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Nowadays, more fashion companies have started to adopt various sustainability practices and communicate these practices through their annual public CSR reports. In this study, we aim to provide a holistic perspective of fashion companies’ sustainable development and investigate the sustainability practices of global fashion companies. A total of 181 CSR reports from 29 fashion companies were collected. A Dictionary approach text classification method, combined with Latent Dirichlet Allocation (LDA), a computer-assisted topic modeling algorithm, was implemented to detect and summarize the themes and keywords of detailed practices disclosed in CSR reports. The findings identified 12 main sustainability practices themes based on the triple bottom line theory and the moral responsibility of corporate sustainability theory. In general, waste management and human rights are the most frequently mentioned themes. The findings also suggest that global fashion companies adopted different sustainability strategies based on their product categories and competitive advantages.

Employing dual-emission thermally activated delayed fluorescence (TADF) molecules to fabricate single emitters in highly efficient white organic light-emitting diodes (WOLEDs) is still a challenge. In this work, a systematic study on the luminescence mechanism of three TADF molecules with blue-orange dual emission is performed. It is found that the quasi-axial (ax) conformer is responsible for blue normal fluorescence (NF), while the quasi-equatorial (eq) conformer is responsible for orange TADF. In addition, the upconversion for the TADF molecules happens from the first triplet state (T

Numerous brands utilize social media to capture consumers’ interests while promoting their sustainability goals. To understand how sustainable fashion brands communicate with their consumers, this study explored the visual and textual information sustainable fashion brands post on social media. Data were collected from sustainable fashion brands’ social media pages, and a total of 1525 images and captions and 140,735 comments were analyzed. By employing color theory and the theory of speech acts, HSV color analysis and the SVM classification model were used to extract information. The results showed that the images and captions posted by all three brands were consistent with their brand identities and sustainability goals. We also found that there were significant differences among the three brands when comparing posts employing expressive and assertive acts with posts using directive and assertive acts. These results indicate that social media users are more likely to leave comments when they read posts containing expressive and directive acts. These findings will allow fashion social media marketers to select appealing images and colors to engage consumers as well as to choose appropriate speech acts to deliver information to achieve their sustainability goals.

With the increasing demand for fashion products, the textile and apparel industry is facing huge challenges in resource management and environmental regulations. Fashion renting provides an option to reuse clothing products. Besides that, it also fulfills an individual’s fashion needs while reducing the production of new clothes. Though some studies have been done to examine consumers’ opinions of fashion renting, most of these studies employed survey methods and did not utilize actual data of consumers’ real renting experiences. The purpose of this study is to evaluate consumers’ actual renting experiences and to identify the motivations and barriers for those consumers to rent fashion products. A data-mining approach was applied in this study. Consumers’ comments on renting experiences from three fashion rental companies, Rent the Runway (RTR), Gwynnie Bee (GB), and Bag Borrow Steal (BBS), were collected as a reliable data source to dig into and identify consumers’ motivations and concerns. Based on the theory of customer value, both benefits and costs for fashion renting were discovered. In addition, a comparison of the three fashion rental companies was also discussed. This study is the first attempt to use a data-mining method to thoroughly investigate the benefits and costs of fashion online renting through real consumers’ feedback of three different types of rental companies. It provides an in-depth text analysis of consumers’ online fashion renting experiences through the use of Latent Dirichlet Allocation (LDA) topic modeling and word co-occurrence networks.

Thermally activated delayed fluorescence (TADF) molecules with dual emission have great potential for use as single emitters in white organic light-emitting diodes (WOLEDs). In this paper, the light-emitting mechanisms of PTZ-TTR and PTZ-Ph-TTR with blue-orange dual emission are studied systematically. The near-planar and near-orthogonal conformations are responsible for the blue and orange emission, respectively. For PTZ-TTR, the near-orthogonal conformation is only generated by the transformation from the near-planar conformation, while the near-orthogonal conformation of PTZ-Ph-TTR can be generated by both excitation and transformation. This results in relatively strong orange emission in PTZ-Ph-TTR. In addition, the TADF mechanism is investigated, and two up-conversion pathways are revealed for both molecules. Based on the comparison of the photophysical properties of PTZ-TTR in toluene and the aggregation state, we find that aggregation could induce a smaller energy gap between the first singlet excited state and the first triplet excited state. Besides, the substitution effect of donors on light-emitting properties is studied, and the design rules for emitters with dual conformations and compensatory emission are proposed. Our theoretical results would favor the understanding of the light-emitting mechanism as well as the design of new-type TADF emitters for WOLEDs.

