Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

A complex networks approach for data clustering

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection algorithms. The network-based clustering approach is applied in two real-world databases and two sets of artificially generated data. The obtained results suggest that the exponential of the Minkowski distance is the most suitable metric to quantify the similarities between pairs of objects. In addition, the community identification method based on the greedy optimization provides the best cluster solution. We compare the network-based clustering approach with some traditional clustering algorithms and verify that it provides the lowest classification error rate. (C) 2012 Elsevier B.V. All rights reserved.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Root niche separation can explain avoidance of seasonal drought stress and vulnerability of overstory trees to extended drought in a mature Amazonian forest

Large areas of Amazonian evergreen forest experience seasonal droughts extending for three or more months, yet show maximum rates of photosynthesis and evapotranspiration during dry intervals. This apparent resilience is belied by disproportionate mortality of the large trees in manipulations that reduce wet season rainfall, occurring after 2-3 years of treatment. The goal of this study is to characterize the mechanisms that produce these contrasting ecosystem responses. A mechanistic model is developed based on the ecohydrological framework of TIN (Triangulated Irregular Network)-based Real Time Integrated Basin Simulator + Vegetation Generator for Interactive Evolution (tRIBS+VEGGIE). The model is used to test the roles of deep roots and soil capillary flux to provide water to the forest during the dry season. Also examined is the importance of "root niche separation," in which roots of overstory trees extend to depth, where during the dry season they use water stored from wet season precipitation, while roots of understory trees are concentrated in shallow layers that access dry season precipitation directly. Observational data from the Tapajo's National Forest, Brazil, were used as meteorological forcing and provided comprehensive observational constraints on the model. Results strongly suggest that deep roots with root niche separation adaptations explain both the observed resilience during seasonal drought and the vulnerability of canopy-dominant trees to extended deficits of wet season rainfall. These mechanisms appear to provide an adaptive strategy that enhances productivity of the largest trees in the face of their disproportionate heat loads and water demand in the dry season. A sensitivity analysis exploring how wet season rainfall affects the stability of the rainforest system is presented. Citation: Ivanov, V. Y., L. R. Hutyra, S. C. Wofsy, J. W. Munger, S. R. Saleska, R. C. de Oliveira Jr., and P. B. de Camargo (2012), Root niche separation can explain avoidance of seasonal drought stress and vulnerability of overstory trees to extended drought in a mature Amazonian forest, Water Resour. Res., 48, W12507, doi:10.1029/2012WR011972.

Reti di partecipazione fra società di capitale in Italia: presenza di topologie caratterizzate da distribuzioni power law

Quando la probabilità di misurare un particolare valore di una certa quantità varia inversamente come potenza di tale valore, il quantitativo è detto come seguente una power-law, conosciuta anche come legge di Zipf o distribuzione di Pareto. Obiettivo di questa tesi sarà principalmente quello di verificare se il campione esteso di imprese segue la power-law (e se sì, in che limiti). A tale fine si configureranno i dati in un formato di rete monomodale, della quale si studieranno alcune macro-proprietà di struttura a livllo complessivo e con riferimento alle componenti (i singoli subnet distinti) di maggior dimensione. Successivamente si compiranno alcuni approfondimenti sulla struttura fine di alcuni subnet, essenzialmente rivolti ad evidenziare la potenza di unapproccio network-based, anche al fine di rivelare rilevanti proprietà nascoste del sistema economico soggiacente, sempre, ovviamente, nei limiti della modellizzazione adottata. In sintesi, ciò che questo lavoro intende ottenere è lo sviluppo di un approccio alternativo al trattamento dei big data a componente relazionale intrinseca (in questo caso le partecipazioni di capitale), verso la loro conversione in "big knowledge": da un insieme di dati cognitivamente inaccessibili, attraverso la strutturazione dell'informazione in modalità di rete, giungere ad una conoscenza sufficientemente chiara e giustificata.

La cooperazione giudiziaria in rete nello spazio di liberta', sicurezza e giustizia

L’oggetto del lavoro si concentra sull’analisi in chiave giuridica del modello di cooperazione in rete tra le autorità nazionali degli Stati membri nel quadro dello Spazio LSG, allo scopo di valutarne il contributo, le prospettive e il potenziale. La trattazione si suddivide in due parti, precedute da una breve premessa teorica incentrata sull’analisi della nozione di rete e la sua valenza giuridica. La prima parte ricostruisce il percorso di maturazione della cooperazione in rete, dando risalto tanto ai fattori di ordine congiunturale quanto ai fattori giuridici e d’ordine strutturale che sono alla base del processo di retificazione dei settori giustizia e sicurezza. In particolare, vengono elaborati taluni rilievi critici, concernenti l’operatività degli strumenti giuridici che attuano il principio di mutuo riconoscimento e di quelli che danno applicazione al principio di disponibilità delle informazioni. Ciò allo scopo di evidenziare gli ostacoli che, di frequente, impediscono il buon esito delle procedure di cooperazione e di comprendere le potenzialità e le criticità derivanti dall’utilizzo della rete rispetto alla concreta applicazione di tali procedure. La seconda parte si focalizza sull’analisi delle principali reti attive in materia di giustizia e sicurezza, con particolare attenzione ai rispettivi meccanismi di funzionamento. La trattazione si suddivide in due distinte sezioni che si concentrano sulle a) reti che operano a supporto dell’applicazione delle procedure di assistenza giudiziaria e degli strumenti di mutuo riconoscimento e sulle b) reti che operano nel settore della cooperazione informativa e agevolano lo scambio di informazioni operative e tecniche nelle azioni di prevenzione e lotta alla criminalità - specialmente nel settore della protezione dell’economia lecita. La trattazione si conclude con la ricostruzione delle caratteristiche di un modello di rete europea e del ruolo che questo esercita rispetto all’esercizio delle competenze dell’Unione Europea in materia di giustizia e sicurezza.

