Sigmoidal Model Research Articles

A mathematical framework for the statistical interpretation of biological growth models

Biological entities are inherently dynamic. As such, various ecological disciplines use mathematical models to describe temporal evolution. Typically, growth curves are modelled as sigmoids, with the evolution modelled by ordinary differential equations. Among the various sigmoid models, the logistic, Gompertz and Richards equations are well-established and widely used for the purpose of fitting growth data in the fields of biology and ecology. The present paper puts forth a mathematical framework for the statistical analysis of population growth models. The analysis is based on a mathematical model of the population–environment relationship, the theoretical foundations of which are discussed in detail. By applying this theory, stochastic evolutionary equations are obtained, for which the logistic, Gompertz, Richards and Birch equations represent a limiting case. To substantiate the models of population growth dynamics, the results of numerical simulations are presented. It is demonstrated that a variety of population growth models can be addressed in a comparable manner. It is suggested that the discussed mathematical framework for statistical interpretation of the joint population-environment evolution represents a promising avenue for further research.

Contribution of ingestive/dietary uptake to bioaccumulation of organics in worms

Ingestive uptake is critical for understanding the accumulation and trophic transfer of chemicals and synthesized particles in general. This study explored the contribution of ingestion in the bioaccumulation of chemicals focusing on worms. Novel theory and equations were developed to derive fractional ingestive contribution, fs, from a broad range of dietary uptake and accumulation studies, and to build a small dataset of fs (n = 43) from relevant toxicokinetic and bioaccumulation measurements. Worm fs could be fitted to log KOW-based sigmoidal models with small errors (RSE < 0.15, RMSE<0.15). The basis and limitations of the applied fs equations were elaborated. These included the assumption that aqueous-based and dietary-based elimination rate constants (kTw and kTS) may be statistically equivalent, as demonstrated using fish and worm data. Bioaccumulation and toxicokinetic parameters obtained at under-exposed conditions can also result in non-sensical, negative fs. The developed fs theory suggested a novel way to model bioaccumulation in the presence of aqueous and solid sources, and the potential to consolidate bioaccumulation data in their variant forms and definitions for assessment, modeling, and benchmarking purposes. While the presented fs-log KOW dependence remained to be explored in other species, the importance of ingestive uptake for high-log KOW chemicals questioned the validity of characterizing and regulating bioaccumulation potential of hydrophobic organics – for which dietary uptake matters – using aqueous-only bioconcentration factor (BCF). This question, along with other less important ones, is yet to be explored in future works.

Rate dependency and fragmentation response of phase field models with micro inertia and micro viscosity terms

We present elliptic, parabolic, and hyperbolic phase field (PF) equations, referred to as EPF, PPF, and HPF, for cohesive zone model (CZM) and Linear Elastic Fracture Mechanics (LEFM) PF models. The micro viscosity and micro inertia terms result in PPF and HPF, that can be solved explicitly in time. The additional advantage of the HPF is implying a finite damage propagation speed and a more favorable time advance limit. The desired micro-viscosity for the HPF is shown to correspond to a damping factor of one. The appropriate length and time scales, nondimensional equations, and (asymptotic) strain-stress solutions are provided for each differential equation. Two sources of rate sensitivity are discussed. First, when the fields are spatially uniform (0D solution) the rate effect arises from the form of differential equation. Asymptotic solutions show that at high loading rates, the energy dissipation has the rates of 2/3 and 1 for the PPF and HPF, respectively, with the former matching the Grady’s rate-sensitivity. The dynamic strength and failure strain show half of this rate. The analysis is extended to damage models with stress-based and bounded driving forces. Second, the rate effect arises from the deviation of 1D from 0D solutions as the damage exceeds a critical value and fragments form. Since this critical value tends to zero for quasi-static loading, this rate-effect is mainly present for low loading rates. This results in lower- and upper-shelf energy limits for the EPF at low and high loading rates, resembling some experimentally observed sigmoidal rate models for energy dissipation.

Concentration-QTcF analysis of quizartinib in patients with newly diagnosed FLT3-internal-tandem-duplication-positive acute myeloid leukemia.

