Budget Model Research Articles

A simulation study of deepwater ventilation in fjords through artificial downwelling

The pumping of less dense surface water into a deep layer, also termed artificial downwelling (AD), has demonstrated its effectiveness in ventilating basin water in a few fjords. Building on a previous experiment in By Fjord, we established a well-calibrated far-field hydrodynamic model to simulate bottom water circulation caused by AD implementation. Our simulations suggest that (1) laterally closed boundaries are crucial for AD to cause spatially uniform compensatory downwelling in the bottom water and that (2) dissolved oxygen delivered through downwelling can be sourced from both surface waters and below the pycnocline to enhance oxygen levels effectively. We propose a physical oxygen budget model to predict the temporal variability of deep-water oxygen levels, offering guidelines on the number of pipes and flow rates required for large-scale oxygenation. For small fjords (∼1 km2), a few pipes (each has a flow rate of 1 m3 s−1) may suffice, while medium-sized basins like Chesapeake Bay (∼1000 km2) may require several hundred pipes.

United States federal indebtedness and fiscal policy trade‐offs

AbstractProjected federal spending and taxes are distributed by birth year, gender, race, and lifetime education, using the Congressional Budget Office's 10‐year budget estimates and the Penn Wharton Budget Models' microsimulation. This enables measurement of conventional federal fiscal imbalance (FI): $162.6 trillion in present value, which is six‐fold larger than current debt held by the public. FI implies a shortfall of 26.1% in federal taxes, alternatively, an excess of 33.4% of federal expenditures. The equi‐proportional shortfall of expenditures plus taxes equals 14.6%. Excluding taxes and transfers of some groups, such as the elderly, increases the measured shortfall.

Impact of patient’s financial burden of COVID-19 treatment on antiviral prescription rates and clinical and economic outcomes

ABSTRACT Background In Japan, medical expenses for COVID-19 treatment transitioned from full public funding support to out-of-pocket (OOP) payment by patients plus partial public support in October 2023, and public support fully ended in March 2024. This study evaluated the clinical and economic impacts of initiating OOP payments. Research design and methods To assess the impact on prescription rates, we compared the prescription rates of antivirals from the 4-month pre- to post-OOP payment initiation period using a claims database. Subsequently, a budget impact model assessed the impacts of a hypothetical decline in the prescription rates on COVID-19-related hospitalizations, deaths, and direct medical costs for antiviral prescription and hospitalization. Results The antiviral prescription rate per 100 patients decreased from 17.5 for the pre-OOP payment initiation period to 11.5 for the post-OOP payment initiation period, that is, a change of − 34.3%. With prescription rate decreases of 40%, 60%, and 80%, the hospitalizations would increase annually by 22,533 (3.3%), 33800 (5.0%), and 45,066 (6.6%), respectively. The total costs would increase by JPY9.5 billion (USD67.3 million; 0.7%), JPY14.3 billion (USD100.9 million; 1.0%), and JPY19.0 billion (USD134.5 million; 1.3%), respectively. Conclusions Higher OOP payment decreased the antiviral prescription rate, potentially leading to clinical and economic loss.

Acclimation Unifies the Scaling of Carbon Assimilation Across Climate Gradients and Levels of Organisation.

The temperature dependence of carbon assimilation-from leaf photosynthesis to ecosystem productivity-is hypothesised to be driven by the kinetics of Rubisco-catalysed carboxylation and electron transport. However, photosynthetic physiology acclimates to changes in temperature, which may decouple temperature dependencies at higher levels of organisation from the acute temperature sensitivity of photosynthesis. Here, we integrate relative growth rate theory, metabolic theory and biochemical photosynthesis theory to develop a carbon budget model of plant growth that accounts for photosynthetic acclimation to temperature. We test its predictions using a novel experimental approach enabling concurrent measurement of the temperature sensitivity of acute photosynthesis, acclimated photosynthesis and growth rate. We demonstrate for the first time that photosynthetic acclimation mediates how carbon assimilation kinetics 'scale up' from leaf photosynthesis to whole-plant growth. We also find that existing models of photosynthetic acclimation are unable to predict features of growth rate responses to temperature in our system.

