An efficient Hopfield network to solve economic dispatch problems with transmission system representation

Economic dispatch (ED) problems have recently been solved by artificial neural network approaches. Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. The ability of neural networks to realize some complex non-linear function makes them attractive for system optimization. All ED models solved by neural approaches described in the literature fail to represent the transmission system. Therefore, such procedures may calculate dispatch policies, which do not take into account important active power constraints. Another drawback pointed out in the literature is that some of the neural approaches fail to converge efficiently toward feasible equilibrium points. A modified Hopfield approach designed to solve ED problems with transmission system representation is presented in this paper. The transmission system is represented through linear load flow equations and constraints on active power flows. The internal parameters of such modified Hopfield networks are computed using the valid-subspace technique. These parameters guarantee the network convergence to feasible equilibrium points, which represent the solution for the ED problem. Simulation results and a sensitivity analysis involving IEEE 14-bus test system are presented to illustrate efficiency of the proposed approach. (C) 2004 Elsevier Ltd. All rights reserved.

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Complex network classification using partially self-avoiding deterministic walks

Complex networks have attracted increasing interest from various fields of science. It has been demonstrated that each complex network model presents specific topological structures which characterize its connectivity and dynamics. Complex network classification relies on the use of representative measurements that describe topological structures. Although there are a large number of measurements, most of them are correlated. To overcome this limitation, this paper presents a new measurement for complex network classification based on partially self-avoiding walks. We validate the measurement on a data set composed by 40000 complex networks of four well-known models. Our results indicate that the proposed measurement improves correct classification of networks compared to the traditional ones. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4737515]

Structure and Ionic Conductivity in the Mixed-Network Former Chalcogenide Glass System [Na2S](2/3)[(B2S3)(x)(P2S5)(1-x)](1/3)

Glasses in the system [Na2S](2/3)[(B2S3)(x)(P2S5)(1-x)](1/3) (0.0 <= x <= 1.0) were prepared by the melt quenching technique, and their properties were characterized by thermal analysis and impedance spectroscopy. Their atomic-level structures were comprehensively characterized by Raman spectroscopy and B-11, P-31, and Na-23 high resolution solid state magic-angle spinning (MAS) NMR techniques. P-31 MAS NMR peak assignments were made by the presence or absence of homonuclear indirect P-31-P-31 spin-spin interactions as detected using homonuclear J-resolved and refocused INADEQUATE techniques. The extent of B-S-P connectivity in the glassy network was quantified by P-31{B-11} and B-11{P-31} rotational echo double resonance spectroscopy. The results clearly illustrate that the network modifier alkali sulfide, Na2S, is not proportionally shared between the two network former components, B and P. Rather, the thiophosphate (P) component tends to attract a larger concentration of network modifier species than predicted by the bulk composition, and this results in the conversion of P2S74-, pyrothiophosphate, Na/P = 2:1, units into PS43-, orthothiophosphate, Na/P = 3:1, groups. Charge balance is maintained by increasing the net degree of polymerization of the thioborate (B) units through the formation of covalent bridging sulfur (BS) units, B S B. Detailed inspection of the B-11 MAS NMR spectra reveals that multiple thioborate units are formed, ranging from neutral BS3/2 groups all the way to the fully depolymerized orthothioborate (BS33-) species. On the basis of these results, a comprehensive and quantitative structural model is developed for these glasses, on the basis of which the compositional trends in the glass transition temperatures (T-g) and ionic conductivities can be rationalized. Up to x = 0.4, the dominant process can be described in a simplified way by the net reaction equation P-1 + B-1 reversible arrow P-0 + B-4, where the superscripts denote the number of BS atoms for the respective network former species. Above x = 0.4, all of the thiophosphate units are of the P-0 type and both pyro-(B-1) and orthothioborate (B-0) species make increasing contributions to the network structure with increasing x. In sharp contrast to the situation in sodium borophosphate glasses, four-coordinated thioborate species are generally less abundant and heteroatomic B-S-P linkages appear to not exist. On the basis of this structural information, compositional trends in the ionic conductivities are discussed in relation to the nature of the charge-compensating anionic species and the spatial distribution of the charge carriers.

