RECEPTIVE AND PRODUCTIVE KNOWLEDGE OF LEXICAL COLLOCATIONS IN THAI UNIVERSITY LEARNERS OF ENGLISH

The present study investigates lexical collocations in first- and fourth-year Thai university learners’ and examines the relationship between receptive and productive knowledge of lexical collocations. A total of 148 students (75 first-year students and 73 fourth-year students) were tested on their lexical collocations, both receptively and productively, using two measures. Descriptive and inferential statistics were used to analyze the quantitative data, and correlational analysis determined the relationship between receptive and productive knowledge. Overall, the results showed that Thai university learners achieved significantly higher performance on tests of receptive knowledge of lexical collocations than on tests of productive knowledge. The data analysis also indicated that the fourth-year learners outperformed the first-year learners on both receptive and productive measures of lexical collocations. Furthermore, the correlational analysis revealed that receptive and productive knowledge of lexical collocations were interrelated. Together, the current findings indicate that Thai university learners’ productive knowledge of lexical collocations is built on receptive knowledge, and lexical collocations result from incremental learning.   Article visualizations

Electrode materials for lithium rechargeable batteries: Synthesis, spectroscopic studies and electrochemical performance.

Three distinct \rm Li\sb{x}V\sb2O\sb5 phases, $\delta, \ \varepsilon,$ and $\gamma$-\rm Li\sb{x}V\sb2O\sb5, were obtained through a chemical intercalation reaction and solid state reactions. Infrared and Raman spectra were recorded for the three phases. The spectral changes were interpreted in terms of the local structural changes of the vanadium-oxygen polyhedra. Although the $\delta$ and $\varepsilon$ phases have very similar powder x-ray diffraction patterns, IR and Raman studies showed these two phases adopt distinctive local structural environments. These results demonstrate that IR and Raman spectroscopy are important techniques for the structural analysis of intercalation materials.For the first time novel mesostructural materials were synthesized as electrode materials for the lithium rechargeable battery. The well-ordered mesostructural materials provide an ideal host for lithium transport processes. The preliminary results on the manganese oxide-based cathode and tin oxide-based anode show that the templating synthesis technique may provide important electrode materials for battery applications.In situ Raman spectra of \rm Li\sb{x}V\sb2O\sb5 were successfully recorded on a operating lithium rechargeable battery. Distinctive spectral changes were observed at different lithium intercalation levels and interpreted in terms of the slight rearrangements of the V-O structural units. The results show that in situ Raman spectroscopy may become an important nondestructive technique in investigating the irreversible structural changes in electrode materials and evaluating battery performance.Single crystals of \rm Li\sb{1.1}V\sb3O\sb8 and \rm\sp6Li\sb{1.1}V\sb3O\sb8 were prepared using solid state synthesis techniques. IR spectra and polarized Raman spectra were recorded on the \rm Li\sb{1.1}V\sb3O\sb8 and \rm\sp6Li\sb{1.1}V\sb3O\sb8 crystals and a lithiated phase, \rm Li\sb4V\sb3O\sb8. Factor group analysis method was used to interpret the spectral changes. These spectroscopic results provide insight into the structural modifications originating from lithium intercalation/deintercalation processes.The lithium rechargeable battery is the newest member of the rechargeable battery family and is best known for its high energy density, long battery life, low self-discharge rate and light weight. This battery may become one of the most important energy sources in consumer market, industrial and military applications. Intercalation compounds play a critical role in determining the overall performance of a lithium rechargeable battery. The common intercalation materials for battery applications are layered structure \rm Li\sb{x}CoO\sb2, spinel \rm Li\sb{x}Mn\sb2O\sb4 and lithium vanadium oxides, \rm Li\sb{x}V\sb2O\sb5 and $\rm Li\sb{x}V\sb3O\sb8.

The Heterogeneous Compensation for the Infiltrative Error of the Binder Jetting Additive Manufacturing Processes

In binder jetting additive manufacturing, such as Three Dimensional Printing (3DP) and Patternless Casting Manufacturing (PCM, a process similar as ExOne and VoxelJet), the building error is mainly caused by the infiltration by the binder in the powder bed, and appear heterogeneous magnitude along different orientations because of the different infiltrating depth of the printed binder between the building direction and the binder printing direction. Current methods to compensating these error are mostly based on the contour equidistant offset and the model shrinkage, which couldn’t deal with the heterogeneous infiltrative error. In this paper, we will propose a novel compensation method, in which the STL model will be counteracted heterogeneously in different directions to compensate the heterogeneous infiltrative distances of the binder in the powder. By this method, a sphere STL model will be transferred into an ellipsoid with variant axis length along different X/Y/Z directions. The method could greatly improve the dimensional accuracy of a series of additive manufacturing techniques which are based on the binder jetting onto powder bed.Mechanical Engineerin

Comparison for Improvements of Singing Voice Detection System Based on Vocal Separation

