Education Journal and the Spread of Modern Western Teaching Methods in China

Education Journal published various articles on western teaching methods. It adopted strategies such as all-round introduction, focusing on publicity and research, in-depth symposium and prize for excellent lesson plans to publicize modern western teaching methods. These practices recorded the changes of the modern western teaching methods in China, promoted the transformation of Chinese teaching methods from tradition to modern, and stimulated the localization of the modern western teaching methods

Participation of Different Forces and Coeducation in Peking University: From Reports of Newspaper Media, 1918-1920

It was an important achievement that Peking University abolished female forbiddance and implemented coeducation in Women’s Liberation Movement during the May 4th New Culture Movement. In this period, newspaper media kept up with the historical trend. As the leader of public opinion, Newspaper media intervened, reported and publicized the abolishment of female forbiddance and the implementation of coeducation in Peking University. It promoted the involvement of different social forces: the Principal of Peking University — Cai Yuanpei, female intellectuals, teachers and students of Peking university. These various forces played different roles in this trend. In their interaction, the abolishment of female forbiddance and the implementation of coeducation in Peking University began with appeals and debates and eventually ended with the realization. Furthermore, it aroused a nationwide impact in the education field. It not only reflects that coeducation in universities is a historical trend of democratization and modernization in higher education, but also indicates that newspaper media plays an indispensable role in Women’s Liberation Movement

Photoluminescent and superparamagnetic reduced graphene oxide-iron oxide quantum dots for dual-modality imaging, drug delivery and photothermal therapy

Reduced graphene oxide–iron oxide quantum dots (QDs) with intrinsic photoluminescent and superparamagnetic properties were synthesized through a green, hydrothermal method that simultaneously reduced and shattered graphene nanosheets to form the dots. The structure, morphology, properties and cell viability of these QDs were investigated. The QDs emitted violet light when excited at 320 nm, possessed no residual magnetization upon magnetic hysteresis tests, and had low cytotoxicity to healthy cells at low concentrations. The suitability of the QDs for fluorescent and magnetic resonance dual-modality imaging was shown by in vitro imaging with dermal fibroblast cells and T2 relaxation time. A drug could be loaded onto the surface of the QDs, with a loading ratio of drug to QD of 0.31:1. The drug achieved a steady but full release from the QDs over 8 h: these drug-loaded QDs could be manipulated by an external magnetic stimulation for targeted drug delivery. The potential for use as a cancer photothermal therapy was demonstrated by both a rapid, ∼50 °C temperature increase by a suspension of 100 μg ml−1 of QDs and the photothermal ablation of HeLa cells in vitro under near infrared irradiation. The stability of the MGQDs in fetal calf serum was shown to improve when an ionic drug was coated on the surface

Scalable modeling and optimization of mode transitions based on decoupled power management architecture

[[abstract]]To save energy, many power management policies rely on issuing mode-change commands to the components of the system. Efforts to date have focused on how these policies interact with the external workload. However, the energy savings are ultimately limited by the set of power-saving modes available to the power manager. This paper exposes new power-saving opportunities to existing system-level power managers by handling each desired mode change in terms of an optimal sequence of mode transitions involving multiple components. We employ algorithms to optimize these transition sequences in polynomial time, making them applicable to static and dynamic policies. The decoupling between policies and mechanisms also makes this approach modular and scalable to devices with complex modes and intricate dependencies on other devices in the system. Experimental results show significant energy savings due to these sequentialized mode-change opportunities that would otherwise be difficult to discover manually even by experienced designers.[[fileno]]2030236030016[[department]]資訊工程學

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects outliers from the trajectory sequence data in both spatial view and temporal view. Specifically, we first use the adaptive spatial clustering algorithm based on the Delaunay triangulation (ASCDT) algorithm to remove the location offset points in the trajectory sequence. After that, based on the most basic attributes of the trajectory points, a 6-dimensional movement feature vector is constructed for each point as an input. The feature-rich trajectory sequence data is reconstructed using the proposed temporal convolutional network autoencoder (TCN-AE), and the Squeeze-and-Excitation (SE) channel attention mechanism is introduced. Finally, the effectiveness of the CTOD method is experimentally verified

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects outliers from the trajectory sequence data in both spatial view and temporal view. Specifically, we first use the adaptive spatial clustering algorithm based on the Delaunay triangulation (ASCDT) algorithm to remove the location offset points in the trajectory sequence. After that, based on the most basic attributes of the trajectory points, a 6-dimensional movement feature vector is constructed for each point as an input. The feature-rich trajectory sequence data is reconstructed using the proposed temporal convolutional network autoencoder (TCN-AE), and the Squeeze-and-Excitation (SE) channel attention mechanism is introduced. Finally, the effectiveness of the CTOD method is experimentally verified

Transformer State Assessment Method Based on Fuzzy and Evidence Theories

Accurate and reliable assessment of power equipment operation state is the premise and basis for maintenance of power system state. This article builds the transformer body state assessment model based on fuzzy and evidence theories taking 500kV oil-immersed transformer as the object of research. The representative parameters in preventive test are selected as state assessment indicators by making reference to the factory values and threshold-crossing values of which the indicator normalization is carried out to determine the degrees of membership of each indicator relative to different state assessment levels using fuzzy evaluation method. These indicators are divided into three sub-evidence bodies, i.e. gas dissolved in oil, oilation test and electrical test, and information combination of these three sub-evidence bodies is carried out using evidence theory to further assess the operation state of transformer body. The effectiveness of this assessment model applied in state assessment of transformer body is verified by example analysis of the data of a 500kV transformer. This assessment model has clear ideas and doesn’t need too much historical data, it provides a new method for transformer state assessment.

Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data

Taxis are an important component of the urban public transportation system, with wide geographical coverage and on-demand services characteristics. Thorough understanding of the built environment affecting taxi ridership can enable transportation authorities to develop targeted policies for transportation planning. Previous studies in this field had few data sources and did not consider the spatiotemporal variability. This study aims to develop an analytical framework for understanding the spatiotemporal correlation between the urban built environment and taxi ridership, which is empirically analyzed in New York City. The built environment is defined through multisource data in terms of density, design, diversity, and destination accessibility. Besides the exploration of travel patterns, the spatiotemporal heterogeneity of taxi ridership is modeled using geographically and temporally weighted regression (GTWR). The result shows that GTWR outperforms ordinary least squares (OLS), geographically weighted regression (GWR), and temporally weighted regression (TWR) in both goodness of fit and explanatory accuracy. More importantly, our study found that land use diversity is negatively correlated with taxi ridership, while transportation diversity is positively correlated with it. A highly accessible road network improves the people’s demand for taxis in the morning rush hours. Moreover, the density of railway stations is positively correlated with taxi ridership on weekdays but adversely on weekends. These findings provide practical insights for urban transportation policy development and taxicab regulation