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The fragmentation of electronic health records in oncology hinders coordinated care, delays diagnoses, and limits therapeutic personalization. Blockchain technology has been proposed as a solution to promote secure, interoperable, and patient-centered data governance; however, patient perceptions regarding its adoption remain underexplored, particularly in middle-income countries such as Brazil. This study assessed oncology patients' opinions, attitudes, and willingness to share clinical information through digital systems, and evaluated the feasibility of a blockchain-based approach to restructuring secure health data sharing within the Brazilian public health context. Three research questions guided the study: (1) What is the level of digital health tool acceptance among oncology patients in Brazil? (2) Which sociodemographic factors are associated with willingness to share health data? (3) Is a blockchain-based approach feasible and acceptable for restructuring secure oncology data sharing? An exploratory, descriptive, cross-sectional self-report survey was conducted at Hospital Santa Izabel, a national oncology reference center in Salvador, Bahia, Brazil, between September and November 2023. A convenience sample of 110 oncology patients was recruited systematically during outpatient visits; data were collected via a QR-code-accessible, self-administered questionnaire hosted on REDCap. The 20-item instrument, developed de novo and validated via expert panel and pilot testing, covered five content domains yielding three composite scoring domains: Self-Management, Adherence, and Governance. Cronbach's alpha assessed internal consistency. Independent t-tests, one-way ANOVA, and Pearson correlations compared domain scores across sociodemographic groups. A chi-square goodness-of-fit test examined trust proportions across recipient types. Of 110 patients approached, 104 provided sufficiently complete responses (response rate: 94.5%). The sample was predominantly female (64.3%), self-identified as Brown/Pardo (65.3%), and of lower income (57.3% earning &#x2264;2 minimum wages). High acceptance of digital technologies was observed: 86.4% were willing to use health apps and 89.1% expressed interest in prevention-focused applications. Trust in data sharing varied significantly across recipient types (&#x3c7;&#xb2;=210.4, df=3, P<.001): 79.1% trusted healthcare professionals, 51.8% trusted hospitals, 15.5% the pharmaceutical industry, and 10.0% the government. Anonymization and encryption significantly increased willingness to share (83.6%). Younger patients (18-59 years) showed significantly higher Adherence scores than those aged &#x2265;60 (mean 76.49 vs 65.83; t=2.29, df=95, P=.024). Domain reliability was good to excellent: Cronbach's &#x3b1;=0.8807 (Adherence), 0.8504 (Self-Management), and 0.7576 (Governance). Oncology patients in Brazil demonstrated high acceptance of digital health tools and openness to data sharing when privacy, security, and governance are guaranteed. These findings support the feasibility of blockchain-based health data management systems, provided they incorporate patient-centered principles, digital inclusion strategies, and robust governance aligned with Brazilian regulations (the General Data Protection Law, LGPD) and the Unified Health System (Sistema &#xda;nico de Sa&#xfa;de, SUS) infrastructure. Importantly, patient support reflected acceptance of blockchain's functional principles: data security, anonymization, and auditability; rather than familiarity with the technology itself, a distinction with direct implications for future implementation studies. Not applicable. RR2-10.2196/89278.

Supply Chain Finance (SCF) enhances financial liquidity, optimizes operational efficiency, and strengthens collaboration among supply chain stakeholders. Traditional SCF systems are vulnerable to economic risks, fraud, and a lack of transparency due to centralized data control and limited real-time monitoring capabilities. Current systems rely on slow central financial bodies and static credit evaluations, lacking dynamic risk assessment and mechanisms to build stakeholder trust. This research proposes Bi-MATRA, a Blockchain-Integrated Multi-Agent Trust and Risk Assessment system. The goal is to create a decentralized, intelligent risk evaluation system for real-time trust computation and automated decision-making in SCF contexts. Bi-MATRA combines blockchain's immutability and transparency with a multi-agent system that automatically monitors and assesses risk. Bi-MATRA uses an adapted Eigen Trust algorithm on the blockchain to calculate trust. Agents rate each other based on interaction history, and transitive trust linkages determine global trust ratings. Smart contracts for dynamic trust-based decision-making update these scores. Smart contracts enforce predefined financial agreements, automating processes and reducing the need for intermediaries. Agent-based modelling was employed to develop a blockchain-based simulation testbed for assessing Bi-MATRA's responsiveness, accuracy, and resilience. The framework was more efficient at trust validation and risk identification than typical SCF systems. Key studies reveal that Bi-MATRA saves transaction clearance time by 35% and improves early risk detection by 28%. EigenTrust-based trust computation builds agent trust and accountability, enhancing financial interactions. In conclusion, the Bi-MATRA framework offers a scalable, decentralized approach to intelligent risk assessment and trust-building in blockchain-enabled supply chain finance systems.

