Search Results (3,165)

Iron, with the unique stability of its nucleus, occupies an unusual position among the elements: its abundance on Earth is not simply a geological accident but a direct consequence of nuclear reactions that happened inside stars billions of years ago. Formed at the final stages of fusion in stars, iron spread through space by supernova explosions and became part of the material that formed Earth, eventually becoming the dominant component of the planet’s core. At the surface, iron’s redox chemistry shaped the early atmosphere and oceans, and its availability as a soluble ferrous ion in the anaerobic Archean ocean made it a natural cofactor for the first enzymatic reactions. That same redox flexibility and the ability of iron to shuttle between Fe²⁺ and Fe³⁺ across a wide range of electrochemical potentials explain why virtually every major metabolic pathway in biology depends on iron in one form or another. Yet iron is also dangerous: free and chelated iron can catalyze the production of toxic hydroxyl radicals through Fenton chemistry, the reactivity of which depends strongly on the nature of the chelating ligand, and every living system must balance its need for iron against the oxidative damage that uncontrolled iron causes. This tension between catalytic necessity and chemical toxicity has driven much of the regulatory complexity we observe in modern iron metabolism. In this review, we first outline iron’s journey from its formation in stars to its role in shaping Earth’s structure and the emergence of early iron-dependent biology. We then discuss in detail how fundamental physical and chemical factors continue to influence living systems. Full article

Building materials and their spatial distribution play a significant role in determining the outcomes of human-caused and natural disasters in urban and peri-urban areas. However, building-level data on building and roofing materials are scarce. Here, we explore the feasibility and performance of a Convolutional Neural Network (CNN) model using spectrally transformed high-resolution multispectral imagery to map roofprints (i.e., classifying and delineating roofing materials at the building footprint-level) in Washington, District of Columbia (D.C.) and Denver, CO, United States. To generate consistent training data, we integrate geospatial vector data of individual building footprints with real estate industry-derived building-level roofing material data to create labeled image data from Planet SuperDove imagery. We compare the CNN classifier to a pixel-based machine learning (ML) model to demonstrate the capability of our roofprints mapping approach. With F1-scores ranging from 0.56 to 0.95 for the most common roof material classes, the CNN model outperformed the pixel-based ML classifier by 15% and 17% in Washington, D.C., and Denver, respectively. Results demonstrate within-domain robustness for the studied metropolitan areas, which are characterized by differing building densities, roof morphologies, and material patterns. While cross-region transferability was not evaluated, our findings provide a controlled comparison of pixel-based and context-aware approaches for rooftop material mapping and highlight the importance of hierarchical representations that integrate spectral information with roof texture, edge characteristics, spatial arrangement, and neighborhood context for improving classification performance. Accurately mapping building materials has the potential to advance urban planning and environmental policies, including assessments of heat exposure, energy demand, as well as hazard risk and community resilience. Full article

Flood susceptibility maps are essential tools for identifying high-risk areas. However, traditional approaches often face limitations in spatial resolution and adaptability under changing climatic conditions, particularly in data-scarce regions. This study addresses these limitations through a data-driven geospatial approach that integrates high-resolution Earth Observation and Geographic Information Systems (GIS) data to improve flood susceptibility assessment in a small river basin in Romania. Ten flood conditioning factors were analyzed, including Elevation, Slope, Topographic Wetness Index (TWI), Topographic Position Index (TPI), Profile Curvature, Aspect, Soil Texture, Distance to the River, Normalized Difference Vegetation Index (NDVI), and Soil Moisture. Historical flood extent data extracted from PlanetScope imagery were used for model training and validation. Two statistical methods, Frequency Ratio (FR) and Weight of Evidence (WoE), were applied to map flood susceptibility at a 12.5 m resolution. Results indicate that both models captured the spatial variability of flood-prone areas, but WoE achieved higher predictive performance (AUC = 0.945) than FR (AUC = 0.876), while FR tended to underestimate flood-prone zones. Half of the basin falls within low to very low susceptibility classes, whereas high and very high susceptibility together occupy about 25–29% of the basin and concentrate along river corridors in the central and southern sectors, overlapping with built-up areas. Consequently, about 38% (WoE) and 30% (FR) of the total built-up area fall within high and very high susceptibility classes. The results demonstrate that integrating high-resolution open-source Earth Observation data with statistical modeling provides a reliable, transferable framework for flood susceptibility assessment and land-use planning in data-scarce environments. Full article

