Modelling insertion behaviour of PVP (Polyvinylpyrrolidone) and PVA (Polyvinyl Alcohol) microneedles.

A comprehensive investigation into the effects of nonlinear material behaviour of polymeric (MN) and skin on the dynamics of the MN insertion in skin was undertaken in this study using experiments and numerical simulations. The nonlinearity of the material behaviour was incorporated by employing the Ramberg-Osgood and neo-Hookean equations for stress-strain relationships for the MN materials and skin, respectively. For this purpose, a characteristic type of dissolving MN array was selected. This type of MN is made by a combination of poly(vinyl alcohol) and poly(vinyl pyrrolidone). The numerical simulations were validated using experimental investigations where the MNs were fabricated using laser-engineered silicone micromould templates technology. Young's modulus, Poisson's ratio, and compression breaking force for the MN polymers were determined using a texture analyser. The alignment between experimental findings and simulation data underscores the accuracy of the parameters determined through mechanical testing and mathematical calculations for both MN materials (PVP/PVA) and skin behaviour during the MN insertion. This study has demonstrated a strong alignment between the experimental findings and computational simulations, confirming the accuracy of the established parameters for MNs and skin interactions for modelling MN insertion behaviour in skin, providing a solid foundation for future research in this area.

Do bitcoin electricity consumption and carbon footprint exhibit random walk and bubbles? Analysis with policy implications.

One of the main current focuses of global economies and decision-makers is the efficiency of energy utilization in cryptocurrency mining and trading, along with the reduction of associated carbon emissions. Understanding the pattern of Bitcoin's energy consumption and its bubble frequency can greatly enhance policy analysis and decision-making for energy efficiency and carbon emission reduction. This research aims to assess the validity of the random walk hypothesis for Bitcoin's electricity consumption and carbon footprint. We employed both traditional methods (ADF and KPSS) and recently proposed unit root techniques that account for structural breaks and non-linearity in the data series. Our analysis covers daily data from July 2010 to December 2021. The empirical results revealed that traditional unit root techniques did not confirm the stationarity of both bitcoin's electricity consumption and carbon footprint. However, novel structural break (SB) and linearity tests conducted enabled us to discover five SB episodes between 2012 and 2020 and non-linearity of the variables, which informed our application of the newly developed non-linear unit root tests with structural breaks. With the new methods, the results indicated stationarity after accommodating the SB and non-linearity. Furthermore, based on Phillips and Shi (2019)'s test, we identified certain bubble episodes in the bitcoin energy and carbon variables between 2013 and 2021. The major drivers of the bubbles in bitcoin energy consumption and carbon footprint are variables relating to the bitcoin and financial markets activities and risks, including the global economic and political risks. The study's conclusion based on the above findings informs several policy implications drawn for energy and environmental management including the encouragement of green investments in cryptocurrency mining and trading.

Examination of nonlinear associations between pulse pressure index and incident prediabetes susceptibility: a 5-year retrospective cohort investigation.

Prediabetes and related complications constitute significant public health burdens globally. As an indicator closely associated with abnormal glucose metabolism and atherosclerosis, the utility of Pulse Pressure Index (PPI) as a prediabetes risk marker has not been explored. We performed a retrospective cohort analysis to investigate this putative association between PPI and prediabetes hazard. Our analysis encompassed 183,517 Chinese adults ≥ 20 years registered within the Rich Healthcare Group 2010-2016. PPI was defined as (systolic blood pressure - diastolic blood pressure)/systolic blood pressure. The relationship between PPI and prediabetes risk was assessed via Cox proportional hazards regression modeling. Non-linearity evaluations applied cubic spline fitting approaches alongside smooth curve analysis. Inflection points of PPI concerning prediabetes hazard were determined using two-piecewise Cox models. During a median follow-up of 3 years (2.17-3.96 years), new-onset prediabetes was documented in 20,607 patients (11.23%). Multivariate regression analysis showed that PPI was an independent risk factor for prediabetes, and the risk of prediabetes increased by 0.6% for every 1% increase in PPI (Hazard Ratio [HR]: 1.006, 95% Confidence Interval [CI] 1.004-1.008, P < 0.001). This association was non-significant for PPI ≤ 37.41% yet exhibited a sharp upsurge when PPI surpassed 37.41% (HR: 1.013, 95% CI 1.005-1.021, P = 0.0029). Our analysis unveils a positive, non-linear association between PPI and future prediabetes risk. Within defined PPI ranges, this relationship is negligible but dramatically elevates beyond identified thresholds.

Upholding or Breaking the Law of Superposition in Pharmacokinetics.

The law of superposition underpins first-order linear pharmacokinetic relationships. Most drugs, therefore, after a single dose can be described by first-order or linear processes, which can be superposed to understand multiple-dose regimen behavior. However, there are a number of situations where drugs could display behaviors after multiple dosing that leads to capacity-limited or saturation non-linear kinetics and the law of superposition is overruled. This review presents a practical guide to understand the equations and calculations for single and multiple-dosing regimens after intravenous and oral administration. It also provides the pharmaceutical basis for saturation in ADME processes and the consequent changes in the area under the concentration-time curve, which represents drug exposure that can lead to the modulation of efficacy and/or toxic effects. The pharmacokineticist must implicitly understand the principles of superposition, which are a central tenet of drug behavior and disposition during drug development.

Bioinspired High-Linearity, Wide-Sensing-Range Flexible Stretchable Bioelectronics Based on MWCNTs/GR/Nd2Fe14B/PDMS Nanocomposites for Human-Computer Interaction and Biomechanics Detection.

In recent years, flexible and stretchable strain sensors have emerged as a prominent area of research, primarily due to their remarkable stretchability and extremely low strain detection threshold. Nevertheless, the advancement of sensors is currently constrained by issues such as complexity, high costs, and limited durability. To tackle the aforementioned issues, this study introduces a lepidophyte-inspired flexible, stretchable strain sensor (LIFSSS). The stretchable bioelectronics composites were composed of multiwalled carbon nanotubes, graphene, neodymium iron boron, and polydimethylsiloxane. Unique biolepidophyted microstructures and magnetic conductive nanocomposites interact with each other through synergistic interactions, resulting in the effective detection of tensile strain and magnetic excitation. The LIFSSS exhibits a 170% tensile range, a linearity of 0.99 in 50-170% strain (0.96 for full-scale range), and a fine durability of 7000 cycles at 110% tensile range. The sensor accurately detects variations in linear tensile force, human movement, and microexpressions. Moreover, LIFSSS demonstrates enhanced efficacy in sign language recognition for individuals with hearing impairments and magnetic grasping for robotic manipulators. Hence, the LIFSSS proposed in this study shows potential applications in various fields, including bioelectronics, electronic skin, and physiological activity monitoring.

An HPLC-UV Method to Assess Human Plasma 25(OH)D3.

The aim of this study was to validate an HPLC-UV method to assess vitamin D status by determining the linearity and precision of the 25-hydroxyvitamin D3 (25(OH)D3) calibration curve, the limits of detection, quantitation and robustness of the method, and its accuracy. A second stock solution of 25(OH)D3 was prepared (500 ng/mL), and working dilutions (5, 10, 20, 30, 40, and 50 ng/mL) were prepared for a calibration curve. The HPLC equipment had a UV-Vis diode-array detector and utilized an AcclaimTM 120 C18 column (5 µm, 4.6 × 250 mm) with a flow rate of 1.2 mL/min, a column temperature of 30 °C, and the standards and samples were maintained at 4 °C, with an injection volume of 100 µL. Detection of 25(OH)D3 was determined at 265 nm, with a retention time of 4.0 min. The validation was conducted according to the FDA Validation of Analytical Procedures: Guidance for Industry. Vitamin D was extracted from plasma samples using acetonitrile (ACN)-0.1% formic acid (2:1 v/v), and the percentage of recovery was calculated. The proposed method conditions gave excellent linearity (R2 = 0.9989) and the linearity coefficient was R2 > 0.99 for 25(OH)D3. The detection and quantification limits were 1.1703 ng/mL and 3.5462 ng/mL, respectively. Decreasing or increasing the reading temperature by 1 °C decreased the response units (AU) of vitamin D, 25(OH)D3. When the current flow rate decreased by 0.2 mL/min (1.0 mL/min), the retention time increased to 4.913 min, whereas an increase of 0.2 mL/min of the proposed flow rate (1.4 mL/min) decreased the retention time to 3.500 min. The percentage of recovery varied from 92.2% to 97.1%. The proposed method to quantify a vitamin D metabolite (25(OH)D3) in human plasma samples was reliable and validated.

Linearity assessment: deviation from linearity and residual of linear regression approaches.

