Model Fitting Performance Research Articles

Analysis of recurrent event data with spatial random effects using a Bayesian approach.

Recurrent event data, which represent the occurrence of repeated incidences, are common in observational studies. Furthermore, collecting possible spatial correlations in health and environmental data is likely to provide more information for risk prediction. This article proposes a comprehensive proportional intensity model considering spatial random effects for recurrent event data using a Bayesian approach. The spatial information for areal data (where the spatial location is known up to a geographic unit such as a county) and georeferenced data (where the location is exactly observed) is examined. A traditional constant baseline intensity function, as well as a flexible piecewise constant baseline intensity function, are both under consideration. To estimate the parameters, a Markov chain Monte Carlo method with the Metropolis-Hastings algorithm and the adaptive Metropolis algorithm are applied. To assess the performance of model fitting, the deviance information criterion and log pseudo marginal likelihood are proposed. Overall, simulation studies demonstrate that the proposed model is significantly better than models that do not consider spatial effects if spatial correlations exist. Finally, our approach is implemented using a dataset related to the recurrence of cardiovascular diseases, which incorporates spatial information.

Simulating multi-scale optimization and variable selection in species distribution modeling

Species distribution modeling (SDM) is a fundamental tool in theoretical and applied ecology. However, relatively little is known about the performance of different approaches for scale optimization, model selection, and algorithmic prediction in the context of nonlinear, multiscale and interactive relationships between environmental variables and species occurrence. Modelers often struggle to optimize a tradeoff between ecological relevance, model robustness, complexity, and overfitting. In this paper, we investigated several methods designed to optimize spatial scale and variable selection in SDMs, in each case evaluating model fitness, parsimony and predictive performance. We used a simulation approach to produce a large pool of alternative underlying habitat relationships that reflect a broad range of realistic habitat associations. We also compared several different modeling algorithms, including logistic regression with a generalized linear model (GLM), Lasso and Elastic-Net Regularized GLMs (GLMNet), and random forest (RF), as well as alternative variable and scale selection methods. We found that GLM methods employing all-subsets dredge routines for variable selection were consistently the best predictors based on all criteria of our model performance assessment and across all attributes of the simulated underlying relationship, including nonlinearity and interaction. We had expected machine learning approaches, such as random forest, to perform better in these more complex forms of species-environment relationships. GLM using dredge variable selection was also the method that included the fewest spurious covariates and included the most correct predictors as a proportion of all predictors. We found that univariate scaling was the most robust method of variable and scale selection, along with Minimal Redundancy Maximal Relevancy (MRMR) which performed equivalently. The simulation experiment presented here provides a robust assessment of simulated multi-species distribution model performance, complexity and fidelity. By simulating a large range of potential habitat relationships with varying spatial scale, effect sizes, linearity, and interactions, we comprehensively evaluated model performance across gradients of complexity of the underlying relationships and violations of classical statistical assumptions. This study provides a valuable assessment and a broader example of the power and utility of controlled simulation experiments in habitat relationships and other ecological spatial predictive modeling.

Personalizing cardiovascular risk prediction for patients with systemic lupus erythematosus

