Automatic extraction of function–behaviour–state information from patents

Patents contain a large quantity of technical information not available elsewhere and therefore very interesting for both academia and industry. The purpose of the research is to try to detect and extract information about the functions, the physical behaviours and the states of the system directly from the text of a patent in an automatic way. The above three categories constitute a well-known set of relevant entities in the theory of engineering design, and their study allows powerful analysis of individual artefacts as well as that of groups of products or technologies. The focus is in providing a handy tool that could speed up and facilitate human analysis and allow tackling also large corpora of documents. A second goal is to develop a protocol based on free software and database resources, so that it could be replicable with limited effort by everyone without having to rely on commercial databases.Extracting technical and design information from a document whose aim is more legal than technical, and that is written using a specific jargon, is not a trivial task. The approach chosen to overcome the various issues is to support state-of-the-art Computational Linguistic tools with a large Knowledge Base. The latter has been constructed both manually and automatically and comprises not only keywords but also concepts, relationships and regular expressions. A case study about a very recent patent describing a mechanical device has been included to show the functioning and output of the entire system.     

Machine learning reveals orbital interaction in materials

We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies of crystalline materials, atomization energies of molecular materials, and local magnetic moments of the constituent atoms in bimetal alloys of lanthanide metal and transition-metal can be predicted with high accuracy using the OFM. Knowledge regarding the role of the coordination numbers of the transition-metal and lanthanide elements in determining the local magnetic moments of the transition-metal sites can be acquired directly from decision tree regression analyses using the OFM.     

An Overview of the State of the Art of Automated Capture of Dietary Intake Information

Significant benefits arise from being able to capture dietary or nutritional intake information automatically or semi-automatically. These include the ability for individuals to know and understand their nutritional intake and hence improve their diet and health. To date, only highly manual processes such as 24-hour recall, food diaries, and food journals have been utilized which have been overly cumbersome for widespread adoption. Emerging informatics, computer vision, mobile computing, and sensor-based approaches are likely to play a role in further automating the capture of dietary intake information and these are becoming increasingly utilizable through such advents as the rapid and ubiquitous uptake of smartphones with built-in digital cameras and other sensors. In this paper, we review the state of the art of technologies for automatic capture of dietary intake information and identify significant outstanding research problems and promising directions.     

Linear guided autoencoder: Representation learning with linearity

In materials science, good representations of materials are important for use with prediction models in order to ensure accurate prediction of the properties of the output. In this paper, in order to address this issue, we use a learning system, linear guided autoencoder (LGAE) we call, which consists of an autoencoder and a linear predictor. For the autoencoder, we adopt a variant of the denoising autoencoder. In the LGAE, the learning addresses the unsupervised and supervised tasks simultaneously. Thus, the LGAE can be regarded as a form of nonlinear partial least squares (PLS) regression. Previous studies have not found the optimal solution for the encoder for an objective that contains both tasks. Our main contributions are a first-order approximation of the optimal solution and determination of the condition for linear solution that applies to the LGAE after training, in order to acquire knowledge from the nonlinear model (i.e., the LGAE). The main drawback of nonlinear PLS regression is that it is difficult to interpret the latent representation. Therefore, we propose a technical method for interpreting the latent representation. Experiments on benchmark datasets are conducted in order to compare the LGAE with kernel PLS regression, which is a powerful nonlinear PLS regression method. We also applied the LGAE to a dataset of methane storage materials in order to interpret the methane uptake based on the input variables and obtained reasonable results.     

SemantEco: A semantically powered modular architecture for integrating distributed environmental and ecological data

We aim to inform the development of decision support tools for resource managers who need to examine large complex ecosystems and make recommendations in the face of many tradeoffs and conflicting drivers. We take a semantic technology approach, leveraging background ontologies and the growing body of linked open data. In previous work, we designed and implemented a semantically enabled environmental monitoring framework called SemantEco and used it to build a water quality portal named SemantAqua. Our previous system included foundational ontologies to support environmental regulation violations and relevant human health effects. In this work, we discuss SemantEco’s new architecture that supports modular extensions and makes it easier to support additional domains. Our enhanced framework includes foundational ontologies to support modeling of wildlife observation and wildlife health impacts, thereby enabling deeper and broader support for more holistically examining the effects of environmental pollution on ecosystems. We conclude with a discussion of how, through the application of semantic technologies, modular designs will make it easier for resource managers to bring in new sources of data to support more complex use cases.     

