The Earth Observation sector is undergoing a structural transformation driven by the convergence of large satellite constellations, historical data archives and in-orbit artificial intelligence. Solutions such as ImageryPack enable seamless access to multi-source satellite imagery through a flexible credit-based model, supporting a wide variety of operational needs. At the same time, the AI-eXpress in-orbit processing platform dramatically shortens the traditional EO value chain by executing analytics directly in space, reducing latency and data transmission requirements. These innovations turn Earth Observation into an actionable geo-intelligence capability for public administrations, infrastructure operators, environmental monitoring, urban planning and emergency management.
Atmospheric carbon dioxide (CO2), a long-lived and well-mixed greenhouse gas, is a key driver of global warming. Accurate, long-term monitoring of its spatiotemporal variability is essential for understanding carbon dynamics. While the Orbiting Carbon Observatory-2 (OCO-2) satellite provides one of the most precise column-averaged CO2 (XCO2) measurements, its limited spatial coverage and short record since 2014 constrain long-term global analysis. Many studies thus highly rely on chemical transport models (e.g. Copernicus Atmosphere Monitoring Service (CAMS) and CarbonTracker) when applying machine learning (ML) approaches. However, their coarse resolutions often lead to spatial smoothing. In this context, we present a novel ML-based framework based on residual learning with the Light Gradient Boosting Machine (LGBM) to reconstruct global, gap-free XCO2 at 0.1° resolution for the period 2003–2019. By explicitly modeling the residuals between high precision OCO-2 observations and the coarse resolution CAMS-EGG4 reanalysis, the proposed framework mitigates spatial smoothing effects and enables the extension of XCO2 estimates beyond the temporal coverage of the OCO-2 mission. The resulting product was strictly validated through internal cross-validation (random, spatial, and temporal) and external in situ validation, showing strong agreement with OCO-2 satellite observations (R2 = 0.93–0.96, RMSE = 0.80–1.11 ppm) and ground-based measurements (R2 = 0.98, RMSE = 1.17 ppm), respectively. Compared to CAMS-EGG4, the LGBM-based XCO2 product also outperforms by offering higher accuracy and resolving the spatial smoothing limitations caused by its coarse resolution. By bridging gaps in satellite data across space and time, this high-resolution XCO2 product enhances applications in climate research, emission source attribution, and greenhouse gas policy assessment.
We present new numerical results on the space of local, unitary, parity-preserving conformal field theories (CFTs) in three dimensions from the stress tensor bootstrap. In bounds maximizing certain OPE coefficients, we find a plethora of sharp features, such as kinks and ridges, as a function of scaling dimensions. We show that some of these features correspond to known theories, but there are many others that are equally strong but do not match known CFTs. We argue that these features are robust to raising numerical order and could then correspond to numerous as yet unknown CFTs. We conclude in proposing a program of "CFT cartography": the systematic exploration of the landscape of CFTs without individual theory targets in mind.
Recent advances in foundational models have yielded reasoning systems capable of achieving a gold-medal standard at the International Mathematical Olympiad. The transition from competition-level problem-solving to professional research, however, requires navigating vast literature and constructing long-horizon proofs. In this work, we introduce Aletheia, a math research agent that iteratively generates, verifies, and revises solutions end-to-end in natural language. Specifically, Aletheia is powered by an advanced version of Gemini Deep Think for challenging reasoning problems, a novel inference-time scaling law that extends beyond Olympiad-level problems, and intensive tool use to navigate the complexities of mathematical research. We demonstrate the capability of Aletheia from Olympiad problems to PhD-level exercises and most notably, through several distinct milestones in AI-assisted mathematics research: (a) a research paper (Feng26) generated by AI without any human intervention in calculating certain structure constants in arithmetic geometry called eigenweights; (b) a research paper (LeeSeo26) demonstrating human-AI collaboration in proving bounds on systems of interacting particles called independent sets; and (c) an extensive semi-autonomous evaluation (Feng et al., 2026a) of 700 open problems on Bloom's Erdos Conjectures database, including autonomous solutions to four open questions. In order to help the public better understand the developments pertaining to AI and mathematics, we suggest quantifying standard levels of autonomy and novelty of AI-assisted results, as well as propose a novel concept of human-AI interaction cards for transparency. We conclude with reflections on human-AI collaboration in mathematics and share all prompts as well as model outputs at https://github.com/google-deepmind/superhuman/tree/main/aletheia.
