Machine learning based models for predicting presentation delay risk among gastric cancer patients.

Objective: Presentation delay of cancer patients prevents the patient from timely diagnosis and treatment leading to poor prognosis. Predicting the risk of presentation delay is crucial to improve the treatment outcomes. This study aimed to develop and validate prediction models of presentation delay risk in gastric cancer patients by using various machine learning models.

Efficient harvesting of triplet excitons multiple fast TTA up-conversion and high-lying reverse intersystem crossing channels for efficient blue fluorescent organic light-emitting diodes.

The efficient harvesting of triplet excitons is key to realizing high efficiency blue fluorescent organic light-emitting diodes (OLEDs). Triplet-triplet annihilation (TTA) up-conversion is one of the effective triplet-harvesting strategies. However, during the TTA up-conversion process, a high current density is necessary due to the competitive non-radiative triplet losses.

Longitudinal Associations Between Sleep Disturbance Trajectories and Internet Gaming Disorder Mediated by Self-Control: A Six-Wave Longitudinal Investigation.

Objective: The present study aims to analyze the heterogeneous trajectories of sleep disturbance (SD) among college students and to examine whether self-control mediates the association between sleep disturbance trajectories and Internet gaming disorder (IGD).

Methods: A total of 4352 students were initially invited to participate, and 4191 ( = 19.12, = 0.

Research Progress of Microneedles in Vaccine Delivery.

Large-scale infectious diseases have become a significant threat to human health and safety. The successful invention of vaccines is the most powerful means for preventing infectious diseases and has greatly improved global human health. Even during the pandemic of COVID-19, which has affected the world, vaccines have played an irreplaceable role.

Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability.

The increasing trend in land surface temperature (LST) and the formation of urban heat islands (UHIs) has emerged as a persistent challenge for urban planners and decision-makers. The current research was carried out to study the land use and land cover (LULC) changes and associated LST patterns in the planned city (Kabul) and the unplanned city (Jalalabad), Afghanistan, using Support Vector Machine (SVM) and Landsat data from 1998 to 2018. Future changes in LULC and LST were predicted for 2028 and 2038 using Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models.