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Critical Virtual Exchange to address teachers’ needs in times of conflict
No full textCritical world events, such as wars and pandemics, have always impacted the field of education. Across the globe, educational contexts have been confronted with challenges emerging from these events, thereby necessitating teachers to respond to the specific needs that arise in disrupted educational settings. Due to its intercultural nature, Virtual Exchange (VE) is a promising practice for contexts where new intercultural spaces are created in educational settings affected by disruption or conflict. In addition, its computer-mediated configuration allows participants from diverse geographic contexts to collaborate on shared goals synchronously or asynchronously. This practice report presents the outcomes of a “Critical Virtual Exchange” project, which brought together teachers from across Europe who were working with or preparing to work with Ukrainian students displaced by the war. By shedding light on the potential of VE to respond to the challenges faced by teachers during periods of educational disruption, this study contributes to leveraging VE as a practice to address critical pedagogical goals in crisis-affected contexts and establish virtual support communities.The research reported in this publication includes data collected by the project Virtual Innovation and Support Networks for Teachers(VALIANT) (626134-EPP-1–2020–2-ESEPPKA3-PIPOLICY). This project is funded by Erasmus+ Key Action 3 (EACEA/38/2019): European policy experimentations in the fields of education, training, and youth led by high-level public authorities. The European Commission’s support for the production of this publicationdoes not constitute an endorsement of the contents, which reflect the views only of theauthors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.https://journal.unicollaboration.org/article/view/4231
Using a genetics-driven phenotypical approach to develop mycelial materials
No full textMycelial materials, derived from filamentous fungi, offer a sustainable alternative to conventional materials due to their biodegradability and low environmental impact. This research explores a rational phenotype-driven approach to enhance the mechanical properties of pure mycelial materials made from Aspergillus nidulans. We employed three strategies to induce phenotypical changes: disruption of the Cell Wall Integrity Signaling pathway (ΔmpkA), absolute inhibition of asexual development (e.g., ΔbrlA, ΔflbA, ΔfluG, fadAᴳ⁴²ᴿ), and controlled partial inhibition of asexual development using α-difluoromethylornithine (DFMO). Structural and biochemical analyses revealed reduced conidiation led to denser hyphal packing and altered cell wall composition, contributing to the increased mechanical strength. Increases in strain at failure was observed in mutants with autolytic dominant phenotypes, suggesting a correlation between autolysis, cell wall composition and mechanical properties. Asexual development was proportionally inhibited by the concentration of DFMO present, however increases in mechanical strength were not linear – displaying our ability to tune specific phenotypes to generate specific mechanical changes, and revealing further studies are required to characterize correlation between reduced asexual development and increased mechanical strength. These findings demonstrate that modulating asexual development in fungi can systematically enhance mycelial material properties, offering a rational strategy for engineering sustainable biomaterials with tailored mechanical performance. This work advances the potential of fungal-based materials for applications in scalable, eco-friendly material solutions aligned with circular economy principles
Towards Deployment of Computer Vision Neural Networks for Scene Understanding
No full textScene understanding is a cornerstone of autonomous operation for robotics and edge computing platforms. However, deploying advanced computer vision neural networks on these platforms presents two central challenges: the need for vast amounts of meticulously labeled training data, and the stringent energy and compute constraints imposed by embedded hardware. Meeting these requirements demands models that achieve both high accuracy and efficiency, balancing performance with limited latency, memory, and power budgets. This thesis addresses both of these barriers to real-world deployment. First, we propose a novel synthetic-to-real domain adaptation framework that substantially reduces the need for large volumes of labeled real-world data, enabling effective image segmentation and robust scene understanding with minimal annotation effort. Second, we introduce Squeezed Edge YOLO, a lightweight object