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The total organic carbon (TOC) content is an important index for source rock evaluation. However, due to the scarcity of rock samples, the vertical continuous TOC change curve cannot be obtained, and the test cost is high, which bring great limitations to the evaluation of source rocks. Predecessors have also studied some TOC prediction models, such as the Δlg R method, support vector regression (SVR), and back-propagation neural network, but the TOC predicted by the Δlg R method and SVR has low accuracy and is difficult to calculate. However, back-propagation neural networks always have problems such as local optimal solution and slow convergence speed. In this article, a convolution neural network prediction scheme is proposed. Taking the source rocks of Pinghu Formation in the Xihu Sag as the research object, the advantages of this method are proved by comparing the prediction results of the Δlg R method, SVR, and BP neural network method. The results show that the prediction accuracy of this method is more than 90%, meeting the prediction requirements of TOC. By predicting the TOC curve of Well A, the TOC variation characteristics of Pinghu Formation are finally obtained.

UAVs are often limited by limited resources when performing flight tasks, especially the contradiction between storage resources and computing resources when the huge YOLOv3 model is deployed on the edge UAVs. In this paper, we tend to compress YOLOv3 model in different aspects to achieve load availability at the edge. In this paper, deep separable convolution is introduced to reduce the computation of the model. Then, PR regularization term is used as the regularization term of sparse training to better distinguish scaling factors, and then the hybrid pruning combining channel pruning and layer pruning is carried out on the model according to scaling factors, in order to reduce the number of model parameters and the amount of calculation. Finally, since the training data is a 32-bit floating point number, DoReFa-Net quantization method is used to quantify the model, so as to compress the storage capacity of the model. The experimental results show that the compression scheme proposed in this paper can effectively reduce the number of parameters by 97.5 % and the calculation amount by 82.3 %, and can maintain the original detection efficiency of UAVs.

This paper explores a periodic watermark-based replay attack detection method for Unmanned Marine Vehicles modeled in the framework of the Takagi–Sugeno fuzzy system. The precise detection of replay attacks is crucial for ensuring the security of Unmanned Marine Vehicles; however, traditional timestamp-based or encoded measurement-dependent detection approaches often sacrifice system performance to achieve higher detection rates. To reduce the potential performance degradation, a periodic watermark-based detection scheme is developed, in which a compensation signal together with a periodic Gaussian watermark signal is integrated into the actuator. By compensation calculations conducted with all compensatory signals in each period, the position corresponding to a minimum value of the detection function can be derived. Then, the time that the attacks occurred can be ensured with the aid of the comparison between this position with the watermark signal in the same period. An application on a UMV is shown to demonstrate the effectiveness of the presented scheme in detecting replay attacks while minimizing control costs.

Transition-metal-catalyzed directed C–H functionalization with various carbene precursors has been widely employed for constructing a wide range of complex and diverse active molecules through metal carbene migratory insertion processes. Among various carbene precursors, iodonium ylides serve as a novel and emerging carbene precursor with features including easy accessibility, thermal stability and high activity, which have attracted great attention from organic chemists and have achieved tremendous success in organic transformation. In this review, recent progress on the application of iodonium ylides with multifunctional coupling characteristics in C–H bond activation reactions is summarized, and the potential of iodonium ylides is discussed.

Seasonal delta is a sandy fan-shaped sedimentary system formed by seasonal river flooding in continental basins. Modern sedimentary finds that seasonal deltas mainly develop in an arid-semi-arid climate environment with gentle terrain. However, the study of seasonal deltas in ancient rock records is still insufficient, leading to misinterpretations as other sedimentary systems, which poses challenges to the correct understanding of the origin and spatial distribution of sand body deposits. In this study, the red bed deposits of the Cretaceous Baxigai Formation in the west of the Tabei Uplift were selected as the research object. Based on core observation and description, a comprehensive analysis of the sedimentary environment, sedimentary characteristics and sedimentary model of the Baxigai Formation was carried out using data such as well-logging and analytical testing, combined with modern depositional characteristics. Research shows that the sedimentary sequence of the Baxigai Formation red bed developed a seasonal delta sedimentary system in the arid climate. The seasonal delta deposits provide an opportunity to understand the influencing of river flow and total sedimentary loading vary greatly on the sedimentary model, sedimentary mechanism, and distribution of sand bodies in arid climates. The sedimentary process of the seasonal delta in the Baxigai Formation was controlled by the dual-stage sedimentation process in an arid paleoclimate. During the flood period, the supercritical flow sedimentary structures are widely developed in the major distributary channels, and the distributary channels migrate and bifurcate frequently. On the delta plain, crevasse channels are formed through the avulsion of major distributary channels. During the dry period, due to the consumption of water seepage and evaporation transfer, terminal distributary channels are abandoned, crevasse channels are not developed, and the sediment is exposed. The density of the distributary channels is reduced, and the total sedimentary loading is low. The distributary channels sand body is the skeleton sand body of the seasonal delta. This study is of great significance for accurately explaining the formation process of seasonal deltas and provides a useful reference for further exploration and deployment of oil and gas reservoirs and paleogeographic reconstruction in the study area.

