Search Results (1,133)

Self-cleansing intrusion-tolerant systems mitigate attacker intrusions and control through periodic recovery, thereby enhancing both availability and security. However, vulnerabilities in the control link render these systems susceptible to request forgery attacks. Furthermore, existing research on the modeling and performance analysis of such systems remains insufficient. To address these issues, this paper introduces an authentication mechanism to fortify control link security and employs Generalized Stochastic Petri Nets for system evaluation. We constructed Petri net models for three distinct scenarios: a traditional system, a system compromised by forged controller requests, and a system fortified with authentication mechanism. Subsequently, isomorphic Continuous-Time Markov Chains were derived to facilitate theoretical analysis. Quantitative evaluations were performed by deriving steady-state probabilities and conducting simulations on the PIPE platform. To further assess practicality, we conduct scalability analysis under varying system scales and parameter settings, and implement a prototype in a virtualized testbed to experimentally validate the analytical findings. Evaluation results indicate that authentication mechanism ensures the reliable execution of cleansing strategies, thereby improving system availability, enhancing security, and mitigating data leakage risks. Full article

This work studies the formation control problem of general linear multi-agent systems (MASs) under hybrid attacks that include man-in-the-middle attacks (MITM) and denial-of-service attacks (DoS). First, an improved Diffie–Hellman key exchange (DHKE)-based encryption–decryption mechanism is proposed. This mechanism combines the challenge–response mechanism and hash function, which can achieve identity authentication, detect MITM attacks and ensure the confidentiality and integrity of information. Second, considering that DoS attacks on different channels are independent, a division model for distributed DoS attacks is established, which can classify attacks into different patterns. Third, an edge-based event-triggered (ET) formation control scheme is proposed. This control method only relies on the information of neighbor agents, which not only saves communication resources but also resists distributed DoS attacks. Finally, sufficient conditions for the implementation of formation control for MASs under hybrid attacks are provided, and the effectiveness and advantages of the proposed strategy are verified by simulation. Full article

As new technologies, such as electronic medical records (EMRs), are introduced into healthcare services, we need to consider how they may be incorporated into simulated environments, so as to maintain and enhance authenticity and learning opportunities. While EMRs have revolutionised clinical practice, many education settings continue to rely on paper-based documentation in simulation, creating a widening gap between educational environments and real-world clinical workflows. This disconnect limits learners’ ability to engage authentically with the tools and resources that underpin contemporary healthcare, impeding the transfer of knowledge to the clinical environment. This practice-based commentary draws on institutional experience from a large, multi-disciplinary simulation-based education facility that explored approaches to integrating EMRs into simulation-based education. It describes the decision points and efforts made to integrate an EMR into simulation-based education and concludes that while genuine EMR systems increase fidelity, their technical rigidity and data governance constraints reduce authenticity. To overcome this, Adelaide Health Simulation adopted an academic EMR (AEMR), a purpose-built digital platform designed for education. The AEMR maintains the functional realism of clinical systems while offering the pedagogical flexibility required to control data, timelines, and learner interactions. Drawing on this experience, this commentary highlights how authenticity in simulation-based education is best achieved not through technological replication alone, but through deliberate use of technologies that align with clinical realities while supporting flexible, learner-centred design. Purpose-built AEMRs exemplify how digital tools can enhance both fidelity and authenticity, fostering higher-order thinking, clinical reasoning, and digital fluency essential for safe and effective contemporary healthcare practice. Here, we argue that advancing simulation-based education in parallel with health service innovations is required if we want to adequately prepare learners for contemporary clinical practice. Full article

This study addresses the ongoing challenge of accurately classifying beers by fermentation type and product category, an issue of growing importance for quality control, authenticity assessment, and product differentiation in the brewing sector. We applied a multiblock chemometric framework that integrates phenolic profiling obtained via GC–MS, antioxidant and antiradical activity derived from in vitro assays, and complementary colorimetric and physicochemical measurements. Principal Component Analysis (PCA) revealed clear compositional structuring within the dataset, with p-coumaric, gallic, syringic, and malic acids emerging as major contributors to variance. Supervised machine-learning classification demonstrated robust performance, achieving approximately 93% accuracy in discriminating top- from bottom-fermented beers, supported by a well-balanced confusion matrix (25 classified and 2 misclassified samples per group). When applied to ale–lager categorization, the model retained strong predictive ability, reaching 90% accuracy, largely driven by the C* chroma value and the concentrations of tyrosol, acetic acid, homovanillic acid, and syringic acid. The integration of multiple analytical blocks significantly enhanced class separation and minimized ambiguity between beer categories. Overall, these findings underscore the value of multi-block chemometrics as a powerful strategy for beer characterization, supporting brewers, researchers, and regulatory bodies in developing more reliable quality-assurance frameworks. Full article

