Search Results (2,613)

Secure transmission and storage of color images remain challenging tasks due to strong inter-pixel correlations and high data volume. This work proposes a one-dimensional sine-tent-logistic-exponential map (STLEM) equipped with numerical boundary correction rules to mitigate finite-precision numerical degradation so as to enhance the unpredictability of chaos-driven cryptosystems. We benchmark STLEM against classic logistic, tent, and sine maps via Lyapunov exponents, autocorrelation, approximate entropy, permutation entropy, Lempel-Ziv complexity, and Kolmogorov–Sinai entropy. Bifurcation diagrams, the 0–1 test, and NIST statistical tests are further adopted to characterize its chaotic dynamics and randomness. Comparative results verify that STLEM achieves improved dynamical complexity and randomness performance. Built upon the proposed STLEM, this paper constructs a color-image encryption scheme that employs a 256-bit master key and two groups of chaotic parameters to produce key-related chaotic sequences. The cryptosystem integrates dynamic edge expansion, chaotic permutation, position-dependent adaptive DNA encoding, DNA-domain chained diffusion, and two successive row-column permutation phases. HMAC-SHA-256 is utilized to generate plaintext-aware initial conditions and perform ciphertext authentication prior to decryption. Experimental validations demonstrate complete plaintext recovery under valid secret inputs, while authentication rejects invalid keys and tampered ciphertexts. Ciphered images exhibit high information entropy, negligible adjacent-pixel correlations, and satisfactory number of pixel change rate (NPCR) and unified average changing intensity (UACI) metrics. Benefiting from a sufficiently large key space and $O\left(MN\log \right(MN\left)\right)$ computational complexity, the proposed scheme is resilient against brute-force attacks and well suited for secure color-image communication scenarios, rather than acting as a general-purpose replacement for standard block ciphers. Full article

Oxidative stress and chronic inflammation are major contributors to skin photoaging, highlighting the need for natural antioxidants with multifunctional bioactivities. This study developed an ultrasound-assisted extraction (UAE) strategy to recover an astaxanthin-rich extract from the underutilized freshwater macroalga Chara corallina and evaluated its antioxidant, anti-inflammatory, and anti-photoaging properties. Different solvent systems were evaluated for extraction efficiency, and astaxanthin-equivalent recovery was quantified by high-performance liquid chromatography with photodiode array detection (HPLC–PDA). Antioxidant activity was evaluated using the DPPH radical scavenging assay, whereas biological activities were assessed in human dermal fibroblasts and RAW264.7 macrophages using cell viability assays, quantitative real-time PCR, and extracellular matrix-related enzyme inhibition assays. The selected solvent system (48% ethanol in ethyl acetate) provided the highest astaxanthin-equivalent recovery (0.2598 ± 0.0086% w/w). The selected CCE exhibited DPPH radical-scavenging activity, modulated antioxidant- and inflammation-associated gene expression, increased COL1A2 mRNA expression, and inhibited collagenase, elastase, and hyaluronidase within the evaluated concentration range. HPLC–PDA analysis revealed a chromatographic component with retention-time and UV–visible spectral characteristics corresponding to those of an authentic astaxanthin reference standard; however, comprehensive structural and phytochemical characterization was not performed. Because CCE is a chemically complex extract, the observed biological responses cannot be attributed exclusively to astaxanthin. Overall, these findings provide an initial basis for further investigation of C. corallina as an underexplored freshwater source of carotenoid-containing bioactive extracts rather than establishing it as a commercially competitive source of natural astaxanthin. Full article

