Measurement and Interpretation of the Effect of Electrical Sliding Speed on Contact Characteristics of On-Load Tap Changers

This paper analyzes the effect of sliding speed on the electrical conductivity and friction properties of the contact pair of an on-load tap changer (OLTC). Reciprocating current-carrying tribological tests were carried out on a rod–plate–copper–tin–copper contact galvanic couple at different sliding speeds in air and insulating oil media. The results show that as the sliding speed increases from 24 mm/s to 119 mm/s, the average contact resistance in air increases from 0.2 Ω to 0.276 Ω, and the average contact resistance in insulating oil also increases from 0.2 Ω to 0.267 Ω. At 119 mm/s, the maximum contact resistance in insulating oil reaches 0.3 Ω. The micro-topography images obtained by scanning electron microscopy show that with the increase in sliding speed, the wear mechanisms in the air are mainly abrasive wear and adhesive wear, and the wear mechanisms in oil are mainly layered wear and erosion craters; high sliding speed and arcing promote contact surface fatigue and crack generation. X-ray photoelectron spectroscopy was used to analyze the surface. The copper oxide in the air and the cuprous sulfide in the insulating oil cause the surface film resistance, and the total contact resistance increases accordingly. In addition, the test shows that 119 mm/s in air and 95 mm/s in insulating oil are the speed thresholds. Below these speed thresholds, the increase in contact resistance is mainly caused by mechanical wear. Above these thresholds, the increase in contact resistance is mainly caused by arc erosion and chemical oxidation processes. Non-mechanical factors exacerbate the deterioration of the contact surface and become the main factor for the increase in contact resistance

Characterization of transcriptome dynamics during watermelon fruit development: sequencing, assembly, annotation and gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Cultivated watermelon [<it>Citrullus lanatus </it>(Thunb.) Matsum. & Nakai var. <it>lanatus</it>] is an important agriculture crop world-wide. The fruit of watermelon undergoes distinct stages of development with dramatic changes in its size, color, sweetness, texture and aroma. In order to better understand the genetic and molecular basis of these changes and significantly expand the watermelon transcript catalog, we have selected four critical stages of watermelon fruit development and used Roche/454 next-generation sequencing technology to generate a large expressed sequence tag (EST) dataset and a comprehensive transcriptome profile for watermelon fruit flesh tissues.</p> <p>Results</p> <p>We performed half Roche/454 GS-FLX run for each of the four watermelon fruit developmental stages (immature white, white-pink flesh, red flesh and over-ripe) and obtained 577,023 high quality ESTs with an average length of 302.8 bp. <it>De novo </it>assembly of these ESTs together with 11,786 watermelon ESTs collected from GenBank produced 75,068 unigenes with a total length of approximately 31.8 Mb. Overall 54.9% of the unigenes showed significant similarities to known sequences in GenBank non-redundant (nr) protein database and around two-thirds of them matched proteins of cucumber, the most closely-related species with a sequenced genome. The unigenes were further assigned with gene ontology (GO) terms and mapped to biochemical pathways. More than 5,000 SSRs were identified from the EST collection. Furthermore we carried out digital gene expression analysis of these ESTs and identified 3,023 genes that were differentially expressed during watermelon fruit development and ripening, which provided novel insights into watermelon fruit biology and a comprehensive resource of candidate genes for future functional analysis. We then generated profiles of several interesting metabolites that are important to fruit quality including pigmentation and sweetness. Integrative analysis of metabolite and digital gene expression profiles helped elucidating molecular mechanisms governing these important quality-related traits during watermelon fruit development.</p> <p>Conclusion</p> <p>We have generated a large collection of watermelon ESTs, which represents a significant expansion of the current transcript catalog of watermelon and a valuable resource for future studies on the genomics of watermelon and other closely-related species. Digital expression analysis of this EST collection allowed us to identify a large set of genes that were differentially expressed during watermelon fruit development and ripening, which provide a rich source of candidates for future functional analysis and represent a valuable increase in our knowledge base of watermelon fruit biology.</p

International recruitment and selection practices of South Korean multinationals in China

South Korean multinational enterprises (MNEs) have developed rapidly since the late 1950s. However, there is little research on, and hence little is known about, how South Korean MNEs manage human resources in overseas operations. To fill this gap, in the literature the current study investigates South Korean MNEs’ international recruitment and selection policies and practices in their Chinese operations. It reveals that South Korean MNEs tend to adopt the polycentric approach or a mixed approach of being polycentric and ethnocentric to international staffing, with the number of expatriates dropping gradually over time. South Korean MNEs adopt ‘one-way selection’ in recruiting and selecting expatriates, and localise recruitment procedures and selection criteria for host-country nationals. South Korean MNEs have paid inadequate attention to: first, expatriates’ career development; and second, personal and family issues emerging from expatriation and repatriation.

A Land Use Classification Model Based on Conditional Random Fields and Attention Mechanism Convolutional Networks

Land use is used to reflect the expression of human activities in space, and land use classification is a way to obtain accurate land use information. Obtaining high-precision land use classification from remote sensing images remains a significant challenge. Traditional machine learning methods and image semantic segmentation models are unable to make full use of the spatial and contextual information of images. This results in land use classification that does not meet high-precision requirements. In order to improve the accuracy of land use classification, we propose a land use classification model, called DADNet-CRFs, that integrates an attention mechanism and conditional random fields (CRFs). The model is divided into two modules: the Dual Attention Dense Network (DADNet) and CRFs. First, the convolution method in the UNet network is modified to Dense Convolution, and the band-hole pyramid pooling module, spatial location attention mechanism module, and channel attention mechanism module are fused at appropriate locations in the network, which together form DADNet. Second, the DADNet segmentation results are used as a priori conditions to guide the training of CRFs. The model is tested with the GID dataset, and the results show that the overall accuracy of land use classification obtained with this model is 7.36% and 1.61% higher than FCN-8s and BiSeNet in classification accuracy, 11.95% and 1.81% higher in MIoU accuracy, and with a 9.35% and 2.07% higher kappa coefficient, respectively. The proposed DADNet-CRFs model can fully use the spatial and contextual semantic information of high-resolution remote sensing images, and it effectively improves the accuracy of land use classification. The model can serve as a highly accurate automatic classification tool for land use classification and mapping high-resolution images