Search Results (128)

Modern enterprise information systems must simultaneously support complex organizational structures, ensure robust security, and remain scalable and maintainable over time. Traditional Role-Based Access Control (RBAC) models, while effective for permission management, operate primarily as post-design security layers and do not provide a unified methodology for structuring system architecture. This paper introduces the Zoned Role-Based (ZRB) model, a mathematically formalized and comprehensive framework that integrates organizational modeling, system design, implementation, access control, and long-term maintenance. ZRB models an organization as a hierarchy of zones, each containing its own roles, applications, operations, and users, forming a recursive Zone Tree that directly mirrors real organizational semantics. Through formally defined role hierarchies, zone-scoped permission sets, and inter-zone inheritance mappings, ZRB provides a context-aware permission calculus that unifies authentication and authorization across all zones. The paper presents the theoretical foundations of ZRB, a multi-phase engineering methodology for constructing integrated enterprise systems, and a complete implementation architecture with permission inference, navigation design, administrative subsystems, and deployment models. Primary validation and evaluations across several developed systems demonstrate significant improvements in permission accuracy, administrative efficiency, scalability, and maintainability. ZRB thus offers a rigorously defined and practically validated framework for building secure, scalable, and organizationally aligned enterprise information systems. Full article

With increasing contamination in aquatic ecosystems, effective monitoring is crucial to preserve biodiversity and protect public health. This study quantified bioindicators (red swamp crayfish (Pcla), Genji-firefly (Lcr2), Ayu fish (Paa), and caddisfly (Sma)), microbial source tracking markers (ruminants (BacR), pigs (Pig2Bac), and humans (gyrB)), and a fecal indicator bacterium (Escherichia coli (sfmD)) using quantitative PCR on river water samples collected every 2 h between 21 and 22 July 2023 (from the Omo and Bingushi Rivers in Yamanashi Prefecture, Japan). Initially, the optimal filter sizes of 1.0, 0.65, and 0.22 µm were evaluated, where the 0.65 µm filter yielded higher Paa concentrations (Kruskal–Wallis test, p < 0.05) and was used subsequently. BacR and Paa exhibited 100% detection in the Omo (13/13) and Bingushi (13/13) Rivers with concentrations of 5.0 log₁₀ and 5.5 log₁₀ copies/L, respectively. These concentrations were used to assess 24 h temporal variability, but no significant fluctuations or cyclical trends between morning, afternoon, evening, and night were observed in either river. The BacR–Paa pair exhibited perfect positive detection correlation (Φ = 1.0) and complete similarity (Jaccard Index = 1.0), but a moderate negative correlation of mean concentrations highlights the importance of considering habitat overlaps and behavioral synchronicity. Full article

Background/Objectives: The mammalian microbiota constitutes a reservoir of antimicrobial resistance genes (ARGs), which can be shaped by environmental and anthropogenic factors. Although bat-associated bacteria have been reported to harbor diverse ARGs globally, the ecological and evolutionary determinants driving this diversity remain unclear. Methods: To characterize ARG diversity in wildlife exposed to contrasting levels of human influence, we analyzed homologs of resistance mechanisms from the Comprehensive Antibiotic Resistance Database in shotgun metagenomes of bat guano. Samples were collected from a colony exposed to continuous anthropogenic activity in Spain (Salamanca) and from a wild, non-impacted bat community in China (Guangdong). Metagenomic analyses revealed marked differences in taxonomic and resistome composition between sites. Results: Salamanca samples contained numerous hospital-associated genera (e.g., Mycobacterium, Staphylococcus, Corynebacterium), while Guangdong was dominated by Lactococcus, Aeromonas, and Stenotrophomonas. β-lactamases and MurA transferase homologs were the most abundant ARGs in both datasets, yet Salamanca exhibited higher richness and functional diversity (median Shannon index = 1.5; Simpson = 0.8) than Guangdong (Shannon = 1.1; Simpson = 0.66). Salamanca also showed enrichment of clinically relevant ARGs, including qacG, emrR, bacA, and acrB, conferring resistance to antibiotics critical for human medicine. In contrast, Guangdong exhibited a more restricted resistome dominated by β-lactamase and MurA homologs. Beta diversity analysis confirmed significant compositional differences between resistomes (PERMANOVA, R² = 0.019, F = 1.33, p = 0.001), indicating ecological rather than stochastic structuring. Conclusions: These findings suggest that anthropogenic exposure enhances the diversity and evenness of resistance mechanisms within bat-associated microbiomes, potentially increasing their role as reservoirs of antimicrobial resistance. Full article

