Skewness Research Articles

Level of social security expenditures and economic growth rate based on econometric regression modelling: New evidence from OECD countries

This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.

Dynamics of capital structure determinants: empirical evidence from GCC countries

AbstractThe study empirically examines the factors affecting the financing decisions of non-financial listed companies in Gulf Cooperation Council (GCC) countries. Using static and dynamic two-step generalized method of moments techniques, it analyzes unbalanced panel data from 364 non-financial companies across six GCC countries from 2011 to 2021. The findings partially support optimal capital structure theories, highlighting significant internal factors such as profitability, market-to-book ratio, firm size, earnings volatility, and growth opportunities that influence financing decisions. While no single theory fully explains the financing choices, the association of internal factors with book and market leverage is consistent. The study provides robust and generalizable results, aiding financial institutions and policymakers in formulating pro-development policies and regulations. This research facilitates better coordination between corporate managers and financial institutions, supporting the region’s economic transition. It is the first study to use extensive data from GCC non-financial firms to investigate financing decisions, offering valuable insights for investors and a basis for further analysis of capital structure choices in the region and beyond.

A double-layered fully automated insomnia identification model employing synthetic data generation using MCSA and CTGAN with single-channel EEG signals.

Insomnia was diagnosed by analyzing sleep stages obtained during polysomnography (PSG) recording. The state-of-the-art insomnia detection models that used physiological signals in PSG were successful in classification. However, the sleep stages of unbalanced data in small-time intervals were fed for classification in previous studies. This can be avoided by analyzing the insomnia detection structure in different frequency bands with artificially generated data from the existing one at the preprocessing and post-processing stages. Hence, the paper proposes a double-layered augmentation model using Modified Conventional Signal Augmentation (MCSA) and a Conditional Tabular Generative Adversarial Network (CTGAN) to generate synthetic signals from raw EEG and synthetic data from extracted features, respectively, in creating training data. The presented work is independent of sleep stage scoring and provides double-layered data protection with the utility of augmentation methods. It is ideally suited for real-time detection using a single-channel EEG provides better mobility and comfort while recording. The work analyzes each augmentation layer's performance individually, and better accuracy was observed when merging both. It also evaluates the augmentation performance in various frequency bands, which are decomposed using discrete wavelet transform, and observed that the alpha band contributes more to detection. The classification is performed using Decision Tree (DT), Ensembled Bagged Decision Tree (EBDT), Gradient Boosting (GB), Random Forest (RF), and Stacking classifier (SC), attaining the highest classification accuracy of 94% using RF with a greater Area Under Curve (AUC) value of 0.97 compared to the existing works and is best suited for small datasets.

Weather Prediction Using Machine Learning Classifiers

This study evaluates machine learning algorithms to increase weather predicting accuracy. However, traditional NWP models are computationally time-consuming and may fail to capture the nonlinear complexities inherent in meteorological observations. This research studies three machine learning algorithms—XGBoost, Gradient Boosting, and Random Forest. For the prediction of future conditions based on detailed patterns of past weather, these methods are adopted. The XGBoost model recorded an accuracy rate of 82.25% which is higher than the Random Forest and Gradient Boosting models. All models performed well in normal weather conditions such as "sun" and "rain," but were least competent at predicting less common phenomena like "drizzle," "fog," and "snow." This demonstrates the challenge of making predictions when unbalanced data sets are used in weather forecasting exercises. The incorporation of such machine learning techniques with conventional NWP models has the potential to result in improved prediction performance in various applications.

The causal relationship between government investment and economic development in ASEAN countries

The purpose of this article is to evaluate the impact of public investment on economic growth. The data was used from the annual data of the Asian Development Bank (ADB) for 6 countries in ASEAN during 2000-2022. The methodology used in the project is to apply the unit root test to the stationarity properties of individual time series and establish the cointegration relationship between non-stationary variables using methods related to cointegration testing in the long-run. The error correction model (ECM) considers the impact of the variables public investment, public expenditure and budget revenue from taxes on short-term economic growth and long-term growth thereby calculating the adjustment speed and adjustment time of the model and using the Granger causality test with fixed effects for analysis of unbalanced panel data to comprehensively analyze the relationship between public investment and economic growth. Data analysis and processing were performed using Stata software version 17. The findings that public investment has a long-term impact on economic growth and has a two-way causal relationship in all countries. The authors provide policy implications to make public investment more effective contributing to promoting the country's socio-economic development, reviewing and improving the legal system on public investment, restructuring public investment while strongly enhancing the efficiency and quality of public investment, inviting and attracting foreign direct investment (FDI) capital, diversifying capital sources and creating more motivation for the private economic sector to develop and promulgate solutions to develop high-quality human resources.

