Bayesian Information Criterion Values Research Articles

Stratifying Mortality Risk in Intensive Care: A Comprehensive Analysis Using Cluster Analysis and Classification and Regression Tree Algorithms

Abstract Background Machine learning (ML) can be promising for stratifying patients into homogeneous groups and assessing mortality based on score combination. Using ML, we compared mortality prediction performance for clustered and non-clustered models and tried to develop a simple decision algorithm to predict the patient’s cluster membership with classification and regression trees (CART). Methods Retrospective study involving patients requiring ICU admission (1st January 2011–16th September 2022). Clusters were identified by combining Charlson Comorbidity Index (CCI) plus Simplified Acute Physiology Score II (SAPS II) or Sequential Organ Failure Assessment (SOFA). Intercluster and survival analyses were performed. We analyzed the relationship with mortality with multivariate logistic regressions and receiver operating characteristic curves (ROC) for models with and without clusters. Nested models were compared with Likelihood Ratio Tests (LRT). Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were compared for non-nested models. With the best model, we used CART to build a decision tree for patient’s membership. Results Our sample consisted of 2605 patients (mortality 59.7%). For both score combinations, we identified two clusters (A and B for CCI + SAPS II, α and β for CCI + SOFA). Belonging to cluster B/β was associated with shorter survival times (Peto-Peto p-values < 0.0001) and increased mortality (Odds-ratio 4.65 and 5.44, respectively). According to LRT and ROC analysis, clustered models performed better, and CCI + SOFA showed the lowest AIC and BIC values (AIC = 3021.21, BIC = 3132.65). Using CART (β cluster positive case) the accuracy of the decision tree was 94.8%. Conclusion Clustered models significantly improved mortality prediction. The CCI + SOFA clustered model showed the best balance between complexity and data fit and should be preferred. Developing a user-friendly decision-making algorithm for cluster membership with CART showed high accuracy. Further validation studies are needed to confirm these findings.

Comparison of Nonlinear Models to Describe the Growth of Tuj and Romanov x Tuj (F1) Lambs

This study aimed to identify the most suitable model for explaining weight changes in purebred Tuj (n=35) and Romanov x Tuj (RoxTuj) (F1) (n=25) lambs using non-linear models. Single-born lambs of both breeds and genders were included in the evaluation. Five different non-linear growth models were compared: Brody, Gompertz, Logistic, Richards, and Weibull. The best model for describing growth was chosen based on four criteria: coefficient of determination (R²), mean square error (MSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Models with the highest R² and the lowest MSE, AIC, and BIC values were considered the best fit for the data. It was observed that the Brody model had the highest R2 and lowest MSE, AIC and BIC values for Tuj and ROxTuj (F1) female and male lambs. The Gompertz, Logistic, and Richards models exhibited similar predictive performance. In contrast, the Weibull model produced significantly different results compared to the other models when predicting weight changes. Therefore, the Brody model was identified as the most effective model for explaining growth patterns in both Tuj and RoxTuj (F1) lambs.

Determining causality in travel mode choice

This article presents one of the pioneering studies on causal modeling in travel mode choice decision-making using causal discovery algorithms. These models are a major advancement from conventional correlation-based techniques. We propose a novel methodology that combines causal discovery with structural equation modeling (SEM). This modeling approach overcomes some of the limitations of SEM by combining the strengths of both causal discovery and SEM. Causal discovery algorithms determine causal graphs from observational data and domain knowledge, and SEMs estimate direct causal effects and test the performance of causal discovery algorithms. In this study, we test four causal discovery algorithms: Peter-Clark (PC), Fast Causal Inference (FCI), Fast Greedy Equivalence Search (FGES), and Direct Linear Non-Gaussian Acyclic Models (DirectLiNGAM). The results show that DirectLiNGAM based SEM model best captures causality in mode choice behavior. It passes several goodness-of-fit tests, including Root Mean Square Error of Approximation (RMSEA) and Goodness-of-Fit Index (GFI), and it achieves the lowest Bayesian Information Criterion (BIC) value. The analyses are conducted on data collected from the 2017 National Household Travel Survey in the New York Metropolitan area.

