A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams

High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience

The stop-schedules for passenger trains are important to the operation planning of high-speed trains, and they decide the quality of passenger service and the transportation efficiency. This paper analyzes the specific manifestation of passenger travel convenience and proposes the concepts of interstation accessibility and degree of accessibility. In consideration of both the economic benefits of railway corporations and the travel convenience of passengers, a multitarget optimization model is established. The model aims at minimizing stop cost and maximizing passenger travel convenience. Several constraints are applied to the model establishment, including the number of stops made by individual trains, the frequency of train service received by each station, the operation section, and the 0-1 variable. A hybrid genetic algorithm is designed to solve the model. Both the model and the algorithm are validated through case study

Evaluation of Alpha-Ketoglutarate Supplementation on the Improvement of Intestinal Antioxidant Capacity and Immune Response in Songpu Mirror Carp (Cyprinus carpio) After Infection With Aeromonas hydrophila

As an intermediate substance of the tricarboxylic acid cycle and a precursor substance of glutamic acid synthesis, the effect of alpha-ketoglutarate on growth and protein synthesis has been extensively studied. However, its prevention and treatment of pathogenic bacteria and its mechanism have not yet been noticed. To evaluate the effects of alpha-ketoglutarate on intestinal antioxidant capacity and immune response of Songpu mirror carp, a total of 360 fish with an average initial weight of 6.54 ± 0.08 g were fed diets containing alpha-ketoglutarate with 1% for 8 weeks. At the end of the feeding trial, the fish were challenged with Aeromonas hydrophila for 2 weeks. The results indicated that alpha-ketoglutarate supplementation significantly increased the survival rate of carp after infection with Aeromonas hydrophila (P < 0.05), and the contents of immune digestion enzymes including lysozyme, alkaline phosphatase and the concentration of complement C4 were markedly enhanced after alpha-ketoglutarate supplementation (P < 0.05). Also, appropriate alpha-ketoglutarate increased the activities of total antioxidant capacity and catalase and prevented the up-regulation in the mRNA expression levels of pro-inflammatory cytokines including tumor necrosis factor-α, interleukin-1β, interleukin-6, and interleukin-8 (P < 0.05). Furthermore, the mRNA expression levels of toll-like receptor 4 (TLR4), and nuclear factor kappa-B (NF-κB) were strikingly increased after infection with Aeromonas hydrophila (P < 0.05), while the TLR4 was strikingly decreased with alpha-ketoglutarate supplementation (P < 0.05). Moreover, the mRNA expression levels of tight junctions including claudin-1, claudin-3, claudin-7, claudin-11 and myosin light chain kinases (MLCK) were upregulated after alpha-ketoglutarate supplementation (P < 0.05). In summary, the appropriate alpha-ketoglutarate supplementation could increase survival rate, strengthen the intestinal enzyme immunosuppressive activities, antioxidant capacities and alleviate the intestinal inflammation, thereby promoting the intestinal immune responses and barrier functions of Songpu mirror carp via activating TLR4/MyD88/NF-κB and MLCK signaling pathways after infection with Aeromonas hydrophila

Effect of Bacillus megaterium-Coated Diets on the Growth, Digestive Enzyme Activity, and Intestinal Microbial Diversity of Songpu Mirror Carp Cyprinus specularis Songpu

The present study was conducted to evaluate the effect of a Bacillus megaterium-coated diet on growth performance, digestive enzymes, and intestinal microbial diversity in Songpu mirror carp (Cyprinus specularis Songpu). The fish were manually fed two diets (a control diet and a B. megaterium-coated diet) three times daily until apparent satiation for 56 days. Compared with the control group, supplementation with the B. megaterium-coated diet enhanced the fish growth and significantly reduced the feed conversion ratio (P0.05). The results of sequencing the 16S rDNA genes of the microbiota through high-throughput sequencing showed that the diversity and abundance of the intestinal microflora increased along with Songpu mirror carp growth. The Songpu mirror carp fed a diet coated with B. megaterium displayed increased proportions of intestinal Bacillus and Lactococcus at the genus level, and both were significantly higher than those of the control group (P<0.05). These results therefore suggest that dietary B. megaterium application can improve the growth and digestive enzyme activity of Songpu mirror carp and enrich the beneficial genus composition of its main intestinal microflora

Cloudformer V2: Set Prior Prediction and Binary Mask Weighted Network for Cloud Detection

Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training difficulty, Cloudformer V2 uses Set Attention Block to extract intermediate features as Set Prior Prediction to participate in supervision, which enables the model to converge faster. For the detection of small clouds, Cloudformer V2 decodes the features by a multi-scale Transformer decoder, which uses multi-resolution features to improve the modeling accuracy. In addition, a binary mask weighted loss function (BW Loss) is designed to construct weights by counting pixels classified as clouds; thus, guiding the network to focus on features of small clouds and improving the overall detection accuracy. Cloudformer V2 is experimented on the dataset from GF-1 satellite and has excellent performance

