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The interior design suffers from inefficiency and a lack of aesthetic appeal. With the development of artificial intelligence diffusion models, using text descriptions to generate aesthetically pleasing designs has emerged as a new approach to address these issues. In this study, we propose a novel method based on the aesthetic diffusion model, which can quickly generate visually appealing interior design based on input text descriptions while allowing for the specification of decorative styles and spatial functions. The method proposed in this study creates creative designs and drawings by computer instead of from designers, thus improving the design efficiency and aesthetic appeal. We demonstrate the potential of this approach in the field of interior design through our research. The results indicate that: (1) The method efficiently provides designers with aesthetically pleasing interior design solutions; (2) By modifying the text descriptions, the method allows for the rapid regeneration of design solutions; (3) Designers can apply this highly flexible method to other design fields through fine-tuning. (4) The method optimizes the workflow of interior design.

Because interior design is subject to inefficiency, more creativity is imperative. Due to the development of artificial intelligence diffusion models, the utilization of text descriptions for the generation of creative designs has become a novel method for solving the aforementioned problem. Herein, we build a unique interior decoration style dataset. Thus, we solve the problem pertaining to the need for datasets, propose a new loss function that considers the decoration style, and retrain the diffusion model using this dataset. The trained model learns interior design knowledge and can generate an interior design through text. The proposed method replaces the designer’s drawing with computer-generated creative design, thereby enhancing the design efficiency and creative generation. Specifically, the proposed diffusion model can generate interior design images of specific decoration styles and spatial functions end to end from text descriptions, and the generated designs are easy to modify. This novel and creative design method can efficiently generate various interior designs, promote the generation of creative designs, and enhance the design and decision-making efficiency.

Traditional kite creation often relies on the hand-painting of experienced artisans, which limits the revitalization and innovation of this intangible cultural heritage. This study proposes using an AI-based diffusion model to learn kite design and generate new kite patterns, thereby promoting the revitalization and innovation of kite-making craftsmanship. Specifically, to address the lack of training data, this study collected ancient kite drawings and physical kites to create a Traditional Kite Style Patterns Dataset. The study then introduces a novel loss function that incorporates auspicious themes in style and motif composition, and fine-tunes the diffusion model using the newly created dataset. The trained model can produce batches of kite designs based on input text descriptions, incorporating specified auspicious themes, style patterns, and varied motif compositions, all of which are easily modifiable. Experiments demonstrate that the proposed AI-generated kite design can replace traditional hand-painted creation. This approach highlights a new application of AI technology in kite creation. Additionally, this new method can be applied to other areas of cultural heritage preservation. Offering a new technical pathway for the revitalization and innovation of intangible cultural heritage. It also opens new directions for future research in the integration of AI and cultural heritage.

Interior designers are suffering from a lack of intelligent design methods. This study aims to enhance the accuracy and efficiency of retrieval textures for interior design, which is a crucial step toward intelligent design. Currently, interior designers rely on repetitive tasks to obtain textures from websites, which is ineffective as a interior design often requires hundreds of textures. To address this issue, this study proposes a hybrid deep learning approach, HyNet, which boosts retrieval efficiency by recommending similar textures instead of blindly searching. Additionally, a new indoor texture dataset is created to support the application of artificial intelligence in this field. The results demonstrate that the proposed method’s ten recommended images achieve a high accuracy rate of 91.41%. This is a significant improvement in efficiency, which can facilitate the design industry’s progression towards intelligence. Overall, this study offers a promising solution to the challenges facing interior designers, and it has the potential to significantly enhance the industry’s productivity and innovation.

Background Compassion fatigue can lead to various physical and mental health issues and reduce the work efficiency and motivation of medical professionals. This study explored the prevalence of compassion fatigue among medical students and its relationship to their decision to continue working in clinical medicine after graduation from medical school. Methods A cross-sectional survey was conducted with clinical medicine students in several hospitals in Southwest China using convenience methods. The Chinese version of the Compassion Fatigue Scale was used to measure compassion fatigue. Additionally, the desire to have a career in clinical medicine after graduation was investigated to determine its relationship to compassion fatigue. Results A total of 473 medical students participated in the survey. Among the participants, 46 experienced mild compassion fatigue, 205 experienced moderate compassion fatigue, and 210 experienced severe compassion fatigue. The regression analysis showed that a night shift frequency of 2–3 times/week (odds ratio (OR) = 5.33, 95% confidence interval (CI) [1.35, 21.0]), working 8–10 h per day (OR = 2.30, 95% CI [1.01, 5.22]), or working 10 h per day or more (OR = 8.64, 95% CI [1.99, 37.6]) were factors of severe compassion fatigue. Furthermore, 158 participants reported that they did not often or always want to pursue a career in clinical work after graduation. Regression analysis revealed that low empathy satisfaction was an independent risk factor for students not wanting to continue in clinical practice post-graduation (odds ratio = 2.30, 95% CI [1.00, 5.31]). Conclusion Compassion fatigue is common among medical students and may significantly influence their intention to pursue a medical career after graduation. Educational institutions, medical facilities, and relevant departments should prioritize addressing compassion fatigue in medical students and implementing effective preventive and interventional strategies.

