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This paper tests Becker’s model of preference discrimination. Using Brazilian data, we reject the hypothesis that the black-white conditional wage gap is determined by the average degree of employers’ prejudice. Instead, we show that the racial wage gap is related to the degree of prejudice of the marginal employer, i.e., the employer who most discriminates among those who hire blacks. We also found that the wage gap is positively correlated with the proportion of blacks in the labor market, which means that blacks are more discriminated where there is more social interaction among races

Model criticism, discrimination, and selection methods often rely on calibrated model outputs. Because calibration can be computationally expensive, model criticism can first be undertaken by assessing model outputs obtained from limited prior parameter ensembles. However, such prior‐based methods are often heuristic and do not formalize the notion of balancing model consistency with data and model complexity (i.e., model adequacy). We present a new framework to discriminate among candidate models prior to calibration that formalizes prior‐to‐calibration model adequacy into a metric to implicitly balance prior model output data coverage with model complexity represented by prior output (co)variance. The prior model adequacy metric “Mahalanobis distance deviation” quantifies the deviation of (a) the set of squared Mahalanobis distances of data from a prior model output distribution from (b) the set of squared Mahalanobis distances of data from their own distribution. A new data worth metric “discernment value” is also presented which quantifies the value of data for screening less‐adequate models prior to calibration. Discernment value is calculated from the change in variance of a weighted average of prior model outputs from all candidate models due to less‐adequate model outputs receiving lower weight. The framework is demonstrated using a one‐dimensional groundwater flow model with eight possible configurations. A synthetic data network is used to test the framework. Results show the framework identifies the candidate models most similar to the true model used to create the synthetic data. Discernment values show variation in the value of different data types and locations for screening less‐adequate models.

Panax notoginseng is an important functional health product, and has been used worldwide because of a wide range of pharmacological activities, of which the taproot is the main edible or medicinal part. However, the technologies for origin discrimination still need to be further studied. In this study, an ICP-MS/MS method for the accurate determination of 49 elements was established, whereby the instrumental detection limits (LODs) were between 0.0003 and 7.716 mg/kg, whereas the quantification limits (LOQs) were between 0.0011 and 25.7202 mg/kg, recovery of the method was in the range of 85.82% to 104.98%, and the relative standard deviations (RSDs) were lower than 10%. Based on the content of multi-element in P. notoginseng (total of 89 mixed samples), the discriminant models of origins and cultivation models were accurately determined by the neural networks (prediction accuracy was 0.9259 and area under ROC curve was 0.9750) and the support vector machine algorithm (both 1.0000), respectively. The discriminant models established in this study could be used to support transparency and traceability of supply chains of P. notoginseng and thus avoid the fraud of geographic identification.

Background Artificial intelligence (AI) demonstrates high potential when applied to radiomic analysis of magnetic resonance imaging (MRI) to discriminate sinonasal tumors. This can enhance diagnostic suspicion beyond visual assessment alone and prior to biopsy, leading to expedite the diagnostic timeline and the treatment planning. The aim of the present work is to evaluate the current advancements and accuracy of this technology in this domain. Methods A systematic literature review was conducted following PRISMA guidelines. Inclusion criteria comprised studies utilizing any machine learning approach applied to MRI of patients with sinonasal tumors. For each study, comprehensive data were gathered on the MRI protocols, feature extraction techniques, and classifiers employed to develop the AI model. The performance was assessed based on accuracy and area under the curve (AUC). Results Fourteen studies, published between May 2017 and August 2024, were included. These studies were categorized into three groups: those examining both benign and malignant tumors, those investigating malignant tumor subpopulations, and those focusing on benign pathologies. All studies reported an AUC greater than 0.800, achieving AUC > 0.89 and accuracy > 0.81 when incorporating clinical-radiological variables. Notably, the best discrimination performance was observed in studies utilizing combined conventional MRI sequences, including T1-weighted, contrasted T1-weighted, and T2-weighted images. Conclusion The application of AI and radiomics in analyzing MRI scans presents significant promise for improving the discrimination of sinonasal tumors. Integrating clinical and radiological indicators enhances model performance, suggesting that future research should focus on larger patient cohorts and diverse AI methodologies to refine diagnostic accuracy and clinical utility.

The rapid identification of kudzu powder of different origins is of great significance for studying the authenticity identification of Chinese medicine. The feasibility of rapidly identifying kudzu powder origin was investigated based on laser-induced breakdown spectroscopy (LIBS) technology combined with chemometrics methods. The discriminant models based on the full spectrum include extreme learning machine (ELM), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN) and random forest (RF), and the accuracy of models was more than 99.00%. The prediction results of KNN and RF models were best: the accuracy of calibration and prediction sets of kudzu powder from different producing areas both reached 100%. The characteristic wavelengths were selected using principal component analysis (PCA) loadings. The accuracy of calibration set and the prediction set of discrimination models, based on characteristic wavelengths, is all higher than 98.00%. Random forest and KNN have the same excellent identification results, and the accuracy of calibration and prediction sets of kudzu powder from different producing areas reached 100%. Compared with the full spectrum discriminant analysis model, the discriminant analysis model based on the characteristic wavelength had almost the same discriminant effects, and the input variables were reduced by 99.92%. The results of this research show that the characteristic wavelength can be used instead of the LIBS full spectrum to quickly identify kudzu powder from different producing areas, which had the advantages of reducing input, simplifying the model, increasing the speed and improving the model effect. Therefore, LIBS technology is an effective method for rapid identification of kudzu powder from different habitats. This study provides a basis for LIBS to be applied in the genuineness and authenticity identification of Chinese medicine.

