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This research reviews 56 robots in science education (RSE) research articles published in SSCI (Social Sciences Citation Index) journals in the Web of Science (WoS) database. The analysis results showed that there were three clusters of RSE studies: robots in elementary science education, robot-based STEM activities, and robot-facilitated computational thinking. In terms of the research issues, more studies were conducted across-disciplines. Regarding thematic classifications, "general benefits of educational robots" was the most frequently investigated issue, while "diversity and broadening participation," and "creativity and motivation" were less frequently discussed. The most adopted research methods were quantitative and mixed methods, focusing on measuring learners' cognition and affect. Most of the interventions in the empirical studies adopted pedagogical interventions. In terms of the interaction issue, LEGO was adopted the most, followed by self-made robots; moreover, robots frequently played the role of tools in science education. From the perspective of specific robotics content knowledge, "control" (e.g. programming) comprised the majority. The most adopted learning strategy was project-based learning, followed by issue-based discussion. Regarding assessment issues, the most adopted evaluation approaches to evaluating students' performance were questionnaires, observation, verbal interviews, and artifacts to assess the quality of learning activities.

Second language (L2) writing plays an important role in improving the learners' language skills of English as a Foreign Language (EFL) in terms of language expression and linguistic thinking. Therefore, improving writing skills is still a focus area for EFL learners. To enhance EFL learners' writing ability and optimize their writing quality, an intelligent-based cognitive diagnostic feedback (I-CDF) strategy is proposed based on the Intelligent Writing Critique System (IWCS). IWCS can provide feedback on students' English writing learning components, including lexical, syntactic, rhetorical expression, chapter structure, and discourse intention. Hence, the study intends to assess the effects of feedback strategies based on students' writing scores, self-efficacy, epistemic network structure, and transferability. A quasi-experiment was conducted in two classes at a university in southeastern China, where students were randomly divided into two classes. One (N = 32) was considered an experimental group conducted using the proposed I-CDF strategy, while the other (N = 30) was the control group using the score-based teacher corrective feedback (S-TCF) strategy. The writing experiment lasted for seven weeks. The students would be interviewed at the end of the writing learning activities. The results indicated that the I-CDF strategy improved students' writing scores and self-efficacy. Furthermore, the epistemic network analysis showed that, compared to the control group, the I-CDF strategy encouraged the students to devote more energy to focus on high-level applications of writing skills such as rhetorical expression and sentence structure collation, optimizing the students' writing epistemic network of the writing of the students. The interview revealed that the I-CDF strategy supported the experimental group students' accurate understanding of writing, strengthening the logical structure of the writing. At the same time, the students in the experimental group were satisfied with the I-CDF strategy.

Robotics education has received widespread attention in K-12 education. Studies have pointed out that in robotics courses, learners face challenges in learning abstract content, such as constructing a robot with a good structure and writing programs to drive a robot to complete specific learning tasks. The present study proposed the embodied learning-based computer programming approach and applied it to the LEGO Mindstorms EV3 robotics course. To evaluate its effectiveness, a quasi-experiment was conducted in one public primary school to explore its effects on students' learning achievement, learning motivation, learning attitudes, learning engagement, and cognitive load. The experimental group (40 students) adopted the embodied learning-based computer programming approach, while the control group (40 students) adopted the conventional computer programming approach. The results showed that the experimental group had significantly better learning achievement in robotics than the control group, and that there was no significant difference in the cognitive load of the two groups. In terms of learning motivation, although both groups showed improvement, the experimental group had higher intrinsic learning motivation. In addition, the experimental group outperformed the control group with regard to learning attitudes and learning engagement (including cognitive, behavioral, and emotional engagement). Accordingly, this study could contribute to future research for developing more effective robotics teaching approaches and computer programming activity design.

