OPR-Miner: Order-preserving rule mining for time series

Discovering frequent trends in time series is a critical task in data mining. Recently, order-preserving matching was proposed to find all occurrences of a pattern in a time series, where the pattern is a relative order (regarded as a trend) and an occurrence is a sub-time series whose relative order coincides with the pattern. Inspired by the order-preserving matching, the existing order-preserving pattern (OPP) mining algorithm employs order-preserving matching to calculate the support, which leads to low efficiency. To address this deficiency, this paper proposes an algorithm called efficient frequent OPP miner (EFO-Miner) to find all frequent OPPs. EFO-Miner is composed of four parts: a pattern fusion strategy to generate candidate patterns, a matching process for the results of sub-patterns to calculate the support of super-patterns, a screening strategy to dynamically reduce the size of prefix and suffix arrays, and a pruning strategy to further dynamically prune candidate patterns. Moreover, this paper explores the order-preserving rule (OPR) mining and proposes an algorithm called OPR-Miner to discover strong rules from all frequent OPPs using EFO-Miner. Experimental results verify that OPR-Miner gives better performance than other competitive algorithms. More importantly, clustering and classification experiments further validate that OPR-Miner achieves good performance

Dispersive Liquid-Liquid Microextraction Combined with Ultrahigh Performance Liquid Chromatography/Tandem Mass Spectrometry for Determination of Organophosphate Esters in Aqueous Samples

A new technique was established to identify eight organophosphate esters (OPEs) in this work. It utilised dispersive liquid-liquid microextraction in combination with ultrahigh performance liquid chromatography/tandem mass spectrometry. The type and volume of extraction solvents, dispersion agent, and amount of NaCl were optimized. The target analytes were detected in the range of 1.0–200 µg/L with correlation coefficients ranging from 0.9982 to 0.9998, and the detection limits of the analytes were ranged from 0.02 to 0.07 µg/L (S/N=3). The feasibility of this method was demonstrated by identifying OPEs in aqueous samples that exhibited spiked recoveries, which ranged between 48.7% and 58.3% for triethyl phosphate (TEP) as well as between 85.9% and 113% for the other OPEs. The precision was ranged from 3.2% to 9.3% (n=6), and the interprecision was ranged from 2.6% to 12.3% (n=5). Only 2 of the 12 selected samples were tested to be positive for OPEs, and the total concentrations of OPEs in them were 1.1 and 1.6 µg/L, respectively. This method was confirmed to be simple, fast, and accurate for identifying OPEs in aqueous samples

Effect of grape pomace supplement on growth performance, gastrointestinal microbiota, and methane production in Tan lambs

Grape pomace (GP), a by-product in wine production, is nutritious and can be used as a feed ingredient for ruminants; however, its role in shaping sheep gastrointestinal tract (GIT) microbiota is unclear. We conducted a controlled trial using a randomized block design with 10 Tan lambs fed a control diet (CD) and 10 Tan lambs fed a pelleted diet containing 8% GP (dry matter basis) for 46 days. Rumen, jejunum, cecum, and colon bacterial and archaeal composition were identified by 16S rRNA gene sequencing. Dry matter intake (DMI) was greater (p < 0.05) in the GP than CD group; however, there was no difference in average daily gain (ADG, p < 0.05) and feed conversion ratio (FCR, p < 0.05) between the two groups. The GP group had a greater abundance of Prevotella 1 and Prevotella 7 in the rumen; of Sharpe, Ruminococcaceae 2, and [Ruminococcus] gauvreauii group in the jejunum; of Ruminococcaceae UCG-014 and Romboutsia in the cecum, and Prevotella UCG-001 in the colon; but lesser Rikenellaceae RC9 gut group in the rumen and cecum, and Ruminococcaceae UCG-005 and Ruminococcaceae UCG-010 in the colon than the CD group. The pathways of carbohydrate metabolism, such as L-rhamnose degradation in the rumen, starch and glycogen degradation in the jejunum, galactose degradation in the cecum, and mixed acid fermentation and mannan degradation in the colon were up-graded; whereas, the pathways of tricarboxylic acid (TCA) cycle VIII, and pyruvate fermentation to acetone in the rumen and colon were down-graded with GP. The archaeal incomplete reductive TCA cycle was enriched in the rumen, jejunum, and colon; whereas, the methanogenesis from H2 and CO2, the cofactors of methanogenesis, including coenzyme M, coenzyme B, and factor 420 biosynthesis were decreased in the colon. The study concluded that a diet including GP at 8% DM did not affect ADG or FCR in Tan lambs. However, there were some potential benefits, such as enhancing propionate production by microbiota and pathways in the GIT, promoting B-vitamin production in the rumen, facilitating starch degradation and amino acid biosynthesis in the jejunum, and reducing methanogenesis in the colon

FF-Net: Feature-Fusion-Based Network for Semantic Segmentation of 3D Plant Point Cloud

