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Localized Rift Valley fever virus persistence explains epidemic and interepidemic dynamics and guides control strategies
No full textRift Valley fever (RVF) is an emerging disease with devastating impacts on livestock health and livelihoods. The risk of RVF virus (RVFV) emergence in new regions and the effectiveness of a strategy for preventing establishment are impacted by how infection persists at local scales. Multiple mechanisms have been proposed for its persistence in regions prone to epidemics, including maintenance via transovarial transmission (TOT) but whether and how TOT can support local persistence is not well understood. Through the development of host- and multi-vector climate-driven simulation models to recreate observed patterns of prevalence and outbreak frequency, we show that TOT has the potential to play an important role in local persistence through seasonal cold or dry periods. Local persistence required annual low-level transmission of RVFV concurrently with substantial TOT, whereas the infrequent large outbreaks hampered long-term persistence in our simulations. We show that under this mode of local persistence, large outbreaks can be prevented with low-level vaccination, but that the long-term local persistence can only be interrupted with many years of sustained vaccination. Determining the role of TOT in persistence is critical for designing countermeasures to prevent establishment after emergence.We would like to acknowledge the support provided by the US Department of Defense, Defense Threat Reduction Agenwho supported the underlying research these models are based on: Understanding Rift Valley Fever in the Republic of South A(HDTRA1-14-1-0029; 2014?2019) and Reducing the Threat of Rift Valley Fever through Ecology, Epidemiology and Socio-Economics(HDTRA1-19-0033; 2019?2024). The project depicted is sponsored by the US Department of Defense, Defense Threat Reduction Agency. Thecontent of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement shouldbe inferred. We would also like to acknowledge the support from BBSRC: BB/M003949/1.https://royalsocietypublishing.org/doi/10.1098/rspb.2025.045
Work-in-Progress: Leveraging ChatGPT to Support Technical Communication Skills (Writing) in a Senior Chemical Engineering Laboratory Course
No full text2025 ASEE Annual Conference & Exposition, 22 - 25 June 2025, Montreal, CanadaArtificial Intelligence (AI) tools like ChatGPT (Chat Generative Pre-trained Transformer) have sparked unprecedented interest across various fields since their release in November 2022 [1]. In education, AI is transforming both learning methodologies and administrative processes. The widespread interest in ChatGPT can be attributed to its robust performance across diverse applications, including essay composition, translation, content generation, and text summarization [2-6]. Its unique capability to respond naturally to interactive queries has set it apart from other tools. Recent studies [6] highlight advantages such as accessibility and efficiency, while others have compared traditional and AI-supported teaching methods in areas such as data analysis, teaching materials development, language learning, and plagiarism prevention [7-9]. Studies have explored its role in exam performance analysis, exam question generation and responses [10], and its effectiveness in academic writing, literature review synthesis, and translation [11]. Remarkably, an entire academic article written by ChatGPT (with minor human editing) has also been documented in the literature [12].https://peer.asee.org/work-in-progress-leveraging-chatgpt-to-support-technical-communication-skills-writing-in-a-senior-chemical-engineering-laboratory-cours
The Role of Professional Development in Enhancing Teachers’ Pedagogical Practice: An Evaluation of the Sherman Center Teacher Summer Institute