This study provides research-based evidence to profile: (1) the roles of artificial intelligence in nursing; (2) its research applications; and (3) the research trends for future study. On the basis of the PRISMA statement, a series of AI and nursing education related keywords from the literature were used to retrieve high-quality journal articles from the Web of Science. A total of 112 AI-supported nursing education research articles were analyzed based on a three-dimensional framework, including interaction (e.g. the roles of AI, types of AI systems), research (e.g. methods and fields), and performance (e.g. research groups and measurement foci). The results revealed that AI played a primary role in profiling and prediction in nursing research (63%), and the most used AI system in nursing was intelligent agents (53%). The quantitative approach (87%) was the dominant research method, and the most relevant studies concerned health and medicine (92%). Regarding sample and measurement matters, patients and medical staff (75%) were the two primary research samples, and the performance evaluation of AI-related tools and systems (90%) was the core measurement focus. Additional content analysis across the three research interests was performed and discussed. Directions for future studies are provided.

With the rapid development of generative artificial intelligence (GAI), the performance and usability of related tools, such as ChatGPT, have significantly improved. The advancement has fostered researchers to increasingly focus on students' perceptions and application of the roles, functionalities, and interaction patterns of these tools in higher education. The present study adopted the draw-a-picture technique to explore the viewpoints and conceptions of undergraduates with different growth mindsets regarding the roles and functionalities of ChatGPT in learning. It also analyzed their interaction process with ChatGPT, especially their interaction skills and question types. The results showed that there were significant differences in the conceptions of "locations," "learning content," and "learning activities" of students with different growth mindsets. In the interaction process between undergraduates and ChatGPT, significant differences existed in the interaction skills and question types of students with different growth mindsets. Besides, students with different growth mindsets also had different learning achievements and critical thinking tendencies. The findings revealed the conceptions of students with different growth mindsets regarding the roles and functionalities of ChatGPT in learning, and also provided valuable insights for teachers. These findings are beneficial for educators to more accurately adjust and optimize the application of these tools in teaching activities based on students' different growth mindsets.

Abstract Tetrahydro-β-carboline skeletons are prominent and ubiquitous in an extraordinary range of indole alkaloid natural products and pharmaceutical compounds. Powerful synthetic approaches for stereoselective synthesis of tetrahydro-β-carboline skeletons have immense impacts and have attracted enormous attention. Here, we outline a general chiral phosphoric acid catalyzed asymmetric 1,3-dipolar cycloaddition of 3,4-dihydro-β-carboline-2-oxide type nitrone that enables access to three types of chiral tetrahydro-β-carbolines bearing continuous multi-chiral centers and quaternary chiral centers. The method displays different endo/exo selectivity from traditional nitrone chemistry. The distinct power of this strategy has been illustrated by application to collective and enantiodivergent total syntheses of 40 tetrahydro-β-carboline-type indole alkaloid natural products with divergent stereochemistry and varied architectures.

Abstract Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO–SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO–SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials. Scientific contribution This study verifies the feasibility of applying transfer learning to large language models in different chemical fields to help organic materials perform virtual screening. Through the comparison of transfer learning from different chemical fields to a variety of organic material molecules, the high precision virtual screening of organic materials is realized.

With the rapid development of Artificial Intelligence, automatic writing evaluation (AWE) has received much attention from English Foreign Language (EFL) writing teachers. However, the obstacles and potential problems of integrating AWE in EFL writing instruction have yet to be explored. Scholars have indicated that the effectiveness of AWE in EFL writing instruction depends on the learners' depth of reflection. Hence, this study proposes a learning approach that integrates AWE and peer assessment (PA) based on the knowledge-building theory, with the expectation that learners will be able to strengthen their reflections on AWE feedback through PA, and thereby improve their EFL writing performance. To examine the effectiveness of the proposed approach, a quasi-experiment was conducted in a university EFL writing class. One of the classes (33 students) was the experimental group using the PA-AWE approach, and the other class (31 students) was a control group that studied using the conventional AWE approach (C-AWE approach). Findings revealed that the PA-AWE group outperformed the C-AWE group regarding EFL writing performance, learning motivation, critical thinking, and reduced EFL writing anxiety. In addition, a thematic inductive qualitative analysis of the interview data indicated each approach's benefits and learning conceptions.