Reconciling Santa Rosalia: Both Reproductive Isolation and Coexistence Constrain Diversification.

AbstractUnderstanding patterns of diversification necessarily requires accounting for both the generation and the persistence of species. Formal models of speciation genetics, however, focus on the generation of new species without explicitly considering the maintenance of biodiversity (e.g., coexistence, the focus of ecological studies of diversity). Consequently, it remains unclear whether and how new species will coexist following a speciation event, a gap limiting our ability to understand the rate-limiting controls of diversification over macroevolutionary timescales. To connect coexistence and speciation theory and assess the relative importance of ecological versus genetic constraints in diversification events, we develop a deterministic, three-locus, population-genetic model that includes a skewed distribution of available resources (to generate variation in fitness differences), frequency-dependent competition, and assortative mating. Both ecology and genetics play vital and interacting roles in shaping initial speciation events and long-term eco-evolutionary outcomes. Ecological constraints are especially important when fitness differences are large and competition remains strong among dissimilar phenotypes. Ephemeral species can occur in our model and are typically lost because of competitive exclusion, a result demonstrating that species persistence may serve as the rate-limiting control of long-term diversification rates. More broadly, our model adds evidence that the unification of ecological and evolutionary (including genetic) perspectives on biodiversity is needed to predict large-scale patterns.

Evaluation of Intraocular Pressure, Refraction, Anterior Chamber Depth, Macular Thickness, and Specular Microscopy Post-Neodymium-Doped Yttrium-Aluminum-Garnet Laser in Patients With Posterior Capsular Opacification.

Background Cataract is the leading cause of blindness globally, particularly in India. Despite advancements in surgical techniques, postoperative complications remain common, with posterior capsular opacification (PCO) being the most frequent issue. Although neodymium-doped yttrium-aluminum-garnet (Nd:YAG) laser capsulotomy is recommended for managing PCO, it is associated with various side effects. This study aimed to evaluate the effects of Nd:YAG laser capsulotomy on intraocular pressure (IOP), visual acuity, anterior chamber depth (ACD), macular thickness, and corneal endothelium in Indian patients. Methodology This prospective, hospital-based study was conducted in the ophthalmology department at a tertiary care center in western Maharashtra from September 2022 to June 2024. Approval from the Institutional Scientific and Ethics Committee was obtained before commencing the research. In the study, 72 eyes from 72 patients with PCO following uncomplicated cataract surgery who were undergoing Nd:YAG laser capsulotomy were included, whereas patients with corneal pathology, retinal pathology, complicated cataract surgery, or trauma were excluded. Patients with active uveitis, non-compliant patients, and those unwilling to undergo the procedure were also excluded. Written informed consent was obtained from each patient. Data were managed in Microsoft Excel, and statistical analysis was conducted using the SPSS 26.0 software. As the continuous variables exhibited skewed distribution, the Wilcoxon test was employed to assess categorical variables such as the significance of IOP and endothelial cell differences over time. A significance level of 5% was assumed, with a p-value below 0.05 considered significant. Results The mean age of patients who underwent Nd:YAG capsulotomy was 64 years, with a female predominance of 37 (51.4%). In the study, 37 (51.4%) patients had their left eye treated, while 35 (48.6%) had their right eye treated. Overall, 45 (62.5%) patients had a baseline best-corrected visual acuity (BCVA) of 6/24-6/12. At one hour post-procedure, 46 (63.9%) patients in Group II had a BCVA of 6/24-6/12, and by one week after treatment, 53 (73.6%) patients had a BCVA of >6/12-6/6. ACD was normal in all patients before and after the procedure. Two patients developed macular edema at one hour and one week after the procedure. The mean IOP at baseline, one hour, and one week were 13.5, 13.86, and 13.69 mmHg, respectively. A significant increase in IOP was observed at one hour post-procedure, along with a significant decrease in endothelial cell count compared to baseline, which also persisted at one week. Conclusions Patients undergoing Nd:YAG capsulotomy experienced an initial rise in IOP, followed by a subsequent decline. Improved visual acuity was noted in most patients at one hour and by one week. A significant decline in endothelial cell count was observed following the procedure, and macular edema was noted in two patients. Anterior chamber reaction was observed in nearly all patients, which decreased by one week. With no change in ACD following the procedure up to one week, ocular refraction was not significantly impacted in the short term. Therefore, further large-scale intervention studies are needed to evaluate the effects of Nd:YAG laser capsulotomy size and the energy used on refractive error and post-procedure complications, as well as to explore the long-term effects on IOP.

