哈罗科皮奥大学

As wireless communication evolves, each generation of networks brings new technologies that change how we connect and interact. Artificial Intelligence (AI) is becoming crucial in shaping the future of sixth-generation (6G) networks. By combining AI and Machine Learning (ML), 6G aims to offer high data rates, low latency, and extensive connectivity for applications including smart cities, autonomous systems, holographic telepresence, and the tactile internet. This paper provides a detailed overview of the role of AI in supporting 6G networks. It focuses on key technologies like deep learning, reinforcement learning, federated learning, and explainable AI. It also looks at how AI integrates with essential network functions and discusses challenges related to scalability, security, and energy efficiency, along with new solutions. Additionally, this work highlights perspectives that connect AI-driven analytics to 6G service domains like Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communication (mMTC), and Integrated Sensing and Communication (ISAC). It addresses concerns about standardization, ethics, and sustainability. By summarizing recent research trends and identifying future directions, this survey offers a valuable reference for researchers and practitioners at the intersection of AI and next-generation wireless communication.

To promote climate adaptation and mitigation strategies, it is crucial to understand the perspectives and knowledge gaps of stakeholders involved in functions affected by or addressing land use and climate changes. A large number of stakeholders across 21 European islands were consulted regarding their views on climate change and land use change issues affecting ecosystem services on their island. Climate change characteristics perceptions included variables such as temperature, precipitation, humidity, extremes, and wind. Land use change characteristics perceptions included deforestation, coastal degradation, habitat protection, renewable energy facilities, wetlands and other variables. Other environmental and societal problem perceptions such as invasive species, water or energy scarcity, problems in infrastructures or austerity were also included. Climate and land use change impact perceptions were analysed with machine learning to quantify their importance on the perception outcome. For example if a stakeholder perceives that pollution, coastal degradation, deforestation, precipitation decrease, and increase of humidity are occurring on the island, and austerity is the biggest problem how likely is that the impact of climate change or land use change will be quantified by the stakeholder as negative, unclear, neutral, or positive? The predominant climatic change characteristic is related with temperature, and the predominant land use change characteristic with deforestation. Water-related problems are top priorities for stakeholders. Energy-related problems, such as energy deficiency but also wind and solar energy facilities problems, rank high as combined climate change and land use change risks. Stakeholders generally perceive climate change impacts on ecosystem services as negative, with natural habitat destruction and biodiversity loss identified as the top variables. Land use change impacts are also negative but also more complex to explain, with a higher number of explanatory variables associated with the impact outcome. Stakeholders have common perceptions regarding climate change and land use change impacts on the benefits of biodiversity despite the geographic disparity. Stakeholders differentiate between factors related to climate change impacts and land use change impacts. Water, energy, and renewable energy related issues pose serious concerns to island stakeholders and management measures are needed to address them.

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI implementation.

Intent-Based Networking (IBN) enables agile and policy-driven network management by translating high-level intents into concrete configurations and continuously validating their compliance. A critical limitation in current Intent-Based Network Assurance (IBNA) systems is the lack of real-time application-level awareness, particularly in dynamic edge environments where AI workloads frequently change. In this work, we address this limitation by introducing a lightweight, monitoring-driven pipeline that enables the detection of application transitions and identification of newly active application types on edge devices. In collaboration with Netdata engineers, we develop multimetric data collectors using Netdata, an open-source platform for real-time system and application monitoring. These collectors capture application-agnostic system metrics with minimal overhead, forming the foundation for real-time alerting and dynamic network adaptation. Our proposed pipeline transforms raw monitoring data into fixed-length vectorized multivariate time series. An undercomplete autoencoder is then used to detect changes in system behavior indicative of application transitions, followed by a Random Forest classifier that labels the newly active application based on its resource usage profile. To support reproducibility, we construct and publicly release the AIMED-2025 dataset, which includes monitoring data from seven MediaPipe-based edge AI applications and two idle states, all executed on a Raspberry Pi. Experimental evaluation demonstrates that our method achieves 100 % accuracy in both Application Transition Detection and Application Type Identification using only a three-second observation window. Furthermore, the system exhibits sub-second training times and millisecond-scale inference latency, making it suitable for real-time deployment on resource-constrained edge devices. Once an application change is detected and identified, the IBNA system can automatically alert network administrators and trigger dynamic reconfiguration of network resources to meet the specific performance, security, and connectivity requirements of the active application. By integrating application-level awareness into IBNA, this work advances the state of the art in intent-driven network management and enables more adaptive, efficient, and reliable operation of edge AI systems.

Forest restoration in Europe has a complex history strongly influenced by various social, policy and economic factors. Understanding these influences is essential for shaping effective restoration strategies and avoiding past mistakes, particularly in light of meeting ambitious targets outlined in initiatives such as the EU Nature Restoration Regulation. Here we identify the key social, policy and economic drivers, barriers and enablers that have historically shaped forest restoration across Europe. We analyzed and synthesized detailed information from historical national narratives on forest restoration provided by experts from 18 European countries. Our work details how wars, changes in governance (centralization vs. decentralization) and forest tenure (privatization vs. nationalization), different policy instruments (regulatory, financial, persuasive and organizational), market fluctuations and sociodemographic changes (e.g., rural abandonment, changes in public opinion) have driven the development of forest restoration in Europe. The findings underscore the need to use inclusive and innovative governance mechanisms to reconcile diverging societal paradigms (e.g., rural vs. urban, conservation vs. forestry) partly reflected in incoherent forest-related policies, as well as to address the fragmentation resulting from forest privatization. Ensuring stable funding mechanisms (e.g., remuneration systems for forest ecosystem services) alongside favorable regulatory frameworks will also be key for successful large-scale forest restoration efforts. Policy recommendations are made to ensure the effective implementation of the EU Nature Restoration Regulation, including a hybrid governance model that balances strong national regulatory frameworks with local adaptability to diverse socioecological contexts, integrating socioeconomic metrics, strengthening public engagement, and leveraging market-based and green tax incentives.