Academic Background In the fields of materials science and chemistry, understanding the properties of materials at the atomic level is crucial. However, traditional methods for calculating interatomic potentials, such as Density Functional Theory (DFT), while highly accurate, are computationally expensive and difficult to apply to large-scale syste...
With the rapid development of artificial intelligence (AI) technology, artificial neural networks (ANNs) have achieved significant success in multiple fields. However, traditional neural network training methods—especially the backpropagation algorithm—face numerous challenges in hardware implementation. Although the backpropagation algorithm is ef...
Background Introduction Two-dimensional (2D) materials exhibit significant potential in fields such as nanoelectronics and optoelectronics due to their unique physical and chemical properties. However, structural perturbations at the nanoscale have a profound impact on their performance. Traditional characterization methods like Raman spectroscopy,...
Academic Background With the rapid development of cloud computing technology, the demand for efficient job scheduling in dynamic and heterogeneous cloud environments has grown significantly. Traditional scheduling algorithms perform well in simple systems but are no longer sufficient for modern, complex cloud infrastructures. Issues such as resourc...
In recent years, the significant role of the human gut microbiota (HGM) in the metabolism of drugs and nutrients has gradually been recognized. The gut microbiota not only affects the bioavailability of orally administered drugs but also participates in the biotransformation of drugs and bioactive molecules through its metabolic enzymes, thereby in...
Evaluation of the Generalizability of Oncology Trial Results Using Machine Learning-Based Trial Emulations Academic Background Randomized Controlled Trials (RCTs) are the gold standard for evaluating the efficacy of anti-cancer drugs, but their results often cannot be directly generalized to real-world oncology patients. RCTs typically employ stric...
Academic Background Parkinson’s Disease (PD) is the second most common chronic neurodegenerative disease, primarily affecting individuals aged 65 and above. With the global population aging, the prevalence of Parkinson’s disease is projected to increase from 7 million in 2015 to 13 million by 2040. Currently, the diagnosis of Parkinson’s disease ma...
Machine Learning Study on the Relationship Between Heart Rate and Body Temperature in Pediatric Intensive Care Units Academic Background In the pediatric intensive care unit (PICU), heart rate (HR) and body temperature (BT) are crucial clinical indicators that reflect a patient’s physiological status. Although the relationship between HR and BT has...
Research on Driver Stress Estimation Based on Visual Road Scenes Academic Background Driver stress is a significant factor contributing to traffic accidents, injuries, and fatalities. Studies show that 94% of traffic accidents are related to drivers, with inattention, internal and external distractions, and improper speed control all closely linked...
Multimodal Learning Reveals Genotype–Phenotype Dynamics Background The complex relationship between genotype and phenotype has long been a central question in biology. Genotype refers to the genetic information of an organism, while phenotype is the manifestation of this genetic information in a specific environment. Although Wilhelm Johannsen intr...