Search

[Introduction] During the power grid fault, the low voltage ride through (LVRT) performance of the doubly-fed induction generator (DFIG) depends on the control parameters. At present, the optimization of control parameters is basically in the offline mode, which lies in the fact that it's hard for algorithm optimization to meet real-time control's requirement of the calculation speed. [Method] Therefore, the real-time optimization control method of LVRT following“offline training, online computation” was presented based on the principles of deep neural networks (DNN). Firstly, the appropriate LVRT strategy for optimization control was proposed for different fault severity levels. The parameters were clustered and optimized according to the respective objectives of each strategy, then the parameter list was formed. [Result] At the moment of power grid fault, the input parameters can be directly input into the trained DNN networks to quickly realize the optimization of control scheme and optimal parameters. [Conclusion] The joint simulation results based on PSCAD and MATLAB demonstrate the advantages of the proposed idea in optimization effect and optimization speed and the practicability is also illustrated.

Early learning centers are springing up rapidly in China. However, parents are confronted with high prices and difficulty in assessing the effectiveness of early enrichment programs (EEPs). This study was driven by this phenomenon and aimed to investigate parental decision-making processes of buying EEPs from a social media perspective. We sampled 271 Chinese parents and analyzed data by partial least squares based structural equation modeling (PLS-SEM). The results show that social media use intensity positively predicted injunctive norms and descriptive norms. The mediating analysis confirms that injunctive norms and descriptive norms are internalized into parental attitude to EEPs with varying degrees. This evidence serves as a new interpretation of the contrasting effect sizes of injunctive norms vs. descriptive norms on parental intention. The data however does not confirm that better-educated parents are less influenced by social norms in EEPs decision-making. Theoretical and practical implications are discussed in the paper.

With technology-enhanced learning playing an increasingly important role in higher education, university students are enjoying unprecedented benefits while likely experiencing technostress due to increased requirements and different academic expectations of technology-enhanced learning. This study aimed to develop a psychometric scale to measure university students' levels of technostress in technology-enhanced learning. An initial technostress scale was developed drawing on the person-environment fit theory and prior research on technostress. In total 620 students were recruited from two public universities in China. Three rounds of study were performed to validate the initial technostress scale using factor analysis, the Rasch model, and multilevel linear regression. The finalised technostress scale with 8 items demonstrated robust psychometric properties, including high internal consistency, validity, uni-dimensionality, and measurement invariance across students of different demographics. The development of the technostress scale is an important step in identifying maladapted students and preserving their wellbeing so as to increase their constructive and active participation in technology-enhanced learning.

This study investigated the factors that led to students adopting cloud computing learning resources (CCLR) in underfunded, rural high schools with the aim of informing future work related to effective implementation of CCLR for underprivileged students. Guided by the CCLR adoption model, survey data of 310 students from two high schools in rural districts in China were obtained and were analyzed utilizing partial least square structural equation modeling. The findings indicated that students' use of CCLR was a function of the perceived ease of use of CCLR, external facilitating conditions from schools, and students' voluntariness. The perceived usefulness of CCLR did not predict students' use behavior. Regarding gender differences, voluntariness affected female students more significantly than males, and anxiety in using CCLR affected female students more negatively than males. This study should interest educational practitioners and policy makers seeking to promote equity in education, particularly in rural districts by effectively implementing new learning technologies.

Silicon carbide particle-reinforced aluminum matrix composites (SiCp/Al) have been widely used in many areas due to their good physical and mechanical properties. However, because the material contains SiC particles with high hardness, processing is difficult and the processing quality of milling is poor. Ultrasonic vibration-assisted longitudinal torsion milling (LTUVAM) has great advantages in cutting difficult materials because it can effectively reduce the cutting force and cutting heat and improve the surface quality of the workpiece. Therefore, the kinematic analysis of LTUVAM is carried out, the material removal mechanism of 60% SiCp/Al is studied by the method of simulation and experiment, and the effects of machining parameters on the cutting force, cutting heat, and surface quality of the material were analyzed and using the traditional milling (CM) compared. The simulation and experimental results show that LTUVAM can improve the particle crushing rate, effectively reduce the cutting force and cutting heat under the relevant parameters, and improve the surface quality. [ABSTRACT FROM AUTHOR]

