Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

High‐intensity sweeteners in espresso coffee: ideal and equivalent sweetness and time–intensity analysis

The efficient substitution of sucrose by a sweetener in beverages requires the application of some sensory techniques. First, one must determine the concentrations of the sweeteners under study, equivalent in sweetness to the ideal sucrose concentration. In addition, it is fundamental to determine which is most similar to sucrose. The objectives of this study were to determine the ideal sweetness for espresso coffee and the equivalent concentrations in sweetness of different sweeteners, as well as characterise the time–intensity profile of each sweetener in relation to sweetness. The sweeteners evaluated were sucralose, aspartame, neotame, a cyclamate/saccharin mixture (2:1) and stevia. The sucrose concentration considered ideal by consumers was 12.5% (w/v), and the equivalent concentrations of the sweeteners were 0.0159% for sucralose, 0.0549% for aspartame, 0.0016% for neotame, 0.0359% for the cyclamate/saccharin mixture and 0.0998% for stevia. The time–intensity analysis indicated that possibly the sweeteners neotame, aspartame and sucralose would be the best substitutes for sucrose.     

Failure analysis of aluminum cable steel reinforced (ACSR) conductor of the transmission line crossing the Paraná River

The failure of the conductor of the 460 kV overhead transmission line located along the crossing of the Paraná River was investigated. As a result of this failure, a blackout took place in the southern states of Brazil in January of 2002, reaching approximately 67 million inhabitants. Investigation of the external aluminium layer of the ACSR conductor near the fracture showed typical static deformation marks and dynamic fretting wear tangential marks, both associated with the presence of Al₂O₃ debris. Additionally, encrusted silicon particles were observed on the external surface of the strands. The internal Al layer showed elliptical deformation marks also associated with Al₂O₃ debris. Both Al₂O₃ and Si particles are efficient abrasive material, which associated with slight relative motion of metallic surfaces (clamp/strand and strand/strand) can promote fretting wear. Two types of fracture surface of the Al strands were identified: 45° and quasi-normal surfaces, the former being the predominant type. Delamination and particle detachment were the mechanisms of superficial degradation observed on the Al strands, indicating that the rupture of the strands occurred under a gross slip fretting regime induced by sub-conductor oscillation. Inspection of the internal surface of as-cast Al–10%Si spacer clamps revealed different stages of intense circumferential wear caused by the preferential cracking and particle detachment of the eutectic constituent of the as-cast microstructure. This intense wear reduces the clamping contact pressure, which allows higher displacement amplitude and leads to the critical fretting regime. Finally, a few suggestions are discussed to minimise the occurrence of future failures.     

Silicon-to-steel bonding using rapid thermal annealing

This paper presents the rapid, low-temperature bonding between silicon and steel using the rapid thermal annealing process. Three different thin-film adhesion layer systems including silver, gold, and nickel were utilized as the intermediate bonding material to assist the eutectic Pb/Sn bonding between silicon and steel. The bonding temperature was set at 220/spl deg/C for 20 s, with a 20-s ramp-up time. Five experiments were conducted to determine the strength of the bond, including static tensile and compressive four-point bend tests, axial extension tests, tensile bending fatigue tests, and corrosion resistance tests. The test results have shown that the gold adhesion layer is the most robust, demonstrating minimal creep during fatigue tests, no delamination during the tensile or compressive four-point bend tests, and acceptable strength during the axial extension tests. Additionally, all adhesion layers have withstood four months of submersion in various high-temperature solutions and lubricants without failure. Simulations of the axial stresses and strains that developed during the four-point bend and axial extension tests were performed and showed that the presence of the silicon die provides a local reinforcement of the bond as observed in the experimental tests.