The loess‐soil sequence in northern China contains a near continuous record of Quaternary paleoclimate. Magnetic susceptibility and grain size have so far been the only proxies available to address the long‐term changes of the East‐Asian paleomonsoon extending back to more than one million years. In this study, the ratio of CBD (citrate‐bicarbonate‐dithionite)‐extractable free Fe2O3 (FeD), a measure of iron liberated by chemical weathering, versus the total Fe2O3 available (FeT) was measured on samples at 10 cm intervals taken from two loess sections deposited over the last 1.2 Ma. Variations of this index are highly consistent with other pedological indicators, but in addition provide a quantitative measurement of the degree of pedogenesis in the Loess Plateau. Since chemical weathering in the region mainly depends upon summer precipitation and temperature, weathering intensity primarily reflects changes in the East‐Asian summer monsoon. The new proxy has been used to document a series of summer monsoon changes of global significance, which are not necessarily recorded by magnetic susceptibility.

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The polyimide (PI)/carboxyl-functionalized multi-walled carbon nanotube (MWCNTs-COOH) nanocomposite films were synthesized by situ polymerization method. The results showed that the incorporation of MWCNTs-COOH greatly enhanced thermal stability and mechanical property of PI. PI/MWCNTs-COOH nanocomposites exhibited better tribological properties under seawater lubrication than other conditions because of excellent lubricating effect of seawater. Besides, the wear resistance of PI under seawater lubrication had been greatly improved by filling MWCNTs-COOH, because strong interfacial adhesion between PI matrix and MWCNTs-COOH nanofillers could transfer load effectively between contact surfaces. In particular, when the content of MWCNTs-COOH was 0.7 wt%, the corresponding PI/ MWCNTs-COOH nanocomposites had the best tribological properties under seawater lubrication. Graphical Abstract The polyimide (PI)/carboxyl-functionalized multi-walled carbon nanotube (MWCNTs-COOH) nanocomposite films were synthesized by in situ polymerization using 4,4 0 -oxydianiline (ODA), carboxyl-functionalized multi-walled carbon nanotube (MWCNTs-COOH) and pyromellitic dianhydride (PMDA). The tribological properties of PI/MWCNTs-COOH nanocomposite films were measured under dry sliding condition, pure water condition and seawater lubrication condition. This study can provide some guidance to develop polymer materials with excellent wear resistance suitable for ocean environment.

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

With the increasing availability of location-acquisition technologies, we have better access to collections of large spatio-temporal datasets. This brings new opportunities to location-based services (LBS), especially when knowledge of users' movement behaviour (i.e., mobility profiles) can be extracted from such datasets. For instance, in social networks, friends can be recommended according to similarity scores between user mobility profiles. In this paper, we propose a new approach to construct users' mobility profiles and calculate the mobility similarities between users. We model mobility profiles as traces of places that users frequently visit and use frequent sequential pattern mining technologies to extract them. To compare users' mobility profiles, we first discuss the weakness of a similarity measurement in the literature and then propose our new measurement. We evaluate our work using a real-life dataset published by Microsoft Research Asia and the experimental results show that our approach outperforms the existing works on different aspects.

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