A real-time network traffic classification system using cost-sensitive deep neural networks

Due to the continuous growth of Internet and online applications, network traffic classification is not only becoming one of the most crucial disciplines in network management but is also becoming quintessential for providing advanced tasks such as Quality of Service and network security. Moreover, even though many studies have been undertaken in recent years, real-time encrypted traffic classification continues to be an important challenge in the field of network traffic classification. Therefore, in this paper a real-time network traffic classification system is proposed together with five new models. The real-time classification system classified each incoming real-time packets into appropriate classes of interest and the five new models make use of a cost-sensitive learning strategy to deal with the unbalanced data problem during the training phase. The proposed models, which are called Cost-Sensitive Long-Short Term Memory (CSLSTM), Cost-Sensitive Gated Recurrent Unit (CSGRU), Cost-Sensitive Convolution Neural Network (CSCNN), CSNN with LSTM and CSCNN with GRU, can handle both traffic categorization and application identification. These proposed models were compared with prominent methods in this field and the proposed CSCNN was observed to outperform the researched deep learning models by at least 4% to 16% in correctly classifying packets from the ISCX VPN-nonVPN dataset.

7394 Testicular Function in Young Adults with Growth Hormone (GH) Deficiency Previously Treated with GH: Comparison with Normal Controls

Abstract Disclosure: R. Cannarella: None. A. Crafa: None. S. Sapienza: None. M.M. Caruso: None. A.E. Calogero: None. Background: We have previously reported that treatment with recombinant human growth hormone (rhGH) can influence testicular growth and puberty onset in children with GH deficiency (GHD), and suggested GH as a determinant of testicular growth in childhood (doi: 10.3389/fendo.2021.619895). Limited evidence is available on testicular function in post-pubertal GHD patients. Objective: This prospective controlled study was undertaken to evaluate testicular function in young GHD adults previously undergone to GH treatment. Patients and Methods: Post-pubertal patients with non-syndromic GHD (>18 years), in whom GH therapy was discontinued after reaching the therapeutic goal, were required to have a complete evaluation of testicular function. Those who agreed were enrolled in our study. Age-matched healthy men older than 18 years served as controls. Exclusion criteria were: the presence of varicocele, urogenital infections, cryptorchidism, hypospadias, hypogonadism, testicular trauma or torsion, and other congenital or acquired disorders capable of interfering with testicular function. The study outcomes were: serum gonadotropins and total testosterone (TT), conventional sperm parameters, and testicular volume (TV) measured by ultrasound examination. Between-group analysis was performed using the Student’s t-test for independent samples or the Mann-Whitney test, for normally or non-normally distributed variables, respectively. Data were reported as mean±standard deviation for non-skewed data and median (interquartile range) for skewed data. Results: 26 patients with GHD and 25 age-matched controls (18.8±3.3 years vs. 20.1±3.6 years; p=0.18) were enrolled. Patients and controls did not differ for serum luteinizing hormone [2.6 (1.5-4.9) IU/L vs. 3.5 (2.8-4.8) IU/L); p=0.2], follicle-stimulating hormone [3.4 (2.2-4.3) IU/L vs. 3.1 (2.3-4.3) IU/L); p=1.0], and TT [5.7 (4.7-6.8) ng/dL vs. 6.0 (5.1-7.4) ng/dL); p=0.4] levels. At semen analysis, GHD patients had lower semen volume (2.1±1.4 mL vs. 3.5±1.5 mL; p=0.002), total sperm count [135.0 miL/ejaculate (54.3-229.0) vs. 231.3 miL/ejaculate (124.8-285.0); p=0.03], progressive motility [25.5% (15.0-34.0%) vs. 32.0% (30.0-36.5); p=0.02], and total motility [56.0% (49.0-62.0) vs. 66.5% (62.5-70.0); p=0.0001] compared to controls. No difference was found in sperm concentration [73.0 miL/mL (45.0-92.0) vs. 68.0 miL/mL (39.0-90.0); p=1.0] and normal morphology [8.5% (7.0-12.0) % vs. 10.0% (7.5-14.0); p=0.3]. Importantly, GHD patients had lower right (13.3±3.4 mL vs. 16.9±4.1 mL; p=0.001) and left (12.5±3.4 mL vs. 16.3±3.8 mL; p=0.0005) TV. Conclusion: This study provides the first evidence of lower TV and slightly worse semen parameters in patients with GHD compared to healthy controls. Serum GH levels during growth may be important for achieving normal testicular volume and spermatogenesis in adulthood. Presentation: 6/2/2024

