Effective real-time monitoring technique is crucial for detecting material degradation and maintaining the structural integrity of nuclear systems to ensure both safety and operational efficiency. Traditional physical sensor systems face limitations such as installation challenges, high costs, and difficulties in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a promising solution by enhancing physical sensor capabilities to monitor critical degradation indicators like pressure, velocity, and turbulence. However, conventional machine learning models struggle with real-time monitoring due to the high-dimensional nature of reactor data and the need for frequent retraining. This paper explores the use of Deep Operator Networks (DeepONet) within a digital twin (DT) framework to predict key thermal-hydraulic parameters in the hot leg of an AP-1000 Pressurized Water Reactor (PWR). In this study, DeepONet is trained with different operational conditions, which relaxes the requirement of continuous retraining, making it suitable for online and real-time prediction components for DT. Our results show that DeepONet achieves accurate predictions with low mean squared error and relative L2 error and can make predictions on unknown data 160,000 times faster than traditional finite element (FE) simulations. This speed and accuracy make DeepONet a powerful tool for tracking conditions that contribute to material degradation in real-time, enhancing reactor safety and longevity.

The article presents the results obtained within the DaWetRest project, which focuses on wetlands preservation and restoration along the Danube River, ranging on three demonstrator sites: the Middle Danube Demo (MD DEMO located in Croatia), the Lower Danube Demo (LD DEMO, located in Bulgaria) and the Danube Delta Demo (DD DEMO-located in Romania). The project aims to provide insight into nature-based solutions, ecological characteristics, hydrology, water quality, vegetation, wildlife, soil, land use and other factors that represent a relevant contribution to the restoration efforts and it contributes to the wetland restoration using concreate hydro-technical measures for ecosystem improvement. This study presents the initial results from in situ applied geophysics, where Electric Resistivity Tomographies (ERT) were applied on the MD Demo (main pilot site in the Municipality of Draž, Croatia) which represents the flood area, located east of the Danube River embankment of the State border. This geophysical surveys represents an exploratory approach that aims to contribute for a better understanding and knowledge on groundwater resources and quality through the determination of potential aquifer areas from main geological features of the area of interest and also as a potential contribution to establish carbon sequestration areas.

Real-time monitoring is a foundation of nuclear digital twin technology, crucial for detecting material degradation and maintaining nuclear system integrity. Traditional physical sensor systems face limitations, particularly in measuring critical parameters in hard-to-reach or harsh environments, often resulting in incomplete data coverage. Machine learning-driven virtual sensors offer a transformative solution by complementing physical sensors in monitoring critical degradation indicators. This paper introduces the use of Deep Operator Networks (DeepONet) to predict key thermal-hydraulic parameters in the hot leg of pressurized water reactor. DeepONet acts as a virtual sensor, mapping operational inputs to spatially distributed system behaviors without requiring frequent retraining. Our results show that DeepONet achieves low mean squared and Relative L2 error, making predictions 1400 times faster than traditional CFD simulations. These characteristics enable DeepONet to function as a real-time virtual sensor, synchronizing with the physical system to track degradation conditions and provide insights within the digital twin framework for nuclear systems.

The capture of a rare, critically endangered adult angular rough shark, Oxynotus centrina (Linnaeus, 1758), with abnormal coloration is reported in this paper. The shark exhibited a partial reduction in pigmentation, resulting in an overall pale appearance with white-greyish patches. Since the retinal pigmentation appeared normal, the shark was considered leucistic. This represents the first documented case of leucism in this species and the first colour disorder reported in the family Oxynotidae Gill, 1912. Despite the atypical appearance, the physical health of the shark seemed unaffected, supporting the notion that pigment disorders in deep-sea sharks do not inherently impair survival and growth. Full morphometric characteristics are presented and compared with those of a normal individual of the same sex caught in the same area, showing no differences.

The personality traits of top judo referees are crucial for fair decision-making in elite competitions, shaping the experience for athletes, coaches, and spectators. This study examines potential differences in personality traits among 63 referees from the World Judo Tour between 1 January 2018 and 31 December 2022. Factors analyzed include completing the IJF Academy course Level 1, elite athlete status, number of officiated events, performance ratings, and participation in the Olympic or Paralympic Games. Our research shows that older referees tend to exhibit greater extraversion, whereas less experienced officials show lower levels of this trait. Referees with limited experience generally demonstrate higher agreeableness than their more experienced counterparts. Female referees and those with top performance ratings display greater conscientiousness than male referees. Completing the IJF Academy course is associated with lower neuroticism, while lower performance ratings are linked to higher neuroticism. Openness tends to decrease with increased officiating experience, with less experienced referees showing higher levels of this trait. In conclusion, competitive experience, training completion, and officiating tenure are associated with specific personality traits among judo referees, highlighting the importance of continuous training for effective officiating. The analysis of personality traits revealed no statistically significant differences between male and female referees in the dimensions measured by the BFI (Big Five Inventory). This indicates that the levels of extraversion, agreeableness, conscientiousness, neuroticism, and openness were similar for both genders, with no significant variation in how these traits were expressed.

