Numerical analysis on magnetic leakage field of pipeline defect

Pipeline magnetic flux leakage inspection, mainly used for pipeline defect detection, is an important means of inner examination technology on pipeline. Flux leakage testing can't obtain valid defect identification signals by one hundred percent because of magnetization of the magnetic leakage field, the measured shape, size and location of pipeline defects, materials and operating conditions, and lift-off value, pole pitch and the length of steel brush during the measurement as well as forged pipe fittings such as welding seam, stiffener, flange and tee on the pipeline to be tested. This article reaches a conclusion that magnetic flux density distribution is influenced by the depth and width of defect through respectively researching magnetic leakage field of individual defect and double defects (thickness type) by finite-element method. It also conducts the numerical analysis on pipeline welding seam, stiffener, flange (increased wall thickness type) and tee (compound) leakage magnetic field in detection conditions of the same direction, and concludes their distribution rules of magnetic flux density. The characteristic parameters of distinguishing defect magnetic flux leakage field and the part of the pipeline magnetic flux leakage, derived from analysis and comparison of results on defective pipeline and conduit joint, stiffener, flange and tee magnetic flux leakage, provide a foundation of qualitative identification for accurately recognizing pipeline defect and eliminating the impact of other ancillary fittings on a pipe on pipeline magnetic flux leakage, and they can also offer infallible data to pipeline maintenance as a basis of quantitative analysis

On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states

Tomography of Quantum States from Structured Measurements via quantum-aware transformer

Quantum state tomography (QST) is the process of reconstructing the state of a quantum system (mathematically described as a density matrix) through a series of different measurements, which can be solved by learning a parameterized function to translate experimentally measured statistics into physical density matrices. However, the specific structure of quantum measurements for characterizing a quantum state has been neglected in previous work. In this paper, we explore the similarity between highly structured sentences in natural language and intrinsically structured measurements in QST. To fully leverage the intrinsic quantum characteristics involved in QST, we design a quantum-aware transformer (QAT) model to capture the complex relationship between measured frequencies and density matrices. In particular, we query quantum operators in the architecture to facilitate informative representations of quantum data and integrate the Bures distance into the loss function to evaluate quantum state fidelity, thereby enabling the reconstruction of quantum states from measured data with high fidelity. Extensive simulations and experiments (on IBM quantum computers) demonstrate the superiority of the QAT in reconstructing quantum states with favorable robustness against experimental noise

Employing a novel bioelastomer to toughen polylactide

Biodegradable, biocompatible polylactide (PLA) synthesized from renewable resources has attracted extensive interests over the past decades and holds great potential to replace many petroleum-derived plastics. With no loss of biodegradability and biocompatibility, we highly toughened PLA using a novel bioelastomer (BE)–synthesized from biomass diols and diacids. Although PLA and BE are immiscible, BE particles of ∼1 μm in diameter are uniformly dispersed in the matrix, and this indicates some compatibility between PLA and BE. BE significantly increased the cold crystallization ability of PLA, which was valuable for practical processing and performance. SEM micrographs of fracture surface showed a brittle-to-ductile transition owing to addition of BE. At 11.5 vol%, notched Izod impact strength improved from 2.4 to 10.3 kJ/m2, 330% increment; the increase is superior to previous toughening effect by using petroleum-based tougheners

Highly toughened polylactide with novel sliding graft copolymer by in situ reactive compatibilization, crosslinking and chain extension

YesThe “sliding graft copolymer” (SGC), in which many linear poly-ε-caprolactone (PCL) side chains are bound to cyclodextrin rings of a polyrotaxane (PR), was prepared and employed to toughen brittle polylactide (PLA) with methylene diphenyl diisocyanate (MDI) by reactive blending. The SGC was in situ crosslinked and therefore transformed from a crystallized plastic into a totally amorphous elastomer during reactive blending. Meanwhile, PLA-co-SGC copolymer was formed at interface to greatly improve the compatibility between PLA and SGC, and the chain extension of PLA also occurred, were confirmed by FTIR, GPC, SEM, and TEM. The resulting PLA/SGC/MDI blends displayed super impact toughness, elongation at break and nice biocompatibility. It was inferred from these results the crosslinked SGC (c-SGC) elastomeric particles with sliding crosslinking points performed as stress concentrators and absorbed considerable energy under impact and tension process.This work was supported by the National Natural Science Foundation of China (50933001, 51221002 and 51320105012)

Genome Expression Profile Analysis of the Immature Maize Embryo during Dedifferentiation

Maize is one of the most important cereal crops worldwide and one of the primary targets of genetic manipulation, which provides an excellent way to promote its production. However, the obvious difference of the dedifferentiation frequency of immature maize embryo among various genotypes indicates that its genetic transformation is dependence on genotype and immature embryo-derived undifferentiated cells. To identify important genes and metabolic pathways involved in forming of embryo-derived embryonic calli, in this study, DGE (differential gene expression) analysis was performed on stages I, II, and III of maize inbred line 18-599R and corresponding control during the process of immature embryo dedifferentiation. A total of ∼21 million cDNA tags were sequenced, and 4,849,453, 5,076,030, 4,931,339, and 5,130,573 clean tags were obtained in the libraries of the samples and the control, respectively. In comparison with the control, 251, 324 and 313 differentially expressed genes (DEGs) were identified in the three stages with more than five folds, respectively. Interestingly, it is revealed that all the DEGs are related to metabolism, cellular process, and signaling and information storage and processing functions. Particularly, the genes involved in amino acid and carbohydrate transport and metabolism, cell wall/membrane/envelope biogenesis and signal transduction mechanism have been significantly changed during the dedifferentiation. To our best knowledge, this study is the first genome-wide effort to investigate the transcriptional changes in dedifferentiation immature maize embryos and the identified DEGs can serve as a basis for further functional characterization

Astroglial-Kir4.1 in Lateral Habenula Drives Neuronal Bursts to Mediate Depression

International audienceEnhanced bursting activity of neurons in the lateral habenula (LHb) is essential in driving depression-like behaviours, but the cause of this increase has been unknown. Here, using a high-throughput quantitative proteomic screen, we show that an astroglial potassium channel (Kir4.1) is upregulated in the LHb in rat models of depression. Kir4.1 in the LHb shows a distinct pattern of expression on astrocytic membrane processes that wrap tightly around the neuronal soma. Electrophysiology and modelling data show that the level of Kir4.1 on astrocytes tightly regulates the degree of membrane hyperpolarization and the amount of bursting activity of LHb neurons. Astrocyte-specific gain and loss of Kir4.1 in the LHb bidirectionally regulates neuronal bursting and depression-like symptoms. Together, these results show that a glia–neuron interaction at the perisomatic space of LHb is involved in setting the neuronal firing mode in models of a major psychiatric disease. Kir4.1 in the LHb might have potential as a target for treating clinical depression