Assessment of Risks and Threats of "Green" Entrepreneurship based on Cross-Factor Modeling

The article is a continuation of a number of scientifc studies conducted by the authors within the framework of government assignments and grants, dedicated to identifying the relationship between the economic security of territories and the development of "green" entrepreneurship on them.Purpose: of the study is to model the assessment of the state of economic security of territories under various conditions of the transition to "green" entrepreneurship, taking into account the action of external and internal factors. "Green" entrepreneurship is a priority area for the development of Russian regions, consistent with the achievement of Russia's national strategic goals for the period up to 2030.Methods: in this work, cross-factor modeling and the method of integral analysis are used to calculate the composite index of the state of economic security and its structural components, taking into account the development of "green" entrepreneurship, supplemented by the method of a priori ranking of factors to assess the contribution of each component and justify the priority the selected factors affecting the level of economic security of the regions.Results: a three-component cross-factor model for assessing the state of economic security in certain regions of the Russian Federation (Moscow Region, Leningrad Region, Sverdlovsk Region, Novosibirsk Region, Altai Territory, Krasnodar Territory) had been calculated and tested. The regions were ranked according to the value of each of the components, as well as separately according to the aggregate level of economic security. The necessity of stimulating the development of "green" entrepreneurship in each of the analyzed regions. Have been substantiated and stimulating and discouraging factors that have an impact on the level of economic security, taking into account "green" entrepreneurship, pointwise in each subject, have been identifed.Conclusions and Relevance: the proposed approach to assessing the impact of the identifed factors on the level of economic security of the regions, taking into account the development of "green" entrepreneurship on the basis of the developed cross-factor model, made it possible to build a rating of territories and identify stimulating and discouraging factors in their development. On the basis of which to determine the tools for influencing the economic security in a separate entity.

Multi-Factor Modeling Method of the Load Sharing Ratio under Moving Train Loads

In railway engineering, the load sharing ratio (LSR) is the ratio of the rail seat load (RSL) to the axle load, which is affected by many factors. The LSR can be used in the design and analysis of railway track structures as well as in the research of predicting the dynamic influence of railway tunnels and the environment. The “static loading method” commonly used to study the LSR does not conform to reality; using it, it is difficult to obtain a complete LSR curve, limiting its application. Besides, there is currently a lack of LSR prediction methods considering the impact of multiple factors. Therefore, this paper proposes a “moving loading method” for investigating the LSR under moving train excitation, verified to be rational by comparing with the experimental results. At the same time, a procedure for establishing the LSR multi-factor prediction model is put forward, namely, we (1) determine the LSR function form and the fitting algorithm; (2) perform parameter sensitivity analysis to determine the main influencing parameters of the LSR function; and (3) design a quadratic regression orthogonal test to obtain the prediction formula of the LSR function coefficients. Once establishing the prediction model for a type of train-track system, the LSR of similar systems can be calculated by adjusting the main parameters of the model. Shijiazhuang Metro Line 1 using the A-type vehicle and the monolithic trackbed is taken as a case study to develop a corresponding LSR multi-factor prediction model by the moving loading method and the procedure mentioned above. The results indicate that the proposed method performs well and can be adopted to enhance the accuracy of track design or tunnel and environmental vibration prediction.

Developmental and Etiological Patterns of Substance Use from Adolescence to Middle Age: A Longitudinal Twin Study

Background: Common liability to addiction framework suggests the tendency to use substances is largely a general heritable liability, but little is known about how expression of this liability varies from adolescence to middle age. We evaluated average trajectories of development and covariation underlying commonly used substances using a genetically informative prospective design spanning three decades. Methods: Using a sample of 3,762 twins across 7 prospective waves of assessment spanning ages 14-40, we modeled these relationships using two complementary approaches: common factor modeling and piecewise latent growth modeling with measures of alcohol, tobacco, and marijuana useResults: We found phenotypic (rp ~.3-.9) and genetic covariation (rg ~.3-1) between a single common factor at each age, though the factor explained less shared variance over time. Average substance use increased across adolescence for all phenotypes and either declined in adulthood or remained stable; these trajectories were heritable (~.35-.75) across all stages of development. We also found shared environmental covariation underlying growth model intercepts reflecting use at age 16 (rc ~.7-1). Conclusions: A heritable common factor accounted for co-occurring substance use from mid-adolescence to mid-adulthood, and greater substance specificity emerged with maturation. Similarly, all stages of substance use development were heritable, but correlations between substances weakened across development. These results extend and reinforce prior work examining consumption and problem use, providing new evidence over a broad age range showing that individuals use substances more indiscriminately at younger ages and show preferences later.

