Assessing Callous-Unemotional Traits in Chinese Detained Boys: Factor Structure and Construct Validity of the Inventory of Callous-Unemotional Traits

The Inventory of Callous-Unemotional Traits (ICU) was designed to evaluate multiple facets of Callous-Unemotional (CU) traits in youths. However, no study has examined the factor structure and psychometrical properties of the ICU in Chinese detained juveniles. The current study assesses the factor structure, internal consistency and convergent validity of the ICU in 613 Chinese detained boys. Confirmatory factor analysis results indicated that the original three-factor model with 24 items showed an unacceptable fit to the data, however, the 11-item shortened version of the ICU (ICU-11) with callousness and uncaring dimensions showed the best fit. Moreover, the ICU-11 total score and factor scores had good and acceptable internal consistencies. The convergent and criterion validity of the ICU-11 was demonstrated by comparable and significant associations in the expected direction with relevant external criteria (e.g., psychopathy, aggression, and empathy). In conclusion, present findings indicated that the ICU-11 is a reliable and efficient instrument to replace the original ICU when assessing CU traits in the Chinese male detained juvenile sample.This work was supported by the National Natural Science Foundation of China (Grant Nos. 31800945 and 31400904) and Guangzhou University’s 2017 training program for young topnotch personnels (BJ201715)

Longitudinal measurement invariance of the Child Problematic Trait Inventory in older Chinese children

The Child Problematic Traits Inventory (CPTI) is a newly developed informant-rated instrument to measure psychopathic traits during early childhood. The aim of this study was to examine the longitudinal measurement invariance of the CPTI in a group of Chinese schoolchildren. Mothers of 585 children aged 8 to 12 years (50% girls) completed the CPTI twice with one-year interval. Confirmatory factor analyses showed that the CPTI had strict invariance (i.e., equality of factor patterns, loadings, intercepts, and item uniqueness) across time. Furthermore, the internal consistencies for the CPTI subscales were good at both time points and the stability coefficients over time were moderate. Findings suggest that, in children aged 8 to 12 years old, changes in CPTI scores across time can be attributed to actual changes in the child’s psychopathic personality

Psychometric Properties and Measurement Invariance of the Brief Symptom Inventory-18 Among Chinese Insurance Employees

This study aimed to examine the psychometric properties and factorial invariance of the Brief Symptom Inventory-18 (BSI-18). Confirmatory factor analyses (CFAs) were performed to verify the BSI-18’s factor structure in a large sample of Chinese insurance professionals (N = 2363, 62.7% women; age range = 19–70). Multigroup CFA were performed to test the measurement invariance of the model with the best fit across genders. In addition, structural equation modeling was conducted to test the correlations between the BSI-18 and two covariates – social support perception and grit trait. Results indicated that the bi-factor model best fit the data and was also equivalent across genders. The BSI-18’s general factor, and somatization and depression dimensions were significantly related to social support perception and grit trait, whereas the anxiety dimension was not. Overall, our findings suggested that the BSI-18’s can be a promising tool in assessing general psychological distress in Chinese employees

A fast and accurate identification model for Rhinolophus bats based on fine-grained information

Abstract Bats are a crucial component within ecosystems, providing valuable ecosystem services such as pollination and pest control. In practical conservation efforts, the classification and identification of bats are essential in order to develop effective conservation management programs for bats and their habitats. Traditionally, the identification of bats has been a manual and time-consuming process. With the development of artificial intelligence technology, the accuracy and speed of identification work of such fine-grained images as bats identification can be greatly improved. Bats identification relies on the fine features of their beaks and faces, so mining the fine-grained information in images is crucial to improve the accuracy of bats identification. This paper presents a deep learning-based model designed for the rapid and precise identification of common horseshoe bats (Chiroptera: Rhinolophidae: Rhinolophus) from Southern China. The model was developed by utilizing a comprehensive dataset of 883 high-resolution images of seven distinct Rhinolophus species which were collected during surveys conducted between 2010 and 2022. An improved EfficientNet model with an attention mechanism module is architected to mine the fine-grained appearance of these Rhinolophus. The performance of the model beat other classical models, including SqueezeNet, AlexNet, VGG16_BN, ShuffleNetV2, GoogleNet, ResNet50 and EfficientNet_B0, according to the predicting precision, recall, accuracy, F1-score. Our model achieved the highest identification accuracy of 94.22% and an F1-score of 0.948 with low computational complexity. Heat maps obtained with Grad-CAM show that our model meets the identification criteria of the morphology of Rhinolophus. Our study highlights the potential of artificial intelligence technology for the identification of small mammals, and facilitating fast species identification in the future

