Network signatures based on gene pair expression ratios improve classification and the analysis of muscle-invasive urothelial cancer

Urothelial cancer (UC) is highly recurrent and can progress from non-invasive (NMIUC) to a more aggressive muscle-invasive (MIUC) subtype that invades the muscle tissue layer of the bladder. We present a proof of principle study that network-based features of gene pairs can be used to improve classifier performance and the functional analysis of urothelial cancer gene expression data. In the first step of our procedure each individual sample of a UC gene expression dataset is inflated by gene pair expression ratios that are defined based on a given network structure. In the second step an elastic net feature selection procedure for network-based signatures is applied to discriminate between NMIUC and MIUC samples. We performed a repeated random subsampling cross validation in three independent datasets. The network signatures were characterized by a functional enrichment analysis and studied for the enrichment of known cancer genes. We observed that the network-based gene signatures from meta collections of proteinprotein interaction (PPI) databases such as CPDB and the PPI databases HPRD and BioGrid improved the classification performance compared to single gene based signatures. The network based signatures that were derived from PPI databases showed a prominent enrichment of cancer genes (e.g., TP53, TRIM27 and HNRNPA2Bl). We provide a novel integrative approach for large-scale gene expression analysis for the identification and development of novel diagnostical targets in bladder cancer. Further, our method allowed to link cancer gene associations to network-based expression signatures that are not observed in gene-based expression signatures.

The river solofrana bicycle network: a project of green mobility

The article presents the “LungoSolofrana” project, carried out during the course “Urban and Mobility” in the academic year 2009/2010, held during the bachelor in Environmental Engineering at the University of Naples “Federico II”. The work has also been chosen as a finalist at the “UrbanPromo 2010” contest, the urban and territorial marketing event sponsored by the National Institute of Urban Planning and Urbit which was held in Venice in 2010. The project consists in a green mobility proposal, developed with an approach based on the integration of the environmental redevelopment of a portion of river Solofrana, located in the Salerno Province, and of the renewal of seven local stations of the railway line Mercato San Severino – Nocera Inferiore, including the realization of a cycle-path network for the natural environment fruition. Furthermore the work drew attention to the local and regional administration. The main intent of the project is to integrate sustainable mobility themes with the environment recovery in a territory affected by high environmental troubles. The area includes the municipalities of Nocera Inferiore, Nocera Superiore, Mercato San Severino, Castel San Giorgio and Roccapiemonte, situated in Salerno’s province, with a total population about 114.000 (font Demo ISTAT 2010). The area extension is about 84,30 sqkm and it is crossed by river Solofrana that is the central point of the project idea. The intervention strategy is defined in two kinds of actions: internal and external rail station interventions. The external rail station interventions regard the construction of pedestrian-cycle paths with the scope of increasing the spaces dedicated to cyclists and to pedestrians along the river Solofrana sides and to connect the urban areas with the railway station. In this way, it’s also possible to achieve an urban requalification of the interested area. On the other side, the interventions inside the station , according to Transit Oriented Development principles, aim at redeveloping common spaces with the insertion of new activities and at realizing new automatic cycle parks covered by photovoltaic panels. The project proposal consists of the urban regeneration of small railway stations along the route-Nocera-Codola Mercato San Severino in the province of Salerno, through interventions aimed at improving pedestrian accessibility. The project involves in particular the construction of pedestrian paths protected access to the station and connecting with neighboring towns and installation of innovative bike parking stations in elevation, covering surfaces coated with solar panels and spaces information. The project is aimed to propose a new model of sustainable transport for small and medium shifts as an alternative to private transportation

An heuristic approach for optimal location of gas supply units in transportation system

The best places to locate the Gas Supply Units (GSUs) on a natural gas systems and their optimal allocation to loads are the key factors to organize an efficient upstream gas infrastructure. The number of GSUs and their optimal location in a gas network is a decision problem that can be formulated as a linear programming problem. Our emphasis is on the formulation and use of a suitable location model, reflecting real-world operations and constraints of a natural gas system. This paper presents a heuristic model, based on lagrangean approach, developed for finding the optimal GSUs location on a natural gas network, minimizing expenses and maximizing throughput and security of supply.The location model is applied to the Iberian high pressure natural gas network, a system modelised with 65 demand nodes. These nodes are linked by physical and virtual pipelines – road trucks with gas in liquefied form. The location model result shows the best places to locate, with the optimal demand allocation and the most economical gas transport mode: by pipeline or by road truck.

