Growth Model Research Articles

Industry specialisation of the Kemerovsk region – Kuzbass: trends and problems that affect economic growth and development of the region

The article is devoted to studying the influence of industry specialisation of the Kemerovo region – Kuzbass on the economic growth of the entity. The purpose of the study is to identify the most significant trends and problems of the industry specialisation that affect the development of the region. Using methods of scientific knowledge – description, synthesis and generalisation of the data obtained; literature review, graphical method – the following has been conducted: analysis of the dynamics of structural changes in the gross regional product (hereinafter referred to as GRP) of the Kemerovo region – Kuzbass; assessment of trends in the movement of main industry indicators and indicators of regional industry. The information base of the study consists of foreign and domestic publications devoted to the analysis of the GRP sectoral structure; statistical data from the Federal State Statistics Service and its territorial authority for the Kemerovo Region – Kuzbass. Based on the analysis of the structural changes dynamics, an increase in the contribution of the raw materials sector to the country’s gross domestic product and an increase in the share of mineral extraction in the industry structure of the GRP of the Kemerovo region – Kuzbass have been revealed. The trend of decrease in the industrial production index is analysed, it is shown that it has a trend similar to the dynamics of the decline in mining production, and the reasons for the decline are described. The strengthening trend of economic growth in the Kemerovo region – Kuzbass based on the raw model has been determined. East Asian models of economic growth are considered. The expediency of using international experience in the design of high-tech industries on the basis of private investment in the research and development sector, and its adaptation to the realities of the development of Russian regions is indicated. A conclusion is drawn about the need to support the technological sovereignty of the country and the transition of the development of the Kemerovo region – Kuzbass to a non-primary model.

The Role of External and Internal Markets in the Economic Growth Models of Belarus and Russia

The Role of External and Internal Markets in the Economic Growth Models of Belarus and Russia

Latent Growth Model with Categorical Indicators: Comparison of the Different Estimators

Despite its potential usage in social and behavioral sciences, there has been limited research on Latent Growth Models (LGM) with categorical indicators. The categorical nature of binary or ordinal variables violates the multivariate normality assumption of the traditional maximum likelihood (ML) estimator. Alternative estimators can be employed, such as marginal maximum likelihood (MML), categorical weighted least squares (c-WLS), and categorical diagonally weighted least squares (c-DWLS). A Monte Carlo simulation study was conducted to compare the performance of the three estimators in the context of categorical LGM. The results revealed that the MML estimator performed the best for the correctly specified linear growth model, whereas the robust c-DWLS or c-WLS estimator excelled in correctly specified quadratic growth model. The robust c-DWLS and c-WLS estimators were more sensitive to model misspecifications. The findings of the present study provide important implications and practical recommendations for empirical researchers using categorical LGM.

Examining Immediate and Sustained Effects of Check-In/Check-Out in Finnish Elementary Schools

Check-in/Check-out (CICO) behavioral support has been implemented in Finnish School-Wide Positive Behavior Interventions and Support (SWPBIS) schools to cater to students who require personalized behavior support beyond the universal level. Previous studies have demonstrated the effectiveness of CICO as a behavioral support method. However, further research is needed to investigate its effectiveness in a larger sample and to analyze the timeline for behavioral change. This study focused on 51 elementary school students, assessing their behavior at baseline and during the CICO intervention phase using two data collection methods: the Daily Report Card (DRC) and the School Situation Questionnaire (SSQ). Nonlinear growth modeling was employed to examine the effects of the intervention. The results indicated that CICO yielded significant positive effects on behavior within 1 week of initiating support. After the outcomes stabilized, the behavior change remained stable beyond the first week of the intervention. These effects were detected in both the target behavior measured with DRC and the problem behavior measured with SSQ. These findings suggest that CICO interventions produce rapid and sustained changes in behavior. Further, the effects of CICO were observed in various settings within the school environment, indicating distal outcomes.

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Longitudinal validation of the PROMIS-16 in a sample of adults in the United States with back pain.

