EMDS-7: Environmental microorganism image dataset seventh version for multiple object detection evaluation.

Nowadays, the detection of environmental microorganism indicators is essential for us to assess the degree of pollution, but the traditional detection methods consume a lot of manpower and material resources. Therefore, it is necessary for us to make microbial data sets to be used in artificial intelligence. The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set that is applied in the field of multi-object detection of artificial intelligence. This method reduces the chemicals, manpower and equipment used in the process of detecting microorganisms. EMDS-7 including the original Environmental Microorganism (EM) images and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2,65 images and 13,216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-Region Convolutional Neural Network (Faster-RCNN), YOLOv3, YOLOv4, SSD, and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571.

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation.

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related datasets, not to mention the datasets with ground truth (GT) images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and object detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods. EMDS-6 is available at the https://figshare.com/articles/dataset/EMDS6/17125025/1.

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers.

In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyper-parameters tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet, and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the vision transformer (ViT) model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

The Mediating Roles of Impulsiveness and Maladjustment in the Association of Psychological Resilience with Drug Addiction during Isolated Detoxification.

Background: Studies have shown that psychological resilience is a key factor in drug rehabilitation. To explore the feasibility of developing psychological resilience as an addiction treatment intervention, it is essential to explore the role that it plays in drug addiction. Objectives: This study aimed to investigate the relationship between psychological resilience and drug addiction, as well as to examine the underlying mediational roles of maladjustment and impulsiveness in this association. Methods: We used a cross-sectional design that included a sample of 140 male drug addicts in compulsory isolation centers and used questionnaires and scales to ascertain their level of drug addiction, psychological resilience, maladjustment, impulsiveness, social support, and loneliness. Correlation and mediation effect analyses were performed to determine the roles of impulsiveness and maladjustment in the association of psychological resilience with drug addiction. Results: Psychological resilience was an inverse predictor of drug addiction. The results of the mediation effect analysis showed that maladjustment acted as a mediator between resilience and drug addiction and between impulsiveness and drug addiction. Furthermore, impulsiveness and maladjustment jointly mediated the relationship between psychological resilience and drug addiction. Conclusion: These findings highlight the importance of psychological resilience in maladjustment and impulsiveness for drug addicts and suggest that the role of psychological resilience in drug addiction needs to be further explored.