detector architecture specifically designed to operate within the tight latency and energy budgets of edge computing platforms. Both the domain adaptation framework and the object detector demonstrate strong empirical performance. Our domain adaptation approach is validated on the challenging synthetic-to-real ”SYNTHIA → Cityscapes” and ”GTAV → Cityscapes” benchmarks, where we outperform the previous state of the art, HALO. To evaluate Squeezed Edge YOLO, we deploy it on a nano-UAV and collect real-world measurements, achieving real-time object detection at approximately 8 inferences per second with low power consumption. Together, these contributions advance the deployment of deep neural scene understanding on resource constrained robotic and edge platforms
Detection of the Orbital Modulation of Fe Kα Fluorescence Emission in Centaurus X-3 Using the High-resolution Spectrometer Resolve on board XRISM
No full textThe Fe Kα fluorescence line emission in X-ray spectra is a powerful diagnostic tool for various astrophysical objects to reveal the distribution of cold matter around photoionizing sources. The advent of the X-ray microcalorimeter on board the XRISM satellite will bring new constraints on the emission line. We present one of the first such results for the high-mass X-ray binary Centaurus X-3, which is composed of an O-type star and a neutron star (NS). We conducted a 155 ks observation covering an entire binary orbit. A weak Fe Kα line was detected in all orbital phases at an equivalent width (EW) of 10–20 eV. We found for the first time that its radial velocity (RV) is sinusoidally modulated by the orbital phase. The RV amplitude is 248 ± 13 km s⁻¹, which is significantly smaller than the value (391 km s⁻¹) expected if the emission is from the NS surface, but is consistent if the emission takes place at the O star surface. We discuss several possibilities of the line production site, including the NS surface, O star surface, O star wind, and accretion stream from the O star to the NS. We ran radiative transfer calculation for some of them assuming spherically symmetric density and velocity profiles and an isotropic distribution of X-ray emission from the NS. None of them explains the observed EW and velocity dispersion dependence on the orbital phase, suggesting that more elaborated modeling is needed. In other words, the present observational results have the capability to constrain deviations from these assumptions.The authors thank all those who contributed to the XRISM mission. Richard Mushotzky provided useful comments on the manuscript. The anonymous reviewer pointed out that the stellar wind can be a production site of the Fe Kα emission. This work was supported by the JSPS Core-to-Core Program (grant No. JPJSCCA20220002) and by NASA under the award number 80GSFC21M0002. This research made use of the JAXA's high-performance computing system JSS3. Y.M. is financially supported by the JST SPRING program (grant No. JPMJSP2108), B.V. by the Fund for Scientific Research Flanders (FWO-Vlaanderen, project 11H2121N). Part of this work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. The material is based upon work supported by NASA under award number 80GSFC21M0002. C.D. acknowledges support from STFC through grant ST/T000244/1.https://iopscience.iop.org/article/10.3847/2041-8213/ad946
Identifying the Optimal WRF-ARW Configuration for the Amazon Rainforest
No full textAMS 2025 105th annual meeting , 12-16 January 2025, New Orleans, LAConvection, which depends on the complex interaction between surface and atmosphere, can develop across a wide range of spatial and temporal scales. This complexity partly explains why large-scale models, especially in the tropics, often fail to accurately represent the intensity, timing, and location of convective precipitation compared to observations. One way to better understand the complex mechanisms responsible for developing and organizing convection is to use atmospheric models at cloud-resolving spatial resolutions. For this study, we used the Advanced Research Weather Research and Forecasting (WRF-ARW) model with resolutions of 9 km, 3 km, and 1 km to simulate the diurnal cycle of convection in the Amazon rainforests.The WRF-ARW model offers a wide range of physics and dynamic options. Thus, finding the optimal configuration to reproduce the observations closely enough is crucial. This step is essential for the subsequent use of the model to study of deep convection.We conducted a one-week-long simulation in December 2014, using either the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5 and ERA5-land) or the National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) dataset for the model initialization and boundary conditions. In addition to the reanalysis, we tested the