Time Distribution Research Articles

Effects of concentrated CO2 on the performance of a PBI/H3PO4 high temperature proton exchange membrane fuel cell

In this study, the impact of mimic reformate of formic acid decomposition on the anode polarization of HT-PEMFC is investigated under various operating conditions of the anode, including different concentrations of CO2, N2, CO, and humidification. Polarization curve and EIS measurements indicate that there is no significant difference (less than 5 mV when i < 0.4A/cm2) between the same concentrations of CO2 and N2 up to 75 vol%. No significant poisoning of the anode by CO2 (up to 75 vol%) has been observed, suggesting that the addition of concentrated CO2 acts more as a diluent than a poison. Upon separating the polarization effects, it is found that the oxygen reduction reaction (ORR) is a crucial step limiting the fuel cell performance, with cathodic activation losses reaching as high as 70 % at 0.8A/cm2. However, with 1 vol% CO in a H2/CO2 1/1 mixture, a significant poisoning effect on the fuel cell is observed. DRT (distribution of relaxation times) analyses reveal two peaks related to CO in high-frequency regions above 100 Hz associated with the anode polarization, with the peak integration being five times larger than in pure hydrogen. Humidification doesn't improve the voltage or mitigate CO poisoning. A one-dimensional flux model that includes the anode polarization is developed to analyze the polarizations for both cathode and anode under different concentrations of diluents, well describing the experimental results. When i < 0.8 A/cm2, the predicted values from the model closely match the measured values (within 5–10 mV). Calculations suggest that the anode polarization cannot be ignored, especially when the anode is fed with diluted gases.

Probing the Origin of Overpotential for Sodium‐Oxygen Batteries with Distribution of Relaxation Time

AbstractSodium‐oxygen batteries are emerging as new battery systems. Deep understanding of the origin of overpotential and the kinetic process in sodium‐oxygen batteries remain challenging yet critical. We apply a method of distribution of relaxation time (DRT) to decipher the electrochemical impedance spectroscopy (EIS), allowing us to monitor the changes of different kinetic processes during the discharging and charging. The origin of the overpotential in a battery was further comprehensively investigated combining DRT analysis with differential electrochemical mass spectrometry, Raman and other characterizations. Overpotential is found to primarily stem from oxygen mass transport during discharging, and from poor solid‐solid contact at the electrode surface during charging. Our work demonstrates the study of kinetic processes using DRT analysis, and suggests effective ways to improve the performances of sodium‐oxygen batteries.

Simple bacterial growth model for the formation of spontaneous and triggered dormant subpopulations

Bacterial persistence is a phenomenon where a subpopulation of cells can survive antibiotic treatment, and it is often linked to extremely slow growth or a dormant state. However, the mechanisms and factors that govern dormancy are not well understood. We propose a simplified growth model that treats the main cellular components as discrete variables and allocates resources among them according to different strategies. The model can reproduce some of the observed features of bacterial persistence, such as wide distribution in division times, long division times after a nutrient downshift, and the existence of different dormant phenotypes. We also show how the growth structure, i.e., whether cells grow in a lineage or a branch, affects the dormant cells' occurrence and distribution due to the growth states' mother-daughter correlation. Our model provides a framework to explore the complex interactions between cellular processes and environmental conditions that lead to bacterial persistence. Published by the American Physical Society 2024

Uncertainty modeling and propagation for groundwater flow: a comparative study of surrogates

We compare sparse grid stochastic collocation and Gaussian process emulation as surrogates for the parameter-to-observation map of a groundwater flow problem related to the Waste Isolation Pilot Plant in Carlsbad, NM. The goal is the computation of the probability distribution of a contaminant particle travel time resulting from uncertain knowledge about the transmissivity field. The latter is modelled as a lognormal random field which is fitted by restricted maximum likelihood estimation and universal kriging to observational data as well as geological information including site-specific trend regression functions obtained from technical documentation. The resulting random transmissivity field leads to a random groundwater flow and particle transport problem which is solved realization-wise using a mixed finite element discretization. Computational surrogates, once constructed, allow sampling the quantities of interest in the uncertainty analysis at substantially reduced computational cost. Special emphasis is placed on explaining the differences between the two surrogates in terms of computational realization and interpretation of the results. Numerical experiments are given for illustration.

Insights into COVID-19 epidemiology and control from temporal changes in serial interval distributions in Hong Kong.

The serial interval distribution is used to approximate the generation time distribution, an essential parameter to infer the transmissibility (${R}_t$) of an epidemic. However, serial interval distributions may change as an epidemic progresses. We examined detailed contact tracing data on laboratory-confirmed cases of COVID-19 in Hong Kong during the five waves from January 2020 to July 2022. We reconstructed the transmission pairs and estimated time-varying effective serial interval distributions and factors associated with longer or shorter intervals. Finally, we assessed the biases in estimating transmissibility using constant serial interval distributions. We found clear temporal changes in mean serial interval estimates within each epidemic wave studied and across waves, with mean serial intervals ranged from 5.5 days (95% CrI: 4.4, 6.6) to 2.7 (95% CrI: 2.2, 3.2) days. The mean serial intervals shortened or lengthened over time, which were found to be closely associated with the temporal variation in COVID-19 case profiles and public health and social measures and could lead to the biases in predicting ${R}_t$. Accounting for the impact of these factors, the time-varying quantification of serial interval distributions could lead to improved estimation of ${R}_t$, and provide additional insights into the impact of public health measures on transmission.

An active fractional Ornstein–Uhlenbeck particle: diffusion and dissipation

In this work, we consider a particle subjected to active fluctuations due to the presence of an active bath. The active fluctuation driving the particle is modeled as an Ornstein–Uhlenbeck process with memory, due to the recent experimental result in that a bacterial bath under a chemotactic mechanism exhibits a large distribution of persistence time, which may result in long-range persistence of a probe particle that cannot be captured by the conventional Ornstein–Uhlenbeck model for active noise. As a paradigmatic model, we consider introducing a fractional Ornstein–Uhlenbeck process with a power-law kernel, which includes a fractional order variable 0<α⩽1 , to model an active noise acting on a particle. We present the relevant properties of the active noise and its implications for the diffusion of free and harmonically confined single particles. This is also supported by the derived active Smoluchowski description. More importantly, we find that the effect of α can either reduce or increase the effective temperature at long time, depending on the competition between the harmonic timescale and persistence time. Lastly, the effects of presented active noise on the spectral density of the energy dissipation rate and the dissipation energy from both the thermal and active bath are explored.

On the Arrival Time Distribution of Reacting Systems in Ion Mobility Spectrometry.

Ion mobility spectrometry (IMS) is a widely used gas-phase separation technique, particularly when coupled with mass spectrometry (MS). Modern IMS instruments often apply elevated reduced field strengths for improved ion separation and ion focusing. These alter the collision dynamics and further drive ion reaction processes that can change the analyte's structure. As a result, the measured arrival time distribution (ATD) can change with the applied reduced field strengths. In this work, we systematically study how the ion collision dynamics and the ion reaction dynamics, as a function of the reduced field strength, can alter the ATD. To this end, we investigate 2,6-di-tert-butylpyridine, methanol, and ethyl acetate using a home-built drift tube IMS coupled to a home-built MS and extensive first-principles Monte Carlo modeling. We show how elevated reduced field strengths can actually lower resolving power through increased ion diffusion and how the field dependency of the ion mobility can introduce uncertainties to collision cross sections (CCS) calculated from the measured mobilities. On top of the collision dynamics, we show how chemical transformation processes that alter the analyte's CCS, e.g., dynamic clustering or fragmentation, can lead to broadened, shifted, or non-Gaussian ATDs and how sensitive these processes are to the applied field strengths. We highlight how first-principles ion dynamics simulations can help to understand and even harness the mentioned effects.

A new extended δ‐shock model with the consideration of shock magnitude

AbstractIn this article, a new ‐shock model that takes into account the magnitude of shocks is introduced and studied from reliability perspective. According to the new model, the system breaks down if either a shock after non‐critical shock occurs in a time length less than or a shock after a critical shock occurs in a time length less than where . The distribution of the system's lifetime is studied for both discrete and continuous intershock time distributions. It is shown that a new model is useful to describe a certain cold standby repairable system.

