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This study explores the effectiveness of chatbots empowered by generative artificial intelligence (GAI) in assisting university students’ creative problem-solving (CPS). We used quasi-experiments to compare the performance of dialogue dynamics, learner perceptions, and practical competencies in CPS during students’ interactions with: (1) a GAI chatbot, and (2) their peers. In total, 80 postgraduate students participated. The assigned CPS task was the creation of an innovative research proposal. We found that there were significant differences in the dialogic exchanges observed between the two types of interaction. Student-GAI chatbot interactions featured more knowledge-based dialogue and elaborate discussions, with less subjective expression compared to student-peer interactions. Notably, students contributed significantly less dialogue when interacting with a GAI chatbot than they did during peer interactions. The dialogic exchanges arising from student-GAI chatbot interactions tended to follow distinct patterns, while those from student-peer interactions were less predictable. The students perceived interacting with a GAI chatbot as more useful and easier than interacting with peers. Furthermore, they exhibited higher intention levels when utilising a GAI chatbot to tackle the CPS task compared to engaging in discussions with their peers. Ultimately, practical performance was significantly enhanced through interactions with a GAI chatbot. This study implies that the prudent use of GAI-based techniques can facilitate university students’ learning achievement.

Photocatalysis has emerged a promising strategy to remedy the current energy and environmental crisis due to its ability to directly convert clean solar energy into chemical energy. Bismuth tungstate (Bi 2 WO 6 ) has been shown to be an excellent visible light response, a well-defined perovskite crystal structure, and an abundance of oxygen atoms (providing efficient channels for photogenerated carrier transfer) due to their suitable band gap, effective electron migration and separation, making them ideal photocatalysts. It has been extensively applied as photocatalyst in aspects including pollutant removal, carbon dioxide reduction, solar hydrogen production, ammonia synthesis by nitrogen photocatalytic reduction, and cancer therapy. In this review, the fabrication and application of Bi 2 WO 6 in photocatalysis were comprehensively discussed. The photocatalytic properties of Bi 2 WO 6 -based materials were significantly enhanced by carbon modification, the construction of heterojunctions, and the atom doping to improve the photogenerated carrier migration rate, the number of surface active sites, and the photoexcitation ability of the composites. In addition, the potential development directions and the existing challenges to improve the photocatalytic performance of Bi 2 WO 6 -based materials were discussed.

This study sought to explore the potential causal relationships among immune cell traits, Guillain-Barre syndrome (GBS) and metabolites. Employing a two-sample Mendelian randomization (MR) approach, the study investigated the causal associations between 731 immune cell traits, 1400 metabolite levels and GBS leveraging summary-level data from a genome-wide association study (GWAS). To ensure the reliability of our findings, we further assessed horizontal pleiotropy and heterogeneity and evaluated the stability of MR results using the Leave-one-out method. This study revealed a causal relationship between CD3 on activated & secreting Tregs and GBS. Higher CD3 on activated and secreting Regulatory Tregs increased the risk of GBS (primary MR analysis odds ratio (OR) 1.31/SD increase, 95% confidence interval (CI) 1.08-1.58, = 0.005). There was no reverse causality for GBS on CD3 on activated & secreting Tregs ( = 0.36). Plasma metabolite N-Acetyl-L-Alanine (ALA) was significantly positively correlated with GBS by using the IVW method (OR = 2.04, 95% CI, 1.26-3.30; = 0.00038). CD3 on activated & secreting Tregs was found to be positively associated with ALA risk (IVW method, OR, 1.04; [95% CI, 1.01-1.07], = 0.0078). Mediation MR analysis indicated the mediated proportion of CD3 on activated & secreting Tregs mediated by ALA was 10% (95%CI 2.63%, 17.4%). In conclusion, our study identified a causal relationship between the level of CD3 on activated & secreting Tregs and GBS by genetic means, with a considerable proportion of the effect mediated by ALA. In clinical practice, thus providing guidance for future clinical research.

Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly to acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces the problems of serious noise, sidelobes, and clutters, which will bring down the accuracy of SAR target classification. Different from the MF-based result, a sparse image shows better quality with less noise and higher image signal-to-noise ratio (SNR). Therefore, theoretically using it for target classification will achieve better performance. In this paper, a novel contrastive domain adaptation (CDA) based sparse SAR target classification method is proposed to solve the problem of insufficient samples. In the proposed method, we firstly construct a sparse SAR image dataset by using the complex image based iterative soft thresholding (BiIST) algorithm. Then, the simulated and real SAR datasets are simultaneously sent into an unsupervised domain adaptation framework to reduce the distribution difference and obtain the reconstructed simulated SAR images for subsequent target classification. Finally, the reconstructed simulated images are manually labeled and fed into a shallow convolutional neural network (CNN) for target classification along with a small number of real sparse SAR images. Since the current definition of the number of small samples is still vague and inconsistent, this paper defines few-shot as less than 20 per class. Experimental results based on MSTAR under standard operating conditions (SOC) and extended operating conditions (EOC) show that the reconstructed simulated SAR dataset makes up for the insufficient information from limited real data. Compared with other typical deep learning methods based on limited samples, our method is able to achieve higher accuracy especially under the conditions of few shots.