To explore the effects of different withering methods on the quality of Congou black tea, this study focused on five different withering methods: natural withering, warm-air withering, sun–natural combined withering, sun withering, and shaking withering. Gas chromatography‒mass spectrometry (GC‒MS), high-performance liquid chromatography (HPLC), and ion-exchange chromatography techniques were used to analyze the nonvolatile and volatile components and composition of the tea. The results revealed significant differences (p < 0.05) in the contents of nonvolatile constituents including caffeine, polyphenols, soluble sugars, free amino acids and their components, theaflavins, thearubigins, and catechins among the five different withering methods, with varying degrees of correlation between these components. A total of 227 aroma compounds were detected, and significant differences in the contents of alcohols, aldehydes, and ketones were observed. A relative odor activity value (ROVA) analysis of the aroma compounds revealed that 19 compounds had an ROVA > 1. Among them, benzylaldehyde, trans-2-decenal, decanal, benzaldehyde, nonanal, hexanal, trans-linalool, and geraniol from the shaking withering method had significantly higher ROVA values than those from the other withering methods, which may be the reason for the prominent floral and fruity aroma of shaking withering. This study revealed the impact of different withering methods on the quality of Congou black tea, providing a scientific basis for the development of Congou black tea with different flavors and the improvement of Congou black tea processing techniques. [ABSTRACT FROM AUTHOR]

To meet the requirements for high-risk testing scenarios in the safety assessment of advanced driver assistance systems (ADASs), this study selected 239 vehicle-to-powered two-wheeler accidents occurring in Xiangtan County from the China In-depth Mobility Safety Study—Traffic Accident (CIMSS-TA) database. Seven scenario elements were selected from the levels of static road elements, dynamic traffic participants and environment elements. The accident characteristics were obtained through statistical analysis. The results showed that 75.73% of accidents occurred at T-junctions/intersections, and 34.73% were caused by visual obstructions. Six typical vehicle-to-powered two-wheeler crash scenarios were established by cluster analysis, including two straight road scenarios (scenarios 3 and 6), two T-junction scenarios (scenarios 1 and 5) and two intersection scenarios (scenarios 2 and 4). Furthermore, six vehicle-to-powered two-wheeler testing scenarios, which represent the traffic environment characteristics of Chinese county-level districts, were constructed by analysing the vehicle/powered two-wheeler speed, the first collision point of the vehicle, the obstacle type and location, etc. The results showed that the vehicle speed in scenario 4 was higher than that in scenario 2, with average values of 34.32 km/h and 21.99 km/h, respectively. The results of this study provide testing scenarios for the safety assessment of ADASs, and contribute to improving vehicle active safety technology. [ABSTRACT FROM AUTHOR]

With the large-scale development of solar power generation, highly uncertain photovoltaic (PV) power output has an increasing impact on distribution networks. PV power generation has complex correlations with various weather factors, while the time series embodies multiple temporal characteristics. To more accurately quantify the uncertainty of PV power generation, this paper proposes a short-term PV power probabilistic forecasting method based on the combination of decomposition prediction and multidimensional variable dependency modeling. First, a seasonal and trend decomposition using a Loess (STL)-based PV time series feature decomposition model is constructed to obtain periodic, trend, and residual components representing different characteristics. For different components, this paper develops a periodic component prediction model based on TimeMixer for multi-scale temporal feature mixing, a long short-term memory (LSTM)-based trend component extraction and prediction model, and a multidimensional PV residual probability density prediction model optimized by Vine Copula optimized with Q-Learning. These components' results form a short-term PV probabilistic forecasting method that considers both temporal features and multidimensional variable correlations. Experimentation with data from the Desert Knowledge Australia Solar Center (DKASC) demonstrates that the proposed method reduced root mean square error (RMSE) and mean absolute percentage error (MAPE) by at least 14.8% and 22%, respectively, compared to recent benchmark models. In probability interval prediction, while improving accuracy by 4% at a 95% confidence interval, the interval width decreased by 19%. The results show that the proposed approach has stronger adaptability and higher accuracy, which can provide more valuable references for power grid planning and decision support. [ABSTRACT FROM AUTHOR]

Radical C–H functionalization represents a useful means of streamlining synthetic routes by avoiding substrate preactivation and allowing access to target molecules in fewer steps. The first-row transition metals (Ti, V, Cr, Mn, Fe, Co, Ni, and Cu) are Earth-abundant and can be employed to regulate radical C–H functionalization. The use of such metals is desirable because of the diverse interaction modes between first-row transition metal complexes and radical species including radical addition to the metal center, radical addition to the ligand of metal complexes, radical substitution of the metal complexes, single-electron transfer between radicals and metal complexes, hydrogen atom transfer between radicals and metal complexes, and noncovalent interaction between the radicals and metal complexes. Such interactions could improve the reactivity, diversity, and selectivity of radical transformations to allow for more challenging radical C–H functionalization reactions. This review examines the achievements in this promising area over the past decade, with a focus on the state-of-the-art while also discussing existing limitations and the enormous potential of high-value radical C–H functionalization regulated by these metals. The aim is to provide the reader with a detailed account of the strategies and mechanisms associated with such functionalization. [ABSTRACT FROM AUTHOR]