9277 Testicular Function in Young Adults with Growth Hormone (GH) Deficiency Previously Treated with GH: Comparison with Normal Controls

Abstract Disclosure: R. Cannarella: None. A. Crafa: None. S. Sapienza: None. M.M. Caruso: None. A.E. Calogero: None. Background: We have previously reported that treatment with recombinant human growth hormone (rhGH) can influence testicular growth and puberty onset in children with GH deficiency (GHD), and suggested GH as a determinant of testicular growth in childhood (doi: 10.3389/fendo.2021.619895). Limited evidence is available on testicular function in post-pubertal GHD patients. Objective: This prospective controlled study was undertaken to evaluate testicular function in young GHD adults previously undergone to GH treatment. Patients and Methods: Post-pubertal patients with non-syndromic GHD (>18 years), in whom GH therapy was discontinued after reaching the therapeutic goal, were required to have a complete evaluation of testicular function. Those who agreed were enrolled in our study. Age-matched healthy men older than 18 years served as controls. Exclusion criteria were: the presence of varicocele, urogenital infections, cryptorchidism, hypospadias, hypogonadism, testicular trauma or torsion, and other congenital or acquired disorders capable of interfering with testicular function. The study outcomes were: serum gonadotropins and total testosterone (TT), conventional sperm parameters, and testicular volume (TV) measured by ultrasound examination. Between-group analysis was performed using the Student’s t-test for independent samples or the Mann-Whitney test, for normally or non-normally distributed variables, respectively. Data were reported as mean±standard deviation for non-skewed data and median (interquartile range) for skewed data. Results: 26 patients with GHD and 25 age-matched controls (18.8±3.3 years vs. 20.1±3.6 years; p=0.18) were enrolled. Patients and controls did not differ for serum luteinizing hormone [2.6 (1.5-4.9) IU/L vs. 3.5 (2.8-4.8) IU/L); p=0.2], follicle-stimulating hormone [3.4 (2.2-4.3) IU/L vs. 3.1 (2.3-4.3) IU/L); p=1.0], and TT [5.7 (4.7-6.8) ng/dL vs. 6.0 (5.1-7.4) ng/dL); p=0.4] levels. At semen analysis, GHD patients had lower semen volume (2.1±1.4 mL vs. 3.5±1.5 mL; p=0.002), total sperm count [135.0 miL/ejaculate (54.3-229.0) vs. 231.3 miL/ejaculate (124.8-285.0); p=0.03], progressive motility [25.5% (15.0-34.0%) vs. 32.0% (30.0-36.5); p=0.02], and total motility [56.0% (49.0-62.0) vs. 66.5% (62.5-70.0); p=0.0001] compared to controls. No difference was found in sperm concentration [73.0 miL/mL (45.0-92.0) vs. 68.0 miL/mL (39.0-90.0); p=1.0] and normal morphology [8.5% (7.0-12.0) % vs. 10.0% (7.5-14.0); p=0.3]. Importantly, GHD patients had lower right (13.3±3.4 mL vs. 16.9±4.1 mL; p=0.001) and left (12.5±3.4 mL vs. 16.3±3.8 mL; p=0.0005) TV. Conclusion: This study provides the first evidence of lower TV and slightly worse semen parameters in patients with GHD compared to healthy controls. Serum GH levels during growth may be important for achieving normal testicular volume and spermatogenesis in adulthood. Presentation: 6/2/2024

Financial Risk Early Warning Model Combining SMOTE and Random Forest for Internet Finance Companies

At present, there have been many achievements on enterprise financial risk early warning model, but relatively few early warning studies focusing on the Internet industry. Research shows that the model has stable recognition accuracy and good prediction performance. The improved SMOTE algorithm based on PCA can realize the equalization of unbalanced data sets and use random forest as a classifier to classify and predict geological data. Because the noise data in the original data set may cause the change of the data distribution after interpolation, it is proposed to combine the PCA algorithm and the SMOTE algorithm, first perform noise reduction and dimension reduction, and then perform data interpolation to improve the classification performance of imbalanced data sets. my country's Internet financial listed companies conduct experiments on research samples, algorithm can better improve the classification accuracy and provide new ideas for the classification and prediction of unbalanced data.

