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Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, owing to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, Dynamic Global Vegetation Model (DGVM) simulations, and machine learning techniques, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982-2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r > 0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r = 0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extra-tropical southern hemisphere (Trop+SH) and extra-tropical northern hemisphere (NH), we found that, during 1982-2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual GPP was dominated by the CO2 fertilization effect (Global: 83.9 %), albeit a large uncertainty in magnitude among individual simulations. However, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. Trends after 2000 were different from the full time-series, showing that satellite-based GPP products suggested weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs kept increasing. The inconsistencies are very likely caused by the contrasting performances between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. [ABSTRACT FROM AUTHOR]

To prevent the spread of the COVID-19 epidemic, restrictions such as lockdown, were conducted globally, which led to significant reduction in fossil fuel emissions, especially in urban regions. However, CO2 concentrations in urban regions are affected by many factors, such as weather and background CO2 fluctuations. Thus, it is difficult to directly observe the reductions in CO2 concentrations with sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the prohibitions, to determine the reduction signals. We conducted six on-road CO2 observations in Beijing using mobile platforms before (BC), during (DC) and after COVID-19 prohibitions (AC). To reduce the weather and background impacts, we chose trips with the most similar weather as possible and calculated the enhancement, which mean the difference in the CO2 concentration between on-road and the background level measured at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP) tower. The results showed that DC CO2 enhancement decreased by 41 parts per million (ppm) and 26 ppm compared to those during BC and AC, respectively, after eliminating the fluctuations in CO2 concentrations on polluted days. Detailed analysis showed that, during COVID, there was no difference between weekdays and weekends. The enhancements during rush hours were almost twice those during working hours, indicating that emissions during rush hours were much higher. Compared with DC and BC, the reductions in the enhancements during rush hours were much larger than those during working hours. Our findings showed a clear decrease during COVID, which are consistent with the CO2 concentration and emissions reductions due to the pandemic. The enhancement way used in this study is an effective method to reduce the impacts of weather and background fluctuation and should be regularly and more frequently conducted in future work. [ABSTRACT FROM AUTHOR]

The concept of an intelligent manufacturing system is proposed and described based on a comparison of manufacturing and biological organism systems. To promptly eliminate uncertain disturbances produced during the intelligent manufacturing system's operation, an immune monitoring model for an intelligent manufacturing system is proposed that is analogous to a biological immune system mechanism. The implementation of immune recognition, assessment, learning, memory, and control regulation in response to abnormal disturbance factors is investigated. The system's immune evaluation model based on the gray correlation analysis method is carefully analyzed. A neural network-based immune response method is used to deal with the disturbance. Finally, the paper shows how the effectiveness of the proposed method was verified experimentally through a simulation. [ABSTRACT FROM AUTHOR]

From the interdisciplinary point of manufacturing engineering, biology engineering as well as control engineering, a new type of bio-inspired manufacturing system architecture is presented based on the neuroendocrine regulation mechanism of the biological organism, and especially the general principles of hormone release control are used for adaptive production planning and control. First, a concept of organic manufacturing cell is proposed, and then a reference framework of bio-inspired manufacturing system is established based on recursive control and regulation of organic manufacturing cells. Mimicking the hormone secretion and release behavior, a multiple feedback control model of bio-inspired manufacturing system is set up, which can enable the adaptive scheduling and production control to deal with the changes and stimulus on the job shop floor. [ABSTRACT FROM AUTHOR]

Background: Quantifying CO2 emissions from cities is of great importance because cities contribute more than 70% of the global total CO2 emissions. As the largest urbanized megalopolis region in northern China, the Beijing-Tianjin-Hebei (Jing-Jin-Ji, JJJ) region (population: 112.7 million) is under considerable pressure to reduce carbon emissions. Despite the several emission inventories covering the JJJ region, a comprehensive evaluation of the CO2 emissions at the prefectural city scale in JJJ is still limited, and this information is crucial to implementing mitigation strategies. Results: Here, we collected and analyzed 8 published emission inventories to assess the emissions and uncertainty at the JJJ city level. The results showed that a large discrepancy existed in the JJJ emissions among downscaled country-level emission inventories, with total emissions ranging from 657 to 1132 Mt CO2 (or 849 ± 214 for mean ± standard deviation (SD)) in 2012, while emission estimates based on provincial-level data estimated emissions to be 1038 and 1056 Mt. Compared to the mean emissions of city-data-based inventories (989 Mt), provincial-data-based inventories were 6% higher, and national-data-based inventories were 14% lower. Emissions from national-data-based inventories were 53–75% lower in the high-emitting industrial cities of Tangshan and Handan, while they were 47–160% higher in Beijing and Tianjin than those from city-data-based inventories. Spatially, the emissions pattern was consistent with the distribution of urban areas, and urban emissions in Beijing contributed 50–70% of the total emissions. Higher emissions from Beijing and Tianjin resulted in lower estimates of prefectural cities in Hebei for some national inventories. Conclusions: National-level data-based emission inventories produce large differences in JJJ prefectural city-level emission estimates. The city-level statistics data-based inventories produced more consistent estimates. The consistent spatial distribution patterns recognized by these inventories (such as high emissions in southern Beijing, central Tianjin and Tangshan) potentially indicate areas with robust emission estimates. This result could be useful in the efficient deployment of monitoring instruments, and if proven by such measurements, it will increase our confidence in inventories and provide support for policy makers trying to reduce emissions in key regions. [ABSTRACT FROM AUTHOR]