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Social media and digital platforms have become essential tools for the new generation of youth activists. However, these tools subject youth to both new (and old) forms of surveillance and control. Drawing on in-depth interviews and social media walkthroughs with 61 youth activists, I examine hybrid tactics that these youth employ to resist surveillance and other forms of digitally mediated control as they participate in politics and social movements. I show that even in democracies like the United States and Canada, for individuals along intersecting axes of marginalization (e.g. race, gender), public political acts do not capture the full range of young people's political repertoires. Young people, especially those from marginalized groups, adopt hidden, under-the-radar tactics in response to pressures of social, state, and corporate surveillance. I develop the concept of "digital infrapolitics" referring to the ways in which digital politics and activism go below the radar under surveillance and control. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

The amount of energy available in a system constrains large-scale patterns of abundance. Here, we test the role of temperature and net primary productivity as drivers of flying insect abundance using a novel continental-scale data source: weather surveillance radar. We use the United States NEXRAD weather radar network to generate a near-daily dataset of insect flight activity across a gradient of temperature and productivity. Insect flight activity was positively correlated with mean annual temperature, explaining 38% of variation across sites. By contrast, net primary productivity did not explain additional variation. Grassland, forest and arid-xeric shrubland biomes differed in their insect flight activity, with the greatest abundance in subtropical and temperate grasslands. The relationship between insect flight abundance and temperature varied across biome types. In arid-xeric shrublands and in forest biomes the temperature–abundance relationship was indirectly (through net primary productivity) or directly (in the form of precipitation) mediated by water availability. These results suggest that temperature constraints on metabolism, development, or flight activity shape macroecological patterns in ectotherm abundance. Assessing the drivers of continental-scale patterns in insect abundance and their variation across biomes is particularly important to predict insect community response to warming conditions. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Large-span, post-tensioned (PT) beams play a crucial role in maximizing the benefits of post-tensioning techniques. Bonded and unbonded systems are prevalent, with the latter being more widespread in the United States. While bonded systems are advantageous for creating long spans when multiple tendons are grouped in ducts, limited studies in the literature exist on their demolition. With a case study, this paper addresses the unique challenge of demolishing large-span-bonded, post-tensioned beams that occurs due to a building's functional change. Emphasizing insights for engineers, it explores the use of cutting and dismantling methods, thereby considering the presence of prestressed cables. The demolition process is distinctive due to the presence of numerous prestressed cables along the beams, necessitating a specialized and cautious cutting approach. This is accomplished through the use of a drilling technique that selectively distresses the tendons, ensuring they are not all affected simultaneously. An intriguing observation discussed in this paper pertains to the occurrence of horizontal cracks accompanied by loud sounds following the drilling process, thereby offering insights from the design perspective of PT systems. This paper details an innovative method for safely demolishing large-span, bonded PT beams using ground-penetrating radar and computer models to navigate structural complexities and ensure nearby structures' safety. [ABSTRACT FROM AUTHOR]

Accurate and timely inland waterbody extent and location data are foundational information to support a variety of hydrological applications and water resources management. Recently, the Cyclone Global Navigation Satellite System (CYGNSS) has emerged as a promising tool for delineating inland water due to distinct surface reflectivity characteristics over dry versus wet land which are observable by CYGNSS's eight microsatellites with passive bistatic radars that acquire reflected L-band signals from the Global Positioning System (GPS) (i.e., signals of opportunity). This study conducts a baseline 1-km comparison of water masks for the contiguous United States between latitudes of 24°N-37°N for 2019 using three Earth observation systems: CYGNSS (i.e., our baseline water mask data), the Moderate Resolution Imaging Spectroradiometer (MODIS) (i.e., land water mask data), and the Landsat Global Surface Water product (i.e., Pekel data). Spatial performance of the 1-km comparison water mask was assessed using confusion matrix statistics and optical high-resolution commercial satellite imagery. When a mosaic of binary thresholds for 8 sub-basins for CYGNSS data were employed, confusion matrix statistics were improved such as up to a 34% increase in F1-score. Further, a performance metric of ratio of inland water to catchment area showed that inland water area estimates from CYGNSS, MODIS, and Landsat were within 2.3% of each other regardless of the sub-basin observed. Overall, this study provides valuable insight into the spatial similarities and discrepancies of inland water masks derived from optical (visible) versus radar (Global Navigation Satellite System Reflectometry, GNSS-R) based satellite Earth observations. [ABSTRACT FROM AUTHOR]

Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work demonstrates an approach to estimate terrain elevations from 12 m TanDEM-X by using a convolutional neural network (CNN) trained with ground elevations from ICESat-2 – a spaceborne laser altimeter. This approach demonstrated the ability to estimate terrain elevations from TanDEM-X DEMs for the greater North Carolina area. The CNN estimated terrain saw an improvement in RMSE from 11.28 m to 4.42 m within the entire area of interest, and a focused improvement in RMSE from 12.78 m to 4.95 m in forested areas when compared to ICESat-2. The CNN model outperformed linear, random forest, and gradient boosted regression models using comparable model inputs. This work combines 12-m TanDEM-X data with ICESat-2 profiles, resulting in a new DTM product with accuracy approaching that of reference elevations obtained from satellite laser altimetry in the southeastern United States. • Investigates use of CNN to create DTM with TanDEM-X and ICESat-2 in North Carolina. • CNN outperformed linear, random forest, and gradient boosted regression models tested. • CNN reduced RMSE from 11.28 m to 4.42 m with convolutional neural network. • Establishes possible framework for creating global DTMs. [ABSTRACT FROM AUTHOR]

