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A systematic comparison of ACE-FTS δD retrievals with airborne in situ sampling
No full textThe isotopic composition of water vapor in the upper troposphere and lower stratosphere (UTLS) can be used to understand and constrain the budget and pathways of water transport into that region of the atmosphere. Measurements of the water isotopic composition help further understanding of the region's chemistry, radiative budget, and the sublimation and growth of polar stratospheric clouds and high-altitude cirrus, both of which are also important to stratospheric chemistry and Earth's radiation budget. Here we present the first intercomparison of water isotopic composition δD using in situ measurements from the ChiWIS, Harvard ICOS, and Hoxotope instruments and satellite retrievals from ACE-FTS. The in situ data comes from the AVE-WIIF, TC4, CR-AVE, StratoClim, and ACCLIP field campaigns, and satellite retrievals of isotopic composition are derived from the ACE-FTS v5.2 data set. We find that in all campaign intervals, the satellite retrievals above about 14 km altitude are depleted by up to 150 ‰ with respect to in situ measurements. We also use in situ measurements from the ChiWIS instrument, which has flown in both the Asian Summer Monsoon (AM) and the North American Monsoon (NAM), to confirm the isotopic enhancement in δD observed in satellite retrievals above the NAM.This work was supported by the National Science Foundation through the Partnerships in International Research and Education program under grant number OISE 1743753 This research has been supported by the StratoClim project of the European Community’s Seventh Framework Programme FP7 2007 2013 under grant agreement no 603557 The ACCLIP campaign was supported by NSF NASA and NOAAhttps://egusphere.copernicus.org/preprints/2025/egusphere-2025-1190
On the Conjecture by David Foster and Turkay Yolcu (Math. Mag. ¨98 (2025), no. 2)
No full textIn this note, we establish the validity of a conjecture recently proposed in Mathematics Magazine and connect it to the existing interesting resultsResearch supported in part by BSF grant 2020063.https://www.tandfonline.com/doi/full/10.1080/0025570X.2025.253354
Addressing the Gap in Genetic Testing Rates for Ovarian Cancer Patients
No full textOvarian cancer remains a leading cause of cancer-related mortality in women, with genetic testing playing a crucial role in guiding treatment and identifying hereditary risks. Despite professional guidelines recommending universal genetic testing for patients with ovarian cancer, provider adherence remains suboptimal, contributing to missed therapeutic and preventive opportunities. This Doctor of Nursing Practice (DNP) project aimed to address this gap by implementing an evidence-based intervention to improve genetic testing referral rates among newly diagnosed ovarian cancer patients.
This evidence-based practice (EBP) implementation project utilized nudge theory to enhance provider adherence to genetic testing guidelines within an electronic health record (EHR) system. The intervention was conducted in a Mid-Atlantic women’s cancer center and targeted gynecologic oncology providers. The primary outcome was an increase in documented genetic testing referrals within a 3-month implementation period compared to baseline practice. The Iowa Model of Evidence-Based Practice guided the project implementation. Nudges included EHR-based SmartPhrases for documentation, peer comparison emails, and educational resources for providers. Data from the pre-implementation and implementation phases were analyzed using descriptive and inferential statistics. Referral rates increased significantly from 14% in the pre-implementation group to 67% post-intervention. However, completed genetic counseling and testing rates remained unchanged. These findings support the sustainability of the intervention but highlight persistent systemic barriers requiring institutional and policy-level changes. Future efforts should explore expanded genetic counseling access to enhance patient follow-through
Utilizing PBL Height Data from Multiple Observing Systems in the GEOS System (I): Assimilation Framework
No full textIn this study, a strategy and framework are developed to build a global Planetary Boundary Layer (PBL) height (PBLH) analysis and monitoring capability from multiple observing systems in the NASA Global Earth Observing System (GEOS) data assimilation system. To facilitate this effort, PBLH are derived from radiosonde and Global Navigation Satellite System Radio Occultation (GNSS-RO) refractivity data. As PBLH can be sensitive to potentially disparate observables and retrieval algorithms, new model PBLH definitions consistent with each observation type are added to the forecast model for the calculation of first guess departures from observations (OmF). These model definitions are augmented to the control variable vector, interacting with other control variables through flow-dependent ensemble background error covariance component. Moreover, to capture capping inversions, methods are explored using PBLH data to improve background error covariance through inflation of ensemble spread and adjustment of vertical localization length scale for virtual temperature and relative humidity variables. Experiments are conducted to assess the separate and combined impacts of these methods and the correlation relationships between PBLH and other control variables in the background error covariance. Preliminary results show that these changes are beneficial to the assimilation of other observations to improve the PBL thermodynamic structure.This study was supported by the National Aeronautics and Space Administration, NNH21ZDA001N-DSI, Decadal Survey Incubation program. Resources supporting this work were also provided by the NASA High-End Computing (HEC) Program through the NASA Center for Climate Simulation (NCCS) at Goddard Space Flight Center.https://journals.ametsoc.org/view/journals/mwre/aop/MWR-D-24-0141.1/MWR-D-24-0141.1.xm
Will Montgomery County's Only Charter School Open in Time?