This study proposes EQAS (Empowered Question-Answering System), a hybrid framework designed to support context-aware question-answering and intelligent feedback generation in domain-specific knowledge environments. EQAS integrates fine-tuned transformer-based models, instruction-guided large language models, domain-specific knowledge graphs, and LangChain-based vector retrieval to improve contextual relevance, response quality, and feedback consistency. To evaluate the proposed framework, a benchmark dataset consisting of 10,000 real-world question–answer pairs was constructed from authentic user interactions and domain-related information resources. Experimental evaluation across established transformer-based architectures and recent instruction-tuned large language models showed that the Llama-3.3-70B-Instruct baseline achieved the highest standalone QA performance (F1: 77.42; EM: 44.60), while the EQAS transformer-based configuration achieved an F1 score of 75.48 and an Exact Match score of 41.80. A controlled ablation analysis using Llama-3.3-70B-Instruct as the fixed QA backbone further showed that incorporating knowledge graph enhancement increased the F1 score from 77.42 to 79.93 and the Exact Match score from 44.60 to 46.82, corresponding to absolute improvements of 2.51 and 2.22 points, respectively. A complementary human-centered evaluation of 1000 generative responses by three NLP researchers yielded an overall quality score of 4.34/5 across correctness, clarity, sufficiency, and helpfulness, with an overall Krippendorff’s $\alpha$ of 0.80. Furthermore, the framework provides context-sensitive explanatory feedback that can support user understanding and knowledge acquisition during information-seeking interactions. The findings suggest that EQAS offers a scalable solution for intelligent question-answering, feedback support, and knowledge assistance in complex information environments. The proposed framework highlights the potential of combining large language models with structured knowledge representations to support context-aware question-answering and explanatory feedback generation in domain-specific information environments. Full article

Next-generation Intelligent Transportation Systems (ITSs) require ultra-reliable, low-latency Vehicle-to-Everything (V2X) communication frameworks that support safety-critical vehicular services. Conventional centralized, monolithic architectures suffer from excessive transmission latency, limited scalability, and authentication overheads that are ill-suited to the highly dynamic and dense vehicular environment. This paper presents a secure and low-latency edge–cloud architecture for intelligent V2X communications based on a lightweight microservice-driven design paradigm. A formal latency-constrained model is presented to ensure that the end-to-end delay satisfies tight real-time constraints. The proposed framework is lightweight and includes HMAC-based authentication, nonce-based replay protection, timestamp validation, and short-lived encrypted session tokens in a stateless architecture using the Laravel framework deployed at the edge layer. Security validation is performed at edge gateways, and asynchronous SQLite-backed job queues support non-blocking telemetry processing and scalable service orchestration. Experimental evaluation shows that the edge-based deployment achieves a mean response time of 2.58 ms with small variance under repeated request conditions, while centralized processing exhibits significantly higher latency. The results demonstrate that secure authentication and telemetry exchange can be achieved without breaching strict latency requirements. The proposed solution creates a deployable, scalable, and security-aware foundation for next-generation V2X ecosystems and Intelligent Transportation Systems (ITSs) in real time. Full article

Fact checking is a key task in the field of natural language processing, and aims to judge the authenticity of a given claim by using external evidence. Existing multi-agent-based fact-checking systems achieve significant improvements on multiple benchmark datasets, but still have two core limitations: firstly, they lack a full-process faithfulness-verification mechanism to test the faithfulness of intermediate products at each stage, resulting in the amplification of hallucination errors cascading through the pipeline; secondly, when both supportive and refuting evidence exist, the system often relies on the implicit preferences of the model to make judgments. To address these issues, this paper proposes a multi-agent fact-checking framework called FRAME (Faithful, Reproducible, Agent-based, Multi-stage, and Evidence perception). FRAME addresses these limitations through two core designs: firstly, it designs a three-stage faithfulness-gating mechanism, embedding a faithfulness-verifier agent into the pipeline for faithfulness verification, and triggering targeted repairs when unfaithfulness is detected; secondly, it constructs a Bayesian-inspired structured perception framework for evidence-conflict perception, classifying the retrieved evidence, performing multi-attribute reweighting and weighted synthesis, and, finally, outputting a structured decision with a complete reasoning chain. FRAME is systematically evaluated on multiple benchmark datasets, and the experimental results demonstrate the advantages, generalization ability, and fault tolerance of FRAME when dealing with different types of datasets. Full article