Monitoring vegetation moisture conditions is paramount to better understand and assess drought impacts on vegetation, enhance crop yield predictions, and improve ecosystem models. Passive microwave remote sensing allows retrievals of the vegetation optical depth (VOD; [unitless]), which is directly proportional to the vegetation water content (VWC; in units of water mass per unit area [kg/m2]). However, VWC is largely dependent on the dry biomass and structure imprints on the VOD signal. Previously, statistical models have been used to isolate the water component from the biomass and structure components. Physically-based approaches have not yet been proposed for this goal. In this study, we present a multi-sensor semi-physical approach to retrieve the vegetation moisture from the VOD and express it as Live Fuel Moisture Content (LFMC [%]; the percentage of water mass per dry biomass unit). The study is performed in the western United States for the period April 2015 – December 2018. There, in situ LFMC samples are available for assessment. We rely on a VOD model based on vegetation height data from GEDI/Sentinel-2 and radar backscatter from Sentinel-1, which account for the biomass and structure components. Vegetation moisture is retrieved at L-, X- and Ku-bands by minimizing the difference between the modeled VOD and the VOD estimates from SMAP (L-band) and AMSR-2 (X- and Ku-band) satellites. Results show that the LFMC retrievals are independent of canopy height, land cover, and radar backscatter, demonstrating the capability of the proposed algorithm to separate water dynamics from the biomass/structure component in VOD. LFMC estimates at X- and Ku-bands reproduce well the expected spatio-temporal dynamics of in situ LFMC. Results show good agreement with in situ at a regional scale, with Pearson's correlations (r) between in situ LFMC samples and LFMC estimates of 0.64 (Ku-band), 0.60 (X-band) and 0.47 (L-band). Similar results are obtained independently for shrub and forest sites at X- and Ku-bands. In most comparisons between in situ and estimated LFMC, biases are below 10% of the dynamic range of LFMC. Performance at L-band is limited by the fact that this frequency senses the full vertical extent of the canopy, while in situ samples are taken only from top of canopy leaves to which X- and Ku-bands are much more sensitive. More insight will be needed for grasslands (r = 0.44 at X-band) using time-dynamic canopy height data. Furthermore, a pixel-scale assessment is conducted, showing a good agreement in most sites (r > 0.6). The proposed method can be tailored to exploit the synergies of past (e.g. , AMSR-E), current (e.g. , AMSR-2) and future satellite sensors such as CIMR and ROSE-L for global vegetation moisture mapping at different canopy layers. [Display omitted] • A multi-sensor semi-physical algorithm is applied to retrieve vegetation moisture. • The water component in vegetation optical depth is disentangled from dry biomass. • Vegetation moisture retrievals at X- and Ku-bands capture the in situ dynamics. • L-band has poorer performance due to lack of in situ samples in wood canopy layers. [ABSTRACT FROM AUTHOR]

Wintertime precipitation poses many observational and forecasting challenges, especially in the complex topography of the western United States where radar beam blockage and difficulty siting in situ observations yields more sparse observations than in the eastern United States. Uncertainty in western U.S. winter precipitation is known to be high, so much so that some studies have found model simulated precipitation to produce similar or better large-scale estimates of annual precipitation than gridded observational products during climatologically anomalous years. This study evaluates high-resolution gridded precipitation estimates from Multi-Radar Multi-Sensor (MRMS) and Stage IV as well as forecasts from NOAA's High-Resolution Rapid Refresh (HRRR) model in the Colorado Rocky Mountains. Gridded precipitation estimates and forecasts are compared with in situ SNOTEL measurements for two seasons of wintertime precipitation. The influence of forecast length, lead time, and model elevation on seasonal precipitation predictions from the HRRR are investigated. Additional comparisons are made with the relatively dense network of observations deployed in Colorado's East River Watershed during the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Gridded products and forecasts are found to underestimate cold-season precipitation by 25%–65% relative to in situ and aircraft measurements, with longer forecast periods and lead times (6–24 h) having smaller biases (25%–30%) than shorter forecast periods and lead times (55%–65%). The assessment of multiple years of observations indicates that these biases are related more to the data and methods used to create the gridded products and forecasts than to precipitation characteristics. Significance Statement: In the mountainous western United States, it is very challenging to both observe and forecast wintertime precipitation, yet snowfall plays an important role in providing the region's annual water supply. This study aims to increase our understanding of the biases in observations and forecasts of snowfall in the Colorado Rocky Mountains, which can in turn impact forecasts of water availability for the ensuing warm season. In this study we find high-resolution gridded precipitation estimates and forecasts to underestimate cold-season precipitation when compared with in situ observing stations, with longer-range forecasts (e.g., daily) being the least biased. These findings were consistent over two years of study and have broad implications for the hydrologic modeling and water management communities. [ABSTRACT FROM AUTHOR]

A unique storm identification and tracking method is analyzed in varying storm environments within the United States spanning 273 hours in 2018. The methodology uses a quantity calculated through fusion of radar-based vertically integrated liquid (VIL) and satellite-based GLM flash rate density (FRD) called VILFRD to identify storms in space and time. This research analyzes GLM data use within VILFRD for the first time (method original: O), assesses four modifications to VILFRD implementation to find a more stable storm size with time (method new: N), larger storms (method original dilated: OD), or both (method new dilated: ND), and compares VILFRD methods with storm tracking using the 35-dBZ isosurface at −10°C (method non-VILFRD: NV). A case study analysis from 2019 is included to assess methods on a smaller scale and introduce a "lightning only" (LO) version of VILFRD. Large study results highlight that VILFRD-based storm identification produces smaller storms with more lightning than the NV method, and the NV method produces larger storms with a more stable size over time. Methods N and ND create smaller storm size fluctuations, but size changes more often. Dilation (OD, ND) creates larger storms and almost double the number of storms identified relative to nondilated methods (O, N, NV). The case study results closely resemble the large sample results and show that the LO method identifies storms with more lightning and shorter durations. Overall, these findings can aid in choice of storm tracking method based on desired user application and promote further testing of a lightning-only version of VILFRD. [ABSTRACT FROM AUTHOR]

