AIC Value Research Articles

Exploring time-to-event analysis models: A study of accelerated failure time and weighted least square estimation

Background: This study explores time-to-event analysis models, focusing on the comparison between accelerated failure time (AFT) and weighted least square estimation (WLSE) methods in analyzing survival data, emphasizing the challenge of heteroscedasticity in real-world datasets. Methods: Using the North Central Cancer Treatment Group (NCCTG) Lung Cancer dataset, five parametric AFT distributions (exponential, Weibull, log-logistic, log-normal, and generalized gamma) were evaluated. The log-likelihood method was utilized to calculate the AIC and BIC values for each distribution to determine the best fit model. Results: Among the different distributions tested, the Weibull distribution was identified as the best fit model based with the lowest AIC and BIC values of 2311.70 and 2318.56, respectively. This distribution played a crucial role in determining factors associated with the cancer data. Specifically, the variablecalories consumed at a meal (meal.cal) was associated with cancer patient recovery, with an estimate of 0.00004 and a standard error of 0.0002. Results indicated homoscedastic data through the Breusch-Pagan test. Moreover, the results demonstrated that when dealing with homoscedastic data, the Weibull AFT model outperformed WLSE in producing statistically significant effects and precise estimates. Conclusion: For data exhibiting homoscedasticity, the AFT method yielded superior results compared to WLSE in terms of statistical significance and precision. Therefore, it is advisable to employ AFT for homoscedastic data and WLSE for heteroscedastic data to enhance the accuracy of covariate effect estimates in survival analysis.

The Number of Episodes of Subtherapeutic Tacrolimus Blood Level Is Independently Associated With Reduced Kidney Graft Survival

ABSTRACTBackgroundTacrolimus blood level variability is associated with reduced graft survival among kidney transplant recipients. To date, no practical approach for reducing variability has been validated. We defined specific tacrolimus blood level patterns correlated with variability and evaluated their independent association with reduced graft survival.MethodsIn this single‐center retrospective study, we predefined 12 patterns that exhibited correlation with high tacrolimus blood level variability. Subsequently, we utilized a multivariate Cox proportional hazard model, in conjunction with the Akaike information criteria, to evaluate the association between the predefined patterns and decreased graft survival.ResultsOur cohort included 1305 kidney transplant recipients. The primary outcome of this trial was graft loss, defined as the initiation of chronic dialysis or the need for retransplantation. The secondary outcome was the combination of death‐censored graft loss and death with a functioning graft. During the study's follow‐up period, there were 131 events of graft loss. The number of episodes of subtherapeutic tacrolimus level during the first‐year posttransplantation was significantly associated with graft loss (HR 1.208 per episode, 95% CI 1.075–1.356, p = 0.001) and significantly improved the relative likelihood of the model compared to the multivariate model as demonstrated by the delta AIC value (8.256, p = 0.016).ConclusionIn addition to increased tacrolimus blood level variability, the number of episodes of subtherapeutic tacrolimus levels is independently associated with decreased graft survival among kidney transplant recipients.

Effect of heat stress on semen characteristics and genetics in Thai native grandparent roosters

Grandparent roosters are crucial in poultry breeding programs and significantly influence future bird generations' genetic makeup and performance. However, these roosters face considerable challenges from heat stress, which can adversely affect their reproductive performance, semen quality, and overall health and welfare. Our study aimed to investigate the effects of heat stress on the genetics of semen characteristics, identify the appropriate temperature and humidity indices (THI), and determine the threshold point of heat stress to prevent thermal stress. We analyzed data from 3,895 records of 242 Thai native grandparent roosters in conjunction with the THI using 7 THI functions and the regression method. The threshold point of heat stress, genetic parameters, rate of decline of semen characteristics per level of THI, estimated breeding values and selection index values were analyzed using the multivariate test-day model in the AIREML and BLUPF90 programs. Based on the regression coefficient and statistical criteria of the lowest -2logL and AIC values, the results showed that a THI of 78 was considered the threshold point of heat stress. The estimated heritability values ranged from 0.023 to 0.032, 0.066 to 0.069, 0.047 to 0.057, and 0.022 to 0.024 for mass movement, semen volume, sperm concentration, and the semen index, respectively. The reduction rates of mass movement, semen volume, sperm concentration, and semen index at a THI of 78 were -0.009, -0.003, -0.170, and -0.083 per THI, respectively. The genetic correlations among the semen traits were moderately to strongly positive and ranged from 0.562 to 0.797. The genetic correlations between semen traits and heat stress were negative and ranged from -0.437 to -0.749. The permanent environmental correlations among the semen traits (0.648-0.929) were positive and greater than the genetic correlations. Permanent environmental correlations between semen traits and heat stress were negative and ranged from -0.539 to -0.773. The results of the selection indices showed that the higher the selection intensity was, the greater the degree to which the selection index corresponded to genetic progress. The recommendation for animal genetic selection is that the top 10% is appropriate because it seems most preferred. Therefore, using a multivariate test-day model and selection index for the high genetic potential of semen traits and heat tolerance in Thai native grandparent roosters makes it possible to achieve genetic assessment in a large population.

