Growth Model Research Articles

Trajectory patterns and predictors of cancer-related fatigue in postoperative lung cancer patients receiving chemotherapy.

Cancer-related fatigue (CRF) is a chronic symptom that can affect the overall functioning of lung cancer patients throughout the course of the disease. However, there is limited research on the trajectory and predictors of CRF specifically in lung cancer patients. Furthermore, few studies have investigated the predictive role of positive psychological and social factors in relation to CRF. This study aimed to explore the trajectory of CRF and its predictors in postoperative chemotherapy patients with lung cancer. A total of 202 lung cancer patients who underwent surgery and received adjuvant chemotherapy were recruited for this study. Baseline questionnaires were completed, covering sociodemographic information, disease details, CRF levels, personality traits, psychological resilience, and social support. CRF was assessed at three time points: first chemotherapy (T1), 3months after chemotherapy (T2), and 6months after chemotherapy (T3). Latent class growth modeling (LCGM) was used to identify distinct developmental trajectories of CRF. Logistic regression analysis was employed to examine predictors of CRF within different patient groups. The LCGM analysis revealed three distinct CRF trajectories: persistent high fatigue group (30.7%), rising fatigue group (30.7%), and no fatigue group (38.6%). Cancer stage (OR = 7.563, 95% CI = 2.468-23.182, P < 0.001), melancholic personality (OR = 6.901, 95% CI = 1.261-37.764, P = 0.026), and high psychological resilience (OR = 0.171, 95% CI = 0.041-0.706, P = 0.015) were associated with the CRF trajectory. On the other hand, sanguine personality (OR = 0.254, 95% CI = 0.071-0.916, P = 0.036) and high social support (OR = 0.168, 95% CI = 0.045-0.627, P = 0.008) were associated with the increasing fatigue trajectory. This study demonstrated that 60% of lung cancer patients experienced persistent fatigue throughout the assessment period. Moreover, it confirmed the heterogeneity of CRF trajectories among lung cancer patients. The severity of CRF was found to be higher in patients with advanced clinical stages, depressive personality traits, and lower psychological resilience.

Association between developmental patterns of single and concurrent externalizing behaviors and internalizing problems over the preschool years.

The present study investigated whether distinct developmental patterns of externalizing behaviors (EBs: hyperactivity-impulsivity, noncompliance, physical aggression) based on parent reports were repeatedly and differentially associated with separate dimensions of internalizing problems such as general anxiety, separation anxiety, and depressive symptoms across the early, middle, and late preschool years in a population birth cohort (N = 2,057, 50.7% boys). Six high trajectory classes obtained by latent growth modeling were used as longitudinal indicators of single EB and co-occurrent EBs. Children following low or moderate trajectories for all EBs served as the reference class. Results revealed that children in trajectory classes reflecting high levels of co-occurring EBs showed higher levels of general anxiety, separation anxiety, and depressive symptoms across the preschool years. In contrast, children in trajectory classes reflecting single EB manifested higher levels of some, but not all, dimensions of internalizing problems. In addition, their scores varied from one period to another. No sex differences were observed in the above associations. These results underline the need for comprehensive assessments across distinct types of EBs and internalizing problems to better reflect the characteristics that distinguish individual children. Finally, results suggest that children showing early co-occurrent EBs and internalizing symptoms may be an important group to target for in-depth assessment and possibly preventive intervention. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The entrepreneurial transformation process of BTS: initiation, development, growth and expansion

