Near-Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass in the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep convolutional neural networks for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We applied transfer learning and fine-tuning on these pre-existing deep convolutional networks, and from the results that we obtained, the potential of using deep convolutional neural networks in the process of asteroid classification can be seen. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we lose the least number of valid asteroids.

Near Earth Asteroids represent potential threats to human life because their trajectories may bring them in the proximity of the Earth. Monitoring these objects could help predict future impact events, but such efforts are hindered by the large numbers of objects that pass through the Earth’s vicinity. Additionally, there is also the problem of distinguishing asteroids from other objects in the night sky, which implies sifting through large sets of telescope image data. Within this context, we believe that employing machine learning techniques could greatly improve the detection process by sorting out the most likely asteroid candidates to be reviewed by human experts. At the moment, the use of machine learning techniques is still limited in the field of astronomy and the main goal of the present paper is to study the effectiveness of deep CNNs for the classification of astronomical objects, asteroids in this particular case, by comparing some of the well-known deep convolutional neural networks, including InceptionV3, Xception, InceptionResNetV2 and ResNet152V2. We have applied transfer learning and fine-tuning on these pre-existing deep convolutional networks and from the results that we have obtained one can see the potential of using deep convolutional neural networks in the process of asteroid classification. The InceptionV3 model has the best results in the asteroid class, meaning that by using it, we loose the least number of valid asteroids.

Biodiversity informatics has made occurrence records widely accessible, yet many databases remain optimized for human browsing rather than structured machine consumption by large language models (LLMs) and AI agents. This creates a bottleneck: analytical capacity is accelerating while biodiversity evidence remains fragmented, weakly documented, and difficult to cite reproducibly. Here we introduce evidence manifestation, a design approach that transforms raw occurrence data into versioned, citable, machine-readable knowledge artifacts with explicit provenance. Using World of Crayfish®, we implement this concept through Species Exposure Bundles (SEBs) that package curated occurrence snapshots, spatial layers, derived range metrics (extent of occurrence, area of occupancy), geographic summaries (basins, administrative units, protected areas), and machine-readable metadata exposed via stable identifiers (JSON/GeoJSON). To validate real-world utility, we conducted controlled LLM query tests contrasting closed-book baseline (T0), web search (T1), and manifest-directed retrieval with SEB access (T2) across four AI applications. Nine endemic crayfish species with minimal online documentation were compared against one high web-visibility control species (Procambarus virginalis). For endemic taxa, closed-book performance was low (mean 1.78/8; 47% complete failures), web search improved but remained variable (mean 4.22/8; 19% failures), and manifest-directed retrieval produced consistently high performance (mean 6.97/8; 0% failures; 50.0% perfect scores). The control species achieved 5.75/8 under closed-book conditions (T0), exceeding endemic web-search performance and approaching endemic manifest-directed performance, highlighting accessibility gaps for underrepresented taxa. These results position evidence manifestation as a practical, reproducible infrastructure pattern for AI-assisted biodiversity synthesis and conservation workflows.

The project ChemiNova, funded by the Horizon Europe program, is dedicated to the development of an integrated suite of five software modules to enhance the conservation, analysis, and monitoring of cultural heritage assets. Using non-invasive data acquisition technologies, we propose a workflow that spatially integrates heterogeneous datasets to generate enriched 3D (e3D) models. These models combine geometric information with multiple co-registered data layers and can be accessed and visualized through three purpose-built interactive applications: ChemiAnalysis (desktop-based analysis), ChemiInspection (augmented reality-based on-site inspection), and ChemiSensing (a collaborative virtual environment). The suite is completed by ChemiPortal, a shared data management gateway, and ChemiAI, an automated damage prediction module, which operate as back-end components supporting the three interactive applications. This paper presents the current workflow together with the underlying database architecture that supports the storage, linkage, and retrieval of complex multimodal data. The methodology is demonstrated on selected cultural heritage elements within La Nau, the Cultural Center of the University of Valencia and one of ChemiNova’s pilot sites. The interdisciplinary collaboration involved in designing and assessing the workflow is also discussed.

The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential growth of the digital cameras' performances and to be able to process (in near real-time) data coming from large surveys. This paper presents a software platform called NEARBY that supports automated detection of moving sources (asteroids) among stars from astronomical images. The detection procedure is based on the classic "blink" detection and, after that, the system supports visual analysis techniques to validate the moving sources, assisted by static and dynamical presentations.