Environmental tracers, including both elemental concentrations and isotope ratios, are widely used to reconstruct the movement patterns of animals throughout landscapes. The methodology involves creating a map that describes the distribution of the environmental tracer across the landscape, an isoscape and then matching the values of the same tracer in the tissue of the animal (teeth, fish otolith, feathers) to determine provenance at one or more life stages. Classification models are commonly used to assign an individual to different areas of the isoscape. However, many of the current classification models are data intensive and may not account for (i) spatial autocorrelation (i.e. where an animal has moved is a function of where it was previously) inherent to data sets that use environmental tracers, (ii) species' movement ability which can influence region assignment or (iii) the propagation of errors from misallocation of locations early in the otolith time series. Here, we introduce a Bayesian classification model to estimate large‐scale movement patterns over the lifetime of freshwater fish that has relatively low data requirements, integrates spatial autocorrelation, offers an avenue to include movement capabilities and quantifies the uncertainty associated with the classification of fish movement throughout its life. We use a simulation study to test the accuracy of this model and then demonstrate functionality using a small otolith microchemistry data set (four species of fish collected at two sites) and a 87Sr/86Sr isoscape from the Mitchell River (Queensland, Australia) that accounts for spatial and temporal variation in water 87Sr/86Sr using water and mussel shell samples. The probabilistic framework of the Monte Carlo simulation allows uncertainty to be incorporated at each life stage, reducing the cumulative impact of misclassification and providing a more reliable reconstruction of lifetime movement patterns.

The overall rate of a heterogeneous catalytic process may be limited by the rate of a chemical transformation on the catalyst surface, reactant transport to either external or internal catalyst surface, or by the interplay between these factors. In this paper, we consider each case concerning the influence of diffusion limitations on the overall process rate. The internal, external, and overall effectiveness factors are obtained for various Langmuir–Hinshelwood kinetic rate equations and catalyst shapes via numerical simulations. It is shown that different kinetic rate equations provide an equally good description of the experimental data obtained under reaction-rate control. In contrast, the internal, external, and overall effectiveness factors may obey dissimilar trends for various kinetic rate equations. The obtained findings are of practical interest since the external and internal diffusion limitations can be achieved by simply changing the feed flow rate in a chemical reactor, catalyst particle size, or temperature increase. Therefore, the presented simulations deliver an easy and comprehensive tool for kinetic model discrimination based on the comparison of the overall effectiveness factors derived in the frame of various rate equations with the experimental one. This result represents a new utilization of an old concept, which is the effectiveness factor, for the selection between the plausible kinetic models.

An origin discrimination model for rice from five production regions in Heilongjiang Province was constructed based on the combination of confocal microscopy Raman spectroscopy and chemometrics. A total of 150 field rice samples were collected from the Fangzheng, Chahayang, Jiansanjiang, Xiangshui, and Wuchang production areas. The optimal sample processing conditions, instrument parameter settings, and spectrum acquisition techniques were identified by investigating the influencing factor. The Raman spectra of milled rice within the range of 100–3200 cm −1 were selected as the raw data, and the optimal preprocessing method combination consisting of normalization, Savitzky–Golay smoothing, and multivariate scatter correction was identified. Subsequently, the competitive adaptive reweighted sampling and discrete binary particle swarm optimization algorithms were employed to optimize the feature wavelength selection, resulting in the screening of 226 and 1899 feature wavelength variables, respectively. Using the full Raman spectrum data and feature wavelength data as inputs, partial least squares discriminant analysis, support vector machine and extreme learning machine origin discrimination models were constructed. The results indicated that the BPSO-GA-SVM model exhibited the best predictive ability, achieving a testing set accuracy of 86.67%.

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called adaptive design optimization, adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate expected utility, weighted expected utility, original prospect theory, and cumulative prospect theory models. This paper was accepted by Teck Ho, decision analysis.

The paper provides a neurophilosophical assessment of a controversy between two neuroeconomic models that compete to identify the putative object of neural utility: goods or actions. We raise two objections to the common view that sees the ‘good-based’ model prevailing over the ‘action-based’ model. First, we suggest extending neuroeconomic model discrimination to all of the models’ neurophilosophical assumptions, showing that action-based assumptions are necessary to explain real-world value-based decisions. Second, we show that the good-based model’s presumption of introducing a normative neural definition of economic choice would arbitrarily restrict the domain of economic choice and consequently of economics.