The digitalization of food safety management systems (FSMS) represents a crucial strategy for mitigating persistent pathogen contamination and foodborne disease outbreaks. The ISO 22000-based FSMS, incorporating hazard analysis and critical control points (HACCP), relies on periodic verification, retrospective microbiological testing, and manual records, leading to reactive risk management, delayed corrective actions, and limited adaptability to evolving contamination dynamics. Key Industry 4.0 technologies, including digital twins (DT), the Internet of Things (IoT), artificial intelligence (AI), and blockchain, have individually demonstrated the capacity to simulate pathogen contamination dynamics (DT), monitor pathogens in real-time with high sensitivity and predictive accuracy (IoT), enable predictive risk assessment (AI), and reduce traceback from days to seconds (blockchain). However, empirical applications remain limited, with most studies addressing them individually or in pairwise combinations and focusing primarily on supply chain logistics, authenticity, or quality assurance rather than their convergent role in combating foodborne pathogens and supporting HACCP implementation. Following a PRISMA methodology, this review critically examines the potential of DT-centered integration of IoT, AI, and blockchain for pathogen-focused FSMS, which remains underexplored. A unified DT-centered framework linking IoT-based sensing, AI-driven predictive analytics, and blockchain-enabled traceability enables continuous monitoring of critical process and environmental parameters, simulation of contamination dynamics, early risk detection, predictive risk assessment, and enhanced traceability. However, widespread implementation depends on addressing challenges, including heterogeneous data synchronization, interoperability, validation, cybersecurity, implementation costs, and regulatory alignment. Overall, this study provides a pathogen-focused assessment of DT-based systems and outlines future directions for building an intelligent farm-to-fork FSMS.

The fast growth of smart farming technologies and Internet of Things (IoT) sensors has transformed the agricultural sector, but has brought some fundamental problems, such as data privacy, scalability, and trust in distributed systems. In this paper, AgriChain-FL, a federated blockchain architecture, is introduced and guarantees privacy-preserving, transparent, and energy-efficient collaboration in agricultural ecosystems. The framework integrates federated learning (FL) of decentralized model training with a hybrid Practical Byzantine Fault Tolerance-Proof of Authority (PBFT-PoA) blockchain to allow aggregating data safely, auditing, and participation through incentives. The proposed system was developed based on the SmartFarm Sensor Dataset. Models are trained individually at each local farm node. The hybrid consensus protocol is more scalable, latency-reduced, and uses less energy. The experimental results indicated that AgriChain-FL was more accurate (92.4%), had a throughput of 2350 TPS, and a block latency of 1.0&#xa0;s, which is also more favorable to analogous frameworks in learning and blockchain performance. Privacy leakage and energy cost were minimized by 73% and 45% respectively. AgriChain-FL is a successful balance between federated intelligence and blockchain trust that provides a privacy-conscious and sustainable smart farming platform. These findings confirm its possibilities as the basis of secure, decentralized, and energy-aware digital agricultural systems.

Decentralized oracle networks pose significant security risks to blockchain systems due to transaction malleability, which can lead to double-spending and integrity issues. While existing solutions such as DAON, SegWit, and SecPLF improve specific aspects of security, they do not address Oracle-driven transaction malleability on a transaction level. DAON focuses on decentralized oracle consensus and reputation mechanisms, but it does not support the cryptographic binding of Oracle metadata to transactions. SegWit reduces signature malleability at the Bitcoin protocol level, but it does not protect the integrity of Oracle-fed data or require validation before transactions are added to the blockchain. SecPLF protects loanable-fund protocols from Oracle manipulation, but it lacks a comprehensive transaction-level solution to prevent Oracle-driven malleability. OracleTrust, on the other hand, uses a dual-layer scheme to bind Oracle metadata and signatures to transactions via provenance tracking and a smart contract validation layer. The first layer encodes transactions into verifiable provenance records, and the second layer dynamically verifies these records with salted Keccak hashing and ECDSA recovery to bind the Oracle signature. A time-constrained commit-reveal mechanism with penalty enforcement ensures that the data is tamper-resistant. OracleTrust outperforms existing solutions in detecting malleable transactions, reducing latency, and memory consumption. This demonstrates its superior robustness and efficiency in blockchain.