In traditional channel sand body modeling methods, the method based on target modeling is difficult to condition, and multipoint geostatistics have difficulty reproducing the shape of continuous channel sand bodies, which makes the modeling results difficult to directly apply to oilfield production. Through depth learning, the depth generation model can generate a sufficiently accurate channel sand body model, reproduce the complex channel sand body shape, meet the well point conditions, and compensate for the shortcomings of the traditional modeling algorithm. In the process of modeling, 20 000 channel sand body models and corresponding condition sets are established based on the target simulation method and computer matching operation. Combined with the theory of variational autoencoders (VAE) and generative adversarial networks (GAN), a depth generation model is established, including a classifier, encoder, decoder and discriminator. The condition data and the real model are input into the depth generation model to obtain the corresponding channel sand body model. Through extensive training, a generator that can generate channel sand bodies that meet the well point conditions is established. Finally, the well point data are input into the generator to establish the corresponding channel sand body model. The results show that compared with the traditional modeling algorithm, the depth generation model modeling algorithm not only shows a continuous and clear channel sand body but can also generate multiple channel sand body models under given conditions. This modeling method overcomes the shortcomings of traditional channel sand modeling methods and provides a new solution for channel sand reservoir modeling. The established channel model can provide a reference for the oilfield development stage.

This paper surveys the recent advances in dynamic positioning (DP) control for marine crafts. DP of marine crafts means that a craft can maintain a fixed position and heading, or move along a predetermined trajectory slowly without the anchoring system, using only its own thruster system to counteract ocean disturbances. The survey is by no means exhaustive but provides a survey of some of the major technological advancements in DP controller design over the years of research and development. Firstly, the model of marine crafts and some difficult problems in DP control are introduced including the impact of multiple source disturbance, unavailable velocity measurement information, resource conservation and performance optimization, destabilizing impact of faults and network security and compound multi-constraint restrictions. Then, the DP control schemes in recent years are summarized and classified in detail. Finally, some theoretical and technical problems are proposed, including online data-driven model-free control, man–machine combination intelligent control and composite hierarchical anti-disturbance control to guide future investigations.

Abstract Background Plants synthesize metabolites to adapt to a continuously changing environment. Metabolite biosynthesis often occurs in response to the tissue-specific combinatorial developmental cues that are transcriptionally regulated. Polyphyllins are the major bioactive components in Paris species that demonstrate hemostatic, anti-inflammatory and antitumor effects and have considerable market demands. However, the mechanisms underlying polyphyllin biosynthesis and regulation during plant development have not been fully elucidated. Results Tissue samples of P. polyphylla var. yunnanensis during the four dominant developmental stages were collected and investigated using high-performance liquid chromatography and RNA sequencing. Polyphyllin concentrations in the different tissues were found to be highly dynamic across developmental stages. Specifically, decreasing trends in polyphyllin concentration were observed in the aerial vegetative tissues, whereas an increasing trend was observed in the rhizomes. Consistent with the aforementioned polyphyllin concentration trends, different patterns of spatiotemporal gene expression in the vegetative tissues were found to be closely related with polyphyllin biosynthesis. Additionally, molecular dissection of the pathway components revealed 137 candidate genes involved in the upstream pathway of polyphyllin backbone biosynthesis. Furthermore, gene co-expression network analysis revealed 74 transcription factor genes and one transporter gene associated with polyphyllin biosynthesis and allocation. Conclusions Our findings outline the framework for understanding the biosynthesis and accumulation of polyphyllins during plant development and contribute to future research in elucidating the molecular mechanism underlying polyphyllin regulation and accumulation in P. polyphylla.

Paris polyphylla Smith is a perennial medicinal herb with records from around 2000 years ago. Polyphyllins are the main bioactive compounds of this herb, which are found to have remarkable effects on bacteriostatic, antiphlogistic, sedative, and antitumor. However, the market demand for P. polyphylla is sharply increasing, and the wild resources are threatened by plundering exploitation. Integrating molecular data of P. polyphylla can benefit the sustainable resource exploitation. Here, we constructed PPDP (Paris polyphylla Data Portal) to provide a data platform for polyphyllin biosynthesis and germplasm resource research. PPDP integrates related molecular data resources, functional genomics analysis, and morphological identification. The database provides abundant data (transcriptome, CDS, lncRNA, alternative splicing, gene family, SSR, and chloroplast genome) and practical analytical tools (network construction, heatmap of expression profiles, enrichment, and pathway search) with a user-friendly interface. So far, PPDP is the first biomolecular database for the genus Paris plants. In the future, we will gradually add genomic data and other necessary molecular biological information to improve the database.

