Log-linear Relationship Research Articles

Scale matters: Large language models with billions (rather than millions) of parameters better match neural representations of natural language.

Recent research has used large language models (LLMs) to study the neural basis of naturalistic language processing in the human brain. LLMs have rapidly grown in complexity, leading to improved language processing capabilities. However, neuroscience researchers haven't kept up with the quick progress in LLM development. Here, we utilized several families of transformer-based LLMs to investigate the relationship between model size and their ability to capture linguistic information in the human brain. Crucially, a subset of LLMs were trained on a fixed training set, enabling us to dissociate model size from architecture and training set size. We used electrocorticography (ECoG) to measure neural activity in epilepsy patients while they listened to a 30-minute naturalistic audio story. We fit electrode-wise encoding models using contextual embeddings extracted from each hidden layer of the LLMs to predict word-level neural signals. In line with prior work, we found that larger LLMs better capture the structure of natural language and better predict neural activity. We also found a log-linear relationship where the encoding performance peaks in relatively earlier layers as model size increases. We also observed variations in the best-performing layer across different brain regions, corresponding to an organized language processing hierarchy.

Deep Neural Network-Based Accelerated Failure Time Models Using Rank Loss.

An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of model has been considered a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the non-linearity of predictors when modeling the mean. Deep neural networks (DNNs) have received much attention over the past few decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing non-linearity. Here, we propose applying a DNN to fit AFT models using Gehan-type loss combined with a sub-sampling technique. Finite sample properties of the proposed DNN and rank-based AFT model (DeepR-AFT) were investigated via an extensive simulation study. The DeepR-AFT model showed superior performance over its parametric and semiparametric counterparts when the predictor was nonlinear. For linear predictors, DeepR-AFT performed better when the dimensions of the covariates were large. The superior performance of the proposed DeepR-AFT was demonstrated using three real datasets.

Hybrid multi-stage steel footprinting unveils a more interdependent material foundation of the global economy

Steel is foundational to modern society, yet tracking its socio-economic metabolism is challenging due to the complex global trade networks. Traditional indicators, such as domestic material consumption (DMC) and consumption-based material footprints (MF), typically focus on metal ore extraction, overlooking the multiple stages of steel production and consumption. To address this, we integrate multi-national anthropogenic steel cycles and international trade networks of steel-containing products into a global monetary multi-regional input-output (MRIO) model. We construct material efficiency indicators based on footprints of iron ores, crude steel, castings, finished steel, and fabricated steel products, comparing them with conventional economy-wide material flow indicators like domestic material production (DMP) and DMC. Our findings reveal that per capita multi-stage MF indicators exhibit more robust log-linear relationships with per capita GDP with an average R2 value of 0.72 compared to 0.10 and 0.18 for DMP and DMC. Shares of Embodied trade in total global production exceed direct trade by 24 percentage points on average, emphasizing the significance of international embodied metal transfers. Using multi-stage MF indicators also reduces disparities in material efficiency between developed and developing countries. This study unravels the intricacies of global steel supply chains and the true interdependencies of steel-containing products among countries.

Urbanization moderates the transitional linkages between energy resource use, greenhouse gas emissions, socio-economic and human development: Insights from subnational analyses in China

Sustainable energy use and management are key to improving human well-being, while rapid urbanization intersecting with economic development makes it challenging. Extensive cross-national analyses on this topic can inform the global agenda; however, far fewer subnational explorations consider the impact of urbanization in moderating the relationships between energy resource use, greenhouse gas emissions, socio-economic and human development. Therefore, this research analyzed Chinese subnational data from 2000 to 2019 to reveal the dynamic relationship between energy use and objective human well-being and explored how urbanization moderated the transitional linkages from energy use to economic development to human need satisfiers and to human well-being. This research found a diminishing return from energy use (both primary energy extraction and final energy use) to the Human Development Index (HDI) from 2000 to 2019. Moreover, in later years, there was neither a linear correlation nor a log-linear relationship between energy use and HDI at the subnational level. In contrast, HDI was consistently strongly correlated with GDP per capita and household income at the subnational level in China over time. Two distinct energy-GDP pathways, i.e., high energy use-low GDP and low energy use-high GDP, were observed at the subnational level, which explained why the log-linear relationship between HDI and energy use/GHGs diminished over time. Urbanization level had a significant moderating impact on the linkages between human need satisfiers and HDI, as well as life expectancy. Subnational policies should prioritize different human need satisfiers in different urbanization periods.

