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In the brain imaging based on electrical impedance tomography, it is sometimes not able to attach 16 electrodes due to space restriction caused by craniotomy. As a result of this, the number of boundary measurements decreases, and spatial resolution of reconstructed conductivity distribution is reduced. The purpose of this study is to enhance reconstruction quality in cases of limited measurement.

A new data expansion method based on the shallow convolutional neural network is proposed. An eight-electrode model is built from which fewer boundary measurements can be obtained. To improve the imaging quality, shallow convolutional neural network is constructed which maps limited voltage data of the 8-electrode model to expanded voltage data of a quasi-16-electrode model. The predicted data is compared with the quasi-16-electrode data. Besides, image reconstruction based on L1 regularization method is conducted.

The results show that the predicted data generally coincides with the quasi-16-electrode data. It is found that images reconstructed with the data of eight-electrode model are the poorest. Nevertheless, imaging results when the limited data is expanded by the proposed method show large improvement, and there is a minor difference with the images recovered with the quasi-16-electrode data. Also, the impact of noise is studied, which shows that the proposed method is robust to noise.

To enhance reconstruction quality in the case of limited measurement, a new data expansion method based on the shallow convolutional neural network is proposed. Both simulation work and phantom experiments have demonstrated that high-quality images of cerebral hemorrhage and cerebral ischemia can be obtained when the limited measurement is expanded by the proposed method.

This study aims to review earned value management (EVM)-relative methods, including the original EVM, earned schedule method (ESM) and earned duration management (EDM(t)). This study then proposes a general implementation procedure and some basic principles for the selection of EVM-relative methods.

After completing an intensive literature review, this study conducts a case study to examine the forecasting performance of project duration using the EVM, ESM and EDM(t) methods.

When the project is expected to finish on time, ESM with a performance factor equal to 1 is the recommended method. EDM(t) would be the most reliable method during a project's entire lifetime if EDM(t) is expected to be delayed based on past experience.

As this research conducts a case study with only one building construction project, the results might not hold true for all types of construction projects.

EVM, ESM and EDM(t) are simple and data-accessible methods. With the help of a general implementation procedure, applying all three methods would be better. The power of the three methods is definitely larger than that of choosing only one for complex construction projects.

Previous studies have discussed the advantages and disadvantages of EVM, ESM and EDM(t). This study amends the available outcomes. Thus, for schedulers or researchers interested in implementing EVM, ESM and EDM(t), this study can provide more constructive instructions.

Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

The purpose of this study is to investigate the effectiveness of university-driven knowledge transfer initiatives in enhancing the capabilities and performance of local suppliers in the garment industry. By focusing on the impact of UDIs in Myanmar, this research aims to provide empirical evidence on how these initiatives can foster supplier development and performance improvement through targeted capability enhancement strategies.

This study utilizes a combination of surveys and an experimental design to evaluate the impact of university-driven supplier development interventions (UDIs) based on Lean principles in Myanmar’s garment industry. Nine garment suppliers were assessed before and after the UDI program. The research employed partial least squares structural equation modeling (PLS-SEM) to analyze the direct, indirect and mediating effects of UDIs on supplier performance, focusing on the role of supplier capability enhancement as a mediating factor.

The study found that the UDI program significantly improved supplier capabilities, which in turn led to enhanced performance. The analysis revealed partial mediation, indicating that while UDIs directly impact supplier performance, their effect is significantly amplified through the enhancement of supplier capabilities. These findings highlight the critical role of targeted capability development in achieving substantial performance improvements among local suppliers.

This research contributes to the literature by providing empirical evidence on the effectiveness of university-driven supplier development initiatives in a developing country context. It validates the indirect role of UDIs in boosting supplier performance via capability enhancement, emphasizing the importance of industry-specific and capability-focused development strategies. The findings underscore the value of structured knowledge transfer programs in supporting local suppliers, offering practical insights for policymakers and educational institutions aiming to enhance industrial performance through strategic interventions.

This study presents evidence of the role of performance measurement and management (PMM) in sustainable supply chain governance. This study tests a model hypothesizing whether it is the PMM itself or the mediating effect of supply chain governance that is essential for both business and sustainability performance.

This study builds on a survey of 274 SMEs in Finland.

The findings indicate that PMM does not directly contribute to SMEs’ business or sustainability performance. Supply chain governance mediates the relationship between PMM and business performance. Business performance also enhances sustainability.

These findings can guide managers in managing company relationships with customers and suppliers. The mediating role of supply chain governance highlights the potential of PMM to enhance performance. Without supply chain governance, the PMM, while efficient in traditional business practices, may lose its effectiveness because of the pressure to advance sustainability values within firm operations.

The role of PMM in enhancing supply chain sustainability is frequently overlooked in the existing research, necessitating an empirical evaluation of PMM’s impact on supply chain sustainability. This study addresses this gap by focusing on the SME context, where the pressure to adopt sustainable practices is increasing, yet SMEs employ PMM less frequently than larger firms.

Compressing project timelines represents a prevalent temporal tactic aimed at accelerating the innovation process. However, empirical evidence on the impact of such time constraints on innovation remains inconclusive. This study aims to investigate the relationship between a prevalent organizational time mechanism—Performance Appraisal Interval (PAI)—and employee exploratory innovation behavior. Additionally, we explore the boundary conditions that may influence this relationship: the moderating effects of future work self salience and supervisory developmental feedback.

