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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. We explain the phenomena as driven by the formation of platinum oxide by reaction of the platinum with atmospheric oxygen, with platinum oxide evaporating to the environment. We found that the use of Pt provides much better controllability than the use of Au. Due to the high vapor pressure of platinum oxide, the engulfment of the Pt nanoparticles into oxidized silicon (SiO2) substrates is faster than of Au nanoparticles. At high temperature annealing we also find that the aggregation of Pt nanoparticles on the substrate surface is insignificant. As a result, the Pt nanoparticles are uniformly engulfed into the substrates, leading to an opportunity for patterning dense nanopore arrays. Moreover, the use of oxidized Si substrates enables us to precisely control the depth of the nanopores since the engulfment of Pt nanoparticles stops at a short distance above the SiO x /Si interface. After subsequent etching steps, a membrane with dense nanopore through-holes with diameters down to sub-30 nm is obtained. With its simple operation and high controllability, this fabrication method provides an alternative for rapid patterning of dense arrays of solid-state nanopores at low-cost.
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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. Our review and qualitative analysis covered application setting, target users, underlying technology, whether MT was used in isolation or in combination with human editing, languages tested, evaluation methods, findings, and identified gaps. RESULTS: Of 27 studies, a majority focused on MT systems for use in clinical settings (nâ¯=â¯18), and eight of these involved speech-based MT systems for facilitating patient-provider communications. Text-based MT systems (nâ¯=â¯19) aimed at generating a range of multilingual health materials. Almost a third of all studies (nâ¯=â¯8) pointed to MT's potential as a starting point before human input. Studies employed a variety of human and automatic MT evaluation methods. In comparison studies, statistical machine translation (SMT) systems were more accurate than rule-based systems when large corpora were available. For a variety of systems, performance was best for translations of simple, less technical sentences and from English to Western European languages. Only one system has been fully deployed. CONCLUSIONS: MT is currently being developed primarily through pilot studies to improve multilingual communication in health settings and to increase access to health resources for a variety of languages. However, continued concerns about accuracy limit the deployment of MT systems in these settings. The variety of piloted systems and the lack of shared evaluation criteria will likely continue to impede adoption in health settings, where excellent accuracy and a strong evidence base are critical. Greater translation accuracy and use of standard evaluation criteria would encourage deployment of MT into health settings. For now, the literature points to using MT in health communication as an initial step to be followed by human correction.
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Comunicación en Salud , Traducción , Biología Computacional , Humanos , Lenguaje , Aprendizaje Automático , Modelos Estadísticos , Redes Neurales de la ComputaciónRESUMEN
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. RESULTS: The study resulted in a detailed description of work related to translation of PH materials, an information workflow diagram, and a description of attitudes towards MT technology. We identified a number of themes that hold design implications for incorporating MT in PH translation practice. A PH translation tool prototype was designed based on these findings. DISCUSSION: This study underscores the importance of understanding the work context and information workflow for which systems will be designed. Based on themes and translation information workflow processes, we identified key design guidelines for incorporating MT into PH translation work. Primary amongst these is that MT should be followed by human review for translations to be of high quality and for the technology to be adopted into practice. CONCLUSION: The time and costs of creating multilingual health promotion materials are barriers to translation. PH personnel were interested in MT's potential to improve access to low-cost translated PH materials, but expressed concerns about ensuring quality. We outline design considerations and a potential machine translation tool to best fit MT systems into PH practice.
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Educación en Salud/métodos , Práctica de Salud Pública , Traducción , Flujo de Trabajo , Acceso a la Información , Algoritmos , Comunicación , Procesamiento Automatizado de Datos , Grupos Focales , Promoción de la Salud , Accesibilidad a los Servicios de Salud , Humanos , Lenguaje , Aprendizaje Automático , Administración en Salud Pública , Informática en Salud Pública , Garantía de la Calidad de Atención de Salud , WashingtónRESUMEN
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. DESIGN: A quality comparison of 25 MT and HT documents was performed with public health translators. The public health professionals involved were queried about the workflow, costs, and time for HT of 11 English public health documents over a 20-month period. Three recently translated documents of similar size and topic were then machine translated, the time for human postediting was recorded, and a blind quality analysis was performed. SETTING: Seattle/King County, Washington. PARTICIPANTS: Public health professionals. MAIN OUTCOME MEASURES: (1) Estimated times for various HT tasks; (2) observed postediting times for MT documents; (3) actual costs for HT; and (4) comparison of quality ratings for HT and MT. RESULTS: Human translation via local health department methods took 17 hours to 6 days. While HT postediting words per minute ranged from 1.58 to 5.88, MT plus human postediting words per minute ranged from 10 to 30. The cost of HT ranged from $130 to $1220; MT required no additional costs. A quality comparison by bilingual public health professionals showed that MT and HT were equivalently preferred. CONCLUSIONS: MT with human postediting can reduce the time and costs of translating public health materials while maintaining quality similar to HT. In conjunction with postediting, MT could greatly improve the availability of multilingual public health materials.