Numerical Analysis of Load-sharing Behavior of Combined Pile Raft Foundation System

This study investigates the application of combined pile raft foundations in the soft soil of Kathmandu Valley, taking into account varying pile spacing and length. Both embedded beams and volume piles were used in numerical analysis to assess the load-sharing behavior of the raft and pile under different conditions. The settlement results from the volume pile, and embedded beam element was comparable, with less than a 6.21% difference, indicating that both methods are equally suitable for evaluation. The embedded beam element was selected for further analysis. The analysis suggests that pile load sharing is primarily influenced by spacing, while an increase in pile stiffness due to increased pile length has a minor effect. The load-sharing factor follows a log-linear relationship when the spacing exceeds 3D. Regarding the normalized vertical settlement of the pile, the power rule governs the load-sharing factor. This power function related to normalized settlement is highly beneficial for designing the CPRF and choosing the appropriate pile spacing and length. Finally, the capacity utilization curve suggests that a spacing greater than three times the diameter results in higher pile capacity utilization. Conversely, a decrease in spacing significantly reduces the mobilized strength of the piles.

Determining the relationship between mobile phone network signal strength and radiofrequency electromagnetic field exposure: protocol and pilot study to derive conversion functions

Mobile phones continuously monitor and evaluate indicators of the received signal strengths from surrounding base stations to optimise wireless services. These signal strength indicators (SSIs) offer the potential for assessing radiofrequency electromagnetic field (RF-EMF) exposure on a population scale, as they can be related to exposure from both base stations and handset devices. Within the ETAIN (Exposure To electromAgnetic fields and plaNetary health) project, an open-access RF-EMF exposure app for smartphones, named "5G Scientist Monitor”, has been developed using citizen science. This paper delineates a measurement protocol for deriving formulas to convert the app SSIs into electric field values to estimate RF-EMF exposure. It presents pilot study results from measurements taken at four locations in Lyon, France (FR), and 14 locations in the Netherlands (NL), using three different phone models and the most common network providers in each country. The measurements were conducted while executing different usage scenarios, such as calls or data transmission. The exposimeter ExpoM-RF4 and on-body electric field probes were used to measure exposure from far-field sources and the handset, respectively. Two-minute aggregates were considered the sample unit for analyses (n=891 in NL and n=395 in FR). Regression analyses showed a positive log-linear relationship between Long Term Evolution (LTE) SSIs and far-field RF-EMF exposure when aggregating data by location (coefficients for the normalised RSSI: 0.91 [95% CI: 0.55 - 1.28] in FR, 1.09 [95% CI: 0.96 - 1.22] in NL). Negative log-linear trends were observed for handset-related RF-EMF exposure at the ear (-0.31 [95% CI: -0.46 - -0.16]) and chest (-0.20 [95% CI: -0.37 - -0.03]) during data transmission scenarios. These results demonstrate that the 5G-Scientist-Monitor app can be implemented for smartphone-based RF-EMF estimation. However, uncertainties in individual measurement points highlight the need for further data collection and analysis to improve the accuracy of exposure estimates.

Loss of cognitive function in Mexican children due to lead exposure and the associated economic costs

