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DEAR FINAL YEAR STUDENT, We would like to inform you that, along with your official university diploma, you have the opportunity to receive a printed PORTFOLIO OF COMPETENCIES. This is an official university-issued document recognizing the skills and competencies you have developed through activities for which VILNIUS TECH digital badges can be awarded. To explore the full range of VILNIUS TECH digital badges, please click here: https://www.badgecraft.eu/en/organisations/16808. To receive the Portfolio of Competencies, you must have at least 3 VILNIUS TECH digital badges in your badge wallet and ensure that you are registered with your full name (using Latin characters) on the badgecraft.eu platform or the BadgeWallet app. If you have collected badges using a different email address, please add your VILNIUS TECH email in the settings. If you already have at least three badges, register no later than June 11th, 2026 (inclusive) by completing this form FORM TO RECEIVE COMPETENCY PORTFOLIO for DIGITAL BADGES (2026) – Fill out form You will receive the printed Portfolio of Competencies along with your university diploma. A digital version (.pdf format) will also be sent to your email. DON’T HAVE ENOUGH BADGES? Many of them can be awarded “for the past.” All you need to do is log in to the VILNIUS TECH Digital Badge System, review your past activities, submit evidence, and wait for approval. Need a clearer plan? Visit https://www.badgecraft.eu/en/organisations/16808. Select the area of activity you were involved in. Check which digital badges you could qualify for. If eligible, log in (or create an account). Complete the tasks associated with the desired badges. * Wait for program administrators to approve (or reject) your evidence. Enjoy your new badges and check if you can earn even more through other badge programs. Collect all the VILNIUS TECH digital badges you’re entitled and get a proof of your activity. *If you see the “Start Mission” button, submit the required evidence, wait for approval, and your new badge will appear in your wallet! If the “Start Mission” button is not available, reply to this email and list all the badges (with links) that you believe you should receive. Include a brief description (1–2 sentences) indicating the year and nature of the activity. Badge program administrators will review your evidence and issue the badge if approved. For more information about the VILNIUS TECH digital badge system, visit: https://vilniustech.lt/studies/digital-badge-system/362195 If you have questions or need assistance, feel free to ask by replying to this email at badge@vilniustech.lt. VILNIUS TECH Digital Badge Team

VILNIUS TECH Library invites you to follow the published new dissertations. The dissertation „Investigation of recurrent neural networks-based methods for early fault detection and short-term power forecasting in wind energy applications“ prepared at VILNIUS TECH by Mindaugas Jankauskas. The dissertation was prepared in 2021–2026. Scientific consultant – Prof. Dr Artūras Serackis. The dissertation was defended at the public meeting of the Dissertation Defence Council of the Scientific Field of Electrical and Electronic Engineering in the Aula Doctoralis Meeting Hall of Vilnius Gediminas Technical University at 10 a. m. on 5 June 2026. The increasing role of wind energy in modern power systems creates a growing need for reliable turbine operation, accurate short-term power forecasting, and computationally efficient data-driven methods. This dissertation addresses two related problems: early fault detection in wind turbines using supervisory control and data acquisition (SCADA) time-series data, and short-term wind farm power forecasting using meteorological forecasts. The dissertation aims to develop and investigate data-driven methods that improve the accuracy, efficiency, and practical applicability of short-term wind power forecasting and early wind turbine fault detection using SCADA and meteorological forecast data. The first part of the dissertation develops and investigates a virtual-sensor-based method for condition monitoring and early fault detection in wind turbines using SCADA time-series data, including the selection of the most informative features and the evaluation of factors affecting prediction accuracy. The second part of the dissertation analyzes and optimizes recurrent neural-network structures for the virtual sensor by evaluating feature-sequence formation, training schemes, and alternative activation functions to increase accuracy and reduce the computational cost relevant for practical deployment. The third part of the dissertation develops and investigates a bidirectional long short-term memory (BiLSTM) based method for short-term wind farm power forecasting using meteorological forecast data, and evaluates the impact of different numerical weather prediction (NWP) sources and the suitability of an objective function with a normalized Nord Pool price multiplier for day-ahead energy production forecasts. The dissertation contributes to the fields of wind energy and artificial intelligence by proposing and validating data-driven methods for virtual sensing, residual-based early fault detection, recurrent-model optimization, computationally efficient activation-function selection, and economically meaningful short-term wind power forecasting. The research results have been published in three peer-reviewed scientific journals and one conference proceeding, and were presented at seven conferences and seminars. Doctoral dissertation readers can search via VILNIUS TECH Virtual Library.