Chen-Long Li, Chang-Shun Yuan, Xiao-Shuang Ma, Wen-Liang Chen and Jun Wang
This paper aims to provide a novel integrated fault detection method for industrial process monitoring.
Abstract
Purpose
This paper aims to provide a novel integrated fault detection method for industrial process monitoring.
Design/methodology/approach
A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output.
Findings
The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process.
Originality/value
The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.
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Dongmei Cao, Maureen Meadows and Xiao Ma
Despite the extensive stimulus–organism–response (SOR) literature, little attention has been paid to the role of marketing activity as a key environmental stimulus, and there is a…
Abstract
Purpose
Despite the extensive stimulus–organism–response (SOR) literature, little attention has been paid to the role of marketing activity as a key environmental stimulus, and there is a dearth of research examining the interplay between emotions and cognition on consumer behaviour, as well as the sequential effects of emotions on cognition. To address these gaps, this study aims to develop a revised SOR model by incorporating Kahneman’s fast and slow thinking theory to investigate the impulse buying of affordable luxury fashion (ALF).
Design/methodology/approach
The authors use outlet stores at Bicester village (BV) in England as the research context for ALF shopping. Partial least squares structural equation modelling was used to analyse a survey sample of 633 consumers with a BV shopping experience.
Findings
The authors find that impulse buying of ALF arises from the interplay of emotional and cognitive factors, as well as a sequential and dual process involving in-store stimuli affecting on-site emotion and in-store browsing.
Research limitations/implications
This study reveals that brand connection has a significant and negative influence on the relationship between on-site emotion and in-store browsing, advancing the SOR paradigm and reflecting the interactive effect of human emotion and reasoning on the impulse buying of ALF items.
Practical implications
Insights into consumers’ impulse buying offer practical implications for luxury brand management, specifically for ALF outlet retailers and store managers.
Originality/value
The results suggest a robust sequential effect of on-site emotion towards in-store browsing on impulse buying, providing updated empirical support for Kahneman’s theory of System 1 and System 2 thinking.
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Xiaohu Wen, Xiangkang Cao, Xiao-ze Ma, Zefan Zhang and Zehua Dong
The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.
Abstract
Purpose
The purpose of this paper was to prepare a ternary hierarchical rough particle to accelerate the anti-corrosive design for coastal concrete infrastructures.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Dong-Heon Kwak, Saerom Lee, Xiao Ma, Jaeung Lee, Khansa Lara and Alan Brandyberry
Mobile loafing, or non-work-related mobile computing, is deviant workplace behavior that can reduce productivity and increase cybersecurity risks. To thwart mobile loafing…
Abstract
Purpose
Mobile loafing, or non-work-related mobile computing, is deviant workplace behavior that can reduce productivity and increase cybersecurity risks. To thwart mobile loafing, organizations often adopt formal controls that encompass rules and policies. These formal controls can serve as a phase-shifting event. Phase shifting is a process where individuals reevaluate and revise their perceptions of the regulation of deviant behaviors. Despite the importance of understanding this process, little research has examined the announcement of formal controls as an impetus for phase shifting. The primary objectives of this study were to induce a phase-shifting perception in an organizational setting and explore its determinants and moderating role in the context of mobile loafing.
Design/methodology/approach
The authors proposed and tested a model using two-wave data collected from 231 Amazon Mechanical Turk workers. To test the research hypotheses, they used covariance-based structural equation modeling and logistic regression.
Findings
The authors found that peer's mobile loafing and neutralization positively influence mobile-loafing intention before and after the announcement of formal controls. This research also shows that the higher an employee's neutralization, the likelier they perceive the announcement of formal controls as phase shifting. Also, the authors found that the moderating effect of phase-shifting perceptions functions in such a way that the relationship between T1 and T2 mobile-loafing intention is weaker when employees perceive the announcement of formal controls as a phase-shifting event.