BackgroundLead exposure reduces the cognitive development and future economic prospects of children. While previous studies in high-income settings have explored productivity losses associated with lead exposure, limited research has focused on low and middle-income countries like Mexico. ObjectivesThis study aims to provide a comprehensive assessment of the economic implications of lead exposure on Mexican children using, for the first time, nationally representative Blood Lead Levels (BLLs) measurements in children aged 1–4, specifically focusing on the costs of forgone lifetime income due to cognitive losses. MethodsBLLs of children aged 1–4 were extracted from the 2018–2019 National Health and Nutrition Survey (ENSANUT). Estimations of cognitive losses were derived from a log-linear relationship between BLLs and IQ loss. Lost lifetime economic productivity per child was calculated, assuming a 2% reduction in lifetime potential productivity for each IQ point lost due to lead exposure, based on previous literature (Attina and Trasande, 2013; Larsen and Sánchez-Triana, 2023). Productivity data were obtained from representative sources for Mexico. ResultsThe estimated economic loss amounted to US $33.02 billion, equivalent to 2.76% of Mexico's Gross Domestic Product (GDP) in 2019 (calculated for a 1-year cohort). On a national scale, the long-term loss of cognition for children 1–4 years old is 4.14 IQ points per child due to lead exposure, with significant variability across States (range: 3.26 to 5.26). Lead-poisoned children (≥5 μg/dL) suffered an average loss of 6.42 IQ points (range: 0 to 6.97). In terms of economic impact, some States like Chiapas experienced losses of 7.08% of its GDP, while others had losses as low as 0.67%. Intriguingly, states with lower Human Development Index (HDIs) exhibited relatively higher economic losses despite lower average blood lead levels. DiscussionThe heterogeneous impact of lead exposure across Mexican states underscores the necessity for tailored regional policies. These findings emphasize the urgency for targeted interventions and informed policy measures to mitigate the socioeconomic consequences of lead exposure on Mexican children.

Bayesian identification and estimation of radon-related increased hazard rates of cancer death in the updated French cohort of uranium miners (1946-2014).

A recent update of the French cohort of uranium miners added seven years of follow-up data. We use these new data to look for new possible radon-related increased risks and refine the estimation of the potential association between cumulative radon exposure and four cancer sites: lung cancer, kidney cancer, brain and central nervous system (CNS) cancer and leukemia (excluding chronic lymphocytic leukemia, which is not radiation-induced). Several parametric survival models are proposed, fitted and compared under the Bayesian paradigm, to perform new and original exposure-risk analyses. In line with recent UNSCEAR recommendations, we consider time-related effect modifiers and exposure rate as potential effect modifying factors. We use Bayesian model selection criteria to identify radon-related increased hazard rates. Under the assumption of a linear exposure-risk relationship, we found a substantial evidence for a strictly positive effect of cumulative radon exposure on the hazard rate of death by lung cancer among French uranium miners. Given the current available data under the assumptions of a linear or log-linear exposure-risk relationship, it is not possible to conclude in favour of the absence or the existence of a strictly positive effect of chronic exposure to radon on the hazard rate of death by kidney cancer. Regarding death by brain and CNS cancer, there is a substantial evidence for the absence of radon-related effect. Finally, under the assumption of a log-linear exposure-risk relationship, a small positive radon-related effect appears when looking at the risk of death by leukemia (excluding CLL). This study investigates the existence of radon-related increased risk of death by lung cancer, kidney cancer, brain and CNS cancer and leukemia under a Bayesian framework and assumptions of linear and log-linear exposure-risk relationships. If there is no doubt in the interpretation of the results for lung cancer and brain and CNS cancer, the conclusion is less clear-cut in the case of kidney cancer and leukemia (excluding CLL). A future update of the French cohort, increasing the follow-up time for miners, may help to reach a clearer conclusion for these two cancer sites.

Enhancing the health benefits of air quality improvement: a comparative study across diverse scenarios.

In many studies, linear methods were used to calculate health benefits of air quality improvement, but the relationship between air pollutants and diseases may be complex and nonlinear. In addition, previous studies using reference number as average number of diseases may overestimate the health benefits. Therefore, the nonlinear model estimation and resetting of the reference number were very important. Hospital admission data for coronary heart disease (CHD), meteorological data, and air pollutant data of Zibo City from 2015 to 2019 were collected. The generalized additive model (GAM) was used to explore the association between air pollutants and hospital admission for CHD, and to evaluate the effects on health benefits under different reference number settings. A total of 21,105 hospitalized cases for CHD were reported in Zibo during the study period. The results of the GAM showed there was a log-linear exposure-response relationship between O3 and hospital admissions for CHD, with RR (relative risk) of 1.0143 (95% CI: 1.0047 ~ 1.0239). There were log-nonlinear exposure-response relationships between PM10, PM2.5, SO2, and hospital admissions for CHD. With the increase of pollutants concentrations, the risk for hospital admission showed a trend of increasing first and then decreasing. Compared with the average hospital admissions as the reference number, health benefits calculated by hospital admissions predicted by the GAM model yielded lower. Using the World Health Organization air quality guidelines as reference, attributable fractions of O3, PM10, and PM2.5 were 1.97% (95% CI: 0.63 ~ 3.40%), 11.82% (95% CI: 8.60 ~ 15.24%), and 11.82% (95% CI: 8.79 ~ 15.04%), respectively. When quantifying health benefits brought by improving air quality, corresponding calculation methods should first be determined according to the exposure-response relationships between air pollutants and outcomes. Then, applying the average hospital admissions as reference number may overestimate health benefits resulting from improved air quality.

