Hellenic Telecommunications Organization (Greece)

This paper proposes a 6G architecture that adopts a multi-technology transport network integrating THz links and optical network technologies to interconnect the 6G radio access network and core domains. The proposed solution operates in a self-organized manner, taking advantage of the software defined networking (SDN) control of the optical network, while suitable SDN control is purposely developed for the THz solution. An end-to-end (E2E) Service Management and Orchestration (SMO) layer is adopted, offering intelligence capabilities in service provisioning and resource allocation across the 6G infrastructure through the adoption of a federated learning (FL) scheme. The proposed architecture and the developed control scheme are validated through an experimental demonstration. To the best of the authors’ knowledge, this is the first experimental demonstration of an autonomously controlled 6G network implementation integrating optical network technologies and THz links in a common transport network. The developed intelligent management framework empowered by FL to jointly optimize THz and multi-vendor optical network equipment supporting 6G services is also experimentally showcased.

The European eel (Anguilla anguilla, Linnaeus 1758) is a catadromous, benthic carnivorous predator species that spends most of its life in freshwater, brackish and coastal habitats. Τhe aims of the present study were to examine the morphometric features of European eel population in five estuarine systems in Northern and Western Greece, to investigate the prevalence of Anguillicola crassus and to identify the risk factors associated with an effect to parasitism. The total length of the examined eels (1,343) ranged from 320 mm to 991 mm and the total weight ranged from 46 g to 2,760 g. Condition factor (K) ranged from 0.02 (Mazoma lagoon) to 0.44 (Vistonis and Evros estuarine systems). From the regression results on Lt-Wt relationship, the coefficient of determination (r2) was found to have a strong relationship between Lt-Wt (p<0.01). The overall prevalence of the parasitism was 27.1% (CI: 26.0-31.0). Among the different sampling sites, the eels collected from Vistonis estuarine system had the highest prevalence (63.5%) (CI: 57.8-68.8) following the two lagoons from Mesolonghi lagoon complex: Kleisova lagoon (36.6%) and Tholi lagoon (35.6%). The multivariate binary logistic model analysis demonstrated that the significant factors associated with the infection of A. crassus into the Greek eel population, were the Condition factor K, gender, life stage and eels originate from Vistonis estuarine system. A high level of A. crassus parasitism in various habitats in the study area was located and the nematode is abundant in all A. anguilla habitats in Greece.

Digital infrastructure is vital for the economy, democracy, and everyday life, yet it is becoming increasingly vulnerable to strategic cyber-attacks. These attacks can lead to significant disruptions, resulting in widespread service outages, financial losses, and a decline in public trust. Ensuring resilience is difficult due to the infrastructure's complexity, the large volume of data involved, and the growing need for quick, coordinated responses. In the EU Horizon project CyberSecDome, we propose a multi-layered framework that provides AI-driven solutions for incident prediction and detection, automated testing, risk assessment, and rapid incident response, supporting continuity amid complex, large-scale cyber threats. Additionally, Cyber-SecDome introduces a virtual reality interface to enhance AI model explainability and provide real-time contextual awareness of ongoing attacks and defense mechanisms. It also enables privacy-aware model sharing across AI systems, fostering secure collaboration among different domes.

Abstract Disclosure: R.D. Paparodis: None. E. Karvounis: None. A. Kapezanou: None. S. Livadas: None. G. Simeakis: None. D.P. Askitis: None. I. Androulakis: None. N.G. Angelopoulos: None. D. Zianni: None. A. Rizoulis: None. A. Boniakos: None. I. Perogamvros: None. D. Bantouna: None. J.C. Jaume: None. Introduction: Diabetes has been linked to increased risk for several cancers and aggressiveness of tumor behavior. A link between thyroid cancer and diabetes appears controversial in the literature as of yet. We designed the present study to assess whether autoimmune (type 1 + LADA = DM1) or type 2 diabetes (DM2) are associated with more aggressive thyroid cancers compared to those found in non-diabetic (non-DM) individuals. Methods: We retrospectively reviewed data from patients undergoing thyroid surgery in 10 Endocrinology and Endocrine Surgery Clinics throughout Greece over 2 years. We collected data on history and type of diabetes, pre-existing to the thyroid surgery, its treatments and duration, the preoperative thyroid function tests and the surgical pathology findings. We compared the incidence of various histological types of thyroid cancer and the features of tumor aggressiveness between patients with diabetes and those with normal glucose homeostasis (non-DM) (DM1 vs. DM2 vs. non-DM). Results: Overall, n=808 subjects with thyroid cancer were included: n=571 were females (70.7%), age 47.3 ± 14.3 years, BMI 27.0 ± 5.0 Kg/m2, mean TSH 1.99 ± 2.21 mIU/L; n= 692 were non-DM, n=10 had DM1 and n=107 had DM2. Histology revealed n=5 poorly differentiated / anaplastic, n=13 medullary, n=12 Hürthle cell, n=17 follicular and n=773 papillary thyroid cancers (PTC); n=20 (2.5%) with aggressive histological subtypes of PTC, n=5 (0.6%) with distant metastases (MET), n=199 (24.6%) with extrathyroidal extension (ETE), n=419 (51.9%) with capsular invasion (CI), n=225 (27.8%) with lymph nodes involvement (LNi) and n=23 with cancer recurrence (CR) (2.8%). The incidence of aggressive histological types, CR, MET or number of I-131 treatments were not different between groups (p>0.05). ETE, CI and LNi were significantly more common in non-DM compared to both diabetes groups, while gross ETE was more common in DM2 over DM1 and non-DM (p<0.001). Conclusions: More DM1 than DM2 seem to confer a protective effect on several features of cancer aggressiveness in affected individuals. On the other hand, more DM2 than DM1 seems to significantly promote gross ETE. No effect was observed pertaining to the risk for more aggressive tumors or histological subtypes of PTC. Although aggressiveness of thyroid cancer seems controlled in DM1 and to a lesser degree in DM2 the complex interplay between autoimmunity, hyperglycemia and thyroid cancer biology requires larger sample size to better determine causative effects. Presentation: 6/2/2024

