An Investigation of Subtest Score Equating Methods under Classical Test Theory and Item Response Theory Frameworks

Test scores are usually equated only at the total score level. If a test mainly measures a single trait, indicating that the test is essentially unidimensional, equating at the total score level could be the best choice. However, when a test is composed of subtests having negligible relationships among them, separate equating for each subtest offers the best choice. Given a moderate amount of correlations among the subtests, performing individual equating for each subtest may be misleading in that it ignores the relationship of the subtests. This study applied and compared several possible subtest score equating methods based on classical test theory and item response theory examining some important factors including correlations among dimensions, different proficiency distributions with skewness or mean shifts, and the number of items and common items. Based on the methods from a classical test theory perspective, the results showed that when the correlations among dimensions were high, using either the total or anchor total score as the anchor could produce better equating results than using the anchor score from each subtest. Among the different input scores for equating—observed scores, weighted averages, and augmented scores—using augmented scores yielded slightly less equating error than the other two methods. Under the item response theory framework, concurrent calibration and separate calibration as well as unidimensional IRT equating and the unidimensional approximation method using multidimensional IRT parameters were applied. The unidimensional approximation method did not perform well compared to unidimensional IRT methods. The proficiency distribution with relatively high skewness or mean shifts yielded the largest equating errors compared to other distributions. Further study is recommended: using more complex models, rather than a simple structure model, to simulate item responses, as well as using direct multidimensional IRT equating rather than the two steps of the unidimensional approximation method and unidimensional IRT equating

Mapping Topics in 100,000 Real-life Moral Dilemmas

Moral dilemmas play an important role in theorizing both about ethical norms and moral psychology. Yet thought experiments borrowed from the philosophical literature often lack the nuances and complexity of real life. We leverage 100,000 threads -- the largest collection to date -- from Reddit's r/AmItheAsshole to examine the features of everyday moral dilemmas. Combining topic modeling with evaluation from both expert and crowd-sourced workers, we discover 47 finer-grained, meaningful topics and group them into five meta-categories. We show that most dilemmas combine at least two topics, such as family and money. We also observe that the pattern of topic co-occurrence carries interesting information about the structure of everyday moral concerns: for example, the generation of moral dilemmas from nominally neutral topics, and interaction effects in which final verdicts do not line up with the moral concerns in the original stories in any simple way. Our analysis demonstrates the utility of a fine-grained data-driven approach to online moral dilemmas, and provides a valuable resource for researchers aiming to explore the intersection of practical and theoretical ethics.Comment: To be published in ICWSM 202

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this study, we predicted ARGs occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (LSTM), LSTMconvolutional neural network (CNN) hybrid model, and input attention (IA)-LSTM. To develop the models, 10 categories of environmental data collected at 30-min intervals and concentration data of 4 types of major ARGs (i.e., aac(6'-Ib-cr), blaTEM, sul1, and tetX) obtained at the Gwangalli Beach in South Korea, between 2018 and 2019 were used. When individually predicting ARGs occurrence, the conventional L STM and IA-L STM exhibited poor R-2 values during training and testing. In contrast, the LSTM-CNN exhibited a 2-6-times improvement in accuracy over those of the conventional L STM and IA-L STM. However, when predicting all ARGs occurrence simultaneously, the IA-LSTM model exhibited a superior performance overall compared to that of LSTM-CNN. Additionally, the influence of environmental variables on prediction was investigated using the IA-LSTM model, and the ranges of input variables that affect each ARG were identified. Consequently, this study demonstrated the possibility of predicting the occurrence and distribution of major ARGs at the beach based on various environmental variables, and the results are expected to contribute to management of ARG occurrence at a recreational beach. (C) 2021 Elsevier Ltd. All rights reserved

Effect of the Hydrofluoroether Cosolvent Structure in Acetonitrile-Based Solvate Electrolytes on the Li^+ Solvation Structure and Li–S Battery Performance

We evaluate hydrofluoroether (HFE) cosolvents with varying degrees of fluorination in the acetonitrile-based solvate electrolyte to determine the effect of the HFE structure on the electrochemical performance of the Li–S battery. Solvates or sparingly solvating electrolytes are an interesting electrolyte choice for the Li–S battery due to their low polysulfide solubility. The solvate electrolyte with a stoichiometric ratio of LiTFSI salt in acetonitrile, (MeCN)_2–LiTFSI, exhibits limited polysulfide solubility due to the high concentration of LiTFSI. We demonstrate that the addition of highly fluorinated HFEs to the solvate yields better capacity retention compared to that of less fluorinated HFE cosolvents. Raman and NMR spectroscopy coupled with ab initio molecular dynamics simulations show that HFEs exhibiting a higher degree of fluorination coordinate to Li+ at the expense of MeCN coordination, resulting in higher free MeCN content in solution. However, the polysulfide solubility remains low, and no crossover of polysulfides from the S cathode to the Li anode is observed

Effect of the Hydrofluoroether Cosolvent Structure in Acetonitrile-Based Solvate Electrolytes on the Li^+ Solvation Structure and Li–S Battery Performance

We evaluate hydrofluoroether (HFE) cosolvents with varying degrees of fluorination in the acetonitrile-based solvate electrolyte to determine the effect of the HFE structure on the electrochemical performance of the Li–S battery. Solvates or sparingly solvating electrolytes are an interesting electrolyte choice for the Li–S battery due to their low polysulfide solubility. The solvate electrolyte with a stoichiometric ratio of LiTFSI salt in acetonitrile, (MeCN)_2–LiTFSI, exhibits limited polysulfide solubility due to the high concentration of LiTFSI. We demonstrate that the addition of highly fluorinated HFEs to the solvate yields better capacity retention compared to that of less fluorinated HFE cosolvents. Raman and NMR spectroscopy coupled with ab initio molecular dynamics simulations show that HFEs exhibiting a higher degree of fluorination coordinate to Li+ at the expense of MeCN coordination, resulting in higher free MeCN content in solution. However, the polysulfide solubility remains low, and no crossover of polysulfides from the S cathode to the Li anode is observed

Metodologias alternativas no ensino de física

Screening a compound library of quinolinone derivatives identified compound 11a as a new P2X7 receptor antagonist. To optimize its activity, we assessed structure-activity relationships (SAR) at three different positions, R_1, R_2 and R_3, of the quinolinone scaffold. SAR analysis suggested that a carboxylic acid ethyl ester group at the R_1 position, an adamantyl carboxamide group at R_2 and a 4-methoxy substitution at the R_3 position are the best substituents for the antagonism of P2X7R activity. However, because most of the quinolinone derivatives showed low inhibitory effects in an IL-1β ELISA assay, the core structure was further modified to a quinoline skeleton with chloride or substituted phenyl groups. The optimized antagonists with the quinoline scaffold included 2-chloro-5-adamantyl-quinoline derivative (16c) and 2-(4-hydroxymethylphenyl)-5-adamantyl-quinoline derivative (17k), with IC_(50) values of 4 and 3 nM, respectively. In contrast to the quinolinone derivatives, the antagonistic effects of the quinoline compounds (16c and 17k) were paralleled by their ability to inhibit the release of the pro-inflammatory cytokine, IL-1β, from LPS/IFN-γ/BzATP-stimulated THP-1 cells (IC_(50) of 7 and 12 nM, respectively). In addition, potent P2X7R antagonists significantly inhibited the sphere size of TS15-88 glioblastoma cells