Resource budget model with Duffing oscillator for dynamics of synchronized biennial-bearing olives in the Levant

We develop and analyze a temporally continuous spatially lumped resource budget model to explain the dynamics of synchronized biennial-bearing olives in the Levant, specifically focusing on Syria, the region’s foremost olive-producing country. The model is a time-continuous counterpart of the celebrated resource budget model. It consists of a Duffing oscillator coupled with a dynamical model of pollination with an external force propelling olive growth by photosynthesis. The reference data are obtained from statistical databases of international organizations and our own observation systems in Jordan, a country neighboring Syria, providing a wealth of information to refine the model structure. An intensive review of Syria’s modern history involving significant shifts in agricultural policy and social stability leads to a conclusion that the model should comprehend the anomaly of olive yield interacting with socio-political factors as an autonomous behavior. The conventional mathematical methodology analyzes the model’s characteristics, such as solutions’ non-negativity, boundedness, and stability. The system is stable during pollination off-season but may become unstable and unbounded during pollination on-season, which is a property that the time-discrete resource budget model cannot reproduce. A significant finding is that coupling individual fruit trees by anemophily is not essential in synchronization, overturning earlier studies in the literature. The values of model parameters that best fit the historical data of olive yield in Syria result in bounded chaos. With alternative parameter values, the model could exhibit periodic oscillation, instability, or blowing up, as clearly shown in bifurcation diagrams.

H3NI: Non-target-specific node injection attacks on hypergraph neural networks via genetic algorithm

Node injection attack is widely used in graph neural networks (GNNs) attacks, which misleads GNNs by injecting nodes. Though hypergraph neural networks (HNNs) are an extension of GNNs, node injection attacks have not yet been studied in HNNs. Since each edge of a hypergraph can connect more than two nodes, existing node injection methods designed for GNNs cannot effectively select the hyperedges connected to the injected nodes when applied to hypergraphs. In this paper, we propose a Hypergraph Neural Network Node Injection attack method called H3NI, which utilizes a genetic algorithm and a predefined budget model to implement the first black-box node injection framework designed for HNNs attacks. We conducted experiments on the datasets of Cora co-authorship and co-citation. Experimental results show the effectiveness and superior performance of H3NI in attacking HNNs, which reduces the model accuracy by 18%–20% within 5% of the total injected nodes and achieves a 2-4X improvement compared to existing gradient-based node injection methods.

PROFITABILITY ANALYSIS OF RICE MILLING AND MARKETING IN BAUCHI STATE, NIGERIA

The study analysed the profitability of rice milling and marketing in Bauchi State, Nigeria. Multi-stage sampling procedure was used for the selection of respondents. The respondents comprised rice processors (millers) and marketers. Data were collected using questionnaire and analysed using descriptive statistics, farm budgeting techniques and multiple regression analysis. The findings revealed that majority (87.5%) of the millers and all (100%) of marketers were male and married with a mean age of 39 and 44 years respectively. However, 43.8% of the millers and 40% of the marketers had a family size of between 6-10 people per household with majority (62.5%) of the millers having tertiary education. The analysis also shows that half (50%) and more than half (53%) of the millers and marketers had a marketing and milling experience of between 11-20 years. The result of the budgeting model revealed that millers had a net income of N714 per bag with a return per naira of N1.46 and marketers had a net income of N1, 672 per bag with a return per naira of N 0.12. The results of the regression analysis revealed that years of milling experience and education level were the factors influencing rice milling while household size and membership of association were significant factors influencing rice marketers’ income. However, high cost of diesel (96.7%) and transportation cost (95%) were the major constraints faced by the millers and marketers in the study area. The study concluded that, milling and marketing of rice is a profitable ventures in the study area. It is therefore, recommended that government together with stakeholders should provide adequate infrastructure and introduce favorable policies to strengthen the rice value chain in the study area.

Reduced CO2 Release from Arctic Soils Due to CO2 Binding to Calcium Forming Aragonite.

