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As global demand for freshwater grows, seawater desalination has become one of the most promising methods for obtaining freshwater. Many coastal nations have included it in their sustainable development plans and are actively advancing related technologies. Compared with traditional desalination methods, such as distillation and membrane-based desalination, seawater freezing desalination offers the benefit of producing large amounts of freshwater at lower costs. This study provides an overview of the main methods and principles of seawater freezing desalination and summarizes the latest research progress. This paper also discusses experimental and simulation studies of different principles. Current research shows that both direct and indirect seawater freezing desalination technologies have become relatively mature, laying a foundation for practical applications. Hydrate-based desalination, eutectic freezing technology, and vacuum freezing technology offer cost-reduction benefits, but existing technologies have limitations, making these areas hot topics in research. Additionally, this paper discusses the experimental progress and simulation methods associated with this, elaborates upon, and analyzes the freezing crystallization process and desalination efficiency from the perspective of the bottom layer of crystal growth, offering valuable insights for future research. It concludes by summarizing and predicting the development of these technologies, emphasizing their great potential due to their low-cost and sustainable features. [ABSTRACT FROM AUTHOR]

Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial, while the academia has invented many traditional physical methods with accurate detection capability, but their detection computational efficiency is low. In recent years, with the increasing application of deep learning in ocean feature detection, many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean data. But it is difficult for them to precisely fit some physical features implicit in traditional methods, leading to inaccurate identification of ocean eddies. In this study, to address the low efficiency of traditional physical methods and the low detection accuracy of deep learning models, we propose a solution that combines the target detection model Faster Region with CNN feature (Faster R-CNN) with the traditional dynamic algorithm Angular Momentum Eddy Detection and Tracking Algorithm (AMEDA). We use Faster R-CNN to detect and generate bounding boxes for eddies, allowing AMEDA to detect the eddy center within these bounding boxes, thus reducing the complexity of center detection. To demonstrate the detection efficiency and accuracy of this model, this paper compares the experimental results with AMEDA and the deep learning-based eddy detection method eddyNet. The results show that the eddy detection results of this paper are more accurate than eddyNet and have higher execution efficiency than AMEDA. [ABSTRACT FROM AUTHOR]

Nostolachma jenkinsii (Hook.f.) Deb & J.Lahiri, a member of the Rubiaceae family, is an endangered wild plant species with potential economic value. In this research, the complete chloroplast genome of N. jenkinsii was sequenced to gain insight into its genome feature and better understand the phylogenetic relationships among the Rubiaceae species. The chloroplast genome, with a total length of 155,036 bp, comprises two inverted repeats (IR) regions spanning 25,692 bp each, a large single-copy (LSC) region measuring 85,437 bp, and a short single-copy (SSC) region measuring 18,215 bp. There is an overall 37% GC content in the chloroplast genome. By annotation analysis,. 54 tRNA genes, 10 rRNA genes, and 107 protein-coding genes were all annotated in N. jenkinsii. Furthermore, we applied phylogenetic analysis that revealed a close relationship between N. jenkinsii, D. fruticosa and D. dubia, placing them together within the Rubiaceae family. [ABSTRACT FROM AUTHOR]

The traditional method for retrieving cloud optical thickness (COT) is carried out through a Look-Up Table (LUT). Researchers must make a series of idealized assumptions and conduct extensive observations and record features in this scenario, consuming considerable resources. The emergence of deep learning effectively addresses the shortcomings of the traditional approach. In this paper, we first propose a daytime (SOZA < 70°) COT retrieval algorithm based on FY-4A AGRI. We establish and train a Convolutional Neural Network (CNN) model for COT retrieval, CM4CR, with the CALIPSO's COT product spatially and temporally synchronized as the ground truth. Then, a deep learning method extended from video prediction models is adopted to predict COT values based on the retrieval results obtained from CM4CR. The COT prediction model (CPM) consists of an encoder, a predictor, and a decoder. On this basis, we further incorporated a time embedding module to enhance the model's ability to learn from irregular time intervals in the input COT sequence. During the training phase, we employed Charbonnier Loss and Edge Loss to enhance the model's capability to represent COT details. Experiments indicate that our CM4CR outperforms existing COT retrieval methods, with predictions showing better performance across several metrics than other benchmark prediction models. Additionally, this paper also investigates the impact of different lengths of COT input sequences and the time intervals between adjacent frames of COT on prediction performance. [ABSTRACT FROM AUTHOR]

Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction. [ABSTRACT FROM AUTHOR]

In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditional lossy compression techniques are hardly suitable due to their inherently random nature. They often impose unpredictable damage to scientific data, which affects the results of data mining and analysis tasks that require certain precision. In this paper, we propose a similarity-based minimum bounding rectangle (SbMBR) tree, a tree-based indexing and compression method, to address the aforementioned problem. Our method can hierarchically select appropriate minimum bounding rectangles according to the given maximum acceptable errors and use the average value contained in each selected MBR to replace the original data to achieve data compression with multi-layer loss control. This paper also provides the corresponding tree construction algorithm and range query processing algorithm for the indexing structure mentioned above. To evaluate the data quality preservation in cross-domain data analysis and mining scenarios, we use mutual information as the estimation metric. Experimental results emphasize the superiority of our method over some of the typical indexing and compression algorithms. [ABSTRACT FROM AUTHOR]

