Exposure to virtual nature: the impact of different immersion levels on skin conductance level,heart rate,and perceived relaxation

Virtual Reality - It is generally accepted that natural environments reduce stress and improve mood. Since access to natural environments is sometimes limited, virtual natural environments,...     

Technological Aspects of Synthesizing Ceramic Coatings by Flow-Through Micro-Arc Oxidation

Refractories and Industrial Ceramics - The paper introduces a promising technology for utilizing a traditional scheme for implementing a flow-through micro-arc oxidation method to restore localized...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

The structure,vacancy characteristics,and magnetic properties of GdMn1-xCrxO3 ceramics

To investigate the evolution of the structural and enhanced magnetic properties of GdMnO₃ systems induced by the substitution of Mn with Cr, polycrystalline GdMn_1-xCr_xO₃ samples were synthesized via solid-state reactions. XRD characterization shows that all GdMn_1-xCr_xO₃ compounds with single-phase structures crystallize well and that Cr³⁺ ions entering the lattice sites of GdMnO₃ induce structural distortion. SEM results indicate that the grain size of the synthesized samples (a few microns) decreases as the Cr substitution concentration increases. Positron annihilation lifetime spectroscopy reveals that vacancy-type defects occur in GdMn_1-xCr_xO₃ ceramics and that the vacancy size and concentration clearly change with the Cr content. The temperature and field dependence of the magnetization curves show that Cr substitution significantly influences the magnetic ordering of the gadolinium sublattice, improving the weak ferromagnetic transition temperature and magnetization of GdMn_1-xCr_xO₃. The enhanced magnetization of GdMn_1-xCr_xO₃ is closely related to the vacancy defect concentration.     

On the Growth of Thin-Film AlGaN/GaN Epitaxial Heterostructures on Hybrid Substrates Containing Layers of Silicon Carbide and Porous Silicon

Semiconductors - Abstract—In our work, we carry out a structural-spectroscopic study of AlGaN/GaN epitaxial layers grown by molecular-beam epitaxy with nitrogen-plasma activation on a hybrid...     

Modern Photodetector IR-Modules

Journal of Communications Technology and Electronics - In recent years, there has been a rapid improvement in photonics products due to the use of multilayer heterostructures grown on the basis of...     

Effect of heat treatment and zircon addition on the properties of low-cement castables based on recycled alumina grog

In this investigation, low-cement castables were prepared using 70% alumina grog aggregates obtained from crushed alumina brick waste. The aggregates were thermally treated at 1550 °C for 3 h. Four types of low-cement castables were prepared with various types of aggregates (alumina grog with or without thermal treatment) and fillers (with or without zircon addition), and they were evaluated in terms of their physical, thermal, and chemical properties. Microstructural analysis via scanning electron microscopy (SEM) was performed on the castables before and after slag attack. Compared to the other fabricated castables, the thermally treated alumina grog castables with zircon showed better physical properties, such as a higher bulk density, cold crushing strength, and modulus of rupture and a lower apparent porosity and water absorption. In addition, they had a higher positive linear thermal expansion, refractoriness under load, permanent linear change, and hot modulus of rupture. The results of the SEM with energy dispersive X-ray analysis of the prepared castables confirmed that the mullite and anorthite phases were predominant when zircon was not added and the zircon–mullite phase additionally appeared upon the incorporation of zircon. A quantitative elemental analysis via X-ray fluorescence spectroscopy was employed to determine the composition of the castables. X-ray diffraction analysis showed that the alumina grog castables had a high mullite and low anorthite content, and the thermally treated alumina grog had a high anorthite, low mullite, and high zircon content. The improvement in the mechanical and thermo-mechanical properties of the castables with thermally treated alumina grog and added zircon can be attributed to the formation of the zircon–mullite phase with a low mullite phase content.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Optimization methods in inverse problems and applications to science and engineering

Optimization and Engineering -     

A Conversion Method for the Preparation of Calcium Formate

Theoretical Foundations of Chemical Engineering - Calcium formate is widely used in construction, tanning, and textile manufacture and as an E238 biological additive in cosmetology and the food...