TP-Transfiner: high-quality segmentation network for tea pest.

Detecting and controlling tea pests promptly are crucial for safeguarding tea production quality. Due to the insufficient feature extraction ability of traditional CNN-based methods, they face challenges such as inaccuracy and inefficiency of detecting pests in dense and mimicry scenarios. This study proposes an end-to-end tea pest detection and segmentation framework, TeaPest-Transfiner (TP-Transfiner), based on Mask Transfiner to address the challenge of detecting and segmenting pests in mimicry and dense scenarios.

Deep Learning-Based Segmentation of Peach Diseases Using Convolutional Neural Network.

Peach diseases seriously affect peach yield and people's health. The precise identification of peach diseases and the segmentation of the diseased areas can provide the basis for disease control and treatment. However, the complex background and imbalanced samples bring certain challenges to the segmentation and recognition of lesion area, and the hard samples and imbalance samples can lead to a decline in classification of foreground class and background class.

L2MXception: an improved Xception network for classification of peach diseases.

Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data.

ClsGAN: Selective Attribute Editing Model based on Classification Adversarial Network.

Attribution editing has achieved remarkable progress in recent years owing to the encoder-decoder structure and generative adversarial network (GAN). However, it remains challenging to generate high-quality images with accurate attribute transformation. Attacking these problems, the work proposes a novel selective attribute editing model based on classification adversarial network (referred to as ClsGAN) that shows good balance between attribute transfer accuracy and photo-realistic images.

Supporting a circular economy: Insights from Taiwan's plastic waste sector and lessons for developing countries.

This study investigates how, in the process of industrialization, Taiwan successfully developed its plastic waste industry into an industrial-level circular economy by leveraging a network-based collective bricolage in conjunction with a framework of adaptive institutional governance. Three conclusions are made: industrialized manufacturing sectors are foundations upon which developing nations can accumulate endogenous social capabilities and can enable the emergence of network-based collective bricolages; for developing nations that are attempting to establish circular economies based on their endogenous small-to-medium enterprises, developing network-based collective bricolages in conjunction with adaptive institutional governance is an essential and effective strategy; and transitioning into green-related sectors can further drive economic development and lead to the creation of new ventures, businesses, and job opportunities while supporting the formation of a circular economy. The approach is especially relevant for developing countries starting their industrialization process and waste management initiatives with few resources.

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization.

Identifying drug-disease associations is integral to drug development. Computationally prioritizing candidate drug-disease associations has attracted growing attention due to its contribution to reducing the cost of laboratory screening. Drug-disease associations involve different association types, such as drug indications and drug side effects.

Pathway Network Analysis of Complex Diseases Based on Multiple Biological Networks.

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network.