Maca (Lepidium meyenii Walp.), a Brassicaceae species native to the high Andes of Peru, has gained global attention as a functional food and herbal medicinal product due to its endocrine-modulating, fertility-enhancing, and neuroprotective properties. Although numerous studies have addressed its biological effects, a systematic and up-to-date summary of its chemical constituents and analytical methodologies is lacking. This review aims to provide a critical overview of the chemical constituents of L. meyenii and to evaluate analytical studies published between 2000 and 2025, focusing on recent advances in extraction strategies and qualitative and quantitative analytical techniques for quality control. Major compound classes include macamides, macaenes, glucosinolates, and alkaloids, each contributing to maca’s multifaceted activity. Ultra-(high-)performance liquid chromatography (U(H)PLC), often coupled with ultraviolet, diode array, or mass spectrometric detection, is the primary and most robust analytical platform due to its sensitivity, selectivity, and throughput, while ultrasound-assisted extraction improves efficiency and reproducibility. Emerging techniques such as metabolomics and chemometric approaches enhance quality control by enabling holistic, multivariate assessment of complex systems and early detection of variations not captured by traditional univariate methods. As such, they provide complementary, predictive, and more representative insights into maca’s phytochemical complexity. The novelty of this review lies in its integration of conventional targeted analysis with emerging approaches, comprehensive comparison of analytical workflows, and critical discussion of variability related to phenotype, geographic origin, and post-harvest processing. By emphasizing analytical standardization and quality assessment rather than biological activity alone, this review provides a framework for quality control, authentication, and safety evaluation of L. meyenii as a functional food and dietary supplement. Full article

Near-infrared (NIR) spectroscopy has emerged as a pivotal non-destructive analytical technique within the cheese industry, offering rapid and precise insights into the chemical composition and quality attributes of various cheese types. This review explores the evolution of NIR spectral sensors, highlighting key technological advancements and their integration into cheese production processes as well as final products already in markets. In addition, the review discusses challenges such as calibration complexities, the influence of sample heterogeneity and the need for robust data and interpretation models through spectroscopy coupled with AI methods. The future potential of NIR spectral sensors, including real-time in-line monitoring and the development of portable devices for on-site analysis, is also examined. This review aims to provide a critical assessment of current NIR spectral sensors and their impact on the cheese industry, offering insights for researchers and industry professionals aiming to enhance quality control and innovation in cheese production, as well as authenticity and fraud studies. The review concludes that the integration of advanced NIR spectroscopy with AI represents a transformative approach for the cheese industry, enabling more accurate, efficient and sustainable quality assessment practices that can strengthen both production consistency and consumer trust. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly used in civilian and military applications, making their communication and control systems targets for cyber attacks. The emerging threat of quantum computing amplifies these risks. Quantum computers could break the classical cryptographic schemes used in current UAV networks. This situation underscores the need for quantum-resilient, privacy-preserving security frameworks. This paper proposes a quantum-resilient federated learning framework for multi-layer cyber anomaly detection in UAV systems. The framework combines a hybrid deep learning architecture. A Variational Autoencoder (VAE) performs unsupervised anomaly detection. A neural network classifier enables multi-class attack categorization. To protect sensitive UAV data, model training is conducted using federated learning with differential privacy. Robustness against malicious participants is ensured through Byzantine-robust aggregation. Additionally, CRYSTALS-Dilithium post-quantum digital signatures are employed to authenticate model updates and provide long-term cryptographic security. Researchers evaluated the proposed framework on a real UAV attack dataset containing GPS spoofing, GPS jamming, denial-of-service, and simulated attack scenarios. Experimental results show the system achieves 98.67% detection accuracy with only 6.8% computational overhead compared to classical cryptographic approaches, while maintaining high robustness under Byzantine attacks. The main contributions of this study are: (1) a hybrid VAE–classifier architecture enabling both zero-day anomaly detection and precise attack classification, (2) the integration of Byzantine-robust and privacy-preserving federated learning for UAV security, and (3) a practical post-quantum security design validated on real UAV communication data. Full article

Nutraceuticals are food-based products that provide health benefits beyond basic nutrition and play an increasingly important role in preventive healthcare. Ensuring their quality, safety, and efficacy is critical as the global market expands. A systematic literature search was conducted in Scopus, Web of Science, and PubMed using keywords such as ‘nutraceuticals,’ functional foods,’ and ‘quality control,’ with studies selected based on their focus on methods for standardization, characterization, and quality assessment. This review summarizes current analytical approaches, including spectroscopic, chromatographic, and techniques for elemental analysis, highlighting their applications in compound identification, quantification, detection of adulterants, and overall quality control. Emerging challenges and future trends, such as the integration of chemometrics and real-time analytical strategies, are also discussed, providing a comprehensive perspective on the evolving field of nutraceutical analysis. Full article

The authentication of wood species is of paramount significance to market regulation and product quality control in the construction industry. While classification based on microscopic wood cell structures serves as a critical reference for this task, the high inter-class similarity of cell structures and the inherent biological anisotropy of fine textures pose significant challenges to existing methods. Due to their generic design, standard deep learning models often struggle to capture these fine-grained directional features and suffer from catastrophic feature loss during global pooling, particularly under limited sample conditions. To bridge this gap between general-purpose architectures and the specific demands of wood texture analysis, this paper proposes CM-EffNet, a lightweight fine-grained classification framework based on an improved EfficientNetV2 architecture. Firstly, a data augmentation strategy is employed to expand a collected dataset of 226 wood species from 3673 to 29,384 images, effectively mitigating overfitting caused by small sample sizes. Secondly, a Coordinate Attention (CA) mechanism is integrated to embed positional information into channel attention. This allows the network to precisely capture long-range dependencies between cell walls and vessels cavities, successfully decoding the challenge of textural anisotropy. Thirdly, a MixPooling strategy is introduced to replace traditional global average pooling, enabling the collaborative extraction of background context and salient fine-grained details to prevent the loss of critical micro-features. Systematic experiments demonstrate that CM-EffNet achieves a recognition accuracy of 96.72% and a training accuracy of 98.18%. Comparative results confirm that the proposed model offers superior learning efficiency and classification precision with a compact parameter size. This makes it highly suitable for deployment on mobile terminals connected to portable microscopes, providing a rapid and accurate solution for on-site timber market regulation and industrial quality control. Full article

Bovine milk is a complex biological fluid whose lipid fraction plays essential roles in nutrition, processing, and product quality. While conventional analyses have traditionally focused on total fat content and fatty acid composition, recent advances in liquid chromatography–mass spectrometry (LC–MS) have unveiled the molecular diversity of polar lipids, particularly phospholipids and sphingolipids. These compounds, largely associated with the milk fat globule membrane (MFGM), include key molecular species such as phosphatidylcholine (PC), phosphatidylethanolamine (PE), sphingomyelin (SM), ceramides (Cer), and lysophospholipids, which collectively contribute to emulsion stability, flavor development, and bioactive functionality. This review summarizes current progress in the determination of sphingolipids and phospholipids in bovine milk, with a specific focus on analytical strategies enabling their accurate detection, identification, and quantification. We discuss how advanced LC–MS platforms have been applied to investigate factors shaping the milk polar lipidome, including lactation stage, animal diet, metabolic and inflammatory stress, and technological processing. Accumulating evidence indicates that specific lipid species and ratios, such as PC/PE balance, SM and ceramide profiles, and Lyso-PC enrichment, act as sensitive molecular indicators of membrane integrity, oxidative status, heat stress, and processing history. From an applied perspective, these lipidomic markers hold strong potential for dairy quality control, shelf-life assessment, and authenticity verification. Overall, advanced lipidomics provides a robust analytical framework to translate molecular-level lipid signatures into actionable tools for monitoring cow health, technological performance, and the nutritional valorization of bovine milk. Full article

LoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 µs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment. Full article

Fermented meat products rely on complex microbial ecosystems in which lactic acid bacteria (LAB) play a central role in safety, quality, and sensory development. In recent years, increasing demand for reduced-nitrite formulations, clean-label products, and improved risk management have driven renewed interest in microbial control strategies beyond traditional fermentation practices. This systematic review aims to synthesize current knowledge on the microbiological aspects of meat fermentation, spanning traditional spontaneous processes and modern approaches to microflora control, including starter cultures, biocontrol strategies, and omics-based tools. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Google Scholar, with the final search performed on 15 May 2025. After screening and eligibility assessment following PRISMA 2020 guidelines, 141 studies were included in the qualitative synthesis. The review integrates evidence on microbial succession, metabolic functions, pathogen inhibition, biogenic amine control, and flavour formation, with particular emphasis on advances in metagenomics, metabolomics, and predictive microbiology. Across studies, LAB-dominated ecosystems—particularly those involving Latilactobacillus sakei, Latilactobacillus curvatus, and Lactiplantibacillus plantarum—consistently emerge as the primary drivers of fermentation stability and safety. The strongest evidence supports the use of selected starter and protective cultures, bacteriocinogenic LAB, and omics-guided predictive control to enhance process reliability, support reduced-nitrite strategies, and mitigate microbiological risks without compromising product quality. Overall, the integration of traditional fermentation knowledge with data-driven microbial management provides a robust framework for developing safe, authentic, and sustainable fermented meat products. Full article

