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Salt rock from salt lakes can serve as a cost-effective material for subgrade filling, as demonstrated in projects like the Qarhan Salt Lake section of the Qinghai-Tibet Railway and the Qarhan Salt Lake section of the G215 Highway. This state-of-the-art paper aims to summarize the engineering properties of salt rock filling and present the advances of its utilization.

This paper collects and analyzes laboratory and field data of salt rock filling from previous studies to present a comprehensive analysis of the engineering properties and utilization of salt rock fillings.

Salt rock primarily contains minerals such as halite and glauberite, which contribute to its unique phase-changing behavior under varying environmental conditions, impacting its mechanical properties. Salt rock filling shrinks when in contact with vapor or unsaturated brine and expands under cooling or evaporation. Its use is particularly recommended for arid regions, with specific restrictions depending on the structure type. This paper discusses suggested countermeasures to mitigate these issues, as well as key quality acceptance indices for salt rock filling compaction. Moisture content after air-drying is recommended as a crucial parameter for construction quality control.

This review aims to support future research and engineering practices in salt rock subgrade applications.

Financial misappropriation is a significant challenge to China’s innovation-driven growth model. This paper investigates the impact of regional-level financial misappropriation on innovation efficiency across 30 provinces and administrative municipalities in China.

The paper uses the Data Envelopment Analysis method to estimate the innovation efficiency at regional level, then, employs panel Tobit and indirect-transmission-channel models to analyze the direct and indirect impact of financial misappropriation on regional innovation efficiency in China.

The findings of the paper suggest that financial misappropriation significantly reduces regional innovation efficiency in China both directly and indirectly. Financial misappropriation hinders the transformation of scientific and technological achievements and, at the same time, it retards high-tech industrial development.

The research adopted the non-parametric approach over the parametric approach due to limitations of data availability. Both approaches have their own criticisms. However, the focus in this generates the efficiency scores that could be used for the analysis principal question of this research.

The results show if the innovation efficiency issues are not addressed at regional levels the national efficiency objecting may achieve suboptimal results.

The benefits of innovation may not flow on to regional economies creating social disparity.

This paper is the first of its nature empirically testing the direct and indirect effects of financial misappropriation on regional innovation efficiency in China by using regional financial corruption data of 30 Chinese provinces and administrative cities.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Drawing on the technology affordance and affinity theories, this study proposes a framework explaining the antecedents and consequences of customers’ smart experiences (CSEs) in the artificial intelligence (AI) chatbot context.

The quantitative approach employing an online survey was adopted to obtain data from chatbot users (N = 761) and analyzed using structural equation modeling.

Results from a survey study show that chatbot affordances, including interactivity (two-way communication, active control and synchronicity), selectivity (customization and localization), information (argument quality and source credibility), association (connectivity and sense of safety) and navigation positively affect CSEs (hedonic and cognitive), leading to customer chatbot stickiness through affinity.

Our study provides evidence that supports and extends the affordances and affinity lens by highlighting the roles of specific chatbot affordances that contribute to a positive-smart experience and subsequently enhances customer chatbot stickiness through affinity.

Fall accidents can cause casualties and economic losses in the construction industry. Fall portents, such as loss of balance (LOB) and sudden sways, can result in fatal, nonfatal or attempted fall accidents. All of them are worthy of studying to take measures to prevent future accidents. Detecting fall portents can proactively and comprehensively help managers assess the risk to workers as well as in the construction environment and further prevent fall accidents.

This study focused on the postures of workers and aimed to directly detect fall portents using a computer vision (CV)-based noncontact approach. Firstly, a joint coordinate matrix generated from a three-dimensional pose estimation model is employed, and then the matrix is preprocessed by principal component analysis, K-means and pre-experiments. Finally, a modified fusion K-nearest neighbor-based machine learning model is built to fuse information from the x, y and z axes and output the worker's pose status into three stages.

The proposed model can output the worker's pose status into three stages (steady–unsteady–fallen) and provide corresponding confidence probabilities for each category. Experiments conducted to evaluate the approach show that the model accuracy reaches 85.02% with threshold-based postprocessing. The proposed fall-portent detection approach can extract the fall risk of workers in the both pre- and post-event phases based on noncontact approach.

First, three-dimensional (3D) pose estimation needs sufficient information, which means it may not perform well when applied in complicated environments or when the shooting distance is extremely large. Second, solely focusing on fall-related factors may not be comprehensive enough. Future studies can incorporate the results of this research as an indicator into the risk assessment system to achieve a more comprehensive and accurate evaluation of worker and site risk.

The proposed machine learning model determines whether the worker is in a status of steady, unsteady or fallen using a CV-based approach. From the perspective of construction management, when detecting fall-related actions on construction sites, the noncontact approach based on CV has irreplaceable advantages of no interruption to workers and low cost. It can make use of the surveillance cameras on construction sites to recognize both preceding events and happened accidents. The detection of fall portents can help worker risk assessment and safety management.

Existing studies using sensor-based approaches are high-cost and invasive for construction workers, and others using CV-based approaches either oversimplify by binary classification of the non-entire fall process or indirectly achieve fall-portent detection. Instead, this study aims to detect fall portents directly by worker's posture and divide the entire fall process into three stages using a CV-based noncontact approach. It can help managers carry out more comprehensive risk assessment and develop preventive measures.

In this paper, we explore the role of education in household financial technology (FinTech) adoption.

