Growth Model Research Articles

Merging Data with Modeling: An Example from Fatigue

It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 107 cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted.

Interfacial characteristics of Nickel-based hybrid structures fabricated using directed energy deposition

The wire-arc directed energy deposition technique was used to develop nickel-based hybrid structures or to repair conventional structures of nickel-718 superalloy. Three different cases were investigated: deposited IN718 and IN625, separately, over the conventional IN718 substrate and deposited IN718 over the previously deposited IN625 substrate. The interfacial characteristics with respect to the microstructural features, residual stresses, compositional changes, etc., were studied using a finite element computational framework and a Monte-Carlo grain growth model. The numerical results were successfully validated with the experimental results. All the hybrid structures revealed excellent interfacial bonding with no defects, as evidenced by the failures outside the interface during their tensile tests. However, the interfacial characteristics differed for each material condition. The results discussed in this study will improve the understanding of developing nickel-based hybrid structures using additive methods and can potentially be beneficial in repairing industrial components.

Evaluating a School-Based Intervention on Adolescents' Ethnic-racial Identity in Sweden.

Finding developmentally appropriate ways to support youth in understanding their own ethnic-racial identity is needed, particularly in contexts like Sweden where such support is not the norm. This preregistered longitudinal study examined whether an 8-week school-based intervention, the Identity Project, impacted youth ethnic-racial identity exploration (participation and search), resolution, private regard, and centrality. Participants were 509 adolescents in the 10th grade (Mage = 16.28, SD = 0.80; 65% self-identified girls; 52% minoritized ethnic background), who were randomized into an intervention or wait-list control group and assessed at baseline and three times post-intervention. The findings indicated an initial and simultaneous effect of the intervention only for exploration participation and resolution but did not show the expected chain of effects with earlier exploration predicting later resolution. Growth models indicated a greater increase in exploration participation over time for the intervention group than the control group. The findings indicate a mixed picture about the effectiveness of the intervention, with effects primarily narrowly targeted to exploration participation, but nevertheless highlight the potential for supporting Swedish youth in engaging with their ethnic-racial identities.

A validated ballistic-He fuzz-growth model for high temperature erosive-plasma regimes

An analytical model of helium-induced fuzz growth on heated tungsten (Patino et al., 2023) is expanded to include the effects of material exposure temperature via an Arrhenius relation and helium-induced sputtering. The expanded model is able to replicate experimental measurements of fuzz thickness for a range of helium fluences from 0.7–110×1026 m−2, sample temperatures from 993–1300 K, and ion energies from 50–500 eV.

Analysis of Export-Led Growth Model for Türkiye Economy with Markov Regime Switching Model

Analysis of Export-Led Growth Model for Türkiye Economy with Markov Regime Switching Model

Leaf Area Index Estimation of Fully and Deficit Irrigated Alfalfa through Canopy Cover and Canopy Height

In arid and semiarid regions, crop production has high irrigation water demands due to low precipitation. Efficient irrigation water management strategies can be developed using crop growth models to assess the effect of different irrigation management practices on crop productivity. The leaf area index (LAI) is an important growth parameter used in crop modeling. Measuring LAI requires specialized and expensive equipment not readily available for producers. Canopy cover (CC) and canopy height (CH) measurements, on the other hand, can be obtained with little effort using mobile devices and a ruler, respectively. The objective of this study was to determine the relationships between LAI, CC, and CH for fully and deficit-irrigated alfalfa (Medicago sativa L.). The LAI, CC, and CH measurements were obtained from an experiment conducted at the Valley Road Field Lab in Reno, Nevada, starting in the Fall of 2020. Three irrigation treatments were applied to two alfalfa varieties (Ladak II and Stratica): 100%, 80%, and 60% of full irrigation demands. Biweekly measurements of CC, CH, and LAI were collected during the growing seasons of 2021 and 2022. The dataset was randomly split into training and testing subsets. For the training subset, an exponential model and a simple linear regression (SLR) model were used to determine the individual relationship of CC and CH with LAI, respectively. Also, a multiple linear regression (MLR) model was implemented for the estimation of LAI with CC and CH as its predictors. The exponential model was fitted with a residual standard error (RSE) and coefficient of determination (R2) of 0.97 and 0.86, respectively. A lower performance was obtained for the SLR model (RSE = 1.03, R2 = 0.81). The MLR model (RSE = 0.82, R2 = 0.88) improved the performance achieved by the exponential and SLR models. The results of the testing indicated that the MLR performed better (RSE = 0.82, R2 = 0.88) than the exponential model (RSE = 0.97, R2 = 0.86) and the SLR model (RSE = 1.03, R2 = 0.82) in the estimation of LAI. The relationships obtained can be useful to estimate LAI when CC, CH, or both predictors are available and assist with the validation of data generated by crop growth models.

