The concept of authenticity has long occupied a central position in English Language Teaching (ELT), particularly within Communicative Language Teaching (CLT), where the use of authentic materials is viewed as essential to meaningful language learning. While traditional approaches prioritized simplified and pedagogically modified input, contemporary perspectives emphasize learners’ exposure to language as it occurs in real-world contexts. Building on this shift, the present paper critically revisits the notion of authenticity, arguing against its treatment as a fixed property of instructional materials. Instead, authenticity is conceptualized as a relational and dynamic process that emerges through learners’ engagement with texts. Drawing on developments in learner-centered pedagogy and technology-mediated learning environments, the paper proposes a three-stage framework of authenticity comprising interaction with the text, decoding and interpretation of meaning, and the production of contextually appropriate and genuinely communicative responses. This reconceptualization aims to offer a more nuanced understanding of authenticity and to inform the principled integration of authentic materials into task design in contemporary ELT contexts.

Optimization of FTIR-PLS models for adulteration detection in sesame oil: a comparative study of genetic algorithm, particle swarm optimization, and a hybrid GA-PSO approach.

A Novel Stateful Authentication Framework Approach With LLM-Based IDS for MQTT Security

SLATSCOG: A secure authentication framework via federated data generation and temporally-enhanced split learning

The security of modern digital infrastructure relies on mathematical problems that are easy to verify but difficult to invert. While the D(1) - Diophantine triple is well-studied, this paper examines a more complex construction using the property D(n), where n=(2m-3)(2m+1)h2 . By linking the elements of the triple to these parameters, a robust framework for authentication and secure key generation is developed. The practical usage of such a framework is exemplified by way of a challenge-response protocol for use on a crew of autonomous unmanned aerial survey drones. This is a practical use case in that a Ground Station has to direct the flight paths of the drones by transmitting numerical challenges A and B, which must be resolved by way of an internal secret polynomial generated by use of the D(n) property to determine a third element C, such that authentic verification is only achievable when the D(n) property is a perfect square.

This study examines a critical intervention in revitalizing intangible cultural heritage through digital symbiosis, focusing on the Sumbawa traditional horse race (Bare Spee) at Angin Laut Biru Arena. As a living embodiment of indigenous values including honor (siri'), communal solidarity (menyama braya), and ethno-ecological wisdom, this tradition faces erosion due to generational disconnect, infrastructural neglect, and rigid management practices. Addressing the lack of integrated digital and cultural frameworks for rural heritage, the study employs an immersive qualitative case study approach. Data were triangulated through participatory observation across two event cycles, in-depth interviews with 22 key stakeholders consisting of ritual elders, kebalan jockeys, samara horse masters, local entrepreneurs, and cultural tourists, as well as digital ethnography. The findings identify a tripartite authenticity framework comprising ritual sanctity, socio-familial reciprocity, and embodied equine knowledge. Based on this analysis, the study proposes a Symbiotic Digital Mediation Model implemented through four strategies, namely community-curated digital storytelling by local youth, context-aware augmented reality using QR-based cultural portals with native-language narratives, an integrated digital ecosystem encompassing a heritage portal, blockchain-based ticketing, and a local product e-marketplace, and the use of appropriate lightweight digital tools for event management and documentation. Crucially, the study demonstrates that digital mediation must adhere to cultural subsidiarity so that technology amplifies rather than replaces indigenous epistemologies. The model supports regenerative cultural governance by enabling community-led digital stewardship and offers broader implications for safeguarding living heritage in the digital era.

Abstract-Traditional authentication mechanisms, rang- ing from knowledge-based passwords to static physio- logical biometrics, face increasing vulnerabilities re- garding theft, spoofing, and irreversibility. This paper proposes the Neuro-Cognitive Identity Authentication (NCIA) framework, a novel conceptual approach that models user identity as a probabilistic cognitive signa- ture rather than a static credential. Unlike standard be- havioral biometrics that focus on motor skills, NCIA authenticates users based on high-level cognitive reason- ing behaviors—such as decision latency, strategy prefer- ence, and error correction—captured during dynamic, structured challenges. We present a layered system ar- chitecture integrating a Dynamic Challenge Generator and a Profile Modeling Engine to extract and verify these cognitive feature vectors. The proposed framework aims to mitigate replay and imitation attacks by leverag- ing the inherent entropy and stability of human problem- solving patterns. This study details the system model, analyzes the security threat landscape, and discusses the feasibility of using cognitive reasoning as a complemen- tary, high-security authentication layer. Keywords: Neuro-Cognitive Authentication, Behavioral Biometrics, Cognitive Security, Identity Verification, Dynamic Challenge, Cybersecurity.

