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Purpose This paper aims to examine the impact of the COVID-19 infodemic on the public’s online information behaviour, offering insights critical for shaping effective informational responses in future public health emergencies. Design/methodology/approach This paper uses a structured online survey with 27 targeted questions using a five-point Likert scale to measure eight variables. Data analysis is conducted through structural equation modelling on 307 valid responses to rigorously test the research hypotheses. Findings This paper indicates that information quality significantly impacts the public’s capacity to select, share and use online information. Additionally, the comprehensibility of information plays a crucial role in shaping the public’s behaviours in terms of online information exchange and usage. The credibility of information sources emerges as a key determinant influencing the public’s online information selection, exchange and utilization behaviour. Moreover, social influence exerts a substantial effect on the public’s online information selection, acquisition, exchange and utilization behaviour. These findings highlight the presence of universality and sociality, mediation and guidance, as well as the purposefulness and selectivity performed by the public’s online information behaviour during an infodemic. Originality/value This paper introduces a novel research model for assessing the influence and identifies the patterns of the public’s online information behaviour during the COVID-19 infodemic. The findings have significant implications for developing strategies to tackle information dissemination challenges in future major public health emergencies.

Open Access (OA) has emerged as a pivotal driver shaping the dissemination scope and academic impact of research findings. To clarify the impact of publishing models such as open access on the citation performance of biochemical papers, this study selects 177,745 biochemistry professional papers included in the core collection of the Web of Science (WoS CC) as the research data; we conduct an analysis of citation and impact advantages in biochemistry research. Employing correlation analysis, baseline regression modeling, and two-way ANOVA, our analysis indicates that: OA publications in biochemistry exhibit notable citation and impact advantages, which are positively correlated with the degree of openness, and the key determinants of the OA advantage encompass funding sources, reference count, and publication region. At present, China accounts for a disproportionately small proportion of OA papers in this field. In the context of the open-science paradigm, Chinese academic journals must systematically address their developmental bottlenecks and formulate publication innovation strategies to enhance the quality of academic publishing.

Considering consumer fairness concerns, this paper investigates an e-commerce platform’s selling scheme choice when it adopts a wholesale selling scheme or an agency selling scheme to create a contract with a manufacturer. We find that the intensity of the fairness concerns and the platform fee are key factors affecting the platform’s optimal selling scheme choice. Specifically, when these two factors are relatively high or low, the wholesale selling scheme outperforms the agency selling scheme in terms of the e-commerce platform’s profit. Otherwise, the e-commerce platform should adopt the agency selling scheme. Moreover, when these two factors are sufficiently large or small, the wholesale selling scheme will yield a win-win result for the players of the e-commerce supply chain. Interestingly, we find that, considering fairness-minded consumers, a larger platform fee may be harmful to the platform. We also extend the baseline model to consider the consumer heterogeneity of fairness concerns, proportional platform fee, fairness concern about the manufacturer’s profit, and endogenous platform fee. We find that the main insights remain qualitatively unchanged under these model extensions.

In the context of global climate governance and the ‘dual carbon’ target, corporate ESG performance has become a key driver of the low-carbon transition. This paper uses traditional econometric models to empirically investigate how corporate ESG performance influences carbon emission reduction performance. Machine learning models are employed to analyze the non-linear relationship, revealing that ESG performance positively affects carbon emission reduction, partly by reducing the shareholding proportion of short-term institutional investors. The study’s robustness is assessed through a variety of methods, including the instrumental variable method. Additionally, a heterogeneity analysis was conducted, which revealed that the ESG effect is more significant for dual-hatted enterprises due to their decision-making efficiency advantage and for enterprises in the eastern region due to their resource endowment advantage. Moreover, machine learning techniques overcome the constraints of conventional linear models by utilizing non-linear regression for hypothesis testing. The CatBoost model quantifies the heterogeneous effects of ESG segmentation dimensions, thereby revealing that ESG’s social dimension exerts a predominant influence on emission reduction. The study confirms the catalyzing effect of corporate ESG performance in empowering emission reduction through financial channels, and conducts a machine learning-based feature importance analysis to highlight the significant roles of social and governance factors in emission reduction, which provides a scientific basis for the precise allocation of ESG resources by enterprises.

User behavior intention is an important evaluation criterion for the construction of mobile library application. To help libraries and mobile application, developers better understand factors influencing user behavior intention and jointly improve the mobile service quality of library. Based on grounded theory, this study experimentally manipulates user behavior intention to use mobile library application related to the survey questionnaire that was designed to obtain data from college teachers and students. The results showed that the user behavior intention to use mobile library application is mainly influenced by system feature (i.e., accessibility, relevance, and system help), interface feature (i.e., screen design, navigation, and term), and individual difference (i.e., performance expectancy, domain knowledge, and social influence). Furthermore, system feature and interface feature are the external driver of user's usage behavior intention, and individual difference is the internal driver of user's usage behavior intention.