Relationship between connectivity and academic productivity

A relevant factor in the growth of academic productivity in the second half of 20th century is the implementation of the internet, particularly in developing countries. One of the first networks in Brazil is the Academic Network at Sao Paulo (ANSP), a regional network implemented in the state of Sao Paulo, which contains the largest concentration of researchers in the country. This study presents a unique metric for analyzing the impact of ANSP in academic productivity in the state of Sao Paulo. We correlate academic production and available bandwidth using Fisher ideal price index with suitable variables to evaluate the impact of the internet on research centers and universities. We find that the members of ANSP show a steady growth in academic productivity compared with other institutions outside of the ANSP network. These results suggest that policies which increase available bandwidth can positively affect academic productivity.

Quantification of Short and Medium Range Order in Mixed Network Former Glasses of the System GeO2-NaPO3: A Combined NMR and X-ray Photoelectron Spectroscopy Study

Glasses in the system xGeO(2)-(1-x)NaPO3 (0 <= x <= 0.50) were prepared by conventional melting quenching and characterized by thermal analysis, Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and P-31 nuclear magnetic resonance (MAS NMR) techniques. The deconvolution of the latter spectra was aided by homonuclear J-resolved and refocused INADEQUATE techniques. The combined analyses of P-31 MAS NMR and O-1s XPS lineshapes, taking charge and mass balance considerations into account, yield the detailed quantitative speciations of the phosphorus, germanium, and oxygen atoms and their respective connectivities. An internally consistent description is possible without invoking the formation of higher-coordinated germanium species in these glasses, in agreement with experimental evidence in the literature. The structure can be regarded, to a first approximation, as a network consisting of P-(2) and P-(3) tetrahedra linked via four-coordinate germanium. As implied by the appearance of P-(3) units, there is a moderate extent of network modifier sharing between phosphate and germanate network formers, as expressed by the formal melt reaction P-(2) + Ge-(4) -> P-(3) + Ge-(3). The equilibrium constant of this reaction is estimated as K = 0.52 +/- 0.11, indicating a preferential attraction of network modifier by the phosphorus component. These conclusions are qualitatively supported by Raman spectroscopy as well as P-31{Na-23} and P-31{Na-23} rotational echo double resonance (REDOR) NMR results. The combined interpretation of O-1s XPS and P-31 MAS NMR spectra shows further that there are clear deviations from a random connectivity scenario: heteroatomic P-O-Ge linkages are favored over homoatomic P-O-P and Ge-O-Ge linkages.

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

Abstract Background Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes. Results Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones. Conclusion We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.

Synaptic connectivity in micropatterned networks of neuronal cells

The presented thesis describes the formation of functional neuronal networks on an underlying micropattern. Small circuits of interconnected neurons defined by the geometry of the patterned substrate could be observed and were utilised as a model system of reduced complexity for the behaviour of neuronal network formation and activity. The first set of experiments was conducted to investigate aspects of the substrate preparation. Micropatterned substrates were created by microcontact printing of physiological proteins onto polystyrene culture dishes. The substrates displayed a high contrast between the repellant background and the cell attracting pattern, such that neurons seeded onto these surfaces aligned with the stamped structure. Both the patterning process and the cell culture were optimised, yielding highly compliant low-density networks of living neuronal cells. In the second step, cellular physiology of the cells grown on these substrates was investigated by patch-clamp measurements and compared to cells cultivated under control conditions. It could be shown that the growth on a patterned substrate did not result in an impairment of cellular integrity nor that it had an impact on synapse formation or synaptic efficacy. Due to the extremely low-density cell culture that was applied, cellular connectivity through chemical synapses could be observed at the single cell level. Having established that single cells were not negatively affected by the growth on patterned substrates, aspects of network formation were investigated. The formation of physical contact between two cells was analysed through microinjection studies and related to the rate at which functional synaptic contacts formed between two neighbouring cells. Surprisingly, the rate of synapse formation between physically contacting cells was shown to be unaltered in spite of the drastic reduction of potential interaction partners on the micropattern. Additional features of network formation were investigated and found consistent with results reported by other groups: A different rate of synapse formation by excitatory and inhibitory neurons could be reproduced as well as a different rate of frequency-dependent depression at excitatory and inhibitory synapses. Furthermore, regarding simple feedback loops, a significant enrichment of reciprocal connectivity between mixed pairs of excitatory and inhibitory neurons relative to uniform pairs could be demonstrated. This phenomenon has also been described by others in unpatterned cultures [Muller, 1997] and may therefore be a feature underlying neuronal network formation in general. Based on these findings, it can be assumed that inherent features of neuronal behaviour and cellular recognition mechanisms were found in the cultured networks and appear to be undisturbed by patterned growth. At the same time, it was possible to reduce the complexity of the forming networks dramatically in a cell culture on a patterned surface. Thus, features of network architecture and synaptic connectivity could be investigated on the single cell level under highly defined conditions.