Singing voice detection is the task to identify the frames which contain the singer vocal or not. It has been one of the main components in music information retrieval (MIR), which can be applicable to melody extraction, artist recognition, and music discovery in popular music. Although there are several methods which have been proposed, a more robust and more complete system is desired to improve the detection performance. In this paper, our motivation is to provide an extensive comparison in different stages of singing voice detection. Based on the analysis a novel method was proposed to build a more efficiently singing voice detection system. In the proposed system, there are main three parts. The first is a pre-process of singing voice separation to extract the vocal without the music. The improvements of several singing voice separation methods were compared to decide the best one which is integrated to singing voice detection system. And the second is a deep neural network based classifier to identify the given frames. Different deep models for classification were also compared. The last one is a post-process to filter out the anomaly frame on the prediction result of the classifier. The median filter and Hidden Markov Model (HMM) based filter as the post process were compared. Through the step by step module extension, the different methods were compared and analyzed. Finally, classification performance on two public datasets indicates that the proposed approach which based on the Long-term Recurrent Convolutional Networks (LRCN) model is a promising alternative.Comment: 15 page

Optimal and Nonlinear Dynamic Countermeasure under a Node-Level Model with Nonlinear Infection Rate

This paper mainly addresses the issue of how to effectively inhibit viral spread by means of dynamic countermeasure. To this end, a controlled node-level model with nonlinear infection and countermeasure rates is established. On this basis, an optimal control problem capturing the dynamic countermeasure is proposed and analyzed. Specifically, the existence of an optimal dynamic countermeasure scheme and the corresponding optimality system are shown theoretically. Finally, some numerical examples are given to illustrate the main results, from which it is found that (1) the proposed optimal strategy can achieve a low level of infections at a low cost and (2) adjusting nonlinear infection and countermeasure rates and tradeoff factor can be conductive to the containment of virus propagation with less cost

Effect of Endocrine Therapy Combined with Trastuzumab Targeted Therapy on Response Rate and Quality of Life in HER2-Positive Metastatic Breast Cancer

Objective: To analyze the effect of endocrine therapy combined with trastuzumab targeted therapy on HER2 (human epidermal growth factor receptor-2) positive metastatic breast cancer on the treatment efficiency and quality of life. Methods: Selected 100 patients with HER2-positive metastatic breast cancer who were treated in our hospital from January 2019 to December 2021, and divided them into a control group and an observation group according to the random number table method, with 50 cases in each group, and were given the clinical effects of single trastuzumab targeted therapy and endocrine therapy combined with trastuzumab targeted therapy were compared. Results: There was no significant difference in the incidence of adverse reactions between the two groups (P>0.05); the remission rate in the observation group was significantly higher than that in the control group (P<0.05); the overall health scale and function scale scores in the observation group were higher than those in the control group, and the individual items Measurement and symptom scale scores were lower than those in the control group (P < 0.05). Conclusion: Endocrine therapy combined with trastuzumab targeted therapy for HER2-positive metastatic breast cancer can effectively relieve the patient's condition and improve the patient's quality of life. The clinical effect is significant, and it is worthy of widespread application

Music Artist Classification with WaveNet Classifier for Raw Waveform Audio Data

Models for music artist classification usually were operated in the frequency domain, in which the input audio samples are processed by the spectral transformation. The WaveNet architecture, originally designed for speech and music generation. In this paper, we propose an end-to-end architecture in the time domain for this task. A WaveNet classifier was introduced which directly models the features from a raw audio waveform. The WaveNet takes the waveform as the input and several downsampling layers are subsequent to discriminate which artist the input belongs to. In addition, the proposed method is applied to singer identification. The model achieving the best performance obtains an average F1 score of 0.854 on benchmark dataset of Artist20, which is a significant improvement over the related works. In order to show the effectiveness of feature learning of the proposed method, the bottleneck layer of the model is visualized.Comment: 12 page

EmoMix: Emotion Mixing via Diffusion Models for Emotional Speech Synthesis

There has been significant progress in emotional Text-To-Speech (TTS) synthesis technology in recent years. However, existing methods primarily focus on the synthesis of a limited number of emotion types and have achieved unsatisfactory performance in intensity control. To address these limitations, we propose EmoMix, which can generate emotional speech with specified intensity or a mixture of emotions. Specifically, EmoMix is a controllable emotional TTS model based on a diffusion probabilistic model and a pre-trained speech emotion recognition (SER) model used to extract emotion embedding. Mixed emotion synthesis is achieved by combining the noises predicted by diffusion model conditioned on different emotions during only one sampling process at the run-time. We further apply the Neutral and specific primary emotion mixed in varying degrees to control intensity. Experimental results validate the effectiveness of EmoMix for synthesizing mixed emotion and intensity control.Comment: Accepted by 24th Annual Conference of the International Speech Communication Association (INTERSPEECH 2023