bluuebunny/crossref_metadata_2025 huggingface dataset:
Using a break prediction model for drinking water networks asset management: From research to practice
Authors: Renaud E. , Le Gat Y. , Poulton M.
Published: August 2012

Break prediction models can help water utility decision-makers to build pipe rehabilitation programs. For many years, using them has been a specialist matter. After more than 15 years of research into the ageing of water pipes, Irstea (formerly Cemagref) has developed the Linear Extension of the Yule Process (LEYP) model based on counting process theory, which relies not only on a pipe's characteristics and environment but also on its age and previous breaks. It was then decided to develop a break prediction tool usable by water utilities: the ‘Casses’ freeware. To make this possible, it was necessary to deal with several constraints. To cope with the diversity of available data for various water utilities, flexible input data formats were designed as well as an importation module which checks the conformity and coherence of data. Tools for data management and an advice module dedicated to model calibration were conceived for non-statistician users. The break prediction results can be used directly to compare break evolution with different rehabilitation strategies and they can also feed multicriteria decision tools. In this case, the ‘Casses’ freeware can work as a ‘slave’ of the integrated application.
Advances in Development of Inhibitors of 17β-Hydroxysteroid Dehydrogenases
Authors: Poirier Donald
Published: August 2012

The 17β-hydroxysteroid dehydrogenases (17β-HSDs) are involved in the regulation of estrogens and androgens by catalyzing the reduction of 17-ketosteroids or the oxidation of 17β-hydroxysteroids. The enzyme activities associated with the different 17β-HSD isoforms are widespread in human tissues, not only in classic steroidogenic tissues but also in a large series of peripheral intracrine tissues. Being involved at the end of steroidogenesis, the numerous members of 17β-HSD family constitute interesting therapeutic targets for controlling the concentration of estrogens and androgens. Thus, inhibitors of reductive 17β-HSD isoforms are attractive to block the formation of hydroxysteroids that stimulate estrogeno-sensitive pathologies (breast, ovarian, and endometrium cancers) and androgenosensitive pathologies (prostate cancer, benign prostatic hyperplasia, acne, and hirsutism). The inhibitors could be used to block the degradation of estradiol, an attractive strategy for treating osteoporosis and Alzheimers disease. In addition to their classical use as anti-cancer agents and therapeutic agents, inhibitors of 17β-HSDs are also useful tools to elucidate the role of these enzymes in particular biological systems. The present review article gives a description of novel inhibitors of 17β-HSDs that were published in 2003-2006.
Formation of levees and en-echelon shear planes during snow avalanche run-out
Authors: Bartelt Perry , Glover James , Feistl Thomas , Bühler Yves , Buser Othmar
Published: August 2012

Snow avalanches often form levees and en-echelon shear planes in the run-out zone. We describe the formation of these depositional structures using a simple model that accounts for the role of granular fluctuations in avalanche motion. A mathematical feature of this model is the existence of a bifurcation saddle point, describing how granular fluctuations control the avalanche velocity in the runout zone. The saddle point discriminates between a flowing and stopping regime and defines the physical boundary between the flow and non-flow regions of the avalanche, i.e. the location of shear planes in the avalanche deposits. The formation of a shear plane depends on the interplay between terrain slope and avalanche mass flux, which varies from avalanche head to tail. Levees can form immediately at the avalanche front or, for steep slopes and low mass fluxes, at the avalanche tail. At ravine and gully shoulders the mass flux is restricted, thus initiating levee formation. We find that the levee lines are parallel to the flow direction when the mass flux is constant; en-echelon shear lines occur when the mass flux is decreasing. We test the model using several case studies where we have accurate laser scans of avalanche deposits. Our results suggest that avalanche flow parameters can be determined from simple levee measurements or, conversely, formation of levees and flow fingers can be predicted once the parameters governing the granular fluctuations are known.
Well-posed boundary conditions for limited-domain models of transient ice flow near an ice divide
Authors: Koutnik Michelle R. , Waddington Edwin D.
Published: August 2012

When an ice-flow model is constrained by data that exist over only a section of an ice sheet, it is computationally advantageous to limit the model domain to only that section. For example, a limited domain near an ice-core site might cross an ice divide, and have no termini. Accurately calculating ice-sheet evolution in response to spatial and temporal variations in climate and ice flow depends on accurately calculating the transient ice flux crossing the limited-domain boundaries. In the absence of information from outside the limited domain, this is an ill-posed problem. Boundary conditions based only on information from inside the limited domain can produce ice-sheet evolution incompatible with the full ice sheet within which we suppose it to exist. We use impulse-response functions to provide boundary values that are informed by the external ice sheet, without conventionally 'nesting' the limited domain in a full ice-sheet model. Evolution within a limited domain can then be consistent with evolution of boundary conditions is designed for future use in affected the limited domain can be inferred from the full ice sheet. Our treatment of limited-domain an inverse problem in which external changes that data from within the limited domain.
Analyzing the microRNA Transcriptome in Plants Using Deep Sequencing Data
Authors: Yang Xiaozeng , Li Lei
Published: August 2012