The discovery of many Earth-like planets has renewed interest in whether life and technological civilizations exist elsewhere. The Search for Extraterrestrial Intelligence (SETI) seeks evidence for technological civilizations via technosignatures across the electromagnetic spectrum. Here, focusing on artificial radio emissions with extremely narrowband signals, we model Earth as a distant, unresolved source and simulate its narrowband transmissions as observed with current and forthcoming radio facilities. Planetary rotation induces small but coherent Doppler drifts (maximum fractional shift of order $10^{-6}$) that imprint a characteristic, time-varying pattern on the spectrum. We develop a forward-inverse framework that exploits this modulation: adopting a population-weighted model for terrestrial transmitters, we compute time-resolved spectra and then apply a new inversion method that reconstructs the underlying transmitter distribution from the temporal pattern of fractional frequency offsets. In noise-added tests, the method recovers the low-order spherical-harmonic structure of the map and retrieves major population centers despite the north-south degeneracy of unresolved observations. The recovered distribution is expected to correlate with continents, climate zones, and population density. This approach moves SETI beyond mere detection, enabling quantitative cartography of a civilization's activity and inference of host-planet properties through sustained, time-resolved spectroscopy.
This book invites readers to see mathematics not just as formulas and rules, but as the deepest expression of human thought. It begins by exploring the timeless idea of mathematics as a universal language, contrasting its precision with the richness of natural speech. From the foundations of pure and applied mathematics to the revolutionary insights of Claude Shannon's information theory, the narrative shows how numbers, symbols, and structures have shaped science, technology, and communication. At the heart of the work lies the Binary Principle -- the idea that zero and one are not merely digits, but primitive building blocks of all mathematical reasoning. By treating absence and presence as fundamental units, the book reveals how complex theories and applications emerge from the simplest distinctions. This perspective makes mathematics more intuitive, more engaging, and more accessible, transforming how it can be taught and understood. Discover the Binary Principle: Mathematics as the True Universal Language. This book invites readers to see mathematics not just as formulas and rules, but as the deepest expression of human thought.
The article presents the results of a study on the transport accessibility of district centers in the Sumy region using mathematical modeling and geoinformation analysis methods. Based on graph theory, a model of the region's transport network was developed, where settlements were considered as nodes and road connections as weighted edges of the graph. Dijkstra’s algorithm was used to calculate the shortest paths and travel time accessibility. The obtained travel time values from district centers to peripheral settlements were interpolated to construct isochrone maps, allowing for an assessment of the spatial heterogeneity of transport provision in the region. The analysis revealed significant geographical differences in transport accessibility levels across the districts. The best accessibility indicators are observed in areas close to the regional center, while peripheral communities, particularly in the Konotop and Shostka districts, are characterized by substantial travel time costs to reach district centers. Special attention is given to territorial contrasts in transport accessibility, which are determined by the spatial structure of the road network and the administrative-territorial features of the region. The use of Python and Google Colab for calculations and visualization enabled efficient modeling and analysis of the region's transport system. The research findings may be useful for spatial planning, optimizing road infrastructure, and improving population mobility within administrative districts.
Abstract. The article is devoted to the study of the possibilities of using the wind resources of the Khizi region in energy production. The study emphasizes territorial geographic characteristics and resource availability as primary determinants for power plant siting. The research database is based on the analysis of information obtained from various sources, such as measure-ments of meteorological stations, the Global Wind Atlas, and NASA Power data. The wind energy potential of the region was assessed based on meteorological indicators, and prospective areas were identified using GIS, taking into account factors such as land use, environmental impact, and energy demand. The collected data was processed using mathematical models, GIS analyses, economic analyses, and cartographic methods. Op-timal areas for the construction of wind power plants (WPP) were identified based on the main wind parameters, including average wind speed, wind direction, and seasonal wind regime, which reflects long-term va-riability and intra-annual fluctuations. As a result, the wind energy potential of the Khizi region was assessed using GIS-based microzoning and 1981–2022 of meteorological data. The selected location covers an area of 250 km², and 20 deployment zones with 40 turbines can be built in this field. The potential wind energy production in the area is possible to be 295.7 GWh, which could contribute 12% to Azerbaijan’s 2030 renewable energy targets.
