Understanding past fire regimes and how they vary with climate, human activity, and vegetation patterns is fundamental to the mitigation and management of changing fire regimes as anthropogenic climate change progresses. Ash-derived trace elements and pyrogenic biomarkers from speleothems have recently been shown to record past fire activity in speleothems from both Australia and North America. This calls for an empirical study of ash geochemistry to aid the interpretation of speleothem palaeofire proxy records. Here we present analyses of leached ashes collected following fires in southwest and southeast Australia. We include a suite of inorganic elemental data from the water-soluble fraction of ash, as well as a selection of organic analytes (pyrogenic lipid biomarkers). We also present elemental data from leachates of soils collected from sites in southwest Australia. We demonstrate that the water-soluble fraction of ash differs from the water-soluble fraction of soils, with trace and minor element concentrations in ash leachates varying with combustion completeness (burn severity) and sample location. Changes in some lipid biomarker concentrations extracted from ashes may reflect burn severity. Our results contribute to building a process-based understanding of how speleothem geochemistry may record fire frequency and severity, and suggest that more research is needed to understand the transport pathways for the inclusion of pyrogenic biomarkers in speleothems. Our results also demonstrate that potential contaminant loads from ashes are much higher than from soils, with implications for the management of karst catchments, which are a critical water resource.

Mid-latitude extreme cold outbreaks are associated with disruptions of the Polar Vortex, which often happen abruptly in connection to a Sudden Stratospheric Warming. Understanding Global Warming (particularly Arctic Amplification) impacts on forecasting such events is challenging for the scientific community. Here we apply clustering analysis on the Northern Annular Mode to identify surface precursors and the governing mechanisms causing polar vortex disruption events. Two clusters of breakdown emerge; 65% of the events are associated with high latitude ocean warming, expressed as North Pacific anomalies and Barents-Kara sea ice loss. Such warming causes large scale modifications of the tropospheric flow that favors a slowdown of the stratospheric vortex. The persistence of ocean surface temperature patterns allows forecasting polar vortex disruptions, and potentially improves prediction skills at the sub-seasonal to seasonal time scales.

Can the current successes of global machine learning-based weather simulators be generalized beyond two-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10-year simulations with a network trained on output from a physics-based global atmosphere model using a grid spacing of approximately 110 km and forced by a repeating annual cycle of sea-surface temperature. Here we show that ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least ten years with similarly small climate biases -- a necessary prerequisite to wider applicability. With a comprehensive analysis from multiple temporal, spatial, and frequency domain perspectives, we show that ACE faithfully represents the spatiotemporal structure of EAMv2 precipitation and related variables. Finally, we show that a pretrained ACE network is able to adapt to a new global climate model simulation dataset with 10x fewer training steps than when starting from random initialization, all while still maintaining low levels of climate bias. Further analysis of these fine-tuning experiments reveal ACE's attractive ability to interpolate between distinct global climate models.

Tsunamis are rare, destructive events, whose generation, propagation and coastal impact processes involve several complex physical phenomena. Most tsunami applications, like probabilistic tsunami hazard assessment, make extensive use of large sets of numerical simulations, facing a systematic trade-off between the computational costs and the modelling accuracy. For seismogenic tsunami, the source is often modelled as an instantaneous sea-floor displacement due to the fault static slip distribution, while the propagation in open-sea is computed through a shallow water approximation. Here, through 1D earthquake-tsunami coupled simulations of large M>8 earthquakes in Tohoku-like subduction zone, we tested for which conditions the instantaneous source (IS) and/or the shallow water (SW) approximations can be used to simulate with enough accuracy the whole tsunami evolution. We used as a reference a time-dependent (TD), multi-layer, non-hydrostatic (NH) model whose source features, duration, and size, are based on seismic rupture dynamic simulations with realistic stress drop and rigidity, within a Tohoku-like environment. We showed that slow ruptures, generating slip in shallow part of subduction slabs (e.g. tsunami earthquakes), and very large events, with an along-dip extension comparable with the trench-coast distance (e.g. mega-thrust) require a TD-NH modelling, in particular when the bathymetry close to the coast features sharp depth gradients. Conversely, deeper, higher stress-drop events can be accurately modelled through an IS-SW approximation. We finally showed to what extent inundation depend on bathymetric geometrical features: (i) steeper bathymetries generate larger inundations and (ii) a resonant mechanism emerges with run-up amplifications associated with larger source size on flatter bathymetries.

