We discuss traditional classroom, e-learning, behavioral engagement and difference between engagements in two kind of instruction environment. Results from variance analyses suggest that there is no significant active learning different classroom conditions, exist differences on higher-level innovative critical thinking. Our findings highlight students' environments have advantage over each other, but e-learning facilitates better.

Aqueous alkaline battery represents a promising energy storage technology with both high density and power as rechargeable batteries. However, the low theoretical capacities, kinetics stability of anode materials have limited their developments commercializations. In this study, we propose novel method to produce two‐dimensional layered bismuth oxide selenium (Bi 2 O Se) reduced graphene (rGO) composites via one‐step hydrothermal method. The volume change caused by phase during rapid...

Outlier detection is an important task in many domains and intensively studied the past decade. Further, how to explain outliers, i.e., outlier interpretation, more significant, which can provide valuable insights for analysts better understand, solve, prevent these detected outliers. However, only limited studies consider this problem. Most of existing methods are based on score-and-search manner. They select a feature subspace as interpretation per queried by estimating outlying scores...

column Share on A survey tree edit distance lower bound estimation techniques for similarity join XML data Authors: Fei Li Harbin Institute of Technology TechnologyView Profile , Hongzhi Wang Jianzhong Hong Gao Authors Info & Claims ACM SIGMOD RecordVolume 42Issue 4December 2013pp 29–39https://doi.org/10.1145/2590989.2590994Published:28 February 2014Publication History 27citation227DownloadsMetricsTotal Citations27Total Downloads227Last 12 Months29Last 6 weeks2 Get Citation AlertsNew Alert...

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering the collective anomalous data both sensitivity robustness of anomaly model, sequential symbolic method is proposed applied to turbine fuel system. A structural Finite State Machine used evaluate posterior probabilities observing sequences most probable state they may locate. Hence an estimation-based model decoding-based are identify anomalies two different ways. Experimental results...

Solar flares strongly influence space weather and human activities, their prediction is highly complex. The existing solutions such as data based approaches model have a common shortcoming which the lack of engagement in forecasting process. An image-case-based reasoning method introduced to achieve this goal. image case library composed SOHO/MDI longitudinal magnetograms, images from exhibit maximum horizontal gradient, length neutral line number singular points that are extracted for...

A multi-model integration method is proposed to develop a multi-source and heterogeneous model for short-term solar flare prediction. Different prediction models are constructed on the basis of extracted predictors from pool observation databases. The outputs base normalized first because these established extract many data resources using different methods. Then weighted used integrated (MIM). weight set that single assign optimized by genetic algorithm. Seven Solar Heliospheric...

Power gating is emerging as a viable solution to reduction of leakage current. However, power gated circuits are different from the conventional designs in sense that power-gated circuit must be brought valid state power-off state, when all nodes at logic zero before useful computation can begin. Thus, estimation maximum current determine possible power-up and normal switching In this paper, we propose cluster-based ATPG algorithm estimate for combinational circuits. Our method achieves...

Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use unlabeled network traffic data improve detection, this paper proposes an method for industrial control systems based improved comparative SimCLR. Firstly, feature extraction is trained SimCLR using data; linear classification layer added to model; small amount used training fine-tuning model parameters. The simulated Secure Water Treatment...

The detection of gas turbine engine anomalies is great significance to its reliable economic operation. Considering the collective anomaly data be detected sensitively, this paper presents a symbolic approach and applies it subsystem. trained finite state machine evaluates posterior probabilities observed symbol sequence. Thus, an strategy based on FSM estimation used detect defects. Experimental results indicate that, despite high performance model, robustness model strong, especially...

The performance conditions of steam turbine regenerative system have important influence on the safety and economy units. It is great significance to doing research monitoring ensure safe economical operation whole coal-fired power plants. In view shortcomings complexity traditional methods, this paper presents a method degradation based Extreme Learning Machine (ELM). training set constructed by using actual running data regenerating in previous year, besides input variables output are...

Anomaly detection plays a significant role in helping gas turbines run reliably and economically. Considering collective anomalous data both sensitivity robustness of the anomaly model, sequential symbolic method is proposed applied to turbine fuel system. A structural Finite State Machine evaluate posterior probabilities observing sequences most probable state they may locate. Hence an estimating based model decoding are used identify anomalies two different ways. Experimental results...

Gas turbine combustion system (GTCS) works in the highly adverse environmental conditions of high temperature and pressure. Because GTCS initial is too to be directly measured, exhaust gas (EGT) an alternative detect performance GTCS. However, various interferences, such as working ambient well compressor efficiencies so on, have a comprehensive effect on EGT, which causes low detection accuracy So, it's necessary build model that can eliminate interferences EGT monitor effectively. Feature...

Partiality for one or some subject(s) is an important issue in higher education and may directly affect the learning outcome of students. In this paper, we will calculate difference between individual average with collaborative filtering personalized recommendation based on binary relation a student all students, use calculation result to find students who are partial need help. Then feedback be given such according their specific situations order help them learn more effective method have...

Abstract With the rapid development of intelligent network vehicles, safety vehicle is taken seriously increasingly, CAN as most widely used car on-board network, its security problem has become one important problemsin made development, communication bus characteristic and existing problems analyzes common means attack, on basis this puts forward anomaly detection model based LSTM And ResNet, through experimental results show that method effectively detect deception attack.

We construct a research model to investigate effects of leadership behavior on organizational performance in university, which includes three dimensions structure, concern and development oriented behavior, satisfaction, teaching performance. Hypotheses were tested using self-perceived data from new faculty university. Results the method SEM suggest that both structure positively significantly affect