Tc-Mapping and Investigation of Water-Initiated Modification of YBa2Cu3O7-x Thin Films by Low Temperature Scanning Electron Microscopy

The Tc -mapping method using low temperature scanning electron microscopy (LTSEM) has been developed to study the spatial distribution of the critical temperature in high temperature superconducting (HTSC) films with a spatial resolution approaching 2 μm. To achieve so high a spatial resolution, a numerical deconvolution method was applied that eliminated distorting effects associated with the thermal diffusion and with the contribution from the absorbed beam current. The Tc -mapping method was used to investigate modification by water of YBa2Cu3O7-x films grown on (100) MgO and (110) LaAIO3 substrates. The rate of modification of a [110]-oriented YBa2Cu3O7-x/LaAIO3 film is found to be 40 times that of a c-axis oriented YBa2Cu3O7-x/MgO epitaxial film. It is argued that water-initiated modification of the films results from penetration of hydrogen into the films, rather than from out-diffusion of oxygen

Risk Modeling in the Oil and Gas Industry

The oil and gas industry is a sector that is prone to risks that can have severe consequences for both the environment and the economy. In this study. the aim is to develop an effective mathematical tool for risk modeling in the oil and gas industry. The research proposes a simulation modeling approach that focuses on two key risk parameters - frequency and severity. By using differentiated distributions. the unique properties of risk in the oil and gas industry can be effectively described, and an algorithm can be developed for practical applications. The findings of this study have significant implications for the oil and gas industry, policymakers, and investors. By using an effective mathematical tool for risk modeling. they can identify and manage risks more effectively, reduce the likelihood of accidents and other events that can have severe consequences. and minimize the potential impact of these events. Overall, this research provides valuable insights into the development of an effective mathematical tool for risk modeling in the oil and gas industry. By using simulation modeling and differentiated distributions. this study proposes an algorithm that can be practically applied to manage risks effectively in this important sector

The External Environment’s Influence on RES Development Intensity

The increasing energy consumption associated with scientific and technological progress has led to environmental concerns. The transition to renewable energy sources is a potential solution to mitigate the negative effects of energy consumption. This study’s objective is to determine the factors influencing the presence of renewable energy in countries’ energy systems and to describe the pattern of their influence. The validated regression model has a high coefficient of determination of 0.9034, indicating the model’s reliability in identifying factors influencing the presence of renewable energy in energy systems. The countries were divided into three groups based on their renewable energy usage level using cluster analysis, indicating the importance of the current usage for further development. The study found that the Human Development Index (HDI) is correlated negatively with the share of renewable energy in energy systems. An increase in the innovation index leads to the development of renewable energy. This study allows for an in-depth analysis of the individual countries in the sample and provides meaningful insights into the current state of renewable energy globally. Overall, this research helps to understand the factors influencing renewable energy usage, and the findings can be used to inform policy decisions regarding renewable energy development

Project of Energy Supply of the URFU Observatory with a Res- Based Microgeneration Plant

В статье рассматриваются перспективные источники энергии, которые могут использоваться вместо горючих ископаемых; приводится анализ метеоданных на территории обсерватории; показаны результаты расчетов для установки альтернативных источников энергии на объекте.The article discusses promising energy sources that can be used instead of fossil fuels; the analysis of meteorological data on the territory of the observatory is given; shows the results of calculations for the installation of alternative energy sources at the facility

Gendered STEM Workforce in the United Kingdom:The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Gendered STEM Workforce in the United Kingdom: The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Chemical characterization of extra layers at the interfaces in MOCVD InGaP/GaAs junctions by electron beam methods

Electron beam methods, such as cathodoluminescence (CL) that is based on an electron-probe microanalyser, and (200) dark field and high angle annular dark field (HAADF) in a scanning transmission electron microscope, are used to study the deterioration of interfaces in InGaP/GaAs system with the GaAs QW on top of InGaP. A CL emission peak different from that of the QW was detected. By using HAADF, it is found that the GaAs QW does not exist any longer, being replaced by extra interlayer(s) that are different from GaAs and InGaP because of atomic rearrangements at the interface. The nature and composition of the interlayer(s) are determined by HAADF. Such changes of the nominal GaAs QW can account for the emission observed by CL

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress towards gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications