A neural network-shaped composite fusing α-MnO and nitrogen-doped graphene (N@Gr/α-MnO) was synthesized a hydrothermal method. The resulting composite demonstrates enhanced electrocatalytic activity for hydrogen peroxide (HO) compared with each sing...
BACKGROUND: The rapid development of large language model chatbots, such as ChatGPT, has created new possibilities for healthcare support. This study investigates the feasibility of integrating self-monitoring of hearing (via a mobile app) with ChatG...
The Baltic Dry Index (BDI) is a critical benchmark for assessing freight rates and chartering activity in the global shipping market. This study forecasts the BDI using diverse financial data, including commodities, currencies, stock markets, and vol...
The internet is one of the essential tools today, and its impact is particularly felt among university students. Internet addiction (IA) has become a serious public health issue worldwide. This multi-class classification study aimed to identify the p...
The rapid development of edge computing and artificial intelligence has brought growing interest in collaborative training. While prior research has addressed technical aspects of resource allocation, less attention has been paid to the underlying ec...
The dynamical growth of cyber threats in IoT setting requires smart and scalable intrusion detection systems. In this paper, a Lean-based hybrid Intrusion Detection framework using Particle Swarm Optimization and Genetic Algorithm (PSO-GA) to select ...
Chloride channel accessory 1 (CLCA1) is considered a potential prognostic biomarker for colon adenocarcinoma (COAD). The objective of this research was to develop two pathomics models to predict CLCA1 expression from hematoxylin-eosin (H&E) stained p...
Diabetic kidney disease (DKD) is a major cause of end-stage renal disease globally, with podocytes being implicated in its pathogenesis. However, the underlying mechanisms of podocyte involvement remain unclear. The aim of the present study was to id...
OBJECTIVES: Predicting optimal coagulation control using heparin in intensive care units (ICUs) remains a significant challenge. This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU ...
BACKGROUND AND OBJECTIVE: Despite advances in intensive care, sepsis remains a leading cause of mortality in intensive care unit (ICU) patients, especially middle-aged and elderly individuals. Given the limitations of conventional scoring systems and...
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