The Q-matrix of a cognitively diagnostic test is said to be complete if it allows for the identification of all possible proficiency classes among examinees. Completeness of the Q-matrix is therefore a key requirement for any cognitively diagnostic test. However, completeness of the Q-matrix is often difficult to establish, especially, for tests with a large number of items involving multiple attributes. As an additional complication, completeness is not an intrinsic property of the Q-matrix, but can only be assessed in reference to a specific cognitive diagnosis model (CDM) supposed to underly the data—that is, the Q-matrix of a given test can be complete for one model but incomplete for another. In this article, a method is presented for assessing whether a given Q-matrix is complete for a given CDM. The proposed procedure relies on the theoretical framework of general CDMs and is therefore legitimate for CDMs that can be reparameterized as a general CDM.

Cognitive diagnosis models (CDMs) for educational assessment are constrained latent class models. Examinees are assigned to classes of intellectual proficiency defined in terms of cognitive skills called attributes, which an examinee may or may not have mastered. The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among psychometricians. Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) are typically used for estimating the Reduced RUM. Commercial implementations of the EM algorithm are available in the latent class analysis (LCA) routines of Latent GOLD and Mplus, for example. Fitting the Reduced RUM with an LCA routine requires that it be reparameterized as a logit model, with constraints imposed on the parameters. For models involving two attributes, these have been worked out. However, for models involving more than two attributes, the parameterization and the constraints are nontrivial and currently unknown. In this article, the general parameterization of the Reduced RUM as a logit model involving any number of attributes and the associated parameter constraints are derived. As a practical illustration, the LCA routine in Mplus is used for fitting the Reduced RUM to two synthetic data sets and to a real-world data set; for comparison, the results obtained by using the MCMC implementation in OpenBUGS are also provided.

Parametric likelihood estimation is the prevailing method for fitting cognitive diagnosis models—also called diagnostic classification models (DCMs). Nonparametric concepts and methods that do not rely on a parametric statistical model have been proposed for cognitive diagnosis. These methods are particularly useful when sample sizes are small. The general nonparametric classification (GNPC) method for assigning examinees to proficiency classes can accommodate assessment data conforming to any diagnostic classification model that describes the probability of a correct item response as an increasing function of the number of required attributes mastered by an examinee (known as the “monotonicity assumption”). Hence, the GNPC method can be used with any model that can be represented as a general DCM. However, the statistical properties of the estimator of examinees’ proficiency class are currently unknown. In this article, the consistency theory of the GNPC proficiency-class estimator is developed and its statistical consistency is proven.

Joint maximum likelihood estimation (JMLE) is developed for diagnostic classification models (DCMs). JMLE has been barely used in Psychometrics because JMLE parameter estimators typically lack statistical consistency. The JMLE procedure presented here resolves the consistency issue by incorporating an external, statistically consistent estimator of examinees’ proficiency class membership into the joint likelihood function, which subsequently allows for the construction of item parameter estimators that also have the consistency property. Consistency of the JMLE parameter estimators is established within the framework of general DCMs: The JMLE parameter estimators are derived for the Loglinear Cognitive Diagnosis Model (LCDM). Two consistency theorems are proven for the LCDM. Using the framework of general DCMs makes the results and proofs also applicable to DCMs that can be expressed as submodels of the LCDM. Simulation studies are reported for evaluating the performance of JMLE when used with tests of varying length and different numbers of attributes. As a practical application, JMLE is also used with “real world” educational data collected with a language proficiency test.

The Polytomous Local Independence Model (PoLIM) by Stefanutti, de Chiusole, Anselmi, and Spoto, is an extension of the Basic Local Independence Model (BLIM) to accommodate polytomous items. BLIM, a model for analyzing responses to binary items, is based on Knowledge Space Theory, a framework developed by cognitive scientists and mathematical psychologists for modeling human knowledge acquisition and representation. The purpose of this commentary is to show that PoLIM is simply a paraphrase of a DINA model in cognitive diagnosis for polytomous items. Specifically, BLIM is shown to be equivalent to the DINA model when the BLIM-items are conceived as binary single-attribute items, each with a distinct attribute; thus, PoLIM is equivalent to the DINA for polytomous single-attribute items, each with a distinct attribute.

