Nanostructured carbons containing FeNi/NiFeO supported over N-doped carbon nanofibers for oxygen reduction and evolution reactions.

Non-precious metal-based electrocatalysts on carbon materials with high durability and low cost have been developed to ameliorate the oxygen-reduction reaction (ORR) and oxygen-evolution reaction (OER) for electrochemical energy applications such as in fuel cells and water electrolysis. Herein, two different morphologies of FeNi/NiFeO supported over hierarchical N-doped carbons were achieved carbonization of the polymer nanofibers by controlling the ratio of metal salts to melamine: a mixture of carbon nanotubes (CNTs) and graphene nanotubes (GNTs) supported over carbon nanofibers (CNFs) with spherical FeNi encapsulated at the tips (G/CNT@NCNF, 1 : 3), and graphene sheets wrapped CNFs with embedded needle-like FeNi (GS@NCNF, 2 : 3). G/CNT@NCNF shows excellent ORR activity (on-set potential: 0.

Engulfment control of platinum nanoparticles into oxidized silicon substrates for fabrication of dense solid-state nanopore arrays.

We found that platinum (Pt) nanoparticles, upon annealing at high temperature of 1000 °C, are engulfed into amorphous fused-silica or thermal oxide silicon substrates. The same phenomenon was previously published for gold (Au) nanoparticles. Similar to the Au nanoparticles, the engulfed Pt nanoparticles connect to the surface of the substrates through conical nanopores, and the size of the Pt nanoparticles decreases with increasing depth of the nanopores.

Development of machine translation technology for assisting health communication: A systematic review.

Objectives: To (1) characterize how machine translation (MT) is being developed to overcome language barriers in health settings; and (2) based on evaluations presented in the literature, determine which MT approaches show evidence of promise and what steps need to be taken to encourage adoption of MT technologies in health settings.

Materials & Methods: We performed a systematic literature search covering 2006-2016 in major health, engineering, and computer science databases. After removing duplicates, two levels of screening identified 27 articles for full text review and analysis.

Colloidally Confined Crystallization of Highly Efficient Ammonium Phosphomolybdate Catalysts.

Nanodroplets in inverse miniemulsions provide a colloidal confinement for the crystallization of ammonium phosphomolybdate (APM), influencing the resulting particle size. The effects of the space confinement are investigated by comparing the crystallization of analogous materials both in miniemulsion and in bulk solution. Both routes result in particles with a rhombododecahedral morphology, but the ones produced in miniemulsion have sizes between 40 and 90 nm, 3 orders of magnitude smaller than the ones obtained in bulk solution.

Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study.

Background: Chinese is the second most common language spoken by limited English proficiency individuals in the United States, yet there are few public health materials available in Chinese. Previous studies have indicated that use of machine translation plus postediting by bilingual translators generated quality translations in a lower time and at a lower cost than human translations.

Objective: The purpose of this study was to investigate the feasibility of using machine translation (MT) tools (eg, Google Translate) followed by human postediting (PE) to produce quality Chinese translations of public health materials.

Machine Assisted Translation of Health Materials to Chinese: An Initial Evaluation.

There is an unmet need for Chinese language health materials in the USA. We investigated the use of machine translation (MT) plus human post-editing (PE) to produce Chinese translations of public health materials. We collected 60 documents that had been manually translated from English to traditional Chinese.

PHAST: A Collaborative Machine Translation and Post-Editing Tool for Public Health.

This paper describes a novel collaborative machine translation (MT) plus post-editing system called PHAST (Public Health Automatic System for Translation, phastsystem.org), tailored for use in producing multilingual education materials for public health. Its collaborative features highlight a new approach in public health informatics: sharing limited bilingual translation resources via a groupware system.

Modeling workflow to design machine translation applications for public health practice.

Objective: Provide a detailed understanding of the information workflow processes related to translating health promotion materials for limited English proficiency individuals in order to inform the design of context-driven machine translation (MT) tools for public health (PH).

Materials And Methods: We applied a cognitive work analysis framework to investigate the translation information workflow processes of two large health departments in Washington State. Researchers conducted interviews, performed a task analysis, and validated results with PH professionals to model translation workflow and identify functional requirements for a translation system for PH.

A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation.

