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Purpose: To investigate the key technologies facilitating the transition towards Industry 5.0 and analysing the contributions of Nvidia, a prominent leader in this field, to these technological advancements.

Significance of the study: Technology companies such as Nvidia play a critical role in this transformation through their innovative solutions. This study addresses the need to understand this evolving landscape and the significant impact of the Nvidia.

Methodology: This study is a qualitative approach that examines the existing literature and secondary case studies pertaining to Industry 5.0, and Nvidia. This study examines Nvidia’s high-performance graphics processing units (GPUs), the digital twin platform Omniverse, and the humanoid robot technology development platform, Isaac.

Findings: The next generation of GPUs with the Blackwell architecture is expected to further advance the development of large language models. The Nvidia Omniverse platform contributes significantly to the development of digital twins, a crucial technology for Industry 5.0. The Nvidia Isaac platform focuses on the development of humanoid robot technology, which is a key component of Industry 5.0. Utilizing realistic simulations with Isaac Sim, imitating human behavior with GR00T, and leveraging the high-performance processing power of Jetson Thor, the platform facilitates the creation of robots capable of safe and effective human–robot collaboration. Nvidia has emerged as a leader in the artificial intelligence (AI), robotics, and gaming sectors because of its innovative and agile company culture.

Practical implications: Companies can leverage Nvidia’s technological solutions to optimize production processes and enhance both efficiency and sustainability. The human–machine collaboration emphasized by Industry 5.0 will necessitate the reshaping of workforce skillsets and operational approaches.

Purpose: Industry 5.0 is a revolution building on the fourth industrial revolution, adopting human-centred production. It aims for a future that enables people to live with high welfare power by combining the power of the technologies it supports and uses with human creativity and skill. The study explores how Industry 5.0 differs from the industrial revolutions that preceded it, highlighting the challenges, benefits, and possible future trends of Industry 5.0 systems.

Need for the study: Industry 5.0 differs from Industry 4.0 – it represents a human-centred and sustainable approach along with the innovations it brings. Examination of the still emerging concept of Industry 5.0 and to investigate the driving forces behind the emergence is needed.

Methodology: The characteristics that distinguish the Industry 5.0 system from the fourth industrial revolution are explored in detail. The areas in which the technologies used in Industry 5.0 provide benefits and the challenges in implementing the system are analysed. The measures to be taken to overcome these difficulties and the deficiencies were investigated.

Findings: The most important principle of Industry 5.0 is human-centred production and technology. In addition, sustainability, personalised production, new business opportunities, more environmentally friendly and renewable energy use, and bioeconomy are shown as innovations brought by Industry 5.0.

Practical implications: The adoption of Industry 5.0 appears to be a mandatory change. In addition, the study emphasises the measures that businesses should take in terms of sustainability, sustainable economy, the importance of the use of renewable energy sources, and much more environmentally friendly and waste-free production.

Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

This study investigates the effects of social media as a social networking tool for donation-based crowdfunding by considering the factors affecting donation intention in the Philippines, such as social influences, motivations, strategic positioning and creative marketing efforts.

A mixed-method approach was used through a survey questionnaire and short focus group discussions (FGD). The researchers used partial least squares structural equation modeling (PLS-SEM) as a statistical tool and qualitative data was analyzed through thematic analysis.

The study found that social experience, perceived donor effectiveness, intrinsic motivation and leveraging creativity positively affected donation intention. In contrast, extrinsic motivation and strategic positioning were found to have no significant effect on donation intention. The qualitative findings support the conclusion by discovering the need to be authentic and transparent to achieve fundraising goals.

Existing research has mainly focused on crowdfunding websites. Also, research regarding the use of social media as a social networking tool for crowdfunding is scarce.

This study provides a deeper understanding of donation crowdfunding and offers insights into developing effective strategies for crowdfunding campaigns through social media.

The findings contribute to the literature on donation crowdfunding by identifying the determinants of a successful campaign through advertised crowdfunding.

Currently, various detection technologies for unmanned underwater vehicles are highly susceptible to environmental impacts. Wake detection technologies have gradually gained attention and development. However, the clarity of detection results remains a challenge. This paper aims to present the design of a MEMS three-dimensional vector wake sensor. Compared to similar sensors, the MEMS three-dimensional vector wake sensor offers improved propeller wake measurement capabilities.

A MEMS three-dimensional vector wake sensor inspired by the fish lateral line system is designed. This paper discusses the working principle of the sensor. Finite element simulation is used to determine the optimal dimensions of the sensor’s sensitive chip and packaging structure. In addition, the wake environment is simulated for performance testing.

Flow velocity calibration test results confirm that the MEMS three-dimensional vector wake sensor exhibits high sensitivity, achieving 1727.6 mV/(m/s). Vector capability tests show that the data consistency in the same direction reaches 91.8%. The sensor demonstrates strong vector detection capability.

The MEMS three-dimensional vector wake sensor plays a critical role in the formation control of unmanned underwater vehicle fleets and target detection.

This study focuses on applications for unmanned underwater vehicles. It enhances the detection capabilities of unmanned underwater vehicles. This is of significant importance for future deep-sea target detection.

This paper aims to investigate the gear performance during meshing. The effects of line load, velocity and rough surfaces (kurtosis and skewness) on the lubrication characteristics and fatigue life of gears were analyzed.