Pure chromatic background images (PCBIs) consist of large uniform regions with minimal structural content, which makes standard perceptual hashing based on low-frequency DCT unreliable. High similarity scores may be assigned to visually different images, leading to false acceptance. In blockchain-based copyright registration systems, such transactions are irreversible once confirmed, necessitating a conservative similarity model. This paper proposes DBpHash, a dual-band perceptual hashing framework for blockchain-based copyright registration and verification of PCBIs. During computation, low-frequency DCT bands are excluded while mid and high-frequency bands are energy-normalized and binarized to create a 128-bit hash. Similarity is calculated independently for each band and fused using inverse-variance weighting. Thresholds are derived from real and imposter distributions. Experiments conducted on a PCBI dataset are further validated via cross-dataset evaluation on BSDS500 and DTD without retraining. False positive rate remains at 0.095 while similarity exceeds 0.92 under common photometric distortions. Statistical analysis confirms strong hash properties, including near-ideal bit balance and entropy. The framework is integrated with blockchain-based registration and off-chain similarity computation. Results indicate that DBpHash provides a reliable and distortion-resilient solution for copyright authentication of PCBIs.

While full ledger access is theoretically possible on public blockchains, in reality it is often not possible. Things that can be seen are limited by storage limitations, client design, indexing services, and off-chain execution pathways. This means that entire ledger objects are rarely used for empirical blockchain analysis; instead, observable projections are typically used. In this research, the observability of blockchain is recast as an inferential problem with incomplete observation. Studying identifiability, information loss, and irreducible uncertainty under coarsened access, the framework defines a full ledger, an observable ledger, and an observability mechanism. Three distinct visibility regimes, independent Bernoulli, clustered, and activity-dependent, are assessed in the simulation study. Reduced visibility raises uncertainty inflation, root mean squared error, variance, and mean squared error across all three regimes. The most severe deterioration happens when the condition of the underlying ledger determines visibility. This empirical study employs Google BigQuery's publicly indexed Ethereum block data spanning blocks 18,000,000 to 18,001,000. Over the chosen Ethereum period, descriptive summaries reveal a large amount of fluctuation in gas utilised, transaction count, and basic charge per gas at the block level. Experiments with controlled missingness on the observed slice reveal that RMSE and trend estimate bias grow with increasing missingness, and that the degree of distortion is significantly affected by whether the incompleteness is MCAR-like, MAR-like, or MNAR-like. This research proves that partial observability isn't just a secondary data issue; it can significantly affect inference on Ethereum block-level summaries.

The fast growth of Internet of Things (IoT) systems has made them very susceptible to advanced cyber-attacks, and an intelligent and privacy-sustainable intrusion detection system is required. Conventional centralized intrusion detection frameworks have the drawbacks of data privacy threats, scale constraints, and a single point of failure, and conventional federated learning still faces the threat of malicious client membership and a lack of trust during model aggregation. To overcome these obstacles, this paper suggests a federated learning (B-FL) system based on a Blockchain to ensure safe and reliable intrusion detection in the distributed Internet of Things. The framework proposed is a combination of federated and blockchain-based trust management to guarantee decentralized collaborative model training and maintain data confidentiality. The use of smart contract-based verification tools and trust-weighted aggregation counteracts the adversarial threats, such as model poisoning, data manipulation, free-rider behavior, and Sybil attacks. Testing is performed on the CICIoT2023 dataset, which consists of traffic produced by 105 IoT devices in 33 different types of attacks, and it allows testing all the aspects of its work in terms of a real and heterogeneous network. Findings reveal that the proposed B-FL model has high detection rates, high convergence stability, and enhanced robustness as compared to traditional methods of centralized and federated intrusion detection. Another study, Receiver Operating Characteristic (ROC) analysis, supports the presence of excellent discriminative ability with respect to several classes of intrusion. Though the integration of the blockchain has a marginal increase in computing overhead, it benefits the system in terms of transparency, reliability, and aggregation security significantly. In general, the suggested framework offers a scalable, privacy-aware, and trust-conscious IoT intrusion detection system in the next generation to enable secure collaborative intelligence in dynamic and adversarial IoT environments such as mining and mineral-processing environments.

