Blockchain technology is enabled by consensus algorithms to manage the relationships among several economic or business operators without human intervention. With the help of consensus algorithms, distributed systems can reliably reach agreement even if part of the system is faulty. Blockchain yields many benefits, among others, traceability, transparency, and security. We consider using the RAFT consensus algorithm to achieve robust and scalable decentralized applications, with focus on healthcare. We propose a stylized healthcare network, enabled by RAFT and built upon Hyperledger Fabric to showcase the use of RAFT in healthcare blockchain. However, RAFT is by no means limited to healthcare record systems, and can be applied to any other record system and value chain. Our paper offers several insights to those working in value chains and information management-related fields. In addition, we end our study with some future research avenues that may inspire managers and scholars to build or refine new decentralized systems in healthcare and other related fields.

New healthcare record management (HRM) systems have been introduced as technology has evolved to provide more efficient care. Since medical data is usually sensitive and must be protected from unauthorized access, attention must be paid to data integrity, patient privacy, and storage. Blockchain technology has been proposed in the literature to integrate healthcare information systems through a decentralized and unified network. However, the literature on blockchain in healthcare is full of promises that may not be true under certain conditions. In our paper, we evaluate the veracity and sophistication of some of the claims made in the literature. We go beyond performing a literature review and shed light on the weak technical aspects claimed about blockchain. In addition, we benefit from our technical assessment and suggest some future research directions to improve healthcare systems that use blockchain and big data solutions.

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange. However, the persistent challenge lies in protecting sensitive information amidst evolving digital landscapes. Among the burgeoning applications of blockchain technology, non-fungible tokens (NFTs) have emerged as digital certificates of ownership, securely recording various types of data on a distributed ledger. Unlike traditional data storage methods, NFTs offer several advantages for secure information exchange. Firstly, their tamperproof nature guarantees the authenticity and integrity of the data. Secondly, NFTs can hold both immutable and mutable data within the same token, simplifying management and access control. Moving beyond their conventional association with art and collectibles, this paper presents a novel approach that utilizes NFTs as dynamic carriers for sensitive information. Our solution leverages the immutable NFT data to serve as a secure data pointer, while the mutable NFT data holds sensitive information protected by steganography. Steganography embeds the data within the NFT, making them invisible to unauthorized eyes, while facilitating portability. This dual approach ensures both data integrity and authorized access, even in the face of evolving digital threats. A performance analysis confirms the approach’s effectiveness, demonstrating its reliability, robustness, and resilience against attacks on hidden data. This paves the way for secure data transmission across diverse industries.

Fermented dairy products are increasingly valued not only for their nutritional content but also for their potential health-promoting properties. However, assessing these functional benefits often requires time-consuming chemical assays that limit scalability. In this study, we investigated whether deep learning (DL) could offer a faster, more efficient alternative. Using liquid chromatography (LC)-MS quadrupole time-of-flight metabolomics, we analyzed 18 fermented milk samples (derived from camel and bovine milk fermented with different bacterial strains) and measured their bioactivity across 9 in vitro assays, including antioxidant capacity (2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), 2,2-diphenyl-1-picrylhydrazyl), enzyme inhibition (angiotensin-converting enzyme, DH), and anticancer activity (HT-29, MDAMB). To address the challenge of limited sample size, we implemented a robust preprocessing pipeline including outlier detection, robust scaling, and data augmentation techniques. We trained a one-dimensional convolutional neural network (1D-CNN; DL) architecture with regularization strategies to predict these bioactivity scores from preprocessed LC-MS data. The model achieved strong performance with a mean absolute error of 0.548 ± 0.089 across all outputs through 3-fold cross-validation, demonstrating effective generalization despite the small dataset. Principal component analysis revealed biologically meaningful structure in the metabolomic data, distinguishing samples by milk type and fermentation condition. Together, these results demonstrate that DL with appropriate regularization and data augmentation can accurately predict the functional bioactivity of fermented milk products from metabolomic signatures, offering a promising path toward scalable, DL-assisted screening in functional food development, even with limited training data.