Bootstrapping Process Research Articles

Pinpointing Conditions for a Metabolic Origin of Life: Underlying Mechanisms and the Role of Coenzymes.

ConspectusFamously found written on the blackboard of physicist Richard Feynman after his death was the phrase, "What I cannot create, I do not understand." From this perspective, recreating the origin of life in the lab is a necessary condition for achieving a deep theoretical understanding of biology. The "metabolism-first" hypothesis is one of the leading frameworks for the origin of life. A complex self-organized reaction network is thought to have been driven into existence as a chemical path of least resistance to release free energy in the environment that could otherwise not be dissipated, rerouting energy from planetary processes to organic chemistry. To increase in complexity, the reaction network, initially under catalysis provided by its geochemical environment, must have produced organic catalysts that pruned the existing flux through the network or expanded it in new directions. This boot-strapping process would gradually lessen the dependence on the initial catalytic environment and allow the reaction network to persist using catalysts of its own making. Eventually, this process leads to the seemingly inseparable interdependence at the heart of biology between catalysts (coenzymes, enzymes, genes) and the metabolic pathways that synthesize them. Experimentally, the primary challenge is to recreate the conditions where such a network emerged. However, the near infinite number of microenvironments and sources of energy available on the early Earth or elsewhere poses an enormous combinatorial challenge. To constrain the search, our lab has been surveying conditions where the reactions making up the core of some of the most ancient chemolithoautotrophic metabolisms, which consist of only a small number of repeating chemical mechanisms, occur nonenzymatically. To give a fresh viewpoint in the first part of this account, we have organized the results of our search (along with important results from other laboratories) by reaction mechanism, rather than by pathway. We expect that identifying a common set of conditions for each type of reaction mechanism will help pinpoint the conditions for the emergence of a self-organized reaction network resembling core metabolism. Many of the reaction mechanisms were found to occur in a wide variety of nonenzymatic conditions. Others, such as carboxylate phosphorylation and C-C bond formation from CO2, were found to be the most constraining, and thus help narrow the scope of environments where a reaction network could emerge. In the second part of this account, we highlight examples where small molecules produced by metabolism, known as coenzymes, mediate nonenzymatic chemistry of the type needed for the coenzyme's own synthesis or that turn on new reactivity of interest for expanding a hypothetical protometabolic network. These examples often feature cooperativity between small organic coenzymes and metal ions, recapitulating the transition from inorganic to organic catalysis during the origin of life. Overall, the most interesting conditions are those containing a reducing potential equivalent to H2 gas (electrochemical or H2 itself), Fe in both reduced and more oxidized forms (possibly with other metals like Ni) and localized strong electric fields. Environments that satisfy these criteria simultaneously will be of prime interest for reconstructing a metabolic origin of life.

Effect of social support on Muslim women's sporting activities: mediating effect of psychological adjustment.

This study explores the relationship between social support and sporting activities of Muslim women and constructs a mediation model through role of psychological adjustment. Using stratified cluster sampling, 301 Muslim women were measured in group psychology using the Social Support Scale and the Sports Activities and Psychological Adjustment Scale. The statistical software SPSS 24.0 and SPSS PROCESS 3.3 were used for statistical processing. The common-method variation test was carried out using the Harman single-factor control test. Finally, the Bootstrap sampling test method and process plug-in were used to test the significance of the intermediary effect. (1) Social support has a significant predictive effect on sports activities (β = 0.32, p < 0.001); (2) psychological adjustment (β = 0.552, p < 0.001) mediates the relationship between social support and sporting activities [social support → psychological adjustment → sporting activities (95% Cl, 0.093, 0.323)]. Social support positively influences sporting participation among Muslim women, and psychological adjustment mediates this relationship. Strengthening social support for Muslim women can enhance their psychological adjustment, thereby improving their participation in sporting activities and offering valuable theoretical and practical guidance.

