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Cohort-Level Protection and Individualized Inference in AI-Based Monitoring
No full textArtificial intelligence (AI) tools are increasingly used to monitor large groups of similar units (e.g., patients, credit cards), but critical decisions must often be made at the individual level.We propose a framework that:• Borrows strength across a cohort for better accuracy,• Enables automated, individualized inference, and• Supports early detection of system breaches (e.g., tumor growth, fraud).Applications include:• Cancer screening via image analysis• Credit card fraud detection• Cybersecurity and ecological monitoring Our work offers a scalable, mathematically grounded approach that blends cohort-level learning with personalized monitoring, forming a key step toward digital twins and precision healthcare.https://indico.bnl.gov/event/28538/contributions/112804/attachments/64680/111127/Cohort-level%20protection%20and%20individualized%20inference%20poster.pd
Joint Retrieval of Cloud properties using Attention-based Deep Learning Models
No full text2025 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2025),3-8 August 2025, Brisbane, AustraliaAccurate cloud property retrieval is vital for understanding cloud behavior and its impact on climate, including applications in weather forecasting, climate modeling, and estimating Earth's radiation balance. The Independent Pixel Approximation (IPA), a widely used physics-based approach, simplifies radiative transfer calculations by assuming each pixel is independent of its neighbors. While computationally efficient, IPA has significant limitations, such as inaccuracies from 3D radiative effects, errors at cloud edges, and ineffectiveness for overlapping or heterogeneous cloud fields. Recent AI/ML-based deep learning models have improved retrieval accuracy by leveraging spatial relationships across pixels. However, these models are often memory-intensive, retrieve only a single cloud property, or struggle with joint property retrievals. To overcome these challenges, we introduce CloudUNet with Attention Module (CAM), a compact UNet-based model that employs attention mechanisms to reduce errors in thick, overlapping cloud regions and a specialized loss function for joint retrieval of Cloud Optical Thickness (COT) and Cloud Effective Radius (CER). Experiments on a Large Eddy Simulation (LES) dataset show that our CAM model outperforms state-of-the-art deep learning methods, reducing mean absolute errors (MAE) by 34% for COT and 42% for CER, and achieving 76% and 86% lower MAE for COT and CER retrievals compared to the IPA method.This research is partially supported by grants from NSF 2238743 and NASA 80NSSC21M0027. This work was carried out using the computational facilities of the High Performance Computing Facility, University of Maryland Baltimore County. - https://hpcf.umbc.edu/http://arxiv.org/abs/2504.0313
XRISM/Xtend Transient Search (XTS) detected an X-ray flare from an AGN candidate
No full textAuthors: M. Yoshimoto (Ehime U.), K. Fukushima, Y. Kanemaru, S. Ogawa (JAXA), M. Audard (U. de Geneve), E. Behar (Technion), S. Inoue (Kyoto U.), Y. Ishihara (Chuo U.), T. Kohmura (TUS), Y. Maeda (JAXA), H. Matsumoto (Osaka U.), M. Mizumoto (UTEF), K. Mori (U. of Miyazaki), N. Nagashima (Chuo U.), M. Nobukawa (NUE), H. Noda (Tohoku U.), K. Pottschmidt (UMBC, NASA GSFC, CRESST), M. Shidatsu (Ehime U.), H. Sugai (Chuo U.), T. Takagi (Ehime U.), H. Takahashi (Hiroshima U.), Y. Terada (Saitama U.), Y. Terashima (Ehime U.), Y. Tsuboi (Chuo U.), H. Uchida (Kyoto U.), T. Yoneyama (Chuo U.)XRISM/Xtend Transient Search (XTS) detected an X-ray brightening from an X-ray source XRISM J0918-1212 on 2025-05-06 TT. The source position is determined to be (R.A., Dec.) = (139.395, -12.200), with a systematic error of ∼ 40 arcsec. A plausible counterpart is the AGN candidate 2XMMi J091734.9-121159, which is located ∼ 2 arcsec apart from the position of XRISM J0918-1212. All statistical uncertainties in this report will be provided as a 90% confidence level unless stated otherwise. The flare started at 2025-05-06 at ~15:23 TT, reached its peak on 2025-05-06 at ∼ 20:40, and decayed on a scale of t⁽⁻⁰˙⁹ ⁺/⁻⁰˙²⁾. The peak flux is calculated as 6 × 10⁻¹³ erg s⁻¹ cm⁻² (0.4 – 10.0 keV). A systematic error of roughly 20% should be added to the statistical error. Corresponding luminosity is 3 × 10⁴² erg s⁻¹ by assuming that XRISM J0918-1212 is a member galaxy of Hydra A cluster, whose distance is z = 0.05 (∼ 300 Mpc). Although the decaying time scale is ∼ t⁻¹, this event might be a possible tidal disruption event. We derived the above systematic error for the flux by comparing our derived values for the sources detected with XTS in several observations with those for the corresponding X-ray counterparts. We estimated the systematic error for the source position from the separations between the detected sources with the corresponding counterparts in the same field of viewhttps://www.astronomerstelegram.org/?read=1717
