Position-sensitive silicon strip detector characterization using particle beams

Silicon strip detectors are fast, cost-effective and have an excellent spatial resolution. They are widely used in many high-energy physics experiments. Modern high energy physics experiments impose harsh operation conditions on the detectors, e.g., of LHC experiments. The high radiation doses cause the detectors to eventually fail as a result of excessive radiation damage. This has led to a need to study radiation tolerance using various techniques. At the same time, a need to operate sensors approaching the end their lifetimes has arisen. The goal of this work is to demonstrate that novel detectors can survive the environment that is foreseen for future high-energy physics experiments. To reach this goal, measurement apparatuses are built. The devices are then used to measure the properties of irradiated detectors. The measurement data are analyzed, and conclusions are drawn. Three measurement apparatuses built as a part of this work are described: two telescopes measuring the tracks of the beam of a particle accelerator and one telescope measuring the tracks of cosmic particles. The telescopes comprise layers of reference detectors providing the reference track, slots for the devices under test, the supporting mechanics, electronics, software, and the trigger system. All three devices work. The differences between these devices are discussed. The reconstruction of the reference tracks and analysis of the device under test are presented. Traditionally, silicon detectors have produced a very clear response to the particles being measured. In the case of detectors nearing the end of their lifefimes, this is no longer true. A new method benefitting from the reference tracks to form clusters is presented. The method provides less biased results compared to the traditional analysis, especially when studying the response of heavily irradiated detectors. Means to avoid false results in demonstrating the particle-finding capabilities of a detector are also discussed. The devices and analysis methods are primarily used to study strip detectors made of Magnetic Czochralski silicon. The detectors studied were irradiated to various fluences prior to measurement. The results show that Magnetic Czochralski silicon has a good radiation tolerance and is suitable for future high-energy physics experiments.

Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Tilasto Suomen eläkkeensaajista kunnittain 2009

Tilasto Suomen eläkkeensaajista kunnittain perustuu Eläketurvakeskuksen ja Kansaneläkelaitoksen rekistereihin. Julkaisua on tuotettu vuodesta 1993 lähtien. Tilaston sisältöä on vuosien varrella jonkin verran uudistettu. Tarkempaa tietoa sisältöön kohdistuneista muutoksista on laatuselosteen kohdassa 4 Tilastojen yhtenäisyys ja vertailukelpoisuus sivulla 93. Julkaisu sisältää kuntakohtaista tietoa kaikista työ- ja kansaneläkkeensaajista sekä eläkemenosta. Tietoja on eläkkeensaajien lukumääristä, eläkkeiden suuruuksista sekä kokonaiseläkemenosta. Luokittelijoina ovat mm. eläkelaji, eläkkeensaajan ikä, eläkkeen suuruus ja eläkejärjestelmä. Koko maata koskevaa tietoa löytyy laajemmin julkaisusta Tilasto Suomen eläkkeensaajista, jota on julkaistu vuodesta 1981 lähtien.

Tilasto Suomen eläkkeensaajista 2009

Tilasto Suomen eläkkeensaajista perustuu Eläketurvakeskuksen ja Kansaneläkelaitoksen rekistereihin. Julkaisua on tuotettu vuodesta 1981 lähtien. Vuosina 1981–1990 kirja julkaistiin erikseen suomen- ja ruotsinkielisenä. Ruotsinkielinen osa sisälsi suppean englanninkielisen liitteen. Vuodesta 1991 lähtien julkaisu on ollut kolmikielinen. Julkaisu sisältää tietoja kaikista työ- ja kansaneläkkeensaajista, eläkkeelle siirtyneistä ja eläkemenosta. Tietoja on kaikista eläkkeensaajista sekä erikseen Suomessa ja ulkomailla asuvista eläkkeensaajista, poikkeuksena eläkkeelle siirtyneet, joista ei ainakaan toistaiseksi tehdä erillistä tilastoa ulkomailla asuvista. Tilasto on ilmestynyt osana Suomen virallista tilastoa vuodesta 2003 lähtien. Laatuseloste on julkaisun liitteessä 1. Julkaisun alussa on kuvaus eläkejärjestelmästä ja eläke-etuuksista sekä tilastossa käytetyistä käsitteistä. Liitteessä 2 on suppea yhteenveto keskeisistä eläkelainsäädännön muutoksista.