Purpose This paper explores the entrepreneurial transformation of Bangtan Sonyeondan or Bangtan Boys (BTS), tracing their journey from initiation through expansion. Despite the global prominence of their music, dance and outreach, many aspects of BTS’s meteoric rise remain underexplored. This study aims to illuminate the strategic and organizational developments that have propelled their unprecedented growth in the global media landscape. Design/methodology/approach Using case study research methods, this paper uses a multimodal approach to examine BTS’s intricate interactions with market dynamics and fan engagement comprehensively. Using entrepreneurial innovation as the overarching theoretical framework, the authors categorize BTS’s transformational processes into drivers, practices and outcomes, thereby elucidating the strategies used to navigate key challenges and pivotal moments in their worldwide ascent. Findings The BTS organization exhibits a distinct entrepreneurial orientation and transformation process across its phases of initiation, development, growth and expansion. The authors identify specific drivers, practices and outcomes along with strategic vision, actionable practices and measurable performance outcomes at each stage. Research limitations/implications While this study draws valuable parallels between BTS’s trajectory and established growth models, it recognizes the limitations inherent in a single-case study approach. The findings highlight the critical role of entrepreneurial leadership in the entertainment industry and suggest further exploration into the universal applicability of these models across different genres and cultural contexts within the music industry. Practical implications This paper underscores the importance of entrepreneurial innovation within entertainment organizations. The conceptual framework provided can assist managers in formulating strategies and policies for the growth and transformation of similar entities. Social implications BTS’s case demonstrates how engaging a global audience with culturally specific content and the universal appeal of youthful energy can be transformative. This engagement is facilitated through effective use of music, dance and dynamic interactions on social media. Originality/value By analyzing BTS from an entrepreneurial leadership perspective, this study offers unique insights by examining their internal motivations and managerial practices, as well as the external outcomes, spanning from 2010 to 2023.

Ethnic identity centrality across the adult lifespan: Aging, cohort, and period effects among majority and minority group members.

Ethnic identity is a major area of study across many disciplines including psychology, sociology, anthropology, and political science. Yet, little is known about changes in ethnic identity across the adult lifespan, and whether such changes are driven by normal aging processes (aging effects), unique societal influences linked with one's formative years (cohort effects), or social changes during a specific time frame (period effects). We address these key oversights by utilizing 13 annual waves of longitudinal panel data from a nationwide random sample of both ethnic majority (N = 49,660) and Indigenous ethnic minority (N = 8,325) group members in New Zealand to examine changes in ethnic identity centrality using cohort-sequential latent growth modeling. This approach helps to identify changes in mean levels of ethnic identity centrality over time and whether such changes are driven by aging, cohort, and/or period effects. Our data reveal that, among both ethnic majority and ethnic minority individuals, changes in ethnic identity centrality were informed by a combination of normative aging processes, societal circumstances that reflected the unique historical context in which people grew to maturity, and societal changes during the 13 annual assessments of our study. Collectively, these results demonstrate for the first time that ethnic identity centrality in adulthood is subject to lifelong changes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An anageometric time scale calculus and its some basic applications

Anageometric calculus is a branch of non-Newtonian calculus introduced by M. Grossman and R. Katz in 1967, in which changes in functional values are measured by linear differences but arguments are measured by ratios. The theory of time scales was initiated by S. Hilger in 1988 to unify continuous and discrete analysis. In this article, we introduce anageometric time scale calculus to discuss dynamic equations in which the values of the independent variable show non-linear behaviour. This newly introduced calculus will be more applicable than other calculi in atomic reactors, growth models, etc. We shall discuss some applications of anageometric time scale calculus on the geometric real field R(G) with its optimality and better accuracy.

Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.

Prior studies have shown that computational fluid dynamics (CFD) simulations help assess patient-specific hemodynamics in abdominal aortic aneurysms (AAAs); patient-specific hemodynamic stressors are frequently used to predict an AAA's growth. Previous studies have utilized both laminar and turbulent simulation models to simulate hemodynamics. However, the impact of different CFD simulation models on the predictive modeling of AAA growth remains unknown and is thus the knowledge gap that motivates this study. Specifically, CFD simulations were performed for 70 AAA models derived from 70 patients' computed tomography angiography (CTA) data with known growth status (i.e., fast-growing [> 5mm/yr] or slowly growing [< 5mm/yr]). We used laminar and large eddy simulation (LES) models to obtain hemodynamic parameters to predict AAAs' growth status. Predicting the growth status of AAAs was based on morphological, hemodynamic, and patient health parameters in conjunction with three classical machine learning (ML) classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and generalized linear model (GLM). Our preliminary results estimated aneurysmal flow stability and wall shear stress (WSS) were comparable in both laminar and LES flow simulations. Moreover, computed WSS and velocity-related hemodynamic variables obtained from the laminar and LES simulations showed comparable abilities in differentiating the growth status of AAAs. More importantly, the predictive modeling performance of the three ML classifiers mentioned above was similar, with less than a 2% difference observed (p-value>0.05). In closing, our findings suggest that two different flow simulations investigated did not significantly affect outcomes of computational hemodynamics and predictive modeling of AAAs' growth status, given the data investigated.