The neural network RTNet exhibits the signatures of human perceptual decision-making.

Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet's ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet's predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.

Accurate Collisional Cross Section Measurement by Multipass Cyclic Ion Mobility Spectrometry.

Ion mobility-mass spectrometry (IM-MS) is a powerful analytical tool for structural characterization. IM measurement provides collision cross section (CCS) values that facilitate analyte identification. While CCS values can be directly calculated from mobility measurements obtained using drift tube ion mobility spectrometry (DT-IMS), this method has limited mobility resolution due to the practical constraints on the length of the ion drift path. Consequently, DT-IMS cannot differentiate analytes with similar mobilities or resolve fine mobility features of individual ions. Cyclic IMS (cIMS) instruments leverage a cyclic path enabled by traveling wave ion mobility (TWIM) technology and offer increased mobility solution to address this challenge. While TWIM devices must first be calibrated to enable CCS measurements, current calibration strategies are primarily tailored for single-pass analyses. This preference is partly attributed to the challenges associated with multipass calibration methods, which require both calibrants and analytes to experience the same number of passes. Achieving this consistency can be complicated due to factors like peak splitting and diffusion, and may not be feasible for online IM-MS analyses. A recent report employed average ion velocities obtained from multiple measurements under different separation pathlengths as a path length-independent metric for CCS calibration. However, the ability to exploit this averaging approach is limited by observed variation in ion drift time/velocity in these measurements. In this study, we introduce a novel calibration strategy designed for multipass cIMS analyses, directly targeting the root cause for the path length- and mobility-dependent variations in ion drift time. With this method, we demonstrate that CCS values derived from multipass measurements closely align with those obtained from single-pass analyses, with an average deviation of 0.1%. We apply this method to characterize four isomeric trisaccharides. Our approach not only results in excellent agreement between our measured cIMSCCS values and the reported DTCCS values, with an average difference of only 0.5%, but also allows us to effectively identify subtle mobility characteristics of each compound and determine their respective CCS values. This level of detail and accuracy was previously unattainable using DT-IMS or single-pass cIMS measurements. We developed an algorithm for reconstructing arrival time distribution in cases where wrap-around has resulted in peak splitting. Collectively, the new calibration strategy and the reconstruction procedure maintain reproducibility and precision in CCS measurements while largely eliminating the need for meticulous selection of separation times. We expect that our method will empower researchers to harness the high mobility resolution offered by multipass cIMS analyses without compromising the accuracy of CCS measurement, making it appropriate for straightforward use across a wide range of applications.

Space–time distribution of intestinal infectious diseases and their association with socioeconomic variables in Ecuador

Space–time distribution of intestinal infectious diseases and their association with socioeconomic variables in Ecuador

Space–time distribution of intestinal infectious diseases and their association with socioeconomic variables in Ecuador

BackgroundIntestinal infectious diseases are a global concern in terms of morbidity, and they are closely linked to socioeconomic variables such as quality of life, weather and access to healthcare services. Despite progress in spatial analysis tools and geographic information systems in epidemiology, studies in Ecuador that evaluate temporal trends, specific geographic groups, and their correlation with socioeconomic variables are lacking. The absence of such information makes it challenging to formulate public health policies. This study sought to identify the spatial and temporal patterns of these diseases in Ecuador, along with their correlation with socioeconomic variables.MethodsIn Ecuador, the study was carried out in a continental territory, focusing on data related to intestinal infectious diseases collected from the National Institute of Statistics and Census (Instituto Nacional de Estadística y Censos) during the period from 2014 to 2019. This study involved spatial and temporal analyses using tools such as the global Moran’s index and Local Indicators of Spatial Association to identify spatial clustering patterns and autocorrelation. Additionally, correlations between morbidity rates and socioeconomic variables were examined.ResultsDuring the investigated period, Ecuador registered 209,668 cases of these diseases. Notable variations in case numbers were identified, with a 9.2% increase in 2019 compared to the previous year. The most impacted group was children under 5 years old, and the highest rates were centered in the southern and southwestern regions of the country, with Limón Indanza and Chunchi being the cantons with the highest rates, notably showing a significant increase in Limón Indanza. Additionally, there were significant correlations between morbidity rates and socioeconomic variables, school dropout rates, low birth weight, and access to water services.ConclusionThis study emphasizes the importance of considering socioeconomic variables when addressing these diseases in Ecuador. Understanding these correlations and geospatial trends can guide the development of health policies and specific intervention programs to reduce the incidence in identified high-risk areas. More specific research is needed to understand the underlying causes of variability in morbidity and develop effective prevention strategies.

