Nonatomic dual bakery algorithm with bounded tokens

A simple mutual exclusion algorithm is presented that only uses nonatomic shared variables of bounded size, and that satisfies bounded overtaking. When the shared variables behave atomically, it has the first-come-first-served property (FCFS). Nonatomic access makes information vulnerable. The effects of this can be mitigated by minimizing the information and by spreading it over more variables. The design approach adopted here begins with such mitigating efforts. These resulted in an algorithm with a proof of correctness, first for atomic variables. This proof is then used as a blueprint for the simultaneous development of the algorithm for nonatomic variables and its proof. Mutual exclusion is proved by means of invariants. Bounded overtaking and liveness under weak fairness are proved with invariants and variant functions. Liveness under weak fairness is formalized and proved in a set-theoretic version of temporal logic. All these assertions are verified with the proof assistant PVS. We heavily rely on the possibility offered by a proof assistant like PVS to reuse proofs developed for one context in a different context.     

SMARTINT: using mined attribute dependencies to integrate fragmented web databases

Many web databases can be seen as providing partial and overlapping information about entities in the world. To answer queries effectively, we need to integrate the information about the individual entities that are fragmented over multiple sources. At first blush this is just the inverse of traditional database normalization problem—rather than go from a universal relation to normalized tables, we want to reconstruct the universal relation given the tables (sources). The standard way of reconstructing the entities will involve joining the tables. Unfortunately, because of the autonomous and decentralized way in which the sources are populated, they often do not have Primary Key–Foreign Key relations. While tables may share attributes, naive joins over these shared attributes can result in reconstruction of many spurious entities thus seriously compromising precision. Our system, SmartInt is aimed at addressing the problem of data integration in such scenarios. Given a query, our system uses the Approximate Functional Dependencies (AFDs) to piece together a tree of relevant tables to answer it. The result tuples produced by our system are able to strike a favorable balance between precision and recall.     

Micro- and nano-scale deformation behavior of nylon 66-based binary and ternary nanocomposites

The primary aim of this paper is to provide an insight on the effect of the location of organoclay on the micro- and nano-scale deformation processes in melt-compounded nylon 66/organoclay/SEBS-g-MA ternary nanocomposites prepared by different blending sequences. In addition, the deformation processes of the ternary nanocomposites were compared to the binary nanocomposites (nylon 66/organoclay and nylon 66/SEBS-g-MA) and neat nylon 66. The incorporation of SEBS-g-MA particles toughened nylon 66 markedly; but the flexural modulus and strength were both reduced. Conversely, the use of organoclay increased the modulus but decreased the fracture toughness of nylon 66. Nylon 66/SEBS-g-MA/organoclay ternary nanocomposites exhibited balanced elastic stiffness and toughness. Stress-whitening studies of the fracture surfaces in terms of gray level were also performed and an attempt was made to correlate the optical reflectivity characteristics with fracture toughness. It was concluded that the capability of SEBS-g-MA particles to cavitate was decreased by the presence of organoclay in the SEBS-g-MA phase, resulting in reduced toughening efficiency. The best micro-structure for toughness and other mechanical properties is thus to maximize the amount of exfoliated organoclay in the nylon 66 matrix rather than to have it embedded in the finely dispersed SEBS-g-MA particles.     

Extraction of S‐parameters from time domain reflectometry and transmission measurements using rational functions

The extraction of the broad band S‐parameter response from transient reflection and transmission measurements is discussed. The proposed method uses the generalized pencil‐of‐function method, recursive deconvolution, and calibration structures to obtain a response using rational functions. A low loss printed circuit board plane and a lossy thin film plane are characterized to capture the two‐port S‐parameters. The results are compared with network analyzer measurements. The effects of the measurement parameters such as the sampling interval, time window, and number of averages are shown. The effect of time jitter on the extracted frequency response is quantified through repeated measurements. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 74–85, 2003.     