AI-based audio generation has advanced rapidly, enabling deepfake audio to reach levels of naturalness that closely resemble real recordings and complicate the distinction between authentic and synthetic signals. While numerous CNN- and Transformer-based detection approaches have been proposed, most adopt a model-centric perspective in which the spectral representation remains fixed. Parallel data-centric efforts have explored alternative representations such as scalograms and CQT, yet the field still lacks a unified framework that jointly evaluates the influence of model architecture, its hyperparameters (e.g., learning rate, number of epochs), and the spectral representation along with its own parameters (e.g., representation type, window size). Moreover, there is no standardized approach for benchmarking custom architectures against established baselines under consistent experimental conditions. FakeVoiceFinder addresses this gap by providing a systematic framework that enables direct comparison of model-centric, data-centric, and hybrid evaluation strategies. It supports controlled experimentation, flexible configuration of models and representations, and comprehensive performance reporting tailored to the detection task. This framework enhances reproducibility and helps clarify how architectural and representational choices interact in synthetic audio detection. Full article

Herba Hyssopi is a key remedy in Uighur medicine for asthma and cough, frequently used as the monarch or minister herb in prescriptions. However, the lack of effective quality assessment methods complicates the detection of adulteration with common substitutes. In this study, UPLC-LTQ-Orbitrap-MS, network pharmacology, molecular docking, and cell experiments were employed to establish scientific and effective quality control methods to differentiate Hyssopus cuspidatus Boiss from its common adulterants. The results showed that a total of 41 chemical constituents were identified from Herba Hyssopi. Network pharmacology analysis revealed 133 potential target genes associated with its therapeutic actions, among which EGFR, MMP9, TNF, PTGS2, MAPK3, ESR1, and TP53 emerged as key targets. Cellular experiments further demonstrated that diosmin, linarin, and rosmarinic acid significantly suppressed nitric oxide (NO) generation and the release of pro-inflammatory cytokines. Based on these findings, a validated HPLC method was established for the simultaneous quantification of these three bioactive markers, providing a reliable tool for the quality assessment and authentication of Herba Hyssopi. This study offers a scientific basis for improving the standardization and quality control of Herba Hyssopi in traditional medicine applications. Full article

The advancement of Automotive Industry 4.0 has promoted the development of Vehicle to Vehicle (V2V) and Internet of Vehicles (IoV) communication, which marks the new era for intelligent, connected and automated transportation. Despite the benefits of this metamorphosis in terms of effectiveness and convenience, new obstacles to safety, inter-connectivity, and cybersecurity emerge. The tire pressure monitoring system (TPMS) is one prominent feature that senses tire pressure, which is closely related to vehicle stability, braking performance and fuel efficiency. However, the majority of TPMSs currently in use are based on the use of insecure and proprietary wireless communication links that can be breached by attackers so as to interfere with not only tire pressure readings but also sensor data manipulation. For this purpose, we design a secure TPMS architecture suitable for real-time IoV sensing. The framework is experimentally implemented using a Raspberry Pi 3B+ (Raspberry Pi Ltd., Cambridge, UK) as an independent autonomous control unit (ACU), interfaced with vehicular pressure sensors and a LoRa SX1278 (Semtech Corporation, Camarillo, CA, USA) module to support low-power, long-range communication. The gathered sensor data are encrypted, their integrity checked, source authenticated by lightweight cryptographic algorithms and sent to a secure server locally. To validate this approach, we show a three-node exhibition where Node A (raw data and tampered copy), B (unprotected copy) and C (secure auditor equipped with alerting of tampering and weekly rotation of the ID) realize detection of physical level threats at top speeds. The validated datasets are further enriched in a MATLAB R2024a simulator by replicating the data of one vehicle by 100 virtual vehicles communicating using over 5G, LoRaWAN and LoRa P2P as communication protocols under urban, rural and hill-station scenarios. The presented statistics show that, despite 5G ultra-low latency, LoRa P2P consistently provides better reliability and energy efficiency and is more resistant to attacks in the presence of various terrains. Considering the lack of private vehicular 5G infrastructure and the regulatory restrictions, this work simulated and evaluated the performance of 5G communication, while LoRa-based communication was experimentally validated with a hardware prototype. The results underline the trade-offs among LoRa P2P and an infrastructure-based uplink 5G mode, when under some specific simulation conditions, as opposed to claiming superiority over all 5G modes. In conclusion, the presented Raspberry Pi–MATLAB hybrid solution proves to be an effective and scalable approach to secure TPMS in IoV settings, intersecting real-world sensing with large-scale network simulation, thus enabling safer and smarter next-generation vehicular systems. Full article