Writing requires both metacognitive support for cognitive monitoring to ensure clear and logical organization of ideas and motivation to sustain this process. These factors complicate writing instruction. In K-12 education, writing instruction often does not provide authentic learning experiences, resulting in low student motivation. Augmented reality (AR), which enhances real-world contexts with interactive virtual elements, has been shown to increase motivation, yet it may also introduce extraneous processing that does not effectively support metacognitive development during writing. By contrast, generative learning strategies (e.g., summarizing, imagining, and self-testing) are well recognized for fostering metacognition by prompting learners to actively organize and integrate knowledge to construct coherent mental representations, but they are often perceived as cognitively effortful and thus struggle to sustain student motivation. To overcome these limitations, this study integrates AR with generative learning strategies to propose a generative-learning-based AR (GLAR) approach. This approach aims to combine the motivational benefits of AR with the metacognitive benefits of generative learning strategies to support students' writing. Additionally, the mediating roles of motivation and metacognition in the effects of pedagogical approach on students' writing performance were examined. The effectiveness of the GLAR approach and the validity of the mediation model were evaluated in a quasi-experimental study involving 117 pupils in China. The students were randomly assigned to one of three groups: the experimental group, which received the GLAR intervention; control group 1, which received a generative learning (GL) intervention without AR; and control group 2, which received an AR-based learning (AL) intervention without GL. The following results were obtained. (1) The GLAR group outperformed the AL group in writing accuracy, organization, and metacognition. (2) The GLAR group outperformed the GL group in writing organization, creativity, motivation, and metacognition. (3) Compared with the AL approach, the GLAR approach improved writing accuracy, organization, and overall writing performance through metacognition as a mediator. (4) Compared with the GL approach, the GLAR approach improved writing creativity and overall writing performance through motivation as a mediator and improved writing organization through the sequential mediation of motivation and metacognition. The present results demonstrated both the effectiveness of the proposed GLAR approach in improving students' writing performance and the mediating role of motivation in its effect on metacognition.

Proficiency in written communication is an essential prerequisite for achievement in both academic settings and broader life contexts, yet many struggle with initiating writing and engaging in deep self-reflection and text revision. To address this challenge, this study integrated augmented reality (AR) technology with formative peer assessment (FPA)-based feedback methods to facilitate students’ writing performance and higher-order thinking (HOT). An empirical study was conducted with 110 Chinese pupils, who were randomly assigned to three groups: a group using formative peer assessment and teacher assessment in a traditional lecture-based learning mode (TFPA); a group using formative peer assessment and teacher assessment in an AR environment (AR-TFPA); and a group using formative peer assessment and automated writing evaluation (AWE) within an AR environment (AR-AFPA). The results indicated that the TFPA approach negatively impacted students’ writing performance and HOT compared to the AR-TFPA approach. While the AR-AFPA approach positively influenced writing performance compared to the AR-TFPA approach, no significant differences were observed in HOT. To better understand how FPA facilitates the writing process, we examined the mediating role of feedback types in the relationships between FPA approaches and writing performance/higher-order thinking. Mediation analysis revealed that the affective and cognitive characteristics of received feedback significantly mediated the relationships between the adopted approaches (i.e., TFPA vs. AR-TFPA; AR-AFPA vs. AR-TFPA) and improvements in writing performance and HOT. The present study aimed to provide insights into how integrating an AWE mechanism into AR-based FPA learning mode can potentially help learners’ writing performance and higher-order thinking.

While the pedagogical use of spherical video-based virtual reality (SVVR) has been evidenced effective in promoting learning outcomes, the impact of students' sense of presence in SVVR-enhanced learning on their motivation remains insufficiently understood, particularly in a cultural learning context. This study examined the relationship between sense of presence and motivation among Hong Kong high school students (n=92) who engaged with SVVR to support cultural learning in a Chinese language course. The findings reveal that students' sense of presence in the process of SVVR-enhanced learning was a significantly positive predictor of their internal, external, and self-regulated motivation. The results inform the pedagogical design of SVVR scenarios, highlighting that strategically cultivating heightened sense of presence can enhance students' motivation and, in turn, optimize learning outcomes.

The multimodal emotion recognition in conversation (ERC) task presents significant challenges due to the complexity of relationships and the difficulty in achieving semantic fusion across various modalities. Graph learning, recognized for its capability to capture intricate data relations, has been suggested as a solution for ERC. However, existing graph-based ERC models often fail to address the fundamental limitations of graph learning, such as assuming pairwise interactions and neglecting high-frequency signals in semantically-poor modalities, which leads to an over-reliance on text. While these issues might be negligible in other applications, they are crucial for the success of ERC. In this paper, we propose a novel framework for ERC, namely multimodal graph learning with framelet-based stochastic configuration networks (i.e., Frame-SCN). Specifically, framelet-based stochastic configuration networks, which employ 2D directional Haar framelets to extract both low- and high-pass components, are introduced to learn the unified semantic embeddings from multimodal data, mitigating prediction biases caused by an excessive reliance on text without introducing an unnecessarily large number of parameters. Also, we develop a modality-aware information extraction module that is able to extract both general and sensitive information in a multimodal semantic space, alleviating potential noise issues. Extensive experiment results demonstrate that our proposed Frame-SCN outperforms many state-of-the-art approaches on two widely used multimodal ERC datasets.

The role of sediment microbial communities in regulating the loss and retention of nutrients in aquatic ecosystems has been increasingly recognised. However, in the Great Lakes, where nutrient mitigation focuses on harmful algal blooms, there are limited studies examining the fundamental role of water/sediment microbes in nutrient biogeochemical cycling. Little is understood in this regard considering the increase in anthropogenic pressure on in-stream biological processes impacting nutrient flux to lakes. In this study, metagenomic and metatranscriptomic approaches were used to investigate the microbial community and gene regulation. The study focused on nitrogen (N) metabolism in a nutrient-polluted watershed of Lake Erie in southwestern Ontario, Canada. Nutrients and microbial analyses of water and sediments were collected in 2020 and 2021 from Sturgeon Creek headwaters to the nearshore of Lake Erie. Results showed no significant shifts in community structure with nutrient concentrations or land use. Metabolically, active genes involved in denitrification (consisting of 32-53% of N metabolic transcripts) showed the highest expression within agricultural and wetland dominant locations. Based on active gene expression patterns, the urbanised location coinciding with peak nitrate (NO3-) concentrations showed the greatest potential for nitrous oxide (N2O) emission and nitrogen loss along this transect. In contrast to denitrification, direct nitrification (5-21% of N metabolic transcripts) increased two-fold approaching downstream and nearshore lake locations. Across this river-lake corridor, expression of key functional genes associated with N transformation showed strong correlation with the change in concentrations of aqueous NO3- and nitrite (NO2-) and the ratio of NO2-/NO3-. Our findings demonstrated a clear link between sediment microbial metabolism and overlying water chemistry in this lotic system. We suggest that future studies assessing nutrient mitigation consider sediment biogeochemical processes and N-metabolising bacteria, and their fundamental role and cooperative relationship with nutrient and hydrological dynamics of overlying waters.

Immersive technology has received extensive attention in both L1 and L2 writing education. Its unique capabilities to offer virtual experiences alongside real-world experiences can create authentic learning environments that support students' experiential learning and enable the observation of events beyond the confines of traditional classrooms. However, there has been a lack of systematic analysis of recent publications in this area. To address this gap and improve the research and practice of writing education, a systematic review was conducted to examine the literature on immersive technology in writing education (ITWE). In this review, 37 articles (30 SSCI-indexed papers from the Web of Science database and seven additional articles from a meticulous forward-backward scan of the references of these studies) were analyzed. The analysis focused on theoretical foundations, participants, types of adopted immersive technology, methods, and research findings. Our review shows that although most studies revealed positive outcomes, a significant number lacked a solid theoretical foundation to interpret the findings in many ITWE studies. Moreover, there is a pressing need for further research on ITWE in middle schools, especially within the realm of English as a foreign language courses. In addition, the review identified some potential negative effects of ITWE, which were often attributed to poorly designed instructional activities. It was observed that conventional research methods like questionnaire surveys and interviews, were commonly used in ITWE. However, the potential benefits of emerging areas like learning analytics and AI in education (e.g., logged actions, facial emotion detection, electroencephalogram (EEG) analysis were rarely used. The article is concluded with the current research evidence on emerging directions and opportunities for future trends in empowering writing education with immersive technology.

Multimodal sentiment analysis has emerged as a critical research area, aiming to analyze complex emotional states using data from multiple sources. While conventional approaches focus on sophisticated fusion techniques to integrate multimodal information, they often struggle with distributional variances among modalities. Bridging this modal distribution gap and optimizing the utilization of modality-sensitive information are crucial challenges. In response, our study presents GM2RC, an innovative framework designed to enhance modality representations for improved multimodal fusion efficacy. GM2RC adopts a two-step approach: intra-modal refinement and intermodal complementation. In the first step, it enhances distinct features within each modality, while the second step fosters the assimilation of shared information across modalities through pairwise learning, thus mitigating inter-modal disparity. This comprehensive approach leads to more accurate sentiment analysis by creating more robust multimodal representations. Further rigorous experiments demonstrate significant boosts of 1.59% in accuracy and 2.03% in w-F1 on the IEMOCAP dataset, and 1.65% in accuracy and 2.73% in w-F1 on the MELD dataset, showcasing the superiority of our proposed GM2RC model.

Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs' performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method.

Cultivated peanut (Arachis hypogaea L.) is a key oil- and protein-providing legume crop of the world. It is full of nutrients, and its nutrient profile is comparable to that of other nuts. Peanut is a unique plant as it showcases a pegging phenomenon, producing flowers above ground, and after fertilization, the developing peg enters the soil and produces seeds underground. This geocarpic nature of peanut exposes its seeds to soil pathogens. Peanut seeds are protected by an inedible pericarp and testa. The pericarp- and testa-specific promoters can be effectively used to improve the seed defense. We identified a pericarp- and testa-abundant expression gene (AhN8DT-2) from available transcriptome expression data, whose tissue-specific expression was further confirmed by the qRT-PCR. The 1827bp promoter sequence was used to construct the expression vector using the pMDC164 vector for further analysis. Quantitative expression of the GUS gene in transgenic Arabidopsis plants showed its high expression in the pericarp. GUS staining showed a deep blue color in the pericarp and testa. Cryostat sectioning of stained Arabidopsis seeds showed that expression is only limited to seed coat (testa), and staining was not present in cotyledons and embryos. GUS staining was not detected in any other tissues, including seedlings, leaves, stems, and roots, except for some staining in flowers. Under different phytohormones, this promoter did not show an increase in expression level. These results indicated that the AhN8DT-2 promoter drives GUS gene expression in a pericarp- and testa-specific manner. The identified promoter can be utilized to drive disease resistance genes, specifically in the pericarp and testa, enhancing peanut seed defense against soil-borne pathogens. This approach has broader implications for improving the resilience of peanut crops and other legumes, contributing to sustainable agricultural practices and food security.

In the digital education landscape, cross-modal retrieval (CMR) from multimodal educational slides represents a significant challenge, particularly because of the complex nature of academic content, which includes images, diagrams, equations, and tables across various subjects such as mathematics and biology. Current CMR systems are primarily designed for “(natural) image to text” interactions (or vice versa) and inadequately address real-world educational scenarios. This study presents EduCross, a novel framework devised to enhance CMR within multimodal educational slides, which is a domain in which traditional retrieval systems fall short. Recognizing the imperative for a system that is tailored to the educational context, EduCross integrates dual adversarial bipartite hypergraph learning, harnessing the capabilities of generative adversarial networks with figure-text dual channels. This powerful combination facilitates robust bidirectional mapping, allowing for the precise association of figures with their descriptive spoken language segments and ensuring a comprehensive CMR experience. Specifically, we develop framelet-based deep bipartite hypergraph neural networks that effectively manage the high-order relationships between diverse educational content types and various types of slide figures. Our experimental results underscore the superior performance of EduCross, demonstrating its effectiveness through the use of the real Multimodal Lecture Presentations dataset that mirrors authentic educational settings. These outcomes highlight the significant advancements of EduCross over existing methods, marking a leap forward in the accurate retrieval of multimodal educational content.

The ability to communicate effectively in writing and produce clear and cohesive text is a necessary skill in both educational settings and the workplace, yet many young students struggle to organize their thoughts and engage in deep thinking. To address these challenges, an augmented reality (AR) application titled “Explore Wild Animals” has been used to help students organize information; however, it may not accommodate different cognitive styles. Integrating formative peer assessment (FPA) strategy into AR-based instruction can enhance knowledge construction and address diverse cognitive needs. This study, conducted from May to June 2023, empirically investigates the effects of FPA in an AR environment on the writing performance of learners with field-independent (FI) and field-dependent (FD) cognitive styles. A total of 89 fifth-grade pupils from China were randomly assigned to two groups: one group adopting FPA in an AR environment (AR-FPA), and the other group adopting FPA in a conventional PowerPoint (PPT) version 2410 environment (FPA). The results of a two-way analysis of covariance (ANCOVA) indicate that the AR-FPA group outperformed the FPA group in writing performance. Specifically, FI learners benefitted more from the AR-FPA approach, while FD learners performed better with the FPA approach. However, multiple linear regression analysis reveals that the peer feedback quality and features showed little to no significant correlation with feedback providers’ writing performance, regardless of cognitive style. These results highlight the effectiveness of integrating AR and FPA in enhancing educational outcomes, providing practical insights for promoting the sustainability of technology-enhanced learning and teaching practices.

Urban land use is central to urban planning. With the emergence of urban big data and advances in deep learning methods, several studies have leveraged graph convolutional networks (GCNs) with local functional characteristics from points of interest data and spatial features from flow data to infer urban land use. However, these studies cannot distinguish spatial interaction and spatial dependence in terms of conceptualization and modeling mechanisms and overlook the inadequacy of GCNs in modeling spatial interaction. This study proposes a novel framework-a heterogeneous graph convolutional network (HGCN)-to explicitly account for the spatial demand and supply components embedded in spatial interaction data. Several experiments, including 19 different models and datasets from Shenzhen and London, were conducted to validate the proposed framework and its generalizability within the same and different spatial contexts. The HGCN can distinguish heterogeneous mechanisms in supply- and demand-related modalities of spatial interactions, incorporating both spatial interaction and spatial dependence for urban land-use inference. Empowered by HGCN, we found that spatial interaction features play a distinctively crucial role in urban land-use inference compared to local attributes and spatial dependence features. In addition, our findings highlight the superiority of HGCN-based models in boosting performance and enhancing model transferability.

Augmented reality (AR) has been regarded as a useful tool in writing education, with the goal of enhancing students’ learning. However, questions still exist about the consistency of student motivation and their writing performance when participating in educational activities driven by AR. This study focused on AR-based writing courses, employing k-means cluster analysis to identify different writing improvement profiles by examining the pre- and post-test scores of 87 primary school students. The study delved into differences in student motivation levels and the frequency of learning behaviors (e.g., disorderly behaviors, raising hands). The analysis identified five different writing improvement profiles, categorized as the Advanced, High-achiever, Persistent, Indifferent, and Diligent groups. Different levels of motivation and learning behavior frequency were observed among these groups. The Advanced, High-achiever, and Diligent groups showed significant improvements in certain dimensions of writing motivation (e.g., curiosity, boredom, competition) in the AR-based writing courses, while such improvements were not observed in the Persistent and Indifferent groups. Additionally, the Indifferent group demonstrated a lack of motivation toward AR-based writing courses, evidenced by increased technical difficulties and disorderly behaviors. Variances were also found in students’ sequential behavior patterns among the different groups. These findings shed light on the dynamics of motivation and learning behaviors among students in different writing improvement profiles in AR-based writing courses, offering valuable insights for a more comprehensive understanding of the dynamics at play in such educational settings.

Understanding commuter traffic in transportation networks is crucial for sustainable urban planning with commuting generation forecasts operating as a pivotal stage in commuter traffic modeling. Overcoming challenges posed by the intricacy of commuting networks and the uncertainty of commuter behaviors, we propose MetroGCN, a metropolis-informed graph convolutional network designed for commuting forecasts in metropolitan areas. MetroGCN introduces dimensions of metropolitan indicators to comprehensively construct commuting networks with diverse socioeconomic features. This model also innovatively embeds topological commuter portraits in spatial interaction through a multi-graph representation approach capturing the semantic spatial correlations based on individual characteristics. By incorporating graph convolution and temporal convolution with a spatial–temporal attention module, MetroGCN adeptly handles high-dimensional dependencies in large commuting networks. Quantitative experiments on the Shenzhen metropolitan area datasets validate the superior performance of MetroGCN compared to state-of-the-art methods. Notably, the results highlight the significance of commuter age and income in forecasting commuting generations. Statistical significance analysis further underscores the importance of anthropic indicators for commuting production forecasts and environmental indicators for commuting attraction forecasts. This research contributes to technical advancement and valuable insights into the critical factors influencing commuting generation forecasts.