Longitudinal Relationships Among Child School Engagement, Parental Monitoring, and Child Prosocial Behavior: A Child-Parent Synergistic Mechanism.

Numerous contextual factors have been identified that impact the development of children's prosocial behavior, yet the influence of child-initiated factors on prosocial behavior and its underlying mechanism remains unclear. This study employed three longitudinal models to examine in depth how children's school engagement may promote the development of their own prosocial behavior. Three-wave longitudinal data from 4691 children (M age = 9.480, SD = 0.507; 48.2% female) with 2-year intervals were used. Sequentially, a cross-lagged panel model, a random intercept cross-lagged panel model, and a parallel process latent growth model were constructed. The findings indicated that children's school engagement consistently predicted the future level, dynamic changes at within-person level, and long-term trends in their prosocial behavior, and these longitudinal relationships were partially mediated by parental monitoring. These results reveal a child-parent synergistic mechanism for the development of prosocial behavior, wherein children's school engagement both directly promotes their own prosocial behavior and simultaneously enhances prosocial behavior through eliciting increased parental monitoring.

Spark plasma sintering of boron carbide (B4C): From characterisation to finite element modeling of sintering

Boron carbide (B4C), known for its unique properties, presents challenges in reaching its full density with sintering process due to low diffusion coefficients. This study explores the application of Spark Plasma Sintering (SPS) to overcome these challenges and enhance the understanding of its impact on pure B4C ceramics. The study outlines a comprehensive methodology for developing an SPS sintering model. Starting with a summary of conducted studies, powder oxidation analysis, characterization methods, and dilatometry curves processing, the article details the derivation of constitutive parameters based on the Skorohod-Olevsky theory. Isothermal profiles at different temperatures, variation in heating regimes, and a stepwise approach for SPS pressure application form the basis of the derivation methods. A grain growth model is developed for a comprehensive simulation of sintering, incorporating microstructural analysis and parameter derivation. Finally, the article addresses the integration of this mechanical sintering model with thermal and electrical considerations into a finite element method (FEM) software for a holistic SPS modeling. Thermo-electrical considerations, including the Peltier effect, are also computed, providing a well-rounded understanding of the SPS process for B4C ceramics. This study contributes valuable insights to optimizing the SPS parameters to achieve enhanced densification and microstructure control in B4C ceramics.

A System-Level Reliability Growth Model for Efficient Inspection Planning of Offshore Wind Farms

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.

Group Cohesion in Experiential Growth Groups: A Multilevel Growth Analysis

ABSTRACT Group cohesion is considered a key group therapeutic factor; however, limited knowledge exists on the session-to-session development of group cohesion in the context of training groups or on the factors that may predict such developments. We utilized multilevel growth modeling to examine the longitudinal changes in 44 counselor trainees’ ratings of group cohesion in seven experiential growth groups across four semesters. We found significant between-person variability in the growth trajectory of group cohesion and identified trainee self-disclosure and gender as significant predictors of group cohesion. The results indicated the complexity of within- and between-person changes in group cohesion in training groups and the need for further research to identify potential predictors of these changes. We also discussed ways for group facilitators to attend to self-disclosure as an important marker of group cohesion.

Group Cohesion in Experiential Growth Groups: A Multilevel Growth Analysis

ABSTRACT Group cohesion is considered a key group therapeutic factor; however, limited knowledge exists on the session-to-session development of group cohesion in the context of training groups or on the factors that may predict such developments. We utilized multilevel growth modeling to examine the longitudinal changes in 44 counselor trainees’ ratings of group cohesion in seven experiential growth groups across four semesters. We found significant between-person variability in the growth trajectory of group cohesion and identified trainee self-disclosure and gender as significant predictors of group cohesion. The results indicated the complexity of within- and between-person changes in group cohesion in training groups and the need for further research to identify potential predictors of these changes. We also discussed ways for group facilitators to attend to self-disclosure as an important marker of group cohesion.

From vulnerability to vitality: Climate change as a catalyst for economic transformation in small island developing states

As climate change intensifies, its associated risks become increasingly important to societal well-being, with small island developing states (SIDS) being particularly susceptible. The challenges faced by these nations transcend the impacts of their greenhouse gas emissions and cannot be mitigated by emission reduction alone. This paper establishes an innovative, interpretable machine learning model for the first time using import and export data from 1970 to 2020 for nine economically advanced Small Island Developing States, to investigate how economic transformation in small islands occurs against specific climatic contexts and to analyse the impact of these climatic challenges in the process of economic transformation. The study finds that when the average annual temperature exceeds 26 °C over the past two years, or when the sea level rises more than 30 mm, certain islands rapidly complete their economic structural transformation. This indicates that climate change is significantly reshaping the developmental trajectories of these small island economies. To confront these challenges, these states are continuously shifting their developmental focus, reconfiguring their economic structures and growth models to address the ongoing challenges posed by climate change.

