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This study aims to introduce an image-based method to determine the processing window for a given alloy system using laser powder bed fusion equipment based on achieving the desired melting mode across multiple materials for powder-free specimens. The method uses a convolutional neural network trained to classify different track morphologies across different alloy systems to select appropriate printing settings. This method is intended for the development of new alloy systems, where the powder feedstock may be unavailable, or prohibitively expensive to manufacture.

A convolutional neural network is designed from scratch to identify the 4 key melting modes that are observed in laser powder bed fusion additive manufacturing across different alloy systems. To increase the prediction accuracy and generalisation accuracy across different materials, the network is trained using a novel hybrid data set that combines fully unsupervised learning with semi-supervised learning.

This study demonstrates that our convolutional network with a novel hybrid training approach can be generalised across different materials, and k-fold validation shows that the model retains good accuracy with changing training conditions. The model can predict the processing maps for the different alloys with an accuracy of up to 96% in some cases. It is also shown that powder-free single-track experiments are a useful indicator for predicting the final print quality of a component.

The “invariant information clustering” (IIC) approach is applied to process optimisation for additive manufacturing, and a novel hybrid data set construction approach that accounts for uncertainty in the ground truth data, enables the trained convolutional model to perform across a range of different materials and most importantly, generalise to materials outside of the training data set. Compared to the traditional cross-sectioning approach, this method considers the whole length of the single track when determining the melting mode.

This study aims to enhance merging of additive manufacturing (AM) techniques with powder injection molding (PIM). In this way, the prototypes could be 3D-printed and mass production implemented using PIM. Thus, the surface properties and mechanical performance of parts produced using powder/polymer binder feedstocks [material extrusion (MEX) and PIM] were investigated and compared with powder manufacturing based on direct metal laser sintering (DMLS).

PIM parts were manufactured from 17-4PH stainless steel PIM-quality powder and powder intended for powder bed fusion compounded with a recently developed environmentally benign binder. Rheological data obtained at the relevant temperatures were used to set up the process parameters of injection molding. The tensile and yield strengths as well as the strain at break were determined for PIM sintered parts and compared to those produced using MEX and DMLS. Surface properties were evaluated through a 3D scanner and analyzed with advanced statistical tools.

Advanced statistical analyses of the surface properties showed the proximity between the surfaces created via PIM and MEX. The tensile and yield strengths, as well as the strain at break, suggested that DMLS provides sintered samples with the highest strength and ductility; however, PIM parts made from environmentally benign feedstock may successfully compete with this manufacturing route.

This study addresses the issues connected to the merging of two environmentally efficient processing routes. The literature survey included has shown that there is so far no study comparing AM and PIM techniques systematically on the fixed part shape and dimensions using advanced statistical tools to derive the proximity of the investigated processing routes.

To address the shortcomings of the traditional back propagation (BP) neural network agent model, such as insufficient fitting accuracy and low computational efficiency, an improved method is proposed.

In this study, an improved sparrow search algorithm (ISSA) is developed to optimize the reliability calculation of the BP neural network (ISSA-BP) using an enhanced BP neural network model. The traditional sparrow search algorithm is enhanced by incorporating a golden sine strategy to improve its position-updating mechanism, thereby overcoming its tendency to converge prematurely to local optima. Additionally, an opposition-based learning strategy is integrated to explore the reverse solution around the optimal solution of the sparrow search algorithm, mitigating the risk of local optima.

The results of the test function demonstrate that the proposed method significantly enhances fitting accuracy while maintaining computational efficiency. Finally, by applying this approach to the metro bogie frame as a case study, the structural reliability of the bogie frame is evaluated using the Monte Carlo method, providing valuable insights for subsequent analysis and structural optimization.

The use of the surrogate model approach for structural reliability analysis significantly improves solution efficiency. Furthermore, the integration of ISSA with the BP neural network enhances both fitting accuracy and computational efficiency, demonstrating the superiority and practicality of the proposed method.

In response to the challenges posed by the social environment, numerous individuals with autism use coping strategies, engaging in a phenomenon known as “camouflaging” to mask their distinctive traits. Despite the growing attention directed toward this area of study, to date, there are no bibliometric studies delineating the evolution of this research theme. This study aims to analyze scientific research on camouflage and the autistic spectrum published in Web of Science (WoS).

Articles on camouflage and autism were retrieved from the WoS. The advanced search interface used was “Title, Abstract, Keywords (autistic OR asperger syndrome OR Autism OR ASD AND camouflage OR camouflaging AND Masking).” The analysis and visualization of the selected records and their data were carried out using MS Excel (v16.0) and Biblioshiny (v2.0, R package).

Following the selection process, a total of 70 articles were included. Notably, the UK emerged as the leading contributor in terms of the number of published articles. Prominent authors in this domain included William Mandy, Laura Hull and Meng-Chuan Lai, who exhibited the highest number of published articles and citations. The Journal of Autism and Developmental Disorders stood out as the most prolific journal. Predominant research themes encompassed camouflage (and its synonyms, masking or compensation), gender differences and mental health.

This study contributes to existing knowledge on this topic, as well as identifying new areas of research related to autism and camouflage. Moreover, it serves as a valuable resource for health-care professionals, including physicians and clinical psychologists, who may seek to augment their understanding and proficiency in the field.

This study aims to empirically test the direct effects of three types of information technology (IT) use on three dimensions of supply chain coordination (SCC). It further explores the spillover effects of IT use on coordination beyond its domain across the SC. Besides, this study probes into the moderating effects of environmental uncertainty (EU).

Structural equation modelling (SEM) is used to examine data collected from 202 firms in Hong Kong. Furthermore, multi-group SEM analysis with a series of invariance tests was used to test the moderating effects of EU.

