Income Status and Life Satisfaction

The importance of both income rank and relative income, as indicators of status, has long been recognised in the literature on life satisfaction and happiness. Recently, several authors have made explicit comparisons of the relative importance of these two measures of income status, and concluded that rank dominates to the extent that reference income becomes insignificant in regressions including both these explanatory variables, and that even absolute or household income, otherwise always positively related to happiness, may lose statistical significance. Here we test this hypothesis with a large UK panel (British Household Panel Survey and Understanding Society) for 1996–2017, split by age and retirement status, and find, contrary to previous results, that rank, household income and reference income are all usually important explanatory variables, but with significant differences between subgroups. This finding holds when rank is in its often-used relative form, and also with absolute rank.

Thin Ice: Teasing Play as a Network-Forming Force in Organizations

Play is a deeply human constitutive activity of organizational life, which may manifest in several distinct processes. In the present study, the author compasses a particular culturally relative form of play, namely teasing in the workplace setting. Conducting a meta-synthesis of adjacent streams of literature, the author reframes the concept of teasing and inspects it through the analytical lens of play. The research unfolds how teasing behaviour may form an invisible meta-network in organizations, which may fundamentally affect group and organizational dynamics.

Face image profiles features extraction for recognition systems

The object of research is the algorithm of piecewise linear approximation when applying it to the selection of facial features and compression of its images. One of the problem areas is to obtain the optimal ratio of the degree of compression and accuracy of image reproduction, as well as the accuracy of the obtained facial features, which can be used to search for people in databases. The main characteristics of the image of the face are the coordinates and size of the eyes, mouth, nose and other objects of attention. Dimensions, distances between them, as well as their relationship also form a set of characteristics. A piecewise linear approximation algorithm is used to identify and determine these features. First, it is used to approximate the image of the face to obtain a graph of the silhouette from right to left and, secondly, to approximate fragments of the face to obtain silhouettes of the face from top to bottom. The purpose of the next stage is to implement multilevel segmentation of the approximated images to cover them with rectangles of different intensity. Due to their shape they are called barcodes. These three stages of the algorithm the faces are represented by two barcode images are vertical and horizontal. This material is used to calculate facial features. The medium intensity function in a row or column is used to form an approximation object and as a tool to measure the values of facial image characteristics. Additionally, the widths of barcodes and the distances between them are calculated. Experimental results with faces from known databases are presented. A piecewise linear approximation is used to compress facial images. Experiments have shown how the accuracy of the approximation changes with the degree of compression of the image. The method has a linear complexity of the algorithm from the number of pixels in the image, which allows its testing for large data. Finding the coordinates of a synchronized object, such as the eyes, allows calculating all the distances between the objects of attention on the face in relative form. The developed software has control parameters for conducting research.

Methodology for assessing the relative tourist attractiveness of the region

Assessment of the tourist attractiveness of a territory is an important analytical area in terms of identifying opportunities for development of regional tourism, as one of the most significant and promising sectors of the economy. This can be the basis for development of appropriate strategic regional programs. The existing scientific discussion in this area has contributed to emergence of a number of methodological approaches that can be used in practice. However, their critical analysis revealed a number of provisions, the improvement of which will make it possible to obtain more objective and relevant results, reflecting the possibility of the destination becoming an object of attraction for tourists. Firstly, the lack of a clear distinction between the categories of “tourist potential”, which reflects the objective state of the resources of the territory, and “tourist attractiveness”, which includes the estimated component of available resources from the perspective of a consumer of tourist services. This leads to the fact that the principle of the subjectivity of the assessment is practically not implemented, thus the possibility of focusing on market demands is not fully used, which implies use of segmentation and positioning tools. Secondly, the results obtained using the methods offered by the scientific community are often distorted by the subjective perception of the researcher, which is associated with use of a large volume of expert assessments. Thirdly, from our point of view, the number of estimated variables, the number of which may approach 100, is not always justified. This, on the one hand, reduces the degree of influence of each of them on the result, and, therefore, the significance, and on the other hand, makes it difficult for the analyst to collect the necessary information. Therefore, the purpose of this study is to develop a methodology that eliminates these disadvantages. The offered author’s development includes objective indicators that do not require involvement of experts in their assessment. The number of these indicators is limited and systematized by groups, in accordance with modern market demands: directly attracting tourists, ensuring the quality of stay and ensuring the availability of the territory. The relative form of the offered metrics shows the degree of difference in the selected variables of some territories in comparison with others, which in the future will make it possible to orient the tourism development in the region to target audiences whose needs can be satisfied by the resources available at the destination.

