The Application of E-learning in Maritime Education and Training in China

E-learning brings the third wave to Internet applications. E-learning is a new training mode with the open characteristics, which is completely different with traditional training. E-learning teaches students the specialized knowledge of theories, work experience and technology by information networks and computer hardware equipment. Students can through a variety of terminal equipment to learn anytime and anywhere, so as to improve student learning results. Maritime education and training must conform to the trend of times to explore E-learning training to improve the training performance. In this paper, based on the theory of E-learning, comparing the advantages and disadvantages of E-learning training. By analyzing current status of maritime education and training, using E-learning to establish China's maritime education and training of the lifelong education system, using E-learning to promote the internationalization of navigation education and training

How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey

Transferability estimation has been attached to great attention in the computer vision fields. Researchers try to estimate with low computational cost the performance of a model when transferred from a source task to a given target task. Considering the effectiveness of such estimations, the communities of natural language processing also began to study similar problems for the selection of pre-trained language models. However, there is a lack of a comprehensive comparison between these estimation methods yet. Also, the differences between vision and language scenarios make it doubtful whether previous conclusions can be established across fields. In this paper, we first conduct a thorough survey of existing transferability estimation methods being able to find the most suitable model, then we conduct a detailed empirical study for the surveyed methods based on the GLUE benchmark. From qualitative and quantitative analyses, we demonstrate the strengths and weaknesses of existing methods and show that H-Score generally performs well with superiorities in effectiveness and efficiency. We also outline the difficulties of consideration of training details, applicability to text generation, and consistency to certain metrics which shed light on future directions.Comment: Accepted by Findings of EMNLP 202

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

Abstract Background Low-resolution images may be acquired in magnetic resonance imaging (MRI) due to limited data acquisition time or other physical constraints, and their resolutions can be improved with super-resolution methods. Since MRI can offer images of an object with different contrasts, e.g., T1-weighted or T2-weighted, the shared information between inter-contrast images can be used to benefit super-resolution. Methods In this study, an MRI image super-resolution approach to enhance in-plane resolution is proposed by exploring the statistical information estimated from another contrast MRI image that shares similar anatomical structures. We assume some edge structures are shown both in T1-weighted and T2-weighted MRI brain images acquired of the same subject, and the proposed approach aims to recover such kind of structures to generate a high-resolution image from its low-resolution counterpart. Results The statistical information produces a local weight of image that are found to be nearly invariant to the image contrast and thus this weight can be used to transfer the shared information from one contrast to another. We analyze this property with comprehensive mathematics as well as numerical experiments. Conclusion Experimental results demonstrate that the image quality of low-resolution images can be remarkably improved with the proposed method if this weight is borrowed from a high resolution image with another contrast. Graphical Abstract Multi-contrast MRI Image Super-resolution with Contrast-invariant Regression Weight

GsAPK, an ABA-Activated and Calcium-Independent SnRK2-Type Kinase from G. soja, Mediates the Regulation of Plant Tolerance to Salinity and ABA Stress

Plant Snf1 (sucrose non-fermenting-1) related protein kinase (SnRK), a subfamily of serine/threonine kinases, has been implicated as a crucial upstream regulator of ABA and osmotic signaling as in many other signaling cascades. In this paper, we have isolated a novel plant specific ABA activated calcium independent protein kinase (GsAPK) from a highly salt tolerant plant, Glycine soja (50109), which is a member of the SnRK2 family. Subcellular localization studies using GFP fusion protein indicated that GsAPK is localized in the plasma membrane. We found that autophosphorylation and Myelin Basis Protein phosphorylation activity of GsAPK is only activated by ABA and the kinase activity also was observed when calcium was replaced by EGTA, suggesting its independence of calcium in enzyme activity. We also found that cold, salinity, drought, and ABA stress alter GsAPK gene transcripts and heterogonous overexpression of GsAPK in Arabidopsis alters plant tolerance to high salinity and ABA stress. In summary, we demonstrated that GsAPK is a Glycine soja ABA activated calcium independent SnRK-type kinase presumably involved in ABA mediated stress signal transduction

A predator-prey interaction between a marine Pseudoalteromonas sp. and Gram-positive bacteria

Predator-prey interactions play important roles in the cycling of marine organic matter. Here we show that a Gram-negative bacterium isolated from marine sediments (Pseudoalteromonas sp. strain CF6-2) can kill Gram-positive bacteria of diverse peptidoglycan (PG) chemotypes by secreting the metalloprotease pseudoalterin. Secretion of the enzyme requires a Type II secretion system. Pseudoalterin binds to the glycan strands of Gram positive bacterial PG and degrades the PG peptide chains, leading to cell death. The released nutrients, including PG-derived D-amino acids, can then be utilized by strain CF6-2 for growth. Pseudoalterin synthesis is induced by PG degradation products such as glycine and glycine-rich oligopeptides. Genes encoding putative pseudoalterin-like proteins are found in many other marine bacteria. This study reveals a new microbial interaction in the ocean

Identification and functional characterization of an N-terminal oligomerization domain for polycystin-2*

Autosomal dominant polycystic kidney disease (ADPKD), the most common inherited cause of kidney failure, is caused by mutations in either PKD1 (85%) or PKD2 (15%). The PKD2 protein, polycystin-2 (PC2 or TRPP2), is a member of the transient receptor potential (TRP) superfamily and functions as a non-selective calcium channel. PC2 has been found to form oligomers in native tissues suggesting that it may form functional homo- or heterotetramers with other subunits, similar to other TRP channels. Our experiments unexpectedly revealed that PC2 mutant proteins lacking the known C-terminal dimerization domain were still able to form oligomers and co-immunoprecipitate full-length PC2, implying the possible existence of a proximal dimerization domain. Using yeast two-hybrid and biochemical assays, we have mapped an alternative dimerization domain to the N terminus of PC2 (NT2-1-223, L224X). Functional characterization of this domain demonstrated that it was sufficient to induce cyst formation in zebrafish embryos and inhibit PC2 surface currents in mIMCD3 cells probably by a dominant-negative mechanism. In summary, we propose a model for PC2 assembly as a functional tetramer which depends on both C- and N-terminal dimerization domains. These results have significant implications for our understanding of PC2 function and disease pathogenesis in ADPKD and provide a new strategy for studying PC2 function

Mathematical model of Scheduler with Semi-Markov input and bandwidth sharing discipline

In this paper, we consider single server queueing system with multiple semi-Markov inputs and buffers. Each request of the flows brings to the system some random amount of information. According to the bandwidth sharing discipline, each buffer has its own part of the throughput and the server transmits the information from buffers simultaneously. The aim of the current research is to derive the probability distribution of the amount of information in single buffer