Lateral organ boundaries domain (LBD) proteins are plant-specific transcription factors that play important roles in organ development and stress response. However, the function of LBD genes has not been reported in Euphorbiaceae. In this paper, we used Jatropha curcas as the main study object and added rubber tree (Hevea brasiliensis), cassava (Manihot esculenta Crantz) and castor (Ricinus communis L.) to take a phylogenetic analysis of LBD genes. Of LBD, 33, 58, 54 and 30 members were identified in J. curcas, rubber tree, cassava and castor, respectively. The phylogenetic analysis showed that LBD members of Euphorbiaceae could be classified into two major classes and seven subclasses (Ia-Ie,IIa-IIb), and LBD genes of Euphorbiaceae tended to cluster in the same branch. Further analysis showed that the LBD genes of Euphorbiaceae in the same clade usually had similar protein motifs and gene structures, and tissue expression patterns showed that they also have similar expression profiles. JcLBDs in class Ia and Ie are mainly expressed in male and female flowers, and there are multiple duplication genes with similar expression profiles in these clades. It was speculated that they are likely to play important regulatory roles in flower development. Our study provided a solid foundation for further investigation of the role of LBD genes in the sexual differentiaion of J. curcas.

Paris polyphylla is an important medicinal plant that can biosynthesize polyphyllins with multiple effective therapies, ranging from anti-inflammation to antitumor; however, the genetic diversity of Paris polyphylla is still unclear. To explore the genetic characteristics of cultivation populations in primary planting areas, we developed 10 expressed sequence tag simple sequence repeat (EST-SSR) markers related to polyphyllin backbone biosynthesis and utilized them in 136 individuals from 10 cultivated populations of P. polyphylla var. yunnanensis. The genetic diversity index showed that ten loci had relatively high genetic polymorphism levels. Shannon information of loci suggested that more information occurred within population and less information occurred among population. In addition, the overall populations exhibited a low degree of differentiation among populations, but maintained a high degree of genetic diversity among individuals, resulting in high gene flow and general hybridization. The genetic structure analysis revealed that 10 populations possibly derived from two ancestral groups and all individuals were found with different levels of admixture. The two groups were different from the cultivation groups at population level, suggesting the cross-pollination among cultivars. These findings will provide insights into the genetic diversity of the germplasm resources and facilitate marker-assisted breeding for this medicinal herb.

Background Low-pass genome sequencing (LP GS) is an alternative to chromosomal microarray analysis (CMA). However, validations of LP GS as a prenatal diagnostic test for amniotic fluid are rare. Moreover, sequencing depth of LP GS in prenatal diagnosis has not been evaluated. Objective The diagnostic performance of LP GS was compared with CMA using 375 amniotic fluid samples. Then, sequencing depth was evaluated by downsampling. Results CMA and LP GS had the same diagnostic yield (8.3%, 31/375). LP GS showed all copy number variations (CNVs) detected by CMA and six additional variant of uncertain significance CNVs (>100 kb) in samples with negative CMA results; CNV size influenced LP GS detection sensitivity. CNV detection was greatly influenced by sequencing depth when the CNV size was small or the CNV was located in the azoospermia factor c (AZFc) region of the Y chromosome. Large CNVs were less affected by sequencing depth and more stably detected. There were 155 CNVs detected by LP GS with at least a 50% reciprocal overlap with CNVs detected by CMA. With 25 M uniquely aligned high-quality reads (UAHRs), the detection sensitivity for the 155 CNVs was 99.14%. LP GS using samples with 25 M UAHRs showed the same performance as LP GS using total UAHRs. Considering the detection sensitivity, cost and interpretation workload, 25 M UAHRs are optimal for detecting most aneuploidies and microdeletions/ microduplications. Conclusion LP GS is a promising, robust alternative to CMA in clinical settings. A total of 25 M UAHRs are sufficient for detecting aneuploidies and most microdeletions/microduplications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technology for waste heat recovery from a coal-fired curing barn flue gas, which can reduce simultaneously the flue gas pollutants. The upper and lower shed tobacco leaves are used as the research object during the curing of tobacco leaves in the rural curing tobacco house. The temperature and the humidity are monitored during the curing process, the change in the appearance of the tobacco leaves is observed. The desulfurization and de-nitration system can improve the uniformity of the overall tobacco leaves temperature and humidity, and can realize the integration of waste heat recovery of coal-fired boilers and flue gas pollutants treatment. An experiment is carried out and the results show that the technology can save energy to the greatest extent while ensuring the quality of tobacco leaves. [ABSTRACT FROM AUTHOR]