Uterine cervix nitric oxide and human papillomavirus infection in women

Most women acquire genital high risk human papillomavirus (HPV) infection during their lifetime, but seldom the infection persists and leads to cervical cancer. However, currently it is not possible to identify the women who will develop HPV mediated cervical cancer and this often results to large scale follow-up and overtreatment of the likely spontaneously regressing infection. Thus, it is important to obtain more information on the course of HPV and find markers that could help to identify HPV infected women in risk for progression of cervical lesions and ultimately cancer. Nitric oxide is a free radical gas that takes part both in immune responses and carcinogenesis. Nitric oxide is produced also by cervical cells and therefore, it is possible that cervical nitric oxide could affect also HPV infection. In the present study, including 801 women from the University of Helsinki between years of 2006 and 2011, association between HPV and cervical nitric oxide was evaluated. The levels of nitric oxide were measured as its metabolites nitrate and nitirite (NOx) by spectrophotometry and the expression of nitric oxide producing enzymes endothelial and inducible synthases (eNOS, iNOS) by Western blotting. Women infected with HPV had two-times higher cervical fluid NOx levels compared with non-infected ones. The expression levels of both eNOS and iNOS were higher in HPV-infected women compared with non-infected. Another sexually transmitted disease Chlamydia trachomatis that is an independent risk factor for cervical cancer was also accompanied with elevated NOx levels, whereas vaginal infections, bacterial vaginosis and candida, did not have any effect on NOx levels. The meaning of the elevated HPV related cervical nitric oxide was evaluated in a 12 months follow-up study. It was revealed that high baseline cervical fluid NOx levels favored HPV persistence with OR 4.1. However, low sensitivity (33%) and high false negative rate (67%) restrict the clinical use of the current NOx test. This study indicated that nitric oxide favors HPV persistence and thus it seems to be one of the cofactor associated with a risk of carcinogenesis.

Ultimate Strength of RC Wall Panels with Openings

Test results of 12 reinforced concrete (RC) wall panels with openings are presented. The panels have been subjected to in-plane vertical loads applied at an eccentricity to represent possible accidental eccentricity that occurs in practice due to constructional imperfections. The 12 specimens consist of two identical groups of six panels each. One group of panels is tested in one-way in-plane action (i.e., supported at top and bottom edges against lateral displacement). The second group of panels is tested in two-way in-plane action (i.e., supported on all the four edges against lateral displacement). Openings in the panels represent typical door and window openings. Cracking loads, ultimate loads, crack patterns, and lateral deflections of the panels are studied. Empirical methods have been developed for the prediction of ultimate load. Also, lateral deflections, cracking loads, and ultimate loads of identical loads tested under one-way and two-way action are compared.

V-CE Sensing for IGBT Protection in NPC Three Level Converters-Causes For Spurious Trippings and Their Elimination

Neutral point clamped (NPC), three level converters with insulated gate bipolar transistor devices are very popular in medium voltage, high power applications. DC bus short circuit protection is usually done, using the sensed voltage across collector and emitter (i.e., V-CE sensing), of all the devices in a leg. This feature is accommodated with the conventional gate drive circuits used in the two level converters. The similar gate drive circuit, when adopted for NPC three level converter protection, leads to false V-CE fault signals for inner devices of the leg. The paper explains the detailed circuit behavior and reasons, which result in the occurrence of such false V-CE fault signals. This paper also illustrates that such a phenomenon shows dependence on the power factor of the supplied three-phase load. Finally, experimental results are presented to support the analysis. It is shown that the problem can be avoided by blocking out the V-CE sense fault signals of the inner devices of the leg.