The health informatics field's pursuit of personalized healthcare continuously faces constraints from patients, clinicians, and resource limitations. Recent advances in artificial intelligence (AI) and machine learning (ML) models have led to their widespread adoption in personalized genomic research for their outstanding predictive capabilities for drug responses to assist in personalized healthcare for tailored therapies and many other applications. Despite their growing use, such models often operate as black boxes, tempering, lacking sources to verify whether a prediction was generated honestly by a model's input or manipulated post hoc. Over these challenges, this study presents a decentralized model that integrates AI predictive modeling with blockchain-based verification to ensure the integrity, traceability, trust, and reproducibility of AI-generated outputs, leading to provable machine learning and trustworthy AI. Our developed scheme computes AI predictions, cryptographic hashes of model inputs, and data hashes to immutably store them on a blockchain via smart contract (SC) using our novel input-output cryptographic hashing technique. This introduces a deterministic tokenization and canonical hashing pipeline that binds each GDSC2 drug-cell line input and its AI prediction output into a salted, on-chain verifiable commit. Later, a verification process has been committed by blockchain's immutability and cross-checking via audit logs, which allows any stakeholder to independently confirm that a specific prediction originated from a known model and reliable source of data without exposing sensitive genomic content, ensuring both the verifiability and honesty of audits to serve the purpose for addressing AI post hoc tampering issue. The experimental results using genomic data inputs derived from the GDSCv2 dataset demonstrate the proposed model's capability to train a Random Forest Regressor (RFR) for accurate AI-driven drug sensitivity prediction, achieving an R&#xb2; of 0.979. Furthermore, 5-Fold Cross-Validation yielded a consistent mean R&#xb2; of 0.977&#x2009;&#xb1;&#x2009;0.001, highlighting the model's strong reliability, robustness, and generalization performance across multiple data partitions. Later, the model can store these predictions on-chain with due patient consent to verify or audit, detect tampering, ensure transparency, and verifiability up to 70% through an audit trial integrity test conducted for 10 samples from the dataset. The findings support the model's applicability in high-stakes personalized medicine and biomedical environments where verifiable AI predictions are paramount.

Self-sovereign identity (SSI) provides a decentralized approach to digital identity management, enabling individuals to control their personal data without reliance on centralized authorities. Blockchain technology offers a tamper-resistant and distributed infrastructure that can support secure and verifiable identity systems. In health care, where identity fragmentation, privacy risks, and interoperability challenges persist, blockchain-enabled SSI (BC-SSI) has been proposed as a potential solution. However, existing research remains heterogeneous, with varying levels of technical maturity and limited evidence of real-world deployment. This study conducts a scoping review to systematically map BC-SSI applications in health care and to analyze their application domains, development stages, study aims, targeted challenges, and technological infrastructures. In addition, this study aims to identify structural gaps in current research and assess the readiness of BC-SSI systems for clinical deployment. This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) methodology. A comprehensive literature search conducted between September 2024 and August 2025 identified 37 peer-reviewed studies that met predefined inclusion criteria. Data were extracted and synthesized using descriptive and thematic analyses across application areas, system maturity, technological components, and reported challenges. The findings indicate that BC-SSI research in health care remains at an early stage of maturity, with most studies proposing conceptual models or prototype implementations and limited real-world validation. Applications predominantly focus on identity verification, credential management, and privacy-preserving data exchange across domains such as electronic health records, mobile health, and access control systems. Commonly used technologies include decentralized identifiers, verifiable credentials, smart contracts, and privacy-enhancing mechanisms such as zero-knowledge proofs and selective disclosure. Despite rapid technical development, persistent challenges include interoperability limitations, governance gaps, usability concerns, and insufficient integration with health care infrastructures. Notably, a structural gap was identified between technological capability and system-level readiness for clinical deployment. BC-SSI technologies demonstrate potential for enabling secure, interoperable, and patient-centric identity management in health care. However, current research is predominantly technology-driven and lacks sufficient system-level validation. This study highlights the need for integrated architectural approaches, governance frameworks, and real-world evaluation to bridge the gap between conceptual innovation and clinical implementation. Advancing BC-SSI toward health care adoption will require coordinated progress across technical, organizational, and regulatory dimensions.