Quizartinib prolongs QT interval through inhibition of the slow delayed rectifier potassium current (IKs). We used non-linear mixed-effects modeling to explore the relationship between quizartinib and its pharmacologically active metabolite AC886 and the Fridericia-corrected QT interval (QTcF) in newly diagnosed acute myeloid leukemia (AML) patients. We evaluated linear and non-linear drug effect models, using triplicate QTcF measurements with available time-matched pharmacokinetic samples from the Phase 3 QuANTUM-First trial. The effect of intrinsic and extrinsic factors on model parameters was tested using stepwise covariate model building. Simulations were conducted to predict the change from baseline in QTcF (ΔQTcF) at the maximum concentration at steady-state (Cmax,ss) for quizartinib maintenance daily doses of 30 and 60 mg. The concentration-QTcF (C-QTcF) relationship was best described by a sigmoidal maximum effect model. After accounting for the effect of quizartinib, including AC886 concentrations did not further explain changes in QTcF. Circadian variations in QTcF were described using an empirical change from baseline based on clock times. Age and hypokalaemia were identified as statistically significant covariates on baseline QTcF; no covariates were found to impact the C-QTcF relationship. The median model-predicted ΔQTcF at Cmax,ss was 18.4 ms (90% confidence interval (CI): 16.3-20.5) at 30 mg and 24.1 ms (90% CI: 21.4-26.6) at 60 mg. In conclusion, in newly diagnosed AML patients, ΔQTcF increased non-linearly with increasing quizartinib concentrations. The predicted ΔQTcF increase at Cmax,ss supports the proposed dose adaptation based on observed QTcF and the dose reduction in case of strong cytochrome P450 3A (CYP3A) inhibitors coadministration.

Population pharmacokinetic and pharmacodynamic model of evogliptin: Severe uremia increases the bioavailability ofevogliptin.

Uremia, a condition characterized by the retention of uremic toxins due to impaired renal function, may affect drug metabolism mediated by CYP3A4 enzymes. Evogliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor diabetic drug that is primarily metabolized by CYP3A4. This study aimed to construct a population pharmacokinetic (PK) and pharmacodynamic (PD) model for evogliptin in patients with varying degrees of renal disease, including end-stage renal disease on hemodialysis. A total of 688 evogliptin concentration and 598 DPP-4 activity data were available from 46 subjects. PK and PD data analyses were performed using a nonlinear mixed-effects model. The PK of evogliptin was optimally described by a two-compartment model with first-order absorption. The significant covariates in the final model included blood amylase and triglyceride on F1 (relative bioavailability). The simulation findings, together with previously reported PK data, provided evidence of a significant inhibition of the first-pass effect of evogliptin in patients with renal impairment. A direct link sigmoidal Emax model was developed to describe the relationship between evogliptin concentration and DPP-4 inhibition. The PD model predicted significant inhibition of DPP-4 at maximum effect (Emax: 88.9%) and a low EC50 value (1.08 μg/L), indicating the high potency and efficacy of evogliptin. The developed PK/PD model accurately predicted exposure and the resulting DPP-4 activity of evogliptin in renal impairment. The findings of this study suggest that renal impairment and associated biochemical changes may impact the bioavailability of CYP3A4-metabolized drugs.

Investigation of the effect of the flow rate of coolant on the kinetics of carbon dioxide hydrate growth using sigmoidal growth model

Relaxation time plays a crucial role in studying reaction kinetics and other processes. This study addresses the importance of relaxation time and suggests methods to minimize it. In this article, we adjust the relaxation time by extracting heat from the hydrate formation process through variations in the flow rate of the cooling fluid. It has been demonstrated that heat transfer plays a dominant role in this process. The kinetics of carbon dioxide hydrate growth were evaluated by increasing the flow rate of cooling fluid using modified sigmoidal growth curve equations, including Logistics and Gompertz. It was conducted a nonlinear least-squares regression analysis to fit sigmoidal functions to the cumulative formation curves of hydrate generated from gas uptake in the static reactor over time. The percentage of error between Areal and Amodel shows that the Gompertz model with Q = 14.59 Lit/min at T = 276 K and Q = 20.25 Lit/min at T = 277 K is the best model for predicting the maximum consumption capacity of CO2. The results also showed that increasing the cooling fluid’s flow rate reduces relaxation time at any temperature. Moreover, increasing the flow rate of the cooling fluid decreased the average relaxation time by 2 % to 60 % at T = 276 K and by 7 % to 22 % at T = 277 K compared to the lowest investigated flow rate. Additionally, reducing the experimental temperature while keeping the flow rate of the cooling fluid constant led to a reduction in the relaxation time.

Model-Informed Approach to Recommend Burosumab Dosing Regimens for Pediatric and Adult Patients With the Ultrarare Disease Tumor-Induced Osteomalacia.

Burosumab is approved for the treatment of hypophosphatemia in persistent tumor-induced osteomalacia. This work exemplifies a model-informed drug development approach that evaluated burosumab pharmacokinetics and pharmacokinetic/pharmacodynamics in the ultrarare tumor-induced osteomalacia population to support adult and pediatric dosing. Data from tumor-induced osteomalacia participants were combined with data from X-linked hypophosphatemia to understand pharmacokinetic and pharmacokinetic/pharmacodynamic characteristics and covariates specific to tumor-induced osteomalacia. Pharmacokinetic and pharmacokinetic/pharmacodynamic simulations were performed using final models to support dosing recommendations for adults and extrapolation to pediatric patients. Burosumab pharmacokinetics were described using a one-compartment model with first-order absorption and body weight as a significant covariate. Pharmacokinetic/pharmacodynamic relationships were described using a sigmoidal Emax model with significant covariates of baseline fibroblast growth factor 23 on baseline fasting serum phosphate and potency of burosumab response and tumor-induced osteomalacia disease state resulting in a steep slope of response; however, the covariates are not clinically meaningful. Simulations demonstrated that, in pediatric patients, starting doses of burosumab 0.3 and 0.4 mg/kg every 2 weeks at steady state would achieve normal serum phosphate levels in ≥ 30% of patients with relatively low risk of hyperphosphatemia (< 3%). In adults, burosumab 0.3 and 0.5 mg/kg every 4 weeks achieves similar percentages of responders and a relative low risk of hyperphosphatemia (< 7%). Serum phosphate titration-based burosumab dosing increased the probability of achieving normal serum phosphate levels. The models supported a model-informed drug development approach for global approvals of titration-based burosumab dosing, guided by monitoring fasting serum phosphate levels.

Degradation kinetics of betalains in red beetroot juices throughout fermentation process and storage

Contemporary customers have increasingly heightened demands regarding their dietary preferences, favoring readily available and healthful ready-to-consume food and beverage alternatives. In order to fulfill this demand, the beverage industry has been exploring innovative products. Red beetroot juice (RBJ) is easily consumed and offers a beneficial advantage due to its betalain content. Both the traditional/spontaneous method and the industrial method are viable options for producing RBJ. The aim of this study was to examine the stability of betalains, the primary bioactive component in RBJ, throughout the fermentation process and during storage. The RBJ samples contained betalain pigments, as indicated by the infrared spectra. No decrease in betalain content was observed in either RBJ juice fermented by two different methods. The total betalain content of the RBJ samples was 594.068 and 535.152 mg/L at the beginning of fermentation through the spontaneous method and the method with the addition of Lacticaseibacillus paracasei 431 (Lc. paracasei), respectively, and 580.151 mg/L and 667.382 mg/L at the conclusion of fermentation, respectively. Mathematical models were employed to represent the degradation of betalains over time during storage. It was found that the sigmoidal model (R2 = 0.974) and other models (R2 = 0.957–0.969) demonstrated a greater potential to describe the degradation of betalains in RBJ samples produced by the spontaneous method compared to the first-order kinetics model (R2 = 0.932). The models used in the study were successful, with R2 values ranging between 0.927 and 0.932 in the RBJ samples produced with the addition of the probiotic Lc. paracasei for predicting betalain degradation.

Dynamic shape changes of dried pasta during cooking via designed surface grooves

The morphing of food during gastronomic treatments is of current interest not only for product innovation and unprecedented human-food sensory interaction but also for sustainable reasons because it would enable to increase the load saturation for food packages with several positive environmental impacts. A shape changing pasta during cooking has been achieved through designed grooves and protrusions on one side of the samples. However, a quantitative understanding of the effects of manufacturing and drying process on the shape morphing is still missing. Here, three pressures applied on the surface of the samples and three thicknesses of the sheeted dough have been analyzed for their effect on the dehydration kinetics of pasta, microstructure properties, water absorption and the kinetic of bending during cooking. Results proved the effectiveness of the proposed strategy to activate the morphing of dried pasta during cooking. The dehydration rates were primarily affected by the thickness of the sheeted dough while the applied pressure did not modify the dehydration kinetics of samples of 1.5 and 1 mm thick. 2D microCT images revealed that by using a constant pressure, the grooves were deeper as sheeted doughs were thick; the greater amount of dough permitted much more upward expansion between the teeth of the plastic stamp leading to higher protrusions/deeper grooves. The rehydration rate was mainly controlled by the height of the remaining layers below the grooves while the pressures applied did not affect the rate of the water absorption over time. The kinetic of bending during cooking was satisfactorily described by using a sigmoidal mathematical model. Results proved that the speed of bending was mainly affected by the thickness of the sheeted dough while the applied pressures affected the speed of bending only for the thinner samples of 1.5 and 1 mm thick while the thicker samples folded at the same speed with no changes among the pressure applied.

Anaerobic digestion performance and kinetics of biomass pretreated with various fungal strains utilizing exponential and sigmoidal equation models

Corn silage was pretreated with white rot fungi to optimize the methane generation capacity of biomass and the effect of the most important pretreatment parameters on the anaerobic digestion performance of material was determined by employing mathematical modelling. Pretreatment with selective fungi led to optimal increment in methane production from 0.301 m3kgVS−1 from corn silage to 0.465 m3kgVS−1 from the 10-day modified material, whereas non-selective fungi had negative effect. A reverse L-shape curve observed for the corn silage indicated an uninterrupted methane production and was not modified when the substrate was pretreated with Dichomitus squalens, Irpex lacteus and Trametes versicolor. Exponential models fitted L-curves better than sigmoidal equations with Cone model providing the most accurate predictions regardless of the selective or non-selective feature of the fungal strain used for the pretreatment of corn silage. Increasing pretreatment time led to a differentiation of the methane yield and adjustment of the curve shape to a stepped outline adequately modelled by Cone and transference models. Contrary to three-parameter sigmoidal models a fourth shape variable included in Richards equation was crucial, allowing for a flexible inflection point that enabled an accurate replication of an S-shape curve obtained for methane production from the hybrid substrate.

Efficient adsorptive removal of methyl green using Fe3O4/sawdust/MWCNT: Explaining sigmoidal behavior

The 4R concept (reduce, reuse, recycle and repurpose) in water management necessitates innovative adsorption techniques that utilize sustainable and natural materials. This study investigates the use of natural sawdust embedded in magnetic iron oxide to treat wastewater. The performance of the newly synthesized Fe3O4/sawdust adsorbent was also compared to the native Fe3O4 and Fe3O4/MWCNT. Methyl Green (MG) was used as a model pollutant due to its wide use and potential toxicity. The new adsorbents demonstrated a high removal efficiency that exceeds 97 % under ambient conditions. The study investigates the effect of pH on adsorption, revealing a significant shift in removal efficiency as pH increases, with an optimal pH of around 7. The pH dependence is explained based on the point-of-zero-charge of the Fe3O4 adsorbent and the structure of the dye. The thermodynamic parameters (ΔH°, ΔS°, and ΔG°) of adsorption were determined through a temperature study. The adsorption equilibrium was found to be endothermic, therefore preferring elevated temperatures. Because the adsorption data of the study exhibited S-shaped-like curves, sigmoidal models were used to describe the adsorption isotherms. This provided new insights into the competitive adsorption mechanisms acting on the heterogeneous Fe3O4/sawdust surfaces. The kinetics study indicates rapid and efficient adsorption with pseudo-second-order reaction. The half-life of the reaction was as low as 4.8 min. The findings suggest a rapid, highly efficient and sustainable method to remove organic pollutants from wastewater.

