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The restoration and strengthening of QT600 is an industry bottleneck challenge. The Co-12 cladding layer has great wear and corrosion resistance. The purpose of this paper is to quantitatively reveal the transient evolution law of the corrosion process of Co-12 cladding layer on QT600 surface.

In this paper, a three-dimensional numerical model of the corrosion process of Co-12 cladding layer by QT600 laser cladding is established. The interaction between pitting pits and corrosion medium is considered to reveal the transient evolution of ion concentration, electrode potential, pH and corrosion rate at different locations.

The calculation shows that the ion concentration in pitting pit changes Cl⁻>Co²⁺>Na⁺, pH value decreases from top to bottom and corrosion rate at bottom is greater than that at top. The electrochemical corrosion test of Co-12 cladding layer was carried out. It is shown that the current density of QT600 increases by an order of magnitude compared to the Co-12 cladding layer, and the corrosion rate is 4.862 times higher than that of the cladding layer.

The results show that Co-12 cladding layer has great corrosion resistance, which provides an effective way for QT600 protection.

Heterogeneous graphs, composed of diverse nodes and edges, are prevalent in real-world applications and effectively model complex web-based relational networks, such as social media, e-commerce and knowledge graphs. As a crucial data source in heterogeneous networks, Node attribute information plays a vital role in Web data mining. Analyzing and leveraging node attributes is essential in heterogeneous network representation learning. In this context, this paper aims to propose a novel attribute-aware heterogeneous information network representation learning algorithm, AAHIN, which incorporates two key strategies: an attribute information coverage-aware random walk strategy and a node-influence-based attribute aggregation strategy.

First, the transition probability of the next node is determined by comparing the attribute similarity between historical nodes and prewalk nodes in a random walk, and nodes with dissimilar attributes are selected to increase the information coverage of different attributes. Then, the representation is enhanced by aggregating the attribute information of different types of high-order neighbors. Additionally, the neighbor attribute information is aggregated by emphasizing the varying influence of each neighbor node.

This paper conducted comprehensive experiments on three real heterogeneous attribute networks, highlighting the superior performance of the AAHIN model over other baseline methods.

This paper proposes an attribute-aware random walk strategy to enhance attribute coverage and walk randomness, improving the quality of walk sequences. A node-influence-based attribute aggregation method is introduced, aggregating neighboring node attributes while preserving the information from different types of high-order neighbors.

A significant amount of ozone-depleting chlorofluorocarbons is emitted during the production of building materials. With the world population expected to increase by 21.3% from 2030 to 2050, the demand for construction materials is set to rise, necessitating a shift toward eco-friendly options to preserve the ecosystem. Bamboo emerges as a promising solution to meet sustainable construction goals. This study aims to investigate bamboo’s potential as a sustainable construction material, evaluating its impact on construction productivity and safety.

A systematic literature review was conducted, using relevant keywords to retrieve journal articles from the Scopus, Web of Science and Google Scholar databases. Articles were screened, and only those meeting the inclusion criteria were reviewed.

Bamboo offers numerous advantages as a construction material, including cost-effectiveness, abundance and strength, making it a viable alternative to traditional building materials with a reduced environmental impact. However, its widespread acceptance encounters significant challenges. The use of bamboo in construction can both positively and negatively affect productivity and safety in construction organizations.

This study proposes a framework for improvement that construction stakeholders can adopt to enhance bamboo’s utilization in construction while maintaining high productivity and safety standards.

While previous studies have advocated for increased bamboo utilization in construction, this study goes further to explore the implications for productivity and safety in construction.

This paper aims to highlight the factors affecting health and safety (H&S) and the SMART Technologies (ST) used to mitigate them in the construction industry through a range of selected papers to encourage readers and potential audiences to consider the need for intelligent technologies to minimize the risks of injuries, illnesses and severe harm in the construction industry.

This paper adopts a double systematic literature review (SLR) to analyse studies investigating the factors affecting H&S and the ST in the construction industry using databases such as Google Scholar, Scopus, Science Direct and Emerald Insight publication.

The SLR identified “fatal or focus five factors” that include objects Fall from heights (FFH) and trapped between objects; Falls, Trips and slips (FTS); Machinery/Equipment Malfunction and Moving Equipment; Pollutants: Chemicals, Airborne Dust, Asbestos; and Electrocution. The ST includes Safety Boots/SMART Glasses/SMART Helmet/SMART Vests/SMART PPE/SMART Watch, Mobile Apps, Building Information Modelling (BIM), Virtual Reality/Augmented Reality (VR/AR), Drones/Unmanned Aerial Vehicles and Wearable Technology/Mobile Sensors help mitigate the risk posed by “Fatal five”. However, other factors within the scope of ST, such as Weather Conditions, Vibrations, Violence, Disease and illness, Fire and Explosion and Over Exertion, are yet to be adopted in the field.