Cross-language internet memes have emerged as a unique and popular mode of online communication, combining bilingual elements with visually textual components. These memes exhibit distinctive characteristics at semantic, syntactic, and pragmatic levels, rendering them a noteworthy semiotic phenomenon in contemporary digital culture. To deepen our understanding of cross-language internet memes, this study investigates user perceptions through a questionnaire, employing SPSS Statistics software for analysis. Applying a social semiotic approach, we decipher the semiotic mechanisms of cross-language memes, shedding light on their potential implications for identity construction. Additionally, we reflect on two prominent trends in internet meme development: the shift from monomodal to multimodal communication and from monolingual to multilingual expressions. This research hopes to provide insights for meme research and online discourse investigations.

The asymptotic classification theory of cognitive diagnosis (ACTCD) provided the theoretical foundation for using clustering methods that do not rely on a parametric statistical model for assigning examinees to proficiency classes. Like general diagnostic classification models, clustering methods can be useful in situations where the true diagnostic classification model (DCM) underlying the data is unknown and possibly misspecified, or the items of a test conform to a mix of multiple DCMs. Clustering methods can also be an option when fitting advanced and complex DCMs encounters computational difficulties. These can range from the use of excessive CPU times to plain computational infeasibility. However, the propositions of the ACTCD have only been proven for the Deterministic Input Noisy Output “AND” gate (DINA) model and the Deterministic Input Noisy Output “OR” gate (DINO) model. For other DCMs, there does not exist a theoretical justification to use clustering for assigning examinees to proficiency classes. But if clustering is to be used legitimately, then the ACTCD must cover a larger number of DCMs than just the DINA model and the DINO model. Thus, the purpose of this article is to prove the theoretical propositions of the ACTCD for two other important DCMs, the Reduced Reparameterized Unified Model and the General Diagnostic Model.

To investigate the associations between dietary patterns and biological ageing, identify the most recommended dietary pattern for ageing and explore the potential mediating role of gut microbiota in less-developed ethnic minority regions (LEMRs). This prospective cohort study included 8288 participants aged 30–79 years from the China Multi-Ethnic Cohort study. Anthropometric measurements and clinical biomarkers were utilised to construct biological age based on Klemera and Doubal’s method (KDM-BA) and KDM-BA acceleration (KDM-AA). Dietary information was obtained through the baseline FFQ. Six dietary patterns were constructed: plant-based diet index, healthful plant-based diet index, unhealthful plant-based diet index, healthy diet score, Dietary Approaches to Stop Hypertension (DASH), and alternative Mediterranean diets. Follow-up adjusted for baseline analysis assessed the associations between dietary patterns and KDM-AA. Additionally, quantile G-computation identified significant beneficial and harmful food groups. In the subsample of 764 participants, we used causal mediation model to explore the mediating role of gut microbiota in these associations. The results showed that all dietary patterns were associated with KDM-AA, with DASH exhibiting the strongest negative association (β = −0·91, 95 % CI (–1·19, −0·63)). The component analyses revealed that beneficial food groups primarily included tea and soy products, whereas harmful groups mainly comprised salt and processed vegetables. In mediation analysis, the Synergistetes and Pyramidobacter possibly mediated the negative associations between plant-based diets and KDM-AA (5·61–9·19 %). Overall, healthy dietary patterns, especially DASH, are negatively associated with biological ageing in LEMRs, indicating that Synergistetes and Pyramidobacter may be potential mediators. Developing appropriate strategies may promote healthy ageing in LEMRs.