A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry.

In the process of collaborative innovation of water pollution control industry chain, there are some problems, such as chain break and lack of chain, which lead to poor effect of water pollution collaborative governance and low economic benefits. Therefore, this paper proposes a research method of collaborative innovation mechanism of water pollution treatment industry chain based on complex scientific management. In view of the problems existing in the current industrial chain of water pollution control, the collaborative economic model and matter-element extension model of the industrial chain of water pollution treatment are constructed to improve the synergy effect of each link in the industrial chain and solve the contradictions of each link in the coordination process, so as to provide guarantee for the collaborative innovation of the industrial chain of water pollution control. The experimental results show that the proposed method can bring high economic benefits in the stage of source emission reduction, process interruption, and end treatment, and the water pollution treatment effect is good, which fully verifies the practical application effect of the method. After using the design method to purify, the maximum purification rate reaches more than 80%, and the purification effect reaches class v. The effect is remarkable, which has practical application value.

Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models. Chemists cannot readily derive useful insights from traditional approaches that rely largely on atom-level decoding in the string representations, because human experts tend to interpret reactions by analyzing substructures that comprise a molecule. It is well-established that some substructures are stable and remain unchanged in reactions. In this paper, we developed a substructure-level decoding model, where commonly preserved portions of product molecules were automatically extracted with a fully data-driven approach. Our model achieves improvement over previously reported models, and we demonstrate that its performance can be boosted further by enhancing the accuracy of these substructures. Analyzing substructures extracted from our machine learning model can provide human experts with additional insights to assist decision-making in retrosynthesis analysis. Retrosynthesis is a critical task for organic chemistry with numerous industrial applications. Here, the authors build a machine learning model to learn the concept of substructures from a large reaction dataset to achieve chemist-like intuitions.

The role of transcription factors and biomolecules in cell type conversion has been widely studied. Yet, it remains unclear whether and how intracellular mechanotransduction through focal adhesions (FAs) and the cytoskeleton regulates the epigenetic state and cell reprogramming. Here, it is shown that cytoskeletal structures and the mechanical properties of cells are modulated during the early phase of induced neuronal (iN) reprogramming, with an increase in actin cytoskeleton assembly induced by Ascl1 transgene. The reduction of actin cytoskeletal tension or cell adhesion at the early phase of reprogramming suppresses the expression of mesenchymal genes, promotes a more open chromatin structure, and significantly enhances the efficiency of iN conversion. Specifically, reduction of intracellular tension or cell adhesion not only modulates global epigenetic marks, but also decreases DNA methylation and heterochromatin marks and increases euchromatin marks at the promoter of neuronal genes, thus enhancing the accessibility for gene activation. Finally, micro‐ and nano‐topographic surfaces that reduce cell adhesions enhance iN reprogramming. These novel findings suggest that the actin cytoskeleton and FAs play an important role in epigenetic regulation for cell fate determination, which may lead to novel engineering approaches for cell reprogramming. Here, it is shown that cytoskeletal structures and the mechanical properties of cells are modulated during the early phase of induced neuronal (iN) reprogramming. The reduction of actin cytoskeletal tension or cell adhesion at the early phase of reprogramming suppresses the expression of mesenchymal genes, promotes a more open chromatin structure, and significantly enhances the efficiency of iN conversion.

Darapladib, an oral inhibitor of lipoprotein-associated phospholipase A2, was compared with placebo in 15,828 patients with stable coronary heart disease. Darapladib did not significantly reduce the risk of cardiovascular death, myocardial infarction, or stroke. Atherosclerotic lesions in humans — in particular, vulnerable 1 and ruptured plaques — are characterized by inflammatory activity and a high expression of lipoprotein-associated phospholipase A 2 . 2 , 3 In atherosclerotic plaques, lipoprotein-associated phospholipase A 2 increases the production of proinflammatory and proapoptotic mediators. 4 – 8 In a meta-analysis of individual records from 79,036 participants in 32 prospective studies, there was a continuous association between lipoprotein-associated phospholipase A 2 activity and the risk of coronary heart disease, with a relative increase in risk of 1.10 (95% confidence interval [CI], 1.05 to 1.16) for each 1-SD increase in lipoprotein-associated phospholipase A 2 activity, . . .