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The advent of autonomous power-limited systems poses a new challenge for early design space exploration. The existing architecture-level power evaluation tools lose accuracy due to ignoring features of circuit-level behaviors and influences of process, voltage, and temperature variations. Although power estimations based on SPICE or PrimeTime PX (PTPX) are accurate enough, they come at the cost of long simulation time and are available only in very late phases of design flow. In this article, a fast and accurate dynamic power evaluation method is proposed, which estimates activity factors at the circuit level. The impact of process variation at the gate level is considered through the proposed effective capacitance model. Activity factors are then estimated by the model and input vectors of the circuit. Input vectors are generated by architecture-level simulations in the form of streaming. For real-time and high-speed power evaluation, a data streaming framework is proposed for massive parallelism. The cross-layer estimation is verified based on the functional units of PULPino processor running SPEC CPU2006 benchmarks. Compared with the SPICE results using SMIC 28-nm PDK, our cycle-by-cycle dynamic power analysis shows an average error of 5.4%. Meanwhile, our approach realizes 65.2% faster than the traditional PTPX simulation and 48.8% faster compared with the state-of-art cross-level evaluation method.

To enhance the antagonistic ability against tobacco bacterial wilt, physical mutagenesis was in this study induced using nitrogen ion beam irradiation, and resulting mutant strain K60 of Bacillus subtilis Pro-2 effectively controlled Ralstonia solanacearum. Through transcriptome sequencing, a total of 1291 significantly different genes were screened, including 424 up-regulated genes and 867 down-regulated genes. After enrichment by GO and KEGG metabolic pathways, there were more differential genes enriched in lipopeptide metabolic pathways. The analysis of significantly changed differential genes showed that, among the three common lipopeptide antibacterial substances of Bacillus (surfactin, iturin and fengycin), the synthetic surfactin genes were significantly increased (P ≤ 0.01). According to transcriptome data, the srfA gene, which is related to surfactin synthetase, had very significant change in transcription (P ≤ 0.01), and transcription of two regulatory factors (Rap, CodY) related to srfA gene expression were reduced, with CodY being significantly downregulated at the transcription level.

A rainfall-induced catastrophic landslide occurred at 03:50 local time UTC + 8 on August 21, 2020 in Zhonghaicun (29°20′30″ N, 102°41′40″ E), Hanyuan County, China. Approximately 1.1 million cubic meters of debris failed instantly along the interface between the overlying soil and the Xigeda mudstone, destroying six buildings and causing nine casualties. The Xigeda stratum near the landslide consists of poorly lithified mudstone, which is characterized by weak cementation and unfavorable dip angle. The Xigeda stratum, as a locally distributed special formation in southwest China, is well known for the feature of “being prone to failure”. In this paper, the mineral composition tests and mechanical strength tests were conducted to determine the physical and mechanical properties of the soil and rock-like stratum. In addition, numerical calculations were carried out to analyze the evolution of the Zhonghaicun landslide. The test results show that the Xigeda mudstone is a weakly swelling rock containing a large amount of clay minerals. The water content of the mudstone plays a significant role in its physico-mechanical properties. The heavy rainfall lasting approximately one week caused the water table to rise and the mudstone to soften, which eventually triggered the Zhonghaicun landslide.

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

Emergency of nanoparticulate drug delivery systems has improved the target, bioavailability, and curative effect of traditional Chinese medicine (TCM). However, the application of nano-preparation has been limited owing to the low content of active ingredients in part TCM. MicroRNAs (miRNAs) regulate plant growth, development, and response to environmental stresses at post-transcriptional regulation level by cleavage or translational inhibition. The molecular functions of miRNAs playing a role in synthesizing active comportments at medicinal plants have been widely researched. Dendrobium nobile is a perennial herb in the orchidaceae family. D. nobile protocorm can produce plant-specific metabolites at a short period. Therefore, it is a good substitute for producing metabolites. To understand the functions of miRNAs in D. nobile protocorm, Illumina sequencing of D. nobile protocorm (Dnp), D. officinale protocorm (Dcp), and D. nobile leaf (Dnl) were carried out. A total of 439, 412, and 432 miRNAs were identified from Dnp, Dcp, and Dnl, respectively. Some specific miRNAs were identified among them. Through combing GO and KEGG function annotation, miRNAs mainly involved metabolic pathways, plant hormone signal transduction, biological regulation, and protein binding. Acetyl-CoA acetyltransferase (AACT), mevalonate kinase (MK), 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), and 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (HDS), synthesizing basic precursor isoprene pyrophosphate (IPP) in terpenoid backbone biosynthesis pathway, were predicted as potential targets of 6 different miRNAs. Twenty-six miRNAs participated in auxin, cytokinin, abscisic acid, jasmonic acid, and salicylic acid signal transduction pathway. This report provided valuable candidate genes in Dnp involved in terpenoid biosynthesis and plant hormone signal transduction pathway. At the same time, it can help accelerate the use of dendrobine into nano preparation.

In heterogeneous catalysis, developing robust catalysts and preventing the metal leaching during the reactions still remain challenging. Herein, a porous honeycomb-like nanostructure with bimetallic Ce–Cr oxide supported on N-doped carbon was prepared via a facile in-situ pyrolysis method. The prepared catalyst was highly active, selective, and stable in the transformation of cyclohexane to KA oil with H2O2 as the oxidant. The N-doped carbon served as an excellent support for the metal nanoparticles and greatly promoted their dispersion, and displayed a comparable or superior catalytic activity compared to the other traditional supports. It was found that the pyridinic N and pyrrolic N species of N-doped carbon were the main active sites for the metal immobilization, and the strong electronic interaction between N-doped carbon and Ce–Cr oxide was formed. The results revealed that the increased low-valence metal species might be responsible for the improved catalytic activity, and EPR analysis indicated that the hydroxyl radicals played a crucial role during the oxidation process. Besides, the catalyst can be easily separated and still remained stable after recycling seven times, making it a promising candidate for the catalytic oxidation of cyclohexane to KA oil under mild conditions.

Pinellia (Pinellia ternata (Thunb.) Breit.), as important medicinal plant, has been used to treat various ailments for a long time. The sixteen ploid plant (2n = 16 * 13 = 208) Pinellia T2Plus line was obtained from an octoploid (2n = 8 * 13 = 104) T2 line by chromosome-doubling technique. Compared with T2 line, the content of various medicinal components (polysaccharide, guanosine, adenosine and ephedrine) was increased in T2Plus line. In this study, the transcriptome of T2 line and T2Plus line were characterized by RNA sequencing (RNA-seq) technology. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis on differential expressed unigenes (DEGs) revealed that multiple metabolic pathway were enriched significantly, such as ‘Starch and sucrose metabolism’, ‘Purine metabolism’, ‘Photosynthesis’ and six transcription factors (MYB, WRKY, bHLH, lateral organ boundaries domain (LBD), homeodomain-zipper (HD-ZIP) and Ethylene-responsive factor (ERF)) play a key role in difference of transcriptome between T2 line and T2Plus line. These metabolic pathways and transcription factors may play an important role in the difference of medicinal components and epigenetic features between these two Pinellia cultivars. This conclusion provides a robust theoretical basis for the mechanism of the formation of medicinal ingredients in Pinellia cultivars.

Class-incremental learning is a model learning technique that can help classification models incrementally learn about new target classes and realize knowledge accumulation. It has become one of the major concerns of the machine learning and classification community. To overcome the catastrophic forgetting that occurs when the network is trained sequentially on a multi-class data stream, a double consolidation class-incremental learning (DCCIL) method is proposed. In the incremental learning process, the network parameters are adjusted by combining knowledge distillation and elastic weight consolidation, so that the network can better maintain the recognition ability of the old classes while learning the new ones. The incremental learning experiment is designed, and the proposed method is compared with the popular incremental learning methods such as EWC, LwF, and iCaRL. Experimental results show that the proposed DCCIL method can achieve better incremental accuracy than that of the current popular incremental learning algorithms, which can effectively improve the expansibility and intelligence of the classification model.

The present study investigated the endophytic biodiversity on different aged Taxus bark using highthroughput sequencing. Fungi from the five Taxus species, including Taxus yunnanensis, T. media, T. chinensis, and two unidentified Taxus species I and II, were identified by Internal Transcribed Spacer sequence analysis. The operational taxonomic units (OTUs) were 141, 114, 98, 67 and 36, respectively, while the genera numbers were 22, 14, 16, 9 and 10 from the species distribution data, respectively. The dominant fungal genera were Mycena (94.33%), Phageophora (43.97%) and Paraconiothyrium (34.02%), followed by Pestalotiopsis (1.58%) and Strelitziana (1.15%). Rankabundance curve and diversity index suggested that the major species from the bark of T. yunnanensis (YN) was endophytic fungi, whereas fungal species from the bark of unidentified Taxus species II (HDS 50) were quite rare. Furthermore, tree age notably influenced the bark endophytic fungal biodiversity.