Why Is Caries Prevention in Children So Difficult? A Narrative Opinion.

Caries is among the most common non-communicable diseases worldwide, yet it is commonly described as preventable. Caries prevention is, however, difficult and complex, since the disease has strong social, parental, behavioral, political, medical/genetic, and psychological elements, and the payment models are targeted at traditional conservative care. The aim of this paper is to discuss some key issues that make caries prevention in children be perceived as "difficult": i) the communication gap between researchers and clinicians, creating unrealistic expectations of intervention efficacy; ii) the skewed distribution of caries and the problem of reaching children with the highest need; iii) limited access to care, which is a threat to oral health, in particular in low-socioeconomic-status, underserviced, and remote communities; and iv) the need to adopt behavior change models to affect the modifiable risk factors that are shared with other non-communicable diseases. Dentists cannot simply rely on fluoride exposure; proper education and training in caries risk assessment, behavior change models targeted at oral hygiene and sugar intake, and collaboration with primary healthcare and local school authorities are avenues that aid in caries prevention and reduce the uneven burden of the disease. Online education and mobile apps may help to promote oral health in areas with shortages of dental work force.

Does financial expertise influence Islamic bank risk-taking?

PurposeThis study investigated how the financial expertise of the board of directors, risk management committee (RMC), audit committee (AC) and Shariá Supervisory Board (SSB) influences the risk-taking of Islamic banks.Design/methodology/approachThe study utilized unbalanced panel data for a sample of 43 full-fledged Islamic banks from 15 countries over 12 years (2010–2021). We employed feasible generalized least squares (FGLS) and heteroskedastic panels corrected standard errors (HPCSE) regression regressions as the primary estimation methods and used a two-step system generalized method of moments (GMM) estimator for robustness checks.FindingsThe results indicate that board financial expertise decreases insolvency and credit risks. Similarly, AC financial expertise and SSB financial expertise reduce insolvency and portfolio risks but increase credit risk. In contrast, RMC financial expertise raises insolvency risk. The remaining relationships are statistically insignificant.Research limitations/implicationsDue to the lack of disclosure regarding the educational and professional background of the board and committee members in the annual reports of some Islamic banks, this research used a sample of only 43 full-fledged Islamic banks operating in fifteen countries from 2010 to 2021.Practical implicationsThe findings can assist both local and international regulators in revising corporate governance codes and risk management guidelines in such a way as to ensure that the financial experts appointed to the board, AC, RMC and SSB, are capable of controlling excessive risk-taking behavior in Islamic banks.Originality/valueThis study contributes to the literature by providing comprehensive empirical evidence that corporate governance financial expertise influences the risk-taking behavior of Islamic banks.

Estimation of soil-to-plant transfer factor across six local government areas of Kwara state, Nigeria

Transfer factors (TFs) are among the necessary parameter values for the mathematical modeling of radiological pollution's environmental impact. In this study, 238U, 232Th, and 40K activity concentrations and TFs were estimated in 48 soil and plant samples through gamma-ray spectrometry NaI(Tl) in six selected areas of local governments in Kwara State, Nigeria. The findings revealed that the soil and plant samples' 238U and 232Th activity concentrations were within the radiological safety limits. The TFs of soil to plant fell within the worldwide ranges. A set of statistical tests were applied to verify the validity of the data, such as descriptive analysis, the analysis of variance (ANOVA) test, frequency distribution, and Spearman's correlation. The study reveals a skewed distribution in data. Spearman correlation coefficient analysis indicates a moderately negative correlation between 238U, 232Th, and 40K's activity concentrations and their respective TFs (TFU, TFTh, and TFK). In terms of future research in the study area, it is hoped that the data from this study will contribute to improved scientific knowledge.

