Viacheslav Vasenev, Robin van Velthuijsen, Marcel R. Hoosbeek
et al.
The development of urban green infrastructures (UGI) is considered among the main nature-based solutions for climate mitigation in cities; however, the role of soils in the carbon (C) balance of UGI ecosystems remains largely overlooked. Urban green spaces are typically dominated by constructed Technosols, created by adding organic materials on top of former natural or agricultural subsoils. The combined effects of land-use history and current UGI management result in a high spatial variation of soil organic carbon (SOC) stocks and soil CO<sub>2</sub> emissions. Our study aimed to explore this variation for the case of Wageningen University campus. Developed on a former agricultural land, the campus area includes green spaces dominated by trees, shrubs, lawns, and herbs, with well-documented management practices for each vegetation type. Across the campus area (~32 ha), a random stratified topsoil sampling (n = 90) was conducted to map the spatial variation of topsoil (0–10 cm) SOC stocks. At the key sites (n = 8), representing different vegetation types and time of development (old, intermediate, and recent), SOC profile distribution was analyzed including SOC fractionation in surface and subsequent horizons, as well as the dynamics in soil CO<sub>2</sub> emissions, temperature, and moisture. Topsoil SOC contents on campus ranged from 1.1 to 5.5% (95% confidence interval). On average, SOC stocks under trees and shrubs were 10–15% higher than those under lawns and herbs. The highest CO<sub>2</sub> emissions were observed from soil under lawns and coincided with a high proportion of labile SOC fraction. Temporal dynamics in soil CO<sub>2</sub> emissions were mainly driven by soil temperature, with the strongest relation (R<sup>2</sup> = 0.71–0.88) observed for lawns. Extrapolating this relationship to the calendar year and across the campus area using high-resolution remote sensing data on surface temperatures resulted in a map of the CO<sub>2</sub> emissions/SOC stocks ratio, used as a spatial proxy for C turnover. Areas dominated by recent and intermediate lawns emerged as hotspots of rapid C turnover, highlighting important differences in the role of various UGI types in the C balance of urban green spaces.
Individual social identities indicate group affiliations and are typically associated with group-typical preferences, signals that indicate group membership, and the propensity to condition actions on the social signals of others, resulting in group-differentiated interaction norms. Past work modeling identity signaling and coordination has typically assumed that individuals belong to one of a discrete set of groups. Yet individuals can simultaneously belong to multiple groups, which may be nested within larger groupings. Here, we introduce the generalized Bach or Stravinsky game, a coordination game with ordered preferences, which allows us to construct a model that captures the overlapping and hierarchical nature of social identity. Our model unifies several prior results into a single framework, including results related to coordination, minority disadvantage, and cross-cultural competence. Our model also allows agents to express complex social identities through multidimensional signaling, which we use to explore a variety of complex group structures. Our consideration of intersectional identities exposes flaws in naive measures of group structure, illustrating how empirical studies may overlook some social identities if they do not consider the behaviors that those identities function to afford.
Study region: The Iberian Peninsula (IP). Study focus: Understanding precipitation and extreme precipitation events is crucial for managing climate impacts on human activities, ecosystems, and environment. Reanalysis datasets such as ERA-Interim and ERA5 are commonly used to studying these patterns; however, their spatial resolution often limits their accuracy. The recent high-resolution Copernicus European Regional ReAnalysis (CERRA) product provides an opportunity to improve precipitation estimates, particularly in regions with complex topography. The present study aims to assess the performance of the CERRA dataset in representing precipitation and extreme precipitation events across the IP. The analysis involves a comparison between CERRA and observational datasets, focusing on several annual and seasonal spatial precipitation indices. Special attention is given to the representation of extreme precipitation and their associated trends. New hydrological insights for the region: The results indicate that CERRA effectively captures the spatial distribution of precipitation across most seasons, with weaker performance during the summer. CERRA reliable represents extreme precipitation and their temporal trends across the IP, especially in regions with complex orography such as Galicia and the Pyrenees. However, it tends to overestimate precipitation in drier areas. Trend analysis also revealed that, while CERRA successfully reflects the general patterns of precipitation extremes, some discrepancies remain, especially in autumn. Overall, CERRA demonstrates strong potential as a valuable dataset for hydrological and climatological studies over the IP.