ObjectiveCardiovascular disease (CVD) risk is increased in SLE and underestimated by general population prediction algorithms. We aimed to develop a novel SLE-specific prediction tool, SLECRISK, to provide a more accurate estimate of CVD risk in SLE. MethodsWe studied patients in the Brigham and Women's Hospital SLE cohort. We collected one-year baseline data including the presence of traditional CVD factors and SLE-related features at cohort enrollment. Ten-year follow-up for the first major adverse cardiovascular event (MACE; myocardial infarction (MI), stroke, or cardiac death) began at day +1 following the baseline period (index date). ICD-9/10 codes identified MACE were adjudicated by board-certified cardiologists. Least absolute shrinkage and selection operator regression selected SLE-related variables to add to the American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Risk Equations 10-year risk Cox regression model. Model fit statistics and performance (sensitivity, specificity, positive/negative predictive value, c-statistic) for predicting moderate/high 10-year risk (≥7.5 %) of MACE were assessed and compared to ACC/AHA, Framingham risk score (FRS), and modified FRS (mFRS). Optimism adjustment internal validation was performed using bootstrapping. ResultsWe included 1,243 patients with 90 MACEs (46 MIs, 36 strokes, 19 cardiac deaths) over 8946.5 person-years of follow-up. SLE variables selected for the new prediction algorithm (SLECRISK) were SLE activity (remission/mild vs. moderate/severe), disease duration (years), creatinine (mg/dL), anti-dsDNA, anti-RNP, lupus anticoagulant, anti-Ro positivity, and low C4. The sensitivity for detecting moderate/high-risk (≥7.5 %) of MACE using SLECRISK was 0.74 (95 %CI: 0.65, 0.83), which was better than the sensitivity of the ACC/AHA model (0.38 (95 %CI: 0.28, 0.48)). It also identified 3.4-fold more moderate/high-risk patients than the ACC/AHA. Patients who were moderate/high-risk according to SLECRISK but not ACC/AHA, were more likely to be young women with severe SLE and few other traditional CVD risk factors. Model performance between SLECRISK, FRS, and mFRS were similar. ConclusionThe novel SLECRISK tool is more sensitive than the ACC/AHA for predicting moderate/high 10-year risk for MACE and may be particularly useful in predicting risk for young females with severe SLE. Future external validation studies utilizing cohorts with more severe SLE are needed.

A nomogram to predict the risk of venous thromboembolism in patients with colon cancer in China.

To create a nomogram for predicting the likelihood of venous thromboembolism (VTE) in colon cancer patients from China. The data of colon cancer patients from Chongqing University Cancer Hospital between 2019 and 2022 were analyzed. Patients were divided into training set and internal validation set by random split-sample method in a split ratio of 7:3. The univariable and multivariable logistic analysis gradually identified the independent risk factors for VTE. A nomogram was created using all the variables that had a significance level of p < 0.05 in the multivariable logistic analysis and those with clinical significance. Calibration curves and clinical decision curve analysis (DCA) were used to assess model's fitting performance and clinical value. Harrell's C-index (concordance statistic) and the area under the receiver operating characteristic curves (AUC) were used to evaluate the predictive effectiveness of models. A total of 1996 patients were ultimately included. There were 1398 patients in the training set and 598 patients in the internal validation set. The nomogram included age, chemotherapy, targeted therapy, hypertension, activated partial thromboplastin time, prothrombin time, platelet, absolute lymphocyte count, and D-dimer. The C-index of nomogram and Khorana score were 0.754 (95% CI 0.711-0.798), 0.520 (95% CI 0.477-0.563) in the training cohort and 0.713 (95% CI 0.643-0.784), 0.542 (95% CI 0.473-0.612) in the internal validation cohort. We have established and validated a nomogram to predict the VTE risk of colon cancer patients in China, which encompasses a diverse age range, a significant population size, and various clinical factors. It facilitates the identification of high-risk groups and may enable the implementation of targeted preventive measures.

Fitting pseudo-Sérsic (Spergel) light profiles to galaxies in interferometric data: The excellence of the uυ-plane

