Cryptocurrencies
Credit Blockchain, a global leader in decentralised finance infrastructure and intelligent digital computing, has announced the official launch of its AI Computing Engine. This next-generation system aims to deliver stable, predictable outcomes for cryptocurrency participants, even during volatile market conditions. The AI Computing Engine leverages machine learning algorithms to forecast
Cryptocurrencies
Credit Blockchain, a leader in decentralised finance infrastructure and intelligent digital computing, has announced the launch of its AI Computing Engine, designed to deliver stable outcomes for cryptocurrency participants even during volatile market conditions. The AI Computing Engine, which leverages machine learning algorithms and predictive analytics, aims to address the
Energy management
Researchers at Nanjing Tech University have developed a new method for optimizing data center energy efficiency by focusing on optimal temperature control of designated active servers. The study finds that the proportion of energy consumed by IT and cooling systems in data centers exceeds 90%, leading to energy inefficiencies due
Energy management
Researchers from North China Electric Power University have conducted a study on the economic efficiency and environmental characteristics of data center integrated energy systems, exploring the feasibility of various energy supply methods and the implementation of optimized operational strategies. The study concluded that adopting an integrated energy system (IES) to
Energy management
A new study from researchers at Kyungpook National University in South Korea has introduced a streamlined data-driven framework for building energy benchmarking. The research, supported by the Korea Institute of Energy Technology Evaluation & Planning (KETEP), aims to enhance energy efficiency and encourage conservation efforts. By integrating publicly available data
Energy management
A recent study highlights the importance of environment-related technologies in achieving sustainable energy growth in China and India. The research, conducted by a team of scholars from Business School, used panel data from 1995 to 2023 to examine the impact of these technologies on non-clean energy, total final energy use,
Energy management
China's urban energy efficiency (EE) has seen significant nationwide improvements, but regional disparities remain, with eastern cities outperforming central and western regions. A new study conducted by researchers at the Shanghai University of Finance and Economics investigated the impact mechanism of green innovation (GI) spatial association on the
Solar energy industry
Researchers at the University of Tenaga Nas in Selangor, Malaysia, have discovered that hexagonal boron nitride (hBN) dispersed in distilled water can significantly enhance the performance of indoor solar photovoltaic thermal systems. The study aimed to improve electrical efficiency, thermal efficiency, and exergy performance while reducing entropy generation. The researchers
Solar energy industry
Researchers at the University of Tenaga Nas in Selangor, Malaysia, have discovered that hexagonal boron nitride (hBN) dispersed in distilled water can significantly enhance the performance of indoor solar photovoltaic thermal systems. The study aimed to improve electrical efficiency, thermal efficiency, and exergy performance while reducing entropy generation. The researchers
Energy management
A recent study highlights the importance of environment-related technologies in achieving sustainable energy growth in China and India. The research, conducted by a team of scholars from Business School, used panel data from 1995 to 2023 to examine the impact of these technologies on non-clean energy, total final energy use,
Energy management
Researchers at Nanjing Tech University have developed a new method for optimizing data center energy efficiency by focusing on optimal temperature control of designated active servers. The study finds that the proportion of energy consumed by IT and cooling systems in data centers exceeds 90%, leading to energy inefficiencies due
Energy management
Researchers at Sichuan University in China have developed a machine learning-based framework to precisely predict power consumption and freshwater production in greenhouse-integrated systems. The framework has shown significant improvements in resource efficiency and sustainable agriculture, particularly in areas with limited water resources or high energy consumption. By combining machine learning
