Societal acceptance of hydrogen for domestic and export applications in Australia

With the urgent need to decarbonise the world's energy system, clean hydrogen is emerging as a potential technological solution. As with any new technology, understanding the public's response to hydrogen is critical to its success. Most studies examining public attitudes towards hydrogen have focused on refuelling stations and transport options. As a first of its kind, using a national survey (N = 2785) we evaluate the Australian public's response towards hydrogen for domestic and export use. In Australia, acceptance of hydrogen in domestic applications was influenced by its relative cost, ability to reduce air pollution and associated health benefits. Further, support for a hydrogen export industry was influenced by levels of trust in the government to manage the associated risks and the industry's commitment to climate protection. The paper concludes that effective, nuanced communication and engagement along with supporting financial policies will be critical in facilitating societal acceptance of hydrogen in Australia.     

Effect of support and reduction temperature in the hydrogenation of CO2 over the Cu–Pd bimetallic catalyst with high Cu/Pd ratio

Metal-support interaction and catalyst pretreatment are important for industrial catalysis. This work investigated the effect of supports (SiO₂, CeO₂, TiO₂ and ZrO₂) for Cu–Pd catalyst with high Cu/Pd ratio (Cu/Pd = 33.5) regarding catalyst cost, and the reduction temperatures of 350 °C and 550 °C were compared. The activity based on catalyst weight follows the order of Si > Ce > Zr > Ti when reduced at 350 °C. The reduction temperature leads to the surface reconstruction over the SiO₂, CeO₂ and TiO₂ catalysts, while results in phase transition over Cu–Pd/ZrO₂. The effect of reduction temperature on catalytic performance is prominent for the SiO₂ and ZrO₂ supported catalysts but not for the CeO₂ and TiO₂ ones. Among the investigated catalysts, Zr-350 exhibits the highest methanol yield. This work reveals the importance of the supports and pretreatment conditions on the physical-chemical properties and the catalytic performance of the Cu–Pd bimetallic catalysts.     

Prediction of pullout interaction coefficient of geogrids by extreme gradient boosting model

Geogrids embedded in fill materials are checked against pullout failure through standard pullout testing methodology. The test determines the pullout interaction coefficient which is critical in fixing the embedment length of geogrids in mechanically stabilized earth walls. This paper proposes prediction of pullout interaction coefficient using data driven machine learning regression algorithms. The study primarily focusses on using extreme gradient boosting (XGBoost) method for prediction. A data set containing 220 test results from the literature has been used for training and testing. Predicted results of XGBoost have been compared with the results of random forest (RF) ensemble learning based algorithm. The predictions of XGBoost model indicates 85% accuracy and that of RF model shows 77% accuracy, indicating significantly superior and robust prediction through XGBoost above RF model. The importance analysis indicates that normal stress is the most significant factor that influences the pullout interaction coefficients. Subsequently pullout tests have been performed on geogrid embedded in four different fill materials at three normal stresses. The proposed XGBoost model gives 90% accuracy in prediction of pullout interaction coefficient compared to laboratory test results. Finally, an open-source graphical user interface based on the XGBoost model has been created for preliminary estimation of the pullout interaction coefficient of geogrid at different test conditions.     

No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

基于光纤光栅技术测力锚杆及保护结构的研究

本文介绍了光纤光栅技术在测力锚杆上的应用,并且基于常用托盘的力学结构,增大了托盘内部的空间、设计了与之相连接的金属保护套筒使之成为一体式结构,从而有效地保护了光纤测力锚杆的主体结构。     

Design and optimization of ceramic membrane structure: From the perspective of flux matching between support and membrane

The support flux was first investigated as a separate influencing factor for its effect on performances of ceramic filtration membranes. Three pre-membranes were prepared by tape-casting and then transfer-coated to supports to form dual-layer ceramic membranes after sintering. Experiments demonstrated that membrane layers with almost the same properties were obtained despite the huge difference in support flux. When the support flux increases from 3.120 to 97.53 m³m^?2h^?1, the flux of these three membrane series have increased by 75%, 186% and 228%, respectively. Experimental rules can provide structural design and evaluation from the perspective of permeability. The limit membrane flux of a certain system was derived according to the resistance distribution law of internal membrane structure and the Darcy's theorem. On this basis, a method for designing support flux was proposed. Furthermore, we present a criterion to quickly and easily evaluate the match between the support and the top layer, which is the ratio of membrane resistance to total resistance. Finally, the filtration resistance of penetration caused by suction of membrane particles into the support was measured for the first time, taking the advantage of the transfer-coating method that inherently free of penetration. Our works are expected to deepen the understanding of the ceramic membrane structure and provided guidance for its rational design and optimization.     

Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

From enfant terrible to problem-solver? Tracing the competing discourse to explain blockchain-related technological diffusion

Since the inception of blockchain-related technologies over a decade ago, investors’ uptake of the technologies has grown rapidly. But even with the advancement in standing from conceptual beginnings to real-world experimentation, mainstream adoption of the technologies in either organizational or individual contexts has yet to eventuate. To resolve this paradox, we examine progression of the technologies’ diffusion instead, and in the process, seek to uncover the underlying dynamics of sentiments at play. Diffusion involves multiple actions by different parties, with social discourses via diverse media playing an essential role. In demonstrating the interplay of support to resistance via media framing and the diffusion of innovation theory, findings from our paper contribute to the technology adoption literature. Understanding resistant behavior toward a new technology area could help resolve potential issues that may arise. This would hopefully lead to better technological implementation outcomes in future.