Direct and deferred sediment-transport events and seafloor disturbance induced by trawling in submarine canyons.

Bottom trawling on marine environments can drastically modify seafloor geomorphology and sedimentary dynamics not only on the fishing grounds but also in adjacent downslope regions, particularly in submarine canyons environments, which are hotspots of benthic biomass and productivity in the deep sea. When this type of fishery occurs along submarine canyon flanks, it can induce sediment gravity flows that descend along tributary gullies towards the main canyon axis. However, these flows had only been clearly identified in the Palamós Canyon, where they could be recorded synchronously with the passage of the trawling fleet. In this study we also recorded trawl-induced sediment gravity flows in the Blanes Canyon, both synchronously and asynchronously with the passage of trawlers. Increases in particulate matter fluxes in other trawled submarine canyons occurring in absence of natural triggering mechanisms, were not directly associated with bottom trawling because of the lack of direct synchronicity of these events with this human activity. Here we show, however, that the practice of bottom trawling along canyon flanks can not only resuspend and directly trigger sediment gravity flows, but they can also pile up disturbed sediment on steep areas, which can become unstable and collapse afterwards, asynchronically with the passage of trawlers. Our study provides evidence that sediment gravity flows in submarine canyons affected by bottom trawling, where the causal mechanisms are presently unidentified, may potentially be linked to instabilities in sediment originating from recurrent bottom trawling, which can precondition these events.

Exchange rate volatility and COVID-19 effects on Indonesia's food products' trade: Symmetric and asymmetric approach.

This study aims to determine the symmetric and asymmetric effects of exchange rate volatility and other explanatory variables (real exchange rate, industrial production index, and COVID-19) on sixteen (16) food products traded between Indonesia and the United States, Indonesia and China. The study used the ARCH/GARCH approach and estimate the volatility of the exchange rate. Linear and nonlinear autoregressive distributed lag (ARDL) were applied to estimate the short- and long-run effect for the period 2009:M1-2020:M12. Findings from the ARDL method indicate that, in the short-term exchange rate volatility has a significant positive/negative effect on many products exported and imported throughout the study period. Different results were found in the Nonlinear ARDL method where a significant effect occurred especially on the food products import. The result further indicates that exchange rate volatility has a more negative effect symmetrically or asymmetrically. These results imply that most Indonesian traders to the United States and China tend to behave as risk-averse in the long run when responding to the phenomenon of exchange rate volatility. As a measure of robustness, a quantile regression further confirms that exchange rate volatility consistently affects food product trade. With this, therefore, stable exchange rate policies are needed to lessen the harmful effect of volatility on trade flows and balance the risk-taking behaviour among importers and exporters.

Modulated turbulent convection: a benchmark model for large scale natural flows driven by diurnal heating.

Our life is strongly affected by turbulent convective flows, driven by time-dependent thermal forcing, especially diurnal heating of the Earth's surface by the Sun. In a laboratory experiment, we investigate their analogues: We study complex and extraordinary properties of turbulent buoyancy driven flows generated due to periodic modulation of the temperature of the plates of a Rayleigh-Bénard cell, with amplitudes both smaller and larger than either the positive or negative mean temperature difference between the top and bottom. We probe the turbulent flow of our working fluid - cryogenic helium gas - using temperature sensors placed in the cell interior and embedded in its plates. We discuss spatial and temporal structure of the heat flow, generalize validity of Nusselt versus Rayleigh number scaling Nu ∝ Ra γ with γ ≈ 1 / 3 at very high Ra for modulated convection and argue that this system represents a benchmark model which helps us understand the energy budget of ocean currents or weather formation on Earth subject to diurnal Sun heating as well as similar natural flows on Earth-like planets.

Laboratory investigations of wave-induced transport of plastic debris over a rippled bottom.