Phage Displayed Short Peptides against Cells of Candida albicans Demonstrate Presence of Species, Morphology and Region Specific Carbohydrate Epitopes

Candida albicans is a commensal opportunistic pathogen, which can cause superficial infections as well as systemic infections in immuocompromised hosts. Among nosocomial fungal infections, infections by C. albicans are associated with highest mortality rates even though incidence of infections by other related species is on the rise world over. Since C. albicans and other Candida species differ in their susceptibility to antifungal drug treatment, it is crucial to accurately identify the species for effective drug treatment. Most diagnostic tests that differentiate between C. albicans and other Candida species are time consuming, as they necessarily involve laboratory culturing. Others, which employ highly sensitive PCR based technologies often, yield false positives which is equally dangerous since that leads to unnecessary antifungal treatment. This is the first report of phage display technology based identification of short peptide sequences that can distinguish C. albicans from other closely related species. The peptides also show high degree of specificity towards its different morphological forms. Using fluorescence microscopy, we show that the peptides bind on the surface of these cells and obtained clones that could even specifically bind to only specific regions of cells indicating restricted distribution of the epitopes. What was peculiar and interesting was that the epitopes were carbohydrate in nature. This gives insight into the complexity of the carbohydrate composition of fungal cell walls. In an ELISA format these peptides allow specific detection of relatively small numbers of C. albicans cells. Hence, if used in combination, such a test could help accurate diagnosis and allow physicians to initiate appropriate drug therapy on time.

Bayesian Decentralized Spectrum Sensing in Cognitive Radio Networks

This paper considers the problem of spectrum sensing, i.e., the detection of whether or not a primary user is transmitting data by a cognitive radio. The Bayesian framework is adopted, with the performance measure being the probability of detection error. A decentralized setup, where N sensors use M observations each to arrive at individual decisions that are combined at a fusion center to form the overall decision is considered. The unknown fading channel between the primary sensor and the cognitive radios makes the individual decision rule computationally complex, hence, a generalized likelihood ratio test (GLRT)-based approach is adopted. Analysis of the probabilities of false alarm and miss detection of the proposed method reveals that the error exponent with respect to M is zero. Also, the fusion of N individual decisions offers a diversity advantage, similar to diversity reception in communication systems, and a tight bound on the error exponent is presented. Through an analysis in the low power regime, the number of observations needed as a function of received power, to achieve a given probability of error is determined. Monte-Carlo simulations confirm the accuracy of the analysis.

Knowledge-based clustering

In the knowledge-based clustering approaches reported in the literature, explicit know ledge, typically in the form of a set of concepts, is used in computing similarity or conceptual cohesiveness between objects and in grouping them. We propose a knowledge-based clustering approach in which the domain knowledge is also used in the pattern representation phase of clustering. We argue that such a knowledge-based pattern representation scheme reduces the complexity of similarity computation and grouping phases. We present a knowledge-based clustering algorithm for grouping hooks in a library.

Link failure detection for maintaining session continuity in packet data networks

A link failure in the path of a virtual circuit in a packet data network will lead to premature disconnection of the circuit by the end-points. A soft failure will result in degraded throughput over the virtual circuit. If these failures can be detected quickly and reliably, then appropriate rerouteing strategies can automatically reroute the virtual circuits that use the failed facility. In this paper, we develop a methodology for analysing and designing failure detection schemes for digital facilities. Based on errored second data, we develop a Markov model for the error and failure behaviour of a T1 trunk. The performance of a detection scheme is characterized by its false alarm probability and the detection delay. Using the Markov model, we analyse the performance of detection schemes that use physical layer or link layer information. The schemes basically rely upon detecting the occurrence of severely errored seconds (SESs). A failure is declared when a counter, that is driven by the occurrence of SESs, reaches a certain threshold.For hard failures, the design problem reduces to a proper choice;of the threshold at which failure is declared, and on the connection reattempt parameters of the virtual circuit end-point session recovery procedures. For soft failures, the performance of a detection scheme depends, in addition, on how long and how frequent the error bursts are in a given failure mode. We also propose and analyse a novel Level 2 detection scheme that relies only upon anomalies observable at Level 2, i.e. CRC failures and idle-fill flag errors. Our results suggest that Level 2 schemes that perform as well as Level 1 schemes are possible.

