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Agricultural waste and food sources are some of the pollutants of the environment. One of these wastes is the peel of fruits that cannot be consumed as food. In this regard, walnut husk (WH) and oleaster peel (PO) are known as two important sources of tannin and are bulky wastes. Because of the high percentage of tannin, these materials can be used as a natural source for the preparation of bio-mordant in the dyeing process.

In this study, Reseda and Madder were used as natural dyes in the presence of a mixture of two bio-mordants. WH and PO were selected as bio-mordant. All natural resources are extracted to obtain the juice. The phenolic percentage of tannin-containing extracts was evaluated and then it was used for wool yarns by premordanting method. The results of evaluating the fastness properties using the ISO method.

The most important achievement of this research is the use of agricultural waste in the dyeing process to reduce environmental pollution and create added value. All compounds rich in tannin have some phenolic components, therefore the amount of phenolic content of these extracts was evaluated. The effect of mixing the mordant on the color characteristics of the yarns was investigated and the results showed that changing the ratio of the combination of two mordant is effective on the K/S values. The results of evaluating the fastness properties using the ISO method showed that the washing, rubbing and light fastness in the presence of mordant is good, good and moderate, respectively.

In this paper, to the best of the authors’ knowledge, for the first time, the combination of two natural extracts obtained from agricultural waste has been used to create a new bio-mordant on fibers and improve stability.

Transitional periods are important for people, such as birth, marriage and death, are important times when ceremonial meals are prepared and served and certain practices are carried out. These periods and the practices constitute our gastronomic cultural heritage. In order to keep our cultural heritage alive and pass it on to future generations, existing values must first be identified and recorded. For this reason, in this study, gastronomic practices of Mentese's transitional periods were examined within the scope of intangible cultural heritage.

In this research, data was collected using ethnographic design, which is one of the qualitative research methods, and document analysis, interviews and focus group studies, as well as participant observation techniques and image/audio materials. The sample was determined using snowball sampling, convenience sampling and maximum diversity sampling methods. In the analysis of the data, themes and codes related to gastronomic practices in transitional periods were created and direct quotations were included with a descriptive approach.

It has been found that traditions are kept more alive in rural areas, while those living in the city centres no longer perform these practices dating back to the ancient times. Participants attribute the main reason for this to the fact that economic conditions are not favourable and that some traditional practices are “unnecessary” today. While wedding meals, which are the main part of the weddings, used to be made by women in the past, they are now mostly made by catering companies.

It was accepted that some of the participants started to give short answers as the duration of the interview increased and that the answers given were limited to what the participants could remember.

This study reveals special day meals and practices, rituals and traditions related to food within the scope of Mentese's culinary culture, which has a very deep-rooted history. In this regard, in addition to making an important contribution to the literature, the study also contributes to the articles about “recording and promoting the culture” mentioned in the Intangible Cultural Heritage convention.

This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications.

An extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis.

The performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature.

Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed.

Nowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).

Two approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.

In Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.

An original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

After working on the assignment questions, the learners can achieve the following learning outcomes: understand the buying behavior towards sustainable products in the context of mindful consumption and product characteristics, appraise the market segmentation and positioning strategy of a sustainable business, understand the application of 5C’s framework for a sustainable business and critically evaluate a new sustainable business’s challenges in the emerging business environment.

Dr Joe Fenn, founder and director of PFoods, with extensive experience in the pharma industry overseas, observed a decline in the consumption of traditional dairy foods. Alternative plant foods come as a savior to people who are lactose intolerant and offer a host of health benefits with low environmental impact. Riding on the waves of veganism and sustainable foods, he saw an opportunity in India. PFoods developed and launched two products, namely, Just Plants (plant-based milk alternative) and Plotein (plant-based protein alternative), in collaboration with scientists at the Indian Institute of Science, a premier scientific institution in India, and PMEDS (PreEmptive Meds), a US-based nutraceutical Company. PFoods launched and pilot-tested Just Plant, a dairy alternative substitute for milk in select reputed organizations in Bangalore. The upcoming challenges for Fenn would be to select the right segment, educate the market and position the product that would resonate well with the target customers.

The case study suits undergraduate and graduate courses such as marketing management, sustainable marketing and sustainable business. The case study can also be used in entrepreneurship management and entrepreneurial marketing courses to introduce the challenges of a sustainable startup. The case study highlights the marketing challenges faced by the disruptive and growing plant-based foods or alternative dairy industry in emerging markets.

Teaching notes are available for educators only.

CSS 8: Marketing.

This research aims to investigate how absorptive capacity impacts artisan businesses' innovation performance in Jammu and Kashmir, India. Additionally, the study examines the role of strategic orientation (customer and technological orientation) as a mediator.