Throughout the summer months in the Southeast United States (SEUS), the initiation of isolated convection (CI) can occur abundantly during the daytime with weak synoptic support (e.g., weak wind shear). Centered around this premise, a dual-summer, limited-area case study of CI events concerning both geographical and meteorological features was conducted. The goal of this study was to help explain SEUS summertime CI in weak synoptic environments, which can enhance CI predictability. Results show that spatial CI nonrandomness event patterns arise, with greater CI event density appearing over high elevation by midday. Later in the day, overall CI event counts subside with other mechanisms/factors emerging (e.g., urban heat island). Antecedent rainfall, instability, and moisture features are also higher on average where CI occurred. In a random forest feature importance analysis, elevation was the most important variable in dictating CI events in the early to midafternoon while antecedent rainfall and wind direction consistently rank highest in permutation importance. The results cumulatively allude to, albeit in a muted, nonsignificant statistical signal, and a degree of spatial clustering of CI event occurrences cross the study domain as a function of daytime heating and contributions of features to enhancing CI probabilities (e.g., differential heating and mesoscale thermal circulations). Significance Statement: Widespread isolated thunderstorms in the Southeast United States summer season with weak synoptic support have been commonly observed. With forecasting these remaining a challenge, a dual-summer intercomparison of geographical/meteorological features with convective initiation events was conducted. Radar data with a minimum threshold for convective initiation detection (35 dBZ) were used. Spatial nonrandomness was discovered with greater event density appearing over higher elevation by midday. Features such as prior rainfall and atmospheric instability/moisture were higher on average where initiation occurred. In a feature importance analysis, elevation ranked higher in the early to midafternoon hours while antecedent rainfall and wind direction ranked highest overall in permutation importance. These results allude to the contribution of localized phenomena to the nonrandomness (e.g., mesoscale circulations). [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are crucial components of the hydrological cycle and often produce flash floods. Given their impact, it is important to understand how they will change under a warming climate. This study uses a satellite‐ and radar‐based MCS tracking algorithm on convection‐permitting climate model simulations and examines changes in MCS properties and precipitation structures between historical and future simulations. An underestimation in MCS total precipitation is evident in historical simulation compared to observations, due to model's depiction of MCS precipitation area and summertime occurrence frequency. Under pseudo‐global warming, increases in MCS frequency and total warm season precipitation are observed, most notably in the southern U.S. The precipitation intensity and precipitating area generated by future MCSs also rises and results in an increase in precipitation volume. MCS precipitation structures are further classified into convective core and stratiform regions to understand how change in these structures contributes to future rainfall changes. In a warmer climate, the stratiform region demonstrates minimal change in size, but increases in mean precipitation rate and mean maximum precipitation rate by 15% and 29% are noted, respectively. A more robust future response is observed in the convective core region, with its size, mean precipitation rate and mean maximum precipitation rate increasing significantly by 24%, 37% and 42%, respectively. Finally, by examining the environmental properties of MCS initial condition, future intensification of convective rain may be attributed to a combined effect of substantial increases in atmospheric instability and moisture availability. Plain Language Summary: Thunderstorms that group together and grow to hundreds of kilometers in size and persist for more than a few hours are called mesoscale convective systems. They play an important role in Earth's water cycle and are often responsible for causing flash flooding events. Given their impact, understanding how they will change due to a warming climate is crucial. By analyzing the outputs from climate models, this study reveals future changes in various aspects of mesoscale storms behaviors, such as their frequency and where they occur. In addition, the rainfall patterns from the present and future mesoscale storms are compared. Our findings show that in a warmer climate, these storms will probably occur more frequently and bring more rain, especially in the southern parts of the United States. Individual mesoscale storms are also expected to contain larger rain areas and release heavier rainfall, which would contribute to an overall increase in rain volume. A shift from lighter rain to more moderate/heavy rain during these storms is also noticed, implying a potential rise in intense mesoscale storm events and heightened chances of flash floods in the future. Key Points: An Mesoscale convective system (MCS) tracking algorithm is applied to regional climate models to examine the potential future changes in their rainfall characteristicsAn evaluation between observations and historical simulations shows that the model underestimates MCS total precipitationFuture MCSs may become more frequent, more intense and produce higher rain volume, primarily due to the large changes in the convective core [ABSTRACT FROM AUTHOR]

Migratory landbirds in North America are experiencing dramatic population declines. Although considerable research and conservation attention have been directed toward these birds' breeding and wintering grounds, far less is known about the areas used as stopover sites during migration. To address this knowledge gap, we used 5 years of weather surveillance radar data to map seasonal stopover densities of landbirds across the eastern United States during spring and autumn migration. We identified stopover hotspots covering 2.47 million ha that consistently support high densities of migratory landbirds in spring or autumn. However, only 16.7% of these sites are hotspots in both seasons. The distribution of hotspots is shifted eastward in autumn compared with spring. Deciduous forest is the most important habitat type in both seasons, with deciduous forest fragments embedded in broadly deforested regions having the highest probability of being hotspots. The concentration of birds in these forest fragments is stronger in spring, especially in the agricultural Midwest. We found generally higher stopover densities in protected areas than in unprotected areas in both seasons. Nonetheless, only one-third of identified stopover hotspots have some sort of protected status, and more than half of these protected hotspots are subject to extractive uses. A well-distributed network of well-protected stopover areas, complementing conservation efforts on the breeding and wintering grounds, is essential to sustaining healthy populations of migratory landbirds in North America. • Weather radar can map seasonal stopover hotspots for landbirds in the eastern US • There is little spatial overlap between spring and autumn hotspots • Birds concentrate in deciduous forest fragments at stopover, especially in spring • Only one-third of identified hotspots are in protected areas Using weather radar, Guo et al. map the seasonal stopover density of migratory landbirds and identify stopover hotspots across the eastern United States. They find little overlap in the distribution of spring versus autumn hotspots. Most of these hotspots do not fall within protected areas, especially for deciduous forest fragments where birds concentrate. [ABSTRACT FROM AUTHOR]

The United States must continue to push the major concentration of its missile defense tracking sensor architecture to space to improve the overall performance of the nation's Missile Defense System against an increasingly diverse missile threat. Without the ability to track the missile payload throughout the flight, especially hypersonic payloads, the ability of a missile defense system to intercept the payload diminishes significantly and the armed forces will be unable to engage and intercept the threat with any meaningful consistency. The country must overcome technological, acquisition, commercial, and policy challenges in order to deploy a space tracking layer and achieve what ultimately may be viewed as the greatest leap in capability that could be achieved in missile defense today. Current development and initial deployments efforts require continued funding and advocacy for satellite and ground system development as well as emphasis on the development of responsive launch capabilities. [ABSTRACT FROM AUTHOR]

Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θe) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events. Significance Statement: Even when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments. [ABSTRACT FROM AUTHOR]

This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this systematic literature review is to examine policy and practice recommendations, along with calls for future research, aimed at addressing food insecurity for community colleges across the U.S. Argument/Proposed Model: This article will provide a detailed methodology for the systematic literature review, as well as the findings gathered from a range of peer-reviewed articles on this topic. The authors analyzed six significant themes that surfaced from the current literature related to policy and practice at the federal, state, local, and institutional levels. Conclusions/Contributions: Six chief themes are discussed in-depth, including but not limited to: important tools and approaches for marketing and communications, data-driven decision-making, and the augmentation of food support with other public benefits and institutional resources. These thematic findings address the issue of food insecurity on community college campuses, and also offer a range of techniques and areas for consideration. This systematic literature review offers a compilation of policy and practice recommendations steeped in actionable strategies for researchers, policymakers, campus leaders, and practitioners alike. The strategies can be implemented and/or tailored to meet the needs and nuances of any community college population. [ABSTRACT FROM AUTHOR]

