Journal of Quality Engineering and Management

Abstract Purpose: This study aims to evaluate and rank collaboration models in the Iranian rubber industry supply chain from the perspective of digitalization quality. Digitalization quality refers to the effective use of Industry 4.0 technologies to improve transparency, integration, agility, resilience, and sustainability. The rubber industry was selected due to its operational complexity and urgent need for digital transformation.
Methodology: A multi-criteria decision-making approach was adopted, combining the Fuzzy Best-Worst Method (BWM) for weighting the evaluation criteria and TOPSIS for ranking the collaboration models. A sensitivity analysis was also conducted to assess the robustness of the results across varying criterion weights.
Findings: The digital supply chain model ranks highest in digitalization quality, with "technology integration" as the most critical criterion. The sensitivity analysis confirms the rankings' robustness and stability across different weight scenarios.
Originality/Value: This research uniquely addresses the comparative assessment of collaboration models in the rubber industry based on digitalization quality. The use of a Fuzzy BWM-TOPSIS hybrid method and comprehensive sensitivity analysis provides a novel, practical framework for strategic decision-making in digital supply chain transformation.

Abstract Purpose: The most important effects of the Internet of Things in healthcare include the ability to exchange information, reduce hospitalization costs, and improve healthcare costs. The primary challenges of the Internet of Things in healthcare are security and privacy, with image transmission particularly crucial for communication and security. The primary objective of this paper is to design a suitable channel for transmitting medical data via chaotic synchronization that employs fuzzy modeling.
Methodology: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order convolutional neural networks based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this scheme, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.
Findings: Simulations have been performed on color and black-and-white medical images. Encrypted and recovered images have been obtained using this scheme. The simulation and accuracy of the proposed method's results have been investigated using MATLAB software. To evaluate the performance of the proposed method, various criteria, including image histogram, signal-to-noise ratio, correlation, and information entropy, were assessed. The results demonstrate the effectiveness of the proposed method in image encryption.
Originality/Value: This paper presents a new method for transmitting medical images to preserve patient information by synchronizing two fractional-order multi-convolutional systems based on polynomial fuzzy modeling. Using chaotic signals as a carrier for medical images and employing a suitable fuzzy controller for synchronization at the receiver enhances security and significantly reduces the likelihood of detection. In this project, a suitable fuzzy controller is designed to establish the stability of the closed-loop system. Then, considering the multi-state synchronization scheme based on the polynomial fuzzy model and its error detection, a chaotic masking method is proposed to encrypt patient-related images.