Genetic structure and connectivity between populations of two common Mediterranean sessile invertebrates

Population genetic and phylogeography of two common mediterranean species were studied in 10 localities located on the coasts of Toscana, Puglia and Calabria. The aim of the study was to verify the extent of genetic breaks, in areas recognized as boundaries between Mediterranean biogeographic sectors. From about 100 sequences obtained from the mitochondrial Cytochrome Oxidase subunit I (COI) gene of Halocynthia papillosa and Hexaplex trunculus genetic diversity, genetic structure at small and large distances and demographic history of both specieswere analyzed. No evidences of genetic breaks were found for the two species in Toscana and Puglia. The genetic structure of H. trunculus evidences the extent of a barrier to gene flow localized in Calabria, which could be represented by the Siculo-Tunisian Strait and the Strait of Messina. The observed patterns showed similar level of gene flow at small distances in both species, although the two species have different larval ecology. These results suggest that other factors, such as currents, local dynamics and seasonal temperatures, influence the connectivity along the Italian peninsula. The geographic distribution of the haplotypes shows that H. papillosacould represent a single genetic pool in expansion, whereas H. trunculus has two distinct genetic pools in expansion. The demographic pattern of the two species suggests that Pleistocene sea level oscillations, in particular of the LGM, may have played a key role in shaping genetic structure of the two species. This knowledge provides basic information, useful for the definition of management plans, or for the design of a network of marine protected areas along the Italian peninsula.

An fmri study of brain functional connectivity in parkinson's disease patients

Argomento del presente lavoro è l’analisi di dati fMRI (functional Magnetic Resonance Imaging) nell’ambito di uno studio EEG-fMRI su pazienti affetti da malattia di Parkinson idiopatica. L’EEG-fMRI combina due diverse tecniche per lo studio in vivo dell’attività cerebrale: l'elettroencefalografia (EEG) e la risonanza magnetica funzionale. La prima registra l’attività elettrica dei neuroni corticali con ottima risoluzione temporale; la seconda misura indirettamente l’attività neuronale registrando gli effetti metabolici ad essa correlati, con buona risoluzione spaziale. L’acquisizione simultanea e la combinazione dei due tipi di dati permettono di sfruttare i vantaggi di ciascuna tecnica. Scopo dello studio è l’indagine della connettività funzionale cerebrale in condizioni di riposo in pazienti con malattia di Parkinson idiopatica ad uno stadio precoce. In particolare, l’interesse è focalizzato sulle variazioni della connettività con aree motorie primarie e supplementari in seguito alla somministrazione della terapia dopaminergica. Le quattro fasi principali dell’analisi dei dati sono la correzione del rumore fisiologico, il pre-processing usuale dei dati fMRI, l’analisi di connettività “seed-based “ e la combinazione dei dati relativi ad ogni paziente in un’analisi statistica di gruppo. Usando ’elettrocardiogramma misurato contestualmente all’EEG ed una stima dell’attività respiratoria, è stata effettuata la correzione del rumore fisiologico, ottenendo risultati consistenti con la letteratura. L’analisi di connettività fMRI ha mostrato un aumento significativo della connettività dopo la somministrazione della terapia: in particolare, si è riscontrato che le aree cerebrali maggiormente connesse alle aree motorie sono quelle coinvolte nel network sensorimotorio, nel network attentivo e nel default mode network. Questi risultati suggeriscono che la terapia dopaminergica, oltre ad avere un effetto positivo sulle performance motorie durante l’esecuzione del movimento, inizia ad agire anche in condizioni di riposo, migliorando le funzioni attentive ed esecutive, componenti integranti della fase preparatoria del movimento. Nel prossimo futuro questi risultati verranno combinati con quelli ottenuti dall’analisi dei dati EEG.

