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Human resources are essential to the survival, success, and long-term growth of a company. Hotel is an industry requiring a high level of human resources for delivering high-quality personal service to the hotel guests to maintain its competitiveness in the business environment. With the rapid economic growth in Macao started in 2002, all the industries have been growing fast and competing fiercely for the limited manpower in Macao. However, the Macao hotel industry has been losing its attractiveness in the Macao labor market and needs to rely on non-local workers with a limited stay in Macao. The management team of the Macao hotel industry is looking for a solution to maintain a stable workforce. Therefore, a study has been conducted on the effectiveness of its employee retention strategies. A questionnaire was designed to collect the preferences of the employees and interviews were conducted to understand the perspective of the management team toward the employee retention strategies. The study shows the employee strategies are focused on key employees’ interests such as career development and prospect. However, the communication between the management team and employees failed and led to employee turnover.
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This study examines the psychometric properties of a Chinese version of the Engaged Teacher Scale (C-ETS). A translated questionnaire with 16 items was administered to a sample of 341 primary and secondary school teachers in Hong Kong. A series of confirmatory factor analyses were performed to assess the construct, convergent, and discriminant validity of the scale in alternative models. Results provide support for a second-order model with teacher engagement as an overarching construct with four hypothesized dimensions: emotional engagement, cognitive engagement, social engagement (students), and social engagement (colleagues). The C-ETS provides a useful measure for teacher engagement in Chinese societies. Contributions and limitations of the study are discussed.
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Neuropeptides are a group of neuronal signaling molecules that regulate physiological and behavioral processes in animals. Here, we used in silico mining to predict the polypeptide composition of available transcriptomic data of Turbinaria peltata. In total, 118 transcripts encoding putative peptide precursors were discovered. One neuropeptide Y/F-like peptide, named TpNPY, was identified and selected for in silico structural, in silico binding, and pharmacological studies. In our study, the anti-inflammation effect of TpNPY was evaluated using an LPS-stimulated C8-D1A astrocyte cell model. Our results demonstrated that TpNPY, at 0.75–3 μM, inhibited LPS-induced NO production and reduced the expression of iNOS in a dose-dependent manner. Furthermore, TpNPY reduced the secretion of proinflammatory cytokines. Additionally, treatment with TpNPY reduced LPS-mediated elevation of ROS production and the intracellular calcium concentration. Further investigation revealed that TpNPY downregulated the IKK/IκB/NF-κB signaling pathway and inhibited expression of the NLRP3 inflammasome. Through molecular docking and using an NPY receptor antagonist, TpNPY was shown to have the ability to interact with the NPY Y1 receptor. On the basis of these findings, we concluded that TpNPY might prevent LPS-induced injury in astrocytes through activation of the NPY-Y1R.
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The number of tourist attractions reviews, travel notes and other texts has grown exponentially in the Internet age. Effectively mining users’ potential opinions and emotions on tourist attractions, and helping to provide users with better recommendation services, which is of great practical significance. This paper proposes a multi-channel neural network model called Pre-BiLSTM combined with a pre-training mechanism. The model uses a combination of coarse and fine- granularity strategies to extract the features of text information such as reviews and travel notes to improve the performance of text sentiment analysis. First, we construct three channels and use the improved BERT and skip-gram methods with negative sampling to vectorize the word-level and vocabulary-level text, respectively, so as to obtain more abundant textual information. Second, we use the pre-training mechanism of BERT to generate deep bidirectional language representation relationships. Third, the vectors of the three channels are input into the BiLSTM network in parallel to extract global and local features. Finally, the model fuses the text features of the three channels and classifies them using SoftMax classifier. Furthermore, numerical experiments are conducted to demonstrate that Pre-BiLSTM outperforms the baselines by 6.27%, 12.83% and 18.12% in average in terms of accuracy, precision and F1-score.
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Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.
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Investor sentiment and emotions have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors for stock price prediction using machine learning. Existing prediction models mostly depend on the analysis of trading data and company profit. few prediction theories have been built based on individual investors' sentiments. The fundamental reason is the difficulty to measure individual investors' sentiment.