Trajectories of Problem Behaviors and Information Technology Use Among Adolescents: A Latent Growth Model Using the Korean Children and Youth Panel Survey.

We investigated the association between computer and mobile phone online activities and adolescents' problem behaviors (e.g., depressive symptoms, withdrawal, somatic complaints, attention deficit, and aggression) using data from the Korean Children and Youth Panel Survey and latent growth model analysis. The results demonstrated that text-related activities lowered withdrawal and attention deficit. Higher use of online communities or personal websites was associated with higher depressive symptoms, withdrawal, somatic symptoms, and aggression. Online gaming increased both attention deficit' initial value and its decrease rate. Taking photos decreased withdrawal. Watching videos increased depressive symptoms, withdrawal, and attention deficit. Listening to music lowered attention deficit' initial value and somatic symptoms' decrease rate. Accessing adult websites increased attention deficit and aggression. Educational information searches reduced attention deficit and aggression. Online transactions increased somatic symptoms. This study indicates that adolescents' problem behaviors may appear differently depending on the type of information technology use.

The effect of sulfuration reaction rates with sulphur concentration gradient dependence on the growth pattern and morphological evolution of MoS2 in laminar flow.

Sulfuration reactions dominate the synthesis of transition-metal dichalcogenides via chemical vapor deposition. A neglected critical issue is the evolution of crystal domain morphology and growth models caused by boundary layer development. In this study, we propose two growth models within a laminar flow field to investigate the kinetic mechanism of uniformly grown MoS2. We used supercritical fluid pre-deposition to obtain a well-distributed and low-crystallinity Mo precursor on the surface of a substrate to avoid non-stoichiometric supply in sulfuration. The development of the boundary layer was suppressed through mainstream force by adjusting the substrate slope angle. For growth within the underdeveloped laminar boundary layer, monolayer MoS2 with a size of 50 μm uniformly distributed on the full substrate with R = 85% (relative change in boundary layer thickness). Moreover, the growth constrained by surface chemical reactions tended to promote spatially uniform growth. However, within the fully developed laminar flow, the crystal domains preferentially grew vertically, which was attributed to the excessive crystal growth rate (g). Our results provide new insights into the controllable preparation of two-dimensional materials.

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

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

Advancing Sustainable Ecosystem Development

Ecosystem management integrates natural and human systems, but understanding early stages remains a challenge. Remote sensing aids in monitoring endangered ecosystems. Ecological growth theories and models offer insights, while energy-related tendencies contribute to succession understanding. Collaboration drives sustainable development, alongside social-ecological system understanding. Adaptive management navigates ecosystem transformation, while resource management ensures sustainable use. Engaging communities, leveraging innovative technologies, and effective governance are crucial for long-term sustainability. Conserving biodiversity is essential for ecosystem health, requiring focused actions like habitat restoration and preservation. Addressing climate change impacts strengthens ecosystem resilience and secures sustainable futures. Promoting environmental education and awareness cultivates community involvement and supports conservation efforts. Incorporating economic incentives and valuing ecosystem services encourages sustainable resource management. Effective policy integration across agriculture, forestry and urban planning sectors is crucial for comprehensive and sustainable ecosystem management.

The Changing Relationship Between Hobby Engagement and Substance Use in Young People: Latent Growth Modelling of the Add Health Cohort.

Cross-sectional and some longitudinal evidence suggests doing hobbies can reduce substance use, but findings have been inconsistent, and whether associations differ across adolescence remains unclear. This study included 7454 Add Health participants (50% female, 77% White, age mean=14.95 and SD = 1.56). Participants were split into three groups, according to whether they were early (aged 11-14 at baseline), mid (aged 15-16), or late (aged 17-20) adolescents at baseline. The trajectories of binge drinking, marijuana, and tobacco use were analysed in latent growth models across Waves 1-5 (1994-2018). Concurrent associations between substance use and hobby engagement were tested at Waves 1-3 separately in the three age groups. Doing hobbies more frequently was associated with lower odds of binge drinking and marijuana and tobacco use in early adolescence. Although there was initially a similar protective association in mid and late adolescence, this had reversed by Wave 3 for binge drinking and marijuana use, when participants were young adults. This change in the association could be a result of differing social contexts, changes in peer influence, or an indication that creative hobbies are particularly beneficial. It could explain previous inconsistent findings and demonstrates the importance of considering developmental differences when investigating engagement in hobbies.