This longitudinal study evaluates whether the Patient-Reported Outcomes Measurement and Information System (PROMIS)-16 domains capture average change over time comparable to the PROMIS-29 + 2 and have similar associations with change in overall health rating and two disability indices. Data were collected using Amazon's Mechanical Turk at baseline, 3 months, and 6 months among individuals reporting chronic low back pain. The analytic sample includes respondents who completed baseline and at least one follow-up assessment (N = 1137). We estimated latent growth models for eight PROMIS domains and compared growth parameters between the PROMIS-16 and PROMIS 29 + 2 with a z-test. Additionally, for each domain, random intercept and slope scores for individuals were computed for the PROMIS-29 + 2 and PROMIS-16 and correlated to estimate concordance. Using growth parameters for physical function and pain interference, we predicted average change in the Oswestry Disability Index (ODI), Roland Morris Disability Questionnaire (RMDQ), the overall health rating, and compared regression coefficients between the PROMIS-16 and PROMIS 29 + 2. All growth models fit the data well. Intercept and slope parameters were statistically comparable (p's > 0.05) in magnitude across all domains between the PROMIS-16 and PROMIS-29 + 2. Correlations between random intercept and slope scores for individuals across domains were high. Additionally, the regression coefficients between slopes for pain interference and physical function and ODI, RMDQ, and overall health rating were statistically comparable (p's > 0.05) between the PROMIS-16 and PROMIS 29 + 2. Results provide between-level support for the longitudinal and predictive validity of the PROMIS-16. Similar average baseline scores and changes over time were observed between PROMIS-16 and PROMIS-29 + 2. Further, average change estimates comparably predicted average change in distal outcomes. This work provides evidence supporting the utility of the PROMIS-16 as a viable, short-profile option for use in clinical and research settings.

A Novel Exploration of Diffusion Process Based on Multi-Type Galton–Watson Forests

Diffusion is a commonly used technique for spreading information from point to point on a graph. The rationale behind diffusion is not clear. The multi-type Galton–Watson forest is a random model of population growth without space or any other resource constraints. In this paper, we use the degenerated multi-type Galton–Watson forest (MGWF) to interpret the diffusion process, corresponding vertices to types and establishing an equivalence relationship between them. With the two-phase setting of the MGWF, one can interpret the diffusion process and the Google PageRank system explicitly. It also improves the convergence behavior of the iterative diffusion process and Google PageRank system. We validate the proposal by experiment while providing new research directions.

Developmental trajectories of interpersonal stress in school and psychological pain contributing to self-harm in adolescents.

This study investigated the trajectories of interpersonal stress and psychological pain and their effects on self-harm behaviors in adolescents and explored the reciprocal associations between interpersonal stress and NSSI/SA. The participants included 1149 adolescents (50.3% women; Mage = 12.86, SD = 0.69) who participated in three waves of data collection. Latent class growth models and cross-lagged panel models were used to identify subgroups of individuals and interactions between interpersonal stress and NSSI/SA. Controlling for sex and age, compared to the low interpersonal stress class, the moderate to high and increasing classes have higher risks of NSSI and SA. Compared to adolescents in the low increasing feelings with high increasing avoidance class, those in the low increasing feelings with low decreasing avoidance class reported lower probabilities of NSSI and SA. Pain avoidance and painful feelings mediated the pathway from interpersonal stress to NSSI/SA, whereas pain arousal mediated the pathway from NSSI/SA to interpersonal stress. Shared impact of interpersonal stress and distinct effects of psychological pain over time on maintaining and distinguishing self-harm behaviors were found. Adolescent crisis interventions should simultaneously focus on building social networks within the school context and regulating maladaptive minds.

Exploring the pyrolysis process of simulated oily sludge: Kinetics, mechanism, product distribution, and S/N elements migration

Pyrolysis is an important method for energy recovery and harmless treatment of oily sludge, definite reaction mechanism and the transformation of S and N elements is a key to improve the pyrolysis products. In this paper, a simulated oil sludge (SOS) was pyrolyzed at various temperatures of 400–700 °C in a tube furnace focusing on pyrolysis process, kinetic parameters, reaction mechanisms, S and N element migration patterns and product distributions. Kinetic parameters were deducted by FWO, KAS, Friedman and Starink methods, and the activation energy were 186.23–231.20 kJ/mol (Avg. 210.71 kJ/mol), 190.18–233.80 kJ/mol (Avg. 212.02 kJ/mol), 196.93–242.01 kJ/mol (Avg. 209.88 kJ/mol) and 184.82–230.95 kJ/mol (Avg. 218.67 kJ/mol), respectively, showed high similarity. All the pre-exponential factors were higher than 109 s−1, which indicated high reactivity of SOS during pyrolysis, and the pyrolysis process followed the nucleation growth model (A3). Pyrolysis temperatures had a significant influence on products distribution. The maximum yields of pyrolysis tar and gas were observed at 550 °C and 700 °C, respectively. Pyrolysis tar was dominated by aromatics and acids, while pyrolysis gas was mainly composed of H2 and CH4. Additionally, high temperatures could facilitate the transfer of more S and N into tar or gas products, and S and N compounds were mainly thiophene-S, sulfoxide-S, pyridine-N and pyrrole-N in char and CS2, CH3SH, COS, SO2, H2S, NH3, HCN and NOx in gas.