Hailstorms are analyzed across the United States using explicit hailstone size calculations from convection-permitting regional climate simulations for historical, mid-century, and end of twenty-first-century epochs. Near-surface hailstones <4 cm are found to decrease in frequency by an average of 25%, whereas the largest stones are found to increase by 15–75% depending on the greenhouse gas emissions pathway. Decreases in the frequency of near-surface severe hail days are expected across the U.S. High Plains, with 2–4 fewer days projected—primarily in summer. Column-maximum severe hail days are projected to increase robustly in most locations outside of the southern Plains, a distribution that closely mimics projections of thunderstorm days. Primary mechanisms for the changes in hailstone size are linked to future environments supportive of greater instability opposed by thicker melting layers. This results in a future hailstone size dichotomy, whereby stronger updrafts promote more of the largest hailstones, but significant decreases occur for a majority of smaller diameters due to increased melting. [ABSTRACT FROM AUTHOR]

In this study we explore homicides and fatal violence against transgender individuals using the framework of the trifecta of violence—violent ideology, violent policies and laws, and violent actions. In recent years, researchers, activists, and the media, particularly the news media, have been warning that the increase in anti-LGBTQ+ rhetoric, especially but not exclusively online, coupled with a surge in anti-transgender legislation, has resulted in increased instances of violence against the LGBTQ+ community, especially transgender people. In this article we examine patterns of transphobic ideology and rhetoric, anti-trans legislation, and fatal violence against transgender people in the United States from 2015 to 2022. We find that increases in all three occurred over this time period, with all three aspects of the "trifecta" correlated. [ABSTRACT FROM AUTHOR]

Introduction: The hepatitis C virus (HCV) causes chronic and curable disease with a substantial burden of morbidity and mortality across the globe. In the United States (US) and other developed countries, incidence of HCV is increasing and people who inject drugs are disproportionately affected. However, HCV treatment rates amongst patients with substance use disorders (SUD) are suboptimal. In this study, we aimed to understand the perspectives of subspecialist physicians who care for substantial numbers of patients with HCV, including addiction medicine, infectious diseases, and hepatology physicians, to better understand barriers and facilitators of HCV treatment. Methods: We recruited subspecialty physicians via purposive and snowball sampling and conducted semi-structured interviews with 20 physicians at 12 institutions across the US. We used a mixed deductive and inductive approach to perform qualitative content analysis with a rapid matrix technique. Results: Three major themes emerged: (1) Perceptions of patient complexity; (2) Systemic barriers to care, and (3) Importance of multidisciplinary teams. Within these themes, we elicited subthemes on the effects of patient-level factors, provider-level factors, and insurance-based requirements. Conclusion: Our results suggest that additional strategies are needed to reach the "last mile" untreated patients for HCV care, including decentralization and leverage of telehealth-based interventions to integrate treatment within primary care clinics, SUD treatment facilities, and community harm reduction sites. Such programs are likely to be more successful when multidisciplinary teams including pharmacists and/or peer navigators are involved. However, burdensome regulatory requirements continue to hinder this expansion in care and should be eliminated. [ABSTRACT FROM AUTHOR]

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In recent years, researchers have become increasingly interested in educational and institutional strategies to limit or eradicate contemporary forms of racism in the United States. They have generally accepted that most people in the modern-day United States do not actually want to be racist, but the various legacies of oppression live on through American institutions, promote unconscious biases, and manifest in unintentionally racist behaviors. However, previous research has largely overlooked the interactive relationship specifically between insidious racists, and the institutions meant to limit and punish their intentional behavior. Insidious racists are conscious of their racist beliefs, they engage in intentional acts of racism, and yet, they hold value in not being deemed racist by American society. In this article I develop a framework of the varying institutional constraints associated with addressing insidious racism. I then deploy this framework on national and local case studies to highlight the contribution of this work. The national case study analyzes the institutional constraints that allowed for loan officers to engage in racist actions leading up to the Great Recession. The local case study analyzes institutional constraints that led to the deviant behavior and racist redistricting efforts coordinated by members of the Los Angeles City Council. Taken together, I show that institutional design and administrator ideologies determine the levels of insidious racism prevalent in a given institution. [ABSTRACT FROM AUTHOR]

Homeland defense does not begin at the water's edge, at least not on the east and west coasts of the United States. In the twenty-first-century geopolitical environment, the definition of homeland defense must now extend well beyond North American shores. The network of US military forces stationed throughout the Indo-Pacific provides a strong military presence in the region that deters potential aggressors and protects US interests. This builds and strengthens partnerships with countries in the region and allows the United States more deployment flexibility. The Chinese government is extending its reach throughout the Pacific, and the United States must do the same. The US military must harden its overseas facilities and continue to expand those facilities to other locations. This will allow greater operational depth and build resilience through sustainment redundancy. [ABSTRACT FROM AUTHOR]

The use of robotic surgery has experienced rapid growth across diverse medical conditions, with a notable emphasis on gastrointestinal cancers. The advanced technologies incorporated into robotic surgery platforms have played a pivotal role in enabling the safe performance of complex procedures, including gastrectomy and pancreatectomy, through a minimally invasive approach. However, there exists a noteworthy gap in high‐level evidence demonstrating that robotic surgery for gastric and pancreatic cancers has substantial benefits compared to traditional open or laparoscopic methods. The primary impediment hindering the broader implementation of robotic surgery is its cost. The escalating healthcare expenses in the United States have prompted healthcare providers and payors to explore patient‐centered, value‐based healthcare models and reimbursement systems that embrace cost‐effectiveness. Thus, it is important to determine what defines the value of robotic surgery. It must either maintain or enhance oncological quality and improve complication rates compared to open procedures. Moreover, its true value should be apparent in patients' expedited recovery and improved quality of life. Another essential aspect of robotic surgery's value lies in minimizing or even eliminating opioid use, even after major operations, offering considerable benefits to the broader public health landscape. A quicker return to oncological therapy has the potential to improve overall oncological outcomes, while a speedier return to work not only alleviates individual financial distress but also positively impacts societal productivity. In this article, we comprehensively review and summarize the current landscape of health economics and value‐based care, with a focus on robotic surgery for gastrointestinal cancers. [ABSTRACT FROM AUTHOR]

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges surrounding groundwater protection in the U.S., with a particular emphasis on rural areas where private wells are a primary source of drinking water. It addresses the lack of federal regulations specifically targeting groundwater quality and highlights the prevalence of nitrate contamination, especially in agricultural regions.