Carbon storage-driven multi-objective spatial planning: simulation and practice at the management unit level in Northeast China

In recent years, global attention to forest ecological environment, carbon, wood yield, and biodiversity has been increasing, which requires forest managers to make reasonable forest planning in time and space to meet the needs of multi-objectives and multi-resources. In this study, the resources of business units in Northeast China were analyzed, and the FPS-ATLAS model was established. A multi-objective decision supporting spatial forest planning scheme for 16629.53 ha of forest in Jingouling forest farm of Wangqing Forestry Bureau in Jilin Province was proposed. In this paper, the volume growth model of six forest types was established, and the carbon storage model was introduced to show the total carbon, biomass carbon, dissolved organic carbon, and total soil carbon of the ecosystem. The dynamic change in carbon during the whole planning period has achieved good results. After 20 time periods, the total soil carbon increased to 7,627,208 Mg (+6.63%), the total ecosystem carbon increased to 14,889,876 Mg (+27.51%), the biomass carbon increased to 5,362,779 Mg (+59.70%), and the dissolved organic carbon increased to 9,531,906 Mg (+14.59%). The purpose of this article is to achieve dynamic management of forest resources, meet multiple constraint settings, achieve multiple management objectives, harvest wood products, and ensure the ecological and social functions of forests so that forests ultimately achieve the ideal state of sustainability.

Non-Linear Relationships between Fine Root Functional Traits and Biomass in Different Semi-Arid Ecosystems on the Loess Plateau of China

As a key soil carbon process, changes in plant root growth may have a dramatic impact on the global ecosystem’s carbon cycle. Fine root functional traits and fine root biomass can be used as important indexes of plant root growth. Compared with the much better understood relationships between aboveground plant functional traits and aboveground biomass, knowledge on the relationships between fine root functional traits and belowground biomass still remains limited. In this study, plant fine roots in 30 abandoned lands, 9 woodlands, 29 alfalfa grasslands, 30 Caragana shrublands and 29 croplands were sampled at 0–20 and 20–40 cm soil depths in Zhonglianchuan, Yuzhong County, Gansu Province, China (36°02′ N, 104°24′ E), to clarify the characteristics of the relationships between fine root functional traits (e.g., diameter, specific root area (SRA) and specific root length (SRL)) and fine root biomass at 0–20 and 20–40 cm soil depths. The results showed that the relationships between the fine root functional traits and fine root biomass in these ecosystems were robust, allowing for the use of an allometric growth model at both 0–20 and 20–40 cm soil depths (p &lt; 0.05). Specifically, the relationship between root diameter and fine root biomass was consistent with highly significant positive power, while highly significant negative power relationships of SRA and SRL with fine root biomass were observed (p &lt; 0.01, except the root diameter–biomass models in the woodlands in the 0–20 cm soil layer (p = 0.017) and 20–40 cm soil layer (p = 0.025)). The results can provide some parameters for these terrestrial ecosystem process models. From this perspective, our study is beneficial in the construction of suitable strategies to increase plant biomass, which will help with the restoration of the semi-arid region of the Loess Plateau of China.

Assessment of Different Frameworks for Addressing Climate Change Impact on Crop Production and Water Requirement

Various methodologies are used to estimate the impact of changing climatic factors, such as precipitation, temperature, and solar radiation, on crop production and water demand. In this study, the changes in rice yield, water demand, and crop phenology were estimated with varying CO2 concentration and an ensemble of general circulation models (GCMs), using a decision support system for agrotechnology transfer (DSSAT), a crop growth model. The measured CO2 concentration of 400 ppm from the Keeling curve, was used as the default CO2 concentration to estimate yield, water demand, and phenology. These outputs, obtained with the default concentration, were compared with the results from climate change scenarios’ concentrations. Further, the outputs corresponding to the ensembled GCMs’ climate data were obtained, and the results were compared with the ensembled crop model outputs simulated with each GCM. The yield was found to increase with the increase in CO2 concentration up to a certain threshold, whereas water demand and phenology were observed to decrease with the increase in CO2 concentration. The two approaches of the ensemble technique to obtain final outputs from DSSAT results did not show a large difference in the predictions.

In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling

Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.