An Accident Prediction Model Based on ARIMA in Kuala Lumpur, Malaysia, Using Time Series of Actual Accidents and Related Data

Recently, there has been an emerging trend to analyse time series data and utilise sophisticated tools for optimally fitting time series models. To date, Malaysian industrial accident data is underutilised and lacks informative records. Thus, this paper aims to investigate the Malaysian accident database and further evaluate the optimal forecasting models in accident prediction. The model’s input was based on available data from the Department of Occupational Safety and Health, Malaysia (DOSH), from 2018 until 2021, with 80% of the dataset to train the models and the remaining 20% for validation. The negative binomial and Poisson distribution prediction showed a mean absolute percentage error (MAPE) of 33% and 51%, respectively. It indicated that the negative binomial performed better than the Poisson distribution in accident frequency prediction. The available time series accident data were gathered for four years, and stationarity was checked in R Studio software for the Augmented Dickey-Fuller test. The lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other error values were used to justify the best model, which was the ARIMA(2,0,2)(2,0,0)(12) model. The ARIMA models were considered after the data showed autocorrelation. The MAPE for both ARIMA in R and manual time series were 40% and 49%, respectively. Therefore, the accident prediction by using R Studio would outperform the manually negative binomial and Poisson distribution. Based on the findings, industrial safety practitioners should report accidents to DOSH truthfully in the era of digitalisation. It could enable future data-driven accident predictions to be carried out.

A Data-driven Approach for Generating Leaf Tissue Nutrient Interpretation Ranges for Greenhouse Lettuce

In the absence of controlled sufficiency studies, foliar interpretations for many horticultural crops are based on survey concentrations from small data sets. In addition, both survey and sufficiency ranges provide little interpretation regarding zones that are above or below the concentration range deemed “sufficient.” While providing a critical initial set of ranges, it was based on a limited set of data and therefore improvements in interpretation of data are needed. This study presents a novel method based on 1950 data points to create data-driven nutrient interpretation ranges by fitting models to provide more refined ranges of deficient (lowest 2.5%), low (2.5% to 25%), sufficient (25% to 75%), high (75% to 97.5%), and excessive (highest 2.5%). Data were analyzed by fitting Normal, Gamma, and Weibull distributions. Corresponding P values were calculated based on the Shapiro-Wilk test for normality for the Normal and Gamma distributions, and the Kolmogorov-Smirnov test was used for the Weibull distribution. The optimal distribution was selected based on the lowest Bayesian Information Criterion (BIC) value and visual fitness. The Weibull distribution best represented nitrogen, phosphorus, potassium, calcium, manganese, zinc, and copper, and the Gamma distribution best represented magnesium, sulfur, iron, and boron. Using the selected distributions, we propose a refined set of nutrient evaluation ranges for greenhouse-grown lettuce. These refined standards will aid growers and technical specialists in more accurately interpreting leaf tissue sample data.

The Generalized Odd Maxwell-Kumaraswamy Distribution: Its Properties and Applications

This study introduces a novel family of continuous probability distributions using Alzaatreh‘s technique to explore the Generalized Odd Maxwell-Generated (GOM-G) distribution family. Within the Generalized Odd Maxwell family, the GOM-Kumaraswamy distribution is presented as extended form of the Kumaraswamy distribution. The investigation thoroughly examines the cumulative distribution function, probability density function, hazard, and survival functions, as well as mixture representations of the Generalized Odd Maxwell-Kumaraswamy distribution. The suggested distribution's structural properties, including moments, skewness, kurtosis, probability-weighted moments, entropies, stress-strength models, and order statistics, are derived. Model parameters are estimated through the maximum likelihood method, and simulation studies evaluate the performance of maximum likelihood estimations using the quantile function. Employing two real-life datasets, goodness-of-fit measures such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion, Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), and chi-square goodness-of-fit tests demonstrate the adaptability and flexibility of the GOM-Kumaraswamy distribution against competing distributions, including Kumaraswamy-Kumaraswamy and Kumaraswamy-Burr III. The results reveal that the GOM-Kumaraswamy distribution exhibits the lowest AIC, CAIC, BIC, and HQIC values and the highest goodness-of-fit values compared to other models, suggesting its superiority as the preferred fit for both dataset forms. This proposed distribution contributes to practical applications, unveiling its potential significance in modeling real-world phenomena within the domains of hydrology and engineering.