MicroRNAs (miRNAs) are 20- to 24-nucleotide endogenous small RNA molecules emerging as an important class of sequence-specific, trans-acting regulators for modulating gene expression at the post-transcription level. There has been a surge of interest in the past decade in identifying miRNAs and profiling their expression pattern using various experimental approaches. In particular, ultra-deep sampling of specifically prepared low-molecular-weight RNA libraries based on next-generation sequencing technologies has been used successfully in diverse species. The challenge now is to effectively deconvolute the complex sequencing data to provide comprehensive and reliable information on the miRNAs, miRNA precursors, and expression profile of miRNA genes. Here we review the recently developed computational tools and their applications in profiling the miRNA transcriptomes, with an emphasis on the model plant Arabidopsis thaliana. Highlighted is also progress and insight into miRNA biology derived from analyzing available deep sequencing data.
Neuroprotective Activity of Lavender Oil on Transient Focal Cerebral Ischemia in Mice
Authors: Wang Dong , Yuan Xuan , Liu Ting , Liu Liangliang , Hu Yanli , Wang Zhenhua , Zheng Qiusheng
Published: August 2012

The air-dried aerial parts of Lavandula angustifolia Mill, a traditional Uygur herbal drug, is used as resuscitation-inducing therapy to treat neurodisfunctions, such as stroke. This study was designed to assess the neuroprotective effects of lavender oil against ischemia/reperfusion (IR) injury in mice. Focal cerebral ischemia was induced by the intraluminal occlusion method with a nylon string. The neurodysfuntion was evaluated by neurological deficit and the infarct area was showed by 2,3,5-triphenyltetrazolium chloride (TTC) staining. The histopathological changes were observed by hematoxylin and eosin staining. The levels of mitochondria-generated reactive oxygen species (ROS), malondialdehyde (MDA) and carbonyl, the ratio of reduced glutathione (GSH)/glutathione disulfide (GSSG), the activities of superoxide dismutase (SOD), catalase (CAT) and glutathion peroxidase (GSH-Px) in brain tissue were measured to estimate the oxidative stress state. Neurological deficit, infarct size, histopathology changes and oxidative stress markers were evaluated after 22 h of reperfusion. In comparison with the model group, treatment with lavender oil significantly decreased neurological deficit scores, infarct size, the levels of MDA, carbonyl and ROS, and attenuated neuronal damage, upregulated SOD, CAT, GSH-Px activities and GSH/GSSG ratio. These results suggested that the neuroprotective effects of lavender oil against cerebral ischemia/reperfusion injury may be attributed to its antioxidant effects.
Microfluidic Approaches to Bacterial Biofilm Formation
Authors: Kim Junghyun , Park Hee-Deung , Chung Seok
Published: August 2012

Bacterial biofilms—aggregations of bacterial cells and extracellular polymeric substrates (EPS)—are an important subject of research in the fields of biology and medical science. Under aquatic conditions, bacterial cells form biofilms as a mechanism for improving survival and dispersion. In this review, we discuss bacterial biofilm development as a structurally and dynamically complex biological system and propose microfluidic approaches for the study of bacterial biofilms. Biofilms develop through a series of steps as bacteria interact with their environment. Gene expression and environmental conditions, including surface properties, hydrodynamic conditions, quorum sensing signals, and the characteristics of the medium, can have positive or negative influences on bacterial biofilm formation. The influences of each factor and the combined effects of multiple factors may be addressed using microfluidic approaches, which provide a promising means for controlling the hydrodynamic conditions, establishing stable chemical gradients, performing measurement in a high-throughput manner, providing real-time monitoring, and providing in vivo-like in vitro culture devices. An increased understanding of biofilms derived from microfluidic approaches may be relevant to improving our understanding of the contributions of determinants to bacterial biofilm development.
Exploring chemical diversity via a modular reaction pairing strategy
Authors: Loh Joanna K , Yoon Sun Young , Samarakoon Thiwanka B , Rolfe Alan , Porubsky Patrick , Neuenswander Benjamin , Lushington Gerald H , Hanson Paul R
Published: August 2012

The efficient synthesis of an 80-member library of unique benzoxathiazocine 1,1-dioxides by a microwave-assisted, intermolecular nucleophilic aromatic substitution (SNAr) diversification pathway is reported. Eight benzofused sultam cores were generated by means of a sulfonylation/SNAr/Mitsunobu reaction pairing protocol, and subsequently diversified by intermolecular SNAr with ten chiral, non-racemic amine/amino alcohol building blocks. Computational analyses were employed to explore and evaluate the chemical diversity of the library.
The Role of Raltegravir in the Treatment of HIV-2 Infections: Evidence from a Case Series
Authors: Peterson Kevin , Ruelle Jean , Vekemans Marc , Siegal Frederick P , Deayton Jane R , Colebunders Robert
Published: August 2012