The Role of Authentic Local Resources and Social Capital in the Development of Sustainable Village Tourism

This study aims to determine the role of authentic local resources and social capital in influencing the development of sustainable village tourism. This research refers to the social exchange theory and the theory of resources base view, using 30 managers of the village tourism "Lontar Sewu" as respondents who then carried out a bootstrapping process of 300 data. WarpPLS processed the collected data. The results revealed that the data collected met the validity and reliability requirements and the fit of the model. Estimation of the model showed that the authenticity of local resources and the social capital of the community had a positive relationship with sustainable village tourism. Likewise, the verification showed that the authenticity of local resources affected sustainable village tourism, and social capital affected sustainable village tourism. The findings showed that the authenticity of local resources and social capital was proven to have an important role in sustainable village tourism, such as the sustainability of the Lontar tree ecosystem (authenticity), the uniqueness of village tourism that is maintained, and local wisdom that makes the community involved in developing village tourism. This research provides a significant theoretical contribution to the sustainable tourism literature. Furthermore, this research has a number of practical implications for village tourism managers in terms of sustainable village tourism management.

Bootstrap prediction inference of nonlinear autoregressive models

The nonlinear autoregressive (NLAR) model plays an important role in modeling and predicting time series. One‐step ahead prediction is straightforward using the NLAR model, but the multi‐step ahead prediction is cumbersome. For instance, iterating the one‐step ahead predictor is a convenient strategy for linear autoregressive (LAR) models, but it is suboptimal under NLAR. In this article, we first propose a simulation and/or bootstrap algorithm to construct optimal point predictors under an or loss criterion. In addition, we construct bootstrap prediction intervals in the multi‐step ahead prediction problem; in particular, we develop an asymptotically valid quantile prediction interval as well as a pertinent prediction interval for future values. To correct the undercoverage of prediction intervals with finite samples, we further employ predictive – as opposed to fitted – residuals in the bootstrap process. Simulation and empirical studies are also given to substantiate the finite sample performance of our methods.

Public Perception of the Effectiveness of Vaccination against the Level of Anxiety Exposed to Covid-19

This research aims to find out how people's perceptions about effectiveness of vaccination on the level of anxiety exposed to Covid-19 which is influenced by knowledge. The sampling technique used accidental sampling. Nine districts and cities in Bali were included in the samples and 190 respondents in all. Questionnaires as research instruments and primary data are the types of data used. The Structural Equation Modeling-Partial Least Square (SEM-PLS) analysis approach was used in this study. The variables in this research consist of exogenous latent variables (knowledge), mediating latent variables (effectiveness), and endogenous latent variables (anxiety). By considering the results of hypothesis testing carried out through the bootstrapping process, the study revealed that better knowledge about Covid-19 can increase the effectiveness of vaccination by 78.9%, better knowledge about Covid-19 can reduce anxiety levels by 23.2%, increased vaccination effectiveness results in a decrease in anxiety levels by 29.4%, and better knowledge about Covid-19 can indirectly reduce anxiety levels through vaccination effectiveness by 59.3%. As a result, H0 gets dismissed in the hypothesis test, which indicates that knowledge significantly affects anxiety, both directly and indirectly through the mediation of vaccination effectiveness.

Factors That Influence Individual Taxpayer Compliance With Tax Sanctions As A Moderating Variable At The Binjai Pratama Tax Service Office

The aim of the research is to determine and analyze the influence of taxpayer awareness, tax knowledge, tax services, tax socialization, and taxpayer financial conditions, on individual taxpayer compliance with tax sanctions as a moderating variable at the Binjai Pratama Tax Service Office. This research was conducted by distributing questionnaires to individual taxpayers registered at the Binjai Pratama Tax Service Office, Binjai City, North Sumatra Province. The sampling method uses probability sampling with an incidental sampling approach, obtaining 100 samples of individual taxpayers from the total population. The analysis method used is SEM-PLS with the help of Smart-PLS software. Hypothesis testing is carried out through a bootstrapping process. The results of the research show that taxpayer awareness and the taxpayer's financial condition have a significant effect on individual taxpayer compliance, however tax knowledge, tax services and tax socialization do not have a significant effect on individual taxpayer compliance at the Binjai Pratama Tax Service Office. Meanwhile, the research results for tax sanctions as a moderating variable are able to moderate taxpayer awareness and tax socialization regarding individual taxpayer compliance at the Binjai Pratama Tax Service Office. However, tax sanctions are unable to moderate tax knowledge, tax services, and the financial condition of taxpayers on individual taxpayer compliance at the Binjai Pratama Tax Service Office

Analysis of Moderating Effect of Turnover Intention on theRelationship of Intrinsic Motivation and Employee Performance

