Long-term prediction for karst spring discharge and petroleum substances concentration based on the combination of LSTM and Transformer models.

Monitoring the quantity and quality of karst springs is essential for groundwater resource management. However, it is challenging to robustly forecast the karst spring discharge and pollutant concentration due to the high complexity and heterogeneity of karst aquifers. Few researchers have addressed the long-term prediction of hourly spring quantity and quality, which is crucial for emergency management.

Beta-sitosterol regulates PTGS1 to inhibit gastric cancer cell proliferation and angiogenesis.

Background: Gastric cancer (GC) is the third leading culprit of cancer-related deaths around the world. Beta-sitosterol (BS) is an important phytosterol that has been proven to have anti-proliferative effects on GC and other tumors. However, mechanisms and targets of BS in cancer are rarely explored.

Protocol for identifying Dicer as dsRNA binding and cleaving reagent in response to transfected dsRNA.

Mammalian Dicer has been proved to be functional on double-stranded RNAs (dsRNAs) and involved in antiviral immunity or immune regulation. Here, we present a protocol for identifying Dicer as a dsRNA binding and cleaving factor to transfected dsRNA in cell lines, based on small RNA sequencing (RNA-seq) and dsRNA-immunoprecipitation (dsRNA-IP). We detail both experimental processes and analysis on small RNA-seq data.

A Deep Learning Approach for Mental Fatigue State Assessment.

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.

Dissimilar Gas Tungsten Arc Welding of (FeCoNi)Al High-Entropy Alloy and Q235 Structural Steel.

(FeCoNi)A high-entropy alloy (HEA) is a new material with a strength similar to that of commercial Q235 structural steel, and its elongation is nearly three times greater than that of Q235 steel. Studying the welding process of the (FeCoNi)Al HEA and Q235 steel is expected to further expand the application range of commercial Q235 structural steel and provide a foundation for the engineering application of the (FeCoNi)Al HEA. This study focuses on the dissimilar welded components of (FeCoNi)Al HEA and Q235 steel and analyzes the forming quality, microstructure, and mechanical properties of dissimilar welded samples under different currents.