Presently, blockchains are widely employed to execute the secure data transmission process among users. However, sharing the sensitive information about the patients among multiple users in the healthcare sector is difficult due to integrity and confidentiality issues. Thus, to tackle these issues in prior models, a secure data-sharing framework for Software-Defined Wireless Body Area Networks (SDWBANs) is designed. In order to ensure privacy and controlled access in SDWBANs, blockchain technology and encryption techniques are considered, which help to protect sensitive medical data. Then, the Adaptive Deep Conditional Random Field (ADCRF) is employed to perform the decision-making procedure. Further, an advanced encryption technique, Optimal Key-based Multi-Authority Attribute-Based Encryption (O-MA-ABE), is employed to ensure that authorized users can access the confidential health data. Here, the Modified Escape Search-based Piranha Foraging Optimization Algorithm (MES-PFOA) is employed to tune the Hyperparameters of ADCRF and keys of O-MA-ABE. Then, the overall performance of the developed framework is compared with classical approaches using metrics such as computational time, decryption time, and decision-making accuracy. In various validations, the developed MES-PFOA-O-MA-ABE&#x2009;+&#x2009;ADCRF-based data sharing model accomplished higher accuracy as 98.7%, precision as 98.3%, minimal encryption time as 210 (ms) and throughput as 275 (TPS) than the recent data sharing models like DTAC-TL-QM, SCCE-DS, BFL-hIoT and PPFL-ICP.

ObjectiveThis research work is designed to solve the problem of patient-centric control, security, and transparency of the healthcare data management. The suggested framework will not only improve patient privacy but also guarantee the data sharing process is in accordance with the regulatory standards.MethodsThe access controls are implemented in the form of programmable smart contracts. Real-life healthcare datasets are evaluated empirically, under varying load conditions, in order to evaluate the system performance characteristics.ResultsBased on the encryption benchmark findings, AES-128 exhibited the least overhead (encryption: 1.3 ms, decryption: 1.1 ms, key generation: 2.1 ms), followed by AES-256 (1.9 ms/1.6 ms/2.9 ms), with RSA-1024 trailing behind as the highest overhead at 2.6 ms, 2.3 ms, and 4.1 ms respectively. Additionally, private and consortium blockchains surpassed public ones in terms of throughput (1,000 TPS and 800 TPS) and latency. In terms of integrity validation, the findings indicated that the Merkle Tree approach was the most efficient (hashing: 0.4 ms, verification: 0.9 ms, energy: 8 mJ).ConclusionThe results show that the combination of cryptographic protection, scalable storage, and blockchain-based access control is a viable and secure solution to healthcare data management.

Blockchain technologies have revolutionized the financial sector through their ability to generate immutable, cryptographically secure records. Clinical trials and health care data possess several synergies with those of the financial sector, specifically pertaining to the importance of tamper-resistant recording of processes. The evolution of blockchain to autonomously execute tasks contingent upon predefined contractual terms via smart contracts (SCs) allows a dynamic chain of interlinked events to unfold independently and in sequence, with time-stamped records. In recent years, mistrust in clinical trial data has grown significantly. Recording the entire clinical trial lifecycle from application, registration, recruitment to conduct, finance management, statistical analysis, and reporting in an immutable, cryptographically secure ledger with SC execution of trial processes could limit the potential for human intervention and tampering. This would produce a time-stamped record of all events within the trial lifecycle. Leveraging the capabilities of SCs could alleviate recruitment challenges and address ongoing concerns regarding data transparency, ownership, and integrity that currently undermine clinical trial processes. This study aimed to review the existing literature on SC applications in clinical trials and propose a system architecture for using SCs to automate key processes throughout the clinical trial lifecycle. A systematic search was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, identifying peer-reviewed studies and open-source repositories pertaining to the implementation of SCs in clinical trials. Data were extracted specific to the stage of the trial lifecycle described, SC architecture, and technical specifications for real-world implementation. Data were synthesized to propose an architecture for automating clinical trial processes within the lifecycle using SCs. A total of 144 records were screened; 10 studies met the inclusion criteria. Most implementations used private Ethereum-based networks (7/10, 70%). Reported applications included automated patient matching (5/10, 50%), consent management with dynamic permissioning (6/10, 60%), protocol enforcement and time-stamped audit logs (9/10, 90%), adverse event reporting (3/10, 30%), and financial or workflow automation (3/10, 30%). SC-based recruitment systems demonstrated rapid matching performance (eg, 6000 simulated patients matched in 2.13 s). However, all included systems were prototypes or simulations, and none were tested in real-world regulatory settings. Scalability, interoperability limitations, regulatory ambiguity (eg, General Data Protection Regulation right-to-erasure conflicts), and high infrastructural complexity were common gaps noted across studies. Current evidence suggests that SCs can enhance transparency, traceability, and automation throughout the clinical trial lifecycle. However, the literature remains dominated by simulation-based prototypes, primarily Ethereum-dependent architectures, and lacks analyses of cost-effectiveness, governance, and integration with institutional workflows. Future research should evaluate hybrid architectures, develop interoperability standards, and assess regulatory and ethical implications in real deployments.

