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When the bearing operates under static load, the lubrication state of the bearing can be well distinguished by the absolute value of the thermoelectric potential. However, when the bearing operates under dynamic loads, the thermoelectric potential is in an alternating state, fluctuating within a high range, making it difficult to straightforwardly characterize the lubrication state through thermoelectric potential. The purpose of this paper is to find the relationship between the thermoelectric potential and the lubrication state under dynamic load.

A thermoelectric simulation is executed using COMSOL Multiphysics to calculate the relationship between thermoelectric potential, load, oil film thickness, oil film pressure and contact pressure. Based on the test rig experimental data, the characteristics of thermoelectric potential in journal bearings under different dynamic loads and operation conditions are investigated and analyzed using feature extraction, clustering and indicator construction methods.

The results showed that the low potential percentage and the percentage of bad operation state cluster can effectively characterize the lubrication state of journal bearings under dynamic load conditions. Specifically, in this experiment, when the low potential percentage reaches 15% and the bad operation state cluster dominates most of the time, it indicates that the bearing is in a poor lubrication state. The results of scanning electron microscopy and energy-dispersive X-ray spectroscopy of bearing specimens under different operation states also validated the above analytical results.

The relationships elucidating the thermoelectric and tribological interactions under dynamic load remain unclear and there is still no efficient method to estimate the lubrication state using thermoelectric potential in previous studies. This paper used simulation and experimental analysis methods to find the relationship between the thermoelectric potential and the lubrication state under dynamic load.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0410/

This study aims to analyze the status and application/use of human–computer interaction (HCI) in libraries by conducting a systematic literature review (SLR).

A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach was used to search Scopus, Web of Science and Google Scholar databases. The search criteria included research studies published in English language between 2010 and 2021, which were 4,167 citations. Out of 4,167 citations, a total of 50 studies were selected for the final analysis.

The results showed a positive attitude of librarians toward HCI applications in libraries worldwide. The results depict that one-third (30%) of the studies were conducted in the USA, followed by four (8%) studies in China. Out of 50 studies, a portion of 15 (30%) studies were based on digital libraries, followed by seven (14%) studies on academic libraries and five (10%) studies on libraries and their websites. HCI was used for searching and retrieving information, users’ interaction, authentication, online help/support, feedback, library web access, web OPAC, virtual access to resources, indigenous repository and virtual services. The most productive year was 2015, and journal of The Electronic Library had more articles on HCI than other journals.

The findings of this study could assist policymakers and library authorities in reconciling the HCI application in libraries for providing effective and efficient access and services to end-users.

This study is unique as no comprehensive study has been conducted on the use of HCI in librarianship using the SLR method.

The study aims to investigate the effects of bodily feelings on preference for robotic service by examining direct and indirect sensations from physical and metaphorically projected bodily feelings.

Through four empirical experiments involving video and recall tasks to metaphorically manipulate participants’ bodily warmth and directly manipulate ambient temperature, the authors explored the mediating role of the need for warmth and the moderating role of robotic features (warmth vs competence) on consumer willingness to engage with and pay for robotic services.

Warmth perception exhibits a positive correlation with robotic services. This relationship is mediated by the need for warmth. Moreover, when customers experience a sensation of physical warmth, they show a greater willingness to pay for a robotic service exhibiting competence versus warmth.

This research contributes to the literature by integrating the feelings-as-information theory and the mind perception view to understand the judgment of robotic services. It extends the application of the embodied cognition theory, highlighting the significance of bodily feelings as a source of information in customer decision-making processes. Furthermore, this research explores the metaphoric influence of service features on bodily responses, providing new insights into the role of embodiment and mental perception in robotic service evaluations.

Managers should consider using different robots based on seasonal settings to meet customers’ need for warmth. Understanding customers’ bodily feelings and the metaphoric influence of service features contributes to the design of more effective and customer-centric robotic services.

This inquiry explores the metaphoric influence of service features on bodily responses, providing new insights into the role of embodiment and mental perception in robotic service evaluations.

With the use of increased number of measures and strategies towards mitigating operational carbon emissions, a greater emphasis has now been placed on reducing the resultant embodied carbon (EC). However, the assessment practice seems cumbersome due to variation in data and methodologies. To this end, this study aims to develop a basis that would facilitate early-stage EC assessment for a proposed building.

This study primarily involved a quantitative analysis of 50 Bill of Quantities (BOQs) of two-story house projects. Additional information such as materials, vehicle and plant and equipment used in construction was obtained from technical specifications, industry practiced norms and databases. The EC emission was calculated using basic statistics.

