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This study aims to enhance singularity detection in non-stationary signals by introducing the frugal wavelet transform (FrugWT), a novel variation of the wavelet transform.

The frugal wavelet transform, based on a modified first-level discrete wavelet transform decomposition, is compared with traditional discrete wavelet transform. The performance of these transforms is evaluated using signals derived from finite element analysis of a functionally graded tapered beam made of porous material.

The frugal wavelet transform significantly outperforms the discrete wavelet transform in detecting singularities within the analyzed signals. It offers more accurate detection of singularities and local abrupt changes, demonstrating its effectiveness for signal analysis.

This paper contributes to the field by proposing the relative frugal wavelet transform as a novel enhancement of the frugal wavelet transform. It provides a significant improvement in detecting subtle singularities in one-dimensional signals, with potential applications in advanced signal processing and analysis across various scientific domains such as electrical engineering, automotive, aerospace engineering, civil engineering, marine engineering and medical signal processing.

This study explores the impact of metaverse knowledge on freelancer engagement and performance within the gig economy, drawing upon the theoretical framework of social cognitive theory. The authors investigate the mediating role of freelancer engagement in the relationship between metaverse knowledge and performance, further examining the moderating influence of freelancer experience on these relationships.

Using a convenient sampling technique, data was collected through questionnaire from 301 freelancers working on various virtual platforms in Pakistan using a five-point Likert scale. Smart PLS 4.0 was used to analyze the data.

The findings reveal positive direct effect of metaverse knowledge on both freelancer engagement and performance. In addition, freelancer engagement significantly mediates the relationship between metaverse knowledge and performance. Furthermore, the findings affirm that the freelancers experience serves as a moderating factor in the relationship between metaverse knowledge, engagement and performance by indicating positive impact.

This study contributes a novel perspective to the gig economy literature by elucidating the underlying mechanisms through which metaverse knowledge drives freelancer performance via engagement. By examining the unique role of the metaverse in the gig context, the study offers valuable theoretical and practical implications for both scholars and practitioners seeking to understand and enhance freelancer engagement and performance in this evolving digital landscape.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

This paper divides the dyadic supply chain into three power structures according to the relative channel power of the supply chain members and consequently examines the optimal supply chain pricing decisions when both suppliers and retailers are concerned with fairness issues.

Three models are constructed, namely the Stackelberg game model with the supplier as the leader, the Nash game model with the balance of power and another Stackelberg game model with the retailer as the leader. The equilibrium solutions are solved, and their results are analyzed.

The retail price of a product increases with an increase in the fairness concerns of the leader in a supply chain in which the supplier or retailer is the leader, while the fairness concerns of the member with less channel power have no effect on the retail price. In a power-balanced supply chain, both suppliers and retailers increase their retail prices as their fairness concerns increase. The relative size of the members’ fairness concerns affects member profits and total supply chain profits.

The main contributions are as follows: First, this paper proposes a new approach to studying supply chain pricing strategy, considering fairness concerns and power structure. Secondly, three game models are constructed. The Nash equilibrium solution is introduced to study the fairness of supply chain participants in pricing decisions and overall supply chain profitability. Finally, the supply chain management theory is expanded by this study on pricing decisions and supply chain performance.

This study aims to report the development and experimental evaluation of two kinds of PANI@semiconductor based photocathodic anti-corrosion coating, for application on stainless steel substrates.

PANI was in situ chemical polymerized on TiO₂ and BiVO₄ particles, and FT-IR and SEM/EDS were used to understand the characteristics and elemental distribution of the composite particles. Composite coatings, which consisted of epoxy, PANI@TiO₂ or PANI@BiVO₄ and graphene, were prepared on the 304L stainless steel. Photoelectrochemical response measurement, electrochemical tests and immersion tests were used to assess the anti-corrosion performance of the prepared coatings in 45°C 3.5 wt.% NaCl solution. And the corrosion protection mechanism was further explained by combining with surface observation.

