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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.

The purpose of this study is to comprehensively evaluate the impact of the prefabrication rate on greenhouse gas (GHG) emissions and sustainability in prefabricated construction. In addition, it aims to identify the optimal prefabrication rate threshold that can promote the transformation of the construction industry toward more environmentally friendly practices.

This study uses an interdisciplinary methodology that combines emergy analysis with an extended input-output model to develop a GHG emission accounting model tailored for prefabricated buildings. The model assesses various construction schemes based on different rates of prefabrication and uses the emergy phase diagram from ecological economics to quantify the sustainability of these schemes.

This study indicates that within a prefabrication rate threshold of 61.27%–71.08%, a 5% increase in the prefabrication rate can significantly reduce emissions by approximately 36,800 kg CO₂(e). However, emissions begin to rise when the prefabrication rate exceeds this threshold. The case analysis identifies steel, concrete and electricity as the primary sources of GHG emissions, suggesting strategies for optimizing their usage and promoting the adoption of clean energy.

This study represents a novel tool for assessing the environmental impact and sustainability of prefabricated buildings. It offers scientific guidance for the construction industry’s environmental protection and sustainable development strategies, thereby contributing to a transition toward more environmentally friendly practices.

In autonomous driving, the inherent sparsity of point clouds often limits the performance of object detection, while existing multimodal architectures struggle to meet the real-time requirements for 3D object detection. Therefore, the main purpose of this paper is to significantly enhance the detection performance of objects, especially the recognition capability for small-sized objects and to address the issue of slow inference speed. This will improve the safety of autonomous driving systems and provide feasibility for devices with limited computing power to achieve autonomous driving.

BRTPillar first adopts an element-based method to fuse image and point cloud features. Secondly, a local-global feature interaction method based on an efficient additive attention mechanism was designed to extract multi-scale contextual information. Finally, an enhanced multi-scale feature fusion method was proposed by introducing adaptive spatial and channel interaction attention mechanisms, thereby improving the learning of fine-grained features.

Extensive experiments were conducted on the KITTI dataset. The results showed that compared with the benchmark model, the accuracy of cars, pedestrians and cyclists on the 3D object box improved by 3.05, 9.01 and 22.65%, respectively; the accuracy in the bird’s-eye view has increased by 2.98, 10.77 and 21.14%, respectively. Meanwhile, the running speed of BRTPillar can reach 40.27 Hz, meeting the real-time detection needs of autonomous driving.

This paper proposes a boosting multimodal real-time 3D object detection method called BRTPillar, which achieves accurate location in many scenarios, especially for complex scenes with many small objects, while also achieving real-time inference speed.

This study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.

The research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.

The analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.

This study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

Postulating that individuals exposed to the threat of contagious diseases respond oversensitively toward other people, the current research aims to investigate its impact on consumers’ preferences for human images—human presence—in product packaging.

Five independent online and offline experiments were conducted. Studies 1, 2a, and 2b employed a three-group (threat: contagious vs. control vs. noncontagious) between-subjects design to investigate the main effect and its underlying mechanism. To further examine the moderation effects, Study 3 used a 2 (threat: contagious vs. control) × 2 (product feature: basic vs. antibacterial) between-subjects design, and Study 4 employed a 2 (threat: contagious vs. control) × 3 (human type: non–human vs. human–adult vs. human–baby) between-subjects design.

Studies 1, 2a, and 2b demonstrate that consumers facing the threat of contagious diseases tend to avoid social interaction, leading to a lower preference for products featuring human presence (vs. non-human presence). Studies 3 and 4 contribute to our hypothesized process by providing boundary conditions. Specifically, when the product incorporates an antibacterial function (Study 3) and the packaging depicts a baby (Study 4), the existing effect can be attenuated.

Despite the prevalence of experiencing epidemics and pandemics, little work has examined how threatened consumers respond to product packaging. The present research addresses this gap by exploring consumers' preferences for products featuring human presence on their packaging. Furthermore, this research contributes to the practical understanding of consumer choices by identifying product features and human types as moderating factors.

This study aims to investigate the impact of firms’ digital orientation (FDO) on corporate green innovation (CGI) among Chinese firms, examining the effects of financial constraint as the mediator and exploring heterogeneous effects across different firm contexts.

Using a sample of 28,697 firm-year observations from Chinese A-share listed companies (2008–2021), we employ a novel multidimensional measure of FDO derived from textual analysis of corporate annual reports. CGI is quantified using patent-based metrics. We utilize fixed-effects panel data models as benchmark regression to quantify FDO’s impact on CGI. Later, we utilize two-stage least squares, alternate measure for core explanatory variable, alternate as well as lead measures for explained variable and propensity score matching to tackle concerns for potential endogeneity.

Our results unveil a substantial positive connection between FDO and CGI. This connection is facilitated through the alleviation of financial constraints. Furthermore, heterogeneity analysis shows that the impact of FDO on CGI is more pronounced for state-owned enterprises, firms in areas with lower financial technology development and politically connected firms.

