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XRISM/Xtend Transient Search (XTS) detected an outburst from a white dwarf candidate
No full textAuthors: N. Nagashima (Chuo 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), M. Mizumoto (UTEF), M. Nobukawa (NUE), K. Pottschmidt (UMBC, NASA GSFC, CRESST), M. Shidatsu (Ehime U.), H. Sugai (Chuo U.), Y. Terada (Saitama U.), Y. Terashima (Ehime U.), Y. Tsuboi (Chuo U.), H. Uchida (Kyoto U.), T. Yoneyama (Chuo U.), M. Yoshimoto (Ehime U.)XRISM/Xtend Transient Search (XTS) detected an X-ray outburst from an X-ray source XRISM J1826-3651 on 2025-04-12 TT. The source position is determined to be (R.A., Dec.) = (276.543, -36.849), with a systematic error of ~40 arcsec. A plausible counterpart is a white dwarf candidate Gaia DR3 6728071049507752320, which is located ~16 arcsec apart from the position of XRISM J1826-3651.
The XRISM observation was started at 2025-04-12T18:07:19 TT, where the flux was estimated to be 8 × 10⁻¹⁴ erg s⁻¹ cm⁻² (0.4 – 10.0 keV). The source flux was increased by an order of magnitude (8 × 10⁻¹³ erg s⁻¹ cm⁻²) by 2025-04-12 at ~22:52 TT. The outburst was continuing at the end of the observation, 2025-04-13T02:55:13 TT. Corresponding luminosities were 3 × D₁.₇ ₖₚ꜀² × 10³¹ erg s⁻¹ and 3 × D₁.₇ ₖₚ꜀² × 10³² erg s⁻¹, respectively by assuming the distance to XRISM J1826-3651 of D₁.₇ ₖₚ꜀.
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 view.https://www.astronomerstelegram.org/?read=1714
SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction
No full textThis paper introduces a high resolution, machine learning-ready heliophysics dataset derived from NASA's Solar Dynamics Observatory (SDO), specifically designed to advance machine learning (ML) applications in solar physics and space weather forecasting. The dataset includes processed imagery from the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI), spanning a solar cycle from May 2010 to July 2024. To ensure suitability for ML tasks, the data has been preprocessed, including correction of spacecraft roll angles, orbital adjustments, exposure normalization, and degradation compensation. We also provide auxiliary application benchmark datasets complementing the core SDO dataset. These provide benchmark applications for central heliophysics and space weather tasks such as active region segmentation, active region emergence forecasting, coronal field extrapolation, solar flare prediction, solar EUV spectra prediction, and solar wind speed estimation. By establishing a unified, standardized data collection, this dataset aims to facilitate benchmarking, enhance reproducibility, and accelerate the development of AI-driven models for critical space weather prediction tasks, bridging gaps between solar physics, machine learning, and operational forecasting.This work is supported by NASA Grant 80MSFC22M004. The Authors acknowledge the National Artificial Intelligence Research Resource (NAIRR) Pilot and NVIDIA for providing support under grant no. NAIRR240178.http://arxiv.org/abs/2508.1410
3D nanoprinting of embryo microinjection needles with anti-clogging features
No full textWide-ranging biomedical applications spanning both research and clinical settings rely on microinjection protocols that involve using a long, hollow microneedle to deliver foreign substances directly into biological targets, such as embryos. Unfortunately, conventional microneedles are prone to clogging—e.g., cytoplasmic material from an embryo becoming lodged inside the needle tip during penetration, thereby obstructing delivery—motivating researchers to use top-down microfabrication techniques to modify needle tips and reduce such failure modes. Recent advancements for the submicron-scale additive manufacturing approach, “Two-Photon Direct Laser Writing (DLW)”, offer a new, bottom-up pathway for re-architecting microneedle tips to address clogging susceptibility via geometric means. Here, we investigate this potential by 3D printing monolithic 650-µm-tall, 15-µm-diameter hollow microneedles comprising architectural features designed to remediate clogging phenomena: (i) a solid, fine-point tip, (ii) multiple side ports (i.e., perpendicular to the insertion direction), and (iii) an internal microfilter. Serial microinjection experiments with live zebrafish embryos reveal that the 3D microneedles yield enhanced delivery performance without any instances of complete blockages that are pervasive among both standard glass and 3D-printed control microneedles. These findings suggest that DLW-based 3D printing holds distinctive promise for high-precision microinjection applications, particularly in scenarios involving extensive serial injections or critical payloads and targets.We greatly appreciate the contributions of Rachel Brewster, Ian B. Rosenthal, Emmett Z. Freeman, Noemi Gonzalez, Michael Restaino, Matthew Kim, Sarah Young, Ladeja Robinson, Chloe Keller, Kay Htut, Allison Orlosky as well as members of the Bioinspired Advanced Manufacturing (BAM) Laboratory and Terrapin Works at the University of Maryland, College Park and the Micro/Nanofabrication Center at the Princeton Institute of Materials. This work was supported in part by U.S. National Institutes of Health (NIH) Award Numbers 1R41GM153053 and 1R41MH135827, U.S. National Science Foundation (NSF) Award Numbers 1943356 and 1938527, and Maryland Industrial Partnerships (MIPS) Award Numbers 6523 and 7422.https://www.nature.com/articles/s41378-025-01005-