This study reviews the journal publications of artificial intelligence-supported online learning (AIoL) in the Web of Science (WOS) database from 1997 to 2019 taking into account the contributing countries/areas, leading journals, highly cited papers, authors, research areas, research topics, roles of AIoL, and adopted artificial intelligence (AI) algorithms. Results indicate that, from 1997 to 2009, AIoL research focused on the combination of intelligent tutoring systems and distance learning. In 2010-2014, AIoL research emphasized learner-oriented learning. In 2015-2019, learner-system interactions to facilitate personalized, adaptive, and collaborative learning became the main focus. "Intelligent tutoring systems" have played the most important role in AIoL, followed by "profiling and prediction," and "adaptive systems with personalization." Accordingly, the roles and research trends as well as several suggestions for future research in the field of AIoL are provided as a reference for researchers and policy makers.

Abstract In order to accurately predict the content and variation trend of dissolved gas in transformer oil and guide the condition maintenance of power transformers, a combined prediction model based on multi‐information fusion is proposed and its effectiveness is analysed. First of all, based on the possibility of pathological and missing historical sample data, a detection and filling method based on variable weight combination samples is established. Second, the authors propose two models. Aiming at the non‐linear and non‐stationary characteristics of gas content, a univariate decomposition prediction mode HBA‐VMD‐TCN which based on the Honey Badger algorithm, variational mode decomposition and time convolutional network (TCN) is established. Then the multivariate Informer prediction model is established for gas content affected by multiple variables. Third, the cross‐entropy theory is used to determine the weight coefficients of the two models, and the multi‐information fusion combined prediction model is formed. Finally, on the basis of the above, a method to determine the time step and the position information of the transition point adaptively in the process of prediction is proposed to further improve the prediction accuracy. The results show that, through a series of simulation experiments of model comparison and transformer anomaly prediction, the accuracy and effectiveness of the combined prediction model are verified.

Abstract Background Circulating miRNAs (c-miR) have been shown to be potential biomarkers in sarcopenia, but the miRNAs response to aerobic exercise in older people remains inconclusive. We sought to examine the exercise benefits on physical fitness and miRNAs, and to explore the mediating effect of miRNAs on training-induced fitness changes. Methods This controlled trial recruited 58 community-dwelling older adults and randomized them into exercise group (EX) and control group (CON). EX received 8-week supervised moderate intensity cycling training 3x/week. C-miR expression (c-miR-21, c-miR-126, c-miR-146a, c-miR-222), physical fitness (body composition, cardiorespiratory fitness, muscular fitness) and physical activity level (PAL, measured as in daily step counts) were evaluated at baseline, post-training, and post-16-week follow-up. The mediating effect of miRNA expression onto exercise-induced physical fitness change was determined by causal mediation analysis (CMA). Results Exercise significantly improved body fat and cardiorespiratory fitness in older people while maintaining muscle mass and strength, and augmented expression of c-miR-126, c-miR-146a, and c-miR-222 for up to 16 weeks post-training. Notably, older people in EX had substantially higher daily step counts than CON throughout the study even after the active training period. However, CMA revealed no significant indirect effect but a potential mediating effect of c-miR-21, but not the rest, onto the body composition, cardiorespiratory fitness, and lower limb strength. Conclusion An eight-week supervised MICT program promoted a higher level of physical activity up to 16 weeks post-training, which induces better cardiorespiratory fitness and resists decline in muscular measures. C-miRNA, especially c-miR-21, potentially mediates the training effect upon fitness.

Abstract The torrent frogs of the genus Amolops represent a great anuran diversification in southern China and Southeast Asia. Previous studies have shown that, the diversity of this genus still remains underestimated. During herpetological surveys from 2021 to 2022, several Amolops specimens were collected from the international border regions of southwestern Yunnan Province, China. Herein, we utilized molecular phylogenetic and morphological data to identify these specimens. Our findings indicate the presence of a separate and previously unknown lineage in the A. viridimaculatus group, which we formally describe as a new species. Furthermore, the specimen from Xishuangbanna National Nature Reserve clustered with A. vitreus from the paratype, supporting the morphological diagnosis. Therefore, we describe a new species and a new species record for China. Our study contributes to the species richness of the genus Amolops as well as the diversity of amphibians in China. Notably, our discovery brings the total number of Amolops species to 85 and the total number of torrent frog species known to occur in China to 53. In addition, our study further confirmed that Yunnan and Indochina Peninsula have similar faunal composition, implying that more studies are needed to achieve a complete understanding of the species diversity and distribution pattern.