MicroRNAs (miRNAs) are endogenous, non-coding RNAs, which are functional in a variety of biological processes through post-transcriptional regulation of gene expression. However, the role of miRNAs in the interaction between Bacillus thuringiensis and insects remains unclear. In this study, small RNA libraries were constructed for B. thuringiensis-infected (Bt) and uninfected (CK) Spodoptera exigua larvae (treated with double-distilled water) using Illumina sequencing. Utilising the miRDeep2 and Randfold, a total of 233 known and 726 novel miRNAs were identified, among which 16 up-regulated and 34 down-regulated differentially expressed (DE) miRNAs were identified compared to the CK. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that potential target genes of DE miRNAs were associated with ABC transporters, fatty acid metabolism and MAPK signalling pathway which are related to the development, reproduction and immunity. Moreover, two miRNA core genes, SeDicer1 and SeAgo1 were identified. The phylogenetic tree showed that lepidopteran Dicer1 clustered into one branch, with SeDicer1 in the position closest to Spodoptera litura Dicer1. A similar phylogenetic relationship was observed in the Ago1 protein. Expression of SeDicer1 increased at 72 h post infection (hpi) with B. thuringiensis; however, expression of SeDicer1 and SeAgo1 decreased at 96 hpi. The RNAi results showed that the knockdown of SeDicer1 directly caused the down-regulation of miRNAs and promoted the mortality of S. exigua infected by B. thuringiensis GS57. In conclusion, our study is crucial to understand the relationship between miRNAs and various biological processes caused by B. thuringiensis infection, and develop an integrated pest management strategy for S. exigua via miRNAs.

We show that for any $\varepsilon \gt 0$ and $\Delta \in \mathbb{N}$, there exists $\alpha \gt 0$ such that for sufficiently large $n$, every $n$-vertex graph $G$ satisfying that $\delta (G)\geq \varepsilon n$ and $e(X, Y)\gt 0$ for every pair of disjoint vertex sets $X, Y\subseteq V(G)$ of size $\alpha n$ contains all spanning trees with maximum degree at most $\Delta$. This strengthens a result of Böttcher, Han, Kohayakawa, Montgomery, Parczyk, and Person.

Previous literature attempted to gain insight into financial abuse involving people with dementia by analysing court cases, but these studies were limited in sample size or scope. This study collected 214 court rulings directly related to the financial decisions of people with dementia to identify characteristics of the financial abuse victim, perpetrators and the types of assets. The models of bystander intervention and routine activity theory were used as conceptual models to guide analysis regarding the role of bank staff as well as the court's decision in cases of financial abuse. The majority of financial abuse perpetrators were family members (73.8%), as opposed to outsiders (19.2%). Transfer of real estate was the most common legal issue, and land was the most common financial asset involved. Difficult intra-family relationships seem to pose a great risk of financial abuse involving people with dementia since adult children were found to be the most likely perpetrators (52.7%) but also plaintiffs accusing financial abuse (57.6%). In accordance with the bystander intervention model, bank staff were more likely to be suspicious of financial abuse when an outsider was regarded as the perpetrator. In accordance with the routine activity theory model, the court was more likely to acknowledge the case as an invalid financial decision when an outsider was regarded as the perpetrator in financial abuse cases. Since people with dementia suffer from greater losses due to their family members, future policies should establish guidelines for front-line bank staff to identify warning signs to reduce the risk of financial abuse involving people with dementia, not only to prevent fraud by outsiders but also exploitation by family members.

Aspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design.

A high image rejection 38 GHz demodulator in TSMC 65-nm CMOS process is presented. To achieve better than −40 dBc image rejection ratio (IRR), a low I/Q mismatch 45° LO power splitter of sub-harmonic mixer is proposed. In this design, the 45° LO power splitter is composed of a Wilkinson divider, a series delay line with electrical length of 45° on one side of the divider, and a shunt 90° transmission line on the other side. This configuration is attractive because of its design simplicity and easy fabrication. Compared with the conventional techniques that utilize capacitors and inductors instead of the shunt 90° transmission line, the proposed LO power splitter can alleviate the issue of process variation. The demodulator demonstrates an IRR lower than −40 dBc from 37.5 to 41.5 GHz. In addition, conversion gain is 1.3 ± 0.9 dB from 33 to 41 GHz with 6 dBm LO power. The total direct current power consumption is 78 mW from 1.0 V supply voltage. At the modulation scheme of 4096-QAM, the proposed demodulator demonstrates 1.7% (−35.1 dB) error vector magnitude (EVM), which is very close to the EVM measurement floor 1.6% (−36 dB) of our millimeter-wave signal analyzer.