Practical implications
The authors’ results provide managers with useful insights into effectively using formal controls to mitigate employees' deviant behavior. To effectively use formal controls, managers should articulate formal controls that can trigger employees to revise their perceptions of counterproductive workplace behavior policies.
Originality/value
This study is one of the first in information systems research to empirically examine the announcement of formal controls as a phase-shifting event and explore its antecedents and moderating role in the context of deviant workplace behavior in general and mobile loafing in particular.
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Xiao-Yan Ma, Yi-Wen Ren, Hui Li, Wei Li, Yanli Liang and Wenjiang Zheng
Silicon-containing groups were introduced into fluoroacrylate polymer to further improve the comprehensive performance of pressure-sensitive adhesive (PSA) for expanded…
Abstract
Purpose
Silicon-containing groups were introduced into fluoroacrylate polymer to further improve the comprehensive performance of pressure-sensitive adhesive (PSA) for expanded polytetrafluoroethylene (ePTFE) bonding.
Design/methodology/approach
A series of silicon-containing fluorinated acrylic copolymers were synthesized through free radical solution polymerization with vinyloxy trimethylsilane, allyltrimethylsilane, 3-(trimethoxysilyl)propyl methacrylate or 1,3,5-tris(3,3,3-trifluoropropyl) methylcyclotrisiloxane as silicon monomers, and comprehensive performance of the copolymers was evaluated based on Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), gel permeation chromatography, glass transition temperatures (Tg), differential scanning calorimetry, thermogravimetric analysis, water contact angle, the track, 180° peel strength, and shear holding power.
Findings
Based on the FTIR and XPS results, it is confirmed that the silicon monomers were successfully introduced into the fluorinated acrylate copolymer. XPS analysis indicated that the silicon groups had the tendency to enrich on the surface of the film, thereby reducing the F content on the film surface. The glass transition temperatures (Tg) of the PSAs increased when silicon monomers were introduced, while the thermal stability declined. The contact angles of the acrylic PSA films were increased with the introduction of silicon monomers. From the perspective of bonding performance, the track, 180° peel strength and shear holding power decreased to varying degrees compared to silicon-free PSA, except significantly elevated holding power with MPS as the silicon monomer.
Originality/value
Silicon-containing fluorinated acrylic copolymers were synthesized, and the comprehensive performance was evaluated as PSAs of ePTFE for the first time.
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Krzysztof Borodako, Jadwiga Berbeka and Michał Rudnicki
The aim of the study is to assess the resilience of tourism enterprises in the face of the pandemic crisis and the war in Ukraine. It was achieved on the base of the results of…
Abstract
The aim of the study is to assess the resilience of tourism enterprises in the face of the pandemic crisis and the war in Ukraine. It was achieved on the base of the results of surveys conducted among Małopolska (Poland) tourism enterprises in the years 2021–2022. The research was conducted using the CAWI technique, it has been collected 517 completed questionnaires. Regression analysis was used to check the relationships between the studied variables. The findings confirm that the surveyed companies react agilely to changing, volatile, uncertain, and complex business conditions. These companies undertook numerous innovative (most often in the scale of the company) solutions in the organisational area. Changes noticed in the behaviour of contractors were also confirmed by constant monitoring of the environment forced by dynamic changes in conditions. Among the factors determining the activities of tourist companies in the context of international factors, one can undoubtedly mention the uncertainty caused by the war in Ukraine (and thus the energy crisis). The research focuses on the important subject of resilience, organisational changes, and agility in adapting to conditions in the face of volatility and uncertainty in the environment.
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John Daniel Mclellan and Esam Moustafa
The aim of this research project is to examine the adoption rate of management accounting tools by businesses in the Gulf Co‐operative Council (GCC) countries and attempts to…
Abstract
Purpose
The aim of this research project is to examine the adoption rate of management accounting tools by businesses in the Gulf Co‐operative Council (GCC) countries and attempts to determine if significant variances in the use of management accounting tools by GCC businesses are contingent on a companies' ownership, legal structure, size or industry sector. The study covers a broad range of businesses, from many different business sectors in six different Arab countries.