Trend, and multivariate decomposition of perinatal mortality in Ethiopia using further analysis of EDHS 2005–2016

BackgroundPerinatal mortality is a global health problem, especially in Ethiopia, which has the highest perinatal mortality rate. Studies about perinatal mortality were conducted in Ethiopia, but which factors specifically contribute to the change in perinatal mortality across time is unknown.ObjectivesTo assess the trend and multivariate decomposition of perinatal mortality in Ethiopia using EDHS 2005–2016.MethodsA community-based, cross-sectional study design was used. EDHS 2005–2016 data was used, and weighting has been applied to adjust the difference in the probability of selection. Logit-based multivariate decomposition analysis was used using STATA version 14.1. The best model was selected using the lowest AIC value, and variables were selected with a p-value less than 0.05 at 95% CI.ResultThe trend of perinatal mortality in Ethiopia decreased from 37 per 1000 births in 2005 to 33 per 1000 births in 2016. About 83.3% of the decrease in perinatal mortality in the survey was attributed to the difference in the endowment (composition) of the women. Among the differences in the endowment, the difference in the composition of ANC visits, taking the TT vaccine, urban residence, occupation, secondary education, and birth attendant significantly decreased perinatal mortality in the last 10 years. Among the differences in coefficients, skilled birth attendants significantly decreased perinatal mortality.Conclusion and recommendationThe perinatal mortality rate in Ethiopia has declined over time. Variables like ANC visits, taking the TT vaccine, urban residence, occupation, secondary education, and skilled birth attendants reduce perinatal mortality. To reduce perinatal mortality more, scaling up maternal and newborn health services has a critical role.

Four-parameter beta mixed models with survey and sentinel 2A satellite data for predicting paddy productivity

Ensuring food security, fostering agricultural sustainability, and driving economic development. However, existing prediction models often overlook the unique characteristics of paddy productivity distribution, which varies between areas, skewed, and bounded within a certain minimum and maximum range, following a four-parameter beta distribution. Consequently, these models yield less accurate and potentially misleading predictions. Additionally, most approaches fail to capture the complex interrelationships among variables that often occur when we incorporate satellite data alongside survey data that has been recognized as a key approach for improving prediction accuracy and optimizing farming practices. To address these shortcomings, this study introduces a four-parameter beta Generalized Linear Mixed Model (GLMM) augmented within a four-parameter beta Generalized Mixed Effect Tree (GMET). The four-parameter beta GMET, an extension of the four-parameter beta GLMM model integrated with a regression tree, offers enhanced flexibility in modeling complex relationships. Application of this methodology to an empirical study in Central Kalimantan and Karawang reveals notable improvements over previous methods, as evidenced by substantially lower AIC and RRMSE values. Notably, the analysis identifies lagged values of band 4, band 8, and NDVI from Sentinel-2A satellite data as significant predictors of paddy productivity, overriding the importance of farmer survey variables. This underscores the potential of satellite data to be utilized in paddy productivity predictions, offering a more efficient and cost-effective alternative to farmer survey-based methods. By enhancing satellite technology, future efforts in paddy productivity prediction can achieve higher efficiency and accuracy, contributing to informed decision-making in agricultural management.