Our findings suggest that managers should view FDO as a strategic posture that can drive sustainable innovation, not just as a technological imperative. Policymakers should consider the role of FDO when designing policies to promote CGI, particularly in less-developed regions.

This study extends current understanding by: (1) Employing a comprehensive multidimensional measure of FDO that goes beyond the existing technologically focused digital transformation matrices. (2) Identifying financial constraints as a key mediating mechanism in the FDO–CGI relationship. (3) Revealing heterogeneous effects across different firm contexts, providing nuanced insights into how institutional and environmental factors moderate this relationship.

The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear.

A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy.

The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism.

This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.

Open innovation platform has become an effective field through which enterprises can acquire valuable knowledge for incremental and breakthrough innovation. However, as more entities join the innovation platform, the knowledge activities in the platform ecosystem are now facing higher complexity and vulnerability due to the differences in the knowledge demands as well as conflicting interest claims of participants. The lack of mature governance mechanisms has caused opportunistic behaviors like knowledge infringement, leakage and hiding, which seriously hinder the in-depth knowledge sharing and effective utilization. What’s more, the enthusiasm for collaborative innovation also reduced among multi-subjects. Therefore, the purpose of this study is to improve platform participants’ innovation ambidexterity under the guidance of scientific design of platform knowledge governance mechanisms through improved knowledge transformation processes.

Therefore, based on knowledge governance theory and knowledge transformation model (SECI, socialization-externalization-combination-internalization), the study explored the influence of relationship and contractual knowledge governance on the innovation ambidexterity of platform participants through the mediation effect of knowledge transformation. To better analyze complex causal relationships among variables and the chain multiple mediation effect, structural equation modeling is used, coupled with bootstrap analysis verification.

Platform contractual governance and relationship governance can positively influence the innovation ambidexterity of participants through knowledge trading and reuse, as well as through knowledge sharing and creation. The findings not only contribute to optimizing the effectiveness of knowledge activities on digital platforms but also provide empirical evidence and practical insights to support enterprises’ incremental and breakthrough innovation according to their own knowledge bases.

The findings offer valuable insights for providing decision-making guidance not only for platform-leading enterprises but also for individual and enterprise users on effectively using open innovation platforms to conduct knowledge seeking, trading or sharing and knowledge reuse or creation to enlarge the incremental innovation value and to trigger breakthrough innovation value in their product and technology developments.

Through diverse knowledge governance mechanisms, platform-leading enterprises do not only act as “economic agents” with private attributes to reduce knowledge asymmetry in the public trading market, diffuse knowledge broadly and mitigate cooperation costs to increase economic value; they also serve as “social actors” for multilateral participants to increase the cohesion of knowledge sharing and creation to provide sustainable knowledge fuel for the higher level of breakthrough innovation. Overall, knowledge arrangement efficiency can be optimized, and breakthrough innovation value can be activated in a well-governed platform, gradually escaping the diminishing marginal benefits of exploitative innovation.

This study has extended the views of the knowledge transformation model under the platform context and proposed dualistic knowledge transformation pathways, named “tacit knowledge socialization” and “explicit knowledge combination,” respectively. Besides, it discovered that under the contractual and relationship knowledge governance mechanisms’ guiding, participants in open innovation platforms may choose different knowledge searching and exchange ways according to their knowledge needs and thus trigger the different knowledge transform process. Then, “tacit knowledge socialization” transformation can show larger positive impact on breakthrough innovation, while “explicit knowledge combination” transformation makes larger impact on incremental innovation.

This study aims to investigate the micro mechanism of macro rheological characteristics for composite modified asphalt.Grey relational analysis (GRA) was used to analyze the correlation between macro rheological indexes and micro infrared spectroscopy indexes.

First, a dynamic shear rheometer and a bending beam rheometer were used to obtain the evaluation indexes of high- and low-temperature rheological characteristics for asphalt (virgin, SBS/styrene butadiene rubber [SBR], SBS/rubber and SBR/rubber) respectively, and its variation rules were analyzed. Subsequently, the infrared spectroscopy test was used to obtain the micro rheological characteristics of asphalt, which were qualitatively and quantitatively analyzed, and its variation rules were analyzed. Finally, with the help of GRA, the macro-micro evaluation indexes were correlated, and the improvement efficiency of composite modifiers on asphalt was explored from rheological characteristics.

It was found that the deformation resistance and aging resistance of SBS/rubber composite modified asphalt are relatively good, and the modification effect of composite modifier and virgin asphalt is realized through physical combination, and the rheological characteristics change with the accumulation of functional groups. The correlation between macro rutting factor and micro functional group index is high, and the relationship between macro Burgers model parameters and micro functional group index is also close.

Results reveal the basic principle of inherent-improved synergistic effect for composite modifiers on asphalt and provide a theoretical basis for improving the composite modified asphalt.