Svetlana Košánová, Andrej Halabuk, Pavol Kenderessy
et al.
This study employed Sentinel-2 satellite data spanning 2018–2024 to conduct a subfield-level yield stability assessment within the Danubian lowland, Slovakia. A robust spatiotemporal multi-crop framework was implemented based on Normalized Difference Vegetation Index (NDVI) classification, followed by multiyear stability analysis. This resulted in the identification of stable high, medium, low, and unstable yield productivity zones. Stable high yielding zones accounted for only 6.5% of the cropland, whereas unstable yield zones comprised almost 47%. The resulting map revealed scattered fine-scale variations in yield stability at the parcel level, while also identifying some general spatial trends related to climatic and elevation gradients. The key factors for distinguishing the two contrasting classes (stable high versus low yield zones) were analysed. Gradient boosting classification models (XGBoost) were implemented to distinguish these classes, achieving a good performance with a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.86. Shapley additive explanation analysis (SHAP) was applied to enhance model interpretation. Notably, elevation, topsoil spectral reflectance (from a Sentinel-2-based bare soil mosaic), topography and field size have been identified as key predictive features for the classification model.
This work introduces a highly efficient implementation of the transformer architecture on a Field-Programmable Gate Array (FPGA) by using the \texttt{hls4ml} tool. Given the demonstrated effectiveness of transformer models in addressing a wide range of problems, their application in experimental triggers within particle physics becomes a subject of significant interest. In this work, we have implemented critical components of a transformer model, such as multi-head attention and softmax layers. To evaluate the effectiveness of our implementation, we have focused on a particle physics jet flavor tagging problem, employing a public dataset. We recorded latency under 2 $μ$s on the Xilinx UltraScale+ FPGA, which is compatible with hardware trigger requirements at the CERN Large Hadron Collider experiments.
In spring 1949 about 70 physicists from eight countries met in Florence to discuss recent trends in statistical mechanics. This scientific gathering, co-organized by the Commission on Thermodynamics and Statistical Mechanics of the International Union of Pure and Applied Physics (IUPAP) and the Italian Physical Society (SIF), initiated a tradition of IUPAP-sponsored international conferences on statistical mechanics that lasts to this day. In 1977, when this conference series took the name of StatPhys, the foundational role of the Florence conference was recognized by retrospectively naming it StatPhys1. This paper examines the dual scientific and social significance of the conference, situating it in the broader contexts of the post-World War II reconstruction in Italian physics and of the revitalization of the international science organization. Through an analysis of IUPAP archives and Italian records, we illustrate how the event's success hinged on the aligned objectives of its organizers. Internationally, it was instrumental in defining the scientific and organizational foundations for the activities of IUPAP commissions during a critical phase of IUPAP's history, when the Union was resurging on the international scene after the inactivity of the interwar period. Nationally, the conference served as a cornerstone in SIF's strategy to re-establish Italian physics' international stature and to aid the domestic revitalization of physics through the internationalization of its activities, notably of its flagship journal, \textit{Il Nuovo Cimento}. This analysis not only sheds light on the conference's impact but also informs recent discussions in the history of science about the multiple roles of international scientific conferences.
Guillermo Rodríguez-Gómez, M. Patrocinio Espigares, Bienvenido Martínez-Navarro
et al.