Aerial Insect Biodiversity Responses to Prescribed Fire in an Eastern Grassland Habitat
No full textGrasslands in the Eastern United States are a biodiverse, but quickly vanishing, habitat type. Public land managers are beginning to incorporate prescribed fire into these fire-adapted systems to achieve cultural and natural resource conservation goals. While the effects of fire on plants are more widely documented, knowledge regarding the effects of fire on insect communities remains sparse. Following a prescribed burn in western Maryland, USA, no changes in community assemblage, species richness, or species diversity were found, but a significant decline in insect abundance was found. Insect species richness and abundance followed common growing season trends, peaking in mid-summer. Although these results follow general trends supported by global meta-analysis of insect community response to fire, the site and habitat specific responses of taxa make it difficult for public land managers to make species-specific management decisions without further detailed research
Assessing Healthcare Delivery and Service Quality in Community Clinics
No full textCommunity clinics (CCs) are integral to rural primary healthcare in Bangladesh, yet concerns remain about whether they deliver services that meet patient expectations. This study evaluates the quality of health service delivery in CCs in Bangladesh using the SERVQUAL model. Data were collected from 414 patients in Daudkandi and Chandina Upazilas of Cumilla district through cluster sampling. Service quality was assessed by comparing patients’ expectations and perceptions across five dimensions: reliability, responsiveness, assurance, empathy and tangibles. Findings showed negative quality gaps in all dimensions, with empathy showing the largest deficit (–1.55) and tangibles the smallest (–1.05). Differences between expectations and perceptions were statistically significant across all dimensions (p < .001). These results indicate that CCs are not meeting patient expectations, particularly in interpersonal care and reliability of services. Addressing these shortcomings requires improved resource allocation, stronger managerial oversight and enhanced provider training. Policy reforms focusing on empathy, responsiveness and reliability are essential to rebuild patient trust and strengthen the role of CCs in rural primary healthcare.https://journals.sagepub.com/doi/10.1177/0972063425137964
Understanding The Tree Of Life: A Fresh Look At Evolution With Biology Professor Kevin Omland
No full textphotographer: Kevin OmlandKevin Omland, professor of biological sciences, has spent 25 years teaching and researching evolution. His new book, Understanding the Tree of Life, is the latest in the “Understanding Life” series published by Cambridge University Press. Omland’s contribution challenges what he views as an outdated understanding of evolution and celebrates the interconnectedness of all species. Below, Omland shares the inspiration behind the book, its surprising insights, and why everyone from nature lovers to seasoned biologists should seek it out.https://umbc.edu/stories/tree-of-life-fresh-look-at-evolution
Analysis of a saline dust storm from the Aralkum Desert – Part 1: Consistency between multisensor satellite aerosol products
No full textThe Aralkum Desert presents a challenging environment for satellite aerosol observations due to its very bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on region-specific aerosol properties. We survey current global satellite algorithms capable of detecting the presence, column burden, and elevation of airborne dust over the Aral Sea basin. Discrepancies and potential biases in retrieved UV aerosol index (UVAI), mid-visible and thermal infrared optical depth (AOD), and layer height due to different assumptions on surface and aerosol properties are assessed. The results indicate that (1) UVAI products consistently delineate dust plume extent but show large positive values over turbid waters and salt flats due to enhanced surface absorption. (2) MODIS and VIIRS total and coarse-mode AOD retrievals show strong agreement over the Caspian Sea despite using different aerosol optical models. Over desert surfaces, all operational AOD products misclassify fresh dust plumes as clouds and exhibit strong nonlinear relationships. The NOAA EPS algorithm retrieves significantly lower AOD than others, although the agreement improves when a dust optical model is used. The MISR research algorithm produces higher, more consistent AOD and improved particle property retrievals compared to the MISR operational product. (3) Among four IASI infrared products, the LMD algorithm performs best in detecting dust plume features over both desert and water surfaces. (4) The EPIC aerosol optical centroid height (AOCH) product overestimates dust layer altitude under low aerosol loadings but exhibits good agreement with CALIOP in detecting the elevated dust characterized by well-defined upper boundaries. MISR height retrievals also align well with CALIOP and EPIC. IASI infrared retrievals are about 0.4 km higher than EPIC over dust-laden scenes. This study underscores the value of a synergistic, multisensor approach leveraging the complementary strengths of satellite aerosol products and calls for their appropriate application and careful interpretation when characterizing saline dust from the Aralkum Desert.This research has been supported by the NASA Land-Cover and Land-Use Change program (grant no. 80NSSC20K1480).https://acp.copernicus.org/articles/25/7403/2025