Abstract Tibetan Buddhist monasteries are widely distributed throughout the Qinghai–Tibet Plateau and are significant locations for human–wildlife coexistence. Given the significance of their historical and contemporary social influence, it is crucial to ascertain whether there exists a non‐negligible correlation between these monasteries and their surrounding wildlife. In contrast to sacred mountains and lakes, there have been few studies that have examined the relationship between Tibetan Buddhist monasteries and wildlife communities. Using birds as a model ecological community, generalized mixed effect models (GLMM) were employed to examine relationships between the presence of Tibetan Buddhist monasteries and the number of individuals (relative abundance), avian species richness and diversity, using data from bird‐watching checklists (n = 262) obtained from the China Bird Report. Our models also included landscape‐wide environmental factors, including the ratio of woodland habitat, grassland habitat and open habitats, human footprint index, distance to nearest human occupation (villages), distance to water, altitude, time, presence of monastery and their interactions. Observation time was included as a model covariate. The presence of Tibetan Buddhist monasteries and the interactions between the presence of monasteries and environmental variables exhibit a significant correlation with the number of individual birds recorded (relative abundance), but not with observed species richness or diversity. Moreover, the relative abundance of birds was found to be significantly higher in areas around monasteries compared to those without monasteries. The abundance of species from eight families, namely Accipitridae, Corvidae, Columbidae, Passeridae, Leiothrichidae, Friningillidae, Musciapidae and Turidae, was significantly positively correlated with the presence of monasteries. Notably, the positive correlation of Accipitridae with the presence of celestial burial grounds around monasteries suggests a relationship between celestial burial practices and Accipitridae abundance at these sites. Our study reveals a significant positive correlation between Tibetan Buddhist monasteries and aspects of avian community composition in the eastern Qinghai–Tibetan Plateau, thus highlights their potential role in the preservation of avian populations in fragmented landscape habitats. We encourage the government and relevant national parks stakeholders to implement targeted management strategies that take into account the unique patterns of human–wildlife coexistence in these areas, and to leverage the cultural advantages of the region to promote ecological conservation. Read the free Plain Language Summary for this article on the Journal blog.

BackgroundThe effectiveness of Tuina therapy has been confirmed in treating pain of patients with cervical spondylosis (CS), however, its therapeutic mechanism is still unclear. This study aimed to observe the changes of regional brain activity following Tuina therapy in patients with painful CS based on resting-state functional magnetic resonance imaging (rs-fMRI) data.MethodsA total of 27 patients with CS and 27 healthy subjects (HCs) were enrolled in this study. All patients received Tuina therapy every 2 days for 2 weeks. The clinical manifestations of patients were evaluated by the Visual Analog Scale (VAS) and Neck Disability Index (NDI) before and after treatment. In addition, rs-fMRI data were collected and preprocessed in all patients before and after treatment, as well as HCs. HCs underwent a 1-time rs-fMRI scan, whereas CS patients underwent 2-times of rs-fMRI scan. The measure of regional homogeneity (ReHo) was calculated and compared between groups. Finally, relationships between altered brain regions and clinical characteristics were evaluated by Pearson’s correlation analysis.ResultsAfter Tuina therapy, VAS and NDI scores of patients decreased. Before treatment, CS patients showed higher ReHo values in the left middle temporal gyrus, left thalamus, right anterior and posterior cingulate gyrus, left inferior parietal gyrus and lower ReHo values in the right gyrus rectus when compared with HCs. After treatment, CS patients exhibited higher ReHo values in the left inferior temporal gyrus, right anterior and posterior cingulate gyrus, left inferior parietal gyrus and lower ReHo values in the right rectus gyrus when compared with HCs. CS patients after treatment demonstrated higher ReHo values in the left inferior occipital gyrus when compared with those before treatment. Positive correlations were found between ReHo values of the right rectus gyrus and VAS, NDI scores in CS patients before treatment. Differences of VAS scores between before and after treatment were negatively correlated with ReHo values of the left inferior temporal gyrus in CS patients after treatment.ConclusionThis study demonstrated the presence of asynchronous activity in certain brain regions in CS patients, which might be associated with pain and cervical spine dysfunction. Tuina therapy might modulate asynchronous activity of abnormal brain regions, which might contribute to the effectiveness of Tuina therapy in alleviating pain and cervical spine dysfunction in CS patients.

