Growth Model Research Articles

Corrosion of Copper-Nickel Alloys in Seawater-Based Electrolytes

Copper nickel materials are used for their corrosion resistance due to the protective film that forms in seawater. The development of this film over time depends on the exposure condition, and it is critically important to understand the aging process for CuNi components used in marine systems to ensure passivity under sulfide contaminated conditions. Here we study the formation of the protective film over time and its effect on corrosion performance. Electrochemical impedance spectroscopy (EIS) measurements during exposure are used to develop a model of film growth on CuNi 70/30 alloys. These electrochemical measurements are coupled with surface analytical techniques such as glow discharge-optical emission spectroscopy (GD-OES) and X-ray photoelectron spectroscopy (XPS) to characterize the composition of the surface oxide. GD-OES results show that as the protective layer forms, nickel mainly remains at the metal surface and the outer layers are copper enriched oxides and chlorides. There is also incorporation of various elements from the seawater within the film. The rate of this film growth is correlated to the resulting decrease in corrosion rate. Different exposure conditions, such as under flow, are investigated.The translation of these protective film mechanisms to additively manufactured CuNi materials is also of interest, to ensure similar performance with these materials. Evaluating corrosion performance of these newly developed materials involves careful comparison to existing knowledge of film growth and potential for breakdown.

The Voltage-Based Implementation of the Tunneling Mechanism in Aging Models for Lithium-Ion Batteries

Lithium-ion batteries (LIBs) have become one of the most crucial power sources for electronics and electric vehicles due to high energy density, long cycle life, environmental friendliness, etc. Precise explanation and prediction of the aging behavior of LIBs is quickly becoming a hotspot. A solid electrolyte interface (SEI) growth is regarded as the dominant factor of capacity losses in LIBs. However, the previous SEI growth and aging models were based on energy levels. That means the energy barrier related to the Fermi level of SEI and electrode was used as the input parameter. It is not straightforward to implement, especially for industrial applications. Furthermore, the state-of-charge (SoC) and the energy barrier were usually assumed to be constant during (dis)charging, contradicting common sense and affecting the calculations' accuracy.The present work proposes and experimentally validates an advanced voltage-based aging model. The voltage of the anode electrode is adopted as an input parameter in this model. It is easier to introduce dependence of aging on the voltage/SoC of the electrode with this model. It is found that deep delithiation of the anodes and lower SoC of LIBs will mitigate electron tunneling, lower SEI growth, and reduce calendar aging. Commonly used anode materials, such as graphite, Li4Ti5O12, and blended Si/C, are presented as interesting cases to investigate the effects of different electrode materials on the tunneling current profiles during (dis)charging for the first time. The model is instructive and can predict aging in LIBs with various electrode materials. This model and related analyses are beneficial for further optimizing LIB's electronic and electrode properties and as interesting limiting cases for testing battery modeling software.

Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models.

Insight into solvent effects on growth morphology of MgCO3·3H2O in CO2 mineralization: Molecular dynamics simulations and DFT studies

Experimental evidence shows that the solvent effect greatly affects the MgCO3·3H2O morphology, but the atomic-level mechanism still remains elusive. In this study, the influence mechanisms of H2O and EtOH on MgCO3·3H2O morphology were systematically investigated by a combination of crystal growth experiments, molecular dynamics (MD) simulations, and DFT calculations. In contrast to the traditional DFT calculations, the interface microenvironments were introduced to the crystal growth model first. The modified attachment energy (MAE) model proved that (1 0 1), (-1 0 1), and (0 1 1) surfaces dominate MgCO3·3H2O morphology in vacuum, H2O, and EtOH/H2O environments. The solvent molecules created an extensive H-bond network at solid-liquid interfaces, which greatly changed diffusion and combination during the crystal growth process. Compared to the H2O molecule, EtOH only forms H-bonds through the hydroxyl group. The CH3- end created some hydrophobic cavities, which led to weaker H-bond connectivity. The weaker H-bond network facilitated the removal of water from the crystal surface and strengthened the connection between the growth unit and surface, while reducing the solvent effect on the growth unit, thereby accelerating crystal growth and changing morphology. This study reveals the significant role of solvent in the crystallization process of MgCO3·3H2O, providing valuable insights for controlling the MgCO3·3H2O morphology at the atomic level.