We like to think that courts are, and have always been, the primary and final interpreters of statutes. As the conventional separation-of-powers wisdom goes, legislatures "make" statutes while judges "interpret" them. In fact, however, legislatures across centuries of American history have thought of themselves as the primary interpreters. They blurred the line between "making" and "interpreting" by embracing a type of legislation that remains overlooked and little understood: "expository" legislation--enactments that specifically interpreted or construed previous enactments. In the most exhaustive historical study of the subject to date, this Article--the first in a series of Articles--unearths and explains that lost tradition of legislative statutory interpretation from an institutional perspective. To do so, it draws on an original dataset of 2,497 pieces of expository legislation passed from 1665 to 2020 at the colonial, territorial, state, and federal levels--the first effort of its kind. It shows how expository legislation originated as a colonial-era British import that Americans came to rely on beyond the creation of new constitutions. Lawmakers used expository statutes to supervise administrative statutory interpretation and to negotiate interpretation in the shadows of courts. Judges accepted and even encouraged legislative statutory interpretation. In the mid-nineteenth century, judges increasingly fought back, emboldened by growing calls for judicial independence. Yet even as the backlash entered into treatises, and even as some lawmakers began to balk, legislatures and judges continued to accept and use legislative interpretations of statutes well into the nineteenth century. The early history of expository legislation offers an alternative constitutional vision to the oft-repeated notion that statutory interpretation is necessarily and has always been an intrinsically and exclusively "judicial" power. As the Article ultimately argues, strict and formalist conceptions of separation of powers in statutory interpretation are misguided, for the extent to which statutory interpretation was considered a judicial power has fluctuated in ways that were intertwined with broader transformations in American society. This history teaches us to think of statutory interpretation as a shared task among branches but exercised in different contexts and domains. It also illuminates the historically contingent nature of legislation, revealing new ways that statutes can contain an inherent interpretive openness. These particular forms of openness raise new questions about the validity of subsequent legislative history. They also reveal how legislatures have embraced a paradoxical concept of original intent and meaning--one that legislatures recognized was rarely a "pure" kind but more often a fictional, dynamic kind intertwined with the changing views of post-enactment interpreters. [ABSTRACT FROM AUTHOR]

Simple Summary: The spotted lanternfly (SLF) is an invasive generalist that is spreading throughout the eastern United States. They feed in large aggregations, but the mechanisms they use to form these aggregations and find each other for mating are poorly understood. Laboratory evidence that they use pheromones is based on volatiles from their bodies and from honeydew excretions, but, due to their broad sensory capabilities, the pheromone components have been difficult to isolate and identify. This is the first evidence gathered in a field study that demonstrates, using naturally collected SLF odors as lures, that volatiles from SLF bodies and honeydew are both required to attract males at mating time and females during oviposition time. Using semiochemicals collected from spotted lanternflies Lycorma delicatula (Hemiptera: Fulgoridae) (SLF) and deployed in the field with circle traps, we demonstrated that SLF responded to SLF pheromones: in particular, this was the case for males while seeking mates and for females while ovipositing. The attractants consisted of SLF body extract emitted from diffuser lures and SLF honeydew on burlap ribbons, collected from heavily infested locations. Traps with attractants were deployed in field sites with very light SLF infestations to avoid competing signals of pre-existing aggregations. The number of SLF equivalents emitted by each diffuser per trapping period was used in a dose–response analysis. Three trees per block received either (1) a control hexane lure and a clean ribbon, (2) a lure containing SLF extract and a clean ribbon, or (3) a lure containing SLF extract and a honeydew-laden ribbon. Ten blocks were sampled three times per week for twelve weeks. We found a significant positive dose–response by males to SLF body extract only in the presence of SLF honeydew, indicating a synergistic effect between honeydew volatiles and body volatiles. This dose–response occurred for five weeks after mating started, after which males no longer responded. Subsequently, females had a significant positive dose–response to SLF extract only in the presence of honeydew when oviposition was their primary activity, continuing for two weeks, suggesting that females may use pheromones to aggregate for oviposition. The extract in the absence of honeydew did not result in a positive dose–response, nor did the hexane control. These findings suggest that SLF respond synergistically to the combination of pheromones present in both SLF honeydew and SLF bodies. Thus, combining key components from both sources may aid the development of semiochemical lures for SLF. [ABSTRACT FROM AUTHOR]

Offshore wind energy development (OWED), while a key strategy for reducing carbon emissions, has potential negative effects to wildlife that should be examined to inform decision making and adaptive management as the industry expands. We present a conceptual framework to guide the long-term study of potential effects to birds and bats from OWED. This framework includes a focus on exposure and vulnerability as key determinants of risk. For birds and bats that are exposed to OWED, there are three main effects of interest that may impact survival and productivity: 1) collision mortality, 2) behavioral responses, including avoidance, displacement, and attraction, and 3) habitat-mediated effects to prey populations. If these OWED effects cause changes in survival and/or breeding success (e.g., fitness), they have the potential for population-level consequences, including changes in population size and structure. Understanding the influence of ecological drivers on exposure and effect parameters can help to disentangle the potential impacts of OWED from other stressors. We use this theoretical framework to summarize existing relevant knowledge and identify current priority research questions (n=22) for the eastern United States, where largescale development of OWED is primarily in the planning and early construction phase. We also identify recommendations for study design and further prioritization of research topics. [ABSTRACT FROM AUTHOR]

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management. [ABSTRACT FROM AUTHOR]

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement: Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts. [ABSTRACT FROM AUTHOR]

The article shows that the submerged nature of the U.S. administrative state in its communications with the public and lack of awareness of policy outputs that undermines public trust in government. Topics discussed include public reputation crisis brought by government failure and ideological assault, as well as legal, resource and political constraints to agency communications, building of salient administrative expertise and enhancement of public participation, and perils of agency salience.