Benzophenone-3 (BP-3), an organic UV filter extensively applied in sunscreens, cosmetics and daily plastic products, is classified as a typical emerging endocrine-disrupting micropollutant. This compound is highly susceptible to bioaccumulation in aquatic organisms, triggers coral bleaching, and incurs oxidative damage to algae, fish and invertebrates. Conventional wastewater treatment processes cannot efficiently eliminate BP-3 from aqueous media, thereby imposing severe ecological risks on freshwater and marine ecosystems. In this study, iron-impregnated biochar (Fe-BC) was synthesized via an impregnation–pyrolysis route using powder of cultivated Chlorella pyrenoidosa (green microalga) as raw feedstock. Batch adsorption experiments revealed that iron impregnation remarkably enhanced the removal efficiency of BP-3. The maximum Langmuir saturated adsorption capacity of Fe-BC reached 91.7 mg/g, considerably exceeding the value of 51.5 mg/g for pristine biochar. Kinetic data exhibited favorable fitting with the pseudo-first-order kinetic model, demonstrating that Fe-BC rapidly captures BP-3 and achieves adsorption equilibrium within 120 min. Solution pH exerted a prominent influence on adsorption performance: the material maintained high BP-3 adsorption capacity at pH 7–10, whereas adsorption capacity declined drastically under strongly acidic (pH < 5) and extreme alkaline conditions (pH > 10.5). Fourier-transform infrared spectroscopy (FTIR) validated the successful loading of iron species onto the biochar surface, as well as the binding of BP-3 onto Fe-BC. Combined with pH-controlled experimental results, hydrogen bonding, hydrophobic interactions, and pore-filling effects are inferred as the dominant adsorption mechanisms for BP-3 removal. Furthermore, Fe-BC retained favorable BP-3 removal performance in simulated seawater matrices, endowing it with preliminary potential for wastewater treatment in coastal zones and tourist scenic areas. This work offers basic laboratory insights into BP-3 adsorption, while further verification concerning environmental low-concentration conditions, authentic water matrices, material reusability and stability is essential for its practical application. Full article

Discrete-Variable Quantum Key Distribution (DV-QKD) provides a mechanism for establishing secret keys between legitimate parties using quantum-state transmission and authenticated classical post-processing. In this work, the underlying quantum layer follows a biased-basis decoy-state BB84 model using phase-randomized weak coherent pulses, while additional implementation-level mechanisms are integrated to support Cyber-Physical System (CPS) communication. Exponential Double Delta Encoding-based Merkle Tree (EDDE-MT) is employed as a receiver-side detection-record integrity mechanism for detecting deletion, insertion, reordering, or modification of records relative to an authenticated committed detection-event batch. It does not establish the completeness of the original TCSPC acquisition, detect records omitted before commitment, detect physical photon loss, or increase the information-theoretic secrecy of the QKD key. Time-Correlated Single Photon Counting (TCSPC) is used for detection-event and timing acquisition, while 2’s Complement Cyclic Redundancy Check-based Low-Density Parity Check (2CCRC-LDPC) supports error reconciliation. Following privacy amplification, the legitimate parties retain matching copies of the distilled QKD key locally. Discrete Variable Quantum Mellin Transform Cryptography (DVQMTC) uses fresh, non-reused segments of this privacy-amplified key for application-layer payload protection; the Mellin-transform component is treated only as implementation-level preprocessing and not as a cryptographic key-generation mechanism. Side-channel monitoring is performed using Gini Cramer’s V Correlation-Stationary Wavelet Transform (GCVC-SWT), Helical Valley-Principal Component Analysis (HV-PCA), and an Entmax-based hyperbolic Tangent exponential Linear Unit-Bidirectional Long Short-Term Memory (E-TeLU-Bi-LSTM) classifier. On the AES-HD benchmark, E-TeLU-Bi-LSTM achieved 99.24% classification accuracy; this value represents benchmark-level classification performance and is not interpreted as experimental validation of physical side-channel protection in a deployed DV-QKD system. Frequency Division Multiple Access (FDMA) and the Halton Quasi-Sequence-Invasive Weed Optimization Algorithm (HQS-IWOA) are further incorporated as classical network-resource segmentation and load-management mechanisms and do not modify the composable QKD security bound. The contribution of the work is therefore positioned as a system-level engineering integration of QKD key establishment, detection-record integrity, reconciliation, application-layer data protection, side-channel monitoring, and network-resource management for CPS. The information-theoretic secrecy claim remains restricted to the underlying finite-key decoy-state BB84 procedure under the stated security assumptions; no new QKD security theorem, formally new cryptographic primitive, or experimentally validated physical quantum communication capability is claimed. Full article

Geographical authentication of medicinal plant materials remains challenging because multispectral variables are often highly collinear and sample grouping can complicate reliable model validation. Existing correlation-based feature-selection strategies also require careful adaptation to multiclass problems to avoid artificial ordering of class labels and information leakage during model development. Therefore, this study aimed to develop a compact and leakage-controlled multispectral learning framework for geographical-origin discrimination. This study analyzed 800 physical Pinellia ternata samples from Gansu Xihe, Sichuan Neijiang, Sichuan Chengdu, and Chongqing Dianjiang (200 samples per origin). Each physical sample was represented by 31 mean grayscale intensities calculated from Otsu-segmented multispectral regions of interest. A Pearson-correlation-guided deep multilayer perceptron (PCG-DeepMLP) was constructed by estimating one-vs-rest band relevance and inter-band redundancy only within the training data. The key methodological innovation is a unified multiclass-aware, relevance–redundancy spectral-learning framework in which class-specific one-vs-rest Pearson relevance is coupled with inter-band redundancy control and embedded within leakage-controlled grouped model development. By learning the spectral subset exclusively from each training partition before nonlinear classification, the framework produces compact and complementary multispectral signatures while preserving multiclass structure and strict independence of held-out groups. Model and feature-selection settings were chosen by three-fold grouped cross-validation within each training partition. PCG-DeepMLP retained 9–21 bands and achieved the highest mean accuracy (0.9812 ± 0.0135), macro-F1 (0.9812 ± 0.0135), Matthews correlation coefficient (MCC; 0.9752 ± 0.0179), and macro-AUC (0.9994 ± 0.0006) among seven models. Its macro-F1 was higher than that of 1D-CNN, 1D-ResNet, full-band MLP, PLS-DA, and random forest after Holm correction. Performance was estimated through a strict nested group-wise internal validation scheme, with every outer test fold remaining isolated from feature selection, preprocessing, and model optimization. These findings demonstrate that multiclass-aware relevance–redundancy learning can retain complementary Pinellia ternata origin-discriminative information in a compact and stable spectral representation, enabling accurate geographical-origin authentication while providing a principled basis for reduced-channel acquisition and future independent multi-batch validation. Full article

Web applications increasingly handle sensitive data in diverse use cases, but conventional encryption implementations apply a uniform level of cryptographic protection to all traffic, regardless of the associated risk. This static approach results in either excessive computational overhead when applying maximum encryption universally, or inadequate protection when using lightweight encryption to preserve performance. This paper proposes an Adaptive Encryption Framework (AEF) designed to bridge the gap between performance and security in web applications. Rather than relying on a static protocol, AEF dynamically adjusts encryption algorithms based on a real-time composite risk score (0–100). This score is derived from six weighted variables: network risk (25%), authentication strength (20%), behavioral risk (20%), device trust (15%), data sensitivity (15%), and temporal risk (5%). Depending on the calculated risk, the system automatically transitions between three distinct security tiers: GREEN (utilizing ChaCha20-Poly1305), YELLOW (AES-256-GCM), or RED (AES-256-GCM with per-request HKDF key derivation for key isolation). All three profiles use exclusively standardized cryptographic primitives. The proposed weighting distribution was evaluated through sensitivity analysis on 27 framework-executed scenarios and further calibrated using 40,000 labeled application requests. Within these experimental conditions, it achieved complete agreement with the expected scenario classifications, and no alternative weight configuration produced better held-out performance. Additional validation on 61,065 HTTP requests from the CSIC 2010 dataset yielded an area under the ROC curve (ROC AUC) of 0.860, with no attack request assigned to the lightweight profile under the evaluated operating conditions. Across three hardware platforms and four payload sizes, all encryption profiles maintained sub-millisecond latency. Extended load testing showed that a four-worker Node.js cluster sustained 4948 requests per second at 2000 concurrent connections, a 7.1-fold improvement over a single process. When hardware cryptographic acceleration was disabled, ChaCha20-Poly1305 became up to 9.1 times faster than AES-256-GCM, supporting its use as the lightweight profile. The framework proposed in this paper operationalizes the qualitative risk assessment guidelines from NIST SP 800-30 and SP 800-63 into a quantitative, automated encryption selection mechanism for web applications, evaluated under the hardware platforms, concurrency levels and traffic assumptions described in this study. Full article