This paper presents an analysis of the moderating effect of turnover intention on the relationship between intrinsic motivation and the employee performance model. The moderation effect analysis was conducted using SmartPLS software version 3.0. The model was constructed within the software, and the analysis was conducted in two stages. The first stage involved a bootstrapping process to generate and determine the significance level and strength of the path. The second stage employed the PLS Algorithm process, generating a simple slope graph. Results from the bootstrapping process revealed a noticeable moderating effect attributed to turnover intention, supported by a t-statistics value of 2.514 and a statistically significant p-value of 0.006. Investigating the specifics of the path coefficient, the moderating impact on the relationship was quantified at -0.097, indicating a negative moderating influence. In practical terms, this suggests that turnover intention exerts a dampening effect on the connection between motivation and employee job performance. Regarding the simple slope analysis, it was found that the moderation effect of turnover intention on the relationship between intrinsic motivation and employee performance is particularly robust when turnover intention is lower. However, this effect diminishes to some extent when turnover intention is higher. These findings provide valuable insights into how the dynamics of these variables interact under varying levels of turnover intention.

Effects of physical health beliefs on college students’ physical exercise behavior intention: mediating effects of exercise imagery

ObjectiveThis study explores the relationship between physical health beliefs and physical exercise behavior intention of college students and constructs a mediation model through the mediation role of exercise imagery.MethodsUsing the stratified cluster sampling method, 1356 college students were measured in group psychology by using the Physical Health Beliefs Scale, Exercise Imagery Inventory, and Physical Exercise Behavior Intention Scale. The statistical software Mplus 8.1, SPSS 22.0 and SPSS PROCESS 3.3 were used for statistical processing. The common method deviation test was carried out by Harman single-factor control method. Finally, the bootstrap sampling test method and process plug-in were used to test the significance of intermediary effect.Results(1) physical health beliefs have a significant predictive effect on physical exercise behavior intention (β = 0.32, p < 0.001); (2) exercise imagery (β = 0.13, p < 0.001) mediate the relationship between physical health beliefs and physical exercise behavior intention (physical health beliefs → exercise imagery → physical exercise behavior intention (95% Cl: 0.14, 0.32)).Conclusionphysical health beliefs can directly improve the physical exercise behavior intention of college students, which can also affect college students’ physical exercise behavior intention indirectly through exercise imagery. The findings suggest that exercise imagery are important variables that mediate the effect of the college students’ physical health beliefs on their physical exercise behavior intention.

Circulating miR-129-3p in combination with clinical factors predicts vascular calcification in hemodialysis patients.

Vascular calcification (VC) commonly occurs and seriously increases the risk of cardiovascular events and mortality in patients with hemodialysis. For optimizing individual management, we will develop a diagnostic multivariable prediction model for evaluating the probability of VC. The study was conducted in four steps. First, identification of miRNAs regulating osteogenic differentiation of vascular smooth muscle cells (VSMCs) in calcified condition. Second, observing the role of miR-129-3p on VC in vitro and the association between circulating miR-129-3p and VC in hemodialysis patients. Third, collecting all indicators related to VC as candidate variables, screening predictors from the candidate variables by Lasso regression, developing the prediction model by logistic regression and showing it as a nomogram in training cohort. Last, verifying predictive performance of the model in validation cohort. In cell experiments, miR-129-3p was found to attenuate vascular calcification, and in human, serum miR-129-3p exhibited a negative correlation with vascular calcification, suggesting that miR-129-3p could be one of the candidate predictor variables. Regression analysis demonstrated that miR-129-3p, age, dialysis duration and smoking were valid factors to establish the prediction model and nomogram for VC. The area under receiver operating characteristic curve of the model was 0.8698. The calibration curve showed that predicted probability of the model was in good agreement with actual probability and decision curve analysis indicated better net benefit of the model. Furthermore, internal validation through bootstrap process and external validation by another independent cohort confirmed the stability of the model. We build a diagnostic prediction model and present it as an intuitive tool based on miR-129-3p and clinical indicators to evaluate the probability of VC in hemodialysis patients, facilitating risk stratification and effective decision, which may be of great importance for reducing the risk of serious cardiovascular events.

