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This study examines the importance of income income-shifting arrangements of US multinational corporations (MNCs) on future stock price crash risk.

This study employs a sample of 7,641 corporation-year observations over the 2005–2017 period and uses ordinary least squares regression analysis.

The authors find that the income-shifting arrangements of MNCs are positively and significantly associated with stock price crash risk after controlling for corporate tax avoidance and other known determinants of stock price crash risk in the regression model. This result is robust to alternative measures of stock price crash risk and income-shifting, and several endogeneity tests. The authors also observe that income-shifting arrangements increase stock price crash risk both directly and indirectly through the information opacity channel. Finally, in cross-sectional analyses, the authors find that the positive association between income-shifting and stock price crash risk is more pronounced for MNCs that use tax haven subsidiaries and have weak corporate governance mechanisms.

The authors provide new empirical evidence that MNCs will likely face significant capital market consequences regarding their income-shifting arrangements.

Rural–urban migration has led to an increase in the community’s need for housing in the migration area. The demand for housing is getting higher while the land availability does not increase so that house prices will continue to increase. This study aims to estimate the impact of immigration on urban housing prices in Indonesia.

This study examines the effect of immigration on urban housing prices at the city level in Indonesia by using 14 major cities data from 2012 to 2020 to build a panel data model. The model also incorporates urban economic conditions as control variables.

From the national level, the authors find that inter-regional migration has a significant and positive impact on urban housing prices. Based on the results, this paper suggested addressing the volatility of house prices through the provision of decent and affordable housing improvement to meet the growing needs and demands of the immigrant population.

This study still has several limitations: the sample of cities used is not comprehensive enough, and the time period used is not long enough; the spatial impact on house prices is not taken into account, and the effect of migrant characteristics in each city has not been considered.

There is limited research on the impact of immigration on urban housing prices in city levels, especially in the case of Indonesia. In addition, recent migration is used to proxy the immigration pattern. This paper provides a valuable contribution to the empirical literature on the effect of immigration at the city level in developing countries.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.

The development of new advanced materials, such as photopolymerizable resins for use in stereolithography (SLA) and Ti6Al4V manufacture via selective laser melting (SLM) processes, have gained significant attention in recent years. Their accuracy, multi-material capability and application in novel fields, such as implantology, biomedical, aviation and energy industries, underscore the growing importance of these materials. The purpose of this study is oriented toward the application of new advanced materials in stent manufacturing realized by 3D printing technologies.

The methodology for designing personalized medical devices, implies computed tomography (CT) or magnetic resonance (MR) techniques. By realizing segmentation, reverse engineering and deriving a 3D model of a blood vessel, a subsequent stent design is achieved. The tessellation process and 3D printing methods can then be used to produce these parts. In this context, the SLA technology, in close correlation with the new types of developed resins, has brought significant evolution, as demonstrated through the analyses that are realized in the research presented in this study. This study undertakes a comprehensive approach, establishing experimentally the characteristics of two new types of photopolymerizable resins (both undoped and doped with micro-ceramic powders), remarking their great accuracy for 3D modeling in die-casting techniques, especially in the production process of customized stents.

A series of analyses were conducted, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, mapping and roughness tests. Additionally, the structural integrity and molecular bonding of these resins were assessed by Fourier-transform infrared spectroscopy–attenuated total reflectance analysis. The research also explored the possibilities of using metallic alloys for producing the stents, comparing the direct manufacturing methods of stents’ struts by SLM technology using Ti6Al4V with stent models made from photopolymerizable resins using SLA. Furthermore, computer-aided engineering (CAE) simulations for two different stent struts were carried out, providing insights into the potential of using these materials and methods for realizing the production of stents.

This study covers advancements in materials and additive manufacturing methods but also approaches the use of CAE analysis, introducing in this way novel elements to the domain of customized stent manufacturing. The emerging applications of these resins, along with metallic alloys and 3D printing technologies, have brought significant contributions to the biomedical domain, as emphasized in this study. This study concludes by highlighting the current challenges and future research directions in the use of photopolymerizable resins and biocompatible metallic alloys, while also emphasizing the integration of artificial intelligence in the design process of customized stents by taking into consideration the 3D printing technologies that are used for producing these stents.

The aim of this article is two-fold. First, it offers a unique account of San Min, the prototype of the current Chinese educational principle proposed by Yan Fu (1854–1921) that aimed at improving people’s physical, intellectual and moral capacities. This system of educational thinking has received only marginal attention in Anglophone research literature. Second, given the influence of Yan Fu’s interpretation and promulgation of Herbert Spencer’s educational philosophy during that period, it investigates the extent to which San Min is derived from Spencer’s educational thought (the “Spencerian Triad”). This article focusses on how Yan Fu adapted the ideas of San Min from Spencer’s account.

This article considers Yan Fu’s principle of San Min in relation to Spencer’s educational triad through a close reading and comparison of key primary texts (including Yan Fu’s original writing). It explores the similarities and differences between each account of education’s goals and its proposed means of attainment.

Yan Fu’s principle of San Min is shown to have been adapted from the Spencerian Triad. However, using the theory of Social Organism, Yan Fu re-interpreted Spencer’s individual liberty as liberty for the nation. While Spencer’s goal was to empower individuals, Yan Fu aimed to serve collective independence, wealth and power.

This article addresses oversights concerning San Min’s Western origins in the Spencerian Triad and its influence on Chinese education under Yan Fu’s sway. It is significant because San Min is still at the core of the current Chinese educational policy.

This paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.

The two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.

The results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.

The conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.

This paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.

The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

Disruptions at ports may destroy the planned ship schedules profoundly, which is an imperative operation problem that shipping companies need to overcome. This paper attempts to help shipping companies cope with port disruptions through recovery scheduling.

This paper studies the ship coping strategies for the port disruptions caused by severe weather. A novel mixed-integer nonlinear programming model is proposed to solve the ship schedule recovery problem (SSRP). A distributionally robust mean conditional value-at-risk (CVaR) optimization model was constructed to handle the SSRP with port disruption uncertainties, for which we derive tractable counterparts under the polyhedral ambiguity sets.

The results show that the size of ambiguity set, confidence level and risk-aversion parameter can significantly affect the optimal values, decision-makers should choose a reasonable parameter combination. Besides, sailing speed adjustment and handling rate adjustment are effective strategies in SSRP but may not be sufficient to recover the schedule; therefore, port skipping and swapping are necessary when multiple or longer disruptions occur at ports.

Since the port disruption is difficult to forecast, we attempt to take the uncertainties into account to achieve more meaningful results. To the best of our knowledge, there is barely a research study focusing on the uncertain port disruptions in the SSRP. Moreover, this is the first paper that applies distributionally robust optimization (DRO) to deal with uncertain port disruptions through the equivalent counterpart of DRO with polyhedral ambiguity set, in which a robust mean-CVaR optimization formulation is adopted as the objective function for a trade-off between the expected total costs and the risk.

The core of the successful implementation of the integrated project delivery (IPD) mode is to establish a high-quality relationship of cooperation, trust and sharing among participants. This paper proposes recommendations to improve the relationship quality of participants from the owner's perspective. The results provide the theoretical basis and practical guidance for the popularization and application of the IPD mode.

This paper analyzes the dynamic relationship among participants in IPD mode based on supply chain theory and similarity theory. A tripartite game model of the owner, designer and the contractor is constructed to obtain the evolutionary equilibrium strategies under different parameter constraints. Then, numerical simulations under various scenarios are conducted to explore the dynamic evolution and the influencing factors of the relationship quality among the participant in the IPD mode.

The results show that (1) the relationship quality under certain conditions gradually improves as the project progresses until stable and high-quality cooperation is formed and (2) the owner's positive supervision cost, the distribution coefficient of incentive and punishment of participants and the scale of incentive pool are important factors influencing the relationship quality.

This study incorporates the following three innovations. First, analyzing the relationship quality among the participants of IPD mode based on supply chain theory. Second, the evolutionary game theory is applied to the relationship quality analysis. Third, conclusion innovation. The authors conclude that the relationship quality may progress, decrease or cycle with the progress of the project and targeted recommendations are presented based on the results.

Different external supply scenarios faced by the retailers will affect their choice of strategy when supply is disrupted and becomes far less than demand, urgently. This study focuses on analyzing both demand and supply side response strategies to meet customer demand and reduce the impact of the shortage during supply disruptions.

According to the quantity of products that the external market can provide, the external supply scenarios were divided into sufficient-type external supply and learning-type external supply. A two-echelon perishable goods supply chain was analyzed, and three kinds of contingency strategy models for downstream retailers were investigated. First, in the sufficient external supply scenario, the optimal price and transshipment quantity to maximize retailer's profits is discussed. Second, in the scenario of learning-type external supply, this study analyzes the optimal decision in three mechanisms of the hybrid strategy and their application: price priority mechanism, quantity priority mechanism and price–quantity balance mechanism. Furthermore, the influence of penalty cost and supply on the priority orders of different mechanisms was studied.

Results show that comparing the two pure strategies (pricing strategy and transshipment strategy)it was noted that the hybrid strategy produces the best results in sufficient-type external supply scenario. In the learning-type external supply scenario, a numerical study has shown the existence of three areas in case of penalty cost and supplier's capacity, and each areas has different priority orders of the three mechanisms. Under the situation of learning external supply, the retailer's optimal strategy is affected by parameters such as penalty cost and supply volume.

The main innovation of the work lies in the following: First; the external supply situation was divided into sufficiency type and learning type, which improves the external situation faced by retailers after the outbreak of emergencies, helps retailers understand the external situation, conforms to the actual situation and has certain practical application value. Second; in the context of learning external supply, there are three coping strategies for retailers, including: Price priority mechanism, Quantity priority mechanism and Pricing and transshipment balance mechanism. This will help retailers make strategic choices, make more scientific management decisions and improve the supply chain emergency management theory. Third; the demand side response was managed through the change of external supply during supply side recovery period and supply disruption. The proposed model enables managing and analyzing supply disruption efficiently and effectively via handling uncertainty by considering all aspects of decision-making process. The proposed model can be applied in various fields such as vegetable and fruit, fresh food, etc.