ORCID Profile
0000-0003-1366-3324
Current Organisation
Hong Kong University of Science and Technology
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Publisher: Wiley
Date: 31-12-2019
DOI: 10.1111/GCB.14904
Abstract: Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to bio ersity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on in idual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
Publisher: Springer International Publishing
Date: 2018
Publisher: JMIR Publications Inc.
Date: 06-05-2021
DOI: 10.2196/28413
Abstract: Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. We aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models. We found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.
Publisher: Informa UK Limited
Date: 07-02-2022
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Project MUSE
Date: 2021
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Springer Nature Singapore
Date: 26-09-2019
Publisher: JMIR Publications Inc.
Date: 04-03-2021
Abstract: mproving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. e aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. ased on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models. e found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. achine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 22-05-2023
DOI: 10.1057/S41599-023-01677-Z
Abstract: Nouns in human languages mostly profile concrete and abstract entities. But how much eventive information can be found in nouns? Will such eventive information found in sensory nouns have anything to do with the cognitive representation of the basic human senses? Importantly, is there any ontological and/or cognitive motivation that can account for this noun–verb dichotomy via body-and-world interactions? This study presents the first comprehensive investigation of sensory nouns in Mandarin Chinese, examining their qualia structures formalised in the Generative Lexicon Theory, as well as the time-dependent (endurant–perdurant) properties encoded in their sensory modalities. This study fills the gap in sensorial studies by highlighting the pivotal position of nouns in sensory experiences and provides insights into the interactions between perception, cognition, and language. Further, it establishes, for the first time, the cognitive motivation of the categorial noun–verb bifurcation without presupposing any a priori knowledge of grammatical categories.
Publisher: Walter de Gruyter GmbH
Date: 25-07-2023
DOI: 10.1515/LINGVAN-2022-0020
Abstract: Linguistic synesthesia links two concepts from two distinct sensory domains and creates conceptual conflicts at the level of embodied cognition. Previous studies focused on constraints on the directionality of synesthetic mapping as a way to establish the conceptual hierarchy among the five senses (i.e., vision, hearing, taste, smell, and touch). This study goes beyond examining the directionality of conventionalized synesthetic terms by adopting a Conceptual Metaphor Theory approach (i.e., the Conceptual Mapping Model) to test if conventional synesthetic directionality still holds when it comes to novel metaphorical expressions. The subjects, 308 native English speakers, are asked to judge the degree of commonness, appropriateness, understandability, and figurativeness in order to measure the degree of comprehensibility of novel synesthetic metaphors. Our findings demonstrate that novel synesthetic metaphors that follow conventional directionality are considered more common, more appropriate, and easier to comprehend than those that violate conventional mapping principles they are also judged as more literal than those that do not follow conventional directionality. This study explores linguistic synesthesia from the perspective of comprehension of novel synesthetic metaphors, posits a pivotal position for mapping principles in synesthetic directionality, and supports an embodied account of linguistic synesthesia.
No related grants have been discovered for Yin Zhong.