With the popularization of big data technology, intelligent translation technology gradually rises and is applied in daily life, and this paper explores its utility in English writing. English ...writing can benefit from the auxiliary utility of Big Data intelligent translation The grammatical error correction model for intelligent English translation is constructed based on statistical classification and deep classification to optimize the English translation system. Through the performance testing of the intelligent translation system, the performance of the algorithm model of this paper is improved by 3.03, 2.64, and 8.55 compared with CYK, CYK-PU, and the shift-and-return algorithm, based on which the intelligent translation system is applied to English writing for the evaluation and analysis of the effect, and the reliability coefficients of the three scales are greater than 0.9 through the reliability and validity test and correlation analysis. Finally, a practical analysis of the auxiliary utility of intelligent translation in English writing is conducted. Finally, the auxiliary utility of intelligent translation in English writing is practically analyzed, and in the final achievement test, its various ability test scores and comprehensive achievement scores are all greater than 0.9. An intelligent translation system can help cultivate and improve English writing ability, which enhances the realistic reference significance of intelligent translation to aid English writing.
Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the ...integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.
Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in ...explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.
Understanding global communications among cells requires accurate representation of cell-cell signaling links and effective systems-level analyses of those links. We construct a database of ...interactions among ligands, receptors and their cofactors that accurately represent known heteromeric molecular complexes. We then develop CellChat, a tool that is able to quantitatively infer and analyze intercellular communication networks from single-cell RNA-sequencing (scRNA-seq) data. CellChat predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Through manifold learning and quantitative contrasts, CellChat classifies signaling pathways and delineates conserved and context-specific pathways across different datasets. Applying CellChat to mouse and human skin datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat and a web-based Explorer ( http://www.cellchat.org/ ) will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.
Multilayered thermoplastic polyurethane/graphene (PUG) composites were fabricated by stacking single-layered PUG foams together. The arrangement of single-layered PUG foams in different orders could ...realize the gradient concentration of graphene, which was proved to be a facile approach to enhance the microwave-absorbing (MA) property of PUG composites, rather than their electromagnetic interference (EMI) shielding effectiveness (SE). Further sandwiching a wave-transmitting layer between two pieces of PUG foams formed a sandwich structure, by which the SE of the two samples could be significantly improved and the resultant composites still possessed strong MA performance. The SE increment was assigned to the enhanced reflection loss induced by constructive interference, and largely dependent on the thickness of the interlayer d. Particularly, when the values of the d were zero and the quarter of the largest wavelength in the testing frequency range, the minimum and maximum SE could be obtained, respectively. These results are critical to the fabrication of high-performance polymer-based shielding materials with strong MA property.
The regional financial risks prevention (RFRP) is indispensable in managing the regional traditional financial risks (TFR) or preventing the regional financial systemic risks (FSR). With the growing ...of big data and the uncertainty of financial risk types, traditional econometrics methods are facing insurmountable difficulties in terms of the efficiency, accuracy, and application of financial risk prevention modeling. Today, increasing future methods and technologies for machine learning (ML) for RFRP prevention have been paid much attention by researchers. A new scientific classification of RFRP prevention and conceptual basis of ML methods are firstly put forward.Secondly, this paper summarizes the ML methods and application of regional TFR prevention, compares their key logic, model algorithm and learning effect of the representative literature, and categorizes the advantages, limitations and traditional scenarios of ML methods. Thirdly, it combs the ML methods and application of regional FSR prevention, an
This study is dedicated to exploring the influence of perceived overall injustice on employee anger and deviant behavior. Based on fairness heuristic theory and cognitive appraisal theory of emotion, ...a model was developed to investigate the relationship between perceived overall injustice, anger and organizational deviance. Based on social cognitive theory, the moderating role of moral disengagement was proposed.
The data were collected from three Chinese manufacturing corporations with a total effective sample size of 264. SPSS 26 and Mplus 8.3 were adopted to analyze data. Confirmatory factor analysis, descriptive statistics analysis and correlation results were illustrated. Hierarchical regression was used to test the model.
Statistical results showed that there is a significant positive relationship among perceived overall injustice, anger and organizational deviance. The moderating effect of moral disengagement on the relationship between perceived overall injustice and organizational deviance is significant, while that on the relationship between anger and organizational deviance is insignificant.
This study built a model to discuss the emotional and behavioral influences of perceived overall injustice. The findings suggest that individuals feel more anger as the level of perceived overall injustice increases, which thus lead to higher level of organizational deviance. Morally disengaged employees are more likely to engage in organizational deviance after being treated unfairly. However, the moderating effect of moral disengagement on the relationship of anger and organizational deviance was insignificant. The reason might be because anger is an aggressive emotion and individual experiencing anger may lead to impulsive behavior regardless of moral rules. Implications and limitations have been discussed.
•Segmented micro-flow extraction is developed using a CFI and a phase separator.•Shorter time and better extraction efficiency are obtained in microflow.•Regular segments are formed at slow velocity ...and in- and outlet match.•Specific interfacial area are measured from the recorded slug dimension.•Mass transfer performance are identified from experimental results.
The segmented micro-flow extraction and separation of the adjacent elements of Co from Ni sulphate solution with Cyanex 272 is developed using a micro-scale coiled flow inverter (CFI). This is compared with the conventional batch extraction in parallel. Continuous operation of a process involving liquid-liquid extraction and then two-phase separation is achieved instantly. For the latter a micro-scale separator is used. Compared with batch extraction, the segmented micro-flow extraction process shows order-of-magnitude faster extraction times, higher extraction ability for Co, lower extraction for Ni, and then a better selectivity between Co and Ni at industrial-matching concentration. Regular columnar slugs are observed at low flow rates and found to be a Gaussian function of the flow rates. Matched in- and outlet flows indicates that extraction in micro-flow is dependent on mass transfer by molecular diffusion. Extraction efficiency is enhanced by the internal circulation flow generated within slugs. Additionally, the characteristic parameters, overall volumetric mass transfer coefficient kLα and overall mass transfer coefficient kL, are measured to determine the mass transfer performance. Compared with batch, much higher kLα of Co (0.26–0.017s−1) and smaller of Ni (0.053–0.013s−1) are investigated in CFI. Meanwhile, 4.5 times increasement of the kL value of Co to Ni is observed in CFI indicating the asynchronous extraction between Co and Ni.
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