The k-th order mean-deviation model for route choice under uncertainty

This study introduces the k-th order mean-deviation model for optimizing route choice within large, stochastic, and time-variant networks. This model addresses the limitations of the traditional mean-standard deviation approach by better handling extreme outcomes in travel times. It features an objective function called the “travel time budget”, which combines the average path travel time with a safety margin. This margin is defined by a trade-off coefficient and a selected deviation measure (total or semi) of the travel time. The model is divided into three variations: 1) The mean-total deviation (MTD) model for symmetric travel time distributions, 2) The mean-upper-semi-deviation (MUSD) model for asymmetric distributions prioritizing upper semi-deviations, suitable for risk-averse travelers, and 3) The mean-lower-semi-deviation (MLSD) model for asymmetric distributions focusing on lower semi-deviations, preferred by risk-prone individuals. We explore these models’ alignment with the stochastic dominance (SD) rule and develop a solution methodology based on SD principles. Numerical experiments in two real-world transportation networks demonstrate the models’ effectiveness and show how the choice of deviation affects route selection decisions.

RoGeR v3.0.5 – a process-based hydrological toolbox model in Python

Abstract. Although water availability and water quality are equally important for effective water resources management at various spatial and temporal scales, to date, a combined representation of soil water balance components and water quality components in Python is not available. The new RoGeR toolbox contains models that can be used for not only the quantification of hydrological processes, fluxes and stores, but also solute transport processes based on StorAge selection (SAS). This study presents the code structure and functionalities of RoGeR developed as a scientific model toolbox following defined open-source software guidelines. RoGeR uses five different computational backends covering just-in-time compilation, parallelism and graphical processing units (GPUs) that might be used for optimizing computational performance. We show that graphical processing unit computing has the greatest potential to improve computation time and energy usage, especially for large modelling experiments. A simple modelling experiment highlights the capabilities of the new RoGeR model toolbox. We simulated the soil water balance, stable water isotope (18O) transport, and corresponding travel time distributions of the Eberbaechle catchment, Germany, for a 3-year period. Due to the current limitations of a variety of process components, further development of RoGeR as a scientific software is needed. Future modifications are easily possible due to the open software architecture of RoGeR.

Responding to the workforce crisis: consensus recommendations from the Second Workforce Summit of the American Society of Pediatric Nephrology.

Pediatric patients with complex medical problems benefit from pediatric sub-specialty care; however, a significant proportion of children live greater than 80 mi. away from pediatric sub-specialty care. To identify current knowledge gaps and outline concrete next steps to make progress on issues that have persistently challenged the pediatric nephrology workforce. Workforce Summit 2.0 employed the round table format and methodology for consensus building using adapted Delphi principles. Content domains were identified via input from the ASPN Workforce Committee, the ASPN's 2023 Strategic Plan survey, the ASPN's Pediatric Nephrology Division Directors survey, and ongoing feedback from ASPN members. Working groups met prior to the Summit to conduct an organized literature review and establish key questions to be addressed. The Summit was held in-person in November 2023. During the Summit, work groups presented their preliminary findings, and the at-large group developed the key action statements and future directions. A holistic appraisal of the effort required to cover inpatient and outpatient sub-specialty care will help define faculty effort and time distribution. Most pediatric nephrologists practice in academic settings, so work beyond clinical care including education, research, advocacy, and administrative/service tasks may form a substantial amount of a faculty member's time and effort. An academic relative value unit (RVU) may assist in creating a more inclusive assessment of their contributions to their academic practice. Pediatric sub-specialties, such as nephrology, contribute to the clinical mission and care of their institutions beyond their direct billable RVUs. Advocacy throughout the field of pediatrics is necessary in order for reimbursement of pediatric sub-specialist care to accurately reflect the time and effort required to address complex care needs. Flexible, individualized training pathways may improve recruitment into sub-specialty fields such as nephrology. The workforce crisis facing the pediatric nephrology field is echoed throughout many pediatric sub-specialties. Efforts to improve recruitment, retention, and reimbursement are necessary to improve the care delivered to pediatric patients.