We describe five patients with HIV-2 infection (four antiretroviral-experienced and one antiretroviral-naive) treated with a regimen containing raltegravir. All responded to treatment as demonstrated by viral load and CD4+ T-cell count monitoring. Our series confirms the clinical effectiveness of raltegravir in HIV-2-infected patients when given with other antiretrovirals to which the virus is susceptible.
Switching to Tenofovir/Emtricitabine from Abacavir/ Lamivudine in HIV-Infected Adults with Raised Cholesterol: Effect on Lipid Profiles
Authors: Behrens Georg , Maserati Renato , Rieger Armin , Domingo Pere , Abel Florian , Wang Hui , Pearce Gill
Published: August 2012

Background

The aim of this study was to investigate the effect on fasting lipid parameters of switching to tenofovir disoproxil fumarate (TDF) plus emtricitabine (FTC) from abacavir (ABC) plus lamivudine (3TC; both fixed-dose combinations), while maintaining ritonavir-boosted lopinavir (LPV/r).

Methods

This was an open-label randomized two-arm 12-week controlled study in virologically suppressed HIV-infected patients with elevated cholesterol (≥5.2 mmol/l). Patients stable on ABC/3TC plus LPV/r either continued treatment or switched to TDF/FTC plus LPV/r for 12 weeks. Standard efficacy and safety end points (including fasting lipids) were assessed.

Results

In total, 85 subjects were treated ( n=42 ABC/FTC and n=43 TDF/3TC). A statistically significant decrease in total cholesterol was observed in the TDF/FTC group: from median (IQR) 6.22 mmol/l (5.91–6.77) at baseline to 5.75 mmol/l (5.04–6.18) at week 12 (median [IQR] change from baseline -0.73 mmol/l [-1.20— 0.18]; P<0.001). No notable change was observed for the ABC/3TC group. The difference between groups at week 12 was -0.82 mmol/l ( P<0.001). For TDF/FTC (but not for ABC/3TC), statistically significant reductions ( P<0.05) from baseline were observed in total, low-density lipoprotein, high-density lipoprotein (HDL)-and non-HDL cholesterol (at weeks 4 and 12). Statistically significant decreases were observed in median estimated creatinine clearance (Cockcroft–Gault) from baseline to week 12 for patients who switched to TDF/FTC (-5.47 ml/ min) versus the ABC/3TC group (-2.15 ml/min; P=0.016 between groups). Virological suppression was maintained in both groups. No new safety issues were identified.

Conclusions

Switching to TDF/FTC from ABC/3TC was associated with rapid improvements in fasting lipid parameters and continued virological control in patients receiving LPV/r as the third component of antiretroviral therapy. The effect of these changes on clinical end points remains unclear and would need to be evaluated in a longer-term study.
Integrated Model of Inductive-Deductive Inference Based on Finite Predicates and Implicative Regularities
Authors: Zakrevskij Arkadij
Published: August 2012

The theory of Boolean functions, especially in respect to representing these functions in the disjunctive or conjunctive normal forms, is extended in this chapter onto the case of finite predicates. Finite predicates are decomposed by that into some binary units, which will correspond to components of Boolean vectors and matrices and are represented as combinations of these units. Further, the main concepts used for solving pattern recognition problems are defined, namely world model, data, and knowledge. The data presenting information about the existence of some objects with definite combinations of properties is considered, as well as the knowledge presenting information about the existence of regular relationships between attributes. These relationships prohibit some combinations of properties. In this way, the knowledge gives the information about the non-existence of objects with some definite (prohibited) combinations of attribute values. A special form of regularity representation, called implicative regularities, is introduced. Any implicative regularity generates an empty interval in the Boolean space of object descriptions, which do not contradict the data. The problem of plausibility evaluation of induced implicative regularities should be solved by that. The pattern recognition problem is solved by two steps. First, regularities are extracted from the database (inductive inference); second, the obtained knowledge is used for the object recognition (deductive inference).
An Analytical Survey of Current Approaches to Mining Logical Rules from Data
Authors: Naidenova Xenia
Published: August 2012

An analytical survey of some efficient current approaches to mining all kind of logical rules is presented including implicative and functional dependencies, association and classification rules. The interconnection between these approaches is analyzed. It is demonstrated that all the approaches are equivalent with respect to using the same key concepts of frequent itemsets (maximally redundant or closed itemset, generator, non-redundant or minimal generator, classification test) and the same procedures of their lattice structure construction. The main current tendencies in developing these approaches are considered.
Measuring Human Intelligence by Applying Soft Computing Techniques
Authors: Mankad Kunjal , Sajja Priti Srinivas
Published: August 2012