A comparative study of LSTM-ED architectures in forecasting day-ahead solar photovoltaic energy using Weather Data

Solar photovoltaic (PV) energy, with its clean, local, and renewable features, is an effective complement to traditional energy sources today. However, the photovoltaic power system is highly weather-dependent and therefore has unstable and intermittent characteristics. Despite the negative impact of these features on solar sources, the increase in worldwide installed PV capacity has made solar energy prediction an important research topic. This study compares three encoder-decoder (ED) networks for day-ahead solar PV energy prediction: Long Short-Term Memory ED (LSTM-ED), Convolutional LSTM ED (Conv-LSTM-ED), and Convolutional Neural Network and LSTM ED (CNN-LSTM-ED). The models are tested using 1741-day-long datasets from 26 PV panels in Istanbul, Turkey, considering both power and energy output of the panels and meteorological features. The results show that the Conv-LSTM-ED with 50 iterations is the most successful model, achieving an average prediction score of up to 0.88 over R-square (R2). Evaluation of the iteration counts’ effect reveals that the Conv-LSTM-ED with 50 iterations also yields the lowest Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values, confirming its success. In addition, the fitness and effectiveness of the models are evaluated, with the Conv-LSTM-ED achieving the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for each iteration. The findings of this work can help researchers build the best data-driven methods for forecasting PV solar energy based on PV features and meteorological features.

A prognostic nomogram integrating carcinoembryonic antigen levels for predicting overall survival in elderly patients with stage II-III colorectal cancer.

With the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II-III CRC and construct a nomogram to predict patient survival. A total of 3,016 elderly CRC patients with stage II-III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups. The 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001). We constructed a prediction nomogram for stage II-III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Water desorption isotherms and thermodynamic properties of Ora-pro-nóbis (Pereskia aculeata Miller)

Desorption isotherms of ora-pro-nóbis leaves were determined at storage (5 °C, 10 °C, 20 °C, 30 °C, and 40 °C) and drying (40 °C, 50 °C, 60 °C, 70 °C, and 80 °C) temperatures by the gravimetric static method using saturated saline solutions to obtain relative humidities ranging from 5.20% to 98.48%. The net isosteric heat of desorption and thermodynamic properties such as differential enthalpy, differential entropy, and Gibbs free energy were also estimated. The GAB model presented the best parameters of fitting and the lowest Akaike and Bayesian information criteria values. The desorption isotherm patterns presented a sigmoid shape and were categorized as BET Type II sigmoid shape. The monolayer moisture content was estimated in 0.06 kg of water/kg of dry matter using the GAB model, and it remained constant at all temperatures. The leaves presented an optimal water activity equal to 0.35 at storage temperatures. The net isosteric heat of desorption decreased with increasing Xeq, and differential entropy increased with rising equilibrium moisture content (Xeq > 0.07). The enthalpy-entropy compensation was evaluated, and the desorption process is revealed to be entropy-driven and non-spontaneous.

Seasonal Autoregressive Integrated Moving Average (SARIMA) for Melon (Cucumis melo) Yield from 2011 to 2020 Based on Planting Date Period

The main goal of time series modelling is to collect and analyze past values to develop appropriate models that describe the inherent structure and characteristics of the series about the planting date of melon. The data used in this study are the melon yield based on twenty-nine days of planting date in March from the first to the last planting date of March 2011 to 2020, to evaluate the prediction performances of the models, two indices: the root mean squared error (RMSE) and mean absolute percentage error (MAPE) were used to compare the forecasting capabilities of the SARIMA models, Excel was used first for preparing as CSV file and then changed to time series data to make the dataset of yield and R version 4.0.2 (the R Development Core Team) was used to perform ETS and SARIMA models For planting date time series based on yield, seasonal autoregressive integral moving average models (SARIMA) were constructed. The Student's t-test was one of the statistical tests used to evaluate the effectiveness and validity of the SARIMA models Through the use of SARIMA models, it is feasible to create synthetic records that maintain the statistical properties of the historical record. Finally, the results can be applied to different planting dates based on yield. Based on the Bayesian Information Criteria (BIC) values and the overall highest R2 values of 0.94, the optimal SARIMA (0,1,1) model was chosen. No limitations were found. It is the first time that this mathematical method was used to analyze time series data for 10 years of planting date of melon and it could be useful for agronomists and horticulturists to choose the best model.

Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates

Multi-walled carbon nanotubes (MCNTs)-enhanced glass fiber composite (GFC) laminates are among the most promising materials for fulfilling various structural and non-structural requirements. They have also shown exceptional functional applications as excellent electrical and thermal conductors, as well as electromagnetic interference shielding materials. The present work primarily focuses on developing regression models for the drilling process of 0.3 wt% MCNTs-GFC laminates. For experimentation, three different coated drills—carbide, TiCN-coated, and TiAlN-coated—are used under both dry and chilled air cutting environments. The lowest thrust force, torque, and delamination factor were observed at a feed rate of 10 mm min−1 and a speed of 1500 RPM using a TiCN-coated drill in a chilled air environment. Regression analysis reveals that feed rate significantly influences thrust force, as justified by the R2 value, which is above 90% for the selected cutting conditions. The corresponding t and F statistics values indicate the statistical significance of the relevant explanatory factors. The efficiency of the developed models is further validated by considering the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, which are 136.9 and 144.7, respectively. These values indicate a good regression fit and likelihood of the models for data prediction. Additionally, there is a strong correlation (coefficient > 0.85) between thrust force and delamination factor under the selected cutting environments. Concurrently, the developed regression models are simulated and evaluated for random experiments (Nos. 87, 125, 187, 243, 244, and 399), and the predicted responses closely match the experimental values.

A COMPARISON OF STATISTICAL DISTRIBUTIONS FOR THE CRUDE BIRTH RATE DATA

Population statistics and demographic are important indicators to show the country's quality of life, social and health, the status of the population, the change in the population structure, and its effect on economic life. In demography, the crude birth rate is used to measure the growth of a population. In this study, we perform the crude birth rate values by statistical regions in Türkiye with some statistical distributions. When the results are compared, the Gumbel distribution provides the best fit to model the crude birth rate values than Normal, Log-Normal, Exponential, Gamma, and Weibull distributions. Among compared distributions, the Gumbel model is the best model to present the CBR data since log likelihood (logL), Akaike information criteria (AIC) and Bayesian information criteria (BIC) values of the Gumbel distribution are the lowest among all models. In addition, the results with the compared distributions are supported by graphs.

Correlation between the Density of Acipenser sinensis and Its Environmental DNA.

Since the construction of the Gezhouba Dam, Chinese sturgeon (Acipenser sinensis) numbers have gradually declined, rendering this species critically endangered according to the International Union for the Conservation of Nature. Environmental DNA (eDNA) technology plays an important role in monitoring the abundance of aquatic organisms. Species density and biomass have been proven to be estimable by researchers, but the level of accuracy depends on the specific species and ecosystem. In this study, juvenile A. sinensis, an endangered fish, were selected as the research target. Under controlled laboratory conditions in an aquarium, one, two, four, six, and eight juvenile A. sinensis were cultured in five fish tanks, respectively. Water samples were filtered at eight different time points for eDNA content analysis. Additionally, eDNA yield was tested at six different time points after a 0.114 ind./L density of A. sinensis was removed, and the employed degradation model was screened using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The results showed that eDNA content remained stable after 3 days and exhibited a significant positive linear correlation with the density of A. sinensis (R2 = 0.768~0.986). Furthermore, eDNA content was negatively correlated with the 3-day period after the removal of A. sinensis. The power function had the smallest AIC and BIC values, indicating better fitting performance. This study lays a momentous foundation for the application of eDNA for monitoring juvenile A. sinensis in the Yangtze Estuary and reveals the applicability of eDNA as a useful tool for assessing fish density/biomass in natural environments.

Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China.

As a vital component of arbor forests, understory vegetation serves as an essential buffer zone for storing carbon due to its strong capacity for community regeneration. This study aimed to identify the diversity pattern and construction mechanism of Platycladus orientalis and Pinus elliottii understory vegetation based on large-scale sample surveys. The Bayesian Information Criterion value of species abundance distribution (SAD) indicated that the Zipf and Zipf-Mandelbrot models were the best-fitting models. The SAD and gambin fitting results suggested that the Pi. elliottii community had a more balanced structure, with most species being relatively abundant. The multiple regression tree model detected four and six indicator species in P. orientalis and Pi. elliottii communities, respectively. The α-diversity index increased with a rise in altitude and showed a wavy curve with latitude. Linear regression between the β diversity and environmental and geographic distance indicated that the P. orientalis and Pi. elliottii understory communities tended to be dominated by different ecological processes. The partition of β diversity indicated that both communities were dominated by turnover processes, which were caused by environmental classification or spatial constraints. This study helped to understand the diversity maintenance in the P. orientalis and Pi. elliottii understory vegetation communities, and will benefit for diversity restoration and conservation of pure conifer forests.