Competitive electrochemical aptasensor for high sensitivity detection of liver cancer marker GP73 based on rGO-Fc-PANi nanocomposites

Golgi protein 73 (GP73) is a novel tumor marker in the early diagnosis and prognosis of hepatocellular carcinoma (HCC). Herein, a competitive electrochemical aptasensor for detecting GP73 was constructed using reduced graphene oxide-ferrocene-polyaniline nanocomposite (rGO-Fc-PANi) as the biosensing platform. The rGO-Fc-PANi had larger specific surface area, excellent conductivity and outstanding electroactive performance, which served as nanocarrier for GP73 aptamer (GP73Apt) binding and as redox nanoprobe for record electrical signal. Then, a complementary chain (cDNA) was fixed to the electrode by hybridization with GP73Apt. When GP73 was present, a competitive process happened among cDNA, GP73Apt and GP73, formed the GP73-GP73Apt stable chemical structure and made cDNA detach from the sensing electrode, resulting in enhancement of electrical signal. The difference in the corresponding peak current before and after the competition can be used to indicate the quantitative of GP73. Under optimal conditions, the DPV current response showed a good log-linear relationship with GP73 concentrations (0.001 ∼ 100.0 ng/mL) with a detection limit of 0.15 pg/mL (S/N = 3). It was successfully used for GP73 detection in human serum with RSDs ranging from 1.08 % to 6.96 %. Therefore, the aptasensor could provide an innovative technology platform and hold a great potential in clinical application.

Clarity tubes as effective citizen science tools for monitoring wastewater treatment works and rivers.

Improved freshwater resource management requires the implementation of widespread, effective, and timely water quality monitoring. Conventional monitoring methods are often inhibited by financial, infrastructural, and human capacity limitations, especially in developing regions. This study aimed to validate the citizen-scientist-operated transparency or clarity tube (hereafter "clarity tube") for measuring water clarity as a proxy for total suspended solids (TSS) concentration, a critical quality metric in river systems and wastewater treatment works (WWTW) effluent in Southern Africa. Clarity tubes provided a relatively accurate and precise proxy for TSS in riverine lotic systems and WWTW effluent, revealing significant inverse log-linear relationships between clarity and TSS with r2 = 0.715 and 0.503, respectively. We demonstrate that clarity-derived estimates of TSS concentration (TSScde) can be used to estimate WWTW compliance with WWTW effluent TSS concentration regulations. The measurements can then be used to engage with WWTW management, potentially affecting WWTW performance. Overall, these findings demonstrate the usefulness of clarity tubes as low-cost, accessible, and easy-to-use citizen science tools for high spatial and temporal resolution water quality monitoring, not only in rivers in Southern Africa but also in WWTW effluent for estimating compliance, with strong global relevance to the sustainable development goals (SDGs). Integr Environ Assess Manag 2024;20:1463-1472. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Spontaneous recovery from neuromuscular block after a single dose of a muscle relaxant in pediatric patients: A systematic review using a network meta-analytic and meta-regression approach.

