To improve the current oil painting teaching mode in Chinese universities, this study combines deep learning technology and artificial intelligence technology to explore oil painting teaching. Firstly, the research status of individualized education and related research on image classification based on brush features are analyzed. Secondly, based on a convolutional neural network, mathematical morphology, and support vector machine, the oil painting classification model is constructed, in which the extracted features include color and brush features. Moreover, based on artificial intelligence technology and individualized education theory, a personalized intelligent oil painting teaching framework is built. Finally, the performance of the intelligent oil painting classification model is evaluated, and the content of the personalized intelligent oil painting teaching framework is explained. The results show that the average classification accuracy of oil painting is 90.25% when only brush features are extracted. When only color features are extracted, the average classification accuracy is over 89%. When the two features are extracted, the average accuracy of the oil painting classification model reaches 94.03%. Iterative Dichotomiser3, decision tree C4.5, and support vector machines have an average classification accuracy of 82.24%, 83.57%, and 94.03%. The training speed of epochs data with size 50 is faster than that of epochs original data with size 100, but the accuracy is slightly decreased. The personalized oil painting teaching system helps students adjust their learning plans according to their conditions, avoid learning repetitive content, and ultimately improve students\' learning efficiency. Compared with other studies, this study obtains a good oil painting classification model and a personalized oil painting education system that plays a positive role in oil painting teaching. This study has laid the foundation for the development of higher art education.

BACKGROUND: Systematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare decision-makers (eg, practitioners, policymakers, researchers) use SRs to inform decision-making and to explore the potential role of a proposed artificial intelligence (AI) tool to assist in critical appraisal and choosing among SRs.
METHODS: We developed a survey with 21 open and closed questions. We followed a knowledge translation plan to disseminate the survey through social media and professional networks.
RESULTS: Our survey response rate was lower than expected (7.9% of distributed emails). Of the 684 respondents, 58.2% identified as researchers, 37.1% as practitioners, 19.2% as students and 13.5% as policymakers. Respondents frequently sought out SRs (97.1%) as a source of evidence to inform decision-making. They frequently (97.9%) found more than one SR on a given topic of interest to them. Just over half (50.8%) struggled to choose the most trustworthy SR among multiple. These difficulties related to lack of time (55.2%), or difficulties comparing due to varying methodological quality of SRs (54.2%), differences in results and conclusions (49.7%) or variation in the included studies (44.6%). Respondents compared SRs based on the relevance to their question of interest, methodological quality, and recency of the SR search. Most respondents (87.0%) were interested in an AI tool to help appraise and compare SRs.
CONCLUSIONS: Given the identified barriers of using SR evidence, an AI tool to facilitate comparison of the relevance of SRs, the search and methodological quality, could help users efficiently choose among SRs and make healthcare decisions.

OBJECTIVE: This study aimed to quantitatively evaluate optic nerve head and retinal vascular parameters in children with hyperopia in relation to age and spherical equivalent refraction (SER) using artificial intelligence (AI)-based analysis of colour fundus photographs (CFP).
METHODS: This cross-sectional study included 324 children with hyperopia aged 3-12 years. Participants were divided into low hyperopia (SER+0.5 D to+2.0 D) and moderate-to-high hyperopia (SER≥+2.0 D) groups. Fundus parameters, such as optic disc area and mean vessel diameter, were automatically and quantitatively detected using AI. Significant variables (p<0.05) in the univariate analysis were included in a stepwise multiple linear regression.
RESULTS: Overall, 324 children were included, 172 with low and 152 with moderate-to-high hyperopia. The median optic disc area and vessel diameter were 1.42 mm2 and 65.09 µm, respectively. Children with high hyperopia had larger superior neuroretinal rim (NRR) width and larger vessel diameter than those with low and moderate hyperopia. In the univariate analysis, axial length was significantly associated with smaller superior NRR width (β=-3.030, p<0.001), smaller temporal NRR width (β=-1.469, p=0.020) and smaller vessel diameter (β=-0.076, p<0.001). A mild inverse correlation was observed between the optic disc area and vertical disc diameter with age.
CONCLUSIONS: AI-based CFP analysis showed that children with high hyperopia had larger mean vessel diameter but smaller vertical cup-to-disc ratio than those with low hyperopia. This suggests that AI can provide quantitative data on fundus parameters in children with hyperopia.

BACKGROUND: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.
RESULTS: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).
CONCLUSIONS: Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual\'s risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.

The role of knowledge graph encompasses the representation, organization, retrieval, reasoning, and application of knowledge, providing a rich and robust cognitive foundation for artificial intelligence systems and applications. When we learn new things, find out that some old information was wrong, see changes and progress happening, and adopt new technology standards, we need to update knowledge graphs. However, in some environments, the initial knowledge cannot be known. For example, we cannot have access to the full code of a software, even if we purchased it. In such circumstances, is there a way to update a knowledge graph without prior knowledge? In this paper, We are investigating whether there is a method for this situation within the framework of Dalal revision operators. We first proved that finding the optimal solution in this environment is a strongly NP-complete problem. For this purpose, we proposed two algorithms: Flaccid_search and Tight_search, which have different conditions, and we have proved that both algorithms can find the desired results.