In a historically colonial field, what are the possibilities of a geography informed by Indigenous and Anti‐Colonial ethics and onto‐epistemologies? This article suggests engaging in a critical study of data from an Indigenous geographic standpoint, with a focus on imperialism and colonialism in settler nation‐states. I begin by emphasizing the pervasive and long‐standing imposition of geographical data collection in Indigenous life, naming the binds of engaging with the production of data for and with colonial institutions. I then review prominent spatial analytics within critical Indigenous studies, Indigenous geography, and aligned Anti‐Colonial geography, including Indigenous place‐based knowledge and onto‐epistemologies, (racialized) colonial dispossession, sovereignty and recognition, environmental colonialism, and mapping and cartography. Last, I suggest that studies of colonial data dynamics and Indigenous data production strengthen Indigenous and Anti‐Colonial geographies by emphasizing the co‐constitution of good relations and good data. Future research avenues include the need to push beyond the geo‐historical bounds of the category settler colonial, and to build co‐rejections of racial empire with other fields of study including Black, Queer, and Feminist geographies.
In geographical education, interdisciplinary links allow to identify the main elements of educational content, to develop system-forming ideas, concepts, general scientific methods of learning activities, opportunities for students to comprehensively assimilate knowledge from different scientific disciplines. Interdisciplinary links influence the composition and structure of each academic discipline. Therefore, it is very important to emphasize the interdisciplinary connections taken into account in the content of geography and, conversely, in other fields of science besides geography. The aim of this study was to conduct a systematic review of interdisciplinary related science fields that integrate geographic educational technologies. The study used methods of analysis, differentiation, systematisation to identify the interdisciplinary links between geography and sciences such as history; biology; ecology; mathematics; cartography; physics and chemistry. As a result of the study, it was found that these branches of science – form a holistic scientific worldview in the student, strengthen interdisciplinary links. Thus, interdisciplinary links in geographical education are a modern principle of learning, influencing the choice and structure of teaching material in a number of disciplines, and an integrated organisational form that activates teaching methods.
Global and local modeling is essential for image super-resolution tasks. However, current efforts often lack explicit consideration of the cross-scale knowledge in large-scale earth observation scenarios, resulting in suboptimal single-scale representations in global and local modeling. The key motivation of this work is inspired by two observations: 1) There exists hierarchical features at the local and global regions in remote sensing images, and 2) they exhibit scale variation of similar ground objects (e.g. cross-scale similarity). In light of these, this paper presents an effective method to grasp the global and local image hierarchies by systematically exploring the cross-scale correlation. Specifically, we developed a Cross-scale Self-Attention (CSA) to model the global features, which introduces an auxiliary token space to calculate cross-scale self-attention matrices, thus exploring global dependency from diverse token scales. To extract the cross-scale localities, a Cross-scale Channel Attention (CCA) is devised, where multi-scale features are explored and progressively incorporated into an enriched feature. Moreover, by hierarchically deploying CSA and CCA into transformer groups, the proposed Cross-scale Hierarchical Transformer (CHT) can effectively explore cross-scale representations in remote sensing images, leading to a favorable reconstruction performance. Comprehensive experiments and analysis on four remote sensing datasets have demonstrated the superiority of CHT in both simulated and real-world remote sensing scenes. In particular, our CHT outperforms the state-of-the-art approach (TransENet) in terms of PSNR by 0.11 dB on average, but only accounts for 54.8% of its parameters.
Audio scene cartography for real or simulated stereo recordings is presented. This audio scene analysis is performed doing successively: a perceptive 10-subbands analysis, calculation of temporal laws for relative delays and gains between both channels of each subband using a short-time cons\-tant scene assumption and channels inter-correlation which permit to follow a mobile source in its moves, calculation of global and subbands histograms whose peaks give the incidence information for fixed sources. Audio scenes composed of 2 to 4 fixed sources or with a fixed source and a mobile one have been already successfully tested. Further extensions and applications will be discussed. Audio illustrations of audio scenes, subband analysis and demonstration of real-time stereo recording simulations will be given.Paper 6340 presented at the 118th Convention of the Audio Engineering Society, Barcelona, 2005
A review of the CARG Project of the Campania Region (marine counterpart) up to water depths of 200 m is herein proposed referring to the Gulf of Naples (southern Tyrrhenian Sea) aimed at focusing on the main scientific results obtained in the frame of this important project of marine geological cartography. The Gulf of Naples includes several geological sheets, namely n. 464 “Island of Ischia” both at the 1:25,000 and 1:10,000 scale, n. 465 “Island of Procida” at the 1:50,000 scale, n. 466–485 “Sorrento–Termini” at the 1:50,000 scale, n. 446–447 Naples at the 1:50,000 scale, and n. 484 “Island of Capri” at the 1:25,000 scale. The detailed revision of both the marine geological and geophysical data and of the literature data has allowed us to outline new perspectives in marine geology and cartography of Campania Region, including monitoring of coastal zone and individuation of coastal and volcano-tectonic and marine hazards.
