Are Trait–State Fit and State–Situation Fit Informative of Within-Person Processes of Personality State Expression?

Choosing the right behavior can oftentimes be an act of balance between one’s own preferences or tendencies and momentary situational affordances. How do people navigate these potentially different requirements? One way would be to choose the behavior that makes one feel good. Specifically, we argue that fit between personality traits and personality states and fit between personality states and situation characteristics should be associated with positive affect. Thereby, fit may provide feedback to within-person processes of personality state expression. In two experience sampling studies (Study 1: N = 194, 4,244 observations; Study 2: N = 254, 7,667 observations), we employed moderated multilevel polynomial regressions and response surface analysis to examine whether trait–state fit and state–situation fit were associated with state affect. We found a consistent pattern of interactions between personality traits, personality states, and situation characteristics predicting state affect, but did not find evidence for the hypothesized trait–state fit patterns or state–situation fit patterns. These findings have two main implications. First, the results suggest that fit patterns are not involved in associations between personality traits, personality states, situation characteristics, and state affect. However, because theories proposing fit are often imprecise regarding the operationalization of fit, it is difficult to draw concrete conclusions about these theories. Second, the large number of interactions between personality traits, personality states, and situation characteristics suggests that these three constructs should be studied together. The specific role of these interactions for personality processes needs to be further determined in future research.

Specificity of the products of Tyatya volcano eruption in 1973

Based on 198 determinations of the complete silicate analysis of Tyatya volcano (Kunashir Island), we compared the data of the latter (1973) and all previous eruptions to reveal their differences and determine the evolution of Tyatya volcanism in the Holocene. The application of the estimation of the thermobaric parameters of the upper mantle magma chamber of the volcano made it possible to determine the temperature decrease in the magma chamber in 1973. An assumption was made about the polycyclic magmatic activity of the Tyatya volcano. The results of statistical processing of data on the thickness of tephra and the size of its fragments from the 1973 eruption are presented. Polynomial regressions of various degrees are applied. Models of tephra distribution based on three-dimensional trend analysis have been constructed. The results of the study are recommended for the creation of information databases on the petrochemistry of eruption products and the distribution of pyroclastics from volcanoes in the Kuril Island arc.

Omni-Channel Customer Experience (In)Consistency and Service Success: A Study Based on Polynomial Regression Analysis

Drawing on expectation disconfirmation theory, this study explores the dyadic nature of omni-channel consistency on customer experience. Specifically, we propose a conceptual model that focuses on a brand’s offline channel customer experience relative to that of its online channel, and test the influences of customer experience (in)consistency on customer satisfaction, which then improves repurchase intention and word-of-mouth. The results of polynomial regressions on 265 survey respondents indicate that given omni-channel customer experience inconsistency, customers prefer consistent online and offline experiences. For omni-channel consistency at lower levels of customer experience quality, customers prefer consistency at higher levels of quality. For omni-channel inconsistency where offline customer experience quality is lower than that online, customers prefer omni-channel inconsistency, where offline customer experience quality is higher than that online. These findings produce not only theoretical contributions but also insightful suggestions for how customer experience can be taken into consideration in the promotion of a brand’s omni-channel service success.

Trend, Challenges and Opportunities of Illegal Wildlife Trafficking and Trade Activities within and through Ethiopia

Having improved knowledge on global, regional and/or national trends in illegal wildlife trade is required to develop effective combating strategies. In this paper, nine years (2011–2019) data on illegal wildlife trade and trafficking (iwt) seizure records were used to explore trends in iwt activities in Ethiopia and to identify countries involved most in the iwt. Trends of iwt activities were assessed in two indicators: as relative transaction index (ti), and as relative weight index (for ivory [wi]) or as relative number of items for ivory and other wildlife products [ni]. To account for potential yearly variations in seizure rates, proxy variables to law enforcement efforts and effectiveness were used and predictive models that produced bias-adjusted estimates of relative trends in iwt activities were built. To explore the underlying reasons for the trend observed, relevant national officers were also interviewed to assess how well has been wildlife law enforcement working in combatting iwt in the country. Linear or polynomial regressions, where appropriate, were fitted to the seizure data to establish trends in transaction index over the nine years. A total of 842 incidences of iwt seizure instance reports, involving 18 animal species, were recorded in the country during the nine years period. However, the highest seizure (94%) rate was ivory and ivory products. Our results generally showed declining trends in both ti and wi or ni. The observed trend was also supported by the experts’ opinions and likely reflects the positive impacts of the country’s implementation of its wildlife laws. Other interesting findings of this study were that China represented the single most important destination country and Chinese nationalities were the most traffickers. In conclusion, the results provide detailed evidence to inform national and international decision making on key species implicated in the illegal trade. Similar periodic assessment of the situation of iwt within the country is needed to evaluate effectiveness of the country’s past and present measures and to revise its future combatting policies and strategies.

Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions

This paper develops residual-based monitoring procedures for cointegrating polynomial regressions (CPRs), i.e., regression models including deterministic variables and integrated processes, as well as integer powers, of integrated processes as regressors. The regressors are allowed to be endogenous, and the stationary errors are allowed to be serially correlated. We consider five variants of monitoring statistics and develop the results for three modified least squares estimators for the parameters of the CPRs. The simulations show that using the combination of self-normalization and a moving window leads to the best performance. We use the developed monitoring statistics to assess the structural stability of environmental Kuznets curves (EKCs) for both CO2 and SO2 emissions for twelve industrialized countries since the first oil price shock.

A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches

The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.

Using Python and Google Colab to Teach Physical Chemistry During Pandemic

<div> The present manuscript intends to propose using the Google Colab platform to teach and solve physical chemistry problems using Python computational language. Seven Jupyter notebooks were written and made available for the students via Google Colab supplementary material of the physical chemistry course of the chemical engineering course of the Technological Faculty of the Rio de Janeiro State University. These notebooks include several problems extracted from the course bibliography and solved with the use of Python language. The scripts show how the students can perform linear and polynomial regressions, fit math models to given data, perform numerical integration and plot creation using Python and its standards libraries. The Colab platform was chosen because it is free to use, does not require the installation, setup, and configuration of Python packages and their libraries in the students’ personal computers. It is a multiuser and collaborative environment, ideal for remote classes. The notebooks can be shared between instructor and students or between the students, which easy the communication and track of students’ progress. Indeed, this resource can be useful even after the end of the pandemic. This manuscript describes the platform, its advantages, how it was applied in our physical chemistry course, and the students’ feedback at the end of the term. All notebooks are available as Supplementary Material of the manuscript, translated from Portuguese to English since our course is entirely in Portuguese. I hope the material and experience shared in this manuscript can be helpful to chemistry instructors who intend to abroad their pedagogical methods to engage more students in the undergraduate courses. </div>

Using Python and Google Colab to Teach Physical Chemistry During Pandemic

<div> The present manuscript intends to propose using the Google Colab platform to teach and solve physical chemistry problems using Python computational language. Seven Jupyter notebooks were written and made available for the students via Google Colab supplementary material of the physical chemistry course of the chemical engineering course of the Technological Faculty of the Rio de Janeiro State University. These notebooks include several problems extracted from the course bibliography and solved with the use of Python language. The scripts show how the students can perform linear and polynomial regressions, fit math models to given data, perform numerical integration and plot creation using Python and its standards libraries. The Colab platform was chosen because it is free to use, does not require the installation, setup, and configuration of Python packages and their libraries in the students’ personal computers. It is a multiuser and collaborative environment, ideal for remote classes. The notebooks can be shared between instructor and students or between the students, which easy the communication and track of students’ progress. Indeed, this resource can be useful even after the end of the pandemic. This manuscript describes the platform, its advantages, how it was applied in our physical chemistry course, and the students’ feedback at the end of the term. All notebooks are available as Supplementary Material of the manuscript, translated from Portuguese to English since our course is entirely in Portuguese. I hope the material and experience shared in this manuscript can be helpful to chemistry instructors who intend to abroad their pedagogical methods to engage more students in the undergraduate courses. </div>

Innovation and Sustainability Management as a Key Factor in Global Purpose-Driven Micro, Small, and Medium-Sized Bogotanas Companies

This work aims to present the results of the application of the RISE model (innovation and business sustainability route) in 25 global purpose-driven micro, small, and medium-sized Bogotanas companies, beneficiaries of the Innova District project and its impact on the net margin. The working hypothesis is the management of innovation and sustainability has some impact on the net margin of the global purpose-driven micro, small, and medium-sized Bogotanas enterprises. It is a preliminary analysis, the findings and conclusions that were made based on the description of results and development of polynomial regressions only generated conclusive elements for the population of companies analyzed and the characteristics of the research proposed. However, the proposed hypothesis was verified for a group of companies, while, for a second group, the correlation between innovation and sustainability and the net margin was relatively low or almost nil.