Single-cell measurement of microbial growth rate with Raman microspectroscopy.

Rates of microbial growth are fundamental to understanding environmental geochemistry and ecology. However, measuring the heterogeneity of microbial activity at the single-cell level, especially within complex populations and environmental matrices, remains a forefront challenge. Stable Isotope Probing (SIP) is a method for assessing microbial growth and involves measuring the incorporation of an isotopic label into microbial biomass. Here, we assess Raman microspectroscopy as a SIP technique, specifically focusing on the measurement of deuterium (2H), a tracer of microbial biomass production. We correlatively measured cells grown in varying concentrations of deuterated water with both Raman spectroscopy and nanoscale secondary ion mass spectrometry (nanoSIMS), generating isotopic calibrations of microbial 2H. Relative to Raman, we find that nanoSIMS measurements of 2H are subject to substantial dilution due to rapid exchange of H during sample washing. We apply our Raman-derived calibration to a numerical model of microbial growth, explicitly parameterizing the factors controlling growth rate quantification and demonstrating that Raman-SIP can sensitively measure the growth of microorganisms with doubling times ranging from hours to years. The measurement of single-cell growth with Raman spectroscopy, a rapid, non-destructive technique, represents an important step towards application of single-cell analysis into complex sample matrices or cellular assemblages.

A model of long-term population growth with an application to Central West Argentina.

We propose an Ideal Specialization Model to help explain the diversity of population growth trajectories exhibited across archaeological regions over thousands of years. The model provides a general set of expectations useful for guiding empirical research, and we provide a concrete example by conducting a preliminary evaluation of three expectations in Central West Argentina. We use kernel density estimates of archaeological radiocarbon, estimates of paleoclimate, and human bone stable isotopes from archaeological remains to evaluate three expectations drawn from the model's dynamics. Based on our results, we suggest that innovations in the production of food and social organization drove demographic transitions and population expansion in the region. The consistency of population expansion in the region positively associates with changes in diet and, potentially, innovations in settlement and social integration.

M&A contractual innovation in SME sector: the effect of valuation adjustment mechanism clause on SME growth

Identifying and effectively managing growth models for small and medium enterprises (SMEs) poses a significant challenge for entrepreneurs. Prior research has indicated that the dominant model is an acquisition-led growth strategy. Our paper introduces a novel acquisition growth model for SMEs in China, which incorporates the use of valuation adjustment mechanism (VAM) clauses in M&A contracts. We thoroughly analyze the financial implications of this innovative model on SME growth, the specific SME profiles that favor this approach, and the prevalent types of VAM clauses adopted by these SMEs. All hypotheses were rigorously tested using primary data from Chinese public SME companies. The results reveal a positive financial impact of VAM clauses in M&A contracts on SME growth. It was found that SMEs with lower equity concentration or smaller holdings are more likely to adopt the VAM Clauses Model, and the inclusion of equity payments as compensation in the VAM clause is highly common in practice. These findings hold significant implications for both theoretical research on SMEs and the practical strategies of entrepreneurs.

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient journey. In this review article, we summarize contemporary AI techniques and explore the clinical applications of AI-based automated segmentation models in radiotherapy planning, focusing on delineation of organs at risk (OARs), the gross tumor volume (GTV), and the clinical target volume (CTV). Emphasizing the need for precise and individualized plans, we review various commercial and freeware segmentation tools and also state-of-the-art approaches. Through our own findings and based on the literature, we demonstrate improved efficiency and consistency as well as time savings in different clinical scenarios. Despite challenges in clinical implementation such as domain shifts, the potential benefits for personalized treatment planning are substantial. The integration of mathematical tumor growth models and AI-based tumor detection further enhances the possibilities for refining target volumes. As advancements continue, the prospect of one-stop-shop segmentation and radiotherapy planning represents an exciting frontier in radiotherapy, potentially enabling fast treatment with enhanced precision and individualization.

