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Among all the learning resources within MOOCs such as video lectures and homework, the discussion forum stood out as a valuable platform for students’ learning through knowledge exchange. However, peer interactions on MOOC discussion forums are scarce. The lack of interactions among MOOC learners can yield negative effects on students’ learning, causing low participation and high dropout rate. This research aims to examine the extent to which the deep-learning-based natural language generation (NLG) models can offer responses similar to human-generated responses to the learners in MOOC forums. Specifically, under the framework of social support theory, this study has examined the use of state-of-the-art deep learning models recurrent neural network (RNN) and generative pretrained transformer 2 (GPT-2) to provide students with informational, emotional, and community support with NLG on discussion forums. We first trained an RNN and GPT-2 model with 13,850 entries of post-reply pairs. Quantitative evaluation on model performance was then conducted with word perplexity, readability, and coherence. The results showed that GPT-2 outperformed RNN on all measures. We then qualitatively compared the dimensions of support provided by humans and GPT-2, and the results suggested that the GPT-2 model can comparably provide emotional and community support to human learners with contextual replies. We further surveyed participants to find out if the collected data would align with our findings. The results showed GPT-2 model could provide supportive and contextual replies to a similar extent compared to humans.

MicroRNAs (miRNAs), a class of endogenous small noncoding RNAs in eukaryotes, have been recognized as significant regulators of gene expression through post-transcriptional mechanisms. To date, >2000 miRNAs have been identified in the human genome, and they orchestrate a variety of biological and pathological processes. Disruption of miRNA levels correlates with many diseases, including diabetes mellitus, a complex multifactorial metabolic disorder affecting >400 million people worldwide. miRNAs are involved in the pathogenesis of diabetes mellitus by affecting pancreatic β-cell functions, insulin resistance, or both. In this review, we summarize the investigations of the regulatory roles of important miRNAs in diabetes, as well as the potential of circulating miRNAs as diagnostic markers for diabetes mellitus.

One important aspect of digital citizenship, defined as ＂the norms of appropriate, responsible behavior with regard to technology use,＂ is to reinforce ethical online behavior and discourage risky conduct. The purpose of this study was to examine the effects of parental involvement and socioeconomic status on teens digital citizenship, which includes: digital access, digital etiquette, and digital safety. A research–based path model was developed to explain causal relationships between these factors. This model was tested based on data gathered from 270 teens and their parents. The results provided significant evidence in support of the following hypothesized model: teens whose parents were more involved in their technology usage and online activities have higher reported levels of digital etiquette and digital safety; teens whose parents have better socioeconomic status have higher level of digital access, digital etiquette and digital safety. Overall, parental involvement and socioeconomic status was found to positively predict teen digital citizenship. The study findings have the potential for guiding future model development and to further influence positive social change by supporting parents and educators to promote online safety and digital citizenship development.

Both Gram-negative and Gram-positive bacteria can release vesicle-like structures referred to as bacterial extracellular vesicles (BEVs), which contain various bioactive compounds. BEVs play important roles in the microbial community interactions and host-microbe interactions. Markedly, BEVs can be delivered to host cells, thus modulating the development and function of the innate immune system. To clarify the compositions and biological functions of BEVs, we need to collect these vesicles with high purity and bioactivity. Here we propose an isolation strategy based on a broad-spectrum antimicrobial epsilon-poly-L-lysine (ϵ-PL) to precipitate BEVs at a relatively low centrifugal speed (10,000 × g). Compared to the standard ultracentrifugation strategy, our method can enrich BEVs from large volumes of media inexpensively and rapidly. The precipitated BEVs can be recovered by adjusting the pH and ionic strength of the media, followed by an ultrafiltration step to remove ϵ-PL and achieve buffer exchange. The morphology, size, and protein composition of the ϵ-PL-precipitated BEVs are comparable to those purified by ultracentrifugation. Moreover, ϵ-PL-precipitated BEVs retained the biological activity as observed by confocal microscopy studies. And THP-1 cells stimulated with these BEVs undergo marked reprogramming of their transcriptome. KEGG analysis of the differentially expressed genes showed that the signal pathways of cellular inflammatory response were significantly activated. Taken together, we provide a new method to rapidly enrich BEVs with high purity and bioactivity, which has the potential to be applied to BEVs-related immune response studies.

 This study investigates how secondary students engage with an AI teachable agent (TA) during mathematics learning, with particular focus on learners whose performance declined after interacting with the TA system. Using a mixed-methods design, we analyzed dialogue logs from two subgroups: a Declined Group (DG; n = 206), whose post-test scores decreased, and an Improved Group (IG; n = 327), whose scores increased. Analyses examined interaction modes and behavioral, emotional, and cognitive engagement. Passive interaction was most prevalent in DG (36 %), whereas IG more frequently demonstrated constructive interaction (62.78 %). DG exhibited high variability in behavioral engagement: although they completed more sessions and generated more utterances per session, their completion rate (0.35) was lower than that of IG (0.58). Regarding emotional engagement, boredom was the most frequent non-neutral emotion in DG (50.4 %) and tended to rise as sessions progressed, whereas IG expressed more positive (30 %) than negative emotions (17.94 %). For cognitive engagement, most students displayed surface-level knowledge acquisition with limited application to novel or complex tasks. Notably, within DG, greater behavioral activity and positive emotions were sometimes associated with lower learning gains, often when such activity reflected off-task dialogue or superficial goal completion. These findings highlight classroom challenges in AI-supported learning and suggest design implications for TAs that scaffold proactive interaction, detect emerging boredom, and redirect high-volume yet low-yield behaviors toward meaningful engagement.

