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Pine Wilt Disease is one of the most destructive pests affecting coniferous forests. After being infected by the harmful Bursaphelenchus xylophilus nematode, most trees die within one year. The complex spreading pattern of the disease and the tedious hard labor process of diagnosis involving field wood sampling followed by laboratory analysis call for alternative methods to detect and manage the infected areas. Remote sensing comes naturally into play owing to the possibility of covering relatively large areas and the ability to discriminate healthy from sick trees based on spectral characteristics. This paper presents the development of machine learning classification algorithms for the detection of Pine Wilt Disease in Pinus pinaster, performed in the framework of the European Commission’s Horizon 2020 project “Operational Forest Monitoring using Copernicus and UAV Hyperspectral Data” (FOCUS) in two provinces of central Portugal. Five flight campaigns have been carried out in two consecutive years in order to capture a multitemporal variation of disease distribution. Classification algorithms based on a Random Forest approach were separately designed for the acquired very-high-resolution multispectral and hyperspectral data, respectively. Both algorithms achieved overall accuracies higher than 0.91 in test data. Furthermore, our study shows that the early detection of decaying trees is feasible, even before symptoms are visible in the field.

The occurrence of litter in natural areas is nowadays one of the major environmental challenges. The uncontrolled dumping of solid waste in nature not only threatens wildlife on land and in water, but also constitutes a serious threat to human health. The detection and monitoring of areas affected by litter pollution is thus of utmost importance, as it allows for the cleaning of these areas and guides public authorities in defining mitigation measures. Among the methods used to spot littered areas, aerial surveillance stands out as a valuable alternative as it allows for the detection of relatively small such regions while covering a relatively large area in a short timeframe. In this study, remotely piloted aircraft systems equipped with multispectral cameras are deployed over littered areas with the ultimate goal of obtaining classification maps based on spectral characteristics. Our approach employs classification algorithms based on random forest approaches in order to distinguish between four classes of natural land cover types and five litter classes. The obtained results show that the detection of various litter types is feasible in the proposed scenario and the employed machine learning algorithms achieve accuracies superior to 85% for all classes in test data. The study further explores sources of errors, the effect of spatial resolution on the retrieved maps and the applicability of the designed algorithm to floating litter detection.

Technological advancements in algorithmic personalization are widely believed to influence user behavior on online gambling platforms. This study explores how such developments, potentially including AI-driven mechanisms, may affect cognitive and motivational processes, especially in relation to risk perception, decision-making, and betting persistence. Using ordinary least squares (OLS) and panel regression models applied to behavioral data from a gambling platform, we examine patterns that are consistent with increased personalization between two distinct time periods, 2016 and 2021. The datasets do not contain any direct metadata regarding AI interventions. However, we interpret changes in user behavior over time as indicative of evolving personalization dynamics within a broader technological and contextual landscape. Accordingly, our conclusions about algorithmic personalization are inferential and exploratory, drawn from temporal comparisons between 2016 and 2021. Our findings show that users receiving personalized bonuses or making early cash-out decisions tend to adjust their stake sizes and betting frequency in systematic ways, which may reflect indirect effects of technological reinforcement strategies. These behavioral patterns raise important ethical and regulatory questions, particularly regarding user autonomy, algorithmic transparency, and the protection of at-risk users. This research contributes to the literature on digital behavior influencing gambling by framing the analysis as observational and quasi-experimental and suggests that further studies use experimental and log-level data to more specifically analyze the algorithmic effects. However, no causal claims can be made about AI influence as the temporal contradictions are interpreted as broad phenomena of technological developments, since they are not measured as algorithmic interventions. Further studies should also investigate the development of predictive models aimed at countering gambling addiction; evaluate the long-term ethical implications of algorithmic personalization; and discuss potential solutions codeveloped to foster a responsible gambling climate.

