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This study aims to study the distribution characteristics of antibiotic resistance in direct-eating food and analysis of Citrobacter freundii genome and pathogenicity. Residual antibiotics and antibiotic resistance genes (ARGs) in the environment severely threaten human health and the ecological environment. The diseases caused by foodborne pathogenic bacteria are increasing daily, and the enhancement of antibiotic resistance of pathogenic bacteria poses many difficulties in the treatment of disease.

In this study, six fresh fruits and vegetable samples were selected for isolation and identification of culturable bacteria and analysis of antibiotic resistance. The whole genome of Citrobacter freundii isolated from cucumber was sequenced and analyzed by Oxford Nanopore sequencing.

The results show that 270 strains of bacteria were identified in 6 samples. From 12 samples of direct food, 2 kinds of probiotics and 10 kinds of opportunistic pathogens were screened. The proportion of Citrobacter freundii screened from cucumber was significantly higher than that from other samples, and it showed resistance to a variety of antibiotics. Whole genome sequencing showed that Citrobacter freundii was composed of a circular chromosome containing signal peptides, transmembrane proteins and transporters that could induce antibiotic efflux, indicating that Citrobacter freundii had strong adaptability to the environment. The detection of genes encoding carbohydrate active enzymes is more beneficial to the growth and reproduction of Citrobacter freundii in crops. A total of 29 kinds of ARGs were detected in Citrobacter freundii, mainly conferring resistance to fluoroquinolones, aminoglycosides, carbapenem, cephalosporins and macrolides. The main mechanisms are the change in antibiotic targets and efflux pumps, the change in cell permeability and the inactivation of antibiotics and the detection of virulence factors and ARGs, further indicating the serious risk to human health.

The detection of genomic islands and prophages increases the risk of horizontal transfer of virulence factors and ARGs, which spreads the drug resistance of bacteria and pathogenic bacteria more widely.

A shared cognitive schema is the fundamental source of tacit understanding within a team. This study aims to address how such a shared cognitive schema emerges and evolves in an interdisciplinary research team.

This study uses an exploratory single case study to analyze the emergence and evolution of a shared cognitive schema in an interdisciplinary research team systematically. The authors spent more than two years collecting data from the IAM team via semistructured interviews, archival data and observation. Subsequently, a framework for the resulting mechanism model was developed by analyzing the data using a three-step process.

This study shows that as the interdisciplinary research team develops, the shared cognitive schema passes through three stages: overlapping cognitive schema, complementary cognitive schema and synergetic cognitive schema. The mechanisms of overlap, complement and synergy play important roles. The convergent roles of partner-based recruiting, knowledge categorization and following the existing institution facilitate the overlapping of knowledge structures. Complementary cognitive schema sharing is facilitated by interdisciplinary member selection, knowledge stock expansion and the effects of accomplished mentors. The synergetic behaviors of group voice, interactive cognition and adaptive learning facilitate synergetic cognitive schema sharing.

This study is the first to discuss the emergence and evolution of a shared cognitive schema at the microlevel of knowledge structure and belief structure. It offers a new theoretical perspective on the development rules of scientific research teams and provides practical enlightenment regarding the establishment and operation of interdisciplinary research teams.

Green innovation and sustainability are two contemporary initiatives that are gaining more and more attention from researchers, academics and industry professionals as they are considered important business strategies to improve environmental conditions and obtain better organizational performance. Besides, the growth of uncontrolled economic activities leads to an imbalance of economic, social and environmental values in different sectors. However, little is known about the mediating role that economic, social and environmental sustainability has in the relationship between green innovation and firm performance. Previous literature has focused on developed economies, but not on a developing economy such as that of Mexico. Therefore, this research aims to fill this existing gap by exploring the mediating effects of sustainability in the relationship between green innovation and firm performance.

A theoretical research model that theorizes, through ten hypotheses, the antecedents and consequences of the mediating effect of economic, social and environmental sustainability and the occurrence between green innovation and firm performance is proposed. The model is tested through PLS-SEM using data that were collected using a questionnaire survey that was distributed among companies in the automotive industry in Mexico. In total, a sample of 460 responses was obtained.

The results suggest that green innovation has significant positive effects on economic and environmental sustainability, as well as on firm performance, but not on social sustainability. The results also indicate that the relationship between green innovation and firm performance improves considerably with the mediation of economic, social and environmental sustainability.

Despite the present study focused on an industrial sector that is commonly at the forefront of technological development, it was limited to a specific region of Mexico. Thus, its results must be taken with caution as more extensive results including other regions and nations will be required to further validate the results obtained from the present study.

The findings of this study have important implications for both policymakers and managers of manufacturing firms in the automotive industry as they can be used as a basis to formulate better strategies and policies to enhance the capabilities of companies to develop innovations that could reduce environmental risks and other consequences of climate change.

The present study adds to the innovation and sustainability body of knowledge by analyzing and discussing the mediating role of sustainability in the relationship between green innovation and firm performance. It also generates new knowledge about the mediating effect that sustainability has on the relationship between green innovation and firm performance, particularly in the context of a developing economy such as that of Mexico.

Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.

This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.

The results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.

The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.