Healthcare faces a critical challenge: protecting sensitive medical data while enabling necessary clinical access. Evolving user behaviors, dynamic clinical contexts, and strict regulatory requirements demand adaptive access control mechanisms. Despite strict regulations, healthcare remains the most breached industry, consistently facing severe security risks related to unauthorized access. Traditional access control models cannot handle contextual variations, detect credential compromise, or provide transparent decision rationales. To address this, SAFE-GUARD (Semantic Access Control Framework Employing Generative User Assessment and Rule Decisions) is proposed as a two-layer framework that combines behavioral analysis with policy enforcement. The Behavioral Analysis Layer uses Retrieval-Augmented Generation (RAG) to detect contextual anomalies by comparing current requests against historical patterns. The Rule-Based Policy Evaluation Layer independently validates organizational procedures and regulatory requirements. Access is granted only when behavioral consistency and both organizational and regulatory policies are satisfied. We evaluate SAFE-GUARD using simulated healthcare scenarios with three LLMs (GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Flash) achieving an anomaly detection accuracy of 95.2%, 94.1%, and 91.3%, respectively. The framework effectively identifies both compromised credentials and insider misuse by detecting deviations from established behavioral patterns, significantly outperforming conventional RBAC and ABAC approaches that rely solely on static rules. Full article

Large Language Models (LLMs) have demonstrated significant potential in transforming software testing by automating tasks such as test case generation. In this work, we explore the integration of LLMs within a Model-Driven Engineering (MDE) approach to enhance the automation of test case generation for smart contracts. Our focus lies in the use of Role-Based Access Control (RBAC) models as formal specifications that guide the generation of test scenarios. By leveraging LLMs’ ability to interpret both natural language and model artifacts, we enable the derivation of model-based test cases that align with specified access control policies. These test cases are subsequently translated into executable code in Digital Asset Modeling Language (DAML) targeting blockchain-based smart contract platforms. Building on prior research that established a complete MDE pipeline for DAML smart contract development, we extend the framework with LLM-supported test automation capabilities and implement the necessary tooling to support this integration. Our evaluation demonstrates the feasibility of using LLMs in this context, highlighting their potential to improve testing coverage, reduce manual effort, and ensure conformance with access control specifications in smart contract systems. Full article

Because of the advancement of IT, cross-domain environments have emerged where independent clouds with different security policies share data. However, sharing data between clouds with heterogeneous security levels is a challenging task, and most existing access control schemes focus on a single cloud domain. Among various access control models, RBAC is suitable for cross-domain data sharing, but existing RBAC schemes cannot provide strong role privacy and do not support freshness in role verification, so they are vulnerable to replay-based misuse of credentials. In this paper, we propose an RBAC scheme for cross-domain cloud environments based on a hash-chain-augmented zk-SNARK and identity-based signatures. The TA issues IBS-based role signing keys to users, and the user proves, through a zk-SNARK circuit, that there exists a valid role signing key satisfying the access policy without revealing the concrete role information to the CDS. In addition, a synchronized hash chain between the user and the CDS is embedded into the proof so that each proof is tied to the current hash-chain state and any previously used proof fails verification when replayed. We formalize role privacy, replay resistance, and MitM resistance in the cross-domain setting and analyze the proposed scheme by comparing it with Saxena and Alam’s I-RBAC, Xu et al.’s RBAC, MO-RBE, and PE-RBAC. The security analysis shows that the proposed scheme achieves robust role privacy against both the CDS and external attackers and prevents replay and man-in-the-middle attacks. Furthermore, the computational cost evaluation based on the number of pairing, exponentiation, point addition, and hash operations confirms that the verifier-side overhead remains comparable to existing schemes, while the additional prover cost is the price for achieving stronger privacy and security. Therefore, the proposed scheme can be applied to cross-domain cloud systems that require secure and privacy-preserving role verification, such as military, healthcare, and government cloud infrastructures. Full article

Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient control—issues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum Holešky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020–2025 highlights its potential as a scalable solution for next-generation ART information systems. Full article

Molecular toxicity prediction plays a crucial role in drug screening and environmental health risk assessment. Traditional toxicity prediction models primarily rely on molecular fingerprints and other structural features, while neglecting the complex biological mechanisms underlying compound toxicity, resulting in limited predictive accuracy, poor interpretability, and reduced generalizability. To address this challenge, this study proposes a novel molecular toxicity prediction framework that integrates knowledge graphs with Graph Neural Networks (GNNs). Specifically, we constructed a heterogeneous toxicological knowledge graph (ToxKG) based on ComptoxAI. ToxKG incorporates data from authoritative databases such as PubChem, Reactome, and ChEMBL, and covers multiple entities and relationships including chemicals, genes, signaling pathways, and bioassays. We then systematically evaluated six representative GNN models (GCN, GAT, R-GCN, HRAN, HGT, and GPS) on the Tox21 dataset. Experimental results demonstrate that heterogeneous graph models enriched with ToxKG information significantly outperform traditional models relying solely on structural features across multiple metrics including AUC, F1-score, ACC, and balanced accuracy (BAC). Notably, the GPS model achieved the highest AUC value (0.956) for key receptor tasks such as NR-AR, highlighting the critical role of biological mechanism information and heterogeneous graph structures in toxicity prediction. This study provides a promising pathway toward the development of interpretable and efficient intelligent models for toxicological risk assessment. Full article