Laboratory experiments are conducted in a wave flume to investigate the effect of water waves on the transport of plastic pellets over a rippled bottom. The horizontal velocities of plastic debris are analyzed over the rippled bottom for different wave conditions and plastic elements with different properties. Laboratory investigations determined the characteristic transport patterns of wave-induced plastic debris with a density of ∼2.0g/cm3 moving along the rippled bottom. In the first, swing-type motion, the grains move only in the ripple trough with velocities lower than 0.10 m/s. For sliding-type movement, the grains move along the entire rippled surface with velocities in the range of 0.10-0.13 m/s. For higher velocities in the range of 0.15-0.20 m/s, a saltation-type motion becomes dominant. The results show that plastic grains may move up to 2-3 cm above the ripple crest depending on hydrodynamic conditions. The analysis shows that for velocity-skewed flows, sliding-type motion and onshore transport dominate. For acceleration-skewed flows, saltation-type motion and offshore transport dominate, which is attributed to higher boundary layer thickness and phase lag effects. The analysis of the relationship between the particle Reynolds number and the thickness of the turbulent boundary layer reveals that for values of Rep≥1000 and a boundary layer thickness mm saltation-type motion becomes dominant. The direction of transport is affected not only by the density of the sediment and the wave skewness coefficients but also by the dimensions of the bottom ripples. The laboratory investigations also provide insight into the hydrodynamic conditions affecting the transport of plastic debris along the bottom covered with ripples in oscillating nonlinear water flows.

Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques.

We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

A mechanistic modelling approach of the host-microbiota interactions to investigate beneficial symbiotic resilience in the human gut.

The health and well-being of a host are deeply influenced by the interactions with its gut microbiota. Contrasted environmental conditions, such as diseases or dietary habits, play a pivotal role in modulating these interactions, impacting microbiota composition and functionality. Such conditions can also lead to transitions from beneficial to detrimental symbiosis, viewed as alternative stable states of the host-microbiota dialogue. This article introduces a novel mathematical model exploring host-microbiota interactions, integrating dynamics of the colonic epithelial crypt, microbial metabolic functions, inflammation sensitivity and colon flows in a transverse section. The model considers metabolic shifts in epithelial cells based on butyrate and hydrogen sulfide concentrations, innate immune pattern recognition receptor activation, microbial oxygen tolerance and the impact of antimicrobial peptides on the microbiota. Using the model, we demonstrated that a high-protein, low-fibre diet exacerbates detrimental interactions and compromises beneficial symbiotic resilience, underscoring a destabilizing effect towards an unhealthy state. Moreover, the proposed model provides essential insights into oxygen levels, fibre and protein breakdown, and basic mechanisms of innate immunity in the colon and offers a crucial understanding of factors influencing the colon environment.

Combination of an Optically Induced Dielectrophoresis (ODEP) Mechanism and a Laminar Flow Pattern in a Microfluidic System for the Continuous Size-Based Sorting and Separation of Microparticles.

Optically induced dielectrophoresis (ODEP)-based microparticle sorting and separation is regarded as promising. However, current methods normally lack the downstream process for the transportation and collection of separated microparticles, which could limit its applications. To address this issue, an ODEP microfluidic chip encompassing three microchannels that join only at the central part of the microchannels (i.e., the working zone) was designed. During operation, three laminar flows were generated in the zone, where two dynamic light bar arrays were designed to sort and separate PS (polystyrene) microbeads of different sizes in a continuous manner. The separated PS microbeads were then continuously transported in laminar flows in a partition manner for the final collection. The results revealed that the method was capable of sorting and separating PS microbeads in a high-purity manner (e.g., the microbead purity values were 89.9 ± 3.7, 88.0 ± 2.5, and 92.8 ± 6.5% for the 5.8, 10.8, and 15.8 µm microbeads harvested, respectively). Overall, this study demonstrated the use of laminar flow and ODEP to achieve size-based sorting, separation, and collection of microparticles in a continuous and high-performance manner. Apart from the demonstration, this method can also be utilized for size-based sorting and the separation of other biological or nonbiological microparticles.

Fish beta diversity associated with hydrologic and anthropogenic disturbance gradients in contrasting stream flow regimes.

Understanding the role of hydrologic variation in structuring aquatic communities is crucial for successful conservation and sustainable management of native freshwater biodiversity. Partitioning beta diversity into the additive components of spatial turnover and nestedness can provide insight into the forces driving variability in fish assemblages across stream flow regimes. We examined stream fish beta diversity across hydrologic and anthropogenic disturbance gradients using long-term (1916-2016) site occurrence records (n = 17,375) encompassing 252 species. We assessed total beta diversity (Sørensen dissimilarity), spatial turnover, and nestedness of fish assemblages in contrasting stream flow regimes across a gradient of decreasing flow stability: groundwater stable (n = 77), groundwater (n = 67), groundwater flashy (n = 175), perennial runoff (n = 141), runoff flashy (n = 255), and intermittent (n = 63) streams. Differences in total beta diversity among the stream flow regimes were driven predominantly (>86 %) by spatial turnover (i.e. species replacement) as opposed to nestedness (i.e. species loss or gain). Total fish beta diversity and spatial turnover were highest in streams with intermediate flow stability (groundwater flashy), while more flow-stable streams (groundwater stable and groundwater) had lower turnover and higher nestedness. Species turnover was also strongly associated with seasonal variation in hydrology across all flow regimes, but these relationships were most evident for assemblages in intermittent streams. Distance-based statistical comparisons showed significant correlations between beta diversity and anthropogenic disturbance variables, including dam density, dam storage volume and water withdrawals in catchments of groundwater stable streams, while hydrologic variables were more strongly correlated with beta diversity in streams with runoff-dominated and flashy flow regimes. The high spatial turnover of species implies that fish conservation actions would benefit from watershed-focused approaches targeting multiple streams with wide spatial distribution, as opposed to simply focusing on preserving sites with the greatest number of species.

Does the limelight foster the green transition of listed industrial companies? An empirical analysis from Chinese A-share market.

This paper contributes current research by investigating the extent to which equity linkage networks impact enterprise green transition in sustainable development. By adopting a complex network approach, we constructed a common shareholding network based on the top ten shareholders of listed industrial enterprises in Shanghai and Shenzhen A-shares from 2013 to 2022. The empirical results indicate that enterprises closer to the centre of the equity linkage network tend to have higher degrees of green transition. This impact is facilitated through three mechanism channels of capital flow, information exchange, and knowledge transfer within the network. We find enterprises at the core of the network can effectively reduce their financing constraints on enterprises, mitigate risks associated with external environmental uncertainties and managerial myopia, and promote knowledge exchange and innovation cooperation between enterprises. We have also discovered that the centrality of equity network has a greater impact on promoting transition in state-owned, large-scale enterprises, and enterprises with less heavy pollution and the network effect can be enhanced by strong regional environmental regulations. The above findings not only provide policy makers with policy recommendations to guide the enterprises green transition, but also provide industry practitioners with practical paths and directions, which can help promote the green development process of the whole society.

New insights into the effects of small permanent charge on ionic flows: A higher order analysis.

This study investigated how permanent charges influence the dynamics of ionic channels. Using a quasi-one-dimensional classical Poisson-Nernst-Planck (PNP) model, we investigated the behavior of two distinct ion species-one positively charged and the other negatively charged. The spatial distribution of permanent charges was characterized by zero values at the channel ends and a constant charge $ Q_0 $ within the central region. By treating the classical PNP model as a boundary value problem (BVP) for a singularly perturbed system, the singular orbit of the BVP depended on $ Q_0 $ in a regular way. We therefore explored the solution space in the presence of a small permanent charge, uncovering a systematic dependence on this parameter. Our analysis employed a rigorous perturbation approach to reveal higher-order effects originating from the permanent charges. Through this investigation, we shed light on the intricate interplay among boundary conditions and permanent charges, providing insights into their impact on the behavior of ionic current, fluxes, and flux ratios. We derived the quadratic solutions in terms of permanent charge, which were notably more intricate compared to the linear solutions. Through computational tools, we investigated the impact of these quadratic solutions on fluxes, current-voltage relations, and flux ratios, conducting a thorough analysis of the results. These novel findings contributed to a deeper comprehension of ionic flow dynamics and hold potential implications for enhancing the design and optimization of ion channel-based technologies.

Turbulence model study for aerodynamic analysis of the leading edge tubercle wing for low Reynolds number flows.

A turbulence model study was performed to analyze the flow around the Tubercle Leading Edge (TLE) wing. Five turbulence models were selected to evaluate aerodynamic force coefficients and flow mechanism by comparing with existing literature results. The selected models are realizable k-ε, k-ω Shear Stress Transport (SST), ( γ - R e θ ) SST model, Transition k-k l -ω model and Stress- ω Reynolds Stress Model (RSM). For that purpose, the TLE wing model was developed by using the NACA0021 airfoil profile. The wing model is designed with tubercle wavelength of 0.11c and amplitude of 0.03c. Numerical simulation was performed at chord-based Reynolds number of Rec = 120,000. The Computational Fluid Dynamic (CFD) simulation reveals that among the selected turbulence models, Stress- ω RSM estimated aerodynamic forces (i.e. lift and drag) coefficients closest to that of the experimental values followed by realizable k-ε, ( γ - R e θ ) SST model, k-ω SST model and k-k l -ω model. However, at a higher angle of attacks i.e. at 16° & 20° k-ω SST model predicted closest drag and lift coefficient to that of the experimental values. Additionally, the critical observation of pressure contour confirmed that at the lower angle of attack Stress- ω RSM predicted strong Leading Edge (LE) suction followed by realizable k-ε, ( γ - R e θ )SST model, k-ω SST model and k-k l -ω model. Thus, the superiority of Stress- ω RSM in predicting the aerodynamic force coefficients is shown by the flow behavior. In addition to this pressure contours also confirmed that k-k l -ω model failed to predict tubercled wing aerodynamic performance. At higher angles of attacks k-ω SST model estimated aerodynamic force coefficients closest to that of the experimental values, thus k-ω SST model is used at 16° & 20° AoAs. The observed streamline behavior for different turbulence models showed that the Stress- ω RSM model and k-k l -ω model failed to model flow behavior at higher AoAs, whereas k-ω SST model is a better approach to model separated flows that experience strong flow recirculation zone.

Data-Driven Exploration of National Health Service Talking Therapies Care Pathways Using Process Mining: Retrospective Cohort Study.

BACKGROUND: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to "stepped care," in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied. OBJECTIVE: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records. METHODS: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes. RESULTS: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care. CONCLUSIONS: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.

Expanding the focus of the One Health concept: links between the Earth-system processes of the planetary boundaries framework and antibiotic resistance.

The scientific community warns that our impact on planet Earth is so acute that we are crossing several of the planetary boundaries that demarcate the safe operating space for humankind. Besides, there is mounting evidence of serious effects on people's health derived from the ongoing environmental degradation. Regarding human health, the spread of antibiotic resistant bacteria is one of the most critical public health issues worldwide. Relevantly, antibiotic resistance has been claimed to be the quintessential One Health issue. The One Health concept links human, animal, and environmental health, but it is frequently only focused on the risk of zoonotic pathogens to public health or, to a lesser extent, the impact of contaminants on human health, i.e., adverse effects on human health coming from the other two One Health "compartments". It is recurrently claimed that antibiotic resistance must be approached from a One Health perspective, but such statement often only refers to the connection between the use of antibiotics in veterinary practice and the antibiotic resistance crisis, or the impact of contaminants (antibiotics, heavy metals, disinfectants, etc.) on antibiotic resistance. Nonetheless, the nine Earth-system processes considered in the planetary boundaries framework can be directly or indirectly linked to antibiotic resistance. Here, some of the main links between those processes and the dissemination of antibiotic resistance are described. The ultimate goal is to expand the focus of the One Health concept by pointing out the links between critical Earth-system processes and the One Health quintessential issue, i.e., antibiotic resistance.

Cellular flows initiate left-right patterning prior to laterality gene expression in amniotes.