Arctic soils are the largest pool of organic carbon compared with other soils globally and serve as a main source for greenhouse gases, especially in the course of the predicted future temperature increase. With increasing temperatures, substantial thawing of the permafrost layer of soils is expected, altering the availability of calcium in those soils, with an increase by ∼5 mg Ca g-1 DW predicted for Alaska. Here we show for two representative soils in Alaska (initially Ca-poor or Ca-rich) that this increase in Ca availability will lead to decreases in CO2 release by 50% and 57%. It is already well-known that the cation bridging of Ca ions to organic carbon renders this carbon unavailable for microbial respiration and that Ca is altering the transformation of Corg by microbes. Here we show that the decrease of the soil CO2 release may be also due to enhanced aragonite formation (by 300% for Ca-poor and 90-200% for Ca-rich soils), as revealed by synchrotron-based scanning transmission X-ray microscopy. We therefore call upon field experiments for validation of this process and inclusion of this process in global and local carbon budget models.

Development of national post-fire restoration system to assess net GHG impacts and salvage biomass availability

In light of the recent unprecedented wildfires in Canada and the potential for increasing burned areas in the future, there is a need to explore post-fire salvage harvest and restoration and the implications for greenhouse gas (GHG) emissions. Salvage logging and replanting initiatives offer a potential solution by regrowing forests more quickly while meeting societal demands for wood and bioenergy.This study presents a comprehensive modeling framework to estimate post-fire salvage biomass and net GHG emissions relative to a ‘do-nothing’ baseline for all of Canada's harvest-eligible forests. Forest ecosystem carbon emissions and removals were modeled at 1-ha spatial resolution for Canadian forests using the Generic Carbon Budget Model (GCBM) from 1990 to 2070 using several forest inventory data sources with future harvest and wildfires.Building upon previous research, our work integrated the Canadian Forest Fire Danger Rating System fire intensity to estimate fire severity of future wildfires. For 2024 to 2070, we assessed the changes in ecosystem carbon, emissions from harvested wood products, and substitution benefits from avoided emissions-intensive materials, relative to a forward-looking baseline. Our prototype system provides a comprehensive framework, configuration files, links to datasets to quantify the net GHG of post-fire restoration, and sample results for validation.•Developed spatially explicit forest carbon modeling system for all of Canada's forests.•Assessed the net GHG reduction from post-fire restoration.•Used system approach to consider forests, wood products and substitution benefits.

High-accuracy and low-disturbance approach for identifying surface water and groundwater interactions in wetlands

Comprehending the hydrological conditions in wetlands is a critical aspect of successfully enhancing wetland conservation. The interaction between wetland surface water and groundwater is a complex process, requiring detailed onsite hydrological and soil surveys, laboratory experiments, and modeling to clarify this relationship. However, conventional investigation methods often cause significant disruptions and thus may affect the natural environment and compromise data reliability. In this study, a high-accuracy and low-environmental-disturbance (LED) approach was proposed involving modified falling head permeability and modified seepage meter tests to elucidate the groundwater characteristics in an ecological reserve. A water balance (WB) calculation method was employed to examine the performance of the proposed LED approach. The results revealed that the LED performed better than conventional methods in hydraulic conductivity and seepage velocity exploration, thereby improving the accuracy of quantifying groundwater flow. Moreover, the experimental findings and ecohydrological observations were used to assess the groundwater flow regime, and the data were consistent with the field survey results. The contradiction between conducting research and protecting ecological reserves can pose difficulties in the sustainable and effective management of wetlands. The LED approach can be applied broadly, especially in areas where significant disturbance should be avoided. The water budget model can thus be developed to help deduce the interaction between groundwater and surface water. We suggest that these innovative methods are effective tools and can assist both scientists and authorities in formulating corresponding habitat management strategies.