Modern (sub)millimeter interferometers, such as ALMA and NOEMA, offer high angular resolution and unprecedented sensitivity. This provides the possibility to characterize the morphology of the gas and dust in distant galaxies. To assess the capabilities of the current software in recovering morphologies and surface brightness profiles in interferometric observations, we tested the performance of the Spergel model for fitting in the uυ-plane, which has been recently implemented in the IRAM software GILDAS (uv_fit). Spergel profiles provide an alternative to the Sérsic profile, with the advantage of having an analytical Fourier transform, making them ideal for modeling visibilities in the uυ-plane. We provide an approximate conversion between the Spergel index and the Sérsic index, which depends on the ratio of the galaxy size to the angular resolution of the data. We show through extensive simulations that Spergel modeling in the uυ-plane is a more reliable method for parameter estimation than modeling in the image plane, as it returns parameters that are less affected by systematic biases and results in a higher effective signal-to-noise ratio. The better performance in the uυ-plane is likely driven by the difficulty of accounting for a correlated signal in interferometric images. Even in the uυ-plane, the integrated source flux needs to be at least 50 times larger than the noise per beam to enable a reasonably good measurement of a Spergel index. We characterized the performance of Spergel model fitting in detail by showing that parameter biases are generally low (&lt;10%) and that uncertainties returned by uv_fit are reliable within a factor of two. Finally, we showcase the power of Spergel fitting by reexamining two claims of extended halos around galaxies from the literature, showing that galaxies and halos can be successfully fitted simultaneously with a single Spergel model.

Sample Size Requirements of a Pharmaceutical Material Library: A Case in Predicting Direct Compression Tablet Tensile Strength by Latent Variable Modeling.

The material library is an emerging, new data-driven approach for developing pharmaceutical process models. How many materials or samples should be involved in a particular application scenario is unclear, and the impact of sample size on process modeling is worth discussing. In this work, the direct compression process was taken as the research object, and the effects of different sample sizes of material libraries on partial least squares (PLS) modeling in the prediction of tablet tensile strength were investigated. A primary material library comprising 45 materials was built. Then, material subsets containing 5 × i (i = 1, 2, 3, …, 8) materials were sampled from the primary material library. Each subset underwent sampling 1000 times to analyze variations in model fitting performance. Both hierarchical sampling and random sampling were employed and compared, with hierarchical sampling implemented with the help of the tabletability classification index d. For each subset, modeling data were organized, incorporating 18 physical properties and tableting pressure as the independent variables and tablet tensile strength as the dependent variable. A series of chemometric indicators was used to assess model performance and find important materials for model training. It was found that the minimum R2 and RMSE values reached their maximum, and the corresponding values were kept almost unchanged when the sample sizes varied from 20 to 45. When the sample size was smaller than 15, the hierarchical sampling method was more reliable in avoiding low-quality few-shot PLS models than the random sampling method. Two important materials were identified as useful for building an initial material library. Overall, this work demonstrated that as the number of materials increased, the model's reliability improved. It also highlighted the potential for effective few-shot modeling on a small material library by controlling its information richness.

Belt conveyor idler fault diagnosis method based on multi-scale feature fusion and residual mask convolution attention

Existing idler fault diagnosis methods have problems in failing to fully obtain global context information and providing poor diagnostic accuracy. To address these problems, this paper investigates a new method for diagnosing faults in belt conveyor idlers, based on analysis of their acoustic signals. The method is also applied to existing databases of bearing fault data. Firstly, an eight-element microphone array sound signal collector is designed to suppress environmental noise and raise the signal-to-noise ratio of the idler sound signal. Secondly, a multi-scale feature fusion (MSFF) module is constructed to learn complementary information between features at different scales. Then, a residual mask convolutional attention (MCA) module is designed to raise the modelling capability of local features and global contextual information. Finally, the structure of the ResNet-18 network is optimised to improve model fitting performance. Experimental results on self-made and public datasets show that the suggested method outperforms other comparative methods, achieving real-time accurate detection and classification of belt conveyor idler faults and typical bearing faults.

An optimized back propagation neural network on small samples spectral data to predict nitrite in water