A Study on the Growth Model of Natural Forests in Southern China Under Climate Change: Application of Transition Matrix Model

This study establishes a climate-sensitive transition matrix growth model and predicts forest growth under different carbon emission scenarios (representative concentration pathways RCP2.6, RCP4.5, and RCP8.5) over the next 40 years. Data from the Eighth (2013) and Ninth (2019) National Forest Resource Inventories in Chongqing and climate data from Climate AP are utilized. The model is used to predict forest growth and compare the number of trees, basal area, and stock volume under different climate scenarios. The results show that the climate-sensitive transition matrix growth model has high accuracy. The relationships between the variables and forest growth, mortality, and recruitment correspond to natural succession and growth. Although the number of trees, basal area, and stock volume do not differ significantly for different climate scenarios, the forest has sufficient seedling regeneration and large-diameter trees. The growth process aligns with succession, with pioneer species being replaced by climax species. The proposed climate-sensitive transition matrix growth model fills the gap in growth models for natural secondary forests in Chongqing and is an accurate method for predicting forest growth. The model can be used for long-term prediction of forest stands to understand future forest growth trends and provide reliable references for forest management. Forest growth can be predicted for different harvesting intensities to determine the optimal intensity to guide natural forest management in Chongqing City. The results of this study can help formulate targeted forest management policies to deal more effectively with climate change and promote sustainable forest health.

Light Distribution Models for Tree Growth Simulation

AbstractThe simulation and modelling of tree growth is a complex subject with a long history and an important area of research in both computer graphics and botany. For more than 50 years, new approaches to this topic have been presented frequently, including several aspects to increase realism. To further improve these achievements, we present a compact and robust functional‐structural plant model (FSPM) that is consistent with botanical rules. While we show several extensions to typical approaches, we focus mainly on the distribution of light as a resource in three‐dimensional space. We therefore present four different light distribution models based on ray tracing, space colonization, voxel‐based approaches and bounding volumes. By simulating individual light sources, we were able to create a more specified scene setup for plant simulation than it has been presented in the past. By taking into account such a more accurate distribution of light in the environment, this technique is capable of modelling realistic and diverse tree models.

The Landscape as a Pillar of the Metropolis of Tomorrow: Sustainable Strategies for Better Integration in Annaba, Algeria

Objectives: This study aims to address the urban and landscape challenges faced by Annaba, a city in Algeria, due to environmental degradation resulting from rapid urbanisation and rural-to-urban migration. Once celebrated for its natural beauty, Annaba now seeks to restore its appeal and improve the quality of life for its residents. The objective is to demonstrate how integrating the landscape into urban planning can play a crucial role in sustainable development, preserving the city's unique identity, and enhancing its social, ecological, and aesthetic environment. Methods: Our approach involves a thorough analysis of Annaba’s landscape characteristics and biodiversity, exploring strategies for reintegration into urban planning frameworks. We focus on the coastal landscape as a primary asset in planning recommendations. This study reviews existing literature on sustainable urban planning, biodiversity conservation, and landscape integration in cities experiencing similar environmental and demographic shifts. Results: The findings suggest that integrating landscape elements into urban planning in Annaba would significantly enhance its ecological and aesthetic appeal. A sustainable development approach that prioritises the landscape can help to combat environmental degradation, improve the quality of urban life, and strengthen the city’s identity. Specific recommendations emphasize preserving coastal and natural features to support biodiversity and attract residents and tourists alike. Conclusion: Reintegrating landscape considerations into Annaba’s urban development strategy is essential for addressing present and future challenges. This approach would not only protect the city's natural assets but also promote a sustainable and appealing urban environment. By focusing on the landscape as a foundation of urban planning, Annaba can redefine its developmental path, offering a model of sustainable growth for other Mediterranean cities facing similar issues.