At present, with the rapid development of the internet and the gradual promotion of online collaborative learning, the social regulation of learning is receiving increasing attention, which involves socially shared metacognition, one facet of social metacognition. To date, social regulation of learning or socially shared metacognition have been widely studied using qualitative approaches. Although a variety of scales have been developed to measure metacognition in traditional individual learning, little work has been done to develop a scale to measure social metacognition in collaborative learning contexts. This study originally developed a social metacognition inventory consisting of 24 indicators by referring to the literature for assessing beliefs of other persons (BOP), awareness of other persons' thinking (AOPT), judgment of other persons' emotions (JOPE), co-regulation of each other's thinking (CREOT), and evaluation of other persons' thinking (EOPT). After EFA using 218 undergraduates' questionnaires of social metacognition in collaborative argumentation on a social psychological issue from a Sino-Foreign Cooperative Educational Institution, 17 indicators showed good factorability and reliability. After CFA using another 300 questionnaires on social metacognition in collaborative argumentation about the pandemic received from undergraduates who come from 52 countries in the International College of Education, among the 17 indicators derived from the first sample's EFA, three indicators had high correlation with others. Finally, based on the reviews of three experts, these three indicators were deleted. The remaining 14 indicators formed good construct validity with acceptable convergent and discriminant validities. In addition, the multi-group invariance test demonstrated that the structural model of the Social Metacognition Inventory has better configural invariance, which indicates that it can be generalized to other online collaborative argumentation contexts. The Social Metacognition Inventory can be used to quantify social metacognition in online collaborative argumentation when administering a large-scale experiment.

Abstract Background Chronic cough is a common symptom in patients post the coronavirus disease 2019 (COVID-19). In this study, we aimed to investigate the efficacy of inhaled corticosteroids (ICS) and the clinical characteristics of patients with post-COVID-19 chronic cough during the Omicron era. Methods An ambispective, longitudinal cohort study was conducted that included patients with post-COVID-19 who attended the respiratory clinic at our hospital between January 1, 2023, and March 31, 2023 with a complaint of persistent cough lasting more than 8 weeks. At 30 and 60 days after the first clinic visit for post-COVID-19 chronic cough, enrolled patients were prospectively followed up. We compared the changes in symptoms and pulmonary function between patients receiving ICS treatment (ICS group) and those not receiving ICS treatment (NICS group) at the two visits. Results A total of 104 patients with post-COVID-19 chronic cough were enrolled in this study (ICS group, n = 51; NICS group, n = 53). The most common symptoms accompanying post-COVID-19 chronic cough were sputum (58.7%, 61/104) and dyspnea (48.1%, 50/104). Seventy-one (82.6%, 71/86) patients had airway hyperresponsiveness, and 49 patients (47.1%, 49/104) were newly diagnosed with asthma. Most patients (95.2%, 99/104) exhibited improvement at 60 days after the first visit. The pulmonary function parameters of the patients in the ICS group were significantly improved compared to the baseline values (P

With the rapid development of generative artificial intelligence (GAI), the performance and usability of related tools, such as ChatGPT, have significantly improved. The advancement has fostered researchers to increasingly focus on students’ perceptions and application of the roles, functionalities, and interaction patterns of these tools in higher education. The present study adopted the draw-a-picture technique to explore the viewpoints and conceptions of undergraduates with different growth mindsets regarding the roles and functionalities of ChatGPT in learning. It also analyzed their interaction process with ChatGPT, especially their interaction skills and question types. The results showed that there were significant differences in the conceptions of “locations,” “learning content,” and “learning activities” of students with different growth mindsets. In the interaction process between undergraduates and ChatGPT, significant differences existed in the interaction skills and question types of students with different growth mindsets. Besides, students with different growth mindsets also had different learning achievements and critical thinking tendencies. The findings revealed the conceptions of students with different growth mindsets regarding the roles and functionalities of ChatGPT in learning, and also provided valuable insights for teachers. These findings are beneficial for educators to more accurately adjust and optimize the application of these tools in teaching activities based on students’ different growth mindsets.

This study reviewed the mobile technology-supported music education (MTSME) studies published in several academic databases, namely Scopus, WOS, ERIC, and RILM, from 2008-2019. Based on the technology-based learning model, the application domains, research issues, sample groups, research methods, adopted devices, and learning strategies were examined. In addition, visual categorization analysis was conducted to further analyze the keywords adopted in the studies. The results show that the number of MTSME studies increased in the time period. It was also found that a majority of studies mainly focused on learner perceptions (e.g. learning reception, learning attitude, and learning motivation). Tablet computers were the most frequently adopted mobile devices. In terms of learning strategies, guided learning, project-based learning, and inquiry-based learning were extensively used in the MTSME studies. In addition, the results of the keyword analysis showed three thematic clusters of MTSME studies, namely "mobile technology-supported teaching management and learning design," "developing appropriate music educational resources for different educational levels," and "mobile technology facilitated learning motivation in music education." According to the research results, several suggestions for future research are proposed.

For professional development programs, students' professional identity and learning perceptions and earning performances have been recognized as important educational objectives. Scholars have indicated that engaging students in social interactions facilitates their deep and diverse thinking, and hence to promoting their learning outcomes. However, in traditional instruction, students generally learn in an individual learning mode and hesitate to interact with peers and teachers. To address this problem, a mobile team-based competition approach to situate students in social interactions was proposed. To evaluate the effectiveness of the proposed approach, an experiment was conducted in a college curriculum design course. 88 students in a teacher education program participated in the experiment, including 42 in the experimental group learning with the proposed approach, and 46 in the control group learning with the conventional mobile individual competition approach. The results showed that the proposed approach outperformed the mobile individual competition approach in terms of learning attitude, collaborative learning tendency and professional identity. It is found that the students generally perceived the proposed learning approach to be an effective way to encourage knowledge sharing, promote collaboration awareness and improve learning attitudes and motivation.

This research reviewed the publications in the Scopus database in the museum-based mobile learning field based on the technology-based learning review model from 2008 to 2019. The aim of this study was to investigate trends in the field of museum-based mobile learning from the main journals and papers, including countries and areas, participants, research methods, learning domains, the orientation of adopted sensing or location-based technologies, learning strategies, and research topics. The cluster and co-occurrence analysis methods were used to analyze the main key terms and the relationships between them. Three thematic clusters, namely strategies and media for mobile-based museum learning, personalized supports for mobile-based museum learning, and applications of mobile-based museum learning were identified. The results show that inquiry-based learning was the most adopted learning strategy for higher education, while contextual mobile learning was the most frequently employed strategy for elementary school students; such extensively adopted mobile learning strategies as peer assessment, project-based learning, video sharing, and synchronous sharing were not adopted in the studies. In addition, an important finding in the literature of the main research purpose of museum-based mobile learning research is the affective domain, followed by the influence of learner characteristics and the effects of mobile learning.

This study investigates the roles, applications, and trends of concept map-supported education (CMSE) by reviewing the studies published from 1992 to 2020. Several dimensions, such as the media of concept maps, roles of concepts, sample groups, research methods, application domains, learning contexts, research foci, as well as the citation and co-citation relationships, are taken into account based on the technology-based learning model. From the review results, two thematic clusters were found, namely concept map-facilitated knowledge construction in collaborative learning, and concept map-based learning design to improve learning achievement and motivation. The research focused more on the applications in engineering courses, followed by science and language courses, and potential was also found in arts, business or management, and mathematics. Learning contexts for CMSE are mainly in-class activities, followed by online discussion activities; moreover, it was found that the number of applications in digital game-based learning contexts is increasing. In terms of research foci, more studies investigated learners' performance, attitude/motivation, and perceptions, while less emphasis was put on developing learners' skills, higher order thinking, as well as collaboration and communication competences. Accordingly, several suggestions for future research of CMSE are provided as a reference for researchers and policy makers.

