Search Results (2,141)

In recent years, most proposed digital image encryption algorithms have primarily focused on encrypting raw pixel data, often neglecting the integration with image compression techniques. Image compression algorithms, such as JPEG, are widely utilized in internet applications, highlighting the need for encryption methods that are compatible with compression processes. This study introduces an innovative color image encryption algorithm integrated with JPEG compression, designed to enhance the security of images susceptible to attacks or tampering during prolonged transmission. The research addresses critical challenges in achieving an optimal balance between encryption security and compression efficiency. The proposed encryption algorithm is structured around three key compression phases: Discrete Cosine Transform (DCT), quantization, and entropy coding. At each stage, the algorithm incorporates advanced techniques such as block segmentation, block replacement, DC coefficient confusion, non-zero AC coefficient transformation, and RSV (Run/Size and Value) pair recombination. Extensive simulations and security analyses demonstrate that the proposed algorithm exhibits strong robustness against noise interference and data loss, effectively meeting stringent security performance requirements. Full article

The increasing reliance on cloud services demands advanced security mechanisms to protect sensitive data and ensure robust access control. This study addresses critical challenges in cloud security by proposing a novel framework that integrates blockchain-based smart contracts to enhance authorization and authentication processes. Smart contracts, as self-executing agreements embedded with predefined rules, enable decentralized, transparent, and tamper-proof mechanisms for managing access control in cloud environments. The proposed system mitigates prevalent threats such as unauthorized access, data breaches, and identity theft through an immutable and auditable security framework. A prototype system, developed using Ethereum blockchain and Solidity programming, demonstrates the feasibility and effectiveness of the approach. Rigorous evaluations reveal significant improvements in key metrics: security, with a 0% success rate for unauthorized access attempts; scalability, maintaining low response times for up to 100 concurrent users; and usability, with an average user satisfaction rating of 4.4 out of 5. These findings establish the efficacy of smart contract-based solutions in addressing critical vulnerabilities in cloud services while maintaining operational efficiency. The study underscores the transformative potential of blockchain and smart contracts in revolutionizing cloud security practices. Future research will focus on optimizing the system’s scalability for higher user loads and integrating advanced features such as adaptive authentication and anomaly detection for enhanced resilience across diverse cloud platforms. Full article

As a new generation of mobile communication networks, 6G security faces many new security challenges. Vehicle to Everything (V2X) will be an important part of 6G. In V2X, connected and automated vehicles (CAVs) need to frequently share data with other vehicles and infrastructures. Therefore, identity revocation technology in the authentication is an important way to secure CAVs and other 6G scenario applications. This paper proposes an efficient credential revocation scheme with a four-layer architecture. First, a rapid pre-filtration layer is constructed based on the cuckoo filter, responsible for the initial screening of credentials. Secondly, a directed routing layer and the precision judgement layer are designed based on the consistency hash and the dynamic RSA accumulator. By proposing the dynamic expansion of the RSA accumulator and load-balancing algorithm, a smaller and more stable revocation delay can be achieved when many users and terminal devices access 6G. Finally, a trusted storage layer is built based on the blockchain, and the key revocation parameters are uploaded to the blockchain to achieve a tamper-proof revocation mechanism and trusted data traceability. Based on this architecture, this paper also proposes a detailed identity credential revocation and verification process. Compared to existing solutions, this paper’s solution has a combined average improvement of 59.14% in the performance of the latency of the cancellation of the inspection, and the system has excellent load balancing, with a standard deviation of only 11.62, and the maximum deviation is controlled within the range of ±4%. Full article

Background: Overcrowded emergency departments (EDs) create significant challenges for patient management and hospital efficiency. In response, Amiens Picardy University Hospital (APUH) developed the “Prediction of the Patient Pathway in the Emergency Department” (3P-U) model to enhance patient flow management. Objectives: To develop and clinically validate an explainable artificial intelligence (XAI) model for hospital admission predictions, using structured triage data, and demonstrate its real-world applicability in the ED setting. Methods: Our retrospective, single-center study involved 351,019 patients consulting in APUH’s EDs between 2015 and 2018. Various models (including a cross-validation artificial neural network (ANN), a k-nearest neighbors (KNN) model, a logistic regression (LR) model, and a random forest (RF) model) were trained and assessed for performance with regard to the area under the receiver operating characteristic curve (AUROC). The best model was validated internally with a test set, and the F1 score was used to determine the best threshold for recall, precision, and accuracy. XAI techniques, such as Shapley additive explanations (SHAP) and partial dependence plots (PDP) were employed, and the clinical explanations were evaluated by emergency physicians. Results: The ANN gave the best performance during the training stage, with an AUROC of 83.1% (SD: 0.2%) for the test set; it surpassed the RF (AUROC: 71.6%, SD: 0.1%), KNN (AUROC: 67.2%, SD: 0.2%), and LR (AUROC: 71.5%, SD: 0.2%) models. In an internal validation, the ANN’s AUROC was 83.2%. The best F1 score (0.67) determined that 0.35 was the optimal threshold; the corresponding recall, precision, and accuracy were 75.7%, 59.7%, and 75.3%, respectively. The SHAP and PDP XAI techniques (as assessed by emergency physicians) highlighted patient age, heart rate, and presentation with multiple injuries as the features that most specifically influenced the admission from the ED to a hospital ward. These insights are being used in bed allocation and patient prioritization, directly improving ED operations. Conclusions: The 3P-U model demonstrates practical utility by reducing ED crowding and enhancing decision-making processes at APUH. Its transparency and physician validation foster trust, facilitating its adoption in clinical practice and offering a replicable framework for other hospitals to optimize patient flow. Full article

