Simple tests for multi-criteria optimality

In this paper we present two tests which can decide whether a given pointx ₀ ^N is locally efficient or not with respect to a given finite set of real valued continuously differentiable functions defined on ^N . Examples indicate that the tests may fail on a nowhere dense set.     

A class of high performance Maekawa-type algorithms for distributed systems under heavy demand

Summary Distributed Mutual Exclusion algorithms have been mainly compared using the number of messages exchanged per critical section execution. In such algorithms, no attention has been paid to the serialization order of the requests. Indeed, they adopt FCFS discipline. Conversely, the insertion of priority serialization disciplines, such as Short-Job-First, Head-Of-Line, Shortest-Remaining-Job-First etc., can be useful in many applications to optimize some performance indices. However, such priority disciplines are prone to starvation. The goal of this paper is to investigate and evaluate the impact of the insertion of a priority discipline in Maekawa-type algorithms. Priority serialization disciplines will be inserted by means of agated batch mechanism which avoids starvation. In a distributed algorithm, such a mechanism needs synchronizations among the processes. In order to highlight the usefulness of the priority based serialization discipline, we show how it can be used to improve theaverage response time compared to the FCFS discipline. The gated batch approach exhibits other advantages: algorithms are inherently deadlock-free and messages do not need to piggyback timestamps. We also show that, under heavy demand, algorithms using gated batch exchange less messages than Maekawa-type algorithms per critical section excution. Roberto Baldoni was born in Rome on February 1, 1965. He received the Laurea degree in electronic engineering in 1990 from the University of Rome La Sapienza and the Ph.D. degree in Computer Science from the University of Rome La Sapienza in 1994. Currently, he is a researcher in computer science at IRISA, Rennes (France). His research interests include operating systems, distributed algorithms, network protocols and real-time multimedia applications. Bruno Ciciani received the Laurea degree in electronic engineering in 1980 from the University of Rome La Sapienza. From 1983 to 1991 he has been a researcher at the University of Rome Tor Vergata. He is currently full professor in Computer Science at the University of Rome La Sapienza. His research activities include distributed computer systems, fault-tolerant computing, languages for parallel processing, and computer system performance and reliability evaluation. He has published in IEEE Trans. on Computers, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Software Engineering and IEEE Trans. on Reliability. He is the author of a book titled Manufactoring Yield Evaluation of VLSI/WSI Systems to be published by IEEE Computer Society Press.This research was supported in part by the Consiglio Nazionale delle Ricerche under grant 93.02294.CT12This author is also supported by a grant of the Human Capital and Mobility project of the European Community under contract No. 3702 CABERNET     

A systematic literature review on IoT-aware business process modeling views,requirements and notations

Software and Systems Modeling - The Internet of things has been adopted in several sectors both influencing how people work and enhancing organizations’ business processes. This resulted in...     

Level Lines of a Polynomial on a Plane

Programming and Computer Software - A method for calculating the location of all types of level lines of a real polynomial on the real plane is proposed. To this end, the critical points and...     

A Novel Particle/Photon Detector Based on a Superconducting Proximity Array of Nanodots

The current frontiers in the investigation of high-energy particles demand for new detection methods. Higher sensitivity to low-energy deposition, high-energy resolution to identify events and improve the background rejection, and large detector masses have to be developed to detect even an individual particle that weakly interacts with ordinary matter. Here, we will describe the concept and the layout of a novel superconducting proximity array which show dynamic vortex Mott insulator to metal transitions, as an ultra-sensitive compact radiation-particle detector.     

Engineered Multifunctional Albumin‐Decorated Porous Silicon Nanoparticles for FcRn Translocation of Insulin

The last decade has seen remarkable advances in the development of drug delivery systems as alternative to parenteral injection‐based delivery of insulin. Neonatal Fc receptor (FcRn)‐mediated transcytosis has been recently proposed as a strategy to increase the transport of drugs across the intestinal epithelium. FcRn‐targeted nanoparticles (NPs) could hijack the FcRn transcytotic pathway and cross the epithelial cell layer. In this study, a novel nanoparticulate system for insulin delivery based on porous silicon NPs is proposed. After surface conjugation with albumin and loading with insulin, the NPs are encapsulated into a pH‐responsive polymeric particle by nanoprecipitation. The developed NP formulation shows controlled size and homogeneous size distribution. Transmission electron microscopy (TEM) images show successful encapsulation of the NPs into pH‐sensitive polymeric particles. No insulin release is detected at acidic conditions, but a controlled release profile is observed at intestinal pH. Toxicity studies show high compatibility of the NPs with intestinal cells. In vitro insulin permeation across the intestinal epithelium shows approximately fivefold increase when insulin is loaded into FcRn‐targeted NPs. Overall, these FcRn‐targeted NPs offer a toolbox in the development of targeted therapies for oral delivery of insulin.     

Plasmon hybridization engineering in self-organized anisotropic metasurfaces

Nano Research - The engineering of self-organized plasmonic metasurfaces is demonstrated using a maskless technique with defocused ion-beam sputtering and kinetically controlled deposition. The...     

Discrimination between accelerated life models via Approximate Bayesian Computation

Accelerated life testing (ALT) is widely used in high-reliability product estimation to get relevant information about an item's performance and its failure mechanisms. To analyse the observed ALT data, reliability practitioners need to select a suitable accelerated life model based on the nature of the stress and the physics involved. A statistical model consists of (i) a lifetime distribution that represents the scatter in product life and (ii) a relationship between life and stress. In practice, several accelerated life models could be used for the same failure mode and the choice of the best model is far from trivial. For this reason, an efficient selection procedure to discriminate between a set of competing accelerated life models is of great importance for practitioners. In this paper, accelerated life model selection is approached by using the Approximate Bayesian Computation (ABC) method and a likelihood-based approach for comparison purposes. To demonstrate the efficiency of the ABC method in calibrating and selecting accelerated life model, an extensive Monte Carlo simulation study is carried out using different distances to measure the discrepancy between the empirical and simulated times of failure data. Then, the ABC algorithm is applied to real accelerated fatigue life data in order to select the most likely model among five plausible models. It has been demonstrated that the ABC method outperforms the likelihood-based approach in terms of reliability predictions mainly at lower percentiles particularly useful in reliability engineering and risk assessment applications. Moreover, it has shown that ABC could mitigate the effects of model misspecification through an appropriate choice of the distance function.