Semantic segmentation of 3D point clouds has played an important role in the field of plant phenotyping in recent years. However, existing methods need to down-sample the point cloud to a relatively small size when processing large-scale plant point clouds, which contain more than hundreds of thousands of points, which fails to take full advantage of the high-resolution of advanced scanning devices. To address this issue, we propose a feature-fusion-based method called FF-Net, which consists of two branches, namely the voxel-branch and the point-branch. In particular, the voxel-branch partitions a point cloud into voxels and then employs sparse 3D convolution to learn the context features, and the point-branch learns the point features within a voxel to preserve the detailed point information. Finally, an attention-based module was designed to fuse the two branch features to produce the final segmentation. We conducted extensive experiments on two large plant point clouds (maize and tomato), and the results showed that our method outperformed three commonly used models on both datasets and achieved the best mIoU of 80.95% on the maize dataset and 86.65% on the tomato dataset. Extensive cross-validation experiments were performed to evaluate the generalization ability of the models, and our method achieved promising segmentation results. In addition, the drawbacks of the proposed method were analyzed, and the directions for future works are given

Win-Former: Window-Based Transformer for Maize Plant Point Cloud Semantic Segmentation

Semantic segmentation of plant point clouds is essential for high-throughput phenotyping systems, while existing methods still struggle to balance efficiency and performance. Recently, the Transformer architecture has revolutionized the area of computer vision, and has potential for processing 3D point clouds. Applying the Transformer for semantic segmentation of 3D plant point clouds remains a challenge. To this end, we propose a novel window-based Transformer (Win-Former) network for maize 3D organic segmentation. First, we pre-processed the Pheno4D maize point cloud dataset for training. The maize points were then projected onto a sphere surface, and a window partition mechanism was proposed to construct windows into which points were distributed evenly. After that, we employed local self-attention within windows for computing the relationship of points. To strengthen the windows’ connection, we introduced a Cross-Window self-attention (C-SA) module to gather the cross-window features by moving entire windows along the sphere. The results demonstrate that Win-Former outperforms the famous networks and obtains 83.45% mIoU with the lowest latency of 31 s on maize organ segmentation. We perform extensive experiments on ShapeNet to evaluate stability and robustness, and our proposed model achieves competitive results on part segmentation tasks. Thus, our Win-Former model effectively and efficiently segments the maize point cloud and provides technical support for automated plant phenotyping analysis

Flexible, Porous, and Metal–Heteroatom-Doped Carbon Nanofibers as Efficient ORR Electrocatalysts for Zn–Air Battery

Abstract Developing an efficient and durable oxygen reduction electrocatalyst is critical for clean-energy technology, such as fuel cells and metal–air batteries. In this study, we developed a facile strategy for the preparation of flexible, porous, and well-dispersed metal–heteroatom-doped carbon nanofibers by direct carbonization of electrospun Zn/Co-ZIFs/PAN nanofibers (Zn/Co-ZIFs/PAN). The obtained Zn/Co and N co-doped porous carbon nanofibers carbonized at 800 °C (Zn/Co–N@PCNFs-800) presented a good flexibility, a continuous porous structure, and a superior oxygen reduction reaction (ORR) catalytic activity to that of commercial 20 wt% Pt/C, in terms of its onset potential (0.98 V vs. RHE), half-wave potential (0.89 V vs. RHE), and limiting current density (− 5.26 mA cm−2). In addition, we tested the suitability and durability of Zn/Co–N@PCNFs-800 as the oxygen cathode for a rechargeable Zn–air battery. The prepared Zn–air batteries exhibited a higher power density (83.5 mW cm−2), a higher specific capacity (640.3 mAh g−1), an excellent reversibility, and a better cycling life than the commercial 20 wt% Pt/C + RuO2 catalysts. This design strategy of flexible porous non-precious metal-doped ORR electrocatalysts obtained from electrospun ZIFs/polymer nanofibers could be extended to fabricate other novel, stable, and easy-to-use multi-functional electrocatalysts for clean-energy technology

Evaluation of Noah-MP Land-Model Uncertainties over Sparsely Vegetated Sites on the Tibet Plateau

The water budget and energy exchange over the Tibetan Plateau (TP) region play an important role on the Asian monsoon. However, it is not well presented in the current land surface models (LSMs). In this study, uncertainties in the Noah with multiparameterization (Noah-MP) LSM are assessed through physics ensemble simulations in three sparsely vegetated sites located in the central TP. The impact of soil organic matter on energy flux and water cycles, along with the influence of uncertainties in precipitation are explored using observations at those sites during the third Tibetan Plateau Experiment from 1August2014 to31July2015. The greatest uncertainties are in the subprocesses of the canopy resistance, soil moisture limiting factors for evaporation, runoff (RNF) and ground water, and surface-layer parameterization. These uncertain subprocesses do not change across the different precipitation datasets. More precipitation can increase the annual total net radiation (Rn), latent heat flux (LH) and RNF, but decrease sensible heat flux (SH). Soil organic matter enlarges the annual total LH by ~26% but lessens the annual total Rn, SH, and RNF by ~7%, 7%, and 39%, respectively. Its effect on the LH and RNF at the Nagqu site, which has a sand soil texture type, is greater than that at the other two sites with sandy loam. This study highlights the importance of precipitation uncertainties and the effect of soil organic matter on the Noah-MP land-model simulations. It provides a guidance to improve the Noah-MP LSM further and hence the land-atmosphere interactions simulated by weather and climate models over the TP region