Body image dissatisfaction, particularly related to breast appearance, plays an important role in cosmetic breast surgery (CBS) decisions and psychological wellbeing. However, existing measures are often lengthy, overlook healthy women considering CBS, and fail to adequately address the nipple–areola complex (NAC), a critical component of breast satisfaction. This study introduces the 12-item Breast Appearance Concerns Scale (BACS), a brief screening tool developed to address existing gaps and to document breast-specific body image concerns among women considering CBS. Data were collected from a diverse sample of 589 young adult women who completed the BACS along with measures of related constructs such as self-esteem and anxiety. Exploratory and confirmatory factor analyses supported a two-subscale structure: NAC Satisfaction and General Breast Satisfaction. The BACS total score demonstrated strong internal consistency (α = 0.785) and test–retest reliability (r = 0.741). Predictive validity analyses revealed that the General Breast Satisfaction subscale effectively distinguished women who had considered CBS from those who had not (classification accuracy = 72.1%). Receiver Operating Characteristic (ROC) analysis was conducted with the General Breast Satisfaction subscale to establish a preliminary cutoff score. This cutoff provides initial support for use of this subscale as a screening tool to help classify individuals based on their consideration of CBS. Although clinically important, the NAC subscale is still in an early stage of development and requires additional research before cutoff scores can be established to inform surgical decision-making and evaluate patient-reported satisfaction outcomes. Both subscales require further investigation in older populations and clinical settings to support their use as screening tools. These findings position the BACS as a promising screening tool for assessing breast-specific body image concerns, particularly general breast satisfaction, with potential applications in clinical, pre-surgical settings. Full article

Developing secure and well-structured web applications using Spring Boot presents a significant challenge, as it requires developers to manage multiple layers, employ framework-specific annotations, and ensure authentication, authorization, and compliance with architectural standards. These complexities often lead to errors among students and novice developers. Although current low-code platforms reduce coding effort, they frequently compromise clarity, modularity, and maintainability. This paper introduces XBoot, a lightweight framework that utilizes a straightforward XML-based domain-specific language (DSL) to automatically generate modular and secure Spring Boot applications. By providing concise specifications for entities, services, routes, and user roles, XBoot generates database entities, service classes, controllers, user interface templates, and integrated security rules. Validation rules are directly enforced from the DSL, and built-in Swagger documentation facilitates interactive API testing. The evaluation was conducted in two phases. Initially, XBoot was validated by generating applications for student–course and flight-booking domains, where less than 50 lines of DSL resulted in 950–1350 lines of Java and HTML code, complete with security and documentation. Subsequently, 10 undergraduate students utilized XBoot in practice. All participants successfully generated and deployed applications within 2–20 min (average ≈ 7), compared to 45–120 min for manual implementation. On a 5-point Likert scale, students rated the reinforcement of layered architecture at an average of 4.0. These findings suggest that XBoot effectively eliminates common structural and security errors, reduces boilerplate complexity through concise DSL specifications, and maintains modularity and transparency-limitations often observed in traditional coding and other low-code platforms. Full article

The integration of blockchain technology into healthcare offers a robust solution to challenges in secure data sharing, privacy protection, and operational efficiency. Effective exchange of sensitive patient information among hospitals, clinics, insurers, and researchers is essential for better outcomes and medical advancements. Traditional centralized systems often suffer from data breaches, inefficiency, and poor interoperability. This paper presents a blockchain-based secure data-sharing framework tailored for healthcare, addressing these limitations. The framework employs a hybrid blockchain model, combining private and public blockchains: the private chain ensures fast transactions and controlled access, while the public chain fosters transparency and trust. Advanced cryptographic methods—such as asymmetric encryption, hashing, and digital signatures—safeguard patient data and maintain integrity throughout the datalifecycle. Smart contracts automate processes like consent management, access control, and auditing, ensuring dynamic permission enforcement without intermediaries. Role-based access control (RBAC) further limits access to authorized entities, enhancing privacy. To tackle interoperability, standardized data formats and protocols enable smooth communication across diverse healthcare systems. Large files, such as medical images, are stored off-chain, with only essential metadata and logs on the blockchain. This approach optimizes performance, scalability, and suitability for large-scale healthcare deployments. Full article