We develop and analyze a mathematical model of oncolytic virotherapy in the treatment of melanoma. We begin with a special, local case of the model, in which we consider the dynamics of the tumour cells in the presence of an oncolytic virus at the primary tumour site. We then consider the more general regional model, in which we incorporate a linear network of lymph nodes through which the tumour cells and the oncolytic virus may spread. The modelling also considers the impact of hypoxia on the disease dynamics. The modelling takes into account both the effects of hypoxia on tumour growth and spreading, as well as the impact of hypoxia on oncolytic virotherapy as a treatment modality. We find that oxygen-rich environments are favourable for the use of adenoviruses as oncolytic agents, potentially suggesting the use of complementary external oxygenation as a key aspect of treatment. Furthermore, the delicate balance between a virus' infection capabilities and its oncolytic capabilities should be considered when engineering an oncolytic virus. If the virus is too potent at killing tumour cells while not being sufficiently effective at infecting them, the infected tumour cells are destroyed faster than they are able to infect additional tumour cells, leading less favourable clinical results. Numerical simulations are performed in order to support the analytic results and to further investigate the impact of various parameters on the outcomes of treatment. Our modelling provides further evidence indicating the importance of three key factors in treatment outcomes: tumour microenvironment oxygen concentration, viral infection rates, and viral oncolysis rates. The numerical results also provide some estimates on these key model parameters which may be useful in the engineering of oncolytic adenoviruses.
The article discusses the possibility of using Redis key-value NoSQL database to process building data in different BIM-GIS integration solutions. Whichever data integration model is adopted, it will require an efficient serving of building data in Industry Foundation Classes (IFC) format. The author proposed a method of processing building data in the Redis database to support the process of feeding IFC data to his own concept of an integrated BIM-GIS database. However, other approaches to BIM-GIS integration, including the import of IFC data to CityGML, or the construction of an integrated BIM-GIS solution based on data integration at the application server level or client application in client-server environments, also require an efficient IFC data serving mechanism. This article describes three methods of storing IFC data in a Redis database using different data types and formats. The author conducted performance tests of the proposed methods in the processing of fourteen test BIM models. The article contains detailed results of the model processing tests in the Redis database.
In this short note, we provide a criterion for DGA-homotopy of augmentations of Chekanov-Eliashberg algebra of disconnected Legendrian submanifolds. We apply the criterion to obtain the extension of geography results of Bourgeois and Galant concerning bilinearized Legendrian contact homology to the case of disconnected Legendrian submanifolds.
Category theory plays a special character in mathematics - it unifies distinct branches under the same formalism. Despite this integrative power in math, it also seems to provide the proper foundations to the experimental physicist. In this work, we present another application of category in physics, related to the principle of relativity. The operational construction of (inertial) frames of reference indicates that only the movement between one and another frame is enough to differentiate both of them. This fact is hidden when one applies only group theory to connect frames. In fact, rotations and translations only change coordinates, keeping the frame inert. The change of frames is only attainable by boosts in the classical and relativistic regimes for both Galileo and Lorentz (Poincaré) groups. Besides providing a non-trivial example of application of category theory in physics, we also fulfill the presented gap when one directly applies group theory for connecting frames.
In this research, the geographic, observational, functional, and cartographic scale is unified into a single mathematical formulation for the purposes of earth observation image classification. Fractal analysis is used to define functional scales, which then are linked to the other concepts of scale using common equations and conditions. The proposed formulation is called Unified Scale Theorem (UST), and was assessed with Sentinel-2 image covering a variety of land uses from the broad area of Thessaloniki, Greece. Provided as an interactive excel spreadsheet, UST promotes objectivity, rapidity, and accuracy, thus facilitating optimal scale selection for image classification purposes.