Only the chairs can edit The integration of vehicular communications, 5G mobile networks, and edge computing represents a significant shift in intelligent transportation. Key components of Intelligent Transportation Systems, such as Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications, are essential for this transformation. The introduction of 5G improves connectivity, while edge computing brings processing capabilities closer to data sources. This combination has the potential to dramatically enhance transportation efficiency and safety. We focus on developing a sustainable Vehicle-to-Everything (V2X) framework based on experimentation in the Smart Highway testbed, located in Antwerp, focusing on protecting Vulnerable Road Users (VRUs). This study explores the interaction between vehicular communication and edge computing within a 5G network, focusing on the varying distances between On Board Units (OBUs) and Roadside Units (RSUs). The framework applications involve the development of a VRU Safety Mobile Application (SMA) and a Collision Prediction Edge Application (CPEA). Additionally, the research addresses sustainability by analyzing energy consumption in the context of the Central Processing Unit (CPU) load at the RSU using detailed real-world experiments and simulations. The findings indicate that energy consumption remains stable at shorter distances but shows increased variability at longer ranges.

TrialsNet is a project dedicated to enhancing European urban ecosystems through a variety of innovative use cases in domains including Security and Safety, Infrastructure, and Transportation. These use cases are being implemented across different clusters in Italy, Spain, Greece, and Romania, involving real users. This paper provides an overview of the diverse use cases, and of the corresponding network solutions, which leverage advanced functionalities like dynamic slicing management, NFV, MEC, AI/ML, and more. The project aims to identify network limitations, optimize infrastructure, and define new requirements for next-generation mobile networks. Ultimately, TrialsNet seeks to improve urban livability by driving advancements across multiple domains.

Automated vehicles communicating with each other or their surroundings are expected to exchange a large amount of data. With that, the trustworthiness of a shared data item concerning its integrity is raised, as well as the trustworthiness of a vehicle component not having been tampered with by an attacker. Traditional security mechanisms, such as misbehavior detection, can help identify some security violations but cannot assess the overall consequences of a range of vehicle attacks. For this purpose, previous work has already introduced the Trust Assessment Framework, which computes a target entity’s Actual Trustworthiness Level (ATL). This paper focuses on the concept of Required Trustworthiness Level (RTL), which represents the numerical thresholds an ATL needs to reach for an entity to be considered trustworthy. We present a risk-based method to calculate the belief component of an RTL based on the well-established and standardized Threat Analysis and Risk Assessment (TARA). We provide an in-vehicle use case to demonstrate our belief calculation method and discuss the impact of using risk ratings.

The research aims to determine the quantity and quality of Norway spruce and silver fir seed stands in the Federation of Bosnia and Herzegovina and to recommend measures for their improvement to produce the highest quality reproduction material and achieve genetic gain in newly planted forests. For this research, four measured and 19 observed phenotypic traits were analyzed in 11 Norway spruce and 14 silver fir seed stands. The average age of the trees in Norway spruce seed stands was 70 years, and in silver fir stands 85 years. The average diameter of trees in Norway spruce seed stands was 49 cm, and 46 cm for silver fir. The average height of trees for both species was 26 m. In all seed stands of both species, there was a sufficient number of favorably shaped and straight trees. There was a small incidence of forkness and a high incidence of thin and medium-thick branches. The number of branches in the whorl was high in the seed stands of both species. There was a large number of trees growing poorly and trees with poor trunk clearance from dead branches. A small number of damaged trees in seed stands were also registered, and a low percentage of trees with moderate twisting. The appearance of mistletoe was registered in silver fir seed stands. Some of the analyzed seed stands have shown very good quality, and individual selection should be made in them. In some of the analyzed stands, activities of arrangement and removal of part of the trees are necessary. Considering the great ecological-vegetation diversity of Bosnia and Herzegovina, with four ecological-vegetation regions, 14 areas and 20 districts, most of which are also represented in the Federation of BiH, a higher number of seed stands is needed. When selecting new seed stands for spruce and fir, the focus should be on small stands that grow in extreme conditions.

The day-ahead load forecast is essential for the efficient planning and operation of electric power systems, especially in the context of smart grids. This task is becoming increasingly important with the growing integration of variable renewable energy sources. Among the various machine learning-based load forecasting methods, Long Short-Term Memory (LSTM) networks have shown to be particularly effective. This paper analyses the impact of reactive load as an exogenous variable on active load forecasting and vice versa, employing LSTM networks with hyperparameters optimized through Gaussian Process Regression (GPR). The results, validated using dataset from Bangalore, India, demonstrate that including exogenous variables enhances forecasting accuracy. Additionally, the effect of different training/(validation+test) percentage ratios on prediction performance is evaluated finding that a 70%:30% ratio yields a satisfactory balance of accuracy and training efficiency. Finally, a combined forecasting model is used to analyse the forecasting accuracy of a model that is trained using data from one location (Bangalore) and tested using data from another location (Itanagar), proving there is no overfitting in the forecasting model.

The social potential of Urban Air Mobility (UAM) as a greener and faster transportation system in and around urban environments is indisputable. Nevertheless, the success of UAM introduction and its wide use will strongly depend on acceptance by the citizens and future UAM users. The impact on overall quality of life, as a multidimensional concept that encompasses physical health, mental and emotional well-being, economic status, education, and the environment, is becoming a significant issue. This paper aims to describe the performance framework for the assessment of the social and environmental impact of UAM. The specific objectives are to identify the full range of UAM’s impacts on citizens’ quality of life and to propose a set of indicators that enables the quantification and assessment of the identified impacts. Firstly, the main issues (focus areas) were identified, namely, noise, visual pollution, and privacy concerns, followed by access and equity, economic aspect, emissions, public safety, and impact on wildlife. In the next step, for each identified focus area, performance indicators were defined along with the several cross-cutting areas for a geographical, temporal, demographic, socioeconomic, and behavioral resolution. The proposed performance framework could enable more efficient mitigation measures and possibly contribute to wider adoption of the UAM operations.

Ex vivo human skin models are valuable tools in skin research due to their physiological relevance. Traditionally, standard cultivation is performed in a cell culture incubator with a defined temperature of 37 °C and a specific atmosphere enriched with CO2 to ensure media stability. Maintaining the model under these specific conditions limits its flexibility in assessing exposures to which the skin is exposed to in daily life, for example changes in atmospheric compositions. In this study we demonstrated that the foreskin-derived skin model can be successfully cultured at room temperature outside a CO2 incubator using a CO2-independent, serum-free media. Over a cultivation period of three days, the integrity of the tissue and the preservation of immune cells is well maintained, indicating the model’s stability and resilience under the given conditions. Exposing our Medical University of Graz – human Organotypic Skin Explant Culture (MUG-hOSEC) model to cytotoxic and inflammatory stimuli results in responses analyzable within the supernatant. Besides the common analysis of released proteins upon treatment, such as cytokines and enzymes, we have included extracellular vesicle to obtain a more comprehensive picture of cell communication.

This research work presents a comprehensive overview of four traits related to the head, with the aim of assessing the statistical phenotypic association among them. The traits examined in this study encompass earlobe type, tongue rugosity, cleft chin and tongue rolling. The primary objective was to investigate the potential associations between these traits and understand their interrelationships. The study focused on examining specific traits in a diverse group of 7431 unrelated individuals, where the genders were almost evenly distributed. To facilitate a comprehensive analysis, three distinct groups were created for each characteristic, comprising the total population, as well as male and female subsets. The selection of subjects was carefully done to ensure a fair representation across different geographical regions within Bosnia and Herzegovina, thereby accurately reflecting the nation's national and ethnic diversity. The association among these traits was assessed for statistical significance using the Chi-squared test, with Fisher's exact test used as a supplementary method to examine the connection between each pair of observed traits. Additionally, the Chi-squared test was applied to examine gender-based differences in the frequencies of the phenotypic characteristics of the head. Following traits were shown to have a statistically significant association: tongue rugosity - tongue rolling, tongue rugosity – earlobe type, cleft chin – earlobe type, cleft chin – tongue rolling and earlobe type – tongue rolling. Investigation into the variations in the frequencies of observed phenotypic traits of the head, with respect to gender, revealed statistically significant results for every trait examined.