Internal, supplier and customer IT use have direct effects on internal, supplier and customer coordination, respectively. Besides, IT use generates forward spillover effects beyond its domain along the forward physical flow in an SC. EU positively moderates the relationships between external IT use and SCC but does not shape the effectiveness of internal IT use in enhancing SCC. Moreover, under a high EU, the spillover effects of IT use on coordination can be intensified. Besides, the spillover effects further expand to benefit coordination on a larger scale of the SC under a high EU.

This study contributes by revealing that in addition to direct effects, IT use in a specific domain could generate spillover effects on coordination beyond its domain throughout an SC. More importantly, it contributes by explaining the difference in the effectiveness of IT use under different levels of EU using multi-group SEM analysis.

The ethical requirement for nurses during the COVID-19 pandemic reached the peak. Healthcare professionals were asked to provide creative services to patients while making ethical decisions. This study aims to understand how ethical leadership and ethical climate interact with help seeking behavior to influence nurses’ creativity.

In this longitudinal study, the authors employed 193 nurses from a large hospital in China. Eligible nurses completed two surveys (one month apart) during the COVID-19 pandemic.

This study found that help seeking during problem-solving mediates the relationship between ethical leadership and creativity while ethical climate moderates the relationship between ethical leadership and help seeking in nurses.

Scholars have rarely studied the relationship between ethical leadership and creativity in the healthcare setting. More importantly, this study unveiled the compounded effect of ethical leadership and ethical climate on creativity via nurses’ help seeking behavior in China.

Drawing on power approach-inhibition theory, this study aims to theorize a cross-level model to examine how team member personal power (i.e. expert power and referent power) impacts shared leadership through activating their taking charge behaviors in R&D teams, as well as the moderating effect of team learning orientation on the relationship between team member taking charge behaviors and shared leadership.

With multisource data collected from 264 employees in 58 R&D teams from 13 companies, this study tested the hypotheses of the cross-level theoretical model using Mplus 7.4.

The results showed that team member expert power was positively related to their taking charge behaviors, which in turn led to shared leadership, while team member referent power was not significantly related to their taking charge behaviors. Furthermore, the positive relationship between team member taking charge behaviors and shared leadership was strengthened by team learning orientation.

This paper offers suggestions regarding how vertical leaders should pay attention to team member power to promote their change-oriented taking charge behaviors and address team learning to strengthen the effect of team member taking charge behaviors on shared leadership.

By echoing the changing focus towards a shared leading process among team members in leadership literature, this paper provides important insights for both scholars and practitioners to understand the role that power plays in activating team member taking charge behaviors which in turn improves shared leadership.

In contrast to other Chinese sports, which attract enormous commercial interests and relates to a kind of political kudos and ideological significance, Chinese baseball represents a singular case for the author to investigate the way in which China responds to sport globalisation due to its peripheral position in, and limited resource from, the domestic sport system and powerful stakeholders from the global society. The study examines the development of Chinese baseball and its interplay with the global sport giants and international events (i.e. the Olympic Games and the Major League Baseball [MLB]). Given the influence of sport globalisation, the author also identifies the consistent dominance of Chinese sport authorities and the spectrum of local stakeholders' reaction towards globalisation. The chapter ends with a discussion of the relationship between marginal position of Chinese baseball in the local sport system and the rationale of its response to sport globalisation.

The purpose of this study is to investigate the technology-focused and technology-supported dilemmas that firms have encountered and their digital orientation from a nuanced perspective to answer the following research questions: What digital orientations do companies take in launching digital initiatives? How does the choice between a proactive digital orientation (Pro-DO) and a reactive digital orientation (Rea-DO) influence firm value?

The authors adopted machine learning and a quantitative research approach using observations from China’s listed companies from 2010 to 2020 and applied statistical techniques and regression analysis to examine the effect of digital orientation alternatives on firm value.

The findings of this study indicate that firms with a Pro-DO exhibit a positive effect on firm value. In contrast, firms with a Rea-DO do not demonstrate the same positive relationship with firm value. Additionally, this study reveals that firms with better corporate governance practices and lower financing constraints are more responsive to the positive effects of Pro-DO on firm value.

We elucidate two primary perspectives of digital orientation: Pro-DO and Rea-DO. Additionally, we empirically showcase their nuanced influences on firm value, thereby enriching knowledge in the fields of strategic orientation and digital transformation. Moreover, our findings underscore the importance of corporate governance and financing constraints as moderators.

Coordinating low-carbonization and digitalization is a practical implementation pathway to achieve high-quality economic development. Regions are under great emission reduction pressure to achieve low-carbon development. However, why and how regional emission reduction pressure influences enterprise digital transformation is lacking in the literature. This study empirically tests the impact of emission reduction pressure on enterprise digital transformation and its mechanism.

This article takes the data of non-financial listed companies from 2011 to 2020 as a sample. The digital transformation index is measured by entropy value method. The bidirectional fixed effect model was used to test the hypothesis.

The research results show that emission reduction pressure forces enterprise digital transformation. The mechanism lies in that emission reduction pressure improves digital transformation by promoting enterprise innovation, and digital economy moderates the nexus between emission reduction pressure and digital transformation. Furthermore, the effect of emission reduction pressure on digital transformation is more significant for non-state-owned, mature and high-tech enterprises.

This paper discusses the mediating role of enterprise innovation between carbon emission reduction pressure and enterprise digital transformation, as well as the moderating role of digital economy. The research expands the body of knowledge about dual carbon targets, digitization and technological innovation. The author’s findings help update the impact of regional digital economy development on enterprise digital transformation. It also provides theoretical guidance for the realization of digital transformation by enterprise innovation.