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Procesamiento Automatizado de Datos , Promoción de la Salud , Informática en Salud Pública , Práctica de Salud Pública , Control de Calidad , Traducción , Acceso a la Información , Procesamiento Automatizado de Datos/economía , Humanos , Lenguaje , Aprendizaje Automático/economía , Informática en Salud Pública/economía , Factores de TiempoRESUMEN
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. Traditional methods of soliciting feedback--such as focus groups and convenience sample interviews--are expensive and time consuming. As a result, adequate feedback from target populations is often insufficient due to the time and resource constraints characteristic to public health. OBJECTIVE: To describe a pilot study investigating the use of crowdsourcing technology as a method to gather rapid and relevant feedback on the design of health promotion messages for oral health. Our goal was to better describe the demographics of participants responding to a crowdsourcing survey and to test whether crowdsourcing could be used to gather feedback from English-speaking and Spanish-speaking participants in a short period of time and at relatively low costs. METHODS: We developed health promotion materials on pediatric dental health issues in four different formats and in two languages (English and Spanish). We then designed an online survey to elicit feedback on format preferences and made it available in both languages via the Amazon Mechanical Turk crowdsourcing platform. RESULTS: We surveyed 236 native English-speaking and 163 native Spanish-speaking participants in less than 12 days, at a cost of US $374. Overall, Spanish-speaking participants originated from a wider distribution of countries than the overall Latino population in the United States. Most participants were in the 18- to 29-year age range and had some college or graduate education. Participants provided valuable input for the health promotion material design. CONCLUSIONS: Our results indicate that crowdsourcing can be an effective method for recruiting and gaining feedback from English-speaking and Spanish-speaking people. Compared with traditional methods, crowdsourcing has the potential to reach more diverse populations than convenience sampling, while substantially reducing the time and cost of gathering participant feedback. More widespread adoption of this method could streamline the development of effective health promotion materials in multiple languages.
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Servicios Contratados/métodos , Promoción de la Salud , Multilingüismo , Salud Pública , Adolescente , Adulto , Servicios de Salud Dental , Grupos Focales , Humanos , Salud Bucal , Proyectos Piloto , Estados Unidos , Adulto JovenRESUMEN
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/NiFe2O4 supported over hierarchical N-doped carbons were achieved via 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: 0.948 V vs. RHE) and methanol tolerance, whilst GS@NCNF exhibited significantly lower over-potential of only 230 mV at 10 mA cm-2 for OER. Such high activities are due to the synergistic effects of bimetallic NPs encapsulated at CNT tips and N-doped carbons with unique hierarchical structures and the desired defects.
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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. X-ray diffraction and transmission electron microscopy (TEM) show that the resulting plates form via a two-step process, where Cu(OH) 2 rods precipitate first. These rods aggregate and fuse into plates with a width/height ratio of about 1.9. High-resolution TEM and electron diffraction show that the plates are single crystals and exhibit only some defects, which most likely originate from the assembly and fusion of the primary rods.
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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. The catalytic activity of the materials is studied by taking the epoxidation of cis-cyclooctene as a model reaction. The miniemulsion route yields APM particles catalytically much more active than analogous samples produced in bulk solution, which can be explained by their higher dispersibility in organic solvents, their higher surface area, and their higher porosity. Inorganic phosphate salt precursors are compared with organic phosphate sources. APM nanoparticles prepared in miniemulsion from d-glucose-6-phosphate and O-phospho-dl-serine yield a conversion in the epoxidation reaction of more than 90% after only 1 h, compared to 30% for materials prepared in bulk solution. In addition, the catalysts prepared in miniemulsion display a promising recyclability.
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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. The English versions were translated to Chinese using MT and assessed for errors and time required to correct via PE. Results suggest poor initial translation may explain the lack of quality translations despite PE.
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Promoción de la Salud , Traducción , China , Promoción de la Salud/métodos , Humanos , Lingüística , Procesamiento de Lenguaje Natural , Programas Informáticos , TraduccionesRESUMEN
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. We report here on the design methods and requirements used to develop PHAST and on its evaluation with potential public health users. Our results indicate such a system could be a feasible means of increasing the production of multilingual public health materials by reducing the barriers of time and cost. PHAST's design can serve as a model for other communities interested in assuring the accuracy of MT through shared language expertise.