Automated writing feedback supported by artificial intelligence (AI) techniques has attracted the attention of English as Foreign Language (EFL) researchers. However, there is insufficient evidence and inconsistent conclusions on the actual impacts of AI on students' writing skills. In addition, in the field of EFL writing, there are few studies which have integrated appropriate teaching strategies to promote the effectiveness of EFL writing teaching and learning. Furthermore, it remains unclear how learners can effectively benefit from AI technology-based English writing. To solve this problem, this study proposed a reflective thinking promotion mechanism-based AI-supported English writing (RTP-AIEW) approach to deepen learners' thinking and improve their EFL writing quality. To investigate the effectiveness of this learning approach, a quasi-experiment was conducted in two EFL writing classes in a university. One class (50 students) was the experimental group learning with the proposed RTP-AIEW approach, while the other class (53 students) was the control group learning with conventional AI-supported EFL writing. The results indicated that the proposed approach not only significantly improved the experimental group students' English writing performance, but also improved their self-efficacy and self-regulated learning, and significantly reduced their cognitive load. In addition, students' learning experience and perceptions are also discussed.

Abstract An efficient one-pot strategy for the facile synthesis of double boron–oxygen-fused polycyclic aromatic hydrocarbons (dBO-PAHs) with high regioselectivity and efficient skeletal editing is developed. The boron–oxygen-fused rings exhibit low aromaticity, endowing the polycyclic aromatic hydrocarbons with high chemical and thermal stabilities. The incorporation of the boron–oxygen units enables the polycyclic aromatic hydrocarbons to show single-component, low-temperature ultralong afterglow of up to 20 s. Moreover, the boron–oxygen-fused polycyclic aromatic hydrocarbons can also serve as ideal n-type host materials for high-brightness and high-efficiency deep-blue OLEDs; compared to single host, devices using boron–oxygen-fused polycyclic aromatic hydrocarbons-based co-hosts exhibit dramatically brightness and efficiency enhancements with significantly reduced efficiency roll-offs; device 9 demonstrates a high color-purity (Commission International de l’Eclairage CIEy = 0.104), and also achieves a record-high external quantum efficiency (28.0%) among Pt(II)-based deep-blue OLEDs with Commission International de l’Eclairage CIEy

Abstract Background Androgenetic alopecia (AGA), one of the most common types of hair loss, is associated with oxidative stress, inflammation and aging. Derinat, a transient receptor potential canonical channels (TRPCs) inhibitor, restrains TRPCs-mediated increase intracellular Ca2+ signaling, which initiates the skin aging process with intracellular reactive oxygen species (ROS) accumulation. This study investigated whether Derinat protected skin from oxidative stress-induced damage and aging, thus inhibiting AGA pathogenesis. Methods The lifespan of Caenorhabditis elegans was measured to examine the capacity of Derinat to oppose the oxidative stress induced-aging process, which drives the hair cycle from anagen to catagen phase. The experiments that used BALB/c-nu and C57BL/6 mice determined the effects of Derinat on hair cycle and oxidative stress in skin. To further apply Derinat to clinical study, the resulting relationship between AGA pathogenesis and TRPCs-regulated oxidative stress was confirmed using the bioinformatics approach. We consequently used the parameters of hair density, hair diameter, hair recovery and quality of life index to evaluate the effect of Derinat treatment on AGA subjects. Results Derinat restrained the oxidative stress induced-aging process sufficiently to extend the lifespan of worms. Derinat also changed the hair growth patterns of mice by maintenance of the hair cycle at the anagen phase. This efficacy was due to reduction of TRPCs-mediated ROS accumulation. Because the bioinformatics analysis found that AGA pathogenesis is associated with TRPCs-regulated oxidative stress and inflammation, treatment with Derinat in AGA subjects increased positive outcomes of oral medication while mitigating the impairment of AGA subjects’ quality of life. Conclusions Derinat restrains AGA pathogenesis and may provide a new therapeutic approach for treating AGA. ClinicalTrials.gov Identifier NCT05450861, https://register.clinicaltrials.gov , date of registration 07/11/2022. Trial registration ClinicalTrials.gov Identifier NCT05450861, https://register.clinicaltrials.gov , date of registration 07/11/2022