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In this paper, we developed three ACT-R cognitive models to simulate the learning process of abacus gestures. Abacus gestures are mid-air gestures, each representing a number between 0 and 99. Our models learn to predict the response time of making an abacus gesture. We found the accuracy of a model's predictions depends on the structure of its declarative memory. A model with 100 chunks cannot simulate human response, whereas models using fewer chunks can, as segmenting chunks increase both the frequency and recency of information retrieval. Furthermore, our findings suggest that the mind is more likely to represent abacus gestures by dividing attention between two hands rather than memorizing and outputting all gestures directly. These insights have important implications for future research in cognitive science and human-computer interaction, particularly in developing vision and motor modules for mental states in existing cognitive architectures and designing intuitive and efficient mid-air gesture interfaces.

The electrochemical corrosion mechanisms of Mg alloys were extensively studied in previous investigations of different chemical compositions, modified surface states and various electrolyte conditions. However, recent research focused on the active state of Mg dissolution, leading to unresolved effects of secondary phases adjacent to a stable α-solid solution passive layer. The present study investigates the fundamental electrochemical corrosion mechanisms of three different Laves phases with varying phase morphologies and phase fractions in the passive state of Mg-Al-Ca alloys. The microstructure was characterized by (transmission-) electron microscopy and synchrotron-based transmission X-ray microscopy. The electrochemical corrosion resistance was determined with a standard three-electrode setup and advanced in-situ flow cell measurements. A new electrochemical activity sequence (C15>C36>α-Mg>C14) was obtained, as a result of a stable passive layer formation on the α-solid solution. Furthermore, nm-scale Mg-rich precipitates were identified within the Laves phases, which tend to inhibit the corrosion kinetics.

The fields resulting from the brain's neural activities provide essential information in diagnosing and treating brain diseases such as epilepsy, convulsions, and brain tumors. Recording brain magnetic field signals is one of the non-invasive brain functional imaging methods, which usually requires magnetic shielding besides expensive and bulky instruments. Although atomic magnetometers are inherently less sensitive than superconducting quantum interference devices, they are considered the best candidate for measuring bio-magnetic fields due to their low manufacturing cost, small size, and no need for cryogenic equipment. Atomic magnetometers measure the low-strength brain magnetic fields based on detecting Zeeman energy splitting and recording changes in the laser light intensity passing through an alkali vapor cell. To improve the sensitivity of these magnetometers, it is common to remove homogeneous noises in two magnetometer channels. For this purpose, we have presented a gradiometer to suppress unwanted magnetic noises. This gradiometer consists of two atomic magnetometers capable of detecting the field produced by the human brain in an unshielded environment in the presence of the Earth's magnetic field. The gradiometer has a sensitivity of 900 fT⁄√Hz. The designed and built gradiometer is suitable for detecting brain magnetic fields, which can be expanded as a multichannel to record the map of the brain's magnetic field.

The aim of the current research was to explain the structural model of the relationship between personality traits and self-worth with academic self-efficacy through the mediation of students' motivation to progress. In a structural equation and correlation design, 306 male students of the second secondary school in Gonabad city were selected in the academic year of 2021-2022 with a multi-stage cluster random sampling method, and the questionnaires of Neo's personality traits (1985), self-esteem questionnaires of Kroker et al. (2003) , Morgan and Jinks' (1999) academic self-efficacy and Hermans' (1977) achievement motivation completed. Data analysis was done with Pearson correlation and structural equation modeling with SPSS.27 and AMOS.24 software. The hypothesized model had a good fit with the data. The results showed that personality traits and self-esteem have a significant direct and indirect effect on academic self-efficacy (P≥0.01). And the direct effect of these variables on academic self-efficacy is more than its indirect effect. Also, the comparison of direct and indirect effects shows that motivation to progress in relation to personality traits and self-worth plays a mediating role with academic self-efficacy, and self-worth (P0.05) has a greater indirect effect on academic self-efficacy. Based on this, it can be concluded that students' academic self-efficacy can be increased due to the relationship between personality traits and self-worth with the motivation to progress, and by strengthening the motivation to progress, the relationship between personality traits and self-worth can be strengthened with students' academic self-efficacy.

