Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction

The successful launch of the Landsat 8 satellite continues the Earth observation of the Landsat series, which has been taking place for nearly 40 years. With the increase in the band number and the improved spectral range compared with the previous Landsat imagery, it will be possible to expand the application of the new Landsat 8 imagery. The purpose of this study is to explore water extraction based on the new Landsat 8 Operational Land Imager (OLI) imagery. According to the specific inland water conditions (clear water, turbid water, and eutrophic water), a number of highly adaptable water indices are assessed for water extraction using Landsat OLI imagery. The results show that clear water is the easiest to extract among the different types of waterbodies, with the highest average accuracy of 97%. The highest-accuracy methods are the automated water extraction index for shadow pixels (AWEI_sh), the normalized difference water index using bands 4 and 7 (NDWI₄₇), and the normalized difference water index using bands 3 and 7 (NDWI₃₇), with accuracies of 98.55%, 95.50%, and 96.61%, corresponding to clear water, turbid water, and eutrophic water, respectively. Through the analysis of the different methods for optimal band selection, the seventh band OLI7 (shortwave infrared 2, SWIR-2) of Landsat OLI shows the best performance in water identification. When applying the water indices to water extraction, Otsu’s algorithm has been used to automatically select the water threshold. Using extensive experiments with Otsu’s algorithm and a manual method, it was found that Otsu’s algorithm can replace manual selection and has the ability to select an accurate threshold for water extraction.     

Activity recognition and intensity estimation in youth from accelerometer data aided by machine learning

Physical activity monitoring for youth is an area of increasing scientific and public health interest due to the high prevalence of obesity and downward trend in physical activity. However, accurate assessment of such activity remains a challenging problem because of the complex nature in which certain activities are performed. In this study, we formulated the issue as a machine learning problem—using a diverse set of 19 physical activities commonly performed by youth—via two approaches: activity recognition and intensity estimation. With the aid of training data, we implemented a distance metric learning method called DML-KNN that utilizes time-frequency features and is capable of effectively classifying both continuous and intermittent movement in youth subjects. Four different time-frequency feature extraction methods were then systematically evaluated. Our results show that the DML-KNN method performed competitively, especially when using features extracted by the Tamura method for intensity estimation, and by the Square Coefficient method for activity recognition.     

资源受限网络中高效XML交换的性能评估

为了研究高效XML交换(EXI)在资源受限网络中的使用情况,文中基于传感器观测服务的真实数据,对四种交换模式下的数据大小进行了对比:XML、高效XML、Gzip压缩的XML以及开启压缩模式的高效XML。通过在ns-2中建模,对资源受限网络环境下各交换格式的平均成功率、平均延时、平均能量消耗等性能进行了分析。实验结果表明,无论是否开启压缩模式,高效XML比XML具有更小的体积、更高的成功率、更短的延时和更少的能量消耗。高效XML交换更加适合于资源受限网络。     

Surface tension calculation of the Ni3S2---FeS---Cu2S mattes

The surface tension of the Ni₃S₂-FeS-Cu₂S ternary mattes have been calculated from those of the Ni₃S₂-FeS·FeS-Cu₂S and Cu₂S-Ni₃S₂ pseudo-binary boundary systems by using geometric models (Kohler and Toop model) and a general solution model (Chou model). Taking account of the experimental accuracy of ±2.5%, the respective calculated results are in agreement with the experimental data. At the same time, surface tensions of molten Ni₃S₂-FeS-Cu₂S mattes and their pseudo-binary boundary systems are predicted based on Butler's original treatment with great attention to the model parameter β. The predicted results also agree with the experimental data. Therefore, the resulting iso-surface tension curves in molten Ni₃S₂-FeS-Cu₂S mattes at 1473K, especially calculated by using Kohler, Toop and Chou models, are generally acceptable.     

Comparison between on- and off-campus behaviour and adaptability in online learning: a case from China

More and more universities and colleges are providing online courses not only for on-campus students but also for off-campus students. Tutors have to consider the differences between on- and off-campus students in order to improve effective instruction. Comparisons are made in this paper between on- and off-campus performances in online learning from four areas: learning time, path of browsing courseware, intercommunication and adaptability towards online learning. The last two areas are emphasized. Multiple approaches were adopted to collect data, which include questionnaires, posted documents, online logs, interviews and observations. This study shows that the rush time of online learning, paths of browsing courseware and favourite intercommunication means of on- and off-campus students are similar. But there are also some differences between these two groups such as competence of self-learning, enthusiasm of interpersonal exchange, dependence on tutors, feeling of learning stress, etc.     

Synthesis of Single-Crystalline Silicon Nitride Nanobelts Via Catalyst-Assisted Pyrolysis of a Polysilazane

Recently, one-dimensional nanostructures have attracted extensive attention since they are potentially important for both applications and fundamental research. In this paper, we report the synthesis of ultra-long single crystal Si₃N₄ nanobelts via catalyst-assisted pyrolysis of polymeric precursors. The obtained products contain both α- and β-Si₃N₄ nanobelts, which are 50–100 nm in thickness, 400–1000 nm in width, and a few hundreds of micrometers to several millimeters in length. Different from previous techniques for synthesizing one-dimensional structures, the current nanobelts are synthesized through confined crystallization of an amorphous phase. A solid–liquid–gas–solid reaction/crystallization growth mechanism is proposed. The formation of nanobelts instead of nanowires is attributed to the anisotropy growth at an earlier stage.     

Identification of Breast Cancer Prognosis Markers via Integrative Analysis

In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.