Digital health interventions (DHIs) have frequently been highlighted as one way to respond to increasing levels of mental health problems in children and young people. Whilst many are developed to address existing mental health problems, there is also potential for DHIs to address prevention and early intervention. However, there are currently limitations in the design and reporting of the development, evaluation and implementation of preventive DHIs that can limit their adoption into real-world practice. This scoping review aimed to examine existing evidence-based DHI interventions and review how well the research literature described factors that researchers need to include in their study designs and reports to support real-world implementation. A search was conducted for relevant publications published from 2013 onwards. Twenty-one different interventions were identified from 30 publications, which took a universal (n = 12), selective (n = 3) and indicative (n = 15) approach to preventing poor mental health. Most interventions targeted adolescents, with only two studies including children aged ≤10 years. There was limited reporting of user co-design involvement in intervention development. Barriers and facilitators to implementation varied across the delivery settings, and only a minority reported financial costs involved in delivering the intervention. This review found that while there are continued attempts to design and evaluate DHIs for children and young people, there are several points of concern. More research is needed with younger children and those from poorer and underserved backgrounds. Co-design processes with children and young people should be recognised and reported as a necessary component within DHI research as they are an important factor in the design and development of interventions, and underpin successful adoption and implementation. Reporting the type and level of human support provided as part of the intervention is also important in enabling the sustained use and implementation of DHIs.

Attention models are proposed in sentiment analysis because some words are more important than others. However, most existing methods either use local context based text information or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. A reading prediction model is first built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition based attention (CBA) layer for neural sentiment analysis. As a comprehensive model, We can capture attentions of words in sentences as well as sentences in documents. Different attention mechanisms can also be incorporated to capture other aspects of attentions. Evaluations show the CBA based method outperforms the state-of-the-art local context based attention methods significantly. This brings insight to how cognition grounded data can be brought into NLP tasks.

Affective lexicon is one of the most important resource in affective computing for text. Manually constructed affective lexicons have limited scale and thus only have limited use in practical systems. In this work, we propose a regression-based method to automatically infer multi-dimensional affective representation of words via their word embedding based on a set of seed words. This method can make use of the rich semantic meanings obtained from word embedding to extract meanings in some specific semantic space. This is based on the assumption that different features in word embedding contribute differently to a particular affective dimension and a particular feature in word embedding contributes differently to different affective dimensions. Evaluation on various affective lexicons shows that our method outperforms the state-of-the-art methods on all the lexicons under different evaluation metrics with large margins. We also explore different regression models and conclude that the Ridge regression model, the Bayesian Ridge regression model and Support Vector Regression with linear kernel are the most suitable models. Comparing to other state-of-the-art methods, our method also has computation advantage. Experiments on a sentiment analysis task show that the lexicons extended by our method achieve better results than publicly available sentiment lexicons on eight sentiment corpora. The extended lexicons are publicly available for access.

Modality exclusivity norms have been developed in different languages for research on the relationship between perceptual and conceptual systems. This paper sets up the first modality exclusivity norms for Chinese, a Sino-Tibetan language with semantics as its orthographically relevant level. The norms are collected through two studies based on Chinese sensory words. The experimental designs take into consideration the morpho-lexical and orthographic structures of Chinese. Study 1 provides a set of norms for Mandarin Chinese single-morpheme words in mean ratings of the extent to which a word is experienced through the five sense modalities. The degrees of modality exclusivity are also provided. The collected norms are further analyzed to examine how sub-lexical orthographic representations of sense modalities in Chinese characters affect speakers’ interpretation of the sensory words. In particular, we found higher modality exclusivity rating for the sense modality explicitly represented by a semantic radical component, as well as higher auditory dominant modality rating for characters with transparent phonetic symbol components. Study 2 presents the mean ratings and modality exclusivity of coordinate disyllabic compounds involving multiple sense modalities. These studies open new perspectives in the study of modality exclusivity. First, links between modality exclusivity and writing systems have been established which has strengthened previous accounts of the influence of orthography in the processing of visual information in reading. Second, a new set of modality exclusivity norms of compounds is proposed to show the competition of influence on modality exclusivity from different linguistic factors and potentially allow such norms to be linked to studies on synesthesia and semantic transparency.

