University tutors’ beliefs about and practices in assessing undergraduates’ writing - A New Zealand case study

Although teacher cognition has been explored widely, university tutor cognition of professional activities, such as evaluating and giving written feedback on students’ written work, has rarely been explored. Very few studies on teacher cognition of giving feedback have included data of real practice collected by think-aloud, observation, and stimulated recall. Traditional teacher cognition studies mainly focus on individual teachers’ beliefs and practices without in-depth study on how individual cognition evolves through and interacts with its social context in which individual teachers participate. It is the research space above that this thesis seeks to occupy, through an in-depth case study of the beliefs and practices of sixteen New Zealand university tutors who were employed in one of the university’s faculties to provide feedback on undergraduates’ assignments. In addition to exploring the beliefs and practices of this specific group of tutors, and the factors that influence these, the study aims to contribute to both the theoretical and methodological construction of teacher cognition studies by employing a holistic socio-cultural frame work based on Vygotsky’ s key notions of cognition, distributed cognition, and an activity theory approach. Data were collected chronologically across an academic year by five methods: preliminary survey for bio-data of participants and their general attitudes to giving feedback across the faculty, individual interviews for beliefs on giving feedback, think-aloud sessions on the actual practice of giving feedback, stimulated recall discussions as reflection in action, and focus group discussion as a means of collective reflection of various factors underlying their beliefs and practices. Data were firstly transcribed, stored, and open coded by NVivo8 for preliminary analysis and then analysed manually for deeper understanding of themes. Constant comparisons were made through the whole process of data analysis between data from different participants and between different sources of data. The findings reveal that there were convergences and divergences among tutors between their beliefs and practices about providing assessment feedback to the written work by undergraduate students. The convergences and divergences were due to the contextual factors in the activity system and tutors' previous experiences. The convergences and divergences of tutors’ beliefs resulted in emotional reactions. Tutors’ emotion interacts with cognition and actions (ECA interaction). The ECA interaction is affected by contextual factors in the activity system. The contradictions of the activity system constrain tutors’ cognition, cause negative emotions, and are often barriers to tutors’ work, but also form the potential of cognitive development. Co-operative effort is needed in the wider context of the activity to facilitate tutors’ cognitive development, promote positive emotions, and achieve a better outcome for the activity. It is concluded that a holistic socio-cultural framework of teacher cognition contributes to the understanding of the complexity of teacher cognition. The study is significant for its practical implications for professional practice of assessing disciplinary writing and tutor development; its contribution to the development of teacher cognition and activity theory regarding the interaction between emotion, cognition, and action at both individual and distributed level; and a multi-method approach to teacher cognition studies

Faulted Feeder Identification Based on Active Adjustment of Arc Suppression Coil and Similarity Measure of Zero-Sequence Currents

Existing faulted feeder identification methods in the resonant grounded distribution network are primarily based on feature extraction of the fault-generated transient currents. The reliability of these approaches is significantly compromised by the fluctuating transient signals and interfering on-off operation of the neighboring switches. To sidestep the problems, a novel method is proposed to identify the faulted feeder by consecutively tuning the arc suppression coil around the full compensation state. Once a series of steady states are reached after tuning, the trajectories of the corresponding zero-sequence currents for both the sound and the faulted feeders are obtained to formulate an adjustment trajectory matrix (ATM). With the ATM, the similarity measure of the adjustment trajectories of all feeders is then employed to identify the faulted feeder based on the selected Deng\u27s grey relational analysis. Results show that the adjustment trajectories of the two sound lines share a high similarity degree, while the similarity between the sound and the faulted lines is much lower. The effectiveness of the proposed method is validated via simulation and some case studies are provided. The results show that the faulted feeder can be correctly identified with high reliability and robustness compared to the existing fault-generated signal-based techniques

Effect of hyperbaric oxygen therapy on cognitive impairment after aneurysm subarachnoid hemorrhage