ANALISIS SPASIAL BERBASIS PEMETAAN MENGGUNAKAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA SEBARAN JUMLAH WISMA DI KABUPATEN LOMBOK BARAT

This research aims to analyze the effect of population density, number of food stalls, and population on the number of homesteads using the Geographically Weighted Regression (GWR) model. GWR modeling is carried out based on spatial data using secondary data sourced from the Central Statistics Agency (BPS). First, multiple linear regression analysis was carried out to evaluate the relationship between variables. The results of multiple linear regression show that only the variable number of food stalls has a significant effect on the number of homesteads. Next, spatial aspects were tested, including spatial autocorrelation tests and spatial heterogeneity tests. Testing spatial aspects shows the existence of spatial autocorrelation and spatial heterogeneity in the data. Therefore, we continued with GWR modeling to obtain a more in-depth picture of the distribution of the number of homesteads in West Lombok Regency. The results of modeling with GWR show that the variables used have a spatially varying influence throughout the West Lombok Regency area. Mapping GWR results produces better modeling of the factors that influence the number of homesteads in each village. A comparison between the multiple linear regression model and the GWR shows that the GWR model is able to explain variations in the number of homesteads with a higher R-square and a smaller AIC value

Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator

Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable. The problem that often occurs in regression analysis is that there are multicollonity and outliers. To deal with such problems can be solved using ridge regression analysis and robust regression. Ridge regression can solve the problem of multicollinearas by assigning a constant k to the matrix Z′Z. But in this method the resulting bias value is still high, so to overcome this problem, the jackknife ridge regression method is used. Meanwhile, to overcome outliers in the data using robust regression methods which have several estimation methods, two of which are the Generalized-M (GM) estimator and the Least Trimmed Square (LTS) estimator. The aim of the study is to solve the problem of multicollinearity and outliers simultaneously using robust jackknife ridge regression method with GM estimators and LTS estimators. The results showed that the robust ridge jackknife regression method with LTS estimator can control multicollinearity and outliers simultaneously better based on MSE, AIC and BIC values compared to the robust ridge jackknife regression method with GM estimators. This is indicated by the value MSE = -6.60371, AIC = 75.823 and BIC = 81.642 on LTS estimators that are of lower value than GM estimators.

Pituitary Adenoma: SSTR2 rs2236750, SSTR5 rs34037914, and AIP rs267606574 Genetic Variants, Serum Levels, and Ki-67 Labeling Index Associations.

Background and Objectives: This study explores the complex pathogenesis of pituitary adenomas (PAs), prevalent intracranial tumors in the pituitary gland. Despite their generally benign nature, PAs exhibit a diverse clinical spectrum involving hormone hypersecretion and varying invasiveness, hinting at multifaceted molecular mechanisms and abnormalities in tumorigenesis and gene regulation. Materials and Methods: The investigation focuses on the Ki-67 labeling index, SSTR2 rs2236750, SSTR5 rs34037914, and AIP rs267606574 polymorphisms, alongside serum levels of SSTR2, SSTR5, and AIP, to discern their association with PAs. The Ki-67 labeling index was assessed using immunohistochemical analysis with the monoclonal antibody clone SP6, representing the percentage of tumor cells showing positive staining. Genotyping was performed via real-time polymerase chain reaction, and serum levels were analyzed using ELISA. The study included 128 PA patients and 272 reference group subjects. Results: The results derived from binary logistic regression analysis revealed an intriguing correlation between the SSTR2 rs2236750 AG genotype and approximately a 1.6-fold increased likelihood of PA occurrence. When analyzing SSTR5 rs34037914, statistically significant differences were found between Micro-PA and the reference group (p = 0.022). Additionally, the SSTR5 rs34037914 TT genotype, compared with CC + CT, under the most robust genetic model (selected based on the lowest AIC value), was associated with a 12-fold increased odds of Micro-PA occurrence. However, it is noteworthy that after applying Bonferroni correction, these findings did not retain statistical significance. Conclusions: Consequently, while this study hinted at a potential link between SSTR2 rs2236750 and pituitary adenoma development, as well as a potential link between SSTR5 rs34037914 and Micro-PA development, it underscored the need for further analysis involving a larger cohort to robustly validate these findings.

