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Article
Publication date: 4 January 2019

Wenhao Wang, Rujing Shi, Wei Zhang, Haibin Sun, Xiaolu Ge and Chengfeng Li

The purpose of this paper is to improve the generation efficiency of singlet oxygen of methylene blue molecules through finely controlling their aggregation states in drug…

224

Abstract

Purpose

The purpose of this paper is to improve the generation efficiency of singlet oxygen of methylene blue molecules through finely controlling their aggregation states in drug carriers.

Design/methodology/approach

As a photosensitiser in photodynamic therapy, methylene blue (MB) was loaded on citrate-modified hydroxyapatite (HAp) through an electrostatic interaction and followed by encapsulation of coordination complexes of tannic acid (TA) and Fe(III) ions. Ultraviolet-visible absorption spectrum of the supernatant after incubation of samples was recorded at certain time interval to investigate the release behaviour of MB. Photodynamic activity of MB was determined by the oxidation reaction of uric acid by singlet oxygen generated by MB under illumination.

Findings

Almost all MB molecules were immediately released from HAp-MB, whilst an initial burst release of MB from HAp-MB@TA was followed by a sustainable and pH-sensitised release. In comparison with HAp-MB, photocatalystic reduction of HAp-MB@TA by titanium dioxide hardly occurred under illumination, indicating the stability against reduction to leukomethylene blue in vitro. Generation efficiency of singlet oxygen by MB released from HAp-MB@TA was significantly higher than that from HAp-MB because of the control of TA and Fe(III) ions complexes on molecular structures of released MB.

Originality/value

A facile method was herein demonstrated to optimise the generation efficiency of singlet oxygen by controlling aggregation states of PS molecules and improve PDT efficiency to damage tumour tissues.

Details

Pigment & Resin Technology, vol. 48 no. 2
Type: Research Article
ISSN: 0369-9420

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Article
Publication date: 13 July 2022

Guangrun Sheng, Xixiang Liu, Zixuan Wang, Wenhao Pu, Xiaoqiang Wu and Xiaoshuang Ma

This paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the…

132

Abstract

Purpose

This paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the system errors introduced by flexural deformation and installing which are difficult to calibrate.

Design/methodology/approach

Based on velocity and attitude matching, redesigning and deducing Kalman filter model by combining double-time observation. By introducing the sampling of the previous update cycle of the strapdown inertial navigation system (SINS), current observation subtracts previous observation are used as measurements for transfer alignment filter, system error in measurement introduced by deformation and installing can be effectively removed.

Findings

The results of simulations and turntable tests show that when there is a system error, the proposed method can improve alignment accuracy, shorten the alignment process and not require any active maneuvers or additional sensor equipment.

Originality/value

Calibrating those deformations and installing errors during transfer alignment need special maneuvers along different axes, which is difficult to fulfill for ships’ poor maneuverability. Without additional sensor equipment and active maneuvers, the system errors in attitude measurement can be eliminated by the proposed algorithms, meanwhile improving the accuracy of the shipboard SINS transfer alignment.

Details

Assembly Automation, vol. 42 no. 4
Type: Research Article
ISSN: 0144-5154

Keywords

Available. Open Access. Open Access
Article
Publication date: 28 June 2022

Wenhao Yu, Jun Li, Li-Ming Peng, Xiong Xiong, Kai Yang and Hong Wang

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered…

1876

Abstract

Purpose

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

Design/methodology/approach

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

Findings

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

Originality/value

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

Details

Journal of Intelligent and Connected Vehicles, vol. 5 no. 3
Type: Research Article
ISSN: 2399-9802

Keywords

Available. Open Access. Open Access
Article
Publication date: 11 April 2023

Wenhao Yi, Mingnian Wang, Jianjun Tong, Siguang Zhao, Jiawang Li, Dengbin Gui and Xiao Zhang

The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock…

332

Abstract

Purpose

The purpose of the study is to quickly identify significant heterogeneity of surrounding rock of tunnel face that generally occurs during the construction of large-section rock tunnels of high-speed railways.

Design/methodology/approach

Relying on the support vector machine (SVM)-based classification model, the nominal classification of blastholes and nominal zoning and classification terms were used to demonstrate the heterogeneity identification method for the surrounding rock of tunnel face, and the identification calculation was carried out for the five test tunnels. Then, the suggestions for local optimization of the support structures of large-section rock tunnels were put forward.

Findings

The results show that compared with the two classification models based on neural networks, the SVM-based classification model has a higher classification accuracy when the sample size is small, and the average accuracy can reach 87.9%. After the samples are replaced, the SVM-based classification model can still reach the same accuracy, whose generalization ability is stronger.

Originality/value

By applying the identification method described in this paper, the significant heterogeneity characteristics of the surrounding rock in the process of two times of blasting were identified, and the identification results are basically consistent with the actual situation of the tunnel face at the end of blasting, and can provide a basis for local optimization of support parameters.