Bird–aircraft strikes are rare but high-impact events that pose persistent safety and operational challenges in aviation. Existing approaches often focus on predictive accuracy, with limited integration of probability calibration, rare-event precision, and decision-making under operational constraints. This paper proposes a calibrated threshold-optimized decision-support framework that anticipates short-term bird strike risk and translates probabilistic forecasts into constrained intervention plans. The framework combines (i) a calibrated occurrence model for short-term strike probability, (ii) a conditional intensity model for expected strike magnitude, and (iii) a constrained planning module that selects a limited number of high-risk time windows per day. The approach is evaluated using EASA bird strike data (2019–2024), enriched with environmental, meteorological, astronomical, tide, and traffic-proxy features. After this stricter temporal reconstruction, the calibrated occurrence model reaches ROC-AUC $\approx 0.636$ and PR-AUC $\approx 0.050$ across forward chain folds, while the OOF aggregate gives ROC-AUC $=0.648$ and PR-AUC $=0.047$ against a forward label prevalence of $0.0246$. Under a strict daily alarm budget ($K=2$), OOF operational Precision@K is $0.063$ and Recall@K is $0.075$, with a median credited lead time of 90 min. The results highlight the importance of calibrated probabilities and decision-oriented evaluation, showing that effective rare-event management requires not only prediction, but also integration with operational planning. Full article

Environmental pollution is becoming an urgent global issue. Countries are implementing various solutions and establishing legal frameworks and institutional policies to promote the use of green energy sources and thereby minimize the adverse environmental impact of economic growth, investment, and consumption. The goal of economies worldwide, including in Asia, is to implement policies that promote sustainable development while optimizing economic performance. The purpose of this study is to evaluate the impact of green energy consumption and institutional quality on environmental quality. Using data from 29 selected Asian countries over the period 1970–2021, the empirical results indicate that green energy consumption does not have a statistically significant impact on environmental quality across the full sample. Improvements in institutional quality can enhance environmental quality and accelerate progress toward carbon neutrality and net-zero emissions targets. The Climate Change Conferences (COPs) have helped raise global awareness of the dangers of climate change, fostering greater international cooperation to protect the planet and promote sustainable development. To capture the influence of international climate commitments, this study employs a dummy variable for the COP period (coded 0 before COP2 in 1996 and 1 thereafter). This variable is interacted with green energy consumption and institutional quality to examine whether the implementation of COP commitments moderates their effects on CO₂ emissions. The empirical results show that greater political stability, particularly during the implementation of carbon emission reduction commitments, contributes to reducing greenhouse gas emissions and promoting sustainable development. The study also confirms that institutional quality plays a significant role in improving environmental quality and accelerating countries’ progress toward carbon neutrality and net-zero emission goals. Full article

The future of the planet depends largely on human beings, who currently occupy a dominant position among living species. This condition highlights the importance of global efforts to ensure that the sustainability of life on Earth remains a central priority, as articulated in the Sustainable Development Goals (SDGs). This paper investigates how sustainability and education have been represented in research publications in Indonesia. This study reviews the development of sustainability and education research in Indonesia using a systematic literature review (SLR) supported by structured content analysis and descriptive mapping. A total of 362 documents were retrieved from the Scopus database using specific keywords. The systematic review reveals an upward trend in publications over the past nine years, with universities in Java playing a dominant role, and a significant acceleration in knowledge production during the last five years. This increase is accompanied by growing diversity in research topics, domains, keywords, and methodological approaches. One of the most notable findings is the prominence of service-based learning (SBL), which appears to be a distinctive feature of higher education pedagogy in Indonesia. Full article

