Site-specific Models Research Articles

Assessing the Lead Accumulation Risks of Wheat Grain by Developing a Source-Specific Accumulation Risk Assessment Model

Characterizing the lead (Pb) transfer in the "source-soil-wheat" chain is of great importance for the prevention and control of the Pb accumulation risk in wheat grain harvested from the croplands of China. In this study, we used the Jiyuan City, northern China, as a case study to investigate the influence of contamination sources and soil factors on the accumulation of Pb in wheat grain. A site-specific source risk assessment model (SRAM), coupling the positive matrix factorization model, Freundlich-type function, and the Monte Carlo simulation method, was developed to estimate the risk of Pb accumulation in wheat grain harvested from different scenarios. Based on the results of the spatial analysis, the effectiveness and potential risk of the control measures applied in the study area was also evaluated. Atmospheric deposition and phosphate fertilizer application were identified as major sources contributing to 29.0% of the Pb accumulations in wheat grain. Soil pH and cation-exchange capacity (CEC) were the primary causative factors affecting the Pb accumulation in wheat grain. Cropping wheat in the high Pb continuation risk areas (western and northwestern areas) of Jiyuan City caused a 10.5% likelihood of Pb to accumulate above the China food standard limit of 0.2 mg·kg-1 (DW). This risk was significantly decreased to 2.39% when the CEC levels of affected soils was improved to 20 cmol·kg-1 and above.

Characterization of soil drainage dynamics on karst terrains by developing a site-specific reservoir cascade scheme hydrological model with preferential flows

Models provide quantitative solutions that enable better understanding of karst soils hydrology. A location in central Spain was selected to implement a 1-D hydrological model that uses a reservoir cascade scheme (RCS) approach to simulate soil water content (SWC) at daily resolution. The model simulates SWC for six soil layers and includes additional modules to reproduce preferential flow regime. The soil profile in the model is 0.5 m deep, developed in silt loam soil on top of dolomite marbles. Three different configurations of the model were considered. Configuration 1 considers a basic RCS module, whereas other configurations include preferential flows. Thus, Configuration 2 considers RCS module together with a continuous preferential flow module, where 1 to 5% of available SWC from each layer is drained every day along preferential pathways. Configuration 3 contains a discontinuous preferential flow module in addition to previous modules. The discontinuous preferential flow is only activated during rainfall events occurring after long dry periods. Simulated SWC values are compared with measured SWC values, so model parameters are iteratively adjusted to optimize the model results. The model produces the best simulations implementing the Configuration 3, when matrix flow and two preferential flow regimes are considered. The model shows that preferential flows significantly contribute to recharge and should not be neglected in soil hydrological models and recharge estimations in karst areas.

Simulating Scanning Electrochemical Microscopy Images of Arbitrarily Shaped Reactive Sites without a Site-Specific Model

Scanning electrochemical microscopy (SECM) yields two-dimensional electrochemical images when the current at a microelectrode is recorded as it moves above a surface generating redox species. As SECM images are often systematically compared to microstructure features obtained by electron microscopy there is a clear need to find ways to integrate electron microscopy images into SECM image simulations.Recently, finite element method simulations of sites where electrochemical reactions take place is employed to evaluate reactive feature size.[1] Current models move the microelectrode or the reacting surface, continuously changing the geometry of the simulation and thus requiring re-meshing, which increases the computation time.We present a modeling approach by which SECM images of arbitrarily-shaped reactive sites can be simulated using a grid of pixels that can be turned ‘on’ and ‘off’ using COMSOL® multiphysics. Specifically, COMSOL®’s Java API is used to change the selection of pixels that are turned ‘on’ to simulate the movement of the electrode without changing the geometry of the simulation. The mesh can be re-used for each data-point in the image, dramatically reducing the time needed to simulate SECM images. This work also presents a mostly automated workflow for using an electron microscopy image of a reactive site to determine which pixels should be turned ‘on’ and fitting the kinetics at these pixels by simulating their SECM image.[1] L. I. Stephens, N. A. Payne, S. A. Skaanvik, D. Polcari, M. Geissler, J. Mauzeroll, Evaluating the Use of Edge Detection in Extracting Feature Size from Scanning Electrochemical Microscopy Images. Analytical Chemistry 2019, 91, 3944-3950.