No full textThe Mecca Business Learning Institute is Montgomery County, Maryland,’s second officially-approved public charter school project and expected to open its doors for the Fall 2025 school year. The school is reporting strong interest from families but as of a few weeks ago the school building was not ready, staff had not been hired, negotiations with the teachers and staff unions incomplete, and finances overly dependent on the county disbursements. Montgomery has long resisted public charter schools even as Washington DC, Prince George’s County, and Baltimore have embraced the idea. Sunil Dasgupta talked with founders LaChaundra Graham and Tracey Cooper about their model and the prospects for opening on time for the next school year. Music by Washington art-pop rock band Catscan! School Website: https://www.mbli-md.org/https://open.spotify.com/episode/4ZOuBRhoJ2kzOVaZYNOG2
VIVAR: learning view-invariant embedding for video action recognition
No full textThe 8th International Conference on Video and Image Processing, 2024, Kuala Lumpur, MalaysiaDeep learning has achieved state-of-the-art video action recognition (VAR) performance by comprehending action-related features from raw video. However, these models often learn to jointly encode auxiliary view (viewpoints and sensor properties) information with primary action features, leading to performance degradation under novel views and security concerns by revealing sensor types and locations. Here, we systematically study these shortcomings of VAR models and develop a novel approach, VIVAR, to learn view-invariant spatiotemporal action features removing view information. In particular, we leverage contrastive learning to separate actions and jointly optimize adversarial loss that aligns view distributions to remove auxiliary view information in the deep embedding space using the unlabeled synchronous multiview (MV) video to learn view-invariant VAR system. We evaluate VIVAR using our in-house large-scale time synchronous MV video dataset containing 10 actions with three angular viewpoints and sensors in diverse environments. VIVAR successfully captures view-invariant action features, improves inter and intra-action clusters’ quality, and outperforms SoTA models consistently with 8% more accuracy. We additionally perform extensive studies with our datasets, model architectures, multiple contrastive learning, and view distribution alignments to provide VIVAR insights. We open-source our code and dataset to facilitate further research in view-invariant systems.This work has been partially supported by the DEVCOM Army Research Laboratory (ARL) under a cooperative agreement (W911NF2120076), ONR Grant #N00014-23-1-2119, NSF CAREER Award #1750936, NSF REU Site Grant #2050999, and NSF CNS EAGER Grant #2233879.https://www.spiedigitallibrary.org/conference-proceedings-of-spie/13558/135580A/VIVAR-learning-view-invariant-embedding-for-video-action-recognition/10.1117/12.3059138.ful
From Arrival to Integration: Understanding the Challenges and Experiences of Afghan Immigrants in the United States
No full textD.P.A. -- The University of Baltimore, 2025Public Scholarship Project submitted to the College of Public Affairs of The University of Baltimore in partial fulfillment of the requirements for the degree of Doctor of Public Administration.Abstract
I conducted this research study to fulfill the requirements for my Doctorate in Public Administration. I explored the resettlement experience and challenges of Afghan immigrants in the United States, specifically those who arrived via the Special Immigrant Visa (SIV) program and resettled in the DMV (District of Columbia, Maryland, and Virginia) area. By conducting 12 qualitative interviews with the Afghan SIVs, I studied essential integration aspects of Afghan immigrants, including employment, language barriers, economic integration, and social and cultural integration. The research findings reveal that, while the initial support from resettlement agencies is essential (including housing), many Afghan immigrants encounter considerable long-term challenges. These include economic instability, unemployment, inadequate employment services, and English language barriers. The study emphasizes that Afghan community support is essential to helping Afghan immigrants adjust to their new environment. The research reveals weaknesses in resettlement programs, specifically long-term housing support, cultural orientation, and customized employment assistance. Through public scholarship contributions, I presented practical policy suggestions to improve resettlement outcomes by advocating for comprehensive pre-arrival orientation initiatives, prolonged housing aid, job skills training programs, and English language instruction. The study demonstrates that Afghan immigrants need a comprehensive resettlement framework to ensure long-term socioeconomic stability and successful integration into U.S. society. I seek to guide policymakers, resettlement agencies, and public administrators in enhancing the existing support mechanisms for immigrant populations while adding to the wider discussions about immigration policy and public administration.https://papers.ssrn.com/sol3/papers.cfm?abstract_id=521869
Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
