Economic growth and development require a consistent supply of energy. Energy which has mainly been supplied from fossil fuels. The impacts of these on the environment such as global warming, raised an alarm on their use. As a result other sources of energy such as wind energy are used as alternatives for electricity production. Wind energy assessment nevertheless faces barriers due to its stochastic nature. This later creates various regimes, which traditional models can't always fit thereby producing poor estimates. In this work, we aim to use Large Language Models (LLMs) to predict the wind potential in a given location. Through this approach, we aim at lifting the barrier on energy problems in developing countries by providing knowledge on the state of wind energy in given locations.

In this paper, we propose a Retrieval-Augmented Generation-based (RAG) approach for fair access to food data in developing countries. This work also contributes to achieving sustainable development goals, specifically goal 2: Zero hunger and goal 3: Ensure healthy lives and promote well-being for all at all ages. Actually, given that a lot of African food data is accessible only in PDF format, we firstly extracted and organized these data using the Open Research Knowledge Graph (ORKG) and the image data using the Firebase database, with the link to the corresponding food description in the ORKG. Currently, more than 1000 foods eaten in around 50 African countries are already documented. Given that Large Language Models necessitate a lot of data and resources to be train/fine-tuned, which we do not have in our university, our idea is to use the food data stored in the ORKG to build a Retrieval-Augmented Generation system (RAG) for improving access to food data in Africa. Its implementation involved the use of multi-embedding models, vector databases (Pinecone), Falcon3 and LangChain. Because we do not have enough resources, this work is made possible thanks to a collaboration with TIB-Hannover who provide access to remote computers.

A constant and consistent supply in electrical energy in a location is a reflection of a good economy. Developing countries nevertheless don't have access to this quality of energy, which slows down their economy and consequently development. Wind is a clean, sustainable and renewable resource which can be used to meet the energy needs in such countries. However, the intermittent nature of wind yields fluctuations on the amount of energy produced by a wind turbine. Coupled with frictional power losses in the wind turbine gearbox bearings, one can't be sure on the exact amount of energy that will be produced. This leads to the distribution and management issues. To tackle this issue, we propose here the use of Large Language models. These are tools which have been proving their potential in various domains till date and whose potential are still to be seen in the field to our knowledge. Taking advantage of their flexibility and adaptability to any model and dataset, we intend to explore its abilities in the fields of wind energy and tribology. Making use of available data, predictions on the wind energy potential and power losses will be carried out using Large Language models such as BERT. The results of this work intends to promote the use of wind energy by lifting barriers in thee management and knowledge of the resource.

In recent years, the research on food information gave rise to the food information engineering domain. The goal of this paper is to provide to the research community with a systematic literature review of methodologies, methods and tools used in this domain.

Large software systems evolve rapidly and these evolutions are usually integrated directly into source code without updating the conceptual model. As a consequence, implementation platforms evolve faster than business logic. Thus, when extracting knowledge to enrich or build an ontology, business logic is not always a complete data source. To solve this problem, some authors have suggested to adopt an ontology learning approach in order to extract knowledge from the source code. In this paper, we show how to realize this task using Hidden Markov Models. experiments on EPICAM, a tuberculosis surveillance system shows the relevance of this approach.