Global Land Data Assimilation System (GLDAS) for soil moisture and temperature. We tested various configurations for microphysics (WSM6, WDM6, Thompson, and Morrison double moment), boundary layer (YSU, MYNN, MYJ), surface layer (MM5, MYNN, MYJ), and land surface (Unified Noah, Noah-MP).The model results were evaluated using the Taylor Skill Score (TSS) and diagram for key variables, including surface heat fluxes and precipitation rates that are integral to deep convection, with observations from the GoAmazon 2014/15 campaign. Among the configurations tested, the combination of ERA5 with ERA5-land, WDM6, YSU, MM5, and Unified Noah showed the best performance, using third-order Runge-Kutta time integration and monotic advection. In addition, enhancing the spatial resolution of the model leads to improved results. For instance, the abovementioned optimal configuration gives a TSS of 0.633 for surface heat flux and 0.632 for precipitation rate in the outer domain (d01), with corresponding values of 0.782 and 0.684 in the innermost (d03). Identifying this optimal configuration provides a valuable basis for further sensitivity experiments to understand the mechanisms contributing to deep convection in this region.https://ams.confex.com/ams/105ANNUAL/meetingapp.cgi/Paper/45613
The 11th Mining and Learning from Time Series (MILETS): From Classical Methods to LLMs
No full textKDD '25, 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2, Toronto, ON Canada, August 3 - 7, 2025Time series data is now pervasive across domains such as healthcare, finance, entertainment, and transportation, driven by advances in sensing technologies that enable continuous data collection. The resulting increase in data volume and complexity poses significant challenges to traditional analysis methods, calling for the development of advanced, interdisciplinary approaches to temporal data mining. This workshop aims to: (1) identify key challenges in learning from time series data, including irregular sampling, spatiotemporal dependencies, and uncertainty quantification; (2) explore recent advances in algorithmic, statistical, theoretical, and systems-based solutions-ranging from classical methods to emerging techniques involving large language models (LLMs); and (3) foster collaboration by highlighting open problems and novel research directions in time series analysis. Bridging theory and practice, the workshop provides a platform for researchers and practitioners from academia, industry, and government to exchange ideas, discuss technical challenges, and showcase practical applications. Contributions from related areas such as AI, machine learning, data science, and statistics are strongly encouraged.https://dl.acm.org/doi/10.1145/3711896.373786
Validation of DSCOVR-EPIC total column O3 retrievals using ground-based Pandora as well as OMPS, OMI, and TEMPO satellite data
No full textThe Earth polychromatic imaging camera (EPIC) onboard the deep space climate observatory (DSCOVR) began obtaining fully illuminated Earth images across 10 wavelength bands on 6 July 2015. The ultraviolet bands 317, 325, 340, and 388 nm are used to retrieve the total column ozone (TCO) values at different local times during the day. On 28 June 2019, the spacecraft experienced a gyroscope failure; after recovery, the EPIC TCO values retrieved from 2021 to 2024 still agree well with those obtained from the ground-based Pandora spectrometer instruments in terms of both the hourly and weekly average basis. The hourly EPIC TCO values show more variability than the matched Pandora TCO values but generally deviate within 2% while tracking the shape of the Pandora daily variations in most cases. At 13:30 hours, the TCO data from the ozone and mapping profiler suite (OMPS) and ozone monitoring instrument (OMI) are also observed to frequently agree with the time-matched Pandora and EPIC TCO values. In addition, comparisons were made with the version-3 (V03) hourly TCO retrievals from the US tropospheric monitoring of pollution (TEMPO) geostationary satellite over two North American sites, namely, Toronto (Canada) and Dearborn (Michigan, United States). The long-term weekly lowess average EPIC and Pandora TCO values agree with deviations of less than 2%, as does the 3-week lowess average of the OMPS TCO value. An analysis of the TCO values from Pandora and 1 year of TEMPO V03 suggests that the noon TCO values are 2%–5% higher than the morning and afternoon values.The author(s) declare that financial support was received for the research and/or publication of this article. This study was funded by the DSCOVR-EPIC project through the University of Maryland Baltimore County.https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1623828/ful
Failing FEMA Flood Maps and New Resilience Planning