Topological aspects of the human protein interaction network

It is currently widely accepted that the understanding of complex cell functions depends on an integrated network theoretical approach and not on an isolated view of the different molecular agents. Aim of this thesis was the examination of topological properties that mirror known biological aspects by depicting the human protein network with methods from graph- and network theory. The presented network is a partial human interactome of 9222 proteins and 36324 interactions, consisting of single interactions reliably extracted from peer-reviewed scientific publications. In general, one can focus on intra- or intermodular characteristics, where a functional module is defined as "a discrete entity whose function is separable from those of other modules". It is found that the presented human network is also scale-free and hierarchically organised, as shown for yeast networks before. The interactome also exhibits proteins with high betweenness and low connectivity which are biologically analyzed and interpreted here as shuttling proteins between organelles (e.g. ER to Golgi, internal ER protein translocation, peroxisomal import, nuclear pores import/export) for the first time. As an optimisation for finding proteins that connect modules, a new method is developed here based on proteins located between highly clustered regions, rather than regarding highly connected regions. As a proof of principle, the Mediator complex is found in first place, the prime example for a connector complex. Focusing on intramodular aspects, the measurement of k-clique communities discriminates overlapping modules very well. Twenty of the largest identified modules are analysed in detail and annotated to known biological structures (e.g. proteasome, the NFκB-, TGF-β complex). Additionally, two large and highly interconnected modules for signal transducer and transcription factor proteins are revealed, separated by known shuttling proteins. These proteins yield also the highest number of redundant shortcuts (by calculating the skeleton), exhibit the highest numbers of interactions and might constitute highly interconnected but spatially separated rich-clubs either for signal transduction or for transcription factors. This design principle allows manifold regulatory events for signal transduction and enables a high diversity of transcription events in the nucleus by a limited set of proteins. Altogether, biological aspects are mirrored by pure topological features, leading to a new view and to new methods that assist the annotation of proteins to biological functions, structures and subcellular localisations. As the human protein network is one of the most complex networks at all, these results will be fruitful for other fields of network theory and will help understanding complex network functions in general.

Multimodal (EEG-fMRI) functional connectivity study of levodopa effect in Parkinson’s disease

Aim: To assess if the intake of levodopa in patients with Parkinson’s Disease (PD) changes cerebral connectivity, as revealed by simultaneous recording of hemodynamic (functional MRI, or fMRI) and electric (electroencephalogram, EEG) signals. Particularly, we hypothesize that the strongest changes in FC will involve the motor network, which is the most impaired in PD. Methods: Eight patients with diagnosis of PD “probable”, therapy with levodopa exclusively, normal cognitive and affective status, were included. Exclusion criteria were: moderate-severe rest tremor, levodopa induced dyskinesia, evidence of gray or white matter abnormalities on structural MRI. Scalp EEG (64 channels) were acquired inside the scanner (1.5 Tesla) before and after the intake of levodopa. fMRI functional connectivity was computed from four regions of interest: right and left supplementary motor area (SMA) and right and left precentral gyrus (primary motor cortex). Weighted partial directed coherence (w-PDC) was computed in the inverse space after the removal of EEG gradient and cardioballistic artifacts. Results and discussion: fMRI group analysis shows that the intake of levodopa increases hemodynamic functional connectivity among the SMAs / primary motor cortex and: sensory-motor network itself, attention network and default mode network. w-PDC analysis shows that EEG connectivity among regions of the motor network has the tendency to decrease after the intake the levodopa; furthermore, regions belonging to the DMN have the tendency to increase their outflow toward the rest of the brain. These findings, even if in a small sample of patients, suggest that other resting state physiological functional networks, beyond the motor one, are affected in patients with PD. The behavioral and cognitive tasks corresponding to the affected networks could benefit from the intake of levodopa.