We present a user error model that simulates a user's errors using an eyes and hands extension to cognitive architectures. We developed a complete interactive cognitive model that performs a spreadsheet task. This model is compared with an existing cognitive model that performs the same task in a different spreadsheet tool. Also, the predictions are compared to human data (N=23) on the same uninstrumented interfaces. The comparison suggests that the interactive cognitive model moves us closer to having a user model that can directly test interfaces by predicting human behavior and performing the task on the same interface that users interact. The error model also allows exploration of error detection, error correction, and different knowledge types.

Introduction: In order to prevent postoperative injuries in the interforaminal region, the dentist must be aware of the anatomical variations in the anterior mandible. The purpose of this study is to CBCT based evaluation of mandibular lingual foramina and incisive canal in selected Iranian population.Materials and Methods: This was a descriptive-cross-sectional study. 214 CBCT images of mandible patients referred to a private oral and maxillofacial radiology center in Tehran were examined. Prevalence and diameter of the lingual foramina, the related canal length and position of its beginning at lingual side of mandible, middle point of alveolar crest or CEJ of teeth and genial Tubercle furthermore prevalence, length and diameter of incisal mandibular canal and its position related to the inferior border, buccal and lingual cortex of mandible were measured. In addition, the effect of patient’s age and gender on the measurement of anatomical dimensions was evaluated. Data were analyzed using SPSS version 24 software.Results: This study examined 83 males and 131 females. 208 (97.2percent) subjects showed the lingual foramina (LF), double MLF and double LLF predominating. 202 (94/0percent) subjects had the MIC. No significant relationship was found between diameter, location of LF and sex, as well as between diameter and length of LF channel and age (P˃0.05). While the average length of the left MIC was higher in men (P˂0.05).Conclusions: This study showed a high prevalence of LF and MIC in the selected Iranian population. Therefore, caution should be taken during the implant treatment at the anterior mandible region.

We use a model to explore the implications of ACT-R's learning and forgetting mechanisms to understand learning and retention on a complex task. The model performs a spreadsheet task that has 14 non-iterated subtasks. The model predicts a learning curve and knowledge decay for different learning stages. The model's learning curve fits the human data well for the first four trials without decay. When decay is examined, however, we have to make modifications to the retention equation for the model's predictions to match data and the shapes predicted by the other learning theories. To fix this anomaly, we modified the effect of time on decay (adjusting time outside the experiment to less than the effect of time in the experiment) and the strength of newly learned memories (less well known than the previous default value). From these results, we learn that training and testing have been confounded in many studies.

Introduction: This study examined the effect of 1.5% NaOCl, 17% EDTA, and curcumin on the proliferation, attachment, and differentiation of dental pulp stem cells (DPSCs) placed on the dentin specimens. Methods: MTT assay was performed to evaluate the proliferation of DPSCs on the dentin specimens treated with different concentrations of NaOCl, 17% EDTA, and curcumin (0.97–250 μM). Cell-adhering ability of DPSCs was tested via the LDH assay to calculate the attached DPSCs. In addition, the western blotting assay was performed to investigate the expression levels of fibronectin as a cell-adhesion marker and analyze the expressions level of differentiation markers, including DMP-1, OCN, ALP, and DSPP, to detect the odontogenic potential of hDPCs. Results: NaOCl had lower toxicity on DPSCs at lower concentrations (P

In this study, we investigate how high- and low-performancelearners (N=12) act differently while using a cognitive tutoringsystem. We examine three research questions: (1) Can wepredict learners’ performance using only their visual attention(eye movement data)? (2) Can we predict learners’performance from visual attention data and initialperformance? (3) Are age, gender, first language, where theylook, and the sequence of Areas of Interests (AOIs) significantfactors in the learners’ performance? Learners more correctlyanswer questions taken from larger rather than smaller AOIs.Our results show that high-performance learners pay moreattention to the content that contains answers to later questions.Surprisingly, the tutor did not change the learners’ visualsearch to a goal-oriented search. Our analyses can helpinstructional designers create a more productive learningexperience because visual search behavior as part of a learnermodel with acceptable accuracy in early stages can be used inadaptive tutors. Additionally, we trained a classifier on the eyemovement data to predict learners’ performance for eachquestion. Its results provide a list of suggestions for designingmore productive learning experiences, such as enticing userattention by increasing the size of the content that containsanswers and changing the order of contents.