Stock return prediction for quantitative trading in U.S. equity markets has evolved from parametric econometric modeling toward data-driven deep learning systems that must jointly capture temporal dynamics, discontinuous jumps, and evolving cross-asset dependencies. Existing approaches still face three key challenges in deep learning-based stock return prediction: jump-aware temporal modeling is often missing or handled by ad hoc heuristics; higher-order stock relations are frequently encoded by static graphs/hypergraphs that do not adapt across market conditions, and temporal and relational learning are commonly implemented as sequential blocks with limited bidirectional interaction. We propose LévyHyper, an end-to-end framework that unifies jump-aware temporal encoding with regime-adaptive dynamic hypergraph learning and multi-scale hypergraph reasoning. LévyHyper integrates a neural jump-aware temporal layer motivated by Lévy jump-diffusion modeling, a regime-weighted fusion of predefined and learned hyperedges via a differentiable constructor, and a multi-scale hypergraph convolution module for hierarchical temporal aggregation. Experiments on S&P 500 data (463 stocks, 10 evaluation phases, prediction horizon τ=5 trading days) show that LévyHyper improves IC/RankIC and portfolio-level Sharpe ratio over strong baselines on average. We additionally report uncertainty estimates, significance tests, and transaction-cost sensitivity to support robust conclusions.

The annotations used during the training process are crucial for the inference results of remote sensing images (RSIs) based on a deep learning framework. Unlabeled RSIs can be obtained relatively easily. However, pixel-level annotation is a process that necessitates a high level of expertise and experience. Consequently, the use of small sample training methods has attracted widespread attention as they help alleviate reliance on large amounts of high-quality labeled data and current deep learning methods. Moreover, research on small sample learning is still in its infancy owing to the unique challenges faced when completing semantic segmentation tasks with RSI. To better understand and stimulate future research that utilizes semantic segmentation tasks with small data, we summarized the supervised learning methods and challenges they face. We also reviewed the supervised approaches with data that are currently popular to help elucidate how to efficiently utilize a limited number of samples to address issues with semantic segmentation in RSI. The main methods discussed are self-supervised learning, semi-supervised learning, weakly supervised learning and few-shot methods. The solution of cross-domain challenges has also been discussed. Furthermore, multi-modal methods, prior knowledge constrained methods, and future research required to help optimize deep learning models for various downstream tasks in relation to RSI have been identified.

Polyphenols from stevia leaves (PPSs) are abundant byproducts from steviol glycoside production, which have been often studied as raw extracts from stevia extracts for their bioactivities. Herein, the PPSs rich in isochlorogenic acids were studied for their antimicrobial and anti-inflammatory properties, as well as their inhibitory effects on digestive enzymes. The PPSs presented stronger antibacterial activity against E. coli, S. aureus, P. aeruginosa, and B. subtilis than their antifungal activity against M. furfur and A. niger. Meanwhile, the PPSs inhibited four cancer cells by more than 60% based on their viability, in a dose-dependent manner. The PPSs presented similar IC50 values on the inhibition of digestive enzyme activities compared to epigallocatechin gallate (EGCG), but had weaker anti-inflammatory activity. Therefore, PPSs could be a potential natural alternative to antimicrobial agents. This is the first report on the bioactivity of polyphenols from stevia rebaudiana (Bertoni) leaves excluding flavonoids, and will be of benefit for understanding the role of PPSs and their application.

The exceptional architecture designed by master architects is a shared treasure of humanity, which embodies their design skills and concepts not possessed by common architectural designers. To help ordinary designers improve the design quality, we propose a new artificial intelligence (AI) method for generative architectural design, which generates designs with specified styles and master architect quality through a diffusion model based on textual prompts of the design requirements. Compared to conventional methods dependent on heavy intellectual labor for innovative design and drawing, the proposed method substantially enhances the creativity and efficiency of the design process. It overcomes the problem of specified style difficulties in generating high-quality designs in traditional diffusion models. The research results indicated that: (1) the proposed method efficiently provides designers with diverse architectural designs; (2) new designs upon easily altered text prompts; (3) high scalability for designers to fine-tune it for applications in other design domains; and (4) an optimized architectural design workflow.

Background IscB (Insertion sequences Cas9-like OrfB) represents a novel class of RNA-guided nucleases, approximately one-third the size of Cas9 proteins. Despite the limited natural efficiency in eukaryotic cells, recent advances have led to the engineering of several IscBs for mammalian genome editing. Results In this study, we screen and identify high-activity IscB variants for rice. A version of pIscB-v3, combining enOgeuIscB and ωRNA-v13, demonstrated superior mutagenesis efficiency compared to other systems. The average editing efficiency of pIscB-v3 is 17.61% from ten endogenous targets, and we obtain edited lines in up to 83.33% of T0 generation with 33.33% of homozygous and bi-allelic mutations. Further analysis reveals that pIscB-v3 exhibits high editing specificity and relaxed target-adjacent motif (TAM) compatibility in rice. Beyond gene knockout systems, we develop cytosine base editors (CBEs) and adenine base editors (ABEs) from pIscB-v3. We find that the ssDNA-targeting SCP1.201 family deaminase Sdd7 outperformed human APOBEC3A in IscB-CBEs for C-to-T conversions in rice. The Sdd7-nIscB achieves precise edits in 22.92% of lines on average, with a maximum frequency of 47.92%. Additionally, TadA8e-nIscB exhibits limited activity. However, fusing an extra copy of TadA-8e to either terminus of TadA8e-nIsc significantly enhances A-to-G conversions. Conclusions Collectively, our results demonstrate the robust capabilities of IscB to develop an efficient and versatile miniature plant genome editing toolkit to substantially facilitate crop breeding.