Structurally informed data assimilation in two dimensions
No full textAccurate data assimilation (DA) for systems with piecewise-smooth or discontinuous state variables remains a significant challenge, as conventional covariance-based ensemble Kalman filter approaches often fail to effectively balance observations and model information near sharp features. In this paper we develop a structurally informed DA framework using ensemble transform Kalman filtering (ETKF). Our approach introduces gradient-based weighting matrices constructed from finite difference statistics of the forecast ensemble, thereby allowing the assimilation process to dynamically adjust the influence of observations and prior estimates according to local roughness. The design is intentionally flexible so that it can be suitably refined for sparse data environments. Numerical experiments demonstrate that our new structurally informed data assimilation framework consistently yields greater accuracy when compared to more conventional approaches.This work is partially supported by the NSF grant DMS #1912685, DOE ASCR #DE-ACO5-000R22725, and DOD ONR MURI grant #N00014-20-1-2595.http://arxiv.org/abs/2510.0636
MONKEY: Masking ON KEY-Value Activation Adapter for Personalization
No full textPersonalizing diffusion models allows users to generate new images that incorporate a given subject, allowing more control than a text prompt. These models often suffer somewhat when they end up just recreating the subject image, and ignoring the text prompt. We observe that one popular method for personalization, the IP-Adapter automatically generates masks that we definitively segment the subject from the background during inference. We propose to use this automatically generated mask on a second pass to mask the image tokens, thus restricting them to the subject, not the background, allowing the text prompt to attend to the rest of the image. For text prompts describing locations and places, this produces images that accurately depict the subject while definitively matching the prompt. We compare our method to a few other test time personalization methods, and find our method displays high prompt and source image alignment.http://arxiv.org/abs/2510.0765
Exploring the role of generative AI in supporting students with disabilities, through the lens of universal design for learning
No full textSpecial educators face challenges managing the time and resources needed to teach students with diverse needs. Creating individualized instructional materials that align with the needs and plans of each student is one of these challenges. In this qualitative study, we investigated how generative Artificial Intelligence (AI) tools can assist special educators in creating learning content tailored to the needs of students with disabilities within the framework of Universal Design for Learning (UDL). Understanding how special educators can utilize generative AI tools to create personalized learning content can inform future technology design and form the basis for developing professional development tools that support the effective use and adoption of these emerging technologies.https://www.ojed.org/STEM/article/view/885
The Surveillance in Infectious Disease Control Role of Genomic
No full textInfectious disease surveillance is central to global public health, enabling the early detection, monitoring, and control of outbreaks. Traditional surveillance methods—relying on clinical diagnoses, laboratory confirmation, and epidemiological investigations—are often constrained by delays, underreporting, and limited resolution in differentiating pathogen strains. The integration of genomic technologies has transformed infectious disease control, offering unprecedented precision and timeliness. High-throughput sequencing, whole-genome sequencing, and metagenomic approaches allow for rapid pathogen identification, antimicrobial resistance (AMR) detection, and real-time outbreak investigation. The COVID-19 pandemic highlighted the power of genomic surveillance in tracking variants, informing vaccine updates, and guiding public health responses. Beyond viral threats, genomics has proven critical in monitoring multidrug-resistant tuberculosis, methicillin-resistant Staphylococcus aureus (MRSA), and foodborne pathogens. Despite these advances, challenges remain, including disparities in global sequencing capacity, data-sharing limitations, privacy concerns, and high infrastructure costs. This review underscores the transformative potential of genomics in infectious disease surveillance while emphasizing the need for equitable access, international collaboration, and ethical governance. As sequencing becomes increasingly affordable and integrated with bioinformatics and artificial intelligence, genomic surveillance is poised to become a cornerstone of resilient, responsive, and precision-driven public health systems worldwide.https://www.multiresearchjournal.com/admin/uploads/archives/archive-1755929811.pd