Different phylogenomic methods support monophyly of enigmatic 'Mesozoa' (Dicyemida + Orthonectida, Lophotrochozoa).

Dicyemids and orthonectids were traditionally classified in a group called Mesozoa, but their placement in a single clade has been contested and their position(s) within Metazoa is uncertain. Here, we assembled a comprehensive matrix of Lophotrochozoa (Metazoa) and investigated the position of Dicyemida (= Rhombozoa) and Orthonectida, employing multiple phylogenomic approaches. We sequenced seven new transcriptomes and one draft genome from dicyemids (Dicyema, Dicyemennea) and two transcriptomes from orthonectids (Rhopalura). Using these and published data, we assembled and analysed contamination-filtered datasets with up to 987 genes. Our results recover Mesozoa monophyletic and as a close relative of Platyhelminthes or Gnathifera. Because of the tendency of the long-branch mesozoans to group with other long-branch taxa in our analyses, we explored the impact of approaches purported to help alleviate long-branch attraction (e.g. taxon removal, coalescent inference, gene targeting). None of these were able to break the association of Orthonectida with Dicyemida in the maximum-likelihood trees. Contrastingly, the Bayesian analysis and site-specific frequency model in maximum-likelihood did not recover a monophyletic Mesozoa (but only when using a specific 50 gene matrix). The classic hypothesis on monophyletic Mesozoa is possibly reborn and should be further tested.

Characterization of the complete chloroplast genome sequences of six Dalbergia species and its comparative analysis in the subfamily of Papilionoideae (Fabaceae).

Dalbergia spp. are numerous and widely distributed in pantropical areas in Asia, Africa and America, and most of the species have important economic and ecological value as precious timber. In this study, we determined and characterized six complete chloroplast genomes of Dalbergia species (Dalbergia obtusifolia, D. hupeana, D. mimosoides, D. sissoo, D. hancei, D. balansae), which displayed the typical quadripartite structure of angiosperms. The sizes of the genomes ranged from 155,698 bp (D. hancei) to 156,419 bp (D. obtusifolia). The complete chloroplast genomes of Dalbergia include 37 tRNA genes, eight rRNA genes and 84 protein-coding genes. We analysed the sequence diversity of Dalberigia chloroplast genomes coupled with previous reports. The results showed 12 noncoding regions (rps16-accD, trnR-UCU-trnG-UCC, ndhE-ndhG, trnG-UCC-psbZ, rps8-rpl14, trnP-UGG-psaJ, ndhH-rps15, trnQ-UUG-rps16, trnS-GCU-psbI, rps12-clpP, psbA-trnK-UUU, trnK-UUU-intron), and four coding regions (rps16, ycf1, rps15 and ndhF) showed many nucleotide variations that could be used as potential molecular markers. Based on a site-specific model, we analysed the selective pressure of chloroplast genes in Dalbergia species. Twenty-two genes with positively selected sites were detected, involving the photosynthetic system (ndhC, adhD, ndhF, petB, psaA, psaB, psbB, psbC, psbK and rbcL), self-replication category of genes (rpoA, rpoC2, rps3, rps12 and rps18) and others (accD, ccsA, cemA, clpP, matK, ycf1 and ycf2). Additionally, we identified potential RNA editing sites that were relatively conserved in the genus Dalbergia. Furthermore, the comparative analysis of cp genomes of Dalbergieae species indicated that the boundary of IRs/SSC was highly variable, which resulted in the size variation of cp genomes. Finally, phylogenetic analysis showed an inferred phylogenetic tree of Papilionoideae species with high bootstrap support and suggested that Amorpheae was the sister of the clade Dalbergieae. Moreover, three genera of the Pterocarpus clade showed a nested evolutionary relationship. These complete cp genomes provided valuable information for understanding the genetic variation and phylogenetic relationship of Dalbergia species with their relatives.