No full textProfessional development (PD) is seen as an avenue to improve student achievement outcomes by modifying teacher knowledge and practices. The present work focuses on evaluating one model of PD, the Teacher Summer Institute (TSI), a Sherman Center for Early Learning in Urban Communities initiative designed for urban early childhood educators in Maryland. The TSI equips teachers with tools, resources, and opportunities to acquire the necessary knowledge and skills to enhance their pedagogical practices. The TSI involves keynotes, PD sessions, collaborative working sessions, research scholars' presentations, and reflection opportunities. Through two studies, we evaluated the TSI’s impact on early childhood educators’ pedagogical practices by analyzing five years of program archival data (Study 1) and conducting a retrospective study (Study 2) in which we fielded an online survey of participating teachers. Implications for future program iterations and subsequent research evaluation efforts are discussed.We thank our partner schools and teachers for making this work possible, the Sherman Family Foundation for supporting this critical work, and the Sherman Center for Early Learning in Urban Communities for funding this study. We also thank the Sherman Center faculty and staff who designed, implemented, and documented this program.https://shermancenter.umbc.edu/wp-content/uploads/sites/523/2025/08/SHER2025Report7.pd
Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data
No full textAccurate maps of Greenland's subglacial bed are essential for sea-level projections, but radar observations are sparse and uneven. We introduce GraphTopoNet, a graph-learning framework that fuses heterogeneous supervision and explicitly models uncertainty via Monte Carlo dropout. Spatial graphs built from surface observables (elevation, velocity, mass balance) are augmented with gradient features and polynomial trends to capture both local variability and broad structure. To handle data gaps, we employ a hybrid loss that combines confidence-weighted radar supervision with dynamically balanced regularization. Applied to three Greenland subregions, GraphTopoNet outperforms interpolation, convolutional, and graph-based baselines, reducing error by up to 60 percent while preserving fine-scale glacial features. The resulting bed maps improve reliability for operational modeling, supporting agencies engaged in climate forecasting and policy. More broadly, GraphTopoNet shows how graph machine learning can convert sparse, uncertain geophysical observations into actionable knowledge at continental scale.http://arxiv.org/abs/2509.0857
Legacies of urbanization and suburbanization on forest patch distribution, ownership, and use: Insights from Baltimore, Maryland
No full textComprehensive high resolution land cover and parcel data provide new abilities to map and identify discrete forest patches across different ownerships and land uses, from urban to rural settings. Mapping of forest patches can inform forest management and protection, to maintain ecological benefits for diverse populations. We use parcel data in combination with high resolution land cover to map and characterize 20,146 unique forest patches covering 581.9km² across urban Baltimore City and suburban and rural portions of Baltimore County. Typical of the eastern United States, initial urban development in this region gave rise to suburban expansion during the 20th century. We found size and number of forest patches, as well as the proportion of forest spanning multiple parcels, all increased when moving outward from urban to rural areas. The number of forest patches went from nearly 2000 in Baltimore City (9.7% of land area) to 7700 forest patches within suburban Baltimore County, within the urban growth boundary (17.9% of land area), to nearly 10,500 in the rural portion of Baltimore County (44.7% of land area). However, ownership type and land use of forest patches, as derived from parcel data, was unique for each region: public ownership was more common within the city, and private ownership, with residential and agricultural land use, more common in suburban and rural Baltimore County, respectively. For all of the regions we considered, most of the forest area and the larger forest patches were distributed across numerous parcels and many types of ownership and land use. For each of these regions, working across parcel boundaries has the potential to expand forest conservation and management, but will involve a range of owners and land use types, including public parks, residential areas, and farmland, from urban to rural settings.This research was supported by the U.S. Department of Agriculture (USDA) Forest Service, Northern Research Station, agreement 17- JV11242308-056. The findings, opinions, and conclusions presented are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.https://www.sciencedirect.com/science/article/pii/S161886672500112
Emotion Recognition via Multimodal Fusion for Human–Robot Interaction Using Deep Learning