In the mixed thermoelastohydrodynamic lubrication (TEHL) model, the distributions of pressure and film thickness are predicted using the Reynolds equation. The elastic–plastic rough contact model is used to calculate the contact force and area. To predict the fatigue life of the gears, the Dang–Van fatigue model is used to obtain the fatigue parameters under different line loads, velocities and rough surfaces.

The skewness of rough surfaces has a more significant effect on gear performance. In addition, according to fatigue parameters, it is found that when the load is too large, it leads to an increase in the probability of gear failure.

In this paper, a mixed TEHL model considering the effects of kurtosis and skewness is developed for the line contact to predict the mixed lubrication problem during gear meshing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2024-0356/

This study aims to improve the sensitivity of magnetic detection. In this article, a multi-frequency modulation technique is used to increase the magnetic detection sensitivity of diamond nitrogen vacancy (NV) centers sensors.

In the field of magnetic detection, NV centers have corresponding advantages due to their unique long coherence property at room temperature. The important indicators for NV centers magnetometers are the magnetic detection sensitivity of the NV centers and the integration of the magnetometer. To solve this problem, the authors propose a multi-frequency modulated magnetic detection technique, using an integrated probe as well as a lock-in amplifier for the double enhancement of sensitivity as well as integration.

The following results can be obtained by processing and calculating the experimental data with an integrated lock-in amplifier circuit with an area of 27.50 cm² and a probe volume of 3.12 cm³. The multi-frequency modulation technique was used to increase the magnetic detection sensitivity of the NV centers from 8.59 nT/Hz1 / 2–2.42 nT/Hz1 / 2.

The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing.

The authors propose a signal modulation technique with an integrated design, which achieves an improvement in the sensitivity of the sensor’s magnetic detection through practical testing. This technique provides new research solution for the subsequent improvement of the magnetic detection sensitivity.

The purpose of this paper is to introduce cyclic electrochemical impedance spectroscopy (EIS) method to understand the corrosion evolution behavior of structural materials in secondary circuit water environments of pressurized water reactor (PWR) system.

The cyclic EIS has been used to understand the corrosion evolution of 304 stainless steel (SS) in simulated secondary circuit water environment. Scanning electron microscopy and X-ray photoelectron spectroscopy have been used to characterize the microstructure and corrosion morphology of 304 SS sample.

Cyclic EIS measurement is applicable in gaining information on the corrosion evolution of 304 SS in high-temperature and high-pressure (HTHP) water environments. Based on analyses of the cyclic EIS data, it is considered that the measured EIS response of 304 SS sample under HTHP water environment mainly comes from the compact inner part of the newly formed oxide layer, which gradually inhibits the progress of electrochemical reactions at the oxide layer/substrate interface.

The cyclic EIS has been introduced into HTHP water environment, and its reliability has been evaluated. It may find a wide application in corrosion studies of materials under HTHP water environments, which is critical for a safe operation in nuclear power plants and beneficial for the development of corrosion-resistant materials in PWR system.

Gamification has been widely applied in mobile fitness apps to motivate users to exercise continuously. Based on the affordances–psychological outcomes–behavioral outcomes framework, this study explores the roles of three specific gamification affordances (competition, visibility of achievement and interactivity) in self-health management (continuous use behavior and health behavior) from the perspectives of achievement satisfaction and gamification exhaustion.

We test the research model using a structural equation model (SEM) with 505 self-reported data points. Furthermore, we apply fuzzy-set qualitative comparative analysis (fsQCA) to explore configurations of gamification affordances associated with self-health management behavior, reinforcing the SEM results.

Results indicate that competition, visibility of achievement and interactivity can enhance achievement satisfaction, which further boosts self-health management behavior. However, competition and interactivity can also cause gamification exhaustion, which undermines self-health management behavior to some extent. Overall, the positive impacts of the three affordances outweigh the negative impacts.

This study provides new insights for relevant practitioners on designing gamification affordances, aiding the sustainable development of mobile fitness apps and their long-term effects on self-health management. Visibility of achievement should be emphasized, and competition and interactivity should be thoughtfully designed to minimize their negative effects.

This study extends the affordances–psychological outcomes–behavioral outcomes framework and the literature on gamification and health management by applying both SEM and fsQCA methodologies to examine the relationship between specific gamification affordances and self-health management behavior.

Effective risk management is critical to successfully developing complex products and systems (CoPS) but is often hampered by the unclear understanding of risks’ effect on outcomes. The purpose of this study is to investigate how do diverse project risks jointly affect innovation performance in both adverse and positive ways within the CoPS context.

This study performs a fuzzy set qualitative comparative analysis (fsQCA) on 98 CoPS projects encompassing eight industries to investigate how diverse risks based on the technology–organization–environment (TOE) framework jointly affect innovation performance within CoPS projects among integrators and complementors.

The results reveal three configurations for high performance. Specifically, technology-oriented and market-driven, technology-oriented and resource-driven for project integrators and technology-oriented, resource and relationship co-driven for project complementors. We also identified four configurations for low performance. Particularly, technology triggered for project integrators, resource and relationship co-triggered, resource triggered and relationship triggered for project complementors.

Theoretically this study makes a valuable contribution to the existing body of literature on risk and performance management in CoPS projects by investigating the correlation between risks and project performance. From a practical perspective, both project integrators and complementors can utilize these insights to enhance their risk-management techniques in CoPS projects.