Multifactorial analysis of factors influencing elite Australian football match outcomes: a machine learning approach

In Australian football (AF), few studies have assessed combinations of pre- game factors and their relation to game outcomes (win/loss) in multivariable analyses. Further, previous research has mostly been confined to association-based linear approaches and post-game prediction, with limited assessment of predictive machine learning (ML) models in a pre-game setting. Therefore, our aim was to use ML techniques to predict game outcomes and produce a hierarchy of important (win/loss) variables. A total of 152 variables (79 absolute and 73 differentials) were used from the 2013–2018 Australian Football League (AFL) seasons. Various ML models were trained (cross-validation) on the 2013–2017 seasons with the–2018 season used as an independent test set. Model performance varied (66.5-73.3% test set accuracy), although the best model (glmnet – 73.3%) rivalled bookmaker predictions in the same period (70.9%). The glmnet model revealed measures of team quality (a player-based rating and a team-based) in their relative form as the most important variables for prediction. Models that contained in-built feature selection or could model non-linear relationships generally performed better. These findings show that AFL game outcomes can be predicted using ML methods and provide a hierarchy of predictors that maximize the chance of winning.

Identificación de áreas quemadas mediante el análisis de series de tiempo en el ámbito de computación en la nube

<p>There are large omission errors in the estimation of burned area in map products that are generated at a global scale. This error is then inherited by other models, for instance, those used to report Greenhouse Gas Emissions using a “bottom up” approach. This study evaluates temporal methods to improve burned area detection using Landsat 5-TM and 8-OLI. In this process, the normalized burn ratio (NBR) was used to highlight burned areas and thresholds to classify burned and non-burned areas. In order to maximize the burned area detection two alternatives to the temporal dNBR method were evaluated: the relative form of the temporal difference RdNBR and the use of time series metrics. The processing, algorithm development and access to Landsat data was made on the Google Earth Engine GEE platform. Three regions of Latin America with large fire occurrence were selected: The Amazon Forest in Colombia, the transition from Chiquitano to Amazon Forest in Bolivia, and El Chaco Region in Argentina. The accuracy assessment of these new products was based on burned area protocols. The best model classified 85% of burned areas in the Chiquitano Forests of Bolivia, 63% of the burned areas of the Amazon Forests of Colombia and 69% of burned areas in El Chaco of Argentina.</p>

Flotation of copper-bearing carbonaceous shale in the presence of amine type frothers

<p>Hexylamine, chloro-derivative of propylamine, and ammonium chloride were used for flotation of Permian carbonaceous copper bearing shale from the Legnica-Glogow Copper Basin region in SW Poland. It was confirmed that naturally hydrophobic shale, known as Kupferschiefer, can be made floatable by application of short chain amines as frothers. It was also established that the short chain amine chloro-derivatives as well as salmiac, the first member of the amine family, can be used as a frother for flotation of the investigated shale sample. It results from the paper that the investigated amine frothers work similarly if the comparison of the flotation results is made using their concentration expressed in relative form taking into account the critical coalescence concentration of the frother.</p>

1961–1990 high-resolution Northern and Central Italy monthly precipitation climatologies

Monthly Northern and Central Italy precipitation climatologies, obtained by means of a procedure aiming at capturing the local dependence of precipitation on elevation, are presented. The procedure consists in considering each cell of a high-resolution grid and in making use of the closest 15 stations in order to perform a weighted linear regression of precipitation versus elevation: the weights are determined by the distances of the stations from the grid point and by the level of similarity between the stations' cells and the considered grid cell in terms of elevation, slope steepness, slope orientation and distance from the sea. The study is based on a dataset of about 3200 1961–1990 monthly precipitation normals. The performance of the procedure is evaluated by reconstructing the monthly precipitation normals of each station and by comparing them with the corresponding observed values. Such comparisons are performed by means of five error estimators: the mean absolute error and the mean error, both evaluated in absolute and relative form, and the root mean squared error. The mean absolute error is slightly more than 10 mm for the monthly precipitation, which turns out to be between 10% and 14% of the monthly precipitation.