Age-related differences in the pharmacokinetics and pharmacodynamics of neuromuscular blocking agents (NMBAs) and the short duration of many surgical procedures put pediatric patients at risk of postoperative residual curarization (PORC). To date, the duration of neuromuscular blocking agent effect in children has not been analyzed in a quantitative review. The current meta-analysis aimed to compare spontaneous recovery following administration of various types and doses of neuromuscular blocking agents and to quantify the effect of prognostic variables associated with the recovery time in pediatric patients. We searched for randomized controlled trials (RCTs) and controlled clinical trials (CCTs) that compared the time to 25% T1 (t25), from 25% to 75% T1 (RI25-75), and to ≥90% train-of-four (tTOF90) neuromuscular recovery between common neuromuscular blocking agent treatments administered as a single bolus to healthy pediatric participants. We compared spontaneous t25, RI25-75, and tTOF90 between (1) neuromuscular blocking agent treatments and (2) age groups receiving a given neuromuscular blocking agent intervention and anesthesia technique. Bayesian random-effects network and pairwise meta-analyses along with meta-regression were used to evaluate the results. We used data from 71 randomized controlled trials/controlled clinical trials including 4319 participants. Network meta-analysis allowed for the juxtaposition and ranking of spontaneous t25, RI25-75, and tTOF90 following common neuromuscular blocking agent interventions. For all neuromuscular blocking agents a log-linear relationship between dose and duration of action was found. With the neuromuscular blocking agent treatments studied, the average tTOF90 (mean[CrI95]) in children (>2-11 y) was 41.96 [14.35, 69.50] and 17.06 [5.99, 28.30] min shorter than in neonates (<28 d) and infants (28 d-12 M), respectively. We found a negative log-linear correlation between age and duration of neuromuscular blocking agent effect. The difference in the tTOF90 (mean[CrI95]) between children and other age groups increased by 21.66 [8.82, 34.53] min with the use of aminosteroid neuromuscular blocking agents and by 24.73 [7.92, 41.43] min with the addition of sevoflurane/isoflurane for anesthesia maintenance. The times to neuromuscular recovery are highly variable. These can decrease significantly with age and are prolonged when volatile anesthetics are administered. This variability, combined with the short duration of many pediatric surgical procedures, makes quantitative neuromuscular monitoring mandatory even after a single dose of neuromuscular blocking agent.

음식 주문 배달 산업의 긴꼬리 효과에 관한 실증 연구

Purpose - This study aims to quantify the long tail effect in the digital economy. It also investigates the role of digital platform before and after the COVID-19 pandemic. Design/methodology/approach - We take advantage of a granular data set from one of the biggest digital platforms in Korea. Rather than computing the absolute number of products sold or the Gini coefficient, we estimate the slope of the log-linear relationship of the non-parametric sales distribution. Findings - We find that the use of online food order and delivery services is positively associated with individual restaurant’s sales growth. We also document that the long tail effect is increasing over time. Long tail effects are clustered in the cross-section where average revenue per order is high or the restaurant belongs to the top 50% of the sales distribution. Research implications or Originality - The findings may indicate that digital platforms are contributing to the development of the digital economy in Korea. Also, we confirm that digital platforms make it possible for small and sole proprietors to go through the difficulties induced by the COVID-19 pandemic.

Bayesian analysis of acoustic emission data for prediction of fatigue crack growth in concrete

Acoustic emission (AE) is a valuable technique for non-destructive testing, enabling real-time monitoring and measurement of fatigue crack growth in engineering structures. The present study focuses on investigating the fatigue crack growth behaviour in plain concrete specimens through Bayesian analysis of AE data collected during the testing of beam specimens under three-point bending. A log-linear relationship is established between parameters extracted from AE waveforms and the fatigue crack growth in concrete. Additionally, AE energy is identified as the most suitable parameter for characterising the fatigue behaviour of concrete. Bayesian regression is employed for estimating model parameters and their posterior distributions. The proposed model is validated through post-processing and Bayesian analysis of experimental data from the literature. Furthermore, it is demonstrated that the estimated model parameters remain unaffected by the frequency of fatigue loading and the size of the specimen.