Allergic disorders are caused by a combination of hereditary and environmental factors. The hygiene hypothesis postulates that early life microbial exposures impede the development of subsequent allergic disease. However, unambiguous evidence that microbes reduce the development of allergic disorders is still lacking. Recently developed ‘wildling’ mice contain a rich and diverse commensal and encounter a repertoire of microbes typical of the wild, with pathogenic potential. Here, we probed the hygiene hypothesis by comparing the development of allergic inflammation in wildlings to that of genetically identical mice lacking diverse microbial exposure. We find that wildlings develop stronger allergic inflammation in response to house dust mites with allergic T cell responses driven not only by cognate peptide antigens, but also by innate cytokines. In all, the results suggest that high microbial content and diversity potentiates, rather than restricts, allergic immune responses.One sentence summaryStrong allergic inflammation in the face of rich and diverse microbial exposures

Intestinal helminth parasites can alter immune responses to vaccines, other infections, allergens and autoantigens, implying effects on host immune responses in distal barrier tissues. We herein show that the skin of C57BL/6 mice infected with the strictly intestinal nematode Heligmosomoides polygyrus contain higher numbers of CD4

Trend forecasting is crucially important and challenging in the fashion industry (Bikhchandani, S., Hirshleifer, D., & Welch,1992),and recently has been an emerging research area in computer vision and machine learning (Vittayakorn et al., 2015; Liu et al., 2016; Han et al., 2017). In the fashion world, trend forecasting is defined as the search for a means to predict mood, behavior, and buying habits of the consumer through identifying trends (Halland & Jones, 2017). With the advent of computational approach, it’s possible to translate the creativity and inspiration of practitioners into a data-driven structure, especially for short-term forecasting which is the main focus of this study.

This paper aims to discover the current needs of adaptive clothing consumers and how consumers assess the adaptive clothing product in the context of online shopping, through a text-mining approach. 253 adaptive clothing product titles and 1060 customer reviews posted on Silvert’s, an adaptive clothing website were collected using Python. The terms associated with needs and target consumers were selected and coded guided by the FEA theory. Then the Latent Dirichlet Allocation (LDA) was implemented to extract the topics from consumer reviews. The finding suggests that the functional needs are most important from both marketers’ and consumers’ perspective in adaptive clothing market. Online marketers mainly use functional words to introduce the products, while only few words are related to aesthetic. For future research, data from other resources, such as social media, will be collected to further explore the online searching habits of adaptive clothing consumers.

Thermally activated delayed fluorescence (TADF) molecules have attracted great attention as high efficient luminescent materials. Most of TADF molecules possess small energy gap between the first singlet excited state (S1) and the first triplet excited state (T1) to favor the up-conversion from T1 to S1. In this paper, a new TADF generation mechanism is revealed based on theoretical simulation. By systematic study of the light-emitting properties of SOBF-OMe in both toluene and in aggregation state, we find that the single SOBF-OMe could not realize TADF emission due to large energy gap as well as small up-conversion rates between S1 and T1. Through analysis of dimers, we find that dimers with intermolecular hydrogen bond (H-bond) are responsible for the generation of TADF, since smaller energy gap between S1 and T1 is found and the emission wavelength is in good agreement with experimental counterpart. The emission properties of SOBF-H are also studied for comparison, which reflect the important role of H-bond. Our theoretical results agree ith experimental results well and confirm the mechanism of H-bond induced TADF.