The chapter focuses on Genetic-Fuzzy Rule Based Systems of soft computing in order to deal with uncertainty and imprecision with evolving nature for different domains. It has been observed that major professional domains such as education and technology, human resources, psychology, etc, still lack intelligent decision support system with self evolving nature. The chapter proposes a novel framework implementing Theory of Multiple Intelligence of education to identify students’ technical and managerial skills. Detail methodology of proposed system architecture which includes the design of rule bases for technical and managerial skills, encoding strategy, fitness function, cross-over and mutation operations for evolving populations is presented in this chapter. The outcome and the supporting experimental results are also presented to justify the significance of the proposed framework. It concludes by discussing advantages and future scope in different domains.
Bimodal Cross-Validation Approach for Recommender Systems Diagnostics
Authors: Ignatov Dmitry I. , Poelmans Jonas
Published: August 2012

Recommender systems are becoming an inseparable part of many modern Internet web sites and web shops. The quality of recommendations made may significantly influence the browsing experience of the user and revenues made by web site owners. Developers can choose between a variety of recommender algorithms; unfortunately no general scheme exists for evaluation of their recall and precision. In this chapter, the authors propose a method based on cross-validation for diagnosing the strengths and weaknesses of recommender algorithms. The method not only splits initial data into a training and test subsets, but also splits the attribute set into a hidden and visible part. Experiments were performed on a user-based and item-based recommender algorithm. These algorithms were applied to the MovieLens dataset, and the authors found classical user-based methods perform better in terms of recall and precision.
Application of Machine Training Methods to Design of New Inorganic Compounds
Authors: Kiselyova Nadezhda , Stolyarenko Andrey , Ryazanov Vladimir , Sen’ko Oleg , Dokukin Alexandr
Published: August 2012

The review of applications of machine training methods to inorganic chemistry and materials science is presented. The possibility of searching for classification regularities in large arrays of chemical information with the use precedent-based recognition methods is discussed. The system for computer-assisted design of inorganic compounds, with an integrated complex of databases for the properties of inorganic substances and materials, a subsystem for the analysis of data, based on computer training (including symbolic pattern recognition methods), a knowledge base, a predictions base, and a managing subsystem, has been developed. In many instances, the employment of the developed system makes it possible to predict new inorganic compounds and estimate various properties of those without experimental synthesis. The results of application of this information-analytical system to the computer-assisted design of inorganic compounds promising for the search for new materials for electronics are presented.
Security Risks/Vulnerability in a RFID System and Possible Defenses
Authors: Chowdhury Morshed U. , Ray Biplob R.
Published: August 2012

Remote technologies are changing our way of life. The radio frequency identification (RFID) system is a new technology which uses the open air to transmit information. This information transmission needs to be protected to provide user safety and privacy. Business will look for a system that has fraud resilience to prevent the misuse of information to take dishonest advantage. The business and the user need to be assured that the transmitted information has no content which is capable of undertaking malicious activities. Public awareness of RFID security will help users and organizations to understand the need for security protection. Publishing a security guideline from the regulating body and monitoring implementation of that guideline in RFID systems will ensure that businesses and users are protected. This chapter explains the importance of security in a RFID system and will outline the protective measures. It also points out the research direction of RFID systems.
Security and Privacy in RFID Systems
Authors: Kamruzzaman Joarder , Azad A. K. M. , Karmakar Nemai Chandra , Karmakar Gour , Srinivasan Bala
Published: August 2012

Security and privacy protection are very critical requirements for the widespread deployment of RFID technologies for commercial applications. In this chapter, the authors first present the security and privacy requirement of any commercial system, and then highlight the security and privacy threats that are unique to an RFID system. The security and privacy preserving protocols for RFID system proposed in literature are elaborately discussed, analyzing their strengths, vulnerabilities, and implementation issues. The open research challenges that need further investigation, especially with the rapid introduction of diverse RFID applications, are also presented.
Managing Tag Collision in RFID Data Streams using Smart Tag Anti-Collision Techniques
Authors: Pupunwiwat Prapassara , Stantic Bela
Published: August 2012