Integrating hydraulic profiling tool pressure logs and hydraulic tomography for improved high-resolution characterization of subsurface heterogeneity

• HPT pressure logs ( P c ) can be rapidly collected in unconsolidated deposits. • P c data provide information on sharp and gradually-varying layer boundaries. • Integrating HPT-derived hydrostratigraphy and HT yields a K field with sharp boundaries. • HPT K estimates require additional evaluations for highly heterogeneous deposits. Hydraulic tomography (HT) has been developed as a robust technique in characterizing subsurface heterogeneity over the last two decades. Hydraulic conductivity ( K ) and specific storage ( S s ) estimated by geostatistically-based HT inversion approaches are typically smooth and lack geological features when observation data density is sparse. Although integrating geological information can be helpful in adding structural features into parameter fields, costly efforts are required to obtain borehole logging through conventional coring and sampling. Also, identification of geological features such as layer boundaries from sampled materials can be a subjective process. In this study, corrected pressure ( P c ) logs collected during hydraulic profiling tool (HPT) surveys are utilized to derive the local hydrostratigraphic layers of the glaciofluvial deposit at the North Campus Research Site (NCRS) in Waterloo, Ontario, Canada. Site-specific geological models representing aquifer and aquitard layers are developed and calibrated to drawdown data of multiple pumping tests. Results show that estimated K fields correctly reflect the variation features of permeameter-estimated K profiles, as well as the spatial distribution of high and low K sediments. The calibrated geological models performed better than the geostatistically-based inversion approach in predicting independent pumping tests used for model validation. Moreover, by including K estimates of geological models as prior means, the estimated K fields reveal both intra- and interlayer heterogeneity, and the performances of drawdown prediction are further improved for the geostatistical inversion approach. Overall, our study suggests that, integration of stratigraphic information derived from HPT P c logs is useful for HT to capture the sharp boundaries in K fields, especially when the pumping test data is sparse.

Relay-assisted handover technique for vehicular VLC networks

In this paper, we propose a relay-assisted handover technique for vehicular visible light communication networks where streetlights are configured to serve as Access Points (APs). In the proposed system, the vehicle is served by a relay terminal in the form of a neighbor vehicle in addition to the serving AP if the measured bit error rate becomes higher than a threshold value. We evaluate the system performance using realistic site-specific channel models developed through non-sequential ray tracing features of OpticStudio for various deployment scenarios. The simulation results show that our technique provides a superior performance compared to conventional handover schemes.

Evaluating Trade-Offs between Using Regional and Site-Specific Allometric Models to Predict Growing Stock Volume of Subtropical Atlantic Forests

Abstract Choosing models to predict volume for individual trees is a relevant step in estimating growing stock volume. When large-area estimates are needed, models should ideally be constructed based on observations acquired across the population. However, due to multiple constraints, models are often constructed using samples collected at one or in a few sites. Therefore, this study aimed to evaluate trade-offs between using models based on a regional dataset (RD) and a model based on a local dataset (LD) and to assess the effects of such models on large-scale estimates of stem volume (V) per unit area. Regional and local V datasets and inventory data acquired throughout the subtropical Brazilian Atlantic Forest were employed. When used to predict V for trees of the RD (n = 1,192), the local model (LM), based on 419 tree observations, presented a mean systematic percentage error (MSPE) of +11%. In turn, when the regional model (RM) (n = 1,192) was used to predict V for trees of the LD (n = 419), it yielded an MSPE of –3%. The estimated mean V ha–1 using the LM was 9.4% greater than the estimated mean using the RM. Moreover, less precise estimates were attained using the LM.

