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A targeted one dimensional fully convolutional autoencoder network for intelligent compression of magnetic flux leakage data
Abstract In response to the issue of massive data volume generated by magnetic flux leakage (MFL) non-destructive testing in oil and gas pipelines, an intelligent data compression method based on a targeted one-dimensional fully convolutional autoencoder network is proposed. Firstly, a data preprocessing module is designed to generate high-quality data required for subsequent processing, taking into account the characteristics of MFL data. Secondly, a data block classification algorithm is developed to calculate peak values for segmented differential data, and based on a predefined targeted threshold, distinguish different types of MFL data. Subsequently, based on the distinct data types, targeted one-dimensional fully convolutional autoencoder models are constructed to effectively achieve dimensionality reduction compression and reconstruction of the MFL data. Through practical experimental analysis, the reconstruction error such as MAE is reduced by about 27.7% and the compression ratio is improved by about 14% compared with traditional methods such as PCA. In addition, compared with ID-AE, the proposed 1D-FCAE reduces 206.8 k, 1.58G, and 80 s in parameters, memory usage, and training time, respectively, and reduces compression and decompression time by 60 ms and 69 ms, respectively, validating that it is easy to be applied in industrial environments with limited resources.
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Endogenous DNA damage at sites of terminated transcripts
Characterization and immune-modulatory roles of branched glucan and acetylated gluco-oligosaccharides produced by glucansucrase 40 from Leuconostoc mesenteroides YTU 40
COVID-19 infection was associated with poor sperm quality: a cross-sectional and longitudinal clinical observation study
Sexual dimorphism and allometric patterns in hawkmoth epiphyses (Lepidoptera: Sphingidae)
Polyvinyl alcohol film comprising biochar modified titanium dioxide nanocomposites as decoloring and disinfectant agents
Abstract In this work, titanium dioxide nanowires were prepared hydrothermally in strong alkaline medium. In parallel, nanostructural biochar was obtained via carbonization of rice husk at relatively high temperature. Then, titanate nanowires were modified with the nanorods of biochar via in-situ and ex-situ approaches in order to determine the best way to produce the nanocomposites with improved properties. Polyvinyl alcohol was used as a commercial matrix to include the superlative nanocomposite obtained and casted as a free-standing nanocomposite film. The synthesized nanowires, nanorods, and their nanocomposites were intensively investigated with transmission electron microscope (TEM), scanning electron microscope (SEM), energy dispersive X-ray (EDX), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and N2 gas sorption. The microscopic images confirmed successful preparation and modification of nanostructures. FTIR showed strong interactions between the surface functional groups of the obtained nanomaterials. XRD exhibited a reduction in the crystallite size upon the treatment step. Also, surface texture analysis of titanate nanowires displayed a significant enhancement, particularly in terms of surface area and total pore volume. These superior properties promote the obtained nanocomposites to be evaluated in the water treatment compared with the pristine. The results confirmed complete removal of methylene blue (20 ppm) from the synthetic wastewater within only 20 min. in dark either by using the nanocomposites as powders or even as films. Kinetics and isotherms indicated that the adsorption process obeyed Langmuir model and follows pseudo-second order. On the other hand, the prepared materials depicted a strong biocidal activity against pathogenic microorganisms. The obtained nanocomposites may open opportunities towards developed adsorbents with superior features and performance for applications in the field of water decontamination.
Preoperative pan-immuno-inflammatory values and albumin-to-globulin ratio predict the prognosis of stage I–III colorectal cancer
Identifying risk factors associated with the health-related quality of life for coronary heart diseases elderly using association rule mining
Effect of melatonin on passive, ex-vivo biomechanical behavior of lamb esophagus
Clarifying interactions between genotype and environment and management in chickpea by focusing on plant and soil attributes
Plant hormone jasmonic acid reduces anxiety behavior in mice
Abstract Anxiety disorders are a leading cause of disability worldwide and major contributors to the global disease burden. In this study, we investigated the anxiolytic-like effects of plant-derived molecules in mice. Jasmonic acid (JA), a major plant hormone, has been identified as an injury response-related hormone in higher plants. We found that the oral, intraperitoneal, and intraventricular administration of JA in mice demonstrated anxiolytic-like effects in an elevated plus maze test. Additionally, JA exhibited anxiolytic-like effects in mice undergoing open field and novel environment feeding suppression tests. In addition, we found that the anxiolytic-like effects of JA were mediated by serotonin 5-HT1A receptors and central dopamine D1 receptor systems. Our findings reveal a novel role of JA in exerting anxiolytic-like effects in animals and suggest that plant hormones, such as JA, could serve as potential compounds for treating anxiety disorders.