A co-evolving memetic wrapper for prediction of patient outcomes in TCM informatics

Traditional Chinese medicine (TCM) relies on the combined effects of herbs within prescribed formulae. However, given the combinatorial explosion due to the vast number of herbs available for treatment, the study of these combined effects can become computationally intractable. Thus feature selection has become increasingly crucial as a pre-processing step prior to the study of combined effects in TCM informatics. In accord with this goal, a new feature selection algorithm known as a co-evolving memetic wrapper (COW) is proposed in this paper. COW takes advantage of recent research in genetic algorithms (GAs) and memetic algorithms (MAs) by evolving appropriate feature subsets for a given domain. Our empirical experiments have demonstrated that COW is capable of selecting subsets of herbs from a TCM insomnia dataset that shows signs of combined effects on the prediction of patient outcomes measured in terms of classification accuracy. We compare the proposed algorithm with results from statistical analysis including main effects and up to three way interaction terms and show that COW is capable of correctly identifying the herbs and herb by herb effects that are significantly associated to patient outcome prediction.     

从文献计量分析看医学信息学的发展

用文献计量学方法分析探讨医学信息学研究发展的动态变化,为我国医学信息学的发展方向提供参考和借鉴。     

数字化技术让沉睡在标本馆里的睡美人活起来——“国家标本资源共享平台及其应用”专刊序言

本期《科研信息化技术与应用》的“国家标本资源共享平台及其应用”专刊刊登了 13 篇文章,其中综述性论文 4 篇,技术性论文 3 篇,应用类论文 6 篇。本专刊全面系统地介绍了我国标本数字化的最新进展及其在各个不同学科不同层次的应用。首先对国内外生物标本资源数字化进展进行了分析和介绍,重点对我国国家标本资源共享平台从不同侧面,系统而全面地进行了分析,并对我国今后标本数字化提出了非常具有建设性的建议;然后基于 NSII 网页浏览日志中标本查询页面中的用户检索关键词进行了分析,一定程度上揭示用户对不同物种、不同地理分布和馆藏分布的关注度,从而确定热点物种;并分别详细介绍了植物、动物和岩矿子平台标本数字化、平台建设、数据资源、专题库建设、以及为相关领域研究提供的服务。国家标本资源共享平台除了提供标本数据服务外,还为各具特色的专题数据库建设、各种专题研究提供了很好的空间和相关技术服务;生物多样性数据 Gap 分析、可视化分析、三维展示技术、多媒体技术等在标本数字化展示和相关研究中得到了很好的应用。     

A web-based e-learning application for wound diagnosis and treatment

Pressure ulcers (PrU) are considered as one of the most challenging problems that Nursing professionals have to deal with in their daily practice. Nowadays, the education on PrUs is mainly based on traditional lecturing, seminars and face-to-face instruction, sometimes with the support of photographs of wounds being used as teaching material. This traditional educational methodology suffers from some important limitations, which could affect the efficacy of the learning process. This current study has been designed to introduce information and communication technologies (ICT) in the education on PrU for undergraduate students, with the main objective of evaluating the advantages an disadvantages of using ICT, by comparing the learning results obtained from using an e-learning tool with those from a traditional teaching methodology. In order to meet this major objective, a web-based learning system named ePULab has been designed and developed as an adaptive e-learning tool for the autonomous acquisition of knowledge on PrU evaluation. This innovative system has been validated by means of a randomized controlled trial that compares its learning efficacy with that from a control group receiving a traditional face-to-face instruction. Students using ePULab gave significantly better (p  <  0.01) learning acquisition scores (from pre-test mean 8.27 (SD 1.39) to post-test mean 15.83 (SD 2.52)) than those following traditional lecture-style classes (from pre-test mean 8.23 (SD 1.23) to post-test mean 11.6 (SD 2.52)). In this article, the ePULab software is described in detail and the results from that experimental educational validation study are also presented and analyzed.