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Undertakes an analysis of electromagnetic forming process. Despite the fact that it is an old process, it is able to treat current problems of advanced manufacturing technology. Primary emphasis is placed on presentation of the physical phenomena, which govern the process, as well as their numerical representation by means of simplified electrical equivalent circuits and fully coupled fields approach of the electromagnetic‐mechanical‐electric phenomena involved. Compares the numerical results with measurements. Finally, draws conclusions and perspectives for future work.

An electromagnet that can produce strong pulsed magnetic fields at kHz frequencies is potentially very favourable to exert a Lorentz force on the metallic workpiece. One of the applications of the pulsed magnetic field is the electromagnetic forming where the design of robust electromagnet is critical. The purpose of this paper is to design a robust electromagnet (coil) for high velocity electromagnetic tube forming operation.

First of all, an analytical model is developed to design the electromagnet and predict the aluminium tube velocity under the action of the estimated pulsed magnetic field. Next, the finite element-based numerical model is used to test the robustness of the designed coil and validate the analytical model. The coil is fabricated and implemented for free forming of aluminium tube. Experimental results of tube displacement are further compared with numerical and analytical model results.

The experimental tube displacement results are showing a good match with analytical and numerical results. The designed electromagnet has generated a peak magnetic field around 14 T at 20 µs rise time and deformed the aluminium tube with a peak velocity of 160 m/s. Robustness of the electromagnet under the action of forming stress is insured by numerical stress analysis and experiments.

Though the designed model in this work is for the 2.4 mm aluminium tube forming, it can also be used for different tube materials, tube dimensions and other electromagnetic forming applications with some modifications.

The research results provide powerful theoretical, numerical simulation and experimental support for the robust electromagnet design.

This article aims to evaluate published food cold chain (FCC) literature against risk management and supply chain sustainability concepts.

The article uses the theory refinement logic proposed by Seuring et al. (2021) to analyze the contents of FCC management-related literature published over the past 20 years. A sample of 116 articles was gathered using Web of Science and subsequently analyzed. The respective articles were then systematically coded against the frameworks of Beske and Seuring (2014) and Vlajic et al. (2012), which focused on building sustainable and robust supply chains, respectively.

The literature review revealed that debates around managing contemporary sources of disruptions/vulnerability and making FCCs more sustainable and resilient are gradually developing. However, an overarching risk management perspective along with incorporating social and environmental dimensions in managing FCCs still needs the adequate attention of the respective research community.

The deductive internal logic of theory refinement approach used in this paper could have been further strengthened by using additional frameworks. This limitation, however, opens avenues for further research. The findings of the paper will stimulate the interest of future researchers to work on expanding our understanding related to sustainability and risk management in FCCs.

The paper is the first attempt to organize published FCC literature along dimensions of supply chain sustainability and risk management. The paper thus provides the respective researchers with a foundation that will help them adopt a focused approach to addressing the research gaps.

Stent malapposition is one of the most significant precursors of stent thrombosis and restenosis. Adverse haemodynamics may play a key role in establishing these diseases, although numerical studies have used idealised drug transport models to show that drug transport from malapposed drug-eluting stent struts can be significant. This paper aims to study whether drug transport from malapposed struts is truly significant. Another aim is to see whether a streamlined strut profile geometry – with a 61% smaller coating but a 32% greater coating-tissue contact area – can mitigate the adverse haemodynamics associated with stent malapposition while enhancing drug uptake.

Two- and three-dimensional computational fluid dynamics simulations were used in this study. Unlike past simulations of malapposed drug-eluting stent struts, a qualitatively validated drug-transport model which simulates the non-uniform depletion of drug within the drug coating was implemented.

It was shown that even a 10-µm gap between the strut and tissue dramatically reduces drug uptake after 24 h of simulated drug transport. Furthermore, the streamlined strut profile was shown to minimise the adverse haemodynamics of malapposed and well-apposed stent struts alike and enhance drug uptake.

Unlike prior numerical studies of malapposed stent struts, which did not model the depletion of drug in the drug coating, it was found that stent malapposition yields negligible drug uptake. The proposed semicircular-profiled strut was also shown to be advantageous from a haemodynamic and drug transport perspective.

This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse.

The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies.

All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement.

The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results.

A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ.

Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse.

The prime aim of this paper is the identification and prioritization of performance indicators, which motivate the development of an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC). Also, this research aims for checking the robustness of obtained results.

Ten performance indicators have been identified based on the five “criteria in the IoT-based traceable system”. Further, based on five criteria, performance indicators were ranked by using grey-based “Additive Ratio Assessment”.

Sustainable practices obtained first rank, and certification of agri-products obtained worst ranking. Further, based on sensitivity analysis, tracking of agri-products and stakeholders' behavior have found high sensitivity. Also, information sharing and global distribution networks have found the least sensitive performance indicators.

This research has some limitations of taking only a few criteria and alternatives. This study may also contribute as a practical insight to the practitioners and managers in decision-making in the adoption of an IoT-based traceable system within the ASC.

This research may motivate the implementation of an IoT-based efficient traceability mechanism that improved the sustainability and consumer's trust in the ASC during different types of hazardous activities and other outbreaks (COVID-19). Also, this research has provided a theoretical insight based on the dynamic capability theory (DCT).

The demand for the cold chain logistics of agricultural products was investigated through demand forecasting; targeted suggestions and countermeasures are provided. This paper aims to discuss the aforementioned statement.

A Markov-optimised mean GM (1, 1) model is proposed to forecast the demand for the cold chain logistics of agricultural products. The mean GM (1, 1) model was used to forecast the demand trend, and the Markov chain model was used for optimisation. Considering Guangxi province as an example, the feasibility and effectiveness of the proposed method were verified, and relevant suggestions are made.

Compared with other models, the Markov-optimised mean GM (1, 1) model can more effectively forecast the demand for the cold chain logistics of agricultural products, is closer to the actual value and has better accuracy and minor error. It shows that the demand forecast can provide specific suggestions and theoretical support for the development of cold chain logistics.

This study evaluated the development trend of the cold chain logistics of agricultural products based on the research horizon of demand forecasting for cold chain logistics. A Markov-optimised mean GM (1, 1) model is proposed to overcome the problem of poor prediction for series with considerable fluctuation in the modelling process, and improve the prediction accuracy. It finds a breakthrough to promote the development of cold chain logistics through empirical analysis, and give relevant suggestions based on the obtained results.

This study aims to investigate the vulnerability of cold chain logistics through a comprehensive assessment and provide targeted control measures.

The index system of the cold chain vulnerability assessment was established with knowledge obtained from three different dimensions, namely, exposure, sensitivity and adaptability. The final index weight was determined through combination of the intuitionistic fuzzy (IF) entropy and compromise ratio approaches, followed by the comprehensive vulnerability assessment through the two-stage grey comprehensive measurement model. The feasibility and effectiveness of the proposed method were verified by evaluation with SF, HNA, China Merchants and COFCO as target examples.

The results revealed that the most influential factors in the cold chain vulnerability problem were the temperature reaching the standard, as well as the storage and preservation levels; through their analysis combined with the overall cold chain vulnerability assessment, the targeted control measures were obtained.

Based on the research perspective of cold chain vulnerability assessment, a novel assessment model of cold chain logistics vulnerability was proposed, which is based on IF entropy two-stage grey comprehensive measurement. It provides more powerful theoretical support to improve the quality management of cold chain products.