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1 – 10 of 84
Article
Publication date: 18 January 2024

Minglang Zhang, Xue Zuo and Yuankai Zhou

The purpose of this paper is to reveal the dynamic contact characteristics of the slip ring. Dynamic contact resistance models considering wear and self-excited were established…

Abstract

Purpose

The purpose of this paper is to reveal the dynamic contact characteristics of the slip ring. Dynamic contact resistance models considering wear and self-excited were established based on fractal theory.

Design/methodology/approach

The effects of tangential velocity, stiffness and damping coefficient on dynamic contact resistance are studied. The relationships between fractal parameters, wear time and contact parameters are revealed.

Findings

The results show that the total contact area decreases with the friction coefficient and fractal roughness under the same load. Self-excited vibration occurs at a low speed (less than 0.6 m/s). It transforms from stick-slip motion at 0.4 m/s to pure sliding at 0.5 m/s. A high stiffness makes contact resistance fluctuate violently, while increasing the damping coefficient can suppress the self-excited vibration and reduce the dynamic contact resistance. The fractal contact resistance model considering wear is established based on the fractal parameters models. The validity of the model is verified by the wear tests.

Originality/value

The results have a great significance to study the electrical contact behavior of conductive slip ring.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2023-0300/

Details

Industrial Lubrication and Tribology, vol. 76 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 7 July 2023

Xiaojie Xu and Yun Zhang

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important…

Abstract

Purpose

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

Design/methodology/approach

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

Findings

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

Originality/value

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

Details

Journal of Financial Management of Property and Construction , vol. 29 no. 1
Type: Research Article
ISSN: 1366-4387

Keywords

Article
Publication date: 18 March 2024

Yu-Xiang Wang, Chia-Hung Hung, Hans Pommerenke, Sung-Heng Wu and Tsai-Yun Liu

This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process…

Abstract

Purpose

This paper aims to present the fabrication of 6061 aluminum alloy (AA6061) using a promising laser additive manufacturing process, called the laser-foil-printing (LFP) process. The process window of AA6061 in LFP was established to optimize process parameters for the fabrication of high strength, dense and crack-free parts even though AA6061 is challenging for laser additive manufacturing processes due to hot-cracking issues.

Design/methodology/approach

The multilayers AA6061 parts were fabricated by LFP to characterize for cracks and porosity. Mechanical properties of the LFP-fabricated AA6061 parts were tested using Vicker’s microhardness and tensile testes. The electron backscattered diffraction (EBSD) technique was used to reveal the grain structure and preferred orientation of AA6061 parts.

Findings

The crack-free AA6061 parts with a high relative density of 99.8% were successfully fabricated using the optimal process parameters in LFP. The LFP-fabricated parts exhibited exceptional tensile strength and comparable ductility compared to AA6061 samples fabricated by conventional laser powder bed fusion (LPBF) processes. The EBSD result shows the formation of cracks was correlated with the cooling rate of the melt pool as cracks tended to develop within finer grain structures, which were formed in a shorter solidification time and higher cooling rate.

Originality/value

This study presents the pioneering achievement of fabricating crack-free AA6061 parts using LFP without the necessity of preheating the substrate or mixing nanoparticles into the melt pool during the laser melting. The study includes a comprehensive examination of both the mechanical properties and grain structures, with comparisons made to parts produced through the traditional LPBF method.

Details

Rapid Prototyping Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 19 October 2023

Huaxiang Song

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition…

Abstract

Purpose

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

Design/methodology/approach

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Findings

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

Originality/value

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Open Access
Article
Publication date: 31 May 2023

Xiaojie Xu and Yun Zhang

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction…

Abstract

Purpose

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

Design/methodology/approach

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

Findings

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

Originality/value

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Details

Asian Journal of Economics and Banking, vol. 8 no. 1
Type: Research Article
ISSN: 2615-9821

Keywords

Article
Publication date: 27 March 2024

Haroon Iqbal Maseeh, Charles Jebarajakirthy, Achchuthan Sivapalan, Mitchell Ross and Mehak Rehman

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal…

Abstract

Purpose

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Design/methodology/approach

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

Findings

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Practical implications

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

Originality/value

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

Details

Information Technology & People, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-3845

Keywords

Article
Publication date: 14 December 2023

Huaxiang Song, Chai Wei and Zhou Yong

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of…

Abstract

Purpose

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

Design/methodology/approach

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

Findings

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

Originality/value

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

Details

International Journal of Web Information Systems, vol. 20 no. 2
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 15 January 2024

Yingying Dong and Lisa Gao

This study aims to identify the decision-making process involved in the purchase of organic wine from consumer values to attitudes to behavioural intention towards organic wine…

Abstract

Purpose

This study aims to identify the decision-making process involved in the purchase of organic wine from consumer values to attitudes to behavioural intention towards organic wine via the value–attitude–behaviour (VAB) model. Involvement in wine is also taken into consideration.

Design/methodology/approach

The data were collected using a snowball sampling method and a closed-ended questionnaire. A total of 209 responses were analysed. Linear regression and PROCESS Macro on SPSS were used to perform data analysis.

