Reliability Assessment of Lifeline Systems with Radial Topology

Abstract: The increased susceptibility of lifeline systems to failure due to aging and external hazards requires efficient methods to quantify their reliability and related uncertainty. Monte Carlo simulation techniques for network‐level reliability and uncertainty assessment usually require large computational experiments. Also, available analytical approaches apply mainly to simple network topologies, and are limited to providing average values, low order moments, or confidence bounds of reliability metrics. This study introduces a closed form technique to obtain the entire probability distribution of a reliability metric of customer service availability (CSA) for generic radial lifeline systems. A special case of this general formulation reduces to a simple sum of products equation, for which a recursive algorithm that exploits its structure is presented. This special‐case algorithm computes the probability mass function (PMF) of CSA for systems with M elements in operations, relative to conventional operations, and opens the possibility of finding recursive algorithms for the general radial case. Parametric models that approximate the CSA metric are also explored and their errors quantified. The proposed radial topology reliability assessment tools and resulting probability distributions provide infrastructure owners with critical insights for informed operation and maintenance decision making under uncertainty.     

ERD-based online brain-machine interfaces (BMI) in the context of neurorehabilitation: optimizing BMI learning and performance

Event-related desynchronization (ERD) of sensori-motor rhythms (SMR) can be used for online brain-machine interface (BMI) control, but yields challenges related to the stability of ERD and feedback strategy to optimize BMI learning.Here, we compared two approaches to this challenge in 20 right-handed healthy subjects (HS, five sessions each, S1-S5) and four stroke patients (SP, 15 sessions each, S1-S15). ERD was recorded from a 275-sensor MEG system. During daily training,motor imagery-induced ERD led to visual and proprioceptive feedback delivered through an orthotic device attached to the subjects' hand and fingers. Group A trained with a heterogeneous reference value (RV) for ERD detection with binary feedback and Group B with a homogenous RV and graded feedback (10 HS and 2 SP in each group). HS in Group B showed better BMI performance than Group A (p < 0.001) and improved BMI control from S1 to S5 (p = 0.012) while Group A did not. In spite of the small n, SP in Group B showed a trend for a higher BMI performance (p = 0.06) and learning was significantly better (p < 0.05). Using a homogeneous RV and graded feedback led to improved modulation of ipsilesional activity resulting in superior BMI learning relative to use of a heterogeneous RV and binary feedback.     

Sparse decomposition and modeling of anatomical shape variation

Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counterparts if constructed carefully. In most medical applications, models are required to have both good statistical performance and a relevant clinical interpretation to be of value. Morphometry of the corpus callosum is one illustrative example. This paper presents a method for relating spatial features to clinical outcome data. A set of parsimonious variables is extracted using sparse principal component analysis, producing simple yet characteristic features. The relation of these variables with clinical data is then established using a regression model. The result may be visualized as patterns of anatomical variation related to clinical outcome. In the present application, landmark-based shape data of the corpus callosum is analyzed in relation to age, gender, and clinical tests of walking speed and verbal fluency. To put the data-driven sparse principal component method into perspective, we consider two alternative techniques, one where features are derived using a model-based wavelet approach, and one where the original variables are regressed directly on the outcome.     

Wetting Behavior and Mechanical Properties of Sn-Zn and Sn-Pb Solder Alloys

A comparative experimental study of the main features of the eutectic Sn-Pb alloy and Sn-Zn alloys was carried out with a view to application of the latter alloys as alternative solder materials. The resulting microstructures, mechanical properties (ultimate tensile strength and elongation), and wettability behavior (spreading area and contact angle) of a hypoeutectic Sn-Zn (Sn-4wt.%Zn), a hypereutectic Sn-Zn (Sn-12wt.%Zn), and the eutectic Sn-9wt.%Zn alloy were examined and compared with the corresponding results of the conventional Sn-40wt.%Pb solder alloy. It was found that, of the Sn-Zn alloys examined, the eutectic Sn-9wt.%Zn alloy offers a compromise between lower wettability and higher mechanical strength.     

Spectroscopic detection of hopping induced mixed valence for Ti and Sc in GdSc1-xTixO3 for x > 0.165

Only two of the first row transition metals have elemental oxides that are either ferro- or ferri-magnetic. These are CrO2 and Fe3O4. The electron spin alignment that promotes the ferro(i)magnetism is associated with a double exchange mechanism that requires mixed valence as well as metallic conductivity. This paper describes a novel way to realize these two necessary, but not sufficient conditions for double exchange magnetism. These are mixed valence and a hopping conductivity that promotes at least intra-plane electron spin alignment in a complex oxide perovskite host, A(B,C)O3. A is an ordinary metal, or a rare earth atom, B is a d0 transition metal, and C is a d(n) transition metal in which n > or = 1, as for example in GdSc1-xTi(x)O3. This article combines X-ray absorption spectroscopy, multiplet theory, charge transfer multiplet theory and degeneracy removal by Jahn-Teller effect mechanisms to demonstrate mixed valence for both Sc and Ti above a percolation threshold, x > 0.16, in which hopping transport gives rise to a metal to insulator transition.     