Improved forage and living conditions in certain parts of Europe over the past few decades have led to alarming levels of ungulate densities. Consequently, the overabundance of red deer, roe deer, and fallow deer in the Western Plains of Romania has begun to generate issues in the development of young oak stands. In addition to causing damage to the agricultural sector and increasing the risk of vehicle collisions, ungulates are increasing pressure on the forestry sector, mainly through the browsing of young saplings. This study quantifies the levels of ungulate browsing in oak stands using a permanent sample grid of 42 plots in both natural and artificial regeneration areas. A total of 3223 individual saplings were measured, revealing browsing intensities of 49.65% in clearcut systems and 12.8% in continuous forest cover systems. With high ungulate densities identified as the main cause, the Sustainable Population Threshold was calculated using a complex set of indices and compared to the actual numbers of ungulates, both of which were translated into stock unit equivalents. A logistic regression model was developed based on silvicultural and wildlife indices to identify other factors influencing browsing occurrence. The findings indicate that the proportion of forested areas in the hunting ground and the type of silvicultural system are significant factors in the occurrence of browsing. The problem of ungulate overabundance clearly influences forest development, and new solutions should be identified in terms of both forestry and wildlife management.

Oil spills in ports are particular cases of oil pollution in water environments that call for specific monitoring measures. Apart from the ecological threats that they pose, their proximity to human activities and the financial losses induced by disturbed port activities add to the need for immediate action. However, in ports, established methods based on short-wave infrared sensors might not be applicable due to the relatively low thickness of the oil layer, and satellite images suffer from insufficient spatial resolution, given the agglomeration of objects in ports. In this study, a lightweight ultraviolet (UV) camera was exploited in both controlled experiments and a real port environment to estimate the potential and limitations of UV imagery in detecting oil spills, in comparison to RGB images. Specifically, motivated by the scarce research literature on this topic, we set up experiments simulating oil spills with various oil types, different viewing angles, and under different weather conditions, such that the separability between oil and background (water) could be better understood and objectively assessed. The UV camera was also used to detect real-world oil spills in a port environment after installing it on a vessel for continuous monitoring. Various separability metrics between water and oil, computed in both scenarios (controlled experiments and port environment), show that the UV cameras have better potential than RGB in detecting oil spills in port environments.

The growing integration of immersive technologies into education is opening new possibilities for teaching and learning, while also raising concerns about the reliability and potential distortion of knowledge in artificial intelligence-mediated environments. Understanding how users perceive and accept artificial intelligence-generated content in immersive learning systems is therefore essential. This study explores the factors that influence user acceptance of artificial intelligence-driven virtual reality educational applications and explains it through a multidimensional framework that extends the Technology Acceptance Model, the Theory of Reasoned Action, and the Theory of Planned Behavior—a new ED-SCALE model. We innovated the previous models by adding an ergonomic dimension, often overlooked in virtual reality-based education. To test the model, we developed an artificial intelligence-driven virtual reality educational escape room designed to simulate adaptive and interactive learning experiences. Data were collected from 213 participants using a questionnaire measuring subjective norms, perceived behavioral control, attitudes toward artificial intelligence-mediated instruction, perceived informational efficacy, and ergonomic quality. The findings show that ergonomic quality, intuitive interfaces, physical comfort, and social influence play an important role in shaping user trust and long-term adoption intentions. The results suggest that the success of artificial intelligence-driven immersive learning systems depends not only on technological performance but also on user experience and social context, confirming our first hypothesis regarding new variables that are conditional for virtual technology acceptance.

The Metaverse concept describes a virtual collective space where users engage with simulated or computer-generated environments, enabled by cutting-edge technologies such as Virtual Reality (VR), Augmented Reality (AR), Blockchain, Artificial Intelligence (AI), and 5G Networks. This convergence allows the creation of complex virtual worlds, enhances user interactions, enables dynamic environments, facilitates seamless integration across platforms, and ensures scalability and persistence, alongside several privacy, security, ethical and legal challenges to be considered. The constant advancement of these technologies is pivotal in shaping a future where the line between physical and virtual realities blurs, unlocking limitless possibilities in realms such as communication, entertainment, education, and business. This leads to novel opportunities for exploration and user engagement. This paper aims to present an overview of the leading technologies driving the transition from the current Internet to the Metaverse, enabling users to interact naturally and intuitively with digital objects and environments. The study explores well-established interactive simulation technologies like virtual reality and augmented reality, along with emerging technologies like somato-sensory interfaces, holographic imaging, and brain-computer interaction interfaces. Emphasizing their potential, the paper demonstrates how these technologies can offer users unparalleled levels of involvement in interactive activities spanning gaming, socializing, education, and business.