This study adopted a corpus-based approach to examine the synaesthetic metaphors of gustatory adjectives in Mandarin. Based on the distribution of synaesthetic uses in the corpus, we found that: (1) the synaesthetic metaphors of Mandarin gustatory adjectives exhibited directionality; (2) the directionality of Mandarin synaesthetic gustatory adjectives showed both commonality and specificity when compared with the attested directionality of gustatory adjectives in English, which calls for a closer re-examination of the claim of cross-lingual universality of synaesthetic tendencies; and (3) the distribution and directionality of Mandarin synaesthetic gustatory adjectives could not be predicted by a single hypothesis, such as the embodiment-driven approach or the biological association-driven approach. Thus, linguistic synaesthesia was constrained by both the embodiment principle and the biological association mechanism.

Attention models are proposed in sentiment analysis and other classification tasks because some words are more important than others to train the attention models. However, most existing methods either use local context based information, affective lexicons, or user preference information. In this work, we propose a novel attention model trained by cognition grounded eye-tracking data. First,a reading prediction model is built using eye-tracking data as dependent data and other features in the context as independent data. The predicted reading time is then used to build a cognition grounded attention layer for neural sentiment analysis. Our model can capture attentions in context both in terms of words at sentence level as well as sentences at document level. Other attention mechanisms can also be incorporated together to capture other aspects of attentions, such as local attention, and affective lexicons. Results of our work include two parts. The first part compares our proposed cognition ground attention model with other state-of-the-art sentiment analysis models. The second part compares our model with an attention model based on other lexicon based sentiment resources. Evaluations show that sentiment analysis using cognition grounded attention model outperforms the state-of-the-art sentiment analysis methods significantly. Comparisons to affective lexicons also indicate that using cognition grounded eye-tracking data has advantages over other sentiment resources by considering both word information and context information. This work brings insight to how cognition grounded data can be integrated into natural language processing (NLP) tasks.

LiNiO2, a promising cobalt-free cathode, suffers from poor cyclic stability and inferior rate performance due to structural degradation. A strategy based on the synergistic effect of a strong Nb-O bond (753 kJ mol-1), high valence state (Nb5+), and larger ionic radius (0.64 Å) is developed to improve the properties of LiNiO2. The synergistic effects can effectively improve lattice stability, lower Li+/Ni2+ cation mixing, expand LiO6-slab spaces, enhance the conductivity and inhibit the H2–H3 phase transition, which can enhance the stability of the layered structure, accelerate Li+ diffusion and alleviate mechanical damage. Therefore, LiN0.99Nb0.01O2 exhibits higher capacity retention (91.4 % after 100 cycles at 0.5 C) and superior rate capability (143 mAh g-1 at 5 C). As a comparison, the data of bare LiNiO2 is 69.2% and 127 mAh g-1 under the same conditions. These results further demonstrate the effectiveness of the proposed doping strategy.

With the increasing importance of the internet to our everyday lives, questions are rightly being asked about how its' use affects our wellbeing. It is important to be able to effectively measure the effects of the online context, as it allows us to assess the impact of specific online contexts on wellbeing that may not apply to offline wellbeing. This paper describes a scoping review of English language, peer-reviewed articles published in MEDLINE, EMBASE, and PsychInfo between 1st January 2015 and 31st December 2019 to identify what measures are used to assess subjective wellbeing and in particular to identify any measures used in the online context. Two hundred forty studies were identified; 160 studies were removed by abstract screening, and 17 studies were removed by full-text screening, leaving 63 included studies. Fifty-six subjective wellbeing scales were identified with 18 excluded and 38 included for further analysis. Only one study was identified researching online wellbeing, and no specific online wellbeing scale was found. Therefore, common features of the existing scales, such as the number and type of questions, are compared to offer recommendations for building an online wellbeing scale. Such a scale is recommended to be between 3 and 20 questions, using mainly 5-point Likert or Likert-like scales to measure at least positive and negative affect, and ideally life satisfaction, and to use mainly subjective evaluation. Further research is needed to establish how these findings for the offline world effectively translate into an online measure of wellbeing.