Graphics processing units (GPUs) underpin high-performance computing, but device partitioning does not ensure that an in-context pointer remains within its allocation. We present a hardware–software co-design whose 16-bit tag uses odd parity and fail-safe class encoding. It combines a variable-precision extent, an aligned CRC checker, and an exact-bounds micro-cache. For 200,000 log-uniform requests from 16 B to 1 TiB, mean Class 4 fragmentation is 1.638%, versus 27.733% for power-of-two encoding. The seven-bit aligned signature has full GF(2) rank and a 1/128 non-adaptive collision rate; its deterministic window is exactly one through three regions and tight at four. Exhaustive testing rejects every one-bit tag corruption, while two-bit analysis demonstrates why parity is not adversarial authentication. A SAT-equivalent endpoint rewrite reduces Class 3 generic depth from 60 to 24 levels. A fail-closed 32-lane Class 4 topology detects nonuniform active-lane tags in hardware; for uniform tags, it reduces generic CMOS cost from 155,200 to 72,872 transistor equivalents (53.05%) and depth from 67 to 61 levels. Official SASS traces provide an analytical exposure bound rather than native simulation; a separate pre-layout 45 nm mapping is reported only as a timing sensitivity experiment. Full article

Archaeological sites with limited surface visibility and low spatial legibility often depend on presentation systems to communicate historical functions and heritage meaning. Using the Site of Xuanquan Posthouse as a case study, this study examined how visual attention and visitor perceptions jointly inform the interpretive effectiveness of such low-visibility archaeological sites. A complementary two-stage quantitative design was adopted. First, 20 adults viewed 12 static presentation images, and visual attention was compared across eight categories of information. Second, 252 valid questionnaires were analyzed using partial least squares structural equation modeling (PLS-SEM) and importance–performance map analysis (IPMA). Archaeological remains, reconstructed architecture or models, and digital media generally showed shorter time-to-first-fixation values and greater cumulative visual attention, whereas explanatory text, diagrams and maps, and bamboo and wooden slip documents were less visually prominent. Interpretation quality, perceived authenticity, and exhibition experience quality were all positively associated with perceived heritage understanding and perceived interpretation effectiveness, with heritage understanding playing a central mediating role. The findings indicate that effective presentation should connect visually prominent entry points with explanatory, spatial, and evidentiary information so that visitors can progress from recognizing physical remains to understanding their historical functions and heritage significance. Full article

Carnosine and anserine are bioactive, histidine-containing dipeptides recognized for their antioxidant, pH-buffering, and neuroprotective functions. However, their precise quantification is frequently compromised by their high structural similarity and complex matrix-induced analytical interferences. This study developed and validated an optimized, simplified extraction protocol coupled with high-performance liquid chromatography diode array detection (HPLC-DAD) for the simultaneous determination of both dipeptides across diverse meat matrices, including Thai native black-bone chicken (Nin-Kaset), commercial broiler, pork, beef, and buffalo meat. Extraction efficiency was systematically optimized by modulating the solution pH. Under optimized conditions, the validated method demonstrated excellent linearity (1–200 µg mL⁻¹, r² ≥ 0.9990) with limits of detection and quantification (LOD/LOQ) values at 0.0093/0.0283 mg g⁻¹ for carnosine and 0.0139/0.0420 mg g⁻¹ for anserine. Method accuracy was confirmed by satisfactory recoveries ranging from 96.64% to 102.68%, with repeatability values (RSDs) of 0.70–14.18%. Application to real matrices revealed distinct dipeptide profiles. Pork contained the highest carnosine level (3.59 ± 0.19 mg g⁻¹), whereas white-feather Nin-Kaset exhibited the highest anserine level (6.77 ± 0.40 mg g⁻¹). Overall, the validated framework offers a rapid, cost-effective, and robust approach for routine dipeptide profiling, supporting future applications in food authentication, nutritional evaluation and functional food development. Full article

