The development of the maize agribusiness system is highly dependent on the role of social capital in facilitating interaction among actors in the chain of activities ranging from the provision of farm supplies to marketing. Therefore, this research aimed to characterize the key elements of social capital specifically bonding, bridging, and linking, as well as to demonstrate their respective roles. Data were collected from farmers and non-farmers actors engaged in various activities in the maize agribusiness system. The data obtained were processed using ATLAS Ti, applying open, axial, and selective coding techniques. The results showed the roles played by bonding, bridging, and linking social capital in the interaction between farmers and multiple actors in activities such as providing farm supplies, farming production, harvesting, post-harvest, and marketing. The combination of these social capital forms acted as the glue and wires that facilitated access to resources, collective decision-making, and reduced transaction costs. These results have theoretical implications, suggesting that bonding, bridging, and linking should be combined with the appropriate role composition for each activity in the agribusiness system.

The provided material presents a priority article on the scientific discovery titled “The phenomenon of simultaneous destruction of water-oil and oil-water emulsions”. The authors propose the corresponding formula: the previously unknown phenomenon of simultaneous destruction of water-oil and oil-water emulsions occurs when polynanostructured surfactant demulsifiers with characteristics akin to crystalline liquids, intramolecular interblock activity, and enduring intramolecular nanomotors (such as block copolymers of ethylene and propylene oxides, which act as sources of oligomer homologues of oxyethylene ethers) are added to crude oil during primary oil processing. This phenomenon is attributed to the redistribution of oligomer homologues, with the most hydrophobic oxyethylene ethers being dispersed in water-oil emulsions and the most hydrophilic ones in oil-water emulsions, resulting in robust nanodispersed phases with crystalline liquid properties.

Recognizing the discipline category of the abstract text is of great significance for automatic text recommendation and knowledge mining. Therefore, this study obtained the abstract text of social science and natural science in the Web of Science 2010-2020, and used the machine learning model SVM and deep learning model TextCNN and SCI-BERT models constructed a discipline classification model. It was found that the SCI-BERT model had the best performance. The precision, recall, and F1 were 86.54%, 86.89%, and 86.71%, respectively, and the F1 is 6.61% and 4.05% higher than SVM and TextCNN. The construction of this model can effectively identify the discipline categories of abstracts, and provide effective support for automatic indexing of subjects.