A fast procedure for the estimation of the hydrogen storage capacity by cryoadsorption of metal-organic framework materials from their available porous properties

In order to identify the best porous materials for the cryogenic physisorption of hydrogen, high-throughput calculations are performed starting, i.e., from the collected information in crystallographic databases. However, these calculations, like molecular simulations, require specific training and significant computational cost. Herein, a relatively simple procedure is proposed to estimate and compare hydrogen uptakes at 77 K and pressure values from 40 bar starting from the porous properties of MOF materials, without involving simulation tools. This procedure uses definitions for adsorption and considers the adsorbed phase as an incompressible fluid whose pressure-density change is that for the liquid phase at 19 K. For the 7000 structures from the CoRE MOF database, the average error of the predictions is only of 1% from reference values at 100 bar, with an SD of ±8%. This accuracy is lower than that from simulation tools, but involving lower computational cost and training.     

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

Evaluation across and within collaborative manufacturing networks: a comparison of manufacturers’ interactions and attributes

Evaluation on collaborative manufacturing network (CMN) structure characteristics has important implications for network operations such as production decision-making, product recovery, creating consensus. Several recent studies suggest that augmenting network structure with nodes’ attributes can provide a more fine-grained understanding of the network. However, there have been few studies to provide a systematic understanding of these effects in a CMN at scale. This gap is bridged using an industrial printing machinery CMN data-set collected on a web-based producing and outsourcing service platform. Novel phenomena with respect to both interaction and attribute metrics across and within the CMNs are observed. Moreover, although many studies employ either interaction or attribute data to study the relative roles of manufacturers in a CMN, relatively little is known about the relationship between these two types of data. This study explores this relationship by comparing two defined metrics (i.e. relational capability and node capability), which evaluate the manufacturers’ interactions and attributes, respectively. We examine to what extent the two metrics of manufacturers correlate with each other, and how possible dissimilarities and similarities can be explained based on the collected industrial CMN data-set. The insights thereby provide a better basis for efficient operations decision-making in CMN.     

Multi-factor impact mechanism on combustion efficiency of a hydrogen-fueled micro-cylindrical combustor

As it is important to achieve higher combustion efficiency for applications of micro-cylindrical combustor, the multi-factor impact mechanism on the combustion efficiency of a hydrogen-fuelled micro-cylindrical combustor is investigated in this work. Firstly, six factors such as hydrogen/air equivalence ratio, inlet velocity, inlet temperature, wall thermal conductivity, wall emissivity and convective heat transfer coefficient of outer wall and five levels of each factor are determined. Orthogonal design table L₂₅(5⁶) is introduced to arrange cases. Secondly, grey relational analysis is adopted to investigate the effects of the six factors on combustion efficiency. Finally, the results of grey relational analysis are validated by analysis of variance. Based on grey relational analysis and analysis of variance, it is determined that the impact ranking from the largest to the smallest is hydrogen/air equivalence ratio, inlet velocity and inlet temperature, followed by the other three factors. The impact of wall thermal conductivity, convective heat transfer coefficient of outer wall and wall emissivity is considered to be equal due to their difference of impact on combustion efficiency is very small. This work provides us significant reference for optimizing combustion efficiency of a hydrogen-fuelled micro-cylindrical combustor.     

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.     

数据挖掘与数据库知识发掘

介绍了数据库析近发展起来的数据挖掘（MD）和数据库发掘（KDD）技术，阐述了DM和KDD之间的区别和联系，综述了DM技术的常用方法，剖析了在处理DM问题时应注意的问题，并指出DM技术的优势和希望所在，最后对这两门交叉学科的发展提出了建议。     

软土地基承载力指标灰色关联分析

该文运用灰色关联分析方法探讨了影响软土地基承载力大小的各种因素。结果表明 :轻型动力触探击数 (N1 0 )对承载力大小变化最为敏感 ,施工过程中用于检测软土地基承载力既简便又较准确。其次为软土的天然密度 (ρ)、压缩模量 (Es)、粘聚力 (c )和塑性指数 (Ip) ,在室内试验中可优先利用这些参数来确定承载力大小。而天然含水量 (w)、孔隙比 (e)和液性指数 (IL)相对较不敏感。     

电测式十字板剪切试验的研究与应用

十字板剪切试验能够明显地提高饱和软粘性土力学指标的准确度,适合沿海等软土分布地区,适用于求算地基承载力、软粘土的灵敏度等重要的地基力学参数。根据电测式十字板剪切试验的原理、标定方法,结合东营沿海软土分布区地基土的建筑经验进行系统地总结和归纳,提出了该地区十字板剪切试验指标应用的相应关系式。     

建筑路径的关联化

当代城市中的建筑尺度越来越大,越来越多的城市功能被纳入建筑,建筑内的路径也越来越长,并且越显重要。然而,当今一部分建筑内的路径被处理为消极的纯交通体,忽视了对人性的关怀。本文提到的关联化是对这一现象的回应。希望通过对建筑内路径的分析研究,探讨如何在有条件的情况下将路径设计得更具人性。     

住宅建筑能耗基准确定及用能评价新方法

针对当前建筑能耗基准确定及用能评价方法仍不完善的现状,提出了一种适用于住宅建筑的新方法。通过灰色关联分析确定了不同影响因素(即特征参数)与建筑能耗的关联度,将关联度作为该因素的权值并结合聚类分析对建筑进行合理分类。在此基础上采用累积频率分布法确定了每类建筑的能耗基准值,并对建筑住户进行用能评价。为验证该方法的可行性,将其应用于日本建筑学会所建立的住宅建筑能耗数据库,为聚类后的每类建筑确定了相应的能耗基准并对住户进行了用能评价。结果表明该方法能够综合考虑不同特征参数的影响大小,科学合理地对建筑细化分类,根据分类后的基准评价能够更好地评估住户的节能潜力并提供可行的节能建议。