In this study, the influence of sewage sludge ash (SSA) waste particle contents on the mechanical properties and interlaminar fracture toughness for mode I and mode II delamination of S-glass fiber-reinforced epoxy composites was investigated. Composite laminate specimens for tensile, flexural double-cantilever beam (DCB), and end-notched fracture (ENF) tests were prepared and tested according to ASTM standards with 5, 10, 15, and 20 wt% SSA-filled S-glass/epoxy composites. Property improvement reasons were explained based on optical and scanning electron microscopy. The highest improvement in tensile and flexural strength was obtained with a 10 wt% content of SSA. The highest mode I and mode II interlaminar fracture toughness’s were obtained with 15 wt% content of SSA. The mode I and mode II interlaminar fracture toughness improved by 33% and 63.6%, respectively, compared to the composite without SSA.

An alternative to CMOS VLSI called Quantum Cellular Automata (QCA) is presently being researched. Although a few basic logical circuits and devices have been examined, very little, if any, research has been done on the architecture of QCA device systems. In the context of nano communication networks, data transmission that is both dependable and efficient is still critical. The technology known as Quantum Dot Cellular Automata (QCA) has shown great promise in the development of nano-scale circuits because of its extremely low power consumption and rapid functioning. This study introduces a unique nano-communication parity-based arithmetic circuit that is reversible, error-detecting, and error-correcting. The minimal outputs are needed for the proposed structure. Based on QCA technology, the proposed nano-communication network makes use of reversible logic gates. The performance increase of the suggested parity generator and checker circuit is significant in terms of clock delay, size, and number of cells.

The micro staring hyperspectral imager can simultaneously acquire two spatial and one spectral images, and only record the external orientation elements of the entire hyperspectral image rather than the external orientation elements of each frame of the image, which avoids the geometric instability during scanning, effectively solves the problem of large geometric deformation of the small line scanning hyperspectral imager, and is suitable for the small UAV load platform with unstable attitude. At present, most of the research focuses on the radio-metric correction method of line scan hyperspectral imager. The application time of staring hyperspectral imager is short, and there is no mature data processing re-search at home and abroad, which hinders the application of UAV micro staring hyperspectral imaging system. In this paper, the calibration method of the linearity and variability of the radiation response of the micro staring hyperspectral imager on the UAV is studied, and the effectiveness of this method is quantitatively evaluated. The results show that the hyperspectral image has obvious vignetting effect and strip phenomenon before the correction of radiation response variability. After the correction, the radiation response variation coefficient of pixels in different bands decreases significantly, and the vignetting effect and image strip decrease significantly. In this paper, a multi-target radiometric calibration method is proposed, and the accuracy of radiometric calibration is verified by comparing the calibrated hyperspectral image spectrum with the measured ground object spectrum of the ground spectrometer. The results show that the calibration results of the multi-target radiometric calibration method show better results, especially for the near-infrared band, and the difference with the surface reflectance measured by the spectrometer is small.

Forest is the main carbon sink of terrestrial ecosystem. Due to the unique growth characteristics of plants, the response of their growth status and physiological activities to climate change will affect the carbon cycle process of forest ecosystem. Based on the local scale CO₂ flux and temperature observation data recorded by the FLUXNET registration site and Harvard Forest FLUX observation tower from 2000 to 2012, combined with the phenological model, this paper analyzes the impact of temperature changes on CO₂ flux in temperate forest ecosystems. The results show that: (1) the maximum NEE in 2000–2012 was 298.13 g·m^-2·a^-1, which occurred in 2010. Except in the 2010 and 2011, the annual NEE in other years was negative. (2) NEE, GPP, temperature and phenology models have good fitting effects (R² > 0.8), which shows that the stable period of photosynthesis in temperate mixed forest ecosystem is mainly concentrated in summer, and vegetation growth is the dominant factor of carbon cycle in temperate mixed forest ecosystem. (3) The linear fitting results of the change time points of air temperature (maximum point, minimum point and 0 point date) and the change time points of NEE and GPP (maximum point, minimum point and 0 point date) show that there is a significant positive correlation between air temperature and CO₂ flux (P < 0.01), and the change of air temperature affects the carbon cycle process of temperate mixed forest ecosystem.

Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.