Evaluating dynamic modulus of cold in-place recycling mixture with foamed bitumen using field core samples

This study evaluates the dynamic modulus of Cold In-Place Recycling with Foamed Bitumen (FB-CIR) pavements, focusing on the influence of various recycled materials—reclaimed asphalt pavement (RAP), reclaimed inorganic binder stabilized aggregate (RAI), and their combination (RAP+RAI). Core samples from three representative FB-CIR projects in China were tested using the MTS-130 system across temperatures of −10°C, 5°C, 20°C, 35°C, and 50°C, and frequencies ranging from 25 Hz to 0.1 Hz. Statistical analysis and the Sigmoidal model were employed to analyze the viscoelastic behavior and establish the master curve for the FB-CIR dynamic modulus. The dynamic modulus of the three FB-CIR mixtures showed pronounced frequency-dependence, decreasing with reduced frequencies, and exhibited temperature sensitivity, with lower values at elevated temperatures. FB-RAI consistently demonstrated higher modulus values due to the hydration of previously unhydrated cement particles, while FB-RAP and FB-RAP+RAI displayed lower values, as RAP behaves similarly to unbound granular material. The construction of the master curve, based on the time-temperature superposition principle, enhanced our understanding of FB-CIR mixtures' mechanical behavior and enabled accurate extrapolation of dynamic modulus values across diverse conditions. Furthermore, FB-RAI's narrow phase angle variation indicates its superior mechanical stability and reduced sensitivity to external changes, making it ideal for areas with extreme climate fluctuations or heavy traffic. The study also identified three distinct phase angle variation patterns in FB-CIR mixtures, underscoring the importance of nuanced pavement design strategies that consider temperature, frequency, and material composition to optimize the performance and durability of FB-CIR pavements.

Deformation-induced martensitic transformation kinetics in TRIP-assisted steels and high-entropy alloys

In this study, the sigmoidal kinetics of deformation-induced martensitic transformation (DIMT) in the transformation-induced plasticity (TRIP)-assisted alloys, including metastable high-entropy alloys (HEAs), austenitic stainless steels, and advanced high-strength steels (AHSSs) such as low-alloyed TRIP steels, medium-Mn steels, and quenching and partitioning steels, were studied using various models. To tune mechanical stability, the influences of stacking fault energy (SFE), chemical composition, austenite grain size, deformation temperature, strain rate, and stress state were considered. The novel Fe47Co30Cr10Ni5V8-xSix (x = 3, 4, and 6 at.%) alloys were the main investigated HEAs in the present work, in which silicon addition effectively led to lowering SFE and faster DIMT kinetics by promoting the formation of body-centered cubic α΄-martensite. The Olson-Cohen, Guimaraes (based on Johnson-Mehl-Avrami-Kolmogorov analysis), Shin, and Ahmedabadi models were analyzed and critically discussed. Moreover, a Hill-based sigmoid model was formulated as fα' / fsat = 1 – p / (p + εq) with the parameters p and q characterizing the stability of austenite, and fsat representing the saturated volume fraction of α΄-martensite. It was demonstrated that χ = p × q can be considered the sole austenite stability parameter. It was concluded that the proposed Hill-based model, demonstrating the highest accuracy among the studied models, can effectively simulate the kinetics of α΄-martensite formation.

Toward Model-Informed Precision Dosing for Remimazolam: A Population Pharmacokinetic-Pharmacodynamic Analysis.

Remimazolam, widely used for procedural sedation and general anesthesia, is a new ultra short-acting benzodiazepine for intravenous sedation and anesthesia. We aim to characterize the pharmacokinetics/pharmacodynamics (PK/PD) of remimazolam and its metabolite CNS 7054 in healthy Chinese volunteers using population analysis and suggest an optimal dosing regimen for sedation therapy. Data were collected from a single-center, placebo-controlled, randomized, and dose-escalation clinical pharmacology study. Forty-six healthy volunteers received a single infusion dose of remimazolam, while nine healthy subjects received a continuous infusion of remimazolam. A population PK/PD model was established and RxODE and Shiny in R were used to design the remimazolam dosing regimens. A three-compartment model best described the PK of remimazolam and a two-compartment model with one transit compartment was adopted for CNS 7054. The relationship between exposure and the bispectral index was best described using an effect compartment model with an inhibitory sigmoid model. Additionally, a web-based dashboard was developed to provide individualized dosing regimens, complemented by a graphical illustration of the PK/PD profiles of the proposed dosing regimen. The established population PK/PD model characterized the dose-exposure-response relationship of remimazolam well, which could be applied to optimize individual dosing regimens.

