Hasil untuk "Environmental Science"

Dipesh Singh Chuphal, Vimal Mishra

Abstract Reliable streamflow projections are essential for effective water‐resource management and climate adaptation. However, streamflow projections are associated with large uncertainties due to divergent precipitation projections from climate models, which directly propagate into hydrological estimates. Observation‐constrained approaches that condition future projections on past observations have been shown to reduce such uncertainties; however, they have not been applied to streamflow projections across the Indian rivers. Using long‐term streamflow and global mean surface temperature observations, climate model projections, hydrological modeling, and a Bayesian detection–attribution framework, we developed observational constrained streamflow projections for nine major Indian rivers. The method reduces the 5–95% confidence interval of future streamflow projections by nearly one‐third compared to raw multimodel ensembles, with constraint strength controlled by internal streamflow variability and inter‐model spread in the unconstrained ensemble. Projection uncertainty is further reduced to ∼20% when considering projections based only on skillful climate models. Constrained projections indicate significant increases in streamflow in the near‐, mid‐, and far‐future periods, except for the Cauvery basin, which shows a near‐term decline. Applying the method to raw precipitation projections reveals comparable constraint strength and increases confidence in the results, given the strong dependence of Indian river flows on precipitation. Our findings underscore the importance of combining skillful climate models with post‐processing constraint methods to substantially reduce model‐based uncertainty. Overall, our results provide critical insights into future streamflow changes across Indian rivers, supporting long‐term water‐resource planning and climate‐resilient management.

LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz et al.

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

W. Furian, T. Sauter

<p>This study investigates simulated glacial lake outburst floods (GLOFs) at five glacial lakes in the Everest region of Nepal using the three-dimensional model OpenFOAM. It presents the evolution of GLOF characteristics in the 21st century considering different moraine breach scenarios and two Shared Socioeconomic Pathways scenarios. The results demonstrate that in low-magnitude scenarios, the five lakes generate GLOFs that inundate between 0.35 and 2.23 km<span class="inline-formula">²</span> of agricultural land with an average water depth of 0.9 to 3.58 m. These GLOFs reach distances of 59 to 84 km, affect 30 to 88 km of roads or trails, and inundate 183 to 1699 buildings with 1.2 to 4.9 m of water. In higher scenarios, GLOFs can extend over 100 km and also affect larger settlements in the foothills. Between 80 and 100 km of roads, 735 to 1989 houses and 0.85 to 3.52 km<span class="inline-formula">²</span> of agricultural land could be inundated, with average water depths of up to 10 m. The high precision of the 3D flood modeling, with detailed simulations of turbulence and viscosity, provides valuable insights into 21st-century GLOF evolution, supporting more accurate risk assessments and effective adaptation strategies.</p>

Melissa Blum, Donato DeIngeniis, Daniela K. Shill et al.

Introduction: Rising ambient temperatures threaten vulnerable populations such as pregnant women, with urban populations bearing a greater risk due to the urban heat island effect. Here, we assessed the independent effects of trimester-specific warm season exposure during pregnancy and neighborhood heat vulnerability on maternal outcomes, including gestational diabetes, hypertensive disorders of pregnancy, genitourinary infections, and operative delivery. Methods: This retrospective study analyzed 819 participants from the Stress in Pregnancy Study (2009–2014), a longitudinal birth cohort study in New York City. Generalized linear models examined associations between trimester-specific warm season exposure, New York City Heat Vulnerability Index (ranging 1-5), and adverse maternal outcomes, adjusting for demographics, parity, and substance use. Results: First trimester warm season exposure was associated with increased odds of gestational hypertension (adjusted odds ratio [AOR] 4.50, 95%CI 1.17-17.27), preeclampsia (AOR 4.38, 95%CI 1.51-12.75), and genitourinary infection (AOR 2.27, 95%CI 1.14-4.51). Each unit increase in heat vulnerability index was associated with increased odds of preeclampsia (AOR 1.38, 95%CI 1.05-1.81) and genitourinary infection (AOR 1.32, 95%CI 1.11-1.57). Conclusions: Both early pregnancy warm weather exposure and neighborhood vulnerability independently increased the risk of adverse maternal complications. Our findings provide evidence in support of targeted heat mitigation strategies to limit heat exposure in at-risk communities as climate change progresses.

Philipp Treu, Dimitra Iltsiou, Rabia Elbuga-Ilica et al.