Installation of subsurface drainage systems has profoundly altered the nitrogen cycle in agricultural regions across the globe, facilitating substantial loss of nitrate (NO3-) to surface water systems. Lack of understanding of the sources and processes controlling NO3- loss from tile-drained agroecosystems hinders the development of management strategies aimed at reducing this loss. The natural abundance nitrogen and oxygen isotopes of NO3- provide a valuable tool for differentiating nitrogen sources and tracking the biogeochemical transformations acting on NO3-. This study combined multi-years of tile drainage measurements with NO3- isotopic analysis to examine NO3- source and transport mechanisms in a tile-drained corn-soybean field. The tile drainage NO3- isotope data were supplemented by characterization of the nitrogen isotopic composition of potential NO3- sources (fertilizer, soil nitrogen, and crop biomass) in the field and the oxygen isotopic composition of NO3- produced by nitrification in soil incubations. The results show that NO3- isotopes in tile drainage were highly responsive to tile discharge variation and fertilizer input. After accounting for isotopic fractionations during nitrification and denitrification, the isotopic signature of tile drainage NO3- was temporally stable and similar to those of fertilizer and soybean residue during unfertilized periods. This temporal invariance in NO3- isotopic signature indicates a nitrogen legacy effect, possibly resulting from N recycling at the soil microsite scale and a large water storage for NO3- mixing. Collectively, these results demonstrate how combining field NO3- isotope data with knowledge of isotopic fractionations can reveal mechanisms controlling NO3- cycling and transport under complex field conditions.

This study investigates natural hydrogen (H2) occurrences in the Paris Basin, using Optical Character Recognition (OCR) technology to analyze an extensive, yet historically underexploited, well database that contains older drilling records. With the growing demand for carbon-free energy, natural hydrogen, produced through processes like serpentinization and water radiolysis, offers a promising alternative to fossil fuels. However, its potential has been largely unexplored in conventional oil and gas wells. Utilizing the BEPH (Office of Exploration and Production of Hydrocarbons) French database, which includes well logs, mudlogs, and End Drilling Reports (EDRs) in PDF image format, we applied the Tesseract-OCR Engine to convert these documents into searchable formats for efficient data analysis. Our analysis revealed several H2-bearing wells across the French sedimentary basins. The hydrogen occurrences in the Aquitaine Basin correlate with the geological context, but those in the Paris Basin present an anomaly, as their H2 occurrences do not align with the expected geological factors. In the Paris Basin, H2 has been detected in four main formations: the Lusitanian aquifer, Dogger aquifer, Triassic aquifer, and the basement. The highest hydrogen concentration (52 vol%) was found in the Dogger formation. These wells are primarily located along the Bray fault and thrust, indicating a geological influence on H2 distribution. This research demonstrates the effectiveness of OCR in reprocessing historical drilling data for natural hydrogen exploration, highlighting the need for comprehensive exploration methodologies in this emerging field.

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2) photon-counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat-2 measurements. Careful planning and dedicated development were accomplished during the pre-launch phase of the mission in preparation for the 2018 launch. Once on-orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat-2’s contributions to earth observation science initiated by NASA and the ICESat-2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat-2’s data capabilities and allowed for advanced science applications beyond the original mission objectives.

Exposure to ionizing radiation from galactic cosmic rays (GCR) and solar energetic particles (SEP) at aircraft flight altitudes can have an adverse effect on human health. Although airline crews are classified as radiation workers by the International Commission on Radiological Protection (ICRP), in most countries, their level of exposure is unquantified and undocumented throughout the duration of their career. As such, there is a need to assess pilot ionizing radiation exposure. The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS), a real-time, global, physics-based model is used to assess such exposure. The Automated Radiation Measurements for Aerospace Safety (ARMAS) measurement dataset consists of high latitude, high altitude, and long-duration aircraft flights between 2013-2023. Here, we characterize radiation exposure at aviation flight altitudes using the NAIRAS model and compare with 45 flight trajectories from the recent ARMAS flight measurement inventory.

Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their vulnerability to permafrost thawing induced by climate change. An ensemble of six terrestrial biosphere models suggests NHL soil N2O emissions doubled since the preindustrial 1860s, increasing on average by 2.0±1.0 Gg N yr-1 (p<0.01). This trend reversed after the 1980s because of reduced nitrogen fertilizer application in non-permafrost regions and increased plant growth due to CO2 fertilization suppressed emissions. However, permafrost soil N2O emissions continued increasing attributable to climate warming; the interaction of climate warming and increasing CO2 concentrations on nitrogen and carbon cycling will determine future trends in NHL soil N2O emissions.

