In the research of age-related performance declines, the value of cross-sectional versus longitudinal data is an ongoing debate. This paper analyses the largest longitudinal master track and field data set ever published to compare the age-related decline in performance between 16 athletics disciplines in cross-sectional and longitudinal data. The data set contained 83,209 results (64,948 from men, 78.1%; 18,261 from women, 21.9%) from 34,132 athletes (26,186 men, 76.7%; 7946 women, 23.3%), aged 35–97 years. In 61 athletes, 20 or more, and in 312 athletes, 15 or more results were available. The data were analyzed by regression statistics/ANCOVA. Men had a higher performance than women, irrespective of discipline in both cross-sectional and longitudinal data (p < 0.001). The performance in cross-sectional data was lower compared with the longitudinal data in all events and at any age (p ≤ 0.007) except for 1000 m men. The average age was lower in the cross-sectional than the longitudinal data (p < 0.001); men 46 and 58 years, women 44 and 56 years, respectively. The annual percentage rate of decline did not differ significantly between cross-sectional and longitudinal data, or between sexes in most disciplines. Performance declines after age 70 were 1.7 times (men) and 1.4 times (women) as steep as before. In conclusion, although longitudinal master athletics data of athletes with 10 and more results has higher average performance and age compared with cross-sectional data, cross-sectional data give a good impression of the annual percentage decline in performance, which was similar in men and women.

No abstract

Learning SQL can be surprisingly difficult, given the relative simplicity of its syntax. Automated tools for teaching and assessing SQL have existed for over two decades. Early tools were only designed for teaching and offered increased feedback and personalised learning, but not summative assessment. More recently, however, the trend has turned towards automated assessment, with learning as a side-effect. These tools offer more limited feedback and are not personalised. In this paper, we present SQL Tester, an online assessment tool and an assessment of its impact. We show that students engaged with SQL Tester as a learning tool, taking an average of 10 practice tests each and spending over 4 hours actively engaged in those tests. A student survey also found that over 90% of students agreed that they wanted to keep trying practice tests until they got a "good" mark. Finally, we present some evidence that taking practice tests increased student achievement, with a strong correlation between the number of practice tests a student took and their score on the assessed test.

Orthogonal frequency division multiplexing (OFDM) is the digital modulation technique used by 4G and many other wireless communication systems. OFDM signals have significant amplitude fluctuations resulting in high peak to average power ratios which can make an OFDM transmitter susceptible to non-linear distortion produced by its high power amplifiers (HPA). A simple and popular solution to this problem is to clip the peaks before an OFDM signal is applied to the HPA but this causes in-band distortion and introduces bit-errors at the receiver. In this paper we discuss a novel technique, which we call the Equation-Method, for correcting these errors. The Equation-Method uses the Fast Fourier Transform to create a set of simultaneous equations which, when solved, return the amplitudes of the peaks before they were clipped. We show analytically and through simulations that this method can, correct all clipping errors over a wide range of clipping thresholds. We show that numerical instability can be avoided and new techniques are needed to enable the receiver to differentiate between correctly and incorrectly received frequency-domain constellation symbols.

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.