Hindcasted Body Temperatures Reveal Underestimated Thermal Stress Faced by Intertidal Species

ABSTRACTAimAs global climate changes, there is a clear mismatch between the temporal and spatial characteristics of body temperature and environmental temperature, confounding the assessment of thermal stress for organisms in many ecological studies. Here, we hindcast the hourly body temperatures of intertidal molluscs to explore the differences between them and environmental temperatures (air and water temperatures) in multiple metrics of thermal stress.LocationIntertidal shores in East Asia (0°–45°N, 100°E–140°E).Time Period40 years, 1980 to 2019.Major Taxa StudiedMollusca.MethodsWe collected habitat zonation data and measured the morphological characteristics of 25 intertidal molluscs living in East Asia. For three different types of intertidal molluscs (i.e., bivalves, limpets and snails), we built corresponding heat budget models (HBMs) to hindcast the hourly body temperatures from 1980 to 2019. We analysed the thermal stress of intertidal species faced in three metrics, annual extreme high temperatures (T99), seasonal daily maximum temperatures (DMT) and heatwaves, and compared them with environmental temperatures.ResultsWe found that T99 of body temperatures and their interannual warming rates are significantly higher than those of environmental temperatures. Moreover, there were non‐negligible mismatches between the seasonal thermal pattern and heatwaves of body temperatures and environmental temperatures, suggesting that the deleterious impacts of global warming on intertidal species are underestimated and cannot be directly revealed by environmental temperatures.Main ConclusionsThermal stress patterns of body temperature were significantly different from those of environmental temperature, and the thermal stress faced by intertidal species had been persistently underestimated. These results emphasise that body temperature should be used as the appropriate metric for evaluating and predicting the impacts of global warming and weather extremes in the intertidal biological system.

Environmental Risk Assessment of Time-Variable Toxicant Exposure with Toxicokinetic-Toxicodynamic Modeling of Sublethal Endpoints and Moving Time Windows: A Case Study with Ceriodaphnia dubia.

Toxicokinetic-toxicodynamic (TKTD) modeling has received increasing attention in terms of the regulatory environmental risk assessment of chemicals. This type of mechanistic model can integrate all available data from individual-level bioassays into a single framework and enable refined risk assessments by extrapolating from laboratory results to time-variable exposure scenarios, based, for instance, on surface water exposure modeling (e.g., FOCUS). Dynamic energy budget (DEB) models coupled with TKTD modules (DEB-TKTD) constitute the leading approach to assess and predict sublethal effects of chemicals on individual organisms. However, thorough case studies are rare. We provide a state-of-the-art example with the standard aquatic test species Ceriodaphnia dubia and the fungicide azoxystrobin, including all steps, from bespoke laboratory toxicity tests to model calibration and validation, through to environmental risk assessment. Following the framework proposed in the European Food Safety Authority Scientific Opinion from 2018, we designed bespoke good laboratory practice-compliant laboratory toxicity studies based on test guideline 211 of the Organisation for Economic Co-operation and Development and then identified robust parameter values from those data for all relevant model parameters through model calibration. The DEB-TKTD model, DEBtox2019, then informed the design of the validation experiment. Once validated, the model was used to perform predictions for a time-variable exposure scenario generated by FOCUS. A moving time-window approach was used to perform the environmental risk assessment. This assessment method reduces uncertainty in the risk assessment while maintaining consistency with the traditional measures of risk. Environ Toxicol Chem 2024;43:2409-2421. © 2024 Syngenta Crop Protection AG. ibacon GmbH and The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Budget impact models for lung cancer interventions: A systematic literature review.

Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence-based clinical decision tool to identify patients with non-small cell lung cancer who are most likely to benefit from immunotherapy. To evaluate the budgetary implications and describe a systematic literature review of published lung cancer BIMs. We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modeling methods used, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines. A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, 4 compared nondrug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time. The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights.

Developing a dynamic energy budget model to project potential effects of deep-sea mining plumes on the Atlantic deep-sea mussel, Bathymodiolus azoricus