Evaluating Non-Stationarity in Precipitation Intensity-Duration-Frequency Curves for the Dallas–Fort Worth Metroplex, Texas, USA

Extreme precipitation has become more frequent and intense with time and space. Infrastructure design tools such as Intensity-Duration-Frequency (IDF) curves still rely on historical precipitation and stationary assumptions, risking current and future urban infrastructure. This study developed IDF curves by incorporating non-stationarity trends in precipitation annual maximum series (AMS) for Dallas–Fort Worth, the fourth-largest metropolitan region in the United States. A Pro-NEVA tool was used to develop non-stationary IDF curves, taking historical precipitation AMS for seven stations that showed a non-stationary trend with time as a covariate. Four statistical indices—the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and Nash–Sutcliffe Efficiency (NSE)—were used as the model goodness of fit evaluation. The lower AIC, BIC, and RMSE values and higher NSE values for non-stationary models indicated a better performance compared to the stationary models. Compared to the traditional stationary assumption, the non-stationary IDF curves showed an increase (up to 75%) in the 24 h precipitation intensity for the 100-year return period. Using the climate change adaptive non-stationary IDF tool for the DFW metroplex and similar urban regions could enable decision makers to make climate-informed choices about infrastructure investments, emergency preparedness measures, and long-term urban development and water resource management planning.

A novel tumor staging system incorporating cN status for stratifying early stage esophageal squamous cell carcinoma patients after trimodal therapy

BackgroundsThe aim of this study is to investigate the prognostic value of cN status for early stage esophageal squamous cell carcinoma (ESCC) patients after neoadjuvant chemoradiotherapy (nCRT) and construct a new staging model for individual survival prediction. MethodsPatients with ESCC who underwent nCRT and esophagectomy were included in this study. Both the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were meticulously ascertained to assess the cogency of each oncological staging system. A discernible abatement in the values of AIC and BIC signifies a model endowed with enhanced predictive prowess and exemplary veracity. ResultsA new staging model was established based on ypTNM stage and cN status by precisely stratifying ypI ESCC patients. The novel ypTNM-cN staging demonstrated superior overall survival trend alignment over the AJCC 8th ypTNM staging, with a notably lower AIC of 3143.014 versus 3149.950. This superiority was supported by a BIC of 3146.605 against 3153.541. In the context of disease-free survival outcomes, the emergent ypTNM-cN staging, with an AIC value registering at 3196.057 and a BIC value at 3199.648, distinctively eclipsed the AJCC 8th ypTNM staging, which documented values of 3203.853 and 3207.444, respectively. ConclusionWe constructed a new staging system based on ypTNM stage and cN status to precisely stratify the patients with ypI stage. Our new ypTNM-cN staging system provides new insights for classifying stage ypI ESCC and shows reliable classification efficacy for all ESCC patients after nCRT and surgery.

Growth curve analysis in different generations of Boer x Central Highland goats using alternative estimation models.