Emergency of nanoparticulate drug delivery systems has improved the target, bioavailability, and curative effect of traditional Chinese medicine (TCM). However, the application of nano-preparation has been limited owing to the low content of active ingredients in part TCM. MicroRNAs (miRNAs) regulate plant growth, development, and response to environmental stresses at post-transcriptional regulation level by cleavage or translational inhibition. The molecular functions of miRNAs playing a role in synthesizing active comportments at medicinal plants have been widely researched. Dendrobium nobile is a perennial herb in the orchidaceae family. D. nobile protocorm can produce plant-specific metabolites at a short period. Therefore, it is a good substitute for producing metabolites. To understand the functions of miRNAs in D. nobile protocorm, Illumina sequencing of D. nobile protocorm (Dnp), D. officinale protocorm (Dcp), and D. nobile leaf (Dnl) were carried out. A total of 439, 412, and 432 miRNAs were identified from Dnp, Dcp, and Dnl, respectively. Some specific miRNAs were identified among them. Through combing GO and KEGG function annotation, miRNAs mainly involved metabolic pathways, plant hormone signal transduction, biological regulation, and protein binding. Acetyl-CoA acetyltransferase (AACT), mevalonate kinase (MK), 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), and 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (HDS), synthesizing basic precursor isoprene pyrophosphate (IPP) in terpenoid backbone biosynthesis pathway, were predicted as potential targets of 6 different miRNAs. Twenty-six miRNAs participated in auxin, cytokinin, abscisic acid, jasmonic acid, and salicylic acid signal transduction pathway. This report provided valuable candidate genes in Dnp involved in terpenoid biosynthesis and plant hormone signal transduction pathway. At the same time, it can help accelerate the use of dendrobine into nano preparation.

To investigate the value of quantitative electroencephalography (qEEG) in the evaluation of thrombolytic efficacy in acute cerebral infarction.A prospective cohort study was conducted. Ninety-four patients with acute cerebral infarction who received intravenous thrombolysis admitted to the department of emergency of Qinhuangdao First Hospital from October 2019 to September 2020 were enrolled. The relative energy values of δ, θ, α and β waves in qEEG before and 2 hours, 24 hours and 7 days after intravenous recombinant tissue plasminogen activator (rt-PA) thrombolysis were dynamically monitored, and the power ratio index [DTABR, DTABR = (δ+θ)/(α+β)] was calculated. The National Institutes of Health stroke scale (NIHSS) score was also recorded. The reduction of NIHSS score ≥ 3 or the disappearance of neurological symptoms were regarded as effective thrombolytic therapy. The changes of DTABR before and after thrombolysis in patients with effective and ineffective thrombolysis were analyzed, and the correlation between DTABR and NIHSS score was analyzed by Pearson method.A total of 94 patients were enrolled, including 64 males and 30 females. The average age was (61.71±10.11) years from 36 to 89 years old. Thrombolysis was effective in 57 cases and ineffective in 37 cases. Compared with before thrombolysis, DTABR of the effective group was significantly decreased at 2 hours, 24 hours and 7 days after thrombolysis (left cerebral infarction: 1.87±1.45, 1.59±0.88, 1.58±0.90 vs. 3.82±2.60; right cerebral infarction: 1.55±0.57, 1.41±0.50, 1.35±0.44 vs. 3.20±1.63, all P0.05). DTABR did not change or increase significantly at 2 hours, 24 hours and 7 days after thrombolysis compared with before thrombolysis (left cerebral infarction: 3.56±2.57, 3.48±2.19, 3.54±2.50 vs. 3.11±1.62; right cerebral infarction: 5.29±3.93, 5.33±3.94, 5.19±4.52 vs. 4.73±2.43, all P0.05). Pearson correlation analysis showed a significant positive correlation between DTABR and NIHSS score in patients with acute cerebral infarction (r = 0.691, P0.01).The quantitative index of qEEG, DTABR, can accurately and quickly monitor the process of thrombolysis in acute cerebral infarction, and can effectively evaluate the effect of thrombolysis in patients.

A series of Cr modified graphitic carbon nitride composites (Cr/g-C3N4) was synthesized via a facile wetness impregnation method. Depending on the doping of Cr, the prepared Cr/g-C3N4 catalysts exhibited excellent catalytic performance in the one-step hydroxylation of benzene to phenol with H2O2 as the oxidant under mild conditions. Subsequently, various characterization techniques, including XRD, FT-TR, TEM, XPS, N2 adsorption–desorption, etc., were used to investigate the physicochemical properties of the prepared materials. The catalyst properties such as crystallinity, the main active Cr species and the surface area have been discussed, providing reasonable correlations for the superior catalyst behavior. With the optimization of the reaction parameters, the catalyst displayed remarkable catalytic ability with the highest phenol yield of 30.9%.

