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.     

Surgery for primary hyperparathyroidism 1962-1996: indications and outcomes

Twenty-two cases of ameloblastoma and ten cases of adenomatoid odontogenic tumour (AOT) were analyzed comparatively by the AgNOR technique. Ameloblastomas were distributed into three groups according to their clinical behaviour: primary lesions without recurrences (PLWTR), 5 cases; primary lesions with recurrences (PLWR), 4 cases; and recurrences, 13 cases. The cases were also regrouped according to their histological pattern: follicular (9 cases), plexiform (7 cases), acanthomatous (4 cases) and unicystic (2 cases). Considering histological patterns, there was a significant statistical difference only between follicular and plexiform types. There were no significant differences between the group of ameloblastomas and the group of AOTs or between the three groups of ameloblastomas with different clinical behaviour. Our results strongly suggest that the distinct clinical behaviour of ameloblastomas and AOT is not correlated with their cellular proliferation ratio. Thus, the infiltrative ability of the ameloblastomas is probably not related to the cellular proliferation index of these tumours.     

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.     

Applications,databases and open computer vision research from drone videos and images: a survey

Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.

     

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.     

Robust Fault Diagnosis for Quadrotor UAVs Using Adaptive Thau Observer

A robust Fault Diagnosis (FD) scheme for a real quadrotor Unmanned Aerial Vehicle (UAV) is proposed in this paper. Firstly, a novel Adaptive Thau observer (ATO) is developed to estimate the quadrotor system states and build a set of offset residuals to indicate actuators’ faults. Based on these residuals, some rules of Fault Diagnosis (FD) are designed to detect and isolate the faults as well as estimate the fault offset parameters. Secondly, a synthetic robust optimization scheme is presented to improve Fault Estimation (FE) accuracies, three key issues include modeling uncertainties, and magnitude order unbalances as well as noises are addressed. Finally, a typical fault of rotors is simulated and injected into one of four rotors of the quadrotor, and experiments for the FD scheme have been carried out. Unlike former research works on the FD schemes for quadrotors, our proposed FD scheme based on the ATO can not only detect and isolate the failed actuators, but also estimate the fault severities. Regardless of roughness of the real flying data, the FD results still have sufficient FE accuracies.     

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.