Energy resource scheduling in a real distribution network managed by several virtual power players

Smart Grids (SGs) appeared as the new paradigm for power system management and operation, being designed to integrate large amounts of distributed energy resources. This new paradigm requires a more efficient Energy Resource Management (ERM) and, simultaneously, makes this a more complex problem, due to the intensive use of distributed energy resources (DER), such as distributed generation, active consumers with demand response contracts, and storage units. This paper presents a methodology to address the energy resource scheduling, considering an intensive use of distributed generation and demand response contracts. A case study of a 30 kV real distribution network, including a substation with 6 feeders and 937 buses, is used to demonstrate the effectiveness of the proposed methodology. This network is managed by six virtual power players (VPP) with capability to manage the DER and the distribution network.

Distribution network short term scheduling in Smart Grid context

The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and “green” operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a Mixed Integer Non-Linear Programming (MINLP) approach.

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Object transportation by multiple mobile robots controlled by attractor dynamics: theory and implementation

Dynamical systems theory is used as a theoretical language and tool to design a distributed control architecture for teams of mobile robots, that must transport a large object and simultaneously avoid collisions with (either static or dynamic) obstacles. Here we demonstrate in simulations and implementations in real robots that it is possible to simplify the architectures presented in previous work and to extend the approach to teams of n robots. The robots have no prior knowledge of the environment. The motion of each robot is controlled by a time series of asymptotical stable states. The attractor dynamics permits the integration of information from various sources in a graded manner. As a result, the robots show a strikingly smooth an stable team behaviour.

Personalized public transportation information

Thesis to obtain the Master of Science Degree in Computer Science and Engineering

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the whole system. The work presented in this paper comprises a methodology able to define the cost allocation in distribution networks considering large integration of DG and DR resources. The proposed methodology is divided into three phases and it is based on an AC Optimal Power Flow (OPF) including the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity.

A Transportation Safety Planning Tool for the City of Ames, August 2011

The historically-reactive approach to identifying safety problems and mitigating them involves selecting black spots or hot spots by ranking locations based on crash frequency and severity. The approach focuses mainly on the corridor level without taking the exposure rate (vehicle miles traveled) and socio-demographics information of the study area, which are very important in the transportation planning process, into consideration. A larger study analysis unit at the Transportation Analysis Zone (TAZ) level or the network planning level should be used to address the needs of development of the community in the future and incorporate safety into the long-range transportation planning process. In this study, existing planning tools (such as the PLANSAFE models presented in NCHRP Report 546) were evaluated for forecasting safety in small and medium-sized communities, particularly as related to changes in socio-demographics characteristics, traffic demand, road network, and countermeasures. The research also evaluated the applicability of the Empirical Bayes (EB) method to network-level analysis. In addition, application of the United States Road Assessment Program (usRAP) protocols at the local urban road network level was investigated. This research evaluated the applicability of these three methods for the City of Ames, Iowa. The outcome of this research is a systematic process and framework for considering road safety issues explicitly in the small and medium-sized community transportation planning process and for quantifying the safety impacts of new developments and policy programs. More specifically, quantitative safety may be incorporated into the planning process, through effective visualization and increased awareness of safety issues (usRAP), the identification of high-risk locations with potential for improvement, (usRAP maps and EB), countermeasures for high-risk locations (EB before and after study and PLANSAFE), and socio-economic and demographic induced changes at the planning-level (PLANSAFE).

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

A game-theoretic network formation model

We study the dynamics of a game-theoretic network formation model that yields large-scale small-world networks. So far, mostly stochastic frameworks have been utilized to explain the emergence of these networks. On the other hand, it is natural to seek for game-theoretic network formation models in which links are formed due to strategic behaviors of individuals, rather than based on probabilities. Inspired by Even-Dar and Kearns (2007), we consider a more realistic model in which the cost of establishing each link is dynamically determined during the course of the game. Moreover, players are allowed to put transfer payments on the formation of links. Also, they must pay a maintenance cost to sustain their direct links during the game. We show that there is a small diameter of at most 4 in the general set of equilibrium networks in our model. Unlike earlier model, not only the existence of equilibrium networks is guaranteed in our model, but also these networks coincide with the outcomes of pairwise Nash equilibrium in network formation. Furthermore, we provide a network formation simulation that generates small-world networks. We also analyze the impact of locating players in a hierarchical structure by constructing a strategic model, where a complete b-ary tree is the seed network.

