First-order Process Research Articles

A computational investigation of diffusivities and heat transfer in the flow of viscoelastic fluid through Darcy–Brinkman–Forchheimer medium

The main objective of this work is to examine the effects of non-linear thermal radiation and induced magnetic fields on the behavior of viscoelastic fluid as it flows through a porous material that does not follow Darcy’s law. Hence, the Darcy–Brinkman–Forchheimer model has been employed when formulating the momentum equation. The research postulates the presence of a chemical process involving two chemical species, A and B. The research utilizes the spectral relaxation method (SRM) for the numerical solution of the altered governing equations. The present algorithm is formulated and executed on computational software MATLAB. The findings demonstrate a high level of concurrence with previously known numerical outcomes in the context of a specific scenario. The findings of this research demonstrate that chemical species B functions as a catalyst in a horizontally subsurface environment. Consequently, the relationship between the homogeneous-heterogeneous scheme may be seen in the context of isothermal cubic autocatalytic events and first-order processes, respectively. The use of the heterogeneous reaction parameter offers significant benefits in reducing the concentration of the bulk fluid while increasing the concentration of the catalyst located on the subsurface. Moreover, the results indicate that the impacts of induced magnetic and viscoelastic factors on velocity and induced magnetic fields are similar in their outcomes but differ in their fundamental nature.

Effects of cross diffusion on radiative MHD flow over a rotating cone through porous medium

This work examines innovative elements of the impacts of thermal radiation, Soret, and Dufour effects on the magnetohydrodynamic (MHD) flow of viscous liquid past a spinning cone via a porous medium from the perspective of advancements in mass as well as heat transfer. Heat generation/absorption, dissipation, thermal flux as well as Joule heating, and other physical factors are taken into account when expressing the energy equation. The physical characteristics of the first-order chemical process that generates entropy are examined. A boundary value problem with reduced dimensions that was obtained from the governing equations is solved for the system using the bvp4c technique. Graphs are used to show how effective factors affect the concentration, entropy generation rate, temperature, and velocity. Based on our study, tangential velocity, as well as azimuthal velocity, is diminishing visually for greater values of the magnetic as well as porosity parameters, but the behavior of the mixed convection parametric exposes the regress behavior. Additionally, when the Dufour parametric increases, parameters of Soret and chemical reaction both show declining behavior.

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Editorial Presentation - Perspectives on innovation governance: challenges and dilemmas

Effect of Blumea Balsamifera Extract on the Kinetics of Calcium Oxalate Monohydrate (COM) Dissolution

Blumea balsamifera is a commercially available herbal drug that has anti-urolithic and diuretic properties. This study quantifies the effect of B. balsamifera extract on the dissolution kinetics of Calcium Oxalate Monohydrate (COM) crystals by modeling the dissolution of Ca2+. COM dissolution follows a first-order diffusion-controlled process. The extract did not change the dissolution phenomena. At 10 ppm, the extract had no significant effect on the rate constant (P = 0.166) and surface concentration (P = 0.372). Increasing the extract to 20 ppm did not change the dissolution phenomena and model parameters. The extract significantly decreases the equilibrium Ca2+ concentration with P = 0.0010 and P = 0.0179 at 10 and 20 ppm of extract, respectively. B. balsamifera extract binds free Ca2+ in the synthetic urine. This increases the amount of Ca2+ that dissolves but does not significantly increase the rate of dissolution suggesting that urine volume is more important for COM stone dissolution.

A Novel Method for Diagnosing Land–Atmosphere Coupling Sensitivity in a Single-Column Model