Radio Frequency Identification (RFID) is considered an emerging technology for advancing a wide range of applications, such as supply chain management and distribution. However, despite the extensive development of the RFID technology in many areas, the RFID tags collision problems remain a serious issue. Collision problems occur due to the simultaneous presence of multiple numbers of tags within the reader zone. To solve collision problems, different anti-collision methods have been mentioned in literature. These methods are either insufficient or too complex, with a high overhead cost of implementation. In this chapter, the authors propose a novel deterministic anti-collision algorithm using combinations of Q-ary trees with the intended goal to minimise memory usage queried by the RFID reader. By reducing the size of queries, the RFID reader can preserve memories, and the identification time can be improved. In addition, the chapter introduces a novel probabilistic group-based anti-collision method to improve the overall performance of the tag recognition process.
5.8 GHz Portable Wireless Monitoring System for Sleep Apnea Diagnosis in Wireless Body Sensor Network (WBSN) Using Active RFID and MIMO Technology
Authors: Yang Yang , Rahim Abdur , Karmakar Nemai Chandra
Published: August 2012

Sleep apnea is a severe, potentially life-threatening condition that requires immediate medical attention. In this chapter, a novel wireless sleep apnea monitoring system is proposed to avoid uncomfortable sleep in an unfamiliar sleep laboratory in traditional PSG-based wired monitoring systems. In wireless sleep apnea monitoring system, signal propagation paths may be affected by fading because of reflection, diffraction, energy absorption, shadowing by the body, body movement, and the surrounding environment. To combat the fading effect in WBSN, the MIMO technology is introduced in this chapter. In addition, the presented active RFID based system is composed of two main parts. The first is an on-body sensor system; the second is a reader and base station. In order to minimize the physical size of the on-body sensors and to avoid interference with 2.4 GHz wireless applications, the system is designed to operate in the 5.8 GHz ISM band. Each on-body sensor system consists of a physiological signal detection circuit, an analogue-to-digital convertor (ADC), a microcontroller (MCU), a transceiver, a channel selection bandpass filter (BPF), and a narrow band antenna.
Recent Advancements in Smart Sensors and Sensing Technology
Authors: Mukhopadhyay Subhas C.
Published: August 2012

The chapter presents the design and development of very low cost planar sensors and sensing systems for measuring fat contents in meat, leather quality assessment, food quality, and biomedical application such as cancer detection, agriculture, and RFID based detection systems. The sensors comprise planar passive microwave integrated circuits in the forms of microstrip meander lines, mesh and inter-digital capacitance. The sensors are excited with voltage controlled oscillators (VCOs) and power supply units. A data acquisition system based on a microcontroller and an op-amp based interfacing circuits complete the sensing system. The results of various characteristics parameters of samples are presented and compared with the results from expensive conventional measurement set up. These low cost sensors bring benefits in the sensing technology with novel and accurate concepts.
Adaptive ESB Infrastructure for Service Based Systems
Authors: González Laura , Ruggia Raúl
Published: August 2012

Service-based systems increasingly need adaptation capabilities to agilely respond to unexpected changes in their business or execution environment. Although service orientation constitutes a promising approach to achieve this goal, current methods and technologies do not fully support automatic and dynamic adaptation at runtime. In turn, the Enterprise Service Bus (ESB), one of the preferred middleware technologies to support the development of service-based systems, provides several built-in mediation capabilities (e.g. message transformation), which can be used to perform adaptation actions. However, the configuration of these capabilities cannot usually be performed at runtime. This chapter proposes an Adaptive ESB Infrastructure which, based on the mediation capabilities provided by ESBs, can respond to adaptation requirements in service-based systems in an automatic and dynamic way at runtime. The chapter also specifies how the proposed solutions can be used to deal with concrete situations including response time degradation, services saturation, and changes in services contracts.
Structural Interoperability as a Basis for Service Adaptability
Authors: Delgado José C.
Published: August 2012

Web Services appeared essentially as an interoperability solution and REST as a closer match to the semantics of protocols such as HTTP. Clearly influenced by the original browsing goals of the Web, these technologies are not native solutions to the service-oriented paradigm, exhibit limitations to interoperability, and behavior has to be implemented in a separate language. Web Services offer a WSDL document to describe them, but assume that complex data use the same schema in both interacting services, which increases their coupling. This chapter discusses interoperability, from the perspective of both the consumer (compliance) and provider (conformance) services, and it argues that compliance is a weaker requirement for service interoperability than conformance and should be the cornerstone to decrease coupling and to favor adaptability. Structural interoperability is used, given that the lifecycles of distributed resources are decoupled. Metrics to quantify adaptability, based on similarity and decoupling, are proposed.
Mining Lifecycle Event Logs for Enhancing Service-based Applications
Authors: Dustdar Schahram , Leitner Philipp , Nardini Franco Maria , Silvestri Fabrizio , Tolomei Gabriele
Published: August 2012