No full textThe focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets are experiencing rapid surface melting and increased freshwater runoff, contributing significantly to global sea level rise. Understanding the mechanisms driving snowmelt in these regions is crucial. ERA5, a widely used reanalysis dataset in polar climate studies, offers extensive climate variables and global data assimilation. However, its snowmelt model employs an energy imbalance approach that may oversimplify the complexity of surface melt. In contrast, the Glacier Energy and Mass Balance (GEMB) model incorporates additional physical processes, such as snow accumulation, firn densification, and meltwater percolation/refreezing, providing a more detailed representation of surface melt dynamics. In this research, we focus on analyzing surface snowmelt dynamics of the Greenland Ice Sheet using feature attribution for anomalous melt events in ERA5 and GEMB models. We present a novel unsupervised attribution method leveraging counterfactual explanation method to analyze detected anomalies in ERA5 and GEMB. Our anomaly detection results are validated using MEaSUREs ground-truth data, and the attributions are evaluated against established feature ranking methods, including XGBoost, Shapley values, and Random Forest. Our attribution framework identifies the physics behind each model and the climate features driving melt anomalies. These findings demonstrate the utility of our attribution method in enhancing the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics.This work is funded by the National Science Foundation (NSF) Award #2118285. The WADI dataset was provided by iTrust, Center for Research in Cyber Security, Singapore University of Technology and Design.http://arxiv.org/abs/2502.0774
An Improved Autoencoder Approach for Nuclei Image Segmentation
No full textThere is a dire need to enable an early diagnosis system to enhance the therapeutic outcome for patients by applying a medical image analysis application. This study proposes an improved auto-encoder model by integrating Squeeze and Excitation (SE) blocks on the different phases of the model for semantic segmentation, which U-Net inspires. We redesigned the model's skip-connection by utilizing Residual Squeez and Excitation (RSE) by employing SE block in a residual way to reduce the semantic gaps and discrepancy between encoder and decoder features. Then, we integrate the Dense Squeeze and Excitation (DSE) block in the model's bottleneck with a densely connected structure. We increase the model's accuracy compared to vanilla U-Net by integrating the discussed module in the model to enhance its capability for feature extraction and obtain more high-level features from the input feature. To evaluate our model's performance, we conducted our experiment on the 2018 Data Science Bowl dataset and compared it with the different approaches that are inspired by U-Net. Our proposed model achieved the Dice and IoU of 92.15% and 85.92% , respectively, surpassing most of the current stateof-the-art models
In-orbit Performance of the Soft X-ray Imaging Telescope Xtend aboard XRISM
No full textWe present a summary of the in-orbit performance of the soft X-ray imaging telescope Xtend onboard the XRISM mission, based on in-flight observation data, including first-light celestial objects, calibration sources, and results from the cross-calibration campaign with other currently-operating X-ray observatories. XRISM/Xtend has a large field of view of 38.5' x 38.5', covering an energy range of 0.4-13 keV, as demonstrated by the first-light observation of the galaxy cluster Abell 2319. It also features an energy resolution of 170--180 eV at 6 keV, which meets the mission requirement and enables to resolve He-like and H-like Fe Kα lines. Throughout the observation during the performance verification phase, we confirm that two issues identified in SXI onboard the previous Hitomi mission -- light leakage and crosstalk events -- are addressed and suppressed in the case of Xtend. A joint cross-calibration observation of the bright quasar 3C273 results in an effective area measured to be ∼420 cm²@1.5 keV and ∼310 cm²@6.0 keV, which matches values obtained in ground tests. We also continuously monitor the health of Xtend by analyzing overclocking data, calibration source spectra, and day-Earth observations: the readout noise is stable and low, and contamination is negligible even one year after launch. A low background level compared to other major X-ray instruments onboard satellites, combined with the largest grasp (Ωeff∼60 cm² degree²) of Xtend, will not only support Resolve analysis, but also enable significant scientific results on its own. This includes near future follow-up observations and transient searches in the context of time-domain and multi-messenger astrophysics.This work is supported by Japan Society for the Promotion of Science JSPS KAKENHI with the Grant number of 19K03915 23K22536 24K21547 HU 19K21884 20H01947 20KK0071 23K20239 24K00672 HN 23K20850 21H01095 KM 20KK0071 24H00253 HN 21J00031 22KJ3059 24K17093 HS 21K13963 24K00638 KH 21K03615 24K00677 MN 20H00175 23H00128 HM 21H04493 15H02090 14079204 TGT 22H01269 TK 24K17105 YK 24KJ1483 SI K K N acknowledges the support by the Yamada Science Foundationhttp://arxiv.org/abs/2503.2018