In this computer simulation study, we examine four different statistical approaches of linearity assessment, including two variants of deviation from linearity (individual (IDL) and averaged (AD)), along with detection capabilities of residuals of linear regression (individual and averaged). From the results of the simulation, the following broad suggestions are provided to laboratory practitioners when performing linearity assessment. A high imprecision can challenge linearity investigations by producing a high false positive rate or low power of detection. Therefore, the imprecision of the measurement procedure should be considered when interpreting linearity assessment results. In the presence of high imprecision, the results of linearity assessment should be interpreted with caution. Different linearity assessment approaches examined in this study performed well under different analytical scenarios. For optimal outcomes, a considered and tailored study design should be implemented. With the exception of specific scenarios, both ADL and IDL methods were suboptimal for the assessment of linearity compared. When imprecision is low (3 %), averaged residual of linear regression with triplicate measurements and a non-linearity acceptance limit of 5 % produces <5 % false positive rates and a high power for detection of non-linearity of >70 % across different types and degrees of non-linearity. Detection of departures from linearity are difficult to identify in practice and enhanced methods of detection need development.

Development and validation of an LC-MSMS method for the quantitation of pacritinib; application of kinetics in rabbits.

BACKGROUND: Accurate and selective LC/ESI-MSMS method development and validation for the quantitation of pacritinib is the primary goal of this study to perform kinetic studies in the healthy rabbit. METHODS: Chromatographic resolution was accomplished with a hypersil/ODS (50 mm × 4.6 mm, 3 µ) analytical C18 column and a mobile phase composition of 0.1% formic acid and ACN in the proportion of 25:75 with a 0.6 ml/min flow of the mobile phasic system from the analytical column. The method was employed by monitoring the established ionic transitions of m/z-473.25/98.09 for Pacritinib and 506.18/57.12 for the internal standard (Amprenavir) in multiple reaction monitoring. RESULTS: The calibration plot regression line was y = 0.0002× + 0.007, with a correction coefficient (r2) of 0.9989. The CV outcomes for the matrix effect at low-QC and high-QC levels were 4.79% and 4.91%, respectively. The percentage average recoveries for Pacritinib in High-QC (12.70 µg/ml), MQC (8.50 µg/ml), and Low-QC (1.19 µg/ml) were 95.87%, 103.64%, and 94.32%, respectively. The obtained values were found between 2.98 and 5.07% for the QC (1.19, 8.50, and 12.70 µg/ml) samples. The established procedure was subjected to kinetics study of Pacritinib after oral administration in rabbits. Cmax, Tmax, and T1/2, of the Pacritinib tablets were 247.25 ± 3.32 ng/ml, 6.0 ± 0.03 h, and 12.24 ± 0.53 h, respectively. AUC0-∞ infinity for Pacritinib tablets was 1691.74 ± 3.67 ng h/ml. CONCLUSION: After oral administration of Pacritinib to healthy rabbits, pharmacokinetic characteristics were presented, and the established technique was effectively verified.

IoT-Based Heartbeat Rate-Monitoring Device Powered by Harvested Kinetic Energy.

Remote patient-monitoring systems are helpful since they can provide timely and effective healthcare facilities. Such online telemedicine is usually achieved with the help of sophisticated and advanced wearable sensor technologies. The modern type of wearable connected devices enable the monitoring of vital sign parameters such as: heart rate variability (HRV) also known as electrocardiogram (ECG), blood pressure (BLP), Respiratory rate and body temperature, blood pressure (BLP), respiratory rate, and body temperature. The ubiquitous problem of wearable devices is their power demand for signal transmission; such devices require frequent battery charging, which causes serious limitations to the continuous monitoring of vital data. To overcome this, the current study provides a primary report on collecting kinetic energy from daily human activities for monitoring vital human signs. The harvested energy is used to sustain the battery autonomy of wearable devices, which allows for a longer monitoring time of vital data. This study proposes a novel type of stress- or exercise-monitoring ECG device based on a microcontroller (PIC18F4550) and a Wi-Fi device (ESP8266), which is cost-effective and enables real-time monitoring of heart rate in the cloud during normal daily activities. In order to achieve both portability and maximum power, the harvester has a small structure and low friction. Neodymium magnets were chosen for their high magnetic strength, versatility, and compact size. Due to the non-linear magnetic force interaction of the magnets, the non-linear part of the dynamic equation has an inverse quadratic form. Electromechanical damping is considered in this study, and the quadratic non-linearity is approximated using MacLaurin expansion, which enables us to find the law of motion for general case studies using classical methods for dynamic equations and the suitable parameters for the harvester. The oscillations are enabled by applying an initial force, and there is a loss of energy due to the electromechanical damping. A typical numerical application is computed with Matlab 2015 software, and an ODE45 solver is used to verify the accuracy of the method.

Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19.

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

Optimization of a deep mutational scanning workflow to improve quantification of mutation effects on protein-protein interactions.

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein-protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

Design of Fluxgate Current Sensor Based on Magnetization Residence Times and Neural Networks.

This study introduces a novel fluxgate current sensor with a compact, ring-shaped configuration that exhibits improved performance through the integration of magnetization residence times and neural networks. The sensor distinguishes itself with a unique magnetization profile, denoted as M waves, which emerge from the interaction between the target signal and ambient magnetic interference, effectively enhancing interference suppression. These M waves highlight the non-linear coupling between the magnetic field and magnetization residence times. Detection of these residence times is accomplished using full-wave rectification circuits and a Schmitt trigger, with a digital output provided by timing sequence detection. A dual-layer feedforward neural network deciphers the target signal, exploiting this non-linear relationship. The sensor achieves a linearity error of 0.054% within a measurement range of 15 A. When juxtaposed with conventional sensors utilizing the residence-time difference strategy, our sensor reduces linearity error by more than 40-fold and extends the effective measurement range by 150%. Furthermore, it demonstrates a significant decrease in ambient magnetic interference.

Calibration - an under-appreciated component in the analytical process of the medical laboratories.

Calibration of an analytical measurement procedure is an important basis for the reliability of patient results. Many publications and as well as procedures on how to estimate quality control and interpret those results have been become available over the years. In this publication we are focusing on the critical part of the calibration as there are no clear communication or guidelines on how to perform it. Usually only the recommendation of the reagent or instrument manufacturer is available. We would like to point out this gap to invite for a discussion and improvement of the current situation.

The complex link between socioeconomic deprivation and COVID-19. Evidence from small areas of Catalonia.

This ecological study assesses the association between the incidence rate of COVID-19 confirmed cases and socioeconomic deprivation in the Catalan small areas for the first six waves of the pandemic. The association is estimated using Poisson regressions and, in contrast to previous studies, considering that the relationship is not linear but rather depends on the degree of deprivation. The results show that the association between deprivation and incidence varied between waves, not only in intensity but also in its sign. Although it was insignificant in the first, third and fourth waves, the association was positive and significant in the second, becoming significantly negative in the fifth and sixth waves. Interestingly, the evidence suggests that the link between both magnitudes was not homogeneous throughout the distribution of deprivation, the pattern also varying between waves. The results are discussed in view of the role of non-pharmacological interventions and vaccination, as well as potential biases (for example that associated with differences between population groups in the propensity to be tested in each wave).

Identifying time-varying dynamics of heart rate and oxygen uptake from single ramp incremental running tests.

Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Enhancing Linearity of Light Response in Avalanche Photodiodes by Suppressing Electrode Size Effect.

The nonlinear characteristics of avalanche photodiodes (APDs) inhibit their performance in high-speed communication systems, thereby limiting their widespread application as optical detectors. Existing theoretical models have not fully elucidated complex phenomena encountered in actual device structures. In this study, actual APD structures exhibiting lower linearity than their ideal counterparts were revealed. Simulation analysis and physical inference based on GaN APDs reveal that electrode size is a noteworthy factor influencing response linearity. This discovery expands the nonlinear theory of APDs, suggesting that APD linearity can be enhanced by suppressing the electrode size effect. A physical model was developed to explain this phenomenon, which is attributed to charge accumulation at the edge of the contact layer. Therefore, we proposed an improved APD design that incorporates an additional gap layer and a buffer layer to stabilize the internal gain under high-current-density conditions, thereby enhancing linearity. Our improved APD design increases the linear threshold for optical input power by 4.46 times. This study not only refines the theoretical model for APD linearity but also opens new pathways for improving the linearity of high-speed optoelectronic detectors.

Dosimetric Evaluation of Semiflex Three-dimensional Chamber under Unflatten Beam in Comparison among Different Detectors.