In this study, the multifaceted asymmetric radiation from the edge (MARFE) was observed in high-density discharges or during impurity gas injection on the EAST tokamak. The MARFE onset indicated by spectral and radiation signals can also be detected by the POlarimeter-INTerferometer (POINT) diagnostic, which measures the horizontal line-integrated density and the Faraday rotation. The fluctuation amplitude of the density signal resulting from the MARFE oscillation increases with the edge safety factor, which is consistent with the thermal instability theory. By combining density and the Faraday rotation, the local current shrinkage in the MARFE region is observed during the MARFE movement. The density and the current profile calculated by the POINT become more peak during the MARFE, which may lead to a strong magnetohydrodynamic instability that can result in disruption. [ABSTRACT FROM AUTHOR]

In order to detect cotton's position on a plant, a laser measurement experiment was designed, which included a crossgirder, and beneath which a model LMS291-S05 laser scanner produced by SICK Co., Ltd was fixed. Then the three-dimensional coordinate values of a single cotton plant can be measured with the devices mentioned above, and the acquired distance image was furthermore processed with computer image processing technology and pattern recognition theory and also with the agronomic characteristics of cotton taken into consideration. The sampling interval of the laser sensor on axis X and axis Y was both 0.004 m, and the sampled points formed a image that illustrated the distance from the laser sensor to the plant surface point cloud. For the purpose of removing the unneeded background of the plant in the image, the acquired plant distance image was first processed with binaryzation at the threshold value of 0.9 m, because that 0.9 m happened to be the longest distance from the laser sensor to the bottom part of the cotton plant surface according to the measured data. Observing the cotton plant's morphological characters in the formerly binarized image, and it was not hard to find out that the cotton part in the image appeared to be circular with a large area, while the stalk part in the image was thin and tiny. On this basis, morphology opening operations were carried out toward the edge of the bimarized image with the structural elements from a circle whose radius ranges from 0.01 m to 0.02 m, which is the usual width of a cotton stalk, in order to remove the unneeded cotton stalk background and extract the binary image of cotton. After a series of comparisons among different distances namely Euclidean, Mahalanobis, Bullock, and Minkowski distances, and considering that the pixel gray value was one-dimensional data, the distance between pixels was found to be insensitive to computing methods. The similarity between pixels in the image was calculated with Euclidean distance, and the centroid distance was chosen, which was also decided among several other different distances like closest distance, longest distance, and sums of squares of deviations, as the between-class distance according to the Cophenetic correlation coefficient. And due to the fact that branches that were 45° to each other usually at least had two more branches in between them vertically, and the minimum vertical distance of two neighboring branches was about 0.05 m, correspondingly the minimum vertical distance of two pieces of overlapped cotton was about 0.17 m, so the cotton image clustered at the threshold of 0.17 m. Then the adhesion or overlapped cotton was segmented, and the three-dimensional coordinate values of a single cotton plant were acquired. The result of a series of experiments showed that the recognition rate of a single cotton plant was as high as 96.67%, the mean measurement error of the cotton was 0.015 m, and the relative error was 2.43%. The correlation coefficient between the laser measurement and manual measurement results was up to 0.9934, which was high enough to provide the picking robot with needed parameters that could be used to determine its movement locus. [ABSTRACT FROM AUTHOR]

Nostolachma jenkinsii (Hook.f.) Deb & J.Lahiri, a member of the Rubiaceae family, is an endangered wild plant species with potential economic value. In this research, the complete chloroplast genome of N. jenkinsii was sequenced to gain insight into its genome feature and better understand the phylogenetic relationships among the Rubiaceae species. The chloroplast genome, with a total length of 155,036 bp, comprises two inverted repeats (IR) regions spanning 25,692 bp each, a large single-copy (LSC) region measuring 85,437 bp, and a short single-copy (SSC) region measuring 18,215 bp. There is an overall 37% GC content in the chloroplast genome. By annotation analysis,. 54 tRNA genes, 10 rRNA genes, and 107 protein-coding genes were all annotated in N. jenkinsii. Furthermore, we applied phylogenetic analysis that revealed a close relationship between N. jenkinsii , D. fruticosa and D. dubia , placing them together within the Rubiaceae family., Competing Interests: No potential conflict of interest was declared by the author(s)., (© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

N 4 -Acetylcytidine (ac 4 C) has been found to affect a variety of cellular and biological processes. For a mechanistic understanding of the roles of ac 4 C in biology and disease, we present an antibody-free, fluorine-assisted metabolic sequencing method to detect RNA ac 4 C, called "FAM-seq". We successfully applied FAM-seq to profile ac 4 C landscapes in human 293T, HeLa, and MDA cell lines in parallel with the reported acRIP-seq method. By comparison with the classic ac 4 C antibody sequencing method, we found that FAM-seq is a convenient and reliable method for transcriptome-wide mapping of ac 4 C. Because this method holds promise for detecting nascent RNA ac 4 C modifications, we further investigated the role of ac 4 C in regulating chemotherapy drug resistance in chronic myeloid leukemia. The results indicated that drug development or combination therapy could be enhanced by appreciating the key role of ac 4 C modification in cancer therapy.