Variational Bayes latent class analysis for EHR-based phenotyping with large real-world data

IntroductionBayesian approaches to patient phenotyping in clinical observational studies have been limited by the computational challenges associated with applying the Markov Chain Monte Carlo (MCMC) approach to real-world data. Approximate Bayesian inference via optimization of the variational evidence lower bound, variational Bayes (VB), has been successfully demonstrated for other applications.MethodsWe investigate the performance and characteristics of currently available VB and MCMC software to explore the practicability of available approaches and provide guidance for clinical practitioners. Two case studies are used to fully explore the methods covering a variety of real-world data. First, we use the publicly available Pima Indian diabetes data to comprehensively compare VB implementations of logistic regression. Second, a large real-world data set, Optum™ EHR with approximately one million diabetes patients extended the analysis to large, highly unbalanced data containing discrete and continuous variables. A Bayesian patient phenotyping composite model incorporating latent class analysis (LCA) and regression was implemented with the second case study.ResultsWe find that several data characteristics common in clinical data, such as sparsity, significantly affect the posterior accuracy of automatic VB methods compared with conditionally conjugate mean-field methods. We find that for both models, automatic VB approaches require more effort and technical knowledge to set up for accurate posterior estimation and are very sensitive to stopping time compared with closed-form VB methods.DiscussionOur results indicate that the patient phenotyping composite Bayes model is more easily usable for real-world studies if Monte Carlo is replaced with VB. It can potentially become a uniquely useful tool for decision support, especially for rare diseases where gold-standard biomarker data are sparse but prior knowledge can be used to assist model diagnosis and may suggest when biomarker tests are warranted.

Comparative Analysis of Machine Learning Algorithms with SMOTE and Boosting Techniques in Accuracy Improvement

This research explores and enhances accuracy in sentiment classification related to Indonesia's Capital City relocation by combining Naive Bayes (NB), Random Forest (RF), SMOTE, and XGBoost. The study addresses challenges of unbalanced data and complexity in social media sentiment analysis. The combination of RF with SMOTE achieved the highest accuracy at 91.25%, demonstrating SMOTE's effectiveness in balancing the dataset and improving minority class detection. While adding XGBoost slightly reduced accuracy (90.92%), it increased the NB model's accuracy from 77.45% to 85.97% when combined with SMOTE. RF alone reached 87.46% and improved to 88.78% with XGBoost. The study underscores the importance of selecting and combining techniques to maximize sentiment prediction accuracy. Future research could explore deep learning or transformer models for even better results, offering deeper insights into public sentiment and aiding effective policy strategy development.

Study on canopy extraction method for narrowband spectral images based on superpixel color gradation skewness distribution features

BackgroundCrop phenotype extraction devices based on multiband narrowband spectral images can effectively detect the physiological and biochemical parameters of crops, which plays a positive role in guiding the development of precision agriculture. Although the narrowband spectral image canopy extraction method is a fundamental algorithm for the development of crop phenotype extraction devices, developing a highly real-time and embedded integrated narrowband spectral image canopy extraction method remains challenging owing to the small difference between the narrowband spectral image canopy and background.MethodsThis study identified and validated the skewed distribution of leaf color gradation in narrowband spectral images. By introducing kurtosis and skewness feature parameters, a canopy extraction method based on a superpixel skewed color gradation distribution was proposed for narrowband spectral images. In addition, different types of parameter combinations were input to construct two classifier models, and the contribution of the skewed distribution feature parameters to the proposed canopy extraction method was evaluated to confirm the effectiveness of introducing skewed leaf color skewed distribution features.ResultsLeaf color gradient skewness verification was conducted on 4200 superpixels of different sizes, and 4190 superpixels conformed to the skewness distribution. The intersection over union (IoU) between the soil background and canopy of the expanded leaf color skewed distribution feature parameters was 90.41%, whereas that of the traditional Otsu segmentation algorithm was 77.95%. The canopy extraction method used in this study performed significantly better than the traditional threshold segmentation method, using the same training set, Y1 (without skewed parameters) and Y2 (with skewed parameters) Bayesian classifier models were constructed. After evaluating the segmentation effect of introducing skewed parameters, the average classification accuracies Acc_Y1 of the Y1 model and Acc_Y2 of the Y2 model were 72.02% and 91.76%, respectively, under the same test conditions. This indicates that introducing leaf color gradient skewed parameters can significantly improve the accuracy of Bayesian classifiers for narrowband spectral images of the canopy and soil background.ConclusionsThe introduction of kurtosis and skewness as leaf color skewness feature parameters can expand the expression of leaf color information in narrowband spectral images. The narrowband spectral image canopy extraction method based on superpixel color skewness distribution features can effectively segment the canopy and soil background in narrowband spectral images, thereby providing a new solution for crop canopy phenotype feature extraction.