Purpose: The goal of this study is to investigate the dosimetric properties of a Semiflex three-dimensional (3D) chamber in an unflatten beam and compare its data from a small to a large field flattening filter-free (FFF) beam with different radiation detectors. Methods: The sensitivity, linearity, reproducibility, dose rate dependency, and energy dependence of a Semiflex 3D detector in flattening filter and filter-free beam were fully investigated. The minimum radiation observed field widths for all detectors were calculated using lateral electronic charged particle equilibrium to investigate dosimetric characteristics such as percentage depth doses (PDDs), profiles, and output factors (OPFs) for Semiflex 3D detector under 6FFF Beam. The Semiflex 3D measured data were compared to that of other detectors employed in this study. Results: The ion chamber has a dosage linearity deviation of +1.2% for <10 MU, a dose-rate dependency deviation of +0.5%, and significantly poorer sensitivity due to its small volume. There is a difference in field sizes between manufacturer specs and derived field sizes. The measured PDD, profiles, and OPFs of the Semiflex 3D chamber were within 1% of each other for all square field sizes set under linac for the 6FFF beam. Conclusion: It was discovered to be an appropriate detector for relative dose measurements for 6 FFF beams with higher dose rates for field sizes more than or equal to 3 cm × 3 cm.

From a false sense of safety to resilience under uncertainty.

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Context-appropriate decision making should enhance system resilience and mitigate extreme risks.

Usability of sand samples in TL Radiation Dosimetry.

This study investigated natural sand thermoluminescence (TL) response as a possible option for retrospective high-dose gamma dosimetry. The natural sand under investigation was collected from six locations with selection criteria for sampling sites covering the highest probability of exposure to unexpected radiation on the Egyptian coast. Dose-response, glow curve, chemical composition, linearity, and fading rate for different sand samples were studied. Energy Dispersive X-ray Spectroscopy (EDX) analysis revealed differences in chemical composition among the various geological sites, leading to variations in TL glow curve intensity. Sand samples collected from Ras Sedr, Taba, Suez, and Enshas showed similar TL patterns, although with different TL intensities. Beach sands of Matrouh and North Coastal with a high calcite content did not show a clear linear response to the TL technique, in the dose range of 10 Gy up to 30 kGy. The results show that most sand samples are suitable as a radiation dosimeter at accidental levels of exposure. It is proposed here that for high-dose gamma dosimetry with doses ranging from 3 to 10 kGy, a single calibration factor might be enough for TL measurements using sand samples. However, proper calibration might allow dose assessment for doses even up to 30 kGy. Most of the investigated sand samples had nearly stable fading rates after seven days of storage. The Ras Sedr sands sample was the most reliable for retrospective dose reconstruction.

Biomechanical Analysis of Age-Dependent Changes in Fontan Power Loss.

The hemodynamics in Fontan patients with single ventricles rely on favorable flow and energetics, especially in the absence of a subpulmonary ventricle. Age-related changes in energetics for extracardiac and lateral tunnel Fontan procedures are not well understood. Vorticity (VOR) and viscous dissipation rate (VDR) are two descriptors that can provide insights into flow dynamics and dissipative areas in Fontan pathways, potentially contributing to power loss. This study examined power loss and its correlation with spatio-temporal flow descriptors (vorticity and VDR). Data from 414 Fontan patients were used to establish a relationship between the superior vena cava (SVC) to inferior vena cava (IVC) flow ratio and age. Computational flow modeling was conducted for both extracardiac conduits (ECC, n = 16) and lateral tunnels (LT, n = 25) at different caval inflow ratios of 2, 1, and 0.5 that corresponded with ages 3, 8, and 15+. In both cohorts, vorticity and VDR correlated well with PL, but ECC cohort exhibited a slightly stronger correlation for PL-VOR (>0.83) and PL-VDR (>0.89) than that for LT cohort (>0.76 and > 0.77, respectively) at all ages. Our data also suggested that absolute and indexed PL increase (p < 0.02) non-linearly as caval inflow changes with age and are highly patient-specific. Comparison of indexed power loss between our ECC and LT cohort showed that while ECC had a slightly higher median PL for all 3 caval inflow ratio examined (3.3, 8.3, 15.3) as opposed to (2.7, 7.6, 14.8), these differences were statistically non-significant. Lastly, there was a consistent rise in pressure gradient across the TCPC with age-related increase in IVC flows for both ECC and LT Fontan patient cohort. Our study provided hemodynamic insights into Fontan energetics and how they are impacted by age-dependent change in caval inflow. This workflow may help assess the long-term sustainability of the Fontan circulation and inform the design of more efficient Fontan conduits.