Multicollinearity Diagnosis Research Articles

Analysis of the Impact of Employees' Own Indicators on Employee Mobility

Abstract. Nowadays, the enterprise employee turnover rate has become an important factor in measuring enterprise knowledge loss, human resource management, enterprise comprehensive competitiveness, and enterprise expenditure. Therefore, as much as possible may affect the enterprise employee turnover data and analyze the influence on employee turnover degree can maintain stable staff and increase its comprehensive competitiveness in the market economy system. Employee mobility is not only affected by corporate and macro environmental factors, such as corporate culture, incentive system, employee relations, economic environment, but also related to employees' factors, such as career development planning, job satisfaction, salary, and burnout. Research on employee mobility and its impression factors should be carried out from multiple aspects and perspectives. This paper refers to the relevant literature on the impression factors of employee mobility in the past, combines the basic data that may affect the employee turnover rate to analyze the Impact of employees' indicators on employee mobility, pre-processes the data, and presents the visual image of the data. And then the paper uses multicollinearity diagnosis to exclude the collinearity influencing variables and conduct a preliminary correlation analysis of the variables. Finally, this text establishes a regression analysis model of the related variables, obtains seven variables related to employee mobility, and gives reasonable suggestions for the enterprise research employee turnover rate.

Effect of Increasing Sample Size on Multi-Collinearity in Multilevel Non-Linear Model

A multilevel logistic regression model demonstrating high correlations among predictor variables is susceptible to multi-collinearity. Multi-collinearity significantly impacts the robustness and interpretability of multilevel non-linear models. In multilevel non-linear models, the effects of multi-collinearity can be amplified, leading to distorted parameter estimates and inflated standard errors. This phenomenon contributes to an escalation in the variances of parameter estimates, thereby resulting in inaccurate inferences regarding the relationships between the response and explanatory factors. The primary objective of this study is to investigate the impact of multi-collinearity on multilevel non-linear models. The research aims to assess whether the quantity of independent variables influences the Multilevel Variance Inflation Factor and to explore the effect of altering the correlation degree at one level on multi-collinearity within a multilevel non-linear model. Additionally, the research seeks to determine how multi-collinearity affects the standard errors of parameters in a multilevel non-linear model. In a 2-level logistic regression, a binary variable was the dependent variable, while pre-established standard variables functioned as regressors. The Monte Carlo analysis incorporated three distinct correlation strengths (0.2, 0.5, and 0.9) and sample sizes (500, 100, and 30). The Multilevel Variance Inflation Factor was employed for multi-collinearity diagnosis. The outcomes revealed that, within the logistic multilevel regression model, an increase in sample size correlated with a reduction in multi-collinearity. Notably, the influence of multi-collinearity on standard errors in a multilevel non-linear model was more pronounced. It was observed that increasing the sample size remains an effective strategy to mitigate multi-collinearity errors in a multilevel non-linear model. This approach is particularly crucial due to the reliance on maximum likelihood estimation in logistic regression, as opposed to ordinary least squares (OLS) regression, which contrasts with the methodology of OLS regression.

Implicações da remoção dos parâmetros do modelo nas relações lineares de ensaios com a aveia

ABSTRACT: This study was conducted with the aim of analyzing the implications of removing the parameters from the mathematical model on the results of path analysis, with oats grown in different years and agricultural scenarios (with and without fungicide). For this, two field trials were conducted in southern Brazil, in five years of growth. The experimental design used in trial I (with fungicide application) was randomized complete blocks (RCB), in a 22 × 4 bifactorial arrangement, characterized by twenty-two oat cultivars and four fungicide applications. For trial II (without fungicide application) the RCB design was used, and the treatments were characterized by twenty-two oat cultivars, with three replications. The traits measured were panicle length, panicle mass, number of spikelets, number of grains, grain mass, and grain yield. For each year, data group, and scenario, the correlation coefficients between the explanatory variables and grain yield were calculated. The diagnosis of multicollinearity indicated violation of the statistical assumption, so it was necessary to proceed with a path analysis under multicollinearity (ridge). The removal of parameters from the mathematical model caused changes in the linear relationships between the oat yield traits, with the maintenance of the linear correlation coefficients in 3.30% and 20% of the situations, for the scenarios with and without fungicide application, respectively. Regarding the path coefficients, it was observed that the direct effects were maintained in 3.30% and 30% and indirect effects in 7.33% and 24.67% of the situations, for the scenarios with and without fungicide application, respectively.