This study develops a formally grounded verification framework for blockchain consensus mechanisms and smart contract behavior using Event-B and the Rodin platform. Unlike prior approaches that rely primarily on simulation or case-based validation of isolated contracts, this work integrates Finite State Machine (FSM) abstraction, invariant-driven proof, refinement modeling, and temporal logic verification to analyze Proof of Work (PoW), Proof of Stake (PoS), and mechanisms for double-spending prevention. Solidity smart contracts are abstracted into FSMs and encoded as Event-B machines, enabling the formal specification of state transitions and safety constraints. Safety properties-including transaction uniqueness, state consistency, access control enforcement, and ledger invariant preservation-are verified through automatically generated proof obligations in Rodin. A total of 312 proof obligations were generated, of which 287 (92%) were automatically discharged, and 25 were proven interactively, resulting in complete invariant coverage. Liveness properties were specified in Computation Tree Logic (CTL) and validated via model checking, confirming deadlock freedom and eventual validator selection under PoS conditions. Double-spending prevention was formally enforced using state-consistent ledger modeling, where uniqueness constraints were proven across all reachable states. Protocol-level consensus logic for PoW and PoS was refined across three abstraction levels, ensuring block integrity and validator correctness through stepwise refinement. The results demonstrate that machine-checked proofs provide verifiable correctness guarantees beyond simulation-based evaluation, establishing a rigorous and reproducible verification pipeline that enhances correctness assurance and protocol-level robustness in blockchain systems.

Security screening systems require reliable and real-time detection of threats in complex X-ray imagery and surrounding environments. Manual inspection of baggage images is often a overhead due to operator fatigue, cluttered objects and overlapping items. Recent advances in deep learning and intelligent sensing technologies provide opportunities for automated threat detection in such environments. In this work, we propose a multi-layer airport security framework integrating three complementary detection modules: YOLO-based X-ray prohibited-item detection, video anomaly detection for behavioural monitoring, and IoT-based environmental anomaly sensing. In addition to this, a blockchain-secured logging mechanism is incorporated to ensure tamper-proof storage of security events. The X-ray detection module employs YOLOv11-s trained on the CLCXray dataset to identify prohibited items in baggage. Behavioural anomalies in surveillance footage are detected using a 3D-CNN autoencoder, while environmental anomalies from IoT sensors are identified using an LSTM autoencoder. Outputs from these modules are integrated using a unified multimodal risk scoring mechanism. Experimental results demonstrate that YOLOv11-s achieves mAP[Formula: see text] of 78.91% and YOLOv8-s achieves mAP[Formula: see text] of 78.49% with strong detection reliability across varying confidence thresholds, both the models have comparable performance and either of it could be chosen. The IoT anomaly detection produces reconstruction errors in the range of 0.019-0.03, with anomalies identified when the error exceeds a threshold of 0.041 and for video anomaly its 0.0040-0.00685. The proposed multimodal fusion module achieves ROC-AUC score of 0.864, which demonstrated an improved detection reliability compared to individual modalities. Also, the framework achieves an average detection latency of 0.014 ms per event, while the blockchain logging module records security events with an average latency of 0.179 ms and supports up to 5723.47 transactions per second. These results demonstrate that the proposed framework provides an effective, scalable, and secure solution for automated airport security monitoring systems.

