High-precision measurement of X-ray spectra is affected by the statistical fluctuation of the X-ray beam under low-counting-rate conditions. It is also limited by counting loss resulting from the dead-time of the system and pile-up pulse effects, especially in a high-counting-rate environment. In this paper a detection system based on a FAST-SDD detector and a new kind of unit impulse pulse-shaping method is presented, for counting-loss correction in X-ray spectroscopy. The unit impulse pulse-shaping method is evolved by inverse deviation of the pulse from a reset-type preamplifier and a C-R shaper. It is applied to obtain the true incoming rate of the system based on a general fast-slow channel processing model. The pulses in the fast channel are shaped to unit impulse pulse shape which possesses small width and no undershoot. The counting rate in the fast channel is corrected by evaluating the dead-time of the fast channel before it is used to correct the counting loss in the slow channel.

The Qinghai–Tibet Plateau (QTP) is a sensor of global climate change and regional human activities, and drought monitoring will help to achieve its ecological protection and sustainable development. In order to effectively control the geospatial scale effect, we divided the study area into eight geomorphological sub-regions, and calculated the Temperature-Vegetation Drought Index (TVDI) of each geomorphological sub-region based on MODIS Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data, and synthesized the TVDI of the whole region. We employed partial and multiple correlation analyses to identify the relationship between TVDI and temperature and precipitation. The random forest model was further used to study the driving mechanism of TVDI in each geomorphological division. The results of the study were as follows: (1) From 2000 to 2019, the QTP showed a drought trend, with the most significant drought trend in the central region. The spatial pattern of TVDI changes of QTP was consistent with the gradient changes of precipitation and temperature, both showing a gradual trend from southeast to northwest. (2) There was a risk of drought in the four seasons of the QTP, and the seasonal variation of TVDI was significant, which was characterized by being relatively dry in spring and summer and relatively humid in autumn and winter. (3) Drought in the QTP was mainly driven by natural factors, supplemented by human factors. The driving effect of temperature and precipitation factors on TVDI was stable and significant, which mainly determined the spatial distribution and variation of TVDI of the QTP. Geomorphological factors led to regional intensification and local differentiation effects of drought, especially in high mountains, flat slopes, sunny slopes and other places, which had a more significant impact on TVDI. Human activities had local point-like and linear impacts, and grass-land and cultivated land that were closely related to the relatively high impacts on TVDI of human grazing and farming activities. In view of the spatial-temporal patterns of change in TVDI in the study area, it is important to strengthen the monitoring and early warning of changes in natural factors, optimize the spatial distribution of human activities, and scientifically promote ecological protection and restoration.

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With the rapid development in online education and the recurrence of COVID-19 around the world, people have temporarily turned to online education. To identify influencing factors of online learning behavior and improve online education, this study used CiteSpace to visually analyze research on influencing factors of online learning behavior on WoS. It discusses the research status, hotspots, and trends. Then, through cluster analysis and literature interpretation, the paper summarizes the types of online learning behavior and the influencing factors of different online learning behaviors from positive and negative dimensions. The findings of this paper are as follows. (1) The number of studies on the influencing factors of online learning behavior has increased in the last decade, especially after the outbreak of COVID-19. The research countries and institutions in this field lack contact and cooperation. (2) Online learning behaviors mainly include online learning engagement behavior, continuous behavior, procrastination behavior, and truancy behavior. (3) Online learning engagement behavior is mainly affected by perceived usefulness, perceived ease of use, individual characteristic differences, and other factors. (4) Online learning continuous behavior is mainly affected by quality, perceived usefulness, learning self-efficacy, and other factors. (5) The influencing factors of online learning procrastination mainly include learning environment, individual characteristics, social support, and pressure. (6) The main influencing factors of online learning truancy behavior are social interaction, participation, and learner control. At the end of this paper, according to the action mode of the influencing factors of online learning behavior, some suggestions for teaching improvement are put forward from the two perspectives of promoting positive online learning behavior and avoiding negative online learning behavior, which can provide a reference for teachers and schools in the future when conducting online education.

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The COVID-19 pandemic has had a significant impact on college education. College students have faced great difficulties in terms of learning and living during the lockdown period, which has brought many negative psychological effects. To explore the psychological states of college students learning during the COVID-19 pandemic and the reasons for these states, this study used CiteSpace to analyze 105 articles on WoS about college students’ learning psychology, and the results of this analysis were combined with an interpretation of the literature to summarize the research hotspots, development trends, learning psychology types, and reasons in this field. The main findings were as follows: (1) During the COVID-19 pandemic, the psychological state of learning college students mainly included academic burnout, learning anxiety, and learning pressure. (2) Academic burnout was affected by perceived usefulness and self-control and was manifested as not accepting online teaching and truancy. (3) Learning anxiety was affected by emotional support factors and was manifested as loneliness, anxiety about lockdown management, and fear of infection. (4) Learning pressure was affected by perceived ease-of-use, environmental support, and self-efficacy and was manifested by difficulties completing online learning tasks, academic performance, and future career uncertainty. Given the above findings, this study proposes corresponding teaching improvement measures from the perspective of the sustainability of the teaching methods of teachers and students’ continuous learning, providing teaching references for schools and teachers, and psychological support for students.

Supercritical water gasification of petrochemical wastewater is a promising technology for clean petrochemical wastewater utilization. In this paper, hydrogen production by noncatalytic partial oxidation of petrochemical wastewater was systematically investigated in supercritical water with a continuous reactor for the first time. The influences of the main operating parameters including residence time, temperature, and oxidant equivalent ratio (ER) on the gasification characteristics of petrochemical wastewater were investigated. The experimental results showed that H 2 yield and carbon gasification efficiency (CE) increased with increasing temperature. CE increased with increasing ER, and H 2 yield peaked when ER equaled 0.2. CE increased quickly within 180 s and then tended to be stable between 240 and 300 s.