The total EC emission in the construction of a two-storey residential building is equivalent to 0.0607 tCO2e per square feet of Gross Internal Floor Area (GIFA). Concrete is the highest contributor in the material production with 36% of emission in the production stage that is responsible for 94% of total EC. The excavation and earthwork is the highest EC emitter during the material transportation stage (93% of total EC emission in transportation stage). During the construction stage, reinforcement shows the highest emission of 85% of total EC emission in construction. The study concludes that the distribution of carbon emission among elements contributes efficient resource allocation towards achieving sustainability in buildings.

This study provides a basis to forecast the EC emitted during cradle-to-end-of-construction stage of a proposed building. From the implication perspective, it is expected that the basis which the study provides would enable to determine the appropriate carbon tax to account the potential client for his contribution to GHGs.

This study aims to understand the relationship between social audit (SA) and sustainable corporate social responsibility (SCSR). Furthermore, this study will investigate the mediating effect of corporate social innovation (CSI) on the relationship between SA and SCSR and the moderating impact of community participation (CP) on the relationship between CSI and SCSR.

This research was conducted in Odisha, a state in India, with 448 respondents and a response rate of 89%. The convenience sampling technique was used, and questionnaires were prepared in English and translated into regional languages during data collection. Psychological separation and Harman’s single-factor test were used to avoid common method bias. Exploratory factor analysis and confirmatory factor analysis were performed step by step to validate the proposed model.

The current study indicates that SA positively impacts SCSR. Findings from the analysis show that CSI partially mediates the relationship between SA and SCSR. Furthermore, this study indicates that CP moderates the relationship between CSI and SCSR.

This study is one of its kind concerning the variables considered, and it can work as a stepping stone for upgrading the delivery mechanism for corporate social responsibility (CSR) programs, which can solve the fundamental problems faced in society more effectively. It will provide new paths for future researchers to study in the area of audit and CSR.

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

The purpose of this paper is to explore how emerging market firms (EMFs), in the face of intense international competition within global value chains (GVCs), continuously accumulate the knowledge and capabilities needed to support leapfrog upgrading. Based on this, this paper aims to construct a process model of the dynamic evolution of EMFs’ control over GVC core activities.

This paper conducts a longitudinal case study of Ninestar Corporation, a leading company in the Chinese printer industry, to investigate its leapfrog upgrading practices. From the perspective of dynamic capabilities theory, the study explores the dynamic processes and intrinsic mechanisms through which EMFs achieve the evolution of their control over GVC core activities.

It is revealed that the dual-dimensional expansion of controlling the core activities of GVC from vertical functional architecture to horizontal product architecture is the pathway for manufacturing enterprises in emerging economies to achieve leapfrogging upgrades. It is also found that the dynamic capabilities of EMFs undergo iterative evolution during the upgrading process. The self-reinforcing logic and the path-creation logic of dynamic capabilities are the internal mechanisms for controlling GVC core activities to achieve the breakthrough in both the vertical functional division chain and the horizontal product division chain.

This paper provides an insightful case for how EMFs can achieve leapfrog upgrading in the new normal of global trade patterns. It explores how these firms can gain control over GVC core activities through the evolution of dynamic capabilities. The research findings extend the boundaries of the theory of firm upgrading.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

A 3D finite element (FE) model based on the double ellipsoidal heat source was developed to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process. This paper aims to investigate the evolution of temperature and stress fields during the multilayer and multi-pass wire and arc additive manufacturing (WAAM) process by developing a 3D finite element (FE) model based on the double ellipsoidal heat source.

Experimental thermal cycle curves and residual stresses were obtained by thermocouples and X-ray diffraction, respectively. The validity of the model was verified by the corresponding experimental results.

The deposition process of the upper pass led to the partial remelting of the lower deposited pass. The thermal process of the current-deposited pass alleviated the stress concentration in the previous-formed passes. A more uniform temperature distribution could be obtained by using the reciprocating deposition path. Compared to the reciprocating deposition path, the peak values of the transverse and longitudinal tensile residual stresses of the deposited sample under the unidirectional deposition path were reduced by 15 MPa and increased by 13 MPa, respectively. The heat conduction in the deposited passes could be improved by extending the inter-pass cooling time appropriately. With an increase in the inter-pass cooling time, the longitudinal residual stress in the middle region of sample along longitudinal and transverse directions showed increase and decrease–increase trends, respectively, while the transverse residual stress exhibited decrease trend.

This study enhances the understanding of temperature and stress fields evolution during the multilayer and multi-pass cold metal transfer-WAAM processes of magnesium alloy and provides the reference for parameter optimization.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.