Due to the consistent lack of Environmental Risk Assessment (ERA) for deep-sea mining scenarios, the potential impacts of this industry on marine ecosystems remain largely unknown. In order to fill this gap, a Dynamic Energy Budget (DEB) model was developed to study the consequences of toxic sediment plumes derived from deep-sea mining on the energy budget of the Atlantic deep-sea mussel, Bathymodiolus azoricus. Model calibration was based on environmental conditions observed at the Menez Gwen (MG) vent field (Mid-Atlantic Ridge- MAR), assuming a B. azoricus lifespan of 10 years and a maximum shell length of 119 mm. Scenario simulations were conducted to mimic the effects of increased concentrations of toxic sediment plumes on mussel filtration rates, the absorption of reduced substrates by their endosymbionts, and the energetic costs associated with metal toxicity. Data were sourced from B. azoricus and, when necessary, from proxy species. One disturbance scenario (EF1) incorporated measured rates and realistic parameters, while the other (EF2) was intentionally designed to encompass cumulative effects and uncertainties, representing a potential worst-case scenario. Both disturbance scenarios were initiated at three different timings (0, 1200 and 2400 days) to accommodate the mining effects at different stages of the mussels' life cycle. Results indicate that B. azoricus is significantly impacted by toxic sediment plumes, particularly during earlier life stages, potentially leading to severe growth impairment and mortality. These results were integrated into a food web model of the MG vent field, revealing that disruptions to the energetic balance of the vent mussel have widespread consequences for the entire ecosystem. Overall, we argue that this numerical framework offers a valuable tool for conducting ERA and Environmental Impact Assessments (EIA) in the context of industrial deep-sea mining.

A Budget Impact Analysis of Adalimumab Biosimilars; The Albanian Context

Adalimumab is used to treat various autoimmune conditions, such as rheumatoid arthritis, ankylosing spondylitis, psoriasis, psoriatic arthritis, Crohn’s disease, and ulcerative colitis. Adalimumab is a tumor necrosis factor (TNF) inhibitor. Adalimumab biosimilars have the potential to reduce costs for the healthcare system. The aim of the study is to develop a drug budget impact analysis to assess the potential financial impact of the availability of the biosimilar adalimumab-aacf in terms of cost savings due to its lower price compared to adalimumab-adaz. Methods: This is a budget impact analysis and cost calculator model in Microsoft Excel based on FDSKSH’s data for 2023. Results: Adalimumab biosimilars accounted for approximately 25% of biologic drug prescriptions in 2023. The reimbursement cost of biologic drugs for autoimmune diseases was ALL 186,523,409 in 2023 (1 ALL = 0.01 USD). Of that cost, 15.21% accounted for treatment with adalimumab biosimilars. Adalimumab-aacf was available at a 15.82% lower price than adalimumab–adaz. The analysis indicated that the replacement of the biosimilar adalimumab-adaz with the biosimilar adalimumab-aacf could have resulted in cost savings of ALL 968,138, and the reimbursement cost of biologic drugs for autoimmune diseases could have been 3% lower. With the potential cost savings, two more patients could have had access to biologic drugs in 2023. Conclusions: The Drug Budget Impact Analysis estimated that the availability of adalimumab-aacf would be more profitable in terms of cost savings compared to adalimumab-adaz. The scenario could result in increased treatment access for patients.

Open-Source Data Formalization through Model-Based Systems Engineering for Concurrent Preliminary Design of CubeSats

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases.

Modelling High Resolution Agricultural Nitrogen Budgets: A Case Study for Germany

Water pollution with nitrogen (N) from agriculture constitutes a persisting environmental problem in intensive farming regions worldwide. Understanding the spatio-temporal interconnection between agricultural N emissions and N inputs to water bodies is key to evaluating and improving existing mitigation policies. Nitrogen flux models are an indispensable tool for addressing these complex research questions in the land use–water nexus, requiring adequate data on agricultural N surpluses. However, high-resolution farm management data are not readily available to the scientific community. We develop a municipality-level agricultural N budget model for Germany based on farm-level administration data from the Integrated Administration and Control System (IACS) and regional expert knowledge. We estimate a total agricultural N surplus of 58 kg N ha−1 of utilised agricultural area as the three-year average for 2014–2016. About 90% of municipalities exhibit N surpluses between 21 and 99 kg N ha−1. Evaluation with collected farm accountancy data revealed a good fit of the modelled (with observed) mineral N quantities applied. Our results highlight the potential of farm-level data for N flux models. Due to the ubiquitous reporting of land use and farming structures in the IACS, our approach can be adapted in other countries of the EU to serve as a harmonised backbone of monitoring and policy impact assessments.