Purpose: To evaluate the effect of hyperbaric oxygen therapy (HBOT) on cognitive impairment after aneurysm subarachnoid hemorrhage (aSAH). Methods: The current study was carried out in a regional neurosurgical center in Taiyuan, Shanxi Province of China from January 2019 to September 2020. A total of 150 patients with persistent cognitive dysfunction at 3 months after aSAH onset were enrolled, which were randomly classified into group A (HBOT) and group B (control) via the random number table method. The outcome was evaluated by Montreal cognitive assessment (MoCA). Results: There were no significant differences between group A and group B with regard to MoCA score and proportions of normal MoCA patients at 3 months after HBOT (p > 0.05). Both groups showed no significant differences in proportions of normal MoCA patients at 6 months after HBOT (p > 0.05). However, there were significant differences between group A and group B with MoCA score of patients at 6 months after HBOT (p < 0.05). There were also significant differences in MoCA score and proportions of normal MoCA patients at 9 months after HBOT. Conclusion: HBOT alleviates cognitive impairment after aSAH, and thus may be used to manage cognitive impairment in patients after aSAH. However, further clinical trials are required prior to application in clinical practice

Combined Primary Frequency and Virtual Inertia Response Control Scheme of Variable-Speed Dish-Stirling System

The potential of variable-speed dish-Stirling (VSDS) solar-thermal generating plant in providing grid frequency support is investigated. In the proposed VSDS frequency support control scheme, the reference speed of the Stirling engine is regulated to track a deloaded power curve which is governed by the solar insolation level. The gain of a supplementary speed-frequency droop controller is then set to meet the primary frequency control requirement. Further uniqueness of the VSDS control scheme pertains to the provision of virtual inertia response by regulating the kinetic energy in the rotating mass of the engine-generator and the thermal energy in the heat absorber/receivers. Small-signal analysis shows that the frequency support scheme is inherently stable, and it will provide higher degree of damping as the penetration level of the VSDS system and/or the solar insolation level increase. The efficacy of the proposed scheme is validated by computer simulation

Control-Oriented Modeling of All-Solid-State Batteries Using Physics-Based Equivalent Circuits

Considered as one of the ultimate energy storage technologies for electrified transportation, the emerging all-solid-state batteries (ASSBs) have attracted immense attention due to their superior thermal stability, increased power and energy densities, and prolonged cycle life. To achieve the expected high performance, practical applications of ASSBs require accurate and computationally efficient models for the design and implementation of many onboard management algorithms, so that the ASSB safety, health, and cycling performance can be optimized under a wide range of operating conditions. A control-oriented modeling framework is thus established in this work by systematically simplifying a rigorous partial differential equation (PDE) based model of the ASSBs developed from underlying electrochemical principles. Specifically, partial fraction expansion and moment matching are used to obtain ordinary differential equation based reduced-order models (ROMs). By expressing the models in a canonical circuit form, excellent properties for control design such as structural simplicity and full observability are revealed. Compared to the original PDE model, the developed ROMs have demonstrated high fidelity at significantly improved computational efficiency. Extensive comparisons have also been conducted to verify its superiority to the prevailing models due to the consideration of concentration-dependent diffusion and migration. Such ROMs can thus be used for advanced control design in future intelligent management systems of ASSBs

An automatic feature extraction method and its application in fault diagnosis

The main challenge of fault diagnosis is to extract excellent fault feature, but these methods usually depend on the manpower and prior knowledge. It is desirable to automatically extract useful feature from input data in an unsupervised way. Hence, an automatic feature extraction method is presented in this paper. The proposed method first captures fault feature from the raw vibration signal by sparse filtering. Considering that the learned feature is high-dimensional data which cannot achieve visualization, t-distributed stochastic neighbor embedding (t-SNE) is further selected as the dimensionality reduction tool to map the learned feature into a three-dimensional feature vector. Consequently, the effectiveness of the proposed method is verified using gearbox and bearing experimental datas. The classification results show that the hybrid method of sparse filtering and t-SNE can well extract discriminative information from the raw vibration signal and can clearly distinguish different fault types. Through comparison analysis, it is also validated that the proposed method is superior to the other methods

