Publisher Profile -- Kanopy

<p>Note: SS: the relevant-same-irrelevant-same condition; SD: the relevant-same-irrelevant-different condition; DS: the relevant-different-irrelevant-same condition; and DD: the relevant-different-irrelevant-different condition.</p

Match-Fixing en tenis : análisis sobre el amaño de partidos en el mundo del tenis

El presente artículo expone, a partir de un hipotético caso práctico, la situación de los amaños deportivos o match-fixing en el ámbito del tenis profesional y cuáles son, a nivel jurídico, la normativa y sanciones que se establecen para los supuestos casos de amaños deportivos

Mean Error Rates (Percent Incorrect) for Experiment 2.

<p>Note: SS: the relevant-same-irrelevant-same condition; SD: the relevant-same-irrelevant-different condition; DS: the relevant-different-irrelevant-same condition; and DD: the relevant-different-irrelevant-different condition.</p

Distribution of number of stickers donated in DG, by social distance levels (between-subject), for the complete sample.

<p>Distribution of number of stickers donated in DG, by social distance levels (between-subject), for the complete sample.</p

Examples of stimulus displays from Experiment 1.

<p>The task was to respond to the color (red or green) or orientation (left or right) of S2 as specified by a task cue, with a C referring to color and an O to orientation. Both the relevant and irrelevant features of S1 and S2 were independent. The figure depicts an example of a color task, in which color was a task relevant feature and orientation a task irrelevant feature. In this example, S1 and S2 had the same color and orientation in the SS condition, the same color but different orientation in the SD condition, the different color but same orientation in the DS condition, and different color and orientation in the DD condition.</p

Mean reaction times for Experiment 2.

<p>A. The color task. B. The orientation task. Partial repetition costs were again found in the color task, but not in the orientation task.</p

Design concept evaluation index system.

The assessment of design concepts presents an efficient and effective strategy for businesses to strengthen their competitive edge and introduce market-worthy products. The widely accepted viewpoint acknowledges this as a intricate multi-criteria decision-making (MCDM) approach, involving a multitude of evaluative criteria and a significant amount of data that is frequently ambiguously defined and subjectively influenced. In order to tackle the problems of uncertainty and fuzziness in design concept evaluation, our research creatively combines interval-valued picture fuzzy set (IVPFS) with an MCDM process of design concept evaluation. Firstly, this study draws on the existing relevant literature and the experience of decision makers to identify some important criteria and corresponding sub-criteria and form a scientific evaluation indicator system. We then introduce the essential operational concepts of interval-valued picture fuzzy numbers (IVPFNs) and the interval-valued picture fuzzy ordered weighted interactive averaging (IVPFOWIA) operator. Thirdly, an entropy weighting method based on IVPFS is proposed in this research to calculate the weights of criteria and sub-criteria, and based on this, an integrated IVPF decision matrix is further constructed based on the presented IVPFOWIA operator. Finally, the best design concept alternative is selected by applying the extended TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach with IVPFS. The IVPFS combined with improved MCDM method have been proven to be superior in complex and uncertain decision-making situations through experiments and comparative assessments. The information ambiguity in the evaluation of design concept is well characterized by our augmentation based on IVPFS.</div

Results of sensitivity analysis.

This figure presents the sensitivity analysis results for IVPF-improved TOPSIS method at different ρ values (ρ = 0.1, 0.3, 0.5, 0.7, 0.9). When ρ = 0.1, due to ρ The value of is relatively small, which may make the model less sensitive to changes in parameters. Therefore, even slight changes in parameters may result in different sorting results. This is because the smaller ρ The value reduces the sensitivity threshold to changes in weights and evaluation values.</p

LM test.

Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China’s resource-based regions’ industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China’s resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development.</div

Hausman test.

Resource-based regions support national economic development and are essential sources of basic energy and raw materials. In the post-pandemic era, however, there are practical situations to deal with, such as a fractured industrial chain, a weaker industrial structure, and a sharp reduction in economic benefits. Based on data collected from 68 cities in China, from 2010 to 2021, with 816 observations, this paper explores the industrial development process of resource-based regions in China and the change in the toughness of the industrial structure under the impact of COVID-19. The paper studies and analyzes industrial development trends, industrial structure toughness, and spatial spillover effects. The methods used are the Markov chain model and the Industrial Structure Advancement Index. By building the spatial Dubin model, the paper analyzes the spatial spillover effect of regional industrial development. It decomposes the spillover effect using the partial differential model based on regression. The results show that, during the study period, the comprehensive development level of industries in resource-based regions in China was slowly improving and tended to stabilize after entering the post-pandemic era. The evolution of an advanced industrial structure is significantly heterogeneous among regions, and each region has different toughness. The impact of COVID-19 has reduced the toughness of China’s resource-based regions’ industrial structure. The spatial spillover effect of regional industrial development is significant. Labor force, technology input, and industrial-structure optimization have different impacts on the industrial development of neighboring regions. In the post-pandemic era, China has used new management methods for more innovation. In order to achieve low-carbon, environmental protection, and sustainable development of resources, realize the rapid recovery of the toughness of industrial structure in China’s resource-based cities, and reduce the impact of the COVID-19 pandemic, China proposes to expand the supply of resources, improve the allocation of resources, optimize the direction, promote the rational flow and efficient aggregation of various factors, and enhance the impetus for innovation and development.</div