BACKGROUND: To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues.
METHODS: A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal-image landmarks and 17 lateral-image landmarks. Automatic landmark detection models were established, which consisted of a high-resolution network, a feature fusion module based on depthwise separable convolution, and a prediction model based on pixel shuffle. Ten measurements for frontal images and eight measurements for lateral images were defined. Test sets were used to evaluate the model performance, respectively. The mean radial error of landmarks and measurement error were calculated and statistically analysed to evaluate their reliability.
RESULTS: The mean radial error was 14.44 ± 17.20 pixels for the landmarks in the frontal images and 13.48 ± 17.12 pixels for the landmarks in the lateral images. There was no statistically significant difference between the model prediction and manual annotation measurements except for the mid facial-lower facial height index. A total of 14 measurements had a high consistency.
CONCLUSIONS: Based on deep learning, we established automatic soft-tissue analysis models for orthodontic facial photographs that can automatically detect 43 frontal-image landmarks and 17 lateral-image landmarks while performing comprehensive soft-tissue measurements. The models can assist orthodontists in efficient and accurate quantitative soft-tissue evaluation for clinical application.

UNASSIGNED: This paper summarizes the research progress into stimulation methods used in rehabilitation equipment for pediatric cerebral palsy (CP) for the past 20 years from 2003 to 2023. We also provide ideas for innovative research and development of artificial intelligence-based rehabilitation equipment.
UNASSIGNED: Through a certain search strategy, Keywords are searched in the China National Knowledge Network Database (CNKI), the Wanfang Database knowledge service platform, the Chongqing VIP information service, PubMed, Web of Science, Cochrane, ScienceDirect, Medline, Embase, and IEEE database. A total of 3,049 relevant articles were retrieved, and 49 articles were included that mentioned research and development of rehabilitation equipment. We excluded articles that were not specific to children with CP, were duplicated or irrelevant literature, were missing data, the full article was not available, the article did not describe the method of stimulation used with the rehabilitation equipment on children with CP, were not Chinese and English, and were the types of reviews and commentaries.
UNASSIGNED: Physical stimulation is the main stimulation method of rehabilitation equipment for children with CP. Force stimulation is the main mode of physical stimulation, and there are 17 articles that have verified the clinical efficacy of force stimulation-based equipment.
UNASSIGNED: Research on the stimulation mode of pediatric cerebral palsy rehabilitation equipment is likely to focus on simulating the force of the Chinese medicine called \"tuina manipulation.\" When this method is combined with artificial intelligence and personalized direction we believe this will lay the foundation for future development of a novel therapy for children with CP.

慢性鼻窦炎（CRS）是累及鼻腔鼻窦的具有不同病理生理机制及临床治疗反应的异质性疾病。近年来，人工智能（AI）技术在医学领域蓬勃发展，基于大数据与AI技术的影像组学、病理组学等研究在CRS的影像识别、辅助诊断、内型鉴定、预后预测等诸多方面有了新的探索和应用，在CRS的精准诊断及个体化治疗方面具有巨大潜力和应用价值。本文按照诊疗环节就AI在CRS中的应用现状进行简要总结，并对其未来发展趋势进行展望。.

Assigning a query individual animal or plant to its derived population is a prime task in diverse applications related to organismal genealogy. Such endeavors have conventionally relied on short DNA sequences under a phylogenetic framework. These methods naturally show constraints when the inferred population sources are ambiguously phylogenetically structured, a scenario demanding substantially more informative genetic signals. Recent advances in cost-effective production of whole-genome sequences and artificial intelligence have created an unprecedented opportunity to trace the population origin for essentially any given individual, as long as the genome reference data are comprehensive and standardized. Here, we developed a convolutional neural network method to identify population origins using genomic SNPs. Three empirical datasets (an Asian honeybee, a red fire ant, and a chicken datasets) and two simulated populations are used for the proof of concepts. The performance tests indicate that our method can accurately identify the genealogy origin of query individuals, with success rates ranging from  93 % to 100 %. We further showed that the accuracy of the model can be significantly increased by refining the informative sites through FST filtering. Our method is robust to configurations related to batch sizes and epochs, whereas model learning benefits from the setting of a proper preset learning rate. Moreover, we explained the importance score of key sites for algorithm interpretability and credibility, which has been largely ignored. We anticipate that by coupling genomics and deep learning, our method will see broad potential in conservation and management applications that involve natural resources, invasive pests and weeds, and illegal trades of wildlife products.