Evaluation of Damage Stress Thresholds and Mechanical Properties of Granite: New Insights from Digital Image Correlation and GB-FDEM

We employed a novel combination of digital image correlation (DIC) and grain-based hybrid finite–discrete element method (GB-FDEM) to improve the comprehension of the relationships between microstructural features and the mechanical properties of granitic rocks. DIC and numerical results showed that macrocracks initiated and propagated along grain boundaries among different minerals driven by the high stiffness contrast between the compliant biotite and the stiffer feldspar/quartz grains. Surface deformation analyses revealed that tensile-dominated macrocracks open at monotonically increased rates before the crack damage threshold, and the opening accelerated afterwards with the increased shear component. The onset of the acceleration of the opening rate of macrocracks can be used to infer the crack damage threshold. Both strain and acoustic emission were used to infer damage stress thresholds in the synthetic numerical samples. Numerical results showed that the damage stress thresholds and uniaxial compressive strength decrease with increasing grain size following log-linear relations. Coarse-grained samples tend to fail by axial splitting, while fine-grained samples fail by shear zone formation. Biotite and quartz contents significantly affect mechanical properties, while quartz to feldspar ratio is positively related to the mechanical properties. Our study demonstrates the capacities of DIC and GB-FDEM in inferring damage conditions in granitic rocks and clarifies the microstructural control of the macroscopic mechanical behaviors. Our results also provide a comprehensive understanding of the systematics of strain localization, crack development, and acoustic emission during the rock progressive failure process.

Development of a SERS aptasensor for the determination of L-theanine using a noble metal nanoparticle-magnetic nanospheres composite.

An ultrasensitive surface-enhanced Raman spectroscopy (SERS) aptamer sensor (aptasensor) using a noble metal nanoparticle-magnetic nanospheres composite was developed for L-theanine detection. It makes use of Fe3O4@Au MNPs and Au@Ag NPs embedded with the Raman reporter 4-mercaptobenzoic acid (4MBA). Au@4MBA@Ag NPs modified by aptamer and Fe3O4@Au MNPs modified by cDNA created the aptasensor with the strongest Raman signal of 4MBA through the specific binding of the aptamer. With the preferred binding of L-theanine aptamer to L-theanine, Au@4MBA@Ag NPs were released from Fe3O4@Au MNPs, causing a linear decrease in SERS intensity to achieve the SERS detection of the L-theanine. The SERS peak of 4MBA at 1078cm-1 was used for quantitative determination. SERS intensity showed a good log-linear relationship within the range 10-10 to 10-6Mof L-theanine. Theaptasensor has a high selectivityfor L-theanine compared with other twelve testedanalytes. Hence, thisaptasensor is a promising analytical tool for L-theanine detection. The developedmethod was appliedto the analysis of real samples, demonstrating excellent performance. The comparison with the standard liquid chromatography mass spectrometry methodshowed an error within 20%.

Sea spray emissions from the Baltic Sea: comparison of aerosol eddy covariance fluxes and chamber-simulated sea spray emissions

Abstract. To compare in situ and laboratory estimates of sea spray aerosol (SSA) production fluxes, we conducted two research campaigns in the vicinity of an eddy covariance (EC) flux tower on the island of Östergarnsholm in the Baltic Sea during May and August 2021. To accomplish this, we performed EC flux measurements for particles with diameters between 0.25 and 2.5 µm simultaneously with laboratory measurements using a plunging jet sea spray simulation chamber containing local seawater sampled close to the footprint of the flux tower. We observed a log-linear relationship between wind speed and EC-derived SSA emission fluxes, a power-law relationship between significant wave height and EC-derived SSA emission fluxes, and a linear relationship between wave Reynolds number and EC-derived SSA emission fluxes, all of which are consistent with earlier studies. Although we observed a weak negative relationship between particle production in the sea spray simulation chamber and seawater chlorophyll-α concentration and a weak positive relationship with the concentration of fluorescent dissolved organic matter in seawater, we did not observe any significant impact of dissolved oxygen on particle production in the chamber. To obtain an estimate of the size-resolved emission spectrum for particles with dry diameters between 0.015 and 10 µm, we combined the estimates of SSA particle production fluxes obtained using the EC measurements and the chamber measurements in three different ways: (1) using the traditional continuous whitecap method, (2) using air entrainment measurements, and (3) simply scaling the chamber data to the EC fluxes. In doing so, we observed that the magnitude of the EC-derived emission fluxes compared relatively well to the magnitude of the fluxes obtained using the chamber air entrainment method as well as the previous flux measurements of Nilsson et al. (2021) and the parameterizations of Mårtensson et al. (2003) and Salter et al. (2015). As a result of these measurements, we have derived a wind-speed-dependent and wave-state-dependent SSA parameterization for particles with dry diameters between 0.015 and 10 µm for low-salinity waters such as the Baltic Sea, thus providing a more accurate estimation of SSA production fluxes.