Risk of obstetric anal sphincter injuries at the time of admission for delivery: A clinical prediction model.

To develop and validate a model to predict obstetric anal sphincter injuries (OASIS) using only information available at the time of admission for labour. A clinical predictive model using a retrospective cohort. A US health system containing one community and one tertiary hospital. A total of 22 873 pregnancy episodes with in-hospital delivery at or beyond 21 weeks of gestation. Thirty antepartum risk factors were identified as candidate variables, and a prediction model was built using logistic regression predicting OASIS versus no OASIS. Models were fit using the overall study population and separately using hospital-specific cohorts. Bootstrapping was used for internal validation and external cross-validation was performed between the two hospital cohorts. Model performance was estimated using the bias-corrected concordance index (c-index), calibration plots and decision curves. Fifteen risk factors were retained in the final model. Decreasing parity, previous caesarean birth and cardiovascular disease increased risk of OASIS, whereas tobacco use and black race decreased risk. The final model from the total study population had good discrimination (c-index 0.77, 95% confidence interval [CI] 0.75-0.78) and was able to accurately predict risks between 0 and 35%, where average risk for OASIS was 3%. The site-specific model fit using patients only from the tertiary hospital had c-stat 0.74 (95% CI 0.72-0.77) on community hospital patients, and the community hospital model was 0.77 (95%CI 0.76-0.80) on the tertiary hospital patients. OASIS can be accurately predicted based on variables known at the time of admission for labour. These predictions could be useful for selectively implementing OASIS prevention strategies.

Soil texture affects the conversion factor of electrical conductivity from 1:5 soil-water to saturated paste extracts

Electrical conductivity (EC) of soil-water extracts is commonly used to assess soil salinity. However, its conversion to the EC of saturated soil paste extracts (EC e ), the standard measure of soil salinity, is currently required for practical applications. Although many regression models can be used to obtain EC e from the EC of soil-water extracts, the application of a site-specific model to different sites is not straightforward due to confounding soil factors such as soil texture. This study was conducted to develop a universal regression model to estimate a conversion factor (CF) for predicting EC e from EC of soil-water extracts at a 1:5 ratio (EC 1:5 ), by employing a site-specific soil texture ( i.e ., sand content). A regression model, CF = 8.910 5e 0.010 6sand /1.298 4 ( r 2 = 0.97, P < 0.001), was developed based on the results of coastal saline soil surveys ( n = 173) and laboratory experiments using artificial saline soils with different textures ( n = 6, sand content = 10%–65%) and salinity levels ( n = 7, salinity = 1–24 dS m -1 ). Model performance was validated using an independent dataset and demonstrated that EC e prediction using the developed model is more suitable for highly saline soils than for low saline soils. The feasibility of the regression model should be tested at other sites. Other soil factors affecting EC conversion factor also need to be explored to revise and improve the model through further studies.

Generation of a non-integrated induced pluripotent stem cell line from urine cells of a Chinese osteogenesis imperfecta type I patient

Osteogenesis imperfecta (OI) is a group of genetic disorders characterized mainly by fractures and bone deformities. It has been established that gene mutations, particularly those in COL1A1 and COL1A2, account for most phenotypes. Here, we generated an induced pluripotent stem cells (iPSCs) line named SMBCi014-A using urine cells (UCs) derived from a 15-year-old female OI type I patient who carried the frame-shift mutation of the COL1A1 gene (exon35:c.2450delC:p.P817fs). The patient had a family history of mild fractures and a blue sclera. Therefore, our study established a patient-derived site-specific cellular model of OI to better understand the osteogenic mechanism.