MIXED GEOGRAPHICALLY WEIGHTED REGRESSION (MGWR) WITH ADAPTIVE WEIGHTING FUNCTION IN POVERTY MODELING IN NTT PROVINCE

Poverty modeling is a crucial economic and social development issue in various regions, including in East Nusa Tenggara (NTT) Province. This research proposes using the Mixed Geographically Weighted Regression (MGWR) model with an adaptive Bisquare weighting function to analyze variables influencing poverty levels in NTT Province. The MGWR model is an extension of the Geographically Weighted Regression (GWR), which allows some variables in the model to have local effects while others have global effects. The adaptive weighting function in the MGWR model enhances the analysis by providing different weights at each location according to its local characteristics, thus making the results more accurate and representative for each area. The data includes economic, social, and infrastructure variables from 22 districts/cities in NTT Province for 2023. The MGWR model with an adaptive weighting function is applied to model the relationship between these variables and poverty levels. The analysis integrates statistical software to manage and analyze spatial data. The study findings show that the MGWR model with an adaptive weighting function offers better estimates than the global regression and GWR models. The results revealed the smallest AIC value for the MGWR model at 104.1888, compared to the global regression model at 140.1427 and the GWR model at 117.6174. This model successfully identifies significant local and global variables and shows variations in influence at different locations in NTT Province. These findings provide valuable insights for policymakers and practitioners in designing and implementing more effective poverty alleviation strategies tailored to local conditions in NTT Province.

Analysis of Economic Growth in East Nusa Tenggara Province Using Spatial Regression Model

This research aims to examine the significant factors influencing economic growth in the East Nusa Tenggara Province. The estimation is carried out through a spatial regression model approach. The research variables were selected based on the economic growth model proposed by Mankiw-Romer-Weil, namely Gross Regional Domestic Product at constant prices (Y), Labor ( X1), Electricity Consumption (X2 ), Regional Original Income (X3 ), Capital Expenditure ( X4), and Schooling Duration (X5 ). The research data consists of secondary data from the year 2022 in 22 regencies/cities obtained from the Central Statistics Agency (BPS) of East Nusa Tenggara Province. Modeling with the Ordinary Least Squares (OLS) regression method resulted in three significant independent variables at α=5%: local revenue, capital expenditure, and average years of schooling. Based on diagnostic tests, there was spatial dependence in lag and error, leading to the use of Spatial Autoregressive Model (SAR) and Spatial Error Model (SEM) for spatial regression. From these spatial models, the significant independent variables influencing economic growth in the 22 regencies/cities in East Nusa Tenggara were local revenue, capital expenditure, and average years of schooling. The queen contiguity weighting was used. Based on R2 and AIC criteria, the best spatial regression model was the Spatial Error Model (SEM) with the highest R2 of 0.837610 and the smallest AIC value of 393.03. For further research, it is recommended to consider local factors that may influence economic growth, such as the sustainability of the agricultural or tourism sector, which may vary in each region.

Response of tomato fruit to consecutive impact loading

Tomato fruits receive successive impacts during harvest and postharvest operations. This paper is a study of the response of tomato fruit to mono and multiple dynamic loadings and the ability to withstand consecutive impacts, which play the most critical role in downgrading and postharvest loss of fresh tomato fruits and designing harvest, and postharvest handling and processing equipment. The fruits were subjected to consecutive impacts by an instrumented pendulum one to three times successive impacts at the same location with the three different impact energy levels: 125, 250, and 500 mJ to establish a comprehensive model and refining by adding various groups of contributing factors to understand better and find out which parameters are more likely to cause and contribute in tomato mechanical damage. Twenty sub-models were evaluated using AIC and R2 values. The parameters of the preferred logistic model consist of response variables (peak contact force, contact time, and Elast), loading conditions (one, two, and three-times impact at the same location), and fruit quality parameter (total soluble solids). Also, another model was suggested for rapid assessment of bruise development.

