Xinhui He, Kun Huang, Guihao Ran, Xiaobiao Mao, Qin Hu, Zhennan Lin, Shuangquan Ran and Tao Hu
This study aims to improve the sensitivity of magnetic detection. In this article, a multi-frequency modulation technique is used to increase the magnetic detection sensitivity of…
Abstract
Purpose
This study aims to improve the sensitivity of magnetic detection. In this article, a multi-frequency modulation technique is used to increase the magnetic detection sensitivity of diamond nitrogen vacancy (NV) centers sensors.
Design/methodology/approach
In the field of magnetic detection, NV centers have corresponding advantages due to their unique long coherence property at room temperature. The important indicators for NV centers magnetometers are the magnetic detection sensitivity of the NV centers and the integration of the magnetometer. To solve this problem, the authors propose a multi-frequency modulated magnetic detection technique, using an integrated probe as well as a lock-in amplifier for the double enhancement of sensitivity as well as integration.
Findings
The following results can be obtained by processing and calculating the experimental data with an integrated lock-in amplifier circuit with an area of 27.50 cm2 and a probe volume of 3.12 cm3. The multi-frequency modulation technique was used to increase the magnetic detection sensitivity of the NV centers from 8.59 nT/Hz1 / 2–2.42 nT/Hz1 / 2.
Research limitations/implications
The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing.
Originality/value
The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing. This technique provides new research solution for the subsequent improvement of the magnetic detection sensitivity.
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This paper aims to investigate and analyze the air-gap field modulation (AGFM) effect on torque contribution in a 6-slot/4-pole high-speed permanent magnet (HSPM) machine. To…
Abstract
Purpose
This paper aims to investigate and analyze the air-gap field modulation (AGFM) effect on torque contribution in a 6-slot/4-pole high-speed permanent magnet (HSPM) machine. To further illustrate the torque generation mechanism, the torque contribution is quantified using the Maxwell stress tensor (MST) method.
Design/methodology/approach
First, a simplified permanent magnet (PM) magnetomotive force model is established to identify the effective main-order working field harmonics. Then, the MST method is used to determine the average torque contributions of the effective main-order working field harmonics. Finally, the influences of various stator and rotor parameters on the AGFM effect are analyzed and optimized to enhance the torque density.
Findings
Apart from the fundamental harmonics, the AGFM harmonics contribute non-negligible average torque on the HSPM machine, and the optimized machine has higher torque density. Finally, a prototype of the 10 kW HSPM machine is manufactured and experimented with to validate the effectiveness of the theoretical analysis.
Originality/value
In this paper, the torque generation mechanism of the HSPM machine is investigated and analyzed. Meanwhile, the AGFM effect of the HSPM machine with different design parameters is analyzed and optimized to enhance the torque density.
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Hong Xue, Sujie Zhang, Zezhou Wu and Lin Zhang
Despite smart construction technology's great potential to improve the productivity of the architectural, engineering and construction (AEC) industry, the implementation of smart…
Abstract
Purpose
Despite smart construction technology's great potential to improve the productivity of the architectural, engineering and construction (AEC) industry, the implementation of smart construction technology has failed to achieve the expected benefits due to the negative usage behaviors of construction enterprise employees. This study aims to identify the determinants and their configuration effects on the smart construction technology usage behavior (SCTUB) based on the Technology-Organization-Environment (TOE) framework. This study then verifies the practical paths to improve the employee's SCTUB from the configuration perspective.
Design/methodology/approach
A mixed-method approach involving survey and qualitative comparative analysis (QCA) is conducted in this study. Based on the detailed literature review and semi-structured interview, this study identifies the factors and proposes the TOE framework to determine the configuration conditions affecting employee's SCTUB and verify practical paths to promote this user behavior.
Findings
The TOE framework's technical, organizational and environmental elements are interdependent. The emergence of a high SCTUB is not determined by a single determinant but by configuration conditions. Four equifinal conditions (e.g. organization-technology type, technology-organization type, environment type and organization-technology balanced type) are verified to promote construction enterprise employee's SCTUB.
Practical implications
The four verified configuration conditions could guide construction enterprises to formulate complementary strategies for promoting the construction enterprises' employees to implement smart construction technology and achieve the enterprise's digital transformation.
Originality/value
The inter-dependence of the three-dimension factors, namely technical, organizational and environmental elements are explored to enrich the literature on the TOE framework. Meanwhile, the configuration effects of these factors on usage behavior are identified, expanding the literature on the information technology acceptance model.
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Munish Gupta, Vikas Sharma and Kshitiz Jangir
Purpose: This study investigates the influence of inflation and oil price fluctuations on stock prices and returns. A thorough bibliometric analysis of research articles published…
Abstract
Purpose: This study investigates the influence of inflation and oil price fluctuations on stock prices and returns. A thorough bibliometric analysis of research articles published in the last 22 years has been conducted to achieve this objective.
