We develop a latent variable selection method for multidimensional item response theory models. The proposed method identifies latent traits probed by items of a multidimensional test. Its basic strategy is to impose an $L_1$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$L_{1}$$\end{document} penalty term to the log-likelihood. The computation is carried out by the expectation–maximization algorithm combined with the coordinate descent algorithm. Simulation studies show that the resulting estimator provides an effective way in correctly identifying the latent structures. The method is applied to a real dataset involving the Eysenck Personality Questionnaire.

Item replenishing is essential for item bank maintenance in cognitive diagnostic computerized adaptive testing (CD-CAT). In regular CAT, online calibration is commonly used to calibrate the new items continuously. However, until now no reference has publicly become available about online calibration for CD-CAT. Thus, this study investigates the possibility to extend some current strategies used in CAT to CD-CAT. Three representative online calibration methods were investigated: Method A (Stocking in Scale drift in on-line calibration. Research Rep. 88-28, 1988), marginal maximum likelihood estimate with one EM cycle (OEM) (Wainer & Mislevy In H. Wainer (ed.) Computerized adaptive testing: A primer, pp. 65–102, 1990) and marginal maximum likelihood estimate with multiple EM cycles (MEM) (Ban, Hanson, Wang, Yi, & Harris in J. Educ. Meas. 38:191–212, 2001). The objective of the current paper is to generalize these methods to the CD-CAT context under certain theoretical justifications, and the new methods are denoted as CD-Method A, CD-OEM and CD-MEM, respectively. Simulation studies are conducted to compare the performance of the three methods in terms of item-parameter recovery, and the results show that all three methods are able to recover item parameters accurately and CD-Method A performs best when the items have smaller slipping and guessing parameters. This research is a starting point of introducing online calibration in CD-CAT, and further studies are proposed for investigations such as different sample sizes, cognitive diagnostic models, and attribute-hierarchical structures.

This study aims to investigate the effects of the vine of Lonicera japonica Thunb (VLT) and marine-derived Bacillus amyloliquefaciens-9 (BA-9) supplementation on the growth performance, antioxidant capacity, and gut microbiota of goat kids. A total of 32 4-week-old kids were randomly assigned into four groups: a control group (CON), a group supplemented with 0.3% BA-9 (BA-9), a group supplemented with 2% VLT (VLT), and a group supplemented with both 0.3% BA-9 and 2% VLT (MIX). The results indicated that VLT supplementation significantly increased both average daily (P < 0.001) and total weight gain (TWG) (P < 0.001), while BA-9 alone had no significant effect (P > 0.05) on the average daily and TWG. Biomarker analysis of oxidative stress revealed that supplementation of VLT or BA-9 alone enhanced antioxidant capacity. The MIX group showing a higher total antioxidant capacity (T-AOC) compared with the CON, VLT, and BA-9 groups (P < 0.05). Plasma albumin (ALB) levels were significantly increased in the both VLT and BA-9 groups. Microbiota analysis revealed significant differences in α-diversity and β-diversity between the MIX and CON groups, with specific genera such as Prevotellaceae_UCG.004 and Rikenellaceae_RC9_gut_group negatively correlated with average daily gain (ADG), while Alistipes was positively correlated with T-AOC. These findings suggest that the combined supplementation of VLT and BA-9 can significantly enhance growth performance and antioxidant capacity in goat kids by modulating the composition of gut microbiota and reducing oxidative stress.

Pro-environmental behavior, including waste sorting and recycling, involves a combination of future-oriented (futureness) and other-oriented (otherness) attributes. Inspired by the perspective of intergenerational choice, this work explores whether eliciting concerns for future others could increase public support for recycling policy and recycling behavior. Study 1 consisted of an online random controlled trial and a laboratory experiment. In Study 1A (N = 400), future other-concern was primed using a static text manipulation, whereas in Study 1B (N = 192), a dynamic virtual manipulation was employed. The results showed that people in the conditions that elicited future other-concern reported greater household recycling intentions and more actual recycling behavior than those in the control conditions. Study 2A (N = 467) and Study 2B (N = 600) generalized this effect on the acceptance of the ‘Certain Time Certain Place’ waste sorting policy in China. Consistent with the intergenerational choice model, envisioning the future of subsequent generations is more impactful in gaining policy approval than merely envisioning a future time. These findings provide a new method for promoting public policy approval and recycling behavior, suggesting that people could become environmentally friendly when they are guided to consider future others.

Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC) = 0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.

Precise pose estimation is crucial to various robots. In this paper, we present a localization method using correlative scan matching (CSM) technique for indoor mobile robots equipped with 2D-LiDAR to provide precise and fast pose estimation based on the common occupancy map. A pose tracking module and a global localization module are included in our method. On the one hand, the pose tracking module corrects accumulated odometry errors by CSM in the classical Bayesian filtering framework. A low-pass filter associating the predictive pose from odometer with the corrected pose by CSM is applied to improve precision and smoothness of the pose tracking module. On the other hand, our localization method can autonomously detect localization failures with several designed trigger criteria. Once a localization failure occurs, the global localization module can recover correct robot pose quickly by leveraging branch-and-bound method that can minimize the volume of CSM-evaluated candidates. Our localization method has been validated extensively in simulated, public dataset-based, and real environments. The experimental results reveal that the proposed method achieves high-precision, real-time pose estimation, and quick pose retrieve and outperforms other compared methods.

Echinococcosis lacks sensitive serological diagnostic tools. The echinococcosis-specific antigens Eg95, AgB8/1 and the Em18 gene sequences were fused and expressed as the novel recombinant antigens rAgB8/1-Em18-Eg95 (T3) and rEm18-Eg95 (T2), used for the diagnosis of hydatid disease, prepared into an enzyme-linked immunosorbent reaction (ELISA) kit, and evaluated for their serological diagnostic value. The relative molecular weight of the T3 protein was 88.1 kDa, the purified concentration was 1.5 mg mL−1, and the purity was 80%. The relative molecular weight of T2 protein was 79.9 kDa, the total protein concentration was 0.5 mg mL−1, and the purity was less than 50%. The overall coincidence rate of T2 protein was low, and it was impossible to distinguish between negative and positive sera. The T3 antigen was coated at 1.0 μg mL−1, the cutoff value was 0.5271, and the serum dilution ratio was 1:400. A T3 ELISA kits (96 tests) was constructed to detect the serum of 272 clinically and pathologically confirmed cases. The sensitivity of T3 was 93.8%, and the specificity was 83.3%. The parasite cross-reaction was 30%. Satisfactorily, the Pearson correlation coefficient between the T3 OD value and lesion diameter was 0.707, showing a strong correlation. T3 exhibits better antigenicity than T2, and the prepared T3 ELISA diagnostic kits reached the laboratory diagnostic level of a commercial kits. T3 can distinguish human cystic echinococcosis (CE) and alveolar echinococcosis (AE) more significantly and predict the diameter of lesions according to the OD value, which provides practical value for drug or surgical efficacy.

Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.

The inverse dynamics model of an industrial robot can predict and control the robot’s motion and torque output, improving its motion accuracy, efficiency, and adaptability. However, the existing inverse rigid body dynamics models still have some unmodelled residuals, and their calculation results differ significantly from the actual industrial robot conditions. The bootstrap aggregating (bagging) algorithm is combined with a long short-term memory network, the linear layer is introduced as the network optimization layer, and a compensation method of hybrid inverse dynamics model for robots based on the BLL residual prediction algorithm is proposed to meet the above needs. The BLL residual prediction algorithm framework is presented. Based on the rigid body inverse dynamics of the Newton–Euler method, the BLL residual prediction network is used to perform error compensation on the inverse dynamics model of the Franka robot. The experimental results show that the hybrid inverse dynamics model based on the BLL residual prediction algorithm can reduce the average residuals of the robot joint torque from 0.5651 N·m to 0.1096 N·m, which improves the accuracy of the inverse dynamics model compared with those of the rigid body inverse dynamics model. This study lays the foundation for performing more accurate operation tasks using industrial robots.

Nano-silicon has been regarded as the most promising anode material for next-generation lithium-ion batteries (LIBs). However, the preparation of nano-silicon suffers from high cost, complex procedures, and low yield, which hinders its commercial application. In this study, porous nano-silicon with particle sizes in the range of 50–100 nm was prepared through molten salt-assisted magnesiothermic reduction using porous nano-silica derived from clay minerals as the precursor. Through combining ball milling and acid activation, the synthesised nano-silica derived from montmorillonite exhibited smaller particle sizes (below 50 nm), higher specific surface area (647 m2 g–1), and total pore volume (0.71 cm3 g–1). This unique structure greatly facilitated the conversion efficiency of silica into nano-silicon by maximising the contact area between silica and magnesium powder and optimising the diffusion kinetics of magnesium atoms. When used as anodes in LIBs, the synthesised nano-silicon materials demonstrated a high specific capacity of up to 1222 mAh g–1 and an excellent capacity retention rate of 79% after 150 cycles at a current density of 0.5 A g–1. This method provides a novel approach for the cost-effective and large-scale production of nano-silicon materials for high-performance anodes.

While tension-type headache (TTH) is the most common primary headache disorder, its effect according to sex, race and ethnicity remains unclear. We investigated disparities in sex, racial and ethnic representation in TTH clinical trials with comparison to global disease burdens. In this cross-sectional analysis, TTH clinical trials had female overrepresentation and racial and ethnic minority underrepresentation, which may affect understanding of the impact of TTH on different populations and personalized treatment development. Trial enrollment that is diverse and reflective of global disease burdens is crucial for improving study generalizability, understanding of diverse clinical presentations, and ensuring healthcare equity.

In practical applications, many robots equipped with embedded devices have limited computing capabilities. These limitations often hinder the performance of existing dynamic SLAM algorithms, especially when faced with occlusions or processor constraints. Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm’s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.