Humean learning (how to learn)

David Hume’s skeptical solution to the problem of induction was grounded in his belief that we learn by means of custom . We consider here how a form of reinforcement learning like custom may allow an agent to learn how to learn in other ways as well. Specifically, an agent may learn by simple reinforcement to adopt new forms of learning that work better than simple reinforcement in the context of specific tasks . We will consider how such a bootstrapping process may lead to a system that includes trial-and-error forms of learning like win-stay/lose-shift, probe and adjust, and simple reinforcement itself together with higher-rationality inferential tools.

Faster Bootstrapping via Modulus Raising and Composite NTT

FHEW-like schemes utilize exact gadget decomposition to reduce error growth and ensure that the bootstrapping incurs only polynomial error growth. However, the exact gadget decomposition method requires higher computation complexity and larger memory storage. In this paper, we improve the efficiency of the FHEWlike schemes by utilizing the composite NTT that performs the Number Theoretic Transform (NTT) with a composite modulus. Specifically, based on the composite NTT, we integrate modulus raising and gadget decomposition in the external product, which reduces the number of NTTs required in the blind rotation from 2(dg + 1)n to 2(⌈dg⌉/2 + 1)n. Furthermore, we develop a modulus packing technique that uses the Chinese Remainder Theorem (CRT) and composite NTT to bootstrap multiple LWE ciphertexts through one blind rotation process.We implement the bootstrapping algorithms and evaluate the performance on various benchmark computations using both binary and ternary secret keys. Our results of the single bootstrapping process indicate that the proposed approach achieves speedups of up to 1.7 x, and reduces the size of the blind rotation key by 50% under specific parameters. Finally, we instantiate two ciphertexts in the packing procedure, and the experimental results show that our technique is around 1.5 x faster than the two bootstrapping processes under the 127-bit security level.

Description of a Clinical Score to Identify PMBL Patients at High Risk of Early-Failure after Rituximab Doxorubicin Back-Bone Chemoimmunotherapy. a FIL Real-World Study

Introduction. Primary Mediastinal B Lymphoma (PMBL) is a rare and aggressive neoplasia that typically occurs in young women and is primarily located in the mediastinum. Although the combination of anthracycline and rituximab immunochemotherapy, and PET-guided consolidation radiotherapy, results in a high cure rate, approximately 10-20% of patients show refractoriness or early failure to front-line treatment with a rapid clinical deterioration and a dismal prognosis. These cases, if promptly identified, could benefit from early treatment shift. Therefore, we addressed the development of a clinical score aimed at estimating the risk of early failure of PMBL patients treated with anthracycline-rituximab-based chemoimmunotherapy in a real-world nationwide setting. Material and Methods This is a retrospective cohort study of an unselected population of 931 adult PMBL patients treated in 37 FIL centers. To qualify for this research, patients had to have a confirmed diagnosis of PMBL, display typical symptoms and have been included in the local databases from 01/01/2007 to 31/12/2019. All patients received treatment with rituximab plus CHOP21 (n=98), CHOP14 (n=181), megaCHOP (n=31), MACOPB (225), VACOPB (n=179), DAEPOCH (n=179). Patients were excluded from this analysis if they received suboptimal treatment (22) or ASCT as consolidation of first-line therapy (12) or were censored within 365 days of treatment initiation (22). We defined early failure (EF) as any relapse or progression within 365 days of treatment initiation. Candidate predictors were clinical factors at the time of diagnosis, including: age, ECOG PS (a 4-level scale), LDH higher than Upper Limit of Normal, Ann Arbor Stage (a 4-level scale), systemic B symptoms, bulky mediastinum &amp;gt; 10 cm, number of Extra-Nodal sites (a 4-level scale: 0, 1, 2, or &amp;gt;=3), and pericardial or pleural effusion. Missing data in the evaluated predictors were multiple-imputed, and combined estimates were obtained from 20 imputed data sets. We used a multivariable logistic model with Backward Selection (level to stay p&amp;lt;0.1). Estimates were adjusted for front-line therapy. Standard errors were adjusted using the clustered sandwich estimator according to the patient's center. The model was internally validated with 1000 bootstrap samples from the original data. The optimism-corrected AUROC was estimated based on the results of the bootstrap process. We constructed a risk score by assigning each factor a weight proportional to the smallest coefficient, rounded to the nearest integer. The discrimination ability of the prognostic score was also evaluated considering the time to progression or relapse (Early Failure Survival) using the Kaplan-Meier method. Results Among the 869 patients included in the present study, 109 (12.5%) were identified as early failures (EF). Four factors were selected after the backward selection process to construct the prognostic score (AUROC 0.744, optimism-corrected 0.710). These factors included Systemic B-symptoms (OR=1.76, p=0.007, weight=1), Bulky mediastinum &amp;gt; 10 cm (OR=2.12, p=0.017, weight=1), AA Stage II (OR=2.20, p=0.018, weight=1), AA Stage III-IV (OR=2.73, p=0.017, weight=2), and Number of Extra-Nodal sites &amp;gt;=2 (OR=2.88, p&amp;lt;0.001, weight=2) (Table 1). Based on the incidence of EF, the score was classified into 3 classes: low (0-1) with 216 patients and an EF incidence of 3.7% (95% CI: 1.6% - 7.2%), intermediate (2-3) with 408 patients and an EF incidence of 11.8% (95% CI: 9.0% - 15.0%), and high (&amp;gt; 4) with 122 patients and an EF incidence of 30.8% (95% CI: 22.3% - 39.3%). (Figure 1A). At 24 months, Early-Failure-Survival (EFS) rates were as follows: High - 63.7% (95% CI: 54.5% - 71.6%), Intermediate - 87.0% (95% CI: 83.6% - 89.7%), and Low - 95.4% (95% CI: 91.5% - 97.5%). Conclusions Based on a large PMBL retrospective case series, we developed a simple and practical prognostic score subdividing patients into three clear-cut groups with different likelihoods of experiencing early failure. We acknowledge that the study has potential biases inherent in retrospective analysis, and further efforts aim to validate these results in external series of patients. If the predictive ability is confirmed, the score could be a valuable tool for treatment planning, interpretation, and comparison of clinical studies.