Need for the Study: Inflation and oil prices are critical macroeconomic variables that significantly impact the economy, including the stock market. It is crucial for investors and policymakers to understand the complex relationship between these economic variables and the stock market.
Methodology: The study employed a bibliometric analysis to identify the most influential authors, institutions, and research themes related to the impact of inflation and oil prices on stock prices.
Findings: The study identified that the fluctuations of the stock market are significantly influenced by both inflation and oil prices. This relationship has been extensively studied in the literature, with several key research themes identified. These themes include the effects of inflation and oil prices on various sectors and industries, the role played by monetary and fiscal policy, and the impact of international trade and global economic conditions.
Practical Implications: The insights that have emerged have significant implications for investors and policymakers. It highlights the need for risk management strategies and mitigation of macroeconomic factors’ impact on stock prices.
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Shuang Tian, Lin Wu and Kulwant S. Pawar
Characterised by simultaneous food waste and shortages, our current food system is far from sustainable. Industry 4.0 has responded with technology-enabled innovations, including…
Abstract
Purpose
Characterised by simultaneous food waste and shortages, our current food system is far from sustainable. Industry 4.0 has responded with technology-enabled innovations, including digital food-sharing platforms aimed at facilitating the efficient redistribution of surplus food. However, potential users often express reluctance to adopt such platforms, prompting this study to explore the underlying reasons for their hesitations.
Design/methodology/approach
This study was conducted in China, the world’s largest platform economy, where food-sharing platforms are notably absent. Using a vignette-based qualitative approach, semi-structured interviews were conducted with 35 potential users. The data were analysed through thematic analysis to uncover insights into adoption intentions.
Findings
The findings highlight the relevance of factors identified in existing technology acceptance theories, such as performance expectancy, effort expectancy, social influence, hedonic motivation, facilitating conditions and price value, in shaping adoption intentions. Additionally, content-specific and context-specific factors – such as trust in other users and the platform, concerns about “losing face” (mianzi) and safety concerns during the pandemic – emerged as critical influences on users' decisions to engage with these platforms.
Originality/value
This study contributes to scholarly discussions on enhancing the effectiveness of new technological innovations for food supply chain sustainability. The theoretical contributions expand the technology acceptance literature by incorporating factors related to platform service content and operating context.
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The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across…
Abstract
Purpose
The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.
Design/methodology/approach
This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.
Findings
(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.
Practical implications
This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.
Originality/value
This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.
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Ruibing Lin, Xiaoyu Lü, Pinghua Xu, Sumin Ge and Huazhou He
To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young…
Abstract
Purpose
To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.
Design/methodology/approach
Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.
Findings
Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.
Originality/value
This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.
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This study aims to explore the impact mechanism of social support on individual health knowledge creation among users in online patient communities, guide and promote the creation…
Abstract
Purpose
This study aims to explore the impact mechanism of social support on individual health knowledge creation among users in online patient communities, guide and promote the creation of health knowledge and provide insights into managing online patient communities.
Design/methodology/approach
A theoretical model was constructed by integrating social impact and social support theories. Data were collected through questionnaires, and 750 valid responses were analysed using a structural equation model.
Findings
This study found the following. (1) Social support influences individual health knowledge creation through the mediating effects of creative self-efficacy and positive emotions. (2) The general rule of the strength of the influencing factors on individual health knowledge creation is that creative self-efficacy > positive emotions. (3) The general pattern of the mediating effect of attitude factors between social support and health knowledge creation is that creative self-efficacy > positive emotions. (4) The key path for social support to influence individual health knowledge creation is “social support → creative self-efficacy → health knowledge creation”. (5) The dimensions of social support in online patient communities can be divided into information, emotional, respect and network support. Individual health knowledge creation can be divided into health knowledge externalisation, combination, socialisation and internalisation.
Originality/value
This study expands the application scope of social influence theory and opens up the “black box” of the impact of social support on individual health knowledge creation behaviour. Simultaneously, the dimensions of social support, individual health knowledge creation and the mediating role between social support and health knowledge creation are discussed.
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Yaqi Zhao, Shengyue Hao, Zhen Chen, Xia Zhou, Lin Zhang and Zhaoyang Guo
Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper…
Abstract
Purpose
Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.
Design/methodology/approach
First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.
Findings
External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.
Originality/value
This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.
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Jingru Lian, Xiaobing Fan, Bin Xu, Shan Li, Zhiqing Tian, Mengdan Wang, Bingli Pan and Hongyu Liu
This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.
Abstract
Purpose
This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.
Design/methodology/approach
PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.
Findings
It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.
Originality/value
This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/