Salmonella enterica subsp. enterica serovar Gallinarum biovar Gallinarum causes fowl typhoid in poultry. In this study, we isolated Salmonella from a Korean retail chicken shell egg and performed whole-genome sequencing, from which we identified one chromosome (4,659,977-bp) and two plasmids (plasmid_1: 87,506 bp and plasmid_2: 2,331 bp). The isolate serotype was confirmed to be Gallinarum, with a biovar type of Gallinarum, which was finally identified as Salmonella enterica subsp. enterica serovar Gallinarum biovar Gallinarum. Multilocus sequence typing confirmed that the isolate was that of sequence type 78. The antimicrobial resistance gene, aac(6')- laa, was identified on the chromosome, and 166 virulence genes were detected on the chromosome and plasmid_1.

Enterococcus species are considered as parts of the indicator strains for fecal contamination on retail meats because they reside in the gastrointestinal tracts of humans and animals. Frequent acquisition and dissemination of antibiotic resistance genes among enterococci have increased their morbidity and mortality rates and thus become a serious public health issue. For example, vancomycin (Van)-resistant and/or multidrug resistant (MDR) enterococci are increased during recent years. Currently, only a few therapeutic options have been approved for linezolid (LZD), daptomycin (DAP), and tigecycline (TGC) to treat VAN-resistant and/or MDR enterococcal infections. In this review, we have updated the recent status of enterococcal resistance to those three last-resort antimicrobials (LZD, DAP, TGC) among livestock animals and retail meats.

This study focused on how retail tech promotes differentiated customer experiences in offline fashion stores. The purpose of this study is to determine the effects of the characteristics of fashion retail tech stores on consumers’ flow and satisfaction. We surveyed Koreans aged 10 to 50 who had experienced offline fashion retail tech stores. The survey was conducted from April 28, 2023, to May 21, 2023. The total number of survey respondents was 200. The quantitative data collected through questionnaires was analyzed using SPSS 25.0. To reveal the effects of fashion retail tech store characteristics on consumer’s flow and satisfaction, frequency analysis, we conducted frequency analysis, factor analysis, reliability analysis, correlation analysis, and regression analysis. The results of this study, figured out that fashion retail tech store’s characteristics, including playfulness, efficiency, interaction, and information provision, have a significant impact on behavior flow, emotional flow, and satisfaction. As a result of analyzing the influence of consumers’ flow led to satisfaction, it was confirmed that emotional flow positively influenced satisfaction, but behavioral flow had no meaningful effect on satisfaction. The results of our study can be used to make a successful marketing strategy and can serve as foundational data for consumer research on retail-tech-applied offline fashion stores.

Several earlier studies have investigated the attitudes and intentions of consumers towards sustainability within both a general (Kim et al., 1998; Nicholls, 2002; Berry & McEachern, 2005) and fashion context (Bray et al., 2011; Henninger et al., 2016; Hosseiunpour et al., 2016; Joergens 2006; Joy et al., 2012; McNeill and Moore, 2015; Reimers et al., 2016; Ritch, 2020; Tey et al., 2018; Bianchi and Gonzalez, 2021). However, there is a paucity of research from the perspective of children (Heo and Muralidharan, 2019; Ritch, 2019; Su et al., 2019; Watkins et al., 2019; Blazquez et. al., 2020; Niinimaki et al., 2020; Riesgo S. B., et al., 2022). There were predictions in 2020 that the global childrenswear market would be worth US$252.2 billion, and was proven to be more resilient than the general fashion sector during the COVID-19 pandemic (Mintel, 2021). Furthermore, the pandemic has seen prominence given to sustainability issues, with consumers increasingly prioritising brands with sustainable credentials (Euromonitor, 2022), yet little is known about children’s attitude towards sustainability. This paper aims to address this shortcoming, by assessing children’s awareness of sustainability. A Theoretical Model is proposed: Children’s sustainability awareness stages infused by educational third places.

This study proposes a new collaborative filtering model that integrates Restricted Boltzmann Machines. The proposed two-stage model is applied to household-level supermarket purchase data. Results show that our model fits the data better and outperforms existing collaborative filtering methods in predicting shopping patterns. The proposed model also improves interpretations of market complexity and common causes of coincidence associated with customers’ multi-category purchases.