Air pollution caused by various anthropogenic activities and biomass burning continues to be a major problem in India. To assess the effectiveness of current air pollution mitigation measures, we used a 3D global chemical transport model to analyze the projected optical depth of carbonaceous aerosol (AOD) in India under representative concentration pathways (RCP) 4.5 and 8.5 over the period 2000-2100. Our results show a decrease in future emissions, leading to a decrease in modeled AOD under both RCPs after 2030. The RCP4.5 scenario shows a 48-65% decrease in AOD by the end of the century, with the Indo-Gangetic Plain (IGP) experiencing a maximum change of ~25% by 2030 compared to 2010. Conversely, RCP8.5 showed an increase in AOD of ~29% by 2050 and did not indicate a significant decrease by the end of the century. Our study also highlights that it is likely to take three decades for current policies to be effective for regions heavily polluted by exposure to carbonaceous aerosols, such as the IGP and eastern India. We emphasize the importance of assessing the effectiveness of current policies and highlight the need for continued efforts to address the problem of air pollution from carbonaceous aerosols, both from anthropogenic sources and biomass burning, in India.

A Proposal for the Center for Undergraduate Research Scholarship

Video games are a versatile and multi-faceted stimulus which can elicit complex player experiences. As a consequence, several datasets have been curated or created for studying human cognition, behaviours, and physiological responses where video games are the primary stimulus. Many of these datasets have a low number of participants or do not have a rich set of modalities and are always recorded in a laboratory setting. To address these issues, we have recorded 256 participants at LAN events while they played the first person shooter, Counter-Strike: Global Offensive. Our dataset consists of several complementary modalities: physiological signals (ECG, EDA, Respiration), behavioural signals (facial expressions, eyetracking, depth images, seat pressure), computer interaction (keyboard and mouse events, game actions), and stimulus information (gameplay video, game logs). We show that the number of participants in our dataset and the variety of modalities recorded is advantageous for training machine learning models.

No Friend but the Mountains is a remarkable and widely acclaimed book on refugee camps. It narrates the author's firsthand experiences in Manus Camp. In this article, we aim to closely examine the author's account and identify his main themes. Boochani portrays the experience of asylum seekers as one of despair and degradation. He vividly depicts how the asylum seekers gradually lost their human dignity and identity during their indefinite and irreversible detention in Manus Camp, where they lived in a state of limbo. Despite having access to basic necessities such as food, clothing, and shelter, the detainees were stripped of any right to depend on them. The theories of Agamben and Arendt provide valuable insights into the plight of the asylum seekers in the camp.

Euler's identity is the most beautiful equation in mathematics. In this paper Euler's identity will be applied to Physics. It will present new beautiful equations of unification of the fundamental interactions. It will calculate new unity formulas that connect the coupling constants of the fundamental forces. Also it will present new beautiful equations of the Dimensionless unification of atomic physics and cosmology and it will prove that the shape of the Universe is Poincaré dodecahedral space. These equations are applicable for all energy scales.

The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter since the significance of health insurance as a research topic has markedly escalated following the pandemic. The dataset employed in this research comprises 1338 observations 7 columns and corresponds to individual medical expenditures in the United States, available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers used machine learning models, including neural networks, XAI, and auto modeling, to determine the correlation between pricing and the attributes. The training process involved partitioning the dataset into an 80-20 ratio for training and evaluation. Consequently, the system achieved an accuracy rate of 97% by Gradient Boosting, but we corrected it to 92% by Gradient Boosting Regressor by encoding and hyper-tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%.

