No abstract

Energy conservation, environmental protection, and intelligence are topics of interest in intelligent buildings. However, the energy requirement of various electrical equipment in intelligent buildings increases energy consumption. This study presents a neural network-based prediction and control system for the regulation of building environmental parameters. Neural network-based soft sensing technology can detect building environmental parameters through few sensors. The proposed system control algorithm can realize the adaptive adjustment of environmental parameters by using a neural network proportional–integral–derivative controller. Zigbee wireless communication is adopted as the information transmission medium to realize the environmental parameter measurement and network control. The soft sensing technique combined with Zigbee communication technology can effectively reduce energy consumption. The central control system analyzes the data coming from the network and regulates the environmental parameter through lifting temperature, ventilation, and switching curtains by using the neural network proportional–integral–derivative algorithm. The regulation of environmental parameters reduces unnecessary energy consumption. Finally, the effectiveness of the system is verified through simulations. Practical applications: This work reports an energy saving scheme. The building communication system constructed by ZigBee can reduce energy consumption and can be easily expanded. The soft sensing technique based on artificial neural network can predict temperatures by using few sensors. The neural network proportional–integral–derivative control algorithm has good performance in the regulation of environmental parameters for a time-varying system. Building energy consumption can be reduced by conducting these measures.

Accumulation of inorganic nanoparticles in living organisms can cause an increase in cellular reactive oxygen species (ROS) in a dose-dependent manner. Low doses of nanoparticles have shown possibilities to induce moderate ROS increases and lead to adaptive responses of biological systems, but beneficial effects of such responses on metabolic health remain elusive. Here, we report that repeated oral administrations of various inorganic nanoparticles, including TiO2, Au, and NaYF4 nanoparticles at low doses, can promote lipid degradation and alleviate steatosis in the liver of male mice. We show that low-level uptake of nanoparticles evokes an unusual antioxidant response in hepatocytes by promoting Ces2h expression and consequently enhancing ester hydrolysis. This process can be implemented to treat specific hepatic metabolic disorders, such as fatty liver in both genetic and high-fat-diet obese mice without causing observed adverse effects. Our results demonstrate that low-dose nanoparticle administration may serve as a promising treatment for metabolic regulation.

Background: Differentiation of suprasellar meningiomas (SSMs) from non-functioning pituitary macroadenomas (NFPMAs) is useful for clinical management. We investigated the utility of 13 N-ammonia combined with 18 F-FDG positron emission tomography (PET)/computed tomography (CT) in distinguishing SSMs from NFPMAs retrospectively. Methods: Fourteen NFPMA patients and eleven SSM patients with histopathologic diagnosis were included in this study. Every patient underwent both 18 F-FDG and 13 N-ammonia PET/CT scans. The tumor to gray matter (T/G) ratios were calculated for the evaluation of tumor uptake. Results: The uptake of 18 F-FDG was higher in NFPMAs than SSMs, whereas the uptake of 13 N-ammonia was obviously lower in NFPMAs than SSMs. The differences of 18 F-FDG and 13 N-ammonia uptake between the two groups were significant respectively (0.92[0.46] vs 0.59[0.29], P < 0.05, 18 F-FDG; 1.58 ± 0.56 vs 2.80 ± 1.45, P < 0.05, 13 N-ammonia). Tumor classification demonstrated a high overall accuracy of 96.0% for differential diagnosis. When the two traces were combined, only 1 SSM was misclassified into the NFPMA group. Conclusion:SSMs and NFPMAs have different metabolic characteristics on 18 F-FDG and 13 N-ammonia PET images. The combination of these two tracers can effectively distinguish SSMs from NFPMAs.