A Novel Similarity Approach for Describing the Bulk Shear in the Atmospheric Surface Layer

The Monin–Obukhov Similarity Theory (MOST) is a cornerstone of boundary layer meteorology and the basis of most parameterizations of the atmospheric surface layer. Due to its significance for observations and modelling, we generalize the dimensional analysis of MOST by considering the bulk gradient directly, enabling the study of any sublayer of the atmospheric surface layer. This results in a family of similarity relations describing all gradients from the local gradient to the full-layer bulk gradient. By applying the profiles derived from the law-of-the-wall and MOST, we are able to derive analytic expressions for this family of similarity relations. Under stable conditions, we discover that the log-linear profile of Businger–Dyer generalizes from the local to the bulk shear where the slope is dependent on the choice of the layer. The simplicity of the general log-linear relation allows for estimating the influence of stability on the non-dimensional gradients. It is shown that bulk gradients are less sensitive to stability than the local gradient. By correctly filtering cases where the full-layer bulk gradient is influenced by stability, we demonstrate that MOST is compatible with the Hockey-Stick Transition. For unstable conditions, the Kader and Yaglom (J Fluid Mech 212(151):637-662, 1990) model represents the local gradient well but was not successful in representing the bulk gradient, demonstrating the need for further analysis of scaling relations for the unstable atmospheric surface layer.

Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds.

Interspecies correlation estimation (ICE) models are log-linear relationships of acute sensitivity between two species that estimate the sensitivity of an untested species from the known sensitivity of a surrogate. As ICE model use increases globally, additional user guidance is required to ensure consistent use across chemicals and applications. The present study expands ICE uncertainty analyses and user guidance with a focus on low toxicity compounds whose acute values (i.e., reported as mg/L) can be greater than those used to develop a model. In these cases, surrogate values may be outside the ICE model domain and require additional extrapolations to predict acute toxicity. We use the extensive, standardized acute toxicity database underlying ICE models to broadly summarize inter-test variability of acute toxicity data as a measure by which model prediction accuracy can be evaluated. Using the data and models found on the USEPA Web-ICE (www3.epa.gov/webice), we created a set of "truncated" models from data corresponding to the lower 75th percentile of surrogate toxicity. We predicted toxicity for chemicals in the upper 25th percentile as both μg/L beyond the model domain and converted to mg/L (i.e., "scaled" value) and compared these predictions with those from cross-validation of whole ICE models and to the measured value. For ICE models with slopes in the range 0.66-1.33, prediction accuracy of scaled values did not differ from the accuracy of the models when data were entered as μg/L within or beyond the model domain. An uncertainty analysis of ICE confidence intervals was conducted and an interval range of two orders of magnitude was determined to minimize type I and II errors when accepting or rejecting ICE predictions. We updated the ICE user guidance based on these analyses to advance the state of the science for ICE model application and interpretation. Integr Environ Assess Manag 2024;20:1554-1565. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Analysis of scaling relationships for flood parameters and peak discharge estimation in a tropical region

Abstract Relationships between peak discharges and catchment size (e.g., flood scaling) in a catchment have the potential to support new river flood forecasting approaches but have not been tested in tropical regions. This study determined flood scaling relationships between peak discharge and nested drainage areas in the La Sierra catchment (Mexico). A statistical power law equation was applied to selected rainfall–runoff events that occurred between 2012 and 2015. Variations in flood scaling parameters were determined in relation to catchment descriptors and processes for peak downstream discharge estimation. Similar to studies in humid temperate regions, the results reveal the existence of log-linear relationships between the intercept (α) and exponent (θ) parameter values and the log–log power–law relationships between (α) and the peak discharge observed from the smallest headwater catchments. The flood parameter values obtained were then factored into the scaling equation (QP = αAθ) and successfully predicted downstream flood peaks, especially highly recurrent flood events. The findings contribute to a better understanding of the nature of flood wave generation and support the development of new flood forecasting approaches in unregulated catchments suitable for non-stationarity in hydrological processes with climate change.