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Sustainability performance of Dutch firms and the role of digitalization: The case of textile and apparel industry

The textiles and apparel industry is a major contributor to economic development while at the same time being one of the most polluting industries due to its lengthy supply chain and resource intensive production operations. To address these sustainability challenges, digitalization is seen as one of the potential solutions. Using the lens of sustainability and digitalization in Supply Chain Management (SCM), this paper analyses the sustainability and digitalization status of Dutch textile and apparel firms. We used a mixed methodology of quantitative text mining of 94 Dutch textile and apparel firms as well as qualitative thematic and coding analysis of experts’ views and opinions on sustainability and digitalization in the Dutch textiles and apparel industry. Quantitative analysis of website data shows that Dutch textile and apparel firms predominantly communicate the environmental, to a lesser extent social, and least of all economic sustainability factors. Keyword analysis also shows that the use of technological keyword indicators is less prominent, while certain technologies such as IoT, sensors and blockchain correlate mostly to environmental sustainability factors. Moreover, qualitative analysis reveals that to address sustainability via digitalization, it is important to link sustainability goals to Key Performance Indicators, which requires data for traceability. We recommend firms to: (1) re-evaluate their business models and assess the extent traceability can be incorporated in their sustainability strategy; (2) enhance stakeholder collaboration within and outside the supply chain to utilize traceability; and (3) proactively use traceability information to improve transparency and accountability to meet legal requirements and address greenwashing. This study contributes to literature by showing the importance of traceability for (a) linking sustainability and digitalization in SCM, b) achieving the ultimate goals of transparency and accountability, and c) predicting demand and supply to address overproduction and waste in the textiles and apparel sector.

Process to extract and purify Δ9-tetrahydrocannabinol

From Novice to Composer

Concerns have been raised over the increased prominence ofgenerative AI in art. Some fear that generative models could replace theviability for humans to create art and oppose developers training generative models on media without the artist's permission. Proponents of AI art point to the potential increase in accessibility. Is there an approach to address the concerns artists raise while still utilizing the potential these models bring? Current models often aim for autonomous music generation. This, however, makes the model a black box that users can't interact with. By utilizing an AI pipeline combining symbolic music generation and a proposed sample creation system trained on Creative Commons data, a musical looping application has been created to provide non-expert music users with a way to start making their own music. The first results show that it assists users in creating musical loops and shows promise for future research into human-AI interaction in art.

Effects of preservation on protein extraction in four seaweed species

Using either freshly pulped or preserved seaweed biomass for the extraction of protein can have a great effect on the amount of protein that can be extracted. In this study, the effect of four preservation techniques (frozen, freeze-dried, and air-dried at 40 and 70 °C) on the protein extractability, measured as Kjeldahl nitrogen, of four seaweed species, Chondrus crispus (Rhodophyceae), Ascophyllum nodosum, Saccharina latissima (both Phaeophyceae) and Ulva lactuca (Chlorophyceae), was tested and compared with extracting freshly pulped biomass. The effect of preservation is species dependent: in all four seaweed species, a differenttreatment resulted in the highest protein extractability. The pellet (i.e., the non-dissolved biomass after extraction) was also analyzed as in most cases the largest part of the initial protein ended up in the pellet and not in the supernatant. Of the four species tested, freeze-dried A. nodosum yielded the highest overall protein extractability of 59.6% with a significantly increased protein content compared with the sample before extraction. For C. crispus extracting biomass air-dried at 40 °C gave the best results with a protein extractability of 50.4%. Preservation had little effect on the protein extraction for S. latissima; only air-drying at 70 °C decreased the yield significantly. Over 70% of the initial protein ended up in the pellet for all U. lactuca extractions while increasing the protein content significantly. Extracting freshly pulped U. lactuca resulted in a 78% increase in protein content in the pellet while still containing 84.5% of the total initial total protein. These results show the importance of the right choice when selecting a preservation method and seaweed species for protein extraction. Besides the extracted protein fraction, the remainingpellet also has the potential as a source with an increased protein content.

Thesaurus based term ranking for keyword extraction

A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite satisfactory results, this measure does not take (semantic) relations between words into account.

Keyword extraction using co-occurrence.

A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite satisfactory results, this measure does not take (semantic) relations between words into account. In this paper we study some alternative relevance measures that do use relations between words. They are computed by defining co-occurrence distributions for words and comparing these distributions with the document and the corpus distribution. We then evaluate keyword extraction algorithms defined by selecting different relevance measures. For two corpora of abstracts with manually assigned keywords, we compare manually extracted keywords with different automatically extracted ones. The results show that using word co-occurrence information can improve precision and recall over tf.idf.