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.     

Logic hybrid simulation-optimization algorithm for distillation design

In this paper, we propose a novel algorithm for the rigorous design of distillation columns that integrates a process simulator in a generalized disjunctive programming formulation. The optimal distillation column, or column sequence, is obtained by selecting, for each column section, among a set of column sections with different number of theoretical trays. The selection of thermodynamic models, properties estimation, etc. is all in the simulation environment. All the numerical issues related to the convergence of distillation columns (or column sections) are also maintained in the simulation environment. The model is formulated as a Generalized Disjunctive Programming (GDP) problem and solved using the logic based outer approximation algorithm without MINLP reformulation. Some examples involving from a single column to thermally coupled sequence or extractive distillation shows the performance of the new algorithm.     

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。     

Nanoinformatics: Emerging Databases and Available Tools

Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research.     

Goldengate实现异构数据库间的数据同步

随着大数据时代的到来,数据之间的关系越来越复杂;人们需要一种强大的数据拓扑结构来提高数据的可用性;Goldengate是一种基于数据库日志的数据复制产品,可以利用极少的系统资源,实时复制数据,提高数据可用性。     

TOWARD AN ARRAY OF PHENOMENOLOGICAL IDEAL-TYPES FOR THE SOCIAL SCIENCES

Beginning with the isolation of four essentially unique mathematical intervals on a continuum, we show the process by which an array of four phenomenological ideal-types can be generated which carry rather specific analytical and administrative implications. The basic vehicle of the paper is deductive inference, employed to generate successively more specific taxonomies of system types, eventuating in the four felt to be a specific interest as real-world references for the social scientist, with empirical point included wherever they can defend the deductions.     

ROUGH KNOWLEDGE DISCOVERY FOR NUCLEAR SAFETY

Large amounts of data are generated in the investigating and maintaining of nuclear energy. Making full use of this data to gain useful insights into, for example, safety issues, presents a tremendous challenge. It is well-known that rough set theory can be applied successfully to rough classification and knowledge discovery. Our work is concerned with finding methods for using rough sets to identify classes in datasets, finding dependencies in relations and discovering rules which are hidden in databases. We believe that these methods can be used to understand and control aspects of nuclear energy generation. So in this paper we illustrate the use of our rough discovery methods by means of a running example using data on nuclear safety from the Belgian Nuclear Reactor 1 (BR1).     

COSTA: A Computerized Support System for the Emergency Technical Advisory Body in Japan

If an accident resulting in an emergency occurs at a nuclear facility, the emergency technical advisory body (ETAB) is asked to make technical advice to the national government for emergency response. In order for the ETAB to carry out its role, a significant amount of materials containing information on nuclear power plants is prepared in the ETAB. These materials are all paper copies and very voluminous. Therefore it is required that they should be computerized for more effective use. Further, methods should be developed which aid the ETAB to understand the status of the affected facility and to predict potential radioactivity release to the environment.

To support the ETAB's activities in an emergency, the materials have been computerized as databases for more effective and quick use of them, and an expert system has been developed to quickly predict accident progression and environmental fission product release with the multivariate analysis method. The databases and the expert system have been unified to be a system of COSTA. The first phase of developing COSTA has been completed in FY1992. This paper describes the present status of development of COSTA.