Selection of popcorn genotypes resistant to Spodoptera frugiperda and identification of resistance-related key traits


 The Spodoptera frugiperda, is one of the most deleterious pests of popcorn and the identification of resistant genotypes is determinant in breeding programs. The objective of this study was to select popcorn genotypes resistant to S. frugiperda and the key traits related to the identification of resistance. The popcorn varieties UEM J1, Composto Márcia, Arachida, Composto Gaúcho, and Zapalote Chico (resistant check) were evaluated in a completely randomized design with 100 replications. The experimental unit consisted of one Petri dish, containing plant material and a caterpillar The following traits were evaluated: larval stage duration (LSt), food intake weight(IW), final larva weight (FW), mean larva weight (MW), feces (F), assimilated (A) and metabolized food weight (M), relative consumption rate (RCR), relative metabolic rate (RMR), relative growth rate (RGR), conversion efficiency of ingested food (CEI), apparent digestibility (AD), conversion efficiency of digested food (CED), and leaf area consumed (LAC). The diagnosis of multicollinearity, analysis of canonical variables, genetic divergence, hierarchical clustering, factor analysis and canonical correspondence analysis were carried out to perform multivariate analysis. After the multicollinearity test, the traits FW, IW, RCR, AD, and LAC were maintained for further analysis. The traits IW, FW and AD were determinant in the resistance by antixenosis expressed by the varieties Zapalote Chico and Arachida, for the varieties Composto Gaúcho and Composto Márcia the determining characteristics were RCR and LAC and for the variety UEM J1 the variable LAC showed greater importance. Variety Arachida was considered resistant to S. frugiperda by antixenosis and can be used in the future as a source of favorable alleles to breed resistant popcorn hybrids. The traits relative consumption rate, apparent digestibility and leaf area consumed were considered key traits in the identification of resistance against S. frugiperda in popcorn genotypes.

Quality Index Approach for Analysis of Urban Green Infrastructure in Himalayan Cities

In fast urbanizing cities, fragmentation of urban green infrastructure (UGI) commonly arises due to lack of efficient planning to maintain the quantity and improve their quality. As ecological processes and landscape patterns are closely intertwined, it is a prerequisite to investigate landscape structure when aiming at better provision of ecosystem services. This study integrates remote sensing, geographic information system, combination of landscape metrics, and multi-variated statistics to delineate structural attributes influencing UGI Quality (UGIQ). We exemplify our methodology in three capital cities of Indian Himalayan states at administrative ward level. The UGIQ is derived by comparing landscape characters defined by nine metrics denoting area, shape, and aggregation attributes. By employing principal component analysis (PCA) and multi-collinearity diagnosis, a set of quality defining metrics are obtained for each city. Further, to gain insightful spatial basis for improving connectivity, Morphological Spatial Pattern Analysis (MSPA) is used to visualize and classify patches into seven morphological classes. Landscape characterization highlights a pattern of low-quality wards having a limited number and area of UGI patches in urban centers, and high-quality wards with complex and aggregated patches towards fringes. PCA identifies the positive influence of area (LPI, AREA_MN) and shape (LSI, FRAC_AM, CONTIG) metrics and negative influence of patch distance (ENN_MN) and fragmentation (PD) on UGIQ in different combinations across the cities. Higher shares of morphological core and edge classes are recognized for overall UGIQ improvement. The results provide quantitative measures to develop integrated spatial planning strategies.

Evaluating Factors Predicting Smooth Transition to the International Public Sector Accounting Standards in Nigeria

