A fair comparison of the performance of computerized adaptive testing and multistage adaptive testing

"The comparison of item-level computerized adaptive testing (CAT) and multistage adaptive testing (MST) has been researched extensively (e.g., Kim & Plake, 1993; Luecht et al., 1996; Patsula, 1999; Jodoin, 2003; Hambleton & Xing, 2006; Keng, 2008; Zheng, 2012). Various CAT and MST designs have been investigated and compared under the same item pool. However, the characteristics of an item pool designed specifically for CAT are different from the characteristics of an item pool designed for MST. If CAT and MST are compared under the same item pool designed for either CAT or MST, the comparison might be unfair to the other test mode. To address this issue, this study focused on comparing the measurement accuracy and averaged test length of MST and CAT, when they were matched on conditional standard error of measurement, exposure rates, IRT scoring method and content specifications, under different item pools designed for MST and CAT, respectively. When designing a MST, multiple factors need to be considered. In this paper, a total of 16 conditions of MST designs (i.e., 1-2-3 and 1-3-3 panel designs; the AMI and DPI routing strategies; the test lengths of 45 and 60 items; forward and backward assembly) were employed. Each condition was compared with the result of the corresponding CAT. A simulation study was conducted to evaluate the performance of MST against the corresponding CAT. The results show similar measurement accuracy between MST and CAT, which implies that the efforts to make a fair comparison where successful. The reason is that both procedures matched similar conditional test information. This fair comparison of MST and CAT provides a reference for testing mode change from CAT to MST in terms of ability recovery and averaged test length. When considering the testing model change from CAT to MST, the backward assembled MST is not suggested even for a classification-oriented test. Whether to change the testing mode depends on the current averaged test length in CAT. If the current CAT has a moderate-length test, switching to a forward assembled MST with 3 stages is plausible and feasible. For a long test, staying in CAT is preferred over switching to MST."--Pages ii-iii.Thesis (Ph. D.)--Michigan State University. Measurement and Quantitative Methods, 2017Includes bibliographical references (pages 81-86

Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents

Mood disorders, including depression and anxiety, often manifest through facial expressions. While previous research has explored the connection between facial features and emotions, machine learning algorithms for estimating mood disorder severity have been hindered by small datasets and limited real-world application. To address this gap, we analyzed facial videos of 11,427 participants, a dataset two orders of magnitude larger than previous studies. This comprehensive collection includes standardized facial expression videos from reading tasks, along with a detailed psychological scale that measures depression, anxiety, and stress. By examining the relationships among these emotional states and employing clustering analysis, we identified distinct subgroups embodying different emotional profiles. We then trained tree-based classifiers and deep learning models to estimate emotional states from facial features. Results indicate that models previously effective on small datasets experienced decreased performance when applied to our large dataset, highlighting the importance of data scale and mitigating overfitting in practical settings. Notably, our study identified subtle shifts in pupil dynamics and gaze orientation as potential markers of mood disorders, providing valuable information on the interaction between facial expressions and mental health. This research marks the first large-scale and comprehensive investigation of facial expressions in the context of mental health, laying the groundwork for future data-driven advancements in this field

Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia

Improved reinforcement learning path planning algorithm integrating prior knowledge.

In order to realize the optimization of autonomous navigation of mobile robot under the condition of partial environmental knowledge known. An improved Q-learning reinforcement learning algorithm based on prior knowledge is proposed to solve the problem of slow convergence and low learning efficiency in mobile robot path planning. Prior knowledge is used to initialize the Q-value, so as to guide the agent to move toward the target direction with a greater probability from the early stage of the algorithm, eliminating a large number of invalid iterations. The greedy factor ε is dynamically adjusted based on the number of times the agent successfully reaches the target position, so as to better balance exploration and exploitation and accelerate convergence. Simulation results show that the improved Q-learning algorithm has a faster convergence rate and higher learning efficiency than the traditional algorithm. The improved algorithm has practical significance for improving the efficiency of autonomous navigation of mobile robots

Comparative liver proteomic analysis of protein expression changes in an acute-on-chronic liver failure mouse model

Purpose: Acute-on-chronic liver failure (ACLF) is a major cause of mortality and morbidity owing to the lack of targeted therapeutic interventions. The molecular mechanisms underlying the complex pathogenesis of ACLF are still unclear. Therefore, in this study, we established an ACLF mouse model using a combination of carbon tetrachloride (CCl 4 ) with lipopolysaccharide (LPS) and D-galactosamine (D-Gal) (CCl 4 + LPS/D-Gal) via intraperitoneal administration. Moreover, the underlying molecular pathogenesis of ACLF was explored. Methods: Four-dimensional label-free mass spectrometry-based quantitative proteomics was used to identify liver proteins that were differentially expressed in liver samples from CCl 4 + LPS/D-Gal-induced ACLF mouse liver samples. Results: Approximately 258, 380, and 471 proteins with a minimum mean expression fold change of 2 were differentially expressed in the liver fibrosis (LF), liver cirrhosis (LC), and ACLF groups, respectively, compared with those in the control (CTRL) group. Furthermore, the mice ACLF showed increased myeloperoxidase (MPO) and coronin-1A (CORO1A) levels, consistent with the phagosome pathway. These two proteins were potential targets for regulating the clinical features and progression of liver injury in ACLF mice. Conclusion: To the best of our knowledge, we are the first to provide a global differential protein expression profile of CCl 4 + LPS/D-Gal-induced ACLF mouse liver samples using label-free mass spectrometry-based quantitative proteomics. Overall, molecular differences in ACLF regulated the phagosome pathway at different disease stages, clarifying ACLF pathogenesis. MPO and CORO1A are the underlying predictors of ACLF severity or prognosis, and their regulation may facilitate timely and intensive clinical intervention

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts.</jats:p