Contract NNM06AA75C from the Marshall Space Flight Center supporting the Juno Mission Science team, under Subcontract 699054X from the Southwest Research Institute.
Adly, Amr A; Abd-El-Hafiz, Salwa K
Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs) have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.
Amr A. Adly
Full Text Available Magnetic materials are considered as crucial components for a wide range of products and devices. Usually, complexity of such materials is defined by their permeability classification and coupling extent to non-magnetic properties. Hence, development of models that could accurately simulate the complex nature of these materials becomes crucial to the multi-dimensional field-media interactions and computations. In the past few decades, artificial neural networks (ANNs have been utilized in many applications to perform miscellaneous tasks such as identification, approximation, optimization, classification and forecasting. The purpose of this review article is to give an account of the utilization of ANNs in modeling as well as field computation involving complex magnetic materials. Mostly used ANN types in magnetics, advantages of this usage, detailed implementation methodologies as well as numerical examples are given in the paper.
Thin film networks (TFNs), designed by Sandia Laboratories, Albuquerque, are manufactured by the Bendix Corporation, Kansas City Division, for DOE programs. The majority of the TFNs fabricated at this division utilize gold films 3 to 9 ..mu..m thick evaporated over a chromium film. The chromium film is evaporated over sputtered tantalum nitride film, and the substrate material is 0.027 in. (0.686 mm) thick alumina. The TFNs must have high film adhesion characteristics, meet high bondability requirements, and allow stable electrical parameters. Various production techniques ensure these high reliability TFNs.
Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans
Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to implement an intervention mix. Data were collected (2009-14) from 29 Dutch public health policy networks. Surveys were used to identify the number of policy sectors, participation of actors, level of trust, networking by the project leader, and intervention strategies implemented. Conditions sufficient for an intervention mix (≥3 of 4 non-educational strategies present) were determined in a fuzzy-set qualitative comparative analysis. A multisectoral policy network (≥7 of 14 sectors present) was neither a necessary nor a sufficient condition. In multisectoral networks, additionally required was either the active participation of network actors (≥50% actively involved) or active networking by the project leader (≥monthly contacts with network actors). In policy networks that included few sectors, a high level of trust (positive perceptions of each other's intentions) was needed-in the absence though of any of the other conditions. If the network actors were also actively involved, an extra requirement was active networking by the project leader. We conclude that the multisectoral composition of policy networks can contribute to the implementation of a variety of intervention strategies, but not without additional efforts. However, policy networks that include only few sectors are also able to implement an intervention mix. Here, trust seems to be the most important condition. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: email@example.com.
Full Text Available Two network-on-chip (NoC designs are examined and compared in this paper. One design applies a bidirectional ring connection scheme, while the other design applies a code-division multiple-access (CDMA connection scheme. Both of the designs apply globally asynchronous locally synchronous (GALS scheme in order to deal with the issue of transferring data in a multiple-clock-domain environment of an on-chip system. The two NoC designs are compared with each other by their network structures, data transfer principles, network node structures, and their asynchronous designs. Both the synchronous and the asynchronous designs of the two on-chip networks are realized using a hardware-description language (HDL in order to make the entire designs suit the commonly used synchronous design tools and flow. The performance estimation and comparison of the two NoC designs which are based on the HDL realizations are addressed. By comparing the two NoC designs, the advantages and disadvantages of applying direct connection and CDMA connection schemes in an on-chip communication network are discussed.
Catalá-López, Ferrán; Tobías, Aurelio; Cameron, Chris; Moher, David; Hutton, Brian
Systematic reviews and meta-analyses of randomized trials have long been important synthesis tools for guiding evidence-based medicine. More recently, network meta-analyses, an extension of traditional meta-analyses enabling the comparison of multiple interventions, use new statistical methods to incorporate clinical evidence from both direct and indirect treatment comparisons in a network of treatments and associated trials. There is a need to provide education to ensure that core methodological considerations underlying network meta-analyses are well understood by readers and researchers to maximize their ability to appropriately interpret findings and appraise validity. Network meta-analyses are highly informative for assessing the comparative effects of multiple competing interventions in clinical practice and are a valuable tool for health technology assessment and comparative effectiveness research.
Ong, Mei-Sing; Olson, Karen L; Cami, Aurel; Liu, Chunfu; Tian, Fang; Selvam, Nandini; Mandl, Kenneth D
Prescription benzodiazepine overdose continues to cause significant morbidity and mortality in the US. Multiple-provider prescribing, due to either fragmented care or "doctor-shopping," contributes to the problem. To elucidate the effect of provider professional relationships on multiple-provider prescribing of benzodiazepines, using social network analytics. A retrospective analysis of commercial healthcare claims spanning the years 2008 through 2011. Provider patient-sharing networks were modelled using social network analytics. Care team cohesion was measured using care density, defined as the ratio between the total number of patients shared by provider pairs within a patient's care team and the total number of provider pairs in the care team. Relationships within provider pairs were further quantified using a range of network metrics, including the number and proportion of patients or collaborators shared. The relationship between patient-sharing network metrics and the likelihood of multiple prescribing of benzodiazepines. Patients between the ages of 18 and 64 years who received two or more benzodiazepine prescriptions from multiple providers, with overlapping coverage of more than 14 days. A total of 5659 patients and 1448 provider pairs were included in our study. Among these, 1028 patients (18.2 %) received multiple prescriptions of benzodiazepines, involving 445 provider pairs (30.7 %). Patients whose providers rarely shared patients had a higher risk of being prescribed overlapping benzodiazepines; the median care density was 8.1 for patients who were prescribed overlapping benzodiazepines and 10.1 for those who were not (p benzodiazepines. Our findings demonstrate the importance of care team cohesion in addressing multiple-provider prescribing of controlled substances. Furthermore, we illustrate the potential of the provider network as a surveillance tool to detect and prevent adverse events that could arise due to fragmentation of care.
Ji, Shenggong; Yeung, Chi Ho; Hu, Yanqing
Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a group of influential spreaders, such that news propagating from them would lead to an extensive coverage and minimal redundancy. The method well utilizes the similarities between the pre-percolated state and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests on the Facebook networks show that this method outperforms conventional methods based on centrality. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation on social networks.
Social network sites are gaining ground with a huge pace. This study takes a group perspective on the phenomenon, investigating the significance of groups on an internationally well-known social network site, Facebook. The consequences of the co-presence of multiple groups with which an individual identifies and the mechanisms that individuals use to cope with the situation are investigated. The study is positioned in the tradition of social identity approach. Special attention is allocated t...
Goldstein, Yaron AB; Bockmayr, Alexander
Background Constraint-based modeling of genome-scale metabolic network reconstructions has become a widely used approach in computational biology. Flux coupling analysis is a constraint-based method that analyses the impact of single reaction knockouts on other reactions in the network. Results We present an extension of flux coupling analysis for double and multiple gene or reaction knockouts, and develop corresponding algorithms for an in silico simulation. To evaluate our method, we perfor...
Lei, Jing; Li, Baoguo; Li, Erbao; Gong, Zhenghui
Multiple access via sparse graph, such as low density signature (LDS) and sparse code multiple access (SCMA), is a promising technique for future wireless communications. This survey presents an overview of the developments in this burgeoning field, including transmitter structures, extrinsic information transform (EXIT) chart analysis and comparisons with existing multiple access techniques. Such technique enables multiple access under overloaded conditions to achieve a satisfactory performance. Message passing algorithm is utilized for multi-user detection in the receiver, and structures of the sparse graph are illustrated in detail. Outlooks and challenges of this technique are also presented.
Dong, L; Chen, C Y; Ning, B; Xu, D L; Gao, J H; Wang, L L; Yan, S Y; Cheng, S
Although many studies have been carried out on monoclonal gammopathy of unknown significances (MGUS), smoldering multiple myeloma (SMM), and multiple myeloma (MM), their classification and underlying pathogenesis are far from elucidated. To discover the relationships among MGUS, SMM, and MM at the transcriptome level, differentially expressed genes in MGUS, SMM, and MM were identified by the rank product method, and then co-expression networks were constructed by integrating the data. Finally, a pathway-network was constructed based on Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and the relationships between the pathways were identified. The results indicated that there were 55, 78, and 138 pathways involved in the myeloma tumor developmental stages of MGUS, SMM, and MM, respectively. The biological processes identified therein were found to have a close relationship with the immune system. Processes and pathways related to the abnormal activity of DNA and RNA were also present in SMM and MM. Six common pathways were found in the whole process of myeloma tumor development. Nine pathways were shown to participate in the progression of MGUS to SMM, and prostate cancer was the sole pathway that was involved only in MGUS and MM. Pathway-network analysis might provide a new indicator for the developmental stage diagnosis of myeloma tumors.
Full Text Available Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed 'quasi-orbits', which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network's firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms.
Full Text Available In the 5G era, the operational cost of mobile wireless networks will significantly increase. Further, massive network capacity and zero latency will be needed because everything will be connected to mobile networks. Thus, self-organizing networks (SON are needed, which expedite automatic operation of mobile wireless networks, but have challenges to satisfy the 5G requirements. Therefore, researchers have proposed a framework to empower SON using big data. The recent framework of a big data-empowered SON analyzes the relationship between key performance indicators (KPIs and related network parameters (NPs using machine-learning tools, and it develops regression models using a Gaussian process with those parameters. The problem, however, is that the methods of finding the NPs related to the KPIs differ individually. Moreover, the Gaussian process regression model cannot determine the relationship between a KPI and its various related NPs. In this paper, to solve these problems, we proposed multivariate multiple regression models to determine the relationship between various KPIs and NPs. If we assume one KPI and multiple NPs as one set, the proposed models help us process multiple sets at one time. Also, we can find out whether some KPIs are conflicting or not. We implement the proposed models using MapReduce.
Hartmann, András; Lemos, João M; Vinga, Susana
The aim of inverse modeling is to capture the systems׳ dynamics through a set of parameterized Ordinary Differential Equations (ODEs). Parameters are often required to fit multiple repeated measurements or different experimental conditions. This typically leads to a multi-objective optimization problem that can be formulated as a non-convex optimization problem. Modeling of glucose utilization of Lactococcus lactis bacteria is considered using in vivo Nuclear Magnetic Resonance (NMR) measurements in perturbation experiments. We propose an ODE model based on a modified time-varying exponential decay that is flexible enough to model several different experimental conditions. The starting point is an over-parameterized non-linear model that will be further simplified through an optimization procedure with regularization penalties. For the parameter estimation, a stochastic global optimization method, particle swarm optimization (PSO) is used. A regularization is introduced to the identification, imposing that parameters should be the same across several experiments in order to identify a general model. On the remaining parameter that varies across the experiments a function is fit in order to be able to predict new experiments for any initial condition. The method is cross-validated by fitting the model to two experiments and validating the third one. Finally, the proposed model is integrated with existing models of glycolysis in order to reconstruct the remaining metabolites. The method was found useful as a general procedure to reduce the number of parameters of unidentifiable and over-parameterized models, thus supporting feature selection methods for parametric models. Copyright © 2014 Elsevier Ltd. All rights reserved.
Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo
Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase.
Cho, Sunghyun; Choi, Ji-Woong; You, Cheolwoo
Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase. PMID:24152920
Full Text Available This paper addresses the problem of projective exponential synchronization for a class of complex spatiotemporal networks with multiple time delays satisfying the homogeneous Neumann boundary conditions, where the network is modeled by coupled partial differential-difference equations (PDDEs. A distributed proportional-spatial derivative (P-sD controller is designed by employing Lyapunov’s direct method and Kronecker product. The controller ensures the projective exponential synchronization of the PDDE network. The main result of this paper is presented in terms of standard linear matrix inequality (LMI. A numerical example is provided to show the effectiveness of the proposed design method.
Goldstein, Yaron Ab; Bockmayr, Alexander
Constraint-based modeling of genome-scale metabolic network reconstructions has become a widely used approach in computational biology. Flux coupling analysis is a constraint-based method that analyses the impact of single reaction knockouts on other reactions in the network. We present an extension of flux coupling analysis for double and multiple gene or reaction knockouts, and develop corresponding algorithms for an in silico simulation. To evaluate our method, we perform a full single and double knockout analysis on a selection of genome-scale metabolic network reconstructions and compare the results. A prototype implementation of double knockout simulation is available at http://hoverboard.io/L4FC.
Veltin, J.; Belfroid, S.P.C.
As Carbon Capture and Storage slowly gets accepted and integrated as a mean for cleaner utilization of fossil fuels, the integration of capture, transport and storage becomes a key component to properly design a CO2 network. While the boundary conditions set by the capture and storage units have
Plan R & D Research and Development RECON Reconnaissance RFD Request for Proposal SAC Strategic Air Caoxnand SAL Strategic Arms Limitation Talks SDSD ...CDF curves intersect, FDSD will be inconclusive, but second degree stochastic domi- nance ( SDSD ) may further screen the set of options. SDSD requires...the DM to be risk averse over the outputs (the utility function must be con- cave). SDSD states that alternative i will dominate j if J (F.(y) - Fi(Y
Wang, Jin; Li, Bin; Xia, Feng; Kim, Chang-Seob; Kim, Jeong-Uk
Traffic patterns in wireless sensor networks (WSNs) usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption. PMID:25196015
Full Text Available Traffic patterns in wireless sensor networks (WSNs usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption.
Wang, Jin; Li, Bin; Xia, Feng; Kim, Chang-Seob; Kim, Jeong-Uk
Traffic patterns in wireless sensor networks (WSNs) usually follow a many-to-one model. Sensor nodes close to static sinks will deplete their limited energy more rapidly than other sensors, since they will have more data to forward during multihop transmission. This will cause network partition, isolated nodes and much shortened network lifetime. Thus, how to balance energy consumption for sensor nodes is an important research issue. In recent years, exploiting sink mobility technology in WSNs has attracted much research attention because it can not only improve energy efficiency, but prolong network lifetime. In this paper, we propose an energy efficient distance-aware routing algorithm with multiple mobile sink for WSNs, where sink nodes will move with a certain speed along the network boundary to collect monitored data. We study the influence of multiple mobile sink nodes on energy consumption and network lifetime, and we mainly focus on the selection of mobile sink node number and the selection of parking positions, as well as their impact on performance metrics above. We can see that both mobile sink node number and the selection of parking position have important influence on network performance. Simulation results show that our proposed routing algorithm has better performance than traditional routing ones in terms of energy consumption.
Full Text Available To determine the capacity of wireless multiple access networks, the interference among the wireless links must be accurately modeled. In this paper, we formalize the notion of the partial interference phenomenon observed in many recent wireless measurement studies and establish analytical models with tractable solutions for various types of wireless multiple access networks. In particular, we characterize the stability region of IEEE 802.11 networks under partial interference with two potentially unsaturated links numerically. We also provide a closed-form solution for the stability region of slotted ALOHA networks under partial interference with two potentially unsaturated links and obtain a partial characterization of the boundary of the stability region for the general M-link case. Finally, we derive a closed-form approximated solution for the stability region for general M-link slotted ALOHA system under partial interference effects. Based on our results, we demonstrate that it is important to model the partial interference effects while analyzing wireless multiple access networks. This is because such considerations can result in not only significant quantitative differences in the predicted system capacity but also fundamental qualitative changes in the shape of the stability region of the systems.
Hashemifar, Somaye; Huang, Qixing; Xu, Jinbo
High-throughput experimental techniques have been producing more and more protein-protein interaction (PPI) data. The PPI network alignment greatly benefits the understanding of evolutionary relationship among species, helps identify conserved subnetworks, and provides extra information for functional annotations. Although a few methods have been developed for multiple PPI network alignment, the alignment quality is still far from perfect, and thus, new network alignment methods are needed. In this article, we present a novel method, denoted as ConvexAlign, for joint alignment of multiple PPI networks by convex optimization of a scoring function composed of sequence similarity, topological score, and interaction conservation score. In contrast to existing methods that generate multiple alignments in a greedy or progressive manner, our convex method optimizes alignments globally and enforces consistency among all pairwise alignments, resulting in much better alignment quality. Tested on both synthetic and real data, our experimental results show that ConvexAlign outperforms several popular methods in producing functionally coherent alignments. ConvexAlign even has a larger advantage over the others in aligning real PPI networks. ConvexAlign also finds a few conserved complexes, which cannot be detected by the other methods.
Linnenbank, G.R.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Mullender, Sape J.; Smulders, P.; van den Meerendonk, H.
This paper describes a cellular multiple-access scheme based on TDMA for multimedia communication networks. The scheme proposes an admission control of two different multimedia application stream types: real-time and non-real-time. We do not consider interference between cells. The proposed
Liu, Hong; Gliese, Ulrik Bo; Dittmann, Lars
In this paper, we propose a knowledge-based multiple access protocol for the extension of wireline ATM to wireless networks. The objective is to enable effecient transmission of all kinds of ATM traffic in the wireless channel with guaranteed QoS.The proposed protocol utilixes knowledge of the main...... guaranteed QoS requirements to a variety of ATM applications....
Anderson, Joan L.
Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models…
Mazvimavi, D.; Meijerink, A.M.J.; Savenije, H.H.G.; Stein, A.
The feasibility of predicting flow characteristics from basin descriptors using multiple regression and neural networks has been investigated on 52 basins in Zimbabwe. Flow characteristics considered were average annual runoff, base flow index, flow duration curve, and average monthly runoff . Mean
Anastasopoulos, Markos P; Tzanakaki, Anna; Georgakilas, Konstantinos; Simeonidou, Dimitra
This paper studies energy efficient planning of multiple concurrent virtual infrastructures over a converged physical infrastructure incorporating integrated optical network and IT resources. An MILP model for virtualization of the underlying physical resources is proposed and validated achieving significant energy savings. © 2011 Optical Society of America
Choupani, R.; Wong, S.; Tolun, M.
Streaming multimedia data on best-effort networks such as the Internet requires measures against bandwidth fluctuations and frame loss. Multiple Description Coding (MDC) methods are used to overcome the jitter and delay problems arising from frame losses by making the transmitted data more error
Guerrero, Josep M.; Kheng Tan, Yen
The chapter covers the smart wireless sensors for microgrids, as well as the energy harvesting technology used to sustain the operations of these sensors. Last, a case study on the multiple distributed smart microgrids with a self-autonomous, energy harvesting wireless sensor network is presented....
Schoonheim, M.M.; Geurts, J.J.G.; Wiebenga, O.T.; de Munck, J.C.; Polman, C.H.; Stam, C.J.; Barkhof, F.; Wink, A.M.
Background: Cognitive dysfunction in multiple sclerosis (MS) has a large impact on the quality of life and is poorlyunderstood.Objective: The aim of this study was to investigate functional network integrity in MS, and relate this to cognitivedysfunction and physical disability.Methods: Resting
Xuegang, Huang; Jensen, Christian Søndergaard; Saltenis, Simonas
for points of interest that are accessible via the road network. Given multiple k nearest neighbor queries, the paper proposes progressive techniques that selectively cache query results in main memory and subsequently reuse these for query processing. The paper initially proposes techniques for the case...
Weng, Tongfeng; Zhang, Jie; Small, Michael; Hui, Pan
We investigate multiple random walks traversing independently and concurrently on complex networks and introduce the concept of mean first parallel passage time (MFPPT) to quantify their search efficiency. The mean first parallel passage time represents the expected time required to find a given target by one or some of the multiple walkers. We develop a general theory that allows us to calculate the MFPPT analytically. Interestingly, we find that the global MFPPT follows a harmonic law with respect to the global mean first passage times of the associated walkers. Remarkably, when the properties of multiple walkers are identical, the global MFPPT decays in a power law manner with an exponent of unity, irrespective of network structure. These findings are confirmed by numerical and theoretical results on various synthetic and real networks. The harmonic law reveals a universal principle governing multiple random walks on networks that uncovers the contribution and role of the combined walkers in a target search. Our paradigm is also applicable to a broad range of random search processes.
Dalgaard, Jens; Pena, Jose; Kocka, Tomas
We propose a method to assist the user in the interpretation of the best Bayesian network model indu- ced from data. The method consists in extracting relevant features from the model (e.g. edges, directed paths and Markov blankets) and, then, assessing the con¯dence in them by studying multiple...
Kamstra, A.; van der Putten, A. A. J.; Vlaskamp, C.
Background: Persons with less severe disabilities are able to express their needs and show initiatives in social contacts, persons with profound intellectual and multiple disabilities (PIMD), however, depend on others for this. This study analysed the structure of informal networks of persons with PIMD. Materials and Methods: Data concerning the…
Cha, Yong Dae; Yoon, Gilwon
A ubiquitous health monitoring system for multiple users was developed based on a ZigBee and wireless local area network (WLAN) dual-network. A compact biosignal monitoring unit (BMU) for measuring electrocardiogram (ECG), photoplethysmogram (PPG), and temperature was also developed. A single 8-bit microcontroller operated the BMU including most of digital filtering and wireless communication. The BMU with its case was reduced to 55 x 35 x 15 mm and 33 g. In routine use, vital signs of 6 bytes/sec (heart rate, temperature, pulse transit time) per each user were transmitted through a ZigBee module even though all the real-time data were recorded in a secure digital memory of the BMU. In an emergency or when need arises, a channel of a particular user was switched to another ZigBee module, called the emergency module, that sent all ECG and PPG waveforms in real time. Each emergency ZigBee module handled up to a few users. Data from multiple users were wirelessly received by the ZigBee receiver modules in a controller called ZigBee-WLAN gateway, where the ZigBee modules were connected to a WLAN module. This WLAN module sent all data wirelessly to a monitoring center. Operating the dual modes of ZigBee/WLAN utilized an advantage of ZigBee by handling multiple users with minimum power consumption, and overcame the ZigBee limitation of low data rate. This dual-network system for LAN is economically competitive and reliable.
Blue, Christine M; Funkhouser, D Ellen; Riggs, Sheila; Rindal, D Brad; Worley, Donald; Pihlstrom, Daniel J; Benjamin, Paul; Gilbert, Gregg H
The purpose of this study was to quantify, within the National Dental Practice-Based Research Network, current utilization of dental hygienists and assistants with expanded functions and quantify network dentists' attitudes toward a new nondentist provider model - the dental therapist. National Dental Practice-Based Research Network practitioner-investigators participated in a single, cross-sectional administration of a questionnaire. Current nondentist providers are not being utilized by network practitioner-investigators to the fullest extent allowed by law. Minnesota practitioners, practitioners in large group practices, and those with prior experience with expanded-function nondentist providers delegate at a higher rate and had more-positive perceptions of the new dental therapist model. Expanding scopes of practice for dental hygienists and assistants has not translated to the maximal delegation allowed by law among network practices. This finding may provide insight into dentists' acceptance of newer nondentist provider models. © 2013 American Association of Public Health Dentistry.
Full Text Available The openness nature of wireless networks allows adversaries to easily launch variety of spoofing attacks and causes havoc in network performance. Recent approaches used Received Signal Strength (RSS traces, which only detect spoofing attacks in mobile wireless networks. However, it is not always desirable to use these methods as RSS values fluctuate significantly over time due to distance, noise and interference. In this paper, we discusses a novel approach, Mobile spOofing attack DEtection and Localization in WIireless Networks (MODELWIN system, which exploits location information about nodes to detect identity-based spoofing attacks in mobile wireless networks. Also, this approach determines the number of attackers who used the same node identity to masquerade as legitimate device. Moreover, multiple adversaries can be localized accurately. By eliminating attackers the proposed system enhances network performance. We have evaluated our technique through simulation using an 802.11 (WiFi network and an 802.15.4 (Zigbee networks. The results prove that MODELWIN can detect spoofing attacks with a very high detection rate and localize adversaries accurately.
Zhou Sheng Jie
Full Text Available A MAC protocol for public bus networks, called Bus MAC protocol, designed to provide high quality Internet service for bus passengers. The paper proposed a multi-channel dual clocks three-demission probability random multiple access protocol based on RTS/CTS mechanism, decreasing collisions caused by multiple access from multiple passengers. Use the RTS/CTS mechanism increases the reliability and stability of the system, reducing the collision possibility of the information packets to a certain extent, improves the channel utilization; use the multi-channel mechanism, not only enables the channel load balancing, but also solves the problem of the hidden terminal and exposed terminal. Use the dual clocks mechanism, reducing the system idle time. At last, the different selection of the three-dimensional probabilities can make the system throughput adapt to the network load which could realize the maximum of the system throughput.
This paper studies the problem of global robust asymptotic stability of the equilibrium point for the class of dynamical neural networks with multiple time delays with respect to the class of slope-bounded activation functions and in the presence of the uncertainties of system parameters of the considered neural network model. By using an appropriate Lyapunov functional and exploiting the properties of the homeomorphism mapping theorem, we derive a new sufficient condition for the existence, uniqueness and global robust asymptotic stability of the equilibrium point for the class of neural networks with multiple time delays. The obtained stability condition basically relies on testing some relationships imposed on the interconnection matrices of the neural system, which can be easily verified by using some certain properties of matrices. An instructive numerical example is also given to illustrate the applicability of our result and show the advantages of this new condition over the previously reported corresponding results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Khan, Fahd Ahmed
Consider a multi-user underlay cognitive network where multiple cognitive users, having limited peak transmit power, concurrently share the spectrum with a primary network with multiple users. The channel between the secondary network is assumed to have independent but not identical Nakagami-m fading. The interference channel between the secondary users and the primary users is assumed to have Rayleigh fading. The uplink scenario is considered where a single secondary user is selected for transmission. This opportunistic selection depends on the transmission channel power gain and the interference channel power gain as well as the power allocation policy adopted at the users. Exact closed form expressions for the momentgenerating function, outage performance and the symbol-error-rate performance are derived. The outage performance is also studied in the asymptotic regimes and the generalized diversity gain of this scheduling scheme is derived. Numerical results corroborate the derived analytical results.
Full Text Available We consider the problem of streaming media transmission in a heterogeneous network from a multisource server to home multiple terminals. In wired network, the transmission performance is limited by network state (e.g., the bandwidth variation, jitter, and packet loss. In wireless network, the multiple user terminals can cause bandwidth competition. Thus, the streaming media distribution in a heterogeneous network becomes a severe challenge which is critical for QoS guarantee. In this paper, we propose a context-aware adaptive streaming media distribution system (CAASS, which implements the context-aware module to perceive the environment parameters and use the strategy analysis (SA module to deduce the most suitable service level. This approach is able to improve the video quality for guarantying streaming QoS. We formulate the optimization problem of QoS relationship with the environment parameters based on the QoS testing algorithm for IPTV in ITU-T G.1070. We evaluate the performance of the proposed CAASS through 12 types of experimental environments using a prototype system. Experimental results show that CAASS can dynamically adjust the service level according to the environment variation (e.g., network state and terminal performances and outperforms the existing streaming approaches in adaptive streaming media distribution according to peak signal-to-noise ratio (PSNR.
