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This paper aims to use two different numerical approaches to simulate the induction welding process for a hybrid thermoplastic material, and the results have been validated experimentally.

The first approach used a numerical model that combines electromagnetism, heat transfer and solid mechanics in the same numerical environment using Hexagon Marc software. Simultaneously, a computationally efficient approach combined steady-state electromagnetism results at specific intervals in the Ansys EM suite with heat transfer and solid mechanics in Ansys Workbench.

The results from both numerical approaches showed a strong correlation with the experimental findings.

The current research offers valuable insights into enhancing induction welding procedures within the aerospace industry, as well as across broader industrial applications. The synergistic combination of numerical simulations and experimental validation served as a robust framework for future research endeavors aimed at enhancing the efficiency, reliability and quality of thermoplastic welding techniques.

Given the current challenges for improving the tribological behavior in automotive engines, which require lubricants that adapt to different operating conditions through replacement mechanisms to reduce friction and wear, this study aims to analyze the use of hexagonal boron nitride nanoparticles blended in the lubricating oil of a diesel engine. The target was to minimize frictional power losses and wear of cylinder liner surfaces to validate what was observed in laboratory and to confirm improvements in thermal efficiency.

Before the definition of the concentration to be used in a real engine environment, tests of sample dispersion were conducted using an ultrasound bath. The mixtures were added to storage bottles in concentrations of 0.1% and 0.5%, as observed in Figure 1. The samples were subsequently analyzed using the dynamic light scattering (DLS) technique. There was a reduction in the hydrodynamic size for the sample with 0.5% of hexagonal boron nitride (hBN), possibly due to sedimentation of the powder during the analysis, which supported this work to continue with the use of 0.1% concentration.

The behavior of hBN as nano additive in a real diesel engine was problematic when compared with laboratory environment, leading to impact in oil temperature. In addition, it was noticed a high amount of deposit formation at the top dead center (TDC) and mid-stroke (MC) and nonsignificative wear at MC and bottom dead center (BDC) of the cylinder, with unusual formation of products from antiwear additives known as zinc dialkyl-dithiophosphate at MC position with the use of hBN. For this reason, this work provides insights into how hBN nanoparticles may not contribute toward the improvement of tribological performance.

The findings of this work aimed to provide a better understanding of the impact of hBN nanoparticles used as additives in real engine environment in terms of performance and tribological impacts. The results of this work indicated that hBN as additive gave poor results in terms of performance and wear prevention.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-02-2024-0047/

The purpose of the study is to examine how risk factors contribute to the occurrence of defects in a process. By analyzing these risk factors in relation to process quality, the study aims to help organizations prioritize their resources and efforts toward addressing the most significant risks. These challenges, integrated with the emerging concept of Quality 4.0, necessitate a comprehensive risk assessment technique.

Fuzzy logic integrated with an analytic network process is used in the process failure mode and effects analysis for conducting risk identification and assessment under uncertainty. Through a mathematical model, the linkage of risk with Six Sigma is established and, finally, a value–risk matrix is developed for illustrating and analysing risk impact on process quality.

A case study on fused filament fabrication demonstrates the proposed methodology’s applicability. The results show its effectiveness in assessing risk factors’ impact on Six Sigma metrics: defects per million opportunities/sigma level.

By integrating qualitative assessments and leveraging available data, this approach enables a more comprehensive understanding of risks and their utilization for an organization’s quality improvement initiatives.

This approach establishes a risk-centric Six Sigma assessment method in accordance with the requirement of ISO 9001:2015 and in the context of Quality 4.0.

This paper aims to explore the role of blockchain (BCT) and Artificial Intelligence (AI) technologies in enhancing and incentivizing environmentally responsible, socially inclusive and economically viable tourism practices within the hospitality and tourism (H&T) industry.

This study is based on a critical reflection research approach that enables a synthesis of information derived from existing literature's insights and the authors' experiences and observations. By examining frameworks and theories in the literature, critical reflection also helps develop a more comprehensive understanding of the topic.

This study portrays how BCT could be used to track the sustainability credentials of tourism providers and how AI can optimize energy usage in hotels. This study depicts how adopting technology-driven sustainable practices in the H&T industry can increase profitability, improve reputation, compliance with regulations, efficiency and a better guest experience.

The study suggests targeted actions and policy frameworks that can be tailored to different stakeholder groups (hospitality businesses, policymakers and tourists) to overcome barriers and maximize the positive societal and environmental impacts of adopting BCT and AI for sustainable tourism.

The originality of this study lies in its ability to offer new perspectives and novel recommendations on the diverse uses of AI and BCT in the context of sustainable tourism. Furthermore, the study provides strategic and policy elements (Targeted actions and policy frameworks) for stakeholders to integrate sustainable tourism practices using BCT and AI successfully. This study differs from earlier review studies that primarily focused on adopting emerging technologies and ignoring the sustainability angle in the use of technology.

This study explores the significance and implementation priorities for Digital Product Passports (DPP) in the context of building renovation projects. It aims to reveal bottlenecks and how a data-driven workflow bridges the DPP understanding/implementation gap, facilitating the transition towards practices aligned with the EU Green Deal goals.

A mixed-methods embedded design was employed for a real-case study exploration. Desk research and field observations ground the two-level analysis combining project documentation, namely the Bill of Quantities (BoQ), with different criteria in digitalisation and sustainability, such as economic ratio, 3D modelling, waste management, hazards, energy performance and facility management. All results were interpreted from the DPP lens.

The analysis revealed a system for identifying building products representing a significant part of the renovation budget. About 11 priority DPPs were found. Some are crucial for both the deconstruction and construction phases, highlighting the need for an incremental and strategic approach to DPP implementation.

The study is limited to a single case study. Constraints are minimised given the sample's archetype representativeness. The outcomes introduce the need for strategic thinking for incremental DPP implementation. Future research will explore additional criteria and cases.

The research has resulted in a classification framework for DPPs' significance and priority, which is provided with case results. The outcome of the framework provides views on concept alignment to make the implementation in construction more straightforward. Its practical use can be replicated in other projects, emphasizing the importance of data structure and management for the circular economy.

High-performance wrought aluminum alloys, particularly AA6061, are pivotal in industries like automotive and aerospace due to their exceptional strength and good response to heat treatments. Investment casting offers precision manufacturing for these alloys, because casting AA6061 poses challenges like hot cracking and severe shrinkage during solidification. This study aims to address these issues, enabling crack-free investment casting of AA6061, thereby unlocking the full potential of investment casting for high-performance aluminum alloy components.

Nanotechnology is used to enhance the investment casting process, incorporating a small volume fraction of nanoparticles into the alloy melt. The focus is on widely used aluminum alloy 6061, utilizing rapid investment casting (RIC) for both pure AA6061 and nanotechnology-enhanced AA6061. Microstructural characterization involved X-ray diffraction, optical microscopy, scanning electron microscopy, differential scanning calorimetry and energy dispersive X-ray spectroscopy. Mechanical properties were evaluated through microhardness and tensile testing.

The study reveals the success of nanotechnology-enabled investment casting in traditionally challenging wrought aluminum alloys like AA6061. Achieving crack-free casting, enhanced grain morphology and superior mechanical properties, because the nanoparticles control grain sizes and phase growth, overcoming traditional challenges associated with low cooling rates. This breakthrough underscores nanotechnology's transformative impact on the mechanical integrity and casting quality of high-performance aluminum alloys.

This research contributes originality and value by successfully addressing the struggles in investment casting AA6061. The novel nano-treating approach overcomes solidification defects, showcasing the potential of integrating nanotechnology into rapid investment casting. By mitigating challenges in casting high-performance aluminum alloys, this study paves the way for advancements in manufacturing crack-free, high-quality aluminum alloy components, emphasizing nanotechnology's transformative role in precision casting.

This paper aims to inform noncataloguers about the current and historical significance of both machine readable cataloguing (MARC) records and traditional cataloguing as well as introduce them to current struggles to modernize.

The approach of this column is to break-down what may appear to be a crisis in the modernization of library cataloguing and metadata by placing the issues in their historical context and considering existing technologies in a user-friendly manner.

MARC-based cataloguing is well-established, robust and widely used in many library applications. It is not easily replaced and attempts to do so have had very slow progress. Although change is expected in the future, it may lay with solutions based on Artificial Intelligence (AI) technologies.

Although there are many papers discussing the merits of traditional cataloguing and the need for new methods and standards, few are written from a nontechnical point of view and for noncataloguers or those not working in systems departments. This column's intended audience is all library workers with an emphasis on those who are not familiar with MARC.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

Stakeholder interests are complex, sensitive and highly uncertain and may influence the development of net zero carbon building (NZCB). However, this study aims to conduct a systematic literature review to explore the stakeholder interests towards achieving NZCB.

A total of 62 articles were identified from the Scopus database and thoroughly reviewed to extract relevant information on stakeholders' interest towards achieving NZCB.

A total of 28 stakeholder interests influencing the development of NZCB were identified from the literature and were classified into six major groups according to their uniqueness (economic, social, environmental, technological, political, regulatory and legal).

The findings of this study provide insight into the specific stakeholder interests towards achieving NZCB. Thus, the findings of this study could serve as a guide for future research, policy formulation and implementation to expedite the practice of building towards net zero carbon (NZC). Empirical studies are suggested in future studies to test and consolidate the theoretical claims of this study.

This paper undertakes a comprehensive systematic review of studies on stakeholder interests towards achieving NZCB, which is the least investigated in the literature.

This paper aims to numerically examine the impact of gyrotactic microorganisms and radiation on heat transport features of magnetic nanoliquid within a closed cavity. Thermophoresis, chemical reaction and Brownian motion are also considered in flow geometry for the moment of nanoparticles.

Finite element method (FEM) was depleted to numerically approximate the temperature, momentum, concentration and microorganisms concentration of the nanoliquid. The present simulation was unsteady state, and the resulting transformed equations are simulated by FEM-based Mathematica algorithm.

It has been found that isotherm patterns get larger with increasing values of the magnetic field parameter. Additionally, numerical codes for rate of heat transport impedance inside the cavity with an increasing Brownian motion parameter values.

To the best of the authors’ knowledge, the research work carried out in this paper is new, and no part is copied from others’ works.