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Basketball is one of the students' favorite ball games, and it is also one of the most popular sports for college students to carry out after class. Especially in recent years, with the spread of NBA culture around the world and the extensive development of CUBA in domestic colleges and universities, Yao Ming has appeared in China to compete in the NBA and achieve brilliant achievements. With the increasing investment in the number of basketball venues, basketball itself, as a sport with low dependence on venues, equipment, and people, has the characteristics of economy, convenience, and remarkable sports effect compared with other sports. College students' basketball skills, basketball awareness, and love for basketball are increasing day by day. Attribute reduction algorithm is one of the core contents of knowledge discovery, which describes whether every attribute in the attribute set of information system is necessary and how to delete unnecessary knowledge. Based on the attribute reduction algorithm, this paper studies the early warning of basketball injury risk. The basketball injury can not only make athletes unable to participate in training or competition, but even cripple or lose their lives, which hinders the normal development of sports. Therefore, we should make a comprehensive and objective analysis of sports training to find out the causes of sports injuries, so as to prevent sports injuries. This algorithm takes the attribute frequency as the heuristic information and solves the attribute selection problem when the attribute frequency is the same. [ABSTRACT FROM AUTHOR]

This paper presents a strategy for segmenting blood vessels based on the threshold, which combines statistics and scale space filter. By incorporating statistical information, the strategy is capable of reducing over-segmentation. We propose a two-stage strategy which involves: (1) optimal selection of window size and (2) optimal selection of scale. We compared our strategy to two commonly used thresholding techniques. Experimental results showed that our method is much more robust and accurate. Our strategy suggested a modification to Otsu's method. In this application the important information to be extracted from images is only the number of blood vessels present in the images. The proposed segmentation technique is tested on manual segmentation, where segmentation errors less than 3% were observed. The work presented in this paper is a part of a global image analysis process. Therefore, these images will be subject to a further morphometrical analysis in order to diagnose and predict automatically malign tumors.

A fast effective inversion algorithm is proposed herein to interpret gravitational responses caused by mineralized/ore sources (sphere, vertical and horizontal cylinders). The algorithm relies on local wavenumber and correlation imaging techniques. The correlation factor (R) between the local wavenumber of observed gravitational field and that of computed field was calculated, and the maximum Rmax was considered to correspond to the best true model (parameters). The proposed algorithm was applied to two theoretical examples, including an example contaminated with regional background and another multisource example. Besides, the proposed approach was used on three different real field cases for mining/ore investigation from Canada and Cuba. From the results obtained from the theoretical and real examples and by comparing the results with drilling and literature information, it was concluded that the method is effective, is applicable even for more than one source, is accurate, and does not necessitate any prior knowledge of the source shape. [ABSTRACT FROM AUTHOR]

Scientific studies confirm the existence of a crisis caused by climate change, in which global causes produce local effects. Despite climate agreements, greenhouse gas emissions continue to fall short of targets to limit global warming. There is still a need for comparable data for Sustainable Development Goal (SDG) 13—Climate Action. The motivation of the research is to provide data for decision-making and to propose solutions to address the climate crisis. The article aims to propose a Fuzzy Logic algorithm to evaluate the SDG13 indicators and to deepen the discussion on climate change. The research is applied explanatory with a combined approach (quantitative-qualitative) through modeling, simulation, and case studies. As a result, the OWA operator ranks 10 Ibero-American countries to SDG13, indicating Colombia, Peru, and Cuba in the first positions. The main contributions are the reduction of identified knowledge gaps and proposals for action for policy and decision-makers. A limitation of this study would be the number of participating countries. The authors indicate future lines of research. [ABSTRACT FROM AUTHOR]

Featured Application: Knowing the departure date of each container is paramount to scheduling an optimal stacking in container terminals, and thus, reducing the fuel consumption of the yard cranes. Supervised classification algorithms are typical for estimating such a dwell time. However, we show that an ordinal regression algorithm outperforms the supervised classification algorithms regarding the mean absolute error and the reshuffles generated. This research has been applied in an inbound yard from Cuba as part of a project for the optimization of the import container flow. Our results can state a baseline for further dwell time estimation systems. The optimal stacking of import containers in a terminal reduces the reshuffles during the unloading operations. Knowing the departure date of each container is critical for optimal stacking. However, such a date is rarely known because it depends on various attributes. Therefore, some authors have proposed estimation algorithms using supervised classification. Although supervised classifiers can estimate this dwell time, the variable "dwell time" takes ordered values for this problem, suggesting using ordinal regression algorithms. Thus, we have compared an ordinal regression algorithm (selected from 15) against two supervised classifiers (selected from 30). We have set up two datasets with data collected in a container terminal. We have extracted and evaluated 35 attributes related to the dwell time. Additionally, we have run 21 experiments to evaluate both approaches regarding the mean absolute error modified and the reshuffles. As a result, we have found that the ordinal regression algorithm outperforms the supervised classifiers, reaching the lowest mean absolute error modified in 15 ( 71 % ) and the lowest reshuffles in 14 ( 67 % ) experiments. [ABSTRACT FROM AUTHOR]

We studied autistics by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma and beta bands, lateralized to the right hemisphere. When comparing VwA versus the V-A in the midline regions, we found significant decrements of absolute PSD for delta, theta and alpha, and increments for the beta and gamma bands. In autistics, VwA versus V-A tended to show lower coherence values in the right hemisphere. An impairment of visual and auditory sensory integration in autistics might explain our results. [ABSTRACT FROM AUTHOR]