Guaranteeing fair competition has been a guiding principle of Union action since the beginnings of the European Economic Community. Anti-competitive activities in the internal market, such as agreements between undertakings, decisions by associations of undertakings and concerted practices which may affect trade between Member States and which have as their object or effect the prevention, […]
Research Papers
A supervised machine learning approach to a predictive model of nanoscale friction
Modelling of nanoscale friction presents a long-lasting challenge. In fact, while there are several generalised models that provide good results for macro- and micro-scale friction, due to the complex concurrent physicochemical interactions in nanoscale contacts, when modelling nanoscale friction there is a clear lack of reliable predicting tools. The modelling methodology proposed in this work […]
Bottom-Up Design Approach for OBOC Peptide Libraries
Evaluation of Design Storms and Critical Rainfall Durations for Flood Prediction in Partially Urbanized Catchments
This study investigates and compares several design storms for flood estimation in partially urbanized catchments. Six different design storms were considered: Euler II, Alternating Block Method, Average Variability Method, Huff’s curves, and uniform rainfall. Additionally, two extreme historical storms have been included for comparison. A small, ungauged, partially urbanized catchment in Novigrad (Croatia) was chosen […]
Thermal Object Detection in Difficult Weather Conditions Using YOLO
The scientific article “Thermal Object Detection in Difficult Weather Conditions Using YOLO” by M. Kristo, M. Ivašić-Kos, M. Pobar was published in journal IEEE Access, 2020, vol. 8, 125459-125476, DOI: 10.1109/ACCESS.2020.3007481 and is available online.
Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification
Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy […]
Comparison of Entropy and Dictionary Based Text Compression in English, German, French, Italian, Czech, Hungarian, Finnish, and Croatian
The rapid growth in the amount of data in the digital world leads to the need for data compression, and so forth, reducing the number of bits needed to represent a text file, an image, audio, or video content. Compressing data saves storage capacity and speeds up data transmission. In this paper, we focus on […]
Does AI Brain Implant Compromise Agency? Examining Potential Harms of Brain-Computer Interfaces on Self-Determination
Novel generations of Brain-Computer Interface (BCI) technologies operated by AI, in particular, predictive neurotechnologies, offer enormous potential to support implanted individuals’ decisions and capacities for self-determination, such as empowering agential cognitive capacities. This chapter examines the ethics of predictive AI BCI, especially the question of potential risks of harm associated with having a predictive AI […]
Informed consent, military medical enhancement, and autonomous AI systems: requirements, implications, concerns
Inspired by the recent development of autonomous artificial intelligence (AI) systems inmilitary and medical applications I envision the use of one such system, an AI-empowered exoskeleton smart-suit called the Praetor Suit, to question the important ethical issues stemming from its use. The Praetor Suit would have the ability to monitor the service member’s physiological and […]
Rapid prediction of earthquake ground shaking intensity using raw waveform data and a convolutional neural network
This study describes a deep convolutional neural network (CNN) based technique to predict intensity measurements (IMs) of earthquake ground shaking. The input data to the CNN model consists of multistation, 3C acceleration waveforms recorded during the 2016 Central Italy earthquake sequence for M ≥ 3.0 events. Using a 10 s window starting at the earthquake origin time, we find […]