Abstract Angiostrongylus cantonensis (Rat Lungworm) is a major pathogen of Eosinophilic Meningitis in humans worldwide. A. cantonensis completes its life-cycle in two hosts: the rodent definitive and the gastropod intermediate hosts. Among the wide range of intermediate gastropod hosts, the invasive Pomacea canaliculata (Golden apple snails), which have caused numerous outbreaks of neuroangiostrongyliasis worldwide, especially China and Taiwan. While there have been numerous surveys on the prevalence of A. cantonensis larvae in P. canaliculata in China, there are an inadequate number of studies in Taiwan. This review gives an overview of the current status of A. cantonensis prevalence and infection in general, along with focusing on the status and developments regarding neuroangiostrongyliasis in Taiwan. Additionally, the present study concentrated on the implications of a well-known invasive vector of the parasite, Pomacea spp., and its effects on disease transmission to humans. Results show that P. canaliculata has been the source of approximately 15.5% infections in Taiwan. Furthermore, due to rapidly growing invasive Pomacea spp. populations in waterlogged areas, disease transmission through water cannot be neglected. Thus, as a precautionary measure, we suggest environmental DNA based monitoring should be implemented to detect parasites.   Keywords: Foodborne disease; invasive species; Angiostrongylus cantonensis; Pomacea spp.; neuroangiostrongyliasis; TaiwanIntroductionAngiostrongylus cantonensis (rat lungworm), the primary causative agent for several outbreaks of eosinophilic meningitis in humans (Tseng et al., 2011), was first described by Chen (1935) based on the worms collected from the pulmonary arteries of infected rats in Guangzhou, China. After 10 years later, the first human infection by this nematode was reported from Taiwan by Nomura and Lin, (Beaver & Rosen, 1964). A. cantonensis infection typically presents as eosinophilic meningitis; however, other manifestations in the form of ocular angiostrongyliasis, encephalitis, and radiculomyelitis have also been reported. Primary symptoms include acute headaches, eosinophilia in the blood and cerebrospinal fluid (CSF), and other symptoms ranging from fever, hyperesthesia, paresthesia, nausea, vomiting and in some rare cases even coma and death (Cowie et al., 2022). There is an additional risk of developing chronic sequelae, which is majorly debilitating. Definitive diagnosis of this infection has proved to be challenging; given visual detection of parasite in patient samples is rare; genetic identification using the nuclear ITS2 region is one of the few reliable methods to identify A. cantonensis (Kaenkaew et al., 2024). Hence, spreading awareness for prevention is just as important as finding a cure.A. cantonensis is a nematode and a parasite, its definitive and intermediate hosts being rodents (particularly Rattus spp.) and a diverse range of gastropods, respectively; however, animals such as shrimp, frogs, and lizards can act as paratenic hosts (Turck et al., 2022) (Figure 1). These hosts are often found in human settlements. Consumption of such hosts have given rise to further cases and outbreaks of A. cantonensis infection in humans in several countries, including Thailand (Eamsobhana, 2014), China (Lv et al., 2009), Taiwan (Tseng et al., 2011), Brazil (Morassutti et al., 2014), USA (Cowie, 2017), Vietnam (McBride et al., 2017), Australia (Barratt et al., 2016), and India (Pandian et al., 2023). A number of cases have occurred in travelers to endemic areas, where consumption of exotic dishes and contaminated foodstuffs result in infection (Federspiel et al., 2020), which highlights the necessity for further awareness about this disease and its causative agent. Indeed, there are concerns as to the extent of infection the rat lungworm can inflict upon humans, where cases have shown presence of the parasite in the lungs of human patients (Jarvi et al., 2023). Nevertheless, as the presence of A. cantonensis in human feces has not yet been identified, hence the spread of A. cantonensis depends largely on its natural hosts, rats and gastropods.

This paper explores the integration of Artificial Intelligence (AI) in access control to enhance enterprise security, focusing on the synergy between human administrators and intelligent systems. It begins by addressing challenges faced by human administrators and emphasizes the role of Explainable AI (XAI) in ensuring transparency [15]. Case studies illustrate the effectiveness of AI in automating policy updates while involving human oversight. The paper concludes by discussing potential advancements and challenges, highlighting the pivotal collaboration between humans and AI in achieving adaptive and robust enterprise security.

A document by Gonçalo Duarte. Click on the document to view its contents.

Key points: 1) In 2023, the temperature of the global ocean was higher than usual, explaining the excess temperature in the eastern tropical Pacific. 2) An announced El Niño event, predicted to be at least moderate, took place in 2023-24, but turned out to be weak. 3) The climate state of the tropical Pacific suggests that a strong or stronger El Niño peaking in the second half of 2024 is likely to occur.

