五配位氧磷烷分子间配体交换反应-RNA水解和融合过程的化学模型（英文）

具有五元环和三配体结构的五配位氧磷烷(ab2)在碱催化条件下自发进行分子间的配体交换反应,产生不同配体组合的全部三种五配位氧磷烷(a3,b3和a2b)。如果把其中a3与b3作为父代分子,其配体交换产生的五配位氧磷烷a2b和ab2可以视作子代分子,从而自发实现了分子结构的多样化。因此,五配位氧磷烷分子间配体交换反应可以作为研究生命过程中具有五配位磷中间体结构化学性质的模型,对理解基因转录和生命信息储存等过程中涉及的RNA分子剪接、水解和融合等重要生命过程的分子机制提供了重要依据

五配位氧磷烷分子间配体交换反应-RNA水解和融合过程的化学模型（英文）

具有五元环和三配体结构的五配位氧磷烷（ab2）在碱催化条件下自发进行分子间的配体交换反应,产生不同配体组合的全部三种五配位氧磷烷（a3,b3和a2b）.如果把其中a3与b3作为父代分子,其配体交换产生的五配位氧磷烷a2b和ab2可以视作子代分子,从而自发实现了分子结构的多样化.因此,五配位氧磷烷分子间配体交换反应可以作为研究生命过程中具有五配位磷中间体结构化学性质的模型,对理解基因转录和生命信息储存等过程中涉及的RNA分子剪接、水解和融合等重要生命过程的分子机制提供了重要依据.Project supported by the National Natural Science Foundation of China(Nos.21778042,41876072,21772163,41576081)the Xiamen Southern Oceanographic Center(No.17GYY002NF02)the Fundamental Research Funds for the Central Universities(No.20720170069)~

Gravitational wave signal denoising and merger time prediction with a deep neural network

The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the short timescale of binary mergers, it is crucial to predict the time of the merger in advance to devise detailed observational plans. The overwhelming noise and slow accumulation of the signal-to-noise ratio in the inspiral phase make this task particularly challenging. To address this issue, we propose a novel deep neural denoising network in this study, capable of denoising a 30-day inspiral phase signal. Following the denoising process, we perform the detection and merger time prediction based on the denoised signals. Our results demonstrate that, for a 30-day inspiral phase data with a signal-to-noise ratio between 10 and 50 occurring no more than 10 days before the merger, our absolute prediction error for the merger time is generally within 24 h

Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network

Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions

Effects of Different Atmospheric Correction Methods on Remote Sensing Monitoring Results of Ulva prolifera

HJ-1 CCD数据具有较高的时间分辨率和空间分辨率,可以实现大范围的浒苔灾害动态监测,大气校正是实现浒苔信息提取的基础,但是不同大气校正方法对同一影像的处理结果会有差异。基于黄海中南部浒苔暴发时期的环境卫星影像,采用FLAASH、6S、COST 3种方法分别对其进行大气校正处理以消除大气影响,并运用NDVI阈值法提取浒苔,通过划分多个研究区以及对光谱特征、NDVI、类间距、混合像元、阈值敏感性、提取结果等多个变化量的统计与分析,比较了3种大气校正方法在浒苔提取中的效果,为后续浒苔定量化监测提供了帮助。结果表明：在采用NDVI阈值法提取浒苔信息时,使用COST大气校正会取得良好的提取效果,其次是FLAASH大气校正方法和6S大气校正方法

Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows

Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as laser interferometer space antenna, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great significance for the global fitting of all resolvable sources, as well as the astrophysical interpretation of gravitational wave signals. However, such analyses usually entail significant computational costs. To address these challenges, inspired by the latest progress in generative models, we explore the application of continuous normalizing flows (CNFs) on the parameter estimation of MBHBs. Specifically, we employ linear interpolation and trig interpolation methods to construct transport paths for training CNFs. Additionally, we creatively introduce a parameter transformation method based on the symmetry in the detector's response function. This transformation is integrated within CNFs, allowing us to train the model using a simplified dataset, and then perform parameter estimation on more general data, hence also acting as a crucial factor in improving the training speed. In conclusion, for the first time, within a comprehensive and reasonable parameter range, we have achieved a complete and unbiased 11-dimensional rapid inference for MBHBs in the presence of astrophysical confusion noise using CNFs. In the experiments based on simulated data, our model produces posterior distributions comparable to those obtained by nested sampling

聚四氟乙烯清洗花篮改良结构

本实用新型涉及一种应用于半导体清洗工艺中的聚四氟乙烯清洗花篮改良结构,其包括把手和一个以上花篮本体,所述把手上设置一个以上卡槽,所述花篮本体的一侧部设置可与所述卡槽紧密插接配合的突出部。所述卡槽优选采用T型卡槽。所述花篮本体上端面上分布二个以上可以容置硅片的浅槽。所述浅槽的槽底部设置复数个竖直通孔。本实用新型在保有现有聚四氟乙烯清洗花篮各种优点的同时,还可用于同时清洗多品种、多规格的多片小片,且其底部设置的通孔还提高了清洗的效率,再者,利用卡槽,尤其是T型卡槽固定花篮本体和把手,使得清洗花篮更加坚固耐用,且便于安装拆卸,并使得清洗花篮还易于运输和收储

芝麻酱感官词典的开发和建立Development and establishment of a sensory lexicon for sesame paste

为建立芝麻酱的感官词典，依据GB/T 16861—1997《感官分析 通过多元分析方法鉴定和选择用于建立感官剖面的描述词》产生芝麻酱的感官描述词，以几何平均值法分析删减描述词以制作芝麻酱感官风味轮，以相关性分析和主成分分析提炼关键性描述词，对关键性描述词添加定义、寻找参比样并进行强度赋值以形成芝麻酱关键性感官属性定量描述词汇表。结果显示，最终提炼20个关键性描述词（7个气味描述词，8个风味描述词，3个口感描述词，2个外观描述词）对芝麻酱感官特征进行定量描述分析。通过芝麻酱感官词典获得了不同芝麻酱的感官属性特征，且不同产地和焙炒条件芝麻酱的感官特征存在差异，这验证了芝麻酱感官词典的有效性。芝麻酱感官词典的开发为芝麻酱感官评价体系的建立提供了基础。In order to establish a sensory lexicon of sesame paste, the sensory descriptors for sesame paste were generated based on GB/T 16861-1997 Sensory analysis-indentification and selection of descriptors for establishing a sensory profile by a multidimensional approach was established. The descriptors were culled by applying the geometric mean method to create a sensory flavor wheel of sesame paste. The key descriptors were extracted by correlation analysis and principal component analysis. The key descriptors were defined, their reference samples were found, and their intensities were assigned to create the key sensory attributes quantitative descriptive lexicon of sesame paste. The results showed that 20 key descriptors (7 odor descriptors, 8 taste descriptors, 3 texture descriptors, and 2 appearance descriptors) were finally extracted to quantitatively describe the sensory characteristics of sesame paste. The sensory attributes characteristics of different sesame pastes were obtained by using the sesame paste sensory lexicon, and there were differences in the sensory characteristics among the sesame paste of different origins and different roasting conditions, which verified the validity of sensory lexicon for sesame paste. The development of the sensory lexicon for sesame paste can provide the basis for the establishment of a sensory evaluation system for sesame paste