Quantifying mountain forest carbon stocks and elucidating their spatially heterogeneous driving mechanisms are both critical for terrestrial carbon management under the global carbon neutrality agenda. Conventional single-source remote sensing approaches can neither fully exploit multi-source data synergies nor adequately resolve spatial heterogeneity in complex terrains. This study develops an integrated framework combining multi-source remote sensing classification, InVEST-based carbon estimation, and multiscale geographically weighted regression (MGWR) and applies it to Zixi County, a subtropical mountainous bamboo-abundant region in southeastern China. Sentinel-2 imagery, PlanetScope data, and DEM derivatives were fused with an optimized Random Forest classifier, achieving an overall accuracy of 0.8565 (Kappa = 0.7065). Carbon stocks were then estimated via the InVEST model. MGWR analysis (adjusted R² = 0.930, AICc = 594.032) substantially outperformed the global OLS model (adjusted R² = 0.795, AICc = 1717.450), confirming strong spatial non-stationarity across all drivers. Canopy density exhibited the strongest positive local effect (coefficient range: 0.343–0.768); slope position showed predominantly negative regulation with localized positive reversals (−0.778 to 0.270); elevation displayed a broad-scale positive gradient (0.133–0.140); and total vegetation cover exhibited bidirectional effects (−0.134 to 0.208) with pronounced east–west divergence. This framework not only provides a robust methodological reference for carbon stock assessment in complex mountain landscapes but also supports targeted forest management and carbon sequestration strategies through spatially explicit driver identification. Full article

The trans-Neptunian region (30–50 AU) is a dynamically structured reservoir of icy planetesimals whose orbital architecture reflects resonant dynamics, chaotic transport, and long-term gravitational sculpting by the giant planets. This review synthesizes recent developments in the dynamical investigation of trans-Neptunian objects (TNOs), with an emphasis on mean-motion and secular resonances, as well as chaotic diffusion, in a system whose growing observational census makes it an ideal testbed for chaos detection methods. Classical indicators, including Lyapunov exponents, MEGNO, SALI/GALI, and frequency map analysis, provide the quantitative backbone for mapping TNO phase space and are complemented by modern approaches such as Lagrangian descriptors, the FAIR resonance identification method, entropy-based chaos indicators, and recurrence plot divergence methods. An anomalous diffusion framework, in which mean squared displacement scales as a power law in time, further enables classification of sub- and superdiffusive orbital transport. Machine learning has emerged as a powerful complement to traditional dynamical methods: surrogate classifiers, deep neural network solvers, and hybrid physics–data-driven frameworks together extend reliable prediction horizons in chaotic regimes and open new routes for Bayesian inference of migration scenarios. The review concludes that the most promising path forward lies in hybrid dynamical–statistical frameworks anchored to Hamiltonian dynamics, enabling efficient exploration of high-dimensional parameter spaces informed by the expanding body of trans-Neptunian observations. Full article

Generating satellite-based deforestation alerts with actionable latency requires frequent imaging, creating an imperative to use different sensors together. We introduce a simple and open-source framework called the Disturbance Index Alert System (DIAS), which is based upon transformation of imagery from different sources into an interoperable stream of Disturbance Index (DI) values. Whereas most alert systems target divergence of forested pixels from historical states, DIAS targets movement of a pixel’s Z-score position relative to the image-wide population of forest pixels along a forest-sensitive axis. This strategy provides the following practical benefits: (1) it reduces the need to process the historical archive; (2) it reduces dependence upon stable sensor calibration; (3) it allows Z-score-based DI values to be combined across sensors; and (4) it accommodates changes to the group of sensors providing measurements. We demonstrated in Madagascar that sensor integration through DIAS can provide more timely alerts than both conventional individual-sensor systems and additive combination of such systems. Across our study sites, using a commercial source of daily imaging (PlanetScope) in conjunction with imagery from public sources (Landsat, Sentinels-1 and -2) allowed high-confidence detection (false alert rate of approximately 20%) of two-thirds of deforestation occurring at 10 m reference pixels within one month; 40% were detected in that timeframe with public data alone. As commercial options for Earth observation proliferate, flexible and computationally lightweight approaches such as DIAS are needed to accommodate diverse and sometimes only loosely calibrated instruments in support of timely forest monitoring. Full article

Geospatial foundation models are a new frontier in artificial intelligence, designed to understand and analyze spatial data at scale. Trained on huge sets of EO data, these models can support a wide range of applications—from monitoring natural disasters to guiding urban development and tracking climate change. To this aim, researchers and practitioners need to fine-tune the foundation models for specific tasks, utilizing a relatively small amount of additional data. As a result, geospatial foundation models are reshaping how we observe, manage, and protect our planet. Fine-tuning a geospatial foundation model requires carefully curated training datasets that reflect specific regions, time periods, or tasks—such as detecting deforestation or mapping urban growth. Yet preparing these datasets is often labor-intensive, involving steps like selecting relevant imagery, aligning spatial formats, and generating accurate labels. In practice, this means that the effectiveness of GFMs hinges on the availability of AI-ready data. This bottleneck limits the accessibility and scalability of GFMs for scientific and operational applications. In this work, we introduce a software library designed to automate these preparatory steps, streamlining the transformation of geospatial datasets into consistent, high-quality inputs for GFM fine-tuning. By reducing technical overhead and ensuring data readiness, the library enables faster, more reliable, and more inclusive adaptation of foundation models to local environmental challenges and specialized domain needs. Full article

