A Data-Driven Analysis Method for the Trajectory of Power Carbon Emission in the Urban Area.

"Industry 4.0" aims to build a highly versatile, individualized digital production model for goods and services. The carbon emission (CE) issue needs to be addressed by changing from centralized control to decentralized and enhanced control. Based on a solid CE monitoring, reporting, and verification system, it is necessary to study future power system CE dynamics simulation technology. In this article, a data-driven approach is proposed to analyzing the trajectory of urban electricity CEs based on empirical mode decomposition, which suggests combining macro-energy thinking and big data thinking by removing the barriers among power systems and related technological, economic, and environmental domains. Based on multisource heterogeneous mass data acquisition, effective secondary data can be extracted through the integration of statistical analysis, causal analysis, and behavior analysis, which can help construct a simulation environment supporting the dynamic interaction among mathematical models, multi-agents, and human participants.

Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access.

This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone's guiding images with the Green Zone's view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F1-score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

Engineering the Pore Size of Pillared-Layer Coordination Polymers Enables Highly Efficient Adsorption Separation of Acetylene from Ethylene.

The pore size of adsorbents plays a vital role in determining the overall separation performance of gas separation and purification by adsorption. In this work, the pore apertures of the coordination pillared layer (CPL) was systematically controlled by adjusting the length of pillared ligands. We used pyrazine, 4,4'-bipyridine, and 1,2-di(4-pyridyl)-ethylene with increased length to synthesize CPL-1 (L = pyrazine), CPL-2 (L = 4,4'-bipyridine), and CPL-5 [L = 1,2-di(4-pyridyl)-ethylene], respectively. The aperture size of these CPLs varies from 4 to 11 Å: CPL-1 (4 × 6 Å2), CPL-2 (9 × 6 Å2), and CPL-5 (11 × 6 Å2). Among the three frameworks, CPL-2 exhibits the highest C2H2 uptake at ambient conditions as it has moderate pore size and porosity. However, CPL-1 has the best separation performance in the breakthrough experiments with binary gas mixture of C2H2/C2H4, thanks to the optimal pore size nearly excluding C2H4, which is only observed in the state-of-the-art UTSA-300a so far. The DFT calculations were carried out to elucidate the specific adsorption sites for both acetylene and ethylene among these frameworks. The modeling results suggest that binding strength is highly related to aperture size and that CPL-1 shows the highest adsorption selectivity owing to the optimal pore size. This work demonstrates that engineering pore size enables us to fabricate the highly efficient metal-organic framework (MOF)-based adsorbents for specific gas separation on the basis of the isoreticular chemistry.