“I Didn’t Understand How Powerful This Could Be”: The Synergy of Arts-Enhanced Literacy Instruction and Multicultural Literature in a Rural Classroom
No full textGuided by culturally sustaining pedagogy, this study examined how an elementary language arts teacher engaged with a yearlong project that focused on enhancing literacy instruction through the synergetic power of using multicultural children’s literature combined with arts. The case study conducted in a context with strict policy guidelines on literacy instruction demonstrated the teacher’s growing understanding of how to integrate multicultural children’s literature and arts in her instruction and powerful shifts in her view of students’ potential for empathy and engagement with complex social issues. The discussions of the books and creative artwork made by her students reshaped the teacher’s view of her students and her professional self. This study confirms that teachers can and do pursue culturally sustaining pedagogy–informed practices despite the restrictions of scripted literacy curricula and policy constraints on the topics of equity and diversityThis study is funded by NCTE Research Granthttps://scholarworks.wmich.edu/reading_horizons/vol64/iss2/
Cloudbursts of the Mid-Atlantic
No full textExtreme short-duration rainfall in the Mid-Atlantic region of the US is examined through polarimetric radar analyses of storms that produced rainfall accumulations exceeding 1,000-year values for time scales less than 3 hr. Polarimetric radar analyses of Mid-Atlantic cloudbursts focus on dynamical processes associated with updrafts and downdrafts, microphysical processes associated with extreme rainfall rates and mesoscale processes associated with structure, motion and evolution of convective systems over short time scales and small spatial scales. Dynamical processes associated with updrafts and downdrafts play a key role in determining the spatial and temporal distribution of extreme rainfall and in dictating errors in radar rainfall estimates through the effects of vertical motion. The microphysics of extreme short-duration rainfall exhibit a mix of cold and warm rain processes, with cold rain processes contributing to cycles of growth and decay in raindrop size distributions. Analyses are designed to address critical research problems linked to modernizing methods for estimating Probable Maximum Precipitation (PMP). Polarimetric radar provides an important path for estimating rainfall for PMP-magnitude storms. We compare rainfall analyses from recent storms in the Mid-Atlantic with cloudburst rainfall from the pre-radar era, including storms that produced record or near-record rainfall accumulations for the US and the world. Rainfall accumulations at time scales shorter than 3 hr for polarimetric era storms are large relative to rainfall frequency results, but modest in comparison with rainfall maxima from historical cloudbursts in the Mid-Atlantic.https://onlinelibrary.wiley.com/doi/abs/10.1029/2025WR04038
Joint Task Offloading and Resource Allocation in RIS-assisted NOMA-VEC Intent-based Networking
No full textIn Intent-based Vehicular Edge Computing (VEC) networking, escalating demands for computational offloading and resource management in dynamic urban environments necessitate innovative solutions. This paper proposes a novel RIS-assisted NOMA-VEC framework that empowers vehicle users (VUs) to offload arbitrary task portions to multiple edge servers via any available subcarrier. This approach overcomes limitations posed by heterogeneous local computing capabilities and stringent latency constraints. By leveraging Reconfigurable Intelligent Surfaces (RIS) to enhance channel conditions through both direct and reflected links, our framework significantly improves communication reliability and offloading efficiency. To minimize the average weighted energy consumption of VUs under time-varying channels and traffic dynamics, we formulate a joint optimization problem integrating offloading decisions, power allocation, and transmission time scheduling. Addressing the problem’s inherent complexity, characterized by multi-variable coupling and non-convex constraints, we develop a two-stage decomposition strategy: Offloading decisions are dynamically adapted to environmental fluctuations using a Proximal Policy Optimization (PPO)-based algorithm, while resource allocation is resolved through a hybrid Genetic Algorithm (GA) and Sequential Least Squares Programming(SLSQP) approach, efficiently navigating combinatorial and non-convex landscapes. Extensive simulations demonstrate that our framework reduces VU energy consumption by 11.12% compared to baseline methods, validating its superior efficiency in RIS-enhanced VEC systems.This work was supported in part by the National Natural Science Foundation of China (62462002), partially supported by the Natural Science Foundation of Guangxi, China (Nos. 2025GXNSFAA069958), and the Key Research and Development Program of Guangxi (No. AD25069071)https://ieeexplore.ieee.org/document/1120189