As digital interactions continue to expand, securing online systems has become a fundamental priority. Multifactor authentication (MFA) plays a pivotal role in modern cybersecurity frameworks. Traditional approaches often exhibit weaknesses such as centralized vulnerabilities and limited adaptability to emerging threats. To address these concerns, this research introduces a novel Blockchain- based Multifactor Authentication (BMFA) system that enhances security, resilience, and scalability. This study provides an in-depth exploration of BMFA's conceptual architecture, operational mechanisms, and potential applications. By decentralizing authentication processes, BMFA reduces single points of failure and fortifies data integrity through cryptographic safeguards. Unlike conventional models, this approach distributes authentication data across multiple blockchain nodes. This reduces the risk of breaches while ensuring continuous availability. Moreover, BMFA improves user privacy via distributed consensus, minimizing dependency on centralized authentication servers. The proposed system demonstrates enhanced load-balancing (LB)capabilities. This makes it more suitable for high-demand environments as compared to existing MFA methods. The proposed system demonstrates improved load-balancing behavior under simulated conditions and distributes authentication verification across multiple nodes. The results indicate potential resilience improvements compared with centralized MFA approaches. However, the findings are based on analytical and simulation evaluation, and real-world deployment assessment remains future work.

The edge computing-based Internet of Things (IoT) system minimizes latency by processing data locally, reducing the distance it needs to travel. Processing data in proximity to its source enables rapid decision-making and real-time reactions. The edge-based IoT has several possible uses, including smart cities, smart healthcare, industrial automation & processing, smart farming, and many more. In this paper, we propose a blockchain-driven machine learning-enabled intrusion-resilient authenticated key agreement scheme for edge-centric IoT systems (in short, BMAS-EIoT), which is equipped with the features of authentication, key management, and machine learning-based intrusion detection. In BMAS-EIoT, we provide the network and threat models to enhance comprehension of the organization and deployment of devices and systems, as well as the potential threats to the system. BMAS-EIoT has been observed to possess protection against a variety of potential attacks during the security investigation. Moreover, it has been observed that BMAS-EIoT outperforms other present schemes in terms of performance comparison. A practical implementation of BMAS-EIoT is provided to evaluate the effectiveness of its key components, including intrusion detection and blockchain implementation. Furthermore, BMAS-EIoT possesses supplementary noteworthy capabilities and enhanced security attributes.

Two decades after its formalization, P4 medicine (predictive, preventive, personalized, participatory) remains more framework than practice. Most implementations stall at single-omics prediction and fail to close the loop across all four dimensions. In this Perspective, we argue that the P4 framework becomes actionable only when each pillar is anchored to a specific, implementable digital technology: multi-omics for prediction, artificial intelligence for prevention, digital twinning for personalization, and blockchain for participation. We propose a tiered multi-omics classification (Tier 1: genomics, measured once; Tier 2: epigenomics/proteomics, periodic; Tier 3: metabolomics/wearables, frequent) and present preliminary metabolomic aging data from 2,072 individuals identifying nine metabolites with linear age associations. We offer a computational definition of personalization requiring baseline state estimation, trajectory prediction, and counterfactual intervention simulation via stochastic digital twin engines. For the participatory pillar, we describe a blockchain architecture enabling patient-controlled data sovereignty and a health data marketplace. These four technologies form a reinforcing flywheel, where longitudinal patient participation enriches upstream data layers. We discuss validation challenges, regulatory gaps, equity concerns, and privacy risks that must be addressed before clinical deployment.

The Internet of Medical Things is revolutionizing the concept of patient care. It is empowering the implementation of remote patient care protocols through the use of body sensors to monitor vital signs. However, it produces vast amounts of information, which raises security and privacy concerns. High-dimensional medical data are essential for diagnosis and treatment, but they are not currently connected to blockchain-based electronic health record systems. To overcome these limitations, the authors present a Hyperledger Fabric-based secure remote patient monitoring model for storing and retrieving medical imaging. The system records patient vital signs using sensors and stores medical images off-chain in the InterPlanetary File System. This model uses two organizations and a single channel, with Raft consensus, to ensure data consistency and high performance. Additionally, this study evaluates the performance of the proposed system in terms of throughput and latency. A test was conducted at 1,200 transactions with varying transfer rates. The results reveal that the throughput was near the send rate, up to 90 TPS. At a send rate of 150 TPS, the system reaches its peak throughput of 117.04 TPS. Moreover, no transactions were lost, which means that the system was able to make all its transactions, representing system reliability. The latency was noted to be 0.21 to 2.24 s, whereas the read operation was always characterized by the same latency of 0.01 s.