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Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

PloS one
Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strate...

An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation.

Journal of environmental management
Evaporation is an important hydrological process in the water cycle, especially for water bodies. Machine Learning (ML) models have become accurate and powerful tools in predicting pan evaporation. Meanwhile, the "black-box" character and the consist...

Mapping global dynamics of benchmark creation and saturation in artificial intelligence.

Nature communications
Benchmarks are crucial to measuring and steering progress in artificial intelligence (AI). However, recent studies raised concerns over the state of AI benchmarking, reporting issues such as benchmark overfitting, benchmark saturation and increasing ...

Graph neural network-based cell switching for energy optimization in ultra-dense heterogeneous networks.

Scientific reports
The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense ...

A Robust Artificial Intelligence Approach with Explainability for Measurement and Verification of Energy Efficient Infrastructure for Net Zero Carbon Emissions.

Sensors (Basel, Switzerland)
Rapid urbanization across the world has led to an exponential increase in demand for utilities, electricity, gas and water. The building infrastructure sector is one of the largest global consumers of electricity and thereby one of the largest emitte...

An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.

Molecules (Basel, Switzerland)
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtaine...

Deep Learning-Based Adaptive Compression and Anomaly Detection for Smart B5G Use Cases Operation.

Sensors (Basel, Switzerland)
The evolution towards next-generation Beyond 5G (B5G) networks will require not only innovation in transport technologies but also the adoption of smarter, more efficient operations of the use cases that are foreseen to be the high consumers of netwo...

Energy consumption prediction using the GRU-MMattention-LightGBM model with features of Prophet decomposition.

PloS one
The prediction of energy consumption is of great significance to the stability of the regional energy supply. In previous research on energy consumption forecasting, researchers have constantly proposed improved neural network prediction models or im...

Water-Immiscible Coacervate as a Liquid Magnetic Robot for Intravascular Navigation.

Journal of the American Chemical Society
Developing magnetic ultrasoft robots to navigate through extraordinarily narrow and confined spaces like capillaries in vivo requires synthesizing materials with excessive deformability, responsive actuation, and rapid adaptability, which are difficu...