Establishment and evaluation of dynamic viscoelasticity constitutive model for asphalt mixtures based on PCA model: Utilizing coal-based synthetic natural gas slag and phosphogypsum whisker as substitute fillers

To promote the secondary utilization of solid waste in asphalt pavement and reduce resource waste and environmental damage, the fine solid waste powder from the secondary treatment of solid waste was used as a substitute filler to prepare asphalt mixtures. To more precisely characterize the effects of filler type and substitution ratio on the dynamic viscoelastic mechanical characteristics of asphalt mixtures, the dynamic modulus test was conducted on asphalt mixtures, while the dynamic modulus master curve of asphalt mixtures was established based on the time-temperature equivalent principle and the Sigmoidal function. The improved generalized Sigmoidal (IGS) model, five-parameter fractional Zener (FFZ) model, six-parameter fractional Zener (SFZ) model and ten-parameter fractional Zener (TFZ) model were used to construct the shrinkage frequency curves of different asphalt mixtures under different constitutive relationships, whereas the applicability of the constitutive models was further evaluated by the principal component analysis (PCA) model. The research results showed that the dynamic modulus and phase angle of the mixture exhibited a similar change rule with the variation of temperature and frequency, and the use of solid waste-based fillers could effectively reduce the effect of frequency variation on the viscoelasticity of the mixture. The dynamic modulus master curve constructed by Sigmoidal function could accurately describe the functional relationship between the dynamic modulus with loading frequency and temperature, and the variation trend of dynamic modulus in a wider frequency range was flattened with the increase in frequency, and this change rule was not affected by the substitution ratio of the filler, while the use of solid waste-based filler could reduce the dependence of the mixture on the temperature. Compared to several fractional constitutive models, the IGS model exhibited a higher fitting accuracy for the dynamic viscoelasticity of several mixtures, and with the use of solid waste-based fillers, the deviation of fitting curves gradually decreased. A comparative study showed that the fractional constitutive model was difficult to accurately describe the dynamic viscoelasticity of the mixture, and the IGS model was preferred to be recommended as a calculation model to investigate the constitutive relationship on the dynamic viscoelasticity of the mixture.

A sigmoidal model for predicting soil thermal conductivity-water content function in room temperature

Apparent thermal conductivity of soil (λ) as a function of soil water content (θ), i.e., λ(θ) is needed to determine the heat flow in soil. The function of λ(θ) can be used in heat and water flow models for simplicity. The objective of this study was to develop a sigmoidal model based on logistic equation for entire range of soil water contents and a wide range of soil textures that can be used in simulation of heat and water flow in respected modes. Further, performance of the developed sigmoidal model along with two other models in literature was evaluated. In the proposed sigmoidal model, the constants of this model are estimated based on empirical multivariate equations by using soil sand content and bulk density. The sigmoidal model was validated with good accuracy for a wide range of soil textures, as the relationship between the measured and predicted λ showed slope and intercept values of nearly 1.0 and 0.0, respectively. Comparison of the results obtained by sigmoidal model with those obtained from Johansen and Lu et al. models indicated that, the sigmoidal model was superior to the other two models in prediction of λ for a wide range of soil textures and soil water contents. Furthermore, comparison with a recently proposed model by Xiong et al. indicated that our sigmoidal model is superior. Therefore, our developed sigmoidal model can be used in heat and water flow models to predict the soil temperature and heat flow.

Interannual variability and seasonality of litterfall in three temperate and boreal forest ecosystems of eastern Canada: A synthesis of long-term monitoring

Litterfall is a major pathway for transferring aboveground biomass to the forest floor and thus plays an important role in building forest soil carbon stocks. However, inter- and intra-annual variability of litterfall remains poorly documented, especially in North American temperate and boreal forests, due to the lack of recent long-term studies at high sampling frequencies. This potentially creates uncertainties in estimates of forest carbon budget models. The objectives of the present study were to 1) quantify the mean annual flux, interannual variability, and seasonality of litterfall in three sites (dominated respectively by sugar maple (Acer saccharum Marsh.), balsam fir (Abies balsamea (L.) Mill. 1768), and black spruce (Picea mariana (Mill.) B.S.P.)) in eastern Canada over a period of 22–32 years, 2) relate the litterfall amounts and temporal variations to the changes in the size of major organic matter pools in these ecosystems, and 3) compare our litterfall estimates with reference values used in national greenhouse gas inventories. Litterfall production decreased from the sugar maple to the balsam fir and black spruce sites, preponderantly due to species composition. Litterfall evolution was related to the aboveground biomass of live trees in both conifer sites; in contrast, in the broadleaf site, changes in forest composition and structure were apparently the main drivers. The litterfall seasonality varied between broadleaf and conifer sites and could be explained by a sigmoidal model. Substantial departures from the seasonality for some given years were likely due to important climatic anomalies. Forest floor biomass remained stable over time at all three sites despite the increase in litterfall at the balsam fir and sugar maple sites and rapid forest floor turnover at the latter site. Our analyses of litterfall suggest that reference values from the literature used for national greenhouse gas inventories underestimate annual litterfall and forest floor carbon stocks for temperate and boreal forests.

