A Sophisticated Method of the Mechanical Design of Cable Accessories Focusing on Interface Contact Pressure

The most critical positions of a prefabricated cable accessory, from the electrical point of view, are the interfaces between the stress cone and its surroundings. Accordingly, the contact pressure on those interfaces needs to be carefully designed to assure both good dielectric strength and smooth installation of the stress cone. Nevertheless, since stress cones made from rubber are under large deformation after installation, their internal stress distribution is neither practical to measure directly by planting sensors, nor feasible to compute accurately with the conventional theory of linear structural mechanics. This paper presents one sophisticated method for computing the mechanical stress distribution in rubber stress cones of cable accessories by employing hyperelastic models in a computation model based on the finite element method. This method offers accurate results for rubber bodies of complex geometries and large deformations. Based on the method, a case study of a composite prefabricated termination for extruded cables is presented, and the sensitivity analysis is given as well

The representation and projection of Hong Kong and Asia through corporation branding : British Airways as case study

published_or_final_versionMedia, Culture and Creative CitiesMasterMaster of Social Sciences in Media, Culture and Creative Citie

New insights into old methods for identifying causal rare variants

The advance of high-throughput next-generation sequencing technology makes possible the analysis of rare variants. However, the investigation of rare variants in unrelated-individuals data sets faces the challenge of low power, and most methods circumvent the difficulty by using various collapsing procedures based on genes, pathways, or gene clusters. We suggest a new way to identify causal rare variants using the F-statistic and sliced inverse regression. The procedure is tested on the data set provided by the Genetic Analysis Workshop 17 (GAW17). After preliminary data reduction, we ranked markers according to their F-statistic values. Top-ranked markers were then subjected to sliced inverse regression, and those with higher absolute coefficients in the most significant sliced inverse regression direction were selected. The procedure yields good false discovery rates for the GAW17 data and thus is a promising method for future study on rare variants

Review of high voltage direct current cables

Increased renewable energy integration and international power trades have led to the construction and development of new HVDC transmission systems. HVDC cables, in particular, play an important role in undersea power transmission and offshore renewable energy integration having lower losses and higher reliability. In this paper, the current commercial feasibility of HVDC cables and the development of different types of HVDC cables and accessories are reviewed. The non-uniform electric field distribution caused by the applied voltage, temperature dependent conductivity, and space charge accumulation is briefly discussed. Current research in HVDC cable for higher operation voltage level and larger power capacity is also reviewed with specific focus on the methodologies of space charge suppression for XLPE extruded cable

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale

Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification) and energy function minimizers that inherit desirable inductive biases and interpretability. However, scaling GNN architectures constructed in this way remains challenging, in part because the convergence of the forward pass may involve models with considerable depth. To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence guarantees in certain settings. We also instantiate a full GNN architecture based on these designs, and the model achieves competitive accuracy and scalability when applied to the largest publicly-available node classification benchmark exceeding 1TB in size

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Film Director: Chen Weijun (陳為軍) Film Release Year: 2007https://commons.ln.edu.hk/ccs_worksheet/1004/thumbnail.jp