This study presents a novel approach to inclusive education by integrating augmented reality (XR) and generative artificial intelligence (AI) technologies into an immersive and adaptive learning platform designed for students with special educational needs. Building upon existing solutions, the approach uniquely combines XR and generative AI to facilitate personalized, accessible, and interactive learning experiences tailored to individual requirements. The framework incorporates an intuitive Unity XR-based interface alongside a generative AI module to enable near real-time customization of content and interactions. Additionally, the study examines related generative AI initiatives that promote inclusion through enhanced communication tools, educational support, and customizable assistive technologies. The motivation for this study arises from the pressing need to address the limitations of traditional educational methods, which often fail to meet the diverse needs of learners with special educational requirements. The integration of XR and generative AI offers transformative potential by creating adaptive, immersive, and inclusive learning environments. This approach ensures real-time adaptability to individual progress and accessibility, addressing critical barriers such as static content and lack of inclusivity in existing systems. The research outlines a pathway toward more inclusive and equitable education, significantly enhancing opportunities for learners with diverse needs and contributing to broader social integration and equity in education.

Spectral unmixing of urban land cover relies on representative endmember libraries. For repeated mapping of multiple cities, the use of a generic spectral library, capturing the vast spectral variability of urban areas, would constitute a more operational alternative to the tedious development of image-specific libraries prior to mapping. The size and heterogeneity of such a generic library requires an efficient pruning technique to extract site-specific spectral libraries. We propose the “Automated MUsic and spectral Separability based Endmember Selection technique” (AMUSES), which selects endmember subsets with respect to the image to be processed, while accounting for internal redundancy. Experiments on simulated hyperspectral data from Brussels (Belgium) showed that AMUSES selects more relevant endmembers compared to the conventional Iterative Endmember Selection (IES) approach. This ultimately improved mapping results (kappa increased from 0.71 to 0.83). Experiments on real HyMap data from Berlin (Germany) using a combination of libraries from different cities underlined the potential of AMUSES for handling libraries with increasing levels of generality (RMSE decreased from 0.18 to 0.15, while only using 55% of the number of spectra compared to IES). Our findings contribute to the value of generic spectral databases in the development of efficient urban mapping workflows.

The accounting profession is fundamentally oriented towards meeting the dynamic needs of the business environment and society. Considering the European Union’s recent sustainability reporting requirements, this study examines the main sustainability topics covered in the accounting curricula of prestigious European higher education institutions (HEIs) to highlight the contribution of the academic environment in maintaining the accounting profession’s relevance in sustainability reporting. Based on the QS ranking, 40 HEIs offering programmes in accounting have been identified, of which 65% include sustainability items in their curriculum. Through a content analysis of 157 syllabi from the final sample of 26 universities, from 39 bachelor’s and 55 master’s programmes, the results of the study indicate the development of an extensive framework of sustainability elements included in accounting education, where mainly technical aspects are prevalent, to the detriment of social and ethical ones. The analysis identified six dimensions of sustainable development topics, with sustainability reporting as the primary dimension, facilitated by secondary dimensions intended to provide context in terms of needs, drivers, and control mechanisms. The study’s findings contribute to the existing literature on sustainability accounting education by exploring this phenomenon in prestigious European HEIs, which, in an uncertain context, assumed a pioneering role in maintaining the accounting profession’s relevance. The proposed framework provides a roadmap for other universities, encouraging the development of mimetic factors in updating the accounting education. Moreover, this study has important implications for the profession and the business environment.