A novel authentication framework using multimodal-based periocular biometrics data with feature integration and adaptive deep learning network

The rapid digitisation of healthcare services presents challenges in guaranteeing safe, scalable, and privacy-preserving access to sensitive medical information. This article presents BBAS, a blockchain-based authentication system for e-Health. BBAS incorporates a multi-factor authentication (MFA) framework that includes password hashing, one-time passwords (OTP), and biometric verification, with a hybrid access control model that combines role-based access control (RBAC) and attribute-based access control (ABAC). To guarantee enduring security, BBAS utilises post-quantum digital signatures (CRYSTALS-Dilithium) and exploits the InterPlanetary file system (IPFS) for off-chain data storage, assuring tamper-resistance and scalability. We implemented the system using solidity smart contracts on a permissioned Ethereum network and assessed via 500 authentication iterations. Results show BBAS outperforms benchmark models across all critical metrics: authentication success rate (ASR: 98.6%), latency (0.05s), throughput (19,000 req/s), gas cost (35,000 gas/req), block confirmation time (10s), and storage overhead (0.03 KB/record). Biometric error rates-false acceptance rate (FAR: 0.5%), false rejection rate (FRR: 1.2%), and equal error rate (EER: 0.85%)-are markedly decreased, therefore improving both security and usability. This research validates BBAS as a reliable, scalable, and quantum-resistant authentication framework for contemporary e-Health systems.

Background/Objectives: The global health functional food (HFF) market is expanding rapidly, driven by increasing consumer interest in preventive healthcare and evidence-based nutrition. The Republic of Korea has established a systematic regulatory framework for HFFs, through the Individually Recognized Functional Ingredient (IRFI) system introduced in 2004. Designed to accommodate innovative physiologically active ingredients beyond standardized categories, the IRFI system is increasingly discussed as a regulatory model for evidence-based functional foods. This study examines the IRFI system within a comparative regulatory context and evaluates its implications for drug-dietary supplement interactions (DDSIs). Methods: Functional categories were defined according to guidelines issued by the Ministry of Food and Drug Safety (MFDS). Data on Korean IRFIs were obtained from the Food Safety Korea database. A literature search was conducted in PubMed using regulatory keywords which identified through Google searches. Regulatory frameworks in the United States, European Union, Japan, and China were comparatively analyzed. DDSIs were reviewed based on MFDS-approved IRFIs and the relevant literature. Results: The IRFI represents a hybrid regulatory model that combines rigorous pre-market scientific evaluation, including GLP-compliant safety testing and human clinical evidence, with regulatory incentives such as expedited review and temporary market exclusivity. Compared with post-market-oriented systems in the United States and Japan, the stringent authorization framework in the European Union, and the dual-track health food system in Chinese, IRFI integrates clinical evidence requirements within a structured pre-market approval process. Conclusions: The IRFI framework establishes a comparatively stringent evidentiary standard for functional foods while providing a structured basis for evaluating potential DDSIs. Its applicability depends on alignment and mutual recognition of scientific and clinical evaluation criteria across regulatory jurisdictions. DDSIs were reviewed based on MFDS-approved IRFIs and the relevant literature.

Honey fraud represents a persistent and analytically challenging form of food adulteration, driven by globalised supply chains, strong economic incentives and asymmetries in regulatory oversight and analytical capacity. Conventional physicochemical, spectroscopic and isotopic methods provide legally robust tools for routine control, yet increasingly struggle to detect sophisticated adulteration strategies that are compositionally optimised to mimic authentic honey profiles. These challenges are amplified in a global context, where heterogeneous enforcement landscapes and fragmented analytical infrastructures create exploitable vulnerabilities across international trade networks. This narrative review synthesises current knowledge on honey fraud typologies and critically evaluates established analytical approaches alongside emerging omics-based authentication strategies, including genomics, metabolomics, proteomics and microbiome profiling. Omics-based approaches extend authenticity assessment beyond single-marker paradigms by capturing multidimensional biological and compositional signatures, thereby improving sensitivity to subtle and system-aware fraud (i.e., adulteration strategies that adapt to prevailing analytical detection methods and regulatory thresholds) strategies. To maintain evidentiary clarity, this review explicitly distinguishes between analytically demonstrated vulnerabilities, technically feasible adulteration scenarios and fraud practices documented in regulatory or enforcement contexts. Advanced technology-driven strategies are therefore discussed as potential system-level risks rather than confirmed large-scale honey fraud cases. This differentiation not only safeguards evidentiary precision but also highlights the structural limits of purely analytical solutions. Beyond analytical performance, honey authentication is framed as a systemic challenge embedded in global food systems. This review highlights the need for integrated, data-driven and scalable authentication frameworks that align analytical innovation with reference harmonisation, governance structures and international regulatory cooperation to support resilient and globally robust honey authenticity control.