Using online survey data collected in two waves from 426 employees working in hi-tech companies in China, we tested all the hypotheses.

(1) PAI demonstrates an inverted U-shaped influence on employees exploratory innovation behavior; (2) Employees’ future work self salience serves as a moderator that enhances the positive nature of this inverted U-shaped relationship; (3) Supervisory developmental feedback amplifies the moderating role of future work self salience, and the synergistic effect of PAI, future work self salience, and supervisory developmental feedback significantly enhances exploratory innovation behavior.

By providing insights that are attuned to the temporal aspects of performance appraisal, this study aids organizations in making more informed, strategic decisions that enhance both the effectiveness of performance assessments and the cultivation of an environment that encourages exploratory innovation. Additionally, it is recommended that organizational leaders incorporate future-oriented interventions and developmental feedback into their management practices to further promote employees' engagement in exploratory innovation.

Drawing on the interactive theory of performance, this study introduces a novel perspective on how an organizational temporal mechanism influences exploratory innovation and advances our understanding of the non-linear link between time constraints and employees' innovative behaviors.

The paper aims to clarify the influence of the equivalent particles number (EPN) change on the flow velocity characteristic.

The paper opted for an exploratory study using PIV technology to obtain the transient flow toxicity vector of oil in the square pipeline.

The paper provides empirical insights about the influence of EPN on the flow average velocity which is most prominent in the middle of the pipeline, and smaller EPN values have a greater impact.

These influence laws of EPN can be used to obtain the dynamic characteristics of oil, which provides theoretical support for oil pollution control and effective treatment measures and lays a preliminary foundation for the online monitoring of particles in oil.

Drawing on social information processing theory, this study constructs a multilevel moderated mediation model. This model seeks to delve into the intricate and previously overlooked interplay between supervisor bottom-line mentality (BLM) and knowledge hiding. Within this context, we introduce self-interest as a mediating factor and incorporate performance climate as a team-level moderating variable.

The time-lagged data involve 336 employees nested in 42 teams from 23 automobile sales companies in five regions of China. The analysis was meticulously executed using Hierarchical Linear Modeling, complemented by bias-corrected bootstrapping techniques.

The findings reveal that self-interest acts as a full mediator in the positive link between supervisor BLM and knowledge hiding. Furthermore, the performance climate plays a moderating role in both the relationship between supervisor BLM and self-interest, and the entire mediation process. Notably, these relationships are intensified in environments with a high performance climate compared to those with a low one.

This research stands as one of the pioneering efforts to integrate supervisor BLM into the discourse on knowledge hiding, elucidating the underlying psychological mechanisms and delineating the boundary conditions that shape the “supervisor BLM–knowledge hiding” relationship. Further, our insights provide organizations with critical guidance on strategies to curtail knowledge hiding among their employees.

The aim of this study is to examine the relationships between taking charge, bootlegging innovation and innovative job performance, and to explore the moderating roles of felt responsibility for constructive change (FRCC) and creative self-efficacy (CSE).

Data for this research was collected from 503 employees working in a chain company. Through a longitudinal study design, a three-wave survey with 397 valid data provided support for the proposed theoretical model.

The results maintain a positive association between taking charge, bootlegging innovation and innovative job performance, indicating the mediating effect of bootlegging innovation. Additionally, both the FRCC and CSE facilitate the indirect effect of taking charge on innovative job performance through bootlegging innovation. Furthermore, the integrated moderated mediation model analysis suggested that FRCC is more vital in improving employees' innovative job performance.

This research aims to break the black box between taking charge and innovative job performance, which has been relatively unexplored. Drawing from self-determination theory (SDT) and the proactive motivation model, the authors verify the bridge-building role of bootlegging innovation and the dual-facilitating effects of FRCC and CSE while employees conduct taking charge. This study’s results provide new insight for managers to foster, encourage and support employees' proactive behavior.

In the context of growing efforts by online businesses to enhance consumer connections, understanding consumer engagement behaviors is imperative. This study explores consumer engagement within online shopping platforms, specifically introducing and examining the roles of crowdsourcing and crowdsending.

The study developed and validated measurement scales for crowdsourcing and crowdsending engagement across transactional, multi-sided and inspirational platforms.

Identifying five sub-dimensions within crowdsourcing and crowdsending, the results unveiled nuances in consumer–platform interactions, emphasizing the value of co-creation. Crowdsourcing entails transaction-oriented engagements such as knowledge gathering, utilitarian and hedonic browsing, interaction and co-shopping. The findings revealed that crowdsourcing significantly influenced platform commitment, surpassing the impact of crowdsending on transactional platforms. Conversely, crowdsending involves knowledge sharing, feedback, participation, advocacy and reciprocity, fostering active engagement and shared value within the platform ecosystem. Notably, the results showed that crowdsending strengthened commitment to inspirational platforms more than to conventional shopping platforms.

This study contributes to the theoretical understanding of a range of consumer engagement experiences in online shopping environments and presents practical applications, offering valuable insights for commerce businesses aiming to optimize their digital strategies.