Open Access

In recent years, companies have become increasingly aware of the importance of social responsibility and sustainability in their operations. Social enterprises have emerged as a concept that aims to blend social objectives with profit-making characteristics. For social enterprises to succeed, a well-designed marketing strategy is essential. Ideally, this strategy should clearly communicate unique purposes and future visions, while justifying any high prices associated with their commitment. Patagonia is an example of a social enterprise that implements a purpose-driven marketing strategy.

Natural language processing (NLP) offers the potential to automate quality assurance of software requirement specifications. Especially large-scale projects involving numerous suppliers can benefit from this improvement. However, due to privacy restrictions and domain- and project-specific vocabulary, as such in the aerospace domain, the availability of SRS documents for training NLP tools is severely limited. To provide a sufficient amount of data, we studied algorithms for the augmentation of textual data. Four algorithms have been studied by expanding a given set of requirements from European Space projects generating correct and incorrect requirements. The study yielded data of poor quality due to insufficient accuracy caused by the domain-specific vocabulary, yet, laid the foundation for the algorithms improvement, which, eventually, resulted in an increased set of requirements, which is 20 times the size of the seed set. Finally, an explorative experiment demonstrated the usability of augmented requirements to support AI-based quality assurance.

Sleep deprivation is a significant contributor to various diseases, leading to poor cognitive function, decreased performance, and heart disorders. Insomnia, the most prevalent sleep disorder, requires more effective diagnosis and screening for proper treatment. Actigraphic data and its combination with physiological sensors like electroencephalogram (EEG), electrocardiogram (ECG), and body temperature have proven significant in predicting insomnia using machine learning methods. Studies focusing solely on actigraphic data achieved an accuracy of 84%, combining it with other wearable devices increased accuracy to 88%, and 2-channel EEG alone yielded an accuracy of 92%, but limits scalability and practicality in real-world settings. Here we show that using the hybrid approach of incorporating both recursive feature elimination (RFE) and principal component analysis (PCA) on sleep and heart data features yields outstanding results, with the multi-layer perception (MLP) achieving an accuracy of 95.83% and an F1 score of 0.93. The top-ranked features are predominantly sleep-related and time-domain RR interval. The dependent variables in our study have been extracted from the self-report Pittsburgh Sleep Quality Index questionnaire responses. Our findings emphasize the importance of tailoring feature sets and employing appropriate reduction techniques for optimal predictive modeling in sleep-related studies. Our results demonstrate that the ensemble classifiers generalize well on the dataset regardless of the feature count, while other algorithms are hindered by the curse of dimensionality.

Our previous article was focusing on the challenges of hybrid work environments. In this continuation of our exploration, we delve deeper into the strategies essential for success in hybrid work environments.

Energy consumption aspects of machine learning classifiers are important for research and practice as well. Due to sparse research in this area, a prototype of a recommender system was developed to provide energy consumption recommendations of different possible classifiers. The prototype is demonstrated as well as discussed and future research points are derived.