The rapid expansion of remote work, cloud services, and distributed enterprise infrastructures has significantly increased cybersecurity risks, rendering traditional perimeter-based security models inadequate. Zero-Trust Architecture (ZTA) has emerged as a modern security paradigm that assumes no implicit trust and continuously verifies user identity and device integrity. However, conventional authentication mechanisms such as passwords and one-time verification methods are insufficient to ensure persistent security throughout a user session. This paper proposes a Deep Learning-Based Continuous Authentication Framework tailored for Zero-Trust enterprise environments. The system leverages behavioral biometrics, including keystroke dynamics, mouse movements, and user interaction patterns, to continuously verify user identity in real time. Advanced deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) are employed to model temporal and spatial behavioral patterns. The framework integrates risk-based scoring and adaptive access control policies to dynamically enforce authentication decisions. Experimental evaluation demonstrates improved detection accuracy, reduced false acceptance rates, and minimal user disruption compared to traditional authentication systems. The proposed solution strengthens enterprise security by enabling adaptive, non-intrusive, and real-time identity verification aligned with Zero-Trust principles

Extending ERC721: Design and implementation of a novel secure NFT framework for IoT asset authentication in cyber-physical systems

A blockchain-integrated PUF framework for secure authentication and communication

Wheat flour tortilla authenticity verification using targeted elemental profiling-based multifunction classification strategies.

ABSTRACT The Vehicular Internet of Things (VIoT) is revolutionizing the way devices and digital assets communicate, enabling seamless data sharing without the need for human involvement. One of the standout features of VIoT is the immense volume of data generated by end‐user devices, which must be efficiently processed and transmitted to the cloud. Unfortunately, this process can be slow and detrimental to network performance. To tackle these challenges effectively, edge computing emerges as a powerful solution. By decentralizing information processing to the network edge, this innovative architecture significantly enhances operational efficiency, especially within the automotive sector. However, the substantial volume of data produced by these devices can significantly hinder processing efficiency and compromise overall network performance. Despite its advantages, vehicular networking is vulnerable to malicious attacks, underscoring the urgent need for a strong and secure authentication framework. A lattice‐based cryptographic approach offers exceptional privacy and security, making it an ideal choice for VIoT‐enabled vehicles and enhancing the future of smart transportation. In this paper, we introduce an innovative authentication and access control protocol based on a lattice‐based cryptosystem for wireless vehicular communication. Our analysis shows that our proposed scheme is highly efficient and effective compared to existing methods, paving the way for safer and smarter transportation systems.

LGTWAFIOD: PUF and Fuzzy extractor based lightweight authentication framework for internet of drones

The literature repeatedly reports that the unique nature of individual brainwave patterns makes them suitable for identification and authentication, because they are difficult to replicate or forge. Therefore, many researchers have utilized brainwaves for authentication by training traditional deep learning and machine learning models. However, the internal decision processes of these black-box models have not been evaluated in terms of biases, overfitting, large training data requirements, and handling complex data structures, which keep them in a fuzzy state. To address these limitations, a smart system is needed to be develop that could be capable of making the authentication process user-friendly, robust, and reliable. In this paper, we present a deep reinforcement learning-based biometric authentication framework known as "BrainAuth" for personal identification using the gamma ($\gamma$) and beta ($\beta$) brainwaves. This approach improves the accuracy of authentication by using the (i) Dyna framework and a dual estimation technique. Both these technique helps to maintain the integrity of brainwave patterns, which are needed for authentication and understanding of spoofing activities. (ii) We also introduce a layered structure architecture in the proposed model to reduce the time needed for exploration using two deep neural networks. These networks work together to handle the complex data while making decisions in delay sensitive environment. (iii) We evaluate the model on seen and unseen data to verify its robustness. During analysis, the model achieved an equal error rate (EER) of $\approx$ 0.07% for seen data and $\approx$ 0.15% for unseen data, respectively. Furthermore, the analysis metrics such as true positive (TP), false positive (FP), true negative (TN), and false negative (FN) followed by false acceptance rate (FAR), false rejection rate (FRR), true acceptance rate (TAR) revealed significant improvements compared to existing schemes.

Compound-specific hydrogen isotope analysis of lignin methoxy groups for tea provenance discrimination: Comparison with dual-water equilibration.

A Multiphase Zero-Trust Authentication Framework Using Replicated and Homomorphic Encryption