In ecological systems, be it a Petri dish or a galaxy, populations evolve from some initial value (say zero) up to a steady-state equilibrium, when the mean number of births and deaths per unit time are equal. This equilibrium point is a function of the birth and death rates, as well as the carrying capacity of the ecological system itself. We show that the occupation fraction versus birth-to-death rate ratio is S-shaped, saturating at the carrying capacity for large birth-to-death rate ratios and tending to zero at the other end. We argue that our astronomical observations appear inconsistent with a cosmos saturated with extraterrestrial intelligences, and thus search for extraterrestrial intelligence optimists are left presuming that the true population is somewhere along the transitional part of this S-curve. Since the birth and death rates are a-priori unbounded, we argue that this presents a fine-tuning problem. Further, we show that if the birth-to-death rate ratio is assumed to have a log-uniform prior distribution, then the probability distribution of the ecological filling fraction is bi-modal – peaking at zero and unity. Indeed, the resulting distribution is formally the classic Haldane prior, conceived to describe the prior expectation of a Bernoulli experiment, such as a technological intelligence developing (or not) on a given world. Our results formally connect the Drake equation to the birth–death formalism, the treatment of ecological carrying capacity and their connection to the Haldane perspective.

Ambient air pollution remains a global challenge, with adverse impacts on health and the environment. Addressing air pollution requires reliable data on pollutant concentrations, which form the foundation for interventions aimed at improving air quality. However, in many regions, including the United Kingdom, air pollution monitoring networks are characterized by spatial sparsity, heterogeneous placement, and frequent temporal data gaps, often due to issues such as power outages. We introduce a scalable data-driven supervised machine learning model framework designed to address temporal and spatial data gaps by filling missing measurements within the United Kingdom. The machine learning framework used is LightGBM, a gradient boosting algorithm based on decision trees, for efficient and scalable modeling. This approach provides a comprehensive dataset for England throughout 2018 at a 1 km2 hourly resolution. Leveraging machine learning techniques and real-world data from the sparsely distributed monitoring stations, we generate 355,827 synthetic monitoring stations across the study area. Validation was conducted to assess the model’s performance in forecasting, estimating missing locations, and capturing peak concentrations. The resulting dataset is of particular interest to a diverse range of stakeholders engaged in downstream assessments supported by outdoor air pollution concentration data for nitrogen dioxide (NO2), Ozone (O3), particulate matter with a diameter of 10 μm or less (PM10), particulate matter with a diameter of 2.5 μm or less PM2.5, and sulphur dioxide (SO2), at a higher resolution than was previously possible.

Several new foraminiferal taxa are described from the Changhsingian carbonates of southern Turkey, and their evolutionary relationships are discussed within the middle to late Permian time frame. Comprising Retroseptellina, Septoglobivalvulina, and Paraglobivalvulinoides, Retroseptellininae n. subfam. originated in the Wordian with thin and dense microgranular walls and became diverse and abundant in Changhsingian strata. Paraglobivalvulina? intermedia n. sp. appeared in the late Capitanian, survived into the Changhsingian, and gave way to completely involute tests of Paraglobivalvulininae. From the class Miliolata, Midiellidae n. fam., consisting of Midiella and Pseudomidiella, is characterized by sigmoidal coiling, and Pseudomidiella sahini n. sp. is probably the youngest known Changhsingian descendant. Glomomidiellopsis? okayi n. sp., which is interpreted as an evolutionary link between Capitanian Hemigordiopsis and Lopingian Glomomidiellopsis, survived into the Changhsingian. In the class Nodosariata, from the fully coiled Robuloides lineage of Robuloididae, Robuloides lata n. sp. and Plectorobuloides taurica n. gen. n. sp. most likely originated from R. lens in the Changhsingian. The R. acutus lineage, characterized by the reduction of laterally thickened hyaline wall and the appearance of evolute coiling, yielded Robuloides? rettorii n. sp. and Pseudorobuloides reicheli n. gen. n. sp. Calvezina anatolica n. sp. and Eomarginulinella galinae n. sp. are interpreted to have evolved from weakly coiled lineages in Robuloididae, whereas Pseudocryptomorphina amplimuralis n. gen. n. sp. is a poorly understood taxon and requires further study. Robustopachyphloia farinacciae n. sp. is interpreted as a descendant of some species within the genus Pachyphloia. The presence of canal-like pores in the wall of some Pachyphloia specimens is suggestive of a new morphological structure in the evolutionary history of the Changhsingian foraminifera.