Untangling the Organizational Ambiguities in Supply Chain Risk Recognition, Assessment, and Response: The Role of Network Embeddedness

In this thesis, I focus on supply chain risk related ambiguity, which represents the ambiguities firms exhibit in recognizing, assessing, and responding to supply chain disruptions. I, primarily, argue that ambiguities associated with recognizing and responding to supply chain risk are information gathering and processing problems. Guided by the theoretical perspective of bounded rationality, I propose a typology of supply chain risk related ambiguity with four distinct dimensions. I, also, argue that the major contributor to risk related ambiguity is often the environment, specifically the web of suppliers. Hence, I focus on the characteristics of these supplier networks to examine the sources of ambiguity. I define three distinct elements of network embeddedness – relational, structural, and positional embeddedness – and argue that the ambiguity faced by a firm in appropriately identifying the nature or impacts of major disruptions is a function of these network properties. Based on a survey of large North American manufacturing firms, I found that the extent of the relational ties a firm has and its position in the network are significantly related to supply chain risk related ambiguity. However, this study did not provide any significant support for the hypothesized relationship between structural embeddedness and ambiguity. My research contributes towards the study of supply chain disruptions by using the idea of bounded rationality to understand supply chain risk related ambiguity and by providing evidence that the structure of supply chain networks influences the organizational understanding of and responses to supply chain disruptions.

Potentiel d’une approche populationnelle orientée vers la reconfiguration des environnements urbains pour améliorer la sécurité des piétons

INTRODUCTION : En milieu urbain, l’amélioration de la sécurité des piétons pose un défi de santé publique. Pour chaque décès attribuable aux collisions routières, il y a des centaines de personnes blessées et, dans les pays riches, la diminution du nombre annuel de piétons décédés s’expliquerait en partie par la diminution de la marche. Les stratégies préventives prédominantes n’interviennent pas sur le volume de circulation automobile, un facteur pourtant fondamental. De plus, les interventions environnementales pour améliorer la sécurité des infrastructures routières se limitent habituellement aux sites comptant le plus grand nombre de décès ou de blessés. Cette thèse vise à décrire la contribution des volumes de circulation automobile, des pratiques locales de marche et de la géométrie des routes au nombre et à la répartition des piétons blessés en milieu urbain, et d’ainsi établir le potentiel d’une approche populationnelle orientée vers la reconfiguration des environnements urbains pour améliorer la sécurité des piétons. MÉTHODE : Le devis est de type descriptif et transversal. Les principales sources de données sont les registres des services ambulanciers d’Urgences-santé (blessés de la route), l’enquête Origine-Destination (volumes de circulation automobile), la Géobase du réseau routier montréalais (géométrie des routes) et le recensement canadien (pratiques locales de marche, position socioéconomique). Les analyses descriptives comprennent la localisation cartographique (coordonnées x,y) de l’ensemble des sites de collision. Des modèles de régression multi-niveaux nichent les intersections dans les secteurs de recensement et dans les arrondissements. RÉSULTATS : Les analyses descriptives démontrent une grande dispersion des sites de collision au sein des quartiers. Les analyses multivariées démontrent les effets significatifs, indépendants du volume de circulation automobile, de la présence d’artère(s) et d’une quatrième branche aux intersections, ainsi que du volume de marche dans le secteur, sur le nombre de piétons blessés aux intersections. L’analyse multi-niveaux démontre une grande variation spatiale de l’effet du volume de circulation automobile. Les facteurs environnementaux expliquent une part substantielle de la variation spatiale du nombre de blessés et du gradient socioéconomique observé. DISCUSSION : La grande dispersion des sites de collision confirme la pertinence d’une approche ne se limitant pas aux sites comptant le plus grand nombre de blessés. Les résultats suggèrent que des stratégies préventives basées sur des approches environnementales et populationnelle pourraient considérablement réduire le nombre de piétons blessés ainsi que les inégalités observées entre les quartiers.