Evaluating a sigmoid dark energy model to explain the Hubble tension

AbstractIn this study, we analyze Type Ia supernovae (SNe Ia) data sourced from the Pantheon+ compilation to investigate late‐time physics effects influencing the expansion history, , at redshifts . Our focus centers on a time‐varying dark energy (DE) model that introduces a rapid transition in the equation of state, at a specific redshift, , from the baseline, , value to the present value, . The change in the equation of state is implemented as a transition in the DE density scale factor driven by a sigmoid function. The constraints obtained for the DE sigmoid phenomenological parametrization have broad applicability for dynamic DE models that invoke late‐time physics. Our analysis indicates that the sigmoid model provides a slightly better, though not statistically significant, fit to the SNe Pantheon+ data compared to the standard cold dark matter () model. The fit results, assuming a flat geometry and maintaining constant at the 2018‐Planck value of , are as follows: km s Mpc, , . The errors represent statistical uncertainties only. The available SN dataset lacks sufficient statistical power to distinguish between the baseline model and the alternative sigmoid models. A feature of interest offered by the sigmoid model is that it identifies a specific redshift, , where a potential transition in the equation of state could have occurred. The sigmoid model does not favor a DE in the phantom region (). Further constraints to the dynamic DE model have been obtained using CMB data to compute the distance to the last scattering surface. While the sigmoid DE model does not completely resolve the tension, it offers a transition mechanism that can still play a role alongside other potential solutions.

Emergence Pattern of Argemone mexicana, Brassica tournefortii, and Rapistrum rugosum in Eastern Australia

A study assessed the potential for using cumulative growing degree days (CGDD) to predict the weed emergence periodicity of three weed species: Argemone mexicana, Brassica tournefortii, and Rapistrum rugosum. Weed emergence was monitored regularly by placing 200 fresh seeds of each weed species on the soil surface. Weed emergence data was fit using a three-parameter sigmoidal Gompertz model. The CGDD required for 50% emergence of A. mexicana ranged from 3380 to 5302, depending upon the seasonal variation in temperature and rainfall. The majority of emergence appeared from March to June. The seeds of A. mexicana exhibited dormancy, as the majority of seeds germinated in the second season. The CGDD required for 50% emergence of B. tournefortii ranged from 824 to 2311, depending upon the seasonal variation in temperature and intensity of rainfall. Most cohorts of B. tournefortii appeared in the first season from February to June, indicating little dormancy in seeds. The CGDD required for 50% emergence of R. rugosum ranged from 2242 to 2699, depending upon weather parameters (temperature and rainfall). The main cohorts of R. rugosum appeared from February to June, and 60% of seeds germinated in the first season, while 40% germinated in the second season, indicating dormancy in seeds. The coefficients of determination for the model verification on the emergence pattern of three weeds were > 85%, suggesting that CGDD are good predictors for the emergence of these weeds. These results suggest that forecasting the emergence of three weed species on the basis of CGDD and rainfall patterns will help growers to make better weed management decisions.

Вопросы энергоиндексного моделирования фенологии растительности

The article is devoted to energy index modeling of vegetation phenology. Based on the dual sigmoidal model representation of the phenological cycle, an energy-index approach to phasing the stages of vegetation development and deactivation is proposed. The optimal laws for changing the amplitude of the model in the phases of development and decontamination have been determined, at which the energy released by vegetation into the environment can reach a maximum. The relationship between the LAI index and the phenological state of vegetation has been investigated. The study of the relationship of LAI with another phenological indicator of vegetation, in particular the prognostic phenological state of vegetation P. It is shown that despite the inverse logarithmic relationship between LAI and FPAR, and also due to the direct dependence of P and FPAR, averaged over all possible values of fv, the value of P increases in the presence of a direct relationship between LAI and fv.