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The purpose of this paper is to propose and optimize plastic optical fiber (POF) probe with macro-bending biconical tapered structure for the relative humidity (RH) sensing.

In this study, the principle is the evanescent wave power modulated by the ambient humidity. The probe is fabricated by using fused biconical taper and heat-setting method and then coated with a fluorescent moisture-sensitive film.

The probe’s sensing performance can be optimized by changing the probe’s curvature radius, biconical tapered transition length and taper waist diameter. The result shows that the sensitivity of the probe is up to 1.60 and 3.40 mV/ per cent, respectively, at low humidity (10-45 per cent) and high humidity (45-90 per cent). Also, this probe has good linearity, repeatability, photostability and long-term stability.

The proposed probe can improve the sensitivity and linearity of RH sensing without complex devices, which is necessary for mass production, remote measurement and convenient operation.

POF probe with macro-bending biconical tapered structure is investigated in this paper, which is proved to be effective in improving the sensitivity and linearity.

The purpose of this study is to construct a scale that is contextually suitable for measuring early childhood leadership in China.

Following a standard scale development procedure, both qualitative and quantitative research approaches were addressed. Qualitative data was collected from 21 semistructured interviews with formal and informal teacher leaders in Xiamen City, China. Using survey data of 120 respondents and 305 respondents, an exploratory factor analysis was conducted twice to determine the underlying factorial structure of the scale. A further sample of 317 respondents were used to test the latent structure and validity of the scale using confirmatory factor analysis.

Based on the results from reliability and validity tests, this study indicates that the scale demonstrates sound psychometric properties. A three-factor model was determined, including staff management and development, peer learning and support and communication with parents.

The scale is the first of its kind for measuring early childhood leadership in China.

The purpose of this paper is to apply analysis of public discourses on Ze Xiao to explore and interpret the power relationships shaping inequality in admission to public junior high schools in urban China.

This study first introduces the rise of Ze Xiao as an educational phenomenon in China. It then elucidates power relationships in public school admission by analyzing continuities and changes in stakeholders’ interaction in public school admission. It concludes by discussing educational reform for equal public school admission in urban China. Data were collected from written and spoken texts about public school admission, including newspaper articles from the 1980s to the 2000s, policy documents and interviews with relevant stakeholders.

Findings demonstrate that multi-layered power relationships caused diverse inequalities in admission to public secondary education in urban China. These are represented by political and institutional privileges and an imbalance in education development during the social transition from a profit-driven approach in the 1990s to a balance-centered one after 2000. Arguably, there is a necessity to further promote a systematic reform to terminate the privileges and imbalance for an equal and balanced public secondary education in urban China post-2015.

This study attempts to make a contribution toward reconstructing the meaning of inequality in admission to public junior high schools in urban areas by revealing the power relationships among stakeholders constituted through their interactions in public education during the different stages of socio-economic development in urban China.

This article aims to develop a fast numerical method for solving the one-dimensional heat and mass transfer problem within a desiccant rotor.

The collocation method is used for discretizing the axial dimension and reducing the number of dependent variables. The resulting system of equation is then solved through backward differentiation formulas.

The numerical results obtained here focus on verifying the accuracy and the computation time of the proposed method with respect to the finite difference method. The proposed numerical solution method resulted faster than, and as much accurate as, the finite difference method, over a large range of operating conditions that are of interest in desiccant cooling applications.

For heat and mass transfer analysis, constant average transfer coefficients are used. The results are calculated for NTU between 2 and 15 and for Le number between 0.5 and 2.

The results can be used in designing desiccant heat exchangers and desiccant cooling systems including complex rotor arrangements.

Different from other simplified solution techniques, the proposed method relies on few parameters that retain physical meaning and applies also to complex rotor configurations.

This paper aims to research the effect of different concentrations for Nd(NO₃)₃ and Ce(NO₃)₃ on the microstructures and corrosion resistance of Ni-W-P composite coatings through electroless plating method.

Scanning electron microscope, attached energy dispersive spectroscopy system and X-ray diffraction were used in this work. Meanwhile, the immersion test and electrochemical tests were used to characterize the corrosion behavior of the coating.

The coatings prepared at 1.00 g·L⁻¹ Nd(NO₃)₃ exhibit a dense structure and high phosphorus content (12.38 wt.%). In addition, compared to the addition of Ce(NO₃)₃, when Nd(NO₃)₃ was introduced at a concentration of 1.00 g·L⁻¹, the minimum corrosion rate of the coating was 1.209 g·m⁻²·h⁻¹, with a noble E_corr (−0.29 V) and lower I_corr (8.29 × 10⁻⁴ A·cm⁻²).

The effects of rare earths on the deposition and corrosion resistance mechanisms of Ni-W-P composite coatings were explored, with the rare earth elements promoting the deposition of nickel and tungsten atoms. Simultaneously, the amorphization of the coating increases, which excellently enhances the corrosion resistance of the coating.

The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.

