我在Applied Sciences（综合性、交叉性期刊，CiteScore=3.70；IF=2.84）组织了一个Special Issue，大题目是“大数据分析进展”，比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎（但不限于）下面四类稿件：（1）数据分析中的基础理论分析，例如一个系统的可预测性（比如时间序列的可预测性）、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析；（2）数据分析的新方法，例如挖掘因果关系的新方法（这和Topic 1也是相关的）、多模态分析的新方法、隐私计算的新方法等等；（3）推出新的、高价值的数据集、数据分析平台、数据分析工具等等；（4）把大数据分析的方法用到自然科学和社会科学的各个分支（并获得洞见），我们特别喜欢用到那些原来定量化程度不高的学科。
投稿链接：https://www.mdpi.com/journal/applsci/special_issues/75Y7F7607U
投稿截止时期为2022年12月20日，我们处理稿件非常快，欢迎大家投稿支持。
其中第三篇论文已经正式发表：
Over recent years, frequent earthquakes have caused huge losses in human life and property. Rapid and automatic earthquake detection plays an important role in earthquake warning systems and earthquake operation mechanism research. Temporal convolution networks (TCNs) are frameworks that use expansion convolution and expansion, which have large and temporal receptive fields and can adapt to time series data. Given the excellent performance of temporal convolution networks using time series data, this paper proposes a deep learning framework based on the temporal convolution network model, which can be used to detect and obtain the accurate start times of seismic phases. In addition, a convolutional neural network (CNN) was added to the temporal convolution network model to automatically extract the deep features of seismic waves and the expansion convolution of each level was added to optimize its structure, which not only reduced the experimental parameters but also produced high-precision seismic phase detection results. Finally, the model was compared to the TCN, CNN-LSTM, SELD-TCN and the traditional AR-AIC methods. Our experimental results showed that the S-TCN method demonstrated great advantages in the accuracy and performance of seismic phase detection.
论文免费下载链接：
https://www.mdpi.com/2076-3417/12/19/9547
