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To quantify how the quality of US medical students accepted to radiation oncology (RO) training programs, as defined by National Resident Matching Program (NRMP) metrics, has changed over time.
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Coyotes (Canis latrans) are a highly adaptable canid species whose behavioral plasticity has allowed them to persist in a wide array of habitats throughout North America. As generalists, coyotes can alter movement patterns and change territorial strategies between residency (high site fidelity) and transiency (low site fidelity) to maximize fitness. Uncertainty remains about resident and transient coyote movement patterns and habitat use because research has reached conflicting conclusions regarding patterns of habitat use by both groups. We quantified effects of habitat on resident and transient coyote movement behavior using first passage time (FPT) analysis, which assesses recursive movement along an individual's movement path to delineate where they exhibit area-restricted search (ARS) behaviors relative to habitat attributes. We quantified monthly movement rates for 171 coyotes (76 residents and 53 transients) and then used estimated FPT values in generalized linear mixed models to quantify monthly habitat use for resident and transient coyotes. Transients had greater movement rates than residents across all months except January. Resident FPT values were positively correlated with agricultural land cover during fall and winter, but negatively correlated with agriculture during spring. Resident FPT values were also negatively correlated with developed habitats during May–August, deciduous land cover during June–August, and wetlands during September–January except November. FPT values of transient coyotes were positively correlated with developed areas throughout much of the year and near wetlands during July–September. Transient FPT values were negatively correlated with agriculture during all months except June and July. High FPT values (ARS behavior) of residents and transients were generally correlated with greater densities of edge habitat. Although we observed high individual variation in space use, our study found substantive differences in habitat use between residents and transients, providing further evidence that complexity and plasticity of coyote habitat use is influenced by territorial strategy.
Groundwater residence time distributions provide fundamental insights on the hydrological processes within watersheds. Yet, observations that can constrain groundwater residence times over broad timescales remain scarce in mountain catchment studies. We use environmental tracers (CFC-12, SF 6 , 3 H, and 4 He) to investigate groundwater residence times along a hillslope in the East River Watershed, Colorado, USA. We develop a Bayesian inference framework that applies a Markov-chain Monte Carlo (MCMC) approach to estimate noble gas recharge temperature, elevation, and excess-air parameters and the resulting environmental tracer concentrations. MCMC is then used to propagate the environmental tracer uncertainties to estimates of groundwater mean residence times inferred with lumped parameter models. All samples contain 3 H, CFC-12, and SF 6 in addition to terrigenic 4 He, suggesting a mixture of water characterized by modern and premodern residence times. 4He exponential mean residence times range from hundreds of years at the upslope well to thousands of years at the toe-slope well assuming average crustal production rates. We find that binary mixing residence time distributions with separate young and old mixing fractions are needed to predict the 4 He, CFC-12, SF 6 , and 3 H observations, supporting the importance of flow path mixing in this bedrock system. Our findings that the fractured bedrock hosts groundwater with a mixture of residence times ranging from decades to millennia suggest variable recharge dynamics and flow path mixing along the hillslope and highlight the importance of characterizing groundwater systems with observations that are sensitive to transport over a broad range of residence times.
Multiple efforts have been made in recent years to establish national benchmarks for research productivity among US radiation oncology residents. Morgan et al found a mean of 1.01 first-author, PubMed-searchable articles published by US radiation oncology residents over 4 years of residency between 2002 and 2007, whereas Verma et al found a mean of 1.97 first-author, PubMed-searchable articles published by members of the graduating US radiation oncology residency classes of 2014 and 2015. In this study, we sought to establish new national benchmarks for US radiation oncology resident research productivity and characterize the scholarly work produced by graduating US radiation oncology residents.
Abstract Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.
This data package is associated with the publication “On the Transferability of Residence Time Distributions in Two 10-km Long River Sections with Similar Hydromorphic Units” submitted to the Journal of Hydrology (Bao et al. 2024).Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface, along with their residence time distributions (RTDs) in the subsurface, is crucial for managing water quality and ecosystem health in dynamic river corridors. However, directly simulating high-spatial resolution HEFs and RTDs can be a time-consuming process, particularly for watershed-scale modeling. Efficient surrogate models that link RTDs to hydromorphic units (HUs) may serve as alternatives for simulating RTDs in large-scale models. One common concern with these surrogate models, however, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this, we evaluated the HEFs and the resulting RTD-HU relationships for two 10-kilometer-long river corridors along the Columbia River, using a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework that we previously developed. Applying this framework to the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. This data package includes the model inputs files and the simulation results data. This data package contains 10 folders. The modeling simulation results data are in the folders 100H_pt_data and 300area_pt_data, for the study domain Hanford 100H and 300 area respectively. The remaining eight folders contain the scripts and data to generate the manuscript figures. The file-level metadata file (Bao_2024_Residence_Time_Distribution _flmd.csv) includes a list of all files contained in this data package and descriptions for each. The data dictionary file (Bao_2024_Residence_Time_Distribution _dd.csv) includes column header definitions and units of all tabular files.