Defining Ideal External Female Genital Anatomy Via Crowdsourcing Analysis

Background Despite existing anthropometric data in the literature regarding the variation of female external genital anatomy, the ideal aesthetic characteristics have yet to be defined. Objectives Authors used crowdsourcing in order to better evaluate preferred anatomic characteristics of external female genitalia. Methods Fifty-six total images were digitally created by altering the proportions of the labia minora, labia majora, and clitoral hood. Images with differing ratios were presented in pairs to Amazon Mechanical Turk (Seattle, WA, USA) raters. Three different experiments were performed with each varying two of the three image characteristics to permit two factor modeling. The Bradley-Terry-Luce model was applied to the pairwise comparisons ratings to create a rank order for each image. Preferences for each anatomic variable were compared using chi-squared tests. Results A total of 5000 raters participated. Experiment 1 compared differing widths of the labia majora and labia minora and determined a significant preference for larger labia majora width and mid-range labia minora width (p=0.007). Experiment 2 compared labia minora width versus clitoral hood length and showed a statistically significant preference for wider majoras (p<.001), but no significant preference in clitoral hood length (p=0.54). Experiment 3 compared clitoral hood length versus labia minora width and showed a statistically significant preference for mid-range labia minora widths (p<.001) but no significant preference in clitoral hood length (p=0.78). Conclusions Raters preferred a labia majora to labia minora width ratio of 3:1 with minimal preference in clitoral hood length.

Issues in Estimating Interpretable Lower Order Factors in Second-Order Hierarchical Models: Commentary on Clark et al. (2021)

In a previous issue of Clinical Psychological Science, Clark and colleagues asserted that lower order factors in second-order models are comparable with specific factors in bifactor models when residualized on the general factor. Modeling simulated data demonstrated that residualized lower order factors are correlated with bifactor-specific factors only to the extent that factor loadings are proportional. Modeling actual data with violations of proportionality showed that specific and residualized lower order factors are not always highly correlated and have differential correlations with criterion variables even when both models fit acceptably. Because proportionality constraints limit only second-order models, bifactor models should be the first option for hierarchical modeling.

Friendship Network Satisfaction: A multifaceted construct scored as a unidimensional scale

Although satisfying friendships are crucial for well-being throughout adulthood, measures of friendship satisfaction have been limited by: (1) item content relevant to children only, (2) a focus on single relationships rather than the friendship network, and (3) disagreement about the number of dimensions necessary to capture the construct. To overcome these limitations, we assembled an item pool from a number of existing measures, created additional items drawn from research on friendships, and then examined the structure and psychometric properties of those items in two online surveys of over 2000 respondents each. Factor analyses consistently identified two correlated factors—closeness and socializing—but bi-factor modeling revealed that scores on both subscales load strongly on a general factor, suggesting that the multifaceted content can be scored efficiently as a unidimensional composite. Analyses using item response theory (IRT) supported the creation of a reliable 14-item instrument that demonstrated adequate convergent and predictive validity. Thus, the Friendship Network Satisfaction (FNS) Scale is a psychometrically sound tool to advance research on friendships across the lifespan.

Factor Modeling Of Russian Women’s Perceptions Of Combining Family And Career

Sociologists and demographers explain late childbearing by the transformation of the life values of modern women. This is considered as one of the reasons for the decline in the birth rate. Our study aims to reveal perceptions of the relationship between career and family in the life strategies of working Russian women by using factor analysis. We collected data in a sociological survey of working women living in the Ural region. We asked respondents to rate 10 statements about work, family and children. We constructed 3-factors model of Russian women’s perceptions of combining family and career. Then we used correlationanalysis to assess the relationship between these factors and the social and demographic parameters of the respondents. We concluded that the use of factor analysis made it possible to model a wide range of Russian women’s perceptions of combining family and career. Considering the results obtained may contribute to improving the regulation of interaction of two important societal spheres of professional and parental activities and create conditions for increasing the birth rate in Russia.