In-class Activities for Improving English Listening and Speaking in Cross Cultural Communication

This study presents a comprehensive framework for designing in-class activities aimed at enhancing English listening and speaking skills in cross-cultural communication in the age of self media. The study emphasizes the crucial connection between in-class activities and the broader curriculum design, giving special importance to the alignment of activities with course objectives. Drawing from established principles of curriculum design, constructivist theories, and key findings in cross-cultural communication, the study outlines a systematic approach that consists of four distinct steps-recognizing cultural self-awareness, interpreting and explaining foreign cultures, comparing and contrasting cultural elements, and evaluating cross-cultural content. Overall, the study offers a structured and practical approach to designing in-class activities that align with the objective of improving English listening and speaking skills in the context of cross-cultural communication under the help of self-media. By following this comprehensive framework, educators and students alike can enhance their cross-cultural competence and contribute to more effective cross-cultural communication teaching and learning

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

A Novel Image Processing Platform Development For Preprocessing Images With Two-Phase Microstructures Containing Isolated Particles

A novel image processing platform is developed in this thesis for pre-processing the images of two-phase microstructures containing isolated particles based on Matlab®. The two phases in this work represent martensite particles and ferrite matrix in Dual Phase steels that are developed for lightweight automotive body structures. An automated image processing tool is useful for obtaining statistical microstructure characteristics, important for better understanding material\u27s deformation and fracture behavior. However, a generalized commercial software can not automatically realize the microstructural features of special interest, and a user-specified pre-processing tool is needed. This platform utilizes the Matlab® GUI technique, Matlab image processing functions, fuzzy technology to distinguish martensites from ferrites, optimize the intermediate martensite results, analyze and generate the martensite distribution information and volume fraction. The multiple region thresholding technique is proposed to enhance the image segmentation. A fuzzy linguistic system is presented to utilize experts\u27 experience to remove noise and fill the unexpected holes on the image. The examples show that with this platform the pre-processing on images of two phase microstructures containing isolated particles is more convenient, fast and accurate

Postoperative Radiotherapy and N2 Non-small Cell Lung Cancer Prognosis: A Retrospective Study Based on Surveillance, Epidemiology, and End Results Database

The purpose of this study is to clarify the significance of postoperative radiotherapy for N2 lung cancer. This study aimed to investigate the effect of postoperative radiotherapy on the survival and prognosis of patients with N2 lung cancer. Data from 12,000 patients with N2 lung cancer were extracted from the Surveillance, Epidemiology, and End Results database (2004-2012). Age at disease onset and 5-year survival rates were calculated. Survival curves were plotted using the Kaplan-Meier method. The univariate log-rank test was performed. Multivariate Cox regression were used to examine factors affecting survival. Patients’ median age was 67 years (mean 66.46 ± 10.03). The 5-year survival rate was 12.55%. Univariate analysis revealed age, sex, pathology, and treatment regimen as factors affecting prognosis. In multivariate analysis, when compared to postoperative chemotherapy, postoperative chemoradiotherapy was better associated with survival benefits (hazard ratio [HR]= 0.85, 95% confidence interval [CI]: 0.813-0.898, P <0.001). Propensity score matching revealed that patients who had received postoperative chemoradiotherapy had a better prognosis than did patients who had received postoperative chemotherapy (HR=0.869, 95% CI: 0.817-0.925, P <0.001). Female patients and patients aged <65 years had a better prognosis than did their counterparts. Patients with adenocarcinoma had a better prognosis than did patients with squamous cell carcinoma. Moreover, prognosis worsened with increasing disease T stage. Patients who had received postoperative chemoradiotherapy had a better prognosis than did patients who had received postoperative chemotherapy. Postoperative radiotherapy was an independent prognostic factor in this patient group

Effect of high-intensity interval training protocol on abdominal fat reduction in overweight chinese women: a randomized controlled trial

The objective of the study was to compare the whole-body and abdominal fat loss resulting from high-intensity interval training (HIIT) with that from moderate-intensity continuous training (MICT) with equivalent oxygen cost in overweight women. Forty-three overweight women with matched anthropometric characteristics were randomly assigned to participate in: (1) HIIT [4 x 4-minute running at 85–95% HRpeak, 10-minute recovery], (2) MICT [33-minute running at 60–70% HRpeak] with oxygen cost equivalent to HIIT, and (3) no training [control], for 12 weeks, 4 d·wk-1. Dietary energy intake and habitual energy expenditure were not altered during the intervention. After the intervention, whole-body fat reduction and serum lipid profile modification were similar in the HIIT and MICT groups. With regard to the abdominal visceral (AVFA) and subcutaneous (ASFA) fat areas revealed by computed tomography scans, a greater reduction in ASFA was found in the HIIT than in the MICT group (p=.038). Moreover, a significant reduction in AVFA was found only in the HIIT group. No variables were changed in the control group. Twelve-week HIIT and MICT programmes with equivalent oxygen cost resulted in similar whole-body fat loss in overweight women. Nonetheless, HIIT appears to be more effective than MICT for controlling abdominal visceral and subcutaneous fat

Nanoindentation induced anisotropy of deformation and damage behaviors of MgF2 crystals

The competition mechanism between the slip motions and cleavage fractures is related to the anisotropy of deformation behaviors, which is essential to manufacture complex optical components. To identify competition mechanism between the slip motions and cleavage fractures and reveal the anisotropy of deformation and damage behaviors of MgF2 crystals, the nanoindentation tests were systematically conducted on different crystal planes. In addition, the stress induced by the nanoindentation was developed and decomposed along the slip systems and cleavage planes, and cleavage factors and Schmid factors were calculated. The stress, cleavage factors and Schmid factors indicated that the activation degree of the slip motions and cleavage fractures determined the indentation morphologies. Under the same indentation conditions, the nanoindentation of the (001) crystal plane activated most slip motions, so the plastic deformation is most prone to occur on this crystal plane. The nanoindentation of the (010) crystal plane activated less slip motions and most cleavage fractures, resulting in the severest brittle fractures on the (010) crystal plane. The theoretical results consisted well with the experimental results, which provides the theoretical guidance to the low-damage manufacturing of MgF2 components