Experimental investigation of the complex loading of alloy 01570 along paths in the form of two-segment broken lines at different temperatures

The results of experimental investigations of tubular alloy 01570 specimens under complex loading are presented. The vector and scalar properties of the material are investigated at various temperatures and fracture angles. V. G. Zubchaninov's hypothesis that active loading processes at fracture angles to 90° can be considered quasisimple is confirmed.Translated from Problemy Prochnosti, No. 3, pp. 35–39, March, 1993.     

Efficient GPU-based parallelization of solvation calculation for the blind docking problem

The Journal of Supercomputing - Molecular docking techniques are widely used in computational drug discovery. Most of these techniques simulate the way that a ligand interacts with a protein target...     

Analysis of the electrical transport,conductivity, and dielectric relaxation behavior of La0.75 Ba0.10 Sr0.15 Fe O2.875-δ (δ = 0.375 and 0.50) brownmillerite oxides

The topotactic reduction of La_0.75Ba_0.10Sr_0.15FeO_2.875 with titanium metal leads to a new isostructural material of the composition La_0.75Ba_0.10Sr_0.15FeO_2.875-δ (δ?=?0.375 and 0.50). XRD analysis of phases confirms that the obtained compound adopts a brownmillerite-type structure. A slight distorted monoclinic P2/m was found to describe the crystal structure. The transport properties have been investigated by current–voltage (I–V). The electric and dielectric measurements were carried out covering a wide range of temperature (300–600 K). Our materials display semiconducting properties as well as mixed ionic and electronic conductivity. At high temperatures, the activation energy values proved to be around 907–630 meV, which refers basically to oxygen vacancies conduction. The analysis of dielectric properties and dielectric losses (ε’, tanδ) of both compounds vs temperature at different frequencies demonstrates two relaxer attitudes. A low dielectric loss and low electrical conductance were displayed. Relying upon these values, these materials stand for perfect candidates for micro-electronics devices.

     

Automatic source scanner identification using 1D convolutional neural network

In this digital world, digitized documents can be considered original or a piece of evidence; checking the authenticity of any suspicious image has become an unavoidable concern to preserve the trust in its legitimacy. However, identifying the source of a digital image without any prior embedded information is a very challenging task. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN) model to solve the source scanner identification (SSI) problem blindly. Unlike traditional methods based on handcrafted features, the proposed framework can dynamically learn and extract scanner device-specific features. This work, comprised of the 1D-CNN and a support vector machine (SVM) as a classifier, was trained on nine scanners of different brands and models. The experimental result shows that our model achieves 98.15% accuracy on full images and overall accuracy of 93.13% on segments from test images, outperforming other state-of-art approaches. Our model also proves to be able to distinguish between scanners of the same model. Furthermore, the SVM classifier improved the 1D-CNN accuracy by approximately 3% compared to its original configuration.

     

Correction to: Introspective interviewing for work activities: applying subjective digital ethnography in a nuclear industry case study

Cognition, Technology & Work - A correction to this paper has been published: https://doi.org/10.1007/s10111-021-00678-9     

Study of the Electromagnetic Coupling in an Inductively Coupledplasma Torch at Atmospheric Pressure

This work is devoted to study the behaviour of an inductively coupled plasma torch using argon,hydrogen and oxygen at atmospheric pressure.Thermal plasmas are generally used to form highly reactive species and to transfer enthalpy.Precise characterisation of the plasma behaviour is thus relevant to evaluate chemical and thermal effects of the plasma.For equivalent configurations in terms of torch geometry and gas flows,a transition between two different coupling modes is identified affecting atomic species distribution and energy transmitted to a copper target.These two modes are affected by total electrical power input and oxygen and hydrogen flow rates,showing up hysteresis behaviour.Electrical current and frequency progress are analysed to highlight the transition between the two coupling modes.Optical spectroscopy is used to measure temperature profiles and radial atomic oxygen concentration profiles for different coupling modes.We observe that-depending on the coupling mode-temperature profile,atomic species distribution as well as the power transmission to a copper target are affected.     

First principles study of structural,electronic and optical properties of AgSbS2

In this work, we study the structural, electronic and optical properties of AgSbS₂, using full-potential linearized augmented plane wave and the pseudopotential plane wave scheme in the frame of generalized gradient approximation. Features such as the lattice constant, bulk modulus and its pressure derivative are reported. Our results suggest a phase transition from AF-IIb phase to rocksalt (B1) phase under high pressure. The calculated band structure and density of states show that the material under load has an indirect energy band gap X→(LГ) for AF-IIb phase (semiconductor) and a negative band gap W→(ГX) for B1 phase (semimetal). The optical properties are analyzed and the origin of some peaks in the spectra is discussed. Besides, the dielectric function, refractive index and extinction coefficient for radiation up to 14 eV have also been reported and discussed.     

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.