Bulk viscous cosmological model in [formula omitted] modified gravity

This article explores the impact of bulk viscosity on understanding the universe’s accelerated expansion within the context of modified f(T,T) gravity, which is an extension of the f(T) gravitational theory, allowing a broad coupling between the energy–momentum scalar T and the torsion scalar T. We consider two f(T,T) functions, specifically f(T,T)=αT+βT and f(T,T)=α−T+βT, where α and β are arbitrary constants, along with the fluid part incorporating the coefficient of bulk viscosity ζ=ζ0>0. We calculate the analytical solutions of the corresponding field equations for a flat FLRW environment, and then we constrain the free parameters of the obtained solution using CC, Pantheon+, and the CC+Pantheon+ samples. We perform the Bayesian statistical analysis to estimate the posterior probability utilizing the likelihood function and the MCMC random sampling technique. Further, to assess the effectiveness of our MCMC analysis, we estimate the corresponding AIC and BIC values, and we find that there is strong evidence supporting the assumed viscous modified gravity models for all three data sets. Also, we find that the linear model precisely mimics the ΛCDM model. We also investigate the evolutionary behavior of some prominent cosmological parameters. We observe that the effective equation of state parameter for both models predict the accelerating behavior of the cosmic expansion phase. In addition, from the statefinder test, we find that the parameters of the considered MOG models favor the quintessence-type behavior. Further, we observe that the behavior of Om(z) curves corresponding to both models represent a consistent negative slope across the entire domain. We infer that our cosmological setting utilizing f(T,T) gravity models with the viscous matter fluid embodies quintessence-like behavior and can successfully describe the late-time scenario.

Bootstrap-after-bootstrap for autoregressive models: an application to Indonesian value of export oil and gas

This research focuses on predicting the value of oil and gas exports in Indonesia, employing a hybrid methodology that combines autoregressive models and a bootstrap approach. Specifically, this research applies the bootstrap-after-bootstrap approach to showcase its effectiveness in improving the accuracy of parameter estimates. Analysis results indicate that the autoregressive model with an order of p=2 minimizes the AIC, BIC, and HQ values, yielding AIC=9.833775, BIC=10.03125, and HQ=9.883440, respectively. Consequently, the AR(2) model emerges as the optimal choice for predicting Indonesia's export value of oil and gas. This research utilizes varying numbers of bootstrap replications (B=100, 250, 500, 1000, and 10000) to assess the impact on prediction intervals. Prediction intervals exhibit less smoothness for B=100 and B=250, whereas B=500 and B=1000 result in a considerably smoother pattern. The highest level of smoothness is achieved for B=10000. The findings underscore that bootstrap-after-bootstrap prediction intervals provide the most accurate and conservative assessment of future uncertainty. Moreover, predictive analysis for the upcoming five periods indicates a projected decline in the export value of oil and gas in Indonesia. Overall, this research demonstrates the efficacy of the bootstrap-after-bootstrap approach in enhancing the precision of predictions and providing robust insights into future uncertainties surrounding Indonesia's oil and gas export market.

PEMODELAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) PADA KEJADIAN STUNTING DIiKABUPATEN BARITO KUALA TAHUN 2022

Stunting is a condition of malnutrition in toddlers that causes their height to be lower than other children their age. In 2022, South Kalimantan Province has a stunting prevalence of 24.6% and ranks fifteenth in Indonesia. Barito Kuala Regency, one of the regions in South Kalimantan Province, has the highest stunting rate at 33.6% which is included in the Chronic- Acute category (≥ 20%). This study uses the GWNBR model to characterize the factors that cause stunting in Barito Kuala Regency. The GWNBR model will make it easier for researchers to find out the factors that affect stunting in each sub-district. The weight matrix used is a fixed kernel function and an adaptive kernel function. The predictor variables used were the percentage of infant history of complete basic immunization, history of exclusive breastfeeding in infants <6 months, history of low birth weight babies, new visits to pregnant women (K1), sixth antenatal care (ANC) visit (K6), history of pregnant women who received blood supplement tablets, history of infants 6-11 months who received vitamin A, active posyandu and households with access to appropate sanitary facilities.(healthy latrines). The best model results obtained with adaptive gaussian weighting with an AIC value of 167.25. Keywords: Stunting Cases, GWNBR model.