Abstract The response of boundary layer properties and cloudiness to changes in surface evaporative fraction (EF) is investigated in a single-column model to quantify the locally coupled impact of subgrid surface variations on the atmosphere during summer. Sensitive coupling days are defined when the model atmosphere exhibits large variations across a range of EFs centered on the analyzed value. Coupling sensitivity exists as both positive feedback (cloudiness increases with EF) and negative feedback (clouds increase with decreasing EF) regimes. The positive regime manifests in shallow convection situations, which are capped by a strengthened inversion and subsidence, restricting the vertical extent of convection to just above the boundary layer. Surfaces with larger EF (greater surface latent heat flux) can inject more moisture into the vertically confined system, lowering the cloud base and an increasing cloud liquid water path (LWP). Negative feedback regimes tend to manifest when large-scale deep convection, such as from mesoscale convective systems and fronts, is advected through the domain, where convection strengthens over surfaces with a lower EF (greater surface sensible heat flux). The invigoration of these systems by the land surface leads to an increase in LWP through strengthened updrafts and stronger coupling between the boundary layer and the free atmosphere. These results apply in the absence of heterogeneity-induced mesoscale circulations, providing a one-dimensional dynamical perspective on the effect of surface heterogeneity. This study provides a framework of intermediate complexity, lying between parcel theory and high-resolution coupled land–atmosphere modeling, and therefore isolates the relevant first-order processes in land–atmosphere interactions. Significance Statement Cloud formation, distribution, and other properties may be sensitive to heterogeneous surfaces depending on the strength and location of such heterogeneities and the background atmospheric state. This may drive differences in the cloud population depending on which part of the domain one is located. This may also lead to mesoscale circulations, which may strengthen or weaken this effect. Currently, climate models act on scales (∼100 km) that are too large to explicitly represent these processes, which are strongest at smaller scales (around 5–40 km). Therefore, subgrid-scale heterogeneity is neglected, and any predictability and model fidelity it may provide is lost. We use a simple model to diagnose sensitivity of the local atmosphere to surface variations meant to represent possible subgrid heterogeneity, providing a first-order estimate of its effect. We conclude that preferentially sensitive atmospheric states exist that lead to positive and/or negative feedback between land and atmosphere. This information is valuable to future climate model parameterizations aimed at improving the representation of these feedbacks.

Grounding our Understanding of the Impacts of Boreal Forest Expansion on Shallow Cumulus Clouds with a Simple Modeling Framework

Abstract The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first-order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL–mixed-layer height compensation in our idealized modeling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed-layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns. Significance Statement Clouds and vegetation are both important components of the climate system that interact across a range of scales. These interactions are central to understanding how changes at the land surface feedback on climate. For example, if a forest expands or recedes, diagnosing how that will impact clouds will determine whether you predict warming or cooling temperatures from that shift in the forest area. These predictions are often made with complex Earth system models, but we look to a more idealized representation of the land–atmosphere system to diagnose how shallow clouds should respond to changes in surface properties with different scenarios of boreal forest expansion at a more foundational level. This both grounds our understanding of previous analysis and provides helpful direction for future studies of this relevant and impactful land cover change.

CYP3A5*3 and CYP3A4*22 Cluster Polymorphism Effects on LCP-Tac Tacrolimus Exposure: Population Pharmacokinetic Approach.

The aim of the study is to develop a population pharmacokinetic (PopPK) model and to investigate the influence of CYP3A5/CYP3A4 and ABCB1 single nucleotide polymorphisms (SNPs) on the Tacrolimus PK parameters after LCP-Tac formulation in stable adult renal transplant patients. The model was developed, using NONMEM v7.5, from full PK profiles from a clinical study (n = 30) and trough concentrations (C0) from patient follow-up (n = 68). The PK profile of the LCP-Tac formulation was best described by a two-compartment model with linear elimination, parameterized in elimination (CL/F) and distributional (CLD/F) clearances and central compartment (Vc/F) and peripheral compartment (Vp/F) distribution volumes. A time-lagged first-order absorption process was characterized using transit compartment models. According to the structural part of the base model, the LCP-Tac showed an absorption profile characterized by two transit compartments and a mean transit time of 3.02 h. Inter-individual variability was associated with CL/F, Vc/F, and Vp/F. Adding inter-occasion variability (IOV) on CL/F caused a statistically significant reduction in the model minimum objective function MOFV (p < 0.001). Genetic polymorphism of CYP3A5 and a cluster of CYP3A4/A5 SNPs statistically significantly influenced Tac CL/F. In conclusion, a PopPK model was successfully developed for LCP-Tac formulation in stable renal transplant patients. CYP3A4/A5 SNPs as a combined cluster including three different phenotypes (high, intermediate, and poor metabolizers) was the most powerful covariate to describe part of the inter-individual variability associated with apparent elimination clearance. Considering this covariate in the initial dose estimation and during the therapeutic drug monitoring (TDM) would probably optimize Tac exposure attainments.

Electron Injection via Modified Diffusive Shock Acceleration in High-Mach-number Collisionless Shocks

The ability of collisionless shocks to efficiently accelerate nonthermal electrons via diffusive shock acceleration (DSA) is thought to require an injection mechanism capable of preaccelerating electrons to high enough energy where they can start crossing the shock front potential. We propose, and show via fully kinetic plasma simulations, that in high-Mach-number shocks electrons can be effectively injected by scattering in kinetic-scale magnetic turbulence produced near the shock transition by the ion Weibel, or current filamentation, instability. We describe this process as a modified DSA mechanism where initially thermal electrons experience the flow velocity gradient in the shock transition and are accelerated via a first-order Fermi process as they scatter back and forth. The electron energization rate, diffusion coefficient, and acceleration time obtained in the model are consistent with particle-in-cell simulations and with the results of recent laboratory experiments where nonthermal electron acceleration was observed. This injection model represents a natural extension of DSA and could account for electron injection in high-Mach-number astrophysical shocks, such as those associated with young supernova remnants and accretion shocks in galaxy clusters.

