Multicomponent Diamond-Like Semiconductors Based on the InBV–CdS System: Bulk and Surface Properties

Semiconductors - According to developed methods, in the fields of the mutual solubility of initial binary compounds (InP, InSb, and CdS), solid solutions of the InP–CdS and InSb–CdS...     

Electrical and magnetic properties of double perovskite: Y2CoMnO6

In this communication, the structural, micro-structural, dielectric, electrical, magnetic, and leakage-current characteristics of a double perovskite (Y₂CoMnO₆) ceramic material have been reported. The material was synthesized via a high-temperature mixed-oxide route. The compound crystallizes in a monoclinic structure which is confirmed from preliminary X-ray structural study. The morphological study by using scanning electron micrograph reveals the almost homogeneous distribution of grains throughout the surface of the sample. The nature of frequency-dependence of dielectric constant has been described by the Maxwell-Wagner model. The occurrence of a dielectric anomaly in the temperature dependence of dielectric permittivity study demonstrates the ferroelectric-paraelectric phase transition in the material. From the Nyquist plots, we found the existence of both grain and grain boundary effects. The frequency dependence of conductivity was studied by the Jonscher’s Power law, and the conduction phenomenon obeys the large overlapping polaron tunneling model. By using the Arrhenius equation, the activation energy has been calculated which is nearly equal to the energy required for the hoping of the electron. Both impedance and conductivity analysis demonstrate that the sample exhibits negative temperature coefficient of resistance (NTCR) properties indicating the semiconducting type of material at high temperatures. The anti-ferromagnetic character of the material is observed from the nature of magnetic hysteresis loop. The leakage current analysis suggests that the conduction process in the material follows the space charge limited conduction phenomenon. Such material will be helpful for modern electronic devices and spintronic applications.     

Synthesis of Light Hydrocarbons from Biogas and Hydrogen: Investigation of a Fe-Mn-K/MgO Catalyst

Fischer-Tropsch synthesis of the CO₂ in biogas aims at producing light hydrocarbons and increasing its calorific value for feeding into the grid. Fe catalysts with Mn and K as promoters are supposed to yield high amounts of light hydrocarbons. Using a Fe-Mn-K/MgO catalyst, a parameter screening and long-term experiments were carried out. The catalyst shows, within the examined range, the highest selectivity to C₂–C₄ hydrocarbons at 450 °C, 8 bar(a), and a gas hourly space velocity of 350 h⁻¹. Calcination of the catalyst resulted in a significant drop of activity and an almost complete loss of selectivity to hydrocarbons. Admixture of steam to the reactant gas lowers the tendency to carbon deposition but also promotes the water-gas shift reaction and results in lower yields of hydrocarbons.     

An index structure supporting rule activation in pervasive applications

World Wide Web - Rule mechanism has been widely used in many areas, such as databases, artificial intelligent and pervasive computing. In a rule mechanism, rule activation decides which rules are...     

超亲水毛细芯环路热管启动及热性能分析

为解决电子设备高热通量下的散热问题，采用H₂O₂氧化法对烧结毛细芯进行了超亲水改性，研究了毛细芯表面润湿性对吸液性能的影响。并将改性后的超亲水毛细芯应用到环路热管内，研究了倾斜角度及加热功率对超亲水毛细芯环路热管的换热特性的影响。实验结果表明：超亲水毛细芯的吸液速度增加，吸液时间较亲水毛细芯减小了3.52ms；与普通亲水毛细芯环路热管相比，在加热功率Q=200W时，超亲水毛细芯环路热管蒸发器中心温度降低了约6.0℃，在Q=20W时启动时间与温度分别降低了33s与2.5℃。同时发现超亲水毛细芯环路热管在正重力状态时的运行温度更低，热阻较小，最低热阻仅为0.084℃/W。     

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.     

Potential of pulsed electric field to control Aspergillus parasiticus,aflatoxin and mutagenicity levels: Sesame seed quality

Seed processing technologies are essential for seed safety and functionality through protection of physicochemical quality, pathogen inactivation, aflatoxin detoxification and alleviation of mutagenicity. Design of a pilot-scale unit of pulsed electric fields (PEF) to treat sesame seeds with respect to quality parameters, Aspergillus parasiticus inactivation and aflatoxin reduction as well as alleviation of aflatoxin mutagenicity were prompted in this study. PEF energy ranged from 0.97 to 17.28 J achieved maximum reductions of peroxide value and acidity number of 67.4 and 85.7%, respectively, and did not change color L*, a*, b* and hue values. A 60% reduction of A. parasiticus counts occurred at the maximum PEF energy. Aflatoxins G1, G2, B1, and B2 contents decreased by 94.7, 92.7, 86.9, and 98.7%, respectively. Except for the samples treated by 2.16 J with 100 μg/plate and by 6.80 J with 10 μg/plate, PEF treatment provided elimination of aflatoxin mutagenity. It is concluded that PEF treatment can be used to treat sesame seeds with preservation of physicochemical properties, inactivation of A. parasiticus and decomposition of aflatoxins with reduced mutagenicity.     

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.