Layered reasoning for randomized distributed algorithms

This paper adopts the communication closed layer (CCL) concept of Elrad and Francez to the formal reasoning of randomized distributed algorithms. We do so by enriching probabilistic automata (PA) with a layered composition operator, an intermediate between parallel and sequential composition. Layered composition is used to establish probabilistic counterparts of the CCL laws that exploit independence and/or precedence conditions between the constituent PA. The probabilistic CCL laws enable partial order (po-) equivalence when layered composition is replaced by sequential composition. Such po-equivalence induces a purely syntactic partial-order state space reduction via layered separation in compositions of PA while preserving probabilistic next-free linear-time properties. The feasibility of such layered separation is demonstrated on a randomized mutual exclusion algorithm by Kushilevitz and Rabin, complementing an algebraic approach (for analyzing this algorithm) by McIver, Gonzalia, Cohen, and Morgan.     

Every team deserves a second chance: an extended study on predicting team performance

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.     

Diffusion of innovation model of consumer behaviour - Ideas to accelerate adoption of renewable energy sources by consumer communities in India

Renewable Energy (RE) sources form a minuscule portion of India’s overall Energy consumption today. India continues to rely on fast depleting fossil fuel and expensive Oil imports to satisfy the energy demands of the economy. But this is hardly sustainable and India has to quickly get RE sources to play a major role in servicing the energy needs of its population. Despite the best efforts the adoption of RE sources by consumer communities in India is patchy. This article will focus on what needs to be done to create a pull from the market for RE sources, by looking at Consumer Behaviour literature available in the area of Diffusion of Innovation[1]. Demand for RE sources from consumer communities must reach a tipping point[4] quickly; for the sector to take-off on its own and become a self-sustaining business.     

An Energy-efficient Switching Scheme with Reduced Switching Transients for a Wind-driven Induction Generator

This article presents a scheme for improving the power output of grid-connected induction generator commonly used in wind energy conversion systems. Generally, the stator of the induction generator is connected in a star with a line voltage of √3 times the rated winding voltage to reduce the line current and, hence, conductor size. To extend the generating operation over a wider speed range, delta-star switchable stator windings are also in vogue. In such cases, the stator is star connected in the lower speed range and switched to a delta connection above a threshold speed. In this study, a new switching scheme is proposed wherein the stator coils are always connected in a star, while the stator is connected to different voltages in low- and high-speed conditions. At low wind speeds, nominal winding voltage is applied to the stator, whereas at higher speeds, the stator applied voltage is √3 times higher than the rated winding voltage. The efficacy of the scheme is demonstrated experimentally with a suitable microcontroller-based switching arrangement. Typical results indicate an increase in output with reduced switching transients. A case study on a 3-Φ, 50-kW induction generator is presented to emphasize the performance improvement with the proposed scheme.     

Nitrites Derived From Foneiculum Vulgare (Fennel) Seeds Promotes Vascular Functions

Abstract: Recent evidence has demonstrated that nitrites play an important role in the cardiovascular system. Fennel (Foneiculum vulgare) seeds are often used as mouth fresheners after a meal in both the Indian sub‐continent and around the world. The present study aims to quantify the nitrite and nitrates in fennel seeds as well as elucidating the effect of fennel derived‐nitrites on vascular functions. Results from our study show that fennel seeds contain significantly higher amount of nitrites when compared to other commonly used post‐meal seeds. Furthermore our study confirmed the functional effects of fennel derived‐nitrites using in vitro and ex vivo models that describe the promotion of angiogenesis, cell migration, and vasorelaxation. We also showed that chewing fennel seeds enhanced nitrite content of saliva. Thus our study indicates the potential role of fennel derived‐nitrites on the vascular system. Practical Application: This study is focused on determining the effect of fennel‐derived nitrites on angiogenesis (the formation of new blood vessels from pre‐existing ones), cell migration, and vasorelaxation (dilation of blood vessels) thereby preserving cardiovascular health.