Cloudformer: Supplementary Aggregation Feature and Mask-Classification Network for Cloud Detection

Cloud detection is an important step in the processing of optical satellite remote-sensing data. In recent years, deep learning methods have achieved excellent results in cloud detection tasks. However, most of the current models have difficulties to accurately classify similar objects (e.g., clouds and snow) and to accurately detect clouds that occupy a few pixels in an image. To solve these problems, a cloud-detection framework (Cloudformer) combining CNN and Transformer is being proposed to achieve high-precision cloud detection in optical remote-sensing images. The framework achieves accurate detection of thin and small clouds using a pyramidal structure encoder. It also achieves accurate classification of similar objects using a dual-path decoder structure of CNN and Transformer, reducing the rate of missed detections and false alarms. In addition, since the Transformer model lacks the perception of location information, an asynchronous position-encoding method is being proposed to enhance the position information of the data entering the Transformer module and to optimize the detection results. Cloudformer is experimented on two datasets, AIR-CD and 38-Cloud, and the results show that it has state-of-the-art performance

Cloudformer V2: Set Prior Prediction and Binary Mask Weighted Network for Cloud Detection

Cloud detection is an essential step in optical remote sensing data processing. With the development of deep learning technology, cloud detection methods have made remarkable progress. Among them, researchers have started to try to introduce Transformer into cloud detection tasks due to its excellent performance in image semantic segmentation tasks. However, the current Transformer-based methods suffer from training difficulty and low detection accuracy of small clouds. To solve these problems, this paper proposes Cloudformer V2 based on the previously proposed Cloudformer. For the training difficulty, Cloudformer V2 uses Set Attention Block to extract intermediate features as Set Prior Prediction to participate in supervision, which enables the model to converge faster. For the detection of small clouds, Cloudformer V2 decodes the features by a multi-scale Transformer decoder, which uses multi-resolution features to improve the modeling accuracy. In addition, a binary mask weighted loss function (BW Loss) is designed to construct weights by counting pixels classified as clouds; thus, guiding the network to focus on features of small clouds and improving the overall detection accuracy. Cloudformer V2 is experimented on the dataset from GF-1 satellite and has excellent performance

Full-Range Static Method of Calibration for Laser Tracker

This paper focuses on the challenge of the inability to accurately calibrate the static measurement of a laser tracker across the full scale. To address this issue, this paper proposes to add a hollow corner cube prism on a 50 m high-precision composite guide rail to achieve a double-range measurement of the laser tracker. Data analysis indicated that, in the 77 m identical-directional double-range measurement experiment, the maximum indication error of a single-beam laser interferometer was −29.5 μm, and that of a triple-beam laser interferometer was 14.6 μm, and the measurement indication error was obviously small when the Abbe error was eliminated. The single-point repeatability of the tracker was 0.9 μm. In the 50 m identical-directional verification experiment, the results of the direct measurement outperformed those of the double-range measurement, and the indication errors under standard conditions were −4.0 μm and −8.9 μm, respectively. Overall, the method used in the experiment satisfies the requirements of the laser tracker. In terms of the identical-directional measurement, the measurement uncertainty of the tracker indication error is U ≈ 1.0 μm + 0.2L (k = 2) L = (0~77 m). The proposed method also provides insights for length measurements using other high-precision measuring instruments

Isolation, Identification, and Optimization of Culture Conditions of a Bioflocculant-Producing Bacterium Bacillus megaterium SP1 and Its Application in Aquaculture Wastewater Treatment

A bioflocculant-producing bacterium, Bacillus megaterium SP1, was isolated from biofloc in pond water and identified by using both 16S rDNA sequencing analysis and a Biolog GEN III MicroStation System. The optimal carbon and nitrogen sources for Bacillus megaterium SP1 were 20 g L−1 of glucose and 0.5 g L−1 of beef extract at 30°C and pH 7. The bioflocculant produced by strain SP1 under optimal culture conditions was applied into aquaculture wastewater treatment. The removal rates of chemical oxygen demand (COD), total ammonia nitrogen (TAN), and suspended solids (SS) in aquaculture wastewater reached 64, 63.61, and 83.8%, respectively. The volume of biofloc (FV) increased from 4.93 to 25.97 mL L−1. The addition of Bacillus megaterium SP1 in aquaculture wastewater could effectively improve aquaculture water quality, promote the formation of biofloc, and then form an efficient and healthy aquaculture model based on biofloc technology