Barany, Ernest; Krupa, Maciej
A new approach to determine the stability of multiple access network control schemes is presented. A “busy” network (the precise meaning of the term “busy” will be presented in the text) is modelled as a switched single-server hybrid dynamical system whose switching laws are stochastic and are based on typical multiple access network control protocols such as ALOHA and ethernet. The techniques are used to compute the critical ratio of traffic production per network node to total available bandwidth that ensures that data packets will not accumulate unboundedly in waiting queues at each node. This is a measure of stability of the network and is an emergent, global, property determined by decentralized, autonomous behavior of each node. The behavior of each individual node is regarded as “microscopic” and the collective behavior of the network as a whole are emergent consequences of such microscopic laws. The results follow from the stationary distribution property of ergodic Markov chains.
Häuslein, Ina; Cantet, Franck; Reschke, Sarah; Chen, Fan; Bonazzi, Matteo; Eisenreich, Wolfgang
The human pathogen Coxiella burnetii causes Q-fever and is classified as a category B bio-weapon. Exploiting the development of the axenic growth medium ACCM-2, we have now used 13C-labeling experiments and isotopolog profiling to investigate the highly diverse metabolic network of C. burnetii. To this aim, C. burnetii RSA 439 NMII was cultured in ACCM-2 containing 5 mM of either [U-13C3]serine, [U-13C6]glucose, or [U-13C3]glycerol until the late-logarithmic phase. GC/MS-based isotopolog profiling of protein-derived amino acids, methanol-soluble polar metabolites, fatty acids, and cell wall components (e.g., diaminopimelate and sugars) from the labeled bacteria revealed differential incorporation rates and isotopolog profiles. These data served to decipher the diverse usages of the labeled substrates and the relative carbon fluxes into the core metabolism of the pathogen. Whereas, de novo biosynthesis from any of these substrates could not be found for histidine, isoleucine, leucine, lysine, phenylalanine, proline and valine, the other amino acids and metabolites under study acquired 13C-label at specific rates depending on the nature of the tracer compound. Glucose was directly used for cell wall biosynthesis, but was also converted into pyruvate (and its downstream metabolites) through the glycolytic pathway or into erythrose 4-phosphate (e.g., for the biosynthesis of tyrosine) via the non-oxidative pentose phosphate pathway. Glycerol efficiently served as a gluconeogenetic substrate and could also be used via phosphoenolpyruvate and diaminopimelate as a major carbon source for cell wall biosynthesis. In contrast, exogenous serine was mainly utilized in downstream metabolic processes, e.g., via acetyl-CoA in a complete citrate cycle with fluxes in the oxidative direction and as a carbon feed for fatty acid biosynthesis. In summary, the data reflect multiple and differential substrate usages by C. burnetii in a bipartite-type metabolic network, resembling the
Le Petit Christel
Full Text Available Abstract Background The co-morbidity of health conditions is becoming a significant health issue, particularly as populations age, and presents important methodological challenges for population health research. For example, the calculation of summary measures of population health (SMPH can be compromised if co-morbidity is not taken into account. One popular co-morbidity adjustment used in SMPH computations relies on a straightforward multiplicative combination of the severity weights for the individual conditions involved. While the convenience and simplicity of the multiplicative model are attractive, its appropriateness has yet to be formally tested. The primary objective of the current study was therefore to examine the empirical evidence in support of this approach. Methods The present study drew on information on the prevalence of chronic conditions and a utility-based measure of health-related quality of life (HRQoL, namely the Health Utilities Index Mark 3 (HUI3, available from Cycle 1.1 of the Canadian Community Health Survey (CCHS; 2000–01. Average HUI3 scores were computed for both single and co-morbid conditions, and were also purified by statistically removing the loss of functional health due to health problems other than the chronic conditions reported. The co-morbidity rule was specified as a multiplicative combination of the purified average observed HUI3 utility scores for the individual conditions involved, with the addition of a synergy coefficient s for capturing any interaction between the conditions not explained by the product of their utilities. The fit of the model to the purified average observed utilities for the co-morbid conditions was optimized using ordinary least squares regression to estimate s. Replicability of the results was assessed by applying the method to triple co-morbidities from the CCHS cycle 1.1 database, as well as to double and triple co-morbidities from cycle 2.1 of the CCHS (2003–04. Results
Nathan D. Schwalm
Full Text Available The utilization of simple sugars is widespread across all domains of life. In contrast, the breakdown of complex carbohydrates is restricted to a subset of organisms. A regulatory paradigm for integration of complex polysaccharide breakdown with simple sugar utilization was established in the mammalian gut symbiont Bacteroides thetaiotaomicron, whereby sensing of monomeric fructose regulates catabolism of both fructose and polymeric fructans. We now report that a different regulatory paradigm governs utilization of monomeric arabinose and the arabinose polymer arabinan. We establish that (i arabinan utilization genes are controlled by a transcriptional activator that responds to arabinan and by a transcriptional repressor that responds to arabinose, (ii arabinose utilization genes are regulated directly by the arabinose-responding repressor but indirectly by the arabinan-responding activator, and (iii activation of both arabinan and arabinose utilization genes requires a pleiotropic transcriptional regulator necessary for survival in the mammalian gut. Genomic analysis predicts that this paradigm is broadly applicable to the breakdown of other polysaccharides in both B. thetaiotaomicron and other gut Bacteroides spp. The uncovered mechanism enables regulation of polysaccharide utilization genes in response to both the polysaccharide and its breakdown products.
Full Text Available Predicting critical nodes of Opportunistic Sensor Network (OSN can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM. It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better.
Kim, Mihui; Chae, Kijoon
To successfully realize the ubiquitous network environment including home automation or industrial control systems, it is important to be able to resist a jamming attack. This has recently been considered as an extremely threatening attack because it can collapse the entire network, despite the existence of basic security protocols such as encryption and authentication. In this paper, we present a method of jamming attack tolerant routing using multiple paths based on zones. The proposed scheme divides the network into zones, and manages the candidate forward nodes of neighbor zones. After detecting an attack, detour nodes decide zones for rerouting, and detour packets destined for victim nodes through forward nodes in the decided zones. Simulation results show that our scheme increases the PDR (Packet Delivery Ratio) and decreases the delay significantly in comparison with rerouting by a general routing protocol on sensor networks, AODV (Ad hoc On Demand Distance Vector), and a conventional JAM (Jammed Area Mapping) service with one reroute.
Full Text Available To successfully realize the ubiquitous network environment including home automation or industrial control systems, it is important to be able to resist a jamming attack. This has recently been considered as an extremely threatening attack because it can collapse the entire network, despite the existence of basic security protocols such as encryption and authentication. In this paper, we present a method of jamming attack tolerant routing using multiple paths based on zones. The proposed scheme divides the network into zones, and manages the candidate forward nodes of neighbor zones. After detecting an attack, detour nodes decide zones for rerouting, and detour packets destined for victim nodes through forward nodes in the decided zones. Simulation results show that our scheme increases the PDR (Packet Delivery Ratio and decreases the delay significantly in comparison with rerouting by a general routing protocol on sensor networks, AODV (Ad hoc On Demand Distance Vector, and a conventional JAM (Jammed Area Mapping service with one reroute.
Many individuals wait until alcohol use becomes severe before treatment is sought. However, social networks, or the number of social groups an individual belongs to, may play a moderating role in this relationship. Logistic regression examined the interaction of alcohol consumption and social networks as a predictor of treatment utilization while adjusting for sociodemographic and clinical variables among 1,433 lifetime alcohol-dependent respondents from wave 2 of the National Epidemiologic Survey on Alcohol Related Conditions (NESARC). Results showed that social networks moderate the relationship between alcohol consumption and treatment utilization such that for individuals with few network ties, the relationship between alcohol consumption and treatment utilization was diminished, compared to the relationship between alcohol consumption and treatment utilization for individuals with many network ties. Findings offer insight into how social networks, at times, can influence individuals to pursue treatment, while at other times, influence individuals to stay out of treatment, or seek treatment substitutes. Copyright © 2014 Elsevier Inc. All rights reserved.
This compilation of diverse conservation and renewable energy projects across the United States was prepared through the enthusiastic participation of solar and alternate energy groups from every state and region. Compiled and edited by the Center for Renewable Resources, these projects reflect many levels of innovation and technical expertise. In many cases, a critique analysis is presented of how projects performed and of the institutional conditions associated with their success or failure. Some 2000 projects are included in this compilation; most have worked, some have not. Information about all is presented to aid learning from these experiences. The four volumes in this set are arranged in state sections by geographic region, coinciding with the four Regional Solar Energy Centers. The table of contents is organized by project category so that maximum cross-referencing may be obtained. This volume includes information on the Western Solar Utilization Network Region. (WHK)
Full Text Available The smartphones are widely available in recent years. Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. The behavior based forwarding has become a new transmission paradigm for supporting many novel applications. However, the commodities, services, and individuals usually have multiple properties of their interests and behaviors. In this paper, we profile these multiple properties and propose an Opportunistic Dissemination Protocol based on Multiple Behavior Profile, ODMBP, in mobile social networks. We first map the interest space to the behavior space and extract the multiple behavior profiles from the behavior space. Then, we propose the correlation computing model based on the principle of BM25 to calculate the correlation metric of multiple behavior profiles. The correlation metric is used to forward the message to the users who are more similar to the target in our protocol. ODMBP consists of three stages: user initialization, gradient ascent, and group spread. Through extensive simulations, we demonstrate that the proposed multiple behavior profile and correlation computing model are correct and efficient. Compared to other classical routing protocols, ODMBP can significantly improve the performance in the aspect of delivery ratio, delay, and overhead ratio.
Schwalm, Nathan D; Townsend, Guy E; Groisman, Eduardo A
The utilization of simple sugars is widespread across all domains of life. In contrast, the breakdown of complex carbohydrates is restricted to a subset of organisms. A regulatory paradigm for integration of complex polysaccharide breakdown with simple sugar utilization was established in the mammalian gut symbiont Bacteroides thetaiotaomicron, whereby sensing of monomeric fructose regulates catabolism of both fructose and polymeric fructans. We now report that a different regulatory paradigm governs utilization of monomeric arabinose and the arabinose polymer arabinan. We establish that (i) arabinan utilization genes are controlled by a transcriptional activator that responds to arabinan and by a transcriptional repressor that responds to arabinose, (ii) arabinose utilization genes are regulated directly by the arabinose-responding repressor but indirectly by the arabinan-responding activator, and (iii) activation of both arabinan and arabinose utilization genes requires a pleiotropic transcriptional regulator necessary for survival in the mammalian gut. Genomic analysis predicts that this paradigm is broadly applicable to the breakdown of other polysaccharides in both B. thetaiotaomicron and other gut Bacteroides spp. The uncovered mechanism enables regulation of polysaccharide utilization genes in response to both the polysaccharide and its breakdown products. Breakdown of complex polysaccharides derived from "dietary fiber" is achieved by the mammalian gut microbiota. This breakdown creates a critical nutrient source for both the microbiota and its mammalian host. Because the availability of individual polysaccharides fluctuates with variations in the host diet, members of the microbiota strictly control expression of polysaccharide utilization genes. Our findings define a regulatory architecture that controls the breakdown of a polysaccharide by a gut bacterium in response to three distinct signals. This architecture integrates perception of a complex
In this letter, we propose two-way amplify-and-forward relaying in conjunction with adaptive modulation in order to improve spectral efficiency of relayed communication systems while monitoring the required error performance. We also consider a multiple relay network where only the best relay node is utilized so that the diversity order increases while maintaining a low complexity of implementation as the number of relays increases. Based on the best relay selection criterion, we offer an upper bound on the signal-to-noise ratio to keep the performance analysis tractable. Our numerical examples show that the proposed system offers a considerable gain in spectral efficiency while satisfying the error rate requirements. © 2011 IEEE.
Full Text Available Abstract Background In the last decade, a considerable amount of research has been devoted to investigating the phylogenetic properties of organisms from a systems-level perspective. Most studies have focused on the classification of organisms based on structural comparison and local alignment of metabolic pathways. In contrast, global alignment of multiple metabolic networks complements sequence-based phylogenetic analyses and provides more comprehensive information. Results We explored the phylogenetic relationships between microorganisms through global alignment of multiple metabolic networks. The proposed approach integrates sequence homology data with topological information of metabolic networks. In general, compared to recent studies, the resulting trees reflect the living style of organisms as well as classical taxa. Moreover, for phylogenetically closely related organisms, the classification results are consistent with specific metabolic characteristics, such as the light-harvesting systems, fermentation types, and sources of electrons in photosynthesis. Conclusions We demonstrate the usefulness of global alignment of multiple metabolic networks to infer phylogenetic relationships between species. In addition, our exhaustive analysis of microbial metabolic pathways reveals differences in metabolic features between phylogenetically closely related organisms. With the ongoing increase in the number of genomic sequences and metabolic annotations, the proposed approach will help identify phenotypic variations that may not be apparent based solely on sequence-based classification.
Jung, Sun-Young; Jung, Sang-Min; Han, Sang-Kook
Exponentially expanding various applications in company with proliferation of mobile devices make mobile traffic exploded annually. For future access network, bandwidth efficient and asynchronous signals converged transmission technique is required in optical network to meet a huge bandwidth demand, while integrating various services and satisfying multiple access in perceived network resource. Orthogonal frequency division multiplexing (OFDM) is highly bandwidth efficient parallel transmission technique based on orthogonal subcarriers. OFDM has been widely studied in wired-/wireless communication and became a Long term evolution (LTE) standard. Consequently, OFDM also has been actively researched in optical network. However, OFDM is vulnerable frequency and phase offset essentially because of its sinc-shaped side lobes, therefore tight synchronism is necessary to maintain orthogonality. Moreover, redundant cyclic prefix (CP) is required in dispersive channel. Additionally, side lobes act as interference among users in multiple access. Thus, it practically hinders from supporting integration of various services and multiple access based on OFDM optical transmission In this paper, adaptively modulated optical filter bank multicarrier system with offset QAM (AMO-FBMC-OQAM) is introduced and experimentally investigated in uplink optical transmission to relax multiple access interference (MAI), while improving bandwidth efficiency. Side lobes are effectively suppressed by using FBMC, therefore the system becomes robust to path difference and imbalance among optical network units (ONUs), which increase bandwidth efficiency by reducing redundancy. In comparison with OFDM, a signal performance and an efficiency of frequency utilization are improved in the same experimental condition. It enables optical network to effectively support heterogeneous services and multiple access.
Wang, Jinlian; Zuo, Yiming; Liu, Lun; Man, Yangao; Tadesse, Mahlet G; Ressom, Habtom W
Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. In this article, we present a Bayesian network classifier that is customized to (1) increase the ability to integrate diverse information from different sources, (2) effectively predict protein-protein interactions, (3) infer aberrant networks with scale-free and small-world properties, and (4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model in discovering protein biomarkers of hepatocellular carcinoma leads to the identification of functional modules that provide insights into the mechanism of the development and progression of hepatocellular carcinoma. These functional modules include cell cycle deregulation, increased angiogenesis (eg, vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. The discoveries and conclusions derived from our customized Bayesian network classifier are consistent with previously published results. The proposed approach for determining Bayesian network structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.
Eibl, Guido; Rozengurt, Enrique
Pancreatic ductal adenocarcinoma (PDAC) continues to be a lethal disease with no efficacious treatment modalities. The incidence of PDAC is expected to increase, at least partially because of the obesity epidemic. Increased efforts to prevent or intercept this disease are clearly needed. Mutations in KRAS are initiating events in pancreatic carcinogenesis supported by genetically engineered mouse models of the disease. However, oncogenic KRAS is not entirely sufficient for the development of fully invasive PDAC. Additional genetic mutations and/or environmental, nutritional, and metabolic stressors, e.g. inflammation and obesity, are required for efficient PDAC formation with activation of KRAS downstream effectors. Multiple factors "upstream" of KRAS associated with obesity, including insulin resistance, inflammation, changes in gut microbiota and GI peptides, can enhance/modulate downstream signals. Multiple signaling networks and feedback loops "downstream" of KRAS have been described that respond to obesogenic diets. We propose that KRAS mutations potentiate a signaling network that is promoted by environmental factors. Specifically, we envisage that KRAS mutations increase the intensity and duration of the growth-promoting signaling network. As the transcriptional activator YAP plays a critical role in the network, we conclude that the rationale for targeting the network (at different points), e.g. with FDA approved drugs such as statins and metformin, is therefore compelling. Copyright © 2017 Elsevier Ltd. All rights reserved.
Liu, Hong; Dittmann, Lars; Gliese, Ulrik Bo
The furture broadband wireless asynchronous transfer mode (ATM) networks must provide seamless extension of multimedia services from the wireline ATM networks. This requires an effecient wireless access protocol to fulfill varying Quality-og-Service (QoS) requirements for multimedia applications....... In this paper, we propose a multiple access protocol using centralized and distributed channel access control techniques to provide QoS guarantees for multimedia services by taking advantage of the characteristics of different kinds of ATM traffics. Multimedia traffic, including constant bit rate (CBR...
Tan, Mei-Fang; Gao, Ting; Liu, Wan-Quan; Zhang, Chun-Yan; Yang, Xi; Zhu, Jia-Wen; Teng, Mu-Ye; Li, Lu; Zhou, Rui
Acquisition and metabolism of carbohydrates are essential for host colonization and pathogenesis of bacterial pathogens. Different bacteria can uptake different lines of carbohydrates via ABC transporters, in which ATPase subunits energize the transport though ATP hydrolysis. Some ABC transporters possess their own ATPases, while some share a common ATPase. Here we identified MsmK, an ATPase from Streptococcus suis, an emerging zoonotic bacterium causing dead infections in pigs and humans. Genetic and biochemistry studies revealed that the MsmK was responsible for the utilization of raffinose, melibiose, maltotetraose, glycogen and maltotriose. In infected mice, the msmK-deletion mutant showed significant defects of survival and colonization when compared with its parental and complementary strains. Taken together, MsmK is an ATPase that contributes to multiple carbohydrates utilization and host colonization of S. suis. This study gives new insight into our understanding of the carbohydrates utilization and its relationship to the pathogenesis of this zoonotic pathogen.
Liu, X.Y; Zhang, C.Y.; Liu, Q.S.; Birkholzer, J.T.
In many underground nuclear waste repository systems, such as at Yucca Mountain, water flow rate and amount of water seepage into the waste emplacement drifts are mainly determined by hydrological properties of fracture network in the surrounding rock mass. Natural fracture network system is not easy to describe, especially with respect to its connectivity which is critically important for simulating the water flow field. In this paper, we introduced a new method for fracture network description and prediction, termed multi-point-statistics (MPS). The process of the MPS method is to record multiple-point statistics concerning the connectivity patterns of a fracture network from a known fracture map, and to reproduce multiple-scale training fracture patterns in a stochastic manner, implicitly and directly. It is applied to fracture data to study flow field behavior at the Yucca Mountain waste repository system. First, the MPS method is used to create a fracture network with an original fracture training image from Yucca Mountain dataset. After we adopt a harmonic and arithmetic average method to upscale the permeability to a coarse grid, THM simulation is carried out to study near-field water flow in the surrounding waste emplacement drifts. Our study shows that connectivity or patterns of fracture networks can be grasped and reconstructed by MPS methods. In theory, it will lead to better prediction of fracture system characteristics and flow behavior. Meanwhile, we can obtain variance from flow field, which gives us a way to quantify model uncertainty even in complicated coupled THM simulations. It indicates that MPS can potentially characterize and reconstruct natural fracture networks in a fractured rock mass with advantages of quantifying connectivity of fracture system and its simulation uncertainty simultaneously.
Full Text Available One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made
Full Text Available We are concerned with the control of quality of service (QoS in wireless cellular networks utilizing linear receivers. We investigate the issues of fairness and total performance, which are measured by a utility function in the form of a weighted sum of link QoS. We disprove the common conjecture on incompatibility of min-max fairness and utility optimality by characterizing network classes in which both goals can be accomplished concurrently. We characterize power and weight allocations achieving min-max fairness and utility optimality and show that they correspond to saddle points of the utility function. Next, we address the problem of the difference between min-max fairness and max-min fairness. We show that in general there is a (fairness gap between the performance achieved under min-max fairness and under max-min fairness. We characterize the network class for which both performance values coincide. Finally, we characterize the corresponding network subclass, in which both min-max fairness and max-min fairness are achievable by the same power allocation.
Zhang, Shizong; Gu, Rentao; Ji, Yuefeng; Wang, Hongxiang
Fast bandwidth growth urges large-scale high-density access scenarios, where the multiple Passive Optical Networking (PON) system clustered deployment can be adopted as an appropriate solution to fulfill the huge bandwidth demands, especially for a future 5G mobile network. However, the lack of interaction between different optical line terminals (OLTs) results in part of the bandwidth resources waste. To increase the bandwidth efficiency, as well as reduce bandwidth pressure at the edge of a network, we propose a centralized flexible PON architecture based on Time- and Wavelength-Division Multiplexing PON (TWDM PON). It can provide flexible affiliation for optical network units (ONUs) and different OLTs to support access network traffic localization. Specifically, a dynamic ONU grouping algorithm (DGA) is provided to obtain the minimal OLT outbound traffic. Simulation results show that DGA obtains an average 25.23% traffic gain increment under different OLT numbers within a small ONU number situation, and the traffic gain will increase dramatically with the increment of the ONU number. As the DGA can be deployed easily as an application running above the centralized control plane, the proposed architecture can be helpful to improve the network efficiency for future traffic-intensive access scenarios.
Pulvirenti, Luca; Ticconi, Francesca; Pierdicca, Nazzareno
The Integral Equation Model with multiple scattering (IEMM) represents a well-established method that provides a theoretical framework for the scattering of electromagnetic waves from rough surfaces. A critical aspect is the long computational time required to run such a complex model. To deal with this problem, a neural network technique is proposed in this work. In particular, we have adopted neural networks to reproduce the backscattering coefficients predicted by IEMM at L- and C-bands, thus making reference to presently operative satellite radar sensors, i.e., that aboard ERS-2, ASAR on board ENVISAT (C-band), and PALSAR aboard ALOS (L-band). The neural network-based model has been designed for radar observations of both flat and tilted surfaces, in order to make it applicable for hilly terrains too. The assessment of the proposed approach has been carried out by comparing neural network-derived backscattering coefficients with IEMM-derived ones. Different databases with respect to those employed to train the networks have been used for this purpose. The outcomes seem to prove the feasibility of relying on a neural network approach to efficiently and reliably approximate an electromagnetic model of surface scattering.
Bolaños, Marcos E; Bernat, Edward M; Aviyente, Selin
The functional connectivity of the human brain may be described by modeling interactions among its neural assemblies as a graph composed of vertices and edges. It has recently been shown that functional brain networks belong to a class of scale-free complex networks for which graphs have helped define an association between function and topology. These networks have been shown to possess a heterogenous structure composed of clusters, dense regions of strongly associated nodes, which represent multivariate relationships among nodes. Network clustering algorithms classify the nodes based on a similarity measure representing the bivariate relationships and similar to unsupervised learning is performed without a priori information. In this paper, we propose a method for partitioning a set of networks representing different subjects and reveal a community structure common to multiple subjects. We apply this community identifying algorithm to functional brain networks during a cognitive control task, in particular the error-related negativity (ERN), to evaluate how the brain organizes itself during error-monitoring.
Wu, Zhengping; Wang, Lifeng
Gustavo Adolfo Puerto Leguizamón
Full Text Available This paper presents a data traffic based study aiming at evaluating the impact of dynamic wavelength allocation on a Gigabit capable Passive Optical Network (GPON. In Passive Optical Networks (PON, an Optical Line Terminal (OLT feeds different PONs in such a way that a given wavelength channel is evenly distributed between the Optical Network Units (ONU at each PON. However, PONs do not specify any kind of dynamic behavior on the way the wavelengths are allocated in the network, a completely static distribution is implemented instead. In thispaper we evaluate the network performance in terms of packet losses and throughput for a number of ONUs being out-of-profile while featuring a given percentage of traffic in excess for a fixed wavelength distribution and for multiple dynamic wavelength allocation. Results show that for a multichannel operation with four wavelengths, the network throughput increases up to a rough value of 19% while the packet losses drop from 22 % to 1.8 % as compared with a static wavelength distribution.
This report makes first a status about the historical specificities, the present day situation and the perspectives of evolution of public utilities in networks with respect to the European directive of 1996 and to the 4 sectors of electricity, gas, railway transport and postal service. Then, it wonders about the new institutions and regulation procedures to implement to conciliate the public utility mission with the honest competition. (J.S.)
Mata, Angélica S
We show that the susceptible-infected-susceptible (SIS) epidemic dynamics running on the top of networks with a power law degree distribution can exhibit multiple phase transitions. Three main transitions involving different mechanisms responsible by sustaining the epidemics are identified: A short-term epidemics concentrated around the most connected vertex; a long-term (asymptotically stable) localized epidemics with a vanishing threshold; and an endemic phase occurring at a finite threshold. The different transitions are suited through different mean-field approaches. We finally show that the multiple transitions are due to the activations of different domains of the network that are observed in rapid (singular) variations of both stationary density of infected vertices and the participation ratio against the infection rate.
Wan, Yan; Roy, Sandip; Saberi, Ali; Stoorvogel, Antonie Arij
We are engaged in a major effort to design decentralized controllers for modern networks, that is fundamentally based on 1) applying feedback of multiple derivatives of local observations and 2) implementing these derivative feedbacks using multiple-delay controllers. Here, we fully motivate and
Bernard, Lisa J.; Rittle, Carrie; Roberts, Kathy
This article presents an account of how the Charleston County School District responded to an event by utilizing the PREPaRE model (Brock, et al., 2009). The acronym, PREPaRE, refers to a range of crisis response activities: P (prevent and prepare for psychological trauma), R (reaffirm physical health and perceptions of security and safety), E…
Wang, Ruijia; Chen, Jie; Wang, Xing; Sun, Bing
Retransmission deception jamming seriously degrades the Synthetic Aperture Radar (SAR) detection efficiency and can mislead SAR image interpretation by forming false targets. In order to suppress retransmission deception jamming, this paper proposes a novel multiple input and multiple output (MIMO) SAR structure range direction MIMO SAR, whose multiple channel antennas are vertical to the azimuth. First, based on the multiple channels of range direction MIMO SAR, the orthogonal frequency division multiplexing (OFDM) linear frequency modulation (LFM) signal was adopted as the transmission signal of each channel, which is defined as a sub-band signal. This sub-band signal corresponds to the transmission channel. Then, all of the sub-band signals are modulated with random initial phases and concurrently transmitted. The signal form is more complex and difficult to intercept. Next, the echoes of the sub-band signal are utilized to synthesize a wide band signal after preprocessing. The proposed method will increase the signal to interference ratio and peak amplitude ratio of the signal to resist retransmission deception jamming. Finally, well-focused SAR imagery is obtained using a conventional imaging method where the retransmission deception jamming strength is degraded and defocused. Simulations demonstrated the effectiveness of the proposed method.