Despite much research on machine translation (MT) evaluation, there is surprisingly little work that directly measures users' intuitive or emotional preferences regarding different types of MT errors. However, the elicitation and modeling of user preferences is an important prerequisite for research on user adaptation and customization of MT engines. In this paper we explore the use of conjoint analysis as a formal quantitative framework to assess users' relative preferences for different types of translation errors.

Design and fabrication of branched polyamine functionalized mesoporous silica: an efficient absorbent for water remediation.

A novel branched polyamine (polyethyleneimine, PEI) functionalized mesoporous silica (MS) adsorbent is developed via a facile "grafting-to" approach. X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FT-IR) spectroscopy verified the effective surface functionalization of MS with monolayer and polymer. The transmission electron microscopy (TEM) was employed to reveal the morphology of the fabricated materials.

A comparison of human and machine translation of health promotion materials for public health practice: time, costs, and quality.

Context: Most local public health departments serve limited English proficiency groups but lack sufficient resources to translate the health promotion materials that they produce into different languages. Machine translation (MT) with human postediting could fill this gap and work toward decreasing health disparities among non-English speakers.

Objectives: (1) To identify the time and costs associated with human translation (HT) of public health documents, (2) determine the time necessary for human postediting of MT, and (3) compare the quality of postedited MT and HT.

Using crowdsourcing technology for testing multilingual public health promotion materials.

Background: Effective communication of public health messages is a key strategy for health promotion by public health agencies. Creating effective health promotion materials requires careful message design and feedback from representatives of target populations. This is particularly true when the target audiences are hard to reach as limited English proficiency groups.

Organic field-effect transistors based on highly ordered single polymer fibers.

Ultrahigh-mobility organic field-effect transistors (OFETs) based on a CDT-BTZ donor-acceptor copolymer are realized by reaching high molecular order and pronounced alignment in single fibers within a short OFET channel via solution processing. The macromolecules directionally self-assemble in a quasi crystal-like order in the fibers providing in this way an unhindered charge carrier pathway with mobilities as high as 5.5 cm(2) V(-1) s(-1).

Application of statistical machine translation to public health information: a feasibility study.

Objective: Accurate, understandable public health information is important for ensuring the health of the nation. The large portion of the US population with Limited English Proficiency is best served by translations of public-health information into other languages. However, a large number of health departments and primary care clinics face significant barriers to fulfilling federal mandates to provide multilingual materials to Limited English Proficiency individuals.

CuO particles from ionic liquid/water mixtures: evidence for growth via Cu(OH)2 nanorod assembly and fusion.

Tetrabutylammonium hydroxide/water mixtures are efficient reaction media for the fabrication of nanoscale metal oxides and hydroxides. Uniform CuO nanoplates, among others, can be grown on a large scale. This work shows that after 30 s at temperatures above 40 degrees C, CuO formation is already essentially complete.

Statistical properties of infant-directed versus adult-directed speech: insights from speech recognition.

Previous studies have shown that infant-directed speech ('motherese') exhibits overemphasized acoustic properties which may facilitate the acquisition of phonetic categories by infant learners. It has been suggested that the use of infant-directed data for training automatic speech recognition systems might also enhance the automatic learning and discrimination of phonetic categories. This study investigates the properties of infant-directed vs.

LANDMARK-BASED SPEECH RECOGNITION: REPORT OF THE 2004 JOHNS HOPKINS SUMMER WORKSHOP.

Three research prototype speech recognition systems are described, all of which use recently developed methods from artificial intelligence (specifically support vector machines, dynamic Bayesian networks, and maximum entropy classification) in order to implement, in the form of an automatic speech recognizer, current theories of human speech perception and phonology (specifically landmark-based speech perception, nonlinear phonology, and articulatory phonology). All three systems begin with a high-dimensional multiframe acoustic-to-distinctive feature transformation, implemented using support vector machines trained to detect and classify acoustic phonetic landmarks. Distinctive feature probabilities estimated by the support vector machines are then integrated using one of three pronunciation models: a dynamic programming algorithm that assumes canonical pronunciation of each word, a dynamic Bayesian network implementation of articulatory phonology, or a discriminative pronunciation model trained using the methods of maximum entropy classification.