Correction to: A Comparative Study of Mechanical Properties,Thermal Conductivity,Residual Stresses,and Wear Resistance of Aluminum-Alumina Composites Obtained by Squeeze Casting and Powder Metallurgy

Metallurgical and Materials Transactions A -     

A Universal Randomized Packet Scheduling Algorithm

We give a memoryless scale-invariant randomized algorithm ReMix for Packet Scheduling that is e/(e?1)-competitive against an adaptive adversary. ReMix unifies most of previously known randomized algorithms, and its general analysis yields improved performance guarantees for several restricted variants, including the s-bounded instances. In particular, ReMix attains the optimum competitive ratio of 4/3 on 2-bounded instances. Our results are applicable to a more general problem, called Item Collection, in which only the relative order between packets’ deadlines is known. ReMix is the optimal memoryless randomized algorithm against adaptive adversary for that problem.     

Controllable Generation of a Submillimeter Single Bubble in Molten Metal Using a Low-Pressure Macrosized Cavity

Metallurgical and Materials Transactions B - We develop a method for generation of a single gas bubble in a pool of molten metal. The method can be useful for applications and research studies...     

Copper-mediated intramolecular oxidative N-dealkylation of a bis(ferrocenyl)pyridyl ligand

Spontaneous reduction of copper(II) by a bis(ferrocenyl)pyridyl diamide ligand was observed which causes a novel intramolecular N-dealkylation as well as N-oxidation in air. Also observed was the unusual formation of vinyl ferrocene which was confirmed by HPLC and mass spectral techniques. Interestingly, the phenyl analogue of the pyridyl ligand does not show any such transformation. The absorption spectral and electrochemical properties were also reported.     

Interface fracture of polymer films: blister test experiments and modelling

The interface fracture between a rigid substrate and polymer film is investigated in this work using pressurised blister test experiments and modelling. The interface crack growth is studied for two different types of polymer films: stiff and compliant ones. The pressurised blister test is used to provide critical pressure-crack length curves for different loading media (water and electrolyte solutions) and loading rates. Two different analytical approaches and a numerical modelling concept are used to determine the critical total energy release rate as a function of the crack length (crack resistance curve or R-curve). A relatively flat R-curve is observed for the system with the stiff polymer film, whilst R-curve for the compliant film system exhibits an increasing tendency. The mixed-mode fracture behaviour occurs for both investigated polymer film systems, as shown by the value of the mixed-mode angle that is constant for all investigated crack lengths. R-curves are nearly unaffected by different loading media, whereas the loading rate has a strong influence on the interface fracture of the compliant file system. Finite element method-based prediction of the total energy release rate is in good agreement with that obtained from analytical expressions.     

A Novel Biocidal Nanocomposite: Spherical Silica with Silver Ions Anchored at the Surface

This article is devoted to a novel class of antimicrobial agents: nanocomposites composed of spherical silica and silver ions located at the silica’s surface with the assumed distribution. Such materials are in high demand due to the increasing threat from bacterial strains that are becoming resistant to currently known antibiotics. In particular, we focus on materials that make it possible to limit the growth of bacterial colonies on a variety of tactile surfaces. In this paper, we present a method for preparing a silica-based nanocomposite containing silver ions and the analysis of their antimicrobial properties. Our research revealed that the presence of tested nanocomposite induces very high oxidative stress in the bacteria cell, damaging and modifying bacterial DNA, creating oxidized guanines, cytosines, or adenines, which causes its very rapid destruction, leading to cell death.     

Age- and Lifespan-Dependent Differences in GO Caused DNA Damage in Acheta domesticus

The rising applicability of graphene oxide (GO) should be preceded by detailed tests confirming its safety and lack of toxicity. Sensitivity to GO of immature, or with different survival strategy, individuals has not been studied so far. Therefore, in the present research, we focused on the GO genotoxic effects, examining selected parameters of DNA damage (total DNA damage, double-strand breaks—DSB, 8-hydroxy-2′-deoxyguanosine-8-OHdG, abasic site—AP sites), DNA damage response parameters, and global methylation in the model organism Acheta domesticus. Special attention was paid to various life stages and lifespans, using wild (H), and selected for longevity (D) strains. DNA damage was significantly affected by stage and/or strain and GO exposure. Larvae and young imago were generally more sensitive than adults, revealing more severe DNA damage. Especially in the earlier life stages, the D strain reacted more intensely/inversely than the H strain. In contrast, DNA damage response parameters were not significantly related to stage and/or strain and GO exposure. Stage-dependent DNA damage, especially DSB and 8-OHdG, with the simultaneous lack or subtle activation of DNA damage response parameters, may result from the general life strategy of insects. Predominantly fast-living and fast-breeding organisms can minimize energy-demanding repair mechanisms.     

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.