GW241011 and GW241110: Exploring Binary Formation and Fundamental Physics with Asymmetric, High-spin Black Hole Coalescences
No full textWe report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO–Virgo–KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, nonnegligible spin–orbit misalignment, and unequal mass ratios between their constituent black holes. These properties are characteristic of binaries in which the more massive object was itself formed from a previous binary black hole merger and suggest that the sources of GW241011 and GW241110 may have formed in dense stellar environments in which repeated mergers can take place. As the third-loudest gravitational-wave event published to date, with a median network signal-to-noise ratio of 36.0, GW241011 furthermore yields stringent constraints on the Kerr nature of black holes, the multipolar structure of gravitational-wave generation, and the existence of ultralight bosons within the mass range 10⁻¹³–10⁻¹² eV.This material is based upon work supported by NSF’s LIGO Laboratory, which is a major facility fully funded by the National Science Foundation. The authors also gratefully acknowledge the support of the Science and Technology Facilities Council (STFC) of the United Kingdom, the MaxPlanck-Society (MPS), and the State of Niedersachsen/ Germany for support of the construction of Advanced LIGO and construction and operation of the GEO 600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. The authors gratefully acknowledge the Italian Istituto Nazionale di Fisica Nucleare (INFN), the French Centre National de la Recherche Scientifique (CNRS) and the Netherlands Organization for Scientific Research (NWO) for the construction and operation of the Virgo detector and the creation and support of the EGO consortium. The authors also gratefully acknowledge research support from these agencies as well as by the Council of Scientific and Industrial Research of India, the Department of Science and Technology, India, the Science & Engineering Research Board (SERB), India, the Ministry of Human Resource Development, India, the Spanish Agencia Estatal de Investigación (AEI), the Spanish Ministerio de Ciencia, Innovación y Universidades, the European Union NextGenerationEU/PRTR (PRTR-C17.I1), the ICSC—CentroNazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by the European Union NextGenerationEU, the Comunitat Autonòma de les Illes Balears through the Conselleria d’Educació i Universitats, the Conselleria d’Innovació, Universitats, Ciència i Societat Digital de la Generalitat Valenciana and the CERCA Programme Generalitat de Catalunya, Spain, the Polish National Agency for Academic Exchange, the National Science Centre of Poland and the European Union—European Regional Development Fund; the Foundation for Polish Science (FNP), the Polish Ministry of Science and Higher Education, the Swiss National Science Foundation (SNSF), the Russian Science Foundation, the European Commission, the European Social Funds (ESF), the European Regional Development Funds (ERDF), the Royal Society, the Scottish Funding Council, the Scottish Universities Physics Alliance, the Hungarian Scientific Research Fund (OTKA), the French Lyon Institute of Origins (LIO), the Belgian Fonds de la Recherche Scientifique (FRS-FNRS), Actions de Recherche Concertées (ARC) and Fonds Wetenschappelijk Onderzoek—Vlaanderen (FWO), Belgium, the Paris I^le-de-France Region, the National Research, Development and Innovation Office of Hungary (NKFIH), the National Research Foundation of Korea, the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Foundation for Innovation (CFI), the Brazilian Ministry of Science, Technology, and Innovations, the International Center for Theoretical Physics South American Institute for Fundamental Research (ICTP-SAIFR), the Research grants Council of Hong Kong, the National Natural Science Foundation of China (NSFC), the Israel Science Foundation (ISF), the US-Israel Binational Science Fund (BSF), the Leverhulme Trust, the Research Corporation, the National Science and Technology Council (NSTC), Taiwan, the United States Department of Energy, and the Kavli Foundation. The authors gratefully acknowledge the support of the NSF, STFC, INFN, and CNRS for provision of computational resources. This work was supported by MEXT, the JSPS Leading-edge Research Infrastructure Program, JSPS Grant-in-Aid for Specially Promoted Research 26000005, JSPS Grant-in-Aid for Scientific Research on Innovative Areas 2402: 24103006, 24103005, and 