Talent identification and development in soccer

In this review, we attempt to integrate the main research findings concerned with talent identification and development in soccer. Research approaches in anthropometry, physiology, psychology and sociology are considered and, where possible, integrated. Although some progress has been made in identifying correlates of playing success, it appears that no unique characteristics can be isolated with confidence. Both biological and behavioural scientists have indicated a strong genetic component in performance of sports such as soccer; nevertheless, the influence of systematic training and development programmes should not be underestimated. Weconclude that the sport and exercise sciences have an important support role in the processes of identifying, monitoring and nurturing talented soccer players towards realizing their potential.     

Profiling Teacher/Teaching Using Descriptors Derived from Qualitative Feedback: Formative and Summative Applications

Considerable work has been done on student evaluation of teaching/teachers, but reservations remain about its use for summative purposes. Student ratings are not universally accepted as being reliable, nor can they provide really meaningful information. Qualitative comments can provide a better understanding but they tend not to be user-friendly from lack of structure and connectedness. This study attempts to devise a method for ‘quantifying’ students’ comments to increase their usefulness in complementing/confirming ratings. The quantified results enable the profile construction of what students regard as an effective/ineffective teacher, and enable identification of strengths and weaknesses. Our findings counter some commonly held assumptions, including those which held that high ratings are dependent on small class size and ‘dumbing down’ of courses and the consequent expectation of high grades. The findings also indicate that students value teaching quality more than teacher characteristics, suggesting their ability to make valid judgments about teaching effectiveness.

Profiling the sport of stand-up paddle boarding

Stand-up paddle boarding (SUP) is a rapidly growing activity where only anecdotal evidence exists for its proposed health and fitness benefits. The purpose of this study was to profile elite and recreational SUP with respect to anthropometric, physiological and musculoskeletal measurements. A total of 30 SUP participants (15 recreational, 15 elite) and 15 sedentary controls participated in this study. Elite and recreational (rec) SUP participants had significantly lower body fat than sedentary (sed) individuals, elite had significantly higher HDL and significantly lower triglycerides than other groups during lipid profiling (P > 0.05). There were significant differences (P > 0.05) between all groups in maximal oxygen uptake (elite 43.7, s = 5.89 ml · kg^–1 · min^–1 vs. rec 31.9, s = 7.7 ml · kg^–1 · min^–1 vs. sed 20.4, s = 3.7 ml · kg^–1 · min^–1) and anaerobic power outputs (35.7, s = 11.1 W vs. 25.0, s = 11.7 W vs. 13.5, s = 7.1 W). The elite group displayed significantly longer endurance than the recreational and sedentary group in the prone bridge (elite 253.4, s = 67.6 s vs. rec 165.6, s = 42.2 s vs. sed 69.7, s = 31.2 s), right-sided bridge (elite 107.9, s = 34.0 s vs. recreational 68.2, s = 24.1 s vs. sed 34.6, s = 15.5 s), left-sided bridge (elite 99.8, s = 24.9 s vs. rec 68.2, s = 27.2 s vs. sed 32.5, s = 15.2 s) and Biering Sorensen test (elite 148.8, s = 35.4 s vs. rec 127.2, s = 43.2 s vs. sed 71.1, s = 32.9 s). Elite SUP had significantly better static and dynamic postural control when compared to the other groups. This study demonstrates the anthropometric, physiological and musculoskeletal values representative of elite and recreational SUP. SUP appears to be associated with increased levels of aerobic and anaerobic fitness, increased static and dynamic balance and a high level of isometric trunk endurance.     

汉语名词陈述化概说

汉语名词陈述化历史悠久,形式灵活。汉语名词陈述化是语言表达的客观需要:表意的需要和创新的需要。汉语名词陈述化的可能性是隐性语义的存在。汉语名词陈述化的实现方式是突显和转指,实现机制是转喻和意象图式等。     

BizPro: Extracting and categorizing business intelligence factors from textual news articles

Company movements and market changes often are headlines of the news, providing managers with important business intelligence (BI). While existing corporate analyses are often based on numerical financial figures, relatively little work has been done to reveal from textual news articles factors that represent BI. In this research, we developed BizPro, an intelligent system for extracting and categorizing BI factors from news articles. BizPro consists of novel text mining procedures and BI factor modeling and categorization. Expert guidance and human knowledge (with high inter-rater reliability) were used to inform system development and profiling of BI factors. We conducted a case study of using the system to profile BI factors of four major IT companies based on 6859 sentences extracted from 231 news articles published in major news sources. The results show that the chosen techniques used in BizPro – Naïve Bayes (NB) and Logistic Regression (LR) – significantly outperformed a benchmark technique. NB was found to outperform LR in terms of precision, recall, F-measure, and area under ROC curve. This research contributes to developing a new system for profiling company BI factors from news articles, to providing new empirical findings to enhance understanding in BI factor extraction and categorization, and to addressing an important yet under-explored concern of BI analysis.     

What do we know about child abuse and neglect patterns of co-occurrence? A systematic review of profiling studies and recommendations for future research

Latent class (LCA) and latent profile (LPA) analysis represent methodological approaches to identify subgroups of maltreated individuals. Although research examining child abuse and neglect (CAN) profiles is still rare, the application of person-centered techniques to clarify CAN types co-occurrence has substantially increased in recent years. Therefore, the aim of the present study was to provide a summary and critical evaluation of the findings of LCA/LPA child maltreatment research to: (a) systemize the current understanding of patterns of maltreatment across populations and (b) elucidate interactive effects of CAN types on psychosocial functioning. A search in PsychInfo, Eric, PubMed, Scopus, and Science Direct, and Google Scholar was performed. Sixteen studies examining the co-occurrence between child physical abuse, emotional abuse, sexual abuse, neglect, and/or exposure to domestic violence were identified. A critical review of the studies revealed inconsistent findings as to the number of CAN classes, but most research uncovered a poly-victimized and a low abuse group. Further, multiple victimization was associated with most adverse internalizing and externalizing outcomes, especially when sexual abuse was present. Exposure to physical and emotional abuse was frequently reported to lead to behavioural problems. Based on the present study results, we provide a set of recommendations for surpassing the current methodological and conceptual limitations in future research.     

Preventing profiling for ethical fake news detection

A news article’s online audience provides useful insights about the article’s identity. However, fake news classifiers using such information risk relying on profiling. In response to the rising demand for ethical AI, we present a profiling-avoiding algorithm that leverages Twitter users during model optimisation while excluding them when an article’s veracity is evaluated. For this, we take inspiration from the social sciences and introduce two objective functions that maximise correlation between the article and its spreaders, and among those spreaders. We applied our profiling-avoiding algorithm to three popular neural classifiers and obtained results on fake news data discussing a variety of news topics. The positive impact on prediction performance demonstrates the soundness of the proposed objective functions to integrate social context in text-based classifiers. Moreover, statistical visualisation and dimension reduction techniques show that the user-inspired classifiers better discriminate between unseen fake and true news in their latent spaces. Our study serves as a stepping stone to resolve the underexplored issue of profiling-dependent decision-making in user-informed fake news detection.