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Flow-type membraneless hydrogen peroxide fuel cell is fabricated using buckypaper-based electrodes and eddy-inducing cell structure.

With the advent of social media technology for sharing commonly asked questions and answers among end users, there is rapidly growing interest in understanding the characteristics as well as utilizing social question and answer (QA) data. Not only SQL (NoSQL) is a popular technical topic on social question answering Web sites and is gaining popularity with emerging demands for scalable databases of big data. Despite the great interest of users in NoSQL technology, an attempt to analyze how the actual users react to NoSQL has not yet been made. Thus, in the present work, we utilize the QA data acquired from Stack Overflow (a QA Web site that works as a large knowledge repository) to understand how people perceive NoSQL technology. To this end, latent Dirichlet allocation (LDA) topic modeling techniques are used to discover the trend of NoSQL databases. In addition, we examine a weighted LDA model to reflect the influence of answers and finally propose the topic discrimination value in an attempt to find topics that distinguish each NoSQL database.

Hydrophilic bridging of an enzyme using sulfhydryl conjugation to produce an aggregate is used to enhance the activity and operational lifetime of a glucose oxidase (GOx)-based biocatalyst. 1,8-Bismaleimido-diethyleneglycol (BMPEG) is used as an alternative to the conventional cross-linker glutaraldehyde (GA), which suffers from downsides in terms of catalytic activity and safety. To evaluate the effect of adopting BMPEG as the cross-linker, a reduced graphene oxide (RGO) and iron phthalocyanine (FePc) composite (RGO/FePc) support material was used as the under layer in the preparation of an [RGO/FePc]/[GOx-BMPEG] electrode for the hydrogen peroxide oxidation reaction. Its electrochemical behavior was verified by comparison with electrodes prepared with GOx alone ([RGO/FePc]/GOx) and a conventional GOx-GA aggregate ([RGO/FePc]/[GOx-GA]). [RGO/FePc]/[GOx-BMPEG] exhibited higher sensitivity (62.4 μW mM−1 cm−2) and longer operating duration (94.5% for 3h) than [RGO/FePc]/[GOx-GA] (52.3 μW mM−1 cm−2, 67.4% for 3 h), even though the amount of GOx on the [RGO/FePc]/[GOx-BMPEG] electrode was found to be around half that on [RGO/FePc]/[GOx-GA] in physiological condition (pH 7.4, 36.5 °C). The excellent performance of the [RGO/FePc]/[GOx-BMPEG] electrode was attributed to the hydrophilicity of the BMPEG cross-links, which mitigates the hindrance of mass transfer typically caused by hydrophobic cross-linkers such as GA, and the bonding of the thiol groups on GOx to BMPEG, which protects the GOx from inactivation. Thus, GOx-BMPEG is a superior alternative to GOx-GA for use in implantable bioelectronics.

In today’s era of information explosion, extracting entities and their relations in large-scale, unstructured collections of text to better represent knowledge has emerged as a daunting challenge in biomedical text mining. To respond to the demand to automatically extract scientific knowledge with higher precision, the public knowledge discovery tool PKDE4J (Song et al., 2015) was proposed as a flexible text-mining tool. In this study, we propose an extended version of PKDE4J to represent scientific knowledge for literature-based knowledge discovery. Specifically, we assess the performance of PKDE4J in terms of three extraction tasks: entity, relation, and event detection. We also suggest applications of PKDE4J along three lines: (1) knowledge search, (2) knowledge linking, and (3) knowledge inference. We first describe the updated features of PKDE4J and report on tests of its performance. With additional options in the processes of named entity extraction, verb expansion, and event detection, we expect that the enhanced PKDE4J can be utilized for literature-based knowledge discovery.

NoSQL (Not only SQL) has gained popularity with emerging demands of scalable database with big data. Despite the great interest of users toward NoSQL technology, an attempt to analyze how the actual users react to NoSQL has not been made yet. Thus, the present work utilizes question-answer data acquired from Stack Overflow, a question and answer site that works as a large knowledge repository to understand how people perceive NoSQL technology. To this end, LDA topic modeling techniques are used to find out the trend of NoSQL databases. In addition, we proposed topic discrimination value in attempt to find topics that distinguish each NoSQL databases.