Service-Oriented Architectures (SOAs), and traditional enterprise systems in general, record a variety of events (e.g., messages being sent and received between service components) to proper log files, i.e., event logs. These files constitute a huge and valuable source of knowledge that may be extracted through data mining techniques. To this end, process mining is increasingly gaining interest across the SOA community. The goal of process mining is to build models without a priori knowledge, i.e., to discover structured process models derived from specific patterns that are present in actual traces of service executions recorded in event logs. However, in this work, the authors focus on detecting frequent sequential patterns, thus considering process mining as a specific instance of the more general sequential pattern mining problem. Furthermore, they apply two sequential pattern mining algorithms to a real event log provided by the Vienna Runtime Environment for Service-oriented Computing, i.e., VRESCo. The obtained results show that the authors are able to find services that are frequently invoked together within the same sequence. Such knowledge could be useful at design-time, when service-based application developers could be provided with service recommendation tools that are able to predict and thus to suggest next services that should be included in the current service composition.
From SOA to Pervasive Service Ecosystems
Authors: Viroli Mirko , Zambonelli Franco , Stevenson Graeme , Dobson Simon
Published: August 2012

Emerging pervasive computing scenarios require open service frameworks promoting situated adaptive behaviors and supporting diversity in services and long-term ability to evolve. The authors argue that this calls for a nature-inspired approach in which pervasive services are modeled and deployed as autonomous individuals in an ecosystem of other services, data sources, and pervasive devices. They discuss how standard service-oriented architectures have to evolve to tackle the above issues, present a general architecture based on a shared spatial substrate mediating interactions of all the individual services of the pervasive computing system, and finally show that this architecture can be implemented relying primarily on standard W3C Semantic Web technologies, like RDF and SPARQL. A use case of adaptive pervasive displays for crowd steering applications is exploited as reference example.
Adaptive Future Internet Applications
Authors: Marquezan Clarissa Cassales , Metzger Andreas , Pohl Klaus , Engen Vegard , Boniface Michael , Phillips Stephen C. , Zlatev Zlatko
Published: August 2012

Adaptive capabilities are essential to guarantee the proper execution of Web services and service-oriented applications once dynamic changes are not exceptions but the rule. The importance of adaptive capabilities significantly increases in the context of Future Internet (FI) applications will have to autonomously adapt to changes on service provisioning, availability of things and content, computing resources, and network connectivity. Current solutions for adaptive Web services and adaptive service-based applications will be challenged in such a setting because they fall short to support essential characteristics of FI applications. This chapter analyzes and justifies the need for the transition from adaptive Web services and service-based applications to adaptive FI applications. Based on two real-world use cases from multimedia and logistics, the authors examine where current solutions fall short to properly address the adaptive needs of FI applications. They propose future research challenges that should be considered in adaptive FI applications.
Intelligent System Monitoring
Authors: García Claudia Maria
Published: August 2012

A general methodology for intelligent system monitoring is proposed in this chapter. The methodology combines degradation hybrid automata to system degradation tracking and a nonlinear adaptive model for model-based diagnosis and prognosis purposes. The principal idea behind this approach is monitoring the plant for any off-nominal system behavior due a wear or degradation. The system degradation is divided in subspaces, from fully functional, nominal, or faultless mode to no functionality mode, failure. The degradation hybrid automata, uses a nonlinear adaptive model for continuous flow dynamics and a system condition guard to transition between modes. Error Filtering On- line Learning (EFOL) scheme is introduced to design a parametric model and adaptive low in such a way that the unknown part of the adaptive model function is on-line approximated; the on-line approximation is via a Radial Basis Function Neural Network (RBFNN). To validate the proposed methodology, a complete conveyor belt simulator, based on a real system, is designed on Simulink; the degradation is characterized using the Paris-Erdogan crack growth function. Once the simulator is designed the measured current, i’s, and velocity of the IM, ?m, are used to modeling the simplified adaptive IM model. EFOL scheme is used to on-line approximate the unknown TL function. The simplified adaptive model estimates the IM velocity, , as output. and the measured IM velocity ?m are compared to detect any deviation from the nominal system behavior. When the degradation automata detect a system condition change the adaptive model on-line approximate the new TL.
Data Driven Prognostics for Rotating Machinery
Authors: Bechhoefer Eric
Published: August 2012

A prognostic is an estimate of the remaining useful life of a monitored part. While diagnostics alone can support condition based maintenance practices, prognostics facilitates changes to logistics which can greatly reduce cost or increase readiness and availability. A successful prognostic requires four processes: 1) feature extraction of measured data to estimate damage; 2) a threshold for the feature, which, when exceeded, indicates that it is appropriate to perform maintenance; 3) given a future load profile, a model that can estimate the remaining useful life of the component based on the current damage state; and 4) an estimate of the confidence in the prognostic. This chapter outlines a process for data-driven prognostics by: describing appropriate condition indicators (CIs) for gear fault detection; threshold setting for those CIs through fusion into a component health indicator (HI); using a state space process to estimate the remaining useful life given the current component health; and a state estimate to quantify the confidence in the estimate of the remaining useful life.
Identifying Suitable Degradation Parameters for Individual-Based Prognostics
Authors: Coble Jamie , Hines J. Wesley
Published: August 2012