The study is an evaluation of factors affecting smooth transition from Statement of Accounting Standards to the International Public Sector Accounting Standards (IPSAS) by Nigerian public sector entities. The predictive variables were analyzed using the proxies of public perceptions and attitudes towards IPSAS, skills and competencies of accounting practitioners and adequacies of legal and regulatory frameworks to drive the adoption. Scholars argued that IPSAS will lead to more quality and transparent financial reports in the public sector.The adoption journeys in Nigeria, has being marked with wobbling attempts at cash based to accrual based IPSAS due to unpreparedness and institutional failures leading to several shift of deadline for mandatory adoption. The study employed theoretical framework of institutional adequacy and attitudinal change, and agency theories. Survey research non experimental design aligns with the behaviours of the variables under study and so was adopted. To check for reliability of the instrument, I calculated the Cronbach’s alpha and the bivariate correlation coefficient to determine how closely linked items are with each other in the scale. The result fall along acceptable Cronbach of .76. The use of Likert Scale correlates with its long standing history of reliability in social science research. Descriptive statistics was employed to specify the nature of the variables prior to main effect analysis. Multicollinearity diagnosis was adopted to check for any overlapping relationship between the variables and Z-score statistics to test for outliers in the data set and clean up my data set by testing for all statistical assumptions. Stratified sampling strategy was found most appropriate to draw representative samples from the sampling frames. 640 respondents were sampled with 20 non respond. This represents mere 3.125% as missing data which is considered of no significant effect on the data. Data was analyzed using multiple regressions and Pearson Correlation coefficient to test the hypothesis and assess the relationship between the variables. The result indicates that there exists significant relationship between the independent and dependent variables, and that negative public perception and low political will are strong barriers to successful implementation of same in Nigeria. Respondents held that Nigeria accountant are not skilled and competent to handle the migration to accrual IPSAS and legislative and regulatory framework is not adequate enough to support IPSAS adoption. The study recommends curriculum overhauls, enacting and amendments of relevant laws to accommodate the teaching and regulation of IPSAS adoption in the public service.

Linear relationships between biometric variables in lettuce seedlings

Seedling production is an important step in growing vegetables. The study of relationships between variables has applications in practically all areas of study and is useful in identifying relationships among quality variables in vegetable seedlings. Thus, this work aims to estimate linear relationships between biometric variables in lettuce seedlings. The seedlings were grown in expanded polystyrene trays (n = 4) with 128 and 200 cells each. Leaf area, stem diameter, shoot height, length, area, volume of the root, dry and fresh shoot mass variables were measured and evaluated with Pearson's correlation analysis, multicollinearity diagnosis, condition number and the variance inflation factor, path analysis and canonical correlations. The results show linear relationships between the variables. For the 128 cell trays, there was a relationship between the total fresh mass and fresh shoot mass variables and between the total fresh mass and dry shoot mass variables. For the 200-cell trays, significant correlation was observed between the characters total fresh mass and fresh shoot mass variables and between root area and root volume variables, indicating that the greater the total fresh mass, the greater the fresh shoot mass and the greater the root area, the greater the root volume.

Ploidy Identification by Flow Cytometry and Application of the Method to Characterize Seasonal Ploidy Variation of Wild Populations of the Red Alga Gracilariopsis lemaneiformis.

Gracilariopsis lemaneiformis (Gp. lemaneiformis) is an economically important alga. At present, there is no way to quickly and easily determine its ploidy and life cycle dynamics in wild populations, which affects the process of genetic breeding. In this study, we developed and verified a ploidy identification method using flow cytometry and then used it to explore the seasonal fluctuation of the ploidy ratio and the environmental factors that influence it in wild populations of this species. Of the three methods we tested for nucleus extraction, quick chopping was the best because of its high extraction efficiency, low debris background, obvious subcellular scatter plot, and clear typical histogram. Samples from the tip of the alga were more suitable for preparing the nuclear suspension than samples from the base. Generalized linear model analysis based on diagnosis of multicollinearity revealed a negative correlation between temperature and ploidy ratio. Among the environmental factors tested, temperature had the greatest influence on the ploidy ratio, whereas precipitation and sunshine duration had no effect on the ploidy ratio fluctuation. Our study will be useful for material collection and studies of utilization and life cycle dynamics. Moreover, understanding of ploidy dynamics may provide a theoretical basis for improving variety and breeding of Gp. lemaneiformis in the future.

Activity prediction of anti-cancer drug candidate ER<bold>α</bold> inhibitor

Breast cancer is the most common malignancy which threats the women's health worldwide. Studies have revealed that the estrogen receptor alpha subtype (ERα) plays an important role in breast development and is considered as an important target for breast cancer treatment. Compounds that can antagonize ERα activity may be candidates for breast cancer treatment. A quantitative structure-activity relationship prediction model is proposed to predict the bioactivity of compounds that can be applied to anti-breast cancer drugs under small samples and multi-characteristic conditions. First, the descriptive statistics and multicollinearity diagnosis are performed on the information of 729 molecular descriptors of 1 974 compounds, and the random forest method is used to screen 20 significant variables with variable importance measure that is greater than 0.01. Then, a CNN-based two-dimensional feature matrix is constructed, and a Bayesian hyperparametric optimization (BHO) method is used to perform hyperparametric optimization of the Bi-LSTM model. Finally, the prediction effect of model is analyzed and evaluated. The results show that compared with the GBDT integrated learning method, the prediction effect of Mul-BHO-Bi-LSTM integrated machine learning prediction model is better, and the model error indexes MSE, NRMSE, error mean, and error std are less than 0.15, and the correlated indicators <italic>R</italic>² and <italic>r</italic> are above 0.99, indicating that the integrated machine learning predictionmodel of Mul-BHO-Bi-LSTM has the good robustness and generalization, and the model can provide a method for the screening and design of anti-breast cancer drugs.

Miniscrews for orthodontic anchorage: analysis of risk factors correlated with the progressive susceptibility to failure

The phenomenon of orthodontic anchorage miniscrews loosening after being implanted several times happens in daily clinical practice, and the reasons need to be traced. This study aimed to investigate the underlying risk factors influencing the progressive susceptibility of orthodontic miniscrews to failure. Overall, 889 miniscrews were successively inserted into 347 patients because some loosened or fell off once, twice, or more before achieving their purposes. The number of miniscrew failures (ie, once, twice, or more) was defined as progressive susceptibility to failure. The clinical indicators were assessed via univariate analysis, multicollinearity diagnosis, and Poisson log-linear regression model with stepwise calculation to screen out. The progressive susceptibility of miniscrews to failure was proved to be affected by the age of patients, the onset of force application, site of placement, and appliance type. Age and onset of force application presented a negative relationship with susceptibility. Miniscrews inserted in the palatal region appeared to be more stable than the forepart of the arch. In contrast, the retromaxillary and retromandibular areas obtained the lowest stability. The patients with fixed appliances were more unlikely to suffer progressive failure than removable appliances. In addition, the larger number of screws inserted in each patient, the greater probability of failure. Younger people with removable appliances that miniscrews inserted in the retromaxillary or retromandibular regions and earlier onsets of loading had a higher progressive susceptibility to loosening. Meanwhile, the failure rate was elevated with the increasing number of screws per patient received.

A comparison of performance measures of three machine learning algorithms for flood susceptibility mapping of river Silabati (tropical river, India)

Flood is the most common phenomenon causing extensive disruption to the environment, socio-economy, infrastructure and many other aspects of human life. Flood susceptibility mapping (FSM) is a crucial step for policymakers in disaster management. However, in the present study, we applied three ensemble machine learning models, namely, Random Forest (RF), Naive Bayes (NB), and Extreme Gradient Boosting (XGB) for FSM of Silabati river (tropical river, India). A total of 500 historical flood points and field observations with considering set of twelve flood influencing factors (rainfall, elevation, slope, curvature, stream power index (SPI), Sediment Transport Index (STI), Terrain ruggedness index (TRI), topographic wetness index (TWI), clay content in soil (SC), distance from the river (DFR), drainage density (DD), and land use and land cover (LULC) for generating the training and validation datasets. To investigate and perceive the flood vulnerability of the study basin, five factors, such as elevation, DD, rainfall, DFR and SC turn out to be the most dominating factors out of the adopted twelve factors considered for the present study in all models. It is found that an area of around 36.08% of the total basin comes under the very high to high FSM. The prediction ability and performance efficiency of three models were comparison and validation measures by statistical techniques such as multicollinearity diagnosis test, Kappa index, MAE, (Mean absolute error), RMSE (Root mean square error), Pearson's correlation coefficients and receiver operating characteristic (ROC). The highest ROC was achieved by the RF model (84.7%), followed by the XGB model (83.1%), and NB model (82.1%) respectively. The RF model performs better for FSM then the other models.

Factors affecting care of elderly patients among nursing staff at the Ho teaching hospital in Ghana: Implications for geriatric care policy in Ghana.