Population Pharmacokinetics of Ozoralizumab in Patients with Rheumatoid Arthritis.

Ozoralizumab is a bispecific NANOBODY compound that binds tumor necrosis factor alpha (TNFα) and human serum albumin. Ozoralizumab inhibits the TNFα physiological activity while maintaining long-term plasma retention owing to its human serum albumin-binding ability. A population pharmacokinetic (PK) model was developed using data from 494 Japanese patients with rheumatoid arthritis in Phase II/III and Phase III trials to assess the effects of potential PK covariates. The ozoralizumab PK after subcutaneous administration was described using a 1-compartment model with first-order absorption and first-order elimination processes. A proportional error model was used for inter- and intra-individual variabilities, with covariance set between inter-individual variabilities of the apparent clearance and apparent distribution volume. Body weight, sex, antidrug antibody status, estimated glomerular filtration rate, and concomitant methotrexate use were identified as covariates for apparent clearance, while body weight and sex were covariates for apparent distribution volume in the final model. Body weight had the greatest effect on the PK of ozoralizumab, while the other covariates had minor effects. When administered at 30 mg every 4 weeks, the predicted steady-state plasma trough concentration in a patient weighing 83.2 kg exceeded the trough concentration required to maintain efficacy of ozoralizumab, and the estimated exposure in a patient weighing 42.5 kg did not exceed the mean exposure at 80 mg, a well-tolerated dose, throughout 52 weeks. We developed a population PK model that adequately described the ozoralizumab PK in Japanese patients with rheumatoid arthritis, and none of the evaluated covariates showed clinically relevant effects on the PK of ozoralizumab.

Hair relaxation after shaping - A kinetic approach.

Hair fibres have been shaped via either a thermal route or via a chemical route. The time-relaxation transients of the shaped hairs in air, and in water, respectively, were evaluated. The collected data were kinetically modelled in order to reveal information about the rate controlling mechanism of the recovery process. Hair fibres were thermally shaped at different temperatures between heated plates and left to relax in an environment of controlled humidity and temperature. Different hair fibres were chemically shaped and left to relax in water of different controlled temperatures. Relaxation data were used for modelling the kinetics of the recovery processes by using exponential and logarithmic kinetic laws. The fitting of the models to the two sets of data has been checked by using the residual sum of squares for matching the proper model to each set of data. The processes of shaping and recovery were assimilated with a sequence of two successive quasi-chemical reactions, occurring at the used temperatures. Based on chemical and physical assumptions, the two groups of experiments were modelled by two different laws: an exponential law, suggesting a first-order process as the rate-determining step of the relaxation of thermally shaped fibres, and a logarithmic law, suggesting a slow relaxation, based on percolation theory, for the chemically shaped fibres. This allowed use of chemical kinetics tools for calculating the values of the activation energy in each case. The evaluated values of activation energy of the relaxation processes for both thermal and chemical shaping were found to be close to each other, in spite of the different methods of shaping. The kinetic analysis suggests that despite different reaction sequences occurring during the different shaping-relaxation processes, the rate-controlling mechanism that manages the recovery process is the same in all cases; and this process is proposed to be the thiol-disulphide reformation of intra-protein bonds inside hair.

Model-based assessment of neutrophil-mediated phagocytosis and digestion of bacteria across in vitro and in vivo studies.

Neutrophil granulocytes are key components of the host response against pathogens, and severe neutropenia, with neutrophil counts below 0.5 × 106 cells/mL, renders patients increasingly vulnerable to infections. Published in vitro (n = 7) and in vivo (n = 5) studies with time-course information on bacterial and neutrophil counts were digitized to characterize the kinetics of neutrophil-mediated bacterial killing and inform on the immune systems' contribution to the clearance of bacterial infections. A mathematical model for the in vitro dynamics of bacteria and the kinetics of neutrophil-mediated phagocytosis and digestion was developed, which was extended to in vivo studies in immune-competent and immune-compromised mice. Neutrophil-mediated bacterial killing was described by two first-order processes-phagocytosis and digestion-scaled by neutrophil concentration, where 50% of the maximum was achieved at neutrophil counts of 1.19 × 106 cells/mL (phagocytosis) and 6.55 × 106 cells/mL (digestion). The process efficiencies diminished as the phagocytosed bacteria to total neutrophils ratio increased (with 50% reduction at a ratio of 3.41). Neutrophil in vivo dynamics were captured through the characterization of myelosuppressive drug effects and postinoculation neutrophil influx into lungs and by system differences (27% bacterial growth and 9.3% maximum capacity, compared with in vitro estimates). Predictions showed how the therapeutically induced reduction of neutrophil counts enabled bacterial growth, especially when falling below 0.5 × 106 cells/mL, whereas control individuals could deal with all tested bacterial burdens (up to 109 colony forming units/g lung). The model-based characterization of neutrophil-mediated bacterial killing simultaneously predicted data across in vitro and in vivo studies and may be used to inform the capacity of host-response at the individual level.