A bilateral body plan is predominant throughout the animal kingdom. Bilaterality of amniote embryos becomes recognizable as midline morphogenesis begins at gastrulation, bisecting an embryonic field into the left and right sides. Soon after, left-right asymmetry also starts. While a series of laterality genes expressed after the left-right compartmentalization has been extensively studied, the laterality patterning prior to and during midline morphogenesis has remained unclear. Here, through a biophysical quantification in a high spatial and temporal resolution, applied to a chick model system, we show that a large-scale bilateral counter-rotating cell flow, termed as 'polonaise movements', display left-right asymmetries in early gastrulation. This cell movement starts prior to the formation of the primitive streak, which is the earliest midline structure, and earlier than expression of laterality genes. The cell flow speed and vorticity unravel the location and timing of the left-right asymmetries. The bilateral cell flow exhibited a Left side asymmetry at the beginning, but a transition towards Right dominance. Mitotic arrest that diminishes primitive streak formation resulted in changes in the bilateral flow pattern, but the Right dominance persisted. Our data indicate that the left-right asymmetry in amniote gastrula becomes detectable prior to the point when the asymmetric regulation of the laterality signals at the node leads to the left-right patterning. More broadly, our results suggest that physical processes can play an unexpected but significant role in influencing left-right laterality during embryonic development.

Effects of buoyancy on the dispersion of drugs released intrathecally in the spinal canal.

This paper investigates the transport of drugs delivered by direct injection into the cerebrospinal fluid (CSF) that fills the intrathecal space surrounding the spinal cord. Because of the small drug diffusivity, the dispersion of neutrally buoyant drugs has been shown in previous work to rely mainly on the mean Lagrangian flow associated with the CSF oscillatory motion. Attention is given here to effects of buoyancy, arising when the drug density differs from the CSF density. For the typical density differences found in applications, the associated Richardson number is shown to be of order unity, so that the Lagrangian drift includes a buoyancy-induced component that depends on the spatial distribution of the drug, resulting in a slowly evolving cycle-averaged flow problem that can be analysed with two-time scale methods. The asymptotic analysis leads to a nonlinear integro-differential equation for the spatiotemporal solute evolution that describes accurately drug dispersion at a fraction of the cost involved in direct numerical simulations of the oscillatory flow. The model equation is used to predict drug dispersion of positively and negatively buoyant drugs in an anatomically correct spinal canal, with separate attention given to drug delivery via bolus injection and constant infusion.

Schlieren imaging and video classification of alphabet pronunciations: exploiting phonetic flows for speech recognition and speech therapy.

Speech is a highly coordinated process that requires precise control over vocal tract morphology/motion to produce intelligible sounds while simultaneously generating unique exhaled flow patterns. The schlieren imaging technique visualizes airflows with subtle density variations. It is hypothesized that speech flows captured by schlieren, when analyzed using a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) network, can recognize alphabet pronunciations, thus facilitating automatic speech recognition and speech disorder therapy. This study evaluates the feasibility of using a CNN-based video classification network to differentiate speech flows corresponding to the first four alphabets: /A/, /B/, /C/, and /D/. A schlieren optical system was developed, and the speech flows of alphabet pronunciations were recorded for two participants at an acquisition rate of 60 frames per second. A total of 640 video clips, each lasting 1 s, were utilized to train and test a hybrid CNN-LSTM network. Acoustic analyses of the recorded sounds were conducted to understand the phonetic differences among the four alphabets. The hybrid CNN-LSTM network was trained separately on four datasets of varying sizes (i.e., 20, 30, 40, 50 videos per alphabet), all achieving over 95% accuracy in classifying videos of the same participant. However, the network's performance declined when tested on speech flows from a different participant, with accuracy dropping to around 44%, indicating significant inter-participant variability in alphabet pronunciation. Retraining the network with videos from both participants improved accuracy to 93% on the second participant. Analysis of misclassified videos indicated that factors such as low video quality and disproportional head size affected accuracy. These results highlight the potential of CNN-assisted speech recognition and speech therapy using articulation flows, although challenges remain in expanding the alphabet set and participant cohort.

Optimizing Interhospital Transfers in Orthopedics and Trauma Surgery: Challenges, Insights, and Proposals for Standardized Care in Germany.