Site-specific Risk Stratification Models for Postoperative Recurrence and Survival Prediction in Patients with Upper Tract Urothelial Carcinoma Undergoing Radical Nephroureterectomy: Better Stratification for Adjuvant Therapy

BackgroundSite-specific postoperative risk models for localized upper tract urothelial carcinoma (UTUC) are unavailable. ObjectiveTo create specific risk models for renal pelvic urothelial carcinoma (RPUC) and ureteral urothelial carcinoma (UUC), and to compare the predictive accuracy with the overall UTUC risk model. Design, setting, and participantsA multi-institutional database retrospective study of 1917 UTUC patients who underwent radical nephroureterectomy (RNU) between 2000 and 2018 was conducted. Outcome measurements and statistical analysisA multivariate hazard model was used to identify the prognostic factors for extraurinary tract recurrence (EUTR), cancer-specific death (CSD), and intravesical recurrence (IVR) after RNU. Patients were stratified into low-, intermediate-, high-, and highest-risk groups. External validation was performed to estimate a concordance index of the created risk models. We investigated whether our risk models could aid decision-making regarding adjuvant chemotherapy (AC) after RNU. Results and limitationsThe UTUC risk models could stratify the risk of cumulative incidence of three endpoints. The RPUC- and UUC-specific risk models showed better stratification than the overall UTUC risk model for all the three endpoints, EUTR, CSD, and IVR (RPUC: concordance index, 0.719 vs 0.770, 0.714 vs 0.794, and 0.538 vs 0.569, respectively; UUC: 0.716 vs 0.767, 0.766 vs 0.809, and 0.553 vs 0.594, respectively). The UUC-specific risk model can identify the high- and highest-risk patients likely to benefit from AC after RNU. A major limitation was the potential selection bias owing to the retrospective nature of this study. ConclusionsWe recommend using site-specific risk models instead of the overall UTUC risk model for better risk stratification and decision-making for AC after RNU. Patient summaryUpper tract urothelial carcinoma comprises renal pelvic and ureteral carcinomas. We recommend using site-specific risk models instead of the overall upper tract urothelial carcinoma risk model in risk prediction and decision-making for adjuvant therapy after radical surgery.

Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Site Amplification at High Spatial Resolution from Combined Ambient Noise and Earthquake Recordings in Sion, Switzerland

Abstract Earthquake site effects have a major impact on the seismic hazard. However, evaluating the site response over a broad frequency range and with a high spatial resolution remains difficult. Therefore, there is a high demand from the engineering seismology community in having cost-effective and reliable approaches to evaluate the site response. Empirical site effect assessment has shown good reliability up to high frequencies but relies on earthquake recordings that require long station deployments. In contrast, seismic ambient noise can be rapidly recorded anywhere at any time. The hybrid standard spectral ratio (SSRh) combines both the spectral ratio from earthquake recordings at a few sites and the spectral ratio from ambient noise recordings at many sites from short duration deployments. The objective of our study is to evaluate the applicability of the SSRh to a large urban area. Our studied area is the Rhône valley around the city of Sion (Switzerland), which is prone to complex 2D and 3D site effects in a broad frequency range caused by the soft, thick, and variable sedimentary deposit of the Rhône river. We benefit from four permanent and five long-term seismic stations present in the area, and we performed 299 short ambient noise measurements on a regular 300 m side grid. At the permanent stations, we find good agreement between the SSRh and the empirical amplification function estimates using earthquake observations, and we observe a good stability of the SSRh over time, frequency, and space, despite the strong variability of the ambient noise. Based on that, we estimate the SSRh for every ambient noise recording point in the area to compute a high-resolution amplification model and subsequently a site-specific seismic hazard model for the Sion area. Our models are in good agreement with the geological information.