The Early Pleistocene sites of Orce in southeastern Spain, including Fuente Nueva-3 (FN3), Barranco León (BL) and Venta Micena (VM), provide important insights into the earliest hominin populations and Late Villafranchian large mammal communities. Dated to approximately 1.4 million years ago, FN3 and BL preserve abundant Oldowan tools, cut marks and a human primary tooth, indicating hominin activity. VM, approximately 1.6 million years old, is an outstanding site because it preserves an exceptionally rich assemblage of large mammals and predates the presence of hominins, providing a context for pre-human conditions in the region. Research suggests that both hominins and giant hyenas were essential to the accumulation of skeletal remains at FN3 and BL, with secondary access to meat resources exploited by saber-toothed felids. This aim of this study aims to correlate the relative abundance of large herbivores at these sites with their estimates of Carrying Capacity (CC) and Total Available Biomass (TAB) using the PSEco model, which incorporates survival and mortality profiles to estimate these parameters in paleoecosystems. Our results show: (i) similarities between quarries VM3 and VM4 and (ii) similarities of these quarries with BL-D (level D), suggesting a similar formation process; (iii) that the role of humans would be secondary in BL-D and FN3-LAL (Lower Archaeological Level), although with a greater human influence in FN3-LAL due to the greater presence of horses and small species; and (iv) that FN3-UAL (Upper Archaeological Level) shows similarities with the expected CC values for FN3/BL, consistent with a natural trap of quicksand scenario, where the large mammal species were trapped according to their abundance and body mass, as there is a greater presence of rhinos and mammoths due to the greater weight per unit area exerted by their legs. Given the usefulness of this approach, we propose to apply it first to sites that have been proposed to function as natural traps.
The quantum-classical correspondence between local minima on the classical energy landscape and excited eigenstates in the energy spectrum is studied within the context of many-body quantum spin systems. In mean-field approximations of a quantum problem, one usually focuses on attaining the global minimum of the resulting energy function, while other minimum solutions are usually ignored. For frustrated systems, a strict distinction between global and local minimum is often not tenable since first-order type transitions can interchange the roles played by two different minima. This begs the question of whether there is any physical interpretation for the local minima encountered in mean-field approximations of quantum systems. We look at the problem from the perspective of quantum spin systems. Two models are studied, a frustrated model with quenched disorder, and a pure system without frustration. Accurate classical energies of the minima are compared with the full spectrum of energy levels, allowing us to search for signs of correspondence between them. It is found that the local minima can generally be interpreted as excited energy eigenstates. Instances of spurious minima are also reported.
Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In this paper, we revisit the problem of location recommendation and point out that explicitly modeling temporal information is a great help when the model needs to predict not only the next location but also further locations. In addition, state-of-the-art methods do not make effective use of geographic information and suffer from the hard boundary problem when encoding geographic information by gridding. To this end, a Temporal Prompt-based and Geography-aware (TPG) framework is proposed. The temporal prompt is firstly designed to incorporate temporal information of any further check-in. A shifted window mechanism is then devised to augment geographic data for addressing the hard boundary problem. Via extensive comparisons with existing methods and ablation studies on five real-world datasets, we demonstrate the effectiveness and superiority of the proposed method under various settings. Most importantly, our proposed model has the superior ability of interval prediction. In particular, the model can predict the location that a user wants to go to at a certain time while the most recent check-in behavioral data is masked, or it can predict specific future check-in (not just the next one) at a given timestamp.
V. Balaram, Lorenzo Copia, U. Saravana Kumar
et al.
Different natural and anthropogenic processes cause pollution of various water bodies worldwide creating numerous health problems for humans. This causes serious concern as water is a basic necessity to all living beings, and needs to be adequately monitored and managed to prevent its contamination. If found contaminated, the water is to be cleaned using suitable water treatment methods keeping in view of WHO regulations before using it for the intended purpose (drinking, irrigation, etc.). Application of different ICP-MS techniques such as classical ICP-MS, ICP-MS/MS, ICP-TOF-MS, HR-ICP-MS, MH-ICP-MS, and MC-ICP-MS for the qualitative and quantitative determination of toxic metals, isotopes and, metal species, and effective monitoring of various other pollutants in drinking water, surface water, groundwater, industrial effluents, and water used for irrigation, aquaculture and for various industries is the focal point of this article. In general, the concentrations of major, minor, and trace element composition of water samples are influenced mainly by the bedrock geology of the point of emergence, as well as by the residence time and the depth of the aquifer. For better management of water resources, it is necessary to have a comprehensive understanding of the quality (with respect to physical, chemical, and biological) requirements, and sustainability of groundwater from a particular source. Identifying the sources of contamination, understanding the health risks associated, and the application of suitable water treatment technique(s) before it is supplied for public consumption. It is also necessary to intensify our studies on the metal species (e.g., As3+, Cr6+ and methyl mercury) in drinking water and their effects on human health, and their regulatory limits in drinking water.