Accurate detection of pollutant levels in water bodies using fusion algorithms combined with spectral data has become a critical issue for water conservation. However, the number of samples is too small and the model is unstable, which often leads to poor prediction and fails to achieve the measurement goal well. To address these challenges, this paper proposes a practical and effective method to precisely predict the concentrations of nitrite pollution in aquatic environments. The proposed method consists of three steps. Firstly, the dimension of the spectral data is reduced using Kernel Principal Component Analysis (KPCA), followed by sample augmentation using Generative Adversarial Network (GAN) to reduce calculation cost and increase the diversity and scale of the data. Secondly, several improvement strategies, including multi-cluster competitive and adaptive parameter updating, are introduced to enhance the capability of the Particle Swarm Optimization (PSO) algorithm. The improved PSO algorithm is then applied to optimize the initialization weights and biases of the Back Propagation neural network, thereby improving the model fitting and training performance. Finally, the developed prediction model is employed to predict the test set samples. The result suggests that the R2, RMSE, and MAE values are 0.976290, 0.008626, and 0.006617, which outperform the state-of-the-art and provided a promising model for the prediction of nitrite concentration in water.

Prediction and reliability analysis of reservoir lithology spatial distribution

Reliable lithology spatial distribution directly reflects the geological situation of the reservoir, which is the basis of stratigraphic correlation, sedimentary modeling, and other geological research. Under the condition of limited reservoir data, it is a challenging task to accurately depict the lithology spatial distribution and provide a quantitative reliability analysis of the results. In this study, we propose a flexible spatial distribution prediction and model reliability analysis method. Firstly, the method develops a spatially dependent deep Kriging technology to fit the heterogeneous characteristics of the reservoir lithology, and adopts the extracted spatial key information and related reservoir attributes to invert lithology spatial distribution intelligently. Then, it focuses on the real-time assimilation of non-Gaussian data in the reliability modeling and quantitatively analyzes the reliability of the prediction system under the non-Gaussian hypothesis. Finally, the method is applied to the actual heterogeneous reservoir, good results are achieved in the prediction accuracy, model fitting degree, model reliability, and time performance compared with other methods. The method is conducive to finding future mineral deposits locations and reducing exploration costs.

Adversarial Modeling Regularization for Modeling and Linearization of Power Amplifiers

Overfitting problem is very common in the coefficient identification process of digital predistortion (DPD) technique. Especially in some related DPD topics, the overfitting problem seriously limits the linearization performance. In this article, to improve the robustness of coefficient identification, we present the adversarial modeling regularization (AMR) technique. By regarding the linear-in-coefficient power amplifier (PA) behavioral models as the prior knowledge of the PA itself, the proposed regularization constrains the PA model’s coefficients by evaluating the fitting performance of other adversarial models. In addition, we present two complexity reduction strategies for the AMR. The AMR is extended and validated under four related tasks that are prone to overfitting: the few-sample learning, the piecewise DPD technique, the aliasing-sampling technique, and the band-limited technique. The experiment results show that the proposed technique can dramatically improve the forward behavioral modeling and linearization performance compared with the other state-of-the-art algorithms in all tasks.

Nonparametric functional analysis under joint estimation with applications to identifying highly cited papers

This article introduces a nonparametric methodology combining the strengths of binary regression and latent variable formulations, while overcoming their disadvantages. The mathematical results are implemented through a novel Bayesian Hierarchical estimation methodology called Latent Adaptive Hierarchical Expectation Maximization Like algorithm. Requiring minimal assumptions, it extends extant methodologies, and in simulation studies gives better prediction and inference performances for asymmetric data generating processes. A new classification statistic, called Adjusted Receiver Operating Curve Statistic is also introduced. Utilizing it we demonstrate better overall model fit, inference and prediction performance of the proposed methodology over widely used existing methods in the sciences. In addition, the methodology can be used to perform model diagnostics for any model specification. This is a highly useful result, and it extends existing work for categorical model diagnostics broadly across the sciences. Furthermore, the mathematical results also highlight important new findings regarding the interplay of statistical significance and scientific significance. Finally, the methodology is applied to identifying highly-cited papers in the social sciences in a joint estimation framework. The results indicate that the methodology outperforms widely used existing artificial intelligence and machine learning models with very few Monte Carlo iterations. In Scientometric application, it finds Journal Impact Factor to be more important than Keyword Popularity parameters for explaining citation outcomes in select social science fields. It further finds that the percentage change in Published Popularity may also help to explain citation outcomes in the field. The findings appear to be new to the Scientometric field.

