The Agricultural Research Institute (ARI) aimed to adapt a computational pipeline to High-Performance Computing (HPC) to enhance agricultural research. The initial focus was on the feasibility of installing the RStudio Server on the CYCLONE HPC infrastructure, taking cues from successful setups at Princeton University and Iowa State University. The goal was to harness RStudio’s statistical computing alongside CYCLONE’s computational resources for managing extensive agricultural datasets. Upon confirming the feasibility, the next step was adapting the computational pipeline for HPC. This involved detailed planning and execution to ensure the seamless integration and utilization of HPC resources. The objective was to establish a scalable, efficient, and robust pipeline to manage diverse datasets crucial for modern agriculture.
We were privileged to provide strategic guidance to C.B.B.Lifeline Biotech Ltd, with a central focus on capitalizing on High-Performance Computing (HPC) for advanced biological data analysis. This evolving field produces copious amounts of complex information, demanding effective computation methods for thorough interpretation and study.
Our advisory journey began by familiarizing the firm with procedures for managing this complex biological data. We highlighted the necessity for scalable strategies, emphasizing the advantages of implementing parallel computing techniques and systems based on data decomposition to process extensive datasets efficiently.
Due to its geographical position in the eastern Mediterranean Sea basin, Cyprus, a global biodiversity hotspot, is directly threatened by climate change and desertification. The country also has Europe’s second-highest population growth rate, emphasizing the need to optimize primary production strategies. These strategies aim to ensure food security and minimize environmental impact cost-effectively and timely. Given the population growth and the recent registration of locally produced cheese (Halloumi) as a Protected Designation of Origin, the demand for goat and sheep milk is expected to surge dramatically. Current milk production scenarios suggest that the goat and sheep livestock population should double (from ~500k to 1000k) to meet the anticipated needs. An alternative approach to meet this demand could be to enhance the existing livestock using genetic data and modelling.
The EuroCC team has collaborated with the Department of Psychology and Neuroscience at the University of Cyprus (UCY), to develop the first multimodal machine learning models for the prediction of Alexithymia in literature.