Transforming Brown Practices into Green Jobs: Training Intervention and Green Job Motivation of Agripreneuship Trainees

Purpose: Within the backdrop of labor market uncertainty for young people and environmental externalities, the study tested the effect of training intervention on green collar job motivation of agripreneuship trainees in a green growth project in Mezam, North West Cameroon. Focus was on training, attitude and social pressure as determinants of green job motivation off agripreneurs. Materials and Methods: The study adopted a quantitative survey design with the goal of data on green collar job training and motivation and a sample of 125 participants (71 females; 54 males) was recruited from a training intervention initiative from Mezam, North West Cameroon. An instrument with internal reliability consistency (α=0.74) was used to gather information. The information was entered into SPSS version 27, and correlation and hierarchical regression were used as test statistics. Findings: According to the results, it was reported as a significant predictor of green job motivation, p < 0.05. In the second model capacity building and attitude significantly determined green job motivation p < 0.05. While the third model (capacity enhancement, attitude, social pressure) was significant, p < 0.05, social pressure as a separate dimension failed to determine green job motivation, p > 0.05, of agripreneurs trainees. From the results of the study it was reported that training factors were capable of predicting green job motivation and the need for transforming brown occupations was also recognised within green growth venture. Implications to Theory, Practice and Policy: Green economic activities have undergone evolution with demands for new skills and training should be recognized as a sustainable measure of green collar job behaviours. Considering the position of green growth models in developing green collar mindset, it asserts that greener occupations are capable of catalysing wealth creation, environmental justice and sustainable and should be promoted. Therefore, advocates of green employment should draw from policy strategies and enrich training programmes to be more responsive to changing environmental, suggesting transformation from brown to green through green mindset building. While the paper contributes to scarce body of knowledge in green jobs, implications for policy and practice have been presented with areas for future research

Mathematical Modeling of Tumor Growth in Preclinical Mouse Models with Applications in Biomarker Discovery and Drug Mechanism Studies.

Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. While past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. Here, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semi-parametric generalized additive model (GAM) on fitting tumor volume data from over 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line-derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models, the latter is often considered the standard growth model. On the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that eGaIT, a GAM derived efficacy metric, is equivalent to eGR, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on Paclitaxel, anti-PD-1 antibody, Cetuximab, Irinotecan, and Sorafenib, we show that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared eGR-based association analysis and exponential modeling approach in biomarker discovery and found they complement each other. Modeling methods herein are implemented in an open-source R package freely available at https://github.com/hjzhou988/TuGroMix.

Scaling Limit of Multi-Type Invariant Measures via the Directed Landscape

Abstract This paper studies the large scale limits of multi-type invariant distributions and Busemann functions of planar stochastic growth models in the Kardar–Parisi–Zhang (KPZ) class. We identify a set of sufficient hypotheses for convergence of multi-type invariant measures of last-passage percolation (LPP) models to the stationary horizon (SH), which is the unique multi-type stationary measure of the KPZ fixed point. Our limit theorem utilizes conditions that are expected to hold broadly in the KPZ class, including convergence of the scaled last-passage process to the directed landscape. We verify these conditions for the six exactly solvable models whose scaled bulk versions converge to the directed landscape, as shown by Dauvergne and Virág. We also present a second, more general, convergence theorem with future applications to polymer models and particle systems. Our paper is the first to show convergence to the SH without relying on information about the structure of the multi-type invariant measures of the prelimit models. These results are consistent with the conjecture that the SH is the universal scaling limit of multi-type invariant measures in the KPZ class.

Next-generation chemotherapy treatments based on black hole algorithms: From cancer remission to chronic disease management