A System-Level Reliability Growth Model for Efficient Inspection Planning of Offshore Wind Farms

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.

Quantification of tree growth change under climate change using National Forest Inventory of Korea

To achieve carbon neutrality in the face of climate change, accurately estimating forest carbon uptake is crucial. Environmental changes, such as temperature increases or precipitation fluctuations caused by climate change, can alter the growth responses of each species and act as a driver for changes in forest productivity. In particular, using a single-species growth model increases uncertainty in predicting stand growth, given the high species diversity in temperate forests in East Asia. Therefore, in this study, we identified the growth curve of 17 species-specific distributed in Korean temperate forest as well as the growth trend over the last 30 years (1976–2005). For this purpose, the species distribution and growth characteristics of 17 major Korean species of trees were analyzed using 13,808 tree-ring series from the 5th National Forest Inventory, which was conducted between 2006 and 2010. The growth characteristics of each species were analyzed using the average growth curve specific to each species, and the yearly growth trend was examined through residual analysis of the average growth curve. The distribution range of the 17 major tree species varied by species, as did the growth rate, ranging from 7 cm² · year⁻¹ to 17 cm² · year⁻¹ for the 30-year-old average basal area increment (BAI). Over time, fast-growing tree species exhibited initially high BAI, followed by a sharp decline from the 2000s. In contrast, broadleaf trees showed a continuous increase. Furthermore, a comparison of the total volume of individual trees and the volume of the yield table revealed that accurately estimating the actual forest volume was challenging. This study contributes to improving the accuracy of productivity predictions for temperate forests, underscoring the necessity to investigate the interaction between climate and forest succession in the future.

Advancing timber construction: historical growth, research frontiers, and time series forecasting

ABSTRACT Lauded for its capacity to curtail building energy consumption and enhance environmental performance compared to traditional mineral-based materials, wood has gained substantial traction in the construction sector. Timber construction, which is a focal point of growing interest in both industry and academia, warrants a reassessment of its historical trajectory and future prospects. This study examines publications on timber construction from 1990 to 2022 to analyze the growth path, shifting hotspots, and potential research directions in this field. By employing scientometric analysis, influential journals, articles, countries, and keywords are identified. Integration into a logistic growth model (LGM) showcases publication trends, while the Prophet model forecasts global growth. Research frontiers and emerging themes are revealed through keyword bursts surge evolution. This study offers a visualized knowledge framework, bridging current research with future trends and enabling practitioners and researchers to comprehensively grasp the evolution of timber construction.

Influence of inorganic solid particles on the stability of water-in-crude oil emulsions: Evaluating the role of surface charge

This study examines the effect of inorganic solid particles on the stability of water-in-oil (W/O) emulsions formed during the transportation, refining, and storage of crude oil. The surface properties of inorganic particles, such as kaolinite and calcite, can influence the interface between crude oil and water, affecting the stability of the emulsion. The study evaluates the quantitative effect of these particles on emulsion stability and investigates how surface properties affect the emulsions. Emulsions were prepared using crude oil and synthetic brine, with calcite and kaolinite added as inorganic solid particles. Adding inorganic solids increased the volume of released water and altered the emulsion stability, with high concentrations decreasing the formation of stable emulsions. The dissolution of inorganic solids increased the pH of the solution and promoted demulsification due to the high surface potential of crude oil. The solid particles enhanced the formation of oil-in-water-in-oil (O/W/O) emulsions, generating unstable emulsions. The study also found that increasing temperature and adding inorganic solids decreased the emulsion height over time, which was predicted by the emulsion layer growth model. The coagulation rate constant was a tuning parameter, with a higher value implying higher internal repulsion and stronger coagulation between water droplets, facilitating emulsion instability. It has been observed that elevating the storage temperature and introducing kaolinite accelerates the coagulation rate constant, whereas the addition of calcite decreases this value, ultimately contributing to the stabilisation of the emulsions.

Enhancing irrigation water productivity using short-range ensemble weather forecasts at basin scale: A novel framework for regions with high hydro-climatic variability