No full textOne of the primary challenges in Human-Robot Interaction (HRI) is enabling robots to effectively understand and respond to human emotions. Humans' express emotions through verbal and non-verbal cues, while robots typically rely on pre-programmed algorithms and physical gestures. Our research aims to develop HRI that bridges this gap by leveraging multimodal emotion detection. Emotions play a crucial role in human communication and decision-making, significantly influencing human-robot interactions. We aim for robots to understand and respond to human emotions by integrating neurophysiological and behavioral channels. Initially, we examine unimodal facial expression recognition using Convolutional Neural Networks (CNN) and Vision Transformers (ViT). Next, we enhance the model with a Mixture of Transformers (MiT). Using this enhanced model, we have developed a human-robot interaction perception system. Subsequently, we investigate multimodal emotion recognition in conveying emotions in Human-Robot Interaction (HRI). While unimodal techniques have been used to recognize emotions from various sources, research indicates that emotion recognition is inherently multimodal. Fusion representations provide a more comprehensive view of the emotional state, thereby enhancing emotion recognition accuracy. Therefore, exploring the role of multimodal fusion through computational models and neurophysiological experiments is essential. Our framework uses machine learning and deep learning to interpret complex physiological and facial expression data, enabling nuanced human-robot interactions. We focus on the offline fusion of multimodal methods, combining brain and behavior models, and exploring real-time fusion solutions. These human-robot interactions, based on emotions, will be validated through neurophysiological experiments, aiming for seamless and intuitive interactions based on a thorough understanding of human emotions
Geophysical Trends Inferred From 20 Years of AIRS Infrared Global Observations
No full textDaily spectral radiance observations by NASA's Atmospheric Infrared Sounder contain detailed information about surface and atmospheric temperature and water vapor. We obtain climate geophysical trends from 20 years (2002/09–2022/08) of Atmospheric Infrared Sounder (AIRS) observations using a novel method operating mostly in radiance space. The observations are binned into 3 X 5 degree tiles using 16 day intervals, after which nominally clear scenes are selected for each tile to construct the spectral radiance time series. Deseasonalized spectral trends are then obtained, which are inverted using a physical retrieval to obtain geophysical trends. This approach is distinct from traditional use of radiances whereby trends are generated after operational retrievals or assimilation into Reanalysis models. Our approach rigorously ties the derived geophysical trends to the observed radiance trends, using far fewer computational resources and time. The retrieved trends are compared to trends derived from ERA5 and MERRA2 reanalysis geophysical fields, and NASA Level3 AIRS v7 and CLIMCAPS v2 data. Our retrieved surface temperature trends agree quite well with ERA5, CLIMCAPS, and the GISS surface climatology trends. Atmospheric temperature profile trends exhibit some variability among all these data sets, especially in the polar stratosphere. Water vapor profile trends are nominally similar among the data sets except for the AIRS v7 which exhibits drying trends in the mid troposphere. Spectral closure between observed trends and those computed by running the reanalysis and AIRS L3 monthly retrieval products through a radiative transfer code are discussed, with the major differences arising in the water vapor sounding region.This work wasperformed under NASA Grant80NSSC22K0702. The hardware used ispart of the UMBC High PerformanceComputing Facility (HPCF). The facility issupported by the U.S. National ScienceFoundation through the MRI program(Grants CNS–0821258, CNS–1228778,OAC–1726023, and CNS—1920079) andthe SCREMS program (Grant DMS–0821311), with additional substantialsupport from the University of Maryland,Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCFand the projects using its resourceshttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JD04350
The Silicon Strip Detector Subsystem for the Trans-Iron Galactic Element Recorder for the International Space Station (TIGERISS)