Anaerobic digestion is an important technology for energy recovery from organic waste. However, methanogenesis is restricted by some barriers, such as the low-speed bottleneck of interspecies electron transfer (IET), the low hydrogen partial pressure limitation, trace element deficiency, etc., resulting in poor system stability and low methane production. Recently, multiple iron accelerants have been employed to overcome the above challenges and have been proven effective in enhancing methanogenesis. This study reviews the effects of iron accelerants (Fe0, Fe3O4 and magnetite, Fe2O3 and hematite, iron salts and other iron accelerants) on anaerobic digestion in terms of methane production, process stability and the microbial community and elaborates the mechanisms of iron accelerants in mediating the direct interspecies electron transfer (DIET) of the syntrophic methanogenic community, strong reducibility promoting methanogenesis, provision of nutrient elements for microorganisms, etc. The potential engineering application of iron accelerants in anaerobic digestion and the current research advances regarding the environmental impacts and the recovery of iron accelerants are also summarized. Although iron accelerants exhibit positive effects on anaerobic digestion, most of the current research focuses on laboratory and small-scale investigations, and its large-scale engineering application should be further verified. Future research should focus on elucidating the mechanisms of iron accelerants for enhancing anaerobic digestion, developing diverse application methods for different types of anaerobic systems, optimizing large-scale engineering applications, and exploring the environmental impacts and high-efficiency recovery strategies of iron accelerants.

Although image-generative AI (GAI) has sparked heated discussion among engineers and designers, its role in CAD (computer-aided design) education, particularly during the conceptual design phase, remains not sufficiently explored. To address this, we examined the integration of GAI into the early stages of design in a CAD class. Specifically, we conducted an in-class workshop introducing Midjourney for conceptual design and released a home assignment on mood board design for hands-on GAI design practice. Twenty students completed the workshop and assignment from a CAD class at a research-intensive university. We collected and analyzed data from surveys, students' prompts, and design artifacts to explore their perceptions of GAI, prompt behaviors, and design creativity and conducted a correlation analysis between these variables. After the workshop, students significantly rated GAI as more useful and user-friendly in design and found it more supportive in terms of design efficiency and aesthetics, while we did not find a significant difference in design creativity. By analyzing 365 prompts used for completing home tasks, we identified three types of prompt behaviors: generation, modification, and selection, as well as classified three types of workflows: exploration, two-step, and multi-step. The correlation analysis showed that design creativity changes significantly and positively correlated with their prompt behaviors. Students with more multi-step prompts produced more creative artifacts based on the instructors' evaluation. This exploratory study offered valuable insights into the integration of GAI in CAD education and suggested potential directions for future GAI curricula and tools in design education.

Proper exposure settings are crucial for modern machine vision cameras to accurately convert light into clear images. However, traditional auto-exposure solutions are vulnerable to illumination changes, splitting the continuous acquisition of unsaturated images, which significantly degrades the overall performance of underlying intelligent systems. Here we present the neuromorphic exposure control (NEC) system. This system effectively alleviates the longstanding saturation problem at its core by exploiting bio-principles found in peripheral vision to compute a trilinear event double integral (TEDI). This approach enables accurate connections between events and frames in the physics space for swift irradiance prediction, ultimately facilitating rapid control parameter updates. Our experimental results demonstrate the remarkable efficiency, low latency, superior generalization capability, and bio-inspired nature of the NEC in delivering timely and robust neuromorphic synergy for lighting-robust machine vision across a wide range of real-world applications. These applications encompass autonomous driving, mixed-reality, and three-dimensional reconstruction. Proper exposure settings are crucial for modern machine vision cameras. This work develops neuromorphic exposure control using peripheral-vision inspired processing to solve the problem, enhancing performance in applications like autonomous driving and medical imaging.

Knowledge Building has been advanced as a pedagogy of engaged learning where students identify as a community whose purpose is to advance their shared ideas. This approach, which has been studied for three decades (Scardamalia & Bereiter, in: K. Sawyer (ed) Cambridge handbook of the learning sciences, Cambridge University Press, 2014), includes cognitive, social constructivist, and emotional elements (Zhu et al. in User Modeling and User-Adapted Interaction, 29: 789–820, 2019b). This paper investigates how refining Knowledge Building activities based on students’ feedback impacts their social, cognitive, and emotional engagement. Using a design-based research method, we refined successive course activities based on feedback from 23 Masters of Education students. With successive iterations, we found that the density of students’ reading networks increased; they theorized more deeply, introduced more authoritative resources, and made greater efforts to integrate ideas within the community knowledge base. As well, their level of negative affect decreased. These findings suggest that soliciting students’ input into course design can benefit their engagement and disposition toward learning, with implications for curriculum design.

Constructing scientific arguments is an important practice for students because it helps them to make sense of data using scientific knowledge and within the conceptual and experimental boundaries of an investigation. In this study, we used a text mining method called Latent Dirichlet Allocation (LDA) to identify underlying patterns in students written scientific arguments about a complex scientific phenomenon called Albedo Effect. We further examined how identified patterns compare to existing frameworks related to explaining evidence to support claims and attributing sources of uncertainty. LDA was applied to electronically stored arguments written by 2472 students and concerning how decreases in sea ice affect global temperatures. The results indicated that each content topic identified in the explanations by the LDA— “data only,” “reasoning only,” “data and reasoning combined,” “wrong reasoning types,” and “restatement of the claim”—could be interpreted using the claim–evidence–reasoning framework. Similarly, each topic identified in the students’ uncertainty attributions— “self-evaluations,” “personal sources related to knowledge and experience,” and “scientific sources related to reasoning and data”—could be interpreted using the taxonomy of uncertainty attribution. These results indicate that LDA can serve as a tool for content analysis that can discover semantic patterns in students’ scientific argumentation in particular science domains and facilitate teachers’ providing help to students.