Pragmatic Validation of Numerical Models Used for the Assessment of Radioactive Waste Repositories: A Perspective

The safety case for a radioactive waste repository relies heavily on results obtained by numerical models that assess the long-term performance of the engineered and natural barrier systems. Given that important engineering and public policy decisions are based on these models, it is essential that we critically evaluate their abilities and limitations, and thus justify the level of confidence we have in the inferences drawn from the modeling. In this article, we discuss some of the issues surrounding the modeler’s attempts to test, corroborate, confirm, and verify numerical models—a process sometimes referred to as model validation. This wide-ranging topic is approached by first examining its deep roots in the philosophy of science and hypothesis testing. However, the application of these principles to radioactive waste isolation calls for a more pragmatic approach, which has the narrower goal of corroborating site-specific models and their usefulness for a specific purpose. We focus on the practical aspects of validating hydrogeological models that are used to understand the evolution of the repository system. We will make the case that the responsible use of numerical models requires a sufficient understanding of the quality and robustness of the simulation results, with direct implications for how these results need to be interpreted, and how they can (or cannot) be used in support of important policy decisions.

Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering

Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud movement are discovered using K-medoid clustering. To ensure broader variation in cloud movements, neighboring stations were also used that were selected using a dynamic time warping (DTW)-based similarity score. Next, cluster-specific models were constructed. At the prediction time, the current weather condition is first matched with the different weather groups found through clustering, and a cluster-specific model is subsequently chosen. As a result, multiple models are dynamically used for a particular day and solar station, which improves performance over a single site-specific model. The proposed model achieved 19.74% and 59% less normalized root mean square error (NRMSE) and mean rank compared to the benchmarks, respectively, and was validated for nine solar stations across two regions and three climatic zones of India.

Using convolutional neural networks to develop starting models for near-surface 2-D full waveform inversion

SUMMARY Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change near-surface (&amp;lt;30 m) site characterization by enabling the recovery of high-resolution (metre-scale) 2-D/3-D maps of subsurface elastic material properties. Yet, FWI results are quite sensitive to their starting model due to their dependence on local-search optimization techniques and inversion non-uniqueness. Starting model dependence is particularly problematic for near-surface FWI due to the complexity of the recorded seismic wavefield (e.g. dominant surface waves intermixed with body waves) and the potential for significant spatial variability over short distances. In response, convolutional neural networks (CNNs) are investigated as a potential tool for developing starting models for near-surface 2-D elastic FWI. Specifically, 100 000 subsurface models were generated to be representative of a classic near-surface geophysics problem; namely, imaging a two-layer, undulating, soil-over-bedrock interface. A CNN has been developed from these synthetic models that is capable of transforming an experimental wavefield acquired using a seismic source located at the centre of a linear array of 24 closely spaced surface sensors directly into a robust starting model for FWI. The CNN approach was able to produce 2-D starting models with seismic image misfits that were significantly less than the misfits from other common starting model approaches, and in many cases even less than the misfits obtained by FWI with inferior starting models. The ability of the CNN to generalize outside its two-layered training set was assessed using a more complex, three-layered, soil-over-bedrock formation. While the predictive ability of the CNN was slightly reduced for this more complex case, it was still able to achieve seismic image and waveform misfits that were comparable to other commonly used starting models, despite not being trained on any three-layered models. As such, CNNs show great potential as tools for rapidly developing robust, site-specific starting models for near-surface elastic FWI.

Using a clustering algorithm to identify patterns of valve-gaping behaviour in mussels reared under different environmental conditions