An aeolian event and a fluvial episode affected Gale crater, Mars, prior to 3.6 billion years ago. Both were short-lived and catastrophic. The same two events also modified the Southern Highlands of the red planet during the same time interval. We show that events in Gale crater were a part of those that modified vast areas of the southern hemisphere of Mars. As such, the patterns documented in Gale crater are consistent with reshaping of large portions of Mars by short-lived catastrophic events by wind and water, although data from other regions are needed to establish this on a planetary scale. The study is based on data collected by the Curiosity rover during the past 14 years. The aeolian event that excavated Gale crater was lithologically controlled. It formed two distinct morphological provinces with two contrasting rock types. One was the cone-shaped ancestral Aeolis Mons, informally known as Mt. Sharp, that consists of sandstone, siltstone, and mudstone. The other was the nearly flat hollowed margin, the ancestral crater floor, that was initially covered by loose pebbles, cobbles, and boulders which were reworked and lithified to a conglomeratic rock unit later. Commonly reported Martian aeolian erosion rates cannot account for the abrasion and transport of 39,000 km³ of sediments out of Gale crater. This conclusion is supported by little modification of Gale crater during the past 3.6 billion years by ordinary winds. Our evaluation indicates that the excavation of Gale crater took place by a powerful aeolian perturbation that resembled a sand-blasting operation. It was short-lived, had extremely high erosion rates, and occurred during a cold and dry climate. The fluvial episode followed the aeolian event. The study of its sedimentary record indicates that it began with intense precipitation-driven great floods that eroded the ancestral Mt. Sharp, carved large canyons on its slope, and reworked gravels of the ancestral crater floor into giant bedforms. Flood waters also formed a deep lake that experienced one rise and one fall of lake-level and had a dynamic storm-driven sedimentation. The fluvial episode was also short-lived and indicates catastrophic actions of water during a warm and wet climate. As such, this study suggests that the extensive reshaping of the red planet during the Late Noachian period, including formation of valley networks, occurrence of hundreds of crater lakes, and excavation of numerous craters, were also due to short-lived, intense, climate-related perturbations by powerful wind and water rather than by ordinary, slow rate, long-duration processes. Another implication of the study is for the mineralogical evolution of Martian sedimentary rocks. It indicates that the Late Noachian period may have been mostly cold and dry, similar to the modern Mars. Its low water/rock ratio and cold temperatures halted chemical weathering that resulted in preservation of highly unstable minerals such as olivine and pyroxene. The fluvial perturbation with its high water/rock ratio was not long and/or warm enough to alter or significantly affect the mineralogy by weathering at the source region, or during the transport, or at the depositional site. Full article

Debates concerning the Nature of Science (NoS) have increasingly acknowledged its epistemic, cultural, ethical, and social dimensions. Recent scholarship has further foregrounded issues of equity, identity, and justice within science education. While these developments represent significant progress, this article argues that dominant conceptualisations of NoS remain fundamentally anthropocentric and insufficiently responsive to the ecological crises that define the Anthropocene. Drawing on Earth System Science, eco-centric theory, post-human theory and Indigenous and local knowledges, this paper proposes a planetary-conscious reconceptualisation of NoS. This framework retains the methodological rigour and evidential standards of Western science while expanding epistemic boundaries to include relational, place-based, and intergenerational ways of knowing. We argue that eco-centric and post-human theoretical frameworks offer essential pedagogical approaches for supporting young people to develop deeper connections with nature, fostering care-based relationships with the more-than-human world, and building resilience for sustainable futures. Such a reconceptualisation is necessary not only for scientific literacy but for the protection and enhancement of planetary health. Implications for curriculum, pedagogy, and teacher education are discussed, with particular attention to primary science education. Full article