Design/methodology/approach
An online survey on the adoption rate of 41 Management Accounting Tools was used to collect data. The Institute of Management Accountants invited 453 CMA's in the Gulf region to participate in the Survey of Management Accounting Practices in the GCC area. Factor analysis was employed to test the effects of company characteristics on the choice of management accounting tools.
Findings
The study finds that companies in the GCC rely on the more traditional management accounting practices such as budgeting rather than the more recently developed strategically focused tools such as activity based management and the use of the balanced scorecard. The research also shows that company characteristics play a significant role in the use of management accounting tools by businesses. Overall, international ownership and incorporation tend to increase the use of many management accounting practices.
Research limitations/implications
Results should be generalized cautiously due to the small number of responding companies. The use of individual tools may not be completely explained by the chosen independent variables; other factors such as management's preference and/or the cost and benefit of the tool may affect choice.
Originality/value
Management accounting practices of businesses in the GCC have never been studied before. This study updates the literature on the management accounting tools by businesses.
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Mengyao Fan, Xiaojing Ma, Lin Li, Xinpeng Xiao and Can Cheng
In this paper, the complex flow evaporation process of droplet impact on the liquid film in a horizontal falling film evaporator is numerically studied based on smoothed particle…
Abstract
Purpose
In this paper, the complex flow evaporation process of droplet impact on the liquid film in a horizontal falling film evaporator is numerically studied based on smoothed particle hydrodynamics (SPH) method. The purpose of this paper is to present the mechanism of the water treatment problem of the falling film evaporation for the high salinity mine water in Xinjiang region of China.
Design/methodology/approach
To effectively characterize the phase transition problem, the particle splitting and merging techniques are introduced. And the particle absorbing layer is proposed to improve the nonphysical aggregation phenomenon caused by the continuous splitting of gas phase particles. The multiresolution model and the artificial viscosity are adopted.
Findings
The SPH model is validated qualitatively with experiment results and then applied to the evaporation of the droplet impact on the liquid film. It is shown that the larger single droplet initial velocity and the smaller single droplet initial temperature difference between the droplet and liquid film improve the liquid film evaporation. The heat transfer effect of a single droplet is preferable to that of multiple droplets.
Originality/value
A multiphase SPH model for evaporation after the droplet impact on the liquid film is developed and validated. The effects of different factors on liquid film evaporation, including single droplet initial velocity, single droplet initial temperature and multiple droplets are investigated.
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Siyu Zhang, Ze Lin and Wii-Joo Yhang
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)…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
研究目的
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。
研究方法
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。
研究发现
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。
研究创新
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。
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Madhumita Chakraborty and Sowmya Subramaniam
The study examines the cross-sectional and asymmetric relationship of investor sentiment with the stock returns and volatility in India.
Abstract
Purpose
The study examines the cross-sectional and asymmetric relationship of investor sentiment with the stock returns and volatility in India.
Design/methodology/approach
The investor sentiment is captured using a market-based measure Market Mood Index (MMI) and a survey-based measure Consumer Sentiment Index (CSI). The asymmetric effect of the relationship is examined using quantile causality approach and cross-sectional effect is examined by considering indices such as the BSE Sensex, and the various size indices such as BSE Large cap, BSE Mid cap and BSE Small cap.
Findings
The result of the study found that investor sentiment (MMI) cause stock returns at extreme quantiles. Lower sentiment induces fear-induced selling, thereby lowers the returns and high sentiment is followed by lower future returns as market reverts to fundamentals. On the other hand, bullish shifts in sentiment lower the volatility. There exists a positive feedback effect of stock return and volatility in the formation of investor sentiment.
Originality/value
The study captures both asymmetric and cross-sectional relationship of investor sentiment and stock market in an emerging economy, India. The study uses a novel data set (i.e.) MMI which captures the sentiment based on market indicators and are widely disseminated to the public.