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Automatización , Informática en Salud Pública , Programas Informáticos , Traducción , Computadores , HumanosRESUMEN
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. METHODS: From state and national public health websites, we collected 60 health promotion documents that had been translated from English to Chinese through human translation. The English version of the documents were then translated to Chinese using Google Translate. The MTs were analyzed for translation errors. A subset of the MT documents was postedited by native Chinese speakers with health backgrounds. Postediting time was measured. Postedited versions were then blindly compared against human translations by bilingual native Chinese quality raters. RESULTS: The most common machine translation errors were errors of word sense (40%) and word order (22%). Posteditors corrected the MTs at a rate of approximately 41 characters per minute. Raters, blinded to the source of translation, consistently selected the human translation over the MT+PE. Initial investigation to determine the reasons for the lower quality of MT+PE indicate that poor MT quality, lack of posteditor expertise, and insufficient posteditor instructions can be barriers to producing quality Chinese translations. CONCLUSIONS: Our results revealed problems with using MT tools plus human postediting for translating public health materials from English to Chinese. Additional work is needed to improve MT and to carefully design postediting processes before the MT+PE approach can be used routinely in public health practice for a variety of language pairs.
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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. We apply our approach to the analysis of MT output from translating public health documents from English into Spanish. Our results indicate that word order errors are clearly the most dispreferred error type, followed by word sense, morphological, and function word errors. The conjoint analysis-based model is able to predict user preferences more accurately than a baseline model that chooses the translation with the fewest errors overall. Additionally we analyze the effect of using a crowd-sourced respondent population versus a sample of domain experts and observe that main preference effects are remarkably stable across the two samples.
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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. The adsorption behavior of the polyamine functionalized mesoporous silica (MS-PEI) is assessed against anionic dyes. The adsorbent characteristics of MS-PEI are compared with a monolayer platform comprising of 3-aminopropyltriethoxy silane (APTES) functionalized mesoporous silica (MS-APTES). The adsorption behavior of the MS-PEI and MS-APTES toward anionic dyes is further evaluated by studying the effect of adsorbent dosage, pH, contact time, and temperature. Langmuir and Freundlich isotherm models are employed to understand the adsorption mechanism. The obtained kinetic data support a pseudo-second-order adsorption behavior for both monolayer and polymer functionalized MS. The associated thermodynamic parameters (ΔG°, ΔH°, and ΔS°) reveal that the process of adsorption with MS-PEI is more spontaneous and energetically favored as compared to the adsorption with MS-APTES. Taken together, the novel adsorbent system derived from a combination of MS and branched polymer (MS-PEI) shows the higher absorption efficiency and capacity toward the anionic dyes than the monolayer based adsorbent (MS-APTES).
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Poliaminas/química , Dióxido de Silicio/química , Purificación del Agua/instrumentación , Adsorción , Cinética , Porosidad , Agua , Contaminación Química del AguaRESUMEN
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).
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Polímeros/química , Transistores Electrónicos , Tiadiazoles/química , Tiofenos/químicaRESUMEN
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. This article presents a pilot study on the feasibility of using freely available statistical machine translation technology to translate health promotion materials. DESIGN: The authors gathered health-promotion materials in English from local and national public-health websites. Spanish versions were created by translating the documents using a freely available machine-translation website. Translations were rated for adequacy and fluency, analyzed for errors, manually corrected by a human posteditor, and compared with exclusively manual translations. RESULTS: Machine translation plus postediting took 15-53 min per document, compared to the reported days or even weeks for the standard translation process. A blind comparison of machine-assisted and human translations of six documents revealed overall equivalency between machine-translated and manually translated materials. The analysis of translation errors indicated that the most important errors were word-sense errors. CONCLUSION: The results indicate that machine translation plus postediting may be an effective method of producing multilingual health materials with equivalent quality but lower cost compared to manual translations.
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Automatización , Información de Salud al Consumidor , Promoción de la Salud , Multilingüismo , Traducción , Estudios de Factibilidad , Humanos , Internet , Proyectos Piloto , Estados UnidosRESUMEN
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. adult-directed speech from the point of view of the statistical pattern recognition paradigm underlying automatic speech recognition. Isolated-word speech recognizers were trained on adult-directed vs. infant-directed data sets and were tested on both matched and mismatched data. Results show that recognizers trained on infant-directed speech did not always exhibit better recognition performance; however, their relative loss in performance on mismatched data was significantly less severe than that of recognizers trained on adult-directed speech and presented with infant-directed test data. An analysis of the statistical distributions of a subset of phonetic classes in both data sets showed that this pattern is caused by larger class overlaps in infant-directed speech. This finding has implications for both automatic speech recognition and theories of infant speech perception.
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Desarrollo del Lenguaje , Relaciones Madre-Hijo , Fonética , Medición de la Producción del Habla , Software de Reconocimiento del Habla , Conducta Verbal , Adulto , Niño , Análisis Discriminante , Femenino , Humanos , Lactante , Modelos Lineales , Modelos EstadísticosRESUMEN
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. Log probability scores computed by these models are then combined, using log-linear combination, with other word scores available in the lattice output of a first-pass recognizer, and the resulting combination score is used to compute a second-pass speech recognition output.