The oxidative cleavage of biomass-derived vicinal diols holds significant potential for producing valuable renewable carboxylic acids. Fe-ZSM-5 zeolite is a highly effective catalyst for this reaction using mild reaction conditions; however, it suffers from diffusion limitations, particularly with larger substrates. To overcome these challenges, we synthesized hierarchical ZSM-5 zeolite that integrate mesopores within the conventional microporous framework, thereby mitigating diffusion constraints. These hierarchical materials were developed using carbon templating and desilication techniques. Carbon templating led to the creation of well-defined mesopores, while desilication facilitated the formation of hollow crystals. The mesopore-containing hierarchical zeolites led to increased ion-exchange capacity, due to enhanced accessibility of exchange positions for the Fe3 + cations, with the desilicated zeolite exceeding the Fe-loading by 3.5 times that of the microporous parent ZSM-5 material, as observed by UV–vis spectroscopy, EXAFS analysis and elemental analysis by ICP-OES. Catalytic tests revealed that hierarchical Fe-ZSM-5 catalysts exhibit superior performance compared to their purely microporous counterparts. Specifically, desilication improved catalytic activity for smaller substrates, while carbon templating proved more effective for larger vicinal diols. Furthermore, the carbon templated zeolite displayed enhanced activity per Fe-site, highlighting the benefits of hierarchical porosity in optimizing catalytic performance.

Elvis Frimpong Manso, Alfred Arthur, Joseph Osafo Eduah et al.

Despite the differences in soil and fertilizer properties affecting fertilizer effectiveness, farmers often use nationwide blanket formulations, which may not optimize cocoa yield. Previous trials have shown that fertilizer application outcomes vary by soil type, prompting recommendations for site-specific fertilizer formulations. Nonetheless, the complexity of creating these models leaves farmers relying on available blanket fertilizers instead. To enable farmers to select fertilizer types that will best suit their soils, the effects of soil properties and fertilizer types on the solubility, availability of macronutrients, and pH in two cocoa soils were investigated. Five kilograms of ferralsol and acrisol were prepared in nursery bags, with five different fertilizers (A, B, C, D, and E) applied at rates of 375, 500, and 625 kg·ha−1 were set in factorial experiment laid in completely randomized design with four replicates each. Following a 3-week incubation, nutrient analysis was conducted weekly. Water solubility was assessed by weighing 1, 2, and 3 g of each fertilizer in 200 mL of distilled water and shaken for 3 hours. Results indicate that lower solute-to-solvent ratios decreased NPK, Ca, and Mg solubility. Fertilizer A increased soil pH from 6.81 to 7.45 in ferralsol and from 5.78 to 7.50 in acrisol. The different soils showed different release trends though the same fertilizers were applied. Available phosphorus rose from 4.76 to 166.69 mg·kg−1 in ferralsol and from 4.32 to 170.00 mg·kg−1 in acrisol, while total nitrogen rose from 0.22% to 0.30% in ferralsol and from 0.16% to 0.20% in acrisol. The findings highlight that soil properties influence fertilizer solubility and nutrient availability in cocoa soils.

Brady D. Lund, Ting Wang

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Wei-Yin Chang, Xin Wang, De-Sheng Guo et al.

This study aims to explore the effects of forest travel activities on university students’ stress affection. Forty volunteer university students participated in this study. All participants were asked to complete physiological (Heart Rate Variability) and psychological (Brief Profile of Mood State and State–Trait Anxiety Inventory) tests before and after the travel activities. The results reported that students’ heart rates were significantly lower after the forest travel activities than before. All domains of negative mood and anxiety decreased from the pre-test to the post-test. This study found that university students could feel less stressed if they went on forest travel activities.

Edoardo Bizzotto, Guido Zampieri, Laura Treu et al.

Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.

Raphaël Millière

Deep learning has enabled major advances across most areas of artificial intelligence research. This remarkable progress extends beyond mere engineering achievements and holds significant relevance for the philosophy of cognitive science. Deep neural networks have made significant strides in overcoming the limitations of older connectionist models that once occupied the centre stage of philosophical debates about cognition. This development is directly relevant to long-standing theoretical debates in the philosophy of cognitive science. Furthermore, ongoing methodological challenges related to the comparative evaluation of deep neural networks stand to benefit greatly from interdisciplinary collaboration with philosophy and cognitive science. The time is ripe for philosophers to explore foundational issues related to deep learning and cognition; this perspective paper surveys key areas where their contributions can be especially fruitful.

