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The purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.

This research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.

This study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.

Originality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Innovation has become a necessity for the information technology (IT) sector, especially during COVID-19 pandemic. Therefore, this study aims to investigate how knowledge sharing affects employees’ innovative work behavior (IWB). Specifically, the study examined occupational self-efficacy (as mediating mechanism) and entrepreneurial leadership (as boundary condition) to encourage IWB.

The study used social media platforms to collect data from 270 employees working in the IT sector through “google forms” on convenience basis between March and August, 2021. The study applied structural equation modeling in two stages to examine the measurement model (for uni-dimensionality) and the structural model (for hypotheses testing).

The study noted that knowledge sharing positively affects employees’ IWB and occupational self-efficacy positively explains this association. In addition, employees’ perception of entrepreneurial leadership strengthens the association between knowledge sharing and IWB.

The study collected data from a developing country during COVID-19 by using a cross-sectional design that may restrict causality. However, the findings suggest the management not only encourages knowledge sharing environment but also engages employees in various training that motivate them to experiment with new ideas and techniques.

This study extends the existing literature on knowledge sharing and IWB by exploring occupational self-efficacy as mediating mechanism and entrepreneurial leadership as a boundary condition.

The purpose of this paper is to investigate the role of gender in the relationships between employee’s work experience and their chances of obtaining a job promotion.

Five hundred middle to lower management Pakistan employees from service industries were surveyed. Measures used to obtain data included work experience, job promotions and gender. Gender was dummy coded. Moderation analyses via SPSS was used to investigate the moderating effect of gender in the relationship between employee’s work experience and job promotions.

The results indicated that work experience was positively associated with job promotion. However, the results indicated that gender fully moderated the relationship between work experience and job promotion. Specifically, female employees were less likely to get promoted compared to male employees despite having similar work experiences.

Drawing together the human capital theory, social role theory and cultural factors, this study highlighted the socioeconomic/cultural barriers’ impact on Pakistani women’s career ascendance.

There is a lack of empirical evidence about career ascendance among Pakistani women. Therefore, the aim of the present study is to conduct and provide empirical evidence so that relevant agencies within Pakistan can develop more gender-equitable promotional policies and processes.

This study responded to the call for more empirical investigation of career ascendancy among women in developing countries. While this issue has been discussed in many developed western countries, there is a lack of empirical evidence in Muslim Pakistan.

The purpose of this study is to investigate the relationship between sustainable leadership and sustainable project performance. Specifically, the study aims to examine the mediating role of knowledge integration, examining how knowledge integration within an organization influences project outcomes. In addition, the study seeks to explore the moderating role of top management knowledge values, examining how the values and beliefs of top management influence the relationship between sustainable leadership and project performance.

A three-wave survey of 392 employees working in construction sector projects in Pakistan used both hierarchical regression analysis and Hayes’ PROCESS macro method to evaluate the hypotheses.

The study results show that sustainable leadership positively relates to sustainable project performance and knowledge integration mediating this relationship. In addition, the top management knowledge value moderates the indirect effect of sustainable leadership on sustainable project performance via knowledge integration.

Although the model was tested using three-wave data, it is important to note that the data were obtained from a single source. Therefore, it is possible that common method bias may have influenced the results, and this cannot be disregarded.

Organizations seek to prioritize sustainability and integrate sustainability considerations into their project management processes. Organizations can achieve improved sustainable project performance by investing in sustainable leadership development, fostering a culture of knowledge sharing and learning, prioritizing top management support for sustainable performance and integrating sustainable considerations into project management processes.

The study’s grounding on organizational learning theory adds an original and valuable perspective to the relationship between sustainable leadership and sustainable project performance. This investigation is original, as it combines sustainable leadership, knowledge integration and the moderating role of top management knowledge value to understand their impact on sustainable project performance. This unique approach contributes to the literature by providing new insights into these relationships and mechanisms in the construction industry.

With multiple-related organizations, worldwide infections, deep economic recession and public disorder, and large consumption amount of anti-epidemic resources, the coronavirus disease 2019 (COVID-19) has been defined as a public health emergency of international concern (PHEIC). Nowadays, Wuhan has recovered from the pandemic disaster and reentered normalization. The purposes of this study are to (1) summarize organization collaboration patterns, successful experience and latent defects under across-stage evolution of Wuhan's cooperation governance mode against the pandemic, and on the basis, (2) reveal how the COVID-19 development trends and organizations' collaborative behaviors affected each other.

Detailed content analysis of online news reports covering COVID-19 prevention and control measures on the website of Wuhan Municipal Government was adopted to identify organizations and their mutual collaborative interrelationships. Four complex network (CN) models of organization collaboration representing the outbreak, preliminary control, recession and normalization stages, respectively, were established then. Time-span-based dynamic parameter analyses of the proposed networks, comprising network cohesiveness analysis and node centrality analysis, were undertaken to indicate changes of global and local characteristics in networks.

First, the definite collaborative status of Wuhan Headquarters for Pandemic Prevention and Control (WHPPC) has persisted throughout the period. Medical institutions and some other administrations were the most crucial participants collaborating with the WHPPC. Construction-industry organizations altered pandemic development trends twice to make the situation controllable. Media, large-scale enterprises, etc. set about underscoring themselves contributions since the third stage. Grassroots cadres and healthcare force, small and medium-sized enterprises (SMEs), financial institutions, etc. were essential collaborated objects. Second, four evolution mechanisms of organization collaboration responding to the COVID-19 in Wuhan has been proposed.

First, universality of Wuhan-style governance experience may be affected. Second, the stage-dividing process may not be the most appropriate. Then, data source was single and link characteristics were not considered when modeling.

This study may offer beneficial action guidelines to governmental agencies, the society force, media, construction-industry organizations and the market in other countries or regions suffering from COVID-19. Other organizations involved could also learn from the concluded organizations' contributions and four evolution mechanisms to find improvement directions.

This study adds to the current theoretical knowledge body by verifying the feasibility and effectiveness of investigating cooperation governance in public emergencies from the perspectives of analyzing the across-stage organization collaboration CNs.