Polysaccharides from pineapple peel: Structural characterization, film-forming properties and its effect on strawberry preservation.

The growing demand for food safety has stimulated the development of new environmentally friendly food packaging. It is the development trend of food packaging in recent years by using natural polysaccharides as carriers and adding bioactive ingredients extracted from plants to prepare multifunctional films with antioxidant, antimicrobial and biodegradable properties. Herein, three polysaccharide components (PPE40, PPE60, and PPE80) from pineapple peel were extracted by ultrasound-assisted hot water extraction combined with gradient ethanol precipitation method, which all showed a certain scavenging activities against DPPH, ABTS, and hydroxyl radical.

A human activity recognition method based on Vision Transformer.

Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human-computer intelligent interaction. It has emerged as a significant research area in computer vision. GCN (Graph Convolutional networks) have recently been widely used in these fields and have made great performance.

Antiparasitic efficacy of flavonoids identified from Psoralea corylifolia against Tetrahymena piriformis in guppy (Poecilia reticulate).

Tetrahymena piriformis belongs to the ciliated protists (ciliates), causing severe economic losses in aquaculture. Chemical drugs currently used usually have toxic side effects, and there is no specific drug against Tetrahymena. Therefore, it is an urgent need to identify new antiparasitic lead compounds.

Meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network for imbalanced breast cancer histopathological image classification.

Breast cancer histopathological image automatic classification can reduce pathologists workload and provide accurate diagnosis. However, one challenge is that empirical datasets are usually imbalanced, resulting in poorer classification quality compared with conventional methods based on balanced datasets. The recently proposed bilateral branch network (BBN) tackles this problem through considering both representation and classifier learning to improve classification performance.

Improved Robot Path Planning Method Based on Deep Reinforcement Learning.

With the advancement of robotics, the field of path planning is currently experiencing a period of prosperity. Researchers strive to address this nonlinear problem and have achieved remarkable results through the implementation of the Deep Reinforcement Learning (DRL) algorithm DQN (Deep Q-Network). However, persistent challenges remain, including the curse of dimensionality, difficulties of model convergence and sparsity in rewards.

Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix.

Breast cancer is regarded as the leading killer of women today. The early diagnosis and treatment of breast cancer is the key to improving the survival rate of patients. A method of breast cancer histopathological images recognition based on deep semantic features and gray level co-occurrence matrix (GLCM) features is proposed in this paper.

Regional level influenza study based on Twitter and machine learning method.

The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, based on twitter and US Centers for Disease Control's (CDC's) Influenza-Like Illness (ILI) data, are proposed (models 1-3) to verify the factors that affect the spread of the flu. In this work, an Improved Particle Swarm Optimization algorithm to optimize the parameters of Support Vector Regression (IPSO-SVR) was proposed.