Signe Balina

This research article explores the contextualization of information objects in enhancing knowledge management within digital workspaces. It emphasizes the critical role of context in managing unstructured data and presents a systematic approach for extracting context dimensions and attributes for information object context modeling. The research article discusses several implications of context-aware computing for organizational productivity: efficient information retrieval, improved knowledge management, support for remote and hybrid work models, reduced data loss, enhanced user activities, and business-level services. The article emphasizes the CASAD matrix modeling method and proposes the approach of extracting the set of attributes for building a context model. A case study is presented to demonstrate the practical application of this approach in an organizational setting. It is shown how combining large language models (LLMs) and organization-specific metamodels contributes to computing secondary context attributes. The research concludes that contextualizing information objects, supported by artificial intelligence and LLMs, can enhance organizational productivity by providing a ground for personalized digital workspaces, efficient information handling, and improved knowledge management processes. It can also support the evolving needs of organizations, such as remote and hybrid work models.

Predicting the growth of broiler chickens is an essential task in the poultry industry. The data used in the study include both the production environmental indicators (temperature, gas concentration, humidity, and others) and the growth rates of poultry (weight, amount of feed consumed, fall) by analyzing their correlations throughout several production cycles. The proposed approach includes several stages, starting with data pre-processing, broiler weight data augmentation, comparison with a reference model, definition, and detection of uncomfortable and dangerous environmental conditions. For the model-building part, the Long short-term memory (LSTM) artificial neural network is applied. The validation of the forecasting model is done by comparing the forecasted weight provided by the model with the actual weight measurements during the randomly selected bird life cycle and varied environmental conditions. The acquired results showed that the provided forecast accuracy is sufficient for production management.

The functional requirements of a data collection automation tool for medical research projects are based on existing data collection automation tools and the current research project experience of Institute of Clinical and Preventive Medicine of the University of Latvia and were generalized to be applicable to a wider range of medical research projects. The tool use scenarios include design of the whole research project dataset content and its individual elements, as well as the creation of a data-recording interface application. The tool is designed as an Angular-based Web application that is used to provide prolific client-side functionality, NET REST services for server-side functionality, and SQL database and SMTP server for user management.

The presented work proposes a practical approach to bird weight data processing and augmentation to enable production outcome forecast model training, which contributes to higher productivity. We suggest using the parametrized model, where parameter values are found through genetic optimization and thus are closely corresponding to broiler body weight factual measurements. The proposed approach is implemented as a stand-alone software system, exposing the models through containerized web services enabling different use scenarios.