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Article
Publication date: 6 October 2022

Tong Lv, Shi Lefeng and Weijun He

A vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct…

95

Abstract

Purpose

A vital job for one sharing business is dynamically dispatching shared items to balance the demand-supply of different sharing points in one sharing network. In order to construct a highly efficient dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network characteristics.

Design/methodology/approach

To that end, in this paper, the profit-changing process of single sharing points is modeled and analyzed first. And then, the characteristics of the whole sharing network are investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named the Two-step random forest reinforcement learning algorithm) is proposed.

Findings

The authors discover that the sharing points of a common sharing network could be categorized into 6 types according to their profit dynamics; a sharing network that is made up of various combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the characteristics, a specific method for guiding the dynamic dispatch of shared products is developed and validated.

Originality/value

Because the suggested method considers the interaction features between sharing points in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.

Details

Industrial Management & Data Systems, vol. 122 no. 10
Type: Research Article
ISSN: 0263-5577

Keywords

Available. Content available
Article
Publication date: 15 June 2010

Desheng Dash Wu

543

Abstract

Details

Kybernetes, vol. 39 no. 5
Type: Research Article
ISSN: 0368-492X

Available. Content available

Abstract

Details

Industrial Management & Data Systems, vol. 122 no. 10
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 7 August 2017

Chunmei Gan and Weijun Wang

The purpose of this paper is to examine the effects of perceived benefits, i.e. utilitarian value, hedonic value and social value, as well as perceived risk, on purchase intention…

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Abstract

Purpose

The purpose of this paper is to examine the effects of perceived benefits, i.e. utilitarian value, hedonic value and social value, as well as perceived risk, on purchase intention in social commerce context.

Design/methodology/approach

To cast light on the factors motivating users’ intention to purchase in the context of social commerce, data of 277 users of social commerce in China were collected via an online survey.

Findings

Results show that satisfaction significantly and positively affects users’ purchase intention in social commerce context. In addition, utilitarian, hedonic and social values have significant and positive impacts on satisfaction and purchase intention; and utilitarian value is found to be the most salient factor influencing purchase intention, while hedonic value has the greatest effect on satisfaction. Moreover, perceived risk significantly and negatively affects satisfaction.

Originality/value

Extant research on social commerce has mainly focused on investigating how the general perceived value affects user behavior, but has less considered different dimensions of perceived value. Moreover, prior studies have explored the roles of utilitarian and hedonic values on user behavior; however, there is a lack of research on the effect of social value. The current study attempts to fill these research gaps.

Details

Internet Research, vol. 27 no. 4
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 5 November 2024

Weijun Liu, Mengzhen Cao and Wojciech J. Florkowski

This study aims to assess the effects of risk perception and management subject satisfaction on consumers' online meal food safety self-protection behavior during the COVID-19…

37

Abstract

Purpose

This study aims to assess the effects of risk perception and management subject satisfaction on consumers' online meal food safety self-protection behavior during the COVID-19 pandemic.

Design/methodology/approach

This study uses 742 questionnaires collected via a two-stage online survey conducted during the COVID-19 pandemic, between December 2021 and January 2022. The entropy method, descriptive statistics, ordered logit model, stepwise regression models, interaction terms and decentralization method were used in the quantitative analysis. Respondents’ written responses to self-protection behavior were categorized into five groups.

Findings

Less than half of consumers were aware that online food products carry the risk of SARS-COV-2 (44.48%). Between 30 and 40% of consumers took insufficient or no self-protection measures. Risk perception significantly and positively affected self-protection behavior during the COVID-19 pandemic. Consumers' management subject satisfaction has a positive moderating effect on risk perception, with the moderating effect of the satisfaction of online retailers being significant at the 5% level. Risk perception significantly and positively influences consumer self-protection behavior in provinces not affected by the pandemic.

Originality/value

The findings stress the benefits of synergistic interventions by consumers and management subject to food safety measures and the inclusion of tailored interventions during events threatening public health to effectively address food safety. The study offers valuable insights contributing to the improvement of public health outcomes, customer trust and service quality within the online food delivery industry.

Details

British Food Journal, vol. 127 no. 1
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 8 November 2024

Xuejie Ni, Weijun Li, Zhong Xu, Fusheng Liu, Qun Wang, Sinian Wan, Maojun Li and Hong He

This study aims to examine the cutting performance of a coated carbide tool during the boring of 1Cr17Ni2 martensitic stainless steel, with a focus on how the tool’s structural…

17

Abstract

Purpose

This study aims to examine the cutting performance of a coated carbide tool during the boring of 1Cr17Ni2 martensitic stainless steel, with a focus on how the tool’s structural parameters, particularly the nose radius, affect the wear patterns, wear volume and lifetime of the cutting tool, and related mechanisms.

Design/methodology/approach

A full factorial boring experiment with three factors at two levels was conducted to analyze systematically the impact of cutting parameters on the tool wear behavior. The evolution of tool wear over the machining time was recorded, and the influences of the cutting parameters and nose radius on wear behavior of the tool were examined.

