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Basic leucine zipper (bZIP) transcription factors play significant roles in plants' growth and development processes, as well as in response to biological and abiotic stresses. Hypericum perforatum is one of the world's top three best-selling herbal medicines, mainly used to treat depression. However, there has been no systematic identification or functional analysis of the bZIP gene family in H. perforatum. In this study, 79 HpbZIP genes were identified. Based on phylogenetic analysis, the HpbZIP gene family was divided into ten groups, designated A–I and S. The physicochemical properties, gene structures, protein conserved motifs, and Gene Ontology enrichments of all HpbZIPs were systematically analyzed. The expression patterns of all genes in different tissues of H. perforatum (i.e., root, stem, leaf, and flower) were analyzed by qRT-PCR, revealing the different expression patterns of HpbZIP under abiotic stresses. The HpbZIP69 protein is localized in the nucleus. According to the results of the yeast one-hybrid (Y1H) assays, HpbZIP69 can bind to the HpASMT2 (N-acetylserotonin O-methyltransferase) gene promoter (G-box cis-element) to activate its activity. Overexpressing HpbZIP69 in Arabidopsis wild-type lines enhanced their tolerance to drought. The MDA and H2O2 contents were significantly decreased, and the activity of superoxide dismutase (SOD) was considerably increased under the drought stress. These results may aid in additional functional studies of HpbZIP transcription factors, and in cultivating drought-resistant medicinal plants. [ABSTRACT FROM AUTHOR]

In the field of software engineering, applying language models to the token sequence of source code is the state-of-the-art approach to building a code recommendation system. When applying language models to source code, it is difficult for state-of-the-art language models to deal with the data inconsistency problem, which is caused by the free naming conventions of source code. It is common for user-defined variables or methods with similar semantics in source code, to have different names in different projects. This means that a model trained on one project may encounter many words the model has never seen before during another project. Those freely named variables or functions in the code will bring difficulties to the processes of training and prediction and cause a data inconsistency problem between projects. However, we discover that the syntax tree of source code has hierarchical structures. This code structure has strong regularity in different projects and can be used to combat data inconsistency. In this paper, we propose a novel Hierarchical Language Model (HLM) to improve the robustness of the state-of-the-art recurrent language model, in order to be able to deal with data inconsistency between training and testing. The newly proposed HLM takes the hierarchical structure of the code tree into consideration to predict code. The proposed HLM method generates the embedding for each sub-tree according to hierarchies and collects the embedding of each sub-tree in context, to predict the next piece of code. The experiments on inner-project and cross-project datasets indicate that the newly proposed HLM method performs better than the state-of-the-art recurrent language model in dealing with the data inconsistency between training and testing, and achieves an average improvement in prediction accuracy of 11.2%. [ABSTRACT FROM AUTHOR]

Eectromechanical systems built by Simulink or Ptolemy have been widely used in industry fields, such as autonomous systems and robotics. It is an urgent need to ensure the safety and security of those systems. Test case generation technologies are widely used to ensure the safety and security. State-of-the-art testing tools employ model-checking techniques or search-based methods to generate test cases. Traditional search-based techniques based on Simulink simulation are plagued by problems such as low speed and high overhead. Traditional model-checking techniques such as symbolic execution have limited performance when dealing with nonlinear elements and complex loops. Recently, coverage guided fuzzing technologies are known to be effective for test case generation, due to their high efficiency and impressive effects over complex branches of loops. In this paper, we apply fuzzing methods to improve model testing and demonstrate the effectiveness. The fuzzing methods aim to cover more program branches by mutating valuable seeds. Inspired by this feature, we propose a novel integration technology SPsCGF, which leverages bounded model checking for symbolic execution to generate test cases as initial seeds and then conduct fuzzing based upon these worthy seeds. Over the evaluated benchmarks which consist of industrial cases, SPsCGF could achieve 8% to 38% higher model coverage and 3x-10x time efficiency compared with the state-of-the-art works. [ABSTRACT FROM AUTHOR]

Nanopositioning stages with piezoelectric actuators have been widely used in fields such as precision mechanical engineering, but the nonlinear start-up accuracy problem under open-loop control has still not been solved, and more errors will accumulate, especially under open-loop control. This paper first analyzes the causes of the starting errors from both the physical properties of materials and voltages: the starting errors are affected by the material properties of piezoelectric ceramics, and the magnitude of the voltage determines the magnitude of the starting errors. Then, this paper adopts an image-only model of the data separated by a Prandtl-Ishlinskii model (DSPI) based on the classical Prandtl-Ishlinskii model (CPI), which can improve the positioning accuracy of the nanopositioning platform after separating the data based on the start-up error characteristics. This model can improve the positioning accuracy of the nanopositioning platform while solving the problem of nonlinear start-up errors under open-loop control. Finally, the DSPI inverse model is used for the feedforward compensation control of the platform, and the experimental results show that the DSPI model can solve the nonlinear start-up error problem existing under open-loop control. The DSPI model not only has higher modeling accuracy than the CPI model but also has better performance in terms of compensation results. The DSPI model improves the localization accuracy by 99.427% compared to the CPI model. When compared with another improved model, the localization accuracy is improved by 92.763%. [ABSTRACT FROM AUTHOR]

Ultrasonic transducers are the basic core component of diagnostic imaging devices, wherein the piezoelectric materials are the active element of transducers. Recent studies showed that the alternating current poling (ACP) method could develop the properties of piezocomposites, which had great potential to improve transducer performance. Herein, transducers (fc = 3 MHz) made of DCP and ACP 1–3 piezocomposites (prepared by PZT-5H ceramics and PMN-PT single crystals) were fabricated. The effect of the ACP method on the bandwidth and insertion loss (sensitivity) was explored. The results indicate that the ACP method can significantly enhance the bandwidth and slightly increase the insertion loss of transducers. Particularly, a superhigh bandwidth of 142.8% was achieved in the transducer of ACP 1–3 PMN-PT single crystal combined with suitable matching and backing layers. This bandwidth is higher than that of all reported transducers with similar center frequency. Moreover, the optimization mechanism of transducer performance by the ACP method was discussed. The obtained results suggested that the ACP is an effective and convenient technology to improve transducer performances, especially for the bandwidth. [ABSTRACT FROM AUTHOR]

The nanopositioning stage with a piezoelectric driver usually compensates for the nonlinear outer-loop hysteresis characteristic of the piezoelectric effect using the Prandtl–Ishlinskii (PI) model under a single-ring linear voltage, but cannot accurately describe the characteristics of the inner-loop hysteresis under the reciprocating linear voltage. In order to improve the accuracy of the nanopositioning, this study designs a nanopositioning stage with a double-parallel guiding mechanism. On the basis of the classical PI model, the study firstly identifies the hysteresis rate tangent slope mark points, then segments and finally proposes a phenomenological model—the mark-segmented Prandtl–Ishlinskii (MSPI) model. The MSPI model, which is fitted together by each segment, can further improve the fitting accuracy of the outer-loop hysteresis nonlinearity, while describing the inner-loop hysteresis nonlinearity perfectly. The experimental results of the inverse model compensation control show that the MSPI model can achieve 99.6% reciprocating linear voltage inner-loop characteristic accuracy. Compared with the classical PI model, the 81.6% accuracy of the hysteresis loop outer loop is improved. [ABSTRACT FROM AUTHOR]

The nanopositioning stage with a piezoelectric driver usually compensates for the nonlinear outer-loop hysteresis characteristic of the piezoelectric effect using the Prandtl-Ishlinskii (PI) model under a single-ring linear voltage, but cannot accurately describe the characteristics of the inner-loop hysteresis under the reciprocating linear voltage. In order to improve the accuracy of the nanopositioning, this study designs a nanopositioning stage with a double-parallel guiding mechanism. On the basis of the classical PI model, the study firstly identifies the hysteresis rate tangent slope mark points, then segments and finally proposes a phenomenological model-the mark-segmented Prandtl-Ishlinskii (MSPI) model. The MSPI model, which is fitted together by each segment, can further improve the fitting accuracy of the outer-loop hysteresis nonlinearity, while describing the inner-loop hysteresis nonlinearity perfectly. The experimental results of the inverse model compensation control show that the MSPI model can achieve 99.6% reciprocating linear voltage inner-loop characteristic accuracy. Compared with the classical PI model, the 81.6% accuracy of the hysteresis loop outer loop is improved., Competing Interests: The authors declare no conflict of interest.