Abstract Disclosure: R.D. Paparodis: None. E. Karvounis: None. A. Kapezanou: None. S. Livadas: None. G. Simeakis: None. D.P. Askitis: None. I. Androulakis: None. N.G. Angelopoulos: None. D. Zianni: None. A. Rizoulis: None. A. Boniakos: None. I. Perogamvros: None. D. Bantouna: None. J.C. Jaume: None. Introduction: Diabetes has been linked to increased risk for several cancers and aggressiveness of tumor behavior. A link between thyroid cancer and diabetes appears controversial in the literature as of yet. We designed the present study to assess whether autoimmune (type 1 + LADA = DM1) or type 2 diabetes (DM2) are associated with more aggressive thyroid cancers compared to those found in non-diabetic (non-DM) individuals. Methods: We retrospectively reviewed data from patients undergoing thyroid surgery in 10 Endocrinology and Endocrine Surgery Clinics throughout Greece over 2 years. We collected data on history and type of diabetes, pre-existing to the thyroid surgery, its treatments and duration, the preoperative thyroid function tests and the surgical pathology findings. We compared the incidence of various histological types of thyroid cancer and the features of tumor aggressiveness between patients with diabetes and those with normal glucose homeostasis (non-DM) (DM1 vs. DM2 vs. non-DM). Results: Overall, n=808 subjects with thyroid cancer were included: n=571 were females (70.7%), age 47.3 ± 14.3 years, BMI 27.0 ± 5.0 Kg/m[2], mean TSH 1.99 ± 2.21 mIU/L; n= 692 were non-DM, n=10 had DM1 and n=107 had DM2. Histology revealed n=5 poorly differentiated / anaplastic, n=13 medullary, n=12 Hürthle cell, n=17 follicular and n=773 papillary thyroid cancers (PTC); n=20 (2.5%) with aggressive histological subtypes of PTC, n=5 (0.6%) with distant metastases (MET), n=199 (24.6%) with extrathyroidal extension (ETE), n=419 (51.9%) with capsular invasion (CI), n=225 (27.8%) with lymph nodes involvement (LNi) and n=23 with cancer recurrence (CR) (2.8%). The incidence of aggressive histological types, CR, MET or number of I-131 treatments were not different between groups (p>0.05). ETE, CI and LNi were significantly more common in non-DM compared to both diabetes groups, while gross ETE was more common in DM2 over DM1 and non-DM (p<0.001). Conclusions: More DM1 than DM2 seem to confer a protective effect on several features of cancer aggressiveness in affected individuals. On the other hand, more DM2 than DM1 seems to significantly promote gross ETE. No effect was observed pertaining to the risk for more aggressive tumors or histological subtypes of PTC. Although aggressiveness of thyroid cancer seems controlled in DM1 and to a lesser degree in DM2 the complex interplay between autoimmunity, hyperglycemia and thyroid cancer biology requires larger sample size to better determine causative effects. Presentation: 6/2/2024

Data monetisation faces nowadays many challenges. In particular in the agriculture domain there is lack of the data sharing culture, related to luck of the trust in sharing of the data, transparency of the data usage, data security issues, and data ownership issues. We are presenting the approach to address some of the challenges of the data monetisation that can be applied in the agriculture domain. We are describing the architecture of the framework supporting data monetisation, that aims to provide the building blocks to foster data sharing. This effort is work in progress within DATAMITE, an EU funded initiative under the Horizon Europe program.

Heron colonies are dynamic components of wetlands. Therefore, their systematic monitoring is important for the management of both birds and wetlands. During the period 1988–2018, we counted breeding pairs of seven colonial breeding heron species at 65 colonies across 37 wetlands in Greece. We considered as annual variables of a population: (a) years since 1988, (b) Natura 2000 network inclusion, (c) protected area management authority overseeing, (d) wetland type (new or restored), and (e) new colonies (established after 2003). The Cattle Egret Bubulcus ibis and the Squacco Heron Ardeola ralloides had a positive breeding population trend. The Black-crowned Night Heron Nycticorax nycticorax, Little Egret Egretta garzetta, and Grey Heron Ardea cinerea had a negative trend, while the Purple Heron Ardea purpurea population was stable. The Great White Egret Ardea alba bred sporadically at only a few sites which precluded the evaluation of its population trend. The informative population variables differed among species, even of those at the same colony, which suggests trends are also affected by conditions at wintering grounds. The study highlights the need for the systematic monitoring of heron colonies and the protection of foraging/breeding areas in order to reverse the observed negative population trends.

Effective zero-touch management and orchestration (ZSM&O) is crucial for scaling network slicing, particularly transitioning toward Beyond 5G (B5G) and 6G networks. This paper empirically validates the network slicing framework developed under the European Union Horizon 2020 MonB5G project. Building on three years of academia-industry collaboration, MonB5G introduces a flexible slicing model featuring umbrella slices that orchestrate modular, specialized slices across multi-domain environments to address next-generation service demands. For the first time, we evaluate its practicality in a 5G cloud-native testbed through a virtual reality (VR) streaming use case, supported by solutions such as federated learning-based CPU forecasting, anomaly detection, and deep reinforcement learning (DRL) for radio access network (RAN) optimization. The paper offers insights from technically demanding experimental tests and highlights challenges and development paths for managing next-generation mobile networks.