Social capital, depressive symptomatology, and frailty among older adults in the western areas of China.

We aimed to explore the relationship between social capital (SC) and frailty, and the mediation role of depressive symptoms in this relationship. A cross-sectional study among 2,591 older adults aged ≥60 years old was conducted from September 2020 to May 2021. SC, depressive symptoms, and frailty were measured using the social capital scale, the 9-item patient health questionnaire, and the FRAIL scale, respectively. The mediation model was tested by Bootstrap PROCESS. After controlling for socio-demographical covariates, the SC was negatively correlated with frailty (r = -0.07, P = 0.001), and depressive symptomatology (r = -0.08, P<0.001); while the depressive symptomatology was positively correlated with frailty (r = 0.33, P<0.001). Logistic regression results showed that SC was associated with a lower risk of frailty (OR = 0.94; 95% CI: 0.92-0.97; P<0.001). Depressive symptomatology partially mediated (explained 36.4% of the total variance) the association between SC and frailty. Those findings suggest that SC may protect older adults from frailty by reducing depressive symptoms. Prevention and intervention implications were also discussed.

Using statistical analysis to understand creep modelling error and the ramifications for Solar Tower receivers

Concentrated Solar Power (CSP) receivers, especially those adopted in Solar Tower (ST) configurations, operate at high temperatures and low stresses making them susceptible to creep damage. Creep modelling used to predict components' life and therefore the accuracy of creep modelling can significantly impact life estimation, prediction of failures, and ultimately the design. The accuracy of creep modelling is highly variable and depends on the choice of model, the choice of fitting functions, and sometimes depends on ‘judgement’ decisions by the modeller. In this article the Larson-Miller and Wilshire creep models are fitted to three common power generation materials (Grade 22, SS316 and Inconel 740H) with differently sized datasets. Region splitting via activation energy is also examined. The fits are examined for accuracy and bias for stress, temperature and rupture time by a statistical process of bootstrapping to provide a better understanding of the impact of the creep model and dataset size on the design and lifing of CSP receivers.