SLR methodology limitations of not obtaining the most updated field knowledge are critical and are offset by choosing 72% of H&S and 92% of SM review literature post-2017. Limitations to capturing articles because of the restriction of database access: only English language search and journals that are not a part of the databases selected are acknowledged. However, key database search that recognizes rigorous peer-reviewed articles offset these limitations. The researcher’s Bias is acknowledged.

This paper unravels the construction H&S factors and their interlinks with ST, which would aid industry understanding and focus on mitigating associated risks. The paper highlights the Fatal five and trivial 15, which would help better understand the causes of the H&S risks. Further, the paper discusses ST’s connectivity, which would aid the organization’s overall H&S management. The practical and theoretical implications include a better understanding of all factors that affect H&S and ST available to help mitigate concerns. The operating managers could use the ST to reduce H&S risks at every construction process stage. This paper on H&S and ST and relationships can theorize that the construction industry is more likely to identify clear root causes of H&S and ST usage than previously. The theoretical implications include enhanced understanding for academics on H&S factors, ST and gaps in ST concerning H&S, which can be expanded to provide new insights into existing knowledge.

This paper highlights all factors affecting H&S and ST that help mitigate associated risks and identifies the “Fatal five” factors. The paper is the first to highlight the factors affecting H&S combined with ST in use and their interactions. The paper also identified factors within the ST scope that are yet to be explored.

This paper aims to investigate the influence of various laser texture parameters (diameter of pit, depth of pit and area density) on the tribological and tribo-vibration characteristics of tapered roller bearings (TRBs) under full oil lubricate conditions.

The laser surface texture parameters include: the diameter of pit (D: 60 µm, 100 µm, 200 µm), the depth of pit (H: 5 µm, 10 µm, 20 µm) and the area density (S: 6%, 12%, 24%). The outer raceway used laser marking device to prepare many regular pits. The tribological and tribo-vibration characteristics of pitting laser textured TRBs under full oil lubrication were studied by using the MMX-1A universal wear tester machine and vibration testing equipment. Through experiment and analysis, the effects of raceway pitting textures on tribological and tribo-vibration noise performance of TRBs were summarized.

When pit-textured TRBs operate under full oil, compared with the non-textured bearings, the average coefficient of friction and wear amount are significantly reduced. When D = 100 µm, H = 10 µm, S = 12%, average coefficient of friction = 0.00195 and wear amount = 0.12 mg, they are all at their minimum values. Compared to the same condition of non-textured groups, the coefficient of friction decreases by 66.6%, and the wear amount decreases by 79.3%. The energy from time-frequency and power spectrum analyses is mainly concentrated at high frequencies, with the signal power of pitting textured groups being lower than non-textured when the Y-direction is around 3600 Hz.

The experimental work can provide a reference for the investigation on the pitting textured TRBs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0357/

The purpose of this paper is to investigate the unique and interactive effects of hard lean practices (HLPs) and soft lean practices (SLPs) on corporate social performance (CSP), as well as the moderating effect of lean maturity on the relationships between each type of practices and CSP.

Adopting a purely quantitative approach, a survey-based questionnaire methodology was used as a tool to collect data from 127 manufacturing companies located in Tunisia, and a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was employed to analyze the collected data and validate the research hypotheses.

The study findings indicate that, firstly, HLPs negatively affect CSP; secondly, SLPs positively influence CSP; and thirdly, the interaction effect of HLPs and SLPs is negative regarding CSP. Additionally, the moderating effect of lean maturity on the relationship between HLPs, SLPs and CSP was found to be insignificant.

This study is limited to manufacturing companies in Tunisia, and its results cannot reflect other geographical contexts.

The current research provides valuable insights for practitioners by showing the importance of SLPs, in particular, in enhancing CSP.

This research provides new insights into the various findings and conclusions reached by previous studies in the literature regarding the impact of lean manufacturing practices on CSP by highlighting the unique and interactive effect of HLPs and SLPs on CSP.

Financial inclusion is acknowledged as a critical facilitator of the United Nations Sustainable Development Goals agenda for 2030. Therefore, this study aims to examine the asymmetric role of overall globalization on financial inclusion by controlling economic growth, urbanization and population for the selected South Asian countries.

Applying the nonlinear autoregressive distributed lag approach to cointegration explores the impact of overall globalization on financial inclusion in the presence of additional variables like economic growth, urbanization and population in the designed financial inclusion function.

The estimated econometric outcomes show that increasing overall globalization fosters financial inclusion while decreasing overall globalization reduces financial inclusion. Furthermore, a positive (negative) change in economic growth leads to an increase (decrease) in financial inclusion while varying short-run findings. Moreover, both positive and negative changes increase financial inclusion in the long run in connection with urbanization. Although the short-run results are not significant, the study finds that an increase (decrease) in population leads to a decrease (increase) in financial inclusion. Finally, to support the promotion of financial inclusivity throughout South Asia, several policies pertaining to financial inclusion are suggested.

To the best of the authors’ knowledge, this is the first study to examine the asymmetries related to overall globalization on financial inclusion by controlling economic growth, urbanization and population.

In this paper, community identification has been considered as the most critical task of social network analysis. The purpose of this paper is to organize the nodes of a given network graph into distinct clusters or known communities. These clusters will therefore form the different communities available within the social network graph.

To date, numerous methods have been developed to detect communities in social networks through graph clustering techniques. The k-means algorithm stands out as one of the most well-known graph clustering algorithms, celebrated for its straightforward implementation and rapid processing. However, it has a serious drawback because it is insensitive to initial conditions and always settles on local optima rather than finding the global optimum. More recently, clustering algorithms that use a reciprocal KNN (k-nearest neighbors) graph have been used for data clustering. It skillfully overcomes many major shortcomings of k-means algorithms, especially about the selection of the initial centers of clusters. However, it does face its own challenge: sensitivity to the choice of the neighborhood size parameter k, which is crucial for selecting the nearest neighbors during the clustering process. In this design, the Jaya optimization method is used to select the K parameter in the KNN method.

The experiment on real-world network data results show that the proposed approach significantly improves the accuracy of methods in community detection in social networks. On the other hand, it seems to offer some potential for discovering a more refined hierarchy in social networks and thus becomes a useful tool in the analysis of social networks.

This paper introduces an enhancement to the KNN graph-based clustering method by proposing a local average vector method for selecting the optimal neighborhood size parameter k. Furthermore, it presents an improved Jaya algorithm with KNN graph-based clustering for more effective community detection in social network graphs.

Although literature abounds on lean sustainability (LS), its contributions to manufacturing industries’ triple bottom line performance (TBLP) through top management commitment (TMC) remain scanty. This research explores the mediating role of TMC in the nexus between LS and TBLP.

Given the study’s quantitative focus, the causal design was utilised. The structured questionnaire, a survey instrument, was used to gather primary data from 285 manufacturing organisations in Ghana, a developing country. Data analysis was done with structural equation modelling.

It was found that LS and TMC positively influence TBLP, whereas TMC partially mediates the connection between LS and TBLP of Ghanaian manufacturing organisations.

The study concentrates on Ghana’s manufacturing industry and embraces the stakeholder theory and quantitative methods.

This research underlines why top managers must prioritise investment in LS to promote sustainable development and attain their organisations’ TBLP targets. The study also provides key insights for top managers to consistently commit enormous resources towards developing lean practices, contributing favourably to TBLP. By establishing the interplay among LS, TMC and TBLP, manufacturing practitioners and researchers can further advance new strategies to address the growing sustainability concerns and achieve higher economic, social and environmental performance.

The study’s originality lies in analysing the mediation effect of TMC on the linkage between LS and TBLP in a developing economy where manufacturing organisations are continuously exposed to resource and waste management problems and lack adequate commitments from top managers towards sustainability initiatives. It is also the first to establish relationships between top management commitment and TBLP in the manufacturing industries of developing economies, concentrating on Ghana.

The paper aims to clarify the operation rationality of high speed trains (HSTs) under tunnel condition with the speed of 400 km/h through representative aerodynamic factors including running drag, eardrum comfort, carriages noise, aerodynamic loads on tunnel ancillary facilities and HST, micro-pressure waves, and then put forward engineering suggestions for higher speed tunnel operation based on the analysis.

Based on the field measurement data of CR400AF-C and CR400BF-J tunnel operation, correlations between each aerodynamic indicators with HST speed were established. By analyzing the safety reserve of aerodynamic indicators at 350 km/h and the sensitivity of each indicator to HST speed increasing and the indicators’ formation mechanism, the coupling relationship between various indicators was obtained.

The sensitivity of different aerodynamic indicators to speed variation differed. The aerodynamic indicators representing flow field around HST showed a linear relationship with HST speed including noise, eardrum comfort, aerodynamic load on HST body. The positive aerodynamic load on tunnel auxiliary facilities and the micro-pressure wave at the entrance of the tunnel have the same sensitivity to the 3th-power relation of HST speed. The over-limit proportion of micro-pressure wave was the highest among the indicators, and aerodynamic buffering measures were recommended for optimization. The open tunnel pressure relief structure is recommended, while allowing trains to pass through the tunnel at an unconditional speed of 380 km/h.

Comprehensive evaluation of multiple aerodynamic indicators for HST tunnel operation with higher speeds was realized. The main engineering requirements to release aerodynamic effect were identified and the optimization scheme is proposed.