ProblemTherapeutic planning strategies have been developed to enhance the effectiveness of cancer drugs. Nevertheless, their performance is highly limited by the inefficient biological representativeness of predictive tumor growth models, which hinders their translation to clinical practice. ObjectiveThis study proposes a disruptive approach to oncology based on nature-inspired control using realistic Black Hole physical laws, in which tumor masses are trapped to experience attraction dynamics on their path to complete remission or to become a chronic disease. This control method is designed to operate independently of individual patient idiosyncrasies, including high tumor heterogeneities and highly uncertain tumor dynamics, making it a promising avenue for advancing beyond the limitations of the traditional survival probabilistic paradigm. DesignHere, we provide a multifaceted study of chemotherapy therapeutic planning that includes: (1) the design of a pioneering controller algorithm based on physical laws found in the Black Holes; (2) investigation of the ability of this controller algorithm to ensure stable equilibrium treatments; and (3) simulation tests concerning tumor volume dynamics using drugs with significantly different pharmacokinetics (Cyclophosphamide and Atezolizumab), tumor volumes (200 mm3 and 12 732 mm3) and modeling characterizations (Gompertzian and Logistic tumor growth models). ResultsOur results highlight the ability of this new astrophysical-inspired control algorithm to perform effective chemotherapy treatments for multiple tumor-treatment scenarios, including tumor resistance to chemotherapy, clinical scenarios modelled by time-dependent parameters, and highly uncertain tumor dynamics. ConclusionsOur findings provide strong evidence that cancer therapy inspired by phenomena found in black holes can emerge as a disruptive paradigm. This opens new high-impacting research directions, exploring synergies between astrophysical-inspired control algorithms and Artificial Intelligence applied to advanced personalized cancer therapeutics.

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease’s transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country’s epidemic pattern and assist the government in developing better effective interventions.

Modelling Climate Effects on Site Productivity and Developing Site Index Conversion Equations for Jack Pine and Trembling Aspen Mixed Stands

Forest site productivity estimates are crucial for making informed forest resource management decisions. These estimates are valuable both for the tree species currently growing in the stands and for those being considered for future stands. Current models are generally designed for pure stands and do not account for the influence of climate on tree growth. Consequently, site index (SI) conversion equations were developed specifically for jack pine (Pinus banksiana Lamb.) and trembling aspen (Populus tremuloides Michx.) trees grown in naturally originated mixed stands. This work involved sampling 186 trees (93 of each species) from 31 even-aged mixed stands (3 trees per species per site) across Ontario, Canada. Stem analysis data from these trees were utilized to develop stand height growth models by incorporating climate variables for each species. The models were developed using a mixed effects modelling approach. The SI of one species was correlated with that of the other species and climate variables to establish SI conversion equations. The effect of climate on site productivity was evaluated by projecting stand heights at four geographic locations (east, center, west, and far west) in Ontario from 2022 to 2100 using the derived stand height growth models. Height projections were made under three emissions scenarios reflecting varying levels of radiative forcing by the end of the century (2.6, 4.5, and 8.5 watts m−2). Climate effects were observed to vary across different regions, with the least and most pronounced effects noted in the central and far western areas, respectively, for jack pine, while effects were relatively similar across all locations for trembling aspen. Stand heights and SIs of jack pine and trembling aspen trees grown in naturally originated mixed stands can be estimated using the height growth models developed here. Similarly, SI conversion equations enable the estimation of the SI for one species based on the SI of another species and environmental variables.

A Bertalanffy–Richards growth model perturbed by a time-dependent pattern, statistical analysis and applications

We analyze a modification of the Richards growth model by introducing a time-dependent perturbation in the growth rate. This modification becomes effective at a special switching time, which represents the first-crossing-time of the Richards growth curve through a given constant boundary. The relevant features of the modified growth model are studied and compared with those of the original one. A sensitivity analysis on the switching time is also performed. Then, we define two different stochastic processes, i.e. a non-homogeneous linear birth–death process and a lognormal diffusion process, such that their means identify to the growth curve under investigation. For the diffusion process, we address the problem of parameters estimation through the maximum likelihood method. The estimates are obtained via meta-heuristic algorithms (namely, Simulated Annealing and Ant Lion Optimizer). A simulation study to validate the estimation procedure is also presented, together with a real application to oil production in France. Special attention is devoted to the approximation of switching time density, viewed as the first-passage-time density for the lognormal process.

Role of Social Demography Factors in Economic Growth and Sustainability Models: Evidence from selected emerging economies

A country’s social demography characteristics are described by population dynamics and technology-based lifestyle changes. Such unique elements may correlate with economic growth and sustainability. In this study, a static panel data model analysis for economic growth and sustainable development of ten emerging economies was presented. Estimation results revealed that social demography factors exhibited a negative significant impact in both economic models, relative to the technological influence. Therefore, effective macroeconomic policies should be meticulously designed against changing demographic trends. Conclusively, by delivering focused public services to the urban area, more liveable cities will emerge to empower human capital development.

Modulation of mechanosensitive genes during embryonic aortic arch development.