Physiological adaptations for inhabiting transitional environments with strongly variable abiotic conditions can sometimes be displayed as behavioural shifts. A striking example might be found in bivalve species that inhabit estuaries characterised by fluctuations in environment. The opening and closing of their valves, so called gaping activity, represents behaviour that is required for two key physiological functions: food intake and respiration. Linking valve-gaping behaviour to environmental drivers can greatly improve our understanding and modelling of bivalve bioenergetics. Nowadays large data sets on gaping activity can be collected with automated sensors, but interpretation is difficult due to the large amount of environmental drivers and the intra-individual variability. This study aims to understand whether an unsupervised machine learning method (k-means clustering) can be used to identify patterns in gaping activity.Two commercially important congener mussels, Mytilus galloprovincialis and Mytilus edulis inhabiting two transitional coastal areas, the Venice Lagoon and the Wadden Sea, were fitted with sensors to monitor valve-gaping, while a comprehensive set of environmental parameters was also monitored. Data were analysed by applying three times a k-mean algorithm to the gaping time series. In the 1st analyses, the algorithm was applied to the overall gaping time series, including daily variations. We identified at both sites three clusters that were characterised by different average daily gaping aperture. The algorithm was subsequently reapplied to relate daily means of gaping to environmental conditions, being temperatures, oxygen saturation and chlorophyll levels. This 2nd analyses revealed that mean gaping aperture was mainly linked to food availability. A 3rd follow-up analysis aimed at exploring daily patterns. This third analysis again revealed consistent patterns amongst the two sites, where two clusters emerged that showed different degrees of oscillatory behaviour. There was however no obvious relationship between this fine scale oscillatory behaviours and environmental variables, but in the Venice Lagoon there was a site effect. Overall, we show that clustering algorithms can disentangle behavioural patterns within complex series of big data. The latter offers new opportunities to improve site-specific bioenergetic bivalve models by rephrasing the clearance and respiration terms based on the mean gaping aperture, provided that further laboratory experimentations are conducted to extrapolate parameters linking aperture with energy inputs and outputs.

Emergence of local scaling relations in adsorption energies on high-entropy alloys

Alloying has been proposed to circumvent scaling relations between the adsorption energies thus allowing for the complete optimization of multistep reactions. Herein the fidelity of scaling rules on high-entropy alloy (HEA) surfaces is assessed focusing on hydrogen-containing molecules, *AHx for A = C and N (x = 0, 1, 2, 3), A = S (x = 0, 1, 2) and A = O (x = 0, 1). Using an adsorbate- and site-specific deep learning model to rapidly compute the adsorption energies on CoMoFeNiCu HEA surfaces, the energies of *AHx and *A are shown to be linearly correlated if *A and *AHx have identical adsorption site symmetry. However, a local linear dependence emerges between the configuration-averaged adsorption energies irrespective of the site symmetry. Although these correlations represent a weaker form of the scaling relationships, they are sufficient to prohibit the optimization of multistep reactions. The underpinning of this behavior is twofold (1) the nearsightedness principle and (2) the narrow distribution of the adsorption energies around the mean-field value. While the nearsightedness is general for all electronic systems, the second criterion applies in HEAs with relatively strong reactive elements. The present findings strongly suggest that alloys may not generally enable the breaking of scaling relationships.

Assessing the Generalizability of a Clinical Machine Learning Model Across Multiple Emergency Departments.

ObjectiveTo assess the generalizability of a clinical machine learning algorithm across multiple emergency departments (EDs).Patients and MethodsWe obtained data on all ED visits at our health care system’s largest ED from May 5, 2018, to December 31, 2019. We also obtained data from 3 satellite EDs and 1 distant-hub ED from May 1, 2018, to December 31, 2018. A gradient-boosted machine model was trained on pooled data from the included EDs. To prevent the effect of differing training set sizes, the data were randomly downsampled to match those of our smallest ED. A second model was trained on this downsampled, pooled data. The model’s performance was compared using area under the receiver operating characteristic (AUC). Finally, site-specific models were trained and tested across all the sites, and the importance of features was examined to understand the reasons for differing generalizability.ResultsThe training data sets contained 1918-64,161 ED visits. The AUC for the pooled model ranged from 0.84 to 0.94 across the sites; the performance decreased slightly when Ns were downsampled to match those of our smallest ED site. When site-specific models were trained and tested across all the sites, the AUCs ranged more widely from 0.71 to 0.93. Within a single ED site, the performance of the 5 site-specific models was most variable for our largest and smallest EDs. Finally, when the importance of features was examined, several features were common to all site-specific models; however, the weight of these features differed.ConclusionA machine learning model for predicting hospital admission from the ED will generalize fairly well within the health care system but will still have significant differences in AUC performance across sites because of site-specific factors.