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I am concerned with insights heterodox economics, and particularly the new institution economics, can offer regarding interactions between health emergencies and the airline industry. Air services not only facilitates the transmissions of diseases among humans, and between animals, but on the positive side, can expedite the movement of medicines and the transfer of those afflicted during pandemics and epidemics. They can serve to limit the outbreak of disease by providing “mercy flights” to regions poorly served by other modes. Significant challenge confronting carriers during a major event, however, often included immediate financial shocks as well as ensuring their operations do not contribute to the spread of disease. These economic challenges for both commercial carriers and public policymakers involve sources of finance for what is, in this context, a semi-public service as well as the extent to which airlines should retain standby capacity extending resilience to the air-service supply chain. Conventional neoclassical economics, while still at the center of analyzing many of these sorts of issues, has increasingly, because of its rigid assumption and poor forecasting record, been supplemented by heterodox economic thinking. This trend has been reinforced by progress in fields such as psychology. What I demonstrate is that heterodox economics, and especially the role of institutions, offers a more complete picture of the interactions between air transportation and the spread of disease.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

Presently, 6 Degree of Freedom (6DOF) visual pose measurement methods enjoy popularity in the industrial sector. However, challenges persist in accurately measuring the visual pose of blank and rough metal casts. Therefore, this paper introduces a 6DOF pose measurement method utilizing stereo vision, and aims to the 6DOF pose measurement of blank and rough metal casts.

This paper studies the 6DOF pose measurement of metal casts from three aspects: sample enhancement of industrial objects, optimization of detector and attention mechanism. Virtual reality technology is used for sample enhancement of metal casts, which solves the problem of large-scale sample sampling in industrial application. The method also includes a novel deep learning detector that uses multiple key points on the object surface as regression objects to detect industrial objects with rotation characteristics. By introducing a mixed paths attention module, the detection accuracy of the detector and the convergence speed of the training are improved.

The experimental results show that the proposed method has a better detection effect for metal casts with smaller size scaling and rotation characteristics.

A method for 6DOF pose measurement of industrial objects is proposed, which realizes the pose measurement and grasping of metal blanks and rough machined casts by industrial robots.

This research investigates whether focal firms employ strategic supply chain information disclosure, focusing on the concealment of supplier and customer identities, as part of their supply chain environmental risk management strategies (supplier sustainability risk and customer loss risk, respectively).

Using a panel dataset of Chinese listed firms from 2009 to 2019 and utilizing the suppliers’ environmental punishment of peer firms (peer events) as an exogenous shock and employing ordinary least squares (OLS) estimation, this study conducts a regression analysis to test how focal firms disclose the identities of their suppliers and customers.

Our results indicate that focal firms prefer to hide the identities of their suppliers and customers following the environmental punishment of peer firms’ suppliers. In addition, supplier concentration weakens the effect of withholding supplier identities, whereas customer concentration strengthens the effect of hiding customer identities. Mechanism analysis shows that firms hide supplier identities to avoid their reputation being affected and hide customer identities to prevent the deterioration of customers’ reputations and thus impact their market share.

Our study reveals that reputation spillover is another crucial factor in supply chain transparency. It is also pioneering in applying the anonymity theory to explain focal firms’ information disclosure strategy in supply chains.

This review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

This study explores whether media coverage of Chinese food safety guide consumer behaviour and determines its impact.

Using data from the China Family Panel Studies, this study implements unsupervised machine learning methods to quantitatively identify themes in news media coverage of food safety across various provinces and regions. Based on these findings, this study examines the impact of coverage of food safety on consumer behaviour related to FAFH.

We find that media coverage of food safety in the restaurant sector significantly decreases household expenditure on FAFH relative to total expenditure. While negative coverage substantially decreases expenditure on FAFH, non-negative coverage significantly increases it. Reports of food safety incidents outside consumers’ province are negatively correlated with expenditure on FAFH, whereas reports within province significantly increases such spending. Further, the negative impact of media coverage on FAFH spending is less pronounced among higher-income families, households headed by individuals with high educational levels and those with low sensitivity to newspaper information. A robust government information infrastructure also mitigates this negative impact.

The findings have important policy reference value for promoting the healthy development of catering and other life services by improving news reporting and the regulatory system.

This article employs machine learning methods to identify news reports related to food safety in the catering industry quantitatively and incorporates them into the study of household consumption in China. Consequently, this not only fills a gap in the existing literature but also provides a new perspective for interdisciplinary research in economics, sociology and computer science.

Robo-advisory has become an increasingly popular asset management tool in recent decades. This paper studies the association between robo-advisor usage and perceived financial satisfaction.

Using data extracted from the National Financial Capability Study 2015 (NFCS2015), the present study carried out a logistic analysis that examines the association between robo-advisory and perceived financial satisfaction. This model also studies the interaction effect of age on this association.