Responding to the workforce crisis: consensus recommendations from the Second Workforce Summit of the American Society of Pediatric Nephrology.

Pediatric patients with complex medical problems benefit from pediatric sub-specialty care; however, a significant proportion of children live greater than 80 mi. away from pediatric sub-specialty care. To identify current knowledge gaps and outline concrete next steps to make progress on issues that have persistently challenged the pediatric nephrology workforce. Workforce Summit 2.0 employed the round table format and methodology for consensus building using adapted Delphi principles. Content domains were identified via input from the ASPN Workforce Committee, the ASPN's 2023 Strategic Plan survey, the ASPN's Pediatric Nephrology Division Directors survey, and ongoing feedback from ASPN members. Working groups met prior to the Summit to conduct an organized literature review and establish key questions to be addressed. The Summit was held in-person in November 2023. During the Summit, work groups presented their preliminary findings, and the at-large group developed the key action statements and future directions. A holistic appraisal of the effort required to cover inpatient and outpatient sub-specialty care will help define faculty effort and time distribution. Most pediatric nephrologists practice in academic settings, so work beyond clinical care including education, research, advocacy, and administrative/service tasks may form a substantial amount of a faculty member's time and effort. An academic relative value unit (RVU) may assist in creating a more inclusive assessment of their contributions to their academic practice. Pediatric sub-specialties, such as nephrology, contribute to the clinical mission and care of their institutions beyond their direct billable RVUs. Advocacy throughout the field of pediatrics is necessary in order for reimbursement of pediatric sub-specialist care to accurately reflect the time and effort required to address complex care needs. Flexible, individualized training pathways may improve recruitment into sub-specialty fields such as nephrology. The workforce crisis facing the pediatric nephrology field is echoed throughout many pediatric sub-specialties. Efforts to improve recruitment, retention, and reimbursement are necessary to improve the care delivered to pediatric patients.

Phase-controlled synthesis of starch-derived Mo2C–MoC/C heterostructure catalyst for electrocatalytic hydrogen evolution reaction

The electronic structure of molybdenum carbide closely resembles that of Pt, making it a promising candidate material for replacing noble metal catalysts. However, the primary challenge is the controlled and eco-friendly synthesis of molybdenum carbide. Herein, we developed a sustainable and controlled method for synthesizing a heterostructure catalyst for the electrocatalytic hydrogen evolution reaction (HER) using starch as the raw material. Through a hydrothermal redox reaction and subsequent temperature-programmed treatment under an inert atmosphere, the hexagonal Mo2C-cubic MoC heterostructure nanoparticles were dispersed on a carbon matrix. Our investigation into the synthesis process extensively covered the growth of MoO2 nanocrystals and hydrochar during the hydrothermal redox reaction, as well as the phase evolution during the subsequent temperature-programmed treatment. Electrochemical assessments demonstrated the catalyst's remarkable efficiency for the hydrogen evolution reaction across both acidic and alkaline media, achieving a current density of 10 mA/cm2 with an overpotential of 139.4 mV in 1 M KOH solution and 160.7 mV in 0.5 M H2SO4 solution. Combining a series of electrochemical characterizations and applying the distribution of relaxation times (DRT) analysis to operando electrochemical impedance spectroscopy (EIS) data, the superior kinetic performance of the Mo2C–MoC/C was demonstrated, outperforming that of single-phase molybdenum carbide. Furthermore, the catalyst has demonstrated remarkable stability even at high current densities in both acidic and alkaline electrolytes, underscoring its potential as a cathode catalyst in both alkaline water electrolyzers and proton exchange membrane (PEM) electrolyzers.

