The rapidly changing nature of the New Psychoactive Substance (NPS) market and the overall large number of substances that need to be monitored have presented challenges for early warning activities in recent years. Since the number of illicit drug classes is growing exponentially over time, the current challenge is no longer to search substances during an investigation, but rather to provide a sort of preliminary identification of the substance as illicit, i.e., to tag it as such, especially when it is a new type of drug. Today several instruments equipped with up-to-date libraries allow for the rapid identification of drugs of abuse. However, this approach suffers from four main problems: 1) The instruments libraries are not updated regularly; 2) The instruments do not easily overcome the matrix effect; 3) NPSs are trafficked and sold in smaller doses, making their detection and identification more difficult; 4) Routine methods of analysis during forensic analysis are no longer effective in screening drugs of abuse, the parent drugs but also their metabolites, due to the lack of structural information and commercial reference materials. NARCOSIS (Non-tArgeted foRensic multidisCiplinary platfOrm for inveStigatIon of drug-related fatalitieS) will provide a set of features for fast, robust, and reliable multidisciplinary approach to harmonise forensic investigations by means of an up-to-date and updateable diagnostic platform with the following key features: 1) A set of selected orthogonal instruments operable both on-site and in laboratories (Raman/SERS, IR, HSI, HRMS) will be adapted to be used with the NARCOSIS platform; 2) Build a comparable and shareable (cross-organisation) reference spectra (meta-spectra) database for faster detection and identification of drugs of abuse; 3) Create an AI-assisted spectra management and integration toolkit for analytical measurements to support the EU Early Warning System to rapidly detect, assess and respond to NPS.

The overall aim of the proposed project is to develop a comprehensive approach to prevent and counter violent radicalisation and extremism. The PERICLES (Policy recommendation and im-proved communication tools for law enforcement and security agencies preventing violent radicali-sation) project is especially dedicated to transitional processes of radicalisation. To meet its aims, PERICLES will consider violent left-wing and right-wing as well as religious ideologies. A special focus will be set on the risks connected with digital violent propaganda. The PERICLES project will deliver advanced and validated counter-propaganda techniques that are target-group-specific. Furthermore, the cooperation between relevant authorities who have due regard against violent rad-icalisation or support the process of de-radicalisation will be enhanced through the use of the project outputs. The comprehensive PERICLES prevention strategy will therefore largely address law enforcement agencies (LEAs) and security agencies; but will also find use by prisons and social workers, teachers and even relatives of affected people.

PROPHETS will look at redefining new methods to prevent, investigate and mitigate cybercriminal behaviours through the development of a coherent, EU-wide, adaptive SECURITY MODEL, built upon the interplay of the human factors within the new cyber ecosystem and capable of addressing the four fundamental dimensions at the core of the phenomenon: 1. early identification of security threats; 2. investigations within a new public-private governance; 3. Increased complexity of the response due to the expansion of the security perimeter towards new societal fields and the emergence of challenging jurisdictional problems; and, last but not least, 4. perception of security and freedoms among citizens, which requires a new communication strategy for LEAs and security policy makers.

TEMA will greatly improve Natural Disaster Management (NDM, e.g., for wildfires, floods) by automating precise semantic 3D mapping and disaster evolution prediction to achieve NDM goals in near-real-time. It will analyze and fuse many heterogeneous extreme data sources: smart drone and in-situ sensors, remote sensing data, topographical data, meteorological data/predictions and geosocial media data (text, image and videos). TEMA will focus on the extreme nature of the data, due to their varying resolution and quality, very large volume and update rate, different spatiotemporal resolutions and acquisition frequencies, real-time needs and multilingualism. It will develop an integrated, ground-breaking NDM platform, focusing on real-time semantic extraction from multiple heterogeneous data modalities and sources, on-the-fly construction of a meaningful semantically annotated 3D disaster area map, prediction of disaster evolution and improved communication between service providers and end-users, through automated process triggering and response recommendations. Semantic analysis computations will be distributed across the edge-to-cloud continuum, in a federated manner, to minimize latency. Extreme data analytics will be performed in a trustworthy and transparent way, by greatly advancing state-of-the-art AI and XAI approaches. The constantly updated 3D map and the disaster evolution predictions will form the basis for an advanced, interactive, Extended Reality (XR) interface, where the current situation will be visualized and different response strategies will be dynamically evaluated through simulation by NDM personnel. The innovative, scalable and efficient TEMA platform will provide precise NDM support, based on extreme data analytics. It will be validated on two critical disaster use-cases (wildfires and floods), in four EU countries, and will form the basis for the TEMA NDM-Analytics-as-a Service (NDM-AaaS) model.