From BIM to thermal comfort: Leveraging BIM for rapid outdoor comfort assessments

This study introduces a novel system within a Building Information Modeling (BIM) environment to rapidly evaluate Outdoor Thermal Comfort (OTC). Traditionally, direct microclimate analysis in the design phase is hindered by cumbersome and resource-intensive processes. By utilizing information from BIM and integrating the COMFA thermal energy budget model, this approach facilitates immediate OTC evaluation. The system automates the Sky View Factor simulation using 3D modeling techniques and utilizes BIM model data to assess the thermal environment. Tested on a university campus, it effectively processes geospatial BIM data to compute microclimate parameters and estimate OTC using COMFA. This research represents a significant advancement in BIM-integrated OTC analysis, streamlining the design feedback process and aiding practitioners in improving outdoor thermal environments amidst climate change challenges.

Revitalizing Electrochemical Science and Engineering Education to Create a Clean Energy Workforce

Electrochemical science, engineering, and technology education and workforce development are central to the ongoing clean energy transition. In particular, I will highlight battery, hydrogen, and microelectronics industries as drivers. I then highlight a unique Electrochemistry-Technology-focused Graduate program launched through the Oregon Center for Electrochemistry in 2020. At the master’s level, the accelerated program consists of six core electrochemistry science and engineering lecture, laboratory, and project-based courses over either two or three academic quarters. In addition to core electrochemistry knowledge spanning thermodynamics, transport, kinetics, and device technology, emphasis is placed on problem solving, data analysis, programming (particularly in Python) and professional development and this additional content is embedded throughout all the core lecture and laboratory courses. Students are also introduced to continuum-level electrochemical simulations, with an emphasis on understanding the effects of boundary conditions on the simulation output. They also complete a team-based applied project sponsored by industry partners. The program ends with a nine-month paid internship leading to a master’s degree and career in the electrochemistry industry after 15 months.The coursework is also open to PhD students and postdoctoral scholars who go on to serve as near peer mentors and teaching assistants in the program in future years, directly synergistic with their research, and building community around electrochemistry. A new BS-MS program leveraging the above has launched in 2023, allowing University of Oregon students to complete coursework for both BS and MS in just four years yielding an incredibly high return on tuition and time investment in terms of career potential and earnings. Key to the success of the programs is very low administrative overhead with technical faculty/staff leading all efforts and a transparent budget model to fund the program based on tuition return.

Interannual variability and seasonality of litterfall in three temperate and boreal forest ecosystems of eastern Canada: A synthesis of long-term monitoring

Litterfall is a major pathway for transferring aboveground biomass to the forest floor and thus plays an important role in building forest soil carbon stocks. However, inter- and intra-annual variability of litterfall remains poorly documented, especially in North American temperate and boreal forests, due to the lack of recent long-term studies at high sampling frequencies. This potentially creates uncertainties in estimates of forest carbon budget models. The objectives of the present study were to 1) quantify the mean annual flux, interannual variability, and seasonality of litterfall in three sites (dominated respectively by sugar maple (Acer saccharum Marsh.), balsam fir (Abies balsamea (L.) Mill. 1768), and black spruce (Picea mariana (Mill.) B.S.P.)) in eastern Canada over a period of 22–32 years, 2) relate the litterfall amounts and temporal variations to the changes in the size of major organic matter pools in these ecosystems, and 3) compare our litterfall estimates with reference values used in national greenhouse gas inventories. Litterfall production decreased from the sugar maple to the balsam fir and black spruce sites, preponderantly due to species composition. Litterfall evolution was related to the aboveground biomass of live trees in both conifer sites; in contrast, in the broadleaf site, changes in forest composition and structure were apparently the main drivers. The litterfall seasonality varied between broadleaf and conifer sites and could be explained by a sigmoidal model. Substantial departures from the seasonality for some given years were likely due to important climatic anomalies. Forest floor biomass remained stable over time at all three sites despite the increase in litterfall at the balsam fir and sugar maple sites and rapid forest floor turnover at the latter site. Our analyses of litterfall suggest that reference values from the literature used for national greenhouse gas inventories underestimate annual litterfall and forest floor carbon stocks for temperate and boreal forests.