Glycine plays an essential role in a variety of biological and biochemical processes. As the smallest amino acid, glycine is especially important in studies of prebiotic chemistry and chemical evolution. The behaviour of glycine in aqueous solution under ionizing radiation fields is still not well understood. Understanding the reaction mechanism of glycine in an ionizing radiation environment may provide insights into the complex processes involved in prebiotic chemical synthesis. Such reaction conditions could provide clues about the environmental conditions that might favour the emergence of life. Numerical modelling based on reaction kinetics provides information on the feasibility of the reaction mechanisms. In this work, we developed a numerical model in Python that describes the behaviour of glycine, as prototype compound, in aqueous solution under gamma radiation. The model is based on a variety of reaction kinetics pathways that have been proposed to describe the principal reactions between glycine and the water radicals formed by ionizing radiation. The numerical results are consistent with the experiments of other researchers. We obtained similar numerical solutions from different reaction mechanisms that share the same initial reactions. The results suggest that the primary attack of water radicals on the glycine is the main factor that controls the general decay of the molar concentration of glycine and the secondary reactions do not have a strong influence, even at high doses of nearly 200 kGy. The numerical tests of the models indicate their stability with the changing initial condition of the molar concentration of glycine. This work contributes to the advancement of knowledge regarding the behaviour of glycine in aqueous solutions under ionizing radiation from a kinetic perspective. It also provides insights into their stability under conditions that are difficult to replicate in the laboratory. Finally, this work contributes to the evaluation of appropriate numerical methods for solving the system of stiff differential equations that describe the reaction mechanism of organic molecules under high radiation fields.

Saponite-like materials have a wide range of potential applications, especially in heterogeneous catalysis. Despite the simplicity of the synthesis, the mechanisms of the formation of saponite are not well understood yet. The aim of the present study was to investigate a possible correlation between the coordination of Al in the solid phase and in the solution. For this, samples were prepared by varying the initial OH:Si molar ratio from 0.18 to 2.14, leading to a pH in the supernatant after the hydrothermal treatment of 6.7 to 12.7, respectively. The characterization of the material was performed by combining nuclear magnetic resonance (NMR) and X-ray absorption near edge structure (XANES) spectroscopies, and good agreement was obtained between the two techniques. Between pH7 and pH10, 60–65% of aluminum was found to be in tetrahedral coordination, while this percentage increased above pH10 (up to 81%). These results correlated with the speciation of the aluminum in aqueous solution. Indeed, above pH10, all available aluminum was in the soluble form Al(OH)4–.

Understanding the complex dynamics of climate patterns under different anthropogenic emissions scenarios is crucial for predicting future environmental conditions and formulating sustainable policies. Using Dynamic Mode Decomposition with control (DMDc), we analyze surface air temperature patterns from climate simulations to elucidate the effects of various climate-forcing agents. This improves upon previous DMD-based methods by including forcing information as a control variable. Our study identifies both common climate patterns, like the North Atlantic Oscillation and El Niño Southern Oscillation, and distinct impacts of aerosol and carbon emissions. We show that these emissions’ effects vary with climate scenarios, particularly under conditions of higher radiative forcing. Our findings confirm DMDc’s utility in climate analysis, highlighting its role in extracting modes of variability from surface air temperature while controlling for emissions contributions and exposing trends in these spatial patterns as forcing scenarios change.

Layered double hydroxides intercalated with tungstate ions ([WO4]2–) are among the anionic clays with interesting applications due to the physicochemical properties of the element tungsten. Their transformation by calcination into the corresponding derived mixed metal oxides should modify their properties. In view of this, the aim of this study is to compare the light absorption behavior of tungstate intercalated Mg-Al layered double hydroxide (LDH) with that of its derived mixed metal oxide (MMO), as well as their electrical and dielectric properties. The LDH precursor was prepared successfully by the co-precipitation method at pH 10, while MMO was obtained by calcining LDH at 723 K. Subsequently, LDH and MMO were characterized by X-ray diffraction and analyzed by thermal gravimetric analysis/differential thermal analysis and Raman spectroscopy. The electrical response, modeled by an equivalent circuit, was found to be intimately dependent on the structures of LDH and MMO, while the light absorption behavior is mainly due to the presence of the distorted [WO4]2– and the tetragonal MgWO4 in LDH and MMO, respectively. In addition, MMO showed an improvement in the dielectric properties through the large decrease in the dielectric loss tangent and electrical conductivity. However, LDH exhibited greater absorption coefficients in the ultraviolet region with a lower optical energy gap compared with its derived MMO, resulting in energy gaps of 4.23 and 4.35 eV for LDH and MMO, respectively. Results revealed that calcining LDH to form MMO fails to improve light absorption, but does improve the dielectric behavior, which makes possible the use of LDH as a shielding material against UV light and MMO for energy storage applications.

