An improved support vector regression and its modelling of manoeuvring performance in multidisciplinary ship design optimization

In this paper, the combination of the Laplace loss function and Support Vector Regression (SVR) are presented for the estimation of manoeuvring performance in multidisciplinary ship design optimization, and a new SVR algorithm was proposed, which has only one parameter to control the errors and automatically minimized with v, and adds b2/2 b to the item of confidence interval. It is shown that the proposed SVR algorithm in conjunction with the Laplace loss function can estimate the ship manoeuvring performance appropriately compared to the simulation results with Napa software and other approximation methods such as Artificial Neural Network (ANN) and classic SVR. In this article, we also gather enough ship information about the offshore support vessel; the Latin Hypercube Design is employed to explore the design space. Instead of requiring the evaluation of expensive simulation codes, we establish the metamedels of ship manoeuvring performance; all the numerical results show the effectiveness and practicability of the new approximation algorithms

Gene expression profile indicates involvement of NO in Camellia sinensis pollen tube growth at low temperature

DEGs identified from the comparison between control (CsPT-CK) and 4 °C-treated (CsPT-LT) pollen tbues. All of the samples were replicated three times. CK and LT FPKM: fragments per kb per million reads for each unigene in the CK and LT libraries, respectively. The log2Ratio (LT/CK): ratio between the FPKM of LT and CK. The absolute values of log2Ratio > 1 and probability > 0.7 were used as threshold for assigning significance. Annotation of DEGs against NR, NT, Swiss-Prot protein, KEGG, COG and GO were all reported in the tables. “-”: no hit. (XLS 381 kb

TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training

Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions supervised by contrastive loss, making it highly effective for single-label classification. However, it shows poor performance on multi-label datasets because the global feature tends to be dominated by the most prominent class and the contrastive nature of softmax operation aggravates it. In this study, we observe that the multi-label classification results heavily rely on discriminative local features but are overlooked by CLIP. As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags. It comprises three steps: (1) patch-level classification to obtain coarse scores; (2) dual-masking attention refinement (DMAR) module to refine the coarse scores; (3) class-wise reidentification (CWR) module to remedy predictions from a global perspective. This framework is solely based on frozen CLIP and significantly enhances its multi-label classification performance on various benchmarks without dataset-specific training. Besides, to comprehensively assess the quality and practicality of generated tags, we extend their application to the downstream task, i.e., weakly supervised semantic segmentation (WSSS) with generated tags as image-level pseudo labels. Experiments demonstrate that this classify-then-segment paradigm dramatically outperforms other annotation-free segmentation methods and validates the effectiveness of generated tags. Our code is available at https://github.com/linyq2117/TagCLIP.Comment: Accepted by AAAI202

Quantitative analysis of multi-components by single marker method combined with UPLC-PAD fingerprint analysis based on saikosaponin for discrimination of Bupleuri Radix according to geographical origin

Background: Saikosaponins are regarded as one of the most likely antipyretic constituents of Bupleuri Radix, establishing a comprehensive method that can reflect both the proportion of all constituents and the content of each saikosaponin is critical for its quality evaluation.Methods: In this study, the combination method of quantitative analysis of multiple components with a single marker (QAMS) and fingerprint was firstly established for simultaneous determination of 7 kinds of saikosaponins in Bupleuri Radix by ultra-high performance liquid chromatography (UPLC).Results: The results showed that saikosaponin d was identified as the optimum IR by evaluating the fluctuations and stability of the relative calibration factors (RCFs) under four different conditions. The new QAMS method has been confirmed to accurately quantify the 7 kinds of saikosaponins by comparing the obtained results with those obtained from external standard method and successfully classify the 20 batches of Bupleuri Radix from 8 provinces of China. The experimental time of fingerprint was significantly reduced to approximate 0.5 h through UPLC-PAD method, a total of 17 common peaks were identified.Conclusion: The QAMS-fingerprint method is feasible and reliable for the quality evaluation of Bupleuri Radix. This method could be considered to be spread in the production enterprises of Bupleuri Radix

Discovery of electromagnetic polarization in Asian rice wine deterioration process and its applications

Rice wine, known as yellow wine in China and Japan, possesses considerable nutritional value and holds significant global influence. This study addresses the challenge of preserving rice wine, which is prone to rancidity due to its low alcohol content. Conventional storage techniques employing pottery jars often result in substantial spoilage losses. Through rigorous investigation, this research identifies a polarization phenomenon exhibited by degraded rice wine when subjected to high-frequency microwaves(>60GHz), presenting a pioneering method for detecting spoilage, even within sealed containers. Employing a multi-channel microwave radar apparatus, the study delves into the susceptibility of rice wine to electromagnetic waves across various frequencies, uncovering pronounced polarization traits in deteriorated samples within the E-band microwave spectrum. Furthermore, lab-controlled simulations elucidate a direct correlation between physicochemical alterations and high-frequency Radar Cross Section (RCS) signals during the wine’s deterioration process. A novel six-membered Hydrated Cluster hypothesis is proposed, offering insights into the molecular mechanisms underlying this phenomenon. Additionally, dielectric property assessments conducted using vector network analyzers (VNA) reveal noteworthy enhancements in the dielectric constant of deteriorated rice wine, particularly within the high-frequency domain, thereby augmenting detectability. These findings carry implications for refining rice wine preservation techniques and contribute to the advancement of non-destructive testing technologies, enabling the detection of rice wine deterioration or indications thereof, even within sealed vessels

Mapping the Distribution of Water Resource Security in the Beijing-Tianjin-Hebei Region at the County Level under a Changing Context

The Beijing-Tianjin-Hebei (Jingjinji) region is the most densely populated region in China and suffers from severe water resource shortage, with considerable water-related issues emerging under a changing context such as construction of water diversion projects (WDP), regional synergistic development, and climate change. To this end, this paper develops a framework to examine the water resource security for 200 counties in the Jingjinji region under these changes. Thus, county-level water resource security is assessed in terms of the long-term annual mean and selected typical years (i.e., dry, normal, and wet years), with and without the WDP, and under the current and projected future (i.e., regional synergistic development and climate change). The outcomes of such scenarios are assessed based on two water-crowding indicators, two use-to-availability indicators, and one composite indicator. Results indicate first that the water resources are distributed unevenly, relatively more abundant in the northeastern counties and extremely limited in the other counties. The water resources are very limited at the regional level, with the water availability per capita and per unit gross domestic product (GDP) being only 279/290 m3 and 46/18 m3 in the current and projected future scenarios, respectively, even when considering the WDP. Second, the population carrying capacity is currently the dominant influence, while economic development will be the controlling factor in the future for most middle and southern counties. This suggests that significant improvement in water-saving technologies, vigorous replacement of industries from high to low water consumption, as well as water from other supplies for large-scale applications are greatly needed. Third, the research identifies those counties most at risk to water scarcity and shows that most of them can be greatly relieved after supplementation by the planned WDP. Finally, more attention should be paid to the southern counties because their water resources are not only limited but also much more sensitive and vulnerable to climate change. This work should benefit water resource management and allocation decisions in the Jingjinji region, and the proposed assessment framework can be applied to other similar problems.This study is supported by the National Key Research and Development Program of China (2016YFC0401401) and the National Natural Science Foundation of China (51609256, 51609122, 51522907, 51739011, and 51569026). Partial support is also from the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2017QNRC001