Machine learning
A recent study employing machine learning techniques has analyzed hourly electricity consumption for 125 households participating in the People Demand Response (DR) program, a Peak Time Rebate (PTR) initiative in Korea, and found varying effects of DR interventions across different household clusters. The research also discovered learning effects over time
Machine learning
A groundbreaking study has been conducted by researchers at Southeast University in Nanjing, People's Republic of China, to predict the cycle life of supercapacitors using machine learning techniques. The study, funded by the National Key R & D Program of China, proposes a novel feature-enhanced framework that accurately
Machine learning
Researchers from East China Normal University have made significant strides in developing desalination technology, employing machine learning (ML) methods to predict the desalination stability of carbon materials. The study utilized eight ML models, with the categorical boosting model exhibiting the highest prediction accuracy. SHapley Additive exPlanations was employed to evaluate
Machine learning
Researchers at the University of Hong Kong have developed an explainable machine learning framework to understand the relationship between urban morphology and building energy consumption in Dongguan, China. The study integrates large-scale smart meter records with 3D building footprints to construct high-resolution datasets, demonstrating superior predictive performance over comparative methods.
Machine learning
A study published in the Journal of Surgical Research has demonstrated the efficacy of a machine learning algorithm-based tumor-stroma ratio in stratifying the prognosis of intrahepatic cholangiocarcinoma (iCCA) patients after radical surgery. The research, conducted by a team of scientists from Sun Yat-Sen University, aimed to quantify tumor components and
Machine learning
A team of researchers at the University of Ulsan in South Korea has made a breakthrough in simulating high-temperature proton exchange membrane fuel cells (PEMFCs) using a novel deep learning approach. The study aimed to address the high computational cost and data generation challenges in PEMFC simulations by introducing a
Machine learning
Researchers at Galgotias University have unveiled a novel Quantum-Inspired Hybrid Reinforcement Learning and Multi-Objective Optimization Framework (QHRMOF) designed to optimize task scheduling, dynamic load balancing, and server consolidation while minimizing power consumption and enhancing system performance. The framework integrates quantum-inspired evolutionary algorithms, hybrid deep reinforcement learning, and multi-objective optimization techniques
Machine learning
Researchers at Yaba College of Technology have introduced a novel approach for proactive road repair that combines machine learning algorithms with Internet of Things (IoT) devices. The study investigates the effectiveness of this method in scheduling and prioritizing maintenance tasks, reducing expenses, and improving efficiency. By utilizing sensors and machine
Machine learning
A recent study employing machine learning techniques has analyzed hourly electricity consumption for 125 households participating in the People Demand Response (DR) program, a Peak Time Rebate (PTR) initiative in Korea, and found varying effects of DR interventions across different household clusters. The research also discovered learning effects over time
Machine learning
Researchers at the University of Hong Kong have developed an explainable machine learning framework to understand the relationship between urban morphology and building energy consumption in Dongguan, China. The study integrates large-scale smart meter records with 3D building footprints to construct high-resolution datasets, demonstrating superior predictive performance over comparative methods.
Machine learning
Research published by Universite Clermont Auvergne, in collaboration with other institutions, presents a novel approach to energy-efficient information processing. By developing a recurrent hierarchical spiking neural network called Predictive Coding Light (PCL), the team has proposed a new method for unsupervised representation learning. PCL differs from previous predictive coding approaches
Precision medicine
Researchers at Yuncheng Central Hospital affiliated to Shanxi Medical University have developed a novel prognostic signature for lower-grade gliomas (LGGs) using machine learning and multi-omics data. The study, published in Discover Oncology, identifies cell adhesion molecules (CAMs) as crucial regulators of tumor biology and immune remodeling in LGGs. Key Takeaways:
