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With the peak of carbon dioxide emissions and carbon neutrality, China is placing greater emphasis on energy expenditure. Office buildings occupy a prominent position in building energy consumption, which is one of the main energy consumption areas. Taking an administration building in Chengdu as an example, this article simulates the building energy consumption based on Design Builder software, examines the variables influencing energy consumption, and suggests energy-saving strategies combined with fresh ideas for sustainable architectural design. The results showed that the modeling building was a high-energy-consuming building, with an energy consumption of 724,857.59 kWh, and a unit area energy consumption of 288.17 kWh/m2 in Chengdu. For energy conservation and emission reduction, this article proposes the following three energy-saving measures. The first is to apply heat recovery technology for air conditioning systems. The second is photovoltaic glass, which provides partial electricity demand for buildings and reduces dependence on traditional energy sources. The third is roof greening, which utilizes the plants to purify the air and beautify the environment. The results showed that the heat recovery technology in air conditioning systems reduced the total energy consumption of buildings from 642144.04 kWh/m2 to 502937.83 kWh/m2, photovoltaic glass reduced 552243.87 kWh/m2, and roof greening reduced to 635947.35 kWh/m2. All of these have good energy-saving and emission reduction effects. The above three strategies not only help reduce building energy consumption, but also provide substantial support for China to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

The spatiotemporal patterns and shifts of net ecosystem productivity (NEP) play a pivotal role in ecological conservation and addressing climate change. For example, by quantifying the NEP information within ecosystems, we can achieve the protection and restoration of natural ecological balance. Monitoring the changes in NEP enables a more profound understanding and prediction of ecosystem alterations caused by global warming, thereby providing a scientific basis for formulating policies aimed at mitigating and adapting to climate change. The accurate prediction of NEP sheds light on the ecosystem's response to climatic variations and aids in formulating targeted carbon sequestration policies. While traditional ecological process models provide a comprehensive approach to predicting NEP, they often require extensive experimental and empirical data, increasing research costs. In contrast, machine-learning models offer a cost-effective alternative for NEP prediction; however, the delicate balance in algorithm selection and hyperparameter tuning is frequently overlooked. In our quest for the optimal prediction model, we examined a combination of four mainstream machine-learning algorithms with four hyperparameter-optimization techniques. Our analysis identified that the backpropagation neural network combined with Bayesian optimization yielded the best performance, with an R2 of 0.68 and an MSE of 1.43. Additionally, deep-learning models showcased promising potential in NEP prediction. Selecting appropriate algorithms and executing precise hyperparameter-optimization strategies are crucial for enhancing the accuracy of NEP predictions. This approach not only improves model performance but also provides us with new tools for a deeper understanding of and response to ecosystem changes induced by climate change. [ABSTRACT FROM AUTHOR]

Na 3 V 2 (PO 4 ) 3 is a promising high-voltage cathode for aqueous zinc-ion batteries (ZIBs) and organic sodium-ion batteries (SIBs). However, the poor rate capability, specific capacity, and cycling stability severely hamper it from further development. In this work, Na 3 V 2 (PO 4 ) 3 (NVP) with vanadium nitride (VN) quantum dots encapsulated by nitrogen-doped carbon (NC) nanoflowers (NVP/VN@NC) are manufactured as cathode using in situ nitridation, carbon coating, and structural adjustment. The outer NC layer increases the higher electronic conductivity of NVP. Furthermore, VN quantum dots with high theoretical capacity not only improve the specific capacity of pristine NVP, but also serve as abundant "pins" between NVP and NC to strengthen the stability of NVP/VN@NC heterostructure. For Zn-ion storage, these essential characteristics allow NVP/VN@NC to attain a high reversible capacity of 135.4 mAh g -1 at 0.1 A g -1 , and a capacity retention of 91% after 2000 cycles at 5 A g -1 . Meanwhile, NVP/VN@NC also demonstrates to be a stable cathode material for SIBs, which can reach a high reversible capacity of 124.5 mAh g -1 at 0.1 A g -1 , and maintain 92% of initial capacity after 11000 cycles at 5 A g -1 . This work presents a feasible path to create innovative high-voltage cathodes with excellent reaction kinetics and structural stability., (© 2024 Wiley‐VCH GmbH.)