Forests play a crucial role in the Earth’s system processes and provide a suite of social and economic ecosystem services, but are significantly impacted by human activities, leading to a pronounced disruption of the equilibrium within ecosystems. Advancing forest monitoring worldwide offers advantages in mitigating human impacts and enhancing our comprehension of forest composition, alongside the effects of climate change. While statistical modeling has traditionally found applications in forest biology, recent strides in machine learning and computer vision have reached important milestones using remote sensing data, such as tree species identification, tree crown segmentation, and forest biomass assessments. For this, the significance of open-access data remains essential in enhancing such data-driven algorithms and methodologies. Here, we provide a comprehensive and extensive overview of 86 open-access forest datasets across spatial scales, encompassing inventories, ground-based, aerial-based, satellite-based recordings, and country or world maps. These datasets are grouped in OpenForest, a dynamic catalog open to contributions that strives to reference all available open-access forest datasets. Moreover, in the context of these datasets, we aim to inspire research in machine learning applied to forest biology by establishing connections between contemporary topics, perspectives, and challenges inherent in both domains. We hope to encourage collaborations among scientists, fostering the sharing and exploration of diverse datasets through the application of machine learning methods for large-scale forest monitoring. OpenForest is available at the following url: https://github.com/RolnickLab/OpenForest.

The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. Their geometry and buttressing strength are influenced by the local surface accumulation and basal melt rates, governed by atmospheric and oceanic conditions. Contemporary methods quantify one of these rates, but typically not both. Moreover, information about these rates is only available for recent time periods, reaching at most a few decades back since measurements are available. We present a new method to simultaneously infer the surface accumulation and basal melt rates averaged over decadal and centennial timescales. We infer the spatial dependence of these rates along flow line transects using internal stratigraphy observed by radars, using a kinematic forward model of internal stratigraphy. We solve the inverse problem using simulation-based inference (SBI). SBI performs Bayesian inference by training neural networks on simulations of the forward model to approximate the posterior distribution, therefore also quantifying uncertainties over the inferred parameters. We validate our method on a synthetic example, and apply it to Ekström Ice Shelf, Antarctica, for which independent validation data are available. We obtain posterior distributions of surface accumulation and basal melt averaging over up to 200 years before 2022.

A new oxalate mineral species, edwindavisite, ideally Cu(C2O4)(NH3), was discovered in specimens collected from the Rowley mine, Maricopa County, Arizona, USA. It occurs as fans or sprays of bladed or prismatic crystals (up to 0.50 × 0.08 × 0.06 mm), associated intimately with ammineite, a sampleite-like mineral, baryte, ebnerite, wulfenite and quartz. Edwindavisite is green, transparent with a pale green streak and has a vitreous lustre. It is brittle and has a Mohs hardness of ∼2; cleavage is perfect on {100}. No parting or twinning was observed. The measured and calculated densities are 2.55(2) and 2.53 g/cm3, respectively. Optically, edwindavisite is biaxial (+), with α = 1.550(2), β = 1.559(2), γ = 1.755(5), 2Vmeas. = 26(2)° and 2Vcal. = 26.4°. Electron microprobe analyses yielded the empirical formula (based on Cu = 1 apfu) Cu1.00(C2O4)(NH3)0.99.

Edwindavisite is the natural counterpart of synthetic catena-μ-oxalato-ammine-copper(II), Cu(C2O4)(NH3). It is orthorhombic with space group Pbca and unit-cell parameters a = 11.1998(10), b = 9.4307(9), c = 8.3977(7) Å, V = 886.98(14) Å3 and Z = 8. In the edwindavisite structure, each Cu2+ cation is coordinated by (5O + N), forming a rather distorted and elongated octahedron. The Cu-octahedra share corners with one another to form chains extending along [001], which are joined together by oxalate (C2O4)2– groups, giving rise to layers parallel to (100). These layers are linked together by N–H···O hydrogen bonds. Among 37 oxalate minerals documented to date, edwindavisite is the first one that contains ammonia (NH3).