Tornado outbreaks—when multiple tornadoes occur within a short period of time—are rare yet impactful events. Here we developed a two-part stochastic tornado outbreak index for the contiguous United States (CONUS). The first component produces a probability map for outbreak tornado occurrence based on spatially resolved values of convective precipitation, storm relative helicity (SRH), and convective available potential energy. The second part of the index provides a probability distribution for the total number of tornadoes given the outbreak tornado probability map. Together these two components allow stochastic simulation of location and number of tornadoes that is consistent with environmental conditions. Storm report data from the Storm Prediction Center for the 1979–2021 period are used to train the model and evaluate its performance. In the first component, the probability of an outbreak-level tornado is most sensitive to SRH changes. In the second component, the total number of CONUS tornadoes depends on the sum and gridpoint maximum of the probability map. Overall, the tornado outbreak index represents the climatology, seasonal cycle, and interannual variability of tornado outbreak activity well, particularly over regions and seasons when tornado outbreaks occur most often. We found that El Niño–Southern Oscillation (ENSO) modulates the tornado outbreak index such that La Niña is associated with enhanced U.S. tornado outbreak activity over the Ohio River Valley and Tennessee River Valley regions during January–March, similar to the behavior seen in storm report data. We also found an upward trend in U.S. tornado outbreak activity during winter and spring for the 1979–2021 period using both observations and the index. Significance Statement: Tornado outbreaks are when multiple tornadoes happen in a short time span. Because of the rare, sporadic nature of tornadoes, it can be challenging to use observational tornado reports directly to assess how climate affects tornado and tornado outbreak activity. Here, we developed a statistical model that produces a U.S. map of the likelihood that an outbreak-level tornado would occur based on environmental conditions. In addition, using that likelihood map, the model predicts a range of how many tornadoes could occur in these events. We found that "storm relative helicity" (a proxy for potential rotation in a storm's updraft) is especially important for predicting outbreak tornado likelihood, and the sum and maximum value of the likelihood map is important for predicting total numbers for an event. Overall, this model can represent the typical behavior and fluctuations in tornado outbreak activity well. Both the tornado outbreak model and observations agree that the state of sea surface temperature in the tropical Pacific (El Niño–Southern Oscillation) is linked to tornado outbreak activity over the Ohio River Valley and Tennessee River Valley in winter through early spring and that there are upward trends in tornado outbreak activity. [ABSTRACT FROM AUTHOR]

Our study provides findings from cultural leadership project based on healing-centered engagement strategies using a transformative convergent mixed methods research design to address mental well-being among young Bhutanese-Nepali refugee women(N = 36). Using a participatory approach, data were collected to explore the feasibility and acceptability of a culturally responsive healing-centered intervention. While quantitative scores showed a significant decrease in anxiety and depression, qualitative findings revealed increased leadership skills, self-esteem, social support, and resilience after the intervention. We discuss the need to promote a holistic view of healing from collective trauma and offer a culturally grounded asset-driven approach to promote hope and healing among refugees. [ABSTRACT FROM AUTHOR]

Recent advancements in technology and infrastructure have greatly improved the capabilities of the natural hazards community to collect robust samples of building performance following hazard events and make them available to the research community for broad reuse purposes. Yet, there lacks standardized, open access data sets that combine reconnaissance data from multiple individual hazard events into unified, living testbeds that can grow through community participation. The objectives of this study are to (1) synthesize and present a unified, multievent windstorm performance data set (WiSPD) and (2) summarize common damage patterns observed in the WiSPD. The WiSPD currently consists of four hurricanes (occurring between 2017 and 2020) and four tornadoes (occurring between 2011 and 2020), all of which struck the United States. Each event's building performance assessments were collected with similar methodologies and contain details such as location, physical address, basic building attributes, estimated 3 s gust wind speed, basic wind speeds, and component-level damage percentage with a precision of ±5%. In combination, the testbed reveals that roof cover damage dominates in windstorms, regardless of the year of construction or building code enforced. Additionally, tornadoes tend to produce higher damage rates than hurricanes, specifically in fenestration and roof structure damage. Fragility functions for hurricanes exhibit a nonmonotonic relationship between wind speed and damage, potentially evidencing the strong influence of other confounding variables. Ultimately, the unified data set promises to be a rich testbed for further knowledge discovery and model validation by the research community. [ABSTRACT FROM AUTHOR]

Purpose: Suicide risk is higher among violence-involved individuals. Intimate Partner Violence hotline workers are a critical source of support and can potentially be suicide prevention champions. Our primary goal was to examine the effectiveness of disseminating a free, online IPV—Suicide Prevention curriculum, via a randomized control trial, to hotline workers in ten states with the highest suicide and IPV homicide rates. Method: We divided the country into five regions and, based on criterion, chose two states in each region to randomize into the two arms of the study. We examined training participation and engagement between the two approaches: (1) 'dissemination as usual' (control) using a National Domestic Violence Hotline email and a postcard to state/county IPV directors, versus (2) 'enhanced dissemination' (intervention) using a four-point touch method (postcard, phone call, email, and letter) to 'drive' participation. Results: Participation increased in the intervention arm as approaches became more personal (i.e., email and phone calls vs. letters). Results indicate that traditional dissemination strategies such as email announcements and invitations are not as effective as varied and multiple touchpoints for IPV hotline staff. Conclusion: Successful dissemination strategies to promote digital training should consider the value added by personalized connection. Future research is needed to understand how to offer effective and efficient web-based training to those providing IPV and child abuse services. [ABSTRACT FROM AUTHOR]

This Article presents the in-progress development of the laws of space warfare as a case of non-binding international lawmaking and connects it to recent scholarship on non-binding international agreements and to Elinor Ostrom's Nobel Prize winning theory of polycentric governance. A rapid escalatory cycle-from NATO's December 2019 declaration of space as a warfghting domain and the subsequent establishment of the U.S. Space Force to Russia's successful test of a killer satellite capable of destroying spacecrafts, culminating in the first space-cyber war in Ukraine-turned a domain once reservedfor peaceful purposes to a war zone. Yet, these laws of war are the least developed compared to the other war domains (land, sea, and air). The under-supply of rules meets a multilateral system almost incapable of adopting new' legally binding instruments. Is space on its way to becoming a law'less war arena? This Article suggests otherwise. With no new treaty expected in the foreseeable future, the laws of space warfare are incrementally developed by multiple o#-UN forums that introduce nonbinding instruments and agreements. While the lack of a comprehensive approach and legally binding status may cause concern, this Article suggests that this is the best course of action to develop the corpus juris qf space warfare under the conditions of modern global affairs, based on empirically backed principles of polycentric governance. In terms Of policy recommendations, this Article suggests policymakers embrace a polycentric approach and divert governance-building efforts to support initiatives to introduce non-binding rules and agreements. These may complement binding law and create, in the aggregate, a more comprehensive array of rules for space warfare. The Articlefurther suggests membership and compliance as more suitable tests for international agreements than bindingness and proposes that, because non-binding international agreements complement legally binding treaties, they are within the compound of international law. [ABSTRACT FROM AUTHOR]

