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.     

Iron(II) Sulfate(VI) from Titania Production as a Raw Material for Preparation of Hydrogen Sulfide Sorbents

A hybrid sorbent material for removal of hydrogen sulfide from air was developed. The material is based on activated carbon and iron compounds obtained from waste iron(II) sulfate(VI) heptahydrate. The iron salt is deposited on the carbonaceous support and subjected to oxidation (Fe²⁺ to Fe³⁺) using atmospheric oxygen under alkaline conditions. An effect of H₂O₂ addition to the process on the composition of the resultant material was also examined. X-ray diffraction (XRD) analyses confirmed easy conversion of waste FeSO₄·7H₂O to iron oxides Fe₃O₄ and FeOOH. The activated carbon supporting iron oxides revealed a higher efficiency in H₂S elimination from air compared to the commercial activated carbon, without any modification.     

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.     

Related Oscillations of Plasmons in Two Similar Nanocylinders from Silver

Journal of Communications Technology and Electronics - Abstract—The two-dimensional diffraction problem of a plane polarized TM-wave on two identical solid silver nanocylinders is considered....     

Chlorination of Quartz-Leucoxene Concentrate of Yarega Field

Metallurgical and Materials Transactions B - The process of obtaining titanium tetrachloride (TiCl4) from the quartz-leucoxene concentrate (QLC) from the Yaregskoe oil-titanium field was studied....     

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.     

Revisiting rock classification to estimate rock mass properties

This paper presents the results of ongoing research carried out by the author exploring methods to provide a more robust estimate of rock mass properties specifically for use in tunnel design. Data from various large-scale rock mass failures are introduced, including coal pillars. The damage-initiation,spalling-limit approach is compared to the coal pillar database. New comparisons of estimating the geological strength index(GSI) and relationships to estimate the Hoeke Brown failure criterion parameters, mb, s and a, are presented.