ANALISIS PERBANDINGAN MODEL REGRESI LINIER BERGANDA, SPATIAL DURBIN ERROR MODEL (SDEM), DAN SPATIAL LAG X (SLX) DALAM PERMODELAN DATA INDEKS PEMBANGUNAN MANUSIA (IPM) DI PROVINSI KALIMANTAN SELATAN

This research aims to determine the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag In this research there are three independent variables, namely poverty severity (2022), population density (2022) and pure participation rate (2019), while the dependent variable is the human development index (2022). This research data is secondary in nature, namely obtained from the website of the South Kalimantan Central Statistics Agency. Based on the results and discussion, it is concluded that the best model from the comparison of multiple linear regression models, spatial durbin error model (SDEM), and spatial lag x (SLX) in modeling human development index (HDI) data in South Kalimantan province is the spatial durbin error model (SDEM). This is because the spatial durbin error model (SDEM) has the smallest AIC value compared to the multiple linear regression model, and spatial lag x (SLX).

Unravelling the impact of heat stress on daughter pregnancy rate in Mehsana buffalo through innovative breeding interventions

Heat stress profoundly affects the reproductive success of buffaloes, which are vital for the dairy industry due to their unique anatomical and physiological characteristics, necessitating careful evaluation under such conditions. Hence, this guided our search for quantifying heat stress' impact on Mehsana buffaloes using the best THI model and evaluating sires’ performance. Fertility records (days open converted to daughter pregnancy rate) were collected in the span of over 24 years, w.e.f. 1989 to 2012. Finally, 3070 records of first lactation cows, daughters of 117 sires from DURDA, Gujarat, India, were used in the analysis. Meteorological data were retrieved from IMD, Pune, to understand the relationship between daughter pregnancy rate (DPR) and heat stress indicators. Several heat stress models were compared based on R2, adjusted R2, AIC, and BIC values, and the impact of heat stress was quantified. The year was classified into different heat stress zones, viz., Non heat stress zone (NHSZ), Heat stress zone (HSZ), and critical heat stress zone (CHSZ), drawing from the findings of DPR and THI. The THI 4th model based on dry and wet bulb temperature was identified as the best-fit model, and DPR significantly changed (P < 0.01) by 1.14% per unit change in THI value based on the month of calving. The average EBVs of the sires for DPR were found to be 20.78% (NHSZ), 38.09% (HSZ), and 39.08% (CHSZ) using BLUP-SM and 20.78% (NHSZ), 37.30% (HSZ), and 38.87% (HSZ) using BLUP-AM. Subsequently, the optimum sire for each of the zones was prioritized. It is noteworthy that bulls that performed better in NHSZ did not perform as well in HSZ and CHSZ, and vice versa. This supports the possibility of evaluating bulls independently in each heat stress zone.

Comparative Analysis of Time Series Forecasting using ARIMA, and GRNNs Models: A Case Study of Death Rate of Diabetic Mellitus in Canada

This research aims to compare ARIMA and GRNN models alone. For this comparison the death rate for diabetes mellitus time series data of Canada is used. Autoregressive Integrated Moving Average (ARIMA), and Generalized Regression Neural Networks (GRNN) models were applied for time series prediction of the death rate for diabetes mellitus—trained data for two models from 2000 to 2015. Test data was used to compare the precision through data from 2016 to 2021. The ARIMA model was applied using the auto-command through R package which provided the least BIC and AIC values. The mean absolute deviation (MAD), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were employed to measure the forecasting efficiency of the models. The ARIMA model had the highest prediction accuracy as compared to the GRNN model. ARIMA predicts the death rate for diabetes mellitus more accurately and robustly compared to the GRNNs model.

Geographically Weighted Poisson Regression for Modeling the Number of Maternal Deaths in Papua Province

Introduction/Main Objectives: Maternal Mortality Rate (MMR) in Indonesia is one of the main focuses in achieving the third Sustainable Development Goals (SDGs) in 2030. Background Problems: The Central Statistics Agency states that the MMR in Papua Province is the highest, reaching 565. Novelty: Given the diverse geographical conditions of each district/city in Papua Province, an analysis was carried out. Research Methods: Using the Geographically Weighted Poisson Regression (GWPR) method with the response variable being maternal mortality rates and variables predictors of health, social, and environmental factors. Finding/Results: Fixed Gaussian kernel GWPR is the best model with an AIC value of 27.6. Variable significantly influencing MMR include the percentage of households with access to adequate sanitation, the number of recipients of food assistance programs, and the number of doctors.