The study analysed data from 408 artisan entrepreneurs using partial least squares structural equation modelling. The research model was built on the “Dynamic-Capability Theory” of absorptive capacity and the “Resource-Based Theory” of performance.

The study’s findings suggest that both realised and potential absorptive capacity positively and significantly impact innovation performance. Moreover, customer and technology orientations positively and strongly influence innovation performance. Additionally, potential and realised absorptive capacity has a favourable impact on customer and technology orientation. The mediation analysis results indicate that customer and technological orientation have complementary partial mediation between potential absorptive capacity and innovation performance. Finally, mediating variables like customer and technological orientation show complementary partial mediation for realised absorptive capacity.

The research model would enrich the existing literature and offer an improved understanding of how absorptive capacity enhances the innovation performance among artisan entrepreneurs and concurrently validates the theory of “Dynamic-Capability Theory” of absorptive capacity and the “Resource Based Theory” of innovation performance of a firm.

This study aims to explore the applications of natural language processing (NLP) and data analytics in understanding large-scale digital collections in oral history archives.

NLP and data analytics were used to analyse the oral interview transcripts of 904 survivors of the Japanese American incarceration camps collected from Densho Digital Repository, relying specifically on descriptive analysis, keyword extraction, topic modelling and sentiment analysis (SA).

The researchers found multiple geographic areas of large residential communities of ethnic Japanese people and the place names of the concentration camps. The keywords and topics extracted reflect the deplorable conditions and militaristic nature of the camps and the forced labour of the internees. When remembering history, the main focus for the narrators remains the redress and reparation movement to obtain the restitution of their civil rights. SA further found that the forcible removal and incarceration of Japanese Americans during Second World War negatively impacted and brought deep trauma to the narrators.

This case study demonstrated how NLP and data analytics could be applied to analyse oral history archives and open avenues for discovery. Archival researchers and the general public may benefit from this type of analysis in making connections between temporal, spatial and emotional elements, which will contribute to a holistic understanding of individuals and communities in terms of their collective memory.

The objective of this study was to develop wearable suit platforms with various anchoring structure designs with the intention of improving wearability and enhancing user satisfaction.

This study selected fabrics and materials for the suit platform through material performance tests. Two anchoring structure designs, 11-type and X-type are compared with regular clothing under control conditions. To evaluate the comfort level of the wearable suit platform, a satisfaction survey and electroencephalogram (EEG) measurements are conducted to triangulate the findings.

The 11-type exhibited higher values in comfort indicators such as α, θ, α/High-β and lower values in concentration or stress indicators such as β, ϒ, sensorimotor rhythm (SMR)+Mid-β/θ, and a spectral edge frequency of 95% compared to the X-type while walking. The 11-type offers greater comfort and satisfaction compared to the X-type when lifting based on the EEG measurements and the participants survey.

It is recommended to implement the 11-type when designing wearable suit platforms. These findings offer essential data on wearability, which can guide the development of soft wearable robots.

This study aims to investigate the landscape of family-based organic farm businesses in the Kashmir Valley, India, analyzing the factors that either facilitate or hinder their adoption. The research also intends to uncover sources of information seeking. The primary purpose is to provide qualitative evidence to address existing knowledge gaps and offer insights for promoting sustainable farm practices in the region.

The research employs a qualitative approach, drawing on focus group interviews. The study thoroughly explores the background and relevant literature, utilizing a comprehensive research framework. Data is collected from family based farmers engaged in organic farming practices in the Kashmir Valley. The data is analyzed using content analysis ensuring a robust and thorough exploration of the subject matter.

This study reveals a notable transition in the agricultural landscape of the Kashmir Valley, showcasing a widespread adoption of organic farming on considerable land. The study reveals that key facilitators for organic farming among family-based farms are farm productivity, entrepreneurial intention, governance, environmental consciousness, and health concerns. The exchange of information, both through formal and informal channels, is found to be a crucial factor influencing the adoption of organic farming. The study also unveiled significant inhibitors that hinder the adoption of organic farming on commercial scales, including on-farm challenges such as difficulties in acquiring inputs and facing reduced yields, market-related concerns, and a lack of support and assistance from government agencies.

This research contributes significantly to the existing literature by advancing the understanding of organic farm business and agri-entrepreneurship. It unveils key factors that either support or hinder family-based organic farms, identifying crucial information sources and presenting valuable insights for policymakers. Furthermore, this study provides practical guidance for overcoming obstacles, enhancing infrastructure, and translating identified facilitators into successful agri-ventures in the Kashmir region.