Multivariate nonlinear ensemble prediction of daily chaotic rainfall with climate inputs

The basic characteristic of a chaotic system is its sensitivity to the infinitesimal changes in its initial conditions. A limit to predictability in chaotic system arises mainly due to this sensitivity and also due to the ineffectiveness of the model to reveal the underlying dynamics of the system. In the present study, an attempt is made to quantify these uncertainties involved and thereby improve the predictability by adopting a multivariate nonlinear ensemble prediction. Daily rainfall data of Malaprabha basin, India for the period 1955-2000 is used for the study. It is found to exhibit a low dimensional chaotic nature with the dimension varying from 5 to 7. A multivariate phase space is generated, considering a climate data set of 16 variables. The chaotic nature of each of these variables is confirmed using false nearest neighbor method. The redundancy, if any, of this atmospheric data set is further removed by employing principal component analysis (PCA) method and thereby reducing it to eight principal components (PCs). This multivariate series (rainfall along with eight PCs) is found to exhibit a low dimensional chaotic nature with dimension 10. Nonlinear prediction employing local approximation method is done using univariate series (rainfall alone) and multivariate series for different combinations of embedding dimensions and delay times. The uncertainty in initial conditions is thus addressed by reconstructing the phase space using different combinations of parameters. The ensembles generated from multivariate predictions are found to be better than those from univariate predictions. The uncertainty in predictions is decreased or in other words predictability is increased by adopting multivariate nonlinear ensemble prediction. The restriction on predictability of a chaotic series can thus be altered by quantifying the uncertainty in the initial conditions and also by including other possible variables, which may influence the system. (C) 2011 Elsevier B.V. All rights reserved.

Data rectification and detection of trend shifts in jet engine path measurements using median filters and fuzzy logic

Filtering methods are explored for removing noise from data while preserving sharp edges that many indicate a trend shift in gas turbine measurements. Linear filters are found to be have problems with removing noise while preserving features in the signal. The nonlinear hybrid median filter is found to accurately reproduce the root signal from noisy data. Simulated faulty data and fault-free gas path measurement data are passed through median filters and health residuals for the data set are created. The health residual is a scalar norm of the gas path measurement deltas and is used to partition the faulty engine from the healthy engine using fuzzy sets. The fuzzy detection system is developed and tested with noisy data and with filtered data. It is found from tests with simulated fault-free and faulty data that fuzzy trend shift detection based on filtered data is very accurate with no false alarms and negligible missed alarms.

DNA Free Energy-Based Promoter Prediction and Comparative Analysis of Arabidopsis and Rice Genomes

The cis-regulatory regions on DNA serve as binding sites for proteins such as transcription factors and RNA polymerase. The combinatorial interaction of these proteins plays a crucial role in transcription initiation, which is an important point of control in the regulation of gene expression. We present here an analysis of the performance of an in silico method for predicting cis-regulatory regions in the plant genomes of Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) on the basis of free energy of DNA melting. For protein-coding genes, we achieve recall and precision of 96% and 42% for Arabidopsis and 97% and 31% for rice, respectively. For noncoding RNA genes, the program gives recall and precision of 94% and 75% for Arabidopsis and 95% and 90% for rice, respectively. Moreover, 96% of the false-positive predictions were located in noncoding regions of primary transcripts, out of which 20% were found in the first intron alone, indicating possible regulatory roles. The predictions for orthologous genes from the two genomes showed a good correlation with respect to prediction scores and promoter organization. Comparison of our results with an existing program for promoter prediction in plant genomes indicates that our method shows improved prediction capability.