Machine learning-based aggressiveness assessment model construction for crabs: A case study of swimming crab Portunus trituberculatus

Aggressiveness trait-based selection is crucial for alleviating interspecies cannibalism in economic crab species and enhancing survival rates in aquaculture. However, there is a lack of efficient and simple methods for assessing aggressiveness. In this study, we measured aggressiveness of the swimming crab Portunus trituberculatus through repeated mirror tests and fighting experiments. Factor analysis and the K-means algorithm were used to assess aggressiveness quantitatively and qualitatively. A combination of multiple linear regression and support vector machine (SVM) analyses was employed to construct an aggressiveness assessment model for swimming crabs and explore the relationship between aggressiveness and fighting ability. The results showed significant correlations among repeated aggressive behaviors (attacking, chela extending, defending, crossing, reverse walking, and freezing). Aggression score was significantly correlated with fighting behaviors, and there were significant differences in fighting abilities among different levels of aggressiveness. This suggested that aggressive behaviors are consistent within individuals and that aggressiveness, as a personal trait, affects the fighting ability of swimming crabs. Aggression score (Y) and clustering results of K-means can serve as assessment indicators of aggressiveness. The predictive variables for the quantitative assessment model were relative movement distance (X1) and freezing duration (X2). The adjusted R-square of the optimized quantitative model was 0.72, it also had the smallest Sigma, AIC, MSE, and RMSE values and the best fitting regression equation, which was Y = 0.023X1 – 0.001X2 – 0.002. The predictor variables for the qualitative assessment model were relative movement distance, freezing frequency, and duration. SVM was used to construct the qualitative model, and the prediction accuracy was 92%, sensitivity was 84%, and specificity was 100%, indicating the model has a good classification and prediction effect. The machine learning-based aggressiveness assessment model constructed in this study provides a behavioral method for the selection and high-throughput measurement of economic crab species with excellent aggressiveness traits, giving it important industrial application value.

Enhancing Forecasting Accuracy in Commodity and Financial Markets: Insights from GARCH and SVR Models

The aim of this study is to enhance the understanding of volatility dynamics in commodity returns, such as gold and cocoa, as well as the financial market index S&amp;P500. It provides a comprehensive overview of each model’s efficacy in capturing volatility clustering, asymmetry, and long-term memory effects in asset returns. By employing models like sGARCH, eGARCH, gjrGARCH, and FIGARCH, the research offers a nuanced understanding of volatility evolution and its impact on asset returns. Using the Skewed Generalized Error Distribution (SGED) in model optimization shows how important it is to understand asymmetry and fat-tailedness in return distributions, which are common in financial data. Key findings include the sGARCH model being the preferred choice for Gold Futures due to its lower AIC value and favorable parameter estimates, indicating significant volatility clustering and a slight positive skewness in return distribution. For Cocoa Futures, the FIGARCH model demonstrates superior performance in capturing long memory effects, as evidenced by its higher log-likelihood value and lower AIC value. For the S&amp;P500 Index, the eGARCH model stands out for its ability to capture asymmetry in volatility responses, showing superior performance in both log-likelihood and AIC values. Overall, identifying superior modeling approaches like the FIGARCH model for long memory effects can enhance risk management strategies by providing more accurate estimates of Value-at-Risk (VaR) and Expected Shortfall (ES). Additionally, the out-of-sample evaluation reveals that Support Vector Regression (SVR) outperforms traditional GARCH models for short-term forecasting horizons, indicating its potential as an alternative forecasting tool in financial markets. These findings underscore the importance of selecting appropriate modeling techniques tailored to specific asset classes and forecasting horizons. Furthermore, the study highlights the potential of advanced techniques like SVR in enhancing forecasting accuracy, thus offering valuable implications for portfolio management and risk assessment in financial markets.