Wildfires significantly disturb ecosystems by altering forest structure, vegetation ecophysiology, and soil properties. Understanding the complex interactions between topographic and climatic conditions in post-wildfire recovery is crucial. This study investigates the interplay between topography, climate, burn severity, and years after fire on vegetation recovery across dominant land cover types (evergreen forest, shrubs, and grassland) in the Pacific Northwest region. Using Moderate Resolution Imaging Spectroradiometer (MODIS) data, we estimated vegetation recovery by calculating the incremental Enhanced Vegetation Index change during post-fire years. A machine learning technique, random forest (RF), was employed to map relationships between the input features (elevation, slope, aspect, precipitation, temperature, burn severity, and years after fire) and the target (incremental EVI recovery) for each land cover type. Variable importance analysis and partial dependence plots were generated to understand the influence of individual features. The observed and predicted incremental EVI values showed good matches, with 𝑅 2 values of 0.99 for training and 0.89-0.945 for testing. The study found that climate variables, specifically precipitation and temperature, were the most important features overall, while elevation played the most significant role among the topographic factors. Partial dependence plots revealed that lower precipitation tended to cause a reduction in vegetation recovery for varying temperature ranges across land cover types. These findings can aid in developing targeted strategies for post-wildfire forest management, considering the varying responses of different land cover types to topographic, climatic, and burn severity factors.

This report presents a comprehensive investigation into the dynamic relationship between physical activity and cardiovascular response, specifically focusing on heart rate and Beats Per Minute (BPM). The study employed a controlled setting to observe and analyze the physiological changes in an individual subjected to varying intensities and durations of physical exercise.

Surface deformation associated with continental earthquake ruptures includes localized deformation on the faults, as well as deformation in the surrounding medium though distributed and/or diffuse processes. However, the connection of the diffuse part of the surface deformation to the overall rupture process, as well as its underlying physical mechanisms are not yet well understood. Computing high-resolution optical image correlations for the 2021/05/21 Mw7.4 Maduo, Tibet, rupture, we highlight a correlation between the presence of faults and fractures at the surface, and variations in the across-fault displacement gradient, fault zone width, and amplitude of surface displacement. We show that surface slip along primary faults is systematically associated with gradients greater than 1%, and is dominant in regions of greater coseismic surface displacement. Conversely, the diffuse deformation is associated with gradients ≤0.3%, and is dominant in regions of lesser surface displacement. The distributed deformation then occurs for intermediate gradients of 0.3-1%, and at the transition between the localized and diffuse deformation regions. Such patterns of deformation are also described in laboratory experiments of rock deformation, themself supported by field observations. Comparing these experiments to our observations, we demonstrate that the diffuse deformation along the 2021 Maduo rupture corresponds to kilometer-wide plastic yielding of the bulk medium occurring in regions where surface rupture is generally missing. Along the 2021 Maduo rupture, diffuse deformation occurs primarily in the epicentral region, where the dynamic stresses associated with the nascent pulse-like rupture could not overcome the shallow fault zone frictional strength.

The present study focuses on heat transfer in ventilated caves for which the airflow is driven by the temperature contrast between the cave and the external atmosphere. We use a numerical model that couples the convective heat transfer due to the airflow in a single karst conduit with the conductive heat transfer in the rock mass. Assuming dry air and a simplified geometry, we investigate the propagation of thermal perturbations inside the karst massif. We perform a parametric study to identify general trends regarding the effect of the air flowrate and conduit size on the amplitude and spatial extent of thermal perturbations. Numerical results support the partition of a cave into three regions: (1) a short (few meters) diffusive region, where heat mainly propagates from the external atmosphere by conduction in the rock mass; (2) a convective region where heat is mainly transported by the air flow; (3) a deep karst region characterized by quasi-constant temperatures throughout the year. An estimation of the length of the convective region is proposed and compared to field data from a mine tunnel and two caves. Our results provide first estimates to identify climate sensitive regions for speleothem science and/or ecosystemic studies.

This paper investigates the accuracy of a solid-fluid model using the SPH software DualSPHysics v5.2 coupled with ProjectChrono for debris flow modelling. It focuses on different validation steps of the method, both for pure fluid and a mixture of fluid and boulders to build reliability of the model to prepare for the simulation of a simplified debris flow. First, velocity profiles, free surface shape and velocity of surges of a viscous fluid are validated against well documented experimental data. It is, to the best of our knowledge, one of the few validations of the SPH approach for very viscous flows near the creeping threshold (Re~1). Secondly, the influence on the macroscopic viscosity of the introduction of granular elements in a viscous fluid is studied against a semi-empirical formula. Finally, the method is applied to simplified $2D$ debris flow surges with field-like features. Surges are composed of a viscous Newtonian fluid and poly-disperse boulders. The flow of surges of different concentrations is studied and Froude numbers of real field measurements are retrieved. Such complex models are shown to be relevant to the study of debris flow dynamics.