BACKGROUND: Germany's high density of under-equipped hospitals and anticipated surge in orthopedic and trauma surgery-related diseases by 2030, combined with personnel shortages, are expected to increase patient transfers between hospitals, an issue that urgently needs standardized protocols. Despite some existing cooperative agreements, such as between joint-replacement centers or within the Trauma Network DGU®, these measures do not adequately address the full range of patient-transfer cases, including those due to a lack of specialization or staff shortages, resulting in delayed treatment and potential health risks. This study aims to dissect the intricacies of interhospital transfers in orthopedics and trauma surgery across Germany, focusing on understanding the underlying reasons for transfers, comparing the operational structures of small and large hospitals, and laying the groundwork for future standardized protocols to enhance patient care. MATERIAL AND METHODS: A cross-sectional study was conducted in the form of an online survey via SoSci Survey, which was directed at orthopedic surgeons and trauma surgeons working in hospitals in Germany. The 22-question survey gathered information on participants' clinic roles, departmental details, transfer processes, frequent diagnoses, perceptions of transfer quality, and improvement areas. The survey was sent to orthopedic and trauma surgeons in Germany by the specialist society. The data were analyzed using descriptive and inferential statistics to ensure a comprehensive insight into interhospital transfer practices. RESULTS: The study involved 152 participants from various hospital ranks and located in different hospital sizes and types across rural and urban areas. A significant difference was observed between the care structures of basic/regular care and central/maximum care hospitals, especially regarding the available facilities and specialties. These findings suggest improvements such as better patient documentation, increased digital communication, optimized patient distribution, and standardization of transfer requests, among others. CONCLUSIONS: This study highlights the urgent need for improved protocols and resource allocation to eliminate inequalities in transfers between hospitals in orthopedics and trauma surgery in Germany.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

A reduced order model formulation for left atrium flow: an atrial fibrillation case.

A data-driven reduced order model (ROM) based on a proper orthogonal decomposition-radial basis function (POD-RBF) approach is adopted in this paper for the analysis of blood flow dynamics in a patient-specific case of atrial fibrillation (AF). The full order model (FOM) is represented by incompressible Navier-Stokes equations, discretized with a finite volume (FV) approach. Both the Newtonian and the Casson's constitutive laws are employed. The aim is to build a computational tool able to efficiently and accurately reconstruct the patterns of relevant hemodynamics indices related to the stasis of the blood in a physical parametrization framework including the cardiac output in the Newtonian case and also the plasma viscosity and the hematocrit in the non-Newtonian one. Many FOM-ROM comparisons are shown to analyze the performance of our approach as regards errors and computational speed-up.

Analysis of the impacts of climate change, physiographic factors and land use/cover on the spatiotemporal variability of seasonal daily mean flows in southern Quebec (Canada).

The objective of this study is to compare the spatiotemporal variability of seasonal daily mean flows measured in 17 watersheds, grouped into three homogeneous hydroclimatic regions, during the period 1930-2023 in southern Quebec. With regard to spatial variability, unlike extreme daily flows, seasonal daily mean flows are very poorly correlated with physiographic factors and land use and land cover. In fall, they are not correlated with any physiographic or climatic factor. In winter, they are positively correlated with the rainfall and winter daily mean maximum temperatures. In spring, they are strongly correlated positively with the snowfall but negatively with the spring daily mean maximum temperatures. However, in summer, they are better correlated with forest area and, to a lesser extent, with the rainfall. As for their temporal variability, the application of six different statistical tests revealed a general increase in daily mean flows in winter due to early snowmelt and increased rainfall in fall. In summer, flows decreased significantly in the snowiest hydroclimatic region on the south shore due to the decrease in the snowfall. In spring, no significant change in flows was globally observed in the three hydroclimatic regions despite the decrease in the snowfall due to the increase in the rainfall. In fall, flows increased significantly south of 47°N on both shores due to the increase in the rainfall. This study demonstrates that, unlike extreme flows, the temporal variability of seasonal daily average flows is exclusively influenced by climatic variables in southern Quebec. Due to this influence, seasonal daily mean flows thus appear to be the best indicator for monitoring the impacts of changes in precipitation regimes and seasonal temperatures on river flows in southern Quebec.