Assessing the Relevance of Opinions in Uncertainty and Info-Incompleteness Conditions

Researchers are interested in defining decision support systems that can act in contexts characterized by uncertainty and info-incompleteness. The present study proposes a learning model for assessing the relevance of probability, plausibility, credibility, and possibility opinions in the conditions above. The solution consists of an Artificial Neural Network acquiring input features related to the considered set of opinions and other relevant attributes. The model provides the weights for minimizing the error between the expected outcome and the ground truth concerning a given phenomenon of interest. A custom loss function was defined to minimize the Mean Best Price Error (MBPE), while the evaluation of football players’ was chosen as a case study for testing the model. A custom dataset was constructed by scraping the Transfermarkt, Football Manager, and FIFA21 information sources and by computing a sentiment score through BERT, obtaining a total of 398 occurrences, of which 85% were employed for training the proposed model. The results show that the probability opinion represents the best choice in conditions of info-completeness, predicting the best price with 0.86 MBPE (0.61% of normalized error), while an arbitrary set composed of plausibility, credibility, and possibility opinions was considered for deciding successfully in info-incompleteness, achieving a confidence score of 2.47±0.188 MBPE (1.89±0.15% of normalized error). The proposed solution provided high performance in predicting the transfer cost of a football player in conditions of both info-completeness and info-incompleteness, revealing the significance of extending the feature space to opinions concerning the quantity to predict. Furthermore, the assumptions of the theoretical background were confirmed, as well as the observations found in the state of the art regarding football player evaluation.

Supply chain movement risk in the sneaker industry: an empirical study

In light of the COVID-19 pandemic and the Sino–US trade war, this study proposes a grey sharing decision-making evaluation model for production base movement and the sustainable operation of enterprises in the footwear industry. First, a focus group technique was employed; personnel from the footwear industry, corresponding government agencies, and the academic community were invited to jointly identify the most important criteria when considering a production base movement. The group listed seven criteria: labor cost, materials, exchange rate fluctuation, tariff, supply chain, transfer cost, and the government. The grey situation decision-making algorithm based on group knowledge and entropy were used to identify the most suitable country for production base movement.

INDICATORS OF FINANCIAL SUPPORT FOR FOOTBALL PLAYERS’ MIGRATION IN LEADING EUROPEAN LEAGUES

Introduction. Modern football is characterized by increasing trends in migration and transfer activity in the leading European football leagues and a decrease in the number of students of their own children's and youth academies of the leading football clubs of the European Championships for the period 2009-2019. The aim of the study is to investigate and determine the level of financial support for the migration of qualified players in leading European clubs on the basis of their transfer activities (according to open sources). Material and methods: analysis and generalization of data of scientific and methodical literature sources and the Internet, pedagogical observations, content analysis of materials of official websites of football clubs, UEFA and FIFA, methods of mathematical statistics. Results. In the «TOP-20» ranking of leading European football clubs at the end of 2019 in terms of total transfer costs, which determine the migration of qualified players, there is the following distribution: «Barclays Premier League» (England) – 8 football clubs (40%) with transfer costs € 4,726 million (43,40%); «Liga de Futbol Profesional, Primera Division» (Spain) – 4 football clubs (20%) with transfer costs of 2415 million € (22,17%); «Lega Nazionale Professionisti Serie A TIM» (Italy) – 4 football clubs (20%) with transfer costs of € 1,816 million (16,67%); «Le championnat de France de football» (France) – 2 football clubs (10%) with transfer costs of 1261 million € (11,58%); «Fußball-Bundesliga» (Germany) – 2 football clubs (10%) with a transfer cost of € 673 million (6,18%). Conclusions. Analysis of the structure of profits «TOP-20» ranking of leading European clubs, shows that the average is: about 44% – the sale of rights to telecast competitions, about 40% – advertising revenue and about 16% is the share of revenue from tickets and season tickets. Indicators of financial activity of leading European football clubs are monitored by UEFA and FIFA programs for compliance with the requirements and criteria of financial «Fair Play».

The Game Equilibrium of Scientific Crowdsourcing Solvers Based on the Hotelling Model

Scientific crowdsourcing, which can effectively obtain wisdom from solvers, has become a new type of open innovation to address worldwide scientific and research problems. In the crowdsourcing process, the initiator should satisfy his own research needs by selecting a proper solver from the crowd, and the solver must have multiple competitions in order to obtain scientific research tasks from the initiator. The participants in the scientific crowdsourcing are based on the knowledge flow to realize the value added of knowledge. This paper discusses a few factors, including knowledge utility, knowledge transfer cost, knowledge distance, and knowledge trading cost, which all affect the solvers to achieve game equilibrium and win tasks in scientific crowdsourcing. By referring to the concept of Hotelling model, this paper constructs a game model with the solvers as the participants, and analyses solvers’ behaviours in scientific crowdsourcing and their profit impacts by each of the key elements. The results show that from a crowdsourcing solver’s point of view, increasing knowledge utility, controlling knowledge transfer cost, shortening knowledge distance to the initiator, and leveraging with a knowledge trading cost are four effective approaches to wining the competition of a scientific crowdsourcing task. The research conclusions provide a theoretical basis and practice guidance for crowdsourcing solvers to participate in scientific crowdsourcing from the perspective of the knowledge flow process.

Operational cost of mechanized harvesting of first-crop coffee

The use of adapted harvesters for harvesting first-crop coffee requires a lower cost and exhibits a higher efficiency than manual harvesting. In view of this, the present study aimed to analyze the operational cost of mechanized harvesting of first-crop coffee. The experiment was conducted in a factorial scheme (2 × 3) + 1 and outlined in randomized blocks with five replications. There were seven treatments: two automotive harvesters (conventional and adapted) with times of operations for each harvester (1, 2 and 3 time operations) and manual harvesting. We tested these treatments in a young coffee crop planted in Catalão, GO, irrigated by Pivot, with 1.5 m of height. We measured the lost coffee, coffee harvest, remaining coffee before the operation to obtain efficient parameters and with the prices of the operations and the costs of the treatments. When operated once and three times, the adapted harvester required a lower transfer cost than the conventional harvester. Moreover, the adapted harvester showed no difference in cost between each operation. The cost reduction by mechanized harvesting varied from 23.96 to 59.9 %, depending on the frequency of the mechanized operations. In conclusion, it is efficient to harvest the young coffee with the adapted harvesters reducing the cost of coffee harvesting.