Trend forecasting is a challenging and important aspect of the fashion industry. The authors design a novel fashion trend analysis system called “Neo-Fashion,” which provides recommendations to fashion researchers and practitioners about potential fashion trends using computer vision and machine learning. Neo-Fashion includes three modules, a data collection and labeling module, an instance segmentation module and a trend analysis module. Diffusion of innovation theory is used as the main theoretical framework to understand fashion trends. 32,702 catwalk images from 2019 fashion week were collected, and 769 images were labeled as training data. Neo-fashion is able to identify and segment fashion items in the given images, and indicate the fashion trends in colors, styles, clothing combinations, and other fashion attributes. To optimize the system, more data sources can be included to not only reflect trends in even more categories but also aid in understanding the trickle-up or trickle-across process in fashion.

In this study, we aim to provide a holistic perspective of fashion brands sustainable development and investigate the sustainability practices of US fashion brands. To achieve the research goal, CSR reports of 12 US brands from all public companies of the fashion industry were collected. To identify and classify the sustainability practices in these documents, a dictionary approach by Python was used based on the Triple Bottom Line theory. The finding suggests that US fashion implement different sustainability strategies based on product categories and competitive advantages.

Many brands have added social media as an important outlet for communication about sustainability. It is important to understand how sustainable apparel brands utilize social media to advertise their product and service, engage with consumers, and to find where social media marketing for sustainable brandsshould go. By analyzing both visual and textual information, this study aims to investigate the impact of color and text features of brands’ posts on customers’ response, including their emotions and emphasis topics in social media marketing. Color theory and semantic frame theory were used in this study. A system was established to scrape images, captions and comments regarding three fashion brands from the official Cloud application program interface on Instagram. Color analysis results reveal that images brands chose for social media posts are consistent with their values and visions. Results of text analysis show that three brands focus on conveying different topics and values.

Sustainable Supply Chain Management (SCM) indicates to a company’s mission and vision that incorporate environmental and social issues into SCM to enhance the company’s environmental and social performance including its suppliers and customers without hampering its overall economic performance. Past researches have been focused on specific aspects of sustainability from the individualistic view and there is a need for conducting further research focusing on sustainability at the organizational level. To address this issue, 44 of publically available sustainability/corporate social responsibility reports from 11 of the top U.S. fashion brands were reviewed using data mining technique. Results showed that all of these brands are practicing continuous improvement, or Kaizen, inherently, even though the term Kaizen might not be explicitly stated in their sustainability practices. Future research is recommended to see how Kaizen principles could yield improvement in various corporate sustainability goals and structures across different fashion brands worldwide.

With the increasing demand for fashion products, the textile and apparel industry is facing huge challenges in resource management and environmental regulations. Fashion renting provides an option to reuse clothing products and also fulfills an individual’s fashion needs while reducing the production of new clothes. The purpose of this study is to evaluate consumers’ actual renting experiences and to identify the motivations and barriers for those consumers to rent fashion products. A data-mining approach was applied in this study. Consumers’ comments on renting experiences from three fashion rental companies, Rent the Runway (RTR), Gwynnie Bee (GB), and Bag Borrow Steal (BBS), were collected as a reliable data source to dig into and identify consumers’ motivations and concerns. Based on the theory of customer value, both benefits and costs for fashion renting were discovered. In addition, a comparison of the three fashion rental companies was also discussed. This study is the first attempt to use a data-mining method to thoroughly investigate the benefits and costs of fashion online renting through real consumers’ feedback of three different types of rental companies. It provides an in-depth text analysis of online fashion renting consumers’ experiences through the use of LDA topic modeling and word co-occurrence networks.

This article continues the works of references to improve and perfect the sampling theorem of exponential distribution. First, the distribution of the sample range of exponential distribution is derived, and that the sample range is mutually independent of the sample minimum is proven. Then, this article derives the distribution of the difference between sample maximum and mean and demonstrates that the difference of these two statistics is mutually independent of the sample minimum. Thus, three local minimum variance unbiased estimators of the mean could be constructed. The estimator built by sample minimum and the difference between sample mean and minimum is precisely the uniformly minimum-variance unbiased estimator (UMVUE) of the mean. Similarly, three local minimum variance unbiased estimators of the variance are derived. At last, the efficiency comparison is made among the above three local minimum variance unbiased estimators of mean and variance of the exponential distribution.