A kind of micro-nano hydrophobic ternary microparticles was fabricated from SiO₂/halloysite nanotubes (HNTs) and recycled concrete powders (RCPs), which was then mixed with sodium silicate and silane to form an inorganic slurry. The slurry was further sprayed on the concrete surface to construct a superhydrophobic coating (SHC). Transmission electron microscopy and energy-dispersive X-ray spectroscopy mappings demonstrate that the nano-sized SiO₂ has been grafted on the sub-micron HNTs and then further adhered to the surface of micro-sized RCP, forming a kind of superhydrophobic particles (SiO₂/HNTs@RCP) featured of abundant micro-nano hierarchical structures.

The SHC surface presents excellent superhydrophobicity with the water contact angle >156°. Electrochemical tests indicate that the corrosion rate of mild steel rebar in coated concrete reduces three-order magnitudes relative to the uncoated one in 3.5% NaCl solution. Water uptake and chloride ion (Cl^-) diffusion tests show that the SHC exhibits high H₂O and Cl^- ions barrier properties thanks to the pore-sealing and water-repellence properties of SiO₂/HNTs@RCP particles. Furthermore, the SHC possesses considerable mechanical durability and outstanding self-cleaning ability.

SHC inhibits water uptake, Cl^- diffusion and rebar corrosion of concrete, which will promote the sustainable application of concrete waste in anti-corrosive concrete projects.

This systematic literature review analyses the determinants and consequences of executive compensation-related shareholder activism and say-on-pay (SOP) votes. The review covers 71 empirical articles published between January 1995 and September 2017. The studies are reviewed within an empirical research framework that separates the reasons for shareholder activism and SOP voting dissent as input factor on the one hand and the consequences of shareholder pressure as output factor on the other. This procedure identifies the five most important groups of factors in the literature: the level and structure of executive compensation, firm characteristics, corporate governance mechanisms, shareholder structure and stakeholders. Of these, executive compensation and firm characteristics are the most frequently examined. Further examination reveals that the key assumptions of neoclassical principal agent theory for both managers and shareholders are not always consistent with recent empirical evidence. First, behavioral aspects (such as the perception of fairness) influence compensation activism and SOP votes. Second, non-financial interests significantly moderate shareholder activism. Insofar, we recommend integrating behavioral and non-financial aspects into the existing research. The implications are analyzed, and new directions for further research are discussed by proposing 19 different research questions.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

This paper provides a quantitative review of the literature on the repercussions of idiosyncratic information on firms’ cost of equity (CoE) capital. In total, I review the results of 113 unique studies examining the CoE effects of information Quantity, Precision and Asymmetry. My results suggest that the association between firm-specific information and CoE is subject to moderate effects. First, the link between Quantity and CoE is moderated by disclosure types and country-level factors in that firms in comparatively weakly regulated countries tend to enjoy up to four times greater CoE benefits from more expansive disclosure—depending on the type of disclosure—than firms in strongly regulated markets. Second, a negative relationship between Precision and CoE is only significant in studies using non-accrual quality proxies for Precision and risk factor-based (RFB)/valuation model-based (VMB) proxies for CoE. Third, almost all VMB studies confirm the positive association between Asymmetry and CoE, but there is notable variation in the conclusions reached when ex post CoE measurers are used.

This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.

本研究旨在开发一种基于LSTM的强大预测模型, 用于预测仁川国际机场的日常国际游客抵达量, 结合多种预测因素, 包括汇率、WTI原油价格、韩国综合股价指数 (KOSPI) 数据和新冠疫情病例。通过利用深度学习技术和多样化数据集, 研究旨在提升旅游需求预测的准确性和可靠性, 对酒店与旅游领域的理论和实际应用有重要贡献。

本研究通过利用长短期记忆（LSTM）网络引入创新方法, 预测国际游客抵达量。这一先进方法解决了旅游管理中的复杂管理问题, 提供了更精确的预测。方法论包括四个关键步骤: (1) 收集数据集; (2) 数据预处理; (3) 训练LSTM网络; 以及 (4) 预测未来的国际游客抵达量。本文的其余部分结构如下：后续部分详细介绍了提出的LSTM模型, 呈现了实证结果, 并讨论了研究的发现、结论以及在酒店与旅游领域的理论和实际意义。

本研究首次同时使用包括国际游客抵达量、汇率、原油价格、股市数据和新冠疫情病例在内的大数据进行日常预测。研究显示, 整合汇率、原油价格、股市数据和新冠疫情病例显著增强了LSTM网络的预测精度。研究填补了现有研究在使用这些因素预测仁川国际机场国际游客抵达量的狭窄范围。此外, 研究证明了LSTM网络在处理多变量时间序列预测问题上的能力, 为其在酒店与旅游管理中的应用提供了坚实基础。

本研究首次将国际游客抵达量、汇率、WTI原油价格、KOSPI数据和新冠疫情病例整合到日常国际游客抵达量的预测中。它通过提出同时考虑多个预测因素的全面方法, 弥合了现有文献的差距。此外, 研究展示了LSTM网络在处理多变量时间序列预测问题方面的有效性, 为增强旅游需求预测提供了实用见解。通过处理这些关键因素并利用先进的深度学习技术, 本研究在旅游业预测方法的进步中做出了重要贡献, 帮助决策者进行有效的规划和资源配置。