Hybrid model for precise hepatitis-C classification using improved random forest and SVM method

Hepatitis C Virus (HCV) is a viral infection that causes liver inflammation. Annually, approximately 3.4 million cases of HCV are reported worldwide. A diagnosis of HCV in earlier stages helps to save lives. In the HCV review, the authors used a single ML-based prediction model in the current research, which encounters several issues, i.e., poor accuracy, data imbalance, and overfitting. This research proposed a Hybrid Predictive Model (HPM) based on an improved random forest and support vector machine to overcome existing research limitations. The proposed model improves a random forest method by adding a bootstrapping approach. The existing RF method is enhanced by adding a bootstrapping process, which helps eliminate the tree’s minor features iteratively to build a strong forest. It improves the performance of the HPM model. The proposed HPM model utilizes a ‘Ranker method’ to rank the dataset features and applies an IRF with SVM, selecting higher-ranked feature elements to build the prediction model. This research uses the online HCV dataset from UCI to measure the proposed model’s performance. The dataset is highly imbalanced; to deal with this issue, we utilized the synthetic minority over-sampling technique (SMOTE). This research performs two experiments. The first experiment is based on data splitting methods, K-fold cross-validation, and training: testing-based splitting. The proposed method achieved an accuracy of 95.89% for k = 5 and 96.29% for k = 10; for the training and testing-based split, the proposed method achieved 91.24% for 80:20 and 92.39% for 70:30, which is the best compared to the existing SVM, MARS, RF, DT, and BGLM methods. In experiment 2, the analysis is performed using feature selection (with SMOTE and without SMOTE). The proposed method achieves an accuracy of 41.541% without SMOTE and 96.82% with SMOTE-based feature selection, which is better than existing ML methods. The experimental results prove the importance of feature selection to achieve higher accuracy in HCV research.

On the Running Time of Hypergraph Bootstrap Percolation

Given $r\geq2$ and an $r$-uniform hypergraph $F$, the $F$-bootstrap process starts with an $r$-uniform hypergraph $H$ and, in each time step, every hyperedge which "completes" a copy of $F$ is added to $H$. The maximum running time of this process has been recently studied in the case that $r=2$ and $F$ is a complete graph by Bollob\'as, Przykucki, Riordan and Sahasrabudhe [Electron. J. Combin. 24(2) (2017), Paper No. 2.16], Matzke [arXiv:1510.06156v2] and Balogh, Kronenberg, Pokrovskiy and Szab\'o [arXiv:1907.04559v1]. We consider the case that $r\geq3$ and $F$ is the complete $r$-uniform hypergraph on $k$ vertices. Our main results are that the maximum running time is $\Theta\left(n^r\right)$ if $k\geq r+2$ and $\Omega\left(n^{r-1}\right)$ if $k=r+1$. For the case $k=r+1$, we conjecture that our lower bound is optimal up to a constant factor when $r=3$, but suspect that it can be improved by more than a constant factor for large $r$.

An econometric understanding of Fintech and operating performance

PurposeThis study aims to develop a unique methodology to construct a bank’s financial technology (Fintech) score, which captures the degree of digitalization of a bank’s operations. Using the Fintech score as the proxy, this study investigates the effect of Fintech on the operating performance of the top largest Islamic bank.Design/methodology/approachThe methodology used measures the link between the degree of digitization of a bank and its operational performance. This study applies the three-degree polynomial of regression to the largest Islamic bank in which the explanatory variable is the natural logarithm of Fintech score, and the response variable is common operating performance measure. To check the sensitivity of the estimates to the sample size and assumptions’ violation, this study has applied Bootstrapping and Bayesian processes to the three-degree polynomial regressions.FindingsThe study estimates from 2007 to 2021 show that the relationship between the operating performance of the Islamic banks and the Fintech is nonlinear and strongly significant: operating returns increase with the increasing level of Fintech, whereas the operating returns decrease with the increasing Fintech variance. At an aggregate level, this study attributes a significant rise in internet coverage to the emergence of Fintech in the Middle East region.Originality/valueThis study constructs an implicit measure of Fintech that measures the adoption of Fintech by the bank and, consequently, offers the technology to their customers for higher use satisfaction. This study finds that Fintech is linked to the operating performance in a nonlinear fashion, in which Fintech and Fintech variance have the opposite effect on operating performance: Fintech increases the operating profitability, whereas Fintech variance decreases the operating profitability of a bank.

