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In this research, the effect of graphene nanosheets and graphene quantum dots (GQDs) as carbon-based nanofillers on corrosion protection performance of epoxy coatings is considered.

Graphene nanosheets are synthesized via chemical vapor deposition method, and GQDs are synthesized by a simple and gram scale procedure from carbon black. The prepared nanofillers are characterized by X-ray diffraction technique, Fourier transform infrared spectroscopy and transmission electron microscopy. Further, solvent-based epoxy coatings containing 0.1 Wt.% graphene nanosheets and GQDs are prepared, and the corrosion resistance of nanocomposite coatings is considered by electrochemical impedance spectroscopy.

The results indicate that both epoxy/graphene nanosheets and epoxy/GQDs samples have significantly higher corrosion resistance than pure epoxy coating. Meanwhile, GQDs can more effectively enhance the corrosion protection performance of epoxy coatings compared to graphene sheets, which can be attributed to the presence of functional groups on GQDs and improving the dispersion quality in polymer matrice.

In this research, for the first time, the graphene quantum dots (GQDs) prepared by a “top-down” method from carbon black are used as nanofiller in epoxy coatings, and the potential application of graphene nanosheets and GQDs as anti-corrosion nanofiller in epoxy coatings is investigated.

In recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.

In this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.

The results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.

This novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.