This article is about the rise and fall of ocean incineration, a method for the disposal of hazardous chemical waste that was initiated in the late 1960s, developed, tested, and perfected throughout the 1970s, commercialized in the 1980s, and eventually phased out from the 1990s onwards. Ocean incineration consisted in the offshore destruction of toxic liquid substances in specially designed ships outfitted with high-temperature combustion chambers and high stacks. When this technology broke through, it seemed like a panacea. It heralded the safe disposal of noxious compounds such as organochlorines and polychlorinated biphenyls (PCBs), which were ubiquitous by-products of petrochemical industrial processes. It promised to minimize private companies' externalities and ease environmental concerns. But it ultimately failed to provide an alternative to safely disposing of toxic waste. What does explain such a decline? This article answers this question by arguing that the demise of ocean incineration was due to the combination of locally oriented and transnationally driven protests, which emerged across the (North) Atlantic and exposed the dangerous and highly exploitative nature of such a practice. [ABSTRACT FROM AUTHOR]

The article examines the design and implementation of user fee programs across Executive Branch of the U.S. government. Topics discussed include design decisions that must be made and the choices available, relationship between these choices and core values of administrative law, and an explanation of how program designers might make these choices, as a procedural matter and as a substantive matter.

Nation-states take steps to prevent the theft of domestic entities' trade secrets and other intellectual property ("IP"). The United States has issued complaints, passed laws, and implemented initiatives and sanctions targeting China's unfair and illegal economic practices, which include trade-secret theft. Although China has responded by amending its civil, criminal, and administrative trade-secret regime, foreign companies still routinely struggle with thefts connected to China. The semiconductor industry-essential to daily-life, commercial, and military needs-is one of the most brutal battlegrounds of today's IP and tech wars. A leader in the industry is the Taiwanese semiconductor sector, which, along with the Taiwanese government and like-minded democracies, must confront a particularly complex set of economic, security, and geopolitical challenges from China. Currently, poor grasp of cross-border trade secret thefts has greatly weakened sincere efforts to deal with the grave threat posed by China. In this Article, I cross-analyze empirical quantitative and qualitative data regarding U.S. and Taiwanese semiconductor trade-secret litigation to better understand the dynamics of Chinese trade-secret theft. I propose that individuals, companies, governments, and international organizations should respond to trade-secret threats by restoring trust within a geopolitical economic framework, rather than by pursuing purely legalistic IP approaches. [ABSTRACT FROM AUTHOR]

Rain-on-snow (RoS) events in regions of ephemeral snowpack – such as the northeastern United States – can be key drivers of cool-season flooding. We describe an automated algorithm for detecting basin-scale RoS events in gridded climate data by generating an area-averaged time-series and then searching for periods of concurrent precipitation, surface runoff, and snowmelt exceeding pre-defined thresholds. When evaluated using historical data over the Susquehanna River Basin (SRB), the technique credibly finds RoS events in published literature and flags events that are followed by anomalously high streamflow as measured by gage data along the river. When comparing four different datasets representing the same 21-year period, we find large differences in RoS event magnitude and frequency, primarily driven by differences in estimated surface runoff and snowmelt. Using dataset-specific thresholds improves agreement between datasets but does not account for all discrepancies. We show that factors such as meteorological forcing and coupling frequency as well as choice of land surface model play roles in how data products capture these compound extremes and suggest care is to be taken when climate datasets are used by stakeholders for operational decision-making. [ABSTRACT FROM AUTHOR]

Water temperature dynamics in large inland lakes are interrelated with internal lake physics, ecosystem function, and adjacent land surface meteorology and climatology. Models for simulating and forecasting lake temperatures often rely on remote sensing and in situ data for validation. In situ monitoring platforms have the benefit of providing relatively precise measurements at multiple lake depths, but are often sparser (temporally and spatially) than remote sensing data. Here, we address the challenge of synthesizing in situ lake temperature data by creating a standardized database of near-surface and subsurface measurements from 134 sites across 29 large North American lakes, with the primary goal of supporting an ongoing lake model validation study. We utilize data sources ranging from federal agency repositories to local monitoring group samples, with a collective historical record spanning January 1, 2000 through December 31, 2022. Our database has direct utility for validating simulations and forecasts from operational numerical weather prediction systems in large lakes whose extensive surface area may significantly influence nearby weather and climate patterns. [ABSTRACT FROM AUTHOR]

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement: Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models. [ABSTRACT FROM AUTHOR]

Research Summary: We analyze firm‐driven labor market search, where firms "hunt" for talent rather than rely on workers to apply for vacancies. We leverage three approaches. We develop a model of firm‐driven search and derive equilibrium conditions under which firms use this channel. We test our model's predictions using two data sources. Data from a nationally representative survey of 10,000 workers shows that the percentage hired through recruiting has increased from 4.9% in 1991 to 14.3% in 2022. This share is larger for higher‐skilled workers and those with online profiles on LinkedIn. We complement this analysis with data on the near universe of online job postings from 2010 through 2020. Consistent with our model and worker survey evidence, we find firms that demand higher‐skilled workers or operate in labor markets with heavy LinkedIn use are more likely to "hunt for talent." Managerial Summary: We study the phenomenon of "hunting" for talent, where firms fill open positions by searching for workers and inviting them to a recruiting process, rather than relying on workers to apply directly. We find that the percentage of workers hired through hunting has increased from 4.9% in 1991 to 14.3% in 2022. We propose that firms that rely more on high‐skilled workers and/or operate within industries with a higher share of available candidates with online profiles are more likely to hunt for their talent. We find support for this conjecture using two data sets, documenting the worker and firm side of the labor market. Data from a nationally representative survey of 10,000 workers shows they are more likely to have been "hunted" by their employer if they work in an occupation that requires more skills, or if their industry has more candidates with online profiles. Moreover, data on US‐wide job postings over the past decade shows that firms in need of highly skilled workers are more likely to invest in outbound recruiting capabilities. [ABSTRACT FROM AUTHOR]