The population of the aged is increasing globally and in Ghana. In 2020, the population aged over 60 years in Ghana was 2,051,903 and this is expected to reach 2.5 million by 2025 and 6.3 million by 2050. Despite the envisaged increase in the number and life expectancy of the older population in Ghana that will require nursing care, there is a paucity of data on nursing staff knowledge and attitudes toward elderly patients in Ghana. This study, therefore, assessed factors affecting the care of elderly patients among nursing staff in a tertiary referral health facility in the Volta region of Ghana. The study employed a descriptive cross-sectional design using quantitative data collection approaches. A total of 150 nurses were sampled with a response rate of 95%. Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 23. The analysis included logistic regression to predict factors associated with nurses' knowledge and attitude in caring for elderly patients, after multicollinearity diagnosis and controlling the effect of confounding variables. Majority (83.8%) of the nurses demonstrated good knowledge of the aging process, knowledge in the care of the elderly (88.7%), and (84.5%) had a positive caring attitude towards the elderly. Professional education, professional qualification, and knowledge on aged care were significantly associated with nurses' attitude towards the elderly (p<0.001), (p<0.005), and (p<0.010), respectively. Lack of special wards/facilities emerged as the predominantly perceived barrier to caring for the elderly as per the nurses' responses. The majority of nurses demonstrated good knowledge and attitude in the aging process and care of the aged. Lack of special wards/facilities and lack of staff motivation were the leading perceived barriers to rendering care to the elderly. Scaling up gerontological nursing programs and establishing special aged care facilities in Ghana with appropriate policy guidelines and regulations for implementation of care will help improve nurses' knowledge and caring attitudes toward the care of elderly patients. Likewise, a national geriatric care policy would help consolidate standard geriatric care in Ghana.

Canonical correlation among morphological traits and yield components of cowpea

The study of canonical correlation makes it possible to identify and quantify the associations of morphological and productive characters with the performance of cultures. Due to the scarcity of studies on the topic of cowpea, the selection of characters of interest for grain yield is promising. The objective of this study was to verify the associations between morphological traits and yield components in cowpea. The experiment was carried out in 2016 at experimental area of the Embrapa Agropecuária Oeste, municipality of Dourados, State of Mato Grosso do Sul. The experimental design was randomized blocks, with 14 treatments and four replications. Canonical groups were established between production components (group 1) and morphological characteristics (group 2). Canonical correlation analysis was estimated between the group consisting of primary agronomic traits (grain yield, number of pods and pod length) and the group consisting of secondary agronomic traits (beginning of flowering, plant size, value for cultivation and lodging). The data were submitted to analysis of variance at 5% probability and canonical variables. Moreover, it was determined the matrix of phenotypic correlation coefficients and the multicollinearity diagnosis. The number of pods per plant, pod length and plant size showed high and positive magnitude, allowing to conclude that, in cowpea breeding program, plants with the highest number of pods per plant, earliness and optimal plant architecture for mechanized harvesting should be selected to increased grain yield.

PATH ANALYSIS OF PHYSICAL ATTRIBUTES OF CHAYOTE FRUIT

Chayote is originally from southern Mexico and Guatemala and has been a staple food highly appreciated in Brazil and worldwide. This study was carried out on a Red Yellow Latosol in 2020 to investigate the relationship between the physical properties of chayote fruit of the variety Cambray and their mass, aiming to indicate criteria for direct selection of more attractive fruits. The fruit parameters evaluated were mass (MAS), largest transverse diameter (LTD), smallest transverse diameter (STD), length (FTL), volume (FTV), area (FTA), and perimeter (FTP), as well as seed area (SDA) and seed perimeter (SDP). Initially, correlations among these morphological variables were assessed by Pearson's correlation coefficient, and a correlation network was used to express the results graphically. A diagnosis of multicollinearity was performed, and a condition number of 6639 (multicollinearity severe) was found. Path analysis considered the fruit mass as the main dependent variable. Our analyses showed that MAS, FTV, FTA, FTP, and STD are physical attributes with the greatest potential for selection and identification of more attractive chayote fruits of the variety Cambray for commercial purposes.

Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China

Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model.

Model performance analysis for landslide susceptibility in cold regions using accuracy rate and fluctuation characteristics

Considering the increasing number of landslides due to permafrost degradation, this paper reports a performance evaluation of three classical landslide susceptibility models applied to cold regions. A landslide inventory was first constructed through a historical survey and image interpretation. Ten causative factors of landslides were then chosen based on the available data and the local environment. Multicollinearity diagnosis and factor effectiveness test were employed to perform a factor analysis. Subsequently, three evaluation models based on the frequency ratio (FR), logistic regression (LR), and artificial neural network (ANN) were established. These models were applied to obtain landslide susceptibility maps, which were then analyzed and compared. The model performance was evaluated in terms of the accuracy rate and fluctuation characteristics. The results showed no multicollinearity issue between the factors employed. The annual temperature difference and frozen depth are two indispensable factors when assessing landslide susceptibility in cold regions. A comparison between the susceptibility maps generated using the three models showed that the FR model-generated map is most in line with the principle of disaster zoning and has the highest degree of conformity with actual landslide points, followed by the maps generated using the LR and ANN models. An accuracy analysis showed that the ANN model yields the highest AUC value in the training and test states, 0.957 and 0.863, respectively; however, these values were not optimal given the fluctuation. Moreover, the fluctuation in the non-landslide data was greater than that in the landslide data. The fluctuation results revealed the drawback of the AUC value in the analysis of the model performance. In other words, the non-landslide error often covers up the landslide error. This study provides a scientific guidance for evaluating the model performance and for assessing landslide disasters in cold regions.