No full textWith increasing inland flash flooding in the region, a new study from Metropolitan Washington Council of Governments’ Transportation Planning Board finds FEMA flood maps cover only 15 percent of flooding-related road closures in the Washington DC region. Sunil Dasgupta talks with Katherine Rainone, a transportation resilience planner at MWCOG, about the regional vulnerability study and projects to come out it. Flood analysis interactive tool: https://experience.arcgis.com/experience/327843f119204e059fcc50af4154ae67/page/Main/ Full flood analysis report: https://www.mwcog.org/documents/2025/06/30/national-capital-region-inland-flood-analysis/. Music by A Shrewdness of Apeshttps://open.spotify.com/episode/0KYzvqjDNGE5fosei3QeM
VLA FRAMEx. III. Circumnuclear Radio Emission Mechanisms in Hard X-ray Selected Active Galactic Nuclei
No full textWe present Stokes I continuum analysis for a volume-limited sample (< 40 Mpc) of hard X-ray selected active galactic nuclei (AGNs) using 4 − 12 GHz observations with the Karl G. Jansky Very Large Array (VLA). All of the 25 sources analyzed here have previously been observed with the Very Long Baseline Array (VLBA) to probe their subparsec projected physical scales, but detected emission has only been measured for 12 of the sources at C band (4.4 GHz), despite expectations. We determined that coronal emission is unlikely to be a dominant emission mechanism for the sources not detected by the VLBA, and the emission measured with the VLA is likely produced beyond parsec spatial scales. We also explore potential radiation mechanisms for the circumnuclear radio emission that is produced beyond the observable ~parsec physical scales probed with the VLBA but within the ≤ 30−110 parsec spatial scales observed with the VLA. From an energetics perspective, we find that all targets have extranuclear radio emission that is compatible with AGN winds, assuming a maximum of 10% of the bolometric output can supply the mechanical energy observed. We also find that the excess emission is likely too strong for star formation alone when compared to results from optical spectroscopy, but may contribute in smaller capacities.The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. The authors acknowledge use of the Very Long Baseline Array under the US Naval Observatory’s time allocation. This work supports USNO’s ongoing research into the celestial reference frame and geodesy.http://arxiv.org/abs/2509.0346
Effects of Maternal Depression and Sensitivity on Infant Emotion Regulation: The Role of Context
No full textIntroduction/Background: Maternal depression is a significant risk factor for infant emotion regulation (ER), often linked to detrimental mother–infant interactions. Individual effects of maternal depression and maternal sensitivity are known, but their combined influence on infant ER across different emotional contexts remains underexplored. This study investigates concurrent relations among maternal depression, maternal sensitivity, and infant ER in low- and high-arousal contexts in a matched sample of primarily White educated mothers. Methods: We examined 5-month-old infants of clinically depressed and nondepressed mothers. Maternal sensitivity was coded from home observations; infant ER behaviors (e.g., gaze aversion, object-attend, self-soothing) were assessed through observation during modified Still-Face Paradigm (SFP) and fear-eliciting tasks. Results: Clinically depressed mothers exhibited lower maternal sensitivity than nondepressed mothers. Infants of depressed mothers used adaptive ER strategies less—specifically, lower monitoring and gaze aversion in the SFP, and lower gaze aversion and object-attend in the Fear task. Maternal sensitivity moderated the association between maternal depression and infant gaze aversion during the SFP and both gaze avert and object-attend during the Fear task. There was a context-specific regulatory difference for self-soothing; only infants of depressed mothers used self-soothing significantly more during the high-arousal Fear task. Conclusions: These findings underscore the interplay between maternal clinical depression and sensitivity in affecting infant ER. Maternal sensitivity acts as a crucial buffer against the adverse effects of maternal depression on infant ER. The results also indicate that infant emotion regulation varies in different contexts of low and high arousal. Interventions that target maternal sensitivity could significantly improve emotion regulation in infants of depressed mothers.The manuscript preparation did not receive external funding. The original data collection was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH). Support for coding was provided by the University of Maryland, Baltimore County (UMBC).https://www.mdpi.com/2227-9067/12/10/132