Investigations of high-speed projectile impact on symmetric sandwich structures containing solid propellant with core perforations

The response of solid rocket motors (SRMs) to high-speed fragment impacts is crucial for their safety design and operational use in scenarios such as rocket launches and space applications. The visualized Burn to Violent Reaction (BVR) test is used to observe intense reactions induced by high-speed projectile impacts. Employing a two-stage light gas gun and optical diagnostic techniques including high-speed schlieren imaging and direct photography, the impact-induced deflagration/explosion behavior, and reaction growth behavior were investigated. The damage mechanisms of the casing and propellant samples were assessed, and the reaction growth and afterburn effects of the impact-induced fragment cloud were quantitatively analyzed. The results indicate that the ignition delay time is inversely correlated with the impact velocity, decreasing from ms to μs scale. Across a wide range of velocities (1050–2058 m/s), higher projectile velocities induce more sustained and vigorous combustion reactions within the propellant. Furthermore, increasing the propellant air gap to 7.8 cm does not trigger further reactions under the studied configurations. The reaction mechanisms are closely linked to the characteristics of the fragment cloud induced by the impact. The developed Smoothed Particle Hydrodynamics (SPH) method, incorporating material constitutive models, ignition criteria, and reaction growth models, was used to study the influence of projectile velocity on the reaction mechanisms. The simulation results were compared with experimental data, demonstrating satisfactory accuracy.

Single-step hydrothermal synthesis of zinc oxide nanorods for potential use as nano-antibiotics without seeding or bases.

The alarming global rise in antibiotic resistance, driven by the widespread overuse of traditional antibiotics, has created an urgent demand for new antimicrobial solutions. This study presents zinc oxide (ZnO) nanorods as a potential nano-antibiotic agent. ZnO nanorods, with a 2:3 aspect ratio, were synthesized using an efficient one-step hydrothermal method at a low temperature of 100°C, reducing the synthesis time to just 5 hours. The synthesized ZnO nanorods' morphology, structure, and composition were characterized using scanning electron microscopy, x-ray diffraction, and energy dispersive x-ray spectroscopy. The potent antimicrobial activity of these nanorods against common bacterial strains such as Escherichia coli, Bacillus subtilis, and Vibrio parahaemolyticus was examined through optical density at 600 nm (OD600) measurements and inhibition zone analysis, demonstrating substantial inhibition of bacterial growth. In particular, at a concentration of 5 mg/mL, ZnO nanorods achieved a 96% reduction of B. subtilis bacteria in OD600 and an impressive 99.87% reduction in culturing assays within one day, showcasing bactericidal efficiency on par with tetracycline at 0.003 mg/mL. Furthermore, a predictive model of bacterial growth was developed and validated, providing insights into the time-dependent bactericidal efficiency of the synthesized nanorods. These results highlight the potential of ZnO-based composites as a promising solution to combat antibiotic resistance, paving the way for next-generation antimicrobial materials.

Mathematical Modeling of Biogas Production in Sanitary Landfills

Objective: The objective of this study is to evaluate the biogas production potential in sanitary landfills using the Verhulst mathematical model, which is employed to represent natural phenomena constrained by a saturation point. Theoretical Framework: It was based on references on energy use from waste, possible conversion routes, in addition to mathematical models that estimate the energy potential for biogas generation. Method: The methodology adopted for this research involves the use of data collected in an experimental cell, subsequently applying the De Verhulst model through a regression with minimization of errors using the Least Squares Method (LSM). Results and Discussion: The results indicated that the developed model correlates well with the observed data, demonstrating its suitability as a predictive model for biogas production. Research Implications: The research contributes to the construction of a theoretical framework for the adaptation, elaboration and use of mathematical models to predict the energy potential in the generation of biogas in landfills. Originality/Value: This study contributes to the literature by using a population growth model (Verhulst) to predict landfill biogas production.