Growth curve analysis can help to optimize the management, determine nutritional requirements, predict the weight of animals at a specific age, and to select highly productive animals. Therefore, this study aimed to find the best-fitted nonlinear functions to provide a specific shape of the growth curve from birth to yearling age in different generations of Boer x Central Highland goats. Gompertz, Logistic, Brody, Von Bertalanffy, Monomolecular, Negative exponential, and Richards models were evaluated to quantify their ability to describe the biological growth curve. Root mean square error (RMSE), Bayesian information criterion (BIC), adjusted coefficient of determination (AdjR2), and Akaike's information criterion (AIC) were used to evaluate the goodness of fit and flexibility of the models. Data were analyzed using the nonlinear regression procedure of SAS. High AdjR2 and lower AIC, BIC, and RMSE values are indicators of best-fitted model. The best-fitting model for the first filial generation (F1), second filial generation (F2), and male goats' growth data was Brody function, whereas the Richards model, followed by Brody, best described the growth of third filial generation (F3) and female goats. The values of parameter A (asymptotic weight) for F1, F2, F3, female, and male goats based on the Brody model were 30.5±1.32, 28.2±1.38, 24.4±1.04, 27.8±0.94, and 29.8±1.32 kg for F1, F2, F3, female, and male goats, respectively. As per the best-fitted growth function, the asymptotic weight tended to reduce when the filial generation increased. The asymptotic weight for male goats was higher than for female goats. F1 had a slightly small value of parameter K, followed by F2 and F3. Both males and females had similar maturity rates. Based on the Brody function, the correlation between maturation rate and mature weight was high (-0.98, P<0.001). The correlation estimates for A-B and B-K were 0.27 and -0.15, respectively. Brody was best fitted for most goat categories, although Richards, followed by Brody, was best fitted for female and F3 goats. Besides, Brody could be better than Richards due to the ease of interpretation, convergence, and applicability for a small sample size. Therefore, the Brody function can predict the mature body weight, maturation rate, and growth rate of Boer x Central Highland goats and be used to formulate breeding and management strategies for profitable goat farming.

Predicting Potential Evapotranspiration for Kalaburagi District using a Seasonal Arima Model

Forecasting potential evapotranspiration (PET) is of great importance in effectively managing irrigation systems. This article centers around models designed to simulate future PET levels for the Kalaburagi district. The study calculates potential evapotranspiration using temperature data in degrees Celsius, employing the Thornthwaite method, and prediction is performed using the Seasonal Autoregressive Moving Average (SARIMA) method. These models are developed based on autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis. Model selection is based on minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values. The chosen models for different stations in Kalaburagi, Chincholi, Sedam, Chittapur, Aland, Jewargi, and Afzalpur respectively are SARIMA (1,0,1)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA(1,0,0)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA (1,0,1) (2,1,0) 12, and SARIMA(1,0,1)(2,1,0)12. The results indicate that the models developed for Jewargi and Chincholi stations show particular promise compared to the other two stations, with all four models performing well. These models have the potential to significantly enhance decision-making in irrigation planning and command area management practices, contributing to improved water resource management.

Determinants of COVID-19 vaccine intention in a University population: a serial mediation approach

Abstract Background While many studies have used the Health Belief Model (HBM) to understand vaccination intention, few have explored the relationships between the HBM constructs. The aim of this study is to develop a serial mediation model dealing with latent variables to assess direct and indirect effects of the six HBM constructs (perceived susceptibility, severity, benefits, barriers, self-efficacy and cue to action) on COVID-19 vaccine intention. Methods From April to June 2021, a questionnaire on vaccine intention against COVID-19 was administered to staff and students at the University of Liège (Belgium). To evaluate direct and indirect effects of the HBM constructs on vaccine intention (score 0-100), serial mediation models for each latent variable permutation were assessed with Partial Least Squares Path Modeling (PLS-PM). Bayesian information criterion (BIC) was used to compare models. Internal consistency reliability and discriminant validity were evaluated. Sociodemographic variables, health literacy, psychological profile, body mass index, chronic disease and previous COVID-19 infection were included in the models as covariates. Results The sample consisted of 1256 participants. After running all permutation chains, the final causal chain, with the lowest BIC value, was barriers (-0.09 (-0.15 - -0.03))* - severity (-0.13 (-0.20 - -0.07))* - low self-efficacy (0.20 (0.15 - 0.25))* - low susceptibility (-0.55 (-0.60 - -0.51))* - vaccine intention (outcome). This highlighted a significant indirect and direct effect (-0.20 (-0.25 - -0.15))* between barriers and vaccine intention. Constructs benefits and cue to action were removed due to no significant path and weak reliability. Non-significant confounding factors were also removed. *estimate Conclusions The results showed that perceived barriers are a key determinant of COVID-19 vaccine resistance. Public health actors should communicate messages to remove barriers that reduce vaccine intention primarily. Key messages • Serial mediations allow a better understanding of how a vaccination intention works. • Public health communications should primarily focus on messages to remove barriers to get vaccinated.