James Hill, Stuart Black, Alejandro Araujo-Murakami
et al.
Phytolith analysis is a well-established archaeobotanical tool, having provided important insights into pre-Columbian crop cultivation and domestication across Amazonia through the Holocene. Yet, its use as a palaeoecological tool is in its infancy in Amazonia and its effectiveness for reconstructing pre-Columbian land-use beyond archaeological sites (i.e., ‘off-site’) has so far received little critical attention. This paper examines both new and previously published soil phytolith data from SW Amazonia to assess the robustness of this proxy for reconstructing pre-Columbian land-use. We conducted the study via off-site soil pits radiating 7.5 km beyond a geoglyph in Acre state, Brazil, and 50 km beyond a ring-ditch in northern Bolivia, spanning the expected gradients in historical land-use intensity. We found that the spatio-temporal patterns in palm phytolith data across our soil-pit transects support the hypothesis that pre-Columbian peoples enriched their forests with palms over several millennia, although phytoliths are limited in their ability to capture small-scale crop cultivation and deforestation. Despite these drawbacks, we conclude that off-site soil phytolith analysis can provide novel insights into pre-Columbian land use, provided it is effectively integrated with other land-use (e.g., charcoal) and archaeological data.
Meagan Sundstrom, Ashley B. Heim, Barum Park
et al.
Researchers have pinpointed recognition from others as one of the most important dimensions of students' science and engineering identity. Studies, however, have found gender biases in students' recognition of their peers, with inconsistent patterns across introductory science and engineering courses. Toward finding the source of this variation, we examine whether a gender bias exists in students' nominations of strong peers across three different remote, introductory physics courses with varying student populations (varying demographics, majors, and course levels). We also uniquely evaluate possible racial/ethnic biases and probe the relationship between instructional context (whether lecture or laboratory) and recognition. Some of our results replicate previous findings (such as the the association of course grade and small class section enrollment with nominations), while others offer contradictions. Comparing across our three courses and the prior work, results suggest that course level (whether first-year students or beyond-first-year students) might be more associated with a gender bias in peer recognition than other variables. Surprisingly, we also find instances of racial/ethnic biases in favor of students from backgrounds historically underrepresented in science. Finally, we find that the nomination patterns differ when students nominate individuals strong in the lecture material versus laboratory material. This work serves as an important step in determining which courses and contexts exhibit biases in peer recognition, as well as how students' perceptions of one another form in remote teaching environments.
Lijing Wang, Takuya Kurihana, Aurelien Meray
et al.
Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural attenuation. Climate change exacerbates the long-term site management problem because extreme precipitation and/or shifts in precipitation/evapotranspiration regimes could re-mobilize contaminants and proliferate affected groundwater. To quickly assess the spatiotemporal variations of groundwater contamination under uncertain climate disturbances, we developed a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Operator (U-FNO) to solve Partial Differential Equations (PDEs) of groundwater flow and transport simulations at the site scale.We develop a combined loss function that includes both data-driven factors and physical boundary constraints at multiple spatiotemporal scales. Our U-FNOs can reliably predict the spatiotemporal variations of groundwater flow and contaminant transport properties from 1954 to 2100 with realistic climate projections. In parallel, we develop a convolutional autoencoder combined with online clustering to reduce the dimensionality of the vast historical and projected climate data by quantifying climatic region similarities across the United States. The ML-based unique climate clusters provide climate projections for the surrogate modeling and help return reliable future recharge rate projections immediately without querying large climate datasets. In all, this Multi-scale Digital Twin work can advance the field of environmental remediation under climate change.