To investigate the potential of generative artificial intelligence (Gen AI) in sustainable engineering education and laboratory instruction, this study used the direct shear test in soil mechanics as a case study. A total of 112 third-year undergraduate students majoring in hydraulic engineering were assigned to traditional and AI-assisted groups. Their performance in experimental operation, data processing, report writing, presentation of results and problem solving was compared using grade statistics, classroom observations and interview data. The results showed that the AI group generally outperformed the traditional group in Experimental operation, data analysis, report completeness, presentation and defense, and overall scores. The analysis showed that Gen AI can function as a form of cognitive scaffolding in conceptual explanation, data processing, report structuring, and error analysis. Specifically, it can provide stage-specific prompts for understanding, procedural support, and feedback during the learning process, thereby helping students complete experimental learning tasks. However, the qualitative corpus included documented cases of overreliance on Gen AI, including uncritical acceptance of generated outputs and alteration of discrepant data without adequate verification; these behaviours were not tabulated at the pair level, so their prevalence cannot be estimated. These findings suggest that the integration of Gen AI into soil mechanics laboratory instruction should be grounded in teacher guidance, disciplinary knowledge support, data verification awareness, and standardized tool-use practices. These findings also offer implications for the responsible use of Gen AI in sustainable engineering education and for the development of students’ AI literacy, awareness of data authenticity, and responsible technology use competencies. Full article

Vehicular ad hoc networks (VANETs) have become a vital component of intelligent transport systems, with their security concerns increasingly drawing attention. To safeguard user privacy and ensure data authenticity and integrity, researchers have devised numerous certificateless conditional privacy-preserving authentication (CLCPPA) schemes. However, existing schemes generally suffer from insufficient security or high computational and communication overhead. Moreover, most implicitly assume the existence of a secure channel between vehicles and trusted entities during pseudonym generation and transmission, making it difficult to meet the real-time demands and practical deployment requirements of VANETs. To address these issues, this paper constructs a certificateless aggregated conditional privacy-preserving authentication (CL-ACPPA) scheme under elliptic curve cryptography that does not require bilinear operations. Formal security analysis demonstrates that, under the Random Oracle Model and the elliptic curve discrete logarithm problem assumption, the proposed scheme resists adaptive chosen-message attacks from adversaries with varying capabilities. Performance analysis and experimental results demonstrate that, compared with existing schemes, the proposed scheme achieves higher security while maintaining low communication and computational overhead. Full article

Economically motivated adulteration of powdered ginger with low-cost cereal flours represents an increasing concern for food authenticity and quality control. The aim of this study was to develop and validate a rapid, reliable, and non-destructive method for the quantitative determination of powdered ginger adulteration using visible and near-infrared (Vis–NIR) spectroscopy coupled with chemometric modelling. Ginger powder samples were adulterated with wheat, corn, and rice flours at concentrations ranging from 5 to 50% (w/w), with particular emphasis on the low-to-medium adulteration interval (10–25%), where reliable quantification is especially relevant for food fraud detection. Spectral data acquired in the visible (400–700 nm), near-infrared (700–2500 nm), and combined Vis–NIR (400–2500 nm) regions were preprocessed using Savitzky–Golay filtering and evaluated using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR). In addition, Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Random Forest (RF) were compared for sample classification. Among the evaluated approaches, LDA achieved the highest classification accuracy (>95%) using the NIR spectroscopic region, while PLSR models developed from the NIR spectral region provided the best quantitative performance, with validation coefficients of determination above 0.99, prediction errors below 1%, and RPD values greater than 13. The results demonstrate that Vis–NIR spectroscopy combined with chemometric modelling enables accurate discrimination between authentic and adulterated samples, as well as reliable quantification of flour adulteration in powdered ginger without sample preparation or chemical reagents. The proposed methodology constitutes a rapid, environmentally friendly, and cost-effective analytical strategy with strong potential for routine quality control and food fraud prevention. Full article