Correlations between characters and path analysis in sweet sorghum (Sorghum bicolor (L.) Moench) genotypes for juice production

Sweet sorghum presents stems with juice similar to that of sugar cane, rich in fermentable sugars, that may be used in off-season for sugar and ethanol production optimizing the sugar-ethanol sector. The objective of this work was to determine genetic correlations between characters and perform path analysis between juice volume and its components. Twenty-five sweet sorghum genotypes were evaluated in randomized blocks design with three repetitions and studied variables were: number of days to flowering; plant height; number of stems; weight of green mass; weight of dry mass; number of leaves; diameter of stems; volume of extracted juice and percentage of total soluble solids. In order to verify the existence of variability among the genotypes, data were subjected to variance analysis by F-test. Subsequently, genetic parameters were determined, as soon as genetic correlation estimator’s method, performed by t-test, to determine phenotypic correlation and bootstrap method for determining environmental and genetics correlation coefficient. Before performing path analysis a multicollinearity diagnosis was also conducted. The results of genetic correlation and path analysis point weight of green mass as the main variable influencing the juice volume, allowing these characters in indirect selection for increasing juice volume

Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions

Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

Molecular Docking and QSAR studies for Modeling Antifungal Activity of Triazine Analogues against Therapeutic Target NMT of Candida albicans

The s-triazine derivatives have been attracting the attention of researchers due to a broad range of biological applications. Present research deals with a combination of genetic algorithm-multiple linear regression (GA-MLR) based quantitative structure–activity relationships (QSAR) modeling and molecular docking as relevant to triazine analogs in an attempt to investigate their role as novel NMT inhibitors of Candida albicans. A penta-varient model which assure all validation criteria up to considerable echelon (R2 = 0.792, Q2 = 0.679 and = 0.603) supplemented by multicollinearity diagnosis by VIF and tolerance data analysis, signaling the robustness of the QSAR model. The descriptors RDF040v, Ds, Mor04m, X4v, and MATS2p in the projected QSAR model have quantified the role of atomic properties such as topology; atomic van der Waals volume, mass, and polarizability execute vital part to modify the antifungal activity of compounds under investigation. Further, a molecular docking simulation study revealed that three compounds, in particular, showed a superior binding affinity with a re-rank score of -142.594, -138.972, -137.540 kcal/mol. Consequently, this study may turn out to be helpful towards the development and optimization of existing antifungal activity of compounds under investigation.

Relationships between yield and some anatomical and morphological traits in rubber tree progenies

The present work evaluated the relationship of rubber yield with morphological and structural traits of the laticiferous system in rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg] open-pollinated progenies to understand the cause and effect relationships between traits in order to enable future early selection. The progenies were installed in Jaú, Pindorama and Votuporanga in a randomized block design, with five replications and 10 plants per plot at a spacing of 2.0 × 2.0 m. The traits evaluated at three years of age were rubber yield (Yld); girth (Grt), expressed by the stem perimeter; bark thickness (BT); number of latex vessel rings (RG); length (PL), diameter (PD) and petiole index (PI); leaflet length(LFL) and width (LFW); leaf width (LW), area (LA) and index (LI); and number of leaf storeys (NLS). Coefficients of the genotypic correlations were used for a graphical representation of the correlation network created in the Genes program. Subsequently, the diagnosis of multicollinearity, as well as the analysis of the direct and indirect genotypic correlations of bark anatomical traits, leaf morphological traits, girth, and rubber yield, were performed. The traits that showed significant genotypic correlations with rubber yield at the three evaluated sites were girth, bark thickness, and number of leaf storeys. The girth character had the largest direct effect on rubber yield. Canonical correlation analysis showed that selection for the number of leaf storeys may increase at least three of the traits (yield, girth, bark thickness, and number of latex vessel rings) for most locations studied. There is an association between the yield traits (yield, girth, and the laticiferous system) and the morphological traits.