The shape of the trunk of Scots pine in the stands of the Ukrainian Polissia

Due to the transition of Ukraine in 2019 to new regulatory documents on standardisation regarding the classification of round timber by quality classes, it became necessary to develop standards for evaluating volumes in accordance with the new requirements. The purpose of the study was to develop a mathematical model of the forming stem of Scots pine for trees of two groups of diameters. The study is based on the use of empirical material collected in different years by researchers of the Department of forest taxation and forest management of the National University of Life and Environmental Sciences of Ukraine, which is 583 model pine trees. The Kozak model was use as the basic model for modelling the forming Scots pine. A mixed effect model was calibrated to characterise the variability in the tree stem shape of Scots pine. Parameters for the role of random effects were selected based on their coefficients of variation using the bootstrap process. Data were grouped into two groups of diameters by analysing modelling residues and evaluating the quality of models based on the use of the Akaike information criterion (AIC). By dividing the data array into two groups of thickness degrees and developing models of the creator for each of the groups separately, a decrease in the indicator of the Akaike information criterion (AIC) was achieved to the level of AIC=7433.02 for a group (8-28 cm) and AIC=13154.67 for the group (32-76 cm), which increased the accuracy of the developed models. Enabling random effect parameters in model by A. Kozak for the developed mathematical equations allowed more accurately describing the variability of the tree stem of Scots pine. The obtained models give a clear and fairly accurate prediction of the determination of tree stem diameters along the entire length. The use of the developed models can serve as a basis for improving forest traction standards for estimating the volume of tree stem in the process of forestry production and predicting the yield of roundwood volumes by quality classes

The roles of social capital, entrepreneurial orientation and competitive intensity in managerial capability and performance relationship: evidence from an emerging market economy

PurposeThis paper sought to investigate the conditional indirect relationship between managerial capabilities (MCs) and performance amongst small and medium-sized enterprises (SMEs) in the sub-Saharan African economy. The study considered social capital (SC) and entrepreneurial orientation (EO) as parallel mediating mechanisms and competitive intensity as boundary conditions within this relationship. The purpose of this paper is to address this issue.Design/methodology/approachData were obtained from SMEs (n = 206) in a sub-Saharan African nation. Bootstrapping (Process Macro) and hierarchical regression in statistical package for social sciences (SPSS) were used to analyse the data.FindingsThe results demonstrate that whereas EO presents a mechanism through which MC influences performance, SC does not mediate the MC–performance relationship. The results further demonstrate that competitive intensity provides various interaction effects such that at high levels of competitive intensity the indirect effect of MC on performance through SC is weakened and strengthened through EO. The study, therefore, provides clarity to the intricate power of interactions of external factors with firm-specific resources.Originality/valueThe study demonstrates that varying combinations of resources influence performance differently. The authors consider the influence of these mediators simultaneously in attempts to extend theory by buttressing the bundling effect of MC on SC and EO in driving performance. They also highlight the impact of the boundary conditions created by competitive intensity (CI) on these mediated relationships.

The Influence of Job Stress, Transformational Leadership, and Knowledge Sharing Toward Innovative Work Behavior at the Tourism Office of the Province of West Sumatra

Entering the era of globalization with various challenges that exist requires us to be able to adjust. One way to be able to adjust to dynamic changes is to innovate. Innovation is changing for the better. As one of the agencies that require innovation is the West Sumatra Tourism Office in order to boost the Indonesian economy, one of which is in the tourism sector. This study aims to examine and build a conceptual model of the influence of Job Stress, Transformational Leadership, and Knowledge sharing towards innovative work behavior on ASN employees of the West Sumatra Provincial Tourism Office. This study used a non-probability sampling technique with a sample used of 58 ASNs through the distribution of questionnaires. The data analysis technique used is path analysis using Smart PLS software. Research hypothesis testing uses T-statistical tests obtained through the bootstrapping process. The results show that Job stress, Transformational Leadership are not able to directly affect innovative work behavior. Job stress affects knowledge sharing directly. Transformational leadership affects Knowledge sharing, and knowledge sharing affects innovative work behavior. The West Sumatra Tourism Office has not been able to implement Innovative work behavior without going through knowledge sharing. This is evidenced by the absence of a direct influence of job stress and transformational leadership on Innovative work behavior. This is because the employees of the West Sumatra Tourism Office have been working for a long time and have more experience and the ability to manage job stress. So that the existence of job stress does not affect the innovative work behavior of employees because they have a high ability to manage job stress on Innovative work behavior.