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Review of Hikari Hori. Promiscuous Media. Film and Visual Culture in Imperial Japan, 1926-1945.
No full text學校組織中倫理型領導對教師建言行為與組織沈默影響之研究：心理安全感與組織公平的雙重中介效果
No full text[[abstract]]本研究旨在建構與驗證學校組織中倫理型領導對教師建言行為與組織沉默影響之研究，並探討心理安全感與組織公平的雙重中介效果。研究對象為臺灣地區公私立國民中學教師，採分層隨機抽樣選取受試者；以驗證性因素分析倫理型領導、教師建言行為、組織沉默、心理安全感與組織公平等各變項問卷資料，確定量表的統計適配資料，透過結構方程模式，分析潛在變項的預期路徑，以及相對支持強度。另採用多重中介模型驗證組織公平與心理安全感對校長倫理型領導與教師建言行為與組織沉默的雙重中介效果。研究結論如下：一、校長倫理型領導與教師建言行為具有正向關聯，與組織沉默具有負向關聯，但均不具有顯著的直接影響作用；二、組織沉默與教師建言行為之間呈現互為消長；三、教師心理安全感對校長倫理型領導與教師建言行為具有正向的完全中介效果，對校長倫理型領導與組織沉默具有負向的完全中介效果。本研究同時探究心理安全感與組織公平對倫理型領導與教師建言行為和組織沉默之中介效果，預期對學校倫理型領導建構一完整的實證模型架構，可作為學校倫理型領導在教師建言行為與組織沉默的實務工作參考
The Impact of Integrating Board Games into Chinese Teaching in the Elementary School on Learning Efficiency -An Example of the Indigenous Fifth and Sixth Graders in a Remote Area of Nantou County
No full textAleatory-aware deep uncertainty quantification for transfer learning
No full text[[abstract]]The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty. Therefore, this paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then we determine the probability of happening a certain class from K-nearest output posteriors of the same DNN in training samples. We name this probability as opacity score, as the paper focuses on the detection of opacity on X-ray images. This score reflects the level of aleatory on the sample. When the NN is certain on the classification of the sample, the probability of happening a class becomes much higher than the probabilities of others. Probabilities for different classes become close to each other for a highly uncertain classification outcome. To capture the epistemic uncertainty, we train multiple DNNs with different random initializations, model selection, and augmentations to observe the effect of these training parameters on prediction and uncertainty. To reduce execution time, we first obtain features from the pre-trained NN. Then we apply features to the ensemble of fully connected layers to get the distribution of opacity score during the test. We also train several ResNet and DenseNet DNNs to observe the effect of model selection on prediction and uncertainty. The paper also demonstrates a patient referral framework based on the proposed uncertainty quantification. The scripts of the proposed method are available at the following link
Development of a Multilayer Perceptron Neural Network for Optimal Predictive Modeling in Urban Microcellular Radio Environments
No full text[[abstract]]Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models
Automated Emotion Recognition: Current Trends and Future Perspectives
No full text[[abstract]]Background: Human emotions greatly affect the actions of a person. The automated emotion recognition has applications in multiple domains such as health care, e-learning, surveillance, etc. The development of computer-aided diagnosis (CAD) tools has led to the automated recognition of human emotions.
Objective: This review paper provides an insight into various methods employed using electroencephalogram (EEG), facial, and speech signals coupled with multi-modal emotion recognition techniques. In this work, we have reviewed most of the state-of-the-art papers published on this topic.
Method: This study was carried out by considering the various emotion recognition (ER) models proposed between 2016 and 2021. The papers were analysed based on methods employed, classifier used and performance obtained.
Results: There is a significant rise in the application of deep learning techniques for ER. They have been widely applied for EEG, speech, facial expression, and multimodal features to develop an accurate ER model.
Conclusion: Our study reveals that most of the proposed machine and deep learning-based systems have yielded good performances for automated ER in a controlled environment. However, there is a need to obtain high performance for ER even in an uncontrolled environment
Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques
No full text[[abstract]]Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow
Biomechanical Analyses of Porous Designs of 3D-Printed Titanium Implant for Mandibular Segmental Osteotomy Defects
No full text[[abstract]]Clinically, a reconstruction plate can be used for the facial repair of patients with mandibular segmental defects, but it cannot restore their chewing function. The main purpose of this research is to design a new three-dimensionally (3D) printed porous titanium mandibular implant with both facial restoration and oral chewing function reconstruction. Its biomechanical properties were examined using both finite element analysis (FEA) and in vitro experiments. Cone beam computed tomography images of the mandible of a patient with oral cancer were selected as a reference to create 3D computational models of the bone and of the 3D-printed porous implant. The pores of the porous implant were circles or hexagons of 1 or 2 mm in size. A nonporous implant was fabricated as a control model. For the FEA, two chewing modes, namely right unilateral molar clench and right group function, were set as loading conditions. Regarding the boundary condition, the displacement of both condyles was fixed in all directions. For the in vitro experiments, an occlusal force (100 N) was applied to the abutment of the 3D-printed mandibular implants with and without porous designs as the loading condition. The porous mandibular implants withstood higher stress and strain than the nonporous mandibular implant, but all stress values were lower than the yield strength of Ti-6Al-4V (800 MPa). The strain value of the bone surrounding the mandibular implant was affected not only by the shape and size of the pores but also by the chewing mode. According to Frost’s mechanostat theory of bone, higher bone strain under the porous implants might help maintain or improve bone quality and bone strength. The findings of this study serve as a biomechanical reference for the design of 3D-printed titanium mandibular implants and require confirmation through clinical investigations
A Longitudinal Study of Nurses’ Work-Life Balance: A Case of A Regional Teaching Hospital in Taiwan
No full text[[abstract]]Work-life balance is essential for nurses who are in direct contact with patients in healthcare organizations. This study employs the measurement of nurses’ behaviors rather than the measurement of their perceptions to identify critical demographic variables influencing the work-life balance. A work-life balance dimension measured by a four-point frequency scale from the Chinese version of the Safety Attitudes Questionnaire is used to assess nurses’ behaviors in practice from a longitudinal viewpoint based on a regional teaching hospital in Taiwan. The results show that experience in organization, respondents reporting events in the past 12 months, and experience in position are three critical factors to impact nurses’ work-life balance. Moreover, “work all day without break,” “change the individual or family plan because of the work,” and “work overtime” are the most critical questions in the work-life balance that can be the targets for hospital management to enhance the balance of nurses’ work-life conditions in the hospital