Urban air pollution, especially the problem of PM_2.5, is one of the major health challenges facing the planet today. To provide accurate PM_2.5 predictions despite data noise and missing data, the authors proposed a deep learning model. We constructed a Stacked Autoencoder–Convolutional Neural Network–Bidirectional Long Short-Term Memory–Long Short-Term Memory (SAE-CNN-BiLSTM-LSTM) model that (1) utilises convolutional layers to extract spatial features from the input data, (2) employs bidirectional LSTM layers to capture long-term temporal dependencies, and (3) utilises an autoencoder to learn latent representations of the data to mitigate the effects of missing data. The model was trained on a large dataset of hourly measurements of air quality and meteorological parameters collected between 2018 and 2020 in Klang, Malaysia. The performance of the model on data that were not used during training was evaluated using a range of metrics. The SAE-CNN-BiLSTM-LSTM model achieved a test RMSE of approximately 11.97 µg/m³ and an R² statistic of approximately 0.85 for PM_2.5 concentrations, outperforming the other models tested on the same datasets. The additional metrics of MAE, MAPE, Mean Bias Error, and Index of Agreement confirmed the model’s accuracy and low bias in the prediction of air pollution levels. Statistical tests, such as the Diebold–Mariano test, confirmed the significance of the model’s accuracy over the CNN-LSTM models. These findings indicate that the proposed model effectively captures the dynamics of the air pollution data. The proposed model structure efficiently achieved an accurate and lightweight model for urban air pollution forecasting. Full article

Introduction: Long-term ecological changes in estuarine communities are primarily driven by anthropogenic and environmental pressures. While abundance-based indicators are commonly used to assess these shifts, they often mask underlying ecological aspects related to age and/or size dynamics that may not necessarily be reflected in the abundance-based approach. Objective: This work tested a size-based indicator approach to examine the long-term changes in the size structure of the Mondego estuarine fish community (Portugal), using a 22-year dataset (2003 to 2025). Methodology: To capture the whole size structure, eight size-based indicators were applied, including mean length (MeanL), length at the 10th percentile (L10), median length (MedianL), length at the 90th percentile (L90), mean length of the 90th percentile (Lmax), size spectrum, the Large Fish index, and the Shannon index of length classes, at community and species levels and subsequently considered these in relation with with local and large-scale environmental factors. Results: Linear models identified a sharp, consistent decline in the overall size of the community, significantly correlated with the North Atlantic Oscillation index (NAO) and increasing estuarine water temperatures. A dynamic factor analysis (DFA) further identified one common trend across species for all indicators, corroborating the decrease in the overall size of the community while also acknowledging contrasting responses from different species, suggesting a heterogenous response across the fish community. Conclusions: These results highlight the importance of size-based indicators when assessing long-term ecological changes in marine ecosystems, allowing us to better understand how size structures shift, their relationship with a changing environment, and the long-term ecological outcomes in terms of community stability, resilience, recruitment, and ecosystem functioning. Full article

Introduction: Skates (Rajidae) are cornerstone elasmobranchs, yet their intrinsic biological constraints, like slow growth, late maturation, and low fecundity, render them exceptionally susceptible to anthropogenic pressure. Despite their ecological and economic importance, tracking their population trajectories is historically hindered by “taxonomic blurring” and aggregated reporting in commercial fisheries. Objective: This study evaluates long-term conservation trends and exploitation dynamics of Rajidae species in the Northeast Atlantic and the Mediterranean Sea. Methodology: We analyzed 31 Rajidae species across the Northeast Atlantic and the Mediterranean Sea (FAO Areas 27 and 37) by integrating IUCN Red List assessments, species-specific life-history traits (maximum body size and depth distribution), and FAO fisheries landing data from 1992 to 2023. Descriptive analyses and Spearman correlations were used to assess temporal trends in conservation status and exploitation patterns. Results: Our synthesis reveals that some species show improvements in IUCN Red List category assessments, likely driven by recent management interventions such as species-specific reporting, catch quotas, and targeted retention bans. However, we also identify a critical mismatch between policy and biology: current Total Allowable Catches (TACs) and minimum landing sizes often do not explicitly incorporate species-specific life-history traits, inadvertently favoring smaller, less-marketable taxa while leaving larger, vulnerable species at risk. While FAO landings offer a valuable broad-scale overview of exploitation, the results highlight the limitations of aggregated fisheries statistics for species-level conservation assessments. Conclusions: These findings underline the need to adopt more precise and species-specific fisheries management approaches for Rajidae, including expanded regional monitoring programs, the use of data collected by on-board observers or electronic monitoring tools, and improved control of data reporting procedures, to prevent continued aggregation of species-level data. Full article