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. Traditionally, individual-based prognostic methods form a measure of degradation which is used to make lifetime estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single prognostic parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique to identify an appropriate parameter. This parameter may be used with a parametric extrapolation model to make prognostic estimates for an individual unit. The proposed methods are illustrated with an application to simulated turbofan engine data.
Modeling Multi-State Equipment Degradation with Non-Homogeneous Continuous-Time Hidden Semi-Markov Process
Authors: Moghaddass Ramin , Zuo Ming J , Zhao Xiaomin
Published: August 2012

The multi-state reliability analysis has received great attention recently in the domain of reliability and maintenance, specifically for mechanical equipment operating under stress, load, and fatigue conditions. The overall performance of this type of mechanical equipment deteriorates over time, which may result in multi-state health conditions. This deterioration can be represented by a continuous-time degradation process with multiple discrete states. In reality, due to technical problems, directly observing the actual health condition of the equipment may not be possible. In such cases, condition monitoring information may be useful to estimate the actual health condition of the equipment. In this chapter, the authors describe the application of a general stochastic process to multi-state equipment modeling. Also, an unsupervised learning method is presented to estimate the parameters of this stochastic model from condition monitoring data.
Combining Health Monitoring and Control
Authors: Escobet Teresa , Quevedo Joseba , Puig Vicenç , Nejjari Fatiha
Published: August 2012

This chapter proposes the combination of system health monitoring with control and prognosis creating a new paradigm, the health-aware control (HAC) of systems. In this paradigm, the information provided by the prognosis module about the component system health should allow the modification of the controller such that the control objectives will consider the system’s health. In this way, the control actions will be generated to fulfill the control objectives, and, at the same time, to extend the life of the system components. HAC control, contrarily to fault-tolerant control (FTC), adjusts the controller even when the system is still in a non-faulty situation. The prognosis module, with the main feature system characteristics provided by condition monitoring, will estimate on-line the component aging for the specific operating conditions. In the non-faulty situation, the control efforts are distributed to the system based on the proposed health indicator. An example is used throughout the chapter to illustrate the ideas and concepts introduced.
Prognostics and Health Management of Choke Valves Subject to Erosion
Authors: Gola Giulio , Nystad Bent H.
Published: August 2012

Oil and gas industries are constantly aiming at improving the efficiency of their operations. In this respect, focus is on the development of technology, methods, and work processes related to equipment condition and performance monitoring in order to achieve the highest standards in terms of safety and productivity. To this aim, a key issue is represented by maintenance optimization of critical structures, systems, and components. A way towards this goal is offered by Condition-Based Maintenance (CBM) strategies. CBM aims at regulating maintenance scheduling based on data analyses and system condition monitoring and bears the potential advantage of obtaining relevant cost savings and improved operational safety and availability. A critical aspect of CBM is its integration with condition monitoring technologies for handling a wide range of information sources and eventually making optimal decisions on when and what to repair. In this chapter, a CBM case study concerning choke valves utilized in Norwegian offshore oil and gas platforms is proposed and investigated. The objective is to define a procedure for optimizing maintenance of choke valves by on-line monitoring their condition and determining their Remaining Useful Life (RUL). Choke valves undergo erosion caused by sand grains transported by the oil-water-gas mixture extracted from the well. Erosion is a critical problem which can affect the correct valve functioning, resulting in revenue losses and cause environmental hazards.
Prognostics and Health Management of Industrial Equipment
Authors: Zio E.
Published: August 2012

Prognostics and health management (PHM) is a field of research and application which aims at making use of past, present, and future information on the environmental, operational, and usage conditions of an equipment in order to detect its degradation, diagnose its faults, and predict and proactively manage its failures. This chapter reviews the state of knowledge on the methods for PHM, placing these in context with the different information and data which may be available for performing the task and identifying the current challenges and open issues which must be addressed for achieving reliable deployment in practice. The focus is predominantly on the prognostic part of PHM, which addresses the prediction of equipment failure occurrence and associated residual useful life (RUL).
Fatigue Damage Prognostics and Life Prediction with Dynamic Response Reconstruction Using Indirect Sensor Measurements
Authors: He Jingjing , Guan Xuefei , Liu Yongming
Published: August 2012

This study presents a general methodology for fatigue damage prognostics and life prediction integrating the structural health monitoring system. A new method for structure response reconstruction of critical locations using measurements from remote sensors is developed. The method is based on the empirical mode decomposition with intermittency criteria and transformation equations derived from finite element modeling. Dynamic responses measured from usage monitoring system or sensors at available locations are decomposed into modal responses directly in time domain. Transformation equations based on finite element modeling are used to extrapolate the modal responses from the measured locations to critical locations where direct sensor measurements are not available. The mode superposition method is employed to obtain dynamic responses at critical locations for fatigue crack propagation analysis. Fatigue analysis and life prediction can be performed given reconstructed responses at the critical location. The method is demonstrated using a multi degree-of-freedom cantilever beam problem.
A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem
Authors: Roy Rahul , Dehuri Satchidananda , Cho Sung Bae
Published: August 2012