Dynamic utility and price based radio resource management for rate adaptive traffic

This paper explores analytical Radio Resource Management models where the relationship between users and services is mapped through utility functions. Compared to other applications of these models to networking, we focus in particular on specific aspects of multimedia systems with adaptive traffic, and propose a novel framework for describing and investigating dynamic allocation of resources in wireless networks. In doing so, we also consider economic aspects, such as the financial needs of the provider and the users’ reaction to prices. As an example of how our analytical tool can be used, in this paper we compare different classes of RRM strategies, e.g., Best Effort vs. Guaranteed Performance, for which we explore the relationships between Radio Resource Allocation, pricing, provider’s revenue, network capacity and users’ satisfaction. Finally, we present a discussion about Economic Admission Control, which can be applied in Best Effort scenarios to further improve the performance. Part of this work has been presented at the conference ACM/IEEE MSWiM 2004, Venice (Italy). Leonardo Badia received a Laurea degree (with honors) in electrical engineering and a Ph.D. in information engineering from the University of Ferrara, Italy, in 2000 and 2004, respectively. He was a Research Fellow at the University of Ferrara from 2001 to 2006. During these years, he also had collaborations with the University of Padova, Italy, and Wireless@KTH, Royal Institute of Technology, Stockholm, Sweden. In 2006, he joined the “Institutions Markets Technologies” (IMT) Institute for Advanced Studies, Lucca, Italy, where he is currently a Research Fellow. His research interests include wireless ad hoc and mesh networks, analysis of transmission protocols, optimization tools and economic models applied to radio resource management. Michele Zorzi received a Laurea degree and a Ph.D. in electrical engineering from the University of Padova in 1990 and 1994, respectively. During academic year 1992–1993, he was on leave at UCSD, attending graduate courses and doing research on multiple access in mobile radio networks. In 1993 he joined the faculty of the Dipartimento di Elettronica e Informazione, Politecnico di Milano, Italy. After spending three years with the Center for Wireless Communications at UCSD, in 1998 he joined the School of Engineering of the University of Ferrara, Italy, where he became a professor in 2000. Since November 2003 he has been on the faculty at the Information Engineering Department of the University of Padova. His present research interests include performance evaluation in mobile communications systems, random access in mobile radio networks, ad hoc and sensor networks, energy constrained communications protocols, and broadband wireless access. He was Editor-In-Chief of IEEE Wireless Communications, 2003–2005, and currently serves on the Editorial Boards of IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, Wiley’s Journal of Wireless Communications and Mobile Computing, and ACM/URSI/Kluwer Journal of Wireless Networks, and on the Steering Committee of the IEEE Transactions on Mobile Computing. He has also been a Guest Editor of special issues in IEEE Personal Communications (Energy Management in Personal Communications Systems) and IEEE Journal on Selected Areas in Communications (Multimedia Network Radios).     

Consistency,availability, and partition tolerance in blockchain: a survey on the consensus mechanism over peer-to-peer networking

Annals of Telecommunications - Blockchain is a disruptive technology that relies on the distributed nature of the peer-to-peer network while performing an agreement, or consensus, a mechanism to...     

Manipulation of Training Sets for Improving Data Mining Coverage-Driven Verification

The constant pressure for making functional verification more agile has led to the conception of coverage driven verification (CDV) techniques. CDV has been implemented in verification testbenches using supervised learning techniques to model the relationship between coverage events and stimuli generation, providing a feedback between them. One commonly used technique is the classification- or decision-tree data mining, which has shown to be appropriate due to the easy modeling. Learning techniques are applied in two steps: training and application. Training is made on one or more sets of examples, which relate datasets to pre-determined classes. Precision of results by applying the predictive learning concept has shown to be sensitive to the size of the training set and the amount of imbalance of associated classes, this last meaning the number of datasets associated to each class is very different from each other. This work presents experiments on the manipulation of data mining training sets, by changing the size and reducing the imbalances, in order to check their influence on the CDV efficiency. To do that, a circuit example with a large input space and strong class imbalance was selected from the application domain of multimedia systems and another one, with a small input space that affects the coverage occurrences, was selected from the communication area.     

Nanotechnology‐Enabled Closed Loop Insulin Delivery Device: In Vitro and In Vivo Evaluation of Glucose‐Regulated Insulin Release for Diabetes Control

Recently, a new multifunctional, bio‐inorganic nanocomposite membrane with the ability to self‐regulate the release of insulin in response to blood glucose (BG) levels was reported. Herein, the application of this material as part of a small, implantable, closed‐loop insulin delivery device designed to continuously monitor BG concentrations and regulate insulin release is proposed. The insulin delivery device consists of a nanocomposite glucose‐responsive plug covalently bound to an insulin reservoir made of surface‐modified silicone. The plug is prepared with crosslinked bovine serum albumin (BSA) and enzymes (glucose oxidase (GOx) and catalase (CAT)), pH‐responsive hydrogel nanoparticles, and multifunctional MnO₂ nanoparticles. The plug functions both as a glucose sensor and controlled delivery unit to release higher rates of insulin from the reservoir in response to hyperglycemic BG levels and basal insulin rates at normal BG concentration. The surfaces of the device are modified by silanization followed by PEGylation to ensure its safety and biocompatibility and the stability of encased insulin. Our results show that insulin release can be modulated in vitro in response to glucose concentrations. In vivo experiments show that the glycemia of diabetic rats can be controlled with implantation of the prototype device. The glucose‐responsiveness of the device is also demonstrated by rapid drop in BG level after challenging diabetic rats with bolus injection of glucose solution. In addition, it is demonstrated that surface PEGylation of the device is necessary for reducing the immune response of the host to the implanted foreign object and maintaining insulin stability and bioactivity. With this molecular architecture and the bio‐inorganic nanocomposite plug, the device has the ability to maintain normal BG levels in diabetic rats.