Personalized courses recommendation technology is one of the hotspots in online education field. A good recommendation algorithm can stimulate learners’ enthusiasm and give full play to different learners’ learning personality. At present, the popular collaborative filtering algorithm ignores the semantic relationship between recommendation items, resulting in unsatisfactory recommendation results. In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. Firstly, the knowledge graph representation learning method is used to embed the semantic information of the items into a low‐dimensional semantic space; then, the semantic similarity between the recommended items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. This algorithm increases the performance of recommendation at the semantic level. The results show that the proposed algorithm can effectively recommend courses for learners and has higher values on precision, recall, and F1 than the traditional recommendation algorithm.

With the rapid development of social networks, location based social network gradually rises. In order to retrieve user’s most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic and industry. Aiming at the problem of low accuracy in personalized tourism recommendation system, this paper presents a personalized algorithm for tourist attraction recommendation – RecUFG Algorithm, which combines user collaborative filtering technology with friends trust relationships and geographic context. This algorithm fully exploits social relations and trust friendship between users, and by means of the geographic information between user and attraction location, recommends users most interesting attractions. Experimental results on real data sets demonstrate the feasibility and effectiveness of the algorithm. Compared with the existing recommendation algorithm, it has a higher prediction accuracy and customer satisfaction.

The advent of digital technology has fundamentally altered the traditional modes of information exchange within enterprises, thereby impacting the concentration of supply chains. This study presents an empirical examination of the effects and underlying mechanisms by which digital transformation influences supply chain concentration, utilizing data from Chinese A-share listed companies spanning 2011 to 2021. The findings reveal that digital transformation enhances information transparency and reduces transaction costs, consequently contributing to a decrease in the concentration of enterprise supply chains. It is observed that the adoption of digital transformation leads to a significant diminution in supply chain concentration, a conclusion that retains its significance even under robustness testing. Further investigation indicates a more pronounced impact of digital transformation on diminishing the concentration of state-owned enterprise supply chains compared to their nonstate-owned counterparts. This research enriches the understanding of the impact of digital transformation on supply chain concentration, offering theoretical support for the acceleration of a unified large market development in China. The study underscores the transformative role of digital technology in reshaping enterprise supply chains, highlighting the necessity for strategic digital integration in modern business practices.

Introduction: Artificial Intelligence (AI) has become a key driving force for promoting the development of Manufacturing 4.0 and enhancing new quality productivity - an economic model that emphasizes high efficiency, innovation-driven growth, and sustainable development. Although the transformative potential of AI is increasingly recognized, how to effectively unleash its role in enhancing new quality productivity at the enterprise level remains a question that requires in-depth research. This study aims to investigate the impact of AI applications on the new quality productivity of Chinese manufacturing enterprises, explore its underlying mechanisms, and assess its heterogeneous effects and spatial spillover effects across different regions and industries. Methods: This paper is based on the panel data of Chinese A-share listed manufacturing enterprises from 2015 to 2023, and employs multiple econometric models to analyze the relationship between AI and new quality productivity. The empirical strategies include: a benchmark regression model to estimate the basic impact; a spatial Durbin model (SDM) constructed under a 0-1 adjacency matrix and a geographical distance matrix to capture spatial spillover effects; a dynamic panel GMM model to address endogeneity and dynamic persistence issues; heterogeneity and mechanism analysis through group regression and mediation effect tests. The robustness of the results is ensured through a series of robustness tests and endogeneity tests. Results: The research findings indicate that AI has significantly enhanced the new quality productivity of manufacturing enterprises, and this conclusion remains robust under different model settings and endogeneity treatments. The mechanism analysis reveals that AI boosts new quality productivity through three pathways: promoting green innovation, alleviating financing constraints for enterprises, and optimizing the structure of human capital. Heterogeneity tests show that the promoting effect of AI is more pronounced in enterprises in the eastern region, non-heavy-polluting industries, and high-tech industries. Additionally, the spatial econometric results confirm that AI not only contributes to the improvement of local new quality productivity but also generates significant positive spatial spillover effects in surrounding areas. Discussion: The above findings highlight the crucial role of artificial intelligence as a key engine driving the development of new quality productivity, especially demonstrating remarkable effectiveness in promoting the green transformation of manufacturing and the development of a circular economy. To fully unleash the potential of artificial intelligence in driving green productivity, policymakers should accelerate the construction of a “digital-green” collaborative innovation ecosystem. At the same time, the leading and radiating role of advanced regions should be fully leveraged to build cross-regional technology sharing platforms and data resource networks, forming a regional development pattern of “leading by points and advancing in coordination”, and comprehensively promoting the realization of new industrialization and the dual carbon goals.

Purpose: Adverse clinical events cause significant morbidity in patients with GBM (GBM). We examined whether genomic alterations were associated with AE (AE) in patients with GBM. Experimental Design: We identified adults with histologically confirmed IDH-wild-type GBM with targeted next-generation sequencing (OncoPanel) at Dana Farber Cancer Institute from 2013 to 2019. Seizure at presentation, lymphopenia, thromboembolic events, pseudoprogression, and early progression (within 6 months of diagnosis) were identified as AE. The biologic function of genetic variants was categorized as loss-of-function (LoF), no change in function, or gain-of-function (GoF) using a somatic tumor mutation knowledge base (OncoKB) and consensus protein function predictions. Associations between functional genomic alterations and AE were examined using univariate logistic regressions and multivariable regressions adjusted for additional clinical predictors. Results: Our study included 470 patients diagnosed with GBM who met the study criteria. We focused on 105 genes that had sequencing data available for ≥ 90% of the patients and were altered in ≥10% of the cohort. Following false-discovery rate (FDR) correction and multivariable adjustment, the TP53, RB1, IGF1R, and DIS3 LoF alterations were associated with lower odds of seizures, while EGFR, SMARCA4, GNA11, BRD4, and TCF3 GoF and SETD2 LoF alterations were associated with higher odds of seizures. For all other AE of interest, no significant associations were found with genomic alterations following FDR correction. Conclusions: Genomic biomarkers based on functional variant analysis of a routine clinical panel may help identify AE in GBM, particularly seizures. Identifying these risk factors could improve the management of patients through better supportive care and consideration of prophylactic therapies.