Metacognitive Psychophysics in Humans, Animals, and AI: A Research Agenda for Mapping Introspective Systems

Kammerer and Frankish (this issue) propose an exciting new research programme on the computational form of introspective systems. Pursuing this goal requires measures that can isolate introspective capacity from response biases and first-order processes. I suggest that metacognitive psychophysics is well placed to meet this challenge, allowing the mapping of introspective architectures in humans, animals, and artificial systems.

Impulse response function analysis for Markov switching var models

We exactly derive the regime-dependent impulse response functions for a Markov switching vector autoregression (VAR) model in terms of neat matrix expressions in closed form. The key is to recognize that the latent first-order Markov switching process in the model has a VAR(1) representation, and that the model can be cast into a state-space form. Using such a representation, the regime-dependent impulse response function analysis can be processed with respect to either an asymmetric discrete shock or to a symmetric continuous shock. Our results extend and correct those obtained by Ehrmann et al. (2003) and coincide with those by Hamilton (1994) for the case of standard VAR models.

고령층 소득의 비선형적 지속성 분석

This study estimates income risk observed among the elderly using the Korean Retirement and Income Study data. In order to decompose household income into permanent and transitory components, the quantile-based panel data framework is employed. The permanent component is assumed to follow a first-order Markov process, and the transitory component is assumed to be independent over time and also independent of the persistent components. I estimate the income persistence from the estimated permanent income component, and analyze the dynamic effects of permanent income shocks on income through simulations. The results show the presence of nonlinear persistence in the household income of the elderly. Furthermore, it is observed that the dynamic effects of permanent income shocks vary depending on the past income history. These findings differ from studies focused on the age groups participating in the labor market. In particular, the permanent income shocks have a significant impact on the income of elderly households. In particular, the permanent income shocks have a significant impact on the income of elderly households.

Impact of pulsed electric field treatment on the viability of Tenebrio molitor insect biomass, and on the following pressing and drying processes

This work explores the impact of pulsed electrical fields (PEF) on false mortality or latent vitality of yellow mealworms (Tenebrio molitor larvae) and following processes of hydraulic pressing (at pressure P = 6 MPa and temperature Tp = 60 °C) and air drying in a ventilated oven (Td = 50–70 °C). The observed PEF effects were compared with data for blanched and frozen-thawed samples. Rinsing the larvae before the treatment significantly increased larval mortality. Analysis of SEM images revealed the presence of large-scale damages such as necrotic spots, after applying severe PEF treatment conditions to achieve complete mortality. In the pressing experiments, remarkable improvements were observed, with pressing yield increasing from 65% in untreated larvae up to 80% in PEF-treated larvae. Simultaneously, the water content of the resulting press cake decreased from 58% to 46%, with all studied samples displaying an inverse linear correlation between the press cake's water content and the pressing yield. Regarding the drying process, it was observed that blanched samples exhibited the slowest drying rate, whereas PEF-treated samples displayed a first-order kinetic process with a noteworthy acceleration in drying. These findings indicate that PEF treatment improves and boosts the overall drying process. Furthermore, PEF treatment resulted in a notable decrease in the activation energy of larvae, dropping from 80 kJ/mol for blanched and frozen-thawed samples to a range of 40–50 kJ/mol for PEF-treated larvae. This reduction in activation energy highlights the potential of PEF treatment in optimizing the overall efficiency of larval drying. Industrial relevanceT. molitor larvae were approved by the European Food Safety Authority as a novel food. It is also a promising candidate for industrial-scale production, with relatively low contamination levels and high nutrient values as food sources for humans and domestic animals (such as birds, fish, and pets). It was demonstrated that PEF treatments significantly affect larval viability and can significantly improve the pressing and drying processes of T. molitor insect biomass. Obtained data may be useful for the industrial optimization of these processes.