The photoelectrochemical response tests revealed the good photocathodic effect of the coatings, and the reversible oxidation-reduction properties of PANI (pseudocapacitive effect) leading to the repeated usage of the coatings. Consequently, the anti-corrosion mechanism of the composite coating is attributed to the physical barrier effect of the coating, the anodic protection effect of PANI and the photocathodic and energy store effect.

These kind coatings could prevent corrosion from day to night for stainless steel, which has great engineering application prospects on stainless steel corrosion protection.

The failure rate of international construction joint venture (ICJV) projects has been noted to be high in developing countries due to the complexity and risky nature of construction projects in the international market. The purpose of this study is to identify and evaluate the risks facing ICJV projects in Ghana.

A risk register was developed through a comprehensive literature review. The identified risks were then used in a questionnaire survey involving local and foreign partners in ICJV projects in Ghana.

From a total of 74 risks identified, categorized into country-level risks, market-level risks and project-level risks, the “top ten” risks found to be the most critical risks facing ICJV projects in Ghana include unstable currency exchange rates, inflation, design changes, high-interest rate, budget overrun, cash flow problems of the client, economy fluctuation, difficulty in obtaining approval of projects from host government authorities/bureaucracy, potential financial distress of JV partner and bribery and corruption.

This study provides a comprehensive list of risks ICJV partners are likely to encounter on their projects in developing countries. Furthermore, this study improves on one of the major limitations of previous ICJV studies by collecting data from both partners of the ICJV, appropriate for cross-cultural examination and comparison.

The study aims to calculate the recall ratio of selected MSEs and provide a comprehensive ranking for MSEs using features, precision and recall analysis.

The study was divided into three consecutive sections: Keyword selections and checking demographic searchability; recall calculation among the MSEs and third calculating the Equal Weighted Score by allotting equal weight (0.25) to all MSEs to rank the MSEs based on the re-ranking aggregation approach.

The study clearly shows all the four MSEs considered—Dogpile, Metacrawler, DuckDuckGo and Startpage—Metacrawler (71%) ranked highest for recall, followed by DuckDuckGo (68%), Dogpile (63%) and Startpage (60%). The re-ranking aggregation approach results show DuckDuckGo (2) ranked 1st, followed by Startpage (2.5), Dogpile (2.75) and Metacrawler (2.75); lower scores indicate better performance. The findings indicate that DuckDuckGo is the best MSE regarding user experience (UX) and search quality.

The study used a re-ranking aggregation approach confined to past rankings and limited to four MSEs, limiting its generalizability.

The finding helps users and developers understand the strengths and weaknesses of the different MSEs, enabling more informed decision-making and enhancing UX.

The study selected a novel approach for assessing the MSEs, and no similar study conducted in the past used different performance metrics to rank the MSEs.

With the current developments within the sphere of Internet of Things (IoT) technology, many conventional articles are all being fitted with smart functionalities, ranging from chairs, beds, shoes and caps to underwear. Bags which are utility as well as fashion items have not been left out of this smart craze, albeit to a less popular degree. The purpose of this study is to fill the research gap on the subject of smart bags research and applications and to contribute to the general discourse on IoT.

This study adopts literature search and database review, concept mapping as well as synthesis methodologies. Relevant literature form databases such as Web of Science, Google Scholar and Bing Scholar were interrogated. Manual sifting was done to eliminate papers that do not fit the set inclusion criteria. Literature on smart bags was organized into structured frameworks using concept mapping methodology. Applying a synthesis methodology enabled an exploration of the different technological trends in smart bag research and their areas of application.

The study identified about 15 different smart bag applications and functionalities. Discussed in this study is a classification of bags based on a number of points such as way of carrying, size, utility and fabrication materials. Also discussed are the description of what constitute a smart bag, relevant technologies for smart bag design and engineering and subsequently the current trends in smart bag applications. This study also discovered that the air travel industry tend to have some difficulties with this smart bag technologies, specifically with their built-in batteries.

The results of this study will provide researchers and other stakeholders with key information about existing problems and opportunities in smart bag research and applications. This will go a long way to help in guiding future research as well as policymaking in smart bag design and application.

To the best of the authors’ knowledge, this is the first review on the subject of smart bags even though smart bag research and commercial product design continue to gain momentum in recent years.

This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.