Horizontal screw reactors are utilized in biorefineries for acid-catalyzed hydrolysis of xylan, which is a multi-step chemical reaction requiring accurate residence-time control. However, it is difficult to obtain online analytical measurement of reactant species. In this work, a residence-time distribution (RTD) is exhibited whose characteristics influence species yields. Sensitivity of product yield to RTD was investigated to understand the relative importance of operating control vs. inherent reactor dispersion. We find that reactor operation using a commonly used theoretical residence-time relationship can result in substantial yield losses. Instead, a model that accounts for the actual reactor RTD provides much improved results. The dispersion caused by reactor conditions only slightly hinders achieving theoretical optimal xylose yield (less than 3% yield loss for coefficient of variation less than 0.35), provided a validated RTD model is used to target the desired mean residence-time. In contrast, neglecting to account for the RTD by using the simplistic theoretical calculation results in xylose yields that are as much as 16% lower than the theoretical maximum.
Groundwater residence times provide fundamental descriptions of hydrologic dynamics and mixing processes in mountainous watersheds. Yet, few observational datasets that can constrain groundwater residence times over broad timescales are available in high elevation mountain systems. Here we present field observations from May 2021 of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the Pumphouse Lower Montane study site (wells PLM1, PLM6, and PLM7) within the East River Watershed, Colorado. The presented noble gas (PLM_noblegas_2021.csv) and environmental tracer (PLM_tracers_2021.csv) observation datasets, along with the associated modeling scripts, aide in quantifying groundwater residence times and recharge conditions in a high elevation mountain system. Furthermore, the modeling scripts quantify groundwater residence time and noble gas recharge condition uncertainties using a novel Markov-chain Monte Carlo approach. All data modeling scripts are written in the Python code.
Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175-250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol-water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol-water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol-water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.
Choosing the right HVAC system or the right algorithm of implementing demand response may create significant energy and environmental gains while maintaining resident comfort. But these questions are closely related to the concept of user comfort, which in turn requires a reasonable fit between user preferences and temperature setpoints. While setting the temperature right is a well-researched question, systems in transition from one setpoint to another are currently not thoroughly addressed in research. But how tolerant the residents really are if a system spends a large share of time outside of the comfort setpoint. This study gives some early insights on how the deviation of temperature from the setpoint affect perceived resident comfort. We use two weeks of data for a smart neighborhood located in Atlanta, GA. We find that the system spends 20% - 50% of time deviating from the setpoint by more than 1℉. However, we do not find that increasing deviations cause resident complaints or increasing overrides.
Solvolytic extraction of lignin from biomass is a critical step in lignin-first biorefining, including the reductive catalytic fractionation (RCF) process. Key to optimal RCF processing is the ability to rapidly extract lignin from biomass at high delignification extents and transfer the lignin molecules to a catalyst surface in a time frame that minimizes lignin condensation reactions. Here, we use a flow-through reactor to study the effects of temperature (175–250 °C), residence time (9 to 36 min), and solvent composition (methanol and methanol–water) on lignin extraction and condensation. We evaluated three metrics at each condition: total delignification, delignification rate, and extent of condensation, the latter measured by a decrease in monomer yield for batch hydrogenolysis reactions of solvolysis liquor compared to batch RCF reactions. We observe that delignification is predominantly determined by temperature, while residence time dictates the lignin condensation extent. Moreover, the extent of both extraction and condensation increased in the methanol–water solvent system compared to that in the methanol system. Lignin extracted in methanol is stable up to 18-min residence times at or below 225 °C, while a majority of the lignin extracted in methanol–water is condensed with a 9-min residence time at 200 °C. These results can inform reactor designs and solvent selection for lignin-first biorefining processes that aim to physically separate the biomass and catalyst.
The Salish Sea, located in the Pacific Northwest region of North America has complex currents and circulation features distributed over numerous interconnected deep basins with islands. Increased risk of exposure to oil spills and untreated wastewater from maritime emergencies and treatment plant failures have led to a need for quantifying residence and flushing characteristics at a sub-basin scale using a shoreline resolving hydrodynamic model. An unstructured grid model of the Salish Sea was developed using FVCOM with a ≈75-100m shoreline resolution. In addition to 7 tides and 23 salinity and temperature monitoring stations, an extensive currents data set from 135 stations collected over a span of three years was used for skill assessment and validation. Explicit forward computations were then conducted to define and quantify residence and flushing times in various sub-basins of interest using (a) Lagrangian particles and (b) Numerical/virtual dye experiments. The results in most basins show expected seasonal variability with longer flushing time associated with summer lower tides and lower freshwater inflows. However, contrary to expectation, flushing time is significantly longer in wintertime in large fjord-like basins such as Hood Canal (≈138 days), likely due to increased stratification and reduced mixing. The flushing time for the Puget Sound region of the Salish Sea is ≈115 days, while Georgia Basin is 240 days when analyzed as stand-alone basins with zero background concentrations. When examined as part of the flushing of the entire Puget Sound filled with virtual dye, the compounded flushing times for embedded sub-basins can be significantly longer in order of magnitudes and largely dictated by the flushing time of Puget Sound. The computed residence and flushing time scales tabulated over 36 sub-basins provide an improved understanding of water renewal in the system, informing pollution management actions.