Insights into kinetics and reaction mechanism of acid-catalyzed transesterification synthesis of diethyl oxalate

The catalytic performance of different acidic catalysts for diethyl oxalate synthesis from the one-step transesterification of dimethyl oxalate and ethanol was evaluated. The effects of different factors (e.g., acidity, electron accepting capacity, cations type and crystalline water) on the catalytic activity of acidic catalysts were investigated respectively. It was proposed and confirmed that the transesterification reaction catalyzed by a Lewis acid (FeCl3) and a Bronsted acid (H2SO4) follows a first-order kinetic reaction process. In addition, the Lewis acid-catalyzed transesterification processes with different ester structures were used to further explore and understand the speculated reaction mechanism. This work enriches the theoretical understanding of acid-catalyzed transesterification reactions and is of great significance for the development of highly active catalysts for diethyl oxalate synthesis, diminishing the industrial production cost of diethyl oxalate, and developing downstream bulk or high-value-added industrial products.

Orders-of-coupling representation achieved with a single neural network with optimal neuron activation functions and without nonlinear parameter optimization

Orders-of-coupling representations (representations of multivariate functions with low-dimensional functions that depend on subsets of original coordinates corresponding to different orders of coupling) are useful in many applications, for example, in computational chemistry and other applications, especially where integration is needed. Examples include N-mode approximations and many-body expansions. Such representations can be conveniently built with machine learning methods, and previously, methods building the lower-dimensional terms of such representations with neural networks [e.g. Comput. Phys. Commun. 180 (2009) 2002] and Gaussian process regressions [e.g. Mach. Learn. Sci. Technol. 3 (2022) 01LT02] were proposed. Here, we show that neural network models of orders-of-coupling representations can be easily built by using a recently proposed neural network with optimal neuron activation functions computed with a first-order additive Gaussian process regression [arXiv:2301.05567] and avoiding non-linear parameter optimization. Examples are given of representations of molecular potential energy surfaces.

First-order random coefficient binomial AR process with dependent counting series

A first-order random coefficient binomial AR process with dependent counting series is introduced. We derive some basic properties of the process. Conditional least squares, weighted conditional least squares and conditional maximum likelihood methods are employed to estimate the parameters of the process. Some asymptotic properties are established. Numerical results of the estimators are studied to assess the performances. An example is provided for practical application.

Modeled Forest Conversion Influences Humid Tropical Watershed Hydrology More than Projected Climate Change

In the humid tropics, forest conversion and climate change threaten the hydrological function and stationarity of watersheds, particularly in steep terrain. As climate change intensifies, shifting precipitation patterns and expanding agricultural and pastoral land use may effectively reduce the resilience of headwater catchments. Compounding this problem is the limited long-term monitoring in developing countries for planning in an uncertain future. In this study, we asked which change, climate or land use, more greatly affects stream discharge in humid tropical mountain watersheds? To answer this question, we used the process-based, spatially distributed Soil Moisture Routing model. After first evaluating model performance (Ns = 0.73), we conducted a global sensitivity analysis to identify the model parameters that most strongly influence simulated watershed discharge. In particular, peak flows are most influenced by input model parameters that represent shallow subsurface soil pathways and saturation-excess runoff while low flows are most sensitive to macropore hydraulic conductivity, soil depth and porosity parameters. We then simulated a range of land use and climate scenarios in three mountain watersheds of central Costa Rica. Our results show that deforestation influences streamflow more than altered precipitation and temperature patterns through changes in first-order hydrologic hillslope processes. However, forest conversion coupled with intensifying precipitation events amplifies hydrological extremes, reducing the hydrological resilience to predicted climate shifts in mountain watersheds of the humid tropics. This finding suggests that reforestation can help mitigate the effects of climate change on streamflow dynamics in the tropics including impacts to water availability, flood pulses, channel geomorphology and aquatic habitat associated with altered flow regimes.

Multivariate threshold integer-valued autoregressive processes with explanatory variables

To capture the multivariate count time series showing piecewise phenomena, we introduce a class of first-order multivariate threshold integer-valued autoregressive process. The component of autoregressive coefficient is driven by explanatory variables via a logistic link function. Basic probabilistic and statistical properties of the model are discussed. The model parameters are estimated by means of conditional least squares and conditional maximum likelihood methods. A new algorithm to estimate the threshold parameter of the model is also provided. Moreover, the nonlinearity test of the model and existence test of explanatory variables are well addressed using a Wald-type test. Forecasting problem and model extension problem are also considered. Finally, some numerical results of the estimates and a real data example are presented.