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The Mesozoic gold deposits in the North China Craton (NCC) were hosted by the Precambrian basement and Mesozoic intrusions. Thus, most researchers consider that these gold deposits were genetically linked to the Mesozoic intrusions. However, we suggest that a metamorphic devolatilization model provides an alternative based on a combined Fe and in-situ S isotopes study on auriferous pyrites from the Baiyun gold deposit in the NCC. The Triassic Baiyun gold deposit contains the quartz vein and altered rock ores that were developed in the Paleoproterozoic metavolcanic-sedimentary rocks (the Liaohe Group). Our in-situ S isotopic analyses show that pyrites from the quartz vein ores are characterized by negative δ34S values (-10.7 ∼ -5.5‰), while those from the altered rock ores have two distinct groups of δ34S values, one being positive (+13.5 ∼ +16.2‰) and the other negative (-10.6 ∼ -3.0‰). We suggest that pyrite grains with positive δ34S values should be relicts from the host rocks, because they show comparable δ34S values with those from the host rocks schists (+3.3 ∼ +16.1‰). Thus, only the negative δ34S values of pyrites in ores (-10.7 ∼ -3.0‰) and the Fe isotopes of the quartz vein ores (δ56Fe = +0.30 ∼ +0.48‰) can represent the isotopic characteristics of ore-forming fluids at Baiyun. Our study shows that the sulfur were probably from the pyritic volcanic-sedimentary sequences of the Liaohe Group, rather than from magmas. The calculated δ56Fe values of the ore-forming fluids (-0.78 ∼ -0.37‰; pyrite-fluid isotope fractionation) could be modelled in a metamorphic devolatilization model with Fe-species (pyrite&magnetite) of the Liaohe Group as sources. Therefore, our combined S- and Fe- isotope data indicate that the metamorphic devolatilization of the Liaohe Group could account for the genesis of the Baiyun gold deposit.
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Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25%, 11.13% and 12.29%, respectively, compared with the baseline models in average, which proves that our model has better performance.
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This dissertation is a comprehensive academic examination of the characteristics and complex historical progress within the Pentecostal and Charismatic Movement in Macau Protestant Churches. Since Macau hosted the first Pentecostal and Charismatic missionary, Thomas J. McIntosh, who entered China in 1907, the history of the Pentecostal and Charismatic Movement in Macau lacks a consistent and synthesized research until now. Thus, primary and secondary resources have been analyzed and reconstructed and historically interpreted to gain a better and deeper understanding of the emergence and development of the Pentecostal and Charismatic Movement within Macau’s Protestant Churches from the beginning up to the present day. This academic review makes an important contribution to Macau with respect to its historical development and will fill the gap in knowledge within the Global church history of the Pentecostal and Charismatic Movement. Key Words: Charismatic, Christianity in China, History of Missionaries in China, Macao, Macau, Pentecostalism, Protestant, T. J. McIntosh
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This study presents the intrinsic value of Moody’s Corporation, a leading credit rating agency in the U.S. The results of the valuation were compared to the market value of Moody’s Corporation of the same date. The aim of the research is to provide a perspective to the investors on whether the actual value of the Company was overvalued or undervalued in the market, and how much the volatility of the stock price by the change of some factors. Both qualitative and quantitative analyses were applied in the research. The historical data, economic outlook, and the Company’s strategies were collected to be the metrics to determine the intrinsic value and provide an analysis of the prospects of Moody’s Corporation. Three valuation models were applied in the research to estimate the intrinsic value of the Company’s common stock. The cost of debt, cost of equity, the weighted average cost of capital, and the market risk premium were introduced and calculated in the research as they were the critical components in the valuation process. Since the valuation was based on assumptions and historical data to determine future growth, which indicates that the results could be changed due to uncertain factors. This study demonstrates that there was some discrepancy between the stock’s market price and the intrinsic value per share of Moody’s Corporation as of December 31, 2021
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Starbucks Corporation (hereinafter “Starbucks” or “the Company”) is a worldwide coffee retailer, which operates over 33,000 stores located in over 83 countries nowadays. The purpose of the study is to estimate Starbucks’ intrinsic value as of December 31, 2021 and identify whether the Company was overvalued or undervalued. Several analyses give investors and shareholders an insight into how the Company may develop or identify the ability to generate positive returns from investing in Starbucks. This study is mainly separated into two aspects. The first part specifically discusses the Company overview, industry analysis, and economic outlook, which includes SWOT analysis, PESTEL analysis, Porter's five forces analysis, and value chain analysis to identify external and internal factors that may influence the Company. The second part focuses on financial analyzes, including both historical and forecasted financial statement. Three valuation models and a sensitive analysis are applied to understand the Company’s financial conditions and performance. Starbucks’ intrinsic value is derived from the three discounted cash flow models, indicating the market overvalued the Company’s stock price as of December 31, 2021. Finally, investors and shareholders can understand more about Starbucks’ capital structure, financial highlights, and intrinsic value, because this set of information is critical for existing investors and potential investors to make investment decisions
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