Genomic instability markers are important hallmarks of aging, as previously evidenced within the European study of biomarkers of human aging, MARK-AGE; however, establishing the specific metabolic determinants of vascular aging is challenging. The objective of the present study was to evaluate the impact of the susceptibility to oxidation of serum LDL particles (LDLox) and the plasma metabolization products of nitric oxide (NOx) on relevant genomic instability markers. The analysis was performed on a MARK-AGE cohort of 1326 subjects (635 men and 691 women, 35–75 years old) randomly recruited from the general population. The Inverse Probability of Treatment Weighting causal inference algorithm was implemented in order to assess the potential causal relationship between the LDLox and NOx octile-based thresholds and three genomic instability markers measured in mononuclear leukocytes: the percentage of telomeres shorter than 3 kb, the initial DNA integrity, and the DNA damage after irradiation with 3.8 Gy. The results showed statistically significant telomere shortening for LDLox, while NOx yielded a significant impact on DNA integrity. Overall, the effect on the genomic instability markers was higher than for the confirmed vascular aging determinants, such as low HDL cholesterol levels, indicating a meaningful impact even for small changes in LDLox and NOx values. Full article

Trust evaluation has become a major challenge in the quickly developing Internet of Things (IoT) environment because of the vulnerabilities and security hazards associated with networked devices. To overcome these obstacles, this study offers a novel approach for evaluating trust that uses IOTA Tangle technology. By decentralizing the trust evaluation process, our approach reduces the risks related to centralized solutions, including privacy violations and single points of failure. To offer a thorough and reliable trust evaluation, this study combines direct and indirect trust measures. Moreover, we incorporate IOTA-based trust metrics to evaluate a node’s trust based on its activity in creating and validating IOTA transactions. The proposed framework ensures data integrity and secrecy by implementing immutable, secure storage for trust scores on IOTA. This ensures that no node transmits a wrong trust score for itself. The results show that the proposed scheme is efficient compared to recent literature, achieving up to +3.5% higher malicious node detection accuracy, up to 93% improvement in throughput, 40% reduction in energy consumption, and up to 24% lower end-to-end delay across various network sizes and adversarial conditions. Our contributions improve the scalability, security, and dependability of trust assessment processes in Internet of Things networks, providing a strong solution to the prevailing issues in current centralized trust models. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article

Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems. Full article

The Industrial Internet of Things (IIoT), a key enabler of Industry 4.0, integrates advanced communication technologies with the industrial economy to enable intelligent manufacturing and interconnected systems. Secure and reliable identity authentication in the IIoT becomes essential as connectivity expands across devices, systems, and domains. Blockchain technology presents a promising solution due to its decentralized, tamper-resistant, and traceable characteristics, facilitating secure and transparent identity verification. However, current blockchain-based cross-domain authentication schemes often lack a lightweight design, rendering them unsuitable for latency-sensitive and resource-constrained industrial environments. This paper proposes a lightweight cross-domain authentication scheme that combines blockchain with Chebyshev chaotic mapping. Unlike existing schemes relying heavily on Elliptic Curve Cryptography or bilinear pairing, our design circumvents such computationally intensive primitives entirely through the algebraic structure of Chebyshev polynomials. A formal security analysis using the Real-Or-Random (ROR) model demonstrates the scheme’s robustness. Furthermore, performance evaluations conducted with Hyperledger Fabric and the MIRACL cryptographic library validate the method’s effectiveness and superiority over existing approaches in terms of both security and operational efficiency. Full article

With the increasing scale of industrial devices and the growing complexity of multi-source heterogeneous sensor data, traditional methods struggle to address challenges in fault detection, data security, and trustworthiness. Ensuring tamper-proof data storage and improving prediction accuracy for imbalanced anomaly detection for potential deployment in the Industrial Internet of Things (IIoT) remain critical issues. This study proposes a blockchain-powered Long Short-Term Memory Network (LSTM)–Attention hybrid model: an LSTM-based Encoder–Attention–Decoder (LEAD) for industrial device anomaly detection. The model utilizes an encoder–attention–decoder architecture for processing multivariate time series data generated by industrial sensors and smart contracts for automated on-chain data verification and tampering alerts. Experiments on real-world datasets demonstrate that the LEAD achieves an $F_{0.1}$ score of 0.96, outperforming baseline models (Recurrent Neural Network (RNN): 0.90; LSTM: 0.94; and Bi-directional LSTM (Bi-LSTM, 0.94)). We simulate the system using a private FISCO-BCOS network with a multi-node setup to demonstrate contract execution, anomaly data upload, and tamper alert triggering. The blockchain system successfully detects unauthorized access and data tampering, offering a scalable solution for device monitoring. Full article

