Design of bandpass–bandpass diplexers using rectangular-, T-, and L-shaped resonators for hybrid power amplifier and 5G applications

Analog Integrated Circuits and Signal Processing - In this paper, two bandpass–bandpass diplexers, based on L-, T- and rectangular-shaped resonators are designed and analyzed, which are used...     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

RELAP5/3.2 assessment against low pressure onset of flow instability in parallel heated channels

Best Estimate computer codes have been, so far, developed for safety analysis of nuclear power plants and were extensively validated against a large set of separate effects and integral test facilities experimental data relevant to such kind of reactors. Their application to research reactors is not fully straightforward. Modelling problems generally emerge when applying existing models to low pressure and more particularly to subcooled flow boiling situations. The objective of the present work is to investigate the RELAP5/3.2 system code capabilities in predicting phenomena that could be encountered under abnormal research reactor’s operating conditions. For this purpose, the separate effect related to the static onset of flow instability is investigated. The cases considered herein are the flow excursion tests performed at the Oak Ridge National Laboratory thermal hydraulic test loop (THTL) as well as some representative Whittle and Forgan (W & F) experiments. The simulation results are presented and the capabilities of RELAP5/Mod 3.2 in predicting this critical phenomenon are discussed.     

Study of the solid-liquid equilibria in the LiPO3-Y(PO3)3 binary system

The LiPO₃-Y(PO₃)₃ system has been studied for the first time. Microdifferential thermal analysis (μ-DTA), infrared spectroscopy (IR) and X-ray diffraction were used to investigate the liquidus and solidus relations. The only new compound observed within this system is LiY(PO₃)₄, melting incongruently at 1104 K. An eutectic appears at 4±1 mol% Y(PO₃)₃ at 933 K. LiY(PO₃)₄ crystallizes in the monoclinic system C_2/c with a unit cell: a=16.201(4) Å, b=7.013(2) Å, c=9.573(2) Å, β=125.589(9)°, Z=4 and V=884.5 Å³, which is isostructural to LiNd(PO₃)₄. The infrared absorption spectrum indicates that this salt is a chain polyphosphate.     

Dynex: multiplex ELISA technology

Conventional enzyme-linked immunosorbent assay (ELISA) is a gold standard for screening antibodies and testing for protein or antigen presence. A significant limitation of this assay resides in the fact that only one analyte can be assessed per microplate well. Here, we describe and investigate a new technology consisting of an automated ELISA system in which up to 10 analytes can be measured within one single well, thus improving productivity, accuracy, and repeatability by reducing the amount of human labor required. Another strength of the platform is that a user can load any necessary sets/subsets of beads to perform required assays, with improved flexibility compared to manufactured-loaded arrays for multiplex analysis. We also demonstrate that this system can be used to determine the pathogenicity (i.e., presence of Shiga toxins) and serotype (i.e., Escherichia coli O157) of E. coli isolates.     

PRIVACY LEAKAGE IN HEALTH SOCIAL NETWORKS

Members of health social networks may be susceptible to privacy leaks by the amount of information they leave behind. The threat to privacy increases when members of these networks reuse their pseudonyms in other social networks. The risk of re‐identifying users from such networks requires quantitative estimates to evaluate its magnitude. The estimates will enable managers and members of health social communities to take corrective measures. We introduce a new re‐identification attack, the social network attack, that takes advantage of the fact that users reuse their pseudonyms. To demonstrate the attack, we establish links between MedHelp and Twitter (two popular social networks) based on matching pseudonyms. We used Bayesian networks to model the re‐identification risk and used stylometric techniques to identify the strength of the links. On the basis of our model 7‐11. 8% of the MedHelp members in the sample population who reused their pseudonyms in Twitter were re‐identifiable compared with 1% who did not. The risk estimates were measured at the 5% risk threshold. Our model was able to re‐identify users with a sensitivity of 41% and specificity of 96%. The potential for re‐identification increases as more data is accumulated from these profiles, which makes the threat of re‐identification more serious.     

A preemptive bound for the Resource Constrained Project Scheduling Problem

The Resource Constrained Project Scheduling Problem is one of the most intensively investigated scheduling problems. It requires scheduling a set of interrelated activities, while considering precedence relationships, and limited renewable resources allocation. The objective is to minimize the project duration. We propose a new destructive lower bound for this challenging ${\mathcal {NP}}$ -hard problem. Starting from a previously suggested LP model, we propose several original valid inequalities that aim at tightening the model representation. These new inequalities are based on precedence constraints, incompatible activity subsets, and nonpreemption constraints. We present the results of an extensive computational study that was carried out on 2,040 benchmark instances of PSPLIB, with up to 120 activities, and that provide strong evidence that the new proposed lower bound exhibits an excellent performance. In particular, we report the improvement of the best known lower bounds of 5 instances.     

Effects of frequency dimming electronic ballast on a high‐pressure mercury thallium iodide discharge

This study focuses on the behavior of a cylindrical metal halide discharge lamp powered by frequency‐controlled electronic ballast. Half‐bridge electronic ballast was designed and achieved.     

Supervised Multi‐Rejector of Periodic Disturbances for Nonlinear Systems Using Decoupled State Multimodel

In this paper, a multi‐rejector of periodic disturbances is proposed for discrete‐time nonlinear systems represented by a decoupled state multimodel. We report a decoupled state multimodel repetitive‐predictive control based on a supervised algorithm to ensure reference trajectory tracking and periodic disturbances rejection. Partial predictors associated to the local controllers make the best choice of the most valid partial controller that meets the desired closed loop performances. The effectiveness of the supervised multi‐rejector is shown via a simulation example. The obtained results are satisfactory and show a good rejection of periodic disturbances and reference trajectory tracking.     

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.