Study on the impact of meteorological factors on influenza in different periods and prediction based on artificial intelligence RF-Bi-LSTM algorithm: to compare the COVID-19 period with the non-COVID-19 period.

Objective: At different times, public health faces various challenges and the degree of intervention measures varies. The research on the impact and prediction of meteorology factors on influenza is increasing gradually, however, there is currently no evidence on whether its research results are affected by different periods. This study aims to provide limited evidence to reveal this issue.

W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM.

Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites.

Coronary heart disease prediction method fusing domain-adaptive transfer learning with graph convolutional networks (GCN).

Graph convolutional networks (GCNs) have achieved impressive results in many medical scenarios involving graph node classification tasks. However, there are difficulties in transfer learning for graph representation learning and graph network models. Most GNNs work only in a single domain and cannot transfer the learned knowledge to other domains.

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China.

Background: This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence.

Method: A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods.

Enhancing Interfacial Strength and Wettability for Wide-Temperature Sodium Metal Batteries.

Development of high-performance sodium metal batteries (SMBs) with a wide operating temperature range (from -40 to 55 °C) is highly challenging. Herein, an artificial hybrid interlayer composed of sodium phosphide (Na P) and metal vanadium (V) is constructed for wide-temperature-range SMBs via vanadium phosphide pretreatment. As evidenced by simulation, the VP-Na interlayer can regulate redistribution of Na flux, which is beneficial for homogeneous Na deposition.

Study on the influence of meteorological factors on influenza in different regions and predictions based on an LSTM algorithm.

Background: Influenza epidemics pose a threat to human health. It has been reported that meteorological factors (MFs) are associated with influenza. This study aimed to explore the similarities and differences between the influences of more comprehensive MFs on influenza in cities with different economic, geographical and climatic characteristics in Fujian Province.

Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study.

Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient.

All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks.

Coronary heart disease (CHD) has become one of the most serious public health issues due to its high morbidity and mortality rates. Most of the existing coronary heart disease risk prediction models manually extract features based on shallow machine learning methods. It only focuses on the differences between local patient features and ignores the interaction modeling between global patients.

Synthesis and biological evaluation of novel phosphoramidate derivatives of coumarin as chitin synthase inhibitors and antifungal agents.

A series of novel phosphoramidate derivatives of coumarin have been designed and synthesized as chitin synthase (CHS) inhibitors. All the synthesized compounds have been screened for their chitin synthase inhibition activity and antimicrobial activity in vitro. The bioactive assay manifested that most of the target compounds exhibited good efficacy against CHS and a variety of clinically important fungal pathogens.