Analysis of low load transients for the Vandellòs-II NPP: Application to operation and control support

The Technical University of Catalonia (UPC) has been jointly working with the Asociación Nuclear Ascó-Vandellòs (ANAV) for a number of years in order to establish, qualify and use best estimate (BE) models for the reactors under the control of ANAV. ANAV is the consortium that is responsible for operation of the Ascó and Vandellòs-II reactors. The reactors are Westinghouse-design three-loop PWRs with an approximate electrical power of 1000 MW. The existing integral plant models for each plant are currently used for many different purposes among which are support of plant operation and control. Quite a number of studies have been done in order to improve both safety and plant competitiveness. Most of these dynamic analyses were carried out in relation to transients starting at nominal full power or at least, very close to full power. This paper develops a specific use of the Vandellòs-II plant model for operation and control support at low power involving new ranges of system actuation parameters. It also examines scenarios that are somewhat different from those typically analysed. The study starts showing the results of an assessment case, which is a start-up test and provides some additional qualification, and subsequently attempts to establish calculations to support both an improvement in feed water controls and to set up operating recommendations for low-load manual operation of feed water turbo-pump. Both results hopefully, will produce an outcome, which leads to an improvement in safety and reduces reactor trip probability.     

Perceived Visual Complexity and Visual Search Performance of Automotive Instrument Cluster: A Quantitative Measurement Study

Advancements in technology have spurred the development of new in-vehicle applications. Drivers are faced with different driving contexts due to an increase in the number of devices that provide a wealth of diverse information. However, such a scenario can cause drivers to become distracted. Therefore, research on how the presentation of visual information can affect drivers’ performance is important. In this study, an analysis of quantifiable measurements that affect drivers’ perception of visual complexity and visual search performance was conducted. A questionnaire was administered to assess subjective perception of visual complexity, and a user experiment using eye tracking was designed to explore participants’ visual search performance. The results of subjective visual complexity perception and visual search performance suggested that some objective measurement variables were significantly related only to perceived visual complexity, whereas others affected both subjective and behavioral measurements. Thus it is possible to predict which quantifiable measurement variables affect subjective perception of visual complexity and which affect visual search performance. Therefore, this study allows understanding and explaining of perception of visual complexity by quantifiable measurements and the different ways by which these measurements affect visual search performance.     

Robust network monitoring in the presence of non-cooperative traffic queries

We present the design of a predictive load shedding scheme for a network monitoring platform that supports multiple and competing traffic queries. The proposed scheme can anticipate overload situations and minimize their impact on the accuracy of the traffic queries. The main novelty of our approach is that it considers queries as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Our system only requires a high-level specification of the accuracy requirements of each query to guide the load shedding procedure and assures a fair allocation of computing resources to queries in a non-cooperative environment. We present an implementation of our load shedding scheme in an existing network monitoring system and evaluate it with a diverse set of traffic queries. Our results show that, with the load shedding mechanism in place, the monitoring system can preserve the accuracy of the queries within predefined error bounds even during extreme overload conditions.     

Weight distribution and dual distance

Several results in coding theory (e.g. the Carlitz-Uchiyama bound) show that the weight distributions of certain algebraic codes of lengthn are concentrated aroundn/2 within a range of width n. It is proved in this article that the extreme weights of a linear binary code of sufficiently high dual distance cannot be too close ton/2, the gap being of order n. The tools used involve the Pless identities and the orthogonality properties of Krawtchouk polynomials, as well as estimates on their zeroes. As a by-product upper bounds on the minimum distance of self-dual binary codes are derived.     

EEG signal preprocessing for biometric recognition

Electroencephalography (EEG) has been recently investigated as a biometric modality for automatic people recognition purposes. Several studies have shown that brain signals possess subject-specific traits that allow distinguishing people. Nonetheless, extracting discriminative characteristics from EEG recordings may be a challenging task, due to the significant presence of artifacts in the acquired data. In order to cope with such issue, in this paper we evaluate the effectiveness of some preprocessing techniques in automatically removing undesired EEG contributions, with the aim of improving the achievable recognition rates. Specifically, methods based on blind source separation and sample entropy estimation are here investigated. An extensive set of experimental tests, performed over a large database comprising recordings taken from 50 healthy subjects during three distinct sessions spanning a period of about one month, in both eyes-closed and eyes-open conditions, is carried out to analyze the performance of the proposed approaches.     

Tool integration for flexible simulation of distributed algorithms

Over the last two decades, considerable research has been done in distributed operating systems, which can be attributed to faster processors and better communication technologies. A distributed operating system requires distributed algorithms to provide basic operating system functionality like mutual exclusion, deadlock detection, etc. A number of such algorithms have been proposed in the literature. Traditionally, these distributed algorithms have been presented in a theoretical way, with limited attempts to simulate actual working models. This paper discusses our experience in simulating distributed algorithms with the aid of some existing tools, including OPNET and Xplot. We discuss our efforts to define a basic model‐based framework for rapid simulation and visualization, and illustrate how we used this framework to evaluate some classic algorithms. We have also shown how the performance of different algorithms can be compared based on some collected statistics. To keep the focus of this paper on the approach itself, and our experience with tool integration, we only discuss some relatively simple models. Yet, the approach can be applied to more complex algorithm specifications. Copyright © 2001 John Wiley & Sons, Ltd.     

diaPASEF Proteomics and Feature Selection for the Description of Sputum Proteome Profiles in a Cohort of Different Subtypes of Lung Cancer Patients and Controls

The high mortality, the presence of an initial asymptomatic stage and the fact that diagnosis in early stages reduces mortality justify the implementation of screening programs in the populations at risk of lung cancer. It is imperative to develop less aggressive methods that can complement existing diagnosis technologies. In this study, we aimed to identify lung cancer protein biomarkers and pathways affected in sputum samples, using the recently developed diaPASEF mass spectrometry (MS) acquisition mode. The sputum proteome of lung cancer cases and controls was analyzed through nano-HPLC–MS using the diaPASEF mode. For functional analysis, the results from differential expression analysis were further analyzed in the STRING platform, and feature selection was performed using sparse partial least squares discriminant analysis (sPLS-DA). Our results showed an activation of inflammation, with an alteration of pathways and processes related to acute-phase, complement, and immune responses. The resulting sPLS-DA model separated between case and control groups with high levels of sensitivity and specificity. In conclusion, we showed how new-generation proteomics can be used to detect potential biomarkers in sputum samples, and ultimately to discriminate patients from controls and even to help to differentiate between different cancer subtypes.     

Multisecret-sharing schemes and bounded distance decoding of linear codes

Error-correcting codes are used to correct errors when messages are transmitted through a noisy communication channel. Bounded distance decoding (i.e. decoding up to the error-correcting capacity) is a method of correcting errors that guarantees unique decoding. Secret sharing is a cryptographic protocol that allows a secret known from a person called the dealer to be distributed to n participants. No single participant knows the secret but some special subsets of participants called coalitions can. In this paper, we present a multisecret-sharing scheme based on an error-correcting code where secret reconstruction is made by using bounded distance decoding of the code. This scheme is ideal in the sense that the size of each secret equals the size of any share. Its security improves on that of multisecret-sharing schemes.