Fine-grained image classification on bats using VGG16-CBAM: a practical example with 7 horseshoe bats taxa (CHIROPTERA: Rhinolophidae: Rhinolophus) from Southern China

Abstract Background Rapid identification and classification of bats are critical for practical applications. However, species identification of bats is a typically detrimental and time-consuming manual task that depends on taxonomists and well-trained experts. Deep Convolutional Neural Networks (DCNNs) provide a practical approach for the extraction of the visual features and classification of objects, with potential application for bat classification. Results In this study, we investigated the capability of deep learning models to classify 7 horseshoe bat taxa (CHIROPTERA: Rhinolophus) from Southern China. We constructed an image dataset of 879 front, oblique, and lateral targeted facial images of live individuals collected during surveys between 2012 and 2021. All images were taken using a standard photograph protocol and setting aimed at enhancing the effectiveness of the DCNNs classification. The results demonstrated that our customized VGG16-CBAM model achieved up to 92.15% classification accuracy with better performance than other mainstream models. Furthermore, the Grad-CAM visualization reveals that the model pays more attention to the taxonomic key regions in the decision-making process, and these regions are often preferred by bat taxonomists for the classification of horseshoe bats, corroborating the validity of our methods. Conclusion Our finding will inspire further research on image-based automatic classification of chiropteran species for early detection and potential application in taxonomy

Assessing construct validity of the Grit-S in Chinese employees.

This research examined the psychometric properties and construct validity of the Short Grit Scale (Grit-S) in Chinese insurance employees (N = 2,363; 37% males; mean age = 35.14). Exploratory factor analysis and confirmatory factor analysis (CFA) were used to determine the factor structure of the Grit-S. The resulting model was tested by multi-group CFA for the factorial invariance of the Grit-S across genders and age groups. Results showed that the Grit-S could be best explained by a two-factor model containing consistency of interest (α = .70) and perseverance of effort (α = .75). The factor model was equivalent across genders and age groups. The scores of the Grit-S were significantly correlated with external criteria variables including mental wellbeing and job performance. Overall, our findings suggested that the Grit-S can be a promising assessment of the grit trait in Chinese employees

Psychometric Properties and Measurement Invariance of the Brief Symptom Inventory-18 Among Chinese Insurance Employees

This study aimed to examine the psychometric properties and factorial invariance of the Brief Symptom Inventory-18 (BSI-18). Confirmatory factor analyses (CFAs) were performed to verify the BSI-18’s factor structure in a large sample of Chinese insurance professionals (N = 2363, 62.7% women; age range = 19–70). Multigroup CFA were performed to test the measurement invariance of the model with the best fit across genders. In addition, structural equation modeling was conducted to test the correlations between the BSI-18 and two covariates – social support perception and grit trait. Results indicated that the bi-factor model best fit the data and was also equivalent across genders. The BSI-18’s general factor, and somatization and depression dimensions were significantly related to social support perception and grit trait, whereas the anxiety dimension was not. Overall, our findings suggested that the BSI-18’s can be a promising tool in assessing general psychological distress in Chinese employees.This study was funded by the National Natural Science Foundation of China (Grant No. 31400904), Xinmiao Project of Guangzhou University (2014-27), Foundation of Guangzhou Shishu Gaoxiao Project (Grant No. 1201431330), and Guangzhou University’s 2017 training program for young top-notch personnels (BJ201715)