The present study finds that robo-advisor usage is positively correlated with a person’s perceived financial satisfaction after controlling for covariates related to financial literacy and other demographic factors. Moreover, the present study reveals that age moderates the association between robo-advisory usage and financial satisfaction. The results are robust after regressing financial satisfaction on robo-advisory by different age groups.

This paper extends existing literature on robo-advisory by showing that robo-advisory usage relates to a higher level of financial satisfaction. This finding helps understand the rapidly increasing trend of robo-advisory in the financial industry. Moreover, the present study reveals a moderate effect of age on the association between robo-advisory usage and perceived financial satisfaction.

Blockchain is a developing technology that affects numerous industries, including facility management (FM). Many barriers are associated with adopting blockchain-enabled building information modeling (BEBIM) in FM. This research aims to identify and prioritize the barriers to adopting BEBIM in FM.

To address the knowledge gap, this study employs a two-phase methodology for evaluating the barriers to adopting BEBIM in FM. The first phase involves a comprehensive literature review identifying 14 barriers to BEBIM adoption. Using a Delphi approach, the identified barriers were categorized into 6 groups and finalized by 11 experts, adding 3 more barriers to the list. The best-worst method (BWM) determines the priority weights of identified barriers and sub-barriers in the second phase.

This study reveals that adopting BEBIM for FM in India faces significant hurdles. The most critical barriers are “limited collaboration” and “communication among stakeholders,” “legal constraints in certain jurisdictions” and “challenges in establishing trust and governance models.” To mitigate these barriers, stakeholders should foster collaboration and communication, develop efficient blockchain technology (BT) and establish a trust and governance model.

This work underscores the importance of formulating effective strategies to overcome the identified barriers and emphasizes implications that can assist policymakers and industry stakeholders in achieving successful BEBIM adoption for improved FM practice.

The study provides valuable insights for policymakers, construction industry stakeholders and facility managers interested in leveraging this technology to improve the efficiency and effectiveness of FM practice in India.

This study aims to (1) investigate the influence of firms’ social media utilization on performance through supply chain agility, (2) examine the mediating role of supply chain agility and (3) explore the indirect effect of social media utilization on operational performance via supply chain agility as knowledge transfer increases.

A survey of 298 Chinese manufacturing firms was conducted to assess the proposed relationships, employing moderated mediation analysis with Andrew Hayes (2017) PROCESS macro.

Social media utilization indirectly enhances operational performance through supply chain agility, supporting our mediation hypothesis (H1). Additionally, knowledge transfer moderates the positive impact of social media utilization on supply chain agility (H2). The moderated mediation analysis reveals that the mediating effect of supply chain agility on operational performance is stronger at higher levels of knowledge transfer (H3), shedding light on the intricate relationships between these variables and providing insights for businesses seeking to leverage social media and knowledge transfer to enhance supply chain resilience and operational performance.

This study empirically investigates the role of social media utilization in supply chains within the digital age. We explore how social media enhances supply chain agility and knowledge transfer, highlighting its transformative potential for real-time communication, responsiveness and collaboration across networks. By integrating dynamic capability theory with contemporary digital practices, we demonstrate how leveraging digital platforms alongside traditional supply chain processes can significantly improve manufacturing efficiency. This research bridges existing gaps in the literature and provides valuable insights for businesses navigating complex, rapidly changing environments in the era of digital transformation.

Green innovation in supply chains is crucial for socioeconomic development and stability. Factors that influence collaborative green innovation in the supply chain are complex and diverse. Exploring the main influencing factors and their mechanisms is essential for promoting collaborative green innovation in supply chains. Therefore, this study analyzes how upstream and downstream enterprises in the supply chain collaborate to develop green technological innovations, thereby providing a theoretical basis for improving the overall efficiency of the supply chain and advancing green innovation technology.

Based on evolutionary game theory, this study divides operational scenarios into pure market and government-regulated operations, thereby constructing collaborative green innovation relationships in different scenarios. Through evolutionary analysis of various entities in different operational scenarios, combined with numerical simulation analysis, we compared the evolutionary stability of collaborative green innovation behavior in supply chains with and without government regulation.

Under pure market mechanisms, the higher the green innovation capability, the stronger the willingness of various entities to collaborate in green innovation. However, under government regulation, a decrease in green innovation capability increases the willingness to collaborate with various entities. Environmental tax rates and green subsidy levels promote collaborative innovation in the short term but inhibit collaborative innovation in the long term, indicating that policy orientation has a short-term impact. Additionally, the greater the penalty for collaborative innovation breaches, the stronger the intention to engage in collaborative green innovation in the supply chain.

We introduce the factors influencing green innovation capability and social benefits in the study of the innovation behavior of upstream and downstream enterprises, expanding the research field of collaborative innovation in the supply chain. By comparing the collaborative innovation behavior of various entities in the supply chain under a pure market scenario and government regulations, this study provides a new perspective for analyzing the impact of corresponding government policies on the green innovation capability of upstream and downstream enterprises, enriching theoretical research on green innovation in the supply chain to some extent.