As the primary biological risk threatening safe dairy production, bovine mastitis control highly relies on environmental disinfection measures. However, the mechanisms by which chemical disinfectants influence host–environment microbial interactions remain unclear. This study systematically investigated the disinfection efficacy and regulatory effects on microbial community composition and diversity of glutaraldehyde-benzalkonium chloride (BAC) and glutaraldehyde-didecyl dimethyl ammonium bromide (DAB) at recommended concentrations (2–5%), using 80 environmental samples from intensive dairy farms in Xinjiang, China. Combining 16S rDNA sequencing with culturomics, the results showed that BAC achieved a disinfection rate of 99.33%, higher than DAB’s 97.87%, and reduced the environment–gut microbiota similarity index by 23.7% via a cationic bacteriostatic film effect. Microbiome analysis revealed that BAC selectively suppressed Fusobacteriota abundance (15.67% reduction) and promoted Bifidobacterium proliferation (7.42% increase), enhancing intestinal mucosal barrier function through butyrate metabolism. In contrast, DAB induced Actinobacteria enrichment in the environment (44.71%), inhibiting pathogen colonization via bioantagonism. BAC’s long-acting bacteriostatic properties significantly reduced disinfection costs and mastitis incidence. This study first elucidated the mechanism by which quaternary ammonium compound (QAC) disinfectants regulate host health through “environment-gut” microbial interactions, providing a critical theoretical basis for developing precision disinfection protocols integrating “cost reduction-efficiency enhancement-risk mitigation.” Full article

The blockchain technology is transforming the mining industry by enabling mineral reserve tokenization, improving security, transparency, and traceability. However, controlling access to sensitive mining data remains a challenge. Existing access control models, such as role-based access control, are too rigid because they assign permissions based on predefined roles rather than real-world conditions like mining licenses, regulatory approvals, or investment status. To address this, this paper explores an attribute-based access control model for blockchain-based mineral tokenization systems. ABAC allows access permissions to be granted dynamically based on multiple attributes rather than fixed roles, making it more adaptable to the mining industry. This paper presents a high-level system design that integrates ABAC with the blockchain using smart contracts to manage access policies and ensure compliance. The proposed model is designed for permissioned blockchain platforms, where access control decisions can be automated and securely recorded. A comparative analysis between ABAC and RBAC highlights how ABAC provides greater flexibility, security, and privacy for mining operations. By introducing ABAC in blockchain-based mineral reserve tokenization, this paper contributes to a more efficient and secure way of managing data access in the mining industry, ensuring that only authorized stakeholders can interact with tokenized mineral assets. Full article

This study explores asymmetric volatility structures within multivariate hysteretic autoregressive (MHAR) models that incorporate conditional correlations, aiming to flexibly capture the dynamic behavior of global financial assets. The proposed framework integrates regime switching and time-varying delays governed by a hysteresis variable, enabling the model to account for both asymmetric volatility and evolving correlation patterns over time. We adopt a fully Bayesian inference approach using adaptive Markov chain Monte Carlo (MCMC) techniques, allowing for the joint estimation of model parameters, Value-at-Risk (VaR), and Marginal Expected Shortfall (MES). The accuracy of VaR forecasts is assessed through two standard backtesting procedures. Our empirical analysis involves both simulated data and real-world financial datasets to evaluate the model’s effectiveness in capturing downside risk dynamics. We demonstrate the application of the proposed method on three pairs of daily log returns involving the S&P500, Bank of America (BAC), Intercontinental Exchange (ICE), and Goldman Sachs (GS), present the results obtained, and compare them against the original model framework. Full article

Background: Canine parvovirus (CPV) is a highly pathogenic virus that predominantly affects puppies, with mortality rates exceeding 70%. Although commercial multivalent live attenuated vaccines (MLV) are widely employed, their efficacy is often compromised by maternal antibody interference. Consequently, the development of novel vaccines remains imperative for effective CPV control. Methods: Recombinant CPV VP2 protein (rVP2) and canine interlukine 12 protein (rcIL-12) were expressed using the Bac-to-Bac baculovirus expression system and the biological activity of these proteins was assessed through hemagglutination, Cell Counting Kit-8 (CCK8) and IFN-γ induction assays. The combined immunoenhancement effect of rVP2 and rcIL-12 protein was evaluated in puppies. Results: Both rVP2 and rcIL-12 were successfully expressed and purified, exhibiting confirmed antigenicity, immunogenicity, and bioactivity. Co-administration of rVP2 with rcIL-12 elicited higher neutralizing antibody titer (6–7 times higher), complete challenge protection efficiency (no clinical symptoms and tissue and organ lesions), fewer viral shedding (decreasing significantly 8-day post challenge) and superior viral blockade (lower viral load in the organism) compared to rVP2 alone. Conclusions: Our findings demonstrate that rVP2 co-administered with rcIL-12 induces robust protective immunity in puppies and significantly mitigated the inhibitory effects of maternal antibodies. This represents a promising strategy for enabling earlier vaccination in puppies and rational design of CPV subunit vaccines. Full article