2905: JP17H06358, JP17H06361, and JP17H06364, JSPS Core-to-Core Program A. Advanced Research Networks, JSPS Grants-in-Aid for Scientific Research (S) 17H06133 and 20H05639, JSPS Grant-in-Aid for Transformative Research Areas (A) 20A203: JP20H05854, the joint research program of the Institute for Cosmic Ray Research, University of Tokyo, the National Research Foundation (NRF), the Computing Infrastructure Project of the Global Science experimental Data hub Center (GSDC) at KISTI, the Korea Astronomy and Space Science Institute (KASI), the Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS), the AS Grid Center (ASGC) and the National Science and Technology Council (NSTC) in Taiwan under grants including the Science Vanguard Research Program, the Advanced Technology Center (ATC) of NAOJ, and the Mechanical Engineering Center of KEK.https://iopscience.iop.org/article/10.3847/2041-8213/ae0d5
Surya: Foundation Model for Heliophysics
No full textHeliophysics is central to understanding and forecasting space weather events and solar activity. Despite decades of high-resolution observations from the Solar Dynamics Observatory (SDO), most models remain task-specific and constrained by scarce labeled data, limiting their capacity to generalize across solar phenomena. We introduce Surya, a 366M parameter foundation model for heliophysics designed to learn general-purpose solar representations from multi-instrument SDO observations, including eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products. Surya employs a spatiotemporal transformer architecture with spectral gating and long--short range attention, pretrained on high-resolution solar image forecasting tasks and further optimized through autoregressive rollout tuning. Zero-shot evaluations demonstrate its ability to forecast solar dynamics and flare events, while downstream fine-tuning with parameter-efficient Low-Rank Adaptation (LoRA) shows strong performance on solar wind forecasting, active region segmentation, solar flare forecasting, and EUV spectra. Surya is the first foundation model in heliophysics that uses time advancement as a pretext task on full-resolution SDO data. Its novel architecture and performance suggest that the model is able to learn the underlying physics behind solar evolution.The Authors acknowledge the National Artificial Intelligence Research Resource (NAIRR) Pilot and NVIDIA for providing support under grant no. NAIRR240178. The authors would also like to thank NASA Advanced Supercomputing (NAS) Division for their compute support. Vishal Upendran would like to acknowledge NASA for support under award number 80NSSC25K7956. NVP and TS have been supported, in part, by NASA R2O2R grant 80NSSC22K0270. DVH is grateful for the support provided by the NASA FINESST grant 80NSSC22K0058.http://arxiv.org/abs/2508.1411
Sustainable use of reverse osmosis concentrates as alternative draw solutions for phosphorus recovery by Donnan dialysis
No full textTo reduce operational costs and improve process sustainability, we propose the use of high salinity wastes as alternative draw solutions for orthophosphate (P(V) as H₂PO₄–) recovery by Donnan dialysis. Brackish groundwater, seawater, and reverse osmosis (RO) concentrates have high chloride contents, which can be exploited to induce an electrochemical potential gradient and recover P(V) in Donnan dialysis systems. In this study, a novel framework was developed and employed to predict P(V) recovery by Donnan dialysis with multicomponent draw solutions. Brackish groundwater, brackish water RO concentrate, seawater, and seawater RO concentrate were predicted to achieve 67.7%, 93.8%, 98.2%, and 98.8% P(V) recovery, respectively, from 10 mM P(V) wastewater, with chloride contributing 18.4%, 86.9%, 97.8%, and 98.4%, respectively, to the overall P(V) recovery. The remaining contributions were mostly from bicarbonate and sulfate. Brackish water RO concentrate, a widely available inland resource, was experimentally evaluated as an alternative draw solution for P(V) recovery by Donnan dialysis. Results confirmed 99.8% and 74.5% P(V) were recovered from wastewater containing 1 and 10 mM P(V), respectively. Magnesium and calcium in the brackish water RO concentrate favorably interacted with recovered P(V) to form struvite and hydroxyapatite solids. The precipitation reactions were successfully integrated with the Donnan transport model for systems operated with draw solutions containing synthetic and real RO concentrate. The outcomes highlight new opportunities for sustainable resource recovery via Donnan dialysis with alternative draw solutions composed of saline wastes.We acknowledge funding from the Environmental Engineering and INFEWS N/P/H2O programs at the US National Science Foundation (1706819). We are grateful to the Kay Bailey Hutchison Desalination Plant (El Paso, Texas, USA) for providing brackish water RO concentrate. We thank Victor Fulda for technical support during construction of the Donnan dialysis reactors.http://iopscience.iop.org/article/10.1088/2977-3504/ae158