Safety Devices Based on Smart Alloys

Journal of Machinery Manufacture and Reliability - A device based on smart alloys has been designed for use in machine building items, in particular, in nuclear engineering, for preventing...     

Unsupervised Segmentation of Stereoscopic Video Objects: Constrained Segmentation Fusion Versus Greedy Active Contours

In this paper, we present a novel memory access reduction scheme (MARS) for two-dimension fast cosine transform (2-D FCT). It targets programmable DSPs with high memory-access latency. It reduces the number of memory accesses by: 1) reducing the number of weighting factors and 2) combining butterflies in vector-radix 2-D FCT pruning diagram from two stages to one stage with an efficient structure. Hardware platform based on general purpose processor is used to verify the effectiveness of the proposed method for vector-radix 2-D FCT pruning implementation. Experimental results validate the benefits of the proposed method with reduced memory access, less clock cycle and fewer memory space compared with the conventional implementation.     

Uncertainties in the determination of pyrrolizidine alkaloid levels in naturally contaminated honeys and comparison of results obtained by different analytical approaches

The contamination of honey with hepatotoxic pyrrolizidine alkaloids (PAs) is a well-known hazard for food safety. While management strategies and controls of the honey industry aim to reduce the PA levels, uncertainties remain with regard to the safety of regionally produced and marketed honey. In addition, a previous study showed large differences of results obtained after various periods of storage and apparent differences between the analytical results of different laboratories. Therefore, this study aimed at examining these uncertainties by monitoring the impact of storage on the PA and PA N-oxide (PANO) content of two freshly harvested honeys and on possible demixing effects caused by pollen settling. Additionally, three analytical approaches – target analysis with matrix-matched calibration or standard addition and a sum parameter method – were applied for a comparative analysis of 20 honeys harvested in summer 2016. All samples originated from Schleswig-Holstein in Northern Germany where the PA plant Jacobaea vulgaris is currently observed on a massive scale. The results of the time series analyses showed that PANO levels markedly decreased within a few weeks and practically reached the LOD 16 weeks after harvest. Tertiary PAs, by contrast, remained stable and did not increase as a consequence of PANO decrease. The experiments on a putative demixing, which may result in a heterogeneous distribution of PAs/PANOs, revealed that there was no such effect during storage of up to 12 weeks. A comparison of the PA/PANO levels obtained by different analytical approaches showed that in some cases the sum parameter method yielded much higher levels than the target approaches, whereas in other cases, the target analysis with standard addition found higher levels than the other two methods. In summary, the results of this study highlight uncertainties regarding the validity and comparability of analytical results and consequently regarding health risk assessment.     

Valorisation of potato wastes

Potato processing industry has a high degree of discarding, which currently has low added value being used primarily for animal feed. However, potato wastes offer a broad range of interesting components such as antioxidants, starch, protein or fibre with potential applications in the food and non-food industries. The recovery of these high valuable fractions using efficient multistage and multiproduct processes could be of great interest. This short review provides a general overview on the integral valorisation of potato wastes, offering an updated vision of the main residual parts generated during potato harvesting and processing, the high valuable obtained components focusing on the bioactive ones and the potential of the emerging extraction techniques over conventional ones. In addition, innovative applications are discussed to highlight the scientific and applied interest of these underutilised and undervalued fractions and to emphasise the integral valorisation of raw materials.     

Synthesis and Properties of Azo Compounds Based on Nitroaniline Derivatives of 2,4,6-Trinitrotoluene and 1,3,5-Trinitrobenzene

Theoretical Foundations of Chemical Engineering - New research results, as well as those published earlier by the authors, on the synthesis of azo compounds based on nitroanilines obtained by the...     

Categorical observers for metamerism

A method is proposed to generate categorical colour observer functions (individual colour matching functions) for any field size based on the CIE 2006 system of physiological observer functions. The method combines proposed categorical observer techniques of Sarkar et al with a physiologically-based individual observer model of Asano et al and a clustering technique to produce the optimal set of categorical observers. The number of required categorical observers varies depending on an application with as many as 50 required to predict individual observers' matches when a laser projector is viewed. However, 10 categorical observers are sufficient to represent colour-normal populations for personalized colour imaging. The proposed and recommended categorical observers represent a robust and inclusive technique to examine and quantify observer metamerism in any application of colorimetry.     

Development of a new ion-imprinted polymer (IIP) with Cd2+ ions based on divinylbenzene copolymers containing amidoxime groups

Polymer Bulletin - In this study, we synthesized a new ion-imprinted polymer (IIP) based on introduction of amidoxime groups in acrylonitrile, complexation with Cd2+ ions and polymerization with...     

Technological and sensorial properties of liquid nitrogen ice cream enriched with protein from brewing waste (trub)

Debittered trub (brewing waste) is an important source of protein source (70.26%). Trub and whey protein were used for 5% protein enrichment of ice cream frozen by liquid nitrogen. Three formulations were elaborated: ice cream standard (ICS), ice cream with whey protein (ICW) and ice cream with trub (ICT). Chemical composition, rheological properties, texture, overrun, melting rate, scanning electron microscopy and a sensorial test were performed. Results showed that ICT presented a higher viscosity, obtained on the upward curve up to 6.76 Pa s⁻¹, consistency index (22.96 (Pa s⁻¹)ⁿ), hysteresis area (140.40 mPa s⁻¹) and hardness (31113.33 g) but a lower melting rate (0.38 g min⁻¹), overrun (13.92%) and sensorial acceptability than the other formulations. The addition of trub debittered for protein enrichment improved ice cream properties and demonstrated that it could be used as a food ingredient.     

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.