Experimental studies of instability process and energy evolution of tunnels under true triaxial stresses: The role of pre-existed flaws

In the natural geological environment, there are many joints, faults and cavities. These natural defects will have an impact on the stability of tunnels. This paper investigates different conditions of surrounding rock: intact surrounding rock, surrounding rock with open-flaw and surrounding rock with filled-flaw under the true triaxial test. The effect of different surrounding rock conditions on the internal failure characteristics of tunnel under true triaxial conditions is explored. According to the characteristics of energy evolution and chaos theory, the failure characteristics inside the tunnel is divided into stages. The results show that: 1) The failure characteristics in the tunnel are different for different surrounding rock conditions. The failure characteristics do not represent the stability of the surrounding rock of the tunnel; 2) The trend of energy dissipation is different under different surrounding rock conditions. The elastic stage of the surrounding rock is shortened and the dissipation energy shows an earlier upward trend as its integrity declines. 3) When analysing the tunnel, chaos theory can give early warnings about the instability of the surrounding rock, but it can not give early warning of particle spray and spalling inside the tunnel

An intelligent fault diagnosis method of rotating machinery based on deep neural networks and time-frequency analysis

As the crucial part of the health management and condition monitoring of mechanical equipment, the fault diagnosis and pattern recognition using vibration signal are essential researching contents. The time-frequency representation method cannot identify the fault patterns from time-frequency representation effectively because of the complex work conditions of rotating machinery parts and the interference of strong background noise. Considering these disadvantages, a new reliable and effective method based on the time-frequency representation and deep convolutional neural networks is presented. In this method, the time-frequency features are calculated by the short time Fourier transform (STFT), and the pseudo-color map as the new identification objects. A novel feature learning method based on the sparse autoencode with linear decode is used to extract these time-frequency features, which is an unsupervised feature learning method with the goal of minimizing the loss function. The convoluting and pooling are applied to establish the hierarchical deep convolutional neural networks and filter the useful features layer by layer from the output of sparse autoencode. And a softmax classifier is used to obtain the faults classification. The experimental datasets from roller bearing and gearbox have been taken to verify the reliability and effectiveness of the proposed method for fault diagnosis and pattern recognition. The results show that the proposed method have excellent performance of the recognized objects

An intelligent fault diagnosis method of rotating machinery using L1-regularized sparse filtering

Traditional intelligent fault diagnosis methods take advantage of diagnostic expertise but are labor-intensive and time-consuming. Among various unsupervised feature extraction methods, sparse filtering computes fast and has less hyperparameters. However, the standard sparse filtering has poor generalization ability and the extracted features are not so discriminative by only constraining the sparsity of the feature matrix. Therefore, an improved sparse filtering with L1 regularization (L1SF) is proposed to improve the generalization ability by improving the sparsity of the weight matrix, which can extract more discriminative features. Based on Fourier transformation (FFT), L1SF, softmax regression, a new three-stage intelligent fault diagnosis method of rotating machinery is developed. It first transforms time-domain samples into frequency-domain samples by FFT, then extracts features in L1-regularized sparse filtering and finally identifies the health condition in softmax regression. Meanwhile, we propose employing different activation functions in the optimization of L1SF and feedforward for considering their different requirements of the non-saturating and anti-noise properties. Furthermore, the effectiveness of the proposed method is verified by a bearing dataset and a gearbox dataset respectively. Through comparisons with the standard sparse filtering and L2-regularized sparse filtering, the superiority of the proposed method is verified. Finally, an interpretation of the weight matrix is given and two useful sparse properties of weight matrix are defined, which explain the effectiveness of L1SF