Thoracoscopic surgery, a representative minimally invasive approach in thoracic surgery, is increasingly employed. However, postoperative pain remains a significant barrier to recovery and quality of life. We aimed to quantitatively analyze research on postoperative pain following thoracoscopic surgery over the past decade; to identify the current research landscape, research hotspots, and future trends; and to provide a reference for future studies. Publications on postoperative pain following thoracoscopic surgery from 2015 to 2024 were retrieved from the Web of Science database. CiteSpace, VOSviewer, and Bibliometrix were used to analyze publication trends, journals, authors, institutions, countries, regions, and keywords. The number of publications increased steadily between 2015 and 2024. China and the United States were the leading contributors, forming a global collaboration network with Europe. Major contributing institutions included Tongji Medical College, Guangzhou Medical University, and the Cleveland Clinic. Leading authors included Jianxing He, Hengrui Liang, and Ali Alagoz. Research areas spanned thoracic surgery, pain medicine, and anesthesiology. Frequently cited keywords were \"pain,\" \"rapid recovery,\" \"analgesia,\" \"pain management,\" \"paravertebral nerve block,\" and \"erector spinae plane block.\" Key research themes included multimodal analgesia, chronic pain, and quality of life. Research on postoperative pain after thoracoscopic surgery has evolved from clinical observation to multimodal analgesia, making advancements toward precision medicine and long-term outcomes. Current research hotspots include optimizing analgesic strategies, understanding pain mechanisms, refining surgical techniques, and promoting rapid recovery. Promising areas include regional analgesia techniques, liposomal bupivacaine, chronic pain prevention, opioid-sparing strategies, and spontaneous ventilation anesthesia.

Several studies have investigated the causative role of the microbiome in the development of rheumatoid arthritis (RA), but changes in the gut microbiome in RA patients during drug treatment have been less well studied. Here, we tracked the longitudinal changes in gut bacteria in 22 RA patients who were randomized into two groups and treated with Huayu-Qiangshen-Tongbi formula (HQT) plus methotrexate (MTX) or leflunomide (LEF) plus MTX. There were differences in the gut microbiome between untreated (at baseline) RA patients and healthy controls, with 37 species being more abundant in the RA patients and 21 species (including Clostridium celatum ) being less abundant. Regarding the functional analysis, vitamin K2 biosynthesis was associated with RA-enriched bacteria. Additionally, in RA patients, alterations in gut microbial species appeared to be associated with RA-related clinical indicators through changing various gut microbiome functional pathways. The clinical efficacy of the two treatments was further observed to be similar, but the response trends of RA-related clinical indices in the two treatment groups differed. For example, HQT treatment affected the erythrocyte sedimentation rate (ESR), while LEF treatment affected the C-reactive protein (CRP) level. Further, 11 species and 9 metabolic pathways significantly changed over time in the HQT group (including C. celatum , which increased), while only 4 species and 2 metabolic pathways significantly changed over time in the LEF group. In summary, we studied the alterations in the gut microbiome of RA patients being treated with HQT or LEF. The results provide useful information on the role of the gut microbiota in the pathogenesis of RA, and they also provide potentially effective directions for developing new RA treatments.

Ni–P–WC–BN(h) nanocomposite coatings were fabricated on 20CrMnTi substrates using ultrasonic-assisted pulsed electrodeposition. 20CrMnTi is a low-carbon steel that is commonly used in the manufacturing gears and shaft components. To enhance the wear resistance and extend the service life of such mechanical parts, ultrasonic-assisted pulsed electrodeposition was employed as an effective surface modification technique. The microhardness, phase structure, surface morphology, and wear behavior of the coating were also characterized. An orthogonal experimental design was employed to examine the effects of current density, bath temperature, ultrasonic power, and pulse duty cycle on the microhardness and wear behavior of the coatings, aiming to optimize the deposition parameters. The optimal process combination was identified as a current density of 3 A·dm−2, a bath temperature of 55 °C, an ultrasonic power of 210 W, and a duty cycle of 0.7. Under these conditions, the coatings exhibited enhanced hardness and wear resistance. Based on the optimized parameters, additional tribological tests were conducted under various operating conditions to further evaluate wear performance. The results showed that the dominant wear mechanisms were chemical wear and adhesive wear. This study offers new insights into the fabrication of high-performance nanocomposite coatings and expands the application scope of ultrasonic-assisted pulsed electrodeposition in multiphase composite systems.

With several fragmented literature reviews and meta-analyses on Large-Language Models (LLMs) in education, this study provides a synthesis of these reviews focusing on the role of LLMs to facilitate different pedagogy paradigms to reveal research trends, existing gaps, and future directions. The synthesis adheres to the PRISMA guidelines and AMSTAR checklist to analyze 50 reviews to find out the trends of research in terms of publication year, geographic regions, types of reviews, types of research questions, pedagogy paradigms addressed, and challenges encountered. Findings revealed that constructivism, cognitivism, and connectivism emerged as the most frequently addressed paradigms, indicating the role of LLMs as (i) tools for learning discovery or scaffolding, where learners actively construct knowledge, (ii) tools to improve cognitive tasks (e.g., recall, comprehension, problem-solving), or (iii) tools to facilitate connections between diverse knowledge sources or enable networked learning environments. Additionally, several challenges emerge, which are related to cognitive load and processing, accuracy and comprehension, cultural sensitivity and diversity, learner autonomy and self-expression, among others. This study offers valuable insights into the evolving roles of LLMs in facilitating paradigms that contribute to a deeper understanding of the pedagogical implications, challenges, and opportunities presented by LLM adoption in education.

Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R2) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research.