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

Effects Of Perceived Financial Accessibility Of Colleges And Universities On Studentsâ¿ Assesment Of Institutional Desirability

This study focused on the effects of socioeconomic exclusivity indicators on college students¿ attitudes toward a hypothetical private liberal arts university. Students enrolled in two undergraduate courses in Education at an elite private liberal arts university in the northeast were randomly presented with one of three versions of an admissions brochure describing a fictitious university. The three versions of the brochure varied in their portrayals of the institution¿s financial exclusivity, ranging from high exclusivity to low exclusivity. Each student was asked to review the brochure and respond to a questionnaire, containing items pertaining to the overall desirability of the institution, as well as its student culture, academic program, campus traditions, and alumni network. Based on Thorstein Veblen¿s theory of the leisure class and Pierre Bourdieu¿s theory of social reproduction, it was hypothesized that students would judge the institution most favorably in all areas under the high exclusivity condition and least favorably under the low exclusivity condition. It was further hypothesized that differences in students¿ ratings of institutional desirability would be mediated by their own financial aid statuses. Results of a two-way multivariate analysis of variance (MANOVA) revealed significant (p < .05) interactive effects of institutional exclusivity and student aid status on the perceived desirability of the academic program and campus traditions of the institution. While recipients of need-based financial aid tended to rate more socioeconomically exclusive institutions more favorably on these two variables, those who were not receiving need-based financial aid tended to rate such institutions less favorably. Implications of the findings for student affairs practice are discussed and recommendations for further research are presented.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients

Background Access to health care can be described along four dimensions: geographic accessibility, availability, financial accessibility and acceptability. Geographic accessibility measures how physically accessible resources are for the population, while availability reflects what resources are available and in what amount. Combining these two types of measure into a single index provides a measure of geographic (or spatial) coverage, which is an important measure for assessing the degree of accessibility of a health care network. Results This paper describes the latest version of AccessMod, an extension to the Geographical Information System ArcView 3.×, and provides an example of application of this tool. AccessMod 3 allows one to compute geographic coverage to health care using terrain information and population distribution. Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available. In addition to integrating travelling time, population distribution and the population coverage capacity specific to each health facility in the network, AccessMod can incorporate the influence of landscape components (e.g. topography, river and road networks, vegetation) that impact travelling time to and from facilities. Topographical constraints can be taken into account through an anisotropic analysis that considers the direction of movement. We provide an example of the application of AccessMod in the southern part of Malawi that shows the influences of the landscape constraints and of the modes of transportation on geographic coverage. Conclusion By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities. This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

The central assumption in the literature on collaborative networks and policy networks is that political outcomes are affected by a variety of state and nonstate actors. Some of these actors are more powerful than others and can therefore have a considerable effect on decision making. In this article, we seek to provide a structural and institutional explanation for these power differentials in policy networks and support the explanation with empirical evidence. We use a dyadic measure of influence reputation as a proxy for power, and posit that influence reputation over the political outcome is related to vertical integration into the political system by means of formal decision-making authority, and to horizontal integration by means of being well embedded into the policy network. Hence, we argue that actors are perceived as influential because of two complementary factors: (a) their institutional roles and (b) their structural positions in the policy network. Based on temporal and cross-sectional exponential random graph models, we compare five cases about climate, telecommunications, flood prevention, and toxic chemicals politics in Switzerland and Germany. The five networks cover national and local networks at different stages of the policy cycle. The results confirm that institutional and structural drivers seem to have a crucial impact on how an actor is perceived in decision making and implementation and, therefore, their ability to significantly shape outputs and service delivery.

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

The HIV care cascade in Switzerland: reaching the UNAIDS/WHO targets for patients diagnosed with HIV.

OBJECTIVES To describe the HIV care cascade for Switzerland in the year 2012. DESIGN/METHODS Six levels were defined: (i) HIV-infected, (ii) HIV-diagnosed, (iii) linked to care, (iv) retained in care, (v) on antiretroviral treatment (ART), and (vi) with suppressed viral load (VL). We used data from the Swiss HIV Cohort Study (SHCS) complemented by a nationwide survey among SHCS physicians to estimate the number of HIV-patients not registered in the cohort. We also used Swiss ART sales data to estimate the number of patients treated outside the SHCS network. Based on the number of patients retained in care, we inferred the estimates for levels (i) to (iii) from previously published data. RESULTS We estimate that (i) 15,200 HIV-infected individuals lived in Switzerland in 2012 (margins of uncertainty, 13,400-19,300). Of those, (ii) 12,300 (81%) were diagnosed, (iii) 12,200 (80%) linked, and (iv) 11,900 (79%) retained in care. Broadly based on SHCS network data, (v) 10,800 (71%) patients were receiving ART, and (vi) 10,400 (68%) had suppressed (<200 copies/ml) VLs. The vast majority (95%) of patients retained in care were followed within the SHCS network, with 76% registered in the cohort. CONCLUSIONS Our estimate for HIV-infected individuals in Switzerland is substantially lower than previously reported, halving previous national HIV prevalence estimates to 0.2%. In Switzerland in 2012, 91% of patients in care were receiving ART, and 96% of patients on ART had suppressed VL, meeting recent UNAIDS/WHO targets.

Real-time localization using software defined radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.