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.
A Hybrid Meta-Heuristic Algorithm for Dynamic Spectrum Management in Multiuser Systems
Authors: Cordova Hernan X. , Van Biesen Leo
Published: August 2012

One of the major sources of performance degradation of current Digital Subscriber Line systems is the electromagnetic coupling among different twisted pairs within the same cable bundle (crosstalk). Several algorithms for Dynamic Spectrum management have been proposed to counteract the crosstalk effect but their complexity remains a challenge in practice. Optimal Spectrum Balancing (OSB) is a centralized algorithm that optimally allocates the available transmit power over the tones making use of a Dual decomposition approach where Lagrange multipliers are used to enforce the constraints and decouple the problem over the tones. However, the overall complexity of this algorithm remains a challenge for practical DSL environments. The authors propose a low-complex algorithm based on a combination of simulated annealing and non-linear simplex to find local (almost global) optimum spectra for multiuser DSL systems, whilst significantly reducing the prohibitive complexity of traditional OSB. The algorithm assumes a Spectrum Management Center (at the cabinet side) but it neither relies on own end-user modem calculations nor on messaging-passing for achieving its performance objective. The approach allows furthering reducing the number of function evaluations achieving further reduction on the convergence time (up to ~27% gain) at reasonable payoff (weighted data rate sum).
Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures
Authors: Omkar S. N. , Naik G. Narayana , Patil Kiran , Mudigere Mrunmaya
Published: August 2012

In this paper, a generic methodology based on swarm algorithms using Artificial Bee Colony (ABC) algorithm is proposed for combined cost and weight optimization of laminated composite structures. Two approaches, namely Vector Evaluated Design Optimization (VEDO) and Objective Switching Design Optimization (OSDO), have been used for solving constrained multi-objective optimization problems. The ply orientations, number of layers, and thickness of each lamina are chosen as the primary optimization variables. Classical lamination theory is used to obtain the global and local stresses for a plate subjected to transverse loading configurations, such as line load and hydrostatic load. Strength of the composite plate is validated using different failure criteria—Failure Mechanism based failure criterion, Maximum stress failure criterion, Tsai-Hill Failure criterion and the Tsai-Wu failure criterion. The design optimization is carried for both variable stacking sequences as well as standard stacking schemes and a comparative study of the different design configurations evolved is presented. Performance of Artificial Bee Colony (ABC) is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for both VEDO and OSDO approaches. The results show ABC yielding a better optimal design than PSO and GA.
Adaptive Non-Uniform Particle Swarm Application to Plasmonic Design
Authors: Kessentini Sameh , Barchiesi Dominique , Grosges Thomas , Giraud-Moreau Laurence , Lamy de la Chapelle Marc
Published: August 2012

The metaheuristic approach has become an important tool for the optimization of design in engineering. In that way, its application to the development of the plasmonic based biosensor is apparent. Plasmonics represents a rapidly expanding interdisciplinary field with numerous transducers for physical, biological and medicine applications. Specific problems are related to this domain. The plasmonic structures design depends on a large number of parameters. Second, the way of their fabrication is complex and industrial aspects are in their infancy. In this study, the authors propose a non-uniform adapted Particle Swarm Optimization (PSO) for rapid resolution of plasmonic problem. The method is tested and compared to the standard PSO, the meta-PSO (Veenhuis, 2006) and the ANUHEM (Barchiesi, 2009).These approaches are applied to the specific problem of the optimization of Surface Plasmon Resonance (SPR) Biosensors design. Results show great efficiency of the introduced method.
New Evolutionary Algorithm Based on 2-Opt Local Search to Solve the Vehicle Routing Problem with Private Fleet and Common Carrier
Authors: Euchi Jalel , Chabchoub Habib , Yassine Adnan
Published: August 2012

Mismanagement of routing and deliveries between sites of the same company or toward external sites leads to consequences in the cost of transport. When shipping alternatives exist, the selection of the appropriate shipping alternative (mode) for each shipment may result in significant cost savings. In this paper, the authors examine a class of vehicle routing in which a fixed internal fleet is available at the warehouse in the presence of an external transporter. The authors describe hybrid Iterated Density Estimation Evolutionary Algorithm with 2-opt local search to determine the specific assignment of each tour to a private vehicle (internal fleet) or an outside carrier (external fleet). Experimental results show that this method is effective, allowing the discovery of new best solutions for well-known benchmarks.