Taohan Lin, Nataliya Rybnikova

Night-time light (NTL) data have been widely used as a remote proxy for the economic performance of regions. The use of these data is more advantageous than the traditional census approach is due to its timeliness, low cost, and comparability between regions and countries. Several recent studies have explored monthly NTL composites produced by the Visible Infrared Imaging Radiometer Suite (VIIRS) and revealed a dimming of the light in some countries during the national lockdowns due to the COVID-19 pandemic. Here, we explicitly tested the extent to which the observed decrease in the amount of NTL is associated with the economic recession at the subnational level. Specifically, we explore how the association between Gross Domestic Product (GDP) and the amount of NTL is modulated by the pandemic and whether NTL data can still serve as a sufficiently reliable proxy for the economic performance of regions even during stressful pandemic periods. For this reason, we use the states of the US and quarterly periods within 2014–2021 as a case study. We start with building a linear mixed effects model linking the state-level quarterly GDPs with the corresponding pre-processed NTL data, additionally controlling only for a long-term trends and seasonal fluctuations. We intentionally do not include other socio-economic predictors, such as population density and structure, in the model, aiming to observe the ‘pure’ explanatory potential of NTL. As it is built only for the pre-COVID-19 period, this model demonstrates a rather good performance, with R² = 0.60, while its extension across the whole period (2014–2021) leads to a considerable worsening of this (R² = 0.42), suggesting that not accounting for the COVID-19 phenomenon substantially weakens the ‘natural’ GDP–NTL association. At the same time, the model’s enrichment with COVID-19 dummies restores the model fit to R² = 0.62. As a plausible application, we estimated the state-level economic losses by comparing actual GDPs in the pandemic period with the corresponding predictions generated by the pre-COVID-19 model. The states’ vulnerability to the crisis varied from ~8 to ~18% (measured as a fraction of the pre-pandemic GDP level in the 4th quarter of 2019), with the largest losses being observed in states with a relatively low pre-pandemic GDP per capita, a low number of remote jobs, and a higher minority ratio.

Amineh Ghorbani, Saba Siddiki, Giangiacomo Bravo

Wenwen Li

GeoAI, or geospatial artificial intelligence, is an exciting new area that leverages artificial intelligence (AI), geospatial big data, and massive computing power to solve problems with high automation and intelligence. This paper reviews the progress of AI in social science research, highlighting important advancements in using GeoAI to fill critical data and knowledge gaps. It also discusses the importance of breaking down data silos, accelerating convergence among GeoAI research methods, as well as moving GeoAI beyond geospatial benefits.

Adi Patria, Haruo Kimura, Yoshihiro Kitade et al.

Abstract The Median Tectonic Line (MTL) is an arc-parallel strike-slip fault that accommodates much of the arc-parallel component of the oblique convergence of the Philippine Sea and Eurasian plates at the Nankai Trough. The MTL in Shikoku is one of the fastest-slipping faults in Japan, with a late Quaternary right-lateral slip rate of 5–10 mm/yr. To estimate the right-lateral slip amounts of the past faulting events on the MTL, we acquired 2D and pseudo-3D ground-penetrating radar (GPR) sections across the ENE-trending Ikeda fault of the MTL in eastern Shikoku. We conducted the GPR surveys at the Higashi-Miyoshi site, where two terrace riser offsets mark the active fault trace. The 2D lines were about 28–64 m long, and the pseudo-3D data were sized 20 m × 30 m with a 0.5-m inline spacing. We used 50 MHz GPR antennas and conducted wide-angle measurements to estimate the electromagnetic wave velocity. We identified three paleochannels on the final depth-converted GPR sections, and two of them are deflected by the fault. A paleochannel at 0.6–1.4 m depth is observed on all inline sections of the pseudo-3D GPR data. We built a 3D model of this paleochannel and estimated the right-lateral and vertical displacements of ~ 3.5 m and ~ 0.5 m, respectively. This paleochannel offset is probably caused by the most recent surface-rupturing earthquake on the Ikeda fault, which may be the 1596 Keicho-Fushimi earthquake. This study demonstrates the usefulness of the GPR surveys to identify geological features displaced laterally and vertically by the most recent surface-rupturing earthquake.

Ram Sagar

The Aryabhatta Research Institute of Observational Sciences (ARIES), a premier autonomous research institute under the Department of Science and Technology, Government of India has a legacy of about seven decades with contributions made in the field of observational sciences namely atmospheric and astrophysics. The Survey of India used a location at ARIES, determined with an accuracy of better than 10 meters on a world datum through institute participation in a global network of Earth artificial satellites imaging during late 1950. Taking advantage of its high-altitude location, ARIES, for the first time, provided valuable input for climate change studies by long term characterization of physical and chemical properties of aerosols and trace gases in the central Himalayan regions. In astrophysical sciences, the institute has contributed precise and sometime unique observations of the celestial bodies leading to a number of discoveries. With the installation of the 3.6 meter Devasthal optical telescope in the year 2015, India became the only Asian country to join those few nations of the world who are hosting 4 meter class optical telescopes. This telescope, having advantage of geographical location, is well-suited for multi-wavelength observations and for sub-arc-second resolution imaging of the celestial objects including follow-up of the GMRT, AstroSat and gravitational-wave sources.

Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal et al.

Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. NP techniques can interface with symbolic domain knowledge from scientists, such as prior knowledge and experimental context, to produce interpretable outputs. We identify opportunities and challenges between current NP models and scientific workflows, with real-world examples from behavior analysis in science: to enable the use of NP broadly for workflows across the natural and social sciences.

Anne-Sophie Bonnet-Lebrun, Anne-Sophie Bonnet-Lebrun, Maria P. Dias et al.

Every year, billions of birds undertake extensive migrations between breeding and non-breeding areas, facing challenges that require behavioural adjustments, particularly to flight timing and duration. Such adjustments in daily activity patterns and the influence of extrinsic factors (e.g., environmental conditions, moonlight) have received much more research attention in terrestrial than marine migrants. Taking advantage of the widespread deployment in recent decades of combined light-level geolocator-immersion loggers, we investigated diel organisation and influence of the moon on flight activities during the non-breeding season of 21 migrant seabird species from a wide taxonomic range (6 families, 3 orders). Migrant seabirds regularly stopped (to either feed or rest) during migration, unlike some terrestrial and wetland birds which fly non-stop. We found an overall increase for most seabird species in time in flight and, for several species, also in flight bout duration, during migration compared to when resident at the non-breeding grounds. Additionally, several nocturnal species spent more of the day in flight during migration than at non-breeding areas, and vice versa for diurnal species. Nocturnal time in flight tended to increase during full moon, both during migration and at the non-breeding grounds, depending on species. Our study provides an extensive overview of activity patterns of migrant seabirds, paving the way for further research on the underlying mechanisms and drivers.

Lei Li, Hua Yu, Yihao Liu et al.

Abstract Background Previous research using whole-brain neuroimaging techniques has revealed structural differences of grey matter (GM) in alcohol use disorder (AUD) patients. However, some of the findings diverge from other neuroimaging studies and require further replication. The quantity of relevant research has, thus far, been limited and the association between GM and abstinence duration of AUD patients has not yet been systematically reviewed. Methods The present research conducted a meta-analysis of voxel-based GM studies in AUD patients published before Jan 2021. The study utilised a whole brain-based d-mapping approach to explore GM changes in AUD patients, and further analysed the relationship between GM deficits, abstinence duration and individual differences. Results The current research included 23 studies with a sample size of 846 AUD patients and 878 controls. The d-mapping approach identified lower GM in brain regions including the right cingulate gyrus, right insula and left middle frontal gyrus in AUD patients compared to controls. Meta-regression analyses found increasing GM atrophy in the right insula associated with the longer mean abstinence duration of the samples in the studies in our analysis. GM atrophy was also found positively correlated with the mean age of the samples in the right insula, and positively correlated with male ratio in the left middle frontal gyrus. Conclusions GM atrophy was found in the cingulate gyrus and insula in AUD patients. These findings align with published meta-analyses, suggesting they are potential deficits for AUD patients. Abstinence duration, age and gender also affect GM atrophy in AUD patients. This research provides some evidence of the underlying neuroanatomical nature of AUD.

Md. J. U. Khan, F. Durand, F. Durand et al.

<p>The Bay of Bengal is a well-known breeding ground to some of the deadliest cyclones in history. Despite recent advancements, the complex morphology and hydrodynamics of this large delta and the associated modelling complexity impede accurate storm surge forecasting in this highly vulnerable region. Here we present a proof of concept of a physically consistent and computationally efficient storm surge forecasting system tractable in real time with limited resources. With a state-of-the-art wave-coupled hydrodynamic numerical modelling system, we forecast the recent Supercyclone Amphan in real time. From the available observations, we assessed the quality of our modelling framework. We affirmed the evidence of the key ingredients needed for an efficient, real-time surge and inundation forecast along this active and complex coastal region. This article shows the proof of the maturity of our framework for operational implementation, which can particularly improve the quality of localized forecast for effective decision-making over the Bengal delta shorelines as well as over other similar cyclone-prone regions.</p>

Richard D. Gill

I draw attention to statistical, probabilistic, computer science aspects of the highly related topics of the Bell game and of a possible future Quantum Internet.