Effect of aluminium acetyl acetonate on the hydrogen and nitrogen permeation of carbon molecular sieves membranes

With a growing interest in hydrogen as energy carrier, the efficient purification of hydrogen from gaseous mixtures is very important. This paper addresses the separation of hydrogen using Carbon Molecular Sieves Membranes (CMSM), which show an attractive combination of high permeability, selectivity and stability. Supported CMSM containing various amounts of aluminium have been prepared from novolac and aluminium acetyl acetonate (Al(acac)₃) as carbon and alumina precursors. The thickness of the CMSM layers depend on the content of Al(acac)₃ in the dipping solution, which also has influence in the pore size and pore size distribution of the membranes. The permeation properties of the membranes against the Al content in the membrane follows a volcano shape, where the membrane containing 4 wt (%) of Al(acac)₃ has the best properties and was stable during 720 h for hydrogen at 150 °C and 6 bar pressure difference. All the CMSM have permeation properties well above the Robeson Upper limit.     

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.     

面向老龄化社会的产品服务系统设计赋能

目的 针对面向老龄化社会的产品及产品服务系统设计,将赋能的设计理念和价值引入其中,探索设计结果提升老年人能动性和参与性的赋能品质,为设计师开展设计赋能实践提供参考.方法 以设计教学中的学生设计方案作为研究材料,邀请设计研究者参与工作坊,对设计结果的赋能属性进行分析,并通过聚类获得类别化的设计属性,产生了能描述赋能品质的属性词汇表.结果 总结了包含5组形容词组的设计赋能品质集,这一集合包含"顺应性"和"激励性"两个面向.结论 研究结果为设计师进行老龄化设计提供了知识参考,有助于提升设计师的赋能意识.揭示了设计赋能充满矛盾性的品质,提出面向老龄化的设计赋能需要在"顺应"和"激励"之间找到平衡.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

金针菇多糖对微冻大黄鱼及其鱼片品质变化的影响

为研究金针菇多糖（polysaccharide from Flammulina velutipes，FVP）对微冻大黄鱼及鱼片在贮藏期间肌原纤维蛋白性质的变化及水分分布的影响，实验分别选用0.03、0.06、0.09 g/L FVP浸渍处理大黄鱼和鱼片，以无菌水处理为对照组，分析微冻贮藏期间样品的感官指标得分、总挥发性盐基氮含量、总巯基含量、Ca2＋-ATPase活性、蛋白流变学性质以及水分迁移变化规律。结果表明：FVP可有效抑制整鱼总挥发性盐基氮含量上升和感官得分的下降；减缓整鱼及鱼片在微冻过程中总巯基含量、Ca2＋-ATPase活性下降和水分流失；此外FVP还能够延缓大黄鱼因腐败而出现的蛋白凝胶能力减弱。在本实验选取的多糖浓度范围内，0.09 g/L FVP处理组保鲜效果较强。该研究结果可为FVP用于水产品贮运保鲜提供理论参考。     

Thiol-Functionalized Covalent Organic Frameworks as Thermal History Indictor for Temperature and Time History Monitoring

Various products, including foods and pharmaceuticals, are sensitive to temperature fluctuations. Thus, temperature monitoring during production, transportation, and storage is critical. Facile indicators are required to monitor temperature conditions via color changes in real time. This study aimed to prepare and apply thiol-functionalized covalent organic frameworks (COFs) as a novel indicator for monitoring thermal history and temperature abuse. The COFs underwent obvious color changes from bright yellow to purple after exposure to different temperatures for varying durations. The reaction kinetics are analyzed under isothermal conditions, which reveal that the order of reaction rates is k_−20°C < k_4°C < k_20°C < k_35°C < k_55°C. The activation energy (E_a) of the COFs is calculated using the Arrhenius equation as 50.71 kJ moL⁻¹. The COFs are capable of sensitive color changes and offer a broad temperature tracking range, thereby demonstrating their application potential for the monitoring of temperature and time exposure history during production, transportation, and storage. This excellent performance thermal history indicator also shows promise for expanding the application field of COFs.     

Enhanced thermal shrinkage behavior of phenolic-derived carbon aerogel-reinforced by HNTs with superior compressive strength performance

A novel carbon/m-HNTs composite aerogel was synthesized by introducing the modified halloysite nanotubes (m-HNTs) into phenolic (PR) aerogels through chemical grafting, followed with carbonization treatment. In order to explore the best proportion of HNTs to phenolic, the micromorphology of PR/m-HNTs were investigated by SEM before carbonization, confirming 10 wt% of m-HNTs is most beneficial to the porous network of aerogels. The interaction between PR and HNTs was studied by FTIR spectra, and microstructure evolution of the target product-carbon/m-HNTs composite aerogel were illustrated by SEM and TEM techniques. SEM patterns indicated that the carbon/m-HNTs aerogels maintain a stable porous structure at 1000 °C (carbonization temperature), while a ~20 nm carbon layer was formed around m-HNTs generating an integral unit through TEM analysis. Specific surface area and pore size distribution of composite aerogels were analyzed based on mercury intrusion porosimetry and N₂ adsorption–desorption method, the obtained results stayed around 500 m²g^?1 and 1.00 cm³g^?1 (pore volume) without significant discrepancy, compared with pure aerogel, showing the uniformity of pore size. The weight loss rate (26.76%) decreased greatly compared with pure aerogel, at the same time, the best volumetric shrinkage rate was only 30.83%, contributed by the existence of HNTs supporting the neighbor structure to avoid over-shrinking. The highest compressive strength reached to 4.43 MPa, while the data of pure aerogel was only 1.52 MPa, demonstrating the excellent mechanical property of carbon/m-HNTs aerogels.