Revisiting Lorenz’s Error Growth Models: Insights and Applications

This entry examines Lorenz’s error growth models with quadratic and cubic hypotheses, highlighting their mathematical connections to the non-dissipative Lorenz 1963 model. The quadratic error growth model is the logistic ordinary differential equation (ODE) with a quadratic nonlinear term, while the cubic model is derived by replacing the quadratic term with a cubic one. A variable transformation shows that the cubic model can be converted to the same form as the logistic ODE. The relationship between the continuous logistic ODE and its discrete version, the logistic map, illustrates chaotic behaviors, demonstrating computational chaos with large time steps. A variant of the logistic ODE is proposed to show how finite predictability horizons can be determined, emphasizing the continuous dependence on initial conditions (CDIC) related to stable and unstable asymptotic values. This review also presents the mathematical relationship between the logistic ODE and the non-dissipative Lorenz 1963 model.

Modeling Reliability-Driven Software Release Strategy Considering Testing Effort with Fault Detection and Correction Processes: A Control Theoretic Approach

In this paper, we have introduced a software reliability growth model (SRGM) that integrates a Weibull testing effort function (TEF) within the software fault detection process (FDP) and fault correction process (FCP), effectively capturing the intricacies of software testing. Leveraging the least squared estimation method, we estimated both model parameters and the TEF, leading to a notably improved fit with the dataset and enhancing our comprehension of software reliability dynamics. Our approach also involved employing a reliability-based method to identify the optimal time for software release, further reinforcing the robustness of our model. Subsequently, we devised an optimal control model to minimize testing efforts throughout the software development life cycle. This work provides a comprehensive methodological framework for computing the total cost while ensuring debugging costs follow a learning curve pattern. Through numerical simulations involving hypothetical values and the Weibull TEF, we observed that once the software achieves the desired reliability for release, subsequent costs gradually decline to zero. In essence, both testing and future costs converge to zero post-release, reducing instantaneous expenses.

The effect of attachment style on mindfulness: findings from a weekly diary study using latent growth modeling

The effect of attachment style on mindfulness: findings from a weekly diary study using latent growth modeling

Long-term trajectories of low back pain in older men: A prospective cohort study with 10-year analysis of the MrOS Study.

While low back pain (LBP) may persist or recur over time, few studies have evaluated the individual course of LBP over a long-term period, particularly among older adults. Based on data from the longitudinal Osteoporotic Fractures in Men (MrOS) study, we aimed to identify and describe different LBP trajectories in older men and characterize members in each trajectory group. A total of 5,976 community-dwelling men (mean age=74.2) enrolled at six US sites were analyzed. Participants self-reported LBP (yes/no) every 4 months during a maximum of 10 years. Latent class growth modelling was performed to identify unique LBP trajectory groups that explained variation in the LBP data. The association of baseline characteristics with trajectory group membership was assessed using univariable and multivariable multinominal logistic regression. A five-class solution was chosen; no/rare LBP (n=2442/40.9%), low frequency-stable LBP (n=1040/17.4%), low frequency-increasing LBP (n=719/12%), moderate frequency-decreasing LBP (n=745/12.5%) and high frequency-stable LBP (n=1030/17.2%). History of falls (OR=1.52), history of LBP (OR=6.37), higher physical impairment (OR=1.51-2.85) and worse psychological function (OR=1.41-1.62) at baseline were all associated with worse LBP trajectory groups in this sample of older men. These findings present an opportunity for targeted interventions and/or management to older men with worse or increasing LBP trajectories and associated modifiable risk factors, to reduce the impact of LBP and improve quality of life.

Are milk quota prices a rational investment? Modeling quotas as financial assets

AbstractOur objective in this paper is to examine the growth of the price of farm milk quotas in Canada, to shed light on their patterns of growth within the last 15 years. Our quota price model is based on the Gordon growth model, supplemented by some important characteristics of the milk quota market. Our explanatory variables include the quota rental price, interest rate, and expectations of both price and quantity growth, all for the province of Alberta over the 2009–2023 time period. Our economic model of quota prices performs well by giving results with the predicted signs for all variables in our model and high levels of statistical significance except for expectations of quantity allocations when entered separately. The model has two variants, one with the expectation of growth in quota rents and the other with the expectation of growth in the quota asset price. Econometric issues related to time series problems are dealt with. The model allows calculation of policy risk, which shows an interesting pattern over this time period.