Dynamic Autoregressive Liquidity (DArLiQ)

We introduce a new class of semiparametric dynamic autoregressive models for the Amihud illiquidity measure, which captures both the long-run trend in the illiquidity series with a nonparametric component and the short-run dynamics with an autoregressive component. We develop a generalized method of moments (GMM) estimator based on conditional moment restrictions and an efficient semiparametric maximum likelihood (ML) estimator based on an iid assumption. We derive large sample properties for our estimators. Finally, we demonstrate the model fitting performance and its empirical relevance on an application. We investigate how the different components of the illiquidity process obtained from our model relate to the stock market risk premium using data on the S&P 500 stock market index.

Predictive modeling of plant uptake of Pb and Cd: Implications of aerial deposition and the origin of parameterisation data

We developed ordinary least squares regression models to predict uptake of cadmium and lead, two metals that are of public health significance because of their toxicity, in the edible tissues of lettuce. Models were parameterised using data on soil metal concentration, pH, and organic carbon. To assess the impact of physical contamination in form of aerial deposition and soil-splash on the metal concentration in lettuce, separate linear regression models were parameterised for indoor- and outdoor-grown lettuce, assuming the physical contamination to be negligible for indoor conditions. Both Cd models showed high model fit and strong predictive performance, when tested on an independent dataset, suggesting uptake via roots to be dominant. For Pb, the indoor model performed better than the outdoor model, indicating that physical contamination, contributes significantly to metal concentration in lettuce leaves. Our results highlight the importance of the parameterisation data when developing uptake models for predictions and risk assessment. Regression models for predicting Pb concentration in lettuce based on indoor data should not be used for predicting lettuce concentrations cultivated in outdoor conditions unless the contribution of physical contamination is explicitly accounted for.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries’ ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Short-term forecasting of COVID-19 using support vector regression: An application using Zimbabwean data

This study aims to show that including pairwise hierarchical interactions of covariates and combining forecasts from individual models improves prediction accuracy. The least absolute shrinkage and selection operator via hierarchical pairwise interaction is used in selecting variables that are not correlated and with the greatest predictive power in single forecast models (Gradient boosting method [GBM], Generalized additive models [GAMs], Support vector regression [SVR]) are used in the analysis. The best model was selected based on the mean absolute error (MAE), the best key performance indicator for skewed data. Forecasts from the 5 models were combined using linear quantile regression averaging (LQRA). Box and Whiskers plots are used to diagnose the overall performance of fitted models. Single forecast models (GBM, GAMs, and SVRs) show that including pairwise interactions improves forecast accuracy. The SVR model with interactions based on the radial basis kernel function is the best from single forecast models with the lowest MAE. Combining point forecasts from all the single forecast models using the LQRA approach further reduces the MAE. However, based on the Box and Whiskers plot, the SVR model with pairwise interactions has the smallest range. Based on the key performance indicators, combining predictions from several individual models improves forecast accuracy. However, overall, the SVM with pairwise hierarchical interactions outperforms all the other models.

Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate067.

The aim of this study was to compare the performance of different extrapolation modeling techniques and analyze their impact on structural uncertainties in the economic evaluations of cancer immunotherapy. The individual patient data was reconstructed through published Checkmate067 Kaplan Meier curves. Standard parametric models and six flexible techniques were tested, including fractional polynomial, restricted cubic splines, Royston-Parmar models, generalized additive models, parametric mixture models, and mixture cure models. Mean square errors (MSE) and bias from raw survival plots were used to test the model fitness and extrapolation performance. Variability of estimated incremental cost-effectiveness ratios (ICERs) from different models was used to inform the structural uncertainty in economic evaluations. All indicators were analyzed and compared under cut-offs of 3 years and 6.5 years, respectively, to further discuss model impact under different data maturity. R Codes for reproducing this study can be found on GitHub. The flexible techniques in general performed better than standard parametric models with smaller MSE irrespective of the data maturity. Survival outcomes projected by long-term extrapolation using immature data differed from those with mature data. Although a best-performing model was not found because several models had very similar MSE in this case, the variability of modeled ICERs significantly increased when prolonging simulation cycles. Flexible techniques show better performance in the case of Checkmate067, regardless of data maturity. Model choices affect ICERs of cancer immunotherapy, especially when dealing with immature survival data. When researchers lack evidence to identify the 'right' model, we recommend identifying and revealing the model impacts on structural uncertainty.

Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures.

The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.

Exploring Health Effects under Specific Causes of Mortality Based on 90 Definitions of PM2.5 and Cold Spell Combined Exposure in Shanghai, China.

In this study, a total of 90 definitions were set up based on six air pollution definitions, five cold spell definitions, and three combined exposure scenarios. The relative risks (RRs) on all-cause, circulatory, and respiratory mortality were explored by a model combining a distributed linear lag model with quasi-Poisson regression. The definition in which daily PM2.5 increases more than 75 μg/m3 for at least 2 days and the average temperature falls below the 10th percentile for at least 2 days produced the best model fit performance in all-cause mortality. The high peaks of the health effect were generally observed around the lag days 6-9. The cumulative relative risks (CRRs) were more significant in the simultaneous-exposure scenario and higher in respiratory mortality, where the highest CRR (12.15, 3.69-40.03) was observed in definition P1T5, in which daily PM2.5 increases more than 75 μg/m3, and the average temperature falls below the 2.5th percentile for at least two days. For relative risk due to interaction (RERI), we found positive additive interactions (RERI > 0) between PM2.5 pollution and cold spell, especially in respiratory mortality. Clarifying the definition of combined events can help policymakers to capture health risks and construct more effective risk warning systems.

Using Bayesian Networks for Cognitive Assessment of Student Understanding of Buoyancy: A Granular Hierarchy Model

ABSTRACT Cognitive diagnostic assessment based on Bayesian networks (BN) is developed in this paper to evaluate student understanding of the physical concept of buoyancy. we propose a three-order granular-hierarchy BN model which accounts for both fine-grained attributes and high-level proficiencies. Conditional independence in the BN structure is tested and utilized to validate the proposed model. The proficiency relationships are verified and the initial Q-matrix is refined. Then, an optimized granular hierarchy model is constructed based on the updated Q-matrix. All variants of the constructed models are evaluated on the basis of the prediction accuracy and the goodness-of-fit test. The experimental results demonstrate that the optimized granular-hierarchy model has the best prediction and model-fitting performance. In general, the BN method not only can provide more flexible modeling approach, but also can help validate or refine the proficiency model and the Q-matrix and this method has its unique advantage in cognitive diagnosis.

Toxic Effects of Fungicide Sheathmar to Worm, Tubifex Tubifex

Abstract: The current study sought to determine Sheathmar's acute toxicity as well as its sublethal effects on oxidative stress enzymes in Tubifex tubifex, a benthic oligochaete worm. The results showed that the 96-hour median lethal concentration (LC50) of Sheathmar for Tubifex tubifex is 3186.3 µg/l. According to the model fit performance, the GUTS-SD model can better predict the survival rate of Tubifex tubifex. Sublethal Sheathmar concentrations (10% and 20% of the 96h LC50) significantly changed the oxidative stress enzymes. Reduced glutathione (GSH), glutathione S-transferase (GST), and glutathione peroxidase (GPx) activity all showed a significant initial increase followed by a decline, whereas catalase (CAT) activity and malondialdehyde (MDA) levels increased significantly with increasing Sheathmar concentrations at all exposure times. Furthermore, the effects of Sheathmar on Tubifex tubifex were demonstrated by the development of a potency index and the assessment of integrated biomarker response (IBR). These findings suggest that exposure to Sheathmar influences Tubifex tubifex survival at the acute stage and modifies oxidative stress enzyme alterations at the sublethal level