Large-scale events, such as festivals and public gatherings, pose serious problems in terms of traffic congestion, slow transaction processing, and security risks to transportation planning. This study proposes a blockchain-based solution for enhancing the efficiency and security of intelligent transport systems (ITS) by utilizing state channels and rollups. Throughput is optimized, enabling transaction speeds of 800 to 3500 transactions per second (TPS) and delays of 5 to 1.5 s. Prevent data tampering, strengthen security, and enhance data integrity from 89% to 99.999%, as well as encryption efficacy from 90% to 98%. Furthermore, our system reduces congestion, optimizes vehicle movement, and shares real-time, secure data with stakeholders. Practical applications include fast and safe road toll payments, faster public transit ticketing, improved emergency response coordination, and enhanced urban mobility. The decentralized blockchain helps maintain trust among users, transportation authorities, and event organizers. Our approach extends beyond large-scale events and proposes a path toward ubiquitous, Artificial Intelligence (AI)-driven decision-making in a broader urban transit network, informing future operations in dynamic traffic optimization. This study demonstrates the potential of blockchain to create more intelligent, more secure, and scalable transportation systems, which will help reduce urban mobility inefficiencies and contribute to the development of resilient smart cities. Full article

This study investigates the effects of ultrasonic nanocrystalline surface modification (UNSM) on the formation of nitride layers in Ti-6Al-4V alloy during ion-plasma nitriding (IPN). Various UNSM parameters, including vibration amplitude, static load, and processing temperature, were systematically varied to evaluate their influence on microstructure, hardness, elastic modulus, and tribological behavior. The results reveal that pre-treatment with optimized UNSM conditions significantly enhances nitrogen diffusion, leading to the formation of dense and uniform TiN/Ti₂N layers. Samples pre-treated under high-load and elevated-temperature UNSM exhibited the greatest improvements in surface hardness (up to 25%), elastic modulus (up to 18%), and wear resistance, with a reduced and stabilized friction coefficient (~0.55). Scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses confirmed microstructural densification, grain refinement, and increased nitride phase intensity. These findings demonstrate not only the scientific relevance but also the practical potential of UNSM as an effective surface activation technique. The hybrid UNSM + IPN approach may serve as a promising method for extending the service life of load-bearing biomedical implants and engineering components subjected to intensive wear. Full article

This paper presents an attack detection, response, and recommendation framework designed to protect the integrity and operational continuity of IoT-based critical infrastructure, specifically focusing on an energy use case. With the growing deployment of IoT-enabled smart meters in energy systems, ensuring data integrity is essential. The proposed framework monitors smart meter data in real time, identifying deviations that may indicate data tampering or device malfunctions. The system comprises two main components: an attack detection and prediction module based on machine learning (ML) models and a response and adaptation module that recommends countermeasures. The detection module employs a forecasting model using a long short-term memory (LSTM) architecture, followed by a dense layer to predict future readings. It also integrates a statistical thresholding technique based on Tukey’s fences to detect abnormal deviations. The system was evaluated on real smart meter data in a testbed environment. It achieved accurate forecasting (MAPE < 2% in most cases) and successfully flagged injected anomalies with a low false positive rate, an effective result given the lightweight, unsupervised, and real-time nature of the approach. These findings confirm the framework’s applicability in resource-constrained energy systems requiring real-time cyberattack detection and mitigation. Full article

Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems. Full article

Background/Objectives: Histological follow-up still lacks consensus in the long-term management of adult patients with celiac disease (CD) adhering to a gluten-free diet (GFD). Despite clinical and serological improvement, a significant proportion of patients continue to have persistent villous atrophy. We aimed to synthesize current evidence regarding histological outcomes after GFD treatment in adult CD, focusing on mucosal healing rates, assessment methods, and remission criteria. Methods: We conducted a literature search with extraction and analysis of published cohort studies that included adult patients with CD on GFD with follow-up biopsy data. Extracted parameters included demographic details, baseline histology, GFD duration and adherence, serologic status, and histologic recovery rates with corresponding remission criteria. Results: Data from 46 studies comprising 15,530 patients were analyzed. The overall mean age was 41 years, and 73.3% were female. Mean histologic remission across cohorts was 58.8%, with considerable interstudy variation. Remission criteria also varied widely, ranging from strict Marsh 0 control histology to more inclusive definitions that considered Marsh 1 or even non-atrophic mucosa (Marsh < 3) as indicative of recovery, while some studies relied on quantitative villous height-to-crypt depth ratio thresholds, substantially influencing reported remission rates. Longer GFD duration and rigorous diet adherence assessment using validated questionnaires and accurate laboratory tools were associated with higher remission rates. Conclusions: Histologic remission in GFD-treated adult patients with CD is highly variable and strongly influenced by remission definitions and adherence assessment methods. Standardized reporting using validated metrics for histologic outcome and dietary compliance is essential for harmonizing follow-up strategies in adult CD. Full article