Weather forecasts can enhance the utilization of scientific irrigation scheduling tools, crucial for maximizing agricultural water use efficiency. This study employed quantitative weather forecasts of 3-, 7- and 14-day lead times from a weather application programming interface (API) to generate irrigation schedules using the AquaCrop-OSPy model for maize, cotton and sorghum under different regulated deficit irrigation scenarios. The study aimed to determine the suitability of forecast lengths for irrigation scheduling under varying pumping capacities of center pivots (114 m3h−1, 182 m3 h−1 and 250 m3 h−1) in the Texas High Plains and Rio Grande Basin regions, United States. A comparative analysis was carried out to evaluate the irrigation schedules and corresponding crop yields simulated using forecasted and observed weather data. Results indicated that using shorter forecast time allowed the crop model to capture more precise variations in weather patterns, however, shorter lead times also caused over-irrigation in some scenarios. Use of longer lead times tended to be less suitable for scheduling irrigation during water-sensitive growth stages. Center pivots with large pumping capacities and application rates benefited more from longer forecast lengths due to their ability to adapt to weather fluctuations. Unplanned irrigation application occurred in some instances, primarily attributed to uncertainties in weather forecasts and limitations of the crop model. The approach developed and evaluated in this study supports water conservation efforts by promoting scientific irrigation scheduling in weather-data-poor and low adoption regions. [ABSTRACT FROM AUTHOR]

Older adult victims of abuse and crime have significant unmet mental health needs, with high rates of depression and anxiety. But few victims are offered mental health care, and there are no standardized, effective programs to address their specific mental health needs. PROTECT, a manualized therapy offered as an adjunct to victim support services was developed and tested and is unique in its demonstrated effectiveness to reduce depression among victims. PROTECT, which is offered remotely (by video and phone), has been found to be effective, offering a shift in delivery that increases its potential dissemination beyond current use in New York City. [ABSTRACT FROM AUTHOR]

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps. [ABSTRACT FROM AUTHOR]

The surface and air temperature gradient (TS00 − Tair) drives the development of the convective boundary layer and the occurrence of clouds and precipitation. However, its variability is still poorly understood due to the lack of high-quality observations. This study fills in this gap by investigating the diurnal to decadal variability in TS00 − Tair from 2002 to 2022 based on hourly observations collected at over 100 stations of the U.S. Climate Reference Network. It is found that TS00 − Tair reaches its maximum at noon with an average of 6.85°C over the contiguous United States, which decreases to 4.28°C when the soil moisture exceeds 30%. The daily minimum of TS00 − Tair has an average of −2.08°C, which generally occurs in the early evening but is postponed as the cloud fraction decreases. Moreover, while existing studies have used the near-surface soil temperature, such as the 5-cm soil temperature (TS05), to calculate TS05 − Tair, we find that TS00 − Tair and TS05 − Tair have opposite diurnal cycles, and their amplitudes differed drastically. The daily minimum of TS00 − Tair has a significant decreasing trend (−0.50° ± 0.007°C decade−1) from 2002 to 2022 due to Tair increasing at a higher rate than TS00 during the nighttime. The occurrence frequency of near-surface stable conditions (TS00 − Tair < 0) increases significantly, and the frequency of unstable conditions (TS00 − Tair > 0) decreases notably throughout the year except for winter. When it is stable, the magnitude of TS00 − Tair tends to decrease while the TS00 − Tair tends to increase when it is unstable, which is consistent with the drying condition caused by the precipitation deficit. This study provides the first observational evidence on how TS00 − Tair responds to warming. Significance Statement: The impact of global warming on surface-air temperature gradients is a crucial scientific issue that needs to be addressed. These gradients determine changes in cloud and precipitation, affecting water resources. However, traditional surface temperature measurements from weather stations are of high uncertainty due to direct exposure to the insolation. Satellite retrieval of surface temperature is limited by the availability of clear sky conditions, with low accuracy for temperature gradient calculations. Despite their importance, high-accuracy data of land surface temperature are still lacking. To address this issue, the U.S. Climate Reference Network (USCRN) uses infrared radiometers to continuously monitor surface temperature with high accuracy and sampling frequency. This study reports on surface-air temperature gradients at more than 100 U.S. stations, providing insight into diurnal to decadal variability over the contiguous United States. The study also highlights the significant difference between the land surface temperature–air temperature gradient and soil temperature–air temperature gradient. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is a significant pig disease causing substantial annual losses exceeding half a billion dollars to the United States pork industry. The cocirculation and emergence of genetically distinct PRRSV-2 viruses hinder PRRS control, especially vaccine development. Similar to other viral infections like seasonal flu and SARS-CoV-2, predictive tools for identifying potential emerging viral variants may prospectively aid in preemptive disease mitigation. However, such predictions have not been made for PRRSV-2, despite the abundance of relevant data. In this study, we analyzed a decade's worth of virus ORF5 sequences (n = 20,700) and corresponding metadata to identify phylogenetic-based early indicators for short-term (12 months) and long-term (24 months) variant emergence. Our analysis focuses on PRRSV-2 Lineage 1, which was the predominant lineage within the U.S. during this period. We evaluated population expansion, spatial distribution, and genetic diversity as key success metrics for variant emergence. Our findings indicate that successful variants were best characterized as those that underwent population expansion alongside widespread geographical spread but had limited genetic diversification. Conditional logistic regression revealed the local branching index as the sole informative indicator for predicting population expansion (balanced accuracy (BA) = 0.75), while ancestral branch length was strongly linked to future genetic diversity (BA = 0.79). Predicting spatial dispersion relied on the branch length and putative antigenic difference (BA = 0.67), but their causal relationships remain unclear. Although the predictive models effectively captured most emerging variants (sensitivity = 0.58–0.81), they exhibited relatively low positive predictive value (PPV = 0.09–0.16). This initial step in PRRSV-2 prediction is a crucial step for more precise prevention strategies against PRRS in the future. [ABSTRACT FROM AUTHOR]