Findings

The results show that higher cutting parameters lead to significant wear or plastic deformation at the tool nose. When the cutting depth is less than the nose radius, the tool wear tends to be minimized. Larger nose radius tools have weaker chip-breaking but greater strength and wear resistance. Higher cutting parameters reduce wear for the tools with larger nose radius, maintaining their integrity. Wear mechanisms are primarily abrasive, adhesive and diffusion wear. Furthermore, the full-factorial analysis of variance revealed that for the tool with rε = 0.4 mm and 0.8 mm, the factors contributing the most to tool wear were cutting speed (38.76%) and cutting depth (86.43%), respectively.

Originality/value

This study is of great significance for selection of cutting tools and cutting parameters for boring 1Cr17Ni2 martensitic stainless-steel parts.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0266/

Details

Industrial Lubrication and Tribology, vol. 76 no. 10
Type: Research Article
ISSN: 0036-8792

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Article
Publication date: 24 February 2025

Jiaxin Liang, Vishnupriya Vishnupriya, An Le and Xiong Shen

The building industry is a critical sector that must significantly reduce its carbon emissions for New Zealand (NZ) to meet its 2050 zero-carbon goals. Green Star NZ, a leading…

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Abstract

Purpose

The building industry is a critical sector that must significantly reduce its carbon emissions for New Zealand (NZ) to meet its 2050 zero-carbon goals. Green Star NZ, a leading Green Building Rating System in NZ, offers a structured framework for assessing and certifying building environmental performance. This research investigates industry professionals' perspectives on Green Star NZ’s effectiveness in achieving NZ’s zero-carbon goals, addressing gaps in existing literature.

Design/methodology/approach

Through qualitative analysis of semi-structured interviews, the research identified key areas where Green Star NZ either supports or falls short of zero-carbon practices, according to 22 practising professionals. A thematic analysis method was used to analyse the data.

Findings

The results indicate that while Green Star NZ suits NZ, it faces adoption challenges due to few supportive policies, complex certification and material supply issues with sustainable materials. The study addressed these barriers through targeted policies, streamlined processes and market support for sustainable technologies. Moreover, cost is directly or indirectly tied to Green Star NZ.

Originality/value

This study offers insights and recommendations to improve Green Star NZ, assisting NZGBC and stakeholders in advancing towards a zero-carbon future. Implementing these suggestions can boost Green Star NZ’s effectiveness. Through the project experience and the viewpoints of industry professionals, it fills the research gap by assessing Green Star NZ’s framework, identifying challenges and proposing improvements. The findings also position NZ’s experience as a possible model, advancing global green building practices and providing policymakers with recommendations.

Details

International Journal of Building Pathology and Adaptation, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-4708

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Article
Publication date: 20 September 2024

Ming-Hui Liu, Jianbin Xiong, Chun-Lin Li, Weijun Sun, Qinghua Zhang and Yuyu Zhang

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to…

49

Abstract

Purpose

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

Design/methodology/approach

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

Findings

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

Research limitations/implications

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Originality/value

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Details

Robotic Intelligence and Automation, vol. 44 no. 6
Type: Research Article
ISSN: 2754-6969

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Article
Publication date: 14 August 2024

Qiqi Zhang, Weijun Zhen, Quansheng Ou, Yusufu Abulajiang and Gangshan Ma

The objective was to investigate the utility of cottonseed oil (CSO) as a raw material for the synthesis of CSO water-based alkyd resin. The synthesis involved the polymerization…

29

Abstract

Purpose

The objective was to investigate the utility of cottonseed oil (CSO) as a raw material for the synthesis of CSO water-based alkyd resin. The synthesis involved the polymerization of CSO, trimethylolpropane, phthalic anhydride (PA) and trimellitic anhydride (TMA). The prepared resin coating material was subsequently applied to the surface of steel structure material.

Design/methodology/approach

This study aimed to synthesize water-based alkyd resins using CSO. Therefore, the alkyd resin was introduced with TMA containing carboxyl groups and neutralized with triethylamine (TEA) to form a water-soluble salt. Then, the esterification kinetics of CSO water-based alkyd resin were investigated, and finally, the basic properties of CSO water-based alkyd resin coating were evaluated.

Findings

It was demonstrated that CSO water-based alkyd resin exhibited excellent water solubility and that the esterification kinetic of the synthesis reaction could be described by a second-order reaction. The coating properties of the material were investigated and found to have good basic properties, with 40% resin addition having the best corrosion resistance. Consequently, it could be effectively applied to the surface of steel structural materials.

Originality/value

This study not only met the requirement of environmentally friendly development but also expanded the application of CSO through the synthesis of CSO water-based alkyd resin via alcoholysis. Compared to fatty acid process, the alcoholysis reduced the need for fatty acid pre-extraction, simplifying the alkyd resin synthesis process. Thus, economic costs are effectively reduced.

Details

Pigment & Resin Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0369-9420

Keywords

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Article
Publication date: 15 June 2010

Yongxiu He, Weijun Tao, Aiying Dai, Lifang Yang, Rui Fang and Furong Li

The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

732

Abstract

Purpose

The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

Design/methodology/approach

A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.

Findings

The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.

Originality/value

The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

Details

Kybernetes, vol. 39 no. 5
Type: Research Article
ISSN: 0368-492X

Keywords

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