Early embryonic aortic arches (AA) are a dynamic vascular structures that are in the process of shaping into the great arteries of cardiovascular system. Previously, a time-lapsed mechanosensitive gene expression map was established for AA subject to altered mechanical loads in the avian embryo. To validate this map, we investigated effects on vascular microstructure and material properties following the perturbation of key genes using an in-house microvascular gene knockdown system. All siRNA vectors show a decrease in the expression intensity of desired genes with no significant differences between vectors. In TGFβ3 knockdowns, we found a reduction in expression intensities of TGFβ3 (≤76%) and its downstream targets such as ELN (≤99.6%), Fbn1 (≤60%), COL1 (≤52%) and COL3 (≤86%) and an increase of diameter in the left AA (23%). MMP2 knockdown also reduced expression levels in MMP2 (≤30%) and a 6-fold increase in its downstream target COL3 with a decrease in stiffness of the AA wall and an increase in the diameter of the AA (55%). These in vivo measurements were confirmed using immunohistochemistry, western blotting and a computational growth model of the vascular extracellular matrix (ECM). Localized spatial genetic modification of the aortic arch region governs the vascular phenotype and ECM composition of the embryo and can be integrated with mechanically-induced congenital heart disease models.

General Fractional Economic Dynamics with Memory

For the first time, a self-consistent mathematical approach to describe economic processes with a general form of a memory function is proposed. In this approach, power-type memory is a special case of such general memory. The memory is described by pairs of memory functions that satisfy the Sonin and Luchko conditions. We propose using general fractional calculus (GFC) as a mathematical language that allows us to describe a general form of memory in economic processes. The existence of memory (non-locality in time) means that the process depends on the history of changes to this process in the past. Using GFC, exactly solvable economic models of natural growth with a general form of memory are proposed. Equations of natural growth with general memory are equations with general fractional derivatives and general fractional integrals for which the fundamental theorems of GFC are satisfied. Exact solutions for these equations of models of natural growth with general memory are derived. The properties of dynamic maps with a general form of memory are described in the general form and do not depend on the choice of specific types of memory functions. Examples of these solutions for various types of memory functions are suggested.

Understanding patient-derived tumor organoid growth through an integrated imaging and mathematical modeling framework.

Patient-derived tumor organoids (PDTOs) are novel cellular models that maintain the genetic, phenotypic and structural features of patient tumor tissue and are useful for studying tumorigenesis and drug response. When integrated with advanced 3D imaging and analysis techniques, PDTOs can be used to establish physiologically relevant high-throughput and high-content drug screening platforms that support the development of patient-specific treatment strategies. However, in order to effectively leverage high-throughput PDTO observations for clinical predictions, it is critical to establish a quantitative understanding of the basic properties and variability of organoid growth dynamics. In this work, we introduced an innovative workflow for analyzing and understanding PDTO growth dynamics, by integrating a high-throughput imaging deep learning platform with mathematical modeling, incorporating flexible growth laws and variable dormancy times. We applied the workflow to colon cancer organoids and demonstrated that organoid growth is well-described by the Gompertz model of growth. Our analysis showed significant intrapatient heterogeneity in PDTO growth dynamics, with the initial exponential growth rate of an organoid following a lognormal distribution within each dataset. The level of intrapatient heterogeneity varied between patients, as did organoid growth rates and dormancy times of single seeded cells. Our work contributes to an emerging understanding of the basic growth characteristics of PDTOs, and it highlights the heterogeneity in organoid growth both within and between patients. These results pave the way for further modeling efforts aimed at predicting treatment response dynamics and drug resistance timing.

Declining research productivity and income inequality: A centenary perspective

Research productivity has been sharply declining since 1920. This decline has significant implications for inequality. Using an open-economy Schumpeterian growth model, we show that domestic research productivity influences income inequality through two opposing forces: a positive effect from increased asset returns and a negative effect from the erosion of innovation rents through creative destruction. In contrast, foreign research productivity affects inequality solely through the asset returns channel. By constructing a long historical data series for 21 OECD countries, we find that the asset returns channel has been the dominant driver of inequality over the past century. The reduction in both domestic and foreign R&D productivity accounts for 25% to 35% of the observed downward trend in income inequality over this period.