No full text39th International Cosmic Ray Conference (ICRC2025),July 15-24,2025,Geneva, SwitzerlandAuthors: TIGERISS Collaboration, H. Allen, R. F. Borda, R. G. Bose, D. L. Braun, J. Calderon, Z. Campbell, N. W. Cannady, R. M. Caputo, M. Clark, J. Coldsmith, S. Coutu, G. A. de Nolfo, T. Forstmeier, M. Fratta, P. Ghosh, ,, S. Graham, J. F. Krizmanic, W. Labrador, L. Lisalda, J. V. Martins, M. P. McPherson, J. G. Mitchell, J. W. Mitchell, S. I. Mognet, A. Moiseev, ,, T. L. Ng, S. Nutter, N. Osborn, M. Pant, I. M. Pastrana, D. Radomski, B. F. Rauch, H. Salmani, M. Sasaki, ,, G. E. Simburger, S. Smith, H. A. Tolentino, Y. Tufail, D. Washington, T. Widmyer, L. Williams, W. V. ZoberThe Trans-Iron Galactic Element Recorder for the International Space Station (TIGERISS) is under construction and is planned for launch in 2027 and will be attached at the SOX location on the Columbus module on the ISS. TIGERISS will make the first definitive measurements of Ultra-Heavy Galactic Cosmic Rays (UHGCRs; Z >29) on an individual element basis past barium (⁵⁶Ba), through the lanthinides, and to lead (⁸²Pb). TIGERISS has a geometry factor of 1.06 m² sr and is comprised of four planes of single-sided silicon strip detectors (SSDs) arranged in orthogonal X-Y layers with an X-Y pair above and an X-Y pair below two large-area Cherenkov detectors. The top Cherenkov detector is comprised of a mosaic of aerogel radiators (n =1.05) while the bottom Cherenkov detector has an acrylic radiator (n = 1.49). The combination of the Cherenkov velocity measurements with the precise measurements of the ionization and trajectory of the traversing cosmic rays leads to highly accurate charge measurements of < 0.25 c.u. over the entire elemental range of ⁵B through ⁸²Pb. These TIGERISS measurements are highly sensitive in determining the strength of s-process, r-process, and rp-processes of Galactic nucleosynthesis while providing critical data needed for multi-messenger studies to determine the contributions of extreme phenomena, including supernovae (SN) and Neutron Star Mergers (NSMs), in the production of galactic matter. The science goals of TIGERISS, mission status, instrument design and performance of the TIGERISS SSD subsystem in relation to the measurements and science goals of TIGERISS are discussed in this paper.This work is supported by NASA awards 21-PIONEERS21-0012 at NASA GSFC and 80NSSC22M0299 at Northern Kentucky Universityhttps://pos.sissa.it/501/06
What Infant Research can-and Cannot-Tell us About Human Universals
No full textThe search for human universals is firmly grounded in the study of infancy. Infants are viewed as unconditioned by social experience and therefore a source of insight into the initial state of psychological organization. This paper outlines three constraints on this approach focusing on limited sample diversity, insufficient predictive and convergent validation of methods, and overreliance on single exposures or unreplicated findings. It argues for a shift from an emphasis on universality towards a focus on variation. Large-scale multi-site collaborations, longitudinal designs, and cross-method convergence across culturally diverse settings as key components of this goal. These approaches can advance a more ecologically valid and culturally situated science of infancy.https://osf.io/preprints/psyarxiv/urebt_v
XRISM/Xtend Transient Search (XTS) detected an X-ray flare from EQ CVn
No full textAuthors: Y. Maeda, T. Yoshida, K. Fukushima, K. Hayashi, 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.), Y. Terada (Saitama U.), Y. Terashima (Ehime U.), Y. Tsuboi (Chuo U.), H. Uchida (Kyoto U.), T. Yanagi (Chuo U.), T. Yoneyama (Chuo U.), M. Yoshimoto (Osaka U.)XRISM/Xtend Transient Search (XTS) detected an X-ray flare from an X-ray source XRISM J1226+3347 on 2024-11-10 TT. The source position is determined to be (R.A., Dec.) = (186.488, 33.781), with a systematic error of ∼ 40 arcsec. A Plausible counterpart is an eclipsing G0V type star EQ CVn which corresponds to an X-ray source RX J1225.9+3346. EQ CVn is located ∼ 10 arcsec apart from the position of XRISM J1226+3347. All statistical uncertainties in this report will be provided as a 90% confidence level unless stated otherwise.
The flare started at 2024-11-10 at ∼ 16:30 TT. The flare reached its peak on 2024-11-10 at ∼ 17:40. The flare exponentially decayed in 10⁴ sec. In order to estimate the source flux, we fit the spectrum in the flare peak phase with an absorbed APEC model with a temperature of kT = 0.9 keV and hydrogen column density NH = 4 × 10²¹ cm⁻². Then, the model flux is calculated as 2 × 10⁻¹² 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 4 × D₄₀₀ₚ꜀ × 10³¹ erg s⁻¹ by assuming the distance to XRISM J1226+3347 of D₄₀₀ₚ꜀.
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=1690