Writing is a fundamental skill linked closely with academic achievement, day‐to‐day communication, formal negotiations, and more. However, due to their lack of contextual experience, learning to write has been a demanding and complex cognitive process for most learners. As a result, learners struggle to exhibit positive learning behaviours and cognitive engagement in writing, let alone embrace deep and autonomous learning. To solve these problems, in the present study, a spherical video‐based virtual reality (SVVR) learning environment is developed to provide students with a contextual learning experience. Moreover, a peer feedback strategy is used to guide students in deep learning in writing. A quasi‐experimental study was conducted to verify the effectiveness of the proposed approach. A total of 79 students from one primary school in southeast China were recruited. The students were assigned to either the experimental group (EG) exposed to the peer‐feedback‐based SVVR (PF‐SVVR) approach or the control group (CG) exposed to the conventional SVVR (C‐SVVR) approach. The results show that PF‐SVVR had more positive effects than C‐SVVR in terms of students' writing performance, cognitive engagement and autonomous learning tendency. In addition, the PF‐SVVR approach was found to be more beneficial for triggering deep learning in writing than the C‐SVVR approach. This study further found that students in the PF‐SVVR group tended to exhibit less disorderly behaviours than those in the C‐SVVR group. Our study contributes to the prior literature by exploring the educational potential of the PF approach in the context of SVVR‐enabled writing learning. Practitioner notesWhat is already known about this topic Peer feedback approach provides opportunities for writing learners to regulate their longer‐term cognitive and behavioural competencies. Spherical video‐based virtual reality (SVVR) not only provides an authentic experiential learning context for learners, but it also significantly reduces the cost and the need for the high‐tech capabilities of traditional VR. What this paper adds A peer feedback‐based SVVR approach is proposed to promote students in learning to write. It was found that students' quality of learning to write can be improved by applying the PF‐SVVR approach in writing courses. Implications for practice and/or policy It is worth promoting the application of the peer feedback strategy and SVVR in school settings. The PF‐SVVR approach is useful for promoting young writing learners' cognitive engagement, autonomous learning tendency and deep learning. Further investigations on the effects of employing the PF‐SVVR approach in writing, with anonymous strategies in the process of giving or receiving peer feedback, are expected.

Objective: The significant impact of fatigue on the lives of patients with chronic conditions has demanded a response. One response has been the development and testing of self-management programs. Little is known about what these programs have in common or how they differ. This scoping review compared the key components of fatigue self-management programs. Methods: Scoping review methodology was employed. Databases of CINAHL, Academic Search Premier, PsycINFO, Cochrane and Medline were searched to identify relevant sources. Results: Included fatigue programs were compared using a three-component framework: 1) self-management strategies; 2) active patient participation; and 3) self-management support. Although all programs included some aspects of these components, the extent varied with only a few domains of these components found across all programs. Conclusion: The three self-management components employed in this study showed potential benefits in identifying similarities and differences across fatigue programs with comparable and distinct underlying theories. This three-component framework could facilitate identification of domains associated with positive outcomes. Practice implications: It is essential that authors of programs provide detailed descriptions to enable inter-program comparison. The three-component framework chosen for this review was capable of describing and comparing fatigue self-management programs, paving the way for more effective interventions.

Chirality of structure is prevalent in nature, which is usually manifest as the inability to coincide with its mirror structure by translation or rotation. Because spectrum detection techniques can reflect the abundant information of the interaction between light and matter, chiroptical spectroscopy has become a common method to investigate and identify chiral substances. Chiral metasurface can be artificially designed to achieve strong circular dichroism (CD), which is a research hot spot in the fields of matter detection and sensing. We propose a terahertz chiral metasurface that can dynamically control the CD response while achieving high sensing performance. The metasurface is based on a flexible material, and its top and bottom surfaces are J-shaped metal structures with four-fold rotational symmetry. The simulation results show that the chiral metasurface can produce a high CD value up to 0. 805 at 0. 760 THz. And by equally proportional stretching in two-dimensions, the CD peak redshifts from 0. 760 THz to 0. 650 THz maintaining a strong CD signal. Meanwhile, its sensing sensitivity can reach 327 GHz/ RIU, and its chiral response and sensing performance are well maintained during the stretching process with the relative stretching deformation up to 20%. The designed chiral metasurface has potential applications in the field of dynamic multifunctional devices and wearable sensors.

Adeno-associated virus (AAV) is a widely used gene therapy vector. The intact packaged genome is a critical quality attribute and necessary for an effective therapeutic. In this work, charge detection mass spectrometry (CDMS) was used to measure the molecular weight (MW) distribution for the genome of interest (GOI) extracted from recombinant AAV (rAAV) vectors. The measured MWs were compared to sequence masses for a range of rAAV vectors with different GOIs, serotypes, and production methods (Sf9 and HEK293 cell lines). In most cases, the measured MWs were slightly larger than the sequence masses, a result attributed to counterions. However, in a few cases, the measured MWs were significantly smaller than the sequence masses. In these cases, genome truncation is the only reasonable explanation for the discrepancy. These results suggest that direct analysis of the extracted GOI by CDMS provides a rapid and powerful tool to evaluate genome integrity in gene therapy products.