Equal mass fermionic trimers with two different spin components near the unitary limit are shown to possess a universal van der Waals bound or resonance state near $s$-wave unitarity, when $p$-wave interactions are included between the particles with equal spin. Our treatment uses a single-channel Lennard-Jones interaction with long range two-body van der Waals potentials. While it is well-known that there is no true Efimov effect that would produce an infinite number of bound states in the unitary limit, we demonstrate that another type of universality emerges for the symmetry $L^Π=1^{-}$. The universality is a remnant of Efimov physics that exists in this system at $p$-wave unitarity, and it leads to modified threshold and scaling laws in that limit. Application of our model to the system of three lithium atoms studied experimentally by Du, Zhang, and Thomas [Phys. Rev. Lett. {\bf 102}, 250402 (2009)] yields a detailed interpretation of their measured three-body recombination loss rates.
<p>Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing “well enough”
in the context of its specific purpose. The decision of reasonable
expectations is necessarily subjective even if the evaluation criteria are
quantitative. Our objective is to provide recommendations for improving the
evaluation of groundwater representation in continental- to global-scale
models. We describe current modeling strategies and evaluation practices,
and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or
other state or flux variables (observation-based evaluation), (2) comparing
several models with each other with or without reference to actual
observations (model-based evaluation), and (3) comparing model behavior with
expert expectations of hydrologic behaviors in particular regions or at
particular times (expert-based evaluation). Based on evolving practices in
model evaluation as well as innovations in observations, machine learning,
and expert elicitation, we argue that combining observation-, model-, and
expert-based model evaluation approaches, while accounting for
commensurability issues, may significantly improve the realism of
groundwater representation in large-scale models, thus advancing our ability
for quantification, understanding, and prediction of crucial Earth science
and sustainability problems. We encourage greater community-level
communication and cooperation on this quest, including among global
hydrology and land surface modelers, local to regional hydrogeologists, and
hydrologists focused on model development and evaluation.</p>
Describing the evolution of science is a salient work not only for revealing the scientific trend but also for establishing a scientific classification system. In this paper, we investigate the evolution of science by observing the structure and change of keyword co-occurrence networks. Starting from seven target physics fields and their initial keywords selected by experts from the Korean Physical Society, we generate keyword co-occurrence networks better to capture topological structure with our proposed approach. In this way, we can construct a more relevant and abundant keyword network from a small set of initial keywords. With these networks, we successfully identify the scientific sub-field by detecting communities and extracting core keywords of each community. Furthermore, we trace the temporal evolution of sub-fields with the time-snapshot keyword network, the resultant temporal change of the community membership explains the evolution of the research field well. Our approach for tracing the evolution of the research field with a keyword co-occurrence network can shed light on identifying and assessing the evolution of science.
In my doctoral thesis, I have focussed my attention on radiation processes in high-energy astrophysics connected with the accretion flow physics around compact objects. Generally, a radiation field beside to exert an outward radiation pressure, there is also the presence of a radiation drag force, which both can drastically change or even halt the motion of the surrounding matter. The radiation drag force, known as Poynting-Robertson effect, acts as a dissipative force against the matter's orbital motion, removing very efficiently angular momentum and energy from it. The thesis is organised in three parts: (1) for ray-tracing purposes, I have developed a mathematical method for deriving a set of high-accurate approximate polynomial formulae to easily integrate photon geodesics in a Schwarzschild spacetime; (2) I gave two fundamental contributions in the field of the general relativistic treatment of the Poynting-Robertson effect (Lagrangian formulations and extension of the model in three dimensions); (3) I reduced the data of three accreting millisecond X-ray pulsars: IGR J00291+5934, IGR J18245-2452, and SAX J1748.9-2021. This thesis offers innovative ideas in the field of radiation processes involving the Poynting-Robertson effect in high-energy astrophysics, opening thus up future interesting perspectives both in theoretical and observational physics.