Environmental Microorganisms (EMs) serve as vital bioindicators for monitoring environmental pollution monitoring. However, their classification is challenged by severe class imbalance and a lack of labeled data. Precise identification of EMs is essential for timely ecological risk assessment, yet conventional methods struggle in few-shot learning (FSL) contexts due to poor generalization and overfitting. To overcome these challenges, we introduce a novel Cross-stage Multi-branch Deep Feature Extraction (CMDFE) framework, which integrates deep feature extraction (Re) with a lightweight multi-branch module (C2f). This design improves feature diversity and mitigates overfitting, while maintaining a favorable balance between accuracy and computational efficiency. Experimental results on the EMDs7 dataset demonstrate the effectiveness of CMDFE, achieving$\mathbf{6 8. 4 0 \%}$accuracy in 1-shot and$\mathbf{7 7. 0 0 \%}$in 5-shot tasks, surpassing most baseline methods. Additionally, CMDFE significantly reduces memory usage, making it well-suited for deployment in resource-constrained environments. Overall, CMDFE offers a robust and scalable approach for few-shot EM classification and holds promise for intelligent environmental monitoring applications.

Histopathological image classification in colorectal cancer (CRC) is hindered by class imbalance, where limited data for some classes and overlapping features lead to degraded performance. Traditional methods struggle to adequately augment minority classes or resolve these uncertainties. To alleviate these problems, we propose a novel data synthesis and adaptive loss-based histopathological image classification framework that integrates two components: 1) a global context generative adversarial network (GCGAN) to generate realistic images for under-represented classes, thereby enriching dataset diversity; 2) an adaptive focal loss (AFL) to dynamically adjust the loss function and mitigate feature overlap. Extensive experiments on the CRC dataset demonstrate the effectiveness of our approach, outperforming other state-of-the-art (SOTA) methods. Additionally, combining GCGAN and AFL benefits these SOTA methods, with performance improvements on cervical (CC) and breast cancer (BC) datasets.

Recently, the development of artificial intelligence technologies has accelerated advancements in threshold segmentation techniques based on intelligent optimization algorithms, which have demonstrated good performance across various types of medical images. However, research specifically focused on the segmentation of Prostate Cancer Pathology Image (PCPI) remains limited. To fill this gap and improve the efficiency of prostate cancer diagnosis, this study proposes a threshold segmentation scheme based on intelligent optimization algorithms, MOLMVO-Rényi, specifically designed for PCPI. This scheme first enhances the optimization capabilities of the multi-verse optimizer algorithm using multiple strategies (multi-population topological structure and orthogonal learning) to propose MOLMVO. Then it utilizes MOLMVO to optimize the thresholds for image segmentation, aiming to maximize the Rényi entropy of the segmented images. Experimental validation on 67 privately collected PCPIs indicates that the proposed scheme shows significant competitive advantages, providing strong support for the early diagnosis and treatment of prostate cancer.

This study proposes a Generative Adversarial Network (GAN)-based data augmentation method to tackle the class imbalance issue in histopathological image classification. The proposed GAN generates high-quality images for minority classes, improving data diversity and classification performance. By integrating Global Context Attention (GCA) and UpBlock-CompRes modules, the GAN generates images that closely resemble real data, achieving superior results in FID, IS, PSNR, and SSIM compared to FastGAN. Experimental results demonstrate that the proposed GAN outperforms both the baseline without data augmentation and FastGAN, with noticeable improvements across key metrics. These enhancements highlight the effectiveness of our GAN in generating high-quality images and achieving more balanced classification results.

Based on the data obtained from face-to-face interview and the measurements using global positioning system (GPS) and geographical information system (GIS) technology, the village pattern evolution of the WL village in the middle of the Huang-Huai-Hai plain was studied. The main results are shown as follows. Firstly, the WL village goes through a series of evolution pattern including the absolute tardiness development before the establishment of People's Republic of China in 1949, the relative tardiness development before the implementation of the economic reform in 1978, the rapid expansion in the 1980s, the steady scale in the 1990s and the recessionary development in the early 21st century. In the meantime, the roads and ponds in the village have also been changed correspondingly. The village scale changed little before the implementation of reform and opening- up in 1978, the dilation of it was prominent from 1978 to the 1990s, and the empty and abandoned houses have increased largely since about 2000. Secondly, main factors influencing the village pattern evolution include the economic improvement, the change of society structure, urbanization and the effect of national policy, while population variation is still the decisive factor. Thirdly, the previous village expansion attributed to four aspects as follows: No family planning after the national liberation in 1949 resulted in population increasing enormously; the economic development enabled people to improve their habitations; the status of nuclear families (namely a family consisting of few people which only include husband, wife and their children in general.) was enhanced step by step; farmers were not conscious of the importance of protecting plantations. Finally, the urbanization and market economy develop further, the profit of planting grains is too small and the fast increase of population has been successfully controlled by the policy of family planning since the 1980s. All these drive the abandoned houses increasing and the village hollowing at present.