Lessons from Three-Dimensional Imaging of Electrical Trees

Electrical trees are artifacts resulting from aging of polymeric insulation in high electrical fields. Whilst there is some debate concerning the mechanism by which they grow, there is no doubt that their growth can lead to the ultimate failure of the host insulation. Studying electrical trees is mainly confined to measurement of associated partial discharges and observing the physical growth of the tree structure optically. This paper reviews developments in observations of the growth of trees in the laboratory. In particular, consideration is given to the benefits of generating three-dimensional replicas of real trees from X-ray computer tomography (XCT) and serial block face scanning electron microscopy (SBFSEM), and how these can facilitate better understanding of tree development mechanisms. It is concluded that both two- and three-dimensional imaging are required, and these need correlating with partial discharge measurements to develop models of tree growth and effective asset management tools

Effect of Doping on the phase stability and Superconductivity in LaH10

We present a computational investigation into the effects of chemical doping with 15 different elements on phase stability and superconductivity in the LaH10 structure. Most doping elements were found to induce softening of phonon modes, enhancing electron-phonon coupling and improving critical superconducting temperature while weakening dynamical stability. Unlike these dopants, Ce was found to extend the range of dynamical stability for LaH10 by eliminating the van Hove singularity near the Fermi level. The doped compound, La0.75Ce0.25H10, maintains high-temperature superconductivity. We also demonstrate that different Ce doping configurations in the LaH10 structure have a minimal effect on energetic stability and electron-phonon coupling strength. Our findings suggest that Ce is a promising dopant to stabilize LaH10 at lower pressures while preserving its high-temperature superconductivity

WebRPG: Automatic Web Rendering Parameters Generation for Visual Presentation

In the era of content creation revolution propelled by advancements in generative models, the field of web design remains unexplored despite its critical role in modern digital communication. The web design process is complex and often time-consuming, especially for those with limited expertise. In this paper, we introduce Web Rendering Parameters Generation (WebRPG), a new task that aims at automating the generation for visual presentation of web pages based on their HTML code. WebRPG would contribute to a faster web development workflow. Since there is no existing benchmark available, we develop a new dataset for WebRPG through an automated pipeline. Moreover, we present baseline models, utilizing VAE to manage numerous elements and rendering parameters, along with custom HTML embedding for capturing essential semantic and hierarchical information from HTML. Extensive experiments, including customized quantitative evaluations for this specific task, are conducted to evaluate the quality of the generated results.Comment: Accepted at ECCV 2024. The dataset and code can be accessed at https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/DocumentUnderstanding/WebRP

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

The Effects of Labeling on Environmental Self-identity

Labeling refers to the behavior that a person uses specific words to label and tends to describe according to the label. For example, when labeled as “environmentally-conscious”, one will enhance his/her pro-environmental behaviors. Thus that will strengthen his/her environmental self-identity, which, in turn, will promote pro-environmental behaviors. This paper looks into the effects of four labels on pro-environmental behaviors. The study into elephant protection found that negative labels can significantly encourage elephant protection and discourage the consumption of elephant-related products. This research will provide a policy tool for environmental organizations and relevant governmental departments

The Effects of Labeling on Environmental Self-identity

Labeling refers to the behavior that a person uses specific words to label and tends to describe according to the label. For example, when labeled as “environmentally-conscious”, one will enhance his/her pro-environmental behaviors. Thus that will strengthen his/her environmental self-identity, which, in turn, will promote pro-environmental behaviors. This paper looks into the effects of four labels on pro-environmental behaviors. The study into elephant protection found that negative labels can significantly encourage elephant protection and discourage the consumption of elephant-related products. This research will provide a policy tool for environmental organizations and relevant governmental departments.</jats:p

Analysis of a main cabin ventilation system in a jack-up offshore platform Part I: Numerical modelling

This work aims to measure the thermodynamics of a main cabin ventilation system in a JU-2000E jack-up offshore platform. A three-dimensional (3D) physical model of the ventilation system was established, and the computational fluid dynamics (CFD) software (ANSYS FLUENT) was used to calculate the model thermodynamics. Numerical analysis was performed to investigate the influence mechanisms of the ventilation factors such as ventilation temperature and volume on the ventilation performance. The analysis results demonstrate that (1) top-setting of the exhaust vents is more effective than the side-setting in terms of high temperature reduction, (2) small ventilation temperature and volume can improve the ventilation efficiency, and (3) proper shutdown selection of the backup diesel engine can enhance the ventilation performance. Furthermore, the effect of humidity for the ventilation air was investigated. Lastly, an experimental platform was developed based on the simulation model. Experimental tests were carried out to evaluate the shutdown selection of the backup engine and have shown consistent results to that of the simulation model. The findings of this study provide valuable guidance in designing the ventilation system in the JU-2000E jack-up offshore platform

Observer-based control for piecewise-affine systems with both input and output quantization

This technical note is concerned with the problem of simultaneous design of observers and controllers for a class of piecewise-affine systems against signal quantization occurring in both measurement output and control input channels. The general scenario is considered that system state and estimated state may not be in the same operating region. By a novel quantization-error-dependent Lyapunov function, the stability and H∞ performance criteria are first established for the augmented system composed of a closed-loop control system and an estimation error system with the aid of S-procedure involving the region partition information of the original system. Then, by the cone complementary linearization algorithm, the desired observer and controller gains are solved simultaneously such that the resulting closed-loop system is asymptotically stable with a prescribed H∞ performance index. Finally, a networked single-link robot arm is utilized to demonstrate the effectiveness of the proposed control strategy

Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire monitoring, this paper proposes a forest fire monitoring system based on drones and deep learning. The proposed system aims to solve the shortcomings of traditional forest fire monitoring systems, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. The image processing techniques are used to determine whether the frame returned by a drone contains fire. This process is accomplished in real time, and the resultant information is used to decide whether a rescue operation is needed. The proposed method has simple operations, high operating efficiency, and low operating cost. The experimental results indicate that the relative accuracy of the proposed algorithm is 81.97%. In addition, the proposed technique provides a digital ability to monitor forest fires in real time effectively. Thus, it can assist in avoiding fire-related disasters and can significantly reduce the labor and other costs of forest fire disaster prevention and suppression.</jats:p