Integrating short-term weather forecasts with crop growth models has emerged as a valuable decision-support tool for enhancing irrigation water productivity in water-intensive crops. Our primary focus lies in formulating a simplified Quasi-Farmer Behavior Routine to utilize ensemble weather forecasts to provide irrigation guidance. The study proposes the concept of Rainfall Confidence Quotient (RCQ) that is optimized based on the forecast uncertainty levels for rainfall categories that affect the irrigation decision. We utilize two publicly available ensemble weather forecasts from NCEP-GEFS and ECM and a regionally fine-tuned high-resolution Weather Research and Forecasting (WRF) ensemble forecast system with a 3-day lead time. We have refined our methodology by conducting 800 simulation experiments across diverse scenarios, each reflecting distinct climate regimes, soil types, cropping seasons, and management practices. The proposed method consists of a sequential 3-checkpoint algorithm incorporating ensemble rainfall forecasts, antecedent soil moisture, and evapotranspiration. During the wet crop season of 2015–16, the enhanced methodology yielded a substantial increase in irrigation water productivity (IWP), averaging around 20–30 % increase across spatial locations, in contrast to the conventional irrigation practices that do not consider weather forecasts. However, during 2016–17, a dry year, the improvement in IWP only ranged between 2–10 %. On average, water savings (at field scale) of 200–500 mm were recorded in the wet year crop season of 2015–16, while 100–300 mm of water savings were achieved during the dry year crop season of 2016–17, without any considerable reductions in the crop yield. The robustness and adaptability of the developed approach have been established through comprehensive evaluation with field observation and remote sensing techniques, suggesting its potential scalability to similar hydro-climatic typologies across the world.

Fabrication of tips for scanning probe magnetometry by diamond growth

The use of quantum sensors is promising detailed insights into physical phenomena such as magnetism or superconductivity. One example of such quantum sensors is a microscopic diamond tip containing nitrogen vacancy (NV) centers, which is capable of producing correlated measurements of vectorial magnetic fields and the sample topography on the nanoscale. In this study, we present a chemical vapor deposition (CVD) process to produce diamond tips with NV centers by overgrowing microstructured diamond substrates. The resulting diamond tips exhibit a radius of curvature of approximately 10 nm, suitable for use as a probe in an atomic force microscope. The magnetic sensitivity of the CVD-grown diamond tips is characterized with pulsed measurements of the optically detected magnetic resonance, which yield a minimum magnetic sensitivity of 60 µT Hz−1 . The growth of the diamond microstructures is observed to differ from the commonly used geometric model predicting CVD growth of bulk diamond crystals. We identify an empirical model for the growth behavior of the microstructures by taking into account processes described in the step flow growth model for crystals. Additionally, we demonstrate the applicability of the developed CVD growth process to membrane substrates required for the preparation of magnetometry-capable diamond tips.

Pyrolysis of high-density polyethylene: Degradation behaviors, kinetics, and product characteristics

Pyrolysis is a promising technology for converting plastic waste into valuable raw materials while offering a potential solution to the global plastic pollution crisis. In this study, the thermal pyrolysis of high-density polyethylene (HDPE) is investigated in a drop tube reactor under nearly isothermal conditions. The impact of reaction temperature and gas/volatile residence time on carbon conversion and product distribution is examined across a range of 500–900 °C and 3.6–32.2 s, respectively. Non-condensable gas products detected by online mass spectrometry are H2, CH4, C2H4, C2H6, C3H6, and C3H8. At elevated temperatures and prolonged residence time, H2 yield reaches as high as 8.6 wt% of the initial HDPE mass due to intensified cracking reactions of C2–C3 hydrocarbons and long-chain aliphatic compounds. Consequently, pyrolysis tars consist mainly of polycyclic aromatic hydrocarbons (PAHs) with 5–7 rings, accompanied by visible coke deposition within the reactor. HDPE decomposition to volatiles is an endothermic process and it is complete at a temperature between 492 °C and 525 °C, depending on the heating rate employed, from non-isothermal thermogravimetric analysis and differential scanning calorimetry (TGA-DSC) measurements. The thermal degradation of HDPE pellets follows the two-dimensional nucleation growth model for conversion levels up to 0.8 with an apparent activation energy of 259–270 kJ/mol and a pre-exponential factor of 4.83 × 1017–1.37 × 1019 min−1, determined from various isoconversional methods such as Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), and Starink, along with Criado's master plots. These findings provide valuable insights into optimizing process parameters and refining reactor design for pyrolysis, which can be integrated with gasification and reforming processes to enhance hydrogen production on a larger scale.

Exploring newcomer voice and silence dynamics: The role of organizational socialization

AbstractThis paper examines the temporal dynamics of newcomer voice and silence in organizations, focusing on the role of organizational socialization. While prior research has explored the antecedents and effects of newcomer voice behaviour, little attention has been given to how voice and silence evolve over time as newcomers become more familiar with their roles and the organizational context. Drawing on multiple goal pursuit theory, this study investigates how organizational socialization influences the trajectories of newcomer promotive voice, prohibitive voice and employee silence. We test our hypotheses on a sample of 198 organizational newcomers over four waves of data, employing random coefficient growth models. The findings suggest that employee voice and silence follow distinct trajectories and provide partial support for the influence of organizational socialization on these changes in newcomer behaviour. Implications for theory and practice are discussed.