Numerical study of aluminum combustion with agglomerate size distribution in solid rocket motor

The aluminum agglomerate size distribution plays an important role in influencing the particle distributed combustion in motor, which subsequently affects the performance of solid rocket motor significantly. In this work, a three-phase model to describe distributed combustion of aluminum agglomerates is established based on the Eulerian-Lagrangian method. Then, the agglomerate size, including mono size and distributed size, is studied to reveal its effect on aluminum combustion and motor flow field. The simulated results indicate that the increasing agglomerate mono size observes the obvious decrease of the average temperature inside the motor combustion chamber, implying the low combustion efficiency of large agglomerate size. When considering the agglomerate size distribution, the size distribution mode and the mean size D43 determine the combustion efficiency together. In particular, even the mean size is similar, with different distribution mode, like skewed distribution, bimodal or trimodal distribution, the combustion efficiency and flow field parameters are nonnegligible different. However, when the size distribution mode is the same and the peak range is similar, the mean size D43 becomes the only and predominant factor as the mono size.

Association Between Nickel Exposure and Metabolic Syndrome: Data from NHANES 2017-2018.

Previous studies have found a possible association between nickel and metabolic syndrome (MetS), but with conflicting results. No studies have determined whether nickel exposure increases the prevalence of MetS in the general U.S. population. Therefore, we used data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between urinary nickel and MetS. Since urinary nickel levels were presented as a skewed distribution, they were normalized using a logarithmic transformation. Weighted multivariate logistic models, restricted cubic spline, threshold effect analysis, and subgroup analyses were used to examine the association between urinary nickel concentration and the risk of MetS and its components. Based on data from 1577 participants, individuals in the second, third, and fourth quartiles of urinary nickel had an adjusted OR for MetS of 1.42 (95% CI: 0.88, 2.28), 2.00 (95% CI: 1.22, 3.28), and 1.68 (95% CI: 1.05, 2.70), respectively, representing an inverted "L"-shaped nonlinear dose-response relationship with an inflection point at 0.2141ng/L. Patients over the age of 40, males, less educated, and smokers are more susceptible to nickel exposure. In addition, there were significant associations between nickel and most components of the MetS, with the strongest to weakest correlations being high fasting glucose, reduced high-density lipoprotein, abdominal obesity, and elevated blood pressure; however, there was no significant correlation between nickel and hyperlipidemia. In conclusion, environmental nickel exposure increases the prevalence of MetS in U.S. adults, particularly in males over 40years of age, those with less education, and smokers.

Influence of tidal asymmetry on local scour near the offshore platform

An in-situ mooring measurement was conducted using a profiling sound of navigation and ranging (SONAR) system to reveal the influence of tidal asymmetry on the local scour near an artificial offshore platform, Southwest Offshore Windfarm Complex, Korea. During the mooring period, the flood tide was significantly dominated by positive tidal duration asymmetry and skewness. The lengths of scour hole were 9.8m and 5.1m along the flood and ebb directions, respectively. Flood-dominated currents intensified the horseshoe vortex, resulting in an approximately 10% deeper scour hole on the flood face than the ebb face. The periodic occurrence of bidirectional currents generated vortices that facilitated the mixing and redistribution of seabed sediments, forming a gentle slope within the scour hole. The average slope angles ranged from 14° to 16° on the flood face and 6° to 8° on the ebb face. Despite the strong influence of local scour, variations in scour depth (Ds) consistently remained around −2.15m, suggesting the exposure of the underlying consolidated sediment layer. During the intermediate period from spring to neap tide, the Ds on the lower slope of scour hole increased by 0.12m, while it decreased by 0.11m from neap to spring tide, suggesting that the net Ds gradually approached zero over the tidal cycle. These findings underscore the importance of understanding tidal impacts on the local scour morphology to enhance the stability and design of offshore wind turbine foundations.