A Study on Future Population Dynamics of Indigenous Earthworm Species in Golaghat District of Assam, India

This study presents a pioneering exploration into the population dynamics of indigenous earthworm species in the Golaghat district of Assam, India, through the lens of mathematical modeling. Recognizing the integral role of earthworms in enhancing soil productivity and ecosystem health, we embarked on a detailed longitudinal analysis spanning from 2018 to 2023 to assess their population trends across different seasons and subdivisions within the district. Utilizing the principles of Mathematical Biology, we employed the Malthus Growth model and the Logistic Growth model to estimate future population trajectories of earthworm species, integrating biological insights with mathematical rigor. Our methodology, combining extensive field data collection with sophisticated mathematical modeling, provides a replicable framework for similar ecological studies. This study contributes to the growing field of Mathematical Biology, offering a novel approach to understanding the complex interactions within ecosystems and the impact of environmental changes on key species. The outcomes offer valuable insights into sustainable agricultural practices and biodiversity conservation in tropical and subtropical regions, emphasizing the critical role of indigenous earthworm species in maintaining soil health and ecosystem services. Our findings reveal significant seasonal variations and highlight the resilience of these species in the face of ecological changes. The models suggest distinct population growth patterns, with the Logistic model providing a more realistic projection considering environmental constraints and resource availability. The bifurcation diagram is drawn for the Logistic map that presents the phenomena inside the chaotic region.

Predicted effect of incidental pulmonary nodule findings on Non-Small Cell Lung Cancer mortality

Despite the reduction in mortality shown by Low dose computer tomography (LDCT) lung cancer screening, the uptake is still low. Patients undergo chest imaging for several other medical reasons, and this is a unique opportunity to detect lung nodules. In a cohort of NSCLC patients from the Surveillance, Epidemiology and End Results (SEER)-Medicare linked data, tumor size at previous imaging was calculated as: VDT = [(T2-T1)·ln2]/ln(V2/V1), solving for diameter of V1. V1 and V2 are tumor volume at times T1 (previous imaging) and T2 (diagnostic procedure) according to three different growth models. 10-year lung cancer-specific mortality was calculated as: lung cancer survival rate = (-0.0098 × maximum tumor diameter) + 1. 1,007 patients who had a chest imaging performed up to one year prior to lung cancer diagnosis. The median size of the tumor at diagnosis was 25 mm, the predicted median tumor size at previous imaging was 12.16 mm, 17.3 mm, and 20.42 mm under the fast, medium and slow growth model. Under the fast growth model, a detection of the nodule at previous imaging would have yield a decrease in mortality of 7.79%; the corresponding values for the medium growth model is 4.5%, for the slow growth model 2.45%. Identifying malignant lung nodules in imaging performed for other clinical reasons can help decreasing the burden of NSCLC, especially for non-LDCT eligible patients and the medically vulnerable. We show here that clinical benefits, especially among patients with aggressive disease, can be considerable.

A Kaleckian growth model of secular stagnation with induced innovation

AbstractThis paper works out a demand‐led growth model that draws on the Kaleckian‐Steindlian tradition to examine the relationships between income distribution, capacity utilization, and capital accumulation; on Goodwin‐type growth cycle models to investigate the dynamic interaction between labour market and distributive conflict; and on the induced innovation literature to link labour productivity growth to income distribution. We find that: (i) an increase in the labour share driven by stronger workers' bargaining power leads to faster capital accumulation and labour productivity growth in the long run, irrespective of the short‐run growth regime of the economy; (ii) a positive institutional shock to the labour share can either increase or decrease the long‐run employment rate, depending on the short‐run growth regime; and (iii) an increase in the labour share driven by negative technology shocks leads to faster capital accumulation and labour productivity growth in the long run only if the economy exhibits a short‐run wage‐led regime. This model then unifies classical/neo‐Goodwinian and Kaleckian views on the long‐run relationships between the labour share, employment, and growth within a more general Kaleckian framework of a labour‐constrained economy with induced technical change.

Spatial externalities, R&D spillovers, and endogenous technological change

Forward-looking economic agents operating in a finite continuous geographic area choose how much to innovate at each point in time and space. Based on this assumption, the present study incorporates spatial interactions in endogenous growth models, addressing the criticism that such models are inconsistent with empirical evidence. More specifically, we introduce spatial production spillovers, knowledge diffusion across space, and the capability for spatial heterogeneity into a standard expanding variety growth model based on R&D. We study the properties of equilibrium and optimal allocations and argue that the characteristics are different from those of the non-spatial model, which alter the appropriate policy measures. Finally, we provide numerical examples demonstrating the importance of spatial dependent policy measures in achieving a balanced regional development.

Two-layer multi-state SPH modelling of momentum growth and its feedback in viscous debris flow on wet bed sediment

Two-layer multi-state SPH modelling of momentum growth and its feedback in viscous debris flow on wet bed sediment