Underwater automatic target recognition (ATR) is a challenging task for marine robots due to the complex environment. The existing recognition methods basically use hand-crafted features and classifiers to recognize targets, which are difficult to achieve ideal recognition accuracy. In this paper, we proposed a novel method to realize accurate multiclass underwater ATR by using forward-looking sonar—Echoscope and deep convolutional neural networks (DCNNs). A complete recognition process from data preprocessing to network training and image recognition was realized. Firstly, we established a real, measured Echoscope sonar image dataset. Inspired by the human visual attention mechanism, the suspected target region was extracted via the graph-based manifold ranking method in image preprocessing. Secondly, an end-to-end DCNNs model, named EchoNet, was designed for Echoscope sonar image feature extraction and recognition. Finally, a network training method based on transfer learning was developed to solve the problem of insufficient training data, and mini-batch gradient descent was used for network optimization. Experimental results demonstrated that our method can implement efficiently, and the recognition accuracy on a nine-class underwater ATR task reached 97.3%, outperforming traditional feature-based methods. The proposed method is expected to be a potential novel technology for the intelligent perception of autonomous underwater vehicles.

A temporary immersion bioreactor system (TIBS) has been explored as a possible way for Bletilla striata pseudobulbs enlargement and accumulation of polysaccharides under a fed-batch culture method. The culture was established in TIBS to optimize feeding parameters for polysaccharides accumulation and pseudobulb enlargement under controlled conditions, included 1/2 MS, 40 g/L sucrose; 60 g/L potato lixivium; 0.5 mg/L NAA and an immersion rate of 3 min at every 6-hour interval. Results showed that the combined feeding and elicitation strategy (2% sucrose and 0.25 mmol/L Me-JA) was suitable for the enlargement of pseudobulbs (diameter: 6.78 ± 0.16 mm) and the biosynthesis of polysaccharides (14.49 ± 0.45 mg/g). The optimum plant growth (plant height: 123.97 ± 5.42 mm, leaf width: 7.78 ± 0.32 mm and stem diameter: 2.10 ± 0.07 mm) was obtained by the inoculation of nitrogen feed source of NO3−: NH4+ with a 3:1 ratio. At this stage, the corresponding B. striata pseudobulb enlargement and polysaccharide content were decreased to 6.1 ± 0.2 mm and 8.51 ± 0.1 mg/g, respectively, while the highest pseudobulb enlargement (7.21 ± 0.17 mm) and polysaccharide content (12.94 ± 0.35 mg/g) were optimized when inoculated the NO3−: NH4+ with molar ratio of a 2:2. In conclusion, the optimal carbon and nitrogen source supplements effectively induced the pseudobulb enlargement and polysaccharides accumulation. Overall, the present combination of TIBS and feeding strategy constitutes advancement for the industrial production of pseudobulbs as well as polysaccharides, as comparison with the conventional process.

Culturing factors regarding to B. striata plantlets growth and pseudobulb development through temporary immersion bioreactor system (TIBS) were studied. Immersion frequency, sucrose concentration and inoculant amount were analyzed for their influence on seedling development. An immersion frequency of 3 min per 2 h was found to substantially promote the development of pseudobulbs. Seedling growth and development were also affected by the immersion frequency. The best seedling development was observed among those cultures that were immersed at a rate of 3 min per 6 h. Similarly, sucrose concentration was identified to affect the development of seedlings and pseudobulb formation obviously, with the best development recorded at 40 g/L weight ratio. A prominent seedling development, in terms of stem diameter, plant height and leaf width, were observed when 300 explants was loaded in the TIBS tank. The development of pseudobulbs was also found to be obviously enhanced at this explants loadings. The current findings verified the optimal immersion frequency, sucrose concentration and explant loads for improved seedling growth and pseudobulb development strategy for B. striata.

In deeply nano-scaled technology nodes, the number of process variations of the circuits will be up to thousands. Although dimensionality reduction can reduce the complexity for circuit modeling, it always needs SPICE and Monte Carlo simulations. In this paper, a weight model of process variations for each individual cell is proposed, which is fed with the operation conditions. After that, a path process variation reduction method is proposed by considering the influence of the cell delay deviations and their weights. The proposed method is verified under ISCAS85 benchmark suite. When selecting the top 20% critical variations, the mean and standard deviation errors of the delay distribution in our method are merely 0.06% and 0.56% compared with the results from SPICE simulations. The model realizes a fast quantification of the process variation effects on any circuit without time-consuming training processes.

The impact of process variations on timing has become significant in advanced technology nodes. In this paper, a multivariate adaptive regression splines (MARS) delay model is proposed that considers both global and local process variations to characterize this impact more accurately. In order to obtain MAX operation results, the first three moments of MARS gate delay distribution are calculated, converting the timing distribution to a skew-normal representation. Eventually, based on an approximation MAX operation, the block-based SSTA propagates the arrival time through the timing diagram. Tested with 10 ISCAS85 benchmark circuits, the average mean squared error and standard deviation error of the path delay calculation are 0.52% and 0.88%.