Connectivity management and dynamic context grouping in mobile heterogenous systems

In the last 10 years the number of mobile devices has grown rapidly. Each person usually brings at least two personal devices and researchers says that in a near future this number could raise up to ten devices per person. Moreover, all the devices are becoming more integrated to our life than in the past, therefore the amount of data exchanged increases accordingly to the improvement of people's lifestyle. This is what researchers call Internet of Things. Thus, in the future there will be more than 60 billions of nodes and the current infrastructure is not ready to keep track of all the exchanges of data between them. Therefore, infrastructure improvements have been proposed in the last years, like MobileIP and HIP in order to facilitate the exchange of packets in mobility, however none of them have been optimized for the purpose. In the last years, researchers from Mid Sweden University created The MediaSense Framework. Initially, this framework was based on the Chord protocol in order to route packets in a big network, but the most important change has been the introduction of PGrids in order to create the Overlay and the persistence. Thanks to this technology, a lookup in the trie takes up to 0.5*log(N), where N is the total number of nodes in the network. This result could be improved by further optimizations on the management of the nodes, for example by the dynamic creation of groups of nodes. Moreover, since the nodes move, an underlaying support for connectivity management is needed. SCTP has been selected as one of the most promising upcoming standards for simultaneous multiple connection's management.

Simulated switching of the resting state functional connectivity in mouse brain using a real mesoscale connectome

Capire come modellare l'attività del cervello a riposo, resting state, è il primo passo necessario per avvicinarsi a una reale comprensione della dinamica cerebrale. Sperimentalmente si osserva che, quando il cervello non è soggetto a stimoli esterni, particolari reti di regioni cerebrali presentano un'attività neuronale superiore alla media. Nonostante gli sforzi dei ricercatori, non è ancora chiara la relazione che sussiste tra le connessioni strutturali e le connessioni funzionali del sistema cerebrale a riposo, organizzate nella matrice di connettività funzionale. Recenti studi sperimentali mostrano la natura non stazionaria della connettività funzionale in disaccordo con i modelli in letteratura. Il modello implementato nella presente tesi per simulare l'evoluzione temporale del network permette di riprodurre il comportamento dinamico della connettività funzionale. Per la prima volta in questa tesi, secondo i lavori a noi noti, un modello di resting state è implementato nel cervello di un topo. Poco è noto, infatti, riguardo all'architettura funzionale su larga scala del cervello dei topi, nonostante il largo utilizzo di tale sistema nella modellizzazione dei disturbi neurologici. Le connessioni strutturali utilizzate per definire la topologia della rete neurale sono quelle ottenute dall'Allen Institute for Brain Science. Tale strumento fornisce una straordinaria opportunità per riprodurre simulazioni realistiche, poiché, come affermato nell'articolo che presenta tale lavoro, questo connettoma è il più esauriente disponibile, ad oggi, in ogni specie vertebrata. I parametri liberi del modello sono stati scelti in modo da inizializzare il sistema nel range dinamico ottimale per riprodurre il comportamento dinamico della connettività funzionale. Diverse considerazioni e misure sono state effettuate sul segnale BOLD simulato per meglio comprenderne la natura. L'accordo soddisfacente fra i centri funzionali calcolati nel network cerebrale simulato e quelli ottenuti tramite l'indagine sperimentale di Mechling et al., 2014 comprovano la bontà del modello e dei metodi utilizzati per analizzare il segnale simulato.

Hierarchical Approach for Survivable Network Design

A central design challenge facing network planners is how to select a cost-effective network configuration that can provide uninterrupted service despite edge failures. In this paper, we study the Survivable Network Design (SND) problem, a core model underlying the design of such resilient networks that incorporates complex cost and connectivity trade-offs. Given an undirected graph with specified edge costs and (integer) connectivity requirements between pairs of nodes, the SND problem seeks the minimum cost set of edges that interconnects each node pair with at least as many edge-disjoint paths as the connectivity requirement of the nodes. We develop a hierarchical approach for solving the problem that integrates ideas from decomposition, tabu search, randomization, and optimization. The approach decomposes the SND problem into two subproblems, Backbone design and Access design, and uses an iterative multi-stage method for solving the SND problem in a hierarchical fashion. Since both subproblems are NP-hard, we develop effective optimization-based tabu search strategies that balance intensification and diversification to identify near-optimal solutions. To initiate this method, we develop two heuristic procedures that can yield good starting points. We test the combined approach on large-scale SND instances, and empirically assess the quality of the solutions vis-à-vis optimal values or lower bounds. On average, our hierarchical solution approach generates solutions within 2.7% of optimality even for very large problems (that cannot be solved using exact methods), and our results demonstrate that the performance of the method is robust for a variety of problems with different size and connectivity characteristics.