Using representative nationwide household data from the 2017 China Household Finance Survey, we employ the change in China’s compulsory schooling law in the 1980s as an instrumental variable for educational attainment.

We find that among Chinese households, education has statistically significant and economically important effects on the use of various FinTech services, including digital banking, mobile payment, digital wealth management and digital consumer credit. Further analysis indicates that exogeneous increases in education lead to higher levels of financial literacy and social trust, both of which are potential drivers of FinTech adoption. Our findings provide new insights into the importance of education for household financial decision-making and technology adoption.

The contribution of our study is mainly twofold. First, we provide evidence on the role of education in household financial decision making. Second, this study adds to the literature on household adoption of technological innovation in finance. Our findings are also policy-relevant.

This paper aims to explore the relationship between digital transformation, international competition and productivity progress by examining the dual margins of export, thereby identifying the digital development pathway for key industrial chains within the context of regional medical and health cooperation.

By constructing models incorporating both the extensive margin and intensive margin, this paper delves into the reasons underlying changes in product trade value and variety within international market competition. Utilizing export data for medical products from China to Belt and Road Initiative partner countries spanning from 2007 to 2020, an industry–destination–time panel benchmark model is established to assess the impact of digital transformation on product expansion and international competition.

The findings reveal the following insights. First, digital transformation positively impacts the intensive margin of medical products, whereas it does not exert a positive influence on the extensive margin. Second, the impact mechanism test indicates that digital transformation fosters the intensive export margin by enhancing the technical efficiency of exports but exhibits an inverted U-shaped effect on technological progress. Third, digital transformation demonstrates a notable non-linear characteristic, with a substantial increase in the lifting effect once a certain threshold is surpassed.

While previous research has extensively explored the effects of digital transformation on export trade, this study uniquely integrates the concepts of the intensive and extensive margins of export, thereby enriching the research insights derived from existing literature. It focuses on the nonlinear effects and threshold dynamics of digital transformation, particularly in relation to total factor productivity. Additionally, the paper makes a contribution to the understanding of the Health Silk Road by incorporating the health index.

This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.

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.

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.

This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/

The purpose of this paper is to investigate the role of Customer Participation (CP) in the effectiveness of New Service Development (NSD) by examining the moderating roles of Customer Empowerment (CE) and Customer Satisfaction (CS). The research reduces the risk of failure of the NSD process and/or improves the NSD processes used by companies through the consideration of the results in the practical dimension.

This study investigates the effects of CP at different stages of NSD using a quantitative approach. Data were collected through an online survey questionnaire. Smart PLS was used to analyse the data collected from 509 newsreaders and users of the news agency’s application.

The model confirmed that CE has an impact on the effectiveness of NSD in the idea generation and commercialization stages, but not in the development stage. Empowerment and customer satisfaction did not influence the three stages of NSD indirectly but directly. The results show that CP, CS and CE do not always have a direct or indirect effect on the development of new services. Therefore, in order to design new service development projects, media news companies need to determine the level of user cooperation.

The lack of objective data, especially on company performance, forces researchers to use questionnaires to analyse NSD effectiveness. Another limitation is that newspaper users answered the questionnaires, which creates “common method variance.”

Researchers on NSD effectiveness must use questionnaires due to a lack of objective data, especially on company performance. Another limitation is “common method variance” from newspaper users answering questionnaires.

This paper is a response to a perceived need for an examination of how new service development can be successful and effective.

This paper aims to develop a methodology for the spatiotemporal analysis of urban household waste data and a geographic information system (GIS)-based dashboard for interactive outcomes that identifies emerging trends and spatial distribution.

The study visualized the emerging hotspot analysis of household waste data covering the waste in selected areas from 2014 to 2019 in New South Wales, Australia. Through analyses in ArcGIS Pro, multiple maps and diagrams can be created to display these results in ArcGIS Insights. To enable the spatial waste analysis outcomes accessible, a GIS-based dashboard including maps and charts, spatiotemporal visualization of household waste tonnage, and emerging hotspots was created.

Based on the development of the dashboard in the ArcGIS Suites, there is an accessible data pipeline from ArcGIS Pro to Insights. The cloud-mapping system in ArcGIS online serves as a foundation for temporary data storage. The results also show the emerging hotspots of recyclable, residual and organic (RRO) waste in the Greater Sydney Region, Wollongong, Newcastle and Tweed. This study found an emerging cold spot in Wagga Wagga.

A dashboard for monitoring waste streams can be developed to enable GIS specialists to use historical spatiotemporal datasets in ArcGIS suites easily. Policymakers, strategy developers, urban waste managers and organizations dealing with urban waste can utilize this analytical dashboard to identify the issues, patterns and trends concerning urban waste for better decision-making in allocating required resources to overcome the identified issues to make informed decisions and develop strategies to alleviate the trends and patterns of ongoing problems. Indeed, the GIS-based dashboard developed in this research provides deep analysis and insights from the spatial waste data, allowing them to understand the included insights at a glance quickly.

Deriving location information for urban household waste data is crucial for waste management since it offers a better understanding of urban household waste data patterns, issues and historical trends. Small-scale studies have examined spatial waste patterns, but the investigation of urban household waste focusing on RRO waste is limited. Moreover, there is a lack of GIS-based dashboard development to enable spatiotemporal waste analysis outcomes to be publicly accessible.