Nonlinear Absorption of Submicrometer Grain‐Size Cobalt‐Doped Magnesium Aluminate Transparent Ceramics

Transparent cobalt‐doped magnesium aluminate spinel (Co:MgAl₂O₄) ceramics with a submicrometer grain size were prepared by spark plasma sintering. For the first time, the nonlinear absorption of Co:MgAl₂O₄ transparent ceramics was experimentally demonstrated. Both ground state absorption (σ_GSA) and excited state absorption (σ_ESA) were estimated using the solid‐state slow saturable absorber model based on absorption saturation measurements performed at 1.535 μm. σ_GSA and σ_ESA for 0.03 at.% Co:MgAl₂O₄ were found to be 4.1 × 10^?19 cm² and 4.0 × 10^?20 cm², respectively. In the case of 0.06 at.% Co:MgAl₂O₄ ceramics, σ_GSA = 2.6 × 10^?19 cm² and σ_ESA= 5.3 × 10^?20 cm² were determined.     

Chemical Composition of Seed Oils Recovered from Different Pear (Pyrus communis L.) Cultivars

Lipophilic bioactive compounds in oils recovered from the seeds of eight pear (Pyrus communis L.) cultivars were studied. Oil yield in pear seeds ranged between 16.3 and 31.5 % (w/w) dw. The main fatty acids were palmitic acid (6.13–8.52 %), oleic acid (27.39–38.17 %) and linoleic acid (50.73–63.78 %), all three representing 96–99 % of the total detected fatty acids. The range of total tocochromanols was between 120.5 and 216.1 mg/100 g of oil. Independent of the cultivar, the γ‐tocopherol was the main tocochromanol and constituted approximately 88 %. The contents of the carotenoids and squalene were between 0.69–2.99 and 25.5–40.8 mg/100 g of oil, respectively. The β‐sitosterol constituted 83.4–87.6 % of total sterols contents, which ranged between 276.4 and 600.1 mg/100 g of oil. Three significant correlations were found between oil yield and total contents of sterols (r = ?0.893), tocochromanols (r = ?0.955) and carotenoids (r = ?0.685) in pear seed oils.     

Machine Learning-Based Predictions of Customers’ Decisions in Car Insurance

Predicting customer decisions allows companies to obtain higher profits due to better resource management. The accuracy of those predictions can be currently boosted by the application of machine learning algorithms.

We propose a new method to predict a car driver’s decision about taking a replacement car after a vehicle accident happens. We use feature engineering to create attributes of high significance. The generated attributes are related to time (e.g., school holidays), place of collision (e.g., distance from home), time and conditions (e.g., weather), vehicles (e.g., vehicle value), addresses of both the victim and the perpetrator. Feature engineering involves external sources of data.

Five machine learning methods of classification are considered: decision trees, multi-layer perceptrons, AdaBoost, logistic regression and gradient boosting. Algorithms are tested on real data from a Polish insurance company. Over 80% accuracy of prediction is achieved. Significance of the attributes is calculated using the linear vector quantization method.

Presented work shows the applicability of machine learning in the car insurance market.     

Discrete time sliding mode control with reduced switching – a new reaching law approach

In this paper, a novel reaching law for discrete‐time variable structure systems is proposed. It ensures that the representative point (state) of the controlled plant approaches the switching plane in finite time and then crosses it in every subsequent step. Moreover, the proposed reaching law ensures that for the nominal plant the absolute value of the sliding variable asymptotically decreases to zero, and for the perturbed plant, it converges to a smaller interval around zero than with the application of previously proposed reaching laws. The control method proposed in this paper guarantees asymptotic stability of the nominal system and uniform ultimate boundedness of the perturbed one. Furthermore, the method ensures that the sliding variable rate of change (i.e. the difference between its values at any two subsequent sampling instants) is bounded by design parameters, which do not depend on the system initial conditions. This is a highly desirable property, as it results in a priori specified, ‘almost’ constant convergence rate of the sliding variable when the system state is far off the switching plane and helps enforce state constraints in the system. Copyright © 2014 John Wiley & Sons, Ltd.     

Randomized mutual exclusion on a multiple access channel

In this paper we consider the mutual exclusion problem on a multiple access channel. Mutual exclusion is one of the fundamental problems in distributed computing. In the classic version of this problem, n processes execute a concurrent program that occasionally triggers some of them to use shared resources, such as memory, communication channel, device, etc. The goal is to design a distributed algorithm to control entries and exits to/from the shared resource (also called a critical section), in such a way that at any time, there is at most one process accessing it. In our considerations, the shared resource is the shared communication channel itself (multiple access channel), and the main challenge arises because the channel is also the only mean of communication between these processes. We consider both the classic and a slightly weaker version of mutual exclusion, called \(\varepsilon \)-mutual-exclusion, where for each period of a process staying in the critical section the probability that there is some other process in the critical section is at most \(\varepsilon \). We show that there are channel settings, where the classic mutual exclusion is not feasible even for randomized algorithms, while the \(\varepsilon \)-mutual-exclusion is. In more relaxed channel settings, we prove an exponential gap between the makespan complexity of the classic mutual exclusion problem and its weaker \(\varepsilon \)-exclusion version. We also show how to guarantee fairness of mutual exclusion algorithms, i.e., that each process that wants to enter the critical section will eventually succeed.     

Evoking emotions in virtual reality: schema activation via a freeze-frame stimulus

Virtual Reality - Virtual reality can be used for educational purposes, particularly in demanding professions such as firefighting. Such virtual training may be useful for preparing trainees for...     

Adaptive rank weighted switching filter for impulsive noise removal in color images

Journal of Real-Time Image Processing - In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on...     

Robust gossiping with an application to consensus

We study deterministic gossiping in synchronous systems with dynamic crash failures. Each processor is initialized with an input value called rumor. In the standard gossip problem, the goal of every processor is to learn all the rumors. When processors may crash, then this goal needs to be revised, since it is possible, at a point in an execution, that certain rumors are known only to processors that have already crashed. We define gossiping to be completed, for a system with crashes, when every processor knows either the rumor of processor v or that v has already crashed, for any processor v. We design gossiping algorithms that are efficient with respect to both time and communication. Let t<n be the number of failures, where n is the number of processors. If , then one of our algorithms completes gossiping in O(log²t) time and with O(npolylogn) messages. We develop an algorithm that performs gossiping with O(n^1.77) messages and in O(log²n) time, in any execution in which at least one processor remains non-faulty. We show a trade-off between time and communication in gossiping algorithms: if the number of messages is at most O(npolylogn), then the time has to be at least . By way of application, we show that if n−t=Ω(n), then consensus can be solved in O(t) time and with O(nlog²t) messages.