Findings

Both biospheric-altruistic values and egoistic values are positively associated with attitudes towards organic wine. Attitude is found to mediate the relationship between biospheric-altruistic/egoistic values and behavioural intention. Egoistic values are found to significantly predict behavioural intention in the organic wine purchase context. Involvement was found to moderate the relationship between egoistic values and attitudinal loyalty.

Originality/value

This study identifies the decision-making hierarchy from consumer values to attitudes to behavioural intention, theoretically confirming the robustness of the VAB model in the organic wine consumption context. It also makes a practical contribution by indicating the marketing emphasis of organic wine and segmenting potential consumers according to their values and levels of wine involvement.

Details

British Food Journal, vol. 126 no. 4
Type: Research Article
ISSN: 0007-070X

Keywords

Book part
Publication date: 14 March 2024

Giulia Pavone and Kathleen Desveaud

This chapter provides an overview of the strategic implications of chatbot use and implementation, including potential applications in marketing, and factors affecting customer…

Abstract

This chapter provides an overview of the strategic implications of chatbot use and implementation, including potential applications in marketing, and factors affecting customer acceptance. After presenting a brief history and a classification of conversational artificial intelligence (AI) and chatbots, the authors provide an in-depth review at the crossroads between marketing, business, and human–computer interaction, to outline the main factors that drive users' perceptions and acceptance of chatbots. In particular, the authors describe technology-related factors and chatbot design characteristics, such as anthropomorphism, gender, identity, and emotional design; context-related factors, such as the product type, task orientation, and consumption contexts; and users-related factors such as sociodemographic and psychographic characteristics. Next, the authors detail the strategic importance of chatbots in the field of marketing and their impact on consumers' perceived service quality, satisfaction, trust, and loyalty. After discussing the ethical implications related to chatbots implementation, the authors conclude with an exploration of future opportunities and potential strategies related to new generative AI technologies, such as ChatGPT. Throughout the chapter, the authors offer theoretical insights and practical implications for incorporating conversational AI into marketing strategies.

Details

The Impact of Digitalization on Current Marketing Strategies
Type: Book
ISBN: 978-1-83753-686-3

Keywords

Content available
Article
Publication date: 2 February 2022

Fangxuan (Sam) Li, Jianan Ma and Yun Tong

This study aims to explore tourism live streamers’ motivations of sharing their travel experiences based on the grounded theory.

1946

Abstract

Purpose

This study aims to explore tourism live streamers’ motivations of sharing their travel experiences based on the grounded theory.

Design/methodology/approach

The use of purposive and snowball sampling methods was used to conduct 22 in-depth semi-structured interviews. The manuscript was analyzed based on the grounded theory.

Findings

This study identifies five tourism live streamers’ motivations of sharing their travel experience, including information sharing, entertainment, self-presentation, monetary incentives and socialization. Information sharing and entertainment are identified as the most important motivations of travel livestreaming (TLS) among the motivations. Monetary incentive is identified as a new motivation for tourism live streamers compared to other social media users.

Research limitations/implications

This study provides valuable suggestions for livestreaming platforms and tourism product providers to attract more tourism live streamers and better serve them.

Originality/value

To the best of the authors’ knowledge, this is one of the first studies to offer empirical findings and discussions on tourism live streamers’ motivations of sharing their travel experiences.

目的

本研究旨在基于扎根理论探讨旅游直播主分享旅游体验的动机。

设计/方法

使用目的性和滚雪球抽样方法进行了22个深入的半结构化访谈。 本研究采用扎根理论对数据进行分析。

发现

本研究发现了五种旅游直播主分享旅游体验的动机, 包括信息共享、娱乐、自我展示、金钱激励和社交。信息共享和娱乐被认为是旅游直播最重要的动机。与其他社交媒体的用户相比, 货币激励被认为是旅游直播的新动机。

研究意义

本研究为直播平台和旅游产品提供商提供有用的建议, 以吸引更多的旅游直播者并更好地为他们服务。

创意/价值

这是对旅游直播主分享旅游体验的动机提供实证研究结果和讨论的首批研究之一。

Propósito

este estudio tiene como objetivo explorar las motivaciones de los transmisores en vivo del turismo para compartir sus experiencias de viaje según la teoría fundamentada.

Diseño/metodología/enfoque

Des méthodes d'échantillonnage raisonné et boule de neige ont été utilisées pour mener 22 entrevues semi-structurées approfondies. Le manuscrit a été analysé sur la base de la théorie ancrée.

Hallazgos

este estudio identifica las motivaciones de cinco transmisores en vivo del turismo para compartir su experiencia de viaje, incluido el intercambio de información, el entretenimiento, la autopresentación, los incentivos monetarios y la socialización. El intercambio de información y el entretenimiento se identifican como las motivaciones más importantes de la transmisión en vivo de viajes (TLS) entre las motivaciones. El incentivo monetario se identifica como una nueva motivación para el transmisor en vivo del turismo en comparación con los usuarios de otras redes sociales.

Limitaciones/implicaciones de la investigación

este estudio proporciona sugerencias útiles para que las plataformas de transmisión en vivo y los proveedores de productos turísticos atraigan a más transmisores turísticos en vivo y les brinden un mejor servicio.

Originalidad/valor

este es uno de los primeros estudios que ofrece hallazgos empíricos y debates sobre las motivaciones de los transmisores en vivo del turismo para compartir sus experiencias de viaje.

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