The research performed in this thesis aims to improve our understanding about one of the most malignant tumors of the human brain – glioma. From the early stages of my career I was confronted with the cruel reality of losing patients due to this devastating disease. The studies performed over the last four years involve extensive data analysis in different clinical and laboratory settings. The direct application of different analysis methods and tools in order to investigate the glioma infiltration delineation has potentially lead to direct applications of our results in the clinical setting. The overall approach of the study is based on three primary outcome measures, i.e., neuronal, electrophysiological and genetic/molecular features for distinguishing infiltrated and non-infiltrated zones within specifically peritumoral tissue (PT) and, more extensively, across the radiologically-defined boundaries of healthy, peritumoral and tumoral tissues. As such, we propose for the first time an objective demarcation and characterization of the PT and we detail how the genetic and epigenetic alterations within the tumoral and peritumoral area are linked with macroscopic functional MRI results. We also describe scale-free features (power law exponent) as well as distinct spectral features and reactivity to external stimulus in the tumoral and adjacent tissue of patients and provide novel insights in terms of glioma’s electrophysiology. The insights gained from these empirical studies further improve our understanding about the pathophysiology of this disease at micro- and macroscopic scales allowing us to envisage novel management methods for patients affected by glioma.

The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU to get a distributed wind speed. The distributed wind speed is then used to interpolate the wind power with the help of the power curve of the wind turbine. The interpolated wind power is then compared with the Actual Production Data (APD) to validate the prediction model. The simulation results show that the model works fairly predicting the Annual Energy Production (AEP) on monthly averages for all sites but the model could not follow the APD trend on all cases. The sensitivity analysis shows that the variation in production does not depend on ’the variation in roughness class’ nor ’the difference in distance between the measuring station and the wind farm’. The thesis has been concluded from the results that the model works fairly predicting the AEP for all cases within the variation bounds. The accuracy of the model has been validated only for monthly averages since the APD was available only on monthly averages. But the accuracy could be increased based on future work, to assess the Power law exponent (a) parameter for different terrain and validate the model for different time scales provided if the APD is available on different time scales.