Abstract Quantifying the relative influence of factors and processes controlling riverine ecosystem function is essential to predicting future conditions under global change. Dissolved organic matter (DOM) is a fundamental component of riverine ecosystems that fuels microbial food webs, influences nutrient and light availability, and represents a significant carbon flux globally. The heterogeneous nature of DOM molecular composition and its propensity for interaction (i.e., functional diversity) can characterize riverine ecosystem function across spatiotemporal scales. To investigate fundamental drivers of DOM diversity, we collected seasonal water samples from 42 nested locations within five watersheds spanning multiple watershed sizes (~ 5 to 30,000 km 2 ) across the United States. Patterns in DOM molecular richness, aromaticity, relative abundance of N-containing formulas, and putative biochemical transformations derived from high-resolution mass spectrometry were assessed across gradients of explanatory variables associated with watershed characteristics (e.g., watershed area, water residence time, land cover). We found that putative biochemical transformations were more strongly related to explanatory variables across watersheds than common bulk DOM parameters and that watershed area, surface water residence time and derived Damköhler numbers representing DOM reactivity timescales were strong predictors of DOM diversity. The data also indicate that catchment-specific land cover factors can significantly influence DOM diversity in diverging directions. Overall, the results highlight the importance of considering water residence time and land cover when interpreting longitudinal patterns in DOM chemistry and the continued challenge of identifying generalizable drivers that are transferable across watershed and regional scales for application in Earth system models. This work also introduces a Findable Accessible Interoperable Reusable (FAIR) dataset (> 300 samples) to the community for future syntheses.
Achieving competency as educators is increasingly recognized as a critical part of residents’ training in graduate medical education across specialties. In addition to teaching medical students, radiation oncology residents often play a vital role in peer and interprofessional education. We conducted a survey to identify the needs of radiation oncology residents for developing skills in teaching.
Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be used to estimate impacts to large numbers of homes based on high-level housing characteristics. In addition to reviewing literature sources, we performed an experiment to evaluate interventions to mitigate degraded indoor air quality. We instrumented one residence for one week during an extreme wildfire event in the Pacific Northwest. Outdoor ambient concentrations of PM2.5 reached historic levels, sustained at over 200 μg/m3 for multiple days. Outdoor and indoor PM2.5 were monitored, and data regarding building characteristics, infiltration, and mechanical system operation were gathered to be consistent with the type of information commonly known for residential energy models. Two conditions were studied: a high-capture minimum efficiency rated value (MERV 13) filter integrated into a central forced air (CFA) system, and a CFA with MERV 13 filtration operating with a portable air cleaner (PAC). With intermittent CFA operation and no PAC, indoor corrected concentrations of PM2.5 reached 280 μg/m3, and indoor/outdoor (I/O) ratios reached a mean of 0.55. The measured I/O ratio was reduced to a mean of 0.22 when both intermittent CFA and the PAC were in operation. Data gathered from the test home were used in a modeling exercise to assess expected I/O ratios from both interventions. The mean modeled I/O ratio for the CFA with an MERV 13 filter was 0.48, and 0.28 when the PAC was added. The model overpredicted the MERV 13 performance and underpredicted the CFA with an MERV 13 filter plus a PAC, though both conditions were predicted within 0.15 standard deviation. The results illustrate the ways that models can be used to estimate indoor PM2.5 concentrations in residences during extreme wildfire smoke events.
Mass residence time distribution (RTD) is considered to be an important factor controlling the product selectivity in the pyrolysis of biomass and plastic wastes along with the pyrolysis chemistry. However, due to the complex pyrolysis chemistry of biomass and plastic waste, the coupling between the reaction chemistry, RTD, and product selectivity is challenging to understand. Here, we introduce a reaction molecular dynamics-based method to examine pyrolysis chemistry and species timescales to assess the impact of RTD on product selectivity and yield. To validate this method, reactive molecular dynamics simulations were conducted for polypropylene pyrolysis and its non-equilibrium product selectivity using 6 different RTDs. We find that the RTD and the reaction chemistry control the peak non-equilibrium product concentrations. The peak monomer (C 3 H 6 ) concentration during pyrolysis can be increased by up to 25 % by using a narrow RTD in the case of polypropylene pyrolysis. We also find that product selectivity is strongly affected by the average residence time and RTD. This coupling between the reaction chemistry, RTD, and product selectivity highlights the need to understand detailed reaction chemistry to control RTD and optimize non-equilibrium product selectivity during polymer and biomass pyrolysis. The present method provides a new way to design RTD for reactors to reach maximized product selectivity of plastic waste and biomass.