Details

Railway Sciences, vol. 2 no. 1
Type: Research Article
ISSN: 2755-0907

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Article
Publication date: 15 April 2020

Xiaoliang Qian, Jing Li, Jianwei Zhang, Wenhao Zhang, Weichao Yue, Qing-E Wu, Huanlong Zhang, Yuanyuan Wu and Wei Wang

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which…

250

Abstract

Purpose

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

Design/methodology/approach

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Findings

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

Originality/value

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

Details

Sensor Review, vol. 40 no. 4
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 22 February 2024

Wenhao Zhou and Hailin Li

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough…

458

Abstract

Purpose

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Design/methodology/approach

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

Findings

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Originality/value

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

Details

European Journal of Innovation Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1460-1060

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Article
Publication date: 22 October 2024

Xuerong Peng, Lian Zhang, Seoki Lee, Wenhao Song and Keyan Shou

This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of…

119

Abstract

Purpose

This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of peer-reviewed articles in this field.

Design/methodology/approach

A bibliometric analysis was conducted using VOSviewer software on 2,698 peer-reviewed English-language articles retrieved from the Web of Science database, published between 1995 and 2023. Key contributors were identified based on publication volume, citation, and co-citation analysis. Co-occurrence analysis of index keywords and content analysis of influential articles were used to identify research themes.

Findings

The study identified four distinct research themes in hospitality innovation: (1) digital technology adoption primarily among customers, (2) innovation management within hospitality firms, focusing on knowledge management and eco-innovation, (3) service innovation primarily among employees, and (4) business model innovation involving multiple stakeholders. Additionally, the study determined key contributors, highlighted research gaps, and provided suggestions for future research directions.

Originality/value

This study contributes to the existing literature by providing a systematic and in-depth review of hospitality innovation research. It identifies key contributors, research themes, and potential gaps for future research, offering valuable insights for both industry practitioners and scholars.

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Article
Publication date: 12 November 2024

Yanqiu Xia, Wenhao Chen, Yi Zhang, Kuo Yang and Hongtao Yang

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel…

11

Abstract

Purpose

The purpose of this study is to investigate the effectiveness of a composite lubrication system combining polytetrafluoroethylene (PTFE) film and oil lubrication in steel–steel friction pairs.

Design/methodology/approach

A PTFE layer was sintered on the surface of a steel disk, and a lubricant with additives was applied to the surface of the steel disk. A friction and wear tester was used to evaluate the tribological properties and insulation capacity. Fourier transform infrared spectrometer was used to analyze the changes in the composition of the lubricant, and X-ray photoelectron spectroscopy was used to analyze the chemical composition of the worn surface.

Findings

It was found that incorporating the PTFE film with PSAIL 2280 significantly enhanced both the friction reduction and insulation capabilities at the electrical contact interface during sliding. The system consistently achieved ultra-low friction coefficients (COF < 0.01) under loads of 2–4 N and elucidated the underlying lubrication mechanisms.

Originality/value

This work not only confirm the potential of PTFE films in insulating electrical contact lubrication but also offer a viable approach for maintaining efficient and stable low-friction wear conditions.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0222/

Details

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

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Article
Publication date: 18 July 2023

Wenhao Zhou, Hailin Li, Liping Zhang, Huimin Tian and Meng Fu

The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.

182

Abstract

Purpose

The purpose of this work is to construct a grey entropy comprehensive evaluation model to measure the regional green innovation vitality (GIV) of 31 provinces in China.

Design/methodology/approach

The traditional grey relational proximity and grey relational similarity degree are integrated into the novel comprehensive grey evaluation framework. The evaluation system of regional green innovation vitality is constructed from three dimensions: economic development vitality, innovative transformation power and environmental protection efficacy. The weights of each indicator are obtained by the entropy weight method. The GIV of 31 provinces in China is measured based on provincial panel data from 2016 to 2020. The ward clustering and K-nearest-neighbor (KNN) algorithms are utilized to explore the regional green innovation discrepancies and promotion paths.

Findings

The novel grey evaluation method exhibits stronger ability to capture intrinsic patterns compared with two separate traditional grey relational models. Green innovation vitality shows obvious regional discrepancies. The Matthew effect of China's regional GIV is obvious, showing a basic trend of strong in the eastern but weak in the western areas. The comprehensive innovation vitality of economically developed provinces exhibits steady increasing trend year by year, while the innovation vitality of less developed regions shows an overall steady state of no fluctuation.

Practical implications

The grey entropy comprehensive relational model in this study is applied for the measurement and evaluation of regional GIV, which improves the one-sidedness of traditional grey relational analysis on the proximity or similarity among sequences. In addition, a three-dimensional evaluation system of regional GIV is constructed, which provides the practical guidance for the research of regional development strategic planning as well as promotion paths.

Originality/value

A comprehensive grey entropy relational model based on traditional grey incidence analysis (GIA) in terms of proximity and similarity is proposed. The three-dimensional evaluation system of China's regional GIV is constructed, which provides a new research perspective for regional innovation evaluation and expands the application scope of grey system theory.

Details

Grey Systems: Theory and Application, vol. 13 no. 4
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 5 January 2024

Wenhao Zhou, Hailin Li, Hufeng Li, Liping Zhang and Weibin Lin

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to…

97

Abstract

Purpose

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

Design/methodology/approach

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

Findings

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Originality/value

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

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