From circular to square‐based section spouted beds: Scale‐up and design overview of a multistage thermal unit

AbstractThis work summarizes the results of several research projects to demonstrate the possibility of scaling‐up spouted beds by assembling a number of square‐based jet units. Cylindrical vessels with a bottom cone and parallelepiped ones with a frustum base do not differ to a large extent in terms of hydrodynamic features. The existing correlations can be used to predict these square‐based spouted beds. Spouted beds can be very reasonably described as well‐mixed reactors. By assembling several units, it is possible to approach a plug flow state for solids residence time distribution (RTD) in continuous processes. Spouting stability is fully reached when the modules do not interfere each other: this goal is obtained by positioning the downstream units at a certain lower level and having the solids move by overflow; placing internal vertical baffles between contiguous units is the solution. Two hydrodynamical models are proposed to describe single and multiple spouted beds; their predictions help in choosing the best geometrical configuration for the assemblage. A possible application of a multiple spouted bed reactor was envisaged for gasifying pelletized textile residues to generate syngas which can be directly used in situ as a co‐fuel. Two units were built: a single 0.2 m side square‐based spouted bed furnace and the consequent dual stage scale‐up apparatus suitable for an auto thermal process. In the bottom stage, four units work independently to run combustion/gasification of low‐quality solid residues, while the upper stage had a cascade of four modules to run pyrolysis/gasification of selected biomass and obtain valuable secondary products.

From circular to square‐based section spouted beds: Scale‐up and design overview of a multistage thermal unit

AbstractThis work summarizes the results of several research projects to demonstrate the possibility of scaling‐up spouted beds by assembling a number of square‐based jet units. Cylindrical vessels with a bottom cone and parallelepiped ones with a frustum base do not differ to a large extent in terms of hydrodynamic features. The existing correlations can be used to predict these square‐based spouted beds. Spouted beds can be very reasonably described as well‐mixed reactors. By assembling several units, it is possible to approach a plug flow state for solids residence time distribution (RTD) in continuous processes. Spouting stability is fully reached when the modules do not interfere each other: this goal is obtained by positioning the downstream units at a certain lower level and having the solids move by overflow; placing internal vertical baffles between contiguous units is the solution. Two hydrodynamical models are proposed to describe single and multiple spouted beds; their predictions help in choosing the best geometrical configuration for the assemblage. A possible application of a multiple spouted bed reactor was envisaged for gasifying pelletized textile residues to generate syngas which can be directly used in situ as a co‐fuel. Two units were built: a single 0.2 m side square‐based spouted bed furnace and the consequent dual stage scale‐up apparatus suitable for an auto thermal process. In the bottom stage, four units work independently to run combustion/gasification of low‐quality solid residues, while the upper stage had a cascade of four modules to run pyrolysis/gasification of selected biomass and obtain valuable secondary products.

Estimating travel time in the Helsinki region utilising sequential Bayesian inference

This paper explores Bayesian inference's application to dynamic travel time estimation in the Helsinki region. Accurate travel time prediction is crucial in a wide range of fields, including departure time and routing. Limited real-time data challenges modelling accuracy. To address this, this paper utilises Bayesian inference, particularly sequential Bayesian inference for evolving observed values. Incorporating 2018 real-time data and 2015 information as prior knowledge, travel time distribution will be updated. Validation yields a 4.0% mean absolute error between the updated 2018 distribution and actual travel time. Also, using the 2015 posterior distribution as prior via sequential Bayesian inference yields a 4.6% mean absolute error. Results highlight that sequential Bayesian inference is an effective tool for updating distributions. The paper underscores Bayesian inference's potential in addressing data scarcity and enhancing transportation model accuracy. By supplying precise travel time estimates, this approach benefits congestion relief and travel planning. With evolving travel time data, Bayesian inference promises to advance transportation modelling.