Full Text Available This paper describes the complete integration of a fuzzy control of multiple evaporator systems with the IEEE 802.15.4 standard, in which we study several important aspects for this kind of system, like a detailed analysis of the end-to-end real-time flows over wireless sensor and actuator networks (WSAN, a real-time kernel with an earliest deadline first (EDF scheduler, periodic and aperiodic tasking models for the nodes, lightweight and flexible compensation-based control algorithms for WSAN that exhibit packet dropouts, an event-triggered sampling scheme and design methodologies. We address the control problem of the multi-evaporators with the presence of uncertainties, which was tackled through a wireless fuzzy control approach, showing the advantages of this concept where it can easily perform the optimization for a set of multiple evaporators controlled by the same smart controller, which should have an intelligent and flexible architecture based on multi-agent systems (MAS that allows one to add or remove new evaporators online, without the need for reconfiguring, while maintaining temporal and functional restrictions in the system. We show clearly how we can get a greater scalability, the self-configuration of the network and the least overhead with a non-beacon or unslotted mode of the IEEE 802.15.4 protocol, as well as wireless communications and distributed architectures, which could be extremely helpful in the development process of networked control systems in large spatially-distributed plants, which involve many sensors and actuators. For this purpose, a fuzzy scheme is used to control a set of parallel evaporator air-conditioning systems, with temperature and relative humidity control as a multi-input and multi-output closed loop system; in addition, a general architecture is presented, which implements multiple control loops closed over a communication network, integrating the analysis and validation method for multi
Fatemeh. Dehghani; Shahram. Darooei
Network on chip has emerged as a long-term and effective method in Multiprocessor System-on-Chip communications in order to overcome the bottleneck in bus based communication architectures. Efficiency and performance of network on chip is so dependent on the architecture and structure of the network. In this paper a new structure and architecture for adaptive traffic control in network on chip using Code Division Multiple Access technique is presented. To solve the problem of synchronous acce...
Ayala Solares, Jose Roberto
The optimal transmit power of a wireless sensor network with one transmitter and multiple receivers in a cognitive radio environment while satisfying independent peak, independent average, sum of peak and sum of average transmission rate constraints is derived. A suboptimal scheme is proposed to overcome the frequency of outages for the independent peak transmission rate constraint. In all cases, numerical results are provided for Rayleigh fading channels. © 2012 IEEE.
Collins, H. Dale; Prince, James M.; Davis, Thomas J.
An apparatus and method for imaging of structural characteristics in test objects using radiation amenable to coherent signal processing methods. Frequency and phase multiplication of received flaw signals is used to simulate a test wavelength at least one to two orders of magnitude smaller than the actual wavelength. The apparent reduction in wavelength between the illumination and recording radiation performs a frequency translation hologram. The hologram constructed with a high synthetic frequency and flaw phase multiplication is similar to a conventional acoustic hologram construction at the high frequency.
Schuelke, J.S.; Quirein, J.A.; Sarg, J.F.
This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Comprehensive statistical methods and interpretational/subjective measures are used in the prediction of porosity from seismic attributes. A 3-D volume of seismic derived porosity estimates for the Devonian reservoir provide a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimal placement of horizontal wells and improved field development. 6 refs., 2 figs.
Use of modern electric vehicles and their effective integration into power grids depends on the technologies applied around distribution substations. Distribution substations equipped with energy storing and V2G capability enable peak load shaving and demand response, which will reduce the need to make new investments into building new power sources or power grids to meet peak demand. This paper presents a distribution substation topology for utilizing electric vehicles as energy resource uni...
Lin, Xiangguo; Liu, Zhengjun; Zhang, Jixian; Shen, Jing
In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extract road features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms.
Full Text Available In wireless sensor networks (WSNs, there is a “crowded center effect” where the energy of nodes located near a data sink drains much faster than other nodes resulting in a short network lifetime. To mitigate the “crowded center effect,” rendezvous points (RPs are used to gather data from other nodes. In order to prolong the lifetime of WSN further, we propose using multiple sets of RPs in turn to average the energy consumption of the RPs. The problem is how to select the multiple sets of RPs and how long to use each set of RPs. An optimal algorithm and a heuristic algorithm are proposed to address this problem. The optimal algorithm is highly complex and only suitable for small scale WSN. The performance of the proposed algorithms is evaluated through simulations. The simulation results indicate that the heuristic algorithm approaches the optimal one and that using multiple RP sets can significantly prolong network lifetime.
Gordon, Evan M; Stollstorff, Melanie; Vaidya, Chandan J
Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. Copyright © 2011 Wiley-Liss, Inc.
Khan, Fahd Ahmed
Consider a multi-user underlay cognitive network where multiple cognitive users concurrently share the spectrum with a primary network with multiple users. The channel between the secondary network is assumed to have independent but not identical Nakagami-m fading. The interference channel between the secondary users (SUs) and the primary users is assumed to have Rayleigh fading. A power allocation based on the instantaneous channel state information is derived when a peak interference power constraint is imposed on the secondary network in addition to the limited peak transmit power of each SU. The uplink scenario is considered where a single SU is selected for transmission. This opportunistic selection depends on the transmission channel power gain and the interference channel power gain as well as the power allocation policy adopted at the users. Exact closed form expressions for the moment-generating function, outage performance, symbol error rate performance, and the ergodic capacity are derived. Numerical results corroborate the derived analytical results. The performance is also studied in the asymptotic regimes, and the generalized diversity gain of this scheduling scheme is derived. It is shown that when the interference channel is deeply faded and the peak transmit power constraint is relaxed, the scheduling scheme achieves full diversity and that increasing the number of primary users does not impact the diversity order. © 2014 John Wiley & Sons, Ltd.
Full Text Available A recently developed theory suggests that network coding is a generalization of source coding and channel coding and thus yields a significant performance improvement in terms of throughput and spatial diversity. This paper proposes a cooperative design of a parity-check network coding scheme in the context of a two-source multiple access relay channel (MARC model, a common compact model in hierarchical wireless sensor networks (WSNs. The scheme uses Low-Density Parity-Check (LDPC as the surrogate to build up a layered structure which encapsulates the multiple constituent LDPC codes in the source and relay nodes. Specifically, the relay node decodes the messages from two sources, which are used to generate extra parity-check bits by a random network coding procedure to fill up the rate gap between Source-Relay and Source-Destination transmissions. Then, we derived the key algebraic relationships among multidimensional LDPC constituent codes as one of the constraints for code profile optimization. These extra check bits are sent to the destination to realize a cooperative diversity as well as to approach MARC decode-and-forward (DF capacity.
Gordon, Evan M.; Stollstorff, Melanie; Vaidya, Chandan J.
Many researchers have noted that the functional architecture of the human brain is relatively invariant during task performance and the resting state. Indeed, intrinsic connectivity networks (ICNs) revealed by resting-state functional connectivity analyses are spatially similar to regions activated during cognitive tasks. This suggests that patterns of task-related activation in individual subjects may result from the engagement of one or more of these ICNs; however, this has not been tested. We used a novel analysis, spatial multiple regression, to test whether the patterns of activation during an N-back working memory task could be well described by a linear combination of ICNs delineated using Independent Components Analysis at rest. We found that across subjects, the cingulo-opercular Set Maintenance ICN, as well as right and left Frontoparietal Control ICNs, were reliably activated during working memory, while Default Mode and Visual ICNs were reliably deactivated. Further, involvement of Set Maintenance, Frontoparietal Control, and Dorsal Attention ICNs was sensitive to varying working memory load. Finally, the degree of left Frontoparietal Control network activation predicted response speed, while activation in both left Frontoparietal Control and Dorsal Attention networks predicted task accuracy. These results suggest that a close relationship between resting-state networks and task-evoked activation is functionally relevant for behavior, and that spatial multiple regression analysis is a suitable method for revealing that relationship. PMID:21761505
To verify the molecular markers linked to the genic multiple-allele male-sterile gene Ms, an F1 plant, which was generated by crossing the inbred line a20 and the male-sterile plant of the genic multipleallele male-sterile AB line, was backcrossed with an a20 plant to develop BC4 and BC5 populations.
Stangel, M.; Fredrikson, S.; Meinl, E.; Petzold, A.; Stuve, O.; Tumani, H.
Diagnosis of multiple sclerosis (MS) requires the exclusion of other possible diagnoses. For this reason, the cerebrospinal fluid (CSF) should be routinely analysed in patients with a first clinical event suggestive of MS. CSF analysis is no longer mandatory for diagnosis of relapsing-remitting MS,
Jatropha curcas L. (JCL) is a popular energy crop in tropical countries. The crop has multiple uses including supply of energy. The major source of energy from JCL the seed oil, which can be used in the raw form or as biodiesel. Biodiesel is a first generation energy carrier. Other products obtained from JCL during its ...
Parker, Ronald K.; Halbrook, Mary Carol
In order to investigate developmental changes in multiple classification, a matrix task was administered to 80 kindergarten first, second, and third grade children. Correct solution of the incomplete matrices, comprised of three pictures in a row and three pictures in a column meeting at a blank intersection, required identification and…
Full Text Available The purpose of this study was to introduce an improved tool for automated classification of event-related potentials (ERPs using spatiotemporally parcellated events incorporated into a functional brain network activation (BNA analysis. The auditory oddball ERP paradigm was selected to demonstrate and evaluate the improved tool. Methods: The ERPs of each subject were decomposed into major dynamic spatiotemporal events. Then, a set of spatiotemporal events representing the group was generated by aligning and clustering the spatiotemporal events of all individual subjects. The temporal relationship between the common group events generated a network, which is the spatiotemporal reference BNA model. Scores were derived by comparing each subject’s spatiotemporal events to the reference BNA model and were then entered into a support vector machine classifier to classify subjects into relevant subgroups. The reliability of the BNA scores (test-retest repeatability using intraclass correlation and their utility as a classification tool were examined in the context of Target-Novel classification. Results: BNA intraclass correlation values of repeatability ranged between 0.51 and 0.82 for the known ERP components N100, P200 and P300. Classification accuracy was high when the trained data were validated on the same subjects for different visits (AUCs 0.93 and 0.95. The classification accuracy remained high for a test group recorded at a different clinical center with a different recording system (AUCs 0.81, 0.85 for 2 visits. Conclusion: The improved spatiotemporal BNA analysis demonstrates high classification accuracy. The BNA analysis method holds promise as a tool for diagnosis, follow-up and drug development associated with different neurological conditions.
Full Text Available This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced, and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.
Turner, James C.; Keller, Adrienne
Objective: This description of the College Health Surveillance Network (CHSN) includes methodology, demography, epidemiology, and health care utilization. Participants: Twenty-three universities representing approximately 730,000 enrolled students contributed data from January 1, 2011, through May 31, 2014. Methods: Participating schools uploaded…
Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.
Jiang, Luo-Luo; Li, Wen-Jing; Wang, Zhen
The social influence exists widely in the human society, where individual decision-making process (from congressional election to electronic commerce) may be affected by the attitude and behavior of others belonging to different social networks. Here, we couple the snowdrift (SD) game and the prisoner’s dilemma (PD) game on two interdependent networks, where strategies in both games are associated by social influence to mimick the majority rule. More accurately, individuals’ strategies updating refers to social learning (based on payoff difference) and above-mentioned social influence (related with environment of interdependent group), which is controlled by social influence strength s. Setting s = 0 decouples the networks and returns the traditional network game; while its increase involves the interactions between networks. By means of numerous Monte Carlo simulations, we find that such a mechanism brings multiple influence to the evolution of cooperation. Small s leads to unequal cooperation level in both games, because social learning is still the main updating rule for most players. Though intermediate and large s guarantees the synchronized evolution of strategy pairs, cooperation finally dies out and reaches a completely dominance in both cases. Interestingly, these observations are attributed to the expansion of cooperation clusters. Our work may provide a new understanding to the emergence of cooperation in intercorrelated social systems.
Jiang, Luo-Luo; Li, Wen-Jing; Wang, Zhen
The social influence exists widely in the human society, where individual decision-making process (from congressional election to electronic commerce) may be affected by the attitude and behavior of others belonging to different social networks. Here, we couple the snowdrift (SD) game and the prisoner's dilemma (PD) game on two interdependent networks, where strategies in both games are associated by social influence to mimick the majority rule. More accurately, individuals' strategies updating refers to social learning (based on payoff difference) and above-mentioned social influence (related with environment of interdependent group), which is controlled by social influence strength s. Setting s = 0 decouples the networks and returns the traditional network game; while its increase involves the interactions between networks. By means of numerous Monte Carlo simulations, we find that such a mechanism brings multiple influence to the evolution of cooperation. Small s leads to unequal cooperation level in both games, because social learning is still the main updating rule for most players. Though intermediate and large s guarantees the synchronized evolution of strategy pairs, cooperation finally dies out and reaches a completely dominance in both cases. Interestingly, these observations are attributed to the expansion of cooperation clusters. Our work may provide a new understanding to the emergence of cooperation in intercorrelated social systems.
Leavitt, Victoria M; Wylie, Glenn R; Girgis, Peter A; DeLuca, John; Chiaravalloti, Nancy D
Identifying effective behavioral treatments to improve memory in persons with learning and memory impairment is a primary goal for neurorehabilitation researchers. Memory deficits are the most common cognitive symptom in multiple sclerosis (MS), and hold negative professional and personal consequences for people who are often in the prime of their lives when diagnosed. A 10-session behavioral treatment, the modified Story Memory Technique (mSMT), was studied in a randomized, placebo-controlled clinical trial. Behavioral improvements and increased fMRI activation were shown after treatment. Here, connectivity within the neural networks underlying memory function was examined with resting-state functional connectivity (RSFC) in a subset of participants from the clinical trial. We hypothesized that the treatment would result in increased integrity of connections within two primary memory networks of the brain, the hippocampal memory network, and the default network (DN). Seeds were placed in left and right hippocampus, and the posterior cingulate cortex. Increased connectivity was found between left hippocampus and cortical regions specifically involved in memory for visual imagery, as well as among critical hubs of the DN. These results represent the first evidence for efficacy of a behavioral intervention to impact the integrity of neural networks subserving memory functions in persons with MS.
Lundstrom, Brian Nils
In response to stimulus changes, the firing rates of many neurons adapt, such that stimulus change is emphasized. Previous work has emphasized that rate adaptation can span a wide range of time scales and produce time scale invariant power law adaptation. However, neuronal rate adaptation is typically modeled using single time scale dynamics, and constructing a conductance-based model with arbitrary adaptation dynamics is nontrivial. Here, a modeling approach is developed in which firing rate adaptation, or spike frequency adaptation, can be understood as a filtering of slow stimulus statistics. Adaptation dynamics are modeled by a stimulus filter, and quantified by measuring the phase leads of the firing rate in response to varying input frequencies. Arbitrary adaptation dynamics are approximated by a set of weighted exponentials with parameters obtained by fitting to a desired filter. With this approach it is straightforward to assess the effect of multiple time scale adaptation dynamics on neural networks. To demonstrate this, single time scale and power law adaptation were added to a network model of local field potentials. Rate adaptation enhanced the slow oscillations of the network and flattened the output power spectrum, dampening intrinsic network frequencies. Thus, rate adaptation may play an important role in network dynamics.
Full Text Available Randomized controlled trials (RCTs are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM to understand the patterns of competing treatment selections.We used social network analysis (SNA to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four "steps" within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.
Elkarim, Ghassan Awad; Alotaibi, Naif M; Samuel, Nardin; Wang, Shelly; Ibrahim, George M; Fallah, Aria; Weil, Alexander G; Kulkarni, Abhaya V
OBJECTIVE A recent survey has shown that caregivers of children with shunt-treated hydrocephalus frequently use social media networks for support and information gathering. The objective of this study is to describe and assess social media utilization among users interested in hydrocephalus. METHODS Publicly accessible accounts and videos dedicated to the topic of hydrocephalus were comprehensively searched across 3 social media platforms (Facebook, Twitter, and YouTube) throughout March 2016. Summary statistics were calculated on standard metrics of social media popularity. A categorization framework to describe the purpose of pages, groups, accounts, channels, and videos was developed following the screening of 100 titles. Categorized data were analyzed using nonparametric tests for statistical significance. RESULTS The authors' search identified 30 Facebook pages, 213 Facebook groups, 17 Twitter accounts, and 253 YouTube videos. These platforms were run by patients, caregivers, nonprofit foundations, and patient support groups. Most accounts were from the United States (n = 196), followed by the United Kingdom (n = 31), Canada (n = 17), India (n = 15), and Germany (n = 12). The earliest accounts were created in 2007, and a peak of 65 new accounts were created in 2011. The total number of users in Facebook pages exceeded those in Facebook groups (p media use in the topic of hydrocephalus. Users interested in hydrocephalus seek privacy for support communications and are attracted to treatment procedure and surgical products videos. These findings provide insight into potential avenues of hydrocephalus outreach, support, or advocacy in social media.
Cinnamon S. Bloss
Full Text Available Background. Mobile health and digital medicine technologies are becoming increasingly used by individuals with common, chronic diseases to monitor their health. Numerous devices, sensors, and apps are available to patients and consumers–some of which have been shown to lead to improved health management and health outcomes. However, no randomized controlled trials have been conducted which examine health care costs, and most have failed to provide study participants with a truly comprehensive monitoring system. Methods. We conducted a prospective randomized controlled trial of adults who had submitted a 2012 health insurance claim associated with hypertension, diabetes, and/or cardiac arrhythmia. The intervention involved receipt of one or more mobile devices that corresponded to their condition(s (hypertension: Withings Blood Pressure Monitor; diabetes: Sanofi iBGStar Blood Glucose Meter; arrhythmia: AliveCor Mobile ECG and an iPhone with linked tracking applications for a period of 6 months; the control group received a standard disease management program. Moreover, intervention study participants received access to an online health management system which provided participants detailed device tracking information over the course of the study. This was a monitoring system designed by leveraging collaborations with device manufacturers, a connected health leader, health care provider, and employee wellness program–making it both unique and inclusive. We hypothesized that health resource utilization with respect to health insurance claims may be influenced by the monitoring intervention. We also examined health-self management. Results & Conclusions. There was little evidence of differences in health care costs or utilization as a result of the intervention. Furthermore, we found evidence that the control and intervention groups were equivalent with respect to most health care utilization outcomes. This result suggests there are not large
Melih Tanis, Cemal; Nadir Arslan, Ali
Webcam networks intended for scientific monitoring of ecosystems is providing digital images and other environmental data for various studies. Also, other types of camera networks can also be used for scientific purposes, e.g. usage of traffic webcams for phenological studies, camera networks for ski tracks and avalanche monitoring over mountains for hydrological studies. To efficiently harness the potential of these camera networks, easy to use software which can obtain and handle images from different networks having different protocols and standards is necessary. For the analyses of the images from webcam networks, numerous software packages are freely available. These software packages have different strong features not only for analyzing but also post processing digital images. But specifically for the ease of use, applicability and scalability, a different set of features could be added. Thus, a more customized approach would be of high value, not only for analyzing images of comprehensive camera networks, but also considering the possibility to create operational data extraction and processing with an easy to use toolbox. At this paper, we introduce a new toolbox, entitled; Finnish Meteorological Institute Image PROcessing Tool (FMIPROT) which a customized approach is followed. FMIPROT has currently following features: • straightforward installation, • no software dependencies that require as extra installations, • communication with multiple camera networks, • automatic downloading and handling images, • user friendly and simple user interface, • data filtering, • visualizing results on customizable plots, • plugins; allows users to add their own algorithms. Current image analyses in FMIPROT include "Color Fraction Extraction" and "Vegetation Indices". The analysis of color fraction extraction is calculating the fractions of the colors in a region of interest, for red, green and blue colors along with brightness and luminance parameters. The
Eduardo Freitas Moreira
Full Text Available Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for
Moreira, Eduardo Freitas; Boscolo, Danilo; Viana, Blandina Felipe
Mutualistic plant-pollinator interactions play a key role in biodiversity conservation and ecosystem functioning. In a community, the combination of these interactions can generate emergent properties, e.g., robustness and resilience to disturbances such as fluctuations in populations and extinctions. Given that these systems are hierarchical and complex, environmental changes must have multiple levels of influence. In addition, changes in habitat quality and in the landscape structure are important threats to plants, pollinators and their interactions. However, despite the importance of these phenomena for the understanding of biological systems, as well as for conservation and management strategies, few studies have empirically evaluated these effects at the network level. Therefore, the objective of this study was to investigate the influence of local conditions and landscape structure at multiple scales on the characteristics of plant-pollinator networks. This study was conducted in agri-natural lands in Chapada Diamantina, Bahia, Brazil. Pollinators were collected in 27 sampling units distributed orthogonally along a gradient of proportion of agriculture and landscape diversity. The Akaike information criterion was used to select models that best fit the metrics for network characteristics, comparing four hypotheses represented by a set of a priori candidate models with specific combinations of the proportion of agriculture, the average shape of the landscape elements, the diversity of the landscape and the structure of local vegetation. The results indicate that a reduction of habitat quality and landscape heterogeneity can cause species loss and decrease of networks nestedness. These structural changes can reduce robustness and resilience of plant-pollinator networks what compromises the reproductive success of plants, the maintenance of biodiversity and the pollination service stability. We also discuss the possible explanations for these relationships and
Rachael A Callcut
Full Text Available Delayed notification and lack of early information hinder timely hospital based activations in large scale multiple casualty events. We hypothesized that Twitter real-time data would produce a unique and reproducible signal within minutes of multiple casualty events and we investigated the timing of the signal compared with other hospital disaster notification mechanisms.Using disaster specific search terms, all relevant tweets from the event to 7 days post-event were analyzed for 5 recent US based multiple casualty events (Boston Bombing [BB], SF Plane Crash [SF], Napa Earthquake [NE], Sandy Hook [SH], and Marysville Shooting [MV]. Quantitative and qualitative analysis of tweet utilization were compared across events.Over 3.8 million tweets were analyzed (SH 1.8 m, BB 1.1m, SF 430k, MV 250k, NE 205k. Peak tweets per min ranged from 209-3326. The mean followers per tweeter ranged from 3382-9992 across events. Retweets were tweeted a mean of 82-564 times per event. Tweets occurred very rapidly for all events (<2 mins and represented 1% of the total event specific tweets in a median of 13 minutes of the first 911 calls. A 200 tweets/min threshold was reached fastest with NE (2 min, BB (7 min, and SF (18 mins. If this threshold was utilized as a signaling mechanism to place local hospitals on standby for possible large scale events, in all case studies, this signal would have preceded patient arrival. Importantly, this threshold for signaling would also have preceded traditional disaster notification mechanisms in SF, NE, and simultaneous with BB and MV.Social media data has demonstrated that this mechanism is a powerful, predictable, and potentially important resource for optimizing disaster response. Further investigated is warranted to assess the utility of prospective signally thresholds for hospital based activation.
Callcut, Rachael A; Moore, Sara; Wakam, Glenn; Hubbard, Alan E; Cohen, Mitchell J
Delayed notification and lack of early information hinder timely hospital based activations in large scale multiple casualty events. We hypothesized that Twitter real-time data would produce a unique and reproducible signal within minutes of multiple casualty events and we investigated the timing of the signal compared with other hospital disaster notification mechanisms. Using disaster specific search terms, all relevant tweets from the event to 7 days post-event were analyzed for 5 recent US based multiple casualty events (Boston Bombing [BB], SF Plane Crash [SF], Napa Earthquake [NE], Sandy Hook [SH], and Marysville Shooting [MV]). Quantitative and qualitative analysis of tweet utilization were compared across events. Over 3.8 million tweets were analyzed (SH 1.8 m, BB 1.1m, SF 430k, MV 250k, NE 205k). Peak tweets per min ranged from 209-3326. The mean followers per tweeter ranged from 3382-9992 across events. Retweets were tweeted a mean of 82-564 times per event. Tweets occurred very rapidly for all events (tweets in a median of 13 minutes of the first 911 calls. A 200 tweets/min threshold was reached fastest with NE (2 min), BB (7 min), and SF (18 mins). If this threshold was utilized as a signaling mechanism to place local hospitals on standby for possible large scale events, in all case studies, this signal would have preceded patient arrival. Importantly, this threshold for signaling would also have preceded traditional disaster notification mechanisms in SF, NE, and simultaneous with BB and MV. Social media data has demonstrated that this mechanism is a powerful, predictable, and potentially important resource for optimizing disaster response. Further investigated is warranted to assess the utility of prospective signally thresholds for hospital based activation.
Liu, Jie; Guo, Sheng; Duan, Jin-Ao; Yan, Hui; Qian, Da-Wei; Tang, Hai-Tao; Tang, Ren-Mao
This research is to analyze the resourceful chemical composition in different tissues (root, stem, leaf and flower) of Abelmoschus manihot and evaluate their utilizing value. The flavonoids, soluble polysaccharides, cellulose, nucleosides and amino acids in the different tissues of A. manihot were determined by HPLC coupled with UV-Vis spectrophotpmetry, and UPLC-TQ/MS. The flowers are rich in the resourceful chemical compositions of flavonoids which mainly consist of hyperoside, isoquercitrin, cotton-8-O-glucuronide, myricetin, quercetin-3'-O-glucoside, rutin and quercetin. The total content of these flavonoids is 25.450 mg•g-1 in the flowers, while they are trace in the other tissues.Different tissues of A. manihot are rich in soluble polysaccharides and celluloses and the stems have the highest content(19.76%) of soluble polysaccharides, while the roots have the highest content (29.88%) of cellulose. Total of 21 amino acids and 9 nucleosides were detected in this plant, and the flowers have the highest content of amino acids(4.737 mg•g⁻¹), while the leaves have the highest content of nucleosides (1.474 mg•g⁻¹). A. manihot is rich in the resourceful chemical compositions, and its constituents and contents are various in different tissues of this plant.The results provided a scientific basis for the utilization and industrial development of A. manihot plants. Copyright© by the Chinese Pharmaceutical Association.
Karay, Yassin; Schauber, Stefan K; Stosch, Christoph; Schuettpelz-Brauns, Katrin
Students' motivation to participate is one of the main challenges in formative assessment. The utility framework identifies potential points of intervention for improving the acceptance of formative assessment [Van Der Vleuten C. 1996. The assessment of professional competence: Developments, research and practical implications. Adv Health Sci Educ 1(1):41-67]. At the Medical Faculty of the University of Cologne, the paper-based version of the Berlin Progress Test has been transformed into computer-based version providing immediate feedback. To investigate whether the introduction of computer-based assessment (CBA) enhances the acceptance of formative assessment relative to paper-based assessment (PBA). In a retrospective cohort study (PBA: N = 2597, CBA: N = 2712), students' overall acceptance of the two forms of assessment was surveyed, their comments were analyzed, and we analyzed their test behavior and categorized students into "serious" or "non-serious" test takers. In the preclinical phase of medical education, no differences were found in overall acceptance of the two forms of assessment (p > 0.05). In the clinical phase, differences in favor of CBA were found in overall acceptance (p < 0.05), the proportion of positive comments (p < 0.001), and the proportion of serious participants (p < 0.001). Introduction of immediate feedback via CBA can enhance the acceptance and therefore the utility of formative assessment.
Full Text Available Objective: We aimed to determine the safety and efficacy of flexible ureterorenoscopy and Holmium laser lithotripsy in treating multiple intrarenal stones. Materials and Methods: We identified 32 consecutive patients with multiple intrarenal stones who underwent flexible ureterorenoscopy and/or laser lithotripsy performed by a single surgeon. Informed consent was obtained from all participants before treatment. Each patient was evaluated in terms of stone location, stone number, stone size, stone burden (cumulative stone length, body mass index, operative time, stone-free rate, and perioperative complications. Results: The median age of the patients was 38 years [interquartile range (IQR, 34.25-52.00]. The patients had a total of 75 intrarenal calculi. The average number of stones per patient was 2.50 (IQR, 2.0-3.0. The median total stone burden per patient was 23.0 mm (IQR, 19.0-28.0 mm. Twenty-one patients (65.5% had stone burdens >20 mm, and 11 (34.5% had burdens ≤20 mm. The overall final stone-free rate was 78.1%. The stone-free rates for patients with stone burdens ≤20 mm and >20 mm were 81.8% (9/11 and 76.2% (16/21, respectively (p=0.544. A perioperative complication (urinary leakage occurred in one patient. Postoperative complications were recorded in four (12.5% patients; a urinary tract infection in one, pain requiring parenteral medication in two, and hematuria in one. Conclusion: Flexible ureterorenoscopy combined with laser lithotripsy may be an effective treatment option for patients with multiple intrarenal stones; we noted only a few minor complications. The success rate was higher in patients with stone burdens ≤20 mm.
Moro, M J; Portero, J A; Gascón, A; Hernández, J M; Ortega, F; Jiménez, R; Guerras, L; Martínez, M; Casanova, F; Sanz, M A
To assess the classification of Greipp et al in a group of multiple myeloma (MM) patients, in an attempt to correlate the morphological patterns with the clinico-biological features of the disease. Bone marrow aspirates from 135 patients with multiple myeloma were examined by two different observers. Full accordance existed in 122 cases (90%). The four morphological MM subgroup distribution was: mature, 38%; intermediate, 30%; immature, 18%, and plasmoblastic, 14%. The analysis of the M component types with regard to morphology showed increased IgA cases within the intermediate (40%) and immature (48%) MM (p = 0.01), and Bence-Jones cases within the plasmoblastic MM (32%). On the contrary, no differences were found with regard to the clinical stage, although none of the plasmoblastic MM was in stage I. The incidence of renal insufficiency and of high bone-marrow infiltration progressively increased from mature to plasmoblastic MM, the difference between the extreme morphological groups being significant. The incidence of hypercalcaemia and lower paraprotein rates was higher in plasmoblastic myeloma, with significant difference with respect to mature myeloma (p = 0.05). The median survival was longer in intermediate (27.8 months) and mature (22.5 months) myelomas than in plasmoblastic (17.9 months) and immature (13.6 months) myelomas. After grouping the mature forms (intermediate plus mature) and the immature ones (plasmoblastic plus immature) the survival differences approached statistical significance (p = 0.07). This study suggests that the morphological examination of plasma cells should be included in the prognostic criteria of multiple myeloma.
Contractor, Noshir S.; Monge, Peter R.
Describes a multitheoretical, multilevel (MTML) model to study the management of knowledge networks. Considers theoretical mechanisms for emergence of knowledge networks and presents empirical findings about the emergence of knowledge networks. Concludes that it is necessary to utilize MTML models to integrate multiple social and communication…
Full Text Available During the past decades, a number of studies have demonstrated multiple beneficial health effects of green tea. Polyphenolics are the most biologically active components of green tea. Many targets can be targeted or affected by polyphenolics. In this study, we excavated all of the targets of green tea polyphenolics (GTPs though literature mining and target calculation and analyzed the multiple pharmacology actions of green tea comprehensively through a network pharmacology approach. In the end, a total of 200 Homo sapiens targets were identified for fifteen GTPs. These targets were classified into six groups according to their related disease, which included cancer, diabetes, neurodegenerative disease, cardiovascular disease, muscular disease, and inflammation. Moreover, these targets mapped into 143 KEGG pathways, 26 of which were more enriched, as determined though pathway enrichment analysis and target-pathway network analysis. Among the identified pathways, 20 pathways were selected for analyzing the mechanisms of green tea in these diseases. Overall, this study systematically illustrated the mechanisms of the pleiotropic activity of green tea by analyzing the corresponding “drug-target-pathway-disease” interaction network.
Didino, Daniele; Knops, André; Vespignani, Francesco; Kornpetpanee, Suchada
Simple multiplication facts are thought to be organised in a network structure in which problems and solutions are associated. Converging evidence suggests that the ability for solving symbolic arithmetic problems is based on an approximate number system (ANS). Most theoretical stances concerning the metric underlying the ANS converge on the assumption that the representational overlap between two adjacent numbers increases as the numerical magnitude of the numbers increases. Given a number N, the overlap between N and N+1 is larger than the overlap between N and N-1. Here, we test whether this asymmetric overlap influences the activation spreading within the multiplication associative network (MAN). When verifying simple multiplication problems such as 8×4 participants were slower in rejecting false but related outcomes that were larger than the actual outcome (e.g., 8×4=36) than rejecting smaller related outcomes (e.g., 8×4=28), despite comparable numerical distance from the correct result (here: 4). This effect was absent for outcomes which are not part of either operands table (e.g., 8×4=35). These results suggest that the metric of the ANS influences the activation spreading within the MAN, further substantiating the notion that symbolic arithmetic is grounded in the ANS. Copyright © 2015 Elsevier B.V. All rights reserved.
Magarotto, Valeria; Salvini, Marco; Bonello, Francesca; Bringhen, Sara; Palumbo, Antonio
Novel agents, such as immunomodulantory drugs (IMiDs) and proteasome inhibitors (PI), have significantly improved overall survival of multiple myeloma (MM) patients. Yet, MM remains an incurable disease, relapse inevitably occurs and patients tend to become resistant to subsequent treatments. This led to the evaluation of new treatment strategies. The recent development of monoclonal antibodies is changing the treatment algorithm of MM by increasing the therapeutic armamentarium. Elotuzumab and Daratumumab were shown to be very effective and are likely to be soon approved by the FDA. Other antibodies are in pre-clinical or early clinical phases of evaluation and further investigation and more robust data are needed. This review will give an overview of the most active monoclonal antibodies against MM.
Drake, Julia I; de Hart, Juan Carlos Trujillo; Monleón, Clara; Toro, Walter; Valentim, Joice
Background and objectives: MCDA is a decision-making tool with increasing use in the healthcare sector, including HTA (Health Technology Assessment). By applying multiple criteria, including innovation, in a comprehensive, structured and explicit manner, MCDA fosters a transparent, participative, consistent decision-making process taking into consideration values of all stakeholders. This paper by FIFARMA (Latin American Federation of Pharmaceutical Industry) proposes the deliberative (partial) MCDA as a more pragmatic, agile approach, especially when newly implemented. Methods: Literature review including real-world examples of effective MCDA implementation in healthcare decision making in both the public and private sector worldwide and in LA. Results and conclusion : It is the view of FIFARMA that MCDA should strongly be considered as a tool to support HTA and broader healthcare decision making such as the contracts and tenders process in order to foster transparency, fairness, and collaboration amongst stakeholders.
Doll, Hannah M; Armitage, David W; Daly, Rebecca A; Emerson, Joanne B; Goltsman, Daniela S Aliaga; Yelton, Alexis P; Kerekes, Jennifer; Firestone, Mary K; Potts, Matthew D
Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Diversity profiles encompass many other indices, provide effective numbers of diversity (mathematical generalizations of previous indices that better convey the magnitude of differences in diversity), and can incorporate taxa similarity information. To explore whether these profiles change interpretations of microbial datasets, diversity profiles were calculated for four microbial datasets from different environments spanning all domains of life as well as viruses. Both similarity-based profiles that incorporated phylogenetic relatedness and naïve (not similarity-based) profiles were calculated. Simulated datasets were used to examine the robustness of diversity profiles to varying phylogenetic topology and community composition. Diversity profiles provided insights into microbial datasets that were not detectable with classical univariate diversity metrics. For all datasets analyzed, there were key distinctions between calculations that incorporated phylogenetic diversity as a measure of taxa similarity and naïve calculations. The profiles also provided information about the effects of rare species on diversity calculations. Additionally, diversity profiles were used to examine thousands of simulated microbial communities, showing that similarity-based and naïve diversity profiles only agreed approximately 50% of the time in their classification of which sample was most diverse. This is a strong argument for incorporating similarity information and calculating diversity with a range of
Full Text Available Claudio Gasperini1, Serena Ruggieri2, Carlo Pozzilli21Department of Neurosciences, S Camillo Forlanini Hospital, Rome, Italy; 2Department of Neurological Sciences, University of Rome “La Sapienza”, ItalyAbstract: Multiple sclerosis (MS is a chronic inflammatory disorder of the central nervous system (CNS that represents one of the first causes of neurological disability in young adults. Although the pathogenesis of MS is still unclear, an autoimmune mechanism has been demonstrated. According to this evidence in the last 15 years different treatments acting on the immune system have been developed. Current disease-modifying drugs (DMDs for MS require regular and frequent parenteral administration and are associated with limited long-term treatment adherence. Moreover the clinical efficacy of these disease-modifying drugs is suboptimal. Thus, there is an important need for the development of new therapeutic strategies. Several oral therapies (fingolimod, fumaric acid, teriflunomide, laquinimod are in development; Among these cladribine is the only therapy with the potential for short-course dosing. Cladribine is an immunosuppressant that offers sustained regulation of the immune system through a preferential lymphocyte depleting action. Cladribine has a well-characterized and well-known safety profile, derived from more than 15 years of use of the parenteral formulation both in the oncology field and in MS. This paper reviews the new oral emerging treatments and presents the available data about the use of cladribine in MS and the future perspective of its clinical use.Keywords: multiple sclerosis, disease modifying drugs, oral therapy, treatment adherence, cladribine
Jones Nick S
Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.
A network of three nodes mutually communicating with each other is studied. This multi-way network is a suitable model for three-user device-to-device communications. The main goal of this paper is to characterize the capacity region of the underlying Gaussian three-way channel (3WC) within a constant gap. To this end, a capacity outer bound is derived using cut-set bounds and genie-aided bounds. For achievability, the 3WC is first transformed into an equivalent star channel. This latter is then decomposed into a set of “successive” sub-channels, leading to a sub-channel allocation problem. Using backward decoding, interference neutralization, and known results on the capacity of the star-channel relying of physical-layer network coding, an achievable rate region for the 3WC is obtained. It is then shown that the achievable rate region is within a constant gap of the developed outer bound, leading to the desired capacity approximation. Interestingly, in contrast to the Gaussian two-way channel (TWC), adaptation is necessary in the 3WC. Furthermore, message splitting is another ingredient of the developed scheme for the 3WC, which is not required in the TWC. The two setups are, however, similar in terms of their sum-capacity pre-log, which is equal to 2. Finally, some interesting networks and their approximate capacities are recovered as special cases of the 3WC, such as the cooperative broadcast channel and multiple access channel.
Oliaro, Jane; Van Ham, Vanessa; Sacirbegovic, Faruk; Pasam, Anupama; Bomzon, Ze’ev; Pham, Kim; Ludford-Menting, Mandy J.; Waterhouse, Nigel J.; Bots, Michael; Hawkins, Edwin D.; Watt, Sally V.; Cluse, Leonie A.; Clarke, Chris J.P.; Izon, David J.; Chang, John T.; Thompson, Natalie; Gu, Min; Johnstone, Ricky W.; Smyth, Mark J.; Humbert, Patrick O.; Reiner, Steven L.; Russell, Sarah M.
Asymmetric cell division is a potential means by which cell fate choices during an immune response are orchestrated. Defining the molecular mechanisms that underlie asymmetric division of T cells is paramount for determining the role of this process in the generation of effector and memory T cell subsets. In other cell types, asymmetric cell division is regulated by conserved polarity protein complexes that control the localization of cell fate determinants and spindle orientation during division. We have developed a tractable, in vitro model of naïve CD8+ T cells undergoing initial division while attached to dendritic cells during antigen presentation to investigate whether similar mechanisms might regulate asymmetric division of T cells. Using this system, we show that direct interactions with antigen presenting cells provide the cue for polarization of T cells. Interestingly, the immunological synapse disseminates before division even though the T cells retain contact with the antigen presenting cell. The cue from the antigen presenting cell is translated into polarization of cell fate determinants via the polarity network of the Par3 and Scribble complexes and orientation of the mitotic spindle during division is orchestrated by the Pins/G protein complex. These findings suggest that T cells have selectively adapted a number of evolutionarily conserved mechanisms to generate diversity through asymmetric cell division. PMID:20530266
Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased population estimates under some assumptions about the sampling procedure and population structure. Several seed individuals are typically used to initiate RDS recruitment. However, standard RDS estimation theory assume that all sampled individuals originate from only one seed. We present an estimator, based on a random walk with teleportation, which accounts for the multiple seed structure of RDS. The new estimator can also be used on populations with disconnected social networks. We numerically evaluate our estimator by simulations on artificial and real networks. Our estimator outperforms previous estimators, especially when the proportion of seeds in the sample is large. We recommend our new estimator to be used in RDS studies, in particular when the number of seeds is large or ...
Guo, Yongyi; Qian, Min; Ge, Hao
Multiple dynamic pathways always exist in biological networks, but their robustness against internal fluctuations and relative stability have not been well recognized and carefully analyzed yet. Here we try to address these issues through an illustrative example, namely the Siah-1/beta-catenin/p14/19 ARF loop of protein p53 dynamics. Its deterministic Boolean network model predicts that two parallel pathways with comparable magnitudes of attractive basins should exist after the protein p53 is activated when a cell becomes harmfully disturbed. Once the low but non-neglectable intrinsic fluctuations are incorporated into the model, we show that a phase transition phenomenon is emerged: in one parameter region the probability weights of the normal pathway, reported in experimental literature, are comparable with the other pathway which is seemingly abnormal with the unknown functions, whereas, in some other parameter regions, the probability weight of the abnormal pathway can even dominate and become globally at...
Dogonowski, Anne-Marie; Siebner, Hartwig R; Sørensen, Per Soelberg
BACKGROUND: Multiple sclerosis (MS) impairs signal transmission along cortico-cortical and cortico-subcortical connections, affecting functional integration within the motor network. Functional magnetic resonance imaging (fMRI) during motor tasks has revealed altered functional connectivity in MS...... controls underwent a 20-minute resting-state fMRI session at 3 Tesla. Independent component analysis was applied to the fMRI data to identify disease-related changes in motor resting-state connectivity. RESULTS: Patients with MS showed a spatial expansion of motor resting-state connectivity in deep...... subcortical nuclei but not at the cortical level. The anterior and middle parts of the putamen, adjacent globus pallidus, anterior and posterior thalamus and the subthalamic region showed stronger functional connectivity with the motor network in the MS group compared with controls. CONCLUSION: MS...
Varadarajan, Srivatsan (Inventor); Hall, Brendan (Inventor); Smithgall, William Todd (Inventor); Bonk, Ted (Inventor); DeLay, Benjamin F. (Inventor)
Systems and methods for systematic hybrid network scheduling for multiple traffic classes with host timing and phase constraints are provided. In certain embodiments, a method of scheduling communications in a network comprises scheduling transmission of virtual links pertaining to a first traffic class on a global schedule to coordinate transmission of the virtual links pertaining to the first traffic class across all transmitting end stations on the global schedule; and scheduling transmission of each virtual link pertaining to a second traffic class on a local schedule of the respective transmitting end station from which each respective virtual link pertaining to the second traffic class is transmitted such that transmission of each virtual link pertaining to the second traffic class is coordinated only at the respective end station from which each respective virtual link pertaining to the second traffic class is transmitted.
Sipos, Marton A.; Fitzek, Frank; Roetter, Daniel Enrique Lucani
of random linear network coding to exploit multiple commercially available cloud storage providers simultaneously with the possibility to constantly adapt to changing cloud performance in order to optimize data retrieval times. The main contribution of this paper is a new data distribution mechanisms...... that cleverly stores and moves data among different clouds in order to optimize performance. Furthermore, we investigate the trade-offs among storage space, reliability and data retrieval speed for our proposed scheme. By means of real-world implementation and measurements using well-known and publicly...... accessible cloud service providers, we can show close to 9x less network use for the adaptation compared to more conventional dense recoding approaches, while maintaining similar download time performance and the same reliability....
We use Delay-Tolerant Networking (DTN) to break control loops between space-ground communication links and ground-ground communication links to increase overall file delivery efficiency, as well as to enable large files to be proactively fragmented and received across multiple ground stations. DTN proactive fragmentation and reactive fragmentation were demonstrated from the UK-DMC satellite using two independent ground stations. The files were reassembled at a bundle agent, located at Glenn Research Center in Cleveland Ohio. The first space-based demonstration of this occurred on September 30 and October 1, 2009. This paper details those experiments. Communication, delay-tolerant networking, DTN, satellite, Internet, protocols, bundle, IP, TCP.
Full Text Available Because wireless sensor networks (WSNs are complex and difficult to deploy and manage, appropriate structures are required to make these networks more flexible. In this paper, a reconfigurable testbed is presented, which supports dynamic protocol switching by creating a novel architecture and experiments with several different protocols. The separation of the control and data planes in this testbed means that routing configuration and data transmission are independent. A programmable flow table provides the testbed with the ability to switch protocols dynamically. We experiment on various aspects of the testbed to analyze its functionality and performance. The results demonstrate that sensors in the testbed are easy to manage and can support multiple protocols. We then raise some important issues that should be investigated in future work concerning the testbed.
Chow, James (Inventor); Gender, Thomas K. (Inventor)
The present invention provides a flash memory management system and method with increased performance. The flash memory management system provides the ability to efficiently manage and allocate flash memory use in a way that improves reliability and longevity, while maintaining good performance levels. The flash memory management system includes a free block mechanism, a disk maintenance mechanism, and a bad block detection mechanism. The free block mechanism provides efficient sorting of free blocks to facilitate selecting low use blocks for writing. The disk maintenance mechanism provides for the ability to efficiently clean flash memory blocks during processor idle times. The bad block detection mechanism provides the ability to better detect when a block of flash memory is likely to go bad. The flash status mechanism stores information in fast access memory that describes the content and status of the data in the flash disk. The new bank detection mechanism provides the ability to automatically detect when new banks of flash memory are added to the system. Together, these mechanisms provide a flash memory management system that can improve the operational efficiency of systems that utilize flash memory.
Full Text Available Cortical gray matter (GM damage is now widely recognized in multiple sclerosis (MS. The standard MRI does not reliably detect cortical GM lesions, although cortical volume loss can be measured. In this study, we demonstrate that the gradient echo MRI can reliably and quantitatively assess cortical GM damage in MS patients using standard clinical scanners. High resolution multi-gradient echo MRI was used for regional mapping of tissue-specific MRI signal transverse relaxation rate values (R2* in 10 each relapsing–remitting, primary-progressive and secondary-progressive MS subjects. A voxel spread function method was used to correct artifacts induced by background field gradients. R2* values from healthy controls (HCs of varying ages were obtained to establish baseline data and calculate ΔR2* values – age-adjusted differences between MS patients and HC. Thickness of cortical regions was also measured in all subjects. In cortical regions, ΔR2* values of MS patients were also adjusted for changes in cortical thickness. Symbol digit modalities (SDMT and paced auditory serial addition (PASAT neurocognitive tests, as well as Expanded Disability Status Score, 25-foot timed walk and nine-hole peg test results were also obtained on all MS subjects. We found that ΔR2* values were lower in multiple cortical GM and normal appearing white matter (NAWM regions in MS compared with HC. ΔR2* values of global cortical GM and several specific cortical regions showed significant (p < 0.05 correlations with SDMT and PASAT scores, and showed better correlations than volumetric measures of the same regions. Neurological tests not focused on cognition (Expanded Disability Status Score, 25-foot timed walk and nine-hole peg tests showed no correlation with cortical GM ΔR2* values. The technique presented here is robust and reproducible. It requires less than 10 min and can be implemented on any MRI scanner. Our results show that quantitative tissue-specific R2
Takatori, Y.; Fitzek, Frank; Tsunekawa, K.
MIMO data transmission scheme, which combines Single-Frequency-Network (SFN) with TDD-OFDM-MIMO applied for wireless LAN networks. In our proposal, we advocate to use SFN for multiple access points (MAP) MIMO data transmission. The goal of this approach is to achieve very high channel capacity in both...
Full Text Available stream_source_info Majozi_2010-ABSTRACT ONLY.pdf.txt stream_content_type text/plain stream_size 1539 Content-Encoding UTF-8 stream_name Majozi_2010-ABSTRACT ONLY.pdf.txt Content-Type text/plain; charset=UTF-8 Industrial... & Engineering Chemistry Research Vol. 49(19), pp. 9154–9164 Synthesis and Optimization of Steam System Networks. 2. Multiple Steam Levels Tim Price† and Thokozani Majozi*,†,‡ Department of Chemical Engineering, UniVersity of Pretoria, South Africa...
Bulzacka, Ewa; Delourme, Gwenaëlle; Hutin, Valérie; Burban, Nathalie; Méary, Alexandre; Lajnef, Mohamed; Leboyer, Marion; Schürhoff, Franck
Schizophrenia (SZ) is a chronic, severe disease, which results in misperception of reality, major social withdrawal, and cognitive disturbances. One type of cognitive disturbance, known as executive dysfunction, is widely considered as a primary determinant of functional outcome. However, classic neuropsychological measures of executive functioning (EF) poorly represent patients' functional outcome, and thus seem inappropriate for evaluating the real-world functional impact of diseases such as SZ. We hypothesized that the Multiple Errands Test (MET), an ecological assessment of executive function would show greater ability to measure everyday adaptive functioning SZ, compared to conventional EF assessment methods. 100 clinically stable SZ patients were administered the MET, Wisconsin Card Sorting Test - 64 and a paper version of MET. Correlation analyses were performed between each EF measure and functional outcome, as measured by the Social Autonomy Scale (SAS). After adjusting for age, education, IQ and illness duration, SAS was significantly predicted by MET global score. No other EF measure correlated with SAS. Results from this study suggest that MET offers a valuable prediction of daily life functional outcome in this large sample of SZ patients. Therefore, it could be used as a complementary measure to improve the identification of executive dysfunctions prior to psychosocial interventions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Pompa, Alessandra; Clemenzi, Allessandro; Troisi, Elio; Pace, Luca; Casillo, Paolo; Catani, Sheila; Grasso, Maria Grazia
Lower thermal and discomfort thresholds may predispose multiple sclerosis (MS) patients to chronic pain, but a possible effect of fibromyalgia (FM) comorbidity has never been investigated. Aims were to investigate the thermal and discomfort thresholds in the evaluation of pain intensity between MS patients with FM (PFM+) and MS patients with pain not associated to FM (PFM-). One hundred thirty three MS patients were investigated for chronic pain. FM was assessed according to the 1990 ACR diagnostic criteria. An algometer was used to measure the thresholds in the patients and 60 matched healthy subjects. Chronic pain was present in 88 (66.2%) patients; 12 (13.6%) had neuropathic pain, 22 (17.3%) were PFM+ and 65 (48.9%) PFM-. PFM+ were predominantly female (p = 0.03) and had a greater EDSS (p = 0.01) than NoP; no other significant differences emerged than PFM-. The thresholds were lower in MS patients than controls (p < 0.01), mainly in the PFM+. FM severity influenced the thermal threshold (p < 0.001), while the female gender influenced the discomfort threshold (p < 0.001). Thermal and discomfort thresholds were lower in patients than controls and were the lowest in PFM+. Their more severely impaired thermal threshold supports a neurophysiological basis of such association. © 2015 S. Karger AG, Basel.
Alsharoa, Ahmad M.
In this paper, a multiple relay selection scheme for two-way relaying cognitive radio network is investigated. We consider a cooperative Cognitive Radio (CR) system with spectrum sharing scenario using Amplify-and-Forward (AF) protocol, where licensed users and unlicensed users operate on the same frequency band. The main objective is to maximize the sum rate of the unlicensed users allowed to share the spectrum with the licensed users by respecting a tolerated interference threshold. A practical low complexity heuristic approach is proposed to solve our formulated optimization problem. Selected numerical results show that the proposed algorithm reaches a performance close to the performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions while providing a considerable saving in terms of computational complexity. In addition, these results show that our proposed scheme significantly outperforms the single relay selection scheme. © 2013 IEEE.
Alsharoa, Ahmad M.
In this paper, we investigate a multiple relay selection scheme for two-way relaying cognitive radio networks where primary users and secondary users operate on the same frequency band. More specifically, cooperative relays using Amplifyand- Forward (AF) protocol are optimally selected to maximize the sum rate of the secondary users without degrading the Quality of Service (QoS) of the primary users by respecting a tolerated interference threshold. A strong optimization tool based on genetic algorithm is employed to solve our formulated optimization problem where discrete relay power levels are considered. Our simulation results show that the practical heuristic approach achieves almost the same performance of the optimal multiple relay selection scheme either with discrete or continuous power distributions. Copyright © 2013 by the Institute of Electrical and Electronic Engineers, Inc.
Kuang, Min; Li, Zhengqi; Xu, Shantian; Zhu, Qunyi
Within a Mitsui Babcock Energy Limited down-fired pulverized-coal 350 MW(e) utility boiler, in situ experiments were performed, with measurements taken of gas temperatures in the burner and near the right-wall regions, and of gas concentrations (O(2) and NO) from the near-wall region. Large combustion differences between zones near the front and rear walls and particularly high NO(x) emissions were found in the boiler. With focus on minimizing these problems, a new technology based on multiple-injection and multiple-staging has been developed. Combustion improvements and NO(x) reductions were validated by investigating three aspects. First, numerical simulations of the pulverized-coal combustion process and NO(x) emissions were compared in both the original and new technologies. Good agreement was found between simulations and in situ measurements with the original technology. Second, with the new technology, gas temperature and concentration distributions were found to be symmetric near the front and rear walls. A relatively low-temperature and high-oxygen-concentration zone formed in the near-wall region that helps mitigate slagging in the lower furnace. Third, NO(x) emissions were found to have decreased by as much as 50%, yielding a slight decrease in the levels of unburnt carbon in the fly ash.
Benjamin W. Teh
Full Text Available BackgroundA translational study in patients with myeloma to determine the utility of immune profiling to predict infection risk in patients with hematological malignancy was conducted.MethodsBaseline, end of induction, and maintenance peripheral blood mononuclear cells from 40 patients were evaluated. Immune cell populations and cytokines released from 1 × 106 cells/ml cultured in the presence of a panel of stimuli (cytomegalovirus, influenza, S. pneumoniae, phorbol myristate acetate/ionomycin and in media alone were quantified. Patient characteristics and infective episodes were captured from clinical records. Immunological variables associated with increased risk for infection in the 3-month period following sample collection were identified using univariate analysis (p < 0.05 and refined with multivariable analysis to define a predictive immune profile.Results525 stimulant samples with 19,950 stimulant–cytokine combinations across three periods were studied, including 61 episodes of infection. Mitogen-stimulated release of IL3 and IL5 were significantly associated with increased risk for subsequent infection during maintenance therapy. A lower Th1/Th2 ratio and higher cytokine response ratios for IL5 and IL13 during maintenance therapy were also significantly associated with increased risk for infection. On multivariable analysis, only IL5 in response to mitogen stimulation was predictive of infection. The lack of cytokine response and numerical value of immune cells were not predictive of infection.ConclusionProfiling cytokine release in response to mitogen stimulation can assist with predicting subsequent onset of infection in patients with hematological malignancy during maintenance therapy.
Curtis, D; North, B V; Sham, P C
Single nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. However as SNPs are biallelic they may have relatively little power in association studies compared with the information that would be obtainable if marker haplotypes were available and could be used efficiently. Modelling the evolutionary events leading to linkage disequilibrium is very complex and many methods that seek to use information from multiple markers simultaneously need to make simplifying assumptions and may only be applicable when marker haplotypes, rather than genotypes, are available for analysis. We explore the properties of a simple application of a standard artificial neural network to this problem. The pattern-recognition properties of the network are used in the hope that marker haplotypes implicit in the genotypes will differ between cases and controls in a way which will lead to the network being able to classify the subjects correctly, according to their marker genotype. This method makes no assumptions at all regarding population history or the marker map, and can be applied to genotypes, as would be available from a simple case-control sample, without any need to determine haplotypes. Through application to data simulated under a very wide range of assumptions we show that such an analysis produces a useful augmentation in power above that which would be achieved by testing each marker individually, in particular when more than one mutation has occurred in a disease gene at different points in evolution. The application of neural networks to such problems shows considerable promise and further work could usefully be directed towards optimising the design and implementation of such networks.
Swenson, D.W.H.; Bolhuis, P.G.
The multiple state transition interface sampling (TIS) framework in principle allows the simulation of a large network of complex rare event transitions, but in practice suffers from convergence problems. To improve convergence, we combine multiple state TIS [J. Rogal and P. G. Bolhuis, J. Chem.
This study explored an alternative assessment procedure to examine learning trajectories of matrix multiplication. It took rule-based analytical and cognitive task analysis methods specifically to break down operation rules for a given matrix multiplication. Based on the analysis results, a hierarchical Bayesian network, an assessment model,…
for the Analysis of Biochemical Reaction Networks 12.PERSONAL AUTHOR(S) Captain Stephen E. Kelly 13a.TYPE OF REPORT I 13b.TIME COVERED I14.DATE OF...necessary’apd identify by bldck numbe&)"War A method is proposed for the analysis of possible distributions of pro’ cts in biochemical reaction networks using...consistency and closure in fermentation material balanc-. -iSeveral biochemical reaction networks were examined. A proposed pathway for the biosynthetic
Fushing, Hsieh; Jordà, Òscar; Beisner, Brianne; McCowan, Brenda
What do the behavior of monkeys in captivity and the financial system have in common? The nodes in such social systems relate to each other through multiple and keystone networks, not just one network. Each network in the system has its own topology, and the interactions among the system's networks change over time. In such systems, the lead into a crisis appears to be characterized by a decoupling of the networks from the keystone network. This decoupling can also be seen in the crumbling of the keystone's power structure toward a more horizontal hierarchy. This paper develops nonparametric methods for describing the joint model of the latent architecture of interconnected networks in order to describe this process of decoupling, and hence provide an early warning system of an impending crisis.
Liu, Wenzong; He, Zhangwei; Yang, Chunxue
in an integrated system of microbial electrolysis cell (MEC) and anaerobic digestion (AD) for waste activated sludge (WAS). Microbial communities in integrated system would build a thorough energetic and metabolic interaction network regarding fermentation communities and electrode respiring communities...... and Parabacteroides, which showed a delayed contribution to the extracellular electron transport leading to a slow cascade utilization of WAS. Conclusions: Efficient pretreatment could supply more short-chain fatty acids and higher conductivities in the fermentative liquid, which facilitated mass transfer in anodic...
Tokumitsu, Masahiro; Ishida, Yoshiteru
This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron ﬂux (>2 MeV). The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic ﬁeld and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron ﬂux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.
Agha, Salah R; Alnahhal, Mohammed J
The current study investigates the possibility of obtaining the anthropometric dimensions, critical to school furniture design, without measuring all of them. The study first selects some anthropometric dimensions that are easy to measure. Two methods are then used to check if these easy-to-measure dimensions can predict the dimensions critical to the furniture design. These methods are multiple linear regression and neural networks. Each dimension that is deemed necessary to ergonomically design school furniture is expressed as a function of some other measured anthropometric dimensions. Results show that out of the five dimensions needed for chair design, four can be related to other dimensions that can be measured while children are standing. Therefore, the method suggested here would definitely save time and effort and avoid the difficulty of dealing with students while measuring these dimensions. In general, it was found that neural networks perform better than multiple linear regression in the current study. Copyright © 2012 Elsevier Ltd and The Ergonomics Society. All rights reserved.
Node deployment is one of the most crucial issues in wireless sensor networks, and it is of realistic significance to complete the deployment task with multiple types of application requirements. In this paper, we propose a deployment strategy for multiple types of requirements to solve the problem of deterministic and grid-based deployment. This deployment strategy consists of three deployment algorithms, which are for different deployment objectives. First, instead of general random search, we put forward a deterministic search mechanism and the related cost-based deployment algorithm, in which nodes are assigned to different groups which are connected by near-shortest paths, and realize significant reduction of path length and deployment cost. Second, rather than ordinary nondirection deployment, we present a notion of counterflow and the related delay-based deployment algorithm, in which the profit of deployment cost and loss of transmission delay are evaluated, and achieve much diminishing of transmission path length and transmission delay. Third, instead of conventional uneven deployment based on the distances to the sink, we propose a concept of node load level and the related lifetime-based deployment algorithm, in which node distribution is determined by the actual load levels and extra nodes are deployed only where really necessary. This contributes to great improvement of network lifetime. Last, extensive simulations are used to test and verify the effectiveness and superiority of our findings.
Full Text Available This paper proposes a space weather forecasting system at geostationary orbit for high-energy electron ﬂux (>2 MeV. The forecasting model involves multiple sensors on multiple satellites. The sensors interconnect and evaluate each other to predict future conditions at geostationary orbit. The proposed forecasting model is constructed using a dynamic relational network for sensor diagnosis and event monitoring. The sensors of the proposed model are located at different positions in space. The satellites for solar monitoring equip with monitoring devices for the interplanetary magnetic ﬁeld and solar wind speed. The satellites orbit near the Earth monitoring high-energy electron ﬂux. We investigate forecasting for typical two examples by comparing the performance of two models with different numbers of sensors. We demonstrate the prediction by the proposed model against coronal mass ejections and a coronal hole. This paper aims to investigate a possibility of space weather forecasting based on the satellite network with in-situ sensing.
Ali, Konpal S.
Non-orthogonal multiple access (NOMA) is promoted as a key component of 5G cellular networks. As the name implies, NOMA operation introduces intracell interference (i.e., interference arising within the cell) to the cellular operation. The intracell interference is managed by careful NOMA design (e.g., user clustering and resource allocation) along with successive interference cancellation. However, most of the proposed NOMA designs are agnostic to intercell interference (i.e., interference from outside the cell), which is a major performance limiting parameter in 5G networks. This article sheds light on the drastic negative-impact of intercell interference on the NOMA performance and advocates interference-aware NOMA design that jointly accounts for both intracell and intercell interference. To this end, a case study for fair NOMA operation is presented and intercell interference mitigation techniques for NOMA networks are discussed. This article also investigates the potential of integrating NOMA with two important 5G transmission schemes, namely, full duplex and device-to-device communication. This is important since the ambitious performance defined by the 3rd Generation Partnership Project (3GPP) for 5G is foreseen to be realized via seamless integration of several new technologies and transmission techniques.
Leavitt, Victoria M; Paxton, Jessica; Sumowski, James F
Memory impairment affects 50% of multiple sclerosis (MS) patients. Altered resting-state functional connectivity (FC) has been observed in the default network (DN) of MS patients. No study to date has examined the association of DN FC to its behavioral concomitant, memory. The approach of the present study represents a methodological shift allowing straightforward interpretation of FC alterations in MS, as it presupposes specificity of a network to its paired cognitive function. We examined FC from fMRI collected during rest in the DN of 43 MS patients with and without memory-impairment. Memory-intact patients showed increased DN FC relative to memory-impaired patients. There were no regions of higher FC in memory-impaired patients. DN FC was positively correlated with memory function, such that higher FC was associated with better memory performance. Results were unchanged after controlling for cognitive efficiency, supporting specificity of the DN to memory and not cognitive status more generally. These findings support DN FC as a marker of memory function in MS patients that can be targeted by future treatment interventions. Pairing a functional network with its behavioral concomitant represents a straightforward method for interpreting FC alterations in patients with MS.
Santosh A Helekar
Full Text Available In multiple sclerosis (MS functional changes in connectivity due to cortical reorganization could lead to cognitive impairment (CI, or reflect a re-adjustment to reduce the clinical effects of widespread tissue damage. Such alterations in connectivity could result in changes in neural activation as assayed by executive function tasks. We examined cognitive function in MS patients with mild to moderate cognitive impairment and age-matched controls. We evaluated brain activity using functional magnetic resonance imaging (fMRI during the successful performance of the Wisconsin-card sorting (WCS task by MS patients, showing compensatory maintenance of normal function, as measured by response latency and error rate. To assess changes in functional connectivity throughout the brain, we performed a global functional brain network analysis by computing voxel by voxel correlations on the fMRI time series data and carrying out a hierarchical cluster analysis. We found that during the WCS task there is a significant reduction in the number of smaller size brain functional networks, and a change in the brain areas representing the nodes of these networks in MS patients compared to age-matched controls. There is also a concomitant increase in the strength of functional connections between brain loci separated at intermediate scale distances in these patients. These functional alterations might reflect compensatory neuroplastic reorganization underlying maintenance of relatively normal cognitive function in the face of white matter lesions and cortical atrophy produced by MS.
Baranov, Yuri P.; Yarishev, Sergey N.; Medvedev, Roman V.
Position control of multiple objects is one of the most actual problems in various technology areas. For example, in construction area this problem is represented as multi-point deformation control of bearing constructions in order to prevent collapse, in mining - deformation control of lining constructions, in rescue operations - potential victims and sources of ignition location, in transport - traffic control and traffic violations detection, in robotics -traffic control for organized group of robots and many other problems in different areas. Usage of stationary devices for solving these problems is inappropriately due to complex and variable geometry of control areas. In these cases self-organized systems of moving visual sensors is the best solution. This paper presents a concept of scalable visual sensor network with swarm architecture for multiple object pose estimation and real-time tracking. In this article recent developments of distributed measuring systems were reviewed with consequent investigation of advantages and disadvantages of existing systems, whereupon theoretical principles of design of swarming visual sensor network (SVSN) were declared. To measure object coordinates in the world coordinate system using TV-camera intrinsic (focal length, pixel size, principal point position, distortion) and extrinsic (rotation matrix, translation vector) calibration parameters were needed to be determined. Robust camera calibration was a too resource-intensive task for using moving camera. In this situation position of the camera is usually estimated using a visual mark with known parameters. All measurements were performed in markcentered coordinate systems. In this article a general adaptive algorithm of coordinate conversion of devices with various intrinsic parameters was developed. Various network topologies were reviewed. Minimum error in objet tracking was realized by finding the shortest path between object of tracking and bearing sensor, which set
Jongen, Peter Joseph; Sinnige, Ludovicus G.; van Geel, Bjoern M.; Verheul, Freek; Verhagen, Wim I.; van der Kruijk, Ruud A.; Haverkamp, Reinoud; Schrijver, Hans M.; Baart, Jacoba C.; Visser, Leo H.; Arnoldus, Edo P.; Gilhuis, Herman Jacobus; Pop, Paul; Booy, Monique; Heerings, Marco; Kool, Anton; van Noort, Esther
Background: MSmonitor is an interactive web-based program for self-management and integrated, multidisciplinary care in multiple sclerosis. Methods: To assess the utilization and valuation by persons with multiple sclerosis, we held an online survey among those who had used the program for at least
Turner, James C; Keller, Adrienne
This description of the College Health Surveillance Network (CHSN) includes methodology, demography, epidemiology, and health care utilization. Twenty-three universities representing approximately 730,000 enrolled students contributed data from January 1, 2011, through May 31, 2014. Participating schools uploaded de-identified electronic health records from student health services monthly. During this study, just over 800,000 individuals used the health centers, comprising 4.17 million patient encounters. Sixty percent of visits included primary care, 13% mental health, 9% vaccination, and 31% other miscellaneous services. The 5 most common specific diagnostic categories (with annual rates per 100 enrolled students) were preventive (16); respiratory (12); skin, hair, and nails; infectious non-sexually transmitted infection (5 each); and mental health (4). Utilization and epidemiologic trends are identified among subpopulations of students. CHSN data establish trends in utilization and epidemiologic patterns by college students and the importance of primary and behavioral health care services on campuses.
Eijlers, Anand J C; Meijer, Kim A; Wassenaar, Thomas M; Steenwijk, Martijn D; Uitdehaag, Bernard M J; Barkhof, Frederik; Wink, Alle M; Geurts, Jeroen J G; Schoonheim, Menno M
To investigate how changes in functional network hierarchy determine cognitive impairment in multiple sclerosis (MS). A cohort consisting of 332 patients with MS (age 48.1 ± 11.0 years, symptom duration 14.6 ± 8.4 years) and 96 healthy controls (HCs; age 45.9 ± 10.4 years) underwent structural MRI, fMRI, and extensive neuropsychological testing. Patients were divided into 3 groups: cognitively impaired (CI; n = 87), mildly cognitively impaired (MCI; n = 65), and cognitively preserved (CP; n = 180). The functional importance of brain regions was quantified with degree centrality, the average strength of the functional connections of a brain region with the rest of the brain, and eigenvector centrality, which adds to this concept by adding additional weight to connections with brain hubs because these are known to be especially important. Centrality values were calculated for each gray matter voxel based on resting-state fMRI data, registered to standard space. Group differences were assessed with a cluster-wise permutation-based method corrected for age, sex, and education. CI patients demonstrated widespread centrality increases compared to both HCs and CP patients, mainly in regions making up the default-mode network. Centrality decreases were similar in all patient groups compared to HCs, mainly in occipital and sensorimotor areas. Results were robust across centrality measures. Patients with MS with cognitive impairment show hallmark alterations in functional network hierarchy with increased relative importance (centrality) of the default-mode network. © 2017 American Academy of Neurology.
Valverde, Sergi; Cabezas, Mariano; Roura, Eloy; González-Villà, Sandra; Pareto, Deborah; Vilanova, Joan C; Ramió-Torrentà, Lluís; Rovira, Àlex; Oliver, Arnau; Lladó, Xavier
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from a small (n≤35) set of labeled data of the same MRI contrast, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r≥0.97) also with the expected lesion volume. Copyright © 2017 Elsevier Inc. All rights reserved.
Full Text Available Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.
Chamis, Christos C.; Coroneos, Rula M.
Simulation of divot weight in the insulating foam, associated with the external tank of the U.S. space shuttle, has been evaluated using least squares and neural network concepts. The simulation required models based on fundamental considerations that can be used to predict under what conditions voids form, the size of the voids, and subsequent divot ejection mechanisms. The quadratic neural networks were found to be satisfactory for the simulation of foam divot weight in various tests associated with the external tank. Both linear least squares method and the nonlinear neural network predicted identical results.
Elnahas, Ahmad; Jackson, Timothy D; Okrainec, Allan; Austin, Peter C; Bell, Chaim M; Urbach, David R
In 2009, the Ontario Bariatric Network was established to address the exploding demand by Ontario residents for bariatric surgery services outside Canada. We compared the use of postoperative hospital services between out-of-country surgery recipients and patients within the Ontario Bariatric Network. We conducted a population-based, comparative study using administrative data held at the Institute for Clinical Evaluative Sciences. We included Ontario residents who underwent bariatric surgery between 2007 and 2012 either outside the country or at one of the Ontario Bariatric Network's designated centres of excellence. The primary outcome was use of hospital services in Ontario within 1 year after surgery. A total of 4852 patients received bariatric surgery out of country, and 5179 patients underwent surgery through the Ontario Bariatric Network. After adjustment, surgery at a network centre was associated with a significantly lower utilization rate of postoperative hospital services than surgery out of country (rate ratio 0.90, 95% confidence interval [CI] 0.84 to 0.97). No statistically significant differences were found with respect to time in critical care or mortality. However, the physician assessment and reoperation rates were significantly higher among patients who received surgery at a network centre than among those who had bariatric surgery out of country (rate ratio 4.10, 95% CI 3.69 to 4.56, and rate ratio 1.84, 95% CI 1.34 to 2.53, respectively). The implementation of a comprehensive, multidisciplinary provincial program to replace outsourcing of bariatric surgical services was associated with less use of postoperative hospital services by Ontario residents undergoing bariatric surgery. Future research should include an economic evaluation to determine the costs and benefits of the Ontario Bariatric Network.
Oh, Sang-Il; Kang, Hang-Bong
Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT), which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT) and KITTIbenchmarks.
Full Text Available Multiple-object tracking is affected by various sources of distortion, such as occlusion, illumination variations and motion changes. Overcoming these distortions by tracking on RGB frames, such as shifting, has limitations because of material distortions caused by RGB frames. To overcome these distortions, we propose a multiple-object fusion tracker (MOFT, which uses a combination of 3D point clouds and corresponding RGB frames. The MOFT uses a matching function initialized on large-scale external sequences to determine which candidates in the current frame match with the target object in the previous frame. After conducting tracking on a few frames, the initialized matching function is fine-tuned according to the appearance models of target objects. The fine-tuning process of the matching function is constructed as a structured form with diverse matching function branches. In general multiple object tracking situations, scale variations for a scene occur depending on the distance between the target objects and the sensors. If the target objects in various scales are equally represented with the same strategy, information losses will occur for any representation of the target objects. In this paper, the output map of the convolutional layer obtained from a pre-trained convolutional neural network is used to adaptively represent instances without information loss. In addition, MOFT fuses the tracking results obtained from each modality at the decision level to compensate the tracking failures of each modality using basic belief assignment, rather than fusing modalities by selectively using the features of each modality. Experimental results indicate that the proposed tracker provides state-of-the-art performance considering multiple objects tracking (MOT and KITTIbenchmarks.
Barman-Adhikari, Anamika; Rice, Eric
Little is known about the factors associated with use of employment services among homeless youth. Social network characteristics have been known to be influential in motivating people's decision to seek services. Traditional theoretical frameworks applied to studies of service use emphasize individual factors over social contexts and interactions. Using key social network, social capital, and social influence theories, this paper developed an integrated theoretical framework that capture the social network processes that act as barriers or facilitators of use of employment services by homeless youth, and understand empirically, the salience of each of these constructs in influencing the use of employment services among homeless youth. We used the "Event based-approach" strategy to recruit a sample of 136 homeless youth at one drop-in agency serving homeless youth in Los Angeles, California in 2008. The participants were queried regarding their individual and network characteristics. Data were entered into NetDraw 2.090 and the spring embedder routine was used to generate the network visualizations. Logistic regression was used to assess the influence of the network characteristics on use of employment services. The study findings suggest that social capital is more significant in understanding why homeless youth use employment services, relative to network structure and network influence. In particular, bonding and bridging social capital were found to have differential effects on use of employment services among this population. The results from this study provide specific directions for interventions aimed to increase use of employment services among homeless youth. Copyright © 2014 Elsevier Ltd. All rights reserved.
Full Text Available Amy Guo,1 Michael Grabner,2 Swetha Rao Palli,2 Jessica Elder,1 Matthew Sidovar,1 Peter Aupperle,1 Stephen Krieger3 1Acorda Therapeutics Inc., Ardsley, New York, NY, USA; 2HealthCore Inc., Wilmington, DE, USA; 3Corinne Goldsmith Dickinson Center for MS, Icahn School of Medicine at Mount Sinai, New York, NY, USA Background: Although previous studies have demonstrated the clinical benefits of dalfampridine extended release (D-ER tablets in patients with multiple sclerosis (MS, there are limited real-world data on D-ER utilization and associated outcomes in patients with MS. Purpose: The objective of this study was to evaluate treatment patterns, budget impact, and health care resource utilization (HRU associated with D-ER use in a real-world setting. Methods: A retrospective claims database analysis was conducted using the HealthCore Integrated Research DatabaseSM. Adherence (measured by medication possession ratio, or [MPR] and persistence (measured by days between initial D-ER claim and discontinuation or end of follow-up were evaluated over 1-year follow-up. Budget impact was calculated as cost per member per month (PMPM over the available follow-up period. D-ER and control cohorts were propensity-score matched on baseline demographics, comorbidities, and MS-related resource utilization to compare walking-impairment-related HRU over follow-up. Results: Of the 2,138 MS patients identified, 1,200 were not treated with D-ER (control and 938 were treated with D-ER. Patients were aged 51 years on average and 74% female. Approximately 82.6% of D-ER patients were adherent (MPR >80%. The estimated budget impact range of D-ER was $0.014–$0.026 PMPM. Propensity-score-matched D-ER and controls yielded 479 patients in each cohort. Postmatching comparison showed that the D-ER cohort was associated with fewer physician (21.5% vs 62.4%, P<0.0001 and other outpatient visits (22.8% vs 51.4%, P<0.0001 over the 12-month follow-up. Changes in HRU from follow
characteristics: reproducibility, accuracy, selectivity, aging, and resolution. Artificial neural network (ANN), a mathematical model formed by mimicking the human nervous system, was used to predict the sensor response. Qwiknet (version 2.23) software was used to develop ANNs and according to the results of Qwiknet the prediction performances for training and testing data sets were 75%, and 83.33% respectively. In this dissertation, Chapter 1 describes the worldwide plastic optical fiber (POF) and fiber optic sensor markets, and the existing textile structures used in fiber optic sensing design particularly for the applications of biomedical and structural health monitoring (SHM). Chapter 2 provides a literature review in detail on polymer optical fibers, fiber optic sensors, and occupancy sensing in the passenger seats of automobiles. Chapter 3 includes the research objectives. Chapter 4 presents the response of POF to tensile loading, bending, and cyclic tensile loading with discussion parts. Chapter 5 includes an e-mail based survey to prioritize customer needs in a Quality Function Deployment (QFD) format utilizing Analytic Hierarchy Process (AHP) and survey results. Chapter 6 describes the POF sensor design and the behavior of it under pressure. Chapter 7 provides a data analysis based on the experimental results of Chapter 6. Chapter 8 presents the summary of this study and recommendations for future work.
Full Text Available loud computing is the extension of parallel computing, distributed computing and grid computing. It provides secure, quick, convenient data storage and net computing services through the internet. The services are available to user in pay per-use-on-demand model. The main aim of using resources from cloud is to reduce the cost and to increase the performance in terms of request response time. Thus, optimizing the resource usage through efficient load balancing strategy is crucial. The main aim of this paper is to develop and implement an Optimized Load balancing algorithm in IaaS virtual cloud environment that aims to utilize the virtual cloud resources efficiently. It minimizes the cost of the applications by effectively using cloud resources and identifies the virtual cloud resources that must be suitable for all the applications. The web application is created with many modules. These modules are considered as tasks and these tasks are submitted to the load balancing server. The server which consists our load balancing policies redirect the tasks to the corresponding virtual machines created by KVM virtual machine manager as per the load balancing algorithm. If the size of the database inside the machine exceeds then the load balancing algorithm uses the other virtual machines for further incoming request. The load balancing strategy are evaluated for various QoS performance metrics like cost, average execution times, throughput, CPU usage, disk space, memory usage, network transmission and reception rate, resource utilization rate and scheduling success rate for the number of virtual machines and it improves the scalability among resources using load balancing techniques.
. Qualitative interviews showed that farmers who opt for organic fertilizers do so partially because of pressure from global traders, mediated through external links and amplified by dense and reciprocal relations within their groups. The results highlight the need for environmental management policies to be based on research at multiple scales and demonstrate that, counter-intuitively, increasing global economic interconnectivity may, in some cases, stimulate the adoption of conservation practices via local social networks.
Full Text Available With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.
Ross, Fiona; Avet-Loiseau, Herve; Ameye, Genevieve
The European Myeloma Network has organized two workshops on fluorescence in situ hybridization in multiple myeloma. The first aimed to identify specific indications and consensus technical approaches of current practice. A second workshop followed a quality control exercise in which 21 laboratories...... that the primary clinical applications for FISH analysis were for newly diagnosed cases of MM or frank relapse cases. A range of technical recommendations included: 1) material should be part of the first draw of the aspirate; 2) samples should be sent at suitable times to allow for the lengthy processing...... clearly and must state the percentage of PC involved and the method used for identification; 11) a retrospective European based FISH data bank linked to clinical data should be generated; and 12) prospective analysis should be centralized for upcoming trials based on the recommendations made. The European...
Crop yield has been greatly enhanced during the last century. However, most elite cultivars are adapted to temperate climates and are not well suited to more stressful conditions. In the context of climate change, stress resistance is a major concern. To overcome these difficulties, scientists may help breeders by providing genetic markers associated with stress resistance. However, multi-stress resistance cannot be obtained from the simple addition of single stress resistance traits. In the field, stresses are unpredictable and several may occur at once. Consequently, the use of single stress resistance traits is often inadequate. Although it has been historically linked with the heat stress response, the heat shock protein (HSP)/chaperone network is a major component of multiple stress responses. Among the HSP/chaperone
Jyoti, Vishav; Kaler, Rajinder Singh
A novel virtual user system is modeled for enhancing the security of an optical code division multiple access (OCDMA) network. Although the OCDMA system implementing code shift keying (CSK) is secure against a conventional power detector, it is susceptible to differential eavesdropping. An analytical framework is developed for the CSK-OCDMA system to show eavesdropper's code interception performance for a single transmitting user in the presence of a virtual user. It is shown that the eavesdropper's probability of correct bit interception decreases from 7.1×10-1 to 1.85×10-5 with the inclusion of the virtual user. Furthermore, the results confirm that the proposed virtual user scheme increases the confidentiality of the CSK-OCDMA system and outperforms the conventional OCDMA scheme in terms of security.
Full Text Available This paper is concerned with the H∞ filtering for a class of networked Markovian jump systems with multiple communication delays. Due to the existence of communication constraints, the measurement signal cannot arrive at the filter completely on time, and the stochastic communication delays are considered in the filter design. Firstly, a set of stochastic variables is introduced to model the occurrence probabilities of the delays. Then based on the stochastic system approach, a sufficient condition is obtained such that the filtering error system is stable in the mean-square sense and with a prescribed H∞ disturbance attenuation level. The optimal filter gain parameters can be determined by solving a convex optimization problem. Finally, a simulation example is given to show the effectiveness of the proposed filter design method.
Kulkarni, Aditya; Evers, Wiel H; Tomić, Stanko; Beard, Matthew C; Vanmaekelbergh, Daniel; Siebbeles, Laurens D A
Carrier multiplication (CM) is a process in which a single photon excites two or more electrons. CM is of interest to enhance the efficiency of a solar cell. Until now, CM in thin films and solar cells of semiconductor nanocrystals (NCs) has been found at photon energies well above the minimum required energy of twice the band gap. The high threshold of CM strongly limits the benefits for solar cell applications. We show that CM is more efficient in a percolative network of directly connected PbSe NCs. The CM threshold is at twice the band gap and increases in a steplike fashion with photon energy. A lower CM efficiency is found for a solid of weaker coupled NCs. This demonstrates that the coupling between NCs strongly affects the CM efficiency. According to device simulations, the measured CM efficiency would significantly enhance the power conversion efficiency of a solar cell.
Beard, Matthew C [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Kulkarni, Aditya [Deflt University of Technology; Evers, Wiel H. [Deflt University of Technology; Tomic, Stanko [University of Salford; Vanmaekelbergh, Daniel [University of Utrecht; Siebbeles, Lauren D. A. [Delft University of Technology
Carrier multiplication (CM) is a process in which a single photon excites two or more electrons. CM is of interest to enhance the efficiency of a solar cell. Until now, CM in thin films and solar cells of semiconductor nanocrystals (NCs) has been found at photon energies well above the minimum required energy of twice the band gap. The high threshold of CM strongly limits the benefits for solar cell applications. We show that CM is more efficient in a percolative network of directly connected PbSe NCs. The CM threshold is at twice the band gap and increases in a steplike fashion with photon energy. A lower CM efficiency is found for a solid of weaker coupled NCs. This demonstrates that the coupling between NCs strongly affects the CM efficiency. According to device simulations, the measured CM efficiency would significantly enhance the power conversion efficiency of a solar cell.
Alsharoa, Ahmad M.
This paper studies the energy efficient transmission and the power allocation problem for multiple two-way relay networks equipped with multi-input multi-output antennas where each relay employs an amplify-and-forward strategy. The goal is to minimize the total power consumption without degrading the quality of service of the terminals. In our analysis, we start by deriving closed-form expressions of the optimal powers allocated to terminals. We then employ a strong optimization tool based on the particle swarm optimization technique to find the optimal power allocated at each relay antenna. Our numerical results illustrate the performance of the proposed scheme and show that it achieves a sub-optimal solution very close to the optimal one.
Full Text Available We investigate two important aspects in sensor network design—the throughput and the energy efficiency. We consider the uplink reachback problem where the receiver is equipped with multiple antennas and linear multiuser detectors. We first assume Rayleigh flat-fading, and analyze two MAC schemes: round-robin and slotted-ALOHA. We optimize the average number of transmissions per slot and the transmission power for two purposes: maximizing the throughput, or minimizing the effective energy (defined as the average energy consumption per successfully received packet subject to a throughput constraint. For each MAC scheme with a given linear detector, we derive the maximum asymptotic throughput as the signal-to-noise ratio goes to infinity. It is shown that the minimum effective energy grows rapidly as the throughput constraint approaches the maximum asymptotic throughput. By comparing the optimal performance of different MAC schemes equipped with different detectors, we draw important tradeoffs involved in the sensor network design. Finally, we show that multiuser scheduling greatly enhances system performance in a shadow fading environment.
Yang, Jin; Liu, Fagui; Cao, Jianneng; Wang, Liangming
Mobile sinks can achieve load-balancing and energy-consumption balancing across the wireless sensor networks (WSNs). However, the frequent change of the paths between source nodes and the sinks caused by sink mobility introduces significant overhead in terms of energy and packet delays. To enhance network performance of WSNs with mobile sinks (MWSNs), we present an efficient routing strategy, which is formulated as an optimization problem and employs the particle swarm optimization algorithm (PSO) to build the optimal routing paths. However, the conventional PSO is insufficient to solve discrete routing optimization problems. Therefore, a novel greedy discrete particle swarm optimization with memory (GMDPSO) is put forward to address this problem. In the GMDPSO, particle's position and velocity of traditional PSO are redefined under discrete MWSNs scenario. Particle updating rule is also reconsidered based on the subnetwork topology of MWSNs. Besides, by improving the greedy forwarding routing, a greedy search strategy is designed to drive particles to find a better position quickly. Furthermore, searching history is memorized to accelerate convergence. Simulation results demonstrate that our new protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.
Al-Habob, Ahmed A.
In this paper, we study the performance of simultaneous wireless information and power transfer (SWIPT) technique in a multi-destination dual-hop underlay cognitive relay network with multiple primary receivers. Information transmission from the secondary source to destinations is performed entirely via a decode- and-forward (DF) relay. The relay is assumed to have no embedded power source and to harvest energy from the source signal using a power splitting (PS) protocol and employing opportunistic scheduling to forward the information to the selected destination. We derive analytical expressions for the outage probability assuming Rayleigh fading channels and considering the energy harvesting efficiency at relay, the source maximum transmit power and primary receivers interference constraints. The system performance is also studied at high signal-to-noise ratio (SNR) values where approximate expressions for the outage probability are provided and analyzed in terms of diversity order and coding gain. Monte-Carlo simulations and some numerical examples are provided to validate the derived expressions and to illustrate the effect of various system parameters on the system performance. In contrast to their conventional counterparts where a multi- destination diversity is usually achieved, the results show that the multi-destination cognitive radio relay networks with the SWIPT technique achieve a constant diversity order of one.
Schoonheim, M M; Geurts, Jjg; Wiebenga, O T; De Munck, J C; Polman, C H; Stam, C J; Barkhof, F; Wink, A M
Cognitive dysfunction in multiple sclerosis (MS) has a large impact on the quality of life and is poorly understood. The aim of this study was to investigate functional network integrity in MS, and relate this to cognitive dysfunction and physical disability. Resting state fMRI scans were included of 128 MS patients and 50 controls. Eigenvector centrality mapping (ECM) was applied, a graph analysis technique that ranks the importance of brain regions based on their connectivity patterns. Significant ECM changes were related to physical disability and cognitive dysfunction. In MS patients, ECM values were increased in bilateral thalamus and posterior cingulate (PCC) areas, and decreased in sensorimotor and ventral stream areas. Sensorimotor ECM decreases were related to higher EDSS (rho = -0.24, p = 0.007), while ventral stream decreases were related to poorer average cognition (rho = 0.23, p = 0.009). The thalamus displayed increased connectivity to sensorimotor and ventral stream areas. In MS, areas in the ventral stream and sensorimotor cortex appear to become less central in the entire functional network of the brain, which is associated with clinico-cognitive dysfunction. The thalamus, however, displays increased connectivity with these areas. These findings may aid in further elucidating the function of functional reorganization processes in MS. © The Author(s) 2013.
Dogonowski, Anne-Marie; Siebner, Hartwig R; Sørensen, Per Soelberg; Wu, Xingchen; Biswal, Bharat; Paulson, Olaf B; Dyrby, Tim B; Skimminge, Arnold; Blinkenberg, Morten; Madsen, Kristoffer H
Multiple sclerosis (MS) impairs signal transmission along cortico-cortical and cortico-subcortical connections, affecting functional integration within the motor network. Functional magnetic resonance imaging (fMRI) during motor tasks has revealed altered functional connectivity in MS, but it is unclear how much motor disability contributed to these abnormal functional interaction patterns. To avoid any influence of impaired task performance, we examined disease-related changes in functional motor connectivity in MS at rest. A total of 42 patients with MS and 30 matched controls underwent a 20-minute resting-state fMRI session at 3 Tesla. Independent component analysis was applied to the fMRI data to identify disease-related changes in motor resting-state connectivity. Patients with MS showed a spatial expansion of motor resting-state connectivity in deep subcortical nuclei but not at the cortical level. The anterior and middle parts of the putamen, adjacent globus pallidus, anterior and posterior thalamus and the subthalamic region showed stronger functional connectivity with the motor network in the MS group compared with controls. MS is characterised by more widespread motor connectivity in the basal ganglia while cortical motor resting-state connectivity is preserved. The expansion of subcortical motor resting-state connectivity in MS indicates less efficient funnelling of neural processing in the executive motor cortico-basal ganglia-thalamo-cortical loops.
Yang, Bo; Wei, Qifan; Zhang, Meng
This paper proposes a multi-human locating method for distributed wireless sensor network with binary pyroelectric infrared sensors. The uniformly deployed infrared sensor network consists of one sink node and nine sensor nodes, which can detect infrared information of moving human targets. An anti-logic bearing-crossing location and clustering algorithm is proposed to locate different targets. Firstly, dynamic virtual detection lines are generated based on the angular bisector of sensor's FOV(field of view) and all intersection points of these detection lines are primary measurement points. The location of multi-human targets can be achieved by first clustering the primary measurement points and then assigning these clusters to each target, which can simplify the assignment problem from multiple points to several clusters. Finally an anti-logic primary measurement points filtering method is used to get the location result of each target. Simulation and experimental results have shown that the measurement points can be obtained and assigned to different targets effectively, and our proposed location method can locate and track two human targets well.
Li, Hantao; Liu, Kai; Zhang, Jun
Based on the concept of contention reservation for polling transmission and collision prevention strategy for collision resolution, a fair on-demand access (FODA) protocol for supporting node mobility and multihop architecture in highly dynamic self-organizing networks is proposed. In the protocol, a distributed clustering network architecture formed by self-organizing algorithm and a main idea of reserving channel resources to get polling service are adopted, so that the hidden terminal (HT) and exposed terminal (ET) problems existed in traffic transmission due to multihop architecture and wireless transmission can be eliminated completely. In addition, an improved collision prevention scheme based on binary countdown algorithm (BCA), called fair collision prevention (FCP) algorithm, is proposed to greatly eliminate unfair phenomena existed in contention access of newly active ordinary nodes and completely resolve access collisions. Finally, the performance comparison of the FODA protocol with carrier sense multiple access with collision avoidance (CSMA/CA) and polling protocols by OPNET simulation are presented. Simulation results show that the FODA protocol can overcome the disadvantages of CSMA/CA and polling protocols, and achieve higher throughput, lower average message delay and less average message dropping rate.
Jiao, Bingqing; Zhang, Delong; Liang, Aiying; Liang, Bishan; Wang, Zengjian; Li, Junchao; Cai, Yuxuan; Gao, Mengxia; Gao, Zhenni; Chang, Song; Huang, Ruiwang; Liu, Ming
Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. Copyright © 2017 Elsevier B.V. All rights reserved.
Shen, Jiahui; Zhang, Gaoyan; Zhu, Chaozhe; Yao, Li; Zhao, Xiaojie
Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.
Liang, Yulan; Kelemen, Arpad
Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.
Liu, Xin; Zhang, Ruisheng; Liu, Qidong
Wireless sensor networks (WSNs), which consist of a large number of sensor nodes, have become among the most important technologies in numerous fields, such as environmental monitoring, military surveillance, control systems in nuclear reactors, vehicle safety systems, and medical monitoring. The most serious drawback for the widespread application of WSNs is the lack of security. Given the resource limitation of WSNs, traditional security schemes are unsuitable. Approaches toward withstanding related attacks with small overhead have thus recently been studied by many researchers. Numerous studies have focused on the authentication scheme for WSNs, but most of these works cannot achieve the security performance and overhead perfectly. Nam et al. proposed a two-factor authentication scheme with lightweight sensor computation for WSNs. In this paper, we review this scheme, emphasize its drawbacks, and propose a temporal credential-based mutual authentication with a multiple-password scheme for WSNs. Our scheme uses multiple passwords to achieve three-factor security performance and generate a session key between user and sensor nodes. The security analysis phase shows that our scheme can withstand related attacks, including a lost password threat, and the comparison phase shows that our scheme involves a relatively small overhead. In the comparison of the overhead phase, the result indicates that more than 95% of the overhead is composed of communication and not computation overhead. Therefore, the result motivates us to pay further attention to communication overhead than computation overhead in future research.
Shi, Fanrong; Tuo, Xianguo; Yang, Simon X; Li, Huailiang; Shi, Rui
Wireless sensor networks (WSNs) have been widely used to collect valuable information in Structural Health Monitoring (SHM) of bridges, using various sensors, such as temperature, vibration and strain sensors. Since multiple sensors are distributed on the bridge, accurate time synchronization is very important for multi-sensor data fusion and information processing. Based on shape of the bridge, a spanning tree is employed to build linear topology WSNs and achieve time synchronization in this paper. Two-way time message exchange (TTME) and maximum likelihood estimation (MLE) are employed for clock offset estimation. Multiple TTMEs are proposed to obtain a subset of TTME observations. The time out restriction and retry mechanism are employed to avoid the estimation errors that are caused by continuous clock offset and software latencies. The simulation results show that the proposed algorithm could avoid the estimation errors caused by clock drift and minimize the estimation error due to the large random variable delay jitter. The proposed algorithm is an accurate and low complexity time synchronization algorithm for bridge health monitoring.
Chakraborty, W; Ray, R; Samanta, N; RoyChaudhuri, C
In spite of the rapid developments in various nanosensor technologies, it still remains challenging to realize a reliable ultrasensitive electrical biosensing platform which will be able to detect multiple viruses in blood simultaneously with a fairly high reproducibility without using secondary labels. In this paper, we have reported quantitative differentiation of Hep-B and Hep-C viruses in blood using nanoporous silicon oxide immunosensor array and artificial neural network (ANN). The peak frequency output (fp) from the steady state sensitivity characteristics and the first cut off frequency (fc) from the transient characteristics have been considered as inputs to the multilayer ANN. Implementation of several classifier blocks in the ANN architecture and coupling them with both the sensor chips, functionalized with Hep-B and Hep-C antibodies have enabled the quantification of the viruses with an accuracy of around 95% in the range of 0.04fM-1pM and with an accuracy of around 90% beyond 1pM and within 25nM in blood serum. This is the most sensitive report on multiple virus quantification using label free method. Copyright © 2017 Elsevier B.V. All rights reserved.
This book develops a mathematical framework for modeling and optimizing interference-coupled multiuser systems. At the core of this framework is the concept of general interference functions, which provides a simple means of characterizing interdependencies between users. The entire analysis builds on the two core axioms scale-invariance and monotonicity. The proposed network calculus has its roots in power control theory and wireless communications. It adds theoretical tools for analyzing the typical behavior of interference-coupled networks. In this way it complements existing game-theoretic approaches. The framework should also be viewed in conjunction with optimization theory. There is a fruitful interplay between the theory of interference functions and convex optimization theory. By jointly exploiting the properties of interference functions, it is possible to design algorithms that outperform general-purpose techniques that only exploit convexity. The title “network calculus” refers to the fact tha...
Jungreuthmayer, Christian; Beurton-Aimar, Marie; Zanghellini, Jürgen
Minimal cut sets are a valuable tool for analyzing metabolic networks and for identifying optimal gene intervention strategies by eliminating unwanted metabolic functions and keeping desired functionality. Minimal cut sets rely on the concept of elementary flux modes, which are sets of indivisible metabolic pathways under steady-state condition. However, the computation of minimal cut sets is nontrivial, as even medium-sized metabolic networks with just 100 reactions easily have several hundred million elementary flux modes. We developed a minimal cut set tool that implements the well-known Berge algorithm and utilizes a novel approach to significantly reduce the program run time by using binary bit pattern trees. By using the introduced tree approach, the size of metabolic models that can be analyzed and optimized by minimal cut sets is pushed to new and considerably higher limits.
Johnson, Tricia J; Jones, Art; Lulias, Cheryl; Perry, Anthony
State Medicaid programs need cost-effective strategies to provide high-quality care that is accessible to individuals with low incomes and limited resources. Integrated delivery systems have been formed to provide care across the continuum, but creating a shared vision for improving community health can be challenging. Medical Home Network was created as a network of primary care providers and hospital systems providing care to Medicaid enrollees, guided by the principles of egalitarian governance, practice-level care coordination, real-time electronic alerts, and pay-for-performance incentives. This analysis of health care utilization and costs included 1,189,195 Medicaid enrollees. After implementation of Medical Home Network, a risk-adjusted increase of $9.07 or 4.3% per member per month was found over the 2 years of implementation compared with an increase of $17.25 or 9.3% per member per month, before accounting for the cost of care management fees and other financial incentives, for Medicaid enrollees within the same geographic area with a primary care provider outside of Medical Home Network. After accounting for care coordination fees paid to providers, the net risk-adjusted cost reduction was $11.0 million.
THIS PAGE INTENTIONALLY LEFT BLANK xiii ACRONYMS AND ABBREVIATIONS AES Advanced Encryption Algorithm AO Area...Network Topology VAP Virtual Access Point VIRT Valuable Information at the Right Time VOIP Voice Over Internet Protocol WEP Wired Equivalent...established for 802.11 (WPA and WEP ) have known vulnerabilities45 that compromise their integrity and effectiveness on the battlefield
This paper discusses whether social network services, like Facebook and Twitter, may be used by elderly living in their own homes to enhance communication with their relatives and friends. It introduces a prototype solution based on the iRobot Roomba 560, iRobot, USA, robot vacuum cleaner, which...
Yip, Kevin Y; Yu, Haiyuan; Kim, Philip M; Schultz, Martin; Gerstein, Mark
Biological processes involve complex networks of interactions between molecules. Various large-scale experiments and curation efforts have led to preliminary versions of complete cellular networks for a number of organisms. To grapple with these networks, we developed TopNet-like Yale Network Analyzer (tYNA), a Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks. It also allows one to manage a large number of private and public networks using a flexible tagging system, to filter them based on a variety of criteria, and to visualize them through an interactive graphical interface. A number of commonly used biological datasets have been pre-loaded into tYNA, standardized and grouped into different categories. The tYNA system can be accessed at http://networks.gersteinlab.org/tyna. The source code, JavaDoc API and WSDL can also be downloaded from the website. tYNA can also be accessed from the Cytoscape software using a plugin.
This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage. The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount. First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power. Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input. In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter. Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as
Adil, Fatime Zehra; Konukseven, Erhan İlhan; Balkan, Tuna; Adil, Ömer Faruk
In the design of pilot helmets with night vision capability, to not limit or block the sight of the pilot, a transparent visor is used. The reflected image from the coated part of the visor must coincide with the physical human sight image seen through the nonreflecting regions of the visor. This makes the alignment of the visor halves critical. In essence, this is an alignment problem of two optical parts that are assembled together during the manufacturing process. Shack-Hartmann wavefront sensor is commonly used for the determination of the misalignments through wavefront measurements, which are quantified in terms of the Zernike polynomials. Although the Zernike polynomials provide very useful feedback about the misalignments, the corrective actions are basically ad hoc. This stems from the fact that there exists no easy inverse relation between the misalignment measurements and the physical causes of the misalignments. This study aims to construct this inverse relation by making use of the expressive power of the neural networks in such complex relations. For this purpose, a neural network is designed and trained in MATLAB® regarding which types of misalignments result in which wavefront measurements, quantitatively given by Zernike polynomials. This way, manual and iterative alignment processes relying on trial and error will be replaced by the trained guesses of a neural network, so the alignment process is reduced to applying the counter actions based on the misalignment causes. Such a training requires data containing misalignment and measurement sets in fine detail, which is hard to obtain manually on a physical setup. For that reason, the optical setup is completely modeled in Zemax® software, and Zernike polynomials are generated for misalignments applied in small steps. The performance of the neural network is experimented and found promising in the actual physical setup.
Full Text Available A number of experiments on fluid flow at the micro/nano-scale have demonstrated that flow velocity obviously deviates from the classical Poiseuille’s law due to the micro forces between the wall and the fluid. Based on an oil–water two-phase network simulation model, a three-dimensional pore-scale micro network model with solid–liquid interfacial effects was established. The influences of solid–liquid interface effects including van der Waals force and wettability on the residual oil distribution and relative permeability were investigated through microscopic simulation. The effects of pore radius, pore–throat size ratio, shaping factor, and coordination number on the residual oil distribution were analyzed at the same time. The results showed that the oil recovery would be overestimated by about 4% without van der Waals force in a water-wet reservoir. The impact of van der Waals force on water-wet reservoirs was significantly obvious in contrast with oil-wet reservoirs. In addition, the residual oil distribution was significantly influenced by pore radius in water-wet reservoir, comparatively influenced by pore–throat size ratio in oil-wet reservoir. The present study illustrates the successful application of three-dimensional micro network models considering solid–liquid interfacial effects, and provides new insights for oil recovery enhancement.
Zainudin, Suhaila; Arif, Shereena M.
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5. PMID:28250767
Roberts, Trina E.; Sargis, Eric J.; Olson, Link E.
Multiple unlinked genetic loci often provide a more comprehensive picture of evolutionary history than any single gene can, but analyzing multigene data presents particular challenges. Differing rates and patterns of nucleotide substitution, combined with the limited information available in any data set, can make it difficult to specify a model of evolution. In addition, conflict among loci can be the result of real differences in evolutionary process or of stochastic variance and errors in reconstruction. We used 6 presumably unlinked nuclear loci to investigate relationships within the mammalian family Tupaiidae (Scandentia), containing all but one of the extant tupaiid genera. We used a phylogenetic mixture model to analyze the concatenated data and compared this with results using partitioned models. We found that more complex models were not necessarily preferred under tests using Bayes factors and that model complexity affected both tree length and parameter variance. We also compared the results of single-gene and multigene analyses and used splits networks to analyze the source and degree of conflict among genes. Networks can show specific relationships that are inconsistent with each other; these conflicting and minority relationships, which are implicitly ignored or collapsed by traditional consensus methods, can be useful in identifying the underlying causes of topological uncertainty. In our data, conflict is concentrated around particular relationships, not widespread throughout the tree. This pattern is further clarified by considering conflict surrounding the root separately from conflict within the ingroup. Uncertainty in rooting may be because of the apparent evolutionary distance separating these genera and our outgroup, the tupaiid genus Dendrogale. Unlike a previous mitochondrial study, these nuclear data strongly suggest that the genus Tupaia is not monophyletic with respect to the monotypic Urogale, even when uncertainty about rooting is
Li, Bo; Rui, Xiaoting
Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.
Salleh, Faridah Hani Mohamed; Zainudin, Suhaila; Arif, Shereena M
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C) as a direct interaction (A → C). Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Uncovering causal genes for human inherited diseases, as the primary step toward understanding the pathogenesis of these diseases, requires a combined analysis of genetic and genomic data. Although bioinformatics methods have been designed to prioritize candidate genes resulting from genetic linkage analysis or association studies, the coverage of both diseases and genes in existing methods is quite limited, thereby preventing the scan of causal genes for a significant proportion of diseases at the whole-genome level. To overcome this limitation, we propose a method named pgWalk to prioritize candidate genes by integrating multiple phenomic and genomic data. We derive three types of phenotype similarities among 7719 diseases and nine types of functional similarities among 20327 genes. Based on a pair of phenotype and gene similarities, we construct a disease-gene network and then simulate the process that a random walker wanders on such a heterogeneous network to quantify the strength of association between a candidate gene and a query disease. A weighted version of the Fisher's method with dependent correction is adopted to integrate 27 scores obtained in this way, and a final q-value is calibrated for prioritizing candidate genes. A series of validation experiments are conducted to demonstrate the superior performance of this approach. We further show the effectiveness of this method in exome sequencing studies of autism and epileptic encephalopathies. An online service and the standalone software of pgWalk can be found at http://bioinfo.au.tsinghua.edu.cn/jianglab/pgwalk. © The Author (2015). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. All rights reserved.
Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W
There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects. Copyright © 2016 Elsevier Inc. All rights reserved.
Faridah Hani Mohamed Salleh
Full Text Available Gene regulatory network (GRN reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies were not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors. Hence, this research aims to propose Multiple Linear Regression (MLR to infer GRN from gene expression data and to avoid wrongly inferring of an indirect interaction (A → B → C as a direct interaction (A → C. Since the number of observations of the real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random subnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to assess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed in this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley collinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks obtained satisfactory results, with AUROC values above 0.5.
Baird, Derek E.; Fisher, Mercedes
Raised in the "always on" world of interactive media, the Internet, and digital messaging technologies, today's student has different expectations and learning styles than previous generations. This net-centric generation values their ability to use the Web to create a self-paced, customized, on-demand learning path that includes multiple forms of…
Thien T. T. Le
Full Text Available Currently, wireless body area networks (WBANs are effectively used for health monitoring services. However, in cases where WBANs are densely deployed, interference among WBANs can cause serious degradation of network performance and reliability. Inter-WBAN interference can be reduced by scheduling the communication links of interfering WBANs. In this paper, we propose an interference-aware traffic-priority-based link scheduling (ITLS algorithm to overcome inter-WBAN interference in densely deployed WBANs. First, we model a network with multiple WBANs as an interference graph where node-level interference and traffic priority are taken into account. Second, we formulate link scheduling for multiple WBANs as an optimization model where the objective is to maximize the throughput of the entire network while ensuring the traffic priority of sensor nodes. Finally, we propose the ITLS algorithm for multiple WBANs on the basis of the optimization model. High spatial reuse is also achieved in the proposed ITLS algorithm. The proposed ITLS achieves high spatial reuse while considering traffic priority, packet length, and the number of interfered sensor nodes. Our simulation results show that the proposed ITLS significantly increases spatial reuse and network throughput with lower delay by mitigating inter-WBAN interference.
Marcano, Andrea; Christiansen, Henrik Lehrmann
Among the key technologies that have been identified as capacity boosters for fifth generation - 5G - mobile networks, are millimeter wave (mmWave) transmissions and non-orthogonal multiple access (NOMA). The large amount of spectrum available at mmWave frequencies combined with a more effective...
Ma, Ning; May, Tobias; Brown, Guy J.
This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localization of multiple sources in reverberant environments. DNNs are used to learn the relationship between the source azimuth and binaural cues, consisting...
Muhammad Marsudi; Dzuraidah Abdul Wahab; Che Hasan Che Haron
Modeling of a manufacturing system enables one to identify the effects of key design parameters on the system performance and as a result make the correct decision. This paper proposes a manufacturing system modeling approach using computer spreadsheet software, in which a static capacity planning model and stochastic queuing model are integrated. The model was used to optimize the existing system utilization in relation to product design. The model incorporates a few parameters such as utili...
Full Text Available Genome-wide proximity ligation based assays such as Hi-C have revealed that eukaryotic genomes are organized into structural units called topologically associating domains (TADs. From a visual examination of the chromosomal contact map, however, it is clear that the organization of the domains is not simple or obvious. Instead, TADs exhibit various length scales and, in many cases, a nested arrangement. Here, by exploiting the resemblance between TADs in a chromosomal contact map and densely connected modules in a network, we formulate TAD identification as a network optimization problem and propose an algorithm, MrTADFinder, to identify TADs from intra-chromosomal contact maps. MrTADFinder is based on the network-science concept of modularity. A key component of it is deriving an appropriate background model for contacts in a random chain, by numerically solving a set of matrix equations. The background model preserves the observed coverage of each genomic bin as well as the distance dependence of the contact frequency for any pair of bins exhibited by the empirical map. Also, by introducing a tunable resolution parameter, MrTADFinder provides a self-consistent approach for identifying TADs at different length scales, hence the acronym "Mr" standing for Multiple Resolutions. We then apply MrTADFinder to various Hi-C datasets. The identified domain boundaries are marked by characteristic signatures in chromatin marks and transcription factors (TF that are consistent with earlier work. Moreover, by calling TADs at different length scales, we observe that boundary signatures change with resolution, with different chromatin features having different characteristic length scales. Furthermore, we report an enrichment of HOT (high-occupancy target regions near TAD boundaries and investigate the role of different TFs in determining boundaries at various resolutions. To further explore the interplay between TADs and epigenetic marks, as tumor mutational
Chen, Sharon; Ross, Thomas J; Zhan, Wang; Myers, Carol S; Chuang, Keh-Shih; Heishman, Stephen J; Stein, Elliot A; Yang, Yihong
Group independent component analysis (gICA) was performed on resting-state data from 14 healthy subjects scanned on 5 fMRI scan sessions across 16 days. The data were reduced and aggregated in 3 steps using Principal Components Analysis (PCA, within scan, within session and across session) and subjected to gICA procedures. The amount of reduction was estimated by an improved method that utilizes a first-order autoregressive fitting technique to the PCA spectrum. Analyses were performed using all sessions in order to maximize sensitivity and alleviate the problem of component identification across session. Across-session consistency was examined by three methods, all using back-reconstruction of the single-session or single-subject/session maps from the grand (5-session) maps. The gICA analysis produced 55 spatially independent maps. Obvious artifactual maps were eliminated and the remainder were grouped based upon physiological recognizability. Biologically relevant component maps were found, including sensory, motor and a 'default-mode' map. All analysis methods showed that components were remarkably consistent across session. Critically, the components with the most obvious physiological relevance were the most consistent. The consistency of these maps suggests that, at least over a period of several weeks, these networks would be useful to follow longitudinal treatment-related manipulations.
Chen, Shuheng; Hu, Weihao; Chen, Zhe
Based on generalized chain-table storage structure (GCTSS), a novel power flow method is proposed, which can be used to solve the power flow of weakly meshed distribution networks with multiple distributed generators (DGs). GCTSS is designed based on chain-table technology and its target is to de......Based on generalized chain-table storage structure (GCTSS), a novel power flow method is proposed, which can be used to solve the power flow of weakly meshed distribution networks with multiple distributed generators (DGs). GCTSS is designed based on chain-table technology and its target...... is to describe the topology of radial distribution networks with a clear logic and a small memory size. The strategies of compensating the equivalent currents of break-point branches and the reactive power outputs of PV-type DGs are presented on the basis of superposition theorem. Their formulations...
Sripada, Rebecca K; Bohnert, Amy S B; Teo, Alan R; Levine, Debra S; Pfeiffer, Paul N; Bowersox, Nicholas W; Mizruchi, Mark S; Chermack, Stephen T; Ganoczy, Dara; Walters, Heather; Valenstein, Marcia
Low social support and small social network size have been associated with a variety of negative mental health outcomes, while their impact on mental health services use is less clear. To date, few studies have examined these associations in National Guard service members, where frequency of mental health problems is high, social support may come from military as well as other sources, and services use may be suboptimal. Surveys were administered to 1448 recently returned National Guard members. Multivariable regression models assessed the associations between social support characteristics, probable mental health conditions, and service utilization. In bivariate analyses, large social network size, high social network diversity, high perceived social support, and high military unit support were each associated with lower likelihood of having a probable mental health condition (p social support (OR .90, CI .88-.92) and high unit support (OR .96, CI .94-.97) continued to be significantly associated with lower likelihood of mental health conditions. Two social support measures were associated with lower likelihood of receiving mental health services in bivariate analyses, but were not significant in adjusted models. General social support and military-specific support were robustly associated with reduced mental health symptoms in National Guard members. Policy makers, military leaders, and clinicians should attend to service members' level of support from both the community and their units and continue efforts to bolster these supports. Other strategies, such as focused outreach, may be needed to bring National Guard members with need into mental health care.
For the Blood Transfusion Service North the German Red Cross (Berlin, Federal Republic of Germany) utilizes the waste heat from production facilities and laboratories for heating offices. By doing this, the VRV technology for the realization of this solution was used.
Bayliss, Elizabeth A; Ellis, Jennifer L; Shoup, Jo Ann; Zeng, Chan; McQuillan, Deanna B; Steiner, John F
Lower continuity of care has been associated with higher rates of adverse outcomes for persons with multiple chronic medical conditions. It is unclear, however, whether this relationship also exists within integrated systems that offer high levels of informational continuity through shared electronic health records. We conducted a retrospective cohort study of 12,200 seniors with 3 or more chronic conditions within an integrated delivery system. Continuity of care was calculated using the Continuity of Care Index, which reflects visit concentration with individual clinicians. Using Cox proportional hazards regression permitting continuity to vary monthly until the outcome or censoring event, we separately assessed inpatient admissions and emergency department visits as a function of primary care continuity and specialty care continuity. After adjusting for covariates (demographics; baseline, primary, and specialty care visits; baseline outcomes; and morbidity burden), greater primary care continuity and greater specialty care continuity were each associated with a lower risk of inpatient admission (respective hazard ratios (95% CIs) = 0.97 (0.96, 0.99) and 0.95 (0.93, 0.98)) and a lower risk of emergency department visits (respective hazard ratios = 0.97 (0.96, 0.98) and 0.98 (0.96, 1.00)). For the subgroup with 3 or more primary care and 3 or more specialty care visits, specialty care continuity (but not primary care continuity) was independently associated with a decreased risk of inpatient admissions (hazard ratio = 0.94 (0.92, 0.97)), and primary care continuity (but not specialty care continuity) was associated with a decreased risk of emergency department visits (hazard ratio = 0.98 (0.96, 1.00)). In an integrated delivery system with high informational continuity, greater continuity of care is independently associated with lower hospital utilization for seniors with multiple chronic medical conditions. Different subgroups of patients will benefit from
The uninterruptible power supply (UPS) systems are used to supply quality power to critical systems such as computers, telephone exchanges, etc. The lack of energy in public power lines is of concern and has to be rectified. Quality power is, therefore, a flow of energy containing controlled amount of uncertainty about its continuity. The reserve energy stored in the UPS system carries out the basic reduction of the uncertainty caused by public power lines. The physical structure of the UPS system and the people maintaining the power system will increase the uncertainty. The main objectives of uncertainty management are to ensure that there is enough reserve energy and to minimize the additional uncertainties. This task can be carried out by optimally utilizing both the human beings and machines. The philosophy of the suggested management scheme is based on distributed decision making and centralized verification of these decisions. The on-site supervision facilities take care of distributed decision making by utilizing human reasoning. The alarm messages contain the most probable explanation of the available evidence. The network management facilities of her information in such a form that supports the human way of reasoning and thereby, effectively enables the centralized verification of the situation in the power system. The credibility of alarm issuing is of prime concern and it can be maintained by utilizing human reasoning. Uncertainty management is studied especially in Telecommunications domain, but most of the results obtained are applicable to computer domain also. The sources of the uncertainties are systematically identified and studied by using reliability techniques and finally a network wide solution is suggested.
Full Text Available The apparently simple structure of a four-stroke internal combustion cylinder belies the complicated problem of optimizing valve operation in response to a change in crankshaft rotation speed. The objective of this study was to determine the cylinder pressure for valve event angles in order to determine the optimal strategy for the timing of valve events when independently-actuated valves are available. In this work, an artificial neural network is applied to create a prediction matrix to anticipate the best variable valve timing approach according to rotation speed.
This dissertation develops a novel system for object recognition in videos. The input of the system is a set of unconstrained videos containing a known set of objects. The output is the locations and categories for each object in each frame across all videos. Initially, a shot boundary detection algorithm is applied to the videos to divide them into multiple sequences separated by the identified shot boundaries. Since each of these sequences still contains moderate content variations, we furt...
Noble, Jason; Forooghian, Farzin; Sproule, Melanie; Westall, Carol; O'Connor, Paul
To investigate the utility of the 25-Item National Eye Institute Visual Function Questionnaire (VFQ-25) in assessing visual function in a heterogeneous group of multiple sclerosis (MS) patients and to identify correlations of VFQ-25 scores with clinically relevant objective visual parameters. Comparative cohort study. The VFQ-25 was distributed to 34 patients with clinically definite MS. Patients underwent a comprehensive ophthalmic examination, including Early Treatment Diabetic Retinopathy Study (ETDRS) visual acuity (V(A)), Pelli-Robson contrast sensitivity (CS), Humphrey visual field 30 to 2 (HVF), and Farnsworth-Munsell 100-Hue color vision (100-Hue). Expanded Disability Status Scores (EDSS) were recorded for each patient. Comparative analyses using chi2 tests and t tests were performed. Spearman rank correlation coefficients were computed to identify relationships between VFQ-25 scores and the aforementioned visual parameters. In comparison with a published reference group without ocular disease, MS patients had considerably worse VFQ-25 composite scores (P < .01), being similar to published cohorts of glaucoma and cataract patients. VFQ-25 composite scores were found to be modestly and significantly correlated with several clinical parameters, including: V(A) (r = -0.63, P < .001), CS (r = 0.60, P < .001), HVF (r = 0.53, P = .003), and 100-Hue (r = -0.48, P = .01). EDSS scores, the use of disease modifying agents, and having a history of previous optic neuritis did not correlate significantly with VFQ-25 composite scores. The VFQ-25 questionnaire is a sensitive and useful tool in assessing visual function in MS patients. Such patients have quality of life indices similar to glaucoma and cataract patients, underscoring the significance of visual symptoms in MS.
Shahesmaeili, Armita; Haghdoost, Ali Akbar; Soori, Hamid
Despite the implementation of harm reduction program, some injecting drug users (IDU) continue to engage in high-risk behaviors. It seems that there are some social factors that contribute to risk of human immunodeficiency virus (HIV) transmission in IDUs. The aim of this study was to analysis the social network of IDUs and examines the effect of network location on HIV transmission risk using the multiple membership multilevel models. From October 2013 to March 2014 we conducted face-to-face interviews on 147 IDUs. We asked participants to nominate up to 20 people whom they had more than causal contact with them during the last month and specify if each nominee is drug injector or not. We defined four Network locations as Core and Peripheries of main components. The risk of HIV transmission for each individual was measured based on 7 items scale. We applied Multiple Membership Multilevel Linear Regression analysis to examine the relationship between network location and HIV transmission risk. We used Stata and UCINET software's for the analysis of data. The mean age of participants was 37 ± 9.32. Most of the individuals were male, single and educated up to guidance school. Being a core member of the main component as like as being a member of other small components in comparison with Isolates/unlinked significantly increased the HIV Transmission risk. Engagement in methadone maintenance therapies (MMT) was associated with a decrease in HIV transmission score. Network analysis is a useful guide to find the most influential members of IDUs network and may have a complementary role for harm reduction program. The efficacy of interventions programs can be reinforced by addressing them to core individuals within the network. Furthermore, it provides the harm reduction staff to find the broader number of IDUs who are usually hard to reach by routine outreach case-finding tasks.
Yakovenko, Oleksandr; Jones, Steven J. M.
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource (https://drugdesigndata.org/). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.
Full Text Available This paper presents a generic framework solution for minimizing video distortion of all multiple video streams transmitted over 802.11e wireless networks, including intelligent packet scheduling and channel access differentiation mechanisms. A distortion prediction model designed to capture the multireferenced frame coding characteristic of H.264/AVC encoded videos is used to predetermine the distortion importance of each video packet in all streams. Two intelligent scheduling algorithms are proposed: the “even-loss distribution,” where each video sender is experiencing the same loss and the “greedy-loss distribution” packet scheduling, where selected packets are dropped over all streams, ensuring that the most significant video stream in terms of picture context and quality characteristics will experience minimum losses. The proposed model has been verified with actual distortion measurements and has been found more accurate than the “additive distortion” model that omits the correlation among lost frames. The paper includes analytical and simulation results from the comparison of both schemes and from their comparison to the simplified additive model, for different video sequences and channel conditions.
Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.
Yakovenko, Oleksandr; Jones, Steven J. M.
We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource (https://drugdesigndata.org). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.
Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien; Gu, Xuejun
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.
Full Text Available Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS treatment planning. In this work, we developed a deep learning convolutional neural network (CNN algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.
Costa, David Castro; Sá, Maria José; Calheiros, José Manuel
To analyse the relationship between the social support network (SSN) and health related quality of life (HRQOL) in multiple sclerosis (MS) patients. The sample comprised 150 consecutive MS patients attending our MS clinic. To assess the socio-demographic data, a specifically designed questionnaire was applied. The HRQOL dimensions were measured with the Short-Form Health Survey Questionnaire-SF36 and the SSN with the Medical Outcomes Study Social Support Survey. Spearman's correlation was used to compare the magnitude of the relationship between the SSN and HRQOL. The mean patient age was 41.7 years (± 10.4; range: 18-70 yr); the mean Expanded Disability Status Score was 2.5 (±2.4; range: 0-9). There was a statistically significant correlation between the structure of the SSN and the HRQOL. The composition of the SSN, social group membership and participation in voluntary work have an important role in the HRQOL of patients with MS.
Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.
Levin, Eugene; Ternovskiy, Igor V.
Presently, there are many technological and industrial efforts for development of virtual flight simulators, usually based on networked technologies. In order to solve the problems of real time availability and realistic quality of simulators, source data images and digital terrain models (DTM) should have some generalized structure, which supposes different imagery resolution and different amount of detail on each level of 3D simulation. One of the central problems is geotruthing of satellite imagery with realistic accuracy requirements with respect to DTM. Traditionally such geotruthing can be achieved by means of geo control points measurements. This process is labor intensive and requires special photogrammetric operator skills. In order to avoid such a process an algorithm of terrain and image models singularity's recognition based on Catastrophe theory is investigated in this paper. This approach does not require training but operates with direct comparison of the analytical manifolds from DTM with those actually extracted from the image. The technology described in this paper, the Catastrophe Approach, and algorithms of satellite imagery treatment may be implemented in a multi-level image pyramid flight simulators. Theoretical approaches and practical realization indicates that the Catastrophe Approach is easy- to-use for a final customer and can be implemented on-line to networked flight simulators.
Beaconless position-based forwarding protocols have recently evolved as a promising solution for packet forwarding in wireless sensor networks. However, as the network density grows, the overhead incurred grows significantly. As such, end-to-end energy and delay performance is adversely impacted. Motivated by the need for a forwarding mechanism that is more tolerant to growth in node density, an alternative position-based protocol is proposed in this paper. The protocol is designed such that it completely eliminates the need for potential relays to undergo a relay election process. Rather, any eligible relay may decide to forward the packet ahead, thus significantly reducing the overhead. The operation of the proposed protocol is empowered by exploiting favorable features of orthogonal frequency division multiplexing (OFDM) at the physical layer. End-to-end performance is evaluated here against existing beaconless protocols. It is demonstrated that the proposed protocol is more efficient since it is able to offer lower end-to-end delay for the same amount of energy consumption. © 2011 IEEE.
Vishnubalaji, R; Hamam, R; Abdulla, M-H
Despite recent advances in cancer management, colorectal cancer (CRC) remains the third most common cancer and a major health-care problem worldwide. MicroRNAs have recently emerged as key regulators of cancer development and progression by targeting multiple cancer-related genes; however......, such regulatory networks are not well characterized in CRC. Thus, the aim of this study was to perform global messenger RNA (mRNA) and microRNA expression profiling in the same CRC samples and adjacent normal tissues and to identify potential miRNA-mRNA regulatory networks. Our data revealed 1273 significantly...... in cell proliferation, and migration in vitro. Concordantly, small interfering RNA-mediated knockdown of EZH2 led to similar effects on CRC cell growth in vitro. Therefore, our data have revealed several hundred potential miRNA-mRNA regulatory networks in CRC and suggest targeting relevant networks...
Dubberley, Matthew A.; Walker, Zachary A.; Haldeman, Benjamin J.
Las Cumbres Observatory Global Telescope (LCOGT) is redefining the function of robotic telescopes by deploying 0.4 meter telescopes that act as a highly networked intelligent instrument. The 0.4 meter telescopes, (P4) are optimized for quick and accurate object acquisition and tracking. This minimizes response time and enables the leveraging of the instrument. A single P4 can independently execute multiple science programs concurrently or team up with other P4s for deeper or multi-color observations of a single target. The intelligent control software will optimize the observation schedule for each individual telescope and the entire network. LCOGT is deploying 6 networked clusters consisting of four P4s around the world, providing capacity and versatility beyond the classical observatory. Each P4 has zero slippage, no backlash friction systems, and is currently achieving 20 deg/s slewing. Blind pointing is currently 8 arcsec RMS. Using the AG acquisition routine, the drive will have repeatable pointing to within 0.6 arcsec within 12 seconds from anywhere on the sky. Other features include wind buffet correction, rapid thermalization, dual autoguiders, novel scanning flat fielding device, large 20 kg instrument capacity, high speed instrument changer, and a stiff split ring mount.
López-Barroso, Diana; Ripollés, Pablo; Marco-Pallarés, Josep; Mohammadi, Bahram; Münte, Thomas F; Bachoud-Lévi, Anne-Catherine; Rodriguez-Fornells, Antoni; de Diego-Balaguer, Ruth
Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance. Copyright © 2015 Elsevier Inc. All rights reserved.
Full Text Available on constraints programming satisfaction technology is proposed. The algorithm is tested in OPNET simulation environment using different network models derived from a hypothetical case study of an optical network design for Bellville area in Cape Town, South...
Yoon, Ikjune; Kim, Hyeok; Noh, Dong Kun
A node in a solar-powered wireless sensor network (WSN) collects energy when the sun shines and stores it in a battery or capacitor for use when no solar power is available, in particular at night. In our scheme, each tiny node in a WSN periodically determines its energy budget, which takes into account its residual energy, and its likely acquisition and consumption. If it expects to acquire more energy than it can store, the data which has it has sensed is aggregated with data from other nodes, compressed, and transmitted. Otherwise, the node continues to sense data, but turns off its wireless communication to reduce energy consumption. We compared several schemes by simulation. Our scheme reduced the number of nodes forced to black out due to lack of energy so that more data arrives at the sink node.
Abate, Megersa Abera
for 13 European countries reveals that on about 30 per cent of all trips made the trucks are empty, while the percentage of a truck’s carrying capacity filled with a cargo (that is, the load factor) remained stable at an average of 50 percent over the period 1990-2008 (European Environmental Agency, 2010......). The overall objective of this PhD thesis is to provide economic analyses of some of the drivers and limits of road freight transport, and their implication on the trucking industry’s performance. It is composed of four self-contained chapters which can be read independently. Each chapter addresses...... be achieved by combining the two strands of studies. Chapter 2 looks at two aspects of capacity utilization, namely the extent of empty running and the load factor. It shows that they are explained as a function of truck, haul and carrier characteristics. Chapter 3 analyzes how firms choose the optimal truck...
Chandrasekar, A; Rakkiyappan, R; Cao, Jinde
This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.
Full Text Available MicroRNAs, a new class of key regulators of gene expression, have been shown to be involved in diverse biological processes and linked to many human diseases. To elucidate miRNA function from a global perspective, we constructed a conserved miRNA co-expression network by integrating multiple human and mouse miRNA expression data. We found that these conserved co-expressed miRNA pairs tend to reside in close genomic proximity, belong to common families, share common transcription factors, and regulate common biological processes by targeting common components of those processes based on miRNA targets and miRNA knockout/transfection expression data, suggesting their strong functional associations. We also identified several co-expressed miRNA sub-networks. Our analysis reveals that many miRNAs in the same sub-network are associated with the same diseases. By mapping known disease miRNAs to the network, we identified three cancer-related miRNA sub-networks. Functional analyses based on targets and miRNA knockout/transfection data consistently show that these sub-networks are significantly involved in cancer-related biological processes, such as apoptosis and cell cycle. Our results imply that multiple co-expressed miRNAs can cooperatively regulate a given biological process by targeting common components of that process, and the pathogenesis of disease may be associated with the abnormality of multiple functionally cooperative miRNAs rather than individual miRNAs. In addition, many of these co-expression relationships provide strong evidence for the involvement of new miRNAs in important biological processes, such as apoptosis, differentiation and cell cycle, indicating their potential disease links.
Full Text Available This paper presents a real industrial example in which the steam utility network of a refinery is modelled in order to evaluate potential Heat Integration retrofits proposed for the site. A refinery, typically, has flexibility to optimize the operating strategy for the steam system depending on the operation of the main processes. This paper presents a few examples of Heat Integration retrofit measures from a case study of a large oil refinery. In order to evaluate expected changes in fuel and electricity imports to the refinery after implementation of the proposed retrofits, a steam system model has been developed. The steam system model has been tested and validated with steady state data from three different operating scenarios and can be used to evaluate how changes to steam balances at different pressure levels would affect overall steam balances, generation of shaft power in turbines, and the consumption of fuel gas.
Full Text Available With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.
Baumbach, Jan; Alcaraz, Nicolas; Pauling, Josch K.
We tackle the problem of de-novo pathway extraction. Given a biological network and a set of case-control studies, KeyPathwayMiner efficiently extracts and visualizes all maximal connected sub-networks that contain mainly genes that are dysregulated, e.g., differentially expressed, in most cases...... problems and designed a set of algorithms to tackle the combinatorial explosion of the search space. During the presentation we will demonstrate how to: Import and process the data, set the parameters for the two models, compute and visualize the key pathways, judge and statistically evaluate the results...
M N Hindia
Full Text Available As the enterprise of the "Internet of Things" is rapidly gaining widespread acceptance, sensors are being deployed in an unrestrained manner around the world to make efficient use of this new technological evolution. A recent survey has shown that sensor deployments over the past decade have increased significantly and has predicted an upsurge in the future growth rate. In health-care services, for instance, sensors are used as a key technology to enable Internet of Things oriented health-care monitoring systems. In this paper, we have proposed a two-stage fundamental approach to facilitate the implementation of such a system. In the first stage, sensors promptly gather together the particle measurements of an android application. Then, in the second stage, the collected data are sent over a Femto-LTE network following a new scheduling technique. The proposed scheduling strategy is used to send the data according to the application's priority. The efficiency of the proposed technique is demonstrated by comparing it with that of well-known algorithms, namely, proportional fairness and exponential proportional fairness.
Hindia, M N; Rahman, T A; Ojukwu, H; Hanafi, E B; Fattouh, A
As the enterprise of the "Internet of Things" is rapidly gaining widespread acceptance, sensors are being deployed in an unrestrained manner around the world to make efficient use of this new technological evolution. A recent survey has shown that sensor deployments over the past decade have increased significantly and has predicted an upsurge in the future growth rate. In health-care services, for instance, sensors are used as a key technology to enable Internet of Things oriented health-care monitoring systems. In this paper, we have proposed a two-stage fundamental approach to facilitate the implementation of such a system. In the first stage, sensors promptly gather together the particle measurements of an android application. Then, in the second stage, the collected data are sent over a Femto-LTE network following a new scheduling technique. The proposed scheduling strategy is used to send the data according to the application's priority. The efficiency of the proposed technique is demonstrated by comparing it with that of well-known algorithms, namely, proportional fairness and exponential proportional fairness.
Full Text Available In the paper authors verify two problems of methods of operational research in optical burst switching. The first problem is at edge node, related to the medium access delay. The second problem is at an intermediate node related to buffering delay. A correction coefficient K of transmission speed is obtained from the first analysis. It is used in to provide a full-featured link of nominal data rate. Simulations of the second problem reveal interesting results. It is not viable to prepare routing and wavelength assignment based on end-to-end delay, i.e. link's length or number of hops, as commonly used in other frameworks (OCS, Ethernet, IP, etc. nowadays. Other parameters such as buffering probability must be taken into consideration as well. Based on the buffering probability an estimation of the number of optical/electrical converters can be made. This paper concentrates important traffic constraints of buffered optical burst switching. It allows authors to prepare optimization algorithms for regenerators placement in CAROBS networks using methods of operational research.
Kao, Jehng-Jung; Hsieh, Ming-Ru
An industrial district with polluting factories operating inside poses a potential threat to the air quality in the surrounding areas. Therefore, establishing a proper air quality monitoring network (AQMN) is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilities in reducing pollutants. The geographic layout of such an AQMN should assure the quality of the monitored data. Monitoring stations located at inappropriate sites will likely affect data validity. In this study, a multiobjective approach was explored for configuring an AQMN for an industrial district. A dispersion model was employed to simulate hourly distribution of pollutant concentrations in the study area. Models optimizing pollution detection, dosage, coverage, and population protection were established. Alternative AQMNs with varied station numbers and spatial distributions were obtained using the models. The resulting AQMNs were compared and evaluated for effectiveness in monitoring the temporal and spatial variation of pollutants. Discussion of the differences among the AQMNs is provided. This multiobjective analysis is expected to facilitate a decision-making process for determining an appropriate AQMN.
Wang, J; Wang, F; Liu, Y; Xu, J; Lin, H; Jia, B; Zuo, W; Jiang, Y; Hu, L; Lin, F
Overweight individuals are at higher risk for developing type II diabetes than the general population. We conducted this study to analyze the correlation between blood glucose and biochemical parameters, and developed a blood glucose prediction model tailored to overweight patients. A total of 346 overweight Chinese people patients ages 18-81 years were involved in this study. Their levels of fasting glucose (fs-GLU), blood lipids, and hepatic and renal functions were measured and analyzed by multiple linear regression (MLR). Based the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tansig as the transfer function of the hidden layers nodes, and purelin for the output layer nodes, with training goal of 0.5×10(-5). There was significant correlation between fs-GLU with age, BMI, and blood biochemical indexes (P<0.05). The results of MLR analysis indicated that age, fasting alanine transaminase (fs-ALT), blood urea nitrogen (fs-BUN), total protein (fs-TP), uric acid (fs-BUN), and BMI are 6 independent variables related to fs-GLU. Based on these parameters, the BP-ANN model was performed well and reached high prediction accuracy when training 1 000 epoch (R=0.9987). The level of fs-GLU was predictable using the proposed BP-ANN model based on 6 related parameters (age, fs-ALT, fs-BUN, fs-TP, fs-UA and BMI) in overweight patients. © Georg Thieme Verlag KG Stuttgart · New York.
La Delfa, Nicholas J; Potvin, Jim R
In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations. Copyright © 2016 Elsevier Ltd. All rights reserved.
Wong, Yen F.; Kegege, Obadiah; Schaire, Scott H.; Bussey, George; Altunc, Serhat; Zhang, Yuwen; Patel, Chitra
National Aeronautics and Space Administration (NASA) CubeSat missions are expected to grow rapidly in the next decade. Higher data rate CubeSats are transitioning away from Amateur Radio bands to higher frequency bands. A high-level communication architecture for future space-to-ground CubeSat communication was proposed within NASA Goddard Space Flight Center. This architecture addresses CubeSat direct-to-ground communication, CubeSat to Tracking Data Relay Satellite System (TDRSS) communication, CubeSat constellation with Mothership direct-to-ground communication, and CubeSat Constellation with Mothership communication through K-Band Single Access (KSA).A Study has been performed to explore this communication architecture, through simulations, analyses, and identifying technologies, to develop the optimum communication concepts for CubeSat communications. This paper will present details of the simulation and analysis that include CubeSat swarm, daughter shipmother ship constellation, Near Earth Network (NEN) S and X-band direct to ground link, TDRS Multiple Access (MA) array vs Single Access mode, notional transceiverantenna configurations, ground asset configurations and Code Division Multiple Access (CDMA) signal trades for daughter mother CubeSat constellation inter-satellite crosslink. Results of Space Science X-band 10 MHz maximum achievable data rate study will be summarized. Assessment of Technology Readiness Level (TRL) of current CubeSat communication technologies capabilities will be presented. Compatibility test of the CubeSat transceiver through NEN and Space Network (SN) will be discussed. Based on the analyses, signal trade studies and technology assessments, the functional design and performance requirements as well as operation concepts for future CubeSat end-to-end communications will be derived.
Tramacere, Irene; Del Giovane, Cinzia; Salanti, Georgia; D'Amico, Roberto; Filippini, Graziella
Different therapeutic strategies are available for the treatment of people with relapsing-remitting multiple sclerosis (RRMS), including immunomodulators, immunosuppressants and biologics. Although there is consensus that these therapies reduce the frequency of relapses, their relative benefit in delaying new relapses or disability worsening remains unclear due to the limited number of direct comparison trials. To compare the benefit and acceptability of interferon beta-1b, interferon beta-1a (Avonex, Rebif), glatiramer acetate, natalizumab, mitoxantrone, fingolimod, teriflunomide, dimethyl fumarate, alemtuzumab, pegylated interferon beta-1a, daclizumab, laquinimod, azathioprine and immunoglobulins for the treatment of people with RRMS and to provide a ranking of these treatments according to their benefit and acceptability, defined as the proportion of participants who withdrew due to any adverse event. We searched the Cochrane Multiple Sclerosis and Rare Diseases of the CNS Group Trials Register, which contains trials from CENTRAL (2014, Issue 9), MEDLINE (1966 to 2014), EMBASE (1974 to 2014), CINAHL (1981 to 2014), LILACS (1982 to 2014), clinicaltrials.gov and the WHO trials registry, and US Food and Drug Administration (FDA) reports. We ran the most recent search in September 2014. Randomised controlled trials (RCTs) that studied one or more of the 15 treatments as monotherapy, compared to placebo or to another active agent, for use in adults with RRMS. Two authors independently identified studies from the search results and performed data extraction. We performed data synthesis by pairwise meta-analysis and network meta-analysis. We assessed the quality of the body of evidence for outcomes within the network meta-analysis according to GRADE, as very low, low, moderate or high. We included 39 studies in this review, in which 25,113 participants were randomised. The majority of the included trials were short-term studies, with a median duration of 24 months
Schulz Kenneth F
Full Text Available Abstract Although current electronic methods of scientific publishing offer increased opportunities for publishing all research studies and describing them in sufficient detail, health research literature still suffers from many shortcomings. These shortcomings seriously undermine the value and utility of the literature and waste scarce resources invested in the research. In recent years there have been several positive steps aimed at improving this situation, such as a strengthening of journals' policies on research publication and the wide requirement to register clinical trials. The EQUATOR (Enhancing the QUAlity and Transparency Of health Research Network is an international initiative set up to advance high quality reporting of health research studies; it promotes good reporting practices including the wider implementation of reporting guidelines. EQUATOR provides free online resources http://www.equator-network.org supported by education and training activities and assists in the development of robust reporting guidelines. This paper outlines EQUATOR's goals and activities and offers suggestions for organizations and individuals involved in health research on how to strengthen research reporting.
Full Text Available Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.
She, Ji; Wang, Fei; Zhou, Jianjiang
Radar networks are proven to have numerous advantages over traditional monostatic and bistatic radar. With recent developments, radar networks have become an attractive platform due to their low probability of intercept (LPI) performance for target tracking. In this paper, a joint sensor selection and power allocation algorithm for multiple-target tracking in a radar network based on LPI is proposed. It is found that this algorithm can minimize the total transmitted power of a radar network on the basis of a predetermined mutual information (MI) threshold between the target impulse response and the reflected signal. The MI is required by the radar network system to estimate target parameters, and it can be calculated predictively with the estimation of target state. The optimization problem of sensor selection and power allocation, which contains two variables, is non-convex and it can be solved by separating power allocation problem from sensor selection problem. To be specific, the optimization problem of power allocation can be solved by using the bisection method for each sensor selection scheme. Also, the optimization problem of sensor selection can be solved by a lower complexity algorithm based on the allocated powers. According to the simulation results, it can be found that the proposed algorithm can effectively reduce the total transmitted power of a radar network, which can be conducive to improving LPI performance.
Kleiman, Evan M; Riskind, John H
While perceived social support has received considerable research as a protective factor for suicide ideation, little attention has been given to the mechanisms that mediate its effects. We integrated two theoretical models, Joiner's (2005) interpersonal theory of suicide and Leary's (Leary, Tambor, Terdal, & Downs, 1995) sociometer theory of self-esteem to investigate two hypothesized mechanisms, utilization of social support and self-esteem. Specifically, we hypothesized that individuals must utilize the social support they perceive that would result in increased self-esteem, which in turn buffers them from suicide ideation. Participants were 172 college students who completed measures of social support, self-esteem, and suicide ideation. Tests of simple mediation indicate that utilization of social support and self-esteem may each individually help to mediate the perceived social support/suicide ideation relationship. Additionally, a test of multiple mediators using bootstrapping supported the hypothesized multiple-mediator model. The use of a cross-sectional design limited our ability to find true cause-and-effect relationships. Results suggested that utilized social support and self-esteem both operate as individual moderators in the social support/self-esteem relationship. Results further suggested, in a comprehensive model, that perceived social support buffers suicide ideation through utilization of social support and increases in self-esteem.
Weaver, C G
The University of Nebraska Medical Center (UNMC) uses five different electronic networks for interlibrary loan (ILL) request transmission. The advantages and problems of using electronic networks for ILL request transmission are discussed. Advantages include speed of request transmission, improved capabilities for locating documents, lower labor costs, improved turnaround time, and production of user reports and statistics. Disadvantages include increased work load, additional staff training, coordination of non-standard networks, determining access protocols, and establishing priorities for handling requests.
Borgmann, Hendrik; DeWitt, Sasha; Tsaur, Igor; Haferkamp, Axel; Loeb, Stacy
Twitter use has grown exponentially within the urological community. We aimed to determine the perceptions of the impact of Twitter on users' clinical practice, research, and other professional activities. We performed an 11-item online survey of Twitter contributors during two major urological meetings: the European Association of Urology (EAU) and the American Urological Association (AUA) annual meetings. During the EAU 2014 meeting, we distributed the survey via the meeting official Twitter feed. During the AUA 2014 meeting, we applied a new method by directly sending the survey to Twitter contributors. We performed a subset analysis for assessing the perceived impact of Twitter on the clinical practice of physicians. Among 312 total respondents, the greatest perceived benefits of Twitter among users were for networking (97%) and disseminating information (96%), followed by research (75%), advocacy (74%) and career development (62%). In total, 65% of Twitter users have dealt with guidelines on online medical professionalism and 71% of physician users found that Twitter had an impact on their clinical practice, and 33% had made a clinical decision based on an online case discussion. Our results suggest that Twitter users in the urological community perceive important benefits. These benefits extend to multiple professional domains, particularly networking, disseminating information, remote conference participation, research, and advocacy. This is the first study that has been disseminated to targeted individuals from the urological community directly through tweets, providing a proof of principle for this research method.
Naseem, Muhammad; Philippi, Nicole; Hussain, Anwar; Wangorsch, Gaby; Ahmed, Nazeer; Dandekar, Thomas
Phytohormones signal and combine to maintain the physiological equilibrium in the plant. Pathogens enhance host susceptibility by modulating the hormonal balance of the plant cell. Unlike other plant hormones, the detailed role of cytokinin in plant immunity remains to be fully elucidated. Here, extensive data mining, including of pathogenicity factors, host regulatory proteins, enzymes of hormone biosynthesis, and signaling components, established an integrated signaling network of 105 nodes and 163 edges. Dynamic modeling and system analysis identified multiple cytokinin-mediated regulatory interactions in plant disease networks. This includes specific synergism between cytokinin and salicylic acid pathways and previously undiscovered aspects of antagonism between cytokinin and auxin in plant immunity. Predicted interactions and hormonal effects on plant immunity are confirmed in subsequent experiments with Pseudomonas syringae pv tomato DC3000 and Arabidopsis thaliana. Our dynamic simulation is instrumental in predicting system effects of individual components in complex hormone disease networks and synergism or antagonism between pathways. PMID:22643121
P. G. Tabarro
Full Text Available For the planning and sustainable development of large cities, it is critical to accurately locate and map, in 3D, existing underground utility networks (UUN such as pipelines, cables, ducts, and channels. An emerging non-invasive instrument for collecting underground data such as UUN is the ground-penetrating radar (GPR. Although its capabilities, handling GPR and extracting relevant information from its data are not trivial tasks. For instance, both GPR and its complimentary software stack provide very few capabilities to co-visualize GPR collected data and other sources of spatial data such as orthophotography, DEM or road maps. Furthermore, the GPR interface lacks functionalities for adding annotation, editing geometric objects or querying attributes. A new approach to support GPR survey is proposed in this paper. This approach is based on the integration of multiple sources of geospatial datasets and the use of a Web-GIS system and relevant functionalities adapted to interoperable GPR data acquisition. The Web-GIS is developed as an improved module in an existing platform called GVX. The GVX-GPR module provides an interactive visualization of multiple layers of structured spatial data, including GPR profiles. This module offers new features when compared to traditional GPR surveys such as geo-annotated points of interest for identifying spatial clues in the GPR profiles, integration of city contextual data, high definition drone and satellite pictures, as-built, and more. The paper explains the engineering approach used to design and develop the Web GIS and tests for this survey approach, mapping and recording UUN as part of 3D city model.
Tabarro, P. G.; Pouliot, J.; Fortier, R.; Losier, L.-M.
For the planning and sustainable development of large cities, it is critical to accurately locate and map, in 3D, existing underground utility networks (UUN) such as pipelines, cables, ducts, and channels. An emerging non-invasive instrument for collecting underground data such as UUN is the ground-penetrating radar (GPR). Although its capabilities, handling GPR and extracting relevant information from its data are not trivial tasks. For instance, both GPR and its complimentary software stack provide very few capabilities to co-visualize GPR collected data and other sources of spatial data such as orthophotography, DEM or road maps. Furthermore, the GPR interface lacks functionalities for adding annotation, editing geometric objects or querying attributes. A new approach to support GPR survey is proposed in this paper. This approach is based on the integration of multiple sources of geospatial datasets and the use of a Web-GIS system and relevant functionalities adapted to interoperable GPR data acquisition. The Web-GIS is developed as an improved module in an existing platform called GVX. The GVX-GPR module provides an interactive visualization of multiple layers of structured spatial data, including GPR profiles. This module offers new features when compared to traditional GPR surveys such as geo-annotated points of interest for identifying spatial clues in the GPR profiles, integration of city contextual data, high definition drone and satellite pictures, as-built, and more. The paper explains the engineering approach used to design and develop the Web GIS and tests for this survey approach, mapping and recording UUN as part of 3D city model.
Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi
The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Runchun Mark Wang
Full Text Available We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP and Spike Timing Dependent Delay Plasticity (STDDP. We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2^26 (64M synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted and/or delayed pre-synaptic spike to the target synapse in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2^36 (64G synaptic adaptors on a current high-end FPGA platform.
Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan C; van Schaik, André
We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP) and Spike Timing Dependent Delay Plasticity (STDDP). We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2(26) (64M) synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted or delayed pre-synaptic spike to the post-synaptic neuron in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2(36) (64G) synaptic adaptors on a current high-end FPGA platform.
Valentine, Sarah E; Elsesser, Steven; Grasso, Chris; Safren, Steven A; Bradford, Judith B; Mereish, Ethan; O'Cleirigh, Conall
Previous studies documenting sexual minority women's disproportionate risk for a range of medical, mental health, and substance use disorders have not provided a predictive framework for understanding their interrelations and outcomes. The present study aimed to address this gap by testing the syndemic effect of co-occurring psychosocial problems on 7-year health care costs and utilization among sexual minority women. The sample was comprised of sexual minority women (N = 341) who were seen at an urban LGBT-affirmative community health center. Medical and mental health care utilization and cost data were extracted from electronic medical records. Demographically adjusted regression models revealed that co-occurring psychosocial problems (i.e., childhood sexual abuse, partner violence, substance use, and mental health distress [history of suicide attempt]) were all strongly interrelated. The presence of these indicators had a syndemic (additive) effect on medical costs and utilization and mental health utilization over 7-year follow-up, but no effect on 7-year mental health costs. These results suggest that the presence and additive effect of these syndemic conditions may, in part, explain increased medical costs and utilization (and higher medical morbidity) among sexual minority women.
Yang, Shufan; Wu, Qiang; Li, Renfa
Recent neuropsychological research has begun to reveal that neurons encode information in the timing of spikes. Spiking neural network simulations are a flexible and powerful method for investigating the behaviour of neuronal systems. Simulation of the spiking neural networks in software is unable to rapidly generate output spikes in large-scale of neural network. An alternative approach, hardware implementation of such system, provides the possibility to generate independent spikes precisely and simultaneously output spike waves in real time, under the premise that spiking neural network can take full advantage of hardware inherent parallelism. We introduce a configurable FPGA-oriented hardware platform for spiking neural network simulation in this work. We aim to use this platform to combine the speed of dedicated hardware with the programmability of software so that it might allow neuroscientists to put together sophisticated computation experiments of their own model. A feed-forward hierarchy network is developed as a case study to describe the operation of biological neural systems (such as orientation selectivity of visual cortex) and computational models of such systems. This model demonstrates how a feed-forward neural network constructs the circuitry required for orientation selectivity and provides platform for reaching a deeper understanding of the primate visual system. In the future, larger scale models based on this framework can be used to replicate the actual architecture in visual cortex, leading to more detailed predictions and insights into visual perception phenomenon.
Steven S. W. Lee
Full Text Available We propose a multitree based fast failover scheme for Ethernet networks. In our system, only few spanning trees are used to carry working traffic in the normal state. As a failure happens, the nodes adjacent to the failure redirect traffic to the preplanned backup VLAN trees to realize fast failure recovery. In the proposed scheme, a new leaf constraint is enforced on the backup trees. It enables the network being able to provide 100% survivability against any single link and any single node failure. Besides fast failover, we also take load balancing into consideration. We model an Ethernet network as a twolayered graph and propose an Integer Linear Programming (ILP formulation for the problem. We further propose a heuristic algorithm to provide solutions to large networks. The simulation results show that the proposed scheme can achieve high survivability while maintaining load balancing at the same time. In addition, we have implemented the proposed scheme in an FPGA system. The experimental results show that it takes only few μsec to recover a network failure. This is far beyond the 50 msec requirement used in telecommunication networks for network protection.
Yates, D. N.; Basdekas, L.; Rajagopalan, B.; Stewart, N.
Municipal water utilities often develop Integrated Water Resource Plans (IWRP), with the goal of providing a reliable, sustainable water supply to customers in a cost-effective manner. Colorado Springs Utilities, a 5-service provider (potable and waste water, solid waste, natural gas and electricity) in Colorado USA, recently undertook an IWRP. where they incorporated water supply, water demand, water quality, infrastructure reliability, environmental protection, and other measures within the context of complex water rights, such as their critically important 'exchange potential'. The IWRP noted that an uncertain climate was one of the greatest sources of uncertainty to achieving a sustainable water supply to a growing community of users. We describe how historic drought, paleo-climate, and climate change projections were blended together into climate narratives that informed a suite of water resource systems models used by the utility to explore the vulnerabilities of their water systems.
Zhu, Jun; Sova, Pavel; Xu, Qiuwei; Dombek, Kenneth M; Xu, Ethan Y; Vu, Heather; Tu, Zhidong; Brem, Rachel B; Bumgarner, Roger E; Schadt, Eric E
Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes...
Peles, Slaven; Munsky, Brian; Khammash, Mustafa
.... Multiple time scales in mathematical models often lead to serious computational difficulties, such as numerical stiffness in the case of differential equations or excessively redundant Monte Carlo...
Energy efficiency aspects in cellular networks can significantly contribute to the reduction of greenhouse gas emissions and help to save the environment. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Besides, introducing renewable energies as alternative power sources becomes a real challenge to network operators. In this paper, we propose a method that reduces the energy consumption of BSs by not only shutting down underutilized BSs but also by optimizing the amounts of energy procured from different retailers (Renewable energy and electricity retailers). We formulate an optimization problem that leads to the maximization of the profit of a Long-Term Evolution (LTE) cellular operator, and at the same time to the minimization of CO2 emissions in green wireless cellular networks without affecting the desired Quality of Service. © 2012 IEEE.
... Network Technology -, during the period from 1 July 2001 to 15 January 2002. The future DADS, also known as mini-DADS or micro-DADS, is the next-generation DADS envisioned for 2020 littoral undersea threats...
Rawstron, Andy C.; Orfao, Alberto; Beksac, Meral; Bezdickova, Ludmila; Broolmans, Rik A.; Bumbea, Horia; Dalva, Klara; Fuhler, Gwenny; Gratama, Jan; Hose, Dirk; Kovarova, Lucie; Lioznov, Michael; Mateo, Gema; Morilla, Ricardo; Mylin, Anne K.; Omede, Paola; Pellat-Deceunynck, Catherine; Andres, Martin Perez; Petrucci, Maria; Ruggeri, Marina; Rymkiewicz, Grzegorz; Schmitz, Alexander; Schreder, Martin; Seynaeve, Carine; Spacek, Martin; de Tute, Ruth M.; Van Valckenborgh, Els; Weston-Bell, Nicky; Owen, Roger G.; Miguel, Jesus F. San; Sonneveld, Pieter; Johnsen, Hans E.
The European Myeloma Network (EMN) organized two flow cytometry workshops. The first aimed to identify specific indications for flow cytometry in patients with monoclonal gammopathies, and consensus technical approaches through a questionnaire-based review of current practice in participating
Filippini, Graziella; Del Giovane, Cinzia; Vacchi, Laura; D'Amico, Roberto; Di Pietrantonj, Carlo; Beecher, Deirdre; Salanti, Georgia
Different therapeutic strategies are available for treatment of multiple sclerosis (MS) including immunosuppressants, immunomodulators, and monoclonal antibodies. Their relative effectiveness in the prevention of relapse or disability progression is unclear due to the limited number of direct comparison trials. A summary of the results, including both direct and indirect comparisons of treatment effects, may help to clarify the above uncertainty. To estimate the relative efficacy and acceptability of interferon ß-1b (IFNß-1b) (Betaseron), interferon ß-1a (IFNß-1a) (Rebif and Avonex), glatiramer acetate, natalizumab, mitoxantrone, methotrexate, cyclophosphamide, azathioprine, intravenous immunoglobulins, and long-term corticosteroids versus placebo or another active agent in participants with MS and to provide a ranking of the treatments according to their effectiveness and risk-benefit balance. We searched the Cochrane Database of Systematic Reviews, the Cochrane MS Group Trials Register, and the Food and Drug Administration (FDA) reports. The most recent search was run in February 2012. Randomized controlled trials (RCTs) that studied one of the 11 treatments for use in adults with MS and that reported our pre-speciﬁed efficacy outcomes were considered for inclusion. Identifying search results and data extraction were performed independently by two authors. Data synthesis was performed by pairwise meta-analysis and network meta-analysis that was performed within a Bayesian framework. The body of evidence for outcomes within the pairwise meta-analysis was assessed according to GRADE, as very low, low, moderate, or high quality. Forty-four trials were included in this review, in which 17,401 participants had been randomised. Twenty-three trials included relapsing-remitting MS (RRMS) (9096 participants, 52%), 18 trials included progressive MS (7726, 44%), and three trials included both RRMS and progressive MS (579, 3%). The majority of the included trials were
Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.
Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.
delivered in hardware. The implementation studied in  uses sensing bits to quantize a likelihood ratio ( LR ) for determining which nodes have enough...worthwhile information to disseminate through the network. Using LRs is thereby a means for reducing meaningless observation traffic within the network...itself. The architecture itself consists of eight sub-components: database interface, spectrum map parser , map distributor, clustering, map merger
Reconfigurable intensity modulation and direct detection optical transceivers for variable-rate wavelength-division-multiplexing passive optical networks utilizing digital signal processing-based symbol mapper
Zhang, Zhiguo; Zhang, Bingbing; Chen, Yanxu; Chen, Xue
Variable-rate intensity modulation and direct detection-based optical transceivers with software-controllable reconfigurability and transmission performance adaptability are experimentally demonstrated, utilizing M-QAM symbol mapping implemented in MATLAB® programs. A frequency division multiplexing-based symbol demapping and wavelength management method is proposed for the symbol demapper and tunable laser management used in colorless optical network unit.
Rolf, Linda; Damoiseaux, Jan; Hupperts, Raymond; Huitinga, Inge; Smolders, Joost
Sex-steroids, corticosteroids and vitamin D3-derived molecules have all been subject to experimental studies and clinical trials in a plethora of autoimmune diseases. These molecules are all derived from cholesterol metabolites and are ligands for nuclear receptors. Ligation of these receptors results in direct regulation of multiple gene transcription involved in general homeostatic and adaptation networks, including the immune system. Indeed, the distinct ligands affect the function of both myeloid and lymphoid cells, eventually resulting in a less pro-inflammatory immune response which is considered beneficial in autoimmune diseases. Next to the immune system, also the central nervous system is prone to regulation by these nuclear receptor ligands. Understanding of the intricate interactions between sex-steroids, corticosteroids and vitamin D3 metabolites, on the one hand, and the immune and central nervous system, on the other hand, may reveal novel approaches to utilize these nuclear receptor ligands to full extent as putative treatments in multiple sclerosis, the prototypic immune-driven disease of the central nervous system. Copyright © 2016 Elsevier B.V. All rights reserved.
Nunes, Clarisse; Miranda, Guilhermina Lobato; Amaral, Isabel
This study aimed to analyze how the Social Software tools could respond to the needs of parents and teachers of students with multiple disabilities in improving their practices, as well as provide information and resources related to the topic of multiple disabilities. The study was implemented in Portugal and involved 45 participants: 25 special…
Perumal, Madhumathy; Dhandapani, Sivakumar
Data gathering and optimal path selection for wireless sensor networks (WSN) using existing protocols result in collision. Increase in collision further increases the possibility of packet drop. Thus there is a necessity to eliminate collision during data aggregation. Increasing the efficiency is the need of the hour with maximum security. This paper is an effort to come up with a reliable and energy efficient WSN routing and secure protocol with minimum delay. This technique is named as relay node based secure routing protocol for multiple mobile sink (RSRPMS). This protocol finds the rendezvous point for optimal transmission of data using a "splitting tree" technique in tree-shaped network topology and then to determine all the subsequent positions of a sink the "Biased Random Walk" model is used. In case of an event, the sink gathers the data from all sources, when they are in the sensing range of rendezvous point. Otherwise relay node is selected from its neighbor to transfer packets from rendezvous point to sink. A symmetric key cryptography is used for secure transmission. The proposed relay node based secure routing protocol for multiple mobile sink (RSRPMS) is experimented and simulation results are compared with Intelligent Agent-Based Routing (IAR) protocol to prove that there is increase in the network lifetime compared with other routing protocols.
Full Text Available Data gathering and optimal path selection for wireless sensor networks (WSN using existing protocols result in collision. Increase in collision further increases the possibility of packet drop. Thus there is a necessity to eliminate collision during data aggregation. Increasing the efficiency is the need of the hour with maximum security. This paper is an effort to come up with a reliable and energy efficient WSN routing and secure protocol with minimum delay. This technique is named as relay node based secure routing protocol for multiple mobile sink (RSRPMS. This protocol finds the rendezvous point for optimal transmission of data using a “splitting tree” technique in tree-shaped network topology and then to determine all the subsequent positions of a sink the “Biased Random Walk” model is used. In case of an event, the sink gathers the data from all sources, when they are in the sensing range of rendezvous point. Otherwise relay node is selected from its neighbor to transfer packets from rendezvous point